نبذة مختصرة : In salt marsh ecology, various indicators, including environmental, biological, and anthropogenic factors, have been used to predict the patterns of plant species richness. The potential impact of spatial autocorrelation on this prediction, however, has yet to receive much attention. In this paper, two sets of regression models were developed to predict spatial patterns (in 2006) and temporal changes (from 2006 to 2012) of richness across selected tidal creeks at a Danish salt marsh: (1) traditional ordinary least squares (OLS) using soil and topographic parameters as independent variables and (2) spatial regressions in which spatial filters produced by spatial eigenvector mapping were included into the non-spatial OLS as additional independent variables. Such incorporation led to a general improvement of model outcomes, that is, increases in R-2 and decreases in both Akaike's information criterion and residual autocorrelation. Notably, only spatial filters were always significant independent variables for both the spatial and temporal dynamics of species richness. In contrast, no environmental variables were consistently significant because of the substantial reduction in their regression coefficients after spatial regression. These results imply that identifying the relevant indicators of richness patterns in salt marshes may be a much more complicated job than previously thought. By revealing the new and statistically more rigorous predictive power of these environmental (i.e., non-spatial) variables, the spatially explicit modeling employed in this paper will provide benefits to the literature on ecological indicators. ; OAIID:RECH_ACHV_DSTSH_NO:T201805891 ; RECH_ACHV_FG:RR00200001 ; ADJUST_YN: ; EMP_ID:A080562 ; CITE_RATE:3.983 ; DEPT_NM:지리학과 ; EMAIL:biogeokim@snu.ac.kr ; SCOPUS_YN:Y ; N ; 1
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