Animals

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12 pages, 668 KiB

Open AccessArticle

Video Conference Technology as a Tool for Pair Introduction in Rhesus Macaques

by Cara Stull, Allison Heagerty and Kristine Coleman

Animals 2022, 12(14), 1783; https://doi.org/10.3390/ani12141783 - 12 Jul 2022

Viewed by 1370

Abstract

Pair housing is known to promote welfare for macaques in captivity. However, finding compatible partners can be challenging, particularly when animals are not located near one another. Because macaques show interest in videos of conspecifics, we examined the use of video conference technology [...] Read more.

Pair housing is known to promote welfare for macaques in captivity. However, finding compatible partners can be challenging, particularly when animals are not located near one another. Because macaques show interest in videos of conspecifics, we examined the use of video conference technology (Zoom) as a potential tool to assess compatibility in 84 rhesus macaques (2–22 years old) prior to pair introduction. Monkeys involved in the pairs (12 female–female, 21 male–male, 9 female–male) were unfamiliar with each other. We set up a 10 min Zoom session between potential partners (on an iPad in front of the cage). We scored attention to the screen, anxiety, and prosocial behaviors and examined whether these behaviors predicted future pair success. Monkeys spent relatively little time attending to the tablet (median = 13.3%), and attention did not predict pair success (B = −0.06, NS). However, pairs in which attention was primarily shown by one animal had a higher chance of success than those in which both individuals showed similar levels (B = −4.66. p = 0.03). Neither prosocial (B = 0.89, NS) nor anxiety (B = −1.95, p = 0.07) behavior correlated with pair success. While preliminary, our data suggest that video conferencing technology may be useful as a tool for introducing unfamiliar partners prior to a socialization attempt. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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14 pages, 4945 KiB

Open AccessCommunication

The Future of Artificial Intelligence in Monitoring Animal Identification, Health, and Behaviour

by Jenna V. Congdon, Mina Hosseini, Ezekiel F. Gading, Mahdi Masousi, Maria Franke and Suzanne E. MacDonald

Animals 2022, 12(13), 1711; https://doi.org/10.3390/ani12131711 - 01 Jul 2022

Cited by 9 | Viewed by 23355

Abstract

With many advancements, technologies are now capable of recording non-human animals’ location, heart rate, and movement, often using a device that is physically attached to the monitored animals. However, to our knowledge, there is currently no technology that is able to do this [...] Read more.

With many advancements, technologies are now capable of recording non-human animals’ location, heart rate, and movement, often using a device that is physically attached to the monitored animals. However, to our knowledge, there is currently no technology that is able to do this unobtrusively and non-invasively. Here, we review the history of technology for use with animals, recent technological advancements, current limitations, and a brief introduction to our proposed novel software. Canadian tech mogul EAIGLE Inc. has developed an artificial intelligence (AI) software solution capable of determining where people and assets are within public places or attractions for operational intelligence, security, and health and safety applications. The solution also monitors individual temperatures to reduce the potential spread of COVID-19. This technology has been adapted for use at the Toronto Zoo, initiated with a focus on Sumatran orangutans (Pongo abelii) given the close physical similarity between orangutans and humans as great ape species. This technology will be capable of mass data collection, individual identification, pose estimation, behaviour monitoring and tracking orangutans’ locations, in real time on a 24/7 basis, benefitting both zookeepers and researchers looking to review this information. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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14 pages, 1973 KiB

Open AccessArticle

Apex and ApeTouch: Development of a Portable Touchscreen System and Software for Primates at Zoos

by Christopher Flynn Martin, Akiho Muramatsu and Tetsuro Matsuzawa

Animals 2022, 12(13), 1660; https://doi.org/10.3390/ani12131660 - 28 Jun 2022

Cited by 4 | Viewed by 3354

Abstract

We report on the development and testing of a portable touchscreen apparatus and accompanying software program for primate enrichment, cognitive research, and husbandry applications. For zoos considering using technology to bolster scientific efforts or enhance the welfare of primates in their care, touchscreen [...] Read more.

