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Journal articles on the topic 'Animal behavior'

To see the other types of publications on this topic, follow the link: Animal behavior.

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

SAEED, HA, MJ HAIDER, TS ANSARI, A. FATIMA, A. KHALID, MA Gilani, M. SALEEM, et al. "ROLE OF ANIMAL BEHAVIOUR AND WELFARE IN LIVESTOCK PRODUCTION AND MANAGEMENT." Biological and Clinical Sciences Research Journal 2023, no. 1 (October 7, 2023): 442. http://dx.doi.org/10.54112/bcsrj.v2023i1.442.

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Animal behavior and welfare play pivotal roles in livestock production and management, shaping the well-being of animals and the industry's overall success. The primary aim of the study is to find the role of animal behaviour and welfare in livestock production and management. The comprehensive study was conducted at University of Veterinary & Animal Sciences Lahore from November 2021 to January 2022. Behavioral data were collected from various livestock species, including cattle, poultry, pigs, and sheep. Observations were made in various settings, such as farms, feedlots, and poultry houses, to capture a comprehensive view of animal behavior. A total of 220 animals were included in the research. Observations revealed that cattle exhibited grazing behaviors for an average of 6 hours daily, while poultry engaged in frequent feeding bouts totaling 10 hours daily. Pigs and sheep displayed flexible feeding behaviors with an average of 8 hours per day, consistent with earlier findings. It is concluded that this study plays a pivotal role in animal behavior and welfare in livestock production and management. Understanding and accommodating natural behaviors, mitigating environmental stressors, and adhering to ethical guidelines contribute to healthier, more contented livestock.

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2

Houpt, Katherine A. "Animal behavior and animal welfare." Journal of the American Veterinary Medical Association 198, no. 8 (April 15, 1991): 1355–59. http://dx.doi.org/10.2460/javma.1991.198.08.1355.

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Summary The value of behavioral techniques in assessing animal welfare, and in particular assessing the psychological well being of animals, is reviewed. Using cats and horses as examples, 3 behavioral methods are presented: (1) comparison of behavior patterns and time budgets; (2) choice tests; and (3) operant conditioning. The behaviors of intact and declawed cats were compared in order to determine if declawing led to behavioral problems or to a change in personality. Apparently it did not. The behavior of free ranging horses was compared with that of stabled horses. Using two-choice preference tests, the preference of horses for visual contact with other horses and the preference for bedding were determined. Horses show no significant preference for locations from which they can make visual contact with other horses, but they do prefer bedding, especially when lying down. Horses will perform an operant response in order to obtain light in a darkened barn or heat in an outside shed. These same techniques can be used to answer a variety of questions about an animal's motivation for a particular attribute of its environment.

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3

Thomas, Evan. "Descartes on the Animal Within, and the Animals Without." Canadian Journal of Philosophy 50, no. 8 (November 2020): 999–1014. http://dx.doi.org/10.1017/can.2020.44.

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AbstractDescartes held that animals are material automata without minds. However, this raises a puzzle. Descartes’s argument for this doctrine relies on the claims that animals lack language and general intelligence. But these claims seem compatible with the view that animals have minds. As a solution to this puzzle, I defend what I call the introspective-analogical interpretation. According to this interpretation, Descartes employs introspection to show that certain human behaviors do not depend on thought but rather on automatic bodily processes. Descartes then argues that animal behavior resembles only those behaviors that are automatic in humans. Analogy thus supports the view that the behaviors of animals do not depend on thought but are, rather, automatic. And if animal behavior is automatic, then animals are best regarded as automata.

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4

Fox, Sara A. "Animal Behavior." American Biology Teacher 80, no. 2 (February 1, 2018): 153. http://dx.doi.org/10.1525/abt.2018.80.2.153.

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5

Greer, Kania. "Animal Behavior." American Biology Teacher 80, no. 2 (February 1, 2018): 153–54. http://dx.doi.org/10.1525/abt.2018.80.2.153b.

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6

Hibbitt, Cate. "Animal Behavior." American Biology Teacher 72, no. 2 (February 1, 2010): 128. http://dx.doi.org/10.1525/abt.2010.72.2.14.c.

