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 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.
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Format: | Texto biblioteca |
Language: | eng |
Subjects: | Ecología, Geografía, Distribución espacial, Análisis espacial (Estadística), Cambio medioambiental global, |
Online Access: | https://doi.org/10.1111/j.1600-0587.2009.05717.x |
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Ecología Geografía Distribución espacial Análisis espacial (Estadística) Cambio medioambiental global Ecología Geografía Distribución espacial Análisis espacial (Estadística) Cambio medioambiental global |
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Ecología Geografía Distribución espacial Análisis espacial (Estadística) Cambio medioambiental global Ecología Geografía Distribución espacial Análisis espacial (Estadística) Cambio medioambiental global Bini, L. Mauricio autor Diniz Filho, J. Alexandre F. autor Akre, Thomas S. B. autor Albaladejo, Rafael G. autor Albuquerque, Fabio S. autor Aparicio, Abelardo autor Araújo, Miguel B. autor Baselga, Andrés autor Beck, Jan autor Bellocq, M. Isabel autora Castro Parga, Isabel autora Chown, Steven L. autor Marco, Paulo de autor Dobkin, David S. autor Ferrer Castán, Dolores autora Field, Richard autor Filloy, Julieta autora Fleishman, Erica autora Gómez, Jose F. autor Iverson, John B. autor Kerr, Jeremy T. autor Kissling, W. Daniel autor Kitching, Ian J. autor León Cortés, Jorge Leonel Doctor autor 7292 Lobo, Jorge M. autor Montoya, Daniel autor Morales Castilla, Ignacio autor Moreno, Juan C. autor Coefficient shifts in geographical ecology an empirical evaluation of spatial and non-spatial regression |
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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. |
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Ecología Geografía Distribución espacial Análisis espacial (Estadística) Cambio medioambiental global |
author |
Bini, L. Mauricio autor Diniz Filho, J. Alexandre F. autor Akre, Thomas S. B. autor Albaladejo, Rafael G. autor Albuquerque, Fabio S. autor Aparicio, Abelardo autor Araújo, Miguel B. autor Baselga, Andrés autor Beck, Jan autor Bellocq, M. Isabel autora Castro Parga, Isabel autora Chown, Steven L. autor Marco, Paulo de autor Dobkin, David S. autor Ferrer Castán, Dolores autora Field, Richard autor Filloy, Julieta autora Fleishman, Erica autora Gómez, Jose F. autor Iverson, John B. autor Kerr, Jeremy T. autor Kissling, W. Daniel autor Kitching, Ian J. autor León Cortés, Jorge Leonel Doctor autor 7292 Lobo, Jorge M. autor Montoya, Daniel autor Morales Castilla, Ignacio autor Moreno, Juan C. autor |
author_facet |
Bini, L. Mauricio autor Diniz Filho, J. Alexandre F. autor Akre, Thomas S. B. autor Albaladejo, Rafael G. autor Albuquerque, Fabio S. autor Aparicio, Abelardo autor Araújo, Miguel B. autor Baselga, Andrés autor Beck, Jan autor Bellocq, M. Isabel autora Castro Parga, Isabel autora Chown, Steven L. autor Marco, Paulo de autor Dobkin, David S. autor Ferrer Castán, Dolores autora Field, Richard autor Filloy, Julieta autora Fleishman, Erica autora Gómez, Jose F. autor Iverson, John B. autor Kerr, Jeremy T. autor Kissling, W. Daniel autor Kitching, Ian J. autor León Cortés, Jorge Leonel Doctor autor 7292 Lobo, Jorge M. autor Montoya, Daniel autor Morales Castilla, Ignacio autor Moreno, Juan C. autor |
author_sort |
Bini, L. Mauricio autor |
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 |
url |
https://doi.org/10.1111/j.1600-0587.2009.05717.x |
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KOHA-OAI-ECOSUR:213272024-03-12T12:52:07ZCoefficient shifts in geographical ecology an empirical evaluation of spatial and non-spatial regression Bini, L. Mauricio autor Diniz Filho, J. Alexandre F. autor Akre, Thomas S. B. autor Albaladejo, Rafael G. autor Albuquerque, Fabio S. autor Aparicio, Abelardo autor Araújo, Miguel B. autor Baselga, Andrés autor Beck, Jan autor Bellocq, M. Isabel autora Castro Parga, Isabel autora Chown, Steven L. autor Marco, Paulo de autor Dobkin, David S. autor Ferrer Castán, Dolores autora Field, Richard autor Filloy, Julieta autora Fleishman, Erica autora Gómez, Jose F. autor Iverson, John B. autor Kerr, Jeremy T. autor Kissling, W. Daniel autor Kitching, Ian J. autor León Cortés, Jorge Leonel Doctor autor 7292 Lobo, Jorge M. autor Montoya, Daniel autor Morales Castilla, Ignacio autor Moreno, Juan C. autor textengA 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.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.EcologíaGeografíaDistribución espacialAnálisis espacial (Estadística)Cambio medioambiental globalEcographyhttps://doi.org/10.1111/j.1600-0587.2009.05717.xDisponible para usuarios de ECOSUR con su clave de acceso |