The evaluation strip: A new and robust method for plotting predicted responses from species distribution models |
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| Affiliation: | 1. School of Botany, The University of Melbourne, Parkville, Melbourne, Victoria 3010, Australia;2. New South Wales Department of Environment and Conservation, PO Box 402, Armidale, NSW 2350, Australia;3. Institute of Arctic Biology, Dept. of Biology and Wildlife, 419 IRVING 1 – EWHALE lab-University of Alaska-Fairbanks, Fairbanks, AK 99775, USA;4. National Institute of Water and Atmospheric Research, PO Box 11115, Hamilton, New Zealand;1. Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University, 007 Agriculture Hall, Stillwater, OK 74078, USA;2. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, PR China;3. Department of Biosystems and Agriculture Engineering, Oklahoma State University, 117 Agriculture Hall, Stillwater, OK, 74078, USA;4. Biological Systems Engineering Department, University of Nebraska-Lincoln, 245 L.W. Chase Hall, Lincoln, NE 68583, USA;5. U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife Research Unit, 007 Agriculture Hall, Oklahoma State University, Stillwater, OK 74078, USA;1. Department of Entomology and Plant Pathology, University of Tennessee, 2505 E. J. Chapman Drive, Knoxville, TN, 37996, United States;2. Department of Ecology and Evolutionary Biology, University of Tennessee, 569 Dabney Hall Knoxville, TN 37996, United States;3. National Institute for Mathematical and Biological Synthesis, 1122 Volunteer Boulevard, University of Tennessee, Knoxville, TN 37996, United States;4. Department of Geography, University of Tennessee, Burchfiel Geography Bldg. 1000 Phillip Fulmer Way, Knoxville, TN 37996, United States;5. Department of Agriculture, Nutrition and Veterinary Sciences, University of Nevada, Reno, 1664 North Virginia Street, Reno, NV 89557, United States;1. Pacific Northwest Research Station, U.S. Forest Service, Corvallis, OR, USA;2. Department of Botany, University of Wyoming, Laramie, WY, USA;3. Department of Environmental Sciences, Section of Conservation Biology, University of Basel, Switzerland;1. Department of Forestry, School of Agriculture and Forestry, University of Córdoba, Laboratory of Dendrochronology, DendrodatLab-ERSAF Edf. Leonardo da Vinci, Campus de Rabanales s/n, 14071 Córdoba, Spain;2. Department of Natural Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500 AE Enschede, The Netherlands;3. Department of Systematic Botany and Functional Biodiversity, Institute of Biology, University of Leipzig, Johannisallee 21–23, 04103 Leipzig, Germany;1. Center for Conservation and Center for Conservation and Sustainable Development, Missouri Botanical Garden, Saint Louis, MO 63116, USA;2. BioProtection Research Centre, Burns Building, Lincoln University, Lincoln 7647, Canterbury, New Zealand;3. Department of Integrative Ecology, Estación Biológica de Doñana, Consejo Superior de Investigaciones Científicas, 41092 Sevilla, Spain;4. Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX 77843, USA;5. Senckenberg Biodiversity and Climate Research Center (SBiK-F), Frankfurt am Main, Germany;6. Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology, Okinawa, Japan;1. Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain;2. Department of Ecology & Evolution, University of Lausanne, Biophore Building, 1015 Lausanne, Switzerland;3. Departmento de Botánica, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain;4. Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark;5. Department of Integrative Biology, University of California—Berkeley, 3040 VLSB, Berkeley, CA 94720, USA |
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| Abstract: | Increasing use is being made in conservation management of statistical models that couple extensive collections of species and environmental data to make predictions of the geographic distributions of species. While the relationships fitted between a species and its environment are relatively transparent for many of these modeling techniques, others are more ‘black box’ in character, only producing geographic predictions and providing minimal or untraditional summaries of the fitted relationships on which these predictions are based. This in turn prevents robust evaluation of the ecological sensibility of such models, a necessary process if model predictions are to be treated with confidence. Here we propose a new but simple method for visualizing modeled responses that can be implemented with any modeling method, and demonstrate its application using five common methods applied to the prediction of an Australian tree species. This is achieved by insetting an “evaluation strip” into the spatial data layers, which, after predictions have been made, can be clipped out and used for creating plots of the modelled responses. We present findings of the application strip for algorithms GLMs, GAMs, CLIM, DOMAIN and MARS. Evaluation strips can be constructed to investigate either uni-variate responses, or the simultaneous variation in predicted values in relation to two variables. The latter option is particularly useful for evaluating responses in models that allow the fitting of complex interaction terms. |
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