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7

Redinger, Andrea, and Runkel Scott. "Animal Behavior." American Biology Teacher 72, no. 5 (May 1, 2010): 320. http://dx.doi.org/10.1525/abt.2010.72.5.14.

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8

Narguizian, Paul. "Animal Behavior." American Biology Teacher 72, no. 6 (August 1, 2010): 384–85. http://dx.doi.org/10.1525/abt.2010.72.6.14.b.

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9

Pedretti, Kay. "Animal Behavior." American Biology Teacher 73, no. 7 (September 1, 2011): 431. http://dx.doi.org/10.1525/abt.2011.73.7.11.b.

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10

Iyengar, Erika. "Animal Behavior." American Biology Teacher 74, no. 9 (November 1, 2012): 657–58. http://dx.doi.org/10.1525/abt.2012.74.9.11d.

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11

Narguizian, Paul. "Animal Behavior." American Biology Teacher 76, no. 6 (August 1, 2014): 417. http://dx.doi.org/10.1525/abt.2014.76.6.10.

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12

Snell-Rood, Emilie C. "Animal Behavior." Animal Behaviour 84, no. 1 (July 2012): 290–91. http://dx.doi.org/10.1016/j.anbehav.2012.04.003.

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13

Mykhailo Honchar, Mykhailo. "ANIMAL BEHAVIOR AND MEMORY: EXPLORING THE INTERPLAY." American Journal of Medical Sciences and Pharmaceutical Research 06, no. 04 (April 21, 2024): 25–30. http://dx.doi.org/10.37547/tajmspr/volume06issue04-04.

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The relationship between animal behavior and memory processes is a complex and dynamic interplay that has long fascinated researchers across various disciplines. This paper explores the intricate connections between animal behavior changes and memory processes, shedding light on how alterations in behavior patterns can influence memory formation, retention, and retrieval in different animal species. Drawing upon research from ethology, neuroscience, and cognitive psychology, we examine the mechanisms underlying these interactions and the factors that modulate memory performance in response to behavioral modifications. By elucidating the interplay between animal behavior and memory, this study contributes to a deeper understanding of cognitive processes in non-human organisms and their adaptive significance in natural and laboratory settings.

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14

Fournier, Angela K., Megan French, Elizabeth A. Letson, Joy Hanson, Thomas D. Berry, and Sarah Cronin. "The Behavioral Cost of Care: Changes in Maintenance Behavior during Equine-Assisted Interventions." Animals 14, no. 4 (February 6, 2024): 536. http://dx.doi.org/10.3390/ani14040536.

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This study examined human–animal symbiosis in an animal-assisted intervention through observations of animal maintenance behaviors. The rise of psychotherapy, learning, and recreation incorporating animals warrants exploration of the welfare of the animals involved in these interventions. The analysis of welfare in multispecies engagements can be discussed in terms of symbiosis. Regarding an intervention’s animal provider (e.g., therapy horse) and human recipient (psychotherapy client), the balance of cost and benefit is important. Research describing human and animal interactive behavior during interventions is limited, whether focusing on client outcomes or animal welfare. The present study adapted ethological methods to study humans and animals in an equine-assisted intervention, observing equine maintenance behaviors and equid–human interactive behavior. Maintenance behaviors were recorded before, during, and after equine-assisted (psychosocial) learning sessions with youth, providing 1600 observations. Equine alertness, eating behavior, and ambulation varied significantly before, during, and after the equine-assisted sessions. Such interruptions of typical behavior are an important aspect of welfare and unit of analysis when examining symbiotic relationships. A total of 267 sequences of equid–human approach–response behavior were also recorded, indicating that human–animal interaction was predominantly from humans toward equids. Equids’ dominant response to human approach was no response, followed by avoidance, while humans’ dominant response to equid approach was reciprocation. The findings are discussed in terms of symbiosis and animal welfare.

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15

Preti, Antonio. "Suicide among Animals: A Review of Evidence." Psychological Reports 101, no. 3 (December 2007): 831–48. http://dx.doi.org/10.2466/pr0.101.3.831-848.

