Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Locating Nesting Sites for Critically Endangered Galápagos Pink Land Iguanas (Conolophus marthae)

Invasive alien species control is recognized worldwide as a priority action to preserve global biodiversity. However, a lack of general life history knowledge for threatened species can impede the effectiveness of conservation actions. Galápagos pink land iguanas (Conolophus marthae) are endemic to Wolf Volcano, Galápagos, Ecuador. These iguanas are threatened by invasive alien species, particularly feral cats, that may affect their small population size. To guarantee the long-term survival of C. marthae, the Galápagos National Park Directorate is considering, along with an ongoing campaign of feral cat control, the implementation of a head-start program. However, the success of this management strategy necessarily relies on the identification of pink iguana nesting grounds, which were still unknown at the onset of this study. We modeled the movement patterns of male and female iguanas during the reproductive season, using location data collected from custom-made remote tracking devices installed on adult pink iguanas in April 2021. We first calculated for each individual the vector of distances from its starting location, which was defined as net displacement. We then used net displacement as the response variable in a generalized additive mixed model with day of the year as the predictor. Based on the hypothesis that males and females may behaviorally differ after mating, we looked for female-specific migratory behavior suggesting females were moving toward nesting areas. The results obtained confirmed our hypothesis, as females exhibited a distinct migratory behavior, reaching a small plateau area inside of Wolf Volcano's caldera and ca. 400 m below the volcano's northern rim. Moreover, once inside the caldera, females displayed a more aggregated distribution pattern. The movement data obtained allowed Galápagos National Park rangers to locate individual pink iguana nests and subsequently to sight and collect the first observed hatchlings of the species. This work constitutes a necessary baseline to perform dedicated studies of pink iguana nests and emerging hatchling iguanas, which is an essential step toward the development of an effective head-start program.

Vegetation biomass and topography are associated with seasonal habitat selection and fall translocation behavior in Arctic hares

Habitat selection theory suggests that environmental features selected at coarse scales reveal fundamental factors affecting animal fitness. When these factors vary across seasons, they may lead to large-scale movements, including long-distance seasonal migrations. We analyzed the seasonal habitat selection of 25 satellite-tracked Arctic hares from a population on Ellesmere Island (Nunavut, Canada) that relocated over 100 km in the fall. Since no other lagomorph is known to perform such extensive movements, this population offered an ideal setting to test animal movement and habitat selection theory. On summer grounds hares selected low elevation areas, while on winter grounds they selected high vegetation biomass, high elevation, and steep slopes. During fall relocation, they alternated between stopover and traveling behavioral states (ratio 2:1). Stopover locations were characterized by higher vegetation heterogeneity and lower rugosity than traveling locations, while vegetation biomass and elevation interacted to explain stopover locations in a more complex way. The selected combination of environmental features thus varied across seasons and behavioral states, in a way broadly consistent with predictions based on the changing food and safety needs of hares. Although causality was not demonstrated, our results improve our understanding of long-distance movements and habitat selection in Arctic hares, as well as herbivore ecology in the polar desert. Results also provide strong support to animal movement and habitat selection theory, by showing how some important hypotheses hold when tested in a species phylogenetically distinct from most animal models used in this research field.

Targeting fin whale conservation in the North-Western Mediterranean Sea: insights on movements and behaviour from biologging and habitat modelling

Biologging and habitat modelling are key tools supporting the development of conservation measures and mitigating the effects of anthropogenic pressures on marine species. Here, we analysed satellite telemetry data and foraging habitat preferences in relation to chlorophyll-a productivity fronts to understand the movements and behaviour of endangered Mediterranean fin whales (Balaenoptera physalus) during their spring-summer feeding aggregation in the North-Western Mediterranean Sea. Eleven individuals were equipped with Argos satellite transmitters across 3 years, with transmissions averaging 23.5 ± 11.3 days. Hidden Markov Models were used to identify foraging behaviour, revealing how individuals showed consistency in their use of seasonal core feeding grounds; this was supported by the distribution of potential foraging habitat. Importantly, tracked whales spent most of their time in areas with no explicit protected status within the study region. This highlights the need for enhanced time- and place-based conservation actions to mitigate the effects of anthropogenic impacts for this species, notably ship strike risk and noise disturbance in an area of exceptionally high maritime traffic levels. These findings strengthen the need to further assess critical habitats and Important Marine Mammal Areas that are crucial for focused conservation, management and mitigation efforts.

Finding navigation cues near fishways

Many fish species depend on migration for various parts of their life cycle. Well-known examples include diadromous fish such as salmon and eels that need both fresh water and salt water to complete their life cycle. Migration also occurs within species that depend only on fresh water. In recent decades, anthropogenic pressures on freshwater systems have increased greatly, and have resulted, among other effects, in drastic habitat fragmentation. Fishways have been developed to mitigate the resulting habitat fragmentation, but these are not always effective. To improve fishway efficiency, the variety of navigation cues used by fish must be better understood: fish use a multitude of sensory inputs ranging from flow variables to olfactory cues. The reaction of a fish is highly dependent on the intensity of the cue, the fish species involved, and individual traits. Recently developed monitoring technologies allow us to gain insights into different combinations of environmental and physiological conditions. By combining fish behavioural models with environmental models, interactions among these components can be investigated. Several methods can be used to analyse fish migration, with state-space models, hidden Markov models, and individual-based models potentially being the most relevant since they can use individual data and can tie them to explicit spatial locations within the considered system. The aim of this review is to analyse the navigational cues used by fish and the models that can be applied to gather knowledge on these processes. Such knowledge could greatly improve the design and operation of fishways for a wider range of fish species and conditions.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles

BACKGROUND

Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements.

