Stochastic modelling of animal movement

Tracking and modeling the movement of Queensland fruit flies, Bactrocera tryoni, using harmonic radar in papaya fields

Determining movement parameters for pest insects such as tephritid fruit flies is critical to developing models which can be used to increase the effectiveness of surveillance and control strategies. In this study, harmonic radar was used to track wild-caught male Queensland fruit flies (Qflies), Bactrocera tryoni, in papaya fields. Experiment 1 continuously tracked single flies which were prodded to induce movement. Qfly movements from this experiment showed greater mean squared displacement than predicted by both a simple random walk (RW) or a correlated random walk (CRW) model, suggesting that movement parameters derived from the entire data set do not adequately describe the movement of individual Qfly at all spatial scales or for all behavioral states. This conclusion is supported by both fractal and hidden Markov model (HMM) analysis. Lower fractal dimensions (straighter movement paths) were observed at larger spatial scales (> 2.5 m) suggesting that Qflies have qualitatively distinct movement at different scales. Further, a two-state HMM fit the observed movement data better than the CRW or RW models. Experiment 2 identified individual landing locations, twice a day, for groups of released Qflies, demonstrating that flies could be tracked over longer periods of time.

Consistency in Verreaux's sifaka home range and core area size despite seasonal variation in resource availability as assessed by Enhanced Vegetation Index (EVI)

Primates are adept at dealing with fluctuating availability of resources and display a range of responses to minimize the effects of food scarcity. An important component of primate conservation is to understand how primates adapt their foraging and ranging patterns in response to fluctuating food resources. Animals optimize resource acquisition within the home range through the selection of resource-bearing patches and choose between contrasting foraging strategies (resource-maximizing vs. area-minimizing). Our study aimed to characterize the foraging strategy of a folivorous primate, Verreaux's sifaka (Propithecus verreauxi), by evaluating whether group home range size varied between peak and lean leaf seasons within a seasonally dry tropical forest in Madagascar. We hypothesized that Verreaux's sifaka used the resource maximization strategy to select high-value resource patches so that during periods of resource depression, the home range area did not significantly change in size. We characterized resource availability (i.e., primary productivity) by season at Kirindy Mitea National Park using remotely-sensed Enhanced Vegetation Index data. We calculated group home ranges using 10 years of focal animal sampling data collected on eight groups using both 95% and 50% kernel density estimation. We used area accumulation curves to ensure each group had an adequate number of locations to reach seasonal home range asymptotes. Neither 95% home ranges nor 50% core areas differed across peak and lean leaf resource seasons, supporting the hypothesis that Verreaux's sifaka use a resource maximization strategy. With a better understanding of animal space use strategies, managers can model anticipated changes under environmental and/or anthropogenic resource depression scenarios. These findings demonstrate the value of long-term data for characterizing and understanding foraging and ranging patterns. We also illustrate the benefits of using satellite data for characterizing food resources for folivorous primates.

Grazing livestock move by Lévy walks: Implications for soil health and environment

Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment.

Movement patterns and habitat use for the sympatric species: Gambelia wislizenii and Aspidoscelis tigris

Movement is an important characteristic of an animal's ecology, reflecting the perception of and response to environmental conditions. To effectively search for food, movement patterns likely depend on habitat characteristics and the sensory systems used to find prey. We examined movements associated with foraging for two sympatric species of lizards inhabiting the Great Basin Desert of southeastern Oregon. The two species have largely overlapping diets but find prey via different sensory cues, which link to their differing foraging strategies-the long-nosed leopard lizard, Gambelia wislizenii, is a visually-oriented predator, while the western whiptail, Aspidoscelis tigris, relies more heavily on chemosensory cues to find prey. Using detailed focal observations, we characterized the habitat use and movement paths of each species. We placed markers at the location of focal animals every minute for the duration of each 30-min observation. Afterward, we recorded whether each location was in the open or in vegetation, as well as the movement metrics of step length, path length, net displacement, straightness index, and turn angle, and then made statistical comparisons between the two species. The visual forager spent more time in open areas, moved less frequently over shorter distances, and differed in patterns of plant use compared to the chemosensory forager. Path characteristics of step length and turn angle differed between species. The visual predator moved in a way that was consistent with the notion that they require a clear visual path to stalk prey whereas the movement of the chemosensory predator increased their chances of detecting prey by venturing further into vegetation. Sympatric species can partition limited resources through differences in search behavior and habitat use.

