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The ASPCA® leads studies and collaborates with other organizations on research that advances the welfare of dogs, cats, and horses in animal shelters. These programs and ideas will enable you to help the animals in your community, from fostering and adoption to relocation, emergency response, animal care, and more.

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• Research article
• Open access
• Published: 05 February 2021

Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation

• Janae Bradley 1 &
• Suchithra Rajendran   ORCID: orcid.org/0000-0002-0817-6292 2 , 3  

BMC Veterinary Research volume  17 , Article number:  70 ( 2021 ) Cite this article

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Among the 6–8 million animals that enter the rescue shelters every year, nearly 3–4 million (i.e., 50% of the incoming animals) are euthanized, and 10–25% of them are put to death specifically because of shelter overcrowding each year. The overall goal of this study is to increase the adoption rates at animal shelters. This involves predicting the length of stay of each animal at shelters considering key features such as animal type (dog, cat, etc.), age, gender, breed, animal size, and shelter location.

Logistic regression, artificial neural network, gradient boosting, and the random forest algorithms were used to develop models to predict the length of stay. The performance of these models was determined using three performance metrics: precision, recall, and F1 score. The results demonstrated that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Upon further observation of the results, it was found that age for dogs (puppy, super senior), multicolor, and large and small size were important predictor variables.

The findings from this study can be utilized to predict and minimize the animal length of stay in a shelter and euthanization. Future studies involve determining which shelter location will most likely lead to the adoption of that animal. The proposed two-phased tool can be used by rescue shelters to achieve the best compromise solution by making a tradeoff between the adoption speed and relocation cost.

As the problem of overpopulation of domestic animals continues to rise, animal shelters across the nation are faced with the challenge of finding solutions to increase the adoption rates. In the United States, about 6–8 million dogs and cats enter animal shelters every year, and 3–4 million of those animals are euthanized [ 1 ]. In other words, about 50% of the total canines and felines that enter animal shelters are put to death annually. Moreover, 10–25% of the total euthanized population in the United States is explicitly euthanized because of shelter overcrowding each year [ 2 ]. Though animal shelters provide incentives such as reduced adoption fees and sterilizing animals before adoption, only a quarter of total animals living in the shelter are adopted.

Animal adoption from shelters and rescues

There are various places to adopt an animal, and each potential owner must complete the adoption process and paperwork to take their new animal home [ 3 ]. Public and private animal shelters include animal control, city and county animal shelters, and police and health departments. Staff and volunteers run these facilities. Animals may also be adopted from a rescue organization, where pets are fostered in a home or a private boarding facility. These organizations are usually run by volunteers, and animals are viewed during local adoption events that are held at different locations, such as a pet store [ 3 ].

There could be several reasons for the euthanization of animals in a shelter, such as overcrowding, medical issues (ex. sick, disabled), or behavioral issues (ex. too aggressive). The causes for the overpopulation of animals include failure to spay or neuter animals leading to reckless breeding habits and abandonment or surrender of offspring, animal abandonment from owners who are no longer able to take care of or do not want the animal, and individuals still buying from pet stores [ 4 ]. With the finite room capacity for animals that are abandoned or surrendered, overpopulation becomes a key challenge [ 5 ]. Though medical and behavioral issues are harder to solve, the overpopulation of healthy adoptable animals in shelters is a problem that can be addressed through machine learning and predictive analytics.

Literature review

In this section, we describe the research conducted on animal shelters evaluating euthanasia and factors associated with animal adoption. The articles provide insights into factors that influence the length of stay and what characteristics influence adoption.

Studies have been conducted investigating the positive influence of pre-adoption neutering of animals on the probability of pet adoption [ 2 ]. The author investigated the impact of the cooperation of veterinary medical schools in increasing pet adoption by offering free sterilization. Results demonstrated that the collaboration between veterinary hospitals and local animal shelters decreased the euthanization of adoptable pets.

Hennessy et al. [ 6 ] conducted a study to determine the relationship between the behavior and cortisol levels of dogs in animal shelters and examined its effect in predicting behavioral issues after adoption. Shore et al. [ 7 ] analyzed the reasons for returning adopted animals by owners and obtained insights for these failed adoptions to attain more successful future approvals. The researchers found that prior failed adoption had led to longer-lasting future acceptances. They hypothesized that the failed adoptions might lead owners to discover their dog preferences by assessing their living situation and the type of animal that would meet that requirement.

Morris et al. [ 8 ] evaluated the trends in income and outcome data for shelters from 1989 to 2010 in a large U.S. metropolitan area. The results showed a decrease in euthanasia, adoption, and intake for dogs. For cats, a reduction in intake was observed until 1998, a decrease in euthanasia was observed until 2000, and the adoption of cats remained the same. Fantuzzi et al. [ 9 ] explored the factors that are significant for the adoption of cats in the animal shelter. The study investigated the effects of toy allocation, cage location, and cat characteristics (such as age, gender, color, and activity level). Results demonstrated that the more active cats that possessed toys and were viewed at eye level were more likely to impress the potential adopter and be adopted. Brown et al. [ 10 ] conducted a study evaluating the influence of age, breed, color, and coat pattern on the length of stay for cats in a no-kill shelter. The authors concluded that while color did not influence the length of stay for kittens, whereas gender, coat patterning, and breed were significant predictors for both cats and kittens.

Machine learning

Machine learning is one possible tool that can be used to identify risk factors for animal adoption and predict the length of stay for animals in shelters. Machine learning is the ability to program computers to learn and improve all by itself using training experience [ 11 ]. The goal of machine learning is to develop a system to analyze big data, quickly deliver accurate and repeatable results, and to adapt to new data independently. A system can be trained to make accurate predictions by learning from examples of desired input-output data. More specifically, machine learning algorithms are utilized to detect classification and prediction patterns from large data and to develop models to predict future outcomes [ 12 ]. These patterns show the relationship between the attribute variables (input) and target variables (output) [ 13 ].

Widely used data mining tasks include supervised learning, unsupervised learning, and reinforcement learning [ 14 ]. Unsupervised learning involves the use of unlabeled datasets to train a system for finding hidden patterns within the data [ 15 ]. Clustering is an example of unsupervised learning. Reinforcement learning is where a system is trained through direct interaction with the environment by trial and error [ 15 ]. Supervised learning encompasses classification and prediction using labeled datasets [ 15 ]. These classification and regression algorithms are used to classify the output variable with a discrete label or predict the outcome as a continuous or numerical value. Traditional algorithms such as neural networks, decision trees, and logistic regression typically use supervised learning. Figure  1 provides a pictorial of the steps for developing and testing a predictive model.

Pictorial Representation of Developing a Predictive Model

Contributions to the literature

Although prior studies have investigated the impact of several factors, such as age and gender, on the length of stay, they focus on a single shelter, rather than multiple organizations, as in this study. The goal of this study is to investigate the length of stay of animals at shelters and the factors influencing the rate of animal adoption. The overall goal is to increase adoption rates of pets in animal shelters by utilizing several factors to predict the length of stay. Machine learning algorithms are used to predict the length of stay of each animal based on numerous factors (such as breed, size, and color). We address several objectives in this study that are listed below.

Identify risk factors associated with adoption rate and length of stay

Utilize the identified risk factors from collected data to develop predictive models

Compare statistical models to determine the best model for length of stay prediction

Exploratory Data results

From Fig.  2 , it is evident that the return of dogs is the highest outcome type at 43.3%, while Fig.  3 shows that the adoption of cats is the highest outcome type at 46.1%. Both figures illustrate that the euthanization of both cats and dogs is still prevalent (~ 20%). The results from Table 1 demonstrate that the longest time spent in the shelter is at 355 days by a male cat that is adopted and a female dog that is euthanized. Observing the results, adoption has the lowest variance among all animal types compared to the other outcome types. Adopted male cats have the lowest variance for days spent in the shelter, followed by female dogs. Female cats that are returned have the highest variance for days spent in the shelter.

Distribution of Outcome Types for Dogs

Distribution of Outcome Types for Cats

Figure  4 shows a comparison of cats and dogs for the three different outcome types. It is observed from the data that there are more dogs returned than cats. From Fig.  5 , it is observed that the number of days a dog stays in the shelter decreases as the age increases. This is not expected, as it is predicted that the number of days in a shelter would be lower for younger dogs and puppies. This observation could be due to having more data points for younger dogs.

Comparison of Outcome Types for Cats and Dogs

Age vs. Days in Shelter for Cats and Dogs

Machine learning results

Examining Table 2 , it is clear that the most proficient predictive model is developed by the gradient boosting algorithm for this dataset, followed by the random forest algorithm. The logistic regression algorithm appears to perform the worst with low precision, recall, and F1 score performance metrics for all categories of length of stay. For the prediction of low length of stay in a shelter, the random forest algorithm is the best performing model in comparison to the others at around 64–70% performance for precision, recall, and F1 score. The ANN algorithm is found to be the best when evaluating the precision and F1 score for medium length of stay, while the random forest algorithm is better for assessing recall. However, the performance of these models in predicting the medium length of stay for the given dataset is low for all three-performance metrics. The gradient boosting algorithm performs the best when predicting the high length of stay. Finally, the gradient boosting and random forest algorithms perform well when predicting the very high length of stay at around 70–80%.

Results from Table 2 also demonstrate that the model developed from the gradient boosting algorithm has a higher performance when predicting the high length of stay that leads to adoption, and when the outcome is euthanization. Evaluating the average of all three-performance metrics for all algorithms, the gradient boosting is the most proficient model at almost 60%, while logistic regression appears to be the worst. Table 2 also provides the computational time for each machine learning algorithm. For the given dataset, logistic regression runs the fastest at 9.41 s, followed by gradient boosting, artificial neural network, and finally, random forest running the longest. The gap in the performance measure ( pm ) is calculated by \( \frac{p{m}_{best}-p{m}_{worst}}{p{m}_{best}} \) , and is nearly 34, 39, and 32% for precision, recall, and F1 score, respectively.

Table 3 provides information on the top features or factors from each machine learning algorithm. Observing the table, we find that age (senior, super senior, and puppy), size (large and small), and color (multicolor) has a significant impact or influence on the length of stay. Specifically, we observe that older-aged animals (senior and/or super senior) appear as a significant factor for every algorithm. For the artificial neural network, older age is the #2 and #3 predictor, and super senior is the #2 predictor for the gradient boosting algorithm. Large and small-sized animals are also observed to be important features, as both are shown as the #1 predictor in the gradient boosting and ANN algorithms. The results also demonstrate that gender, animal type, other colors besides multicolor, middle age, and medium-sized animals did not significantly impact the length of stay.

Results from our study provided information on what factors are significant in influencing length of stay. Brown et al. [ 10 ] conducted research that found that age, breed designation, coat color, and coat pattern influenced the length of stay for cats in animal shelters. Similar to these studies, observations from our study also suggest that age and color have a significant impact or influence on the length of stay.

Determining which algorithm will develop the best model for the given set of data is critical to predict the length of stay and minimize the chances of euthanization. The goal of predictive analytics is to develop a model that best approximates the true mapping function for the relationship between the input and output variables. To approximate this function, parametric or non-parametric algorithms can be used. Parametric algorithms simplify the unknown function to a known form. Non-parametric algorithms do not make assumptions about the structure of the mapping function, allowing free learning of any functional form. In this study, we utilize both parametric (logistic regression and artificial neural network) and non-parametric (random forest and gradient boosting) algorithms on the given data. Observing the results from Table 2 , the gradient boosting and random forest (non-parametric algorithms) perform the best on the dataset. It is observed from the results that using a non-parametric approach leads to a better approximation of the true mapping function for the given records. These results also support prior studies on parametric versus non-parametric methods. Neely et al. [ 16 ] detailed the theoretical superiority of non-parametric algorithms for detecting pharmacokinetic and pharmacodynamic subgroups in a study population. The author suggests this superiority comes from the lack of assumptions made about the distribution of parameter values in a dataset. Bissantz et al. [ 17 ] discussed a resampling algorithm that evaluates the deviations between parametric and non-parametric methods to be noise or systematic by comparing parametric models to a non-parametric “supermodel”. Results demonstrate the non-parametric model to be significantly better. The use of algorithms that do not approximate the true function of the relationship between input and output provides better performance results for this application as well.

Current literature also supports the use of ensemble methods to increase prediction accuracy and performance. Dietterich [ 18 ] discussed the ongoing research into developing good ensemble methods as well as the discovery that ensemble algorithms are often more accurate than individual algorithms that are used to create them. Pandey, and S, T [ 19 ]. conducted a study to compare the accuracy of ensemble methodology on predicting student academic performance as research has demonstrated better results for composite models over a single model. This study applied ensemble techniques on learning algorithms (AdaBoost, Random Forest, Rotation Forest, and Bagging). For our study with the given records, the results support this claim. Both the gradient boosting and random forest algorithms are ensemble algorithms and performed the best on the animal shelter data.

Results from Table 2 demonstrate the best performance of the gradient boosting and random forest algorithm when the length of stay was classified as very high or the animal was euthanized. This is beneficial as the models can predict long stays where the outcome is euthanasia. This can lead to shelters identifying at-risk animals and implementing methods and solutions to ensure their adoption. These potential methods are the second phase of this research study, which will involve relocating animals to shelters where they will more likely be adopted. This phase is discussed in the future directions section.

Studies have been conducted evaluating euthanasia-related stress on workers (e.g., [ 1 ]). In other words, overpopulation not only leads to euthanasia but can, in turn, cause mental and emotional problems for the workers. For instance, Reeve et al. [ 20 ] evaluated the strain related to euthanasia among animal workers. Results demonstrated that euthanasia related strain was prevalent, and an increase in substance abuse, job stress, work causing family conflict, complaints, and low job satisfaction was observed. Predicting the length of stay for animals will aid in them being more likely to be adopted and will lead to fewer animals being euthanized, adding value not only to animals finding a home but also less stress on the workers.

The approach developed in this paper could be beneficial not only to reduce euthanasia but also to reduce overcrowding in shelters operated in countries where euthanasia of healthy animals is illegal, and all animals must be housed in shelters until adoption (or natural death). It is essential to develop an information system for a collaborative animal shelter network in which the entities can coordinate with each other, exchanging information about the animal inventory. Another benefit of this study is that it investigates applying machine learning to the animal care domain. Previous studies have looked into what factors influence the length of stay; however, this study utilizes these factors in addition to classification algorithms to predict how long an animal will stay in the shelter. Moreover, the use of a prescriptive analytics approach is discussed in this paper, where the predictions made by the machine learning algorithms will be used along with a goal programming model to decide in what shelter is an animal most likely to be adopted.

Limitations of this study include lack of behavioral data, limited sample size, and the use of simple algorithms. The first limitation, lack of behavioral data of the animal during intake and outcome, would be beneficial to develop a more comprehensive model. Though behavioral problems are harder to solve, having data would provide insight into how long these animals with behavioral issues are staying in shelters and what the outcome is. Studies have shown that behavioral problems play a significant role in preventing bonding between owners and their animals and one of the most common reasons cited for animal surrender [ 21 , 22 ]. These behavioral problems can include poor manners, too much energy, aggression, and destruction of the household. Dogs surrendered to shelters because of behavioral issues have also been shown to be less likely to be adopted or rehomed, and the ones that are adopted are more likely to be returned [ 21 ]. Studies have also been conducted to evaluate the effect of the length of time on the behavior of dogs in rescue shelters [ 23 , 24 , 25 ]. Most of them concluded that environmental factors led to changes in the behavior of dogs and that a prolonged period in a shelter may lead to unattractive behavior of dogs to potential owners. Acquiring information on behavioral problems gives more information for the algorithm to learn when developing the predictive model. This allows more in-depth predictions to be made on how long an animal will stay in a shelter, which could also aid in adoption. This approach can be used to shorten the length of stay, which makes sure that healthy animals are not developing behavioral problems in the shelters. It is not only crucial for the animal to be adopted, but also that the adoption is a good fit between owner and pet. Shortening the length of stay would also lessen the chance that the animal will be returned by the adopter because of behavior. Having this information will also allow shelters to find other shelters close by where animals with behavioral issues are more likely to be adopted. To overcome this limitation of the lack of data on behavioral problems, behavioral issues will be used as a factor and will be specifically asked for when acquiring data from shelters.

Another limitation includes collecting more data from animal shelters across the United States, allowing for more representative data to be collected and inputted into these algorithms. However, this presents a challenge due to most shelters being underfunded and low on staff. Though we reached out to shelters, most replied that they lacked the resources and staff to provide the information needed. Future work would include applying for funding to provide a stipend to staff for their assistance in gathering the data from respective shelters. With more data, the algorithm has more information to learn on, which could improve the performance metrics of the predictive models developed. There may also be other factors that show to be significant as more data is collected.

Finally, the last limitation is the use of simpler algorithms. This study considers basic ML algorithms. Nevertheless, in recent years, there has been development in the ML field of more complex networks. For instance, Zhong et al. [ 26 ] proposed a novel reinforcement learning method to select neural blocks and develop deep learning networks. Results demonstrated high efficiency in comparison to most of the previous deep network search approaches. Though only four algorithms were considered, future work would investigate deep learning networks, as well as bagging algorithms. Using more complex algorithms could ensure that if intricate patterns in the data are present, the algorithm can learn them.

Future direction

Phase 2: goal programming approach for making relocation decisions.

Using the information gathered in this study, we can predict the type of animals that are being adopted the most in each region and during each season of the year. To accomplish this, we utilize a two-phase approach. The first phase was leveraging the machine learning algorithms to predict the length of stay of each animal based on numerous factors (such as breed, size, and color). Phase-2 involves determining the best shelter to transport adoptable animals to increase the adoption rates, based on several conflicting criteria. This criterion includes predicted length of stay from phase-1, the distance between where the animal is currently housed and the potential animal shelters, transportation costs, and transportation time. Therefore, our goal is to increase adoption rates of pets in animal shelters by utilizing several factors to predict the length of stay, as well as determine the optimal animal shelter location where the animal will have the least amount stay in a shelter and most likely be adopted.

After predicting the length of stay of an incoming animal that is currently housed in the shelter l ′ using the machine learning algorithms, the next phase is to evaluate the potential relocation options for that animal. This strategic decision is specifically essential if the length of stay of the animal at its current location is high/very high. Nevertheless, while making this relocation decision, it is also necessary to consider the cost of transporting the animal between the shelters. For instance, if a dog is brought into a shelter in Houston, Texas, and is estimated to have a high/very high length of stay. Suppose if the dog is predicted to have a low length of stay at New York City and a medium length of stay at Oklahoma City, then a tradeoff has to be made between the relocation cost and the adoption speed. The objectives, length of stay, and relocation costs are conflicting and have to be minimized. Phase-2 attempts to yield a compromise solution that establishes a trade-off between these two criteria.

Goal programming (GP) is a widely used approach to solve problems involving multiple conflicting criteria. Under this method, each objective function is assigned as a goal, and a target value is specified for the individual criterion [ 27 ]. These target numbers can be fulfilled by the model with certain deviations, while the objective of the GP model is to minimize these deviations. Pertaining to this study, the desired values for the length of stay and relocation cost is pre-specified in the model and can be fulfilled with deviations. The GP model attempts to minimize these deviations. Thus, this technique attempts to produce a solution that is as close as possible to the targets, and the model solutions are referred to as the “most preferred solution” by prior studies (e.g., [ 28 , 29 ]).

As mentioned earlier, the primary task to be completed using this phase-2 goal programming approach is the relocation decisions considering the adoption speed and the cost of transporting the animal from the current location.

Model notations

Goal programming model formulation, goal constraints.

Objective 1: Minimize the overall length of stay of the animal under consideration (Eq. 1 ).

Goal constraint for objective 1: The corresponding goal constraint of objective 2 is given using Equation [ 30 ].

Objective 2: Minimize the overall relocation cost for transporting the animal under consideration (Eq. 3 ).

Goal constraint for objective 2: The corresponding goal constraint of objective 2 is given using Equation [ 18 ].

Hard constraints

Equation [ 9 ] ensures that the animal can be assigned to only one shelter.

The animal can be accommodated in shelter l only if there are a shelter capacity and type for that particular animal size category, and this is guaranteed using constraint [ 31 ]. It is important to note that both y and s are input parameters , whereas l is the set of shelters.

Equation [ 21 ] sets an upper limit on the length of stay category if the shelter l is assigned as the destination location. This prevents relocating animals to a shelter that might potentially have a high or very high length of stay.

Similarly, Equation [ 32 ] sets an upper limit on the relocation cost, if the shelter l is assigned as the destination location. This prevents relocating animals to a very far location. The current shelter location, l ′ , that is hosting the animal is an input parameter.

Objective function

Since the current problem focuses on minimizing the expected length of stay and relocation cost, the objective function of the goal programming approach is to reduce the sum of the weighted positive deviations given in Equations ([ 18 , 30 ], as shown in Equation [ 6 ].

where w g is the weight assigned for each goal g .

It is necessary to scale the deviation (since the objectives have different magnitudes as well as units) to avoid a biased solution.

If the scaling factors are represented by f g for goal g , then the scaled objective function is given in Equation [ 14 ].

Using this goal programming approach, the potential relocation options are evaluated considering the length of stay from phase-1. This phase-2 goal programming approach is useful, especially if the length of stay of the animal at its current location is high/very high, and a trade-off has to be made between relocation cost and length of stay. Phase-2 acts as a recommendation tool for assisting administrators with relocation decisions.

Nearly 3–4 million animals are euthanized out of the 6–8 million animals that enter shelters annually. The overall objective of this study is to increase the adoption rates of animals entering shelters by using key factors found in the literature to predict the length of stay. The second phase determines the best shelter location to transport animals using the goal programming approach to make relocation decisions. To accomplish this objective, first, the data is acquired from online sources as well as from numerous shelters across the United States. Once the data is acquired and cleaned, predictive models are developed using logistic regression, artificial neural network, gradient boosting, and random forest. The performance of these models is determined using three performance metrics: precision, recall, and F1 score.

The results demonstrate that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Followed closely in second is the random forest algorithm, then the artificial neural network, and then finally, the logistic regression algorithm is the worst performer. We also observed from the data that the gradient boosting performed better when predicting the high or very high length of stay. Further observing the results, it is found that age for dogs (e.g., puppy, super senior), multicolor, and large and small size are important predictor variables.

The findings from this study can be utilized to predict how long an animal will stay in a shelter, as well as minimize their length of stay and chance of euthanization by determining which shelter location will most likely lead to the adoption of that animal. For future studies, we will implement phase 2, which will determine the best shelter location to transport animals using the goal programming approach to make relocation decisions.

Data description

A literature review is conducted to determine the factors that might potentially influence the length of stay for animals in shelters. These factors include gender, breed, age, and several other variables that are listed in Table 4 . These features will be treated as input variables for the machine learning algorithms. Overall, there are eight input or predictor variables and one output variable, which is the length of stay.

Animal shelter intake and outcome data are publicly made available by several state/city governments on their website (e.g., [ 33 , 34 ]), specifically in several southern and south-western states. These online sources provide datasets for animal shelters from Kentucky (150,843 data rows), California (334,016), Texas (155,115), and Indiana (4132). Since there is no nationwide database for animal shelters, information is also collected through individual animal shelters that conduct euthanization of animals. We contacted over 100 animal shelters across the United States and inquired for data on the factors mentioned in Table 4 . We received responses from 20 of the animal shelters that were contacted. Most responses received stated there was not enough staff or resources to be able to provide this information. From the responses that were received back, only four shelters were able to provide any information. Of those four, only two of the datasets contained the factors and information needed, which are Colorado (8488 data rows) and Arizona (4, 667 data rows).

The data that is collected from the database and animal shelters included information such as animal type, intake and outcome date, gender, color, breed, and intake and outcome status (behavior of animal entering the shelter and behavior of animal at outcome type). These records also included information on several types of animals, such as dogs, cats, birds, rabbits, and lizards. For this study, the focus is on dogs and cats. After filtering through these records, we found that only California, Kentucky, Colorado, Arizona, and Indiana had all of the factors needed for the study. Upon downloading data from the database and receiving data from the animal shelters, the acquired data underwent data integration, data transformation, and data cleaning (as detailed in Fig.  1 ). After data pre-processing, there are over 113,000 animal records.

Data cleaning methods

Next, data cleaning methods are utilized to detect discrepancies in the data, such as missing values, erroneous data, and inconsistencies. Data cleaning is an essential step for obtaining unbiased results [ 35 , 36 ]. In other words, identifying and cleaning erroneous data must be performed before inputting the data into the algorithm as it can significantly impact the output results.

