How to account for behavioral states in step-selection analysis: a model comparison.

Step-selection models are widely used to study animals' fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, often depend on the animal's unobserved behavioral states, such as resting or foraging. As this is ignored in standard (integrated) step-selection analyses (SSA, iSSA), different approaches have emerged to account for such states in the analysis. The performance of these approaches and the consequences of ignoring the states in step-selection analysis, however, have rarely been quantified. We evaluate the recent idea of combining iSSAs with hidden Markov models (HMMs), which allows for a joint estimation of the unobserved behavioral states and the associated state-dependent habitat selection. Besides theoretical considerations, we use an extensive simulation study and a case study on fine-scale interactions of simultaneously tracked bank voles (Myodes glareolus) to compare this HMM-iSSA empirically to both the standard and a widely used classification-based iSSA (i.e., a two-step approach based on a separate prior state classification). Moreover, to facilitate its use, we implemented the basic HMM-iSSA approach in the R package HMMiSSA available on GitHub.

Accounting for behaviour in fine-scale habitat selection: A case study highlighting methodological intricacies.

Animal habitat selection-central in both theoretical and applied ecology-may depend on behavioural motivations such as foraging, predator avoidance, and thermoregulation. Step-selection functions (SSFs) enable assessment of fine-scale habitat selection as a function of an animal's movement capacities and spatiotemporal variation in extrinsic conditions. If animal location data can be associated with behaviour, SSFs are an intuitive approach to quantify behaviour-specific habitat selection. Fitting SSFs separately for distinct behavioural states helped to uncover state-specific selection patterns. However, while the definition of the availability domain has been highlighted as the most critical aspect of SSFs, the influence of accounting for behaviour in the use-availability design has not been quantified yet. Using a predator-free population of high-arctic muskoxen Ovibos moschatus as a case study, we aimed to evaluate how (1) defining behaviour-specific availability domains, and/or (2) fitting separate behaviour-specific models impacts (a) model structure, (b) estimated selection coefficients and (c) model predictive performance as opposed to behaviour-unspecific approaches. To do so, we first applied hidden Markov models to infer different behavioural modes (resting, foraging, relocating) from hourly GPS positions (19 individuals, 153-1062 observation days/animal). Using SSFs, we then compared behaviour-specific versus behaviour-unspecific habitat selection in relation to terrain features, vegetation and snow conditions. Our results show that incorporating behaviour into the definition of the availability domain primarily impacts model structure (i.e. variable selection), whereas fitting separate behaviour-specific models mainly influences selection strength. Behaviour-specific availability domains improved predictive performance for foraging and relocating models (i.e. behaviours with medium to large spatial displacement), but decreased performance for resting models. Thus, even for a predator-free population subject to only negligible interspecific competition and human disturbance we found that accounting for behaviour in SSFs impacted model structure, selection coefficients and predictive performance. Our results indicate that for robust inference, both a behaviour-specific availability domain and behaviour-specific model fitting should be explored, especially for populations where strong spatiotemporal selection trade-offs are expected. This is particularly critical if wildlife habitat preferences are estimated to inform management and conservation initiatives.

Wherever I may roam-Human activity alters movements of red deer (Cervus elaphus) and elk (Cervus canadensis) across two continents.

Human activity and associated landscape modifications alter the movements of animals with consequences for populations and ecosystems worldwide. Species performing long-distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual-level movements relative to the environmental context, or movement expression, using the standardized metric Intensity of Use, reflecting both the directionality and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topography, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strongest driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human-dominated landscapes. Our work represents the first comparison of metric-based movement expression across widely distributed populations of a deer genus, contributing to the understanding and prediction of animals' responses to human activity.

Big-data approaches lead to an increased understanding of the ecology of animal movement.

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

Body size and digestive system shape resource selection by ungulates: A cross-taxa test of the forage maturation hypothesis.

The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small-bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.

Do all roads lead to resistance? State road density is the main impediment to gene flow in a flagship species inhabiting a severely fragmented anthropogenic landscape.