METHODS

We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic.

RESULTS

Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence.

CONCLUSIONS

Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Uncovering loggerhead (Caretta caretta) navigation strategy in the open ocean through the consideration of their diving behaviour

While scientists have been monitoring the movements and diving behaviour of sea turtles using Argos platform terminal transmitters for decades, the precise navigational mechanisms used by these animals remain an open question. Until now, active swimming motion has been derived from total motion by subtracting surface or subsurface modelled ocean currents, following the approximation of a quasi-two-dimensional surface layer migration. This study, based on tracking and diving data collected from 25 late-juvenile loggerhead turtles released from Reunion Island during their pre-reproductive migration, demonstrates the importance of considering the subsurface presence of the animals. Using a piecewise constant heading model, we investigate navigation strategy using daily time-at-depth distributions and three-dimensional currents to calculate swimming velocity. Our results are consistent with a map and compass strategy in which swimming movements follow straight courses at a stable swimming speed (approx. 0.5 m s-1), intermittently segmented by course corrections. This strategy, previously hypothesized for post-nesting green and hawksbill turtles, had never been observed in juvenile loggerheads. These results confirm a common open-ocean navigation mechanism across ages and species and highlight the importance of considering diving behaviour in most studies of sea turtle spatial ecology.

Zero-shot model-free learning of periodic movements for a bio-inspired soft-robotic arm

In recent years, soft robots gain increasing attention as a result of their compliance when operating in unstructured environments, and their flexibility that ensures safety when interacting with humans. However, challenges lie on the difficulty to develop control algorithms due to various limitations induced by their soft structure. In this paper, we introduce a novel technique that aims to perform motion control of a modular bio-inspired soft-robotic arm, with the main focus lying on facilitating the qualitative reproduction of well-specified periodic trajectories. The introduced method combines the notion behind two previously developed methodologies both based on the Movement Primitive (MP) theory, by exploiting their capabilities while coping with their main drawbacks. Concretely, the requested actuation is initially computed using a Probabilistic MP (ProMP)-based method that considers the trajectory as a combination of simple movements previously learned and stored as a MP library. Subsequently, the key components of the resulting actuation are extracted and filtered in the frequency domain. These are eventually used as input to a Central Pattern Generator (CPG)-based model that takes over the generation of rhythmic patterns at the motor level. The proposed methodology is evaluated on a two-module soft arm. Results show that the first algorithmic component (ProMP) provides an immediate estimation of the requested actuation by avoiding time-consuming training, while the latter (CPG) further simplifies the execution by allowing its control through a low-dimensional parameterization. Altogether, these results open new avenues for the rapid acquisition of periodic movements in soft robots, and their compression into CPG parameters for long-term storage and execution.

Multi-scale movement syndromes for comparative analyses of animal movement patterns

BACKGROUND

Animal movement is a behavioral trait shaped by the need to find food and suitable habitat, avoid predators, and reproduce. Using high-resolution tracking data, it is possible to describe movement in greater detail than ever before, which has led to many discoveries about the behavioral strategies of particular species. Recently, enough data been become available to enable a comparative approach, which has the potential to uncover general causes and consequences of variation in movement patterns, but which must be scale specific.

METHODS

Here we introduce a new multi-scale movement syndrome (MSMS) framework for describing and comparing animal movements and use it to explore the behavior of four sympatric mammals. MSMS incorporates four hierarchical scales of animal movement: (1) fine-scale movement steps which accumulate into (2) daily paths which then, over weeks or months, form a (3) life-history phase. Finally, (4) the lifetime track of an individual consists of multiple life-history phases connected by dispersal or migration events. We suggest a series of metrics to describe patterns of movement at each of these scales and use the first three scales of this framework to compare the movement of 46 animals from four frugivorous mammal species.

RESULTS

While subtle differences exist between the four species in their step-level movements, they cluster into three distinct movement syndromes in both path- and life-history phase level analyses. Differences in feeding ecology were a better predictor of movement patterns than a species' locomotory or sensory adaptations.

CONCLUSIONS

Given the role these species play as seed dispersers, these movement syndromes could have important ecosystem implications by affecting the pattern of seed deposition. This multiscale approach provides a hierarchical framework for comparing animal movement for addressing ecological and evolutionary questions. It parallels scales of analyses for resource selection functions, offering the potential to connect movement process with emergent patterns of space use.

Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

BACKGROUND

Fish migration has severely been impacted by dam construction. Through the disruption of fish migration routes, freshwater fish communities have seen an incredible decline. Fishways, which have been constructed to mitigate the problem, have been shown to underperform. This is in part due to fish navigation still being largely misunderstood. Recent developments in tracking technology and modelling make it possible today to track (aquatic) animals at very fine spatial (down to one meter) and temporal (down to every second) scales. Hidden Markov models are appropriate models to analyse behavioural states at these fine scales. In this study we link fine-scale tracking data of barbel (Barbus barbus) and grayling (Thymallus thymallus) to a fine-scale hydrodynamic model. With a HMM we analyse the fish's behavioural switches to understand their movement and navigation behaviour near a barrier and fishway outflow in the Iller river in Southern Germany.

