Albatross movement suggests sensitivity to infrasound cues at sea.

The ways in which seabirds navigate over very large spatial scales remain poorly understood. While olfactory and visual information can provide guidance over short distances, their range is often limited to 100s km, far below the navigational capacity of wide-ranging animals such as albatrosses. Infrasound is a form of low-frequency sound that propagates for 1,000s km in the atmosphere. In marine habitats, its association with storms and ocean surface waves could in effect make it a useful cue for anticipating environmental conditions that favor or hinder flight or be associated with profitable foraging patches. However, behavioral responses of wild birds to infrasound remain untested. Here, we explored whether wandering albatrosses, Diomedea exulans, respond to microbarom infrasound at sea. We used Global Positioning System tracks of 89 free-ranging albatrosses in combination with acoustic modeling to investigate whether albatrosses preferentially orientate toward areas of 'loud' microbarom infrasound on their foraging trips. We found that in addition to responding to winds encountered in situ, albatrosses moved toward source regions associated with higher sound pressure levels. These findings suggest that albatrosses may be responding to long-range infrasonic cues. As albatrosses depend on winds and waves for soaring flight, infrasonic cues may help albatrosses to identify environmental conditions that allow them to energetically optimize flight over long distances. Our results shed light on one of the great unresolved mysteries in nature, navigation in seemingly featureless ocean environments.

ExMove: An open-source toolkit for processing and exploring animal-tracking data in R.

Ongoing technological advances have led to a rapid increase in the number, type and scope of animal-tracking studies. In response, many software tools have been developed to analyse animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre-processing steps are often time-consuming and involve a steep learning curve but are crucial for the creation of high-quality, standardised and shareable data. Moreover, decisions made at this early stage can substantially influence subsequent analyses, and in the current age of reproducibility crisis, the transparency of this process is vital. Here we present an open-access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases (found here: https://github.com/ExMove/ExMove). The toolkit comprises well-documented and flexible code to facilitate data processing and user understanding, both of which can increase user confidence and improve the uptake of sharing open and reproducible code. Additionally, we provide an overview website (found here: https://exmove.github.io/) and a Shiny app to help users visualise tracking data and assist with parameter determination during data cleaning. The toolkit is generalisable to different data formats and device types, uses modern 'tidy coding' practices, and relies on a few well-maintained packages. Among these, we perform spatial manipulations using the package sf. Overall, by collating all required steps from data collection to archiving on open access databases into a single, robust pipeline, our toolkit provides a valuable resource for anyone conducting animal movement analyses and represents an important step towards increased standardisation and reproducibility in animal movement ecology.

The role of memory-based movements in the formation of animal home ranges.

Most animals live in spatially-constrained home ranges. The prevalence of this space-use pattern in nature suggests that general biological mechanisms are likely to be responsible for their occurrence. Individual-based models of animal movement in both theoretical and empirical settings have demonstrated that the revisitation of familiar areas through memory can lead to the formation of stable home ranges. Here, we formulate a deterministic, mechanistic home range model that includes the interplay between a bi-component memory and resource preference, and evaluate resulting patterns of space-use. We show that a bi-component memory process can lead to the formation of stable home ranges and control its size, with greater spatial memory capabilities being associated with larger home range size. The interplay between memory and resource preferences gives rise to a continuum of space-use patterns-from spatially-restricted movements into a home range that is influenced by local resource heterogeneity, to diffusive-like movements dependent on larger-scale resource distributions, such as in nomadism. Future work could take advantage of this model formulation to evaluate the role of memory in shaping individual performance in response to varying spatio-temporal resource patterns.

A unified evolutionary framework for understanding parasite infection and host migratory behaviour.

Animal migration impacts organismal health and parasite transmission: migrants are simultaneously exposed to parasites and able to reduce infection for both individuals and populations. However, these dynamics are difficult to study; empirical studies reveal disparate results while existing theory makes assumptions that simplify natural complexity. Here, we systematically review empirical studies of migration and infection across taxa, highlighting key gaps in our understanding. Next, we develop a unified evolutionary framework incorporating different selective pressures of parasite-migration interactions while accounting for ecological complexity that goes beyond previous theory. Our framework generates diverse migration-infection patterns paralleling those seen in empirical systems, including partial and differential migration. Finally, we generate predictions about which mechanisms dominate which empirical systems to guide future studies. Our framework provides an overarching understanding of selective pressures shaping migration patterns in the context of animal health and disease, which is critical for predicting how environmental change may threaten migration.

A simple cognitive model explains movement decisions in zebrafish while following leaders.

