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Cited by 51

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Applying network theory to animal movements to identify properties of landscape space use

Bastille‐Rousseau, Guillaume, Douglas‐Hamilton, Iain, Blake, Stephen, Northrup, Joseph M., Wittemyer, George·2018·Ecological Applications
DOI 10.1002/eap.1697
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Abstract

Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node‐level (local) metrics and graph‐level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node‐level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter‐patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph‐level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large‐scale data sets efficiently, providing a framework for conservationists to analyze movement data. Functions created for the analyses are available within the R package move NT.

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Crossref · 10.1002/eap.1697

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Metadata

Authors
Bastille‐Rousseau, G., Douglas‐Hamilton, I., Blake, S., Northrup, J. M., & Wittemyer, G.
Affiliations
  • Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado 80523 USA
  • Save the Elephants Nairobi Kenya
  • Max Planck Institute for Ornithology Radolfzell Germany
  • Wildlife Research and Monitoring Section Ontario Ministry of Natural Resources and Forestry Peterborough ON Canada K9L 1Z8
ORCID
Bastille‐Rousseau, Guillaume
Venue
Ecological Applications
Publisher
Wiley
Published
2018-04
Volume
28
Issue
3
Pages
854-864
ISSN
  • 1051-0761
  • 1939-5582
DOI
10.1002/eap.1697
Cited by
51
References
53
Language
en
Licence
Funders
  • Colorado Parks and Wildlife
  • National Science Foundation (DEB 1258062)
  • Fundación Charles Darwin
  • National Geographic Society
  • Nature Conservancy of Canada
  • Natural Sciences and Engineering Research Council of Canada

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