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Naturalists have not identified suicide in nonhuman species in field situations, despite intensive study of thousands of animal species. In this review, evidence on suicidal behavior among animals is analyzed to discover analogies with human suicidal behavior. Literature was retrieved by exploring Medline/PubMed and PsychINFO databases (1967–2007) and through manual literature searches. Keyword terms were “suicide or suicidal behavior” and “animal or animal behavior.” Few empirical investigations have been carried out on this topic. Nevertheless, sparse evidence supports some resemblance between the self-endangering behavior observed in the animal kingdom, particularly in animals held in captivity or put under pressure by environmental challenges, and suicidal behavior among humans. Animal models have contributed to the study of both normal and pathological human behaviors: discovering some correlates of suicide among animals could be a valid contribution to the field.

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16

Crerar, Lorelei. "Animal Behavior: Ants." American Biology Teacher 72, no. 8 (October 1, 2010): 518–19. http://dx.doi.org/10.1525/abt.2010.72.8.11.b.

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17

Giardina, Irene. "Collective Animal Behavior." Animal Behaviour 82, no. 3 (September 2011): 608. http://dx.doi.org/10.1016/j.anbehav.2011.06.005.

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18

Huntingford, F. "AN ARRAY OF ANIMAL BEHAVIOUR: The Behavior of Animals: Mechanisms, Function, and Evolution." Journal of Experimental Biology 208, no. 12 (June 15, 2005): 2205–6. http://dx.doi.org/10.1242/jeb.01674.

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19

Orihuela, A. "Animal welfare and sustainable animal production." Advances in Animal Biosciences 7, no. 2 (October 2016): 215–17. http://dx.doi.org/10.1017/s2040470016000157.

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This paper describes the basic principles of animal behavior and how these concepts can be applied to the management and care of farm animal species in a sustainable way. Several examples about how the behavior of animals can be used to increase production and welfare understanding animal needs while solving farm problems, are mentioned. Topics covered include: fostering of orphans, explaining how to substitute dead lambs, or how to add extra lambs to ewes with single births; the breakdown of the cow–calf relationship, covering different forms of weaning, focusing on stress reduction as reproductive efficiency and productivity increases; handling system designs, explaining the basic principles of animal handling and how to leverage this knowledge in the design of facilities for the purpose of moving cattle efficiently, reducing at the same time the risk of injury in humans and animals; the behavior of sick animals, where the physiological processes in order to regain homeostasis through changes in animal behavior are explained, in addition to how those changes in behavior can be used to predict some diseases even before clinical signs appeared, or how these changes might be applied to assess the extent of the pain suffered by a particular individual; and finally, a miscellaneous section covering various behavioral aspects of management of productive animals.

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20

Talyn, Becky, Kelly Muller, Cindy Mercado, Bryan Gonzalez, and Katherine Bartels. "The Herbicide Glyphosate and Its Formulations Impact Animal Behavior across Taxa." Agrochemicals 2, no. 3 (July 10, 2023): 367–408. http://dx.doi.org/10.3390/agrochemicals2030022.

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Use of glyphosate and glyphosate-based herbicides is ubiquitous in US agriculture and widespread around the world. Despite marketing efforts to the contrary, numerous studies demonstrate glyphosate toxicity to non-target organisms including animals, primarily focusing on mortality, carcinogenicity, renal toxicity, reproductive, and neurological toxicity, and the biochemical mechanisms underlying these physiological outcomes. Glyphosate toxicity also impacts animal behavior, both in model systems and in agricultural and environmentally relevant contexts. In this review, we examine the effects of glyphosate and glyphosate-based herbicides on animal behaviors, particularly activity, foraging and feeding, anti-predator behavior, reproductive behaviors, learning and memory, and social behaviors. Glyphosate can be detected both in food and in the environment, and avoided through activity and feeding strategies. However, exposure also reduces activity, depresses foraging and feeding, increases susceptibility to predation, interferes with courtship, mating, fertility and maternal behaviors, decreases learning and memory capabilities, and disrupts social behaviors. Changes in animal behavior as a result of glyphosate toxicity are important because of their sometimes severe effects on individual fitness, as well as ecosystem health. Implications for human behavior are also considered.

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21

Dawkins, Marian Stamp. "Farm animal welfare: Beyond “natural” behavior." Science 379, no. 6630 (January 27, 2023): 326–28. http://dx.doi.org/10.1126/science.ade5437.