We report on the development and testing of a portable touchscreen apparatus and accompanying software program for primate enrichment, cognitive research, and husbandry applications. For zoos considering using technology to bolster scientific efforts or enhance the welfare of primates in their care, touchscreen activities offer a solution that has a long and proven record of primate use in laboratory settings as well as a history of usage in the zoo world. We review the options that are available for zoos to build their own touchscreen systems and we offer as an alternative our pre-built apparatus, Apex, and primate software suite, ApeTouch, both of which are tailored for use in a zoo setting. The efficacy and utility of these tools are demonstrated in a training study with four macaque groups of different species that were previously naïve to touchscreens. All of the groups in the study learned to use the device and displayed a consistent engagement with the touchscreen tasks over 95 daily sessions of exposure. In the final stage of the training, two of the four groups displayed an above-chance level performance on a numerical sequencing task. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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16 pages, 19888 KiB

Open AccessArticle

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

by Matthias Zuerl, Philip Stoll, Ingrid Brehm, René Raab, Dario Zanca, Samira Kabri, Johanna Happold, Heiko Nille, Katharina Prechtel, Sophie Wuensch, Marie Krause, Stefan Seegerer, Lorenzo von Fersen and Bjoern Eskofier

Animals 2022, 12(6), 692; https://doi.org/10.3390/ani12060692 - 10 Mar 2022

Cited by 16 | Viewed by 6418

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 [...] Read more.

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 \pm 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. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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18 pages, 18562 KiB

Open AccessArticle

A Food for All Seasons: Stability of Food Preferences in Gorillas across Testing Methods and Seasons

by Jennifer Vonk, Jordyn Truax and Molly C. McGuire

Animals 2022, 12(6), 685; https://doi.org/10.3390/ani12060685 - 09 Mar 2022

Cited by 6 | Viewed by 2119

Abstract

Decisions about which foods to use during training and enrichment for captive animals may be based on invalid assumptions about individuals’ preferences. It is important to assess the stability of food preferences given that one-time preferences are often used to inform which items [...] Read more.

Decisions about which foods to use during training and enrichment for captive animals may be based on invalid assumptions about individuals’ preferences. It is important to assess the stability of food preferences given that one-time preferences are often used to inform which items are offered over a longer period of time. Presenting preference assessments using images of food items allows control over factors such as size, scent, and inadvertent cueing but requires validation. We presented three male gorillas with choices between randomly selected pairs of actual food items from their morning meal using PVC feeders. We also presented the gorillas with two-alternative forced-choice tests between images of these foods on a touchscreen computer. Ranked preferences were correlated across method and seasons. Furthermore, gorillas selected images of preferred over less preferred foods in a validation task on the touchscreen. However, selections of some food items changed within sessions, suggesting that preference may be relative to other contextual factors. Researchers should assess how choices affect subsequent preferences to understand whether animals demonstrate absolute preferences for particular food items, or prefer variety. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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12 pages, 1628 KiB

Open AccessArticle

Body Condition Score Change throughout Lactation Utilizing an Automated BCS System: A Descriptive Study

by Carissa M. Truman, Magnus R. Campler and Joao H. C. Costa

Animals 2022, 12(5), 601; https://doi.org/10.3390/ani12050601 - 28 Feb 2022

Cited by 5 | Viewed by 3802

Abstract

Body condition scoring (BCS) is a traditional visual technique often using a five-point scale to non-invasively assess fat reserves in cattle. However, recent studies have highlighted the potential in automating body condition scoring using imaging technology. Therefore, the objective was to implement a [...] Read more.

Body condition scoring (BCS) is a traditional visual technique often using a five-point scale to non-invasively assess fat reserves in cattle. However, recent studies have highlighted the potential in automating body condition scoring using imaging technology. Therefore, the objective was to implement a commercially available automated body condition scoring (ABCS) camera system to collect data for developing a predictive equation of body condition dynamics throughout the lactation period. Holstein cows (n = 2343, parity = 2.1 ± 1.1, calving BCS = 3.42 ± 0.24), up to 300 days in milk (DIM), were scored daily using two ABCS cameras mounted on sort-gates at the milk parlor exits. Scores were reported on a 1 to 5 scale in 0.1 increments. Lactation number, DIM, disease status, and 305d-predicted-milk-yield (305PMY) were used to create a multivariate prediction model for body condition scores throughout lactation. The equation derived from the model was: ABCS_ijk = 1.4838 − 0.00452 × DIM_i − 0.03851 × Lactation number_j + 0.5970 × Calving ABCS_k + 0.02998 × Disease Status(neg)_l − 1.52 × 10⁻⁶ × 305PMY_m + e_ijklm. We identified factors which are significant for predicting the BCS curve during lactation. These could be used to monitor deviations or benchmark ABCS in lactating dairy cows. The advantage of BCS automation is that it may provide objective, frequent, and accurate BCS with a higher degree of sensitivity compared with more sporadic and subjective manual BCS. Applying ABCS technology in future studies on commercial dairies may assist in providing improved dairy management protocols based on more available BCS. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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Review

