Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression
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...
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2009
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paper:paper_09067590_v32_n2_p193_Bini2023-06-08T15:49:52Z Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression Bellocq, Maria Isabel Filloy, Julieta Sackmann, Paula abundance body size data set least squares method macroecology range size regression analysis species richness 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. Fil:Bellocq, M.I. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Filloy, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Sackmann, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09067590_v32_n2_p193_Bini http://hdl.handle.net/20.500.12110/paper_09067590_v32_n2_p193_Bini |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
abundance body size data set least squares method macroecology range size regression analysis species richness |
| spellingShingle |
abundance body size data set least squares method macroecology range size regression analysis species richness Bellocq, Maria Isabel Filloy, Julieta Sackmann, Paula Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression |
| topic_facet |
abundance body size data set least squares method macroecology range size regression analysis species richness |
| description |
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. |
| author |
Bellocq, Maria Isabel Filloy, Julieta Sackmann, Paula |
| author_facet |
Bellocq, Maria Isabel Filloy, Julieta Sackmann, Paula |
| author_sort |
Bellocq, Maria Isabel |
| title |
Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression |
| title_short |
Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression |
| title_full |
Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression |
| title_fullStr |
Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression |
| title_full_unstemmed |
Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression |
| title_sort |
coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression |
| publishDate |
2009 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09067590_v32_n2_p193_Bini http://hdl.handle.net/20.500.12110/paper_09067590_v32_n2_p193_Bini |
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