Transect-based trapping for area-wide delimitation of insects

Typical delimitation trapping survey designs for area-wide (nonlocalized) insect populations are regularly spaced grids, and alternative shapes have not been evaluated. We hypothesized that transect-based designs could give similar detection rates with significantly shorter servicing distances. We used the TrapGrid model to investigate novel "trap-sect" designs incorporating crossed, spoked, and parallel lines of traps, comparing them to a regular grid, in single survey and multiple-site scenarios. We calculated minimum servicing distances and simulated mean probabilities of detecting a pest population, judging overall performance of trap network designs using both metrics. For single sites, trap-sect designs reduced service distances by 65-89%, and most had similar detection probabilities as the regular grid. Kernel-smoothed intensity plots indicated that the best performing trap-sect designs distributed traps more fully across the area. With multiple sites (3 side by side), results depended on insect dispersal ability. All designs performed similarly in terms of detection for highly mobile insects, suggesting that designs minimizing service distances would be best for such pests. For less mobile pests the best trap-sect designs had 4-6 parallel lines, or 8 spokes, which reduced servicing distances by 33-50%. Comparisons of hypothetical trap-sect arrays to real program trap locations for 2 pests demonstrated that the novel designs reduced both trap numbers and service distances, with little differences in mean nearest trap distance to random pest locations. Trap-sect designs in delimitation surveys could reduce costs and increase program flexibility without harming the ability to detect populations.

Open problems in PDE models for knowledge-based animal movement via nonlocal perception and cognitive mapping

The inclusion of cognitive processes, such as perception, learning and memory, are inevitable in mechanistic animal movement modelling. Cognition is the unique feature that distinguishes animal movement from mere particle movement in chemistry or physics. Hence, it is essential to incorporate such knowledge-based processes into animal movement models. Here, we summarize popular deterministic mathematical models derived from first principles that begin to incorporate such influences on movement behaviour mechanisms. Most generally, these models take the form of nonlocal reaction-diffusion-advection equations, where the nonlocality may appear in the spatial domain, the temporal domain, or both. Mathematical rules of thumb are provided to judge the model rationality, to aid in model development or interpretation, and to streamline an understanding of the range of difficulty in possible model conceptions. To emphasize the importance of biological conclusions drawn from these models, we briefly present available mathematical techniques and introduce some existing "measures of success" to compare and contrast the possible predictions and outcomes. Throughout the review, we propose a large number of open problems relevant to this relatively new area, ranging from precise technical mathematical challenges, to more broad conceptual challenges at the cross-section between mathematics and ecology. This review paper is expected to act as a synthesis of existing efforts while also pushing the boundaries of current modelling perspectives to better understand the influence of cognitive movement mechanisms on movement behaviours and space use outcomes.

Design and Development of a Family of Integrated Devices to Monitor Animal Movement in the Wild

Monitoring the tortoise Chelonoidis chilensis in the wild, currently in a vulnerable state of conservation in southern Argentina, is essential to gather movement information to elaborate guidelines for the species preservation. We present here the electronic circuit design as well as the associated firmware for animal monitoring that was entirely designed by our interdisciplinary research team to allow the extension of device features in the future. Our development stands out for being a family of low-cost and low-power devices, that could be easily adaptable to other species and contexts. Each device is composed of a sub 1 GHz radiofrequency IoT-compatible transceiver, a global navigation satellite system (GNSS) receiver, a magnetometer, and temperature and inertial sensors. The device does not exceed 5% of the animal's weight to avoid disturbance in their behavior. The board was designed to work as a monitoring device as well as a collecting data station and a tracker, by adding only small pieces of hardware. We performed field measurements to assess the autonomy and range of the radiofrequency link, as well as the power consumption and the associated positioning error. We report those values and discuss the device's limitations and advantages. The weight of the PCB including battery and GNSS receiver is 44.9 g, its dimensions are 48.7 mm × 63.7 mm, and it has an autonomy that can vary between a week and a month, depending on the sampling rates of the sensors and the rate of the RF signal and that of the GNSS receiver. The characterization of the device parameters will favor the open use of this development by other research groups working on similar projects.