The following is a list of commonly used data cleaning techniques in the literature [ 11 ]:

Substitution with Median: Missing or incorrect data are replaced with the median value for that predictor variable.

Substitution with a Unique Value: Erroneous data are replaced with a value that does not fall within the range that the input variables can accept (e.g., a negative number)

Discard Variable and Substitute with a Median: When an input variable has a significant number of missing values, these values are removed from the dataset, and the features that remain with missing or erroneous values are replaced with the median.

Discard Variable and Substitute with a Unique Value: Input variables with a significant number of missing values are removed from the dataset, and the features that remain with missing or erroneous values are coded as − 1.

Remove Incomplete Rows Entirely: Incomplete Rows are removed from the dataset.

Data preprocessing

Some animal breeds are listed in multiple formats and are changed to maintain uniformity. An example of this is a Russian Blue cat, which is formatted in several ways such as “Russian”, “Russian Blue”, and “RUSSIAN BLUE”. Animals with multiple breeds such as “Shih Tzu/mix” or “Shih Tzu/Yorkshire Terr” are classified as the first breed listed. Other uncommon breeds are classified as “other” for simplicity. Finally, all animal breeds are summarized into three categories (small, medium, or large) using the American Kennel Clubs’ breed size classification [ 37 ]. Part of the data cleansing process also includes categorizing multiple colors found throughout the sample size into five distinct color categories (brown, black, blue, white, and multicolor). We classified age into five categories for dogs and cats (puppy or kitten, adolescent, adult, senior, super senior). The puppy or kitten category includes data points 0–1 year, adolescence includes data points 2–3 years old, adulthood includes animals 4–7 years of age, and senior animals are 8–10 years of age. Any animal that is older than ten years are categorized as a super senior, based on the recommendations provided in Wapiti Labs [ 38 ].

As mentioned previously, the output variable is the length of stay and is classified as low, medium, high, and very high/euthanization. The length of stay is calculated by taking the difference between the intake date and outcome date. To remove erroneous data entries and special cases, the number of days in the animal shelter is also capped at a year. The “low” category represents animals that are returned (in which case, they are assigned the days in the shelter as 0) or spent less than 8 days before getting adopted. It is important to keep these animals at the shelter so that the owner may find them or they are transferred to their new homes. Animals that stayed in a shelter for 9–42 days and are adopted are categorized as “medium” length of stay. The “high” category is given to animals that stayed in the shelter for 43–365 days. Finally, animals that are euthanized are categorized as “very high”.

After integrating all data points from each animal shelter, the sample size includes 119,691 records. After the evaluation of these data points, 5436 samples are found to have miscellaneous (such as a negative length of stay) or missing values. After applying data cleaning techniques, the final cleaned dataset includes 114,256 data points, with 50,466 cat- and 63,790 dog-records.

Machine learning algorithms to predict the length of stay

The preprocessed records are then separated into training and testing datasets based on the type of classification algorithm used. Studies have demonstrated the need for testing and comparing machine learning algorithms, as the performance of the models depends on the application. While an algorithm may develop a predictive model that performs well in one application, it may not be the best performing model for another. A comparison between the statistical models is conducted to determine the overall best performing model. In this section, we provide a description as well as the advantages of each classification algorithm that is utilized in this study.

Logistic regression

Logistic regression (LR) is a machine learning algorithm that is used to understand the probability of the occurrence of an event [ 39 ]. It is typically used when the model output variable is binary or categorical (see Fig.  6 ), unlike linear regression, where the dependent variable is numeric [ 40 ]. Logistic regression involves the use of a logistic function, referred to as a “sigmoid function” that takes a real-valued number and maps it into a value between 0 and 1 [ 41 ]. The probability that the length of stay of the animal at a specific location will be low, medium, high, or very high, is computed using the input features discussed in Table 4 .

Pictorial Representation of the Logistic Regression Algorithm

The linear predictor function to predict the probability that the animal in record i has a low, medium, high, and very high length of stay categories is given by Equations ( 11 ) –[ 3 ], respectively.

Where β v , l is a set of multinomial logistic regression coefficients for variable v of the length of stay category l , and x v , i is the input feature v corresponding to data observation i .

Artificial neural network

Artificial Neural Network (ANN) algorithms were inspired by the brain’s neuron, which transmits signals to other nerve cells [ 40 , 42 ]. ANN’s were designed to replicate the way humans learn and were developed to imitate the operational sequence in which the body sends signals in the nervous system [ 43 ]. In an ANN, there exists a network structure with directional links connecting multiple nodes or “artificial neurons”. These neurons are information-processing units, and the ties that connect them represent the relationship between each of the connected neurons. Each ANN consists of three layers - the input layer, hidden layer, and the output layer [ 32 , 44 ]. The input layer is where each of the input variables is fed into the artificial neuron. The neuron will first calculate the sum of multiple inputs from the independent variables. Each of the connecting links (synapses) from these inputs has a characterized weight or strength that has a negative or positive value [ 32 ]. When new data is received, the synaptic weight changes, and learning will occur. The hidden layer learns the relationship between the input and output variables, and a threshold value determines whether the artificial neuron will fire or pass the learned information to the output layer, as shown in Fig.  7 . Finally, the output layer is where labels are given to the output value, and backpropagation is used to correct any errors.

Pictorial Representation of the Artificial Neural Networks

Random Forest

The Random Forest (RF) algorithm is a type of ensemble methodology that combines the results of multiple decision trees to create a new predictive model that is less likely to misclassify new data [ 30 , 45 ]. Decision Trees have a root node at the top of the tree that consists of the attribute that best classifies the training data. The attribute with the highest information gain (given in Eq. 16 ) is used to determine the best attribute at each level/node. The root node will be split into more subnodes, which are categorized as a decision node or leaf node. A decision node can be divided into further subnodes, while a leaf node cannot be split further and will provide the final classification or discrete label. RF algorithm uses mtree and ntry as the two main parameters in developing the multiple parallel decision trees. Mtree specifies how many trees to train in parallel, while ntry defines the number of independent variables or attributes to choose to split each node [ 30 ].. The majority voting from all parallel trees gives the final prediction, as given in Fig.  8 .

Pictorial Representation of the Random Forest Algorithm

Gradient boosting

Boosting is another type of ensemble method that combines the results from multiple predictive algorithms to develop a new model. While the RF approach is built solely on decision trees, boosting algorithms can use various algorithms such as decision trees, logistic regression, and neural networks. The primary goal of boosting algorithms is to convert weak learners into stronger ones by leveraging weighted averages to identify “weak classifiers” [ 31 ]. Samples are assigned an initial uniformed weight, and when incorrectly labeled by the algorithm, a penalty of an increase in weight is given [ 46 ]. On the other hand, samples that are correctly classified by the algorithm will decrease in weight. This process of re-weighing is done until a weighted vote of weak classifiers is combined into a robust classifier that determines the final labels or classification [ 46 ]. For our study, gradient boosting (GB) will be used on decision trees for the given dataset, as illustrated in Fig.  9 .

Pictorial Representation of Boosting Algorithm

Machine learning model parameters

The clean animal shelter data is split into two datasets: training and testing data. These records are randomly placed in the two groups to train the algorithms and to test the model developed by the algorithm. 80% of the data is used to train the algorithm, while the other 20% is used to test the predictive model. To avoid overfitting, a tenfold cross-validation procedure is used on the training data. There are no parameters associated with the machine learning of logistic regression algorithms. However, a grid search method is used to tune the parameters of the random forest, gradient boosting, and artificial neural network algorithms. This allows the best parameter in a specific set to be chosen by running an in-depth search by the user during the training period.

The number of trees in the random forest and gradient boosting algorithms is changed from 100 to 1000 in increments of 100. A learning rate of 0.01, 0.05, and 0.10 is used based on the recommendations of previous studies [ 47 ]. The minimum observations for the trees’ terminal node are set to vary from 2 to 10 in increments of one, while the splitting of trees varies from 2 to 10 in increments of two. A feed-forward method is used to develop the predictive model using the artificial neural network algorithm. The feed-forward algorithm consists of three layers (input, hidden, output) as well as backpropagation learning. The independent and dependent variables represent the input and output layers. Since the input and output layers are already known, an optimal point is reached for the number of nodes when between 1 and the number of predictors. This means that for our study, the nodes of the hidden layer vary from 1 to 8. The learning rate values used to train the ANN are 0.01, 0.05, and 0.10.

To find the optimal setting for each machine learning algorithm, a thorough search of their corresponding parameter space is performed.

Performance measures

In this study, we use three performance measures to evaluate the ability of machine learning algorithms in developing the best predictive model for the intended application. The measures considered are precision, F1 score, and sensitivity/recall to determine the best model given the inputted data samples. Table 5 provides a confusion matrix to define the terms used for all possible outcomes.

Precision evaluates the number of correct, true positive predictions by the algorithm while still considering the incorrectly predicted positive when it should have been negative (Eq. 17 ). By having high precision, this means that there is a low rate of false positives or type I error. Sensitivity or recall evaluates the number of true positives that are correctly predicted by the algorithm while considering the incorrectly predicted negative when it should have been positive (Eq. 18 ). Recall is a good tool to use when the focus is on minimizing false negatives (type II error). F1 score (shown in Eq. 19 ) evaluates both type I and type II errors and assesses the ability of the model to resist false positives and false negatives. This performance metric evaluates the robustness (low number of missed classifications), as well as the number of data points that are classified correctly by the model.

Availability of data and materials

Most of the datasets used and/or analyzed during the current study were publicly available online as open source data. The data were available in the website details given below:

https://data.bloomington.in.gov/dataset

https://data.louisvilleky.gov/dataset

https://data.sonomacounty.ca.gov/Government

We also obtained data from Sun Cities 4 Paws Rescue, Inc., and the Rifle Animal Shelter. No administrative permission was required to access the raw data from these shelters.

Abbreviations

Logistic Regression

Artificial Neural Network

Gradient Boosting

Goal Programming

Coefficient of Variation

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Acknowledgments

We would like to thank the Sun Cities 4 Paws Rescue, Inc., and the Rifle Animal Shelter for providing the length of stay reports in order to complete this study.

This research was not funded by any agency/grant.

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Janae Bradley

Department of Industrial and Manufacturing Systems Engineering, University of Missouri Columbia, Columbia, MO, 65211, USA

Suchithra Rajendran

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Contributions

JB performed data mining, data cleaning and analyses of the animal shelter data and machine learning algorithms. JB was also a major contributor in writing the manuscript. SR performed data mining, cleaning, and analyses of the machine learning algorithms, as well as the goal programming. All authors have read and approved the final manuscript.

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Correspondence to Suchithra Rajendran .

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Bradley, J., Rajendran, S. Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation. BMC Vet Res 17 , 70 (2021). https://doi.org/10.1186/s12917-020-02728-2

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Research & Data

The State of U.S. Animal Sheltering, 2020

For more recent shelter data, check out " The State of U.S. Animal Sheltering, 2021 ."

The year 2020 brought significant changes for the animal welfare industry. The COVID-19 pandemic changed the way our nation’s shelters operated, resulting in an unexpected large leap forward in progress with more shelter than ever reaching the no-kill benchmark of a 90% save rate. This report shares the top-line results of the Best Friends 2020 national dataset and analysis, as well as a five-year trend of total data and specific types of intake and outcomes.

Table of Contents

Executive Summary

• Key Findings

Introduction

The growing no-kill movement and the importance of transparency

Findings: 2020 Details

National progress indicators

Findings by state

Findings by region

Findings by species

Findings by shelter type

Shelter data, social vulnerability and demographics

Looking ahead

Findings: Trend Analysis (2016-2020)

The sample used for trend analysis

• Live outcomes

Lifesaving gap

Shelter type

Conclusions

Research methods and analysis.

• What it means for a shelter, county, state or nation to be no-kill
• Data sources in Best Friends' 2020 dataset

The difference between gross and net intake

• How save rates are calculated

How lifesaving gap is calculated

Gaps in the data

Contributors

Footnotes and references.

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Executive Summary 

Significant changes began to occur in our nation’s shelters during the early weeks of the COVID-19 pandemic. Intake policies, fostering needs and adoption protocols looked different. One of the biggest questions was how long these changes would last. Thankfully, shelters and Best Friends were in a better position than ever before to document and collect data.

For nearly four decades, the animal welfare industry has been working to overhaul outmoded systems and end the needless deaths of millions of dogs and cats. Data collection has always been a challenge in this work. Recent efforts by Best Friends to collect and analyze national shelter data provide the most comprehensive look at our nation’s lifesaving gap and the amazing progress made in just the past five years. 

This progress took an unexpectedly large leap forward during the pandemic. More shelters than ever reached the no-kill benchmark of a 90% save rate. The number of dogs and cats entering shelters and needlessly dying fell by the largest rate ever recorded. This report shares the top-line results of the Best Friends 2020 national dataset and analysis. In addition to national progress, the report covers specific findings by state, region, human population and species, as well as a five-year trend of total data and specific types of intake and outcomes.

By understanding national, state and regional trends, animal welfare professionals can have a benchmark by which to compare their local progress toward no-kill (i.e., achieving positive outcomes for at least 90% of the animals entering a shelter) and better understand the data tools that Best Friends has developed and made available publicly. In today’s environment of limited resources and many competing social causes, it is imperative to spend every dollar as wisely as possible. Strategic plans, resource allocation and lifesaving programs all benefit from a data-driven approach.

Note: Overall, the number of dogs and cats suffering from irremediable medical or behavioral issues that compromise their quality of life and prevent them from being rehomed typically makes up no more than 10% of dogs and cats entering the shelter system. Best Friends uses the term “lifesaving gap” and the phrase “needlessly dying in shelters” to represent progress toward that 90% save rate, which is the commonly recognized no-kill benchmark. The lifesaving gap is the difference between the current save rate and the no-kill benchmark. We use “needlessly dying in shelters” to distinguish between individual dogs and cats who lose their lives because of preventable factors, such as lack of shelter space or other resources, and those who are euthanized, which is an act of true mercy for individual animals who are sick or suffering irremediably. See the Research Methods and Analysis section for more details on defining no-kill.

As a leader in the no-kill movement, Best Friends is careful with its terminology. We refrain from referring to shelters as “kill shelters” when they have not yet achieved no-kill because it implies that the responsibility for becoming no-kill falls on one entity and the caring people within it, rather than acknowledging the need for everyone to work together to end the unnecessary deaths of homeless dogs and cats.

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Key findings

The lifesaving gap decreased more than ever in 2020. The year 2020 ended with a lifesaving gap of 346,622 dogs and cats. While there is still work to be done, this is a 278,778 (44.6%) drop from the 625,400 dogs and cats unnecessarily losing their lives in 2019. This follows a reduction of 14.7% between 2018 and 2019.

Shelter intake plummeted during 2020. Total shelter intake during 2020 fell by 20.5% from 2019, from 5.36 million to 4.26 million (1.9 million dogs, 1.8 million cats, and roughly 500,000 undesignated by the reporting shelter or estimated). From 2018 to 2019, intake rose by 0.2%.

The U.S. shelter lifesaving gap is made up predominantly of cats. More than two cats (68.4% of the lifesaving gap) are unnecessarily dying in U.S. shelters for every dog. This is happening despite dogs making up 51.1% of intake (among pets specifically reported as either a dog or a cat).

More than half of the dogs and cats who need saving are in six states. Texas (52,106), California (39,111), North Carolina (27,031), Florida (24,289), Alabama (16,825) and Louisiana (15,288) account for 50.4% of the lifesaving gap. Of the 4,404 brick-and-mortar shelters in the country, the 100 (2.3%) shelters with the largest gaps collectively account for 42.5% of the dogs and cats who still need saving.

Live outcomes for dogs and cats fell significantly in 2020. Contrary to popular narrative, fewer dogs and cats were saved from U.S. shelters in 2020. Among shelters that reported both 2019 and 2020 data, live outcomes fell by 20.6%. Included in this decrease is adoptions, which fell by 19.6%. Despite a drop in live outcomes, the national save rate for all U.S. shelters increased 4 percentage points, from 79.0% to 83.0%.

The numbers of no-kill shelters and communities continue to increase. Shelters achieving the no-kill benchmark (a save rate of 90%) totaled 2,094, or 47.5% of all shelters nationwide. This is an increase of 3.7 percentage points from 43.8% in 2019. The number of counties achieving no-kill also rose, from 658 in 2019 to 764 in 2020. This is 32.6% of the 2,340 counties with sheltering services (807 counties do not have sheltering services). Note: See the Research Methods and Analysis section for more details on how no-kill is measured.

Human populations tell us a lot about the country’s lifesaving needs. Roughly one dog or cat needs to be saved for every 1,000 people in the United States. While Texas and California contain more of these pets than any other state, they rank just tenth and nineteenth, respectively, in per capita lifesaving gap. Hawaii (6.5 pets per 1,000 population), Alabama (3.4) and Louisiana (3.3) have the most dogs and cats to be saved relative to the number of people who live there. Based on the U.S. Centers for Disease Control and Prevention’s Social Vulnerability Index, 28.8% of the country’s human population lives in highly vulnerable communities. These communities, however, make up half (49.8%) of the country’s lifesaving gap.

For generations, healthy cats and dogs have been dying in our communities’ animal shelters. Until the past few years, millions lost their lives annually. For the past 35 years, the animal welfare industry has been working to overhaul antiquated systems and ways of relating to companion animals. Yet, until recently, the numbers of dogs and cats entering and dying in shelters were largely broad estimates based on limited data.

Because there is no mandated federal reporting of animal shelter performance, it is left to states and municipalities to decide if and how they want to track this data. Very few states require state-level reporting and those that do often collect data in different ways. And in states that do not require reporting, it has been left to the municipalities or the shelters themselves to define, record and track animal statistics.

The industry as a whole has been left to grapple with large data gaps, inconsistent information, disputes over definitions of terms, and a lack of clarity surrounding the number of dogs and cats unnecessarily dying in shelters each year. Even the precise number of animal shelters in the country has remained a matter of considerable speculation.

In the mid-2010s, Best Friends began to compile a database of all U.S. shelters. There were 4,404 brick-and-mortar animal shelters in the country in 2020 1 . In addition to building and maintaining this master shelter list, Best Friends collects data about how many dogs and cats are entering these shelters, the array of outcomes for each, and how many animals need to be saved each year for that shelter and community to reach no-kill status (i.e., achieving positive outcomes for at least 90% of the animals entering a shelter).

In this report, we share the top-line results of Best Friends’ 2020 dataset analysis across four areas of interest: outcomes by species (dog and cat), per capita findings, state and regional differences, and animal sheltering trends over the past five years.

By understanding national, regional and state trends, animal welfare professionals can have a benchmark by which to compare their local progress toward no-kill and better understand the data tools that Best Friends has developed and made available publicly. In today’s environment of limited resources and many competing social causes, it is imperative to spend every dollar as wisely as possible. Strategic plans, resource allocation and lifesaving programs all benefit from a data-driven approach.

Historically, animal welfare has lacked comprehensive data regarding sheltered cats and dogs. A variety of efforts have been undertaken in the past to bridge this long-standing industry gap. Some are still in progress while others have not been maintained or have been limited in their efficacy because of lack of consistency, completeness or knowledge of how best to apply the information. A number of regional efforts were conducted in the 1990s, including the California Sheltering Agencies Survey, the Iowa Federation of Humane Societies Animal Shelter Survey, and the Progressive Animal Welfare Society Report on Washington State Animal Shelter Statistics.

A national effort was initiated in 1993 with the formation of the National Council on Pet Population Study and Policy to gather and analyze data on the number, origin and disposition of companion animals (dogs and cats) in the United States. Unfortunately, only about 1,100 of the estimated 4,700 shelters responded, making it difficult to draw conclusions about intake, outcomes or national trends 2 .

In 2004, 20 leaders representing national organizations and funders in the industry gathered to find common ground in an otherwise divided animal welfare field. The result was the Asilomar Accords. But this effort, too, proved to be problematic in delivering consistent, non-subjective reporting standards that would allow for accurate comparative analyses. In addition, adoption of the Asilomar Accords was insufficient to provide a representative view of sheltering data nationally.

By 2010, after more than a century of animal sheltering in the United States, it was considered unacceptable that the best we could do as an industry was to estimate that millions of dogs and cats enter the nation’s shelters every year and that some large percentage of those animals do not leave alive. As a result of this widespread dissatisfaction, many of the same organizations that collaborated on the Asilomar Accords and others worked together to create the Basic Data Matrix 3 , which currently serves as the industry standard regarding the minimum amount of data that shelters should be collecting and reporting annually.

In 2012, Best Friends joined with these same organizations, collaborating in the creation of Shelter Animals Count (SAC), a national database of self-reported data to serve as the most credible and complete source of data on sheltered animals for the industry — a critical dependency for our mission advancement and assessment.

By 2016, however, Best Friends was narrowing its strategic focus with the ambitious goal of leading the country to no-kill by 2025. This goal required more complete and representative shelter data than SAC had accumulated through its voluntary data reporting. So, in late 2016, Best Friends began its first data collection effort to augment the available industry data and apply an estimation methodology for missing data (see the Research Methods and Analysis section for more information), thereby creating the most comprehensive national dataset to date. In July 2019, Best Friends introduced our public-facing pet lifesaving dashboard 4 for publishing national, state, community and shelter-level data for calendar year 2018.

In June 2021, the pet lifesaving dashboard was updated with the 2020 dataset, the third annual dataset to be displayed publicly by Best Friends. This update made the dashboard the most complete compilation of sheltering data in the industry, including calendar-year data from 3,330 brick-and-mortar shelters. This latest dataset represents 76% of the 4,404 shelters in the country and accounts for an estimated 93% of the dogs and cats entering U.S. shelters in 2020. A total of 2,340 of the 3,147 counties have sheltering services, and 1,529 of those are fully accounted for with collected data. For the remaining 811 counties with sheltering services, the data is either a combination of collected data and estimation or entirely an estimation.

A decade ago, there were fewer than 10 counties known to have achieved the recognized no-kill benchmark of a 90% save rate 5 . For a county to be no-kill, every shelter located within it must reach this benchmark. Today, thanks to considerable lifesaving progress and improved data collection, we know that there are 764 no-kill counties and two no-kill states, Delaware and New Hampshire.

Reporting on what has been accomplished is encouraging, to be sure, but the impetus behind data collection efforts has less to do with looking back than with looking ahead. This comprehensive dataset now available allows for an increasingly detailed map of the work that needs to be done to achieve no-kill by 2025. With a better understanding and identification of the remaining hot spots, collective resources can be better deployed for the greatest lifesaving impact by targeting programs more precisely. This ongoing data collection and analysis allow for program impact measurement, progress tracking and the identification of areas in further need.

Transparency is central to the no-kill philosophy and to the effective implementation of no-kill programs. Like any industry, the animal welfare field is ill-equipped to solve a problem if we cannot define the nature of that problem. And community members — whose engagement is essential for lifesaving progress — cannot help solve a problem they do not know exists.

Animal shelter transparency begins with the reporting of simple numbers: the number of dogs and cats who entered a shelter in a given time period, the number of animals with positive outcomes (e.g., animals who were adopted, animals returned to their owners, community cats returned to the field 6 ) and the number of animals with negative outcomes (e.g., animals who died, were lost in care or were euthanized). The more detailed the data, the more accurately programs can be targeted to particular needs, evaluated for efficacy and refined.

In October 2018, Best Friends was proud to co-author a landmark position statement jointly issued by eight of the nation’s leading animal welfare organizations and foundations. Calling upon every organization in the country that takes companion animals into their care to share their data, Best Friends — along with the American Society for the Prevention of Cruelty to Animals, Michelson Found Animals Foundation, the Humane Society of the United States, Maddie’s Fund, PetSmart Charities, Petco Love (formerly Petco Foundation) and the WaterShed Animal Fund — issued the following joint statement of shared commitment to transparency:

“As national leaders and funders of animal welfare in North America, we believe that organizations should be transparent about the number of animals that come under their care, and the outcome for all of those animals. That is why we support the public availability of key data (the basic data matrix as defined by Shelter Animals Count) from all animal welfare agencies and nonprofits, both publicly and privately funded and whether or not they provide government animal control services or humane law enforcement.” 7

Our 2020 dataset demonstrates the significant lifesaving progress that can be made by managing intake. The COVID-19 pandemic caused many shelters to change how they operate, resulting in fewer dogs and cats entering shelters and even fewer animals dying. This happened despite decreases in live outcomes. While increasing live outcomes is a valuable tool for helping to reduce the number of animals dying in shelters, it is not the only tool available to shelters committed to achieving the no-kill benchmark.

