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Although aggression is associated with several psychiatric disorders, there is no effective treatment nor a rigorous definition for "pathological aggression". Mice make a valuable model for studying aggression. They have a dynamic social structure that depends on the habitat and includes reciprocal interactions between the mice's aggression levels, social dominance hierarchy (SDH), and resource allocation. Nevertheless, the classical behavioral tests for territorial aggression and SDH in mice are reductive and have limited ethological and translational relevance. Recent work has explored the use of semi-natural environments to simultaneously study dominance-related behaviors, resource allocation, and aggressive behavior. Semi-natural setups allow experimental control of the environment combined with manipulations of neural activity. We argue that these setups can help bridge the translational gap in aggression research toward discovering neuronal mechanisms underlying maladaptive aggression., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring., Competing Interests: Declaration of competing interest Authors have no competing interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The article reflects upon the observational practices and methods developed by the early exponents of ethology committed to naturalistic field study and explores how their approaches and techniques influenced a wider field of popular natural-history filmmaking and photography. In doing so, my focus is upon three aspects of ethological field studies: the socio-technical devices used by ethologists to bring birds closer to them, the distinctive observational and representational practices which they forged, and the analogies they used to codify behaviour. This assemblage of elements included hides or screens from which to watch wild birds without disturbing them, optics to extend human vision, pens and paper to sketch and fix patterns of behaviour, watches to record timings, photography to capture action and freeze movement, and illustration and photographs to visualize behaviour. Carried through natural-history networks, the practices, methods and theories of ethologists like Huxley and Tinbergen influenced popular natural-history filmmaking and photography more broadly from the 1940s, driving a behavioural turn in these cultural practices. This popularization of the 'ethological eye' was further facilitated by the convergence of socio-technical devices, forms of observation and dramatization in the work of the early exponents of naturalistic field studies of birds and the popular filmmakers.

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Social behavior is naturally occurring in vertebrate species, which holds a strong evolutionary component and is crucial for the normal development and survival of individuals throughout life. Behavioral neuroscience has seen different influential methods for social behavioral phenotyping. The ethological research approach has extensively investigated social behavior in natural habitats, while the comparative psychology approach was developed utilizing standardized and univariate social behavioral tests. The development of advanced and precise tracking tools, together with post-tracking analysis packages, has recently enabled a novel behavioral phenotyping method, that includes the strengths of both approaches. The implementation of such methods will be beneficial for fundamental social behavioral research but will also enable an increased understanding of the influences of many different factors that can influence social behavior, such as stress exposure. Furthermore, future research will increase the number of data modalities, such as sensory, physiological, and neuronal activity data, and will thereby significantly enhance our understanding of the biological basis of social behavior and guide intervention strategies for behavioral abnormalities in psychiatric disorders., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach, thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure-function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Acoustic signals serve communication within and across species throughout the animal kingdom. Studying the genetics, evolution, and neurobiology of acoustic communication requires annotating acoustic signals: segmenting and identifying individual acoustic elements like syllables or sound pulses. To be useful, annotations need to be accurate, robust to noise, and fast., We here introduce DeepAudioSegmenter ( DAS) , a method that annotates acoustic signals across species based on a deep-learning derived hierarchical presentation of sound. We demonstrate the accuracy, robustness, and speed of DAS using acoustic signals with diverse characteristics from insects, birds, and mammals. DAS comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations. The method can be trained to annotate signals from new species with little manual annotation and can be combined with unsupervised methods to discover novel signal types. DAS annotates song with high throughput and low latency for experimental interventions in realtime. Overall, DAS is a universal, versatile, and accessible tool for annotating acoustic communication signals., Competing Interests: ES, AP, JR, DY No competing interests declared, (© 2021, Steinfath et al.)

Mice have a large visual field that is constantly stabilized by vestibular ocular reflex (VOR) driven eye rotations that counter head-rotations. While maintaining their extensive visual coverage is advantageous for predator detection, mice also track and capture prey using vision. However, in the freely moving animal quantifying object location in the field of view is challenging. Here, we developed a method to digitally reconstruct and quantify the visual scene of freely moving mice performing a visually based prey capture task. By isolating the visual sense and combining a mouse eye optic model with the head and eye rotations, the detailed reconstruction of the digital environment and retinal features were projected onto the corneal surface for comparison, and updated throughout the behavior. By quantifying the spatial location of objects in the visual scene and their motion throughout the behavior, we show that the prey image consistently falls within a small area of the VOR-stabilized visual field. This functional focus coincides with the region of minimal optic flow within the visual field and consequently area of minimal motion-induced image-blur, as during pursuit mice ran directly toward the prey. The functional focus lies in the upper-temporal part of the retina and coincides with the reported high density-region of Alpha-ON sustained retinal ganglion cells., Competing Interests: CH, PS, DW, KV, EM, JS, GB, JK No competing interests declared, (© 2021, Holmgren et al.)

In this perspective paper, we focus on the study of synchronization abilities across the animal kingdom. We propose an ecological approach to studying nonhuman animal synchronization that begins from observations about when, how and why an animal might synchronize spontaneously with natural environmental rhythms. We discuss what we consider to be the most important, but thus far largely understudied, temporal, physical, perceptual and motivational constraints that must be taken into account when designing experiments to test synchronization in nonhuman animals. First and foremost, different species are likely to be sensitive to and therefore capable of synchronizing at different timescales. We also argue that it is fruitful to consider the latent flexibility of animal synchronization. Finally, we discuss the importance of an animal's motivational state for showcasing synchronization abilities. We demonstrate that the likelihood that an animal can successfully synchronize with an environmental rhythm is context-dependent and suggest that the list of species capable of synchronization is likely to grow when tested with ecologically honest, species-tuned experiments. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

Rhythmic behaviour is ubiquitous in both human and non-human animals, but it is unclear whether the cognitive mechanisms underlying the specific rhythmic behaviours observed in different species are related. Laboratory experiments combined with highly controlled stimuli and tasks can be very effective in probing the cognitive architecture underlying rhythmic abilities. Rhythmic abilities have been examined in the laboratory with explicit and implicit perception tasks, and with production tasks, such as sensorimotor synchronization, with stimuli ranging from isochronous sequences of artificial sounds to human music. Here, we provide an overview of experimental findings on rhythmic abilities in human and non-human animals, while critically considering the wide variety of paradigms used. We identify several gaps in what is known about rhythmic abilities. Many bird species have been tested on rhythm perception, but research on rhythm production abilities in the same birds is lacking. By contrast, research in mammals has primarily focused on rhythm production rather than perception. Many experiments also do not differentiate between possible components of rhythmic abilities, such as processing of single temporal intervals, rhythmic patterns, a regular beat or hierarchical metrical structures. For future research, we suggest a careful choice of paradigm to aid cross-species comparisons, and a critical consideration of the multifaceted abilities that underlie rhythmic behaviour. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

The movements an organism makes provide insights into its internal states and motives. This principle is the foundation of the new field of computational ethology, which links rich automatic measurements of natural behaviors to motivational states and neural activity. Computational ethology has proven transformative for animal behavioral neuroscience. This success raises the question of whether rich automatic measurements of behavior can similarly drive progress in human neuroscience and psychology. New technologies for capturing and analyzing complex behaviors in real and virtual environments enable us to probe the human brain during naturalistic dynamic interactions with the environment that so far were beyond experimental investigation. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that application of this methodology will help human neuroscience and psychology extend limited behavioral measurements such as reaction time and accuracy, permit novel insights into how the human brain produces behavior, and ultimately reduce the growing measurement gap between human and animal neuroscience., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms., Competing Interests: BG, AP, HH, GS, VP, VK No competing interests declared, (© 2021, Geuther et al.)

We describe the development, field testing, and results from an automated 3D insect flight detection and tracking system for honey bees (Apis mellifera L.) (Hymenoptera: Apidae) that is capable of providing remarkable insights into airborne behavior. It comprises two orthogonally mounted video cameras with an observing volume of over 200 m3 and an offline analysis software system that outputs 3D space trajectories and inflight statistics of the target honey bees. The imaging devices require no human intervention once set up and are waterproof, providing high resolution and framerate videos. The software module uses several forms of modern image processing techniques with GPU-enabled acceleration to remove both stationary and moving artifact while preserving flight track information. The analysis system has thus far provided information not only on flight statistics (such as speeds and accelerations), but also on subtleties associated with flight behavior by generating heat maps of density and classifying flight patterns according to patrol and foraging behavior. Although the results presented here focus on behavior in the locale of a beehive, the system could be adapted to study a wide range of airborne insect activity., (© The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America.)

Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms' sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60 Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5 and 46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface., Competing Interests: TW, IC No competing interests declared, (© 2021, Walter and Couzin.)

Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together., (Copyright © 2021 the authors.)

Researchers frequently use focal individual sampling to study primate communication. Recent studies of primate gestures have shown that opportunistic sampling offers benefits not found in focal individual sampling, such as the collection of larger sample sizes. What is not known is whether the opportunistic method is biased towards certain signal types or signalers. Our goal was to assess the validity of the opportunistic method by comparing focal individual sampling to opportunistic sampling of facial and gestural communication in a group of captive chimpanzees (Pan troglodytes). We compared: (1) the number of observed facial and gestural signals per signal type and (2) the number of observed facial and gestural signals produced by each signaler. Both methods identified facial signals, gesture signals, and gesture signalers at similar relative rates, but the opportunistic sampling method yielded a more even distribution of signalers and signal types than the focal individual sampling method. In addition, the opportunistic sampling method resulted in larger sample sizes for both facial and gestural communication. However, the opportunistic method did not allow us to calculate the signals per time for each individual, which is easily done with the focal individual method. These results suggest that the opportunistic sampling method is (1) comparable to the focal individual sampling method in multiple important measures, (2) associated with additional sampling benefits, and (3) limited in measuring some variables. Thus, we recommend that future studies use a mixed-methods approach, as focal individual and opportunistic sampling have distinct strengths that complement each other's limitations., (© 2021 S. Karger AG, Basel.)

