A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.
| Title: | A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data. |
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| Authors: | Kaur P; School of Mathematics and Statistics, University College Dublin, Dublin, Ireland. prabhleen.kaur.ucd@gmail.com.; Ciuti S; Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.; Ossi F; Animal Ecology Unit, Research and Innovation Center (CRI), Fondazione Edmund Mach, San Michele all'Adige, Italy.; NBFC, National Biodiversity Future Center, 90133, Palermo, Italy.; Cagnacci F; Animal Ecology Unit, Research and Innovation Center (CRI), Fondazione Edmund Mach, San Michele all'Adige, Italy.; NBFC, National Biodiversity Future Center, 90133, Palermo, Italy.; Morellet N; INRAE, CEFS, Université de Toulouse, Castanet-Tolosan, 31326, France.; LTSER ZA PYRénées GARonne, Auzeville-Tolosane, 31320, France.; Loison A; Alpine Ecology Laboratory, Savoie Mont Blanc University, Chambéry, France.; Atmeh K; Biometrics and Evolutionary Biology Laboratory, Claude Bernard University Lyon 1, Lyon, France.; McLoughlin P; Department of Biology, University of Saskatchewan, Saskatoon, Canada.; Reinking AK; Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, USA.; Department of Ecosystem Science and Management, University of Wyoming, Laramie, USA.; Graduate Degree Program in Ecology, Colorado State University, Fort Collins, USA.; Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, USA.; Beck JL; Department of Ecosystem Science and Management, University of Wyoming, Laramie, USA.; Ortega AC; Program in Ecology, University of Wyoming, Laramie, WY, 82071, USA.; Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, Laramie, USA.; Kauffman M; U.S. Geological Survey, Wyoming Cooperative Fish and Wildlife Research Unit, Laramie, USA.; Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, Laramie, USA.; Boyce MS; Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2R3, Canada.; Haigh A; Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.; David A; Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.; Griffin LL; Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.; Conteddu K; Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.; Faull J; Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.; Salter-Townshend M; School of Mathematics and Statistics, University College Dublin, Dublin, Ireland. michael.salter-townshend@ucd.ie. |
| Source: | Movement ecology [Mov Ecol] 2024 Aug 06; Vol. 12 (1), pp. 55. Date of Electronic Publication: 2024 Aug 06. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101635009 Publication Model: Electronic Cited Medium: Print ISSN: 2051-3933 (Print) Linking ISSN: 20513933 NLM ISO Abbreviation: Mov Ecol Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: London : BioMed Central, 2013- |
| Abstract: | Background: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored.; Methods: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where INLINEMATH of the individuals have been directly monitored.; Results: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples.; Conclusions: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.; (© 2024. The Author(s).) |
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| Grant Information: | 18/CRT/6049 Ireland SFI_ Science Foundation Ireland |
| Contributed Indexing: | Keywords: Bootstrapping; Correlation; GPS-based radiotelemetry; Network metrics; Permutations; Social network analysis; Sub-sampling; Uncertainty |
| Entry Date(s): | Date Created: 20240806 Latest Revision: 20240809 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11304672 |
| DOI: | 10.1186/s40462-024-00494-6 |
| PMID: | 39107862 |
| Database: | MEDLINE |
Journal Article