METHODS

Fish were tracked with acoustic telemetry as they approached a hydropower facility and were presented with a fishway. Tracking resulted in fish tracks with variable intervals between subsequent fish positions. This variability stems from both a variable interval between tag emissions and missing detections within a track. After track regularisation hidden Markov models were fitted using different parameters. The tested parameters are step length, straightness index calculated over a 3-min moving window, and straightness index calculated over a 10-min window. The best performing model (based on a selection by AIC) was then expanded by allowing flow velocity and spatial velocity gradient to affect the transition matrix between behavioural states.

RESULTS

In this study it was found that using step length to identify behavioural states with hidden Markov models underperformed when compared to models constructed using straightness index. Of the two different straightness indices assessed, the index calculated over a 10-min moving window performed better. Linking behavioural states to the ecohydraulic environment showed an effect of the spatial velocity gradient on behavioural switches. On the contrary, flow velocity did not show an effect on the behavioural transition matrix.

CONCLUSIONS

We found that behavioural switches were affected by the spatial velocity gradient caused by the attraction flow coming from the fishway. Insight into fish navigation and fish reactions to the ecohydraulic environment can aid in the construction of fishways and improve overall fishway efficiencies, thereby helping to mitigate the effects migration barriers have on the aquatic ecosystem.

The applicability of human mobility scaling laws on animals-A Herring Gull case study

With the development of sensors, recording and availability of high-resolution movement data from animals and humans, two disciplines have rapidly developed: human mobility and movement ecology. Addressing methodological gaps between these two mobility fields could improve the understanding of movement processes and has been defined as the Integrated Science of Movement. We apply well-known human mobility metrics and data processing methods to Global Positioning System (GPS) tracking data of European Herring Gulls (Larus argentatus) to test the usefulness of these methods for explaining animal mobility behavior. We use stop detection, spatial aggregation, and for the first time on animal movement data, two approaches to temporal aggregation (Next Time-Bin and Next Place). We also calculate from this data a set of movement statistics (visitation frequency, distinct locations over time, and radius of gyration). Furthermore, we analyze and compare the gull and human data from the perspective of scaling laws commonly used for human mobility. The results confirm those of previous studies and indicate differences in movement parameters between the breeding season and other parts of the year. This paper also shows that methods used in human mobility analysis have the potential to improve our understanding of animal behavior.

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.

The Neonicotinoid Imidacloprid Impairs Learning, Locomotor Activity Levels, and Sucrose Solution Consumption in Bumblebees (Bombus terrestris)

Bumblebees carry out the complex task of foraging to provide for their colonies. They also conduct pollination, an ecosystem service of high importance to both wild plants and entomophilous crops. Insecticides can alter different aspects of bumblebee foraging behavior, including the motivation to leave the hive, finding the right flowers, handling flowers, and the ability to return to the colony. In the present study, we assessed how the neonicotinoid imidacloprid affects bumblebees' foraging behavior after exposure to four different treatment levels, including field-realistic concentrations (0 [control], 1, 10, and 100 μg/L), through sucrose solution over 9 days. We observed the behavior of several free-flying bumblebees simultaneously foraging on artificial flowers in a flight arena to register the bees' complex behavior postexposure. To conduct a detailed assessment of how insecticides affect bumblebee locomotor behavior, we used video cameras and analyzed the recordings using computer vision. We found that imidacloprid impaired learning and locomotor activity level when the bumblebees foraged on artificial flowers. We also found that imidacloprid exposure reduced sucrose solution intake and storage. By using automated analyses of video recordings of bumblebee behavior, we identified sublethal effects of imidacloprid exposure at field-realistic doses. Specifically, we observed negative impacts on consumption of sucrose solution as well as on learning and locomotor activity level. Our results highlight the need for more multimodal approaches when assessing the sublethal effects of insecticides and plant protection products in general. Environ Toxicol Chem 2023;42:1337-1345. © 2023 SETAC.

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.

Large-scale movement patterns in a social vulture are influenced by seasonality, sex, and breeding region

Quantifying space use and segregation, as well as the extrinsic and intrinsic factors affecting them, is crucial to increase our knowledge of species-specific movement ecology and to design effective management and conservation measures. This is particularly relevant in the case of species that are highly mobile and dependent on sparse and unpredictable trophic resources, such as vultures. Here, we used the GPS-tagged data of 127 adult Griffon Vultures Gyps fulvus captured at five different breeding regions in Spain to describe the movement patterns (home-range size and fidelity, and monthly cumulative distance). We also examined how individual sex, season, and breeding region determined the cumulative distance traveled and the size and overlap between consecutive monthly home-ranges. Overall, Griffon Vultures exhibited very large annual home-range sizes of 5027 ± 2123 km2, mean monthly cumulative distances of 1776 ± 1497 km, and showed a monthly home-range fidelity of 67.8 ± 25.5%. However, individuals from northern breeding regions showed smaller home-ranges and traveled shorter monthly distances than those from southern ones. In all cases, home-ranges were larger in spring and summer than in winter and autumn, which could be related to difference in flying conditions and food requirements associated with reproduction. Moreover, females showed larger home-ranges and less monthly fidelity than males, indicating that the latter tended to use the similar areas throughout the year. Overall, our results indicate that both extrinsic and intrinsic factors modulate the home-range of the Griffon Vulture and that spatial segregation depends on sex and season at the individual level, without relevant differences between breeding regions in individual site fidelity. These results have important implications for conservation, such as identifying key threat factors necessary to improve management actions and policy decisions.