The 4.26 million dogs and cats who entered shelters in 2020 represents a decrease of 20.5% from 2019’s 5.36 million. This is a dramatic change from the slight increase from 2018 to 2019. Of these pets, 1.9 million were dogs, 1.8 million were cats, and roughly 500,000 were undesignated by the reporting shelter or estimated. This change in intake, coupled with the implementation of new lifesaving policies, helped lead to significant lifesaving progress in 2020:

• The total number of dogs and cats unnecessarily dying in shelters decreased by 44.6%, from 625,400 to 346,622.
• The national save rate increased by 4 percentage points, from 79.0% to 83.0%.
• The 2,094 no-kill shelters represent an increase of 3.7 percentage points, to 47.5% of all U.S. shelters.
• No-kill counties total 764, up from 658 in 2019, or 32.6% of all counties with sheltering services.

Note: Please see the Research Methods and Analysis section for definitions of these metrics.

In 2016, Best Friends declared a goal of helping the country to reach no-kill by 2025. As shown in Figure 1, significant progress has occurred since then.

In recent years, five states accounted for more than half of the nation’s lifesaving gap. By prioritizing help for the states with the most dogs and cats needlessly dying in shelters, Best Friends can have the greatest lifesaving impact. In 2020, as the lifesaving gap fell by 44.6%, that number of states rose from five to six. Texas is the state with the largest lifesaving gap (see Table 1), a reduction of 46.1% from 2019. California, the top state in 2019, is now second, with a reduction of 61.0% from 2019. North Carolina, Florida, Alabama and Louisiana are the other states that account for half of the pets unnecessarily dying in U.S. shelters, but they all have reduced their lifesaving gaps by 29.0% or more. 

The top six states for 2020 have a combined lifesaving gap of 174,650 pets, or 50.4% of the nation’s total. These six states, along with the nine states shown in yellow in Figure 2, account for more than 75% of the pets nationwide in need of saving.

Other state-level highlights from the 2020 dataset:

• New Hampshire joins Delaware as the nation’s second no-kill state.
• Rhode Island and Vermont are very close to becoming no-kill, with a combined lifesaving gap of just 71 dogs and cats.
• In 21 states, more than half of the shelters have reached the no-kill benchmark of a 90% save rate.
• Seven states (Delaware, New Hampshire, Rhode Island, Maine, Connecticut, Vermont and Montana) have aggregated save rates of at least 90%, but these states, except for Delaware and New Hampshire, do still have a lifesaving gap because some shelters are not yet no-kill.
• Hawaii’s 61.0% save rate is the only one below 70%.
• Hawaii’s lifesaving gap per human population is the highest in the nation, at 6.5 pets per 1,000 humans. (See Table 2.)
• In 33 states, the number of dogs and cats needlessly dying in shelters is below 1 per 1,000 human population. (See Table 2.)

Note: This table sorted by lifesaving gap can be found in the appendix.

Best Friends has divided the country into eight regions for programming and strategy implementation (Figure 3). Of the 4,404 brick-and-mortar shelters in the United States, 100 (2.3%) account for 42.5% of dogs and cats needlessly dying in shelters. The 2020 dataset reveals several important regional differences (see Figure 4), including:

• The South Central region continues to account for the largest national share of both intake (18.0%) and lifesaving gap (24.7%).
• For lifesaving gap per 1,000 population, the South Central (2.08), Southeast (1.51) and Mid-Atlantic (1.39) regions are all higher than the nation in aggregate (1.05).
• At 59.0%, the Mountain West region has the highest rate of shelters reaching the no-kill benchmark. In the Midwest (54.3%) and the Northeast (53.1%), most of the shelters have reached a 90% save rate.
• The South Central (-72,992) and Pacific (-70,041) regions had the largest reduction in lifesaving gap and experienced the largest leaps in save rate, at +5.7 and +5.5 percentage points, respectively.

The 2020 dataset reveals some important differences between the outcomes for dogs and cats based on the 3,299 shelters that reported species-specific data. Some of those differences are:

• Cats make up 68.4% of the national lifesaving gap, down slightly from 69.1% in 2019.
• Even though dogs make up more than half (51.1%) of shelter intake, cats make up most of the lifesaving gap — at a ratio of more than 2:1 over dogs. This is down 1.3 percentage points from 2019.
• The national aggregate save rate for dogs is 87.6%, an increase of 2.4 percentage points from 2019. The cat save rate had a greater increase (5.8 percentage points), but is still behind dogs at 80.3%.
• Cats make up most of the lifesaving gap in 40 states.

More than half of the 4,404 shelters in the country are municipal shelters, but they are disproportionately not yet no-kill. Private shelters without a government contract were more likely to be at the no-kill benchmark in 2020 (see Figure 6). Figure 7 shows the share of no-kill shelters within each shelter type.

Note: Within the 2020 national dataset, there are five rescue organizations (groups with hours open to the public but without a brick-and-mortar facility) that hold government contracts, of which three are no-kill.

The communities in which dogs and cats are most vulnerable tend to be those where people are the most vulnerable. The Social Vulnerability Index 8 (SVI), developed by the U.S. Centers for Disease Control and Prevention to measure a community’s ability to respond to stresses and disasters, indicates that 28.8% of the country’s human population lives in high vulnerability counties (SVI percentile of 70 or greater). These communities make up 49.8% of the U.S. shelter lifesaving gap. The pets who are needlessly dying are more likely to be in communities where people have less access to critical quality-of-life services and are more at risk to the impact of economic crises and natural disasters.

Sheltering services within high vulnerability communities contrast with low vulnerability communities (SVI percentile of 30 or below) in several key ways (see Figure 8). Even though more dogs and cats enter shelters in high vulnerability communities as strays, fewer are being adopted back into these communities or returned to their owners. A lower rate of owner surrender in high vulnerability communities further highlights that not everyone is engaged in the sheltering process in the same way.

Overlaying demographics9 with shelter data highlights more specific differences between communities with the most pets needlessly dying in shelters and the rest of the country (see Figure 9). The communities with the largest lifesaving gaps skew more toward Hispanic populations, Spanish speakers and those living in poverty. Given the significant link between human vulnerability and pet vulnerability, this data shows that the animal welfare industry must understand both the human elements associated with pets in need and the importance of including all people in sheltering practices.

Reaching no-kill in every shelter and every community will require particular focus on those that have been systemically left behind in services that extend far beyond animal sheltering. Policies and programs cannot further the marginalization of people in these communities if we wish for their help in reducing the country’s lifesaving gap. Taking steps toward inclusion will save the lives of dogs and cats from these underleveraged communities.

Undoubtedly, the COVID-19 pandemic dramatically impacted the sharp reduction in intake and unnecessary deaths in shelters in 2020. It would be easy to assume that once the pandemic abates, intake and deaths will revert to pre-pandemic levels. This may be an overly cynical view.

Because of the dramatic disruptions caused by the pandemic, many shelters implemented changes that will likely have sustained impact on shelter populations. One of the changes is appointment based managed intake programs, which could result in a permanent reduction in pets entering shelters. And when shelters called on their communities for help, community members stepped up to foster pets. Shelters became more innovative and found new ways to connect pets with adopters in their communities. In many shelters, leadership realized that they had more ability and support for changing shelter policies than they previously thought.

While reopening the country after the pandemic will likely lead to an increase in pets coming into shelters, Best Friends does not anticipate a return to 2019 numbers, since many of the new programs and forms of community support will remain fixtures in the shelter environment for years to come. And while we are forecasting an increase in shelter intake for 2021 (and that forecast will remain well below 2019 numbers), we are also forecasting a continued reduction in animals unnecessarily dying in shelters.

The basis for analyzing sheltering trends is data collected from 1,373 brick-and-mortar shelters that reported data consistently over the past five years (2016–2020). This sample makes up 53.9% of the national intake in the 2020 national dataset and is used to isolate true trends by excluding changes in data collection and estimation rates. The resulting trends could then be used to provide overall insight and differences observed by organization type, region and species. It is important to note that Best Friends Network partners are over-represented in this sample at 52.3% of all shelters, compared to 35.3% in the national dataset.

A total of 1,131 organizations in this sample have year-over-year dog data and 1,101 have year-over-year cat data. These two samples serve as the basis for species-level findings. (See Table 4.) Because of limitations in shelter reporting, smaller samples are used to analyze trends in intake and outcome subtypes. The general subtype sample of five years of data for all species is 754 organizations, with 675 specifically for dogs and 695 for cats.

Between 2016 and 2020, intake decreased by 32.2%. The largest drop in this sample occurred in 2020 with a decrease of 26.3% from 2019. Intake has fallen since 2016 for all regions, ranging from 36.5% in the Northeast to 17.0% in the Great Plains region. (See Table 5.)

Live Outcomes

Live outcomes, down 13.3% from 2016 to 2020, were driven down by lower rates of intake (see Figure 13). All regions of the country had lower live  outcomes in 2020 than in 2016, ranging from a decrease of 22.3% in the Northeast to 2.9% in the Great Plains.

Live outcomes have declined in aggregate volume since 2016, with the exception of return-to-field (see Figure 14). As a percentage of intake, however, all live outcomes grew over the past five years.

The lifesaving gap has declined more dramatically than live outcomes, falling 73.5% since 2016 and 50.7% since 2019. (See Figure 16.) Seven of the eight regions have had decreases in lifesaving gap of at least 70% since 2016, led by the Pacific region at 78.4%. The Great Plains region had the smallest decrease at 52.2%.

Municipal shelters experienced the greatest change in 2020. Fewer dogs and cats entering these government facilities led to an expected drop in live outcomes. The most important data point, however, is that this reduction in intake helped to drive a 53.6% reduction in the number of dogs and cats needlessly dying in municipal shelters.

Private shelters with government contracts had lifesaving improvements closer to those of municipal shelters than private shelters without government contracts.

Analysis of the 2020 dataset shows the magnitude of changes in U.S. sheltering during the COVID-19 pandemic. Changes in intake policy and services became a reality for many shelters during the pandemic.

The intake decrease of 20.5%, the most significant change in intake since Best Friends has been tracking U.S. shelter data, led to dramatically different levels of outcomes. The national lifesaving gap fell from 625,400 to 346,622, a drop of 44.6%. As a result of fewer dogs and cats in shelters, live outcomes dropped 20.6%, with adoptions 19.6% lower than in 2019. All of this combined into a national aggregate save rate increase of 4 percentage points, from 79.0% to 83.0%. The lifesaving emphasis on cats has not changed because more than two cats to every dog (68.4%) are unnecessarily dying in U.S. shelters.

It is the hope of Best Friends that this report will help shelters, policymakers, animal welfare advocates and other stakeholders identify and implement lifesaving programs to save more pets in their communities. Great progress was made in 2020, but there are many dogs and cats who still need to be saved.

The descriptions and definitions provided here are in keeping with Best Friends’ commitment to transparency and desire for those in animal welfare and public policy to better understand the data.

What it means for a shelter, county, state or nation to be no-kill 9

A no-kill community acts on the belief that every dog and cat deserves to live — and focuses on saving lives through pet adoption, transfer and transport, trap-neuter-return, return-to-field and other community support programs. While achieving a certain percentage of lives saved is not the goal, a quantitative benchmark can help guide lifesaving efforts. Saving 90% or more of the dogs and cats who enter shelters is the current benchmark for no-kill.

For a community to be considered no-kill, each of the brick-and-mortar animal shelters located in or servicing that community must be at a 90% save rate or higher for the animals in their care (i.e., dogs and cats combined). “Community” is defined as a U.S. county. In addition, Best Friends must have access to the data for each of these brick-and-mortar shelters.

While the 90% benchmark offers a meaningful measurement by which to gauge the progress of shelters and communities, we recognize that there may be special circumstances in which a community could be successfully implementing no-kill principles and practices but not reach a 90% save rate. These instances are uncommon and are evaluated on an individual basis to determine whether a no-kill exemption is appropriate. 

Data sources in Best Friends’ 2020 dataset

Data is compiled from all brick-and-mortar shelters for which calendar year 2020 data was available (or CY 2019 where CY 2020 wasn’t provided, and likewise using CY 2018 data where more recent data wasn’t available). Data fields are collected consistent with the Basic Data Matrix industry standard 11 and include the following for dogs and cats (data for other species is not collected at this time):

• Live intake: stray/at-large, relinquished by owner, owner-requested euthanasia, transferred in from an agency or other intakes
• Outcomes: adoption, returned to owner, transferred to another agency, returned to field (for community cats), other live outcome, died in care, lost in care, shelter euthanasia or owner requested euthanasia

The master shelter list is an evolving list because shelters open and close all the time. It was compiled through manual research, state by state and county by county, to find all organizations with a physical location that admit and house dogs and cats. Additional research was then conducted to identify the service area coverage for all organizations.

Sources for shelter data include public websites, government-provided data and public records requests, voluntary data submissions, and Shelter Animals Count (SAC) data that is self-reported by an organization that has opted in to a Best Friends–led coalition. Organizations that operate multiple locations or shelters may choose to report their data in aggregate or broken down by location. Their information is presented on the pet lifesaving dashboard (bestfriends.org/2025) as it is reported to us. For SAC-derived data, SAC specifically disclaims all responsibility for any analysis, interpretations, conclusions and opinions contained in the information presented in the map. While Best Friends attempts to validate data sources, we cannot guarantee the accuracy of these sources.

Each data record represents a geographical area, usually an area circumscribed by the county/FIPS code, the unique official code given to each county by the U.S. Census Bureau. This data was collected through the sources listed above and may or may not have included all shelters in that geographical area. Statewide datasets were compiled from these county-level records.

Data from counties for which all shelter data could be collected was used to develop a regional per capita rate for intake and lifesaving gap. To account for counties where data was incomplete or missing entirely, a conservative estimation factor was then applied to the known data, thereby minimizing the likelihood of underestimating the national lifesaving gap. The estimation methodology was formulated by the Best Friends business intelligence and strategy team in consultation with an outside research advisor and economist. Using the master shelter list, this rate was applied to areas lacking complete shelter data as follows:

• If there are no brick-and-mortar shelters in the master shelter list, the county was considered a non-service area and no estimation factor was applied;
• If none of the shelters in a county have verified data, the estimation factor was used for the entire county; and
• If some (but not all) shelter data is known for a county, a modified estimation factor was added to the known data to account for the unknown data.

Gross intake is the total number of live intakes at a shelter and is used for shelter-level data. Net intake, or total intake minus transfers, is used for any level of aggregation beyond the shelter level as a way to avoid the double-counting of transferred dogs and cats.

How save rates are calculated 12

For all individual shelters, a gross save-rate calculation is used:

(Live Intakes – Died in Care – Lost in Care – Shelter Euthanasia – Owner-Requested Euthanasia) / (Live Intakes)

At the state and national levels, a net save-rate calculation is used because it is important to remove transfers between agencies, thereby avoiding the double-counting of these animals:

(Live Intakes – Transfers In – Died in Care – Lost in Care – Shelter Euthanasia – Owner-Requested Euthanasia) / (Live Intakes – Transfers In)

Shelter outcomes in which dogs and cats are not leaving the shelter alive include died in care, lost in care, shelter euthanasia and owner-requested euthanasia. In recognition of the no-kill benchmark of a 90% save rate, Best Friends uses the following calculation to determine the lifesaving gap in a shelter:

(Died in Care + Lost in Care + Shelter Euthanasia + Owner-Requested Euthanasia) – (0.10 × Gross Live Intake)

The total lifesaving gap of a county, state or nation is the sum total of lifesaving gaps of individual shelters.

For 2020, Best Friends has data from 3,330 brick-and-mortar shelters nationwide, 76% of the total number of those identified in our master shelter list. Because our data collection process focuses on high-volume and municipal shelters, the combined intake of collected data is much higher — estimated to account for 93% of total nationwide shelter intake. The estimation process described above was used for the remaining shelters and jurisdictions where data was unavailable, representing an estimated 7% of nationwide shelter intake.

Janice Dankert , statistics specialist, business intelligence and strategy

Jon Davis , manager of analytics, business intelligence and strategy

DeVon Farago , manager of statistics and insights, business intelligence and strategy

Bethany Heins , director of strategy and network operations

Samantha Hill , data analytics specialist, business intelligence and strategy

Vicki Kilmer , director of business intelligence and strategy

Stephanie Macgill , director of development strategy and foundation partnerships

Joani Ross , research coordinator, business intelligence and strategy

Kayla Sebo , manager of network communications

Brent Toellner , senior director of national programs

Peter Wolf , analyst, legislation and advocacy

Lifesaving Gap Ranked by State

Save Rate Bands Distribution in 2020

Organizations in the 2020 national dataset were grouped by their save rates (gross). Outside of the 90%+ save rate band (which accounts for 62.4% of all collected data organizations), the nearly-no-kill save rate band (85% to 89.9%) has the highest percentage of collected organizations (11.2%).

Government animal services organizations account for the majority of shelters in the collected dataset (56.9%) and 76.1% of non-no-kill shelters. Government animal services organizations also account for a disproportionate percentage of organizations in save rate bands of less than 80%, while shelters with a government contract are more concentrated in the higher-than-80% bands.

Shelters with save rates of less than 50% skew to the rural end of the urban/rural spectrum, while shelters in the 80% to 89.9% bands skew more to the urban end.

The State of U.S. Animal Sheltering, 2019 

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• Open access
• Published: 21 January 2022

The impact of returning a pet to the shelter on future animal adoptions

• Lauren Powell 1 ,
• Chelsea L. Reinhard 1 ,
• Donya Satriale 2 ,
• Margaret Morris 2 ,
• James Serpell 1   na1 &
• Brittany Watson 1   na1  

Scientific Reports volume  12 , Article number:  1109 ( 2022 ) Cite this article

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Metrics details

• Animal behaviour
• Human behaviour

Unsuccessful animal adoptions are stressful for many owners and may reduce their willingness to adopt again. The goal of this study was to determine the proportion of return owners who adopted post-return and investigate return characteristics that affected the likelihood of post-return adoption. We analyzed adoption records from a South Carolina animal shelter between 2015 and 2019 ( n  = 1999) using a logistic regression model including post-return adoption (binary) and return reason, species, animal sex and age. We found one in 10 individuals adopted from the shelter within 12 months of return, and post-return adoption was associated with return reason and species. Returns due to owner-related reasons, such as the owner’s health (OR 0.20, 95% CI 0.07, 0.57) or unrealistic expectations (OR 0.42, 95% CI 0.19, 0.94) were associated with significantly lower odds of post-return adoption. Owners who returned due to the animal’s health exhibited four times greater odds of post-return adoption compared with behavioral returns (OR 4.20, 95% CI 2.37, 7.45). Our findings highlight the value of ensuring adopters’ expectations are aligned with the reality of ownership and minimizing adopter-animal behavioral incompatibility as unsuccessful animal adoptions can reduce the owner’s willingness to adopt again and may affect the adopter’s relationship with the shelter.

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Introduction

Pet ownership is popular in the United States with 57% of U.S. households estimated to own a pet. Dogs are the most common companion animal and can be found in 38% of U.S. households, while 25% of U.S. households are cat owners 1 . Approximately 3.2 million animals are adopted from animal shelters each year, and recent reports suggest adoption from an animal shelter is typically the preferred method of pet acquisition among prospective owners 2 , 3 , 4 . Garrison and Weiss 4 found more than 80% of prospective dog owners considered acquiring their dog from a shelter. However, animals are returned to the shelter following adoption in 7% to 20% of all adoptions 5 , 6 , 7 , 8 , 9 , 10 , 11 . Returns occur for a variety of reasons, although animal behavior has been consistently documented as a primary cause of return for both dogs and cats 6 , 7 , 8 , 11 , 12 . Incompatibility with existing pets and owner’s health concerns, particularly allergies among cat adopters, also lead to a significant number of returned adoptions 11 , 12 , 13 .

A considerable body of research has investigated the effects of the shelter environment on animal health and wellbeing 14 , 15 , 16 , 17 , 18 , but the effects of pet relinquishment on owner wellbeing has received less scientific attention. In a study of relinquishing owners, DiGiacomo et al. 19 reported all participants found the decision to surrender their pet very difficult. In the only study to date to investigate owners’ perceptions of returning a newly adopted animal, Shore 20 found most owners thought the experience was very difficult. Forty-one percent of returning owners indicated they would not adopt a pet in the future and a further 13% were unsure whether they would adopt again 20 . These data suggest unsuccessful animal adoptions may detrimentally affect individuals’ desire to own a companion animal in the future, although no empirical evidence exists to support this hypothesis.

The unsuccessful animal adoption experience is likely to vary for each human-animal dyad which could differentially affect the likelihood of owners adopting again post-return. For example, Shore 20 found that some returning owners indicated they would not adopt again—e.g., one adopter whose child was allergic to the pet—while others indicated they would adopt a different animal in the future, such as an adopter whose landlord had a pet weight limit which prevented her from keeping the pet 20 . To our knowledge, the effect of return characteristics on the odds of post-return adoptions has not yet been investigated. The aims of this study were to investigate the rate of post-return adoptions at a large animal shelter in the Southeastern United States, and to determine whether characteristics of unsuccessful animal adoptions affect the likelihood of post-return adoptions.

A 5-year retrospective analysis of adoption records from Charleston Animal Society (South Carolina, USA) showed 1999 owners returned animals to the shelter following adoption, giving an overall return rate of 9.2% 11 . The vast majority of individuals returned one animal ( n  = 1899, 95.0%), although 4.4% returned two animals ( n  = 88), 10 adopters returned three animals and two adopters returned four animals. Individuals who returned more than one animal during the study period were excluded from further analyses ( n  = 100). Cases where more than two animals were adopted prior to return were also excluded from the logistic regression models ( n  = 214). The returned animals included 1486 dogs, 402 cats, nine rabbits and two barnyard animals. The mean length of ownership was 8.68 days (SD 16.70, Fig.  1 ).

Length of adoption (days) for animals returned within 1 month ( n  = 1666).

The characteristics of the returned animals, the reasons for return and the animals’ outcomes post-return have been described in detail elsewhere 11 . In brief, most returned animals were adults (38.2%), 28.6% were young adults, 14.1% were under the age of 6 months and 3.8% were seniors. The majority of returns were also male (54.9%). Behavior was the most common return reason accounting for 32.8% of returns, followed by incompatibility with existing pets at 21.5% and owner circumstances at 11.2%. Return reasons differed between dogs and cats ( X 2  = 138.54, p < 0.001). Post-hoc analyses using standardized residuals showed dogs were returned more frequently than cats for behavior (36.1%) and housing issues (11.3%), and cats were returned more due to the health of the owner (17.8%) and the health of the animal (9.8%). The detailed return reasons are provided in Table 1 .

Post-return adoptions

10.5% of return owners adopted a new animal following return, including 144 individuals who returned dogs, 65 individuals who returned cats and one individual who returned a rabbit. The median length of time between return and post-adoption return was 3.2 months (Fig.  2 ), with no difference between cat and dog returns ( t (207) = 1.24, p  = 0.22). The adopted animals included 107 dogs, 101 cats, one rabbit, and one guinea pig.

Months between return and adoption post-return for all owners who adopted a new animal during the study period ( n  = 210).

A binary logistic regression model revealed the likelihood of post-return adoption within 12 months was associated with species (including cats and dogs only). Individuals who returned cats were 2.45 times more likely to adopt a new animal compared with individuals who returned dogs (OR 2.45, 95% CI 1.62, 3.72). The reasons for return were also associated with the likelihood of post-return adoption. Owners who returned animals due to the animal’s health were four times more likely to adopt a new animal compared with owners who returned animals for behavioral reasons (OR 4.20, 95% CI 2.37, 7.45). Owners who returned animals due to their personal health or circumstances were 80% and 59% less likely to adopt another animal compared with owners who returned animals for behavioral reasons (OR 0.20, 95% CI 0.07, 0.57 and OR 0.41, 95% CI 0.20, 0.82). ‘Unrealistic expectations’ and ‘not compatible with pets’ were also associated with decreased odds of post-return adoption compared with returns due to behavioral reasons (OR 0.42, 95% CI 0.19, 0.94, and OR 0.61, 95% CI 0.38, 0.98). Returns due to incompatibility with the owner or children, housing issues, or ‘unwanted’ were not associated with the likelihood of post-return adoption ( p   ≥0.14). Length of stay in the home ( p  = 0.89) and the sex ( p  = 0.88) of the returned animal did not predict the odds of post-return adoption. The likelihood of post-return adoption was also not significantly associated with the returned animal’s age group, although there was a trend towards an increased likelihood of post-return adoption among adult animals (OR 1.61, 95% CI 0.95, 2.72, p  = 0.08).