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22

Relić, Renata, and Mirjana Đukić-Stojčić. "Influence of Environmental Pollution on Animal Behavior." Contemporary Agriculture 72, no. 4 (December 1, 2023): 216–23. http://dx.doi.org/10.2478/contagri-2023-0029.

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Summary Animals, like humans, act according to physiological patterns of behavior that change in response to various internal and external stimuli. Environmental pollutants represent “negative” stimuli and stressors. Air pollution is among pollution sources that pose the greatest threat to the environment and all living organisms. Exposure to pollutants causes behavioral changes in animals and disruption of organ functions and structures, which are often identical to those of humans working or living under the same conditions as animals (e.g., on a farm or in a city). Aside from a shorter life span and possible premature death, there are some diseases which commonly occur as a result of the exposure. Symptoms indicative of a disease or irritation, such as coughing, lameness, diarrhea, eye discharge and the like, cause the animal to make movements (actions) that are not part of their normal physiological routine and are considered behavioral changes. Behavioral changes are the earliest indicator that the animal is suffering from physical or mental disorders that can negatively affect its health and, in the case of livestock, production results. Various animal species serve as indicators of pollution, and domestic animals, including farm animals, can also serve this purpose. Behavioral changes resulting from exposure to various pollutants include disorientation, problems interacting with humans and other animals, reproductive problems, respiratory, digestive symptoms, etc. This review compiled data from a number of studies on changes in animal behavior after short or long exposure to various environmental pollutants. The focus was on the effects of air pollutants on animals, which are of particular importance to humans as they share living or working space with the animals or breed them for economic interest.

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23

Hoffman, Kate Nicole. "Subjective Experience in Explanations of Animal PTSD Behavior." Philosophical Topics 48, no. 1 (2020): 155–75. http://dx.doi.org/10.5840/philtopics20204818.

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Post-Traumatic Stress Disorder (PTSD) is a mental health condition in which the experience of a traumatic event causes a series of psychiatric and behavioral symptoms such as hypervigilance, insomnia, irritability, aggression, constricted affect, and self-destructive behavior. This paper investigates two case studies to argue that the experience of PTSD is not restricted to humans alone; we have good epistemic reason to hold that some animals can experience genuine PTSD, given our current and best clinical understanding of the disorder in humans. I will use this evidence to argue for two claims. First, because the causal structure of PTSD plausibly requires reference to a traumatic conscious experience in order to explain subsequent behaviors, the fact that animals can have PTSD provides new evidence for animal consciousness. Second, the discovery of PTSD in animals puts pressure on accounts which hold that animal behavior can be fully explained without reference to subjective experience.

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24

Church, Russell M. "Human Models of Animal Behavior." Psychological Science 4, no. 3 (May 1993): 170–73. http://dx.doi.org/10.1111/j.1467-9280.1993.tb00482.x.

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Similarities between the behavior of humans and other animals in analogous tasks suggest that simitar cognitive processes are involved. Human studies provide an efficient way to collect data with low variability, and animal studies provide a way to collect a large amount of data under controlled conditions, study a wide range of manipulations, limit the range of plausible explanations, and develop explanations in terms of brain mechanisms. Examples are taken from studies of timing that used similar procedures for humans and other animals.

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25

Watanabe, Rafael N., Priscila A. Bernardes, Eliéder P. Romanzini, Larissa G. Braga, Thaís R. Brito, Ronyatta W. Teobaldo, Ricardo A. Reis, and Danísio P. Munari. "Strategy to Predict High and Low Frequency Behaviors Using Triaxial Accelerometers in Grazing of Beef Cattle." Animals 11, no. 12 (December 2, 2021): 3438. http://dx.doi.org/10.3390/ani11123438.

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Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest.

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26

Bacqué-Cazenave, Julien, Rahul Bharatiya, Grégory Barrière, Jean-Paul Delbecque, Nouhaila Bouguiyoud, Giuseppe Di Giovanni, Daniel Cattaert, and Philippe De Deurwaerdère. "Serotonin in Animal Cognition and Behavior." International Journal of Molecular Sciences 21, no. 5 (February 28, 2020): 1649. http://dx.doi.org/10.3390/ijms21051649.

