Animal Behavior Research Articles

Dogs understand the role of a human partner in a cooperative task

Humans are exceptionally flexible in cooperation, partly due to our ability to recognize the roles of cooperative partners. While some non-human animals understand the need for a partner in such interactions, it is unclear whether they grasp the consequences of their partner’s actions and adjust accordingly. Previous studies utilizing economic games with non-human animals yielded mixed results. We investigated dogs, known for their close cooperation with humans, in a stag hunt game. Dogs could cooperate for better rewards or defect for lower ones, while their human partners would either cooperate, never cooperate, or act randomly. We control for attraction to food, side bias, and local enhancement. Dogs were more likely to coordinate with their partners when it led to better rewards, suggesting that they understood their partner’s actions. By highlighting this cognitive skill in dogs, we advance our knowledge of the intricate mechanisms driving cooperative behavior in non-human animals.

Parasite infestation patterns differ between ticks and chigger mites on two rodent host species in Taiwan.

Parasites are typically concentrated on a few host individuals, and identifying the mechanisms underlying aggregated distribution can facilitate a more targeted control of parasites. We investigated the infestation patterns of hard ticks and chigger mites on two rodent species, the striped field mouse, Apodemus agrarius, and the lesser ricefield rat, Rattus losea, in Taiwan. We also explored abiotic and biotic factors that were important in explaining variation in the abundance of ticks and chiggers on rodent hosts. Ticks were more aggregated than chiggers on both rodent species. Factors important for the variation in parasitic loads, especially biotic factors, largely differed between ticks and chiggers. Variation partitioning analyses revealed that a larger proportion of variation in chiggers than in ticks can be explained, especially by abiotic factors. If, as proposed, the higher number of parasites in males is due to a larger range area or immunity being suppressed by testosterone, when A. agrarius males host more ticks, they are expected to also host more chiggers, given that chiggers adopt a similar host finding approach to that of ticks. Instead, the similar abundance of chiggers in male and female A. agrarius implies that a large home range or suppressed immunity does not predispose males to inevitably host more parasites. More variations were explained by abiotic than biotic factors, suggesting that controlling practices are more likely to be successful by focusing on factors related to the environment instead of host traits. Our study indicated that the extent of parasitism is rarely determined by a sole factor, but is an outcome of complex interactions among animal physiology, animal behavior, characteristics of parasites, and the environments.

A novel animal model for understanding secondary traumatic stress and visceral pain in male rats.

Empathetic relationships and the social transference of behaviours have been shown to occur in humans, and more recently through the development of rodent models, where both fear and pain phenotypes develop in observer animals. Clinically, observing traumatic events can induce 'trauma and stressor-related disorders' as defined in the DSM 5. These disorders are often comorbid with pain and gastrointestinal disturbances; however, our understanding of how gastrointestinal - or visceral - pain can be vicariously transmitted is lacking. Visceral pain originates from the internal organs, and despite its widespread prevalence, remains poorly understood. We established an observation paradigm to assess the impact of witnessing visceral pain. We utilised colorectal distension (CRD) to induce visceral pain behaviours in a stimulus rodent while the observer rodent observed. Twenty four hours post-observation, the observer rodent's visceral sensitivity was assessed using CRD. The observer rodents were found to have significant hyperalgesia as determined by lower visceral pain threshold and higher number of total pain behaviours compared with controls. The behaviours of the observer animals during the observation were found to be correlated with the behaviours of the stimulus animal employed. We found that observer animals had hypoactivity of the hypothalamic-pituitary-adrenal (HPA) axis, highlighted by reduced corticosterone at 90 minutes post-CRD. Using c-Fos immunohistochemistry we showed that observer animals also had increased activation of the anterior cingulate cortex, and decreased activation of the paraventricular nucleus, compared with controls. These results suggest that witnessing another animal in pain produces a behavioural phenotype and impacts the brain-gut axis.

A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds.

Impact of the gut microbiome composition on social decision-making.

There is increasing evidence for the role of the gut microbiome in the regulation of socio-affective behavior in animals and clinical conditions. However, whether and how the composition of the gut microbiome may influence social decision-making in health remains unknown. Here, we tested the causal effects of a 7-week synbiotic (vs. placebo) dietary intervention on altruistic social punishment behavior in an ultimatum game. Results showed that the intervention increased participants' willingness to forgo a monetary payoff when treated unfairly. This change in social decision-making was related to changes in fasting-state serum levels of the dopamine-precursor tyrosine proposing a potential mechanistic link along the gut-microbiota-brain-behavior axis. These results improve our understanding of the bidirectional role body-brain interactions play in social decision-making and why humans at times act "irrationally" according to standard economic theory.

