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Geographical Ecology (geographical + ecology)
Selected AbstractsCoefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regressionECOGRAPHY, Issue 2 2009L. Mauricio Bini 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. [source] Red herrings remain in geographical ecology: a reply to Hawkins et al. (2007)ECOGRAPHY, Issue 6 2007Colin M. Beale No abstract is available for this article. [source] Measurements of area and the (island) species,area relationship: new directions for an old patternOIKOS, Issue 10 2008Kostas A. Triantis The species,area relationship is one of the strongest empirical generalizations in geographical ecology, yet controversy persists about some important questions concerning its causality and application. Here, using more accurate measures of island surface size for five different island systems, we show that increasing the accuracy of the estimation of area has negligible impact on the fit and form of the species,area relationship, even though our analyses included some of the most topographically diverse island groups in the world. In addition, we show that the inclusion of general measurements of environmental heterogeneity (in the form of the so-called choros model), can substantially improve the descriptive power of models of island species number. We suggest that quantification of other variables, apart from area, that are also critical for the establishment of biodiversity and at the same time have high explanatory power (such as island age, distance, productivity, energy, and environmental heterogeneity), is necessary if we are to build up a more predictive science of species richness variation across island systems. [source] | ||||||||||