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Implications of ignoring telemetry error on inference in wildlife resource use models

Authors

  • Robert A. Montgomery,

    Corresponding author
    1. Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources Building, East Lansing, MI 48824-1222, USA
    • Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources Building, East Lansing, MI 48824-1222, USA.
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  • Gary J. Roloff,

    1. Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources Building, East Lansing, MI 48824-1222, USA
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  • Jay M. Ver Hoef

    1. NOAA National Marine Mammal Laboratory, NMFS Alaska Fisheries Science Center, Fairbanks, AK 99775-7345, USA
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  • Associate Editor: Scott M. McCorquodale.

Abstract

Global Positioning System (GPS) and very high frequency (VHF) telemetry data redefined the examination of wildlife resource use. Researchers collar animals, relocate those animals over time, and utilize the estimated locations to infer resource use and build predictive models. Precision of these estimated wildlife locations, however, influences the reliability of point-based models with accuracy depending on the interaction between mean telemetry error and how habitat characteristics are mapped (categorical raster resolution and patch size). Telemetry data often foster the assumption that locational error can be ignored without biasing study results. We evaluated the effects of mean telemetry error and categorical raster resolution on the correct characterization of patch use when locational error is ignored. We found that our ability to accurately attribute patch type to an estimated telemetry location improved nonlinearly as patch size increased and mean telemetry error decreased. Furthermore, the exact shape of these relationships was directly influenced by categorical raster resolution. Accuracy ranged from 100% (200-ha patch size, 1- to 5-m telemetry error) to 46% (0.5-ha patch size, 56- to 60-m telemetry error) for 10 m resolution rasters. Accuracy ranged from 99% (200-ha patch size, 1- to 5-m telemetry error) to 57% (0.5-ha patch size, 56- to 60-m telemetry error) for 30-m resolution rasters. When covariate rasters were less resolute (30 m vs. 10 m) estimates for the ignore technique were more accurate at smaller patch sizes. Hence, both fine resolution (10 m) covariate rasters and small patch sizes increased probability of patch misidentification. Our results help frame the scope of ecological inference made from point-based wildlife resource use models. For instance, to make ecological inferences with 90% accuracy at small patch sizes (≤5 ha) mean telemetry error ≤5 m is required for 10-m resolution categorical rasters. To achieve the same inference on 30-m resolution categorical rasters, mean telemetry error ≤10 m is required. We encourage wildlife professionals creating point-based models to assess whether reasonable estimates of resource use can be expected given their telemetry error, covariate raster resolution, and range of patch sizes. © 2011 The Wildlife Society.

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