Characteristics of post-return adoptions

Most owners adopted the same species post-return (75.7%, n  = 159), but 24.3% adopted a different species ( n  = 51). Of those who adopted a different species, 84.3% adopted a dog initially and then adopted a cat post-return ( n  = 43), whereas 13.7% adopted a cat initially and then adopted a dog post-return ( n  = 7). One individual returned a dog and then adopted a rabbit. Considering individuals who adopted the same species post-return, almost half adopted an animal of a different sex post-return (43.4%, n  = 69), although the direction of change was split. Thirty-two owners returned a female and then adopted a male animal post-return, and 37 owners returned a male and then adopted a female post-return. Most returning owners adopted from the same age group post-return (84.8%, n  = 178). All owners who adopted an animal of a different age group chose an older animal post-return ( n  = 32).

To date, the impact of unsuccessful animal adoptions on the likelihood of owners adopting a new animal post-return has not been established. At this large animal shelter in the Southeastern United States, one in 10 individuals adopted a new animal from the shelter post-return suggesting the unsuccessful adoption experience decreased adopters’ desire to acquire another pet from the shelter. The majority of individuals who adopted again did not change their animal preferences post-return with the exception of sex, whereby half of returning owners adopted an animal of a different sex following return. We also found one quarter of individuals adopted a different species following return, most of whom returned a dog and adopted a cat. It is possible that individuals who returned dogs were more inclined to adopt cats post-return due to the lower perceived costs and responsibilities of cat ownership compared with dog ownership 21 .

The reasons for return had a significant influence on the likelihood of future adoptions. Owners who returned animals due to the animal’s health were four times more likely to adopt post-return compared with owners who returned animals for behavioral issues. Behavior problems have been associated with greater ownership costs 22 , 23 , 24 , decreased ownership satisfaction 23 , decreased human-animal attachment 25 and poorer mental wellbeing among owners 24 , 25 , 26 . In contrast, compatibility between owners and their pets on key behavioral characteristics, such as enjoying exercise and getting along with peers, has been associated with greater ownership satisfaction and happiness, and decreased stress 27 , 28 . Research also indicates that caring for an animal with medical needs can increase stress and anxiety and reduce owners’ quality of life 29 , 30 , 31 . It is therefore interesting that returns due to animal behavior had such a detrimental effect on the likelihood of future adoptions compared with medical concerns. It seems a mismatch between the animal’s behavioral needs and the owner’s willingness to tolerate behavioral issues could damage the adopter’s long-term relationship with the shelter. Considering that behavior is a leading cause of post-adoption returns 6 , 7 , 11 , 12 , it is imperative that animal shelters aim to minimize behavioral incompatibility between adopters and their animals. The efficacy of adoption counselling and adopter-animal matching programs in reducing returns is a developing area of research that warrants further attention. Preliminary evidence suggests some policies, such as only showing adopters the animals that match their needs, may be associated with reduced return rates but additional research is needed 32 .

Individuals who returned animals for owner-related reasons were considerably less likely to adopt post-return. For example, owners who returned animals due to their health or the health of their family were 80% less likely to adopt again. This finding is logical as health concerns that are exacerbated by pet ownership, such as allergies, may not improve with the introduction of a different pet. Previous research has found owners who relinquished animals due to allergies often viewed their situation as insurmountable 19 . Returns due to owners’ circumstances were also associated with a 60% reduction in the odds of future adoptions. Again, owners’ circumstances, such as personal problems or a lack of time, may not improve with the introduction of a different pet.

Returns due to unrealistic expectations for ownership were associated with a 60% reduction in the likelihood of future adoptions. Ownership satisfaction has been shown to decrease with greater perceived costs of ownership, including lifestyle, time, and financial costs 23 . Adopters who underestimated the effort involved in caring for an animal may have been dissatisfied with pet ownership and therefore, less motivated to adopt again post-return. It is also possible that some owners had unrealistic expectations for benefits attributable to pet ownership. Previous research indicates that individuals with dog ownership history (previous or current owners) are more likely to expect mental and psychosocial health benefits than prospective owners with no prior experience, possibly due to bias arising from their affection towards their previous/current dog 33 . Data regarding the influence of previous ownership history on the risk of returns is mixed. One study found adopters with previous ownership experience returned animals more frequently due to behavioral issues than first-time owners 6 , but other studies have found first-time owners are more likely to return animals 9 . A lack of data regarding adopters’ lifetime pet ownership history precluded us from investigating the role of previous pet ownership on post-return adoptions in the current study. Incompatibility with existing pets was also associated with 40% lower odds of post-return adoption, suggesting that some owners concluded they did not want to upset the current pet dynamic or that their current pet was better suited as the only pet in the household.

Individuals who returned cats were two and a half times more likely to adopt post-return compared with dog adopters. This finding may be attributable to the differences in return reasons between dogs and cats, such as the higher rate of cat returns due to the animal’s health. Cat and dog adopters may also differ in their expectations for ownership which could affect the likelihood of owners adopting post-return 9 , 34 , 35 . For instance, preliminary data suggests cat owners believe their ability to control or modify their cat’s behavior is low 36 , 37 , while most dog owners anticipate the need for training and expect to encounter some difficulties with dog behavior 33 . Cat adopters that experience undesirable behavior may believe the behavior cannot be modified and that a different cat would be better suited to their household. Alternatively, dog adopters that face difficulties with behavior may feel it is their responsibility to work with the dog to modify its behavior. If the behavior is too challenging for the adopter, resulting in the animal’s return to the shelter, the owner may question whether they have the time or resources necessary for dog ownership. It is also possible that individuals who returned dogs to the shelter may have acquired a dog from a different source post-return. Future studies might focus on the differences in adopters’ expectations of ownership based on species and their role in post-return adoptions.

Length of stay in the home was not associated with the odds of future adoptions. The mean length of ownership in this study was relatively short, and it is possible that the provision of adoption vouchers for returns within 30 days incentivized adopters to return the animal within this period. However, this does not explain the significant number of returns that occurred within the first week of ownership. It is also plausible that adopters observed the problem that led to return relatively quickly after bringing the animal home. Previous work has found half of returning adopters observed the problem that led to return immediately after adoption, and a further 17% observed the problem within the first week 20 . The short length of ownership may have limited differences in the strength of the human-animal bond, perhaps reducing any impact of length of ownership on the likelihood of readoption. We also found the sex of the returned animal was not associated with the likelihood of post-return adoption.

The findings presented in this study are subject to several limitations. Firstly, the data reflect a single animal shelter and research across multiple facilities is needed to confirm our findings. The retrospective nature of the study also necessitates cautious interpretation. For example, we could not establish how many owners, if any, had acquired pets through alternative sources, so the true rate of pet acquisition following unsuccessful animal adoptions may be significantly higher than suggested here. Nevertheless, our findings speak to the importance of the unsuccessful adoption experience on individuals’ willingness to adopt from the same animal shelter and indicate potential long-term effects of returns on the shelter-adopter relationship. The retrospective design also resulted in a reliance on owner-reported return reasons which may be subject to bias or inaccuracies 38 , 39 . For example, returns due to behavioral issues are likely affected by the owner’s understanding of animal behavior, and previous research suggests owners’ ability to recognize animal behavior is poor 40 , 41 , 42 . However, given that this study focused on the owner-related effects of unsuccessful animal adoptions, the owner’s perceived return reason is important irrespective of the accuracy of the reason itself.

At this large animal shelter in the Southeastern United States, one in 10 returning owners acquired an animal from the shelter following return. The likelihood of post-return adoption was associated with the reasons for return and species. Individuals who returned animals for owner-related reasons were significantly less likely to adopt post-return compared with owners who returned due to animal-related reasons. However, owners who returned animals due to the animal’s health were four times more likely to adopt post-return compared with those who returned them for behavioral reasons. The experience of an unsuccessful animal adoption appears to suppress an individual’s desire to adopt again, particularly when returns occur due to owner-related reasons or animal behavior. Future, prospective studies might investigate the use of alternative acquisition sources post-return and elucidate possible differences in the experiences of people who adopt again and those who do not.

Shelter characteristics

Charleston Animal Society is a large, open admission animal shelter in South Carolina, United States. Between 2015 and 2019, the shelter’s total live intake included 17,664 dogs and 23,525 cats. Most animals entered the shelter as strays, including 72% of dogs and 89% of cats. Charleston Animal Society employs an open adoption policy that aims to create a trusting and communicative relationship with adopters. The shelter encourages all adopters to return their animal/s to the shelter if necessary and provides a refund voucher for future adoptions if the animal is returned within 30 days (excluding animals adopted during fee-waived promotions). Charleston Animal Society also offers post-adoption support in the form of a free veterinary appointment at a local veterinary clinic and free behavioral support. The shelter’s behavior team conduct follow-up calls for animals with known behavioral problems in the shelter where possible, and adopters have the opportunity to contact the shelter to request behavioral advice and support if needed. The study was determined exempt from review by the University of Pennsylvania Institutional Review Board (protocol number 84837). The study was carried out in accordance with relevant guidelines and regulations.

Data records and variables

Data from individuals who adopted and returned an animal to the Charleston Animal Society between 1st January 2015 and 31st December 2019 were included in the study ( n  = 2073). Cases were excluded if the person’s ID number differed between the adoption and the return ( n  = 74). Data were downloaded from the shelter’s electronic records (PetPoint Data Management System, Version 5, Pethealth Software Solutions Inc., USA) and the following variables were extracted: animal species, sex, known/estimated date of birth, adoption date/s, return date/s and reason/s for return.

Animal shelter staff recorded a single return reason in PetPoint at the time of return. Researchers then categorized the return reasons into the following groups: behavior, owner circumstances, health of owner, health of animal, housing, not compatible with children, not compatible with pets, not compatible with owner, unrealistic expectations, unwanted and other (Table 1 ).

The animal’s age at adoption was calculated as the number of months between the known/estimated date of birth and the date of adoption. Adoption age was then categorized as puppy/kitten (< 6 months), young adult (> 6 months–2 years), adult (> 2–8 years) and senior (> 8 years). Length of stay in the home was calculated as the number of days between the adoption date and the return date. For owners who adopted post-return, the time between return and post-return adoption was calculated as the number of days between the return date and the second animal’s adoption date. A binary variable was created to compare individuals who adopted within 12 months of return and those who did not adopt within 12 months. Twelve months was chosen as the cut-off value as this captured most individuals who adopted post-return while excluding outliers that may have adopted years later.

Statistical analysis

All statistical analyses were conducted in IBM SPSS Statistics for Windows, version 24. A Pearson’s Chi-Square test was used to compare return reasons, and an independent t-test was used to compare the time between return and post-return adoption between dogs and cats. A binary logistic regression model was used to investigate the associations between post-return adoption (within 12 months) and the reasons for return, species (cats and dogs only), sex, length of stay in the home and age group of the returned animal. Returns due to ‘insurance restrictions’ ( n  = 1) or ‘abandoned by owner’ ( n  = 1) were excluded from the model due to the low number of cases. Statistical significance was set at p < 0.05.

Data availability

The data governance arrangements for the study do not allow us to redistribute Charleston Animal Society data to other parties.

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Acknowledgements

We thank Charleston Animal Society for providing their support, expertise, and data throughout this study. We also thank the Bernice Barbour Foundation for their support of Penn Vet Shelter Medicine Program faculty and the Arnall Family Foundation for their support of the program post-doctoral researcher.

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These authors jointly supervised this work: James Serpell and Brittany Watson.

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School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA

Lauren Powell, Chelsea L. Reinhard, James Serpell & Brittany Watson

Charleston Animal Society, North Charleston, SC, USA

Donya Satriale & Margaret Morris

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L.P., C.L.R., B. W. and J.S. conceived and designed the study. D.S. and M.M. provided the original data. L.P. extracted the data, performed the data analyses, and drafted the manuscript. All authors contributed to manuscript revision, read and approved the submitted manuscript.

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Correspondence to Lauren Powell .

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Competing interests.

D.S. and M.M. are paid employees of Charleston Animal Society. Charleston Animal Society did not fund this research or contribute to the study design, data analysis or initial drafting of the manuscript. Salary for C.L.R. was provided by grant funding from the Bernice Barbour Foundation, and salary for L.P. was provided by grant funding from the Arnall Family Foundation. The other authors declare no other competing interests.

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Powell, L., Reinhard, C.L., Satriale, D. et al. The impact of returning a pet to the shelter on future animal adoptions. Sci Rep 12 , 1109 (2022). https://doi.org/10.1038/s41598-022-05101-5

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Rescue groups begin work to rehome 4,000 beagles bred for research

Jaclyn Diaz

Amanda Andrade-Rhoades

Sue Bell holds one of more than a dozen beagles that arrived at the headquarters of animal rescue group Homeward Trails in Fairfax Station, Va., on Thursday, while posing for a portrait. The dogs were a small portion of the roughly 4,000 beagles rescued from a research facility where the conditions were found to be inhumane. Amanda Andrade-Rhoades for NPR hide caption

Sue Bell holds one of more than a dozen beagles that arrived at the headquarters of animal rescue group Homeward Trails in Fairfax Station, Va., on Thursday, while posing for a portrait. The dogs were a small portion of the roughly 4,000 beagles rescued from a research facility where the conditions were found to be inhumane.

The first group of the roughly 4,000 beagles in the custody of a research facility in Virginia have been brought to their new, temporary homes.

More than a dozen beagles arrived at the facility of the Homeward Trails Animal Rescue in Virginia on Thursday. It was the first of many deliveries as hundreds of rescue groups across the country are mobilizing in the coming weeks to rehome the beagles.

Dogs explore toys and their surroundings after they arrived Thursday at animal rescue group Homeward Trails in Fairfax Station, Va. Amanda Andrade-Rhoades for NPR hide caption

Dogs explore toys and their surroundings after they arrived Thursday at animal rescue group Homeward Trails in Fairfax Station, Va.

Eva Brandan takes a beagle off of a bus after over a dozen beagles arrived at Homeward Trails. Amanda Andrade-Rhoades for NPR hide caption

Eva Brandan takes a beagle off of a bus after over a dozen beagles arrived at Homeward Trails.

Animal trainer Janice du Plessis supervises beagles exploring the outdoors after the dogs' arrival. Amanda Andrade-Rhoades for NPR hide caption

Animal trainer Janice du Plessis supervises beagles exploring the outdoors after the dogs' arrival.

The Humane Society of the United States is spearheading the effort to transfer these dogs from their current home at the Envigo facility in Cumberland, Va., to shelters. The organization has just a few weeks to get this done. Earlier this year the Envigo facility, which bred these beagles for pharmaceutical research and testing, was found to be in violation of several federal regulations. A federal judge ordered the dogs to be released within 60 days.

Staff members of Homeward Trails examine one the newly arrived beagles. Amanda Andrade-Rhoades for NPR hide caption

Staff members of Homeward Trails examine one the newly arrived beagles.

Annie Esquivel (left) and Kelsey Moncrief put a temporary collar on a dog, newly named Neema. Amanda Andrade-Rhoades for NPR hide caption

Annie Esquivel (left) and Kelsey Moncrief put a temporary collar on a dog, newly named Neema.

Animal rescue groups from Wyoming, Massachusetts, Virginia and elsewhere are working together to assist in this herculean effort.

"Finding partners who can make space and find homes for around 4,000 dogs in the summer — a time of year when animal shelters already are over-capacity — will be a feat of epic proportions," Kitty Block, president of The Humane Society of the United States, wrote on her blog.

Kindness Ranch, an animal rescue group from Wyoming, also trekked out to Virginia this week. The organization already has named the first of the more than 150 beagles they will be rescuing from Envigo: He's Uno.

Annie Esquivel carries a beagle onto the grass at Homeward Trails. Amanda Andrade-Rhoades for NPR hide caption

Annie Esquivel carries a beagle onto the grass at Homeward Trails.

One of the dogs drinks out of a kiddie pool. Amanda Andrade-Rhoades for NPR hide caption

One of the dogs drinks out of a kiddie pool.

Meghan Hobson lowers a dog onto the ground at Homeward Trails. Amanda Andrade-Rhoades for NPR hide caption

Meghan Hobson lowers a dog onto the ground at Homeward Trails.

It will take some time before these dogs will be up for adoption. The Envigo beagles must be evaluated by each of these rescue groups and vetted for any illnesses or behavioral issues before they get to their forever homes.

In the meantime, the dogs appear to be enjoying their freedom.

Grass! Grass is neat! Amanda Andrade-Rhoades for NPR hide caption

Grass! Grass is neat!

Dogs greet each after arriving. Amanda Andrade-Rhoades for NPR hide caption

Dogs greet each after arriving.

Homeward Trails shared videos of the first moments of the dogs arriving at their facility on social media. The dogs excitedly sniffed the ground, played with toys, and ran on grass for the first time.

And on their first full day of freedom the beagles were treated to a much-needed spa day, the organization posted.

"These cuties enjoyed a bubble bath and massage followed by an afternoon of frolicking in the yard, cuddle time in our Peace Out rooms, frozen kongs and cool nap time," Homeward Trails wrote. "Oh how it feels great to be FREE to be a DOG! For dogs that have spent their entire lives in a kennel, these cuties are truly embracing life and love."

A beagle sits in a corner of the animal rescue group facility. Amanda Andrade-Rhoades for NPR hide caption

A beagle sits in a corner of the animal rescue group facility.

Jessica Powers pets one of the new arrivals. Amanda Andrade-Rhoades for NPR hide caption

Jessica Powers pets one of the new arrivals.

One of the rescued beagles receives some affection. Amanda Andrade-Rhoades for NPR hide caption

One of the rescued beagles receives some affection.

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Everything we do at International Animal Rescue is built upon the best scientific and veterinarian practices. Many of our front line staff have Masters and Phds in their field, or are currently working towards them. Our projects around the world also regularly take in students looking to study the animals in our care.

As a result, International Animal Rescue has helped contribute a large amount of scientific research, some of which you can find below.

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The AVMA supports the adoption of healthy, post-study, research and teaching animals into long-term, private homes as companion animals through the use of adoption programs developed and managed by research institutions. The AVMA encourages research institution adoption programs because they can provide the individualized attention needed by each animal moving from a laboratory home into a private home. Research institution adoption programs typically fall under the purview of an oversight body (e.g., an IACUC) and must also comply with federal regulations or policies that protect the welfare and health of research animals.

The AVMA believes the following factors should be considered when developing a research institution adoption program and determining an animal's potential for adoption:

• The research institution adoption program must take into consideration all applicable federal regulations and state and local laws related to the transfer of animal ownership.
• The research institution's Attending Veterinarian, as the person responsible for the health and well-being of all laboratory animals used at the institution, must be involved with the development and oversight of the program. The involvement of the IACUC or another appropriate committee, is encouraged.
• Each adoption must require approval of the Attending Veterinarian or designee, and the Attending Veterinarian or designee must have the discretion and authority to deny adoption requests.
• Each potential adoption must include expert veterinary guidance. The animal's suitability for adoption as a companion animal should be based upon the animal's species, health status, and behavior.
• When appropriate for the animal's health and welfare, the institution should vaccinate and spay/neuter animals prior to adoption.
• Adopters should be educated about the animal's health status and other pertinent information during the review process and should be provided with a written record of the animal's health history upon transfer.
• Adopters should be willing and able to accept legal and financial responsibility, in writing, for the life-long care of the animal, including veterinary care.
• Adoption programs that collaborate with third parties (e.g., shelters) should consider whether the third party's adoption program is consistent with the institution's mission and values.

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Search form, new research points to social media as important tool for animal shelters and rescues.

NEW YORK, N.Y. (October 9, 2018)  — Today the  ASPCA ® (The American Society for the Prevention of Cruelty to Animals ® ) released the results of a  new survey * conducted by Edge Research which reveals the positive impact social media has had on the animal shelter and rescue community. According to more than 800 shelter, rescue and municipal animal agency professionals and volunteers surveyed, use of social media among this group is on the rise and communication tools like Facebook, Instagram and Twitter have helped generate increased public support and make it possible for them to save the lives of more animals in need. 

More than three quarters of respondents said their social media use has increased in the last year and 70 percent say it will continue to increase in the next year. Among the many positive impacts of social media use, those surveyed indicated it increased general awareness about their organization (86 percent), increased animal adoptions overall (66 percent) and increased adoptions of harder to place animals, like senior pets and those with medical issues (55 percent). Survey respondents also noted that with the pace of change in technology and limited staff, additional support and training would help them expand their use of social media and, hopefully, its positive impact.

With these new insights in mind, the ASPCA has focused the second year of its Adopt a Shelter Dog Month national campaign, # FindYourFido , on providing additional training and social media resources for the more than 500 shelters and rescues registered and on harnessing the power of animal advocates’ social media networks to help more animals find homes. 

“This research validates how using social media creates new opportunities for shelters, rescue organizations and communities to help homeless animals find safe and loving homes,” said Matt Bershadker, president and CEO of the ASPCA. “Through Find Your Fido and other programs, we’re committed to helping shelters and rescues take full advantage of these platforms – and encourage all animal lovers to do the same – to bring greater attention to local animals in need.” 

The #FindYourFido 2018 campaign takes place throughout October (Adopt a Shelter Dog Month) and engages animal advocates across the country to adopt and – if they can’t adopt – step into the role of digital ambassador by using their social media networks to help find homes for dogs in their communities.Profiles of adoptable dogs from participating shelters and rescues can be easily accessed through the #FindYourFido  map . Sharing information about dogs in need helps expand their exposure well beyond the shelter/rescue walls and increases their chances of finding a good match.

This year, the ASPCA has also enhanced its resources and trainings on social media tactics and best practices in collaboration with experts from Facebook and The Dodo. The Dodo serves as the official media content partner for the ASPCA’s #FindYourFido campaign and will be producing content around adoptable dogs across their website and social media channels. 

The ASPCA is also proud to have the support of networking app  Bumble  for #FindYourFido 2018. 

You can learn more at our website  www.aspca.org/helpfido  or search the hashtag #FindYourFido on any social media platform.

Survey Methodology : The results of the survey commissioned by the ASPCA and conducted by Edge Research among their ASPCApro’s list of nearly 64,000 shelter, rescue, and municipal animal agency professionals and volunteers are  available here . The survey included 827 staff and volunteers from those organizations.  

About the ASPCA ® Founded in 1866, the ASPCA ® (The American Society for the Prevention of Cruelty to Animals ® ) was the first animal welfare organization to be established in North America and today serves as the nation’s leading voice for vulnerable and victimized animals. As a 501(c)(3) not-for-profit corporation with more than two million supporters nationwide, the ASPCA is committed to preventing cruelty to dogs, cats, equines, and farm animals throughout the United States. The ASPCA assists animals in need through on-the-ground disaster and cruelty interventions, behavioral rehabilitation, animal placement, legal and legislative advocacy, and the advancement of the sheltering and veterinary community through research, training, and resources. For more information, visit www.ASPCA.org , and follow the ASPCA on Facebook , X , Instagram , and TikTok .

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What Do Dogs Rescued From Research Laboratories Really Need?

It takes a lot of hard work to successfully rehome dogs who have been abused..

Posted March 21, 2019

When researchers say they're going to try to rehome dogs it has to be more than a "feel good" move because it takes a lot of hard work and resources

Recently, many people learned about a horrific experiment in which beagles were force-fed a pesticide to see how they would react to it. It was being conducted in a laboratory in Michigan for a company in Brazil. These sorts of studies were deemed unnecessary by the EPA, but they're still required in Brazil. (See " Why Are Beagles Being Poisoned and Killed? ") Many people worldwide were offended and on March 18 the Michigan company terminated them and released a statement indicating that the dogs would be rehomed . Part of it read, "We've been working to refine, reduce, & replace animal tests for years. Today we’re pleased to announce our efforts resulted in a waiver & we can stop the study. We’ll make every effort to rehome the animals."

Immediately after I posted this information, Vivian Zottola , a Certified Professional Dog Trainer and Certified Behavior Consultant, contacted me because she was deeply concerned that many people might not be aware of how much dedication and money it can take to rehome a former research dog. While we both are extremely glad that this experiment has ended and that the company "will make every effort to rehome the animals," these dogs and other nonhuman animals (animals) who are subjected to horrifically cruel treatment are most likely going to need a lot of care when they finally find what we hope will be a "forever home." Vivian runs a private practice as a canine behavior consultant and training professional. She collaborates with DVMs and DVM Behaviorists in the Boston area working with reactive dogs due to fear , anxiety , and stress . Vivian also is a research assistant for the Center for Canine Behavior Studies and enrolled in the Anthrozoology graduate program at Canisius College .

