Detecting and modeling spatial and temporal dependence in conservation biology

Due to the structuring forces and large-scale physical processes that shape our biosphere, we often find that environmental and ecological data are ei ther spatially or temporally-or both spatially and temporally-dependent. Wh en these data are analyzed statistical techniques and models are frequently applied that were developed for independent data. We describe some of the detrimental consequences, such as inefficient parameter estimators, biased hypothesis test results, and inaccurate predictions, of ignoring spatial an d temporal data dependencies, and we cite an example of adverse statistical results occurring when spatial dependencies were disregarded. We also disc uss and recommend available techniques used to detect and model spatial and temporal dependence including variograms, covariograms, autocorrelation an d partial autocorrelation plots, geostatistical techniques, Gaussian autore gressive models K functions, and ARIMA models, in environmental and ecologi cal research to avoid the aforementioned difficulties.