AIM: Connectivity conservation is ideally based on empirical information on how landscape heterogeneity influences species-specific movement and gene flow. Here, we present the first large-scale evaluation of landscape impacts on genetic connectivity in the European wildcat (Felis silvestris), a flagship and umbrella species for connectivity conservation across Europe. LOCATION: The study was carried out in the core area of the distributional range of wildcats in Germany, covering about 186,000 km2 of a densely populated and highly fragmented landscape. METHODS: We used data of 975 wildcats genotyped at 14 microsatellites and an individual-based landscape genetic framework to assess the importance of twelve landscape variables for explaining observed genetic connectivity. For this, we optimized landscape resistance surfaces for all variables and compared their relative impacts using multiple regression on distance matrices and commonality analysis. RESULTS: Genetic connectivity was best explained by a synergistic combination of six landscape variables and isolation by distance. Of these variables, road density had by far the strongest individual impact followed by synergistic effects of agricultural lands and settlements. Subsequent analyses involving different road types revealed that the strong effect of road density was largely due to state roads, while highways and federal roads had a much smaller, and county roads only a negligible impact. MAIN CONCLUSIONS: Our results highlight that landscape-wide genetic connectivity in wildcats across Germany is strongly shaped by the density of roads and in particular state roads, with higher densities providing larger resistance to successful dispersal. These findings have important implications for conservation planning, as measures to mitigate fragmentation effects of roads (e.g., over- or underpasses) often focus on large, federally managed transportation infrastructures. While these major roads exert local barrier effects, other road types can be more influential on overall connectivity, as they are more abundant and more widespread across the landscape.

Visitation of artificial watering points by the red fox (Vulpes vulpes) in semiarid Australia.

The introduced red fox (Vulpes vulpes) now occupies most of the Australian continent outside the tropics, including arid and semiarid ecosystems. Information on the water requirements of foxes is scant, but free water is not thought to be required if adequate moisture-containing food is available. The frequency and duration of visits by foxes fitted with GPS collars to known artificial watering points in semiarid Australia were recorded for 22 individual foxes across four austral seasons between October 2015 and November 2017, providing >93,000 location fixes. We modeled home range and the distance traveled by range-resident foxes beyond their home range to reach known water sources. We used recurse analysis to determine the frequency of visitation and step-selection functions to model the speed and directionality of movement inside and outside the home range. Our study demonstrates that some foxes in this semiarid environment utilize free-standing water. The findings suggest that artificial watering points can be used as a focal point for conducting strategic fox control in arid and semiarid environments. Additionally, strategies that restrict access to water by foxes may reduce their duration of occupancy and/or long-term abundance in parts of the landscape, thus providing benefits for conservation and agriculture.

A fresh look at an old concept: home-range estimation in a tidy world.

A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed. Here we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators. We show how such a workflow can help to make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily. This allows one to perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions. By providing a standardized implementation of home-range estimators, we hope to equip researchers with the tools needed to explore how estimator choice influences answers to biologically meaningful questions.

A 'How to' guide for interpreting parameters in habitat-selection analyses.

Habitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modelling of both movement and habitat-selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat-selection analyses. Furthermore, we provide a 'how to' guide illustrating the steps required to implement integrated step-selection analyses using the amt package By providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users.

In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal.

There is a growing agreement that conservation needs to be proactive and pay increased attention to common species and to the threats they face. The blue sheep (Pseudois nayaur) plays a key ecological role in sensitive high-altitude ecosystems of Central Asia and is among the main prey species for the globally vulnerable snow leopard (Panthera uncia). As the blue sheep has been increasingly exposed to human pressures, it is vital to estimate its population dynamics, protect the key populations, identify important habitats, and secure a balance between conservation and local livelihoods. We conducted a study in Manang, Annapurna Conservation Area (Nepal), to survey blue sheep on 60 transects in spring (127.9 km) and 61 transects in autumn (134.7 km) of 2019, estimate their minimum densities from total counts, compare these densities with previous estimates, and assess blue sheep habitat selection by the application of generalized additive models (GAMs). Total counts yielded minimum density estimates of 6.0-7.7 and 6.9-7.8 individuals/km2 in spring and autumn, respectively, which are relatively high compared to other areas. Elevation and, to a lesser extent, land cover indicated by the normalized difference vegetation index (NDVI) strongly affected habitat selection by blue sheep, whereas the effects of anthropogenic variables were insignificant. Animals were found mainly in habitats associated with grasslands and shrublands at elevations between 4,200 and 4,700 m. We show that the blue sheep population size in Manang has been largely maintained over the past three decades, indicating the success of the integrated conservation and development efforts in this area. Considering a strong dependence of snow leopards on blue sheep, these findings give hope for the long-term conservation of this big cat in Manang. We suggest that long-term population monitoring and a better understanding of blue sheep-livestock interactions are crucial to maintain healthy populations of blue sheep and, as a consequence, of snow leopards.