Rethinking core affect: the role of dominance in animal behaviour and welfare research

This paper critically examines the philosophical underpinnings of current experimental investigation into animal affect-related decision-making. Animals’ affective states are standardly operationalised by linking positively valenced states with “approach” behaviours and negatively valenced states with “avoidance” behaviours. While this operationalisation has provided a helpful starting point to investigate the ecological role of animals’ internal states, there is extensive evidence that valenced and motivational states do not always neatly align, namely, instances where “liking” does not entail “wanting” (and vice versa). To address this limitation, this paper proposes the (re-)integration of dominance as a dimension of affect. In particular, I argue that dominance, construed as a measure of organisms’ self-assessed behavioural control, can provide the necessary nuance to understand animal affect-related decision-making. Thus, after addressing the theoretical and methodological challenges related to incorporating dominance into a definition of “core affect”, this paper introduces the “Valence-Arousal-Dominance” model of animal affect. This model is explored for its potential applications in two domains. Firstly, in the study of animal affect-related decision-making under predation risk, and secondly, in the study of animal wellbeing. Through these applications, this model aims to bring experimental paradigms of animal affect-related behaviour closer to ecologically relevant scenarios.

Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration.

Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast-paced behaviours (e.g. locomotion), whereas lower-frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals.

Computational modeling of animal behavior in T-mazes: Insights from machine learning

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science.

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Animal behavior: A tale of two apes

Animal behavior: A tale of two apes

Ontogenetic transition from aquatic to amphibious life in the mudskipper, Periophthalmus modestus

Vertebrates first emerged from water to land in the Paleozoic. Our understanding about the process has been steadily refined through paleontological studies, although the soft-body traits and behavior of these early animals remain poorly known. Mudskippers, extant amphibious gobies, could give insight into this question. This study reports on the ontogenetic transition from water to land of the mudskipper Periophthalmus modestus under laboratory conditions. After about 30 days after hatching (dah), the fish gradually changed their preference from water to an artificial shore and then to land. After about five days of periodic volitional emersion, the fish became able to propel themselves on land using the pectoral fins and after a further 13 days they began feeding on land. During the transition, the head morphology altered to suit for terrestrial existence. Tissue contents of triiodothyronine (T3) and thyroxine (T4) sharply increased at 30 dah. Forced underwater confinement of larvae at the last pelagic stage (27–29 dah) for 40–42 days resulted in no statistically significant difference in survival or gross morphology of the body and the gills. Growth was slightly stimulated. Our results show that mudskippers emerge on land with little morphological alteration during ontogenesis, much less than the changes observed for amphibians, and that emersion was not indispensable for survival or growth under our laboratory conditions. Further analysis of how and why mudskippers make their way across the water’s edge will shed valuable light on what morphological, behavioral and physiological traits were needed for, and what environmental conditions may have driven the earliest steps of the water-to-land transition in ancient fishes.

Understanding the research and practical needs required to control toxocariasis in Iran.

Human toxocariasis (HT) is a widespread zoonotic infection globally, notably prevalent in tropical areas. Enhancing our understanding of toxocariasis can lead to increased attention towards the socioeconomic impact and control of this neglected zoonosis. We conducted a comprehensive review of all available articles and official documents on toxocariasis in Iran to identify research gaps and critical needs for its control. This review highlights that despite numerous studies exploring various aspects of toxocariasis in definitive and paratenic hosts, as well as humans and environmental contamination, significant data deficiencies and gaps persist across different regions in the country. These gaps involve investigating the worm burden and reinfection rates in definitive hosts, developing more sensitive methods to detect and differentiate of Toxocara species, and understanding the behavior of definitive host animals. Additionally, identifying potential paratenic hosts for HT and exploring the organ-specific affinity and survival duration of Toxocara larvae within these hosts are essential areas for exploration. It's also imperative to comprehend the sylvatic and domestic cycles of the parasite in paratenic hosts. Furthermore, assessing egg density in the environment, exploring potential new sources such as water, and identifying regions with optimal climatic conditions for the survival and development of Toxocara eggs are crucial for the formulation of effective prevention and control strategies. Identifying at-risk groups, developing early diagnosis techniques, employing imaging methods, and identifying long-term complications in humans are also crucial. Community health organizations should prioritize health education for the public and professionals. Furthermore, accurately estimating definitive host populations, monitoring and preventing their movements in public places, implementing regular deworming practices for pets and stray hosts, and recognizing the infection's significance as a health priority are critical. This comprehensive understanding advocates for a holistic "one health" approach to control of HT.