Reinforced diffusions as models of memory-mediated animal movement

How memory shapes animals' movement paths is a topic of growing interest in ecology, with connections to planning for conservation and climate change. Empirical studies suggest that memory has both temporal and spatial components, and can include both attractive and aversive elements. Here, we introduce reinforced diffusions (the continuous time counterpart of reinforced random walks) as a modelling framework for understanding the role that memory plays in determining animal movements. This framework includes reinforcement via functions of time before present and of distance away from a current location. Focusing on the interplay between memory and central place attraction (a component of home ranging behaviour), we explore patterns of space usage that result from the reinforced diffusion. Our efforts identify three qualitatively different behaviours: bounded wandering behaviour that does not collapse spatially, collapse to a very small area, and, most intriguingly, convergence to a cycle. Subsequent applications show how reinforced diffusion can create movement trajectories emulating the learning of movement routes by homing pigeons and consolidation of ant travel paths. The mathematically explicit manner with which assumptions about the structure of memory can be stated and subsequently explored provides linkages to biological concepts like an animal's 'immediate surroundings' and memory decay.

Joint use of location and acceleration data reveals influences on transitions among habitats in wintering birds

Quantifying relationships between animal behavior and habitat use is essential to understanding animal decision-making. High-resolution location and acceleration data allows unprecedented insights into animal movement and behavior. These data types allow researchers to study the complex linkages between behavioral plasticity and habitat distribution. We used a novel Markov model in a Bayesian framework to quantify the influence of behavioral state frequencies and environmental variables on transitions among landcover types through joint use of location and tri-axial accelerometer data. Data were collected from 56 greater white-fronted geese (Anser albifrons frontalis) across seven ecologically distinct winter regions over two years in midcontinent North America. We showed that goose decision-making varied across landcover types, ecoregions, and abiotic conditions, and was influenced by behavior. We found that time spent in specific behaviors explained variation in the probability of transitioning among habitats, revealing unique behavioral responses from geese among different habitats. Combining GPS and acceleration data allowed unique study of potential influences of an ongoing large-scale range shift in the wintering distribution of a migratory bird across midcontinent North America. We anticipate that behavioral adaptations among variable landscapes is a likely mechanism explaining goose use of highly variable ecosystems during winter in ways which optimize their persistence.

Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease

Introduction

Complex gait disturbances represent one of the prominent manifestations of various neurophysiological conditions, including widespread neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Therefore, instrumental measurement techniques and automatic computerized analysis appears essential for the differential diagnostics, as well as for the assessment of treatment effectiveness from experimental animal models to clinical settings.

Methods

Here we present a marker-free instrumental approach to the analysis of gait disturbances in animal models. Our approach is based on the analysis of video recordings obtained with a camera placed underneath an open field arena with transparent floor using the DeeperCut algorithm capable of online tracking of individual animal body parts, such as the snout, the paws and the tail. The extracted trajectories of animal body parts are next analyzed using an original computerized methodology that relies upon a generalized scalable model based on fractional Brownian motion with parameters identified by detrended partial cross-correlation analysis.

Results

We have shown that in a mouse model representative movement patterns are characterized by two asymptotic regimes characterized by integrated 1/f noise at small scales and nearly random displacements at large scales separated by a single crossover. More detailed analysis of gait disturbances revealed that the detrended cross-correlations between the movements of the snout, paws and tail relative to the animal body midpoint exhibit statistically significant discrepancies in the Alzheimer's disease mouse model compared to the control group at scales around the location of the crossover.