While moving, animals must frequently make decisions about their future travel direction, whether they are alone or in a group. Here we investigate this process for zebrafish (Danio rerio), which naturally move in cohesive groups. Employing state-of-the-art virtual reality, we study how real fish (RF) follow one or several moving, virtual conspecifics (leaders). These data are used to inform, and test, a model of social response that includes a process of explicit decision-making, whereby the fish can decide which of the virtual conspecifics to follow, or to follow in some average direction. This approach is in contrast with previous models where the direction of motion was based on a continuous computation, such as directional averaging. Building upon a simplified version of this model (Sridharet al2021Proc. Natl Acad. Sci.118e2102157118), which was limited to a one-dimensional projection of the fish motion, we present here a model that describes the motion of the RF as it swims freely in two-dimensions. Motivated by experimental observations, the swim speed of the fish in this model uses a burst-and-coast swimming pattern, with the burst frequency being dependent on the distance of the fish from the followed conspecific(s). We demonstrate that this model is able to explain the observed spatial distribution of the RF behind the virtual conspecifics in the experiments, as a function of their average speed and number. In particular, the model naturally explains the observed critical bifurcations for a freely swimming fish, which appear in the spatial distributions whenever the fish makes a decision to follow only one of the virtual conspecifics, instead of following them as an averaged group. This model can provide the foundation for modeling a cohesive shoal of swimming fish, while explicitly describing their directional decision-making process at the individual level.

Microhabitat conditions remedy heat stress effects on insect activity.

Anthropogenic global warming has major implications for mobile terrestrial insects, including long-term effects from constant warming, for example, on species distribution patterns, and short-term effects from heat extremes that induce immediate physiological responses. To cope with heat extremes, they either have to reduce their activity or move to preferable microhabitats. The availability of favorable microhabitat conditions is strongly promoted by the spatial heterogeneity of habitats, which is often reduced by anthropogenic land transformation. Thus, it is decisive to understand the combined effects of these global change drivers on insect activity. Here, we assessed the movement activity of six insect species (from three orders) in response to heat stress using a unique tracking approach via radio frequency identification. We tracked 465 individuals at the iDiv Ecotron across a temperature gradient up to 38.7°C. In addition, we varied microhabitat conditions by adding leaf litter from four different tree species to the experimental units, either spatially separated or well mixed. Our results show opposing effects of heat extremes on insect activity depending on the microhabitat conditions. The insect community significantly decreased its activity in the mixed litter scenario, while we found a strong positive effect on activity in the separated litter scenario. We hypothesize that the simultaneous availability of thermal refugia as well as resources provided by the mixed litter scenario allows animals to reduce their activity and save energy in response to heat stress. Contrary, the spatial separation of beneficial microclimatic conditions and resources forces animals to increase their activity to fulfill their energetic needs. Thus, our study highlights the importance of habitat heterogeneity on smaller scales, because it may buffer the consequences of extreme temperatures of insect performance and survival under global change.

Solar Energy-driven Land-cover Change Could Alter Landscapes Critical to Animal Movement in the Continental United States.

The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km2 of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land-cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.

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.

Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model.

Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic data set and then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.

The Olfactory Landscape Concept: A Key Source of Past, Present, and Future Information Driving Animal Movement and Decision-making.

Odor is everywhere, emitted across the landscape from predators, prey, decaying carcasses, conspecifics, vegetation, surface water, and smoke. Many animals exploit odor to find food, avoid threats, and attract or judge potential mates. Here, we focus on odor in terrestrial ecosystems to introduce the concept of an olfactory landscape: real-time dynamic olfactory contours reflecting the patchy distribution of resources and risks, providing a key source of information used by many animals in their movement and decision-making. Incorporating the olfactory landscape into current frameworks of movement ecology and animal behavior will provide a mechanistic link to help answer significant questions about where, why, and when many animals move, and how they do so efficiently in both space and time. By understanding how animals use the olfactory landscape to make crucial decisions affecting their fitness, we can then manipulate the landscape to modify ecological interactions and, ultimately, ecosystem consequences of these interactions.

Long distance homing in the cane toad (Rhinella marina) in its native range.

Many animals exhibit complex navigation over different scales and environments. Navigation studies in amphibians have largely focused on species with life histories that require accurate spatial movements, such as territorial poison frogs and migratory pond-breeding amphibians that show fidelity to mating sites. However, other amphibian species have remained relatively understudied, leaving open the possibility that well-developed navigational abilities are widespread. Here, we measured short-term space use in non-territorial, non-migratory cane toads (Rhinella marina) in their native range in French Guiana. After establishing site fidelity, we tested their ability to return home following translocations of 500 and 1000 m. Toads were able to travel in straight trajectories back to home areas, suggesting navigational abilities similar to those observed in amphibians with more complex spatial behavior. These observations break with the current paradigm of amphibian navigation and suggest that navigational abilities may be widely shared among amphibians.

