Modeling and Data Analysis in Movement Ecology

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.

Tropical rainforests are biodiversity hotspots, where the seeds of 60%–90% of trees are dispersed by animals, yet movement data needed to understand the behaviors and habitat selection of tropical frugivores remain scarce. We GPS‐tracked four great blue turacos ( Corythaeola cristata ) in the Dja Faunal Reserve of southern Cameroon and predicted spatial patterns of seed dispersal within each bird's home range, which covered up to 2.33 km 2 . Using integrated Step Selection Analyses, we related turaco movements to LiDAR‐derived measurements of 3D vegetation structure and habitat type. One individual preferred areas of tall canopy height, whereas one preferred intermediate canopy height and avoided swamps and areas of greater vertical vegetation complexity. Seed dispersal patterns for the two turacos with the largest home ranges were predicted to be more diffuse, with local maxima almost an order of magnitude smaller than those with smaller home ranges. Although the great blue turaco is a common bird species throughout central African forests, this is the first study to characterize the species' movement ecology using telemetry. The observed individual variation in movement and habitat use underscores the need to track a broader range of species in central African landscapes, which are increasingly threatened by hunting, logging, habitat loss, and climate change.

Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an r package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates.

Context: High-resolution animal movement data are becoming increasingly available, yet having a multitude of trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species' potential to act as a link between patches, populations, or ecosystems are of importance. Objectives: We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of their ecological likelihood. With this approach, we estimated connectivity at the landscape level throughout the annual cycle of bar-headed geese (Anser indicus) in its native range. Methods: We used a tracking dataset of bar-headed geese to parameterise a multi-state movement model and to estimate temporally explicit habitat suitability within the species' range. We simulated migratory movements between range fragments, and estimated their ecological likelihood. The results are compared to expectations derived from published literature. Results: Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The estimated likelihood of simulated migrations was similar to that of empirical trajectories. We found that the predicted connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species. Conclusions: We show how empirical tracking data and environmental information can be fused to make meaningful predictions about future animal movements. These are temporally explicit and transferable even outside the spatial range of the available data. Our integrative framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

The recent debate on both the existence and the cause of fractal (Lévy) patterns in animal movement resonates with much deeper and richer problems in movement ecology: (1) establishing mechanistic links between animal behavior and statistical patterns of movement, and (2) understanding what is the role of randomness (stochasticity) in animal motion. Here, the idea of behavioral intermittence is shown to be crucial to establish mechanistic connections between the behavior of organisms and the statistical properties they generate when moving. Attention is drawn to the fact that some random walk modeling procedures can impair the identification of intermittent biological mechanisms which could govern major statistical properties of movement. This fact, together with some misconceptions and prejudices regarding the role of randomness in animal motion may explain why stochastic processes have been disregarded as a potential source of adaptation in animal movement. In the near future, the advances in biotelemetry together with a more explicit consideration of behavioral intermittence, and the development of novel random walk approaches, could help us to set up the bases for a landscape-level behavioral ecology.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal's home range, and many methods of estimating home ranges have arisen over the past half a century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as kernel density estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian bridge movement models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of appropriately using these two categories of distributions and their estimators. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home ranges estimated using estimators of occurrence distributions are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use estimators of range distributions to quantify home ranges and estimators of occurrence distributions to answer other questions in movement ecology, such as when and where an animal crossed a linear feature, visited a location of interest, or interacted with other animals.

Trait-based approaches are key to understanding eco-evolutionary processes but rarely account for animal behaviour despite its central role in ecosystem dynamics. We propose integrating behaviour into trait-based ecology through movement traits-standardised and comparable measures of animal movement derived from biologging data, such as daily displacements or range sizes. Accounting for animal behaviour will advance trait-based research on species interactions, community structure and ecosystem functioning. Importantly, movement traits allow for quantification of behavioural reaction norms, offering insights into species' acclimation and adaptive capacity to environmental change. We outline a vision for a 'living' global movement trait database that enhances trait data curation by (1) continuously growing alongside shared biologging data, (2) calculating traits directly from individual-level data using standardised, consistent methodology and (3) providing information on multi-level (species, individual, within-individual) trait variation. We present a proof-of-concept 'MoveTraits' database with 52 mammal and 97 bird species, demonstrating calculation workflows for 5 traits across multiple timescales. Movement traits have significant potential to improve trait-based global change predictions and contribute to global biodiversity assessments as Essential Biodiversity Variables. By making animal movement data more accessible and interpretable, this database could bridge the gap between movement ecology and biodiversity policy, facilitating evidence-based conservation.

