Understanding processes underlying cross-taxon congruence in species composition along elevational gradients

Abstract

Changes in species diversity of different taxa along environmental gradients are usually correlated, resulting in a pattern called cross-taxon congruence. This pattern can be due to functional relationships between taxa, a common response to niche-related processes, or stochastic processes. However, it remains unclear the extent to which they contribute to the association among patterns of changes in species composition, (i.e., beta diversity), and whether these changes are related to species nestedness and turnover. Here we described patterns of change in the taxonomic composition of plant and orthopteran assemblages along an elevational gradient in Cordoba province, central Argentina. We assessed cross-taxon congruence and identified the main environmental variables accounting for such patterns. Mantel correlations showed congruence between the patterns of taxonomic dissimilarity of plants and orthopterans. According Generalized disiimilarity models (GDM) the main environmental variables driving the patterns were temperature for both taxa, and changes in soil nutrient content for plants, spatial effects were also found. Beta diversity was mainly due to species turnover for orthopterans and plants, indicating replacement by species adapted to elevational conditions. Niche-related process, such as environmetal filtering, along with neutral processes may have contributed to cross-taxon congruence in beta diversity.

Fig. 1

Elevational dissimilarity patterns of orthopteran and plant species composition for the mountains.

Fig. 2

Orthopteran observed dissimilarity vs GDM-predicted ecological distance, points are each site pair, the line indicating the model predicted dissimilarity (left) and Observed dissimilarity vs. GDM-predicted dissimilarity, featuring the line of equality for reference (right). (a) Model with temperature dissimilarity and geographic distance; (b) model with temperature dissimilarity.

Fig. 3

Orthopteran GDM fitted spline functions for the predictor variables included in the model with lowest AIC. On the x-axis, variables are in their native scales and on the y-axis, the transformed GDM values. The overall importance of each predictor is indicated by the maximum value of the spline function (y-axis). Steeper slopes indicate greater dissimilarity per unit change in the predictor variable.

Fig. 4

Plant observed dissimilarity vs GDM-predicted ecological distance, points are each site pair, the line indicating the model predicted dissimilarity (left) and Observed dissimilarity vs. GDM-predicted dissimilarity, featuring the line of equality for reference (right). (a) Model with soil nutrient component dissimilarity; (b) Model with soil nutrient component dissimilarity and geographic distance; (c) model with temperature dissimilarity.

Fig. 5

Plants GDM fitted spline functions for the predictor variables included in the models with lowest AIC. On the x-axis, variables are in their native scales and on the y-axis, the transformed GDM values. The overall importance of each predictor is indicated by the maximum value of the spline function (y-axis). Steeper slopes indicate greater dissimilarity per unit change in the predictor variable.