A guide for studying among-individual behavioral variation from movement data in the wild

Abstract

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.

Early-life behaviour predicts first-year survival in a long-distance avian migrant

“…Nevertheless, the activity measured here inherently reflects the joint contribution of both intrinsic and extrinsic factors and thus, our referrals to animal personalities are made with caution and should be taken as potential hypotheses. Assessing animal personality from movement data collected from free-ranging animals is a challenging, emerging paradigm that has been recently promoted and demonstrated by linking the fields of animal personality and movement ecology [56][57][58]. We believe that our findings emphasize both the need for and potential merit in future studies integrating these fields towards a better understanding of behaviour and survival in the wild.…”

Biologging reveals individual variation in behavioural predictability in the wild

“…We here show that movement data can reveal significant individual variation in behavioural predictability as they record behaviour of many members of a population over long monitoring durations (Hertel et al, 2020) offering the opportunity to receive numerous repeated measures, a prerequisite when studying predictability (Cleasby et al., 2015). This finding is important given that a key assumption of mixed models, the common analytical approach in animal personality studies, is that individuals are homogenous in variance around their individual means (Cleasby et al., 2015; Schielzeth et al., 2020).…”

Seasonal niche tracking of climate emerges at the population level in a migratory bird

“…Additionally, we found high within-individual compared to between-individual variation repeatability in seasonal niche overlap and niche tracking for both climate and weather and independent of the migratory flyway (figure 4). The high individual plasticity might be the source of emergent patterns at the population level, which can be the signature of adaptive mechanisms [21]. Individual variation might result from fluctuations in the internal state of individuals (energy level [44], health status [45], experience [46]) or extrinsic factors experienced by them [43] (e.g.…”

“…tracking patterns [21]. Additionally, to account for the fact that ecological and environmental processes are scale-dependent -(figure 1b), we estimate seasonal niche overlap and tracking both in terms of climate (long-term average over greater than 15 years) and weather data (defined as the fine-scale conditions over a short period; less than 20 days).…”

“…between eastern and western migrants) and within populations. Knowledge of the presence or absence of such variation is important to understand the potential mechanisms behind the seasonal niche tracking patterns [ 21 ]. Additionally, to account for the fact that ecological and environmental processes are scale-dependent ( figure 1 b ), we estimate seasonal niche overlap and tracking both in terms of climate (long-term average over greater than 15 years) and weather data (defined as the fine-scale conditions over a short period; less than 20 days).…”