In animal groups, individual decisions are best characterized by probabilistic rules. Furthermore, animals of many species live in small groups. Probabilistic interactions among small numbers of individuals lead to a so-called intrinsic noise at the group level. Theory predicts that the strength of intrinsic noise is not a constant but often depends on the collective state of the group; hence, it is also called a state-dependent noise or a multiplicative noise . Surprisingly, such noise may produce collective order. However, only a few empirical studies on collective behaviour have paid attention to such effects owing to the lack of methods that enable us to connect data with theory. Here, we demonstrate a method to characterize the role of stochasticity directly from high-resolution time-series data of collective dynamics. We do this by employing two well-studied individual-based toy models of collective behaviour. We argue that the group-level noise may encode important information about the underlying processes at the individual scale. In summary, we describe a method that enables us to establish connections between empirical data of animal (or cellular) collectives and the phenomenon of noise-induced states, a field that is otherwise largely limited to the theoretical literature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Collective dynamics in animal groups is a challenging theme for the modelling community, being treated with a wide range of approaches. This topic is here tackled by a discrete model. Entering in more details, each agent, represented by a material point, is assumed to move following a first-order Newtonian law, which distinguishes speed and orientation. In particular, the latter results from the balance of a given set of behavioural stimuli, each of them defined by a direction and a weight, that quantifies its relative importance. A constraint on the sum of the weights then avoids implausible simultaneous maximization/minimization of all movement traits. Our framework is based on a minimal set of rules and parameters and is able to capture and classify a number of collective group dynamics emerging from different individual preferred behaviour, which possibly includes attractive, repulsive and alignment stimuli. In the case of a system of animals subjected only to the first two behavioural inputs, we also show how analytical arguments allow us to a priori relate the equilibrium interparticle spacing to critical model coefficients. Our approach is then extended to account for the presence of predators with different hunting strategies, which impact on the behaviour of a prey population. Hints for model refinement and applications are finally given in the conclusive part of the article. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Group-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish, and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and can be described as a combination of interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate datasets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of studies, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummy-nose tetra ( Hemigrammus rhodostomus ) and the zebrafish ( Danio rerio ), which both present a burst-and-coast motion. From the detailed quantitative description of individual-level interactions, it is thus possible to develop a quantitative model of the emergent dynamics observed at the group level, whose predictions can be checked against experimental results. This method can be applied to a wide range of biological and social systems. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Despite the importance of emotional intelligence, its biological mechanism is still not well understood. For this reason, we have developed a rodent detour task which requires an animal to reach a highly desired object placed directly behind a transparent barrier that blocks the direct route to the target. This apparently simple task is highly dependent on the emotional control that is necessary to inhibit prepotent and counterproductive responses driven by the sight of a desired object. The water escape detour task designed for mice enables testing the ability to solve emotionally challenging problems, as well as identification of an impairment termed perseveration. Such a maladaptive reaction to a challenging situation is characterized by difficulty in terminating an unsuccessful response, leading to persistent repetition of inappropriate behavior. This issue is important because perseveration is associated with schizophrenia, drug abuse, and aging. © 2020 Wiley Periodicals LLC. Basic Protocol: Water escape detour task Support Protocol 1: Preparation of escape platform Support Protocol 2: Preparation of the transparent barrier Alternate Protocol: Water escape detour task for testing acute effects., (© 2020 Wiley Periodicals LLC.)

Rationale: Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior that can be ethologically demarcated as either appetitive (rewarding) or reactive (defensive), each with its own behavioral characteristics, functionality, and neural basis that may transition from adaptive to maladaptive depending on genetic and environmental factors. There has been a recent surge in the development of preclinical animal models for studying appetitive aggression-related behaviors and identifying the neural mechanisms guiding their progression and expression. However, adoption of these procedures is often impeded by the arduous task of manually scoring complex social interactions. Manual observations are generally susceptible to observer drift, long analysis times, and poor inter-rater reliability, and are further incompatible with the sampling frequencies required of modern neuroscience methods., Objectives: In this review, we discuss recent advances in the preclinical study of appetitive aggression in mice, paired with our perspective on the potential for machine learning techniques in producing automated, robust scoring of aggressive social behavior. We discuss critical considerations for implementing valid computer classifications within behavioral pharmacological studies., Key Results: Open-source automated classification platforms can match or exceed the performance of human observers while removing the confounds of observer drift, bias, and inter-rater reliability. Furthermore, unsupervised approaches can identify previously uncharacterized aggression-related behavioral repertoires in model species., Discussion and Conclusions: Advances in open-source computational approaches hold promise for overcoming current manual annotation caveats while also introducing and generalizing computational neuroethology to the greater behavioral neuroscience community. We propose that currently available open-source approaches are sufficient for overcoming the main limitations preventing wide adoption of machine learning within the context of preclinical aggression behavioral research.

Behavioral correlations stretching over time are an essential but often neglected aspect of interactions among animals. These correlations pose a challenge to current behavioral-analysis methods that lack effective means to analyze complex series of interactions. Here we show that non-invasive information-theoretic tools can be used to reveal communication protocols that guide complex social interactions by measuring simultaneous flows of different types of information between subjects. We demonstrate this approach by showing that the tandem-running behavior of the ant Temnothorax rugatulus and that of the termites Coptotermes formosanus and Reticulitermes speratus are governed by different communication protocols. Our discovery reconciles the diverse ultimate causes of tandem running across these two taxa with their apparently similar signaling mechanisms. We show that bidirectional flow of information is present only in ants and is consistent with the use of acknowledgement signals to regulate the flow of directional information., Competing Interests: GV, NM, SP, TP, SW No competing interests declared, (© 2020, Valentini et al.)

