Pteridophyte richness, climate and topography in the Iberian Peninsula: comparing spatial and nonspatial models of richness patterns
Article first published online: 12 NOV 2004
Global Ecology and Biogeography
Volume 14, Issue 2, pages 155–165, March 2005
How to Cite
Ferrer-Castán, D. and Vetaas, O. R. (2005), Pteridophyte richness, climate and topography in the Iberian Peninsula: comparing spatial and nonspatial models of richness patterns. Global Ecology and Biogeography, 14: 155–165. doi: 10.1111/j.1466-822X.2004.00140.x
- Issue published online: 26 JAN 2005
- Article first published online: 12 NOV 2004
- generalized least squares models;
- geographical grid system;
- mid-domain effect;
- spatial autocorrelation;
- variance partitioning
Aim To describe the spatial variation in pteridophyte species richness; evaluate the importance of macroclimate, topography and within-grid cell range variables; assess the influence of spatial autocorrelation on the significance of the variables; and to test the prediction of the mid-domain effect.
Location The Iberian Peninsula.
Methods We estimated pteridophyte richness on a grid map with c. 2500 km2 cell size, using published geocoded data of the individual species. Environmental data were obtained by superimposing the grid system over isoline maps of precipitation, temperature, and altitude. Mean and range values were calculated for each cell. Pteridophyte richness was related to the environmental variables by means of nonspatial and spatial generalized least squares models. We also used ordinary least squares regression, where a variance partitioning was performed to partial out the spatial component, i.e. latitude and longitude. Coastal and central cells were compared to test the mid-domain effect.
Results Both spatial and nonspatial models showed that pteridophyte richness was best explained by a second-order polynomial of mean annual precipitation and a quadratic elevation-range term, although the relative importance of these two variables varied when spatial autocorrelation was accounted for. Precipitation range was weakly significant in a nonspatial multiple model (i.e. ordinary regression), and did not remain significant in spatial models. Richness is significantly higher along the coast than in the centre of the peninsula.
Main conclusions Spatial autocorrelation affects the statistical significance of explanatory variables, but this did not change the biological interpretation of precipitation and elevation range as the main predictors of pteridophyte richness. Spatial and nonspatial models gave very similar results, which reinforce the idea that water availability and topographic relief control species richness in relatively high-energy regions. The prediction of the mid-domain effect is falsified.