Equine-Assisted Social Pedagogy is a practice grounded in social pedagogical theory, emphasising the interaction between horse and human as well as the stable community. Its aim is to support clients’ social development, either preventively or rehabilitatively. While research has mostly focused on clients and facilitators, the horse’s point of view has been less studied. The practice, once centred on the stable community, now increasingly relies on the emotional labour of individual horses, making it essential to critically examine their role.This literature review explores the possibilities and constraints of equine agency through Martha Nussbaum’s capabilities approach, integrating perspectives from social pedagogy, animal behaviour science, posthumanism, and animal philosophy. It identifies research gaps and discusses practices that may either hinder or support agency.Findings show that agency is limited by compulsory participation, behavioural misinterpretation, economic interests, and anthropomorphic biases. In contrast, facilitators’ awareness of the power dynamic, reflective practice, and willingness to give horses opportunities to choose and live in an environment that enables speciestypical behaviour, promote agency. The horse’s role in social pedagogy is generally positive, yet remains ethically complex.

Highlights of published papers in Applied Animal Behaviour Science in 2025

Field data collection in veterinary and animal behaviour science often faces practical limitations, including time constraints, restricted resources, and difficulties integrating high-quality data capture into real-world clinical workflows. This paper highlights the need for flexible, efficient, and standardised digital solutions that facilitate the collection of multimodal behavioural data in real-world settings. We present a case example using PetsDataLab, a novel cloud-based, “no code” platform designed to enable researchers to create customized apps for efficient and standardised data collection tailored to the behavioural domain, facilitating capture of diverse data types, including video, images, and contextual metadata. We used the platform to develop an app supporting the creation of the Dog Pain Database, a novel comprehensive resource aimed at advancing research on behaviour-based pain indicators in dogs. Using the app, we created a large-scale, structured dataset of dogs with clinically diagnosed conditions expected to be associated with pain and discomfort, including demographic, medical, and pain-related information, alongside high-quality video recordings for future behavioural analyses. To evaluate the app’s usability and its potential for future broader deployment, 14 veterinary professionals tested the app and provided structured feedback via a questionnaire. Results indicated strong usability and clarity, although agreement with using the app in daily clinic life was lower among external testers, pointing to possible barriers to routine integration. This proof-of-concept case study demonstrates the potential of cloud-based platforms like PetsDataLab to bridge research and practice by enabling scalable, standardised, and clinically compatible behavioural data collection. While developed for veterinary pain research, the approach is broadly applicable across behavioural science and supports open science principles through structured, reusable, and interoperable data collection.

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Laura McHenry is an author on ‘ Sublethal glyphosate exposure reduces honey bee foraging and alters the balance of biogenic amines in the brain’, published in JEB. Laura conducted the research described in this article while a PhD student in Dr Margaret J. Couvillon's lab at Virginia Tech Department of Entomology, USA. Laura is now a Postdoctoral Research Scholar in the lab of Dr Margarita Lòpez-Uribe at The Pennsylvania State University Department of Entomology, USA, investigating animal behavior science to safeguard and promote the livelihoods of both people and wildlife.

New article types in Applied Animal Behaviour Science

Highlights of published papers in Applied Animal Behaviour Science in 2024

Animal linguistics is an interdisciplinary field that integrates animal behavior, linguistics, and cognitive science to explore issues such as (a) what animal signals mean, (b) what cognitive abilities are necessary for the production and understanding of these signals, and (c) how communication systems have evolved. Despite the traditional belief that language evolved through a single mutation in our ancestors, accumulating evidence suggests that many cognitive abilities underlying human language have also evolved in nonhuman animals. For example, several species of birds and nonhuman primates convey conceptual meanings through specific vocalizations and/or combine multiple meaning-bearing calls into sequences using syntactic rules. Using experimental paradigms inspired by cognitive science and linguistics, animal linguistics aims to uncover the cognitive mechanisms underlying animal language and explores its evolutionary principles. This review examines previous studies exploring the meanings and cognitive abilities underlying animal language and introduces key methodologies in this emerging field.

Text mining approach in chicken meat welfare

Abstract Domesticated livestock behavior (DLB) is an advanced animal science lecture course that emphasizes behavior associated with domestication of various animal species such as livestock, equine, companion animals and exotic species. Meat Science (MS) is an introductory dual lecture and laboratory course that focuses on muscle biology, food-borne illnesses, humane livestock harvesting, and fabrication techniques. With the emerging usage of artificial intelligence (AI) generative technologies, an assignment was developed utilizing the free AI photo generation tool, Craiyon. Students in DLB and MS were tasked with completing an AI assignment, generating images relevant to either DLB or MS concepts, to expose students to AI technologies. Therefore, the objective of this study was to determine students’ perceptions, knowledge, concerns, and willingness to use AI generative tools and evaluate differences relative to course enrolled (DLB compared with MS). Students (n = 60) enrolled in DLB and MS completed a post-completion survey instrument that consisted of demographic and 5-point Likert-scale questions to gauge student perceptions of the AI assignment. Student survey responses were compiled online using Qualtrics and subsequent respondent data was analyzed using JMP. To give some demographic information relative to the population of students enrolled in DLB and MS, 81 and 76% of the students were female while 19 and 24% were male, respectively. Both courses contained students further along in their degree-seeking program (> 80% Junior or Senior classification). Approximately, 43% of respondents were first generation college students and 66% commute over 14 km daily. When asked about their previous experience using AI generative technologies, 75% of respondents reported no previous experience using AI generative programs. Students enrolled in MS showed a greater concern (P ≤ 0.03) of AI technologies limiting their opportunity to interact and socialize with others as compared with DLB students. Student experience, knowledge, and willingness to use AI tools was not impacted by course enrolled (P ≥ 0.14). Most students reported that they had a positive experience with the AI assignment and felt the experience would be useful in their future (53 and 73%, respectively). Additionally, a majority of students reported that the AI assignment allowed them to visualize concepts and offered a unique learning experience. Participants seemed to have a solid understanding of AI technologies reporting knowledge in tool limitations and inaccurate output. Students were neutral regarding concerns in becoming over-reliant on AI tools and AI use hindering the development of transferable skills. Students felt that AI image generation tools boosted creativity and could be useful in the realm of DLB and MS. Overall, students possessed a general knowledge of AI generative technologies and saw value in using AI image generation tools as it relates to domesticated animal behavior and meat science principles.

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships.

Citizen science has become a popular means to collect data in many research fields, including animal behaviour science. Using this approach has numerous potential benefits (e.g. larger sample sizes, decreased strain on resources, increasing the public’s engagement in science), but there are also issues and assumptions relating to data quality that should be considered. The aim of this study was to assess the reliability of a battery of game-like tests developed to assess behavioural flexibility in dogs. The battery was administered to samples of shelter dogs and owned dogs who had remained in homes long-term. From the two populations, four test groups were created: shelter dogs assessed by the principal investigator (n=85), shelter dogs assessed by shelter staff/volunteers (n=34), long-term owned dogs assessed by the principal investigator (n=21), and long-term dogs assessed by their owners (n=29). Shelter staff/volunteers and dog owners participated as “citizen scientists”. It is accepted that using less skilled “citizen” researchers increases the error within the data, but often claimed that this is offset by larger sample sizes. This implies that the increased error is random and not systematic. Whether who (citizen versus trained researcher) tested the group in a given context was associated with overall test item outcomes was evaluated. In two of the tests, the Alone Time Test and the Three-Toy Test, tester was found to be associated with outcomes. In the Alone Time Test, tester was associated with four items in the shelter dog samples and with three in the long-term owned dog samples. In the Three Toy Test, a series of subsequent items were contingent on the first, and the outcome of that initial item was related to tester. These results demonstrate that it is unwise to assume that the increased error from citizen science work is largely random and thus evened out by the use of a large sample of researchers. Unexpected systematic error might arise within citizen science projects so controls need to be introduced to test for these effects, so that unsound assumptions are not made. There may also be relatively unique factors, beyond researcher objectivity, to consider when using a citizen science approach to study domestic dogs, such as the bidirectional influence on behaviour of any emotional bond between the owner or shelter staff/volunteer and the dog being tested. These results highlight the need for quality checks and preliminary analysis to ensure the identification of any relevant tester effects.

How to stay within the scope of Applied Animal Behaviour Science when conducting research on ‘laboratory animals’?

An interview with two of the authors of the ASPCA's latest research paper, "Behavioral rehabilitation of extremely fearful dogs: Report on the efficacy of a treatment protocol," published in Applied Animal Behavior Science.

Emotional states of animals influence their cognitive processes as well as their behavior. Assessing emotional states is important for animal welfare science as well as for many fields of neuroscience, behavior science, and biomedicine. This can be done in different ways, e.g. through assessing animals’ physiological states or interpreting their behaviors. This paper focuses on the so-called cognitive judgment bias test, which has gained special attention in the last 2 decades and has become a highly important tool for measuring emotional states in non-human animals. However, less attention has been given to the epistemology of the cognitive judgment bias test and to disentangling the relevance of different steps in the underlying cognitive mechanisms. This paper sheds some light on both the epistemology of the methods and the architecture of the underlying cognitive abilities of the tested animals. Based on this reconstruction, we propose a scheme for classifying and assessing different cognitive abilities involved in cognitive judgment bias tests.

