Local spatial modeling of white-tailed deer distribution |
| |
| Institution: | 1. Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824, USA;2. Department of Forestry, 126 Natural Resources Building, Michigan State University, East Lansing, MI 48824, USA;3. Department of Agricultural Economics, 213 Agriculture Hall, Michigan State University, East Lansing, MI 48824, USA;4. Michigan Department of Natural Resources, Stephens T. Mason Building, Lansing, MI 48909, USA;5. Michigan Department of Natural Resources, Norway, MI 49884, USA;1. Dipartimento di Science e Tecnologie per l’Agricoltura, le Foreste, la Natura e l’Energia,Università degli studi di Viterbo, Via S. Camillo de Lellis snc, Viterbo I-01100, Italy;2. Dipartimento di Matematica e Fisica, Università di Roma Tre, Via della Vasca Navale 84, Rome I-00146, Italy;3. Consiglio per la Ricerca e la sperimentazione in Agricoltura, Unità di ricerca per la Climatologia ela Meteorologia applicate all''Agricoltura, Via del Caravita 7a, Rome I-00186, Italy;4. Consiglio per la Ricerca e la sperimentazione in Agricoltura, Centro per le studio delle Relazioni Pianta-Suolo (CRA-RPS), Via della Navicella 2-4, Rome I-00184, Italy;1. Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA;2. U.S. Forest Service, Northern Research Station, 410 MacInnes Drive, Houghton, MI 49931, USA;1. School of Earth and Environmental Sciences, University of Wollongong, NSW 2522, Australia;2. Centre for Sustainable Ecosystem Solutions, University of Wollongong, NSW 2522, Australia;3. South East Local Land Services, NSW Government, 84 Crown Street, Wollongong, NSW 2500, Australia;4. Landcare Illawarra, Australia;5. Hawthorn Street, Tarrawanna, NSW 2518, Australia;6. Faculty of Science, Medicine and Health, University of Wollongong, NSW 2522, Australia;7. CSIRO Health and Biosecurity, GPO Box 1700, Canberra, Australian Capital Territory 2601, Australia;1. Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstraße 33, 80539 München, Germany;2. Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland;3. Department of Conservation and Research, Bavarian Forest National Park, Freyunger Straße 2, 94481 Grafenau, Germany;4. Chair for Terrestrial Ecology, Department of Ecology and Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany;5. Chair of Wildlife Ecology and Management, University of Freiburg, Faculty of Environment and Natural Resources, Tennenbacher Straße 4, 79106 Freiburg, Germany;1. Beijing Institute of Surveying and Mapping, Haidian, Beijing 100038, China;2. CSIRO, Waite Campus, Urrbrae, SA 5064, Australia |
| |
| Abstract: | Complex spatial heterogeneity of ecological systems is difficult to capture and interpret using global models alone. For this reason, recent attention has been paid to local spatial modeling techniques. We used one local modeling approach, geographically weighted regression (GWR), to investigate the effects of local spatial heterogeneity on multivariate relationships of white-tailed deer distribution using land cover patch metrics and climate factors. The results of these analyses quantify differences in the contributions of model parameters to estimates of deer density over space. A GWR model with local kernel bandwidth was compared to a GWR model with global kernel bandwidth and an ordinary least-squares regression (OLS) model with the same parameters to evaluate their relative abilities in modeling deer distributions. The results indicated that the GWR models predicted deer density better than the traditional ordinary least-squares model and also provided useful information regarding local environmental processes affecting deer distribution. GWR model comparisons showed that the local kernel bandwidth GWR model was more realistic than the global kernel bandwidth GWR model, as the latter exaggerated local spatial variation. The parameter estimates and model statistics (e.g., model R2) of the GWR models were mapped using geographic information systems (GIS) to illustrate local spatial variation in the regression relationship and to identify causes of large-scale model misspecifications and low estimation efficiencies. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