Conspecific acceptance thresholds (Reeve 1989 Am. Nat. 133 , 407-435), which have been widely applied to explain ecological behaviour in animals, proposed how sensory information, prior information and the costs of decisions determine actions. Signal detection theory (Green & Swets 1966 Signal detection theory and psychophysics ; SDT), which forms the basis of CAT models, has been widely used in psychological studies to partition the ability to discriminate sensory information from the action made as a result of it. In this article, we will review the application of SDT in interpreting the behaviour of laboratory animals trained in operant conditioning tasks and then consider its potential in ecological studies of animal behaviour in natural environments. Focusing on the nest-mate recognition systems exhibited by social insects, we show how the quantitative application of SDT has the potential to transform acceptance rate data into independent indices of cue sensitivity and decision criterion (also known as the acceptance threshold). However, further tests of the assumptions underlying SDT analysis are required. Overall, we argue that SDT, as conventionally applied in psychological studies, may provide clearer insights into the mechanistic basis of decision making and information processing in behavioural ecology. This article is part of the theme issue 'Signal detection theory in recognition systems: from evolving models to experimental tests'.

Most predictions of how populations and species of ectotherms will respond to global warming are based on estimates of the temperature at which organisms lose motor control (i.e., CT max - the Critical Thermal Maximum). Here, we describe a non-lethal protocol and ethograms to estimate the relative tolerance of amphibians to increasing temperatures. These methods-suitable for field or laboratory conditions-are replicable, inexpensive and applicable to both post-metamorphic stages and organisms with direct development. We illustrate the use of this standardized protocol for four amphibians from a tropical cloud forest in Veracruz, Mexico with contrasting life histories: a lungless salamander (Aquiloeurycea cafetalera: Plethodontidae), a leaf-litter frog (Craugastor rhodopis: Craugastoridae), a semiaquatic frog (Lithobates berlandieri: Ranidae), and a tree frog (Rheohyla miotympanum: Hylidae). We identified four behavioral responses preceding CT max for all amphibians included in this study: 1) Optimal Activity Range, 2) Supra-optimal Activity Range, 3) Heat Stress Range, and 4) Involuntary Movements Range. Additionally, we identified a fifth parameter associated with resilience to heat shock: 5) Recovery Stage after reaching CT max . We conclude that the behavioral responses preceding the Critical Thermal Maximum are as informative as CT max . Using behavioral responses to estimate thermal tolerance has the additional advantage of reducing the risk of injury or death of amphibians during physiological experiments., Competing Interests: Declaration of competing interest No competing interests declared., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Current preclinical cognitive assessments are highly time intensive, with lengthy assessment procedures. In this regard, a single-day assay that focuses just on assessing learning behavior in a time-effective and relatable manner would be of value. This article describes the box maze as a short-term behavioral procedure to measure learning in mice. The protocol consists of allowing mice to explore an enclosed space that has eight holes. One of these holes leads to a tunnel that connects to an escape cage, and the latency to enter this escape hole is recorded for each mouse. Mice are tested four times within a single day, and the decrease in escape latency over time is used as a measure of learning. Age is a factor that affects escape latency in the box maze. Hence, the box-maze procedure is proposed as an efficient test to probe aging and aging intervention-related research questions. © 2020 by John Wiley & Sons, Inc., (© 2020 John Wiley & Sons, Inc.)

Field studies on freely behaving animals commonly require tagging and often are focused on single species. Weakly electric fish generate a species- and individual-specific electric organ discharge (EOD) and therefore provide a unique opportunity for individual tracking without tagging. Here, we present and test tracking algorithms based on recordings with submerged electrode arrays. Harmonic structures extracted from power spectra provide fish identity. Localization of fish based on weighted averages of their EOD amplitudes is found to be more robust than fitting a dipole model. We apply these techniques to monitor a community of three species, Apteronotus rostratus , Eigenmannia humboldtii and Sternopygus dariensis , in their natural habitat in Darién, Panama. We found consistent upstream movements after sunset followed by downstream movements in the second half of the night. Extrapolations of these movements and estimates of fish density obtained from additional transect data suggest that some fish cover at least several hundreds of meters of the stream per night. Most fish, including E . humboldtii , were traversing the electrode array solitarily. From in situ measurements of the decay of the EOD amplitude with distance of individual animals, we estimated that fish can detect conspecifics at distances of up to 2 m. Our recordings also emphasize the complexity of natural electrosensory scenes resulting from the interactions of the EODs of different species. Electrode arrays thus provide an unprecedented window into the so-far hidden nocturnal activities of multispecies communities of weakly electric fish at an unmatched level of detail., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

Continuous sampling provides the most complete data set for behavioral research; however, it often requires a prohibitive investment of time and labor. The objectives of this study were to validate behavioral observation methods of young broiler chickens using 1) 7 scan sampling intervals (0.5, 1, 3, 5, 10, 15, and 30 min) and 2) an automated tracking software program (EthoVision XT 14) compared to continuous behavioral observation, considered the gold standard for behavior observation. Ten 19-day-old Ross 708 broiler cockerels were included in this study. All behavior was video recorded over an 8-h period, and data were collected using a continuous sampling methodology. The same video files were utilized for analysis for scan sampling and automated tracking software analysis. For both analyses, the following criteria were used to identify which method accurately reflected the true duration and frequency for each behavior, as determined by continuous observation: R2 ≥ 0.9, slope was not different from 1 (P > 0.05), and intercept was not different from 0 (P > 0.05). Active, eating, drinking, and maintenance behaviors were accurately estimated with 0.5-min scan sample intervals. Active, inactive, eating, and maintenance behaviors were accurately estimated with 1-min scan sample intervals. Inactive behavior was accurately estimated with 5-min scan sample intervals. The remainder of sampling intervals examined did not provide accurate estimates, and no scan sampling interval accurately estimated the number of behavior bouts. The automated tracking software was able to accurately detect true duration of inactive behavior but was unable to accurately detect activity. The results of this study suggest that high-frequency behaviors can be accurately observed with instantaneous scan sampling up to 1-min intervals. Automated tracking software can accurately identify inactivity in young broiler chickens, but further behavior identification will require refinement., (© 2019 Poultry Science Association Inc.)