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Serotonin (5-hydroxytryptamine, 5-HT) is acknowledged as a major neuromodulator of nervous systems in both invertebrates and vertebrates. It has been proposed for several decades that it impacts animal cognition and behavior. In spite of a completely distinct organization of the 5-HT systems across the animal kingdom, several lines of evidence suggest that the influences of 5-HT on behavior and cognition are evolutionary conserved. In this review, we have selected some behaviors classically evoked when addressing the roles of 5-HT on nervous system functions. In particular, we focus on the motor activity, arousal, sleep and circadian rhythm, feeding, social interactions and aggressiveness, anxiety, mood, learning and memory, or impulsive/compulsive dimension and behavioral flexibility. The roles of 5-HT, illustrated in both invertebrates and vertebrates, show that it is more able to potentiate or mitigate the neuronal responses necessary for the fine-tuning of most behaviors, rather than to trigger or halt a specific behavior. 5-HT is, therefore, the prototypical neuromodulator fundamentally involved in the adaptation of all organisms across the animal kingdom.

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27

Chen, Yuming, Tianzhe Jiao, Jie Song, Guangyu He, and Zhu Jin. "AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments." Applied Sciences 14, no. 11 (May 27, 2024): 4583. http://dx.doi.org/10.3390/app14114583.

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In recent years, with the rapid development of medicine, pathology, toxicology, and neuroscience technology, animal behavior research has become essential in modern life science research. However, the current mainstream commercial animal behavior recognition tools only provide a single behavior recognition method, limiting the expansion of algorithms and how researchers interact with experimental data. To address this issue, we propose an AI-enabled, highly usable platform for analyzing experimental animal behavior, which aims to provide better flexibility, scalability, and interactivity to make the platform more usable. Researchers can flexibly select or extend different behavior recognition algorithms for automated recognition of animal behaviors or experience more convenient human-computer interaction through natural language descriptions only. A case study at a medical laboratory where the platform was used to evaluate behavioral differences between sick and healthy animals demonstrated the high usability of the platform.

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28

Dewsbury, Donald A. "Animal Learning (& Behavior?)." Bulletin of the Psychonomic Society 29, no. 1 (January 1991): 57–58. http://dx.doi.org/10.3758/bf03334769.

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29

McCartney, M. "Animal behavior follows rewards." Science 345, no. 6204 (September 25, 2014): 1574. http://dx.doi.org/10.1126/science.345.6204.1574-r.

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30

Ash, C. "ANIMAL BEHAVIOR: Turning Turtles." Science 321, no. 5889 (August 1, 2008): 613a. http://dx.doi.org/10.1126/science.321.5889.613a.

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31

Zahn, L. M. "ANIMAL BEHAVIOR: Bee Raves." Science 323, no. 5911 (January 9, 2009): 187c. http://dx.doi.org/10.1126/science.323.5911.187c.

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32

McCartney, M. "Animal Behavior Follows Rewards." Science Signaling 7, no. 345 (September 30, 2014): ec273-ec273. http://dx.doi.org/10.1126/scisignal.2005961.

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33

Neff, Ellen P. "Animal behavior on auto." Lab Animal 48, no. 6 (May 13, 2019): 157–61. http://dx.doi.org/10.1038/s41684-019-0312-z.

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34

No authorship indicated. "Animal Behavior Processes: Editor." Journal of Experimental Psychology: Animal Behavior Processes 29, no. 2 (2003): C2. http://dx.doi.org/10.1037/0097-7403.29.2.c2.

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35

Lorenzen, Kai. "Animal Domestication and Behavior." Fish and Fisheries 4, no. 4 (December 2003): 376. http://dx.doi.org/10.1046/j.1467-2979.2003.0139f.x.

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36

Jensen, Per. "Perspectives on animal behavior." Applied Animal Behaviour Science 45, no. 3-4 (November 1995): 316–17. http://dx.doi.org/10.1016/0168-1591(95)90008-x.