What you see is where you go: visibility influences movement decisions of a forest bird navigating a three-dimensional-structured matrix.

Animal spatial behaviour is often presumed to reflect responses to visual cues. However, inference of behaviour in relation to the environment is challenged by the lack of objective methods to identify the information that effectively is available to an animal from a given location. In general, animals are assumed to have unconstrained information on the environment within a detection circle of a certain radius (the perceptual range; PR). However, visual cues are only available up to the first physical obstruction within an animal's PR, making information availability a function of an animal's location within the physical environment (the effective visual perceptual range; EVPR). By using LiDAR data and viewshed analysis, we modelled forest birds' EVPRs at each step along a movement path. We found that the EVPR was on average 0.063% that of an unconstrained PR and, by applying a step-selection analysis, that individuals are 1.55 times more likely to move to a tree within their EVPR than to an equivalent tree outside it. This demonstrates that behavioural choices can be substantially impacted by the characteristics of an individual's EVPR and highlights that inferences made from movement data may be improved by accounting for the EVPR.

Wave-like Patterns of Plant Phenology Determine Ungulate Movement Tactics.

Animals exhibit a diversity of movement tactics [1]. Tracking resources that change across space and time is predicted to be a fundamental driver of animal movement [2]. For example, some migratory ungulates (i.e., hooved mammals) closely track the progression of highly nutritious plant green-up, a phenomenon called "green-wave surfing" [3-5]. Yet general principles describing how the dynamic nature of resources determine movement tactics are lacking [6]. We tested an emerging theory that predicts surfing and the existence of migratory behavior will be favored in environments where green-up is fleeting and moves sequentially across large landscapes (i.e., wave-like green-up) [7]. Landscapes exhibiting wave-like patterns of green-up facilitated surfing and explained the existence of migratory behavior across 61 populations of four ungulate species on two continents (n = 1,696 individuals). At the species level, foraging benefits were equivalent between tactics, suggesting that each movement tactic is fine-tuned to local patterns of plant phenology. For decades, ecologists have sought to understand how animals move to select habitat, commonly defining habitat as a set of static patches [8, 9]. Our findings indicate that animal movement tactics emerge as a function of the flux of resources across space and time, underscoring the need to redefine habitat to include its dynamic attributes. As global habitats continue to be modified by anthropogenic disturbance and climate change [10], our synthesis provides a generalizable framework to understand how animal movement will be influenced by altered patterns of resource phenology.

Correction to: Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models.

[This corrects the article DOI: 10.1186/s40462-019-0186-0.].

Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models.

BACKGROUND: Habitat fragmentation is a primary driver of wildlife loss, and the establishment of biological corridors is a conservation strategy to mitigate this problem. Identifying areas with high potential functional connectivity typically relies on the assessment of landscape resistance to movement. Many modeling approaches exist to estimate resistance surfaces but to date only a handful of studies compared the outputs resulting from different methods. Moreover, as many species are threatened by fragmentation, effective biodiversity conservation requires that corridors simultaneously meet the needs of multiple species. While many corridor planning initiatives focus on single species, we here used a combination of data types and analytical approaches to identify and compare corridors for several large mammal species within the Panama portion of the Mesoamerican Biological Corridor. METHODS: We divided a large mammal assemblage into two groups depending on the species sensitivity to habitat disturbance. We subsequently used cost-distance methods to produce multi-species corridors which were modeled on the basis of (i) occupancy of nine species derived from camera trapping data collected across Panama, and (ii) step selection functions based on GPS telemetry data from white-lipped peccary Tayassu pecari, puma Puma concolor, and ocelot Leopardus pardalis. In addition to different data sources and species groups, we also used different transformation curves to convert occupancy and step-selection results into landscape resistance values. RESULTS: Corridors modeled differed between sensitive and tolerant species, between the data sets, and between the transformation curves. There were more corridors identified for tolerant species than for sensitive species. For tolerant species, several corridors developed with occupancy data overlapped with corridors produced with step selection functions, but this was not the case for sensitive species. CONCLUSION: Our study represents the first comparison of multispecies corridors parametrized with step selection functions versus occupancy models. Given the wide variability in output corridors, our findings underscore the need to consider the ecological requirements of several species. Our results also suggest that occupancy models can be used for estimating connectivity of generalist species. Finally, this effort allowed to identify important corridors within the MBC (i) at a country scale and (ii) for several species simultaneously to accurately inform the local authorities in conservation planning. The approach we present is reproducible in other sites and/or for other species.

Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation.

Popular frameworks for studying habitat selection include resource-selection functions (RSFs) and step-selection functions (SSFs), estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animals. Conceptually, slopes that vary by individual, that is, random coefficient models, could be used to accommodate inter-individual heterogeneity with either approach. While fitting such models for RSFs is possible with standard software for generalized linear mixed-effects models (GLMMs), straightforward and efficient one-step procedures for fitting SSFs with random coefficients are currently lacking. To close this gap, we take advantage of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood-equivalent to a Poisson model with stratum-specific fixed intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference for random-slope models becomes feasible with standard Bayesian techniques, or with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including models without random slopes and mixed conditional regression models fit using a two-step algorithm. Using data from mountain goats (Oreamnos americanus) and Eurasian otters (Lutra lutra), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to compare different estimation approaches for SSFs and to demonstrate the importance of including individual-specific slopes when estimating individual- and population-level habitat-selection parameters. By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales, but these models had previously been very challenging to fit.

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses.

Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step-selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat- and movement-related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four-step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.

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

Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or 'hidden states'. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis.

The role of the residence-effect on the outcome of intergroup encounters in Verreaux's sifakas.

Intergroup competition has an important impact on the survival and fitness of individuals in group-living species. However, factors influencing the probability of winning an encounter are not fully understood. We studied the influence of numerical advantage and location of the encounter on the chances of winning in eight neighboring groups of Verreaux's sifakas (Propithecus verreauxi), in Kirindy Forest, western Madagascar. Intergroup encounters were inferred from spatial data collected via GPS loggers over a period of two years. Location, i.e., the proximity to the respective core area, rather than the numerical advantage of a group in a given encounter, influenced the probability of winning. Accordingly, the high value that resident groups attribute to exclusive and intensively used areas increased their motivation in defending these locations against intruders. Moreover, losers used the encounter area less often than winners within a month after the encounter, suggesting that losing also entails long-term costs. Thus, our results suggest that in gregarious animals the particular circumstances of each encounter, such as the location, can outweigh group characteristics and predict the chances of winning an intergroup encounter.

Intergroup encounters in Verreaux's sifakas (Propithecus verreauxi): who fights and why?

ABSTRACT: Individuals living in groups have to achieve collective action for successful territorial defense. Because conflicts between neighboring groups always involve risks and costs, individuals must base their decision to participate in a given conflict on an evaluation of the trade-off between potential costs and benefits. Since group members may differ in motivation to engage in group encounters, they exhibit different levels of participation in conflicts. In this study, we investigated factors influencing participation in intergroup encounters in Verreaux's sifakas (Propithecus verreauxi), a group-living primate from Madagascar. Over a period of 12 months, we studied eight adjacent sifaka groups in Kirindy Forest. We observed 71 encounters between known neighboring groups in which adult females and males participated equally as often. No individual participated in every encounter, and non-participation occurred more often in larger groups. Females participated less often in encounters when they had dependent infants, presumably to reduce the risk of infanticide. Male participation was influenced by social status: dominant males participated in most encounters, whereas males with fewer opportunities to reproduce participated less often, hence male participation is influenced by the incentive of maintaining access to females. The number of actively participating individuals in the opponent group positively influenced the participation in both sexes. Thus, sifakas seem to decide joining a given encounter opportunistically, most likely based on a combination of individual incentives and the actual circumstance of each encounter, suggesting that the complexity in intergroup relationships appears to be the product of decisions made by each individual group member. SIGNIFICANCE STATEMENT: Cooperation among group-living animals is often challenged by collective action problems resulting from individual differences in interests in contributing to collective behaviors. Intergroup encounters involve distinguished costs and benefits for each individual despite being in the same social group. Therefore, encounters between groups offer a good opportunity to investigate individual participation in collective action. In this study, we investigate the influence of different incentives on individual participation in intergroup encounters in wild Malagasy primate, Verreaux's sifakas. We propose a novel approach that takes into account the variable circumstances of each conflict, such as the number of individuals fighting in both groups as a predictor for participation. We believe that our study not only provides novel data on wild sifakas, but it also offers new perspectives for the interpretation of intergroup relationships in other taxa.