Depto de Biologia Geral, ICB, Univ. Federal de Goias, CP 131, 74001-970 Goiania, GO, Brazil; Dept of Ecology and Evolutionary Biology, Univ. of Connecticut, Storrs, CT 06269, United States; Dept of Biological and Environmental Sciences, Longwood Univ., Farmville, VA 23909, United States; Depto de Biologia Vegetal y Ecologia, Univ. de Sevilla, c/Prof. Garcia Gonzalez no 2, ES-41012 Sevilla, Spain; Depto de Ecologia, Univ. de Alcala, ES-28871 Alcala de Henares, Madrid, Spain; Depto de Biodiversidad y Biologia Evolutiva, Museo Nacional de Ciencias Naturales (CSIC), ES-28006 Madrid, Spain; Dept of Environmental Sciences, Inst. of Biogeography, Univ. of Basel, St.Johanns-Vorstadt 10, CH-4056 Basel, Switzerland; Depto de Ecologia, Genetica y Evolucio, Facultad de Ciencias Exactas y Naturales, CONICET, Ciudad Universitaria Pab. 2, 1428 Buenos Aires, Argentina; Inst. fur Zoologie, Johannes Gutenberg-Univ. Mainz, Becherweg 13, DE-55099 Mainz, Germany; Depto de Ciencias Agrarias, Univ. dos Acores, CITA A (Azorean Biodiversity Group), Terra Cha, PT- 9700-851 Angra do Heroismo, Terceira, Acores, Portugal; Depto de Ecologia C/Darwin 2, Univ. Autonoma de Madrid, ES-28049 Madrid, Spain; Entomology Section, Forest Research Centre of Sabah, Sepilok, P.O. Box 1407, 90715 Sandakan, Malaysia; DST-NRF Centre of Excellence for Invasion Biology, Stellenbosch Univ., Private Bag XI, Matieland 7602, South Africa; High Desert Ecological Research Inst., 15 S.W. Colorado Ave., Bend, OR 97702, United States; Area de Ecologia, Facultad de Biologia, Univ. de Salamanca, ES-37007 Salamanca, Spain; School of Geography, Univ. of Nottingham, Nottingham NG7 2RD, United Kingdom; National Center for Ecological Analysis and Synthesis, 735 State St, Santa Barbara, CA 93101, United States; NERC Centre for Population Biology, Imperial College, Silwood Park, Ascot SL5 7PY, United Kingdom; Dept of Biology, Earlham College, Richmond, IN 47374, United States; Dept of Biology, Univ. of Ottawa, Ottawa, ON KIN 6N5, Canada; Dept of Entomology, Natural History Museum, Cromwell Road, London SW7 5BD, United Kingdom; Depto de Ecologia y Sistematica Terrestre, El Colegio de la Frontera Sur, Carr. Panamericana y Av. Periferico Sur s/n, San Cristobal de Chiapas 29290, Mexico; Depto de Biologia, Univ. Autonoma de Madrid, C/ Darwin 2, ES-28049 Madrid, Spain; IRD, DMPA, Museum National dHistoire Naturelle, 43 Rue Cuvier, FR-75005 Paris, France; Centro de Investigacion sobre Desertificacion (CIDE, CSIC), Apartado Oficial, ES-46470 Albal, Valencia, Spain; Research and Collections Center, Illinois State Museum, 1011 East Ash Street, Springfield, IL 62703, United States; Center for Macroecology, Dept of Biology, Univ. of Copenhagen, DK-2100 Copenhagen, Denmark; Laboratorio Ecotono, Centro Regional Universitario Bariloche, INIBIOMA-CONICET, Quintral 1250, 8400 Bariloche, Rio Negro, Argentina; Dept of Ecology and Evolutionary Biology, Univ. of Tennessee, Knoxville, TN 37996, United States; UNIFOB Global, Univ. of Bergen, NO-5015 Bergen, Norway; Dept of Ecology and Evolutionary Biology, Univ. of California, Irvine, CA 92697, United States
A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; "OLS models" hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation. © 2009 Ecography.