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37

Friedman, Susan G., Cynthia E. Stringfield, and Marion R. Desmarchelier. "Animal Behavior and Learning." Veterinary Clinics of North America: Exotic Animal Practice 24, no. 1 (January 2021): 1–16. http://dx.doi.org/10.1016/j.cvex.2020.08.002.

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38

Huntingford, Felicity A. "Foundations of animal behavior." Trends in Ecology & Evolution 11, no. 11 (November 1996): 483. http://dx.doi.org/10.1016/0169-5347(96)81159-1.

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39

Hart, Adam G. "Bringing animal behavior together." Trends in Ecology & Evolution 27, no. 5 (May 2012): 259. http://dx.doi.org/10.1016/j.tree.2012.01.008.

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40

Vico, F. J., P. Mir, F. J. Veredas, and J. de La Torre. "Animal-like adaptive behavior." Artificial Intelligence in Engineering 15, no. 1 (January 2001): 5–12. http://dx.doi.org/10.1016/s0954-1810(00)00023-6.

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41

Gutnick, Tamar, and Michael J. Kuba. "Animal Behavior: Socializing Octopus." Current Biology 28, no. 19 (October 2018): R1147—R1149. http://dx.doi.org/10.1016/j.cub.2018.08.006.

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42

Lee, Jin Il, and Joohong Ahnn. "Calcineurin in Animal Behavior." Molecules and Cells 17, no. 3 (June 2004): 390–96. http://dx.doi.org/10.1016/s1016-8478(23)13057-3.

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43

Yuan, Jing, and Ningning Dong. "Rethinking the origins of animal domestication in China." Chinese Archaeology 19, no. 1 (November 26, 2019): 195–200. http://dx.doi.org/10.1515/char-2019-0015.

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Abstract Initial animal domestication in early Neolithic China may have resembled the behavior of raising animals as pets for entertainment. During the domestication process, the ecological characteristics and living habits of each animal and the subjective demands of ancient people jointly led to the formation of animal raising behaviors for utilitarian purposes. These commensal interactions involved both the actions of ancient people and the cooperative reactions of the animals themselves. Domestication processes were gradual and involved multiple repeated and progressive co-evolutionary developments.

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44

Low, MR. "Domestic Animal Behavior for Veterinarians and Animal Scientists." Australian Veterinary Journal 83, no. 7 (July 2005): 411. http://dx.doi.org/10.1111/j.1751-0813.2005.tb13077.x.

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45

Marder, Amy R., and Laura R. Marder. "Human-Companion Animal Relationships and Animal Behavior Problems." Veterinary Clinics of North America: Small Animal Practice 15, no. 2 (March 1985): 411–21. http://dx.doi.org/10.1016/s0195-5616(85)50313-7.

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46

O’Connor, Rachel, Jason B. Coe, Lee Niel, and Andria Jones-Bitton. "Exploratory Study of Adopters’ Concerns Prior to Acquiring Dogs or Cats from Animal Shelters." Society & Animals 25, no. 4 (July 20, 2017): 362–83. http://dx.doi.org/10.1163/15685306-12341451.

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Caretaker expectations for companion-animal guardianship can affect attachment to, and satisfaction with, an animal. Understanding these expectations may help match adopters and companion animals, increasing success of adoptions. Seventeen one-on-one interviews were used to gain a deep understanding of the thoughts and expectations of potential cat or dog adopters at three animal shelters in Ontario, Canada. Thematic analysis was conducted until data saturation was achieved (n = 14). Animal behavior was the most common prior concern held by participants, specifically, unknown history, aggression, incompatibility between animals, and shy or aloof, destructive, or vocal behavior. Participants who identified adoption “deal-breakers” often identified specific traits they wanted and did not want in an animal. In contrast, others indicated they would seek out training or advice for problem behaviors. Participants discussed prior human-related concerns less frequently. Understanding pre-adoption concerns at the time of adoption will assist in better preparing individuals for companion-animal guardianship.

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47

Dhungana, S., D. R. Khanal, M. Sharma, N. Bhattarai, D. T. Tamang, S. Wasti, and R. C. Acharya. "Effect of Music on Animal Behavior: A Review." Nepalese Veterinary Journal 35 (December 31, 2018): 142–49. http://dx.doi.org/10.3126/nvj.v35i0.25251.