Summary1. Understanding human resource extraction is crucial for conservation science, allowing accurate assessments of system sustainability and testing key assumptions about human resource users.2. We apply ecological methods and principles to Global Positioning System (GPS) data on human movement to investigate the ecological and behavioural differences between human hunters and non‐hunters, a method which can be reproduced with any species which routinely return to a central place. The integration of movement ecology and habitat selection can greatly augment the applicability and scope of both disciplines, and we explore the issues that arise from integration, because of the differing data types and methods used by each approach.3. We propose an adaptable methodological framework which can be used to combine movement ecology and habitat selection when using data from GPS tracking devices, whether from human or animal foragers. The methodology is based on three steps and can be implemented in the free downloadable statistical program R, using the full code in the Data S1.4. First, we show that net squared displacement, coupled with nonlinear mixed‐effects models, is suitable for quantifying characteristics of small‐scale movement, such as daily travel patterns, and extracting parts of these journeys for analysis.5. Secondly, having extracted part of a journey, biased random bridges use the auto‐correlated GIS tracking data to create a utilization distribution (UD) for each individual. This method includes movement between known locations to estimate use intensity in an area.6. Finally, the UD can be analysed by a resource utilization function which relates the intensity of use to landscape features of an area to identify habitats selected by an individual. This can be used to predict use of the landscape at larger scales for both individuals and an entire population.7. This methodological framework is a flexible method to accurately assess human and animal resource use and movement through the natural environment.

Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions

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.

BackgroundAnimals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.MethodsWe propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.ResultsWe show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.ConclusionsNot only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

Movement is a fundamental characteristic of life that has been defined as a change across many spatial and temporal scales. It is also a ubiquitous ecological process which influences the structure and dynamics of populations, communities, and ecosystems. Movement has always been at the center of many observations, investigations and studies since the very first attempt to understand where a species may go. Naturalists have investigated the mysteries of movement since the writings of Aristotle (4th century B.C.) searched for common features unifying animal movements. He tried to explain movement such as flight through air and motion of animals in water in general terms on the basis of casual observations. Since then, researchers have traditionally used direct observation as a method to monitor wildlife. The direct observation has also been used to elucidate and describe movement phenomena. Not losing sight of an animal is the most challenging part of this traditional type of research, but this has now been solved by deploying telemetry techniques radar, radio, satellite and Global Positioning System (GPS) tracking. Recent advances in telemetry techniques, such as extensive use of bio-loggers with GPS, have enabled spatiotemporal data to be collected on animals with ever-increasing accuracy. Also, recent advances in movement research have inspired a shift in the study at species-level or population-level patterns to individual-level patterns. This dissertation aims to contribute to the understanding of the movement phenomenon at individual-level pattern. The availability of movement data and recent advances in movement research are key factors to improve our understanding of animals' movement at individual levels. Although the techniques for studying animal movement have been advanced and flourished since earliest attempts, the incorporation of movement studies in other methodological advancements, particularly in species distribution modelling (SDM) has lagged behind. In the context of SDMs, the accessibility of habitats by species or populations has been considered rather than underlying the process of individuals' movement. Part of my dissertation is dedicated to providing evidence that including the individuals' movement of a species and accounting for their potential dispersal, along with environmental dynamics improves the accuracy and credibility of models to predict the potential distribution of species over time under climate change. Also, I illustrate the capacity of individuals’ movement data which can be considered as a reliable source for species distribution modelling. A common hypothesis is that when including the vertical movement of individuals improves the accuracy and credibility of the individual’s range maps. Another part of this dissertation provides evidence for this hypothesis and puts it to use in the utilization distribution model. I incorporated the vertical movement data to the 2D space use analysis and proved that the 3D volumetric analysis is a realistic depiction of species occurrence. To develop efficient nature conservation practices, it is necessary to know where and when wildlife may occur. This approach would give a better information to conservationists and wildlife managers while developing or practising conservation plans. A complex phenomenon like movement involving uncertainties may create confusion for managers. In this dissertation, I also presented the use of movement data to classify different types of movement (i.e. flight). I am confident that application of movement data in wildlife management and conservation practices will continue to expand and improve.