Every scientific practice rests on implicit unrevised theoretical assumptions. Neuroscience, in particular, focuses on a very controversial object of study-the brain and is therefore prone to tacitly embrace philosophical positions in its everyday workings. It is thus, of utmost importance, to develop a critique of the structure of neuroscientific investigation so as to understand what the uncovered pillars of the field are, what pitfalls they may implicate and how we can correct them. In this paper, I gather the first critiques in animal cognitive neuroscience and hope to establish the first step in a continuous process of revision. By applying a conceptual division of neuroscience into cognitive, behavioral and neurobiological theories, I point out the main problems in articulating the three, based on actual scientific practice rather than purely theoretical reasoning. I conclude by proposing developments on behavioral theory and set an initial critique on assumptions on both cognitive and neurobiological theories.

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments., Competing Interests: BLdB is a scientific advisor of FlySorter, LLC. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Chemically induced nociception has not yet been studied intensively in genetically tractable models. Hence, our goal was to establish a Drosophila assay that can be used to study the cellular and molecular/genetic bases of chemically induced nociception. Drosophila larvae exposed to increasing concentrations of hydrochloric acid (HCl) produced an increasingly intense aversive rolling response. HCl (0.5%) was subthreshold and provoked no response. All classes of peripheral multidendritic (md) sensory neurons (classes I-IV) are required for full responsiveness to acid, with class IV making the largest contribution. At the cellular level, classes IV, III and I showed increases in calcium following acid exposure. In the central nervous system, Basin-4 second-order neurons are the key regulators of chemically induced nociception, with a slight contribution from other types. Finally, chemical nociception can be sensitized by tissue damage. Subthreshold HCl provoked chemical allodynia in larvae 4 h after physical puncture wounding. Pinch wounding and UV irradiation, which do not compromise the cuticle, did not cause chemical allodynia. In sum, we developed a novel assay to study chemically induced nociception in Drosophila larvae. This assay, combined with the high genetic resolving power of Drosophila, should improve our basic understanding of fundamental mechanisms of chemical nociception. This article is part of the Theo Murphy meeting issue 'Evolution of mechanisms and behaviour important for pain'.

Behaviour underpins interactions among conspecifics and between species, with consequences for the transmission of disease-causing parasites. Because many parasites lead to declines in population size and increased risk of extinction for threatened species, understanding the link between host behaviour and disease transmission is particularly important for conservation management. Here, we consider the intersection of behaviour, ecology and parasite transmission, broadly encompassing micro- and macroparasites. We focus on behaviours that have direct impacts on transmission, as well as the behaviours that result from infection. Given the important role of parasites in host survival and reproduction, the effects of behaviour on parasitism can scale up to population-level processes, thus affecting species conservation. Understanding how conservation and infectious disease control strategies actually affect transmission potential can therefore often only be understood through a behavioural lens. We highlight how behavioural perspectives of disease ecology apply to conservation by reviewing the different ways that behavioural ecology influences parasite transmission and conservation goals. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Insights into animal behaviour play an increasingly central role in species-focused conservation practice. However, progress towards incorporating behaviour into regional or global conservation strategies has been more limited, not least because standardized datasets of behavioural traits are generally lacking at wider taxonomic or spatial scales. Here we make use of the recent expansion of global datasets for birds to assess the prospects for including behavioural traits in systematic conservation priority-setting and monitoring programmes. Using International Union for Conservation of Nature Red List classifications for more than 9500 bird species, we show that the incidence of threat can vary substantially across different behavioural categories, and that some types of behaviour-including particular foraging, mating and migration strategies-are significantly more threatened than others. The link between behavioural traits and extinction risk is partly driven by correlations with well-established geographical and ecological factors (e.g. range size, body mass, human population pressure), but our models also reveal that behaviour modifies the effect of these factors, helping to explain broad-scale patterns of extinction risk. Overall, these results suggest that a multi-species approach at the scale of communities, continents and ecosystems can be used to identify and monitor threatened behaviours, and to flag up cases of latent extinction risk, where threatened status may currently be underestimated. Our findings also highlight the importance of comprehensive standardized descriptive data for ecological and behavioural traits, and point the way towards deeper integration of behaviour into quantitative conservation assessments. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