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Music is an expression of moods and emotions, which has a history of physical and emotional healings. It is thought to have both analgesic and anxiolytic properties. Various effects of music therapy on the physiology and psychology of human have been documented. The effect of music on physiology and behavior have been studied in animals too. Many of these studies claim that even animals are affected by the music. The potential benefits of music in animals might be through auditory enrichment which modifies the behavior of animals. The milking behavior and milk yield of farm animals including cattle and buffalo are affected by music. The objective of this study was to review the influence of music in animal behavior and discuss its usefulness for stress relief. The available literatures indicated that there is a variation among animals for music preference and their behavior is affected depending upon the animal species.

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48

Pereira, Talmo D., Nathaniel Tabris, Arie Matsliah, David M. Turner, Junyu Li, Shruthi Ravindranath, Eleni S. Papadoyannis, et al. "SLEAP: A deep learning system for multi-animal pose tracking." Nature Methods 19, no. 4 (April 2022): 486–95. http://dx.doi.org/10.1038/s41592-022-01426-1.

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AbstractThe desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

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Fonseca, Luís, Daniel Corujo, William Xavier, and Pedro Gonçalves. "On the Development of a Wearable Animal Monitor." Animals 13, no. 1 (December 28, 2022): 120. http://dx.doi.org/10.3390/ani13010120.

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Animal monitoring is a task traditionally performed by pastoralists, as a way of ensuring the safety and well-being of animals; a tremendously arduous and lonely task, it requires long walks and extended periods of contact with the animals. The Internet of Things and the possibility of applying sensors to different kinds of devices, in particular the use of wearable sensors, has proven not only to be less invasive to the animals, but also to have a low cost and to be quite efficient. The present work analyses the most impactful monitored features in the behavior learning process and their learning results. It especially addresses the impact of a gyroscope, which heavily influences the cost of the collar. Based on the chosen set of sensors, a learning model is subsequently established, and the learning outcomes are analyzed. Finally, the animal behavior prediction capability of the learning model (which was based on the sensed data of adult animals) is additionally subjected and evaluated in a scenario featuring younger animals. Results suggest that not only is it possible to accurately classify these behaviors (with a balanced accuracy around 91%), but that removing the gyroscope can be advantageous. Results additionally show a positive contribution of the thermometer in behavior identification but evidences the need for further confirmation in future work, considering different seasons of different years and scenarios including more diverse animals’ behavior.

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Liu, Jiefei, Derek W. Bailey, Huiping Cao, Tran Cao Son, and Colin T. Tobin. "109 Animal Behavior Analysis Using Unsupervised Machine Learning Techniques." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 2. http://dx.doi.org/10.1093/jas/skad281.002.

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Abstract With recent technological advances, Global Positioning Systems (GPS) and other tracking sensors can collect large amounts of data and transfer information to the cloud and then to producers to remotely monitor livestock health and well-being. Currently, supervised machine learning, such as random forest and linear or quadratic discriminant analysis techniques are used to develop algorithms to identify changes in animal behavior that may be associated with well-being concerns. Supervised machine learning requires behavior observation of monitored animals and may hinder normal expression. However, recording behavioral observations is time consuming and expensive. Our goal was to design a new unsupervised machine learning framework to identify animal behavior without the utilization of human observations. The framework contains two steps. The first step is to segment the tracking data of the animal using time series segmentation, and the second step is to group the segments into clusters where each cluster represents one type of behavior. To validate the applicability of our proposed framework, we utilize GPS tracking data collected at 2-minute intervals from eight cows from May 28 to June 22, 2018, in a 1096 ha rangeland pasture near Prescott, Arizona. After extensive experiments, our framework can partition the movement of the cow using the speed, direction, and distance of the cow from water. These segments were grouped into meaningful behavior clusters using clustering analysis. Speed was the most successful feature for clustering into behaviors. Results are similar to approaches based only on expert knowledge, that rely on speed for classification. Our study demonstrated that we can directly use unlabeled data to group animal behaviors from GPS tracking data. The proposed unsupervised two-step framework allows the analysis of cattle tracking data without direct human observation of behaviors. It is applicable for analyzing the immense amount of data that can be obtained from real-time tracking and sensor devices.

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