Discussion

We expect that the proposed approach, due to its universality, robustness and clear physical interpretation, is a promising direction for the design of applied analysis tools for the diagnostics of various gait disturbances and behavioral aspects in animal models. We further believe that the suggested mathematical models could be relevant as a complementary tool in clinical diagnostics of various neurophysiological conditions associated with movement disorders.

Linking animal migration and ecosystem processes: Data-driven simulation of propagule dispersal by migratory herbivores

Animal migration is a key process underlying active subsidies and species dispersal over long distances, which affects the connectivity and functioning of ecosystems. Despite much research describing patterns of where animals migrate, we still lack a framework for quantifying and predicting how animal migration affects ecosystem processes. In this study, we aim to integrate animal movement behavior and ecosystem functioning by developing a predictive modeling framework that can inform ecosystem management and conservation.

We propose a framework to model individual‐level migration trajectories between populations' seasonal ranges as well as the resulting dispersal and fate of propagules carried by the migratory animals, which can be calibrated using empirical data at every step of the modeling process. As a case study, we applied our framework to model the spread of guava seeds, Psidium guajava, by a population of migratory Galapagos tortoises, Chelonoidis porteri, across Santa Cruz Island. Galapagos tortoises are large herbivores that transport seeds and nutrients across the island, while Guava is one of the most problematic invasive species in the Galapagos archipelago.

Our model can predict the pattern of spread of guava seeds alongside tortoises' downslope migration range, and it identified areas most likely to see establishment success. Our results show that Galapagos tortoises' seed dispersal may particularly contribute to guava range expansion on Santa Cruz Island, due to both long gut retention time and tortoise's long‐distance migration across vegetation zones. In particular, we predict that tortoises are dispersing a significant amount of guava seeds into the Galapagos National Park, which has important consequences for the native flora.

The flexibility and modularity of our framework allow for the integration of multiple data sources. It also allows for a wide range of applications to investigate how migratory animals affect ecosystem processes, including propagule dispersal but also other processes such as nutrient transport across ecosystems. Our framework is also a valuable tool for predicting how animal‐mediated propagule dispersal can be affected by environmental change. These different applications can have important conservation implications for the management of ecosystems that include migratory animals.

High individual repeatability of the migratory behaviour of a long-distance migratory seabird

BACKGROUND

Understanding the evolution of migration requires knowledge of the patterns, sources, and consequences of variation in migratory behaviour, a need exacerbated by the fact that many migratory species show rapid population declines and require knowledge-based conservation measures. We therefore need detailed knowledge on the spatial and temporal distribution of individuals across their annual cycle, and quantify how the spatial and temporal components of migratory behaviour vary within and among individuals.

METHODS

We tracked 138 migratory journeys undertaken by 64 adult common terns (Sterna hirundo) from a breeding colony in northwest Germany to identify the annual spatiotemporal distribution of these birds and to evaluate the individual repeatability of eleven traits describing their migratory behaviour.

RESULTS

Birds left the breeding colony early September, then moved south along the East Atlantic Flyway. Wintering areas were reached mid-September and located at the west and south coasts of West Africa as well as the coasts of Namibia and South Africa. Birds left their wintering areas late March and reached the breeding colony mid-April. The timing, total duration and total distance of migration, as well as the location of individual wintering areas, were moderately to highly repeatable within individuals (repeatability indexes: 0.36-0.75, 0.65-0.66, 0.93-0.94, and 0.98-1.00, respectively), and repeatability estimates were not strongly affected by population-level inter-annual variation in migratory behaviour.

CONCLUSIONS

We found large between-individual variation in common tern annual spatiotemporal distribution and strong individual repeatability of several aspects of their migratory behaviour.

