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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.

SUBMITTER: Hertel AG

PROVIDER: S-EPMC7325061 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Hertel Anne G AG Niemelä Petri T PT Dingemanse Niels J NJ Mueller Thomas T

Movement ecology 20200629

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 par ...[more]

PMID: 32612837

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Project description:BackgroundMovement of animals directly affects individual fitness, yet fine spatial and temporal resolution movement behavior has been studied in relatively few small species, particularly in the tropics. Nectarivorous Hawaiian honeycreepers are believed to be highly mobile throughout the year, but their fine-scale movement patterns remain unknown. The movement behavior of these crucial pollinators has important implications for forest ecology, and for mortality from avian malaria (Plasmodium relictum), an introduced disease that does not occur in high-elevation forests where Hawaiian honeycreepers primarily breed.MethodsWe used an automated radio telemetry network to track the movement of two Hawaiian honeycreeper species, the 'apapane (Himatione sanguinea) and 'i'iwi (Drepanis coccinea). We collected high temporal and spatial resolution data across the annual cycle. We identified movement strategies using a multivariate analysis of movement metrics and assessed seasonal changes in movement behavior.ResultsBoth species exhibited multiple movement strategies including sedentary, central place foraging, commuting, and nomadism , and these movement strategies occurred simultaneously across the population. We observed a high degree of intraspecific variability at the individual and population level. The timing of the movement strategies corresponded well with regional bloom patterns of 'ōhi'a (Metrosideros polymorpha) the primary nectar source for the focal species. Birds made long-distance flights, including multi-day forays outside the tracking array, but exhibited a high degree of fidelity to a core use area, even in the non-breeding period. Both species visited elevations where avian malaria can occur but exhibited little seasonal change in elevation (< 150 m) and regularly returned to high-elevation roosts at night.ConclusionsThis study demonstrates the power of automated telemetry to study complex and fine-scale movement behaviors in rugged tropical environments. Our work reveals a system in which birds can track shifting resources using a diverse set of movement behaviors and can facultatively respond to environmental change. Importantly, fidelity to high-elevation roosting sites minimizes nocturnal exposure to avian malaria for far-ranging individuals and is thus a beneficial behavior that may be under high selection pressure.

Project description:BACKGROUND:Shifts in data sharing policy have increased researchers' access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations. METHODS:We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD. RESULTS:Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed. For our IPDMA it took 6.5?years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues. CONCLUSIONS:Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.

Project description:BackgroundOlder adults struggle to maintain newly initiated levels of physical activity (PA) or sedentary behavior (SB) and often regress to baseline levels over time. This is partly because health behavior theories that inform interventions rarely address how the changing contexts of daily life influence the processes regulating PA and SB or how those processes differ across the behavior change continuum. Few studies have focused on motivational processes that regulate the dynamic nature of PA and SB adoption and maintenance on microtimescales (ie, across minutes, hours, or days).ObjectiveThe overarching goal of Project Studying Maintenance and Adoption in Real Time (SMART) is to determine the motivational processes that regulate behavioral adoption versus maintenance over microtimescales, using a dual process framework combined with ecological momentary assessment and sensor-based monitoring of behavior. This paper describes the recruitment, enrollment, data collection, and analytics protocols for Project SMART.MethodsIn Project SMART, older adults engaging in at least 30 minutes of moderate-to-vigorous intensity PA per week complete 3 data collection periods over 1 year, with each data collection period lasting 14 days. Across each data collection period, participants wear an ActiGraph GT3X accelerometer (ActiGraph, LLC) on their nondominant waist and an ActivPAL micro4 accelerometer (PAL Technologies, Ltd) on their anterior thigh to measure PA and SB, respectively. Ecological momentary assessment questionnaires are randomly delivered via smartphone 10 times per day on 4 selected days in each data collection period and assess reflective processes (eg, evaluating one's efficacy and exerting self-control) and reactive processes (eg, contextual cues) within the dual process framework. At the beginning and end of each data collection period, participants complete a computer-based questionnaire to learn more about their typical motivation for PA and SB, physical and mental health, and life events over the course of the study.ResultsRecruitment and enrollment began in January 2021; enrollment in the first data collection period was completed by February 2022; and all participants completed their second and third data collection by July 2022 and December 2022, respectively. Data were collected from 202 older adults during the first data collection period, with approximate retention rates of 90.1% (n=182) during the second data collection period and 88.1% (n=178) during the third data collection period. Multilevel models and mixed-effects location scale modeling will be used to evaluate the study aims.ConclusionsProject SMART seeks to predict and model the adoption and maintenance of optimal levels of PA and SB among older adults. In turn, this will inform the future delivery of personalized intervention content under conditions where the content will be most effective to promote sustained behavior change among older adults.International registered report identifier (irrid)DERR1-10.2196/47320.

Dataset Information

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

Publications

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

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