A guide to area-restricted search: a foundational foraging behaviour

Area-restricted search is the capacity to change search effort adaptively in response to resource encounters or expectations, from directional exploration (global, extensive search) to focused exploitation (local, intensive search). This search pattern is used by numerous organisms, from worms and insects to humans, to find various targets, such as food, mates, nests, and other resources. Area-restricted search has been studied for at least 80 years by ecologists, and more recently in the neurological and psychological literature. In general, the conditions promoting this search pattern are: (1) clustered resources; (2) active search (e.g. not a sit-and-wait predator); (3) searcher memory for recent target encounters or expectations; and (4) searcher ignorance about the exact location of targets. Because area-restricted search adapts to resource encounters, the search can be performed at multiple spatial scales. Models and experiments have demonstrated that area-restricted search is superior to alternative search patterns that do not involve a memory of the exact location of the target, such as correlated random walks or Lévy walks/flights. Area-restricted search is triggered by sensory cues whereas concentrated search in the absence of sensory cues is associated with other forms of foraging. Some neural underpinnings of area-restricted search are probably shared across metazoans, suggesting a shared ancestry and a shared solution to a common ecological problem of finding clustered resources. Area-restricted search is also apparent in other domains, such as memory and visual search in humans, which may indicate an exaptation from spatial search to other forms of search. Here, we review these various aspects of area-restricted search, as well as how to identify it, and point to open questions.

Movement and habitat selection of a large carnivore in response to human infrastructure differs by life stage

BACKGROUND

The movement extent of mammals is influenced by human-modified areas, which can affect population demographics. Understanding how human infrastructure influences movement at different life stages is important for wildlife management. This is true especially for large carnivores, due to their substantial space requirements and potential for conflict with humans.

METHODS

We investigated human impact on movement and habitat selection by GPS-collared male brown bears (Ursus arctos) in two life stages (residents and dispersers) in central Sweden. We identified dispersers visually based on their GPS locations and used hidden Markov models to delineate dispersal events. We used integrated step selection analysis (iSSA) to infer movement and habitat selection at a local scale (availability defined by hourly relocations), and resource selection functions (RSFs) to infer habitat selection at a landscape scale (availability defined by the study area extent).

RESULTS

Movement of residents on a local scale was facilitated by small forestry roads as they moved faster and selected areas closer to forestry roads, and they avoided areas closer to larger public roads and buildings on both scales. Dispersers were more ambivalent in their response to human infrastructure. Dispersers increased their speed closer to small forestry roads and larger public roads, did not exhibit selection for or against any road class, and avoided areas closer to buildings only at local scale. Dispersers did not select for any features on the landscape, which is likely explained by the novelty of the landscape or their naivety towards it.

CONCLUSION

Our results show that movement in male brown bears is life stage-dependent and indicate that connectivity maps derived from movement data of dispersing animals may provide more numerous and more realistic pathways than those derived from resident animal data alone. This suggests that data from dispersing animals provide more realistic models for reconnecting populations and maintaining connectivity than if data were derived from resident animals alone.

Effects of roads on small-mammal movements: Opportunities and risks of vegetation management on roadsides

Roads can block animal movement and reduce persistence of species living in road surroundings. Movement restrictions on local populations may even increase extinction risk of abundant small mammals. However, road verges (road managed area between the edge of the road and the beginning of private land) may provide refuge and corridors for small mammals when properly managed. Information on the effects of roads and roadside management on small-mammal movement is still scarce for low traffic roads (<20,000 vehicles per day) crossing well-preserved habitats. We aimed to fill this gap by comparing fine-scale movement patterns of wood mice (Apodemus sylvaticus) in a road and in a similar roadless area without management. Both areas consisted of a well-preserved Mediterranean agro-silvo pastoral system. We studied several movement patterns: road crossings, verge use, length, and direction of movement. Additionally, we assessed how roadside management, animals' sex and residency status, season and microhabitat affect movement at the road area. At the roadless area, we defined a virtual road and verges at equivalent locations to the road area for comparison purposes. We gathered capture-mark-recapture data for two years to characterize movement patterns. Wood mice tended to avoid the road by crossing it less often and moving away from it more frequently than from equivalent locations in the roadless area. Wood mice used road verges more frequently than virtual verges and moved more often parallel to the road than to the virtual road. Road crossings were more frequent after firebreak openings (strips of mowed land) in surrounding areas and near taller shrubs. Also, males used road verges more often than females. Differences on several movement patterns between areas and their trends within the road area can be explained mainly by the presence of the road and roadside vegetation management (e.g., firebreaks openings). We suggest roadside vegetation management practices (e.g., avoid land mowing; maintain vegetation strips) to promote the role of verges as refuges and/or corridors for small mammals.

Multi-mode movement decisions across widely ranging behavioral processes

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.

Migratory network reveals unique spatial-temporal migration dynamics of Dunlin subspecies along the East Asian-Australasian Flyway

Determining the dynamics of where and when individuals occur is necessary to understand population declines and identify critical areas for populations of conservation concern. However, there are few examples where a spatially and temporally explicit model has been used to evaluate the migratory dynamics of a bird population across its entire annual cycle. We used geolocator-derived migration tracks of 84 Dunlin (Calidris alpina) on the East Asian-Australasian Flyway (EAAF) to construct a migratory network describing annual subspecies-specific migration patterns in space and time. We found that Dunlin subspecies exhibited unique patterns of spatial and temporal flyway use. Spatially, C. a. arcticola predominated in regions along the eastern edge of the flyway (e.g., western Alaska and central Japan), whereas C. a. sakhalina predominated in regions along the western edge of the flyway (e.g., N China and inland China). No individual Dunlin that wintered in Japan also wintered in the Yellow Sea, China seas, or inland China, and vice-versa. However, similar proportions of the 4 subspecies used many of the same regions at the center of the flyway (e.g., N Sakhalin Island and the Yellow Sea). Temporally, Dunlin subspecies staggered their south migrations and exhibited little temporal overlap among subspecies within shared migration regions. In contrast, Dunlin subspecies migrated simultaneously during north migration. South migration was also characterized by individuals stopping more often and for more days than during north migration. Taken together, these spatial-temporal migration dynamics indicate Dunlin subspecies may be differentially affected by regional habitat change and population declines according to where and when they occur. We suggest that the migration dynamics presented here are useful for guiding on-the-ground survey efforts to quantify subspecies’ use of specific sites, and to estimate subspecies’ population sizes and long-term trends. Such studies would significantly advance our understanding of Dunlin space-time dynamics and the coordination of Dunlin conservation actions across the EAAF.