Modelling group movement with behaviour switching in continuous time.

This article presents a new method for modelling collective movement in continuous time with behavioural switching, motivated by simultaneous tracking of wild or semi-domesticated animals. Each individual in the group is at times attracted to a unobserved leading point. However, the behavioural state of each individual can switch between 'following' and 'independent'. The 'following' movement is modelled through a linear stochastic differential equation, while the 'independent' movement is modelled as Brownian motion. The movement of the leading point is modelled either as an Ornstein-Uhlenbeck (OU) process or as Brownian motion (BM), which makes the whole system a higher-dimensional Ornstein-Uhlenbeck process, possibly an intrinsic non-stationary version. An inhomogeneous Kalman filter Markov chain Monte Carlo algorithm is developed to estimate the diffusion and switching parameters and the behaviour states of each individual at a given time point. The method successfully recovers the true behavioural states in simulated data sets , and is also applied to model a group of simultaneously tracked reindeer (Rangifer tarandus).

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution.

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

Energy-based step selection analysis: Modelling the energetic drivers of animal movement and habitat use.

The energetic gains from foraging and costs of movement are expected to be key drivers of animal decision-making, as their balance is a large determinant of body condition and survival. This fundamental perspective is often missing from habitat selection studies, which mainly describe correlations between space use and environmental features, rather than the mechanisms behind these correlations. To address this gap, we present a novel parameterisation of step selection functions (SSFs), that we term the energy selection function (ESF). In this model, the likelihood of an animal selecting a movement step depends directly on the corresponding energetic gains and costs, and we can therefore assess how moving animals choose habitat based on energetic considerations. The ESF retains the mathematical convenience and practicality of other SSFs and can be quickly fitted using standard software. In this article, we outline a workflow, from data gathering to statistical analysis, and use a case study of polar bears Ursus maritimus to demonstrate application of the model. We explain how defining gains and costs at the scale of the movement step allows us to include information about resource distribution, landscape resistance and movement patterns. We further demonstrate this process with a case study of polar bears and show how the parameters can be interpreted in terms of selection for energetic gains and against energetic costs. The ESF is a flexible framework that combines the energetic consequences of both movement and resource selection, thus incorporating a key mechanism into habitat selection analysis. Further, because it is based on familiar habitat selection models, the ESF is widely applicable to any study system where energetic gains and costs can be derived, and has immense potential for methodological extensions.

Detecting minimum energy states and multi-stability in nonlocal advection-diffusion models for interacting species.

Deriving emergent patterns from models of biological processes is a core concern of mathematical biology. In the context of partial differential equations, these emergent patterns sometimes appear as local minimisers of a corresponding energy functional. Here we give methods for determining the qualitative structure of local minimum energy states of a broad class of multi-species nonlocal advection-diffusion models, recently proposed for modelling the spatial structure of ecosystems. We show that when each pair of species respond to one another in a symmetric fashion (i.e. via mutual avoidance or mutual attraction, with equal strength), the system admits an energy functional that decreases in time and is bounded below. This suggests that the system will eventually reach a local minimum energy steady state, rather than fluctuating in perpetuity. We leverage this energy functional to develop tools, including a novel application of computational algebraic geometry, for making conjectures about the number and qualitative structure of local minimum energy solutions. These conjectures give a guide as to where to look for numerical steady state solutions, which we verify through numerical analysis. Our technique shows that even with two species, multi-stability with up to four classes of local minimum energy states can emerge. The associated dynamics include spatial sorting via aggregation and repulsion both within and between species. The emerging spatial patterns include a mixture of territory-like segregation as well as narrow spike-type solutions. Overall, our study reveals a general picture of rich multi-stability in systems of moving and interacting species.

Emerging infectious diseases may spread across pig trade networks in Thailand once introduced: a network analysis approach.

In Thailand, pork is one of the most consumed meats nationwide. Pig farming is hence an important business in the country. However, 95% of the farms were considered smallholders raising only 50 pigs or less. With limited budgets and resources, the biosecurity level in these farms is relatively low. Pig movements have been previously identified as a risk factor in the spread of infectious diseases. Therefore, the present study aimed to explicitly analyze the pig movement network structure and assess its vulnerability to the spread of emerging diseases in Thailand. We used official electronic records of nationwide pig movements throughout the year 2021 to construct a directed weighted one-mode network. Degree centrality, degree distribution, connected components, network community, and modularity were measured to explore the network architectures and properties. In this network, 484,483 pig movements were captured. In which, 379,948 (78.42%) were moved toward slaughterhouses and hence excluded from further analyses. From the remaining links, we suggested that the pig movement network in Thailand was vulnerable to the spread of emerging infectious diseases. Within the network, we found a strongly connected component (SCC) connecting 1044 subdistricts (38.6% of the nodes), a giant weakly connected component (GWCC) covering 98.2% of the nodes (2654/2704), and inter-regional communities with overall network modularity of 0.68. The disease may rapidly spread throughout the country. A better understanding of the nationwide pig movement networks is helpful in tailoring control interventions to cope with the newly emerged diseases once introduced.

