Ponds are among the most diverse and yet threatened components of freshwater biodiversity. The conservation of ponds would greatly benefit from the identification of surrogate taxa in preliminary assessments aimed at detecting ponds of potentially high biodiversity value. Here, we used predictive co-correspondence analysis (Co-CA) to quantify the strength of plant species composition and plant community types in predicting multivariate patterns in water beetle assemblages, based on data from 54 farmland ponds in Ireland. The predictive accuracy of a number of environmental variables as well as that of plant diversity (species richness and evenness) was calculated using predictive canonical correspondence analysis (CCA-PLS). The study ponds supported over 30% of the Irish water beetle fauna (76 species), with five species having some form of IUCN Red List Status in Ireland, as well as 67 wetland plant species, including a nationally rare one. Co-CA showed that plant species composition had a positive predictive accuracy, which was significantly higher compared to that of data at the plant community type level. Although environmental variables showed a higher predictive capacity compared to that of plant species composition, the difference was not significant. Explanatory CCA analyses showed that plants and beetles both responded to the same subset of environmental conditions, which explained approximately 18% of the variation in both plant and beetle species composition. Regional differences as well as permanency, substratum, and grazing intensity affected the composition of both plant and beetle assemblages. These findings have important implications in conservation planning. First, wetland plants can be effectively used as a surrogate taxon in the identification of conservation-priority ponds. Second, conservation strategies aimed at maintaining and enhancing pond biodiversity should be based on considerations on plant species composition.
The conservation value of farmland ponds: Predicting water beetle assemblages using vascular plants as a surrogate group
BACARO, Giovanni;
2010-01-01
Abstract
Ponds are among the most diverse and yet threatened components of freshwater biodiversity. The conservation of ponds would greatly benefit from the identification of surrogate taxa in preliminary assessments aimed at detecting ponds of potentially high biodiversity value. Here, we used predictive co-correspondence analysis (Co-CA) to quantify the strength of plant species composition and plant community types in predicting multivariate patterns in water beetle assemblages, based on data from 54 farmland ponds in Ireland. The predictive accuracy of a number of environmental variables as well as that of plant diversity (species richness and evenness) was calculated using predictive canonical correspondence analysis (CCA-PLS). The study ponds supported over 30% of the Irish water beetle fauna (76 species), with five species having some form of IUCN Red List Status in Ireland, as well as 67 wetland plant species, including a nationally rare one. Co-CA showed that plant species composition had a positive predictive accuracy, which was significantly higher compared to that of data at the plant community type level. Although environmental variables showed a higher predictive capacity compared to that of plant species composition, the difference was not significant. Explanatory CCA analyses showed that plants and beetles both responded to the same subset of environmental conditions, which explained approximately 18% of the variation in both plant and beetle species composition. Regional differences as well as permanency, substratum, and grazing intensity affected the composition of both plant and beetle assemblages. These findings have important implications in conservation planning. First, wetland plants can be effectively used as a surrogate taxon in the identification of conservation-priority ponds. Second, conservation strategies aimed at maintaining and enhancing pond biodiversity should be based on considerations on plant species composition.Pubblicazioni consigliate
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