A scoping review of the use of bioacoustics to assess various components of farm animal welfare

Recording and analyzing animal sounds, including vocal and non-vocal cues, is a relevant tool to access and monitor different aspects of animal physiology, behaviour and general welfare in a remote, non-invasive and continuous fashion. This field, known as bioacoustics, is considerably growing, notably due to the development of machine learning techniques. Yet, applications of these methods in animal husbandry remain limited, and their potential may not be fully acknowledged. This review aims to present the potential of bioacoustic tools to monitor various aspects of the welfare of farm animals, and focuses on studies published since 2010. The first section of the review introduces the concept of bioacoustics, describes the different parameters that can be extracted from the sounds of animals, and explains how they can be analysed. The potential of these measures to inform about the physical characteristics, individuality, health, physiology, behaviour, and affective states of different species of farm animals are then presented, through a contemporary overview of the research field. Although this analysis identifies a variety of applications of bioacoustics tools in relation to the assessment and monitoring of farm animal welfare, it also points out the current scarcity of methods implemented in practice and available to farm management. Barriers to the development of bioacoustics measures in conventional farming are also discussed, and future directions for this field of research are suggested.

Large-language models facilitate discovery of the molecular signatures regulating sleep and activity.

Sleep, locomotor and social activities are essential animal behaviors, but their reciprocal relationships and underlying mechanisms remain poorly understood. Here, we elicit information from a cutting-edge large-language model (LLM), generative pre-trained transformer (GPT) 3.5, which interprets 10.2-13.8% of Drosophila genes known to regulate the 3 behaviors. We develop an instrument for simultaneous video tracking of multiple moving objects, and conduct a genome-wide screen. We have identified 758 fly genes that regulate sleep and activities, including mre11 which regulates sleep only in the presence of conspecifics, and NELF-B which regulates sleep regardless of whether conspecifics are present. Based on LLM-reasoning, an educated signal web is modeled for understanding of potential relationships between its components, presenting comprehensive molecular signatures that control sleep, locomotor and social activities. This LLM-aided strategy may also be helpful for addressing other complex scientific questions.

Emergence of integrated behaviors through direct optimization for homeostasis

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.

A Network Pharmacology-Based Method to Explore the Therapeutic Effect of Honokiol on Diabetes with Comorbid Depression in Mice

The effective treatment of diabetes with comorbid depression is a big challenge so far. Honokiol, a bioactive compound from the dietary supplement Magnolia officinalis extract, possesses multiple health benefits. The present study aims to propose a network pharmacology-based method to elucidate potential targets of honokiol in treating diabetes with comorbid depression and related mechanisms. The antidepressant-like efficacy of honokiol was evaluated in high-fat diet (HFD) induced diabetic mice using animal behavior testing, immuno-staining and western blotting assay. Through network pharmacology analysis, retinoid X receptor alpha (RXRα) and vitamin D receptor (VDR) were identified as potential targets related to diabetes and depression. The stable binding conformation between honokiol and RXR/VDR was determined by molecular docking simulation. Moreover, hononkiol effectively alleviated depression-like behaviors in HFD diabetic mice, presented anti-diabetic and anti-neuroinflammatory functions, and protected the hippocampal neuroplasticity. Importantly, honokiol could activate RXR/VDR heterodimer in vivo. The beneficial effects of honokiol on HFD mice were significantly suppressed by UVI3003 (a RXR antagonist), while enhanced by calcitriol (a VDR agonist). Additionally, the disruption of autophagy in the hippocampus of HFD mice was ameliorated by honokiol, which was attenuated by UVI3003 but strengthened by calcitriol. Taken together, the data provide new evidence that honokiol exerts the antidepressant-like effect in HFD diabetic mice via activating RXR/VDR heterodimer to restore the balance of autophagy. Our findings indicate that the RXR/VDR-mediated signaling might be a potential target for treating diabetes with comorbid depression.