I wanted to know more about the details of rehoming former research dogs, so I asked Vivian if she could answer a few questions about her concerns Gladly she said she could. Our interview went as follows.

In your first note to me you wrote, "While I am happy to learn this news, I’m hopeful they will pay for each dog to have a proper behavior evaluation and any behavior modification training including, if necessary, medication to help reduce the risk of future suffering." Can you please tell readers more about what these beagles are going to need based on your experience working with animals who have been previously abused?

There is this preconceived notion the act of rehoming an abused nonhuman animal in and of itself equates to improved welfare when, in fact, it’s really only marginally improved, if at all, for these individuals. We need to consider the experience from their perspective and adhere to long term welfare considerations if we truly wish to help these dogs. Consider, for example, that these dogs have been psychologically and physically traumatized and will require mental health rehabilitation. Love, trust, and care are important, however, this is not enough. We don’t know the history of the dogs, for example, if they were born in captivity in a laboratory and living isolated lives, born in commercial breeding establishments, or if they are rescues. The first few months are critical for canine brain development and unnatural conditions do affect behavior. Regardless of where these dogs were sourced from, they endured physical and psychological abuse . Yes, dogs are resilient , however, some may have developed pathologies from the experience or been predisposed to develop them. I don’t know enough about how these dogs were bred or about how long they were tortured. However, the fact is they were abused, suffered, and most likely have cPTSD from this experience.

Certainly, getting these animals out of that horrific environment is an improvement, however, the message I’m trying to convey is while a loving, safe and patient human home is extremely essential, it is not nearly enough. I’ve worked with many rescue dogs over the years and while some are able to habituate, others don’t. Many people rehome these (and shelter) dogs without having them evaluated by the right professionals only to have them continue to suffer from fear/anxiety/stress all the while thinking they will get better over time. In reality, unknowingly the dogs' suffering is prolonged. We need to question animal welfare post- adoption . The act of rehoming is not enough.

Can you provide some specific details about what a person who rehomes dogs like this will have to do and endure? And, what should they be told when they make this wonderful decision?

Sure, I’ll start with making the decision. Making the decision is the most important part of adopting one of these special dogs. And, while reasons vary for each new human caretaker , careful consideration of a few key points will impact long term improved welfare for both the dog and the human in the relationship. Acquiring a dog is an important responsibility and always a two-way street, so to speak. Often people jump in with good intentions only to find months later they are overwhelmed because of unforeseen and sometimes debilitating behavior challenges. So, while intentions to help one of these amazing individuals is good, it’s best to be realistic about expectations upfront so as to avoid potential emotional harm to either dogs or humans. It's important to understand the full scope of considerations starting with why it is that you wish to rehome one of these dogs (go beyond the obvious). It's also important to evaluate your schedule, lifestyle, and home environment. For example, will you have time, patience, and emotional and financial ability to care for the dog(s)?

Welfare considerations may include long term veterinary care and behavior modification training that may necessitate the use of medications. Many people have biases about using medications and this only serves to prolong the animals' suffering. I have seen it happen far too many times. Other considerations include grooming, feeding, providing a stimulating or calm environment depending on the individual dog, participating in mental and physical exercise, having the time to do these and other things a dog might need, and giving them a lot of focused attention . Asking yourself really tough questions upfront is especially important for any rescue dog, since it will reduce the risk of potential injury (psychological and physical), suffering, and most important recidivism, the risk of surrendering back to a shelter.

There is ample evidence the mental health of dogs formally used in commercial breeding establishments, laboratories, and shelters are compromised to some degree. No longitudinal observational studies have been conducted for dogs housed in laboratories, however, there are short term studies. Dogs rehomed from rescues, laboratories, and commercial breeding typically present fearfulness, hyper-vigilance, sound sensitivity, and separation anxiety. These dogs have also demonstrated trouble learning and present learned helplessness that is often interpreted by owners as boredom or laziness. ( Learned helplessness is " behavior that occurs when the subject endures repeatedly painful or otherwise aversive stimuli which it is unable to escape from or avoid . After such experiences, the organism often fails to learn or accept 'escape' or 'avoidance' in new situations where such behavior is likely to be effective. In other words, the organism learned that it is helpless.") Dogs, like us, experience emotions and moods and unfortunately moods are more difficult to detect in individuals such as dogs who don’t use words to communicate. So, expect some level of behavior challenges and reach out for help sooner rather than later. [This is another reason why it's essential for humans who choose to live with dogs become fluent in dog, or dog literate .]

If you decide to pursue acquisition of a former research dog, make sure to hire a kind and qualified professional who doesn't use pain, fear, or intimidation when engaging with your dog. This goes for veterinarians, trainers, groomers, dog walkers, and daycare facilities. Rescue dogs (and of course all dogs) need to engage with humans who will be kind and patient. Your job is to ensure excellent welfare for the animal now under your care, and this means all humans should engage in ways that are free of pain, force, and intimidation of any type, including verbal discipline and equipment (electronic shock, prong, choke collars). Ample studies show the use of aversive equipment and methods increases phobias and aggressive behaviors in dogs, and its best to stay clear of any type of aversive discipline when teaching.

It's also important to make an appointment for a physical and psychological evaluation when you first acquire the dog, and through your first year of living together, periodically meet with this professional to ensure you’re on track. Most dog owners are not versed in identifying potential behavior challenges early on and may not be aware of prevention strategies. The more you understand canine ethology the better your relationship. Also, hire a qualified behavior consultant (advanced training certification) to evaluate your dog in your home and teach you how to hone your observational skills that will help manage expectations for both you and your dog. Meet with them early on in the relationship, surely during the first few months of acquisition. These specially trained individuals will teach you about the “world of the dog” including husbandry; canine ethology, including canine stress signals and body language ; canine psychological, physiological, and social development; antecedent management and desensitization/counter conditioning strategies. They will work with you outside your home and counsel you on walking equipment as well as environmental enrichment strategies. To find qualified professionals see the Certification Council For Professional Dog Trainers and the Internation Association of Animal Behavior Consultants . While these individuals have signed ethical statements, gone through vigorous testing, and approach training using the least intrusive methods with nonhuman animal, remember that dog training is still an unregulated profession . Also, note that while Dr. Google is convenient, there is a lot of bad information out there. Stick with information from canine scientists, Certified Applied Animal Behaviorists (CAABs), and DVM behaviorists.

On average, dog owners report unwanted behaviors and seek professional help anywhere between the first day and up to six months from acquisition. In addition to seeking a kind behavior modification trainer, you’ll want to meet with a veterinarian to ensure there are no health risks. The dog may experience underlying pain from the laboratory housing that may manifest into behavior challenges as well. If possible, seek out a veterinary practice in your area that is American Animal Hospital Association (AAHA) approved, or better yet, an AAHA approved Fear Free Clinic . These practices are staffed with specially trained veterinary technicians and veterinarians who can identify small behavior challenges. They will be able to flag potential problems and most importantly provide consent-based diagnostic testing instead of forcing procedures on the animals. If they identify potentially challenging behaviors, depending on the severity they will direct you to the right mental health professional for assistance including someone like myself, a Canine Behavior Consultant who helps with behavior modification training, or if medication is necessary, a DVM Behaviorist . The current trend is for canine behavior consultants to collaborate with DVM behaviorists. Also, more and more DVMs are taking courses in animal behavior and becoming versed in using behavior medication combined with behavior training for more expedient and successful results. Ask your veterinarian first if they can help because if they don’t have the experience they may be interested in learning and improving their practice. Be aware and prepared that in some cases medication is necessary to reduce stress and aid the individual in learning during behavior modification training.

Are you hopeful that more and more research facilities will stop conducting horrifically abusive studies and opt for more humane non-animal alternatives?

Yes, I have hope in humanity that our moral compass will point us in the right direction, however, nothing will change when incentives are skewed and unless we consumers demand change. The more we continue to turn a blind eye, not asking questions or looking under the covers, the more research facilities will continue to use dogs and other animals to test them. We are allowing the behavior to precipitate. Why should they change what they're doing if they're making money and no one complains? Many products (cosmetics, shampoos, and perfumes, for example) are still tested on nonhuman animals (for example, dogs, rats, and rabbits) using methods that are not only inhumane but also unnecessary. We have the technology to perform tests using alternative methods that do not involve harming living beings. Consumers are willing to pay more for a product they really don’t need if they know a nonhuman animal wasn’t used in its manufacture. It's important to recognize nonhuman animals are living and sentient individuals and, like us, they want to live their lives free of pain and suffering. They are not unfeeling objects without emotions, but rather sentient and feeling beings. Our objectification and commodification of nonhuman animals have got to change or nothing else will.

I was recently reading a study conducted in Germany where funding was provided by a pharmaceutical company in which they were looking for evidence to essentially justify the use of dogs as test subjects. It was concluded that laboratory raised dogs fair better than commercially raised dogs during testing because they are trained to be handled by staff. They also concluded their laboratory dogs were successful candidates to be rehomed because they showed fewer behavioral challenges. They suggested this is a better and more humane alternative than killing the dogs after testing was concluded. However, evidence actually showed that the dogs were less trainable and suffered from fearfulness.

Perhaps the decision for these conglomerates to release their test dogs is a sign of moral integrity? Or, perhaps, it is fear of consumer retribution? It isn’t just one person in a company acting cruelly to these living beings behind closed doors, it is a team of them from manager to staff worker. And, while their action is admirable, will the public trust them to be truthful going forward? Science and animal ethics /welfare are inseparable and certainly, when people become of aware of the horrific ways in which laboratory dogs and other animals are treated, many feel moral outrage. It most likely was public moral outrage that forced the Michigan company to end their experiments. Isn’t that saying something important? I am hopeful the decision to release the dogs will give scientists and others pause to listen to their heart and find what renowned philosopher, the late Mary Midgley , called the “ yuck factor again ." We need to find our humanity and respect for all life, human and nonhuman.

Is there anything else you would like to tell readers?

We need to take a 30,000 foot step back and really evaluate underlying systemic problems that result from our poor decisions and behaviors. An excellent case in point centers on the laboratory beagles who were force-fed poisonous pesticides. We overlook the fact that legally dogs are a commodity, basically objects or products we can legally buy and sell. Is buying and selling dogs the right thing to do? Of course not.

Thank you Vivian for a very important interview and explaining to readers what it really takes to rehome a dog who has been abused. We both hope that the company's decision to stop the project and to try to rehome the dogs will help give them pause and motivate researchers to listen to their hearts and find the “yuck factor" as Mary Midgley called it. And, we hope they will never do research like this again and other research facilities and researchers will follow suit. Science and animal ethics/welfare are inseparable, and seeing the horrific treatment that these beagles endured is what caused moral outrage that in turn most likely affected the company's decision. There's a lot at stake for research facilities in which dogs and other animals are abused when what they're doing goes public.

As the late Gretchen Wyler aptly said, " Cruelty can't stand the spotlight ." In the United States there are approximately 90 million dogs living in 68% of all households . If even a small fraction of these people who are offended by dog (animal) abuse made their voices heard in one way or another, it would make a huge difference for other animals who are used in abusive research, including projects that don't generate useful information such as the one from which these beagles were rescued. Of course, the end goal is to stop abusive research using dogs and other animals altogether. There still is much work to be done.

An important comment was sent to me by Barbara Dwyer, a research associate at the Center for Canine Behavior Studies and Certified Behavior Counselor.

"These kinds of cases just infuriate me, first for the poor dog who is still suffering from fear and anxiety and secondly because the adopter is traumatized. With experiences like this, many people would give up on rescue for fear of a repeat experience, and they'll tell their friends. We need to do a better job of working with serious cases before they go into a typical pet home. Placement does not assure that a dog will have a good life. Unsuccessful placements increase stress to the dog, returns to shelters/rescues and even euthanasia. Lastly, hearing about the bad experience of friends or family can result in fewer folks being willing to take the risk of adopting. Severely fearful, anxious and/or aggressive dogs need time and specialists who with slow, careful behavior modification and possibly medication can teach them to trust us. Dogs deserve a chance at rehabilitation and should not be tossed into a new home to sink or swim. We need to find effective ways to rehabilitate and give them a reasonable chance at a good quality of life. If we can't, we fail to meet basic welfare needs like freedom from pain and fear."

Döring, D., Nick, O., Bauer, A., Küchenhoff, H., & Erhard, M. H. (2017). How do rehomed laboratory beagles behave in everyday situations? Results from an observational test and a survey of new owners. PloS one, 12(7), e0181303.

McMillan, F. D. (2017). Behavioral and psychological outcomes for dogs sold as puppies through pet stores and/or born in commercial breeding establishments: Current knowledge and putative causes. Journal of veterinary behavior, 19, 14-26.

McMillan, F. D., Duffy, D. L., & Serpell, J. A. (2011). Mental health of dogs formerly used as ‘breeding stock’in commercial breeding establishments. Applied Animal Behaviour Science, 135(1-2), 86-94.

Mondelli, F., Prato Previde, E., Verga, M., Levi, D., Magistrelli, S., & Valsecchi, P. (2004). The bond that never developed: adoption and relinquishment of dogs in a rescue shelter. Journal of Applied Animal Welfare Science, 7(4), 253-266.

Marc Bekoff, Ph.D. , is professor emeritus of ecology and evolutionary biology at the University of Colorado, Boulder.

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Conceptual analysis article, critical problems for research in animal sheltering, a conceptual analysis.

• 1 Research Department, Austin Pets Alive!, Austin, TX, United States
• 2 Arkansas State University, Department of Political Science, Jonesboro, AR, United States

Animal shelter research has seen significant increases in participation over the past several decades from academic organizations, private organizations, public entities, and even corporations that aims to improve shelter programs, processes, operations, and outcomes for the various stakeholders/participants involved in a shelter system (animals, humans, the community, wildlife, and the environment). These efforts are scattered through a huge variety of different research areas that are challenging to define and scope for organizations seeking to start new lines of research inquiry. This work aims to enumerate some of the most critical outstanding problems for research in animal sheltering in a conceptual framework that is intended to help direct research conversations toward the research topics of highest impact (with the highest quality outcomes possible). To this end, we define seven (7) key areas for research: animal behavior, adoptions and special needs populations, medical conditions, disease transmission, community, ecology, and wellness (one health), operations, and public-private-academic-corporate collaboration. Within each of these areas, we review specific problems and highlight examples of successes in each area in the past several decades. We close with a discussion of some of the topics that were not detailed in this manuscript but, nonetheless, deserve some mention. Through this enumeration, we hope to spur conversation around innovative methodologies, technologies, and concepts in both research and practice in animal sheltering.

Introduction

Animal Sheltering in Western society, in some form, has existed since the mid-1800's (with the creation of both the Royal Society for the Prevention of Cruelty to Animals and the American Society for the Prevention of Cruelty to Animals in 1824 and 1866, respectively) and has been a constantly evolving field to both the benefit ( 1 , 2 ) and detriment ( 3 , 4 ), of its stakeholders: animals, pet owners, communities, and the organizations that tie these groups together. In the past several decades, a cultural shift has been occurring in which animal welfare ( 5 , 6 ) has received more attention, resources, and scrutiny than in the decades before. Success in sheltering is commonly measured by the Live Release Rate (hereafter LRR) that is obtained by dividing the total number of live animal outcomes (such as adoptions and transfers) by the total number of live animal intakes ( 7 ). Many cities have been able to increase their LRR and those of surrounding counties above 90 and even 95% ( 8 ), yet shelters still struggle with having adequate resources ( 9 ) and rural shelters may be more likely to struggle ( 10 ). The number of conceptual problems in sheltering is enormous, and as awareness of the needs of shelters continues to rise, more and more groups—academic, corporate, non-profit, and private—are looking for ways to contribute to the wider movement of animal welfare using their unique skills and talents. One difficulty for these potential partners is in understanding what the needs of shelters are and what high-value unsolved problems exist in the field.

Some of these are knowledge problems, others, implementation problems, and even more, systemic, cultural, and societal problems. Almost all require some manner of research to elucidate best practices and truths and differentiate them from traditions and myths. To aid interested parties in contributing to these areas of animal sheltering, we seek to enumerate and explain many of the critical problems for research in animal sheltering so that those organizations and interested parties might find a place to contribute. The key areas for future research were developed through a combination of both empirical and a priori traditions. The empirical approach used included input from animal sheltering professionals, including the responses of over 10 working groups representing more than 300 shelter professionals associated with Human Animal Support Services project to the question of what research needs were to advance animal sheltering. A priori observation and reflection of the researchers and reviews of the existing literature also helped to inform a lengthy list of research needs. These research needs were then thematically grouped in to the 7 key areas. Each of the areas was then evaluated on two factors: the degree of potential impact to animal sheltering and the difficulty in studying the problem.

Table 1 presents the 7 key areas identified. It also presents the impact potential for research in these areas by identifying the top-level impacts that advances in each topic area could have on the field of animal sheltering. While not intended to be all encompassing, this list captures the main topics generated by the authors and the consulted professionals.

Table 1 . The 7 key problem areas identified in this work and their impact potential in the space of animal sheltering. While not intended to be all encompassing, this list captures the main topics generated by the authors and the consulted professionals.

Figure 1 provides an additional way of examining these topical areas. This figure provides examples of critical problems for research in animal sheltering and provides a way to compare and evaluate the areas in terms of the relative difficulty of studying the problems as well as the relative magnitude of the potential impact. In addition to evaluating these factors for the key research areas identified herein, this figures also shows a framework through which other researchers could evaluate the relative impacts and difficulty of other possible research topics.

Figure 1 . A Force Directed Graph showing the various problems discussed in this article. The size of a node represents the relative impact a solution would have. The color represents the relative difficulty of studying the problem (with red being more difficult). Links relate the topics. An interactive version can be found here: https://codepen.io/kevroy314/pen/jONoXma . Click to isolate nodes and their neighbors. Drag to move around. Scroll to zoom.

Key Areas for Research

Animal behavior.

Animal behavior is one of the most challenging and complex topics in animal sheltering. Leaving aside controversies surrounding the ethics of adopting out animals with known behavior challenges or the ending of the life of an animal, whether for the protection of the public, retribution for an incident, quality of life, or any other justification related to behavioral issues, such as biting or inappropriate elimination, the practical need to better understand and modify animal behavior to improve the lives of animals and their caregivers to improve their chances of adoption and/or their probability of remaining in the home ( 11 ) is substantial. Here, we highlight 4 key areas in animal behavior that may have the biggest impact in a shelter setting and that may be underrepresented in the literature.

Efforts to form a typology of dog behaviors that may be problematic in the home, and, specifically, dog behavior that may be averse to a successful adoption and retention in a home ( 12 , 13 ) have been attempted in the past ( 14 – 16 ). Despite the interest in canine behavior in general rising sharply in the early 1990s and more recently ( 17 ), no consensus has been reached upon a singular behavioral classification and identification system that can be used to make decisions around best practices with dogs with histories of behavior problems or potential for behavior problems. Such a classification system should have the following properties [( 18 ) for a more detailed discussion of the difficulties surrounding some of these issues]:

• Objective measurability and reproducibility.

• Characterization of common temporal progressions.

• Understood correlations between related behaviors.

• Clinical relevance to predictability and intervention.

Somewhat recent attempts ( 18 , 19 ) at assessing the efficacy of behavioral evaluations have not been as promising as might be hoped given the 50+ year history of the field, and the impact of such a system, especially in establishing new interventions that can help these animals be successfully placed in homes, could be enormous given the extreme difficulty in achieving successful outcomes for dogs with behavior issues.

One key factor in negative animal behaviors, especially as pertaining to the adoptability of animals, is the stress they experience while in a shelter setting ( 20 – 22 ). Studies of animal stress date back to 1926 ( 23 ) with animals have often serving as a model for human stress ( 24 , 25 ). Practical tools are needed to assess the impact of shelter environmental improvements. Using Biomarkers to assess stress ( 26 ) across species ( 27 – 29 ) have shown significant success in recent years. Unfortunately, the practical measurement of such biomarkers in shelter settings remains unlikely due to resource and practical constraints. Non-invasive measures of stress are possible in many species [including thermographic ( 30 , 31 ), salivary ( 32 ), visual ( 33 ), and multimodal ( 34 ) systems], though their efficacy as an intervention target is unclear. A more thorough understanding of best practices around the reduction of stress for animals in shelters will allow for significant improvement in the quality of life of long-stay animals as well as the adoptability of animals that may show fewer behavioral issues once removed from a stressful environment, with some evidence showing changes in cortisol levels, a common biomarker for stress, with even a single night removed from the shelter environment in adult dogs ( 35 ).

A key element in the success of an animal with behavioral issues, post-adoption is not simply the cessation of negative behaviors, but also the match with an adopter who can maintain the environment necessary for permanent improvement in behavior as well as following up with those adopters to ensure continued success is achieved. Preparing adopters and proper matching is key given the frequency of post adoption behavioral issues among shelter animals ( 36 ). This problem comes down to two key sets of questions:

Given evidence suggesting choice of pet is often tied to factors like appearance more than behavioral considerations ( 37 ), how should shelters best match behavioral issues with potential adopters who can handle the maintenance surrounding those issues to reduce the chance of return ( 36 , 38 , 39 ) and adverse incidents such as bites or escape from a yard?

What risks (i.e., environment impact on biting) ( 40 ) exist in the home that might exacerbate issues surrounding behavior?

Finally, when it comes to animal behavior, especially canine behavior, one of the most critical incidents that can occur is a bite incident since these can result in serious injury to persons and potentially result in liability claims against the shelter ( 41 ). The previously mentioned issues all likely contribute to the probability of a bite incident occurring, but predictions of such events, even in aggregate across a city ( 42 ), are challenging at best. A successful bite prediction system would also pose ethical issues as individuals, shelters, and cities may choose to use such a system to decide which animal's lives should be preemptively ended, to avoid the potential risk and liability. It is critical, therefore, that the predictability of bite incidents increase at the same rate as our ability to reasonably intervene to prevent the incidents.

Adoptions and Special Needs Populations

The core problem with adoptions at shelters is always “how do we get as many animals out to good homes as quickly as possible?.” Of course, as with so many seemingly simple problems, the posing of the question in such a general manner means no obvious solutions present themselves. Properly reframing the question often begins to imply solutions. Preventing the surrender of animals to the shelter system is certainly a key component to assuring positive outcomes for animals and people alike. New programs, such as the Human Animal Support Services project, are focusing heavily on programs aimed at keeping animals out of the shelter altogether and in their original homes whenever possible. Further, this paradigm shift has the potential to profoundly impact positive outcomes for community cats, who may not be best served through adoption. Although adoption is not the only possible positive outcome for all animals that enter a shelter system, for many animals (and the humans who manage the systems of sheltering), it remains an important practical and ethical outcome. Here, we review 3 key areas in adoptions that remain complex and difficult despite extensive efforts in the sheltering community. For a more complete list of these challenge areas, see the American Pets Alive! Documentation on the topic (“American Pets Alive! Resources;” https://americanpetsalive.org/resources ) ( 43 ).

First and most critically, large dogs, often considered to be those weighing over approximately 35 pounds or 16 kilograms, consistently have more difficulty in being adopted ( 44 – 46 ). This can be due to factors such as the general public perception around larger breeds ( 47 , 48 ), city ordinances banning ownership of certain breeds ( 49 ), housing restrictions implemented at the facility level ( 50 ), or concerns around safety, behavior, and compatibility with other home residents ( 51 ). These issues are exacerbated by the difficulty in accurately identifying breed information in shelter animal populations ( 52 ). As a result of these complications around getting large breeds out of shelters, shelters often end up with a stagnant population of these animals that has less turnover than other, easier to adopt categories (puppies of any breed, for example). This can create a perception that the only populations present are these large breed animals. These factors result in many of these animals having long stays and, as mentioned in prior sections, increased stress and overall wellness difficulties that further worsen their adoptable potential. Moreover, animals in the shelter are less likely to behave the way they might otherwise in a home ( 53 ), further decreasing their chances for a positive outcome. A strategy around breaking this cycle and helping large dogs would alleviate significant amounts of trapped resources as site maintenance and housing can create substantial costs and reduce flexibility in serving other populations. The importance of providing an equal opportunity for these large breed dogs to stay in their home is one consideration beyond adoption in strategy design. For example, policies that disallow the use of size of a dog as criteria for access to housing (as discussed above) would help keep these animals out of the shelter system in the first place. Adequate access to resources to behavior training could be another community level intervention that could allow more of these animals to stay in their homes.

