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Finally, we also believe that methodological diversity is important, both as a way of broadening the base of evidence that our journal publishes, but also as a way of broadening the perspectives on personality psychology that are represented. Thus, we are open to research that contributes to our understanding of personality processes and individual differences using a broad range of approaches including research that links personality psychology with theories and methodological approaches from other disciplines.
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The first aim of this paper is to provide a literature review of studies that used variance partitioning to study different aspects of individual variation in movement behaviors. Indeed, variance partitioning has already been used to study individual variation in foraging behavior of marine mammals and birds [36,37,38], in fish activity and movements [39,40,41,42], in movement and habitat selection of terrestrial mammals [27, 43,44,45,46], and in partial migration strategies of bats, birds, fish, and mammals [47,48,49,50]. Despite this growing body of literature, research concentrating on individual variation from movement data has developed quite isolated within the different animal taxa and with this systematic review we hope to facilitate a synthesis of existing efforts.
Possible relationships between behavioral types (among-individual variation in intercepts) and behavioral plasticity (non-zero slopes) along environmental gradients for mobile (a, b, c) and stationary (d, e, f) species. Mobile species may be exposed to a wider gradient of environmental conditions than range resident species, allowing to disentangle behavioral plasticity and behavioral types more easily. Individuals may adjust their behavior plastically to the local environment while not differing in behavioral type (a & d). Behavioral differences exist when individuals are not observed over the same environmental gradient (d). Alternatively, individual differences may fully account for behavioral differences with no behavioral plasticity to environmental conditions (b & e). In stationary species this may lead to non-random distribution of behavioral types when individuals choose habitats which match their behavior (e). Most likely, individual differences and behavioral plasticity to environmental conditions jointly contribute to observed behavioral differences (c & f). This figure has been adapted from Sprau and Dingemanse [107] and Niemelä and Dingemanse [26]
Ultimately, the relative contribution of genetic versus permanent environmental effects on among-individual variation can only be assessed by quantifying heritability of movement behaviors [99, 101, 125]. We here gave recommendations how empiricists can disentangle individual variation from reversible behavioral plasticity [26]. We want to stress that results from studies that cannot control for all important co-variates that cause reversible behavioral plasticity are still meaningful as long as possible limitations on study conclusions are appropriately addressed.
While data with a high resolution and long duration over many individuals in a population become increasingly available, this traditionally tends to present a major financial and ethical challenge for movement ecologists. However, not in all cases all conditions (long duration, high resolution, many individuals) need to be met. For example, questions related to annual breeding dispersal distance and breeding site fidelity, or migration patterns require multiannual time series allowing to model individual level variation in behavior over long time spans (Table 1). Estimating individual differences of such broad scale movement patterns on the other hand only requires long GPS fix intervals at the scale of days or even months. At the other extreme we may need short GPS fix intervals to quantify individual variation in the degree of area restricted search behavior (ARS), the duration an animal keeps searching for food in a food patch before giving up and leaving the patch, in particular when habitat patches are small relative to the movement capacity of the species (Table 1). Individuals can vary in their behavioral type from exploratory ones with low ARS to less exploratory ones with high ARS [52]. To study such individual variation in ARS, every foraging patch encounter which results in ARS could be classified as a repeated measure and individual variation in ARS could be already assessed after a few days of data collection (depending on how many patches are encountered per day). When it comes to analyzing whether individuals differ in their plasticity towards the environment (Fig. 1b), the monitoring duration needed is obviously dependent on the temporal scales over which environmental conditions change. Importantly, the behavior of every individual should be measured repeatedly in each context (or over the environmental gradient) in order to account for individual variation in plasticity [98]. For example, variation in diel plasticity of lake depth use warrant repeated dive depth measures at day and night over several days for every individual [42], whereas questions related to e.g. seasonal variation in diel depth use warrant longer monitoring times, respectively [42]. Finally, if we are asking questions whether individuals differ in their behavioral development over age, or whether they differentially use (experiential or social) learning, (spatial) memory [127], exploration, and behavioral innovation [54, 127, 128, 133] to navigate in space we need long durations of monitoring, ideally over the entire lifespan of the animal.
Last, movement data are often collected at different sampling rates across individuals and data may be collected from a mix of older and newer GPS tag models leading to differences in precision and error rates. Especially when tracing individual differences in movement, a careful cleaning of the data is indispensable to avoid the erroneous detection of individual differences caused by measurement error.
We showed how variance partitioning approaches developed in behavioral ecology can be brought to movement ecology and applied to movement data to study not only reversible but also intrinsic variation among individuals. We highlighted the three different forms of among-individual variation formulated in behavioral ecology and their covariance and reviewed the current evidence for such variation in movement behavior. We also discussed ways to disentangle intrinsic individual variation from behavioral plasticity and the inherent limitations to control for all relevant aspects of the environment in the wild. Studying among-individual variation in movement can facilitate ecologically and evolutionary meaningful research. For example the POLS hypothesis (see paragraph on behavioral syndromes) predicts that individuals within populations differ in suites of traits along a fast-slow continuum where fast individuals exhibit faster growth rates and invest in early reproduction at the cost of lower survival as compared to slow individuals [106]. The lower survival of individuals investing into early reproduction is assumed to be mediated by the expression of risk enhancing behaviors which facilitate resource acquisition at the expense of survival [106, 142, 143]. Individual variation in movement may therefore translate into individual variation in resource acquisition, body mass, reproductive output per attempt, and survival [143]. Another example concerns how animal populations will be able to adapt to landscape change. On a global scale, landscape fragmentation and anthropogenic features have recently been shown to restrict animal movements (on a population level) across terrestrial mammals [144]. However, behavioral ecologists predict that individuals may differ in their ability to cope with landscape change and hence how they move through these landscapes; some individuals are expected to move more easily through our modernized landscape [69]. They may for example be quicker to use anthropogenic features developed to aid connectivity, like road crossing structures [145,146,147]. Variation is the key ingredient for selection and evolution and recent evidence for the heritability of movement traits [101] suggests that behaviorally diverse animal populations have the potential to adapt to the challenges of the Anthropocene within a few generations. Movement data is ideal for testing such predictions in elusive wildlife by adopting the theory and statistical tools (i.e. variance partitioning) from behavioral ecology and quantitative genetics [148, 149]. This would bind movement ecology more tightly into an evolutionary ecological framework.
Increasing amounts of movement data from various taxa and species are being collected and deposited into standardized databases, like the Movebank Data Repository ( ) or EUROMAMMALS ( ) to promote collaborative science examining general patterns beyond local populations. This offers unique opportunities to study the extent of individual variation in movement behavior across ecosystems and species. Recognizing individuals with specific movement patters, and how sensitive they are to environmental variation, might further be valuable, for example, when making decisions related to animal conservation. Certain behavioral types may for example be better suited for animal translocations, or cope better with landscape fragmentation and urbanization.
We here show that movement data are a promising data source to reveal individual differences in the behavior of wildlife. To date this individual variation is however rarely systematically analyzed or even considered as biologically interesting phenomenon. Using statistical tools from the modern behavioral ecology literature we demonstrate how partitioning behavioral variance into its among- and within-individual sources can give new insights about the biology hidden behind the population mean trait expression. Individual differences in movement are important because it means that individuals differ in how they move through the landscape and their likelihood to encounter conspecifics, prey or landscape features. These may have important consequences for ecology and evolution of movement behaviors. Ignoring individual differences in movement, while solely interpreting population level effects, may in the worst case misrepresent true underlying mechanisms in how animals move.
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