Animal behavior and welfare science can form the basis of zoo animal management. However, even basic behavioral data are lacking for the majority of amphibian species, and species-specific research is required to inform management. Our goal was to develop the first ethogram for the critically endangered frog Xenopus longipes through observation of a captive population of 24 frogs. The ethogram was applied to produce a diurnal activity budget and to measure the behavioral impact of a routine health check where frogs were restrained. In the activity budget, frogs spent the vast majority of time swimming, resting in small amounts of time devoted to feeding, foraging, breathing, and (in males) amplexus. Using linear mixed models, we found no effect of time of day or sex on baseline behavior, other than for breathing, which had a greater duration in females. Linear mixed models indicated significant effects of the health check on duration of swimming, resting, foraging, feeding, and breathing behaviors for all frogs. This indicates a welfare trade-off associated with veterinary monitoring and highlights the importance of non-invasive monitoring where possible, as well as providing candidates for behavioral monitoring of acute stress. This investigation has provided the first behavioral data for this species which can be applied to future research regarding husbandry and management practices.

This study presents ways elementary teachers can use a science picturebook genre as an avenue into a grammatical structure prevalent within school science books.

In this paper, I present an argument that quantitative behavioural analysis can be used in zoosemiotic studies to advance the field of biosemiotics. The premise is that signs and signals form patterns in space and time, which can be measured and analysed mathematically. Whole organism sign processing is an important component of the semiosphere, with individual organisms in their Umwelten deriving signs from, and contributing to, the semiosphere, and vice versa. Moreover, there is a wealth of data available in the traditional ethology literature which can be reinterpreted semiotically and drawn together to make a cohesive biosemiotic whole. For example, isolated signals, such as structural elements of birdsong, are attributed meaning by an interpreter, thus generating new ideas and hypotheses in both biology and semiotics. Furthermore, animal behaviour science has developed numerous test paradigms that with careful adaptation, could be suitable for use within a Peircean tripartite model, and thus give valuable insights into Umwelten of other species. In my conclusion, I suggest that by bringing together traditional ethology and biosemiotics, it is possible to use the Modern Synthesis to provide context to biosemiosis, thus pragmatic meaning to animal signals. On this basis, I propose updating the Modern Synthesis to a Semiotic Modern Synthesis, which focuses on whole-organism signals and their contexts, the latter being derived from neo-Darwinian theory and the ‘Umwelt’. Thus, there need be no dichotomy; the Modern Synthesis can successfully be integrated with biosemiotics.

Online conferences as an opportunity to enhance inclusiveness in animal behaviour and welfare research: A case study of the ISAE 2020 virtual meeting

Replications are widely considered an essential tool to evaluate scientific claims. However, many fields have recently reported that replication rates are low and - when they are conducted - many findings do not successfully replicate. These circumstances have led to widespread debates about the value of replications for research quality, credibility of research findings, and factors contributing to current problems with replicability. This special issue brings together researchers from various areas within the field of animal behavior to offer their perspective on the status and value of replications in animal behavior science.

While most animal behavior researchers have mastered the process of knowledge creation, generating knowledge that can readily be applied requires a different set of skills. The process and timeframe of fundamental scientific knowledge production is often not relevant to those who might apply it, such as conservation or wildlife managers. Additionally, the complex challenges that policy makers, managers and practitioners face are often not adequately communicated to and among scientists. This mutual disconnect in discourse, relationships, common terms, and practices is especially apparent when animal behavior researchers seek to have applied impact. We argue that bridging the complex implementation gap in animal behavior requires a formalized vision for change. We turn to change model theory, a tool commonly used in other fields for identifying the links between actions and outcomes necessary for enacting large-scale change. We focus on the subfield of conservation behavior with a change model that outlines specific ways to improve collaboration and coordination between animal behavior science and conservation practice. We present this targeted change model, review each strategy the model outlines, and highlight pressing actions that people from various career stages and backgrounds can take. We encourage researchers to further the alignment of science with management needs by developing the proper communication mechanisms for improved cultural exchange and plan future change model efforts directly targeting managers. Beyond the conservation behavior change model we present, we also discuss the broad applicability of change models to enhance the application of academic research to other fields. Fundamental science researchers are increasingly required to show impact of their work on society; the change model process we describe here can enable further impact.