Michael McLeish, Adrián Peláez, Israel Pagán, Rosario Gavilán, Aurora Fraile, Fernando García-Arenal
BACKGROUND: Plant communities of fragmented agricultural landscapes, are subject to patch isolation and scale-dependent effects. Variation in configuration, composition, and distance from one another affect biological processes of disturbance, productivity, and the movement ecology of species. However, connectivity and spatial structuring among these diverse communities are rarely considered together in the investigation of biological processes. Spatially optimised predictor variables that are based on informed measures of connectivity among communities, offer a solution to untangling multiple processes that drive biodiversity.
RESULTS: To address the gap between theory and practice, a novel spatial optimisation method that incorporates hypotheses of community connectivity, was used to estimate the scale of effect of biotic and abiotic factors that distinguish plant communities. We tested: (1) whether different hypotheses of connectivity among sites was important to measuring diversity and environmental variation among plant communities; and (2) whether spatially optimised variables of species relative abundance and the abiotic environment among communities were consistent with diversity parameters in distinguishing four habitat types; namely Crop, Edge, Oak, and Wasteland. The global estimates of spatial autocorrelation, which did not consider environmental variation among sites, indicated significant positive autocorrelation under four hypotheses of landscape connectivity. The spatially optimised approach indicated significant positive and negative autocorrelation of species relative abundance at fine and broad scales, which depended on the measure of connectivity and environmental variation among sites.
CONCLUSIONS: These findings showed that variation in community diversity parameters does not necessarily correspond to underlying spatial structuring of species relative abundance. The technique used to generate spatially-optimised predictors is extendible to incorporate multiple variables of interest along with a priori hypotheses of landscape connectivity. Spatially-optimised variables with appropriate definitions of connectivity might be better than diversity parameters in explaining functional differences among communities.