Simulation-Based Investigation of the Performance of Delimiting Trapping Surveys for Insect Pests

Fully trapped survey designs are widely used to delimit adventive pests populations that can be detected using traps and lures. Delimitation includes verifying the presence of the pest and determining its spatial extent. The size and shape of the survey design and the density of traps can vary; however, resulting variation in detecting efficiency is often unknown. We used a trapping network simulation model with diffusion-based insect movement to investigate delimiting survey trapping design performance for fully trapped and some modified designs. Simulations included randomized outbreak locations in a core area and a duration of 30 d. We assessed impacts of insect dispersal ability, grid size and shape, and trap attractiveness and density on survey performance, measured as mean probability of capturing individual pests [p(capture)]. Most published grids are square, but circles performed equally well and are more efficient. Over different grid sizes, p(capture) increased for insects with greater dispersal ability but was generally unresponsive to size because most captures occurred in central areas. For low dispersing insects, the likelihood of egress was approximately zero with a 3.2-km square grid, whereas an 11.3-km grid was needed to contain highly vagile insects. Trap attractiveness affected p(capture) more strongly than density: lower densities of poorly attractive traps may underperform expectations. Variable density designs demonstrated potential for cost savings but highlighted that resource-intensive outer bands are critical to boundary determination. Results suggesting that many grids are oversized need empirical verification, whereas other principles, such as using circular shapes, are readily adoptable now.

Data Management and Sharing for Collaborative Science: Lessons Learnt From the Euromammals Initiative

The current and future consequences of anthropogenic impacts such as climate change and habitat loss on ecosystems will be better understood and therefore addressed if diverse ecological data from multiple environmental contexts are more effectively shared. Re-use requires that data are readily available to the scientific scrutiny of the research community. A number of repositories to store shared data have emerged in different ecological domains and developments are underway to define common data and metadata standards. Nevertheless, the goal is far from being achieved and many challenges still need to be addressed. The definition of best practices for data sharing and re-use can benefit from the experience accumulated by pilot collaborative projects. The Euromammals bottom-up initiative has pioneered collaborative science in spatial animal ecology since 2007. It involves more than 150 institutes to address scientific, management and conservation questions regarding terrestrial mammal species in Europe using data stored in a shared database. In this manuscript we present some key lessons that we have learnt from the process of making shared data and knowledge accessible to researchers and we stress the importance of data management for data quality assurance. We suggest putting in place a pro-active data review before data are made available in shared repositories via robust technical support and users’ training in data management and standards. We recommend pursuing the definition of common data collection protocols, data and metadata standards, and shared vocabularies with direct involvement of the community to boost their implementation. We stress the importance of knowledge sharing, in addition to data sharing. We show the crucial relevance of collaborative networking with pro-active involvement of data providers in all stages of the scientific process. Our main message is that for data-sharing collaborative efforts to obtain substantial and durable scientific returns, the goals should not only consist in the creation of e-infrastructures and software tools but primarily in the establishment of a network and community trust. This requires moderate investment, but over long-term horizons.

A random walk model that accounts for space occupation and movements of a large herbivore

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Large datasets of invididual locations often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of biological justifications in their design. We propose a simple model of individual movement with explicit parameters, based on a two-dimensional biased and correlated random walk with three forces related to advection (correlation), attraction (bias) and immobility of the animal. These forces can be directly estimated using individual data. We demonstrate the approach by using GPS data of 5 red deer with a high frequency sampling. The results show that a simple random walk template can account for the spatial complexity of wild animals. The practical design of the model is also verified for detecting spatial feature abnormalities and for providing estimates of density and abundance of wild animals. Integrating even more additional features of animal movement, such as individuals’ interactions or environmental repellents, could help to better understand the spatial behavior of wild animals.