Other Special Populations

Beyond these major issues, there are numerous conditions of decreasing commonality that require increasingly complex adaptations of program and policy to accommodate. This article cannot enumerate all such conditions, but the following list, sorted roughly by difficulty, captures some of the most critical special needs populations that require specially trained homes to inhabit, making them more difficult to adopt out:

• Geriatric Animals.

• Animals with Chronic Allergies.

• Hospice Animals.

• Feline Leukemia Virus (FeLV) Cats.

• Kidney Failure Animals.

• Diabetic Animals.

• Behavior Animals.

• Animals with Paralysis and/or Incontinence.

Much of the care, maintenance, and treatment of these populations is well understood, but the problem of placing them in amenable homes is still a significant one. More research around interventions that can increase the likelihood of placement as well as the factors that impact the likelihood of special population placement may provide actionable insights [see ( 54 ) as an example in geriatric animals].

Finally, and significantly, a more thorough understanding of how to match adopters to animals ( 37 , 55 ), how to evaluate homes for safety and longevity of adoption outcomes ( 13 , 56 ), how to optimize placement of animals in homes ( 57 ), and what preferences exist when it comes to adoption practices around marketing, visitation, and engagement is desperately needed. This understanding will likely depend significantly on local cultural distinctions in populations ( 58 , 59 ) and is, therefore, difficult to examine systematically. More best practices around adoption matching and marketing would greatly simplify one of the most critical functions in animal shelters.

Unique Challenges of Cats

Another, potentially less obvious problem in sheltering is the difference in positive outcomes for cats vs. dogs. Best Friends, a national non-profit that provides the most comprehensive summary of annual shelter statistics reports that cats are still dying in shelters at a ratio of 2:1 when compared with dogs ( 60 ) despite approximately one-fourth of US households providing a home for cats ( 61 ). Many shelters consistently report difficulties in adopting out adult cats once they no longer have the appearance of a kitten ( 62 ). Further, shelter or municipal policies around the extermination of community cats ( 63 ) may also be a significant contributor to the numbers of cats not having successful outcomes in shelters.

Approaches to improving live outcomes for cats require shelters to explore ideas outside of the traditional intake to adoption framework. Some strategies that are specifically applicable to cats have been evaluated and shown to be effective such as trap-neuter-return and shelter-neuter-return, which could reduce the number of un-adoptable cats entering the shelter system ( 64 , 65 ), but more research into the social drivers and potential interventions for this issue are warranted. A development of the recognition of the ecology of community cats is an additional issue that is elaborated on in Section Operations.

Medical Conditions

In addition to its capacity as an adoption agency for unowned animals, animal shelters often perform a variety of medical services. These services depend on the location, resources, and risk tolerance each organization has, and it is often difficult for organizations to decide what to treat and what to not treat (whether euthanasia is then called for or not). One critical element of this that remains a challenge for all shelters is the effective, actionable diagnosis of disease [see, ( 66 )]. Many diseases have reliable tests (such as canine parvovirus) while others have a much more complicated history in the development of a reliable test [such as canine distemper, though many strongly claim RNA tests should be considered reliable; ( 67 – 69 )]. Cost is also a critical factor in shelter tests as even a relatively inexpensive (50 dollars) test in an outbreak scenario can be entirely impractical in a population of just a few dozen animals. Further research into low-cost testing is certainly needed for a wide variety of diseases.

Once the disease is identified, shelters often lack the resources for what would be considered “standard” care in a private practice. Some shelters opt to not offer reduced care and, instead, euthanize, while others choose to offer whatever care they can within their own ethical limitations of suffering and quality of life considerations. The need for significantly more research into evidence-based medical guidelines, and especially those that are specifically optimized for triage situations with limited resources and around medical conditions seen in shelters, is widely apparent. Some conditions, such as kitten diarrhea, may be somewhat understood in a general medical sense, but the treatments and time course do not scale appropriately for the model of a medium to large shelter.

Although many diseases could use additional scrutiny for the purposes outlined above, the following are of particular interest due to the costs, in either lives or resources, associated with typical treatment or management (T; indicates specific transmissible disease relevant to Section Disease Transmission):

• (T) Canine parvovirus ( 70 , 71 ).

• (T) Feline panleukemia ( 72 ).

• (T) Canine distemper ( 73 ).

• (T) Feline leukemia virus (FeLV) ( 74 ).

• (T) Feline immunodeficiency virus ( 75 – 78 ).

• Kitten diarrhea ( 79 ).

• Fracture and trauma management.

Disease Transmission

More so than the treatment of disease, the prevention of disease spread in the shelter environment is one of the most challenging, concretely measurable in the form of infection rates, yet ambiguous (difficult to diagnose in source) tasks a shelter may face. Shelters are examples of anthropogenic biological instability due to the housing of transient, displaced mixed-species of animals that may not have prior veterinary care or have been scavenging during times of homelessness ( 80 ). The disease transmission in shelters is further complicated by situation of overcrowding, poor levels of hygiene, and housing of multiple species which can add significant sources of stress for the animals and create a perfect environment for pathogen emergence and transmission ( 80 ). This transmission can quickly lead to a crisis in the shelter ( 81 ). Shelters that treat infectious disease like the canine parvovirus establish isolation areas in which only that disease is treated, but little is known about the ease with which these diseases spread under different quarantine practices.

Although there are many interesting diseases that are typically seen in shelters, some (such as those listed in Section Medical Conditions) are considered more impactful/deadly than others and, therefore, would make excellent targets for more detailed studies of disease spread.

While it is not officially recommended as a best practice ( 82 ), when shelters experience disease outbreaks, some may opt to depopulate, i.e., end the lives of their entire population, ( 83 ) rather than have it persist through many generations of animals flowing through the system. Better understanding of how to stem these outbreaks rapidly, efficiently, safely, in a resource-efficient manner, and given the constraints of a shelter environment (space, staffing, facility design, and the need to maintain normal operations) will allow shelters to avoid mass culling and take an approach that increases lifesaving with more confidence.

Community, Ecology, and Wellness (One Health)

Beyond the scope of the basic operations of a shelter in managing the conditions of individual animals and placing them in appropriate homes, shelters also serve a critical role in the community as providers of services that can enhance public perception and wellbeing ( 84 ). This collaboration requires an engaged community that recognizes the importance of animal welfare in the health and wellness of the larger, shared space. Best practices around establishing this type of engagement are not well identified in the existing body of knowledge. This is further confounded by variation in the distribution of resources and community attitudes in different geographic areas.

As animal shelters continue to evolve in response to societal shifts in attitudes toward animals, the focus of operations are changing from centering on adoptions to centering on the prevention of surrender of animals to the shelter in the first place [see ( 11 ) for a review]. This has already been discussed as it relates to community cats and behavior/health but there are many other human-centered reasons that animals are surrendered to shelters such as guardian health problems, housing insecurity, domestic violence, and many others ( 85 ). Our understanding of how human welfare intersects with animal welfare has the potential to have a dramatic impact on the way shelters operate in their communities. Some communities have hotlines, spay and neuter programs, and other medical/behavioral services that can potentially contribute to this issue, but the efficacy of such systems and the gaps they leave are not well understood. More significant study of the needs of local populations as they relate to shelter success is needed.

Local populations also differ in their perception and support of shelter policies, ethics, and the local system of laws that are intertwined with these efforts. No unified system of ethics is established in animal sheltering, and communities often do not understand the nuances of practices in shelters (especially regarding resource allocations and euthanasia practices). This makes galvanizing community support difficult, even in communities that have achieved remarkably high live release rates. Public perception, messaging, and ethical alignment will undoubtedly continue to be an ever-evolving socio-cultural landscape that is sorely in need of attention.

The mental health of volunteers, staff, and veterinarians ( 86 ) in animal shelters also requires much more attention than it often receives. Individuals that participate in euthanasia are reported to have higher work stress and lower job satisfaction than their counterparts ( 87 ). Suicide rates are significantly higher in the field of animal welfare than other high-stress fields ( 88 , 89 ), and more understanding and support is needing to help those working in these areas receive the help they need to continue to serve the community in a sustainable, healthy manner.

Access to Care

Access to veterinary care is emerging as a critical issue in animal welfare. Access to care is an aspect of the One Health approach to considering animal welfare due to the zoonotic potential of various diseases that can find reservoir in companion animals ( 90 ). In addition to being a risk to public health, lack of access to veterinary care can result in surrender of animals to shelters, stress to the caregiver/family ( 91 ) as well as stress to veterinarians who must counsel caregivers who cannot afford the recommended care ( 92 ). Shelters feel the impact of this as downstream recipients of animals when owners surrender due to an inability to access needed care. This can both drive surrender to shelters and result in a greater financial burden for shelters to meet medical needs that may be complicated by a historic lack of access to preventative or early intervention care. Further, shelters themselves compete in the market to employ veterinary professionals and support staff that may be further complicated by a shortage of veterinarians ( 93 , 94 ).

Access to care can be seen as a problem with multiple causes from cost to lack of transportation to the unequal distribution of veterinary resources across the landscape. Cost was identified as the most common barrier to accessing veterinary care in the Access to Veterinary Care Coalition report on this issue ( 91 ). In the past decade, costs for veterinary care have been outpacing increases in human health care ( 95 ). The average American spends 47% more on equivalent veterinary care today than a decade ago ( 96 ). The functional impact of this increasing cost is that fewer people are seeking care for their pets ( 97 ) resulting in what is considered the greatest current threat to companion animal welfare in the US ( 91 ). More research that identifies efficient, effective, and sustainable solutions to the cost of veterinary care will be key for animal shelters.

Key research questions in access to care can come down to three key areas:

Advances in areas like incremental care or spectrum of care, which are not equivalent but present different perspectives on the issue of cost-benefit analyses in treatment protocols, could reduce costs and prevent shelter surrenders but could also help shelters mitigate the increasing expense of medical treatment for animals in their care.

A deeper understanding of the number of animals surrendered for medical reasons, the types of these conditions and potential treatment routes pre-surrender would also add valuable knowledge to the animal sheltering and animal welfare communities.

Development of community-based solutions that focus on disease prevention when the cost is likely lower than when a disease process is more advanced. This includes the prevention of infectious disease transmission in the community and the development of effective education around other preventable conditions by pet guardians.

Ecology/Environment

The study of the ecology surrounding community cats has received significant attention over the past several years ( 63 , 64 , 98 – 102 ), and debates are likely to continue in this area to determine the most effective ways to ensure the health and safety of community cats and the organisms with which they interact. Additionally, ecological perspectives on the interaction between stray and roaming animals in general and the community are also of interest, but often only actively studied due to concerns over infectious disease spread such as the Rabies virus. Finally, the interaction of wildlife systems with domesticated animals may be of some interest both due to the spread of infectious disease and the more complex interactions these two animal groups may have with one another.

As animal welfare incorporates a One-health approach, further research that identifies strategies to reduce the environmental impact of shelter operations cannot be ignored. Effective ways of cleaning outdoor kennels without contributing to contaminate run-off, ecological disposal of animal waste and the evaluation of how large-scale animal transport can contribute to environmental degradation are just a few examples of the interaction of sheltering and the environment that are open for additional exploration.

In addition to the study of animal-centric, adoption-centric, and community-centric aspects of sheltering, the study of the operations that contribute to the ability of shelters to continually adapt, and advance is of critical importance if we are to have systems robust to disaster and capable of implementing our values and ethics on a global scale. Although blueprints do exist that can guide communities in setting up new shelters and enhancing existing shelters, significant problems remain in the space beyond the distribution of known solution resources. Here, we discuss 4 key operations problem areas with varying levels of complexity.

Data Problems

Shelters need to collect data to know how they are serving their animals, adopters, volunteers, staff, and community, and how to improve operations in all areas of the shelter. While the industry recognizes the need for quality data, significant barriers have been identified such as a lack of training and resources [( 103 ), additionally, see the Associate for Veterinary Informatics (AVI) for additional information on this topic; https://avinformatics.org/ ]. Solutions such as ShelterLuv, Chameleon, and PetPoint for database management go a long way to improving situations for shelters, but the ability to flexibly collect and curate all manner of useful data (including electronic medical records, location-based event history, and other meta-data about entities that comprise shelters) remains an open problem. It is also essential that the prioritization and understanding of the critical importance of data is shared by line staff as well as senior management. When line staff fail to understand the importance of complete data collection this action can be de-prioritized in fast paced shelter environment.

Beyond this, shelters need methods of protecting themselves in the sharing of data with the public, academic institutions, and each other. The public, which support shelters through taxes or donations, show widespread support, for example, for programs that reduce levels of shelter euthanasia shelters ( 104 ). The best practices around of performing data sharing and managing data access for shelters have yet to be established (though some progress has been made in recent months at the Municipal Shelter level). Over time, there have been attempts to create a single authoritative collection of sheltering data but to date, none have achieved high success. The current initiative that has achieved the most progress is Shelters Animals Count (SAC). SAC is a national database that relies on the voluntary participation by shelters and animal rescues to upload monthly sheltering summary statistics. Unfortunately, there is still relatively poor participation. For example, in 2020 there was participation by only 422 municipal shelters, 359 private shelters and 516 rescues ( 105 ). This can be contrasted with a 2014 estimate by the Humane Society of the United States of 3,500 municipal and non-profit shelters and over 10,000 rescue organizations ( 106 ). Despite the move toward increasing transparency in government, only a small handful of states and municipalities require reporting to their state and local governments, with even fewer providing enough clarity as to what should be reported for such reporting to be of use to the wider sheltering community. The result of this paucity and irregularity of data provides a significant challenge to researchers and policymakers in understanding what is happening across the nation regarding sheltering, though the contributions of states in which reporting is mandated effectively have provided a valuable starting point for these efforts.

KPI Problems

Once data is collected, linking that data down to trackable KPIs (Key Performance Indicators) that are useful to shelters in improving outcomes for animals is a challenge in and of itself. The standardization of KPIs and their strict definitions has suffered from some of the disagreement and difficulties surrounding data collection. The most marked attempt to create unified KPIs occurred in 2004 resulting in the Asilomar Accords. The Live Release Rate, and methods of fairly but consistently calculating it materialized as a critical outcome of the Accords ( 107 ). This measure has never been without controversy and is limited, in part, by the wide variance in the various ways in which animal shelters operate in their community and what their priority services are ( 108 ). As the operation of shelters have changed, with more innovative programs designed to prevent animals from ever entering the shelter system appearing, advancements in medical and behavioral interventions, and the geographically biased nature of animal population distributions ( 109 ), the use of a single KPI will likely remain a source of both conflict and difficulty for many shelters. A more diverse set of KPIs will allow for shelters to perform more nuanced comparisons of their successes and failures that will enable better sharing of solutions and resources. What this list of KPIs should entail remains an open problem [see ( 110 )].

Growth Problems

Finally, as some shelters begin to stabilize the animal welfare situation in their cities, adapting to the varying degrees and paces of growth in various organizations to ensure resources are being properly utilized to the benefit of animals and the community is a challenge, to say the least. The field of Health Economics in humans has a rich history ( 111 ), and a similar field in Animal Health Economics ( 112 , 113 ) will likely need to be expanded beyond its traditional focus on production animals so that organizations are not put in a position to blindly guess at the proper allocations or resources toward different intervention programs (such as a canine parvovirus treatment program, FeLV treatment program, behavior program, or kitten foster program).

One particularly challenging program area for shelters to understand in the context of growth, integration, and resource allocation is the management of foster programs. Foster programs have been fantastically successful as a method of expanding the effective capacity of shelters, increasing live outcomes ( 114 ), enhancing community engagement, increasing quality of life of animals in care ( 35 ), and providing special assistance for more difficult to adopt populations. However, a thorough understanding of how to best engage, utilize, and grow foster programs is lacking.

Diversity Equity and Inclusion

Researchers have evidenced that the oppression of non-human animals, disabled humans, and people of color are deeply interconnected ( 115 ). If animal shelters are to continue to function as key members of diverse communities it is essential that they pay increasing attention to issues of diversity, equity, and inclusion in their operations both internal and external. While the community of research in this space has assembled a basic understanding of some inequities that currently exist, many others have yet to be explored in a thorough way. For example, we know that African Americans are underrepresented in leadership positions ( 116 ). The homogeneity of animal shelters is not confined to the workforce alone. Two large survey-based studies found similar results in evaluating the demographics of animal welfare volunteers concluding that most volunteers were White females in the middle to upper middle class ( 117 , 118 ). Questions of why this lack of diversity persists and what successful strategies could be used to improve conditions would be of benefit as representation of communities within organizations that serve them allow those organizations to supply the appropriate services to maximize the community benefit and foster a highly participatory, engaged, fair, enthusiastic, and ethical social system.

Beyond direct engagement with shelters as volunteers or employees, there are fecund areas for research in the provisioning of shelter operations. As increases in public-private partnerships place more animal shelters in the business of providing animal control operations, the enforcement of ordinances becomes a key issue in balancing public demand for action and the ethics and priorities of animal welfare. A recently published commentary on the subject argues that there is inherent bias in the design and enforcement of public policy around animal welfare and urges a shift from enforcement to resource provision ( 119 ). Evaluating policies and enforcement and implementation of these policies and whether biases are leading to unequal burden are not well understood though it is difficult to not draw comparisons to the arena of policing and the long, complicated relationship between marginalized communities and law enforcement personnel. Additional challenges persist in understanding potential inequities between the surrender of animals and the adoption of animals and whether these differences enforce equity imbalances or are based on existing biases and structural inequities ( 120 ).

Public-Private-Academic-Corporate Collaborations

A less visible and virtually unstudied problem in animal sheltering is the ability for organizational entities of different types and with different incentives to collaborate to the benefit of animals, their owners, the community, and each other. The social network analysis of Reese and Ye ( 121 ) is a prime example of the complex collaborative relationships that can emerge between organizations to advance lifesaving in a community. Many questions in this space exist around the best ways for these organizations to interact (i.e., what roles are best served by what organizations, what incentives are best to ensure ethical treatment of all parties, and what restrictions should be put on various types of interactions). Legal restrictions around the use of shelter animals in research may be a barrier that exists to research collaborations between shelters and academic institutions. Dialogue, consensus, and potential legislative change may be needed between animal shelters, the veterinary community, and academia to address the negative consequences of legislation originally intended to protect animals from harm.

Public-private partnerships in other areas of medicine have become increasingly common and valuable ( 122 ), and corporate sponsorship of shelters has become increasingly common. Public-private shelter partnerships are also on the rise with some proposing this structure as the new standard in the field ( 123 ). Academic collaboration with animal shelters, where academic institutions take advantage of the wealth of available subjects and data in shelters, is still a relatively new concept. Though many potential pitfalls exist in these collaborations (including issues with credit attribution, resource allocation, and ethical alignment), the potential to accelerate the state of the art in animal sheltering via these collaborations is huge thanks to the varied strengths of each organizational type.

The seven key areas for research in animal sheltering outlined above are not the only areas that might be of interest to shelter practitioners and their partners. Some additional areas of interest were not mentioned specifically in this manuscript due to the well-researched nature of the topics, the lack of clear definition in the space, and/or their relative distance from the typical practices of an animal shelter. These areas, nonetheless, merit some mention due to their importance to the area of animal welfare research at large and potential intersection with some shelter practices (depending on specific shelter policy, philosophy, and operations).

A variety of interventions have been proposed that might address some of the problems mentioned in this manuscript. On the behavior side, playgroup services have been proposed that may aid in social development and lead to more positive behavioral outcomes for dogs ( 124 ). Moreover, foster programs that take advantage of these and other medical or behavioral services to accelerate positive outcomes for animals deserve significant attention ( 35 , 125 ). Foster programs can serve as an additional reservoir for animal populations, increase community engagement in the shelter system, and encourage positive outcomes for animals in the foster system through positive environmental enrichment in homes. In situations where foster homes are not available, additional environmental enrichment to achieve similar aims may be found through clever building and facility design at the shelter site ( 126 , 127 ). Finally, a variety of programmatic and procedural interventions around lost and found animals, self-service rehoming, and intake-to-placement optimization, and field services optimization that aim to prevent animals from entering the physical shelter facility can serve as systems optimizations that improve outcomes for all parties; though, more research is needed in these areas to examine their efficacy. Each of these intervention areas, and other innovations in sheltering, deserve significantly more attention than can be afforded in this outline, and future work should attempt to address them more directly.

In addition to a variety of community and ecology problems and interventions, ethical problems in the industry of animal sheltering are not specifically addressed in this work as these are not research topics per se , but more in the realm of philosophy. Future work should examine ethical questions surrounding the topics outlined in this manuscript and other sociological research questions related to the ethics of animal shelter practices.

In this work, we present a conceptual organization of topics for research in Animal Sheltering. These topics vary significantly in difficulty and impact but represent a large swath of needed scientific contributions in the literature. Many of these areas are being actively worked upon by various research institutions (i.e., significant work in animal diseases has occurred), but some have received little attention yet (i.e., operations research). Moreover, some of these areas are being examined, but due to resource and/or methodological constraints, progress is slow. By enumerating these problems, the community of researchers attempting to improve the function of shelters for animals, staff, volunteers, and the community can more carefully and wholistically consider the breadth of applicability of their ideas and investigations and hopefully, more productively contribute to the literature.

Author Contributions

The above document was drafted and originated by KH with review, added sections on community cats, DEI, and Access to Care and most edits provided by SN. Figures were generated by KH. Both authors contributed to the article and approved the submitted version.

Funding for publication was provided by the American Pets Alive! Organization, while funding for individual authors and the creation of the work herein was provided by their respective institutions or the authors themselves.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank the Human Animal Support Services organization for their support and encouragement. Additionally, we would like to especially thank Dr. Nipuni Ratnayaka for her contributions to and review of some of the ideas in this document, Dr. Elizabeth Davis for her support in the development of these ideas, Rory Adams for his thoughtful comments around the topics in this document, Steve Porter for his commentary around shelter operations, Dr. Ellen Jefferson for her leadership and support of this work, and all of the staff, volunteers, and communities that continue to advance the state of the art in animal sheltering around the world. This work originally appeared in large part in a blog post from November of 2019. The original blog post can be found at: https://medium.com/@amparesearch/critical-problems-for-research-in-animal-sheltering-404395127b35 .

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Keywords: animal shelters, animal welfare, research problems, animal behavior, shelter adoption, disease transmission, one health

Citation: Horecka K and Neal S (2022) Critical Problems for Research in Animal Sheltering, a Conceptual Analysis. Front. Vet. Sci. 9:804154. doi: 10.3389/fvets.2022.804154

Received: 28 October 2021; Accepted: 22 February 2022; Published: 01 April 2022.

Reviewed by:

Copyright © 2022 Horecka and Neal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kevin Horecka, kevin.horecka@gmail.com

This article is part of the Research Topic

Reimagining Animal Sheltering: Support Services and Community-Driven Sheltering Methods

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Trends in wildlife rehabilitation rescues and animal fate across a six-year period in New South Wales, Australia

Alan b. c. kwok.

1 Independent consultant, Bensville, Australia

Ron Haering

2 New South Wales Department of Planning, New South Wales National Parks and Wildlife Service, Industry and Environment, Parramatta, Australia

Samantha K. Travers

3 New South Wales Department of Planning, Industry and Environment, Parramatta, Australia

4 Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia

Peter Stathis

Associated data.

All relevant data are within the manuscript and its Supporting Information files.

Globally, millions of animals are rescued and rehabilitated by wildlife carers each year. Information gathered in this process is useful for uncovering threats to native wildlife, particularly those from anthropogenic causes. However, few studies using rehabilitation data include a diverse range of fauna, cover large geographical areas, and consider long-term trends. Furthermore, few studies have statistically modelled causes of why animals come into care, and what are their chances of survival. This study draws on 469,553 rescues reported over six years by wildlife rehabilitators for 688 species of bird, reptile, and mammal from New South Wales, Australia. For birds and mammals, ‘abandoned/orphaned’ and ‘collisions with vehicles’ were the dominant causes for rescue, however for reptiles this was ‘unsuitable environment’. Overall rescue numbers were lowest in winter, and highest in spring, with six-times more ‘abandoned/orphaned’ individuals in spring than winter. Of the 364,461 rescues for which the fate of an animal was known, 92% fell within two categories: ‘dead’, ‘died or euthanased’ (54.8% of rescues with known fate) and animals that recovered and were subsequently released (37.1% of rescues with known fate). Modelling of the fate of animals indicated that the likelihood of animal survival (i.e. chance of: being released, left and observed, or permanent care), was related to the cause for rescue. In general, causes for rescue involving physical trauma (collisions, attacks, etc.) had a much lower likelihood of animals surviving than other causes such as ‘unsuitable environment’, ‘abandoned/orphaned’, and this also showed some dependence upon whether the animal was a bird, reptile, or mammal. This suggests rehabilitation efforts could be focused on particular threats or taxa to maximise success, depending on the desired outcomes. The results illustrate the sheer volume of work undertaken by rehabilitation volunteers and professionals toward both animal welfare and to the improvement of wildlife rehabilitation in the future.

