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The exploratory analysis of autocorrelation in animal‐movement studies

Dray, Stéphane, Royer‐Carenzi, Manuela, Calenge, Clément·2010·Ecological Research
DOI 10.1007/s11284-010-0701-7
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Abstract

Studies of animal movements have been popularized for many large and shy species by the increasing use of radio telemetry methods (VHF and GPS technologies). Data are collected with high sampling frequency, and consist of successive observations of the position of an individual animal. The statistical analysis of such data poses several problems due to the lack of independence of successive observations. However, the statistical description of the temporal autocorrelation between successive steps is rarely performed by ecologists studying the patterns of animals movements. The aim of this paper is to warn ecologists against the consequences of failing to consider this aspect. We discuss the various issues related to analyzing autocorrelated data, and show how the exploratory analysis of autocorrelation can both reveal important biological insights and help to improve the accuracy of movement models. We suggest some tools that can be used to measure, test, and adjust for temporal autocorrelation. A short ecological illustration is presented.

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  • Autocorrelation, spatial scale of movement, and mortality risk have well-established and fundamental importance in shaping movement patterns.

    “For example, these models do not account for autocorrelation, spatial scale of movement, or mortality risk, despite the well-established and fundamental importance these factors have in shaping movement patterns5,35–37.”

    From Connectivity modelling in conservation science: a comparative evaluation (2022) · cites this paper as [35]

  • These models do not account for autocorrelation, spatial scale of movement, or mortality risk.

    “For example, these models do not account for autocorrelation, spatial scale of movement, or mortality risk, despite the well-established and fundamental importance these factors have in shaping movement patterns5,35–37.”

    From Connectivity modelling in conservation science: a comparative evaluation (2022) · cites this paper as [35]

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Metadata

Authors
Dray, S., Royer‐Carenzi, M., & Calenge, C.
Affiliations
  • Laboratoire de Biométrie et Biologie Evolutive Université de Lyon, Université Lyon 1, CNRS, UMR 5558 43 boulevard du 11 novembre 1918 Villeurbanne France
  • Evolution Biologique et Modélisation, case 5 Université de Provence, LATP, CNRS‐UMR 6632 Place Victor Hugo Marseille Cedex 3 13331 France
  • Direction des études et de la recherche Office national de la chasse et de la faune sauvage Saint Benoist Auffargis 78610 France
Venue
Ecological Research
Publisher
Wiley
Published
2010-03-09
Volume
25
Issue
3
Pages
673-681
ISSN
  • 0912-3814
  • 1440-1703
DOI
10.1007/s11284-010-0701-7
Cited by
70
References
0
Language
en
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)

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