Ecological Modelling of Insect Movement in Cropping Systems

The spatio-temporal dynamics of insect pests in agricultural landscapes involves the potential of species to move, invade, colonise, and establish in different areas. This study revised the dispersal of the important crop pests Diabrotica speciosa Germar and Spodoptera frugiperda (J.E. Smith) by using computational modelling to represent the movement of these polyphagous pests in agricultural mosaics. The findings raise significant questions regarding the dispersal of pests through crops and refuge areas, indicating that understanding pest movement is essential for developing strategies to predict critical infestation levels to assist in pest-management decisions. In addition, our modelling approach can be adapted for other insect species and other cropping systems despite discussing two specific species in the current manuscript. We present an overview of studies, combining experimentation and ecological modelling, discussing the methods used and the importance of studying insect movement as well as the implications for agricultural landscapes in Brazil.

Intensive longitudinal characterization of multidimensional biobehavioral dynamics in laboratory rats

Rats have been used as animal models for human diseases for more than a century, yet a systematic understanding of basal biobehavioral phenotypes of laboratory rats is still missing. In this study, we utilize wireless tracking technology and videography, collect and analyze more than 130 billion data points to fill this gap, and characterize the evolution of behavior and physiology of group-housed male and female rats (n = 114) of the most commonly used strains (Lister Hooded, Long-Evans, Sprague-Dawley, and Wistar) throughout their development. The resulting intensive longitudinal data suggest the existence of strain and sex differences and bi-stable developmental states. Under standard laboratory 12-h light/12-h dark conditions, our study found the presence of multiple oscillations such as circatidal-like rhythms in locomotor activity. The overall findings further suggest that frequent movement along cage walls or thigmotaxic activity may be a physical feature of motion in constrained spaces, critically affecting the interpretation of basal behavior of rats in cages.

Cancer risk landscapes: A framework to study cancer in ecosystems

Cancer is a family of diseases that has been documented in most metazoan species and ecosystems. Human induced environmental changes are increasingly exposing wildlife to carcinogenic risk factors, and negative repercussions on ecosystems and on the conservation of endangered species are already been observed. It is therefore of key importance to understand the spatiotemporal variability of those risk factors and how they interact with the biosphere to mitigate their effects. Here we introduce the concept of cancer risk landscape that can be applied to understand how species are exposed to, interact with, and modify cancer risk factors. With this publication we aim to provide a framework in order to stimulate a discussion on how to mitigate cancer-causing risk factors.

Employing Very High Frequency (VHF) Radio Telemetry to Recreate Monarch Butterfly Flight Paths

The overwintering population of eastern North American monarch butterflies (Danaus plexippus) has declined significantly. Loss of milkweed (Asclepias sp.), the monarch's obligate host plant in the Midwest United States, is considered to be a major cause of the decline. Restoring breeding habitat is an actionable step towards population recovery. Monarch butterflies are highly vagile; therefore, the spatial arrangement of milkweed in the landscape influences movement patterns, habitat utilization, and reproductive output. Empirical studies of female movement patterns within and between habitat patches in representative agricultural landscapes support recommendations for habitat restoration. To track monarch movement at distances beyond human visual range, we employed very high frequency radio telemetry with handheld antennae to collect movement bearings on a biologically relevant time scale. Attachment of 220-300 mg transmitters did not significantly affect behavior and flight capability. Thirteen radio-tagged monarchs were released in a restored prairie, and locations were estimated every minute for up to 39 min by simultaneous triangulation from four operators. Monarchs that left the prairie were tracked and relocated at distances up to 250 m. Assuming straight flights between locations, the majority of steps within the prairie were below 50 m. Steps associated with exiting the prairie exceeded 50 m with high directionality. Because butterflies do not fly in straight lines between stationary points, we also illustrate how occurrence models can use location data obtained through radio telemetry to estimate movement within a prairie and over multiple land cover types.

Stochastically modeling multiscale stationary biological processes

Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.

Truncated Lévy motion through path integrals and applications to nondiffusive suprathermal ion transport

Fractional Levy motion has been derived from its generalized Langevin equation via path integrals in earlier works and has since proven to be a useful model for nonlocal and non-Markovian processes, especially in the context of nondiffusive transport. Here, we generalize the approach to treat tempered Lévy distributions and derive the propagator and diffusion equation of truncated asymmetrical fractional Levy motion via path integrals. The model now recovers exponentially tempered tails above a chosen scale in the propagator, and therefore finite moments at all orders. Concise analytical expressions for its variance, skewness, and kurtosis are derived as a function of time. We then illustrate the versatility of this model by applying it to simulations of the turbulent transport of fast ions in the TORPEX basic plasma device.

