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. Description Animal tracking in a big data world So-called “big-data” approaches have revolutionized fields of research from astronomy to genetics. Such approaches are not limited to fields that seem inherently technical, because the combination of rapid data collection and advanced analytical techniques could be applied to almost any scientific question. Nathan et al. reviewed how these modern approaches are being applied to the very old field of animal tracking and monitoring. Large-scale data collection can reveal details about how animals use their environment and interact with each other that were impossible to explore previously. Such methodological shifts will open new avenues of research—and conservation—across species. —SNV A review suggests that modern “big-data” techniques are vastly increasing our understanding of animal movement and its ecology. BACKGROUND Movement is ubiquitous across the natural world. All organisms move, actively or passively, regularly or during specific life stages, as a result of varied proximate drivers including energetic demands, social interactions, competition or predation. Movement closely interacts with individual fitness, affects a myriad of ecological processes, and is crucial for animals’ ability to cope with human-induced rapid environmental changes. Driven by advances in analytical methods and technologies for tracking mammals, birds, fish, and other free-ranging vertebrates (hereafter, wildlife), movement ecology is rapidly transforming into a data-rich discipline, following previous developments in fields such as genomics and environmental monitoring. This ongoing revolution is facilitated by cost-effective automated high-throughput wildlife tracking systems that generate massive high-resolution datasets across scales relevant to the ecological context in which animals perceive, interact with, and respond to their environment. ADVANCES Modern tracking technologies efficiently generate copious, accurate information on the movements of multiple individual animals in the wild. Reverse-GPS technologies, which primarily use acoustic signals under water and radio signals over land, are automated high-throughput systems that are highly cost- and power-effective and capable of simultaneous tracking of multiple small animals (e.g., 20-g birds) at high spatiotemporal resolution (e.g., 1-s interval, a few meters) for months, but they require system installation and are usually limited to regional scales (≤100 km wide). GPS-based systems are, by contrast, readily available, longer term, and cover nearly global scales, but are similarly spatially accurate and periodically capable of high-resolution tracking at regional scales. However, they are more cost- and power-demanding, limited to larger animals, and cannot be applied under water. Two other tracking technologies, radar and computer vision, permit high-resolution snapshots of the movement of multiple individuals and can noninvasively track nontagged animals, but are less cost-effective, usually limited to smaller scales, and make individual identification challenging. Combined, these high-throughput technologies enable groundbreaking research in animal behavior, cognitive sciences, evolution, and ecology, facilitating previously infeasible investigation of animal movement ecology. Big movement data can help link interindividual variation in movement to individual behavior, traits, cognition and physiology; divulge fine-scale interactions within or among species; improve evidence-based management of human-wildlife interactions; and elucidate behavioral changes across spatiotemporal scales. OUTLOOK High-throughput wildlife tracking technologies are opening new research frontiers in biology and ecology. Their advantages, however, come with typical big-data costs such as computational load, intensive data management and processing, and challenging statistical analyses. Enlisting fields with a longer history of big data offers new prospects to address these challenges. Progress will arise from combining observational and experimental movement ecology and data-rich studies revealing behavioral shifts across individuals, species, scales, ecosystems, and life stages. High-resolution wildlife tracking is currently infeasible at large to global scales, a key limitation that can be addressed by combining low- and high-rate sampling, increasing interoperability between technologies, standardizing and sharing data, and promoting multidisciplinary international collaboration. Coupling movement and environmental big data could help determine impacts of major environmental and climate changes on animal–environment interactions, whereas real-time movement data could uniquely inform biodiversity conservation and ecosystem management. Why do high-throughput movement data matter? Big movement data are essential for addressing key ecological questions, as conclusions based on traditional lower-resolution data could differ markedly from the correct conclusions. We illustrate several examples for contrasting conclusions derived from lower- versus higher-resolution data of the same tracks from the same number of animals. Higher-resolution data can reveal that bolder birds visit more sites across the landscape and that bird tracks frequently cross each other, suggesting high potential for disease transmission, and that fish avoid fisheries and frequently search locally within small patches. None of these conclusions, however, could have been drawn from lower-resolution data. See also movies S1 to S5.

https://www.science.org/doi/pdf/10.1126/science.abg1780?download=true

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

To address the challenges of biodiversity conservation and commodity production, a framework has been proposed that distinguishes between the integration (“land sharing”) and separation (“land sparing”) of conservation and production. Controversy has arisen around this framework partly because many scholars have focused specifically on food production rather than more encompassing notions such as land scarcity or food security. Controversy further surrounds the practical value of partial trade‐off analyses, the ways in which biodiversity should be quantified, and a series of scale effects that are not readily accounted for. We see key priorities for the future in (1) addressing these issues when using the existing framework, and (2) developing alternative, holistic ways to conceptualise challenges related to food, biodiversity, and land scarcity.

Land sparing versus land sharing: Moving forward

Abstract Reversing biodiversity declines requires a better understanding of organismal mobility, as movement processes dictate the scale at which species interact with the environment. Previous studies have demonstrated that species foraging ranges, and therefore, habitat use increases with body size. Yet, foraging ranges are also affected by other life‐history traits, such as sociality, which influence the need of and ability to detect resources. We evaluated the effect of body size and sociality on potential and realized foraging ranges using a compiled dataset of 383 measurements for 81 bee species. Potential ranges were larger than realized ranges and increased more steeply with body size. Highly eusocial species had larger realized foraging ranges than primitively eusocial or solitary taxa. We contend that potential ranges describe species movement capabilities, whereas realized ranges depict how foraging movements result from interactions between species traits and environmental conditions. Furthermore, the complex communication strategies and large colony sizes in highly eusocial species may facilitate foraging over wider areas in response to resource depletion. Our findings should contribute to a greater understanding of landscape ecology and conservation, as traits that influence movement mediate species vulnerability to habitat loss and fragmentation.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ecy.3809