A framework for integrating inferred movement behavior into disease risk models

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains

Many parasites induce decreased host movement, known as lethargy, which can impact disease spread and the evolution of virulence. Mathematical models have investigated virulence evolution when parasites cause host death, but disease-induced decreased host movement has received relatively less attention. Here, we consider a model where, due to the within-host parasite replication rate, an infected host can become lethargic and shift from a moving to a resting state, where it can die. We find that when the lethargy and disease-induced mortality costs to the parasites are not high, then evolutionary bistability can arise, and either moderate or high virulence can evolve depending on the initial virulence and the magnitude of mutation. These results suggest, firstly, the coexistence of strains with different virulence, which may explain the transient coexistence of low- and high-pathogenic strains of avian influenza viruses, and secondly, that medical interventions to treat the symptoms of lethargy or prevent disease-induced host deaths can result in a large jump in virulence and the rapid evolution of high virulence. In complement to existing results that show bistability when hosts are heterogeneous at the population level, we show that evolutionary bistability may arise due to transmission heterogeneity at the individual host level.

Joint Effects of Fragmentation and Mercury Contamination on Marsh Periwinkle (Littoraria irrorata) Movement

There are different ways contaminants can interact and enhance the effects of habitat fragmentation, such as modifying the movement of organisms. The present study tested the hypothesis that mercury exacerbates the effects of fragmentation by affecting the movement of the marsh periwinkle Littoraria irrorata and reducing the probability of snails crossing fragmented microlandscape experimental systems. How these changes could affect the search efficiency of organisms in the long term was assessed using hidden Markov models and random walks simulations. Bayesian nonlinear models were used to analyze the effects of fragmentation and contamination on the mean speed and mean directional change of organisms. Snail movement for control and two mercury-exposure treatments were recorded in microlandscapes with six different levels of habitat cover and three landscape replicates. The results indicated that exposed organisms had lower probabilities of crossing the landscape, reduced speed, and shifts in step length distributions. Both mercury exposure and habitat fragmentation affected the movement of the marsh periwinkle. Mercury exacerbated the effects of habitat fragmentation by affecting the cognition (e.g., route planning, orientation, and spatial learning) and movement of L. irrorata. Hence, the interaction of these stressors could further reduce the functional connectivity of landscapes and reduce the search efficiency of organisms. Environ Toxicol Chem 2022;41:1742-1753. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Clustering for Automated Exploratory Pattern Discovery in Animal Behavioral Data

Traditional methods of data analysis in animal behavior research are usually based on measuring behavior by manually coding a set of chosen behavioral parameters, which is naturally prone to human bias and error, and is also a tedious labor-intensive task. Machine learning techniques are increasingly applied to support researchers in this field, mostly in a supervised manner: for tracking animals, detecting land marks or recognizing actions. Unsupervised methods are increasingly used, but are under-explored in the context of behavior studies and applied contexts such as behavioral testing of dogs. This study explores the potential of unsupervised approaches such as clustering for the automated discovery of patterns in data which have potential behavioral meaning. We aim to demonstrate that such patterns can be useful at exploratory stages of data analysis before forming specific hypotheses. To this end, we propose a concrete method for grouping video trials of behavioral testing of animal individuals into clusters using a set of potentially relevant features. Using an example of protocol for testing in a “Stranger Test”, we compare the discovered clusters against the C-BARQ owner-based questionnaire, which is commonly used for dog behavioral trait assessment, showing that our method separated well between dogs with higher C-BARQ scores for stranger fear, and those with lower scores. This demonstrates potential use of such clustering approach for exploration prior to hypothesis forming and testing in behavioral research.

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl

BACKGROUND

Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using "automated modelling pipelines".

METHODS

Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter "feature sets": GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the "features" (independent variables) used in models.

RESULTS

Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting).

CONCLUSIONS

Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.

A Standardized Method for Experimental Human Approach Trials on Wild Wolves

As wolves recolonize areas of Europe ranging from moderate to high anthropogenic impact, fear of wolves is a recurring source of conflict. Shared tools for evaluating wolf responses to humans, and comparing such responses across their range, can be valuable. Experiments in which humans approach wild wolves can increase our understanding of how wolves respond to humans, facilitating human-wolf coexistence. We have developed the first standardized protocol for evaluating wolf responses to approaching humans using high-resolution GPS data, and tested it on wild wolves. We present a field protocol for experimentally approaching GPS-collared wolves, a descriptive comparison of two statistical methods for detecting a measurable flight response, a tutorial for identifying wolf flight initiation and resettling positions, and an evaluation of the method when reducing GPS positioning frequency. The field protocol, a data collection form, and the tutorial with R code for extracting flight parameters are provided. This protocol will facilitate studies of wolf responses to approaching humans, applicable at a local, national, and international level. Data compiled in a standardized way from multiple study areas can be used to quantify the variation in wolf responses to humans within and between populations, and in relation to predictors such as social status, landscape factors, or human population density, and to establish a baseline distribution of wolf response patterns given a number of known predictors. The variation in wolf responses can be used to assess the degree to which results can be generalized to areas where GPS studies are not feasible, e.g., for predicting the range of likely wolf behaviors, assessing the likelihood of wolf-human encounters, and complementing existing tools for evaluating reports of bold wolves. Showing how wolves respond to human encounters should help demystify the behavior of wild wolves toward humans in their shared habitat.