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13 pages, 283 KiB

Open AccessReview

The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare

by Brenna Knaebe, Claudia C. Weiss, Jan Zimmermann and Benjamin Y. Hayden

Animals 2022, 12(13), 1648; https://doi.org/10.3390/ani12131648 - 27 Jun 2022

Cited by 5 | Viewed by 2493

Abstract

Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and [...] Read more.

Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare within the context of the three Rs of animal research (reduction, refinement, and replacement), and applying our understanding of animal behavior to increase the “natural” behaviors in captive and wild populations facing human impact challenges. We note that these benefits are often incidental to the designed purpose of these tracking systems, a reflection of the fact that animal welfare is not inimical to research progress, but instead, that the aligned interests between basic research and welfare hold great promise for improvements to animal well-being. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

23 pages, 3327 KiB

Open AccessReview

Affective State Recognition in Livestock—Artificial Intelligence Approaches

by Suresh Neethirajan

Animals 2022, 12(6), 759; https://doi.org/10.3390/ani12060759 - 17 Mar 2022

Cited by 15 | Viewed by 5868 | Correction

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need [...] Read more.

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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Other

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1 pages, 582 KiB

Open AccessCorrection

Correction: Neethirajan, S. Affective State Recognition in Livestock—Artificial Intelligence Approaches. Animals 2022, 12, 759

by Suresh Neethirajan

Animals 2022, 12(14), 1856; https://doi.org/10.3390/ani12141856 - 21 Jul 2022

Viewed by 1323

Abstract

The authors wish to make the following correction to the original paper [...] Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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14 pages, 2649 KiB

Open AccessCommentary

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

by Jason David Wark

Animals 2022, 12(13), 1606; https://doi.org/10.3390/ani12131606 - 22 Jun 2022

Cited by 2 | Viewed by 3027

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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13 pages, 524 KiB

Open AccessSystematic Review

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

by Alessia Diana, Marina Salas, Zjef Pereboom, Michael Mendl and Tomas Norton

Animals 2021, 11(11), 3048; https://doi.org/10.3390/ani11113048 - 25 Oct 2021

Cited by 11 | Viewed by 4971

Abstract

A top priority of modern zoos is to ensure good animal welfare (AW), thus, efforts towards improving AW monitoring are increasing. Welfare assessments are performed through more traditional approaches by employing direct observations and time-consuming data collection that require trained specialists. These limitations [...] Read more.

A top priority of modern zoos is to ensure good animal welfare (AW), thus, efforts towards improving AW monitoring are increasing. Welfare assessments are performed through more traditional approaches by employing direct observations and time-consuming data collection that require trained specialists. These limitations may be overcome through automated monitoring using wearable or remotely placed sensors. However, in this fast-developing field, the level of automated AW monitoring used in zoos is unclear. Hence, the aim of this systematic literature review was to investigate research conducted on the use of technology for AW assessment in zoos with a focus on real-time automated monitoring systems. The search led to 19 publications with 18 of them published in the last six years. Studies focused on mammals (89.5%) with elephant as the most studied species followed by primates. The most used technologies were camera (52.6%) and wearable sensors (31.6%) mainly used to measure behaviour, while the use of algorithms was reported in two publications only. This research area is still young in zoos and mainly focused on large mammals. Despite an increase in publications employing automated AW monitoring in the last years, the potential for this to become an extra useful tool needs further research. Full article

(This article belongs to the Special Issue The Use of New Technology to Enhance Animal Welfare)

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