The potential and realized foraging movements of bees are differentially determined by body size and sociality

Despite being widely used, habitat selection models are rarely reliable and informative when applied across different ecosystems or over time. One possible explanation is that habitat selection is context-dependent due to variation in consumer density and/or resource availability. The goal of this paper is to provide a general theoretical perspective on the contributory mechanisms of consumer and resource density-dependent habitat selection, as well as on our capacity to account for their effects. Towards this goal we revisit the ideal free distribution (IFD), where consumers are assumed to be omniscient, equally competitive and freely moving, and are hence expected to instantaneously distribute themselves across a heterogeneous landscape such that fitness is equalised across the population. Although these assumptions are clearly unrealistic to some degree, the simplicity of the structure in IFD provides a useful theoretical vantage point to help clarify our understanding of more complex spatial processes. Of equal importance, IFD assumptions are compatible with the assumptions underlying common habitat selection models. Here we show how a fitness-maximising space use model, based on IFD, gives rise to resource and consumer density-dependent shifts in consumer distribution, providing a mechanistic explanation for the context-dependent outcomes often reported in habitat selection analysis. Our model suggests that adaptive shifts in consumer distribution patterns would be expected to lead to nonlinear and often non-monotonic patterns of habitat selection. These results indicate that even under the simplest of assumptions about adaptive organismal behaviour, habitat selection strength should critically depend on system-wide characteristics. Clarifying the impact of adaptive behavioural responses may be pivotal in making meaningful ecological inferences about observed patterns of habitat selection and allow reliable transferability of habitat selection predictions across time and space.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2656.13352

Habitat selection patterns are density-dependent under the Ideal Free Distribution.

The biodiversity-ecosystem functioning concept asserts that processes in ecosystems are markedly influenced by species richness and other facets of biodiversity. However, biodiversity-ecosystem functioning studies have been largely restricted to single ecosystems, ignoring the importance of functional links - such as the exchange of matter, energy, and organisms - between coupled ecosystems. Here we present a basic concept and outline three pathways of cross-boundary biodiversity effects on ecosystem processes and propose an agenda to assess such effects, focusing on terrestrial-aquatic linkages to illustrate the case. This cross-boundary perspective of biodiversity-ecosystem functioning relationships presents a promising frontier for biodiversity and ecosystem science with repercussions for the conservation, restoration, and management of biodiversity and ecosystems from local to landscape scales. 

Pathways for cross-boundary effects of biodiversity on ecosystem functioning.

Organismal movement is ubiquitous and facilitates important ecological mechanisms that drive community and metacommunity composition and hence biodiversity. In most existing ecological theories and models in biodiversity research, movement is represented simplistically, ignoring the behavioural basis of movement and consequently the variation in behaviour at species and individual levels. However, as human endeavours modify climate and land use, the behavioural processes of organisms in response to these changes, including movement, become critical to understanding the resulting biodiversity loss. Here, we draw together research from different subdisciplines in ecology to understand the impact of individual-level movement processes on community-level patterns in species composition and coexistence. We join the movement ecology framework with the key concepts from metacommunity theory, community assembly and modern coexistence theory using the idea of micro-macro links, where various aspects of emergent movement behaviour scale up to local and regional patterns in species mobility and mobile-link-generated patterns in abiotic and biotic environmental conditions. These in turn influence both individual movement and, at ecological timescales, mechanisms such as dispersal limitation, environmental filtering, and niche partitioning. We conclude by highlighting challenges to and promising future avenues for data generation, data analysis and complementary modelling approaches and provide a brief outlook on how a new behaviour-based view on movement becomes important in understanding the responses of communities under ongoing environmental change.

Movement-mediated community assembly and coexistence.

Biotelemetry is increasingly used to study animal movement at high spatial and temporal resolution and guide conservation and resource management. Yet, limited sample sizes and variation in space and habitat use across regions and life stages may compromise robustness of behavioral analyses and subsequent conservation plans. Here, we assessed variation in (i) home range sizes, (ii) home range selection, and (iii) fine-scale resource selection of white storks across breeding status and regions and test model transferability. Three study areas were chosen within the Central German breeding grounds ranging from agricultural to fluvial and marshland. We monitored GPS-locations of 62 adult white storks equipped with solar-charged GPS/3D-acceleration (ACC) transmitters in 2013-2014. Home range sizes were estimated using minimum convex polygons. Generalized linear mixed models were used to assess home range selection and fine-scale resource selection by relating the home ranges and foraging sites to Corine habitat variables and normalized difference vegetation index in a presence/pseudo-absence design. We found strong variation in home range sizes across breeding stages with significantly larger home ranges in non-breeding compared to breeding white storks, but variation between regions. Home range selection models had high explanatory power and well predicted overall density of Central German white stork breeding pairs. Also, they showed good transferability across regions and breeding status although variable importance varied considerably. Fine-scale resource selection models showed low explanatory power. Resource preferences differed both across breeding status and across regions, and model transferability was poor. Our results indicate that habitat selection of wild animals may vary considerably within and between populations, and is highly scale dependent. Thereby, home range scale analyses show higher robustness whereas fine-scale resource selection is not easily predictable and not transferable across life stages and regions. Such variation may compromise management decisions when based on data of limited sample size or limited regional coverage. We thus recommend home range scale analyses and sampling designs that cover diverse regional landscapes and ensure robust estimates of habitat suitability to conserve wild animal populations.

Home range size and resource use of breeding and non-breeding white storks along a land use gradient

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Movement-mediated community assembly and coexistence.

Home range size and resource use of breeding and non-breeding white storks along a land use gradient