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Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the 'spatial-social interface' as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial-social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial-social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial-social interface., (© 2023 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.)

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases., (© 2018 John Wiley & Sons Ltd/CNRS.)

Movement is important to all organisms, and accordingly it is addressed in a huge number of papers in the literature. Of nearly 26,000 papers referring to movement, an estimated 34% focused on movement by measuring it or testing hypotheses about it. This enormous amount of information is difficult to review and highlights the need to assess the collective completeness of movement studies and identify gaps. We surveyed 1,000 randomly selected papers from 496 journals and compared the facets of movement studied with a suggested framework for movement ecology, consisting of internal state (motivation, physiology), motion and navigation capacities, and external factors (both the physical environment and living organisms), and links among these components. Most studies simply measured and described the movement of organisms without reference to ecological or internal factors, and the most frequently studied part of the framework was the link between external factors and motion capacity. Few studies looked at the effects on movement of navigation capacity, or internal state, and those were mainly from vertebrates. For invertebrates and plants most studies were at the population level, whereas more vertebrate studies were conducted at the individual level. Consideration of only population-level averages promulgates neglect of between-individual variation in movement, potentially hindering the study of factors controlling movement. Terminology was found to be inconsistent among taxa and subdisciplines. The gaps identified in coverage of movement studies highlight research areas that should be addressed to fully understand the ecology of movement.

Social relationships among animals emerge from interactions in multiple ecological and social situations. However, we seldom ask how each situation contributes to the global structure of a population, and whether different situations contribute different information about social relationships and the position of individuals within the social fabric. Griffon vultures (Gyps fulvus) interact socially in multiple situations, including communal roosting, joint flights, and co-feeding. These social interactions can influence population-level outcomes, such as disease transmission and information sharing that determine survival and response to changes. We examined the unique contribution of each social and ecological situation to the social structure of the population and individuals' positions within the overall social network using high-resolution GPS tracking. We found that the number of individuals each vulture interacted with (degree) was best predicted by diurnal interactions-both during flights and on the ground (such as when feeding). However, the strength of social bonds, that is, the number of interactions an individual had (strength), was best predicted by interactions on the ground-both during the day (e.g., while feeding) and at night (e.g., while roosting) but not by interactions while flying. Thus, social situations differ in their impact on the relationships that individuals form. By incorporating the ecological situations in which social interactions occur we gain a more complete view of how social relationships are formed and which situations are important for different types of interactions.

BackgroundMovement is central to understanding the ecology of animals. The most robustly definable segments of an individual's lifetime track are its diel activity routines (DARs). This robustness is due to fixed start and end points set by a 24-h clock that depends on the individual's quotidian schedule. An analysis of day-to-day variation in the DARs of individuals, their comparisons among individuals, and the questions that can be asked, particularly in the context of lunar and annual cycles, depends on the relocation frequency and spatial accuracy of movement data. Here we present methods for categorizing the geometry of DARs for high frequency (seconds to minutes) movement data.MethodsOur method involves an initial categorization of DARs using data pooled across all individuals. We approached this categorization using a Ward clustering algorithm that employs four scalar "whole-path metrics" of trajectory geometry: 1. net displacement (distance between start and end points), 2. maximum displacement from start point, 3. maximum diameter, and 4. maximum width. We illustrate the general approach using reverse-GPS data obtained from 44 barn owls, Tyto alba, in north-eastern Israel. We conducted a principle components analysis (PCA) to obtain a factor, PC1, that essentially captures the scale of movement. We then used a generalized linear mixed model with PC1 as the dependent variable to assess the effects of age and sex on movement.ResultsWe clustered 6230 individual DARs into 7 categories representing different shapes and scale of the owls nightly routines. Five categories based on size and elongation were classified as closed (i.e. returning to the same roost), one as partially open (returning to a nearby roost) and one as fully open (leaving for another region). Our PCA revealed that the DAR scale factor, PC1, accounted for 86.5% of the existing variation. It also showed that PC2 captures the openness of the DAR and accounted for another 8.4% of the variation. We also constructed spatio-temporal distributions of DAR types for individuals and groups of individuals aggregated by age, sex, and seasonal quadrimester, as well as identify some idiosyncratic behavior of individuals within family groups in relation to location. Finally, we showed in two ways that DARs were significantly larger in young than adults and in males than females.ConclusionOur study offers a new method for using high-frequency movement data to classify animal diel movement routines. Insights into the types and distributions of the geometric shape and size of DARs in populations may well prove to be more invaluable for predicting the space-use response of individuals and populations to climate and land-use changes than other currently used movement track methods of analysis.

Animal movements are important drivers of nutrient redistribution that can affect primary productivity and biodiversity across various spatial scales. Recent work indicates that incorporating these movements into ecosystem models can enhance our ability to predict the spatio-temporal distribution of nutrients. However, the role of animal behaviour in animal-mediated nutrient transport (i.e. active subsidies) remains under-explored. Here we review the current literature on active subsidies to show how the behaviour of active subsidy agents makes them both ecologically important and qualitatively distinct from abiotic processes (i.e. passive subsidies). We first propose that animal movement patterns can create similar ecological effects (i.e. press and pulse disturbances) in recipient ecosystems, which can be equal in magnitude to or greater than those of passive subsidies. We then highlight three key behavioural features distinguishing active subsidies. First, organisms can transport nutrients counter-directionally to abiotic forces and potential energy gradients (e.g. upstream). Second, unlike passive subsidies, organisms respond to the patterns of nutrients that they generate. Third, animal agents interact with each other. The latter two features can form positive- or negative-feedback loops, creating patterns in space or time that can reinforce nutrient hotspots in places of mass aggregations and/or create lasting impacts within ecosystems. Because human-driven changes can affect both the space-use of active subsidy species and their composition at both population (i.e. individual variation) and community levels (i.e. species interactions), predicting patterns in nutrient flows under future modified environmental conditions depends on understanding the behavioural mechanisms that underlie active subsidies and variation among agents' contributions. We conclude by advocating for the integration of animal behaviour, animal movement data, and individual variation into future conservation efforts in order to provide more accurate and realistic assessments of changing ecosystem function.

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviors, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesize the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology, and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions expanding our understanding of host-pathogen dynamics, and improving our predictive capacity for wildlife and even human diseases.

Animal movements exhibit an almost universal pattern of fat-tailed step-size distributions, mixing short and very long steps. The Lévy flight foraging hypothesis (LFFH) suggests a single optimal food search strategy to explain this pattern, yet mixed movement distributions are biologically more plausible and often convincingly fit movement data. To confront alternative explanations for these patterns, we tracked vultures of three species in two very different ecosystems using high-resolution global positioning system/accelerometer tags accompanied by behavioral, genetic, and morphological data. The Lévy distribution fitted the data sets reasonably well, matching expectations based on their sparsely distributed food resources; yet the fit of mixed models was considerably better, suggesting distinct movement modes operating at three different scales. Specifically, long-range forays (LRFs)—rare, short-term, large-scale circular journeys that greatly exceed the typical foraging range and contribute to the tail-fatness of the movement distribution in all three species— do not match an optimal foraging strategy suggested by the LFFH. We also found no support for preferred weather conditions or population genetic structure as alternative explanations, so the hypothesis that LRFs represent failed breeding dispersal attempts to find mates remains our most plausible explanation at this time. We conclude that inference about the mechanisms underlying animal movements should be confronted with complementary data, and suggest that mixed behavioral modes likely explain commonly observed fat-tailed movement distributions.

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Natal dispersal, the movement of an organism from its birthplace to the site of first reproduction, is fundamental to many ecological and evolutionary processes. Mechanistically, individual dispersal decisions can depend on both individual phenotype and environmental cues. In particular, many established evolutionary theories of dispersal highlight the importance of the social environment. More recent research in behavioral ecology has focused on the importance of individual behavioral phenotypes. We reviewed the literature on individual behavioral phenotypes and dispersal and suggest that how individual behavioral phenotypes interact with the immediate social environment experienced by individuals in influencing dispersal is still poorly understood, despite growing interest. We found that very few studies had examined the interaction of individual behavioral phenotypes and social factors, and behavioral phenotypes related to social tendencies were less commonly measured than were behavioral phenotypes related to exploration or response to risk. Further, and unsurprisingly, studies on social behavioral phenotypes and dispersal behaviors during the transience stage of dispersal were underrepresented compared to the departure or settlement stages. Future studies in this area should aim to: a) make explicit links between behavioral traits and their proposed effects on dispersal decisions throughout multiple stages of dispersal, b) integrate more continuous dispersal variables, and c) consider the effects of the spatial distribution and phenotypes of conspecifics (i.e., the social landscape) encountered by individual dispersers. [ABSTRACT FROM AUTHOR]