The impact of environmental change on the reproduction and survival of wildlife is often behaviourally mediated, placing behavioural ecology in a central position to quantify population- and community-level consequences of anthropogenic threats to biodiversity. This theme issue demonstrates how recent conceptual and methodological advances in the discipline are applied to inform conservation. The issue highlights how the focus in behavioural ecology on understanding variation in behaviour between individuals, rather than just measuring the population mean, is critical to explaining demographic stochasticity and thereby reducing fuzziness of population models. The contributions also show the importance of knowing the mechanisms by which behaviour is achieved, i.e. the role of learning, reasoning and instincts, in order to understand how behaviours change in human-modified environments, where their function is less likely to be adaptive. More recent work has thus abandoned the 'adaptationist' paradigm of early behavioural ecology and increasingly measures evolutionary processes directly by quantifying selection gradients and phenotypic plasticity. To support quantitative predictions at the population and community levels, a rich arsenal of modelling techniques has developed, and interdisciplinary approaches show promising prospects for predicting the effectiveness of alternative management options, with the social sciences, movement ecology and epidemiology particularly pertinent. The theme issue furthermore explores the relevance of behaviour for global threat assessment, and practical advice is given as to how behavioural ecologists can augment their conservation impact by carefully selecting and promoting their study systems, and increasing their engagement with local communities, natural resource managers and policy-makers. Its aim to uncover the nuts and bolts of how natural systems work positions behavioural ecology squarely in the heart of conservation biology, where its perspective offers an all-important complement to more descriptive 'big-picture' approaches to priority setting. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Protecting wild places is conservation's most pressing task given rapid contemporary declines in biodiversity and massive land use changes. We suggest that behavioural ecology has a valuable, albeit limited, role to play in this agenda. Behaviourally based empiricism and modelling, especially of animal movements and habitat preferences have enjoyed wide applicability in delineating reserve boundaries. In protected areas that sanction exploitation, it may also be important to understand individuals' behavioural and life-history responses to management decisions. We also argue, however, that the in-depth studies of behavioural ecologists may have an important role in conservation by elevating species' status from mundane to charismatic and often sparking public empathy, and their mere presence in field generates local (or broader) intrigue. More generally behavioural ecologists will only be listened to, and their contributions considered of conservation importance, if they become more involved in decision-making processes as witnessed by several prominent examples that have supported the establishment of protected areas. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Wildlife tracking is one of the most frequently employed approaches to monitor and study wildlife populations. To date, the application of tracking data to applied objectives has focused largely on the intensity of use by an animal in a location or the type of habitat. While this has provided valuable insights and advanced spatial wildlife management, such interpretation of tracking data does not capture the complexity of spatio-temporal processes inherent to animal behaviour and represented in the movement path. Here, we discuss current and emerging approaches to estimate the behavioural value of spatial locations using movement data, focusing on the nexus of conservation behaviour and movement ecology that can amplify the application of animal tracking research to contemporary conservation challenges. We highlight the importance of applying behavioural ecological approaches to the analysis of tracking data and discuss the utility of comparative approaches, optimization theory and economic valuation to gain understanding of movement strategies and gauge population-level processes. First, we discuss innovations in the most fundamental movement-based valuation of landscapes, the intensity of use of a location, namely dissecting temporal dynamics in and means by which to weight the intensity of use. We then expand our discussion to three less common currencies for behavioural valuation of landscapes, namely the assessment of the functional (i.e. what an individual is doing at a location), structural (i.e. how a location relates to use of the broader landscape) and fitness (i.e. the return from using a location) value of a location. Strengthening the behavioural theoretical underpinnings of movement ecology research promises to provide a deeper, mechanistic understanding of animal movement that can lead to unprecedented insights into the interaction between landscapes and animal behaviour and advance the application of movement research to conservation challenges. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

The evolution of a female penis is an extremely rare event and is only known to have occurred in a tribe of small cave insects, Sensitibillini (Psocodea: Trogiomorpha: Prionoglarididae). The female penis, which is protrudable and inserted into the male vagina-like cavity during copulation to receive semen, is thought to have evolved independently twice in this tribe, in the Brazilian Neotrogla and the African Afrotrogla. These findings strongly suggest that there are some factors unique to Sensitibillini that have facilitated female penis evolution. Here, several hypothetical factors are presented that may have enabled the evolution of the female penis in Sensitibillini. The female-female competition for nutritious semen, the oligotrophic environment, and the twin insemination slots with switching valve are considered to be the driving factors for female penis evolution. Additionally, the following factors are considered responsible for relaxing the constraint against female penis evolution: preexistence of the female-above mating position, the elongated duct connecting the female pre-penis with the sperm storage organ, and the small male genital cavity accepting the female genital tubercle bearing the opening of this duct. Understanding the factors enabling female penis evolution may also shed light on the evolution of the male penis., (© 2019 WILEY Periodicals, Inc.)

The analysis of behavior in animal models is an important objective in many research fields, including neuroscience, psychology, toxicology, and neuropsychopharmacology. Animal models have been used for many years, and several behavioral paradigms, such as locomotor activity, social interactions, and cognitive behavior, have been studied in animal models to correlate the behaviors with pharmacological or environmental interventions and with molecular, biochemical, and physiological findings. We reviewed the literature looking for open-source, freely available software to analyze animal behavior and found 12 freely available programs: ToxTrack, EthoWatcher, Mouse Behavior Tracker, Mouse Move, JAABA, wrMTrck, AnimalTracker, idTracker, Ctrax, Mousetracker, VideoHacking, and Cowlog, which were developed with different programs, work on different platforms, and have particular types of inputs and outputs and analysis capabilities. We reviewed some examples of their use, tested some of them, and provided several recommendations for the future development of programs for the automated analysis of behavior in animal models. In conclusion, we show freely available software for the automated analysis of behavior in animal models such as adult zebrafish and provide information for researchers and students looking for quick, easy-to-implement, and inexpensive behavior analysis alternatives.