Nerve tissue regeneration based on magnetic and conductive bifunctional hydrogel scaffold

The growth of neurons is often influenced by magnetic and electrical physical factors, and there is more and more research focused on designing a magnetic and conductive engineering scaffolds for nerve injury repair. In this study, electrospun poly lactic-co-glycolic acid (PLGA) fibers containing Fe3O4 magnetic nanoparticles were cryo-cut into magnetic short fibers (MSFs) and subsequently incorporated into sodium alginate (SA)/carboxymethyl chitosan (CMCS) hydrogel along with the addition of multi-wall carbon nanotubes (MWCNTs). MSFs can also achieve alignment by external magnetic fields within the hydrogel to construct magnetic and conductive bifunctional MSF/MWCNT/SA/CMCS hydrogel with anisotropic structure. The magnetic-conductive bifunctional hydrogel exhibited good magnetic responsiveness, electrically active and mechanical property, in which both conductivity and mechanical property increased with the enhancement of MWCNTs contents. Notably, appearing of aligned structure significantly promoted the mechanical property of MSF/SA/CMCS hydrogel. The MSF/SA/CMCS hydrogel with aligned structure also facilitated greater primary neuron cells proliferation than random ones. Compared with individual magnetic or conductive hydrogel, there was significant increase of cell viability in the primary cultured cortical neurons on MSF/MWCNT/SA/CMCS hydrogel with aligned structure. Simultaneously, electrical stimulation (ES) combined with magnetic stimulation (MS) delivered via magnetic and conductive bifunctional MSF/MWCNT/SA/CMCS hydrogel with aligned structure produced a better effect on promoting neuronal growth compared with individual ES or MS. Besides, hot plate measurement indicated that MSF/MWCNT/SA/CMCS hydrogel with aligned structure and MS significantly decreased the mice licking latency after sciatic nerve injury. Animal behavior studies confirmed the protective role of synergistic MS with magnetic hydrogel. Such hydrogel scaffolds have long term potential for nerve tissue regeneration.

3D-Printed proangiogenic patches of photo-crosslinked gelatin and polyurethane hydrogels laden with vascular cells for treating vascular ischemic diseases

Engineering vascularized tissues remains a promising approach for treating ischemic cardiovascular diseases. The availability of 3D-bioprinted vascular grafts that induce therapeutic angiogenesis can help avoid necrosis and excision of ischemic tissues. Here, using a combination of living cells and biodegradable hydrogels, we fabricated 3D-printed biocompatible proangiogenic patches from endothelial cell-laden photo-crosslinked gelatin (EC-PCG) bioink and smooth muscle cell-encapsulated polyurethane (SMC-PU) bioink. Implantation of 3D-bioprinted proangiogenic patches in a mouse model showed that EC-PCG served as an angiogenic capillary bed, whereas patterned SMC-PU increased the density of microvessels. Moreover, the assembled patterns between EC-PCG and SMC-PU induced the geometrically guided generation of microvessels with blood perfusion. In a rodent model of hindlimb ischemia, the vascular patches rescued blood flow to distal tissues, prevented toe/foot necrosis, promoted muscle remodeling, and increased the capillary density, thereby improving the heat-escape behavior of ischemic animals. Thus, our 3D-printed vascular cell-laden bioinks constitute efficient and scalable biomaterials that facilitate the engineering of vascular patches capable of directing therapeutic angiogenesis for treating ischemic vascular diseases.

Environmental Monitoring in Internet of Things (IOT)

Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Standards like MQTT and communication technologies like Wi-Fi govern IoT implementation. Environmental monitoring involves assessing air and water quality, weather conditions, wildlife behavior, and urban heat islands. Air quality monitoring measures pollutant levels in the atmosphere using IoT-based sensor networks. Water quality monitoring assesses chemical and biological characteristics of water bodies. Weather and climate monitoring observe atmospheric conditions using IoT-based sensor networks. Wildlife monitoring tracks animal movements and behavior using IoT-enabled devices. Urban heat island monitoring assesses temperature distribution in urban areas using IoT-based sensors. IoT revolutionizes environmental monitoring by enabling real-time, remote observation across various domains, contributing to better resource management and sustainability efforts.

Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning.

Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.