Detecting and modeling spatial and temporal dependence in conservation biology

S. S. Carroll, David Pearson

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Due to the structuring forces and large-scale physical processes that shape our biosphere, we often find that environmental and ecological data are either spatially or temporally - or both spatially and temporally - dependent. When 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 and temporal data dependencies, and we cite an example of adverse statistical results occurring when spatial dependencies were disregarded. We also discuss and recommend available techniques used to detect and model spatial and temporal dependence, including variograms, covariograms, autocorrelation and partial autocorrelation plots, geostatistical techniques, Gaussian autoregressive models, K functions, and ARIMA models, in environmental and ecological research to avoid the aforementioned difficulties.

Original languageEnglish (US)
Pages (from-to)1893-1897
Number of pages5
JournalConservation Biology
Volume14
Issue number6
DOIs
StatePublished - 2000

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Nature and Landscape Conservation

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