A Systematic Review of the Use of Technology to Monitor Welfare in Zoo Animals: Is There Space for Improvement?

Human-animal interactions and machine-animal interactions in animals under human care: A summary of stakeholder and researcher perceptions and future directions

Animals under human care are exposed to a potentially large range of both familiar and unfamiliar humans. Human-animal interactions vary across settings, and individuals, with the nature of the interaction being affected by a suite of different intrinsic and extrinsic factors. These interactions can be described as positive, negative or neutral. Across some industries, there has been a move towards the development of technologies to support or replace human interactions with animals. Whilst this has many benefits, there can also be challenges associated with increased technology use. A day-long Animal Welfare Research Network workshop was hosted at Harper Adams University, UK, with the aim of bringing together stakeholders and researchers (n = 38) from the companion, farm and zoo animal fields, to discuss benefits, challenges and limitations of human-animal interactions and machine-animal interactions for animals under human care and create a list of future research priorities. The workshop consisted of four talks from experts within these areas, followed by break-out room discussions. This work is the outcome of that workshop. The key recommendations are that approaches to advancing the scientific discipline of machine-animal interactions in animals under human care should focus on: (1) interdisciplinary collaboration; (2) development of validated methods; (3) incorporation of an animal-centred perspective; (4) a focus on promotion of positive animal welfare states (not just avoidance of negative states); and (5) an exploration of ways that machines can support a reduction in the exposure of animals to negative human-animal interactions to reduce negative, and increase positive, experiences for animals.

Access to veterinary care: evaluating working definitions, barriers, and implications for animal welfare

Humans have a moral obligation to meet the physical and mental needs of the animals in their care. This requires access to resources such as veterinary care, which is integral to achieving animal welfare. However, "access" to veterinary care is not always homogenous across communities and currently lacks a consistent definition. The objectives of this scoping review were to (1) understand how "access" to veterinary care has been defined in the literature, (2) map a broad list of potential barriers that may influence access to veterinary care, and (3) identify how access to care impacts the welfare of companion and livestock animals. The literature search yielded a total of 1,044 publications, 77 of which were relevant to our inclusion criteria, and were published between 2002 and 2022. Studies were most frequently conducted in the United States (n = 17) and Canada (n = 11). Publications defining access to veterinary care (n = 10) or discussing its impacts on animal welfare (n = 13) were minimal. However, barriers to accessing veterinary care were thoroughly discussed in the literature (n = 69) and were categorized into ten themes according to common challenges and keywords, with financial limitations (n = 57), geographic location (n = 35), and limited personnel/equipment (n = 32) being the most frequently reported. The results of this scoping review informed our proposed definition of access to veterinary care. Additionally, our findings identified a need to further investigate several understudied barriers relating to access to care (i.e., veterinarian-client relationship, client identity) and to better understand how they potentially affect animal welfare outcomes.

Zoo Animal Welfare Assessment: Where Do We Stand?

Zoological institutions, such as zoos and aquariums, have made animal welfare a top priority, as it is not only a moral obligation but also crucial for fulfilling their roles in education and conservation. There is a need for science-based tools to assess and monitor animal welfare in these settings. However, assessing the welfare of zoo animals is challenging due to the diversity of species and lack of knowledge on their specific needs. This review aims to discuss the advantages and disadvantages of existing methodologies for assessing zoo animal welfare through: (1) A critical analysis of the main approaches to zoo animal welfare assessment; (2) A description of the most relevant animal-based welfare indicators for zoo animals with a particular focus on behavioural and physiological indicators; and (3) An identification of areas that require further research.

PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears

Automated monitoring systems have become increasingly important for zoological institutions in the study of their animals' behavior. One crucial processing step for such a system is the re-identification of individuals when using multiple cameras. Deep learning approaches have become the standard methodology for this task. Especially video-based methods promise to achieve a good performance in re-identification, as they can leverage the movement of an animal as an additional feature. This is especially important for applications in zoos, where one has to overcome specific challenges such as changing lighting conditions, occlusions or low image resolutions. However, large amounts of labeled data are needed to train such a deep learning model. We provide an extensively annotated dataset including 13 individual polar bears shown in 1431 sequences, which is an equivalent of 138,363 images. PolarBearVidID is the first video-based re-identification dataset for a non-human species to date. Unlike typical human benchmark re-identification datasets, the polar bears were filmed in a range of unconstrained poses and lighting conditions. Additionally, a video-based re-identification approach is trained and tested on this dataset. The results show that the animals can be identified with a rank-1 accuracy of 96.6%. We thereby show that the movement of individual animals is a characteristic feature and it can be utilized for re-identification.