Movement ecology is an emerging discipline within ecology. Researchers addressing ba- sic and applied questions within the movement ecology framework often rely on animal biotelemetry data. Biotelemetry sensors can collect different types of data (from track- ing an animal’s position to measuring its heart rate). Rapid technological advances in satellite based navigation and tracking devices enable researchers to track animals with smaller devices, leading to steadily increasing sampling rates. To characterize space requirements of tracked animals, the concept of a home range is often used. This thesis starts with a general introduction (chapter 1), that connects the following chapters to the wider conceptual and analytical picture. The main parts of this thesis focus on the analysis of animal tracking data using home range analyses (chapters 2 through 5) and how to statistically test for the influence of (environmental) covariates on animal movement (chapter 6). Starting at the level of data management, chapter 2 highlights the need for a data model when working with tracking data. Next, a new package for program R is introduced that implements the previously discussed data model and provides functionality for the analysis of animal tracking data (chapter 3). Within the rhr (reproducible home ranges) package, the most commonly used estimators for home-range analyses are implemented. Further, functionalities for the automatic reporting of results and a graphical user interface are provided. After these technical aspects of tracking data, different methods for the estimation of home-range core areas (chapter 4) and home ranges as such (chapter 5) are discussed. Moving beyond home ranges, chapter 6 introduces a method to test which tests whether an animal’s movement track is influenced by environmental covariates. Finally, this thesis concludes that: (i) amounts of data (tracking data and auxiliary environmental data) will continue to increase in the future; (ii) tools to handle, manage, and analyze them are of great importance (chapter 2 and 3); (iii) researchers should not get lost in methods and lose sight of the wider biological picture, rather, they should use established as well as new methods to answer interesting biological questions (chapter 4 through 6).

Animal tracking and biologging devices record large amounts of data on individual movement behaviors in natural environments. In these data, movement ecologists often view unexplained variation around the mean as “noise” when studying patterns at the population level. In the field of behavioral ecology, however, focus has shifted from population means to the biological underpinnings of variation around means. Specifically, behavioral ecologists use repeated measures of individual behavior to partition behavioral variability into intrinsic among-individual variation and reversible behavioral plasticity and to quantify: a) individual variation in behavioral types (i.e. different average behavioral expression), b) individual variation in behavioral plasticity (i.e. different responsiveness of individuals to environmental gradients), c) individual variation in behavioral predictability (i.e. different residual within-individual variability of behavior around the mean), and d) correlations among these components and correlations in suites of behaviors, called ‘behavioral syndromes’. We here suggest that partitioning behavioral variability in animal movements will further the integration of movement ecology with other fields of behavioral ecology. We provide a literature review illustrating that individual differences in movement behaviors are insightful for wildlife and conservation studies and give recommendations regarding the data required for addressing such questions. In the accompanying R tutorial we provide a guide to the statistical approaches quantifying the different aspects of among-individual variation. We use movement data from 35 African elephants and show that elephants differ in a) their average behavior for three common movement behaviors, b) the rate at which they adjusted movement over a temporal gradient, and c) their behavioral predictability (ranging from more to less predictable individuals). Finally, two of the three movement behaviors were correlated into a behavioral syndrome (d), with farther moving individuals having shorter mean residence times. Though not explicitly tested here, individual differences in movement and predictability can affect an individual’s risk to be hunted or poached and could therefore open new avenues for conservation biologists to assess population viability. We hope that this review, tutorial, and worked example will encourage movement ecologists to examine the biology of individual variation in animal movements hidden behind the population mean.

As global agricultural expansion continues, there is little choice but to incorporate modified landscapes into biodiversity conservation efforts. Increasingly available animal movement data can be leveraged to identify important habitat features and assess population connectivity in fragmented landscapes. We use the shingleback lizard (Tiliqua rugosa) as a case study to demonstrate the utility of GPS-telemetry data to inform conservation decisions. We investigated habitat selection at the fine scale (movement step selection), broad scale (home range selection) and simulated movement trajectories to assess landscape-scale movement. We show that patches of remnant vegetation are focal points for fine-scale movement, as well as for potential population connectivity in the broader landscape. However, shingleback lizards also used edge habitats like paddock edges and roadsides, perhaps utilising altered thermoregulatory opportunities, foraging conditions, or resource subsidies. These results underscore the importance of remnant vegetation patches for sustaining movement and gene flow in modified landscapes. Our study demonstrates how movement ecology, including simulated movement trajectories, can inform conservation strategies to mitigate the impacts of habitat destruction and fragmentation on wildlife populations.