Introduction

Wildlife rescue and rehabilitation broadly involves the rescuing, treatment, and care of injured, sick or orphaned native animals. Ultimately the aim is to allow the animals to regain independence for release back into the wild or more suitable habitat [ 1 , 2 ]. When release is not an option early assessment to limit suffering through humane euthanasia is usually required [ 3 ]. Globally, the rescue and rehabilitation of native animals is primarily supported by the community through individual volunteers or as members of not-for-profit volunteer groups and wildlife rehabilitation centres. In the state of New South Wales in south-eastern Australia, the provision of wildlife rehabilitation services also relies heavily on volunteer participation and pro-bono services from private veterinary practices [ 4 – 6 ]. The sector is regulated under licence by the New South Wales Government (i.e. National Parks and Wildlife Service (NPWS) and within), totalling approximately 50 rehabilitation providers each year of various sizes and capacities. Roughly half of these are volunteer groups, with the remainder either rehabilitation facilities or independent rehabilitators. These providers are comprised of over 5,600 volunteers and operate across specified geographic areas over most of NSW. Home-based multi-species care is the sector’s primary mode of operation. These services are augmented by a small number of central facility-based organisations which are predominantly single species or similar species focused, and wildlife hospitals that also function as animal display establishments (i.e. zoos and aquaria; [ 7 ]).

Reporting protocols for wildlife rehabilitators vary depending on country, region, and organisation, but generally records are maintained for each individual animal rescued and rehabilitated. This valuable information generally includes the species of animal, location of rescue, why it came into care, physical condition including trauma sustained, and a range of other details (e.g. sex, life stage) [ 8 – 11 ].

Wildlife rehabilitation data have been used for a variety of purposes, predominantly elucidating threats to native animals and examining associated outcomes (e.g. [ 2 , 12 – 20 ]). Furthermore, studies have begun using these data to explore factors affecting release success, such as the life stage, condition, and sex of the animal (e.g. [ 2 , 9 , 11 , 21 – 23 ]). Single species studies are also common [ 15 , 24 – 28 ] including those for species with a threatened conservation status [ 2 , 13 , 29 , 30 ]). Wildlife rehabilitation data have also been used more broadly for disease surveillance [ 24 , 31 – 34 ], and in animal health science [ 22 , 35 , 36 ].

Despite a growing recognition of the utility of wildlife rehabilitation data, it remains a largely underutilised source of information. Wildlife rehabilitation likely generates millions of animal records per year globally [ 37 ], representing unique and information-rich datasets that may contain hundreds of species (e.g., [ 2 , 38 , 39 ]). While examining trends in causes for wildlife rescue is one of the more commonly researched topics, to date most studies primarily use descriptive statistics to examine these data, and there have been few studies that have attempted to statistically model rescue outcomes based on taxonomic group and the cause for rescue (though see [ 2 , 9 ]). Statistical models can capture levels of uncertainty around rescue outcomes and provide a more informative picture on the underlying factors and processes that contribute to rescue events and successful outcomes, or even allow predictions to be generated, leading to a greater understanding of the efficacy of the wildlife rehabilitation process and identification of areas for improvement.

In this study, we investigate patterns of wildlife rehabilitation across a six-year period from a broad geographical region in New South Wales using data collated from over 50 wildlife rehabilitation providers. New South Wales has about a third of all active wildlife rehabilitation volunteers in Australia and they annually respond to over 180,000 wildlife rescue assistance calls from the community [ 5 , 7 , 40 ]. To date few studies have modelled broad comprehensive trends across multiple animal taxa in wildlife rehabilitation, with most focusing on single species or a small groups of species (e.g. [ 29 , 41 – 43 ].

Here, we describe the general characteristics of wildlife rescues for birds, mammals, and reptiles in our study area, using both descriptive statistics and statistical models to answer three questions: (1) How do patterns of rescue change over time, both throughout the year and across the six-year period? (2) What are the main causes for rescue for each of the major taxonomic groups (birds, mammals, and reptiles)? (3) If inter-year temporal trends are minimal, what is the probability of survival for each of the animal groups, and how does this vary in relation to the cause for rescue?

Data from this study were obtained for the state of New South Wales (NSW), an area spanning over 800,000 km 2 in south-eastern Australia. All wildlife rehabilitation providers are required to collect data about each animal rescued in a standard spreadsheet template as a condition of their licence [ 44 ] ( S1 File ). Reports are submitted annually to the NSW National Parks and Wildlife Service (NPWS) for each financial year (1st July to 30th June). The report includes data on taxonomic group and species, sex, and age class of each rescued animal as well as information about the rescue encounter (date, location, cause for rescue, animal condition) and the animal’s ultimate fate. For this study we extracted data from six consecutive years of reporting (1 July 2013 to 30 June 2018), submitted from 50 licensed wildlife rehabilitation providers.

The data presented in this study were acquired from volunteer wildlife rehabilitation providers acting in accordance with a licence issued under the NSW Biodiversity Conservation Act 2016. The licence permits these providers to undertake wildlife rehabilitation activities including harm (i.e. pursue, capture and/or euthanasia), possession, and release of protected animals in accordance with prescribed standards outlined in the NSW Department of Planning, Industry and Environment Codes of Practice [ 44 ] and/or where necessary by a licensed veterinarian under the NSW Veterinary Practices Act 2003. Data were obtained as part of licensing agreements with the NSW National Parks and Wildlife Service with written consent to publish approved by the NSW Department of Planning, Industry and Environment (DPIE). The work described in this study was not wildlife research requiring approval from an Animal Ethics Committee as it deals only with data.

Data preparation

The initial reporting data contained 37 causes for rescue and 25 fates ( S1 File ). For the purpose of this study, rescues were pooled into a smaller number of categories (19 causes for rescue and 7 fates) ( S2 File ). Additionally, a binary score of animal fate (‘survived’ or ‘died’) was created for records where this was reported. ‘Survived’ includes animals that were left and observed at a rescue location, relocated to a more suitable habitat, released following care, or placed into permanent care. ‘Died’ includes animals that were found dead, died, or were euthanased.

In total, there were 872,087 records reported during the six-year (2013–14 to 2018–19) study period. Just over 97% of these came from three animal groups–birds, mammals, and reptiles. Of the total number of records, 402,534, (46%) were excluded from the descriptive analysis because they: a) did not contain any information about the animal, or the animal’s identification was ambiguous and could not be placed within a group (e.g. an ‘unidentified animal’); b) contained only sightings of animals and were not attended to in some way by a wildlife rehabilitator; c) were records of amphibians (373 records) or non-vertebrate fauna (e.g. spiders, insects, etc.); d) were non-avian marine vertebrates such as whales, seals, sharks, rays, fish etc; e) were reported as floating, drowned, or washed up animals (deemed an ambiguous cause for rescue, n = 48); f) contained both an ‘unknown’ cause for rescue and an ‘unknown’ fate; or f) were an introduced or spurious species (e.g. extinct, or out of known range). These exclusions resulted in a dataset for descriptive analysis of 469,553 records i.e. 54% of the initially reported amount.

For the descriptive analysis of causes for rescue, all records reported with an ‘unknown’ cause were excluded. Percentages therefore refer to proportions of known causes of rescue.

For statistical modelling of the likelihood of survival (see below), records with an ‘unknown’ cause for rescue were included. However, 107,604 records were excluded as they either reported an ambiguous fate (e.g. ‘unknown’ (50,664 records), ‘in care’ (54,428 records), ‘escaped from rescuer or carer’ (2,403 records), or were species that could not be confidently placed into animal subgroups (110 records). This resulted in a dataset of 361,949 records.

Animal classification

Data were investigated at several scales of classification. Analyses were first conducted at the level of broad taxonomic group (class: bird, mammal, reptile). Subgroups were then created within each taxonomic group to allow for more detailed investigation of the fate of animals ( S3 File ). Subgroup classification was based on taxonomy, behaviour, and/or physical characteristics that could potentially reflect shared responses or reasons for coming into care. Where possible, we used classifications based on those that already exist in the wildlife rehabilitation literature (i.e. mammals, [ 19 ]; birds, [ 2 ]), however modifications and additions were made for the additional species in this study.

Seasonal trends were assessed using broad southern hemisphere seasons: Summer (December, January, February), Autumn (March, April, May), Winter (June, July, August), and Spring (September, October, November).

Data analysis

To estimate the number of species for each survey year and for the entire survey period, species accumulation curves were created using the Chao1 and Jacknife2 indices in the PRIMER (Version 6) software [ 45 ].

We used binomial generalised linear mixed models (GLMMs) to assess the likelihood of survival (fate) for taxonomic groups in relation to causes for rescue. A binary classification of fate was used as a response variable for these models (survived: released, left and observed (represented as ‘1’ in the statistical models), or permanent care; or died: dead, died, or euthanased (‘0’)) to model the likelihood of a survival. Likelihood models were constructed using a negative binomial distribution within the ‘glmmadmb’ function from the ‘ glmmADMB’ package [ 46 ] within R (Version 4.0.3, R Core [ 47 ]. We ran two separate models, one for the fate of birds and mammals, and one for the fate of reptiles due to data limitations with cause for rescue for reptiles. The bird and mammal model was constructed to investigate the additive fixed effects of cause for rescue, the two animal groups (mammals and birds), plus their two-way interaction, with random intercepts for each group and subgroup. The reptile model contained only the fixed effect of 20 causes for rescue, and slope was allowed to vary for each of 8 subgroup levels. For each model, we estimated confidence intervals around the model parameters (fixed and random) using Wald Confidence intervals. In prior model versions, survey year was considered (2013–14 to 2018–19), however this did not improve model fit as there was little variation among years, therefore the most parsimonious model was selected and year was not included in final models.

Reporting trends

During the six-year (2013–2014 to 2018–2019) survey period a total of 227 reports were submitted by wildlife rehabilitation providers to NPWS ( Table 1 ). This represents on average, 37.8 ± 1.4 reports per year. This number is generally lower than the total number of active rehabilitation providers as some volunteer groups were unable to provide data reports each year due to internal governance or other technical reasons. The variation in the number of reports received each year were minor ( Table 1 ).

Overall number of species

In total, there were 469,553 rescues reported during the survey period. Just over half of these (53.4%) were birds, 34.1% mammals, and 12.5% reptiles ( Table 2 , S4 File ). In total, 688 species were reported across all animal groups. Birds were the most species rich (65.6% of all species) followed by reptiles (19.5%,) and mammals (15%) ( Table 2 , S3 File ).

On average, there were 457 ± 7 species reported in total each year during the survey period ( Table 3 ). The number of species reported from each animal group was stable from year to year, with no clear increases or decreases ( Table 3 ). Although new species are reported for all taxonomic groups each year, the rate of new species records accumulating across years is slow and approaching a plateau ( Fig 1 ). This pattern was consistent when each taxonomic group was analysed separately ( S5 File ).

Survey year represents the year during the study period (i.e. 1 = 2013–14; 6 = 2018–19).

Threatened species

Overall, 147 species with a conservation status listed as Threatened under the NSW Biodiversity Conservation Act 2016 (BC Act) were reported across the six-year survey period, representing 21.3% of the total number of species rescued. Of these, 106 (72.1%), were classified as ‘Vulnerable’, 26 (17.7%) were ‘Endangered’, and 15 (10.2%) ‘Critically Endangered’ ( Table 4 ). Across all animal groups, 58.5% of threatened species were birds, 31.3% were mammals, and 10.2% were reptiles ( Table 5 ). The relative proportion of species classified as Threatened in the BC Act within each taxonomic group was variable. Approximately 44.7% of mammal species, 19% of bird species, and 11.2% of reptile species were classified as Threatened ( Table 5 ). Of all individual animals rescued that were a threatened species, 92.2% were mammals, followed by 7.3% birds, and <1% reptiles ( Table 5 ).

Causes for rescue

Overall, 46% of all rescues were attributed to a specific cause, with the remainder being reported as ‘unknown’. Of the known causes for rescue, three were dominant across all taxonomic groups: ‘collisions with vehicles’ (24.3%), ‘abandoned/orphaned’ (20.1%), and ‘unsuitable environment’ (16.8%) ( Table 6 ). Another three causes account for about a further 20% of rescues (7.1% ‘entangled/trapped’; 6.2% ‘collisions with other objects’; and 5.1% ‘diseased’ individuals), with thirteen other causes responsible for the remaining 20.4% of rescues ( Table 6 ).

Values are ranked from highest contributor to lowest based on number of records.

The number of rescues attributed to each cause varied between taxonomic groups ( Table 7 , Fig 2 , S4 File ). The two most common causes for bird and mammal rescues were ‘abandonment/orphaned’ (25.2% for birds and 20.8% for mammals) and ‘collisions with vehicles’, (20.5% for birds and 33.5% mammals). About 12% of birds were found injured from collisions with other objects such as windows and buildings and 12% rescued from an ‘unsuitable environment’ (mammals were about 11%). A further 9.2% of mammal rescues were animals ‘entangled/trapped’ in netting or wire.

For reptiles, ‘unsuitable environment’ was the most common cause (53.2% of rescues) followed by ‘attacks from dogs’ (11.6% of rescues) ( Table 7 ). Reptiles were much more likely to be reported as ‘nuisance’ fauna (8.8%) than mammals (1%) or birds (0.4%) ( Table 7 ).

Within each animal group, the cause for rescue did show some variation according to sub-group ( S5 File ). For example, for honeyeaters and passerines, abandoned/orphaned rescues comprised over 40% of the total number of rescues for that group, while this value was much lower for groups such as parrots and diurnal birds of prey (<12%).

Trends across years

Overall, average annual rescues for all animals combined (78,259 ± 2,962, mean ± SE) increased slightly during the study period, particularly from the last year of the survey (2018–19) ( Table 3 , Fig 3 ). The increase can be attributed to bird and mammal rescues with reptile rescues being mostly stable ( Fig 3 ). Several causes for rescue appear to be increasing (in terms of the number of rescues) ( Fig 4 , S4 File ). Most notable are collisions (both with vehicles, as well as other collisions) which have increased every year during the study period ( Fig 4 ). Other causes for rescues such as ‘entangled’, ‘disease’ and ‘unsuitable environment’ showed slight increases, or inconsistent changes in the number of rescues ( Fig 4 , S4 File ).

Trends across months

Each month there were on average 6523 ± 232 rescues, however there is marked variation between months. The volume of rescues peak in October and November (spring), before steadily decreasing from December to January (early to mid-summer) to the lowest point in June (winter, Fig 5 , S6 File ). There are approximately 2.4 times more rescues in October and November (about 9300) than in June (about 4000 rescues/month) ( Fig 5 ). This pattern is apparent for birds and reptiles. For mammals the peak (August) and low (April) both occur slightly earlier than for the other taxa ( Fig 5 ). These patterns of monthly variation consistently occur for every year of the study period ( Fig 6 , S7 File ).

There was substantial monthly variation in the number of rescues for several causes for rescue ( Fig 7 , S8 File ). For example, there are six times more ‘abandoned/orphaned’ rescues during the peak in November (1180 ± 65.4) than there are in June (197 ± 9), with a similar pattern in ‘unsuitable environment’ rescues. ‘Collisions with vehicles’, gradually increased from March to October, before declining for the following period ( Fig 7 ).

Some causes for rescue also showed monthly variation in the percentage contribution to the total number of rescues ( Fig 8 ). The most noticeable change is an increase in the percentage of rescues due to ‘collisions with vehicles’ from January to June, with a mirrored decline from July onward. This pattern is in direct contrast to the percentage attributed to the ‘abandoned/orphaned’ cause for rescue. The remaining causes display only minor variations throughout the year.

Fate and likelihood of survival

Of the 364,461 rescues for which a fate was known about 92% fell within two categories: Those that were dead, died or euthanased (54.8% of all rescues) and animals that recovered and were subsequently released (37.1% of all rescues) ( Table 8 ). A further 7.2% of animals were reported as left and observed following a rescue callout and the remaining <1% were in long term rehabilitation or permanent care.

When the outcome of an animal’s fate was simplified into survived (released, left and observed, or permanent care) or died (dead, died, or euthanased), and with other outcomes omitted from analysis (i.e. escaped rescuer or from care), 55% of rescues (n = 199,785) survived, and 45% (n = 162,273) died. The proportion of rescues that survived to those that died was stable throughout the study period, though ‘died’ rescues in 2017–18 were slightly higher than other years ( Table 9 , S9 File ). The outcome of a rescue was also dependent upon the taxonomic group ( Fig 9 ). For example, the proportion of animals which had a negative outcome is substantially higher for mammals (65%) and birds (57%) than for reptiles (24%), across all years ( Fig 9 ). The outcome of an animal’s fate also varied depending on the cause for rescue ( Table 10 ), with marked differences between groups.

Predicted likelihood of survival

For birds and mammals combined, 10 causes for rescue generally resulted in a greater than 50% likelihood of survival outcomes ( Fig 10 ). This was strongest for ‘domestic or captivity issues’, ‘nuisance’, and ‘unsuitable environment’.

For birds and mammals combined, 10 of the 20 causes for rescue had less than a 50% likelihood of survival ( Fig 10 ). For many causes for rescue, there was little variation in the probability of survival, particularly for attacks, collisions, and disease ( Fig 10 ). The likelihood of survival did, however, show variation between taxonomic groups ( Fig 11 ). In general, birds have a higher likelihood of survival compared to mammals, and for half of the causes for rescue (‘abandoned/orphaned, ‘attacked by dog’, ‘attacked by other’, ‘collisions with vehicles’, ‘collisions with other, ‘electrocution’, ‘entanglement/trapped’, ‘fouled’, ‘weather–heat’, and ‘weather–unspecified’) this difference was statistically significant. For collisions (with both vehicles, and other) and ‘weather–heat’ in particular, mammals had a significantly lower likelihood survival ( Fig 11 ).

For ‘collisions–other’, ‘fouled’, and ‘weather–heat’, the likelihood of survival was greater than 0.5 for birds and lower than 0.5 for the mammals ( Fig 11 ). In no cases did mammals have a significantly greater likelihood of survival than birds. ‘Nuisance’ rescues and those for animals reported in an unsuitable environment had a consistently high likelihood of survival for both birds and mammals ( Fig 11 ). Predicted outcomes also indicate that the likelihood of survival varies between animal subgroups. For example, echidnas, and the koala generally fare much better than other subgroups such as rodents, and large kangaroos ( Fig 12 ).

For reptiles, the predicted likelihood of survival was generally greater or close to 50% for all causes for rescue, except ‘collisions with vehicle’, which was markedly lower (0.25) ( Fig 13 ). There was, however, high variability in the fate of reptiles for most causes for rescue. As for birds and mammals, the subgroup of the animal influences a reptile’s likelihood of survival with snakes and turtles more likely to survive than other reptiles such as geckos, and bearded dragons ( Fig 14 ).

This six-year study presents the largest single analysis of wildlife rehabilitation data in New South Wales and builds on a growing understanding of the direct factors contributing to the rescue of injured, sick and orphaned Australian native animals and their likelihood of survival. The results shed light on four key outcomes: (1) The relatively high annual number of animal rescues occurring over the study area with birds the most common and most species rich animal group represented; (2) abandonment or orphaning of individuals and collisions with vehicles are the dominant direct reasons why birds and mammals are reported and rescued, accounting for nearly half of all rescues, where a cause was attributed; (3) there is substantial seasonal variation in rescue volume throughout the year, but only minor increases across the study period; (4) for most causes for rescue, the modelled likelihood of survival for birds and mammals is <0.5, but substantially higher for reptiles (>0.6). Additionally, variation in predicted outcomes is low for birds and mammals but is much higher for reptiles. Despite clear trends at the broadest taxonomic level, our data indicate that likelihood of survival also depends on both the group of the animal and the subgroup to which it belongs.

Volume and diversity of animals rescued

We found wildlife rehabilitation providers in New South Wales rescued on average about 78,260 native animals each year between 2013–19. The number of rescues appears have risen slightly over the study period, but this may be a function of improved reporting and an increase in volunteer numbers collecting data [ 48 ]. The annual average is a 41% increase on the 45,000 animals reported by the last study that collated rescue numbers in New South Wales across all animal groups [ 49 ]. In our study, a relatively high proportion of rescues were of birds (53.4% of all rescues), and similar results have been reported previously for rehabilitators [ 49 ] and in private veterinary hospitals [ 6 , 50 ].

Most species rescued were common and widespread, though threatened species represent a substantial number of species and rescues in certain cases. Threatened animals accounted for only 4.6% of the total number of animals rescued but more than a fifth of all species in the study. Nearly half (45%) of all mammal species rescued were threatened species, and the majority of threatened animal rescues were of mammals (92%). The latter figure is due to high number of rescues of two threatened species: the grey-headed flying fox Pteropus poliocephalus [ 42 ] and koala Phascolarctos cinereus [ 48 , 51 ]. The high richness of threatened species rescues in New South Wales is previously unreported, and these data may help inform conservation management programs for these species [ 37 ]. The true number of threatened species rescued is also likely to be higher when marine species are taken into consideration [ 41 , 48 , 51 ].

Seasonal variation in wildlife rescue volume has been well reported in Australia [ 1 , 19 ] and other countries [ 27 , 28 , 39 , 52 , 53 ], with more rescues occurring during Spring-Summer than Autumn-Winter. This generally reflects increased levels of breeding and rearing of young (particularly for birds), as well as greater movement and activity of animals during these periods. In Australia, Spring-Summer can bring periods of extreme heat and rainfall associated flooding events, which can also result in fauna requiring rescue and rehabilitation (e.g. flying-foxes, [ 42 ]). Birds showed the most distinct seasonal pattern which correlates with the high influx of abandoned/orphaned birds rescued during that time. In contrast, for some groups of animals the incidences of rescue can be higher during the colder months. For example, our data indicate that rescues for large macropods are greater in the cooler June-August period, particularly for collisions with vehicles. This aligns with a range of other studies [ 54 , 55 ], including motor vehicle insurance data [ 56 ]. Higher macropod collisions during cooler months is thought to be due to the spatial availability of food (lower growth of food in pastures, and greater growth of food along moister roadside verges during these periods), as well as more night-time driving hours and therefore greater potential contact with nocturnal animals [ 55 ]. These patterns interact with higher mortality for certain causes for rescue, resulting in a lower likelihood of survival for animals such as large macropods (see below).

Our study has shown that in New South Wales the two dominant causes reported for bird and mammal rescues are ‘abandoned/orphaned’, and ‘collisions with vehicles’. Together, these two causes account for about 45% of all known reasons for rescue. ‘Abandoned/orphaned’ animals are often cited as a key cause for rescue in Australia [ 1 , 19 , 49 ] and other countries [ 2 , 10 , 18 , 20 , 27 , 57 – 59 ], often involving healthy juvenile animals with a relatively high likelihood of recovery and future release. High incident rates of wildlife ‘collisions with vehicles’ show some geographic variation but are particularly prevalent in Australia and overseas in studies with increasing road networks and vehicular traffic [ 19 , 29 , 38 – 40 ], and result in relatively high rates of mortality due to the traumatic nature of injuries sustained by affected animals.

Just over 60% of all reptile rescues were assigned as ‘unsuitable environment’ or ‘nuisance’ in this study. In these cases, the animal is not necessarily injured, but the member of the public either perceives them to be in danger (e.g. confined in a yard with a dog or cat), or more likely that they do not want the animal close to their dwelling. This was particularly the case for snakes, which represented most of the ‘nuisance’ records across all animals. This result is consistent with previous studies that found nearly 40% of lizards and over 70% of snakes were rescued in Sydney, New South Wales because residents wanted them removed from their property [ 43 ]. Studies investigating reptiles within wildlife rehabilitation are limited, and those published to date have not included a category alluding to ‘unsuitable environment’ or ‘nuisance’ [ 53 , 60 , 61 ]. This is because animal receiving centres in these studies were mostly wildlife veterinary hospitals treating reptiles affected by trauma related injuries from vehicles and dog and cat attacks. The inclusion of this category of cause for rescue is important for monitoring human-wildlife conflicts which clearly can be animal specific.