Environmental and anthropogenic constraints on animal space use drive extinction risk worldwide.

Animals require a certain amount of habitat to persist and thrive, and habitat loss is one of the most critical drivers of global biodiversity decline. While habitat requirements have been predicted by relationships between species traits and home-range size, little is known about constraints imposed by environmental conditions and human impacts on a global scale. Our meta-analysis of 395 vertebrate species shows that global climate gradients in temperature and precipitation exert indirect effects via primary productivity, generally reducing space requirements. Human pressure, however, reduces realised space use due to ensuing limitations in available habitat, particularly for large carnivores. We show that human pressure drives extinction risk by increasing the mismatch between space requirements and availability. We use large-scale climate gradients to predict current species extinction risk across global regions, which also offers an important tool for predicting future extinction risk due to ongoing space loss and climate change.

Individual-Level Memory Is Sufficient to Create Spatial Segregation among Neighboring Colonies of Central Place Foragers.

AbstractCentral place foragers often segregate in space, even without signs of direct agonistic interactions. Using parsimonious individual-based simulations, we show that for species with spatial cognitive abilities, individual-level memory of resource availability can be sufficient to cause spatial segregation in the foraging ranges of colonial animals. The shapes of the foraging distributions are governed by commuting costs, the emerging distribution of depleted resources, and the fidelity of foragers to their colonies. When colony fidelity is weak and foragers can easily switch to colonies located closer to favorable foraging grounds, this leads to space partitioning with equidistant borders between neighboring colonies. In contrast, when colony fidelity is strong-for example, because larger colonies provide safety in numbers or individuals are unable to leave-it can create a regional imbalance between resource requirements and resource availability. This leads to nontrivial space-use patterns that propagate through the landscape. Interestingly, while better spatial memory creates more defined boundaries between neighboring colonies, it can lower the average intake rate of the population, suggesting a potential trade-off between an individual's attempt for increased intake and population growth rates.

Human activity shapes the wintering ecology of a migratory bird.

Human behavior profoundly affects the natural world. Migratory birds are particularly susceptible to adverse effects of human activities because the global networks of ecosystems on which birds rely are undergoing rapid change. In spite of these challenges, the blackcap (Sylvia atricapilla) is a thriving migratory species. Its recent establishment of high-latitude wintering areas in Britain and Ireland has been linked to climate change and backyard bird feeding, exemplifying the interaction between human activity and migrant ecology. To understand how anthropogenic influences shape avian movements and ecology, we marked 623 wintering blackcaps at 59 sites across Britain and Ireland and compiled a dataset of 9929 encounters. We investigated visitation behavior at garden feeding sites, inter-annual site fidelity, and movements within and across seasons. We analyzed migration tracks from 25 geolocators fitted to a subset of individuals to understand how garden behavior may impact subsequent migration and breeding. We found that blackcaps wintering in Britain and Ireland showed high site fidelity and low transience among wintering sites, in contrast to the itinerant movements characteristic of blackcaps wintering in their traditional winter range. First-winter birds showed lower site fidelity and a greater likelihood of transience than adults. Adults that frequented gardens had better body condition, smaller fat stores, longer bills, and rounder wingtips. However, blackcaps did not exclusively feed in gardens; visits were linked to harsher weather. Individuals generally stayed at garden sites until immediately before spring departure. Our results suggest that supplementary feeding is modifying blackcap winter ecology and driving morphological evolution. Supplemental feeding may have multifaceted benefits on winter survival, and these positive effects may carry over to migration and subsequent breeding. Overall, the high individual variability in blackcap movement and foraging ecology, and the flexibility it imparts, may have allowed this species to flourish during rapid environmental change.

An agent-based model shows zombie ants exhibit search behavior.

Parasites can alter the behavior of animals. Such alterations could be a byproduct of infection or actively controlled and directed by the parasite. Ants infected with zombie ant fungi (Ophiocordyceps sp.) show behavioral changes culminating in the ant dying while biting into vegetation. To investigate the influence of the parasite on behavioral changes, we created an agent-based model that provides a prediction of how fungal infected ants move before death. The model shows how alterations in movement, such as an increased turning rate, within the normal range of ant behavior, can lead a host from the nest to the underside of a leaf. This demonstrates the simplicity in how such behavioral changes could evolve, as the fungal parasite could benefit from the natural behavior of the host, contesting a hypothesis of highly directed manipulation.