The elaborate structure of the cerebellum has been long known, although its contribution to a remarkable diversity of behavior is only recently appreciated. Taking an evolutionary perspective, we consider the wider function of the cerebellum based on insight from the function of so-called cerebellum-like structures. Cerebellum-like structures cancel the effects of self-stimulation, a function that has been well characterized in both elasmobranch and weakly electric fish. This function depends on the implementation of an adaptive filter, which provides an input-output transformation modified by specific learning rules. We argue that the broad contribution of the cerebellum to behavior can be understood by thinking of the cerebellum as an array of adaptive filters that can be coopted to a wide range of tasks. Consistent with this, studies of mammalian cerebellum reveal operations characteristic of an adaptive filter, which we review here. We then describe a model for how a cerebellar adaptive filter architecture could interact with central pattern generators and sensory feedback to coordinate sequentially patterned behavior. Finally, we use the acquisition and production of complex vocal motor sequencing in birdsong as an example to explore how this model of cerebellar adaptive filter-central pattern generator interactions may contribute to other important domains of vertebrate neuroethology. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

This special issue highlights some recent advances in neuroethology based on research presented at the 13th International Congress of Neuroethology and associated satellite symposia in Brisbane, Australia, on July 15-20, 2018. The discipline of neuroethology combines methods and concepts from ethology with those from neurobiology to develop a comparative analysis of the mechanisms of behavior that takes into account a species' ecology and evolutionary history. In his 1951 book The Study of Instinct , Nobel Prize winner Niko Tinbergen called on ethologists and neurophysiologists to join forces to search for mechanisms of motivated behaviors. He used the term "ethophysiology" to describe this integrative research approach, which he felt was crucial for developing a full understanding of behavior in all its complexities. At that time, "the ethologists were as naive about the neurophysiological significance of their findings as the neurophysiologists were about the behavioral implications of theirs" (Hoyle, 1984, p. 371). To rectify these limitations and to move forward, some of the earliest neuroethological research focused on understanding cellular mechanisms underlying the ethological concepts of releasers (sign stimuli) and fixed action patterns in species such as locusts and toads (Ewert, 1980; Hoyle, 1984). The breadth of neuroethological research soon expanded to include many other species, particularly those with distinct sensory or motor specializations (echolocating bats, electric fish, barn owls; Ewert, Capranica, & Ingle, 1983), as well as to understand neural mechanisms of more "general" behaviors, such as learning, memory, navigation, and communication. Molecular genetic, anatomical, and computational approaches are also part of the neuroethologist's toolbox. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Understanding the brain requires understanding behavior. New machine vision and learning techniques are poised to revolutionize our ability to analyze behaviors exhibited by animals in the laboratory. Here we describe one such method, Motion Sequencing (MoSeq), which combines three-dimensional (3D) imaging with unsupervised machine learning techniques to identify the syllables and grammar that comprise mouse body language. This Q&A situates MoSeq within the array of novel methods currently being developed for behavioral analysis, enumerates its relative strengths and weaknesses, and describes its future trajectory.

Studies of odontocete foraging ecology have been limited by the challenges of observing prey capture events and outcomes underwater. We sought to determine whether subsurface movement behavior recorded from archival tags could accurately identify foraging events by fish-eating killer whales. We used multisensor bio-logging tags attached by suction cups to Southern Resident killer whales ( Orcinus orca ) to: (1) identify a stereotyped movement signature that co-occurred with visually confirmed prey capture dives; (2) construct a prey capture dive detector and validate it against acoustically confirmed prey capture dives; and (3) demonstrate the utility of the detector by testing hypotheses about foraging ecology. Predation events were significantly predicted by peaks in the rate of change of acceleration ('jerk peak'), roll angle and heading variance. Detection of prey capture dives by movement signatures enabled substantially more dives to be included in subsequent analyses compared with previous surface or acoustic detection methods. Males made significantly more prey capture dives than females and more dives to the depth of their preferred prey, Chinook salmon. Additionally, only half of the tag deployments on females (5 out of 10) included a prey capture dive, whereas all tag deployments on males exhibited at least one prey capture dive (12 out of 12). This dual approach of kinematic detection of prey capture coupled with hypothesis testing can be applied across odontocetes and other marine predators to investigate the impacts of social, environmental and anthropogenic factors on foraging ecology., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2019. Published by The Company of Biologists Ltd.)

Different rat and mouse models are used in studies of social interactions. Simple behavioral measures, which are commonly used in the laboratory, allow to perform relatively short experiments and to use multiple brain manipulation techniques. However, too much focus on the simplest behavioral models generates a serious risk of reducing ecological validity or even studying phenomena which would never happen outside of the laboratory. In this review, we discuss the suitability of mice and rats as model organisms for studying social behaviors, with focus on social transmission of fear paradigms. First, we briefly introduce the concept of domestication and what impact it had on laboratory rodents. Then, we present two aspects of social behaviors, sociability and dominance, which are crucial for social organization in these species. Finally, we present experimental models used for studying how animals transmit information about danger between each other, and how these models may reflect what happens in the natural environment. We discuss the difficulties that arise from our limited knowledge of rat and mouse ecology, especially their social life. We also explore the subject of balancing ecological validity and controllability in rodent models of social behaviors, the latter being particularly important for studying brain activity. Although it is very challenging, an efficient program for social neuroscience research should, in our opinion, aim at bridging the gap between laboratory and field studies., (© 2018 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.)