Summer habitat use and movements of invasive wild pigs (Sus scrofa) in Canadian agro-ecosystems

Resource selection informs understanding of a species’ ecology and is especially pertinent for invasive species. Since introduced to Canada, wild pigs (Sus scrofa Linnaeus, 1978) remain understudied despite recognized negative impacts to native and agricultural systems globally. Elsewhere in North America, pigs typically use forests and forage in agricultural crops. We hypothesized Canadian wild pigs would behave similarly and using GPS locations from 15 individuals we examined diel and seasonal resource selection and movement in the Canadian prairie region. Forests were predominately selected during the day, while corn (Zea mays L.), oilseeds, and wheat (Triticum aestivum L.) were predominately selected at night. Forests and corn were consistently selected throughout the growing season. Wetlands and forests showed greater use rates than other habitats, with evident trade-offs as crop use increased with the timing of maturation. Activity was consistent with foraging in growing crops. Results indicate diel patterns were likely a function of short-term needs to avoid daytime anthropogenic risk, while seasonal patterns demonstrate how habitats that fill multiple functional roles – food, cover, and thermoregulation – can be optimized. Understanding selection by invasive species is an important step in understanding their potential environmental impacts in novel environments and informs their management.

The effect of habitat reduction by roads on space use and movement patterns of an endangered species, the Cabrera vole Microtus cabrerae

Roads are among the most widespread signs of man’s presence around the globe. From simple low traffic trails to wide and highly used highways, roads have a wide array of effects on wildlife. In the present study, we tested how habitat reduction by roads may affect the space use and movement patterns of the Cabrera vole (Microtus cabrerae), a near-threatened Iberian endemism, often living on road verges. A total of 16 voles were successfully radio-tracked in two habitat patches with different size and proximity to roads. Results showed that individuals from the smaller patch (Verge patch) had smaller and less complex home-ranges than those from the larger patch (Meadow patch). Movement patterns were significantly influenced by the day period but only in individuals from the Verge patch. There was evidence of a barrier effect in both habitat patches, being this effect much more noticeable in the verge population. Overall, this study shows that space use and movement patterns of Cabrera voles near roads may be affected by the degree of habitat reduction imposed by these infrastructures. This suggests that species space use and movement patterns at fine-scale should be accounted for in road planning, even for species that may benefit from road verge habitats as refuges.

Altered Larval Yellow Perch Swimming Behavior Due to Methylmercury and PCB126 Detected Using Hidden Markov Chain Models

Fish swimming behavior is a commonly measured response in aquatic ecotoxicology because behavior is considered a whole organism-level effect that integrates many sensory systems. Recent advancements in animal behavior models, such as hidden Markov chain models (HMM), suggest an improved analytical approach for toxicology. Using both new and traditional approaches, we examined the sublethal effects of PCB126 and methylmercury on yellow perch (YP) larvae (Perca flavescens) using three doses. Both approaches indicate larvae increase activity after exposure to either chemical. The middle methylmercury-dosed larvae showed multiple altered behavior patterns. First, larvae had a general increase in activity, typically performing more behavior states, more time swimming, and more swimming bouts per second. Second, when larvae were in a slow or medium swimming state, these larvae tended to switch between these states more often. Third, larvae swam slower during the swimming bouts. The upper PCB126-dosed larvae exhibited a higher proportion and a fast swimming state, but the total time spent swimming fast decreased. The middle PCB126-dosed larvae transitioned from fast to slow swimming states less often than the control larvae. These results indicate that developmental exposure to very low doses of these neurotoxicants alters YP larvae overall swimming behaviors, suggesting neurodevelopment alteration.

Seasonal movements in caribou ecotypes of Western Canada

BACKGROUND

Several migratory ungulates, including caribou, are dramatically declining. Caribou of the Barren-ground ecotype, which forms its own subspecies, are known to be mainly migratory. By contrast, within the Woodland subspecies, animals of the Boreal ecotype are known to be mainly sedentary, while those within the Northern and Central Mountain ecotypes to be partially migratory, with only some individuals migrating. Promotion of conservation actions (e.g., habitat protection) that are specific to both residents and migrants, as well as to the areas they frequent seasonally (which may be separate for migrants), requires distinguishing migration from other movement behaviours, which might be a challenge.

METHODS

We aimed at assessing seasonal movement behaviours, including migratory, resident, dispersing, and nomadic, for caribou belonging to the Barren-ground and Woodland subspecies and ecotypes. We examined seasonal displacement, both planar and altitudinal, and seasonal ranges overlap for 366 individuals that were GPS-collared in Northern and Western Canada. Lastly, we assessed the ability of caribou individuals to switch between migratory and non-migratory movement behaviours between years.