Behavioral analysis of substrate texture preference in a leech, Helobdella austinensis

Leeches in the wild are often found on smooth surfaces, such as vegetation, smooth rocks or human artifacts such as bottles and cans, thus exhibiting what appears to be a "substrate texture preference". Here, we have reproduced this behavior under controlled circumstances, by allowing leeches to step about freely on a range of silicon carbide substrates (sandpaper). To begin to understand the neural mechanisms underlying this texture preference behavior, we have determined relevant parameters of leech behavior both on uniform substrates of varying textures, and in a behavior choice paradigm in which the leech is confronted with a choice between rougher and smoother substrate textures at each step. We tested two non-exclusive mechanisms which could produce substrate texture preference: (1) a Differential Diffusion mechanism, in which a leech is more likely to stop moving on a smooth surface than on a rough one, and (2) a Smoothness Selection mechanism, in which a leech is more likely to attach its front sucker (prerequisite for taking a step) to a smooth surface than to a rough one. We propose that both mechanisms contribute to the texture preference exhibited by leeches.

Traveling pulse emerges from coupled intermittent walks: A case study in sheep

Monitoring small groups of sheep in spontaneous evolution in the field, we decipher behavioural rules that sheep follow at the individual scale in order to sustain collective motion. Individuals alternate grazing mode at null speed and moving mode at walking speed, so cohesive motion stems from synchronising when they decide to switch between the two modes. We propose a model for the individual decision making process, based on switching rates between stopped / walking states that depend on behind / ahead locations and states of the others. We parametrize this model from data. Next, we translate this (microscopic) individual-based model into its density-flow (macroscopic) equations counterpart. Numerical solving these equations display a traveling pulse propagating at constant speed even though each individual is at any moment either stopped or walking. Considering the minimal model embedded in these equations, we derive analytically the steady shape of the pulse (sech square). The parameters of the pulse (shape and speed) are expressed as functions of individual parameters. This pulse emerges from the non linear coupling of start/stop individual decisions which compensate exactly for diffusion and promotes a steady ratio of walking / stopped individuals, which in turn determines the traveling speed of the pulse. The system seems to converge to this pulse from any initial condition, and to recover the pulse after perturbation. This gives a high robustness to this coordination mechanism.

Ecological metrics and methods for GPS movement data

The growing field of movement ecology uses high resolution movement data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses contribute to various subfields of ecology-inter alia behavioral, disease, landscape, resource, and wildlife-and facilitate facilitate novel exploration in fields ranging from conservation planning to public health. Despite the growing availability and general accessibility of animal movement data, much potential remains for the analytical methods of movement ecology to be incorporated in all types of geographic analyses. This review provides for the Geographical Information Sciences (GIS) community an overview of the most common movement metrics and methods of analysis employed by animal ecologists. Through illustrative applications, we emphasize the potential for movement analyses to promote transdisciplinary GIS/wildlife-ecology research.

Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology

Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host-pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.

Fitness consequences of fish circadian behavioural variation in exploited marine environments