Although humans may have more nuanced reasons for communicating - e.g. to teach or inform, to share or change opinions or attitudes - all animals engage in communication with members of their own as well as other species, and there are more similarities than differences between non-human and human communication. All communication systems are composed of the same basic elements and all face comparable challenges. In this Commentary, we explore the extent to which research investigating how non-human animals communicate with each other (animal communication) overlaps in questions and approaches with research focused on how humans communicate with each other. We place a special focus on human communication involving scientific content, i.e. science communication. We begin with a brief review of the fields of animal communication and science communication. We next synthesize literature from each field to examine the roles, impacts and potential interactions of communication system elements - signaling environments, signalers, signal form and receivers - on effective communication. We find that research examining animal and human communication, including science communication, often has different emphases. Animal communication research, for example, tends to focus more on the role of the signaling environment through quantification of receiver responses. In contrast, science communication research currently emphasizes relationship building between signalers and receivers, and quantifies aspects of the receiver's psychology. Informed by our cross-disciplinary assessment, we propose potentially productive avenues of future research in both animal communication and science communication., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

Machine vision represents an accurate and easily verifiable method for observing live organisms and this technology is constantly evolving in terms of accessibility and cost. Motivated by the complexity of observing small-sized aquatic organisms in experimental systems, and the difficulties related to real-time observation, sampling and counting without interfering with the organisms, we here present a new method for observing behaviour and dispersion of non-sessile zooplankton organisms using a custom-made tank with an associated machine vision system. The system was used in an experiment where the aim was to assess the effect of sound and ultrasound on the phototactic behaviour of copepodite stages of the salmon louse ( Lepeophtheirus salmonis ). The experimental set-up is described, including a triangular test tank designed to create a sound pressure gradient, a mechanized camera movement system and a vision system with dedicated image processing software., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

Gaze behavior offers valuable insights into attention and cognition. However, technological limitations have prevented the examination of animals' gaze behavior in natural, information-rich contexts; for example, during navigation through complex environments. Therefore, we developed a lightweight custom-made logger equipped with an inertial measurement unit (IMU) and GPS to simultaneously track the head movements and flight trajectories of free-flying homing pigeons. Pigeons have a limited range of eye movement, and their eye moves in coordination with their head in a saccadic manner (similar to primate eye saccades). This allows head movement to act as a proxy for visual scanning behavior. Our IMU sensor recorded the 3D movement of the birds' heads in high resolution, allowing us to reliably detect distinct saccade signals. The birds moved their head far more than necessary for maneuvering flight, suggesting that they actively scanned the environment. This movement was predominantly horizontal (yaw) and sideways (roll), allowing them to scan the environment with their lateral visual field. They decreased their head movement when they flew solo over prominent landmarks (major roads and a railway line) and also when they flew in pairs (especially when flying side by side, with the partner maintained in their lateral visual field). Thus, a decrease in head movement indicates a change in birds' focus of attention. We conclude that pigeons use their head gaze in a task-related manner and that tracking flying birds' head movement is a promising method for examining their visual attention during natural tasks., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

Integrating mechanistic models of movement and behavior into large-scale movement ecology and biodiversity research is one of the major challenges in current ecological science. This is mainly due to a large gap between the spatial scales at which these research lines act. Here, we propose to apply trait-based movement models to bridge this gap and generalize movement trajectories across species and ecosystems. We show how to use species traits (e.g., body mass) to generate allometric random walks and illustrate in two worked examples how this facilitates general predictions of species-interaction traits, meta-community structures, and biodiversity patterns. Thereby, allometric random walks foster a closer integration of movement ecology and biodiversity research by scaling up from small-scale mechanistic measurements to a predictive understanding of movement and biodiversity patterns in different landscapes., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Modern decision neuroscience offers a powerful and broad account of human behaviour using computational techniques that link psychological and neuroscientific approaches to the ways that individuals can generate near-optimal choices in complex controlled environments. However, until recently, relatively little attention has been paid to the extent to which the structure of experimental environments relates to natural scenarios, and the survival problems that individuals have evolved to solve. This situation not only risks leaving decision-theoretic accounts ungrounded but also makes various aspects of the solutions, such as hard-wired or Pavlovian policies, difficult to interpret in the natural world. Here, we suggest importing concepts, paradigms and approaches from the fields of ethology and behavioural ecology, which concentrate on the contextual and functional correlates of decisions made about foraging and escape and address these lacunae.

Behaviour is a key interface between an animal's genome and its environment. Repeatable individual differences in behaviour have been extensively documented in animals, but the molecular underpinnings of behavioural variation among individuals within natural populations remain largely unknown. Here, we offer a critical review of when molecular techniques may yield new insights, and we provide specific guidance on how and whether the latest tools available are appropriate given different resources, system and organismal constraints, and experimental designs. Integrating molecular genetic techniques with other strategies to study the proximal causes of behaviour provides opportunities to expand rapidly into new avenues of exploration. Such endeavours will enable us to better understand how repeatable individual differences in behaviour have evolved, how they are expressed and how they can be maintained within natural populations of animals.

Animal collective movements are a key example of a system that links two clearly defined levels of organization: the individual and the group. Most models investigating collective movements have generated coherent collective behaviours without the inclusion of individual variability. However, new individual-based models, together with emerging empirical information, emphasize that within-group heterogeneity may strongly influence collective movement behaviour. Here we (i) review the empirical evidence for individual variation in animal collective movements, (ii) explore how theoretical investigations have represented individual heterogeneity when modelling collective movements and (iii) present a model to show how within-group heterogeneity influences the collective properties of a group. Our review underscores the need to consider variability at the level of the individual to improve our understanding of how individual decision rules lead to emergent movement patterns, and also to yield better quantitative predictions of collective behaviour.This article is part of the theme issue 'Collective movement ecology'., (© 2018 The Author(s).)