RESULTS

We detected migratory behaviour within each of the studied subspecies and ecotypes. However, seasonal ranges overlap (an index of sedentary behaviour) varied, with proportions of clear migrants (0 overlap) of 40.94% for Barren-ground caribou and 23.34% for Woodland caribou, and of 32.95%, 54.87%, and 8.86% for its Northern Mountain, Central Mountain, and Boreal ecotype, respectively. Plastic switches of individuals were also detected between migratory, resident, dispersing, and nomadic seasonal movements performed across years.

CONCLUSIONS

Our unexpected findings of marked seasonal movement plasticity in caribou indicate that this phenomenon should be better studied to understand the resilience of this endangered species to habitat and climatic changes. Our results that a substantial proportion of individuals engaged in seasonal migration in all studied ecotypes indicate that caribou conservation plans should account for critical habitat in both summer and winter ranges. Accordingly, conservation strategies are being devised for the Woodland subspecies and its ecotypes, which were found to be at least partially migratory in this study. Our findings that migration is detectable with both planar and altitudinal analyses of seasonal displacement provide a tool to better define seasonal ranges, also in mountainous and hilly environments, and protect habitat there.

A predictive model and a field study on heterogeneous slug distribution in arable fields arising from density dependent movement

Factors and processes determining heterogeneous (‘patchy’) population distributions in natural environments have long been a major focus in ecology. Existing theoretical approaches proved to be successful in explaining vegetation patterns. In the case of animal populations, existing theories are at most conceptual: they may suggest a qualitative explanation but largely fail to explain patchiness quantitatively. We aim to bridge this knowledge gap. We present a new mechanism of self-organized formation of a patchy spatial population distribution. A factor that was under-appreciated by pattern formation theories is animal sociability, which may result in density dependent movement behaviour. Our approach was inspired by a recent project on movement and distribution of slugs in arable fields. The project discovered a strongly heterogeneous slug distribution and a specific density dependent individual movement. In this paper, we bring these two findings together. We develop a model of density dependent animal movement to account for the switch in the movement behaviour when the local population density exceeds a certain threshold. The model is fully parameterized using the field data. We then show that the model produces spatial patterns with properties closely resembling those observed in the field, in particular to exhibit similar values of the aggregation index.

Classifying behavior from short-interval biologging data: An example with GPS tracking of birds

Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.We apply a framework for using K-means clustering to classify bird behavior using points from short time interval GPS tracks. K-means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K-means clustering to six focal variables derived from GPS data collected at 1-11 s intervals from free-flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior.After filtering for data quality, the K-means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.The K-means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.

Decision rules for determining terrestrial movement and the consequences for filtering high-resolution global positioning system tracks: a case study using the African lion (Panthera leo)

The combined use of global positioning system (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequencies. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy being affected by signal reception. We hypothesized that animal behaviour was the main factor affecting fix inaccuracy, with inherent GPS positional noise (jitter) being most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A movement-verified filtering (MVF) protocol was constructed to compare GPS-derived speed data with dynamic body acceleration, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions (Panthera leo) fitted with collar-mounted GPS units and tri-axial motion sensors recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by greater than 80% prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high-resolution GPS datasets and demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement.

Research sites get closer to field camps over time: Informing environmental management through a geospatial analysis of science in the McMurdo Dry Valleys, Antarctica

As in many parts of the world, the management of environmental science research in Antarctica relies on cost-benefit analysis of negative environmental impact versus positive scientific gain. Several studies have examined the environmental impact of Antarctic field camps, but very little work looks at how the placement of these camps influences scientific research. In this study, we integrate bibliometrics, geospatial analysis, and historical research to understand the relationship between field camp placement and scientific production in the McMurdo Dry Valleys of East Antarctica. Our analysis of the scientific corpus from 1907-2016 shows that, on average, research sites have become less dispersed and closer to field camps over time. Scientific output does not necessarily correspond to the number of field camps, and constructing a field camp does not always lead to a subsequent increase in research in the local area. Our results underscore the need to consider the complex historical and spatial relationships between field camps and research sites in environmental management decision-making in Antarctica and other protected areas.

How often should dead-reckoned animal movement paths be corrected for drift?

BACKGROUND

Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, 'GPS') is typically used to verify an animal's location periodically. Straight lines are typically drawn between these 'Verified Positions' ('VPs') so the interpolation of space-use is limited by the temporal and spatial resolution of the system's measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear.

METHODS AND RESULTS

Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy.

CONCLUSIONS

We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal-barrier interactions and foraging strategies.

Combining Tracking and Remote Sensing to Identify Critical Year-Round Site, Habitat Use and Migratory Connectivity of a Threatened Waterbird Species

We tracked 39 western flyway white-naped cranes (Antigone vipio) throughout multiple annual cycles from June 2017 to July 2020, using GSM-GPS loggers providing positions every 10-min to describe migration routes and key staging areas used between their Mongolian breeding and wintering areas in China’s Yangtze River Basin. The results demonstrated that white-naped cranes migrated an average of 2556 km (±187.9 SD) in autumn and 2673 km (±342.3) in spring. We identified 86 critical stopover sites that supported individuals for more than 14 days, within a 100–800 km wide migratory corridor. This study also confirmed that Luan River catchment is the most important staging region, where white-naped cranes spent 18% of the annual cycle (in both spring and autumn) each year. Throughout the annual cycle, 69% of the tracking locations were from outside of the currently protected areas, while none of the critical staging areas enjoyed any form of site protection. We see further future potential to combine avian tracking data and remote-sensing information throughout the annual range of the white-naped crane to restore it and other such species to a more favourable conservation status.

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.