Wildlife rehabilitation data are often used to show the direct threats to native animals, particularly those related to anthropogenic activities [ 1 , 19 , 57 , 58 ]. Within this context it is important to recognise that threats to the welfare of animals are varied and often there may be more than one reason why an animal needs rescue [ 8 , 13 ]. An animal may be abandoned or orphaned because its parent has been attacked by a dog or has been in a collision with a vehicle. This is particularly the case for marsupials (e.g. kangaroos, possums) as they carry pouch young, but is also relevant for birds which suffer a higher number of attacks from other animals. Underlying these direct threats are also landscape scale impacts from fragmentation and clearing of habitat [ 18 , 19 ] that may be difficult to detect by rehabilitators on ground. For example, increases in wild bird collisions in Spain have been shown to be due to increased road infrastructure and vehicle activity [ 39 ]. Similarly, urban landscape change has been shown to negatively affect body condition and increase the prevalence of Chlamydia disease in koalas in south-east Queensland [ 62 ], and such change would not be reported in rehabilitation data. In general, as is the case with this dataset, a large proportion of wildlife rescue and rehabilitation occurs in urban and peri-urban environments. It is in these environments where both humans are most densely populated, and where the most traumatic threats to wildlife are likely to occur (e.g. collisions).

For many rescued animals, the chances of survival are low. Our study shows that across all animal groups, more than half died or were humanely euthanased, while about 37% were released back to the wild. The release rate is slightly higher than the 31% reported for New South Wales approximately twenty years ago [ 49 ]. The overall mortality rate of about 55% is similar to that found in Queensland, Australia [ 19 ] and in studies elsewhere [ 2 , 57 ]. Anthropogenic driven causes such as all collision, attacks, and entangled/trapped have previously been shown to be associated with high mortality outcomes, and for some mammal species this may be as high as 90% [ 19 , 48 , 51 ]. In contrast, the majority of reptiles are healthy animals in ‘unsuitable environments’ or are a ‘nuisance’, and subsequently had release rates of greater than 75%. Importantly, in our study we were able to utilise a large dataset to create robust statistical models that predict the likelihood of survival for rescued animals. These analyses indicate that not only did many causes for rescue have a low predicted likelihood of survival, but the variation around these predictions was low.

In addition to trends evident for broad animal groups, variability in survivorship is also likely to occur within animal group and for different species. This is related to cause for rescue, the nature of injures, condition at admission [ 9 , 11 ], as well the sex, and age of the animal (e.g. [ 9 , 17 , 42 , 63 ]. Individual species may have particular traits, behaviours, breeding and movement patterns that may expose them to particular threats [ 19 ], and these may also be age-specific (e.g. [ 43 ]. Future analysis at finer taxonomic or functional levels will allow us to gain a better understanding of the cause of rescue events and will assist in the development of strategies both for threat mitigation, and for treatment once in care. This may be particularly the case for threatened and/or migratory species, for which specific conservation actions are often enacted (e.g. [ 51 ]).

Implications for the volunteer wildlife rehabilitation sector

This study demonstrates the significant effort volunteers make to the rescue of injured, sick and orphaned native animals in New South Wales. They are frontline responders often working in challenging environments at significant personal cost and stress [ 5 , 7 , 40 ]. In New South Wales alone, the total value of volunteer services each year was estimated to be in excess of $27 million AUD and for private veterinary hospitals about $1.1 million AUD [ 5 , 6 ]. There are implications for the welfare of animals and their prospects for recovery if they are not adequately assessed and rehabilitated, and consequently there has been a significant investment in the development of minimum standards in the wildlife rehabilitation sector in NSW through the development of Codes of Practice [ 6 ]. These standards are being augmented with the training standards to ensure volunteers across the sector are competent to implement the requirements of each Code of Practice [ 4 ]. These standards need to be complemented by a program of mentoring [ 5 , 64 ] and ongoing volunteer training opportunities such as webinars and conferences that connect volunteers to other veterinary and conservation networks. It is clear from this study that such standards and training are necessary given the volume of rescues being undertaken each year.

This study also highlights the benefits of the State’s fauna authority working collaboratively with the volunteer wildlife rehabilitation sector to standardise data collection processes and report periodically so it can be systematically collated and reported on in a holistic fashion [ 1 , 19 , 58 ]. There are limitations and challenges when utilising wildlife rehabilitation data [ 7 , 9 , 37 , 58 ], including addressing poor species identification and a high proportion of unknown causes for rescue or animal fates. However, this does not negate the potential usefulness of the data so long as these caveats are considered are addressed carefully. Furthermore, it is crucial to demonstrate to the wildlife rehabilitators the potential uses of the data, particularly as to how their reporting is used to inform landscape scale processes such as environmental planning, research and threatened species management [ 37 ]. This is also important in creating an understanding amongst rehabilitators as to the importance of accurate data collection and reporting, which is generally regarded as a low priority by some providers. Additionally, further collaboration should be sought between scientists, rehabilitators, and veterinarians to investigate post-release success of animals on wild populations [ 37 ].

From our study, it is clear that there are discernible trends in wildlife rehabilitation in NSW. Birds, mammals, and reptiles each are subject to specific threats, each with their own probability of a successful rehabilitation outcome. In general, causes for rescue such as collisions or animal attacks that involve some sort of physical trauma result in relatively poor chances for a successful outcome. In contrast, less physically traumatic, such as those for abandoned or orphaned individuals, or where humans simply do not want the animals in their presence, have a much relatively high likelihood of a successful outcome. Detailed statistical analyses indicate that most causes for rescue have very little variation in their likelihood of survival for an animal, suggesting that these data could be used to better inform triage of animals in the future, whilst also allowing focus on those threats for which there are greater chances of success both for individuals and populations.

Supporting information

Acknowledgments.

We are grateful to the wildlife care and rescue volunteers of New South Wales and the groups they belong to for their ongoing passion and dedication to wildlife rehabilitation, and commitment to reporting. We would also like to thank the numerous private veterinary practices who dedicate significant resources to the treatment of wildlife often on a pro-bono basis. We thank Ian Oliver, Martin Predavec and an anonymous reviewer for their helpful and critical comments that strengthened the paper.

Funding Statement

The author(s) received no specific funding for this work.

Data Availability

Research Animals

This comprehensive resource looks at the animals most commonly used in research and dissection, examining issues like the Animal Welfare Act, breeding and transport, research alternatives and more.

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Research on animals costs many millions of lives each year. And millions more animals are kept confined in laboratories and cages, awaiting their turn for experimentation. Biomedical research using animals is a largely secretive process and the public knows little about what goes on in research labs. This exclusive Faunalytics Fundamental examines the use of animals in research, estimating the scope and nature of the problem based on the best available data. We hope you find the information useful in your advocacy for research animals. Please see all the sources here.

Meet the Animals

Animals live rich and complex lives. Primates exhibit deep thought and intricate social structures; their similarity to humans has made using other primates unthinkable for most purposes other than experimentation (and “entertainment”). Dogs like beagles are docile, friendly, and cooperative – traits that make them easier to manage as test subjects. Guinea pigs, which have become synonymous with animal research, are gentle and even purr like cats when they’re happy. Mice and rats are empathetic and studies have shown that they will risk themselves to rescue cage-mates in captivity. There is no doubt that the animals we use for research and dissection are capable of thinking, feeling, and suffering just like we are. Check out the interactive graphics below to explore facts about some of the animals most commonly used in research. Of course, we’re only scratching the surface of what makes these animals unique, sentient, and deserving of their own rights to life and freedom.

Rats & Mice

Show empathy in different forms to other rats and mice in trouble.

• Sentient & Smart – Remember sources of pain and have a long-lasting aversion to them
• Cage Boredom – Can quickly become bored unless provided with enrichment
• Have A Heart – Have gregarious personalities and live in cohesive social groups

Guinea Pigs

Need companionship and enjoy cuddling and sleeping together.

• Active Animals – Love to play and need “boredom breakers” to stay stimulated
• Happy Pop – Known to jump in the air—called “popcorning”—when excited
• A Balancing Act – Have complex and delicately balanced social structures

Used in research because they are gentle, sweet, and curious.

• Mother’s Intinct – Are highly protective of their own puppies and even human babies
• Strength In Numbers – Have a deep pack instinct and struggle with separation anxiety
• Led By The Nose – Have an extraordinary sense of smell and a strong urge to investigate scents

Often become bored, depressed, and aggressive in captivity.

• Healing Touch – Groom each other after conflicts to repair damaged relationships
• Getting Their Point Across – Communicate through vocalizations, facial expressions, and gestures
• Cool Tools – Use a variety of objects as tools to help solve problems

Opinions about the use of animals for research are complicated and often divided when it comes to different purposes or types of research. For instance, a clear majority of people are against the use of animals for testing cosmetics and personal care products . However, if the research is claimed to save or improve human lives, then opinions shift, even if those claims are hypothetical or baseless. Attitudes regarding the use of animals for student dissection are less clear given there are fewer surveys of those audiences. However, the limited research shows that many students and teachers prefer non-animal alternatives to dissection – see our “In The Classroom” section below for a detailed breakdown.

What about trends over time? The Gallup figures shown in the graphic above have shown a consistent decline in the perceived moral acceptability of testing on animals. From 65% saying “acceptable” in 2001 to 51% saying “acceptable” in 2017 (a record low). Through our Animal Tracker survey, Faunalytics has monitored attitudes about animals used for research since 2008. In the selection of Animal Tracker charts below, you can see that feelings about the protection of animals in laboratories has fluctuated since 2008/2009, but only slightly. In summary, belief in the importance of research animal welfare has stayed strong, most people continue to question the adequacy of laws protecting laboratory animals, and belief in the necessity of animal research and dissection appears to be dropping in recent years.

Breeding and Transport

Before they arrive at the laboratory, animals used for research are most often born and housed in large breeding facilities found throughout the world. Some research animals may come from relatively regulated companies such as Charles River or Interfauna, based in countries like the United States, England, or Spain. Other animals, such as monkeys, more often come from international suppliers that operate in Southeast Asia, parts of Africa, and China. Below, we connect some of the dots of the global breeding and transport of research animals, with Southeast Asia as an example.

In the Laboratory

How many animals are bred for research, kept in laboratories, and used in experiments? Unfortunately, under current regulations in virtually any country, it’s impossible to know the exact answer. Estimates for the total number of animals used in research worldwide hover around 115 million to 127 million, while estimates for the U.S. specifically hover around 25 million. In the U.S., researchers are not required to report the numbers of rats, mice, and birds used in experiments, and these species combined make up an estimated 95% of all animals used in research. The chart below provides the best estimates for the numbers of mice, rats, and birds used in the United States in 2015, as well as the other species whose use is covered by the Animal Welfare Act.

Immediately, we can see that the Animal Welfare Act is inadequate and ignores the big picture. Not only does the act not cover these animals, it also does not mandate that researchers maintain any statistics for these species. Therefore, we can only make educated estimates on the numbers of animals being used. Secondly, we see that researchers, who already keep hidden from public view, have very few official controls or oversight for most of the work they do. For animal advocates, it is important to spotlight these facilities and the often egregious suffering that they house.

The above map shows animal experimentation labs and breeding facilities in the U.S. You can look up the animal research facilities in your state using this tool put together by HSUS . The prevalence of such operations throughout the country may be shocking to see. Unlike the farms and feedlots that we often see while driving through the countryside, animal research and breeding facilities are much more hidden from view. Likewise, the laws that govern and regulate experimentation in the U.S. (and many other places in the world) do their part to obscure both the scope and the nature of animal research.

In the Classroom

Conventional wisdom says that the best way for students to learn about biology is through hands-on methods, usually involving the dissection of dead animals. Dissection has been used as a learning tool for many decades, and it’s estimated that 6-12 million animals are used each year – though that is a very loose estimate and no official statistics are kept. Thankfully, scientific advancements and public opinion are starting to help turn the tide. Recently, a study by Faunalytics on behalf of the National Anti-Vivisection Society found that only half of students are interested in dissecting animals and more than a third (37%) of students would prefer to use alternatives to animal dissection. What’s more, a study by the American Anti-Vivisection Society found that 75% of U.S. adults agree that “students taking biology courses should be allowed to choose alternative methods of learning that do not involve dissecting animals.”

Support for dissection isn’t just dwindling. Students are actively demanding alternatives to the use of animals in grade school classrooms. In response, many schools have created what are called “student choice policies,” which allow students to opt-out of dissection for ethical reasons. Unfortunately, such policies are not ubiquitous yet; in the U.S., only 18 states and the District of Columbia allow students to choose alternatives. Even when such policies exist, teachers and students may not be aware of them. The Faunalytics/NAVS study mentioned earlier showed that only 53% of educators in states with student choice policies knew about those policies. The same study found that 38% of students didn’t know if alternatives were available to them. Thanks to the work of anti-vivisection groups, this is changing.

Alternatives

If you aren’t swayed by the ethical arguments against animal research, perhaps this will change your mind: Approximately 100 vaccines have shown effectiveness against HIV-like animal viruses, but none prevent HIV in humans. Up to 1,000 drugs have shown effectiveness for neuroprotection in animals, but none for humans. While the biomedical research industry is quick to claim victories, the reality is less glamourous: nine out of ten drugs fail in clinical studies because they cannot predict how they will behave in people; only 8% of drugs tested on animals are deemed fit for human use; one meta-study found that animal trials overestimate the likelihood that a treatment works by 30% because negative results often go unpublished. Fortunately, using animals in scientific research is not a foregone conclusion. On the contrary, there is a burgeoning field of alternatives to animal research, and many such alternatives are already in use today.

The above graphic shows a small selection of some of the most exciting and promising alternatives to animal research that exist today. There are many others that are available, and even more are on the way. Among the many organizations and institutions working on better alternatives are FRAME , INTERNICHE , and Animalearn who are leading the way in promoting clinical and educational alternatives.

This Faunalytics Fundamental has provided a visual overview of the use of animals in research. The result is a complex picture: public opinion is mixed and context-specific; the number of animals used is a guesstimate due to lack of reporting for certain species; and laws are not keeping pace with students’ interest in dissection alternatives. The scientific establishment has a lot of inertia in favor of continuing the use of animals in research, but is slowly shifting towards alternatives.

Meanwhile, millions of animals are trapped in labs, waiting for us to help them. The data presented here raise many questions for how to invest limited advocacy resources:

• How can advocates galvanize the majority of the public that believes research animal welfare is important and guide them towards supporting elimination and alternatives? Looking a public opinion research such as this and this can help advocates to target different public audiences on a case by case basis.
• What can animal advocates – and members of the public – do to contribute to the development of research alternatives? Education about alternatives and their effectiveness is key.
• Public opinion for various animal research issues is somewhat contradictory and confused. What are the most effective ways that advocates can clarify the issues? First, animal advocates needs to educate themselves on the science , and then communicate that effectively.
• As animal research regulations become more stringent in some parts of the world, “outsourcing” to laboratories in other parts of the world becomes a bigger issue. How can advocates anticipate and prevent this trend?

These are questions advocates should think about and possibly research further.

We hope you find the above information useful in your advocacy for animals used in research. Check out all the sources here.

There is so much more work to be done to give advocates the insight they need to choose the most effective ways to help animals. Please donate generously now to help us bring you and other advocates this crucial information.

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A new beginning for retired laboratory rabbits.

Healthy laboratory animals who are no longer needed in research deserve the chance to be rehomed. The practice of rehoming retired laboratory animals is more common with dogs, but other species are also deserving of this opportunity. A few months ago, 10 female New Zealand white rabbits were granted a new beginning thanks to a collaboration between an animal rescue organization and the research institution where they had been living for approximately three years. 

The Small Animal Rescue Society of British Columbia—a volunteer-run organization specializing in the rescue and rehoming of small animals such as rabbits, guinea pigs, rats, and hedgehogs—offered them a new home at its private shelter. Prior to the rabbits’ release from the laboratory, veterinarians and staff at the originating research institution offered to spay and vaccinate the animals, which would have cost the rescue organization more than $3,000. The research institution also shared the animals’ full medical records with the Small Animal Rescue Society.

The 10 rabbits, who had been living in pairs or trios at the research institution, now all live together in a pen measuring 10 feet by 8 feet. As anyone experienced with rabbits will know, unfamiliar rabbits must be carefully introduced to each other to avoid aggression. Taking newly acquainted rabbits for a car ride together is a proven method of bonding them, so the rescue organization took advantage of this method during the drive between the research institution and the rabbits’ new home: Although individual carriers were brought for each rabbit (just in case), the animals were placed in the van together so they could huddle and seek comfort from each other during the ride (see photo bottom left). 

Upon their arrival at the shelter, the rabbits’ pen was outfitted with an abundance of hiding boxes to ensure that each animal had her own space to retreat to. The rabbits were getting along well, so after a few hours, many of the boxes were removed to further encourage them to interact as a group. Lisa Hutcheon, co-founder and executive director of the Small Animal Rescue Society, said that the rabbits have been getting on well from the beginning. She noted that former laboratory pen mates tended to rest with each other at first, but that now the whole group mingles together. Only one former pair—who were friendly in the laboratory—now actively avoid each other; perhaps with more friends to choose from, they realized they were not so fond of each other after all! With 15 years’ experience running the rescue and shelter, Hutcheon has found that it is much easier to bond larger groups of rabbits (10+ individuals) compared to pairs or trios. 

Hutcheon describes this group of girls as “busy, busy, busy.” Compared to other domestic rabbits residing at the shelter—most of whom were found abandoned or surrendered by caretakers no longer willing or able to keep them—these former laboratory animals are unusually curious and active. They love to chew on any objects placed inside their pen, especially their cardboard hiding boxes, which need to be replaced every three days. These youthful rabbits are always on the move, nudging noses, hopping around, and showing interest in everything that happens at the shelter.

Hutcheon was surprised at how quickly the rabbits settled into their new life. Within one week, they had fully adapted to their new routine; for example, just like the other rabbits residing at the shelter, they circle in excited anticipation of the daily delivery of fresh vegetables and dried cranberry treats. 

Despite the rabbits’ relatively recent arrival, shelter volunteers can already recognize individuals by their unique personalities. One doe has a habit of grooming her companions around the eyes, and another, nicknamed Big Mamma due to her size, can always be seen sitting on top of a box. One rabbit always pokes her nose through the pen gate to socialize with the neighboring bunnies, while another is incredibly curious about people. 

Before taking in these rabbits, Hutcheon was unsure how well they would adjust; she was open to the possibility that they would need to continue to live together at the shelter with other rabbits who may not do well in a private home. However, these girls have surpassed all expectations: They are friendly, outgoing, and will do well with a human family of their own. The rabbits will be up for adoption once the staff get to know each rabbit properly and can be certain of the type of home best suited to each one—for example, whether they will do well with another type of animal or young children in the home. What is certain, however, is that they will capture the heart of any person fortunate enough to take them in. 

For tips on bonding groups of rabbits, Hutcheon encourages readers to contact the Small Animal Rescue Society of British Columbia at [email protected] . Donations to support the care of these rabbits are also welcome. 

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The 25 million animals used in U.S. laboratory tests endure numerous horrors every year. Researchers most often use non-human primates and small mammals like mice, rats, and rabbits, but dogs and cats are also common subjects in their painful, repetitive, and lengthy trials. All types of laboratory animals are usually bred just for research , but they start their lives with the same potential as our treasured pets. Breeders don’t remove their abilities to feel physical and emotional pain, as doing so would of course compromise the study’s findings.

Animals who survive laboratory tests are typically euthanized when a study comes to a close, even if they’re still healthy. Thankfully, some rescue groups are working to change that. Animals need rehabilitation after their traumatizing lab experiences. Many have trouble trusting humans again, and may suffer from PTSD-like symptoms. But the groups below offer hope to these little soldiers, so they can move beyond their painful pasts and enjoy a new life feeling the grass under their feet and the love of compassionate pet parents.

1. New Life Animal Sanctuary

Elsinore, Calif.’s  New Life Animal Sanctuary  takes retired animals from laboratories, willing to offer them a comfortable forever home as an alternative to euthanasia. The sanctuary fights to save a variety of lab animals, from common pigeons and rats to cuddlier bunnies and beagles, and works with researchers and students to lobby for lab animals’ right to life after experimentation. Their Facebook page shares videos of animals enjoying their new home, plus updates on their latest crowdfunding and rescue goals.

In their first month, New Life rescued over 300 rodents from a closing psychology program at Calif. State University-Northridge and adopted all of them, plus the offspring of pregnant animals, to loving homes.

2. The Beagle Freedom Project

Tragically, one of our nation’s favorite dogs is also a popular choice for animal research. The friendly, sweet, trusting, and forgiving nature of the breed makes it ideal for family life and, unfortunately, laboratory trials. It’s tough to imagine what these sweet pups endure in the labs, but we’re thankful that  The Beagle Freedom Project  is there to give these dogs hope for a better future.

The Project began in 2010, when founder Shannon Keith learned that beagles formerly used in laboratory testing would be given a chance at freedom. Since then, she and her team have been removing dogs from laboratory situations and transporting them to forever homes. The organization informs adopters that laboratory dogs can be difficult — they aren’t used to loving humans and, in most cases, have never been outdoors. On the other hand, their transformations and reaction to their new homes will make up for any challenges.

3. Chimp Haven

Let’s not forget the primate survivors of animal research. Because apes can’t be adopted out,  Chimp Haven  aims to Support their long-term needs. The sanctuary was first established in 1995 as a place where chimpanzees could live in spacious outdoor areas and form social groups. In 2000, Chimp Haven earned government funding in tandem with the CHIMP act, which retires chimpanzees from federally funded research programs.

Chimp Haven also supports chimpanzees from non-government programs. “The Price is Right” host Bob Barker recently sponsored five adult chimps who had lived their entire lives in laboratory settings. As seen in this video , they are now on their way to a life of freedom at the sanctuary.

4. Animal Rescue Corps

The  Animal Rescue Corps  (ARC)  specializes in cases involving large numbers of animals, such as natural disasters, puppy mills, dog fighting rings and, of course, laboratory situations.

ARC seizes animals legally, using investigations and partnering with law enforcement and other animal organizations to ensure a successful rescue. Before taking animals, the organization plans ahead to secure a comfortable, safe place for each of them in the appropriate animal shelter, foster home, or sanctuary.

ARC founder Scotlund Haisley built a revolutionary cageless shelter  that allows his animals to enjoy their freedom immediately after rescue from a horrible life of confinement.

5. Kindness Ranch

From a quick glance at their website,  Kindness Ranch  looks like any other animal shelter, with profiles of cute beagles and kitties available for adoption. But the ranch is designed specifically to rehabilitate cats, dogs, sheep, horses, and pigs who are later adopted into loving forever homes. Those who are too sick to leave can live the rest of their days in comfort at the Ranch.

Animals at the Kindness Ranch live in a spacious home-like environment with plenty of space and opportunity to play outside and with toys and other animals. This helps them learn to socialize properly after a former life of isolation. Caretakers work to give the animals round-the-clock care until they are healthy and well-adjusted enough to be adopted out.

Green Monsters: Don’t see your favorite group on the list? Tell us which others you Support with a comment below!

Image source: Kindness Ranch / Facebook

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I just learned about Last Chance Corral. They save nurse foals. Thank all of these amazing organizations that save and protect animals .

hello again

Dogs rescued from war-torn areas near Israel up for adoption

BRUCE TOWNSHIP, Mich. (WWJ) – A group of dogs rescued from war-torn areas in Israel and Palestine are now finding forever homes in the United States.

One of those pups, Cody, has seen a lot.

Kelley LaBonty, director of Detroit Animal Welfare Group, said the organization received Cody along with nine other dogs from the animal protection organization SPCA International .

“Some of these dogs had their ears cut off, and they were full of maggots, and they were abused, and they were neglected, and starved,” she said.

LaBonty said the dogs are of the Canaan breed from the Jordan and Palestinian areas.

“They’re very very resilient,” she said.

Some of the dogs have fortunately already been adopted.

“Intelligent, they are protective but not aggressive; they are great family dogs,” LaBonty said.

Stephanie Grunow and her family adopted a dog from the group named Xena, changing all of their lives forever.

“She needs a good home,” Grunow said. “She needs a good loving safe home and she wasn’t safe there.”

Grunow said her family grieved the loss of their 13-year-old dog named Peanut a month before they found Xena. She said they weren’t planning to get another dog so soon, but when she saw Xena, she also saw Peanut in her, and the decision was obvious.

“We have a lot of love to give each other ... for a lot of years,” Grunow said.

For more information on the dogs and the adoption process, visit the DAWG website .

Copyright 2024 WWJ via CNN Newsource. All rights reserved.

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