The selective properties of fishing that influence behavioural traits have recently gained interest. Recent acoustic tracking experiments have revealed between-individual differences in the circadian behavioural traits of marine free-living fish; these differences are consistent across time and ecological contexts and generate different chronotypes. Here, we hypothesised that the directional selection resulting from fishing influences the wild circadian behavioural variation and affects differently to individuals in the same population differing in certain traits such as awakening time or rest onset time. We developed a spatially explicit social-ecological individual-based model (IBM) to test this hypothesis. The parametrisation of our IBM was fully based on empirical data; which represent a fishery formed by patchily distributed diurnal resident fish that are exploited by a fleet of mobile boats (mostly bottom fisheries). We ran our IBM with and without the observed circadian behavioural variation and estimated selection gradients as a quantitative measure of trait change. Our simulations revealed significant and strong selection gradients against early-riser chronotypes when compared with other behavioural and life-history traits. Significant selection gradients were consistent across a wide range of fishing effort scenarios. Our theoretical findings enhance our understanding of the selective properties of fishing by bridging the gaps among three traditionally separated fields: fisheries science, behavioural ecology and chronobiology. We derive some general predictions from our theoretical findings and outline a list of empirical research needs that are required to further understand the causes and consequences of circadian behavioural variation in marine fish.

Identification of Griffon Vulture's Flight Types Using High-Resolution Tracking Data

Being one of the most frequently killed raptors by collision with wind turbines, little is known about the Griffon vulture's flight strategies and behaviour in a fine scale. In this study, we used high-resolution tracking data to differentiate between the most frequently observed flight types of the Griffon, and evaluated the performance of our proposed approach by an independent observation during a period of 4 weeks of fieldwork. Five passive flight types including three types of soaring and two types of gliding were discriminated using the patterns of measured GPS locations. Of all flight patterns, gliding was classified precisely (precision = 88%), followed by linear and thermal soaring with precision of 83 and 75%, respectively. The overall accuracy of our classification was 70%. Our study contributes a baseline technique using high-resolution tracking data for the classification of flight types, and is one step forward towards the collision management of this species.

Mechanistic movement models to understand epidemic spread

An overlooked aspect of disease ecology is considering how and why animals come into contact with one and other resulting in disease transmission. Mathematical models of disease spread frequently assume mass-action transmission, justified by stating that susceptible and infectious hosts mix readily, and foregoing any detailed description of host movement. Numerous recent studies have recorded, analysed and modelled animal movement. These movement models describe how animals move with respect to resources, conspecifics and previous movement directions and have been used to understand the conditions for the occurrence and the spread of infectious diseases when hosts perform a type of movement. Here, we summarize the effect of the different types of movement on the threshold conditions for disease spread. We identify gaps in the literature and suggest several promising directions for future research. The mechanistic inclusion of movement in epidemic models may be beneficial for the following two reasons. Firstly, the estimation of the transmission coefficient in an epidemic model is possible because animal movement data can be used to estimate the rate of contacts between conspecifics. Secondly, unsuccessful transmission events, where a susceptible host contacts an infectious host but does not become infected can be quantified. Following an outbreak, this enables disease ecologists to identify 'near misses' and to explore possible alternative epidemic outcomes given shifts in ecological or immunological parameters.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

Space partitioning in wild, non-territorial mountain gorillas: the impact of food and neighbours

In territorial species, the distribution of neighbours and food abundance play a crucial role in space use patterns but less is known about how and when neighbours use shared areas in non-territorial species. We investigated space partitioning in 10 groups of wild, non-territorial mountain gorillas (Gorilla beringei beringei). Using location data, we examined factors influencing daily movement decisions and calculated the per cent overlap of annual kernel home ranges and core areas among neighbours. We found that the probability that a group chose an area was positively influenced by both food availability and the previous use of that area by the group. Additionally, groups reduced their overall utilization of areas previously used by neighbouring groups. Lastly, groups used their core areas more exclusively than their home ranges. In sum, our results show that both foraging needs and avoidance of competition with neighbours determined the gorillas' daily movement decisions, which presumably lead to largely mutually exclusive core areas. Our research suggests that non-territorial species actively avoid neighbours to maintain core area exclusivity. Together, these findings contribute to our understanding of the costs and benefits of non-territoriality.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models may provide new insights into understanding, for example, how global warming and habitat disturbance contribute to disease maintenance and emergence. Moving forward, better integration of dynamic, spatial disease models with approaches from movement ecology, landscape genetics/genomics and ecoimmunology may provide new avenues for investigation and aid in the control of zoonotic and emerging infectious diseases.