Zoo Closure Does Not Affect Behavior and Activity Patterns of Palawan Binturong (Arctictis binturong whitei)

Exploring the interaction between humans and animals has become increasingly important in the evaluation of well-being for species housed in zoos and aquaria. The COVID-19 pandemic saw the global closure of zoos and aquaria to visitors. Chester Zoo, U.K., was no exception, with the charity shutting its doors for the longest period in its 90-year history. Whilst access to site was strictly limited to essential animal care staff, recent investment in networked infrared CCTV camera systems allowed some species to be monitored remotely during this extraordinary period of zoo closure. Here, we used this equipment to investigate whether zoo closure influenced activity patterns and behavior of two adult Palawan binturong, Arctictis binturong whitei. The cameras facilitated behavioral monitoring over 24 h enabling the collection of a full activity budget, which revealed a natural crepuscular activity pattern. Overall, visitor presence was found to have a neutral effect on this species, with no significant difference observed in time spent engaging in den use, vigilance or travel behaviors during zoo open and zoo closed conditions. A neutral visitor effect was found when evaluating behavior over a 24 h period and during hours which the zoo would normally be open to visitors (10:00–16:30). This research presents new information on this elusive and understudied species in captivity, and promotes investment in monitoring equipment which enables more comprehensive behavioral sampling than traditional visitor-effect methods.

Power Up: Combining Behavior Monitoring Software with Business Intelligence Tools to Enhance Proactive Animal Welfare Reporting

Simple Summary

Monitoring animal behavior over time is important for zoos and aquariums seeking to continually evaluate animal welfare. Although new digital tools are making behavior monitoring more accessible, analyzing behavior data in a timely manner to draw meaningful insights can be challenging. Business intelligence software has the potential to help address these challenges. Business intelligence software is a class of tools that combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. Here, I highlight features of the Microsoft Power BI platform as an example. Power BI is a leading option in business intelligence software and is freely available. To demonstrate the potential of business intelligence tools for behavior monitoring, I provide two example data dashboards of data recorded using the ZooMonitor behavior recording software. The first dashboard illustrates a simple quarterly behavior summary to track behavior changes in an ongoing manner. The second dashboard visualizes data relating to enrichment evaluation. I hope this introduction to business intelligence software and the Microsoft Power BI platform can provide researchers and managers in zoos and aquariums with new tools to support their evidence-based decision-making processes.

Abstract

Animal welfare is a dynamic process, and its evaluation must be similarly dynamic. The development of ongoing behavior monitoring programs in zoos and aquariums is a valuable tool for identifying meaningful changes in behavior and allows proactive animal management. However, analyzing observational behavior data in an ongoing manner introduces unique challenges compared with traditional hypothesis-driven studies of behavior over fixed time periods. Here, I introduce business intelligence software as a potential solution. Business intelligence software combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. As an example, I provide an overview of the Microsoft Power BI platform, a leading option in business intelligence software that is freely available. With Power BI, users can apply data cleaning and shaping in a stepwise fashion, then build dashboards using a library of visualizations through a drag-and-drop interface. I share two examples of data dashboards built with Power BI using data from the ZooMonitor behavior recording app: a quarterly behavior summary and an enrichment evaluation summary. I hope this introduction to business intelligence software and Microsoft Power BI empowers researchers and managers working in zoos and aquariums with new tools to enhance their evidence-based decision-making processes.

From Soundwave to Soundscape: A Guide to Acoustic Research in Captive Animal Environments

Sound is a complex feature of all environments, but captive animals' soundscapes (acoustic scenes) have been studied far less than those of wild animals. Furthermore, research across farms, laboratories, pet shelters, and zoos tends to focus on just one aspect of environmental sound measurement: its pressure level or intensity (in decibels). We review the state of the art of captive animal acoustic research and contrast this to the wild, highlighting new opportunities for the former to learn from the latter. We begin with a primer on sound, aimed at captive researchers and animal caregivers with an interest (rather than specific expertise) in acoustics. Then, we summarize animal acoustic research broadly split into measuring sound from animals, or their environment. We guide readers from soundwave to soundscape and through the burgeoning field of conservation technology, which offers new methods to capture multiple features of complex, gestalt soundscapes. Our review ends with suggestions for future research, and a practical guide to sound measurement in captive environments.

Welfare Assessment Tools in Zoos: From Theory to Practice

Zoos are increasingly implementing formalized animal welfare assessment programs to allow monitoring of welfare over time, as well as to aid in resource prioritization. These programs tend to rely on assessment tools that incorporate resource-based and observational animal-focused measures. A narrative review of the literature was conducted to bring together recent studies examining welfare assessment methods in zoo animals. A summary of these methods is provided, with advantages and limitations of the approaches presented. We then highlight practical considerations with respect to implementation of these tools into practice, for example scoring schemes, weighting of criteria, and innate animal factors for consideration. It is concluded that there would be value in standardizing guidelines for development of welfare assessment tools since zoo accreditation bodies rarely prescribe these. There is also a need to develop taxon or species-specific assessment tools to complement more generic processes and more directly inform welfare management.

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Simple Summary

Every institution that keeps animals under human care must ensure animal welfare. To analyze the state of an animal, various measurements can be performed, such as blood analysis or fur condition scoring. They also need to be observed as often as possible to gain further insight into their behavior. Such observations are performed manually in most cases, which makes them very labor- and time-intensive and prevent them from being performed on a continual basis. We present a camera-based framework that provides automated observation of animals. The system detects individual animals and analyzes their locations, walking paths, and activity. We test the framework on the two polar bears of the Nuremberg Zoo.

Abstract

The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.