Open AccessCoefficient shifts in geographical ecology: an empirical evaluation of spatial and non‐spatial regression
Author(s) -
Mauricio Bini L.,
DinizFilho J. Alexandre F.,
Rangel Thiago F. L. V. B.,
Akre Thomas S. B.,
Albaladejo Rafael G.,
Albuquerque Fabio S.,
Aparicio Abelardo,
Araújo Miguel B.,
Baselga Andrés,
Beck Jan,
Isabel Bellocq M.,
BöhningGaese Katrin,
Borges Paulo A. V.,
CastroParga Isabel,
Khen Chey Vun,
Chown Steven L.,
De Marco, Jr Paulo,
Dobkin David S.,
FerrerCastán Dolores,
Field Richard,
Filloy Julieta,
Fleishman Erica,
Gómez Jose F.,
Hortal Joaquín,
Iverson John B.,
Kerr Jeremy T.,
Daniel Kissling W.,
Kitching Ian J.,
LeónCortés Jorge L.,
Lobo Jorge M.,
Montoya Daniel,
MoralesCastilla Ignacio,
Moreno Juan C.,
Oberdorff Thierry,
OlallaTárraga Miguel Á.,
Pausas Juli G.,
Qian Hong,
Rahbek Carsten,
Rodríguez Miguel Á.,
Rueda Marta,
Ruggiero Adriana,
Sackmann Paula,
Sanders Nathan J.,
Carina Terribile Levi,
Vetaas Ole R.,
Hawkins Bradford A.
Publication year - 2009
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/j.1600-0587.2009.05717.x
Subject(s) - spatial analysis , ordinary least squares , statistics , spatial ecology , ecology , macroecology , autocorrelation , regression , linear regression , regression analysis , species richness , spatial variability , range (aeronautics) , mathematics , econometrics , biology , materials science , composite material
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.