A methodological roadmap to quantify animal-vectored spatial ecosystem subsidies

Energy, nutrients and organisms move over landscapes, connecting ecosystems across space and time. Meta-ecosystem theory investigates the emerging properties of local ecosystems coupled spatially by these movements of organisms and matter, by explicitly tracking exchanges of multiple substances across ecosystem borders. To date, meta-ecosystem research has focused mostly on abiotic flows-neglecting biotic nutrient flows. However, recent work has indicated animals act as spatial nutrient vectors when they transport nutrients across landscapes in the form of excreta, egesta and their own bodies. Partly due to its high level of abstraction, there are few empirical tests of meta-ecosystem theory. Furthermore, while animals may be viewed as important mediators of ecosystem functions, better integration of tools is needed to develop predictive insights of their relative roles and impacts on diverse ecosystems. We present a methodological roadmap that explains how to do such integration by discussing how to combine insights from movement, foraging and ecosystem ecology to develop a coherent understanding of animal-vectored nutrient transport on meta-ecosystems processes. We discuss how the slate of newly developed technologies and methods-tracking devices, mechanistic movement models, diet reconstruction techniques and remote sensing-that when integrated have the potential to advance the quantification of animal-vectored nutrient flows and increase the predictive power of meta-ecosystem theory. We demonstrate that by integrating novel and established tools of animal ecology, ecosystem ecology and remote sensing, we can begin to identify and quantify animal-mediated nutrient translocation by large animals. We also provide conceptual examples that show how our proposed integration of methodologies can help investigate ecosystem impacts of large animal movement. We conclude by describing practical advancements to understanding cross-ecosystem contributions of animals on the move. Understanding the mechanisms by which animals shape ecosystem dynamics is important for ongoing conservation, rewilding and restoration initiatives around the world, and for developing more accurate models of ecosystem nutrient budgets. Our roadmap will enable ecologists to better qualify and quantify animal-mediated nutrient translocation for animals on the move.

Linking behavioral states to landscape features for improved conservation management

A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat-specific behaviors.Here we use behavioral change point analysis to identify behavioral states of golden eagles (Aquila chrysaetos) in the Sonoran and Mojave Deserts of the southwestern United States, and we identify, for each behavioral state, conservation-relevant habitat associations.We modeled behavior using 186,859 GPS points from 48 eagles and identified 2,851 distinct segments comprising four behavioral states. Altitude above ground level (AGL) best differentiated behavioral states, with two clusters of short-distance movement behaviors characterized by low AGL (state 1 AGL = 14 m (median); state 2 AGL = 11 m) and two associated with longer-distance movement behaviors and characterized by higher AGL (state 3 AGL = 108 m; state 4 AGL = 450 m).Behaviors such as perching and low-altitude hunting were associated with short-distance movements in updraft-poor environments, at higher elevations, and over steeper and more north-facing terrain. In contrast, medium-distance movements such as hunting and transiting were over gentle and south-facing slopes. Long-distance transiting occurred over the desert habitats that generate the best updraft.This information can guide management of this species, and our approach provides a template for behavior-specific habitat associations for other species of management concern.

Mapping marine debris encountered by albatrosses tracked over oceanic waters

Anthropogenic marine debris is a threat to marine organisms. Understanding how this debris spatially distributes at sea and may become associated with marine wildlife are key steps to tackle this current issue. Using bird-borne GPS- and video-loggers on 13 black-footed albatrosses Phoebastria nigripes breeding in Torishima, Japan, we examined the distribution of large floating debris in the Kuroshio Current area, western North Pacific. A total of 16 floating debris, including styrofoam (n = 4), plastic pieces (n = 3), plastic sheet (n = 1), fishery-related items (rope or netting, n = 4), and unidentified debris (n = 4), were recorded across the 9003 km covered by nine birds. The debris was concentrated in the southern area of the Kuroshio Current, where the surface current was weak, and the albatrosses were foraging. The albatrosses displayed changes in flight direction towards the debris when at a mean distance of 4.9 km, similarly to when approaching prey, and one bird was observed pecking at a plastic sheet; indicating that albatrosses actively interacted with the debris. This paper shows the usefulness of studying wide-ranging marine predators through the use of combined biologging tools, and highlights areas with increased risk of debris exposure and behavioral responses to debris items.

Characterizing the landscape of movement to identify critical wildlife habitat and corridors

Landscape planning that ensures the ecological integrity of ecosystems is critical in the face of rapid human-driven habitat conversion and development pressure. Wildlife tracking data provide unique and valuable information on animal distribution and location-specific behaviors that can serve to increase the efficacy of such planning. Given the spatiotemporal complexity inherent to animal movements, the interaction between movement behavior and a location is often oversimplified in commonly applied analyses of tracking data. We analyzed GPS-tracking-derived metrics of intensity of use, structural properties (based on network theory), and properties of the movement path (speed and directionality) with machine learning to define homogeneous spatial movement types. We applied our approach to a long-term tracking data set of over 130 African elephants (Loxodonta africana) in an area under pressure from infrastructure development. We identified 5 unique location-specific movement categories displayed by elephants, generally defined as high, medium, and low use intensity, and 2 types of connectivity corridors associated with fast and slow movements. High-use and slow-movement corridors were associated with similar landscape characteristics associated with productive areas near water, whereas low-use and fast corridors were characterized by areas of low productivity farther from water. By combining information on intensity of use, properties of movement paths, and structural aspects of movement across the landscape, our approach provides an explicit definition of the functional role of areas for movement across the landscape that we term the movescape. This combined, high-resolution information regarding wildlife space use offers mechanistic information that can improve landscape planning.