Disentangling the effects of land use and geo-climatic factors on diversity in European freshwater ecosystems. (January 2016)

• Record Type: Journal Article
• Authors: Feld, Christian K.; Birk, Sebastian; Eme, David; Gerisch, Michael; Hering, Daniel; Kernan, Martin; Maileht, Kairi; Mischke, Ute; Ott, Ingmar; Pletterbauer, Florian; Poikane, Sandra; Salgado, Jorge; Sayer, Carl D.; van Wichelen, Jeroen; Malard, Florian
• Is Part Of: Ecological indicators. Volume 60(2016)
• Page Start: 71
• Page End: 83
• Publication Date: 2016-01
• Subjects: Freshwater ecosystems -- Biodiversity -- Arable land -- Urban area -- Geo-climatic descriptors -- Variance partitioning; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2015.06.024
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Diversity in eleven organism groups across five aquatic ecosystems was quantified. Land use alone explained little variation in aquatic biodiversity. Geo-climatic (natural) descriptors explained significantly more variation. Land use interacted strongly with natural descriptors. Natural descriptor's effects were not driven by spatial gradients. Abstract: Land use effects are considered among the main stressors on freshwater biodiversity, with up to 80% of land in Europe under intensive use. Here, we address the impact of arable and urban landscapes on taxon richness, Shannon–Wiener diversity, taxon rareness and taxonomic distinctness of eleven organism groups encompassing vertebrates, invertebrates and plants, occurring in five freshwater ecosystem types across Europe: rivers, floodplains, lakes, ponds and groundwater. In addition, nine geo-climatic descriptors (e.g. latitude, longitude, precipitation) were used to disentangle land use effects from those of natural drivers of biodiversity. Using a variance partitioning scheme based on boosted regression trees and generalised linear regression modelling, we sought: (i) to partition the unique, shared and unexplained variation in the metrics explained by both groups of descriptor variables, (ii) to quantify the contribution of each descriptor variable to biodiversity variation in the most parsimonious regression model and (iii) to identify interactions of land use and natural descriptors. The variation in biodiversity uniquely described by land use was consistently low across both ecosystem types and organism groups. In contrast, geo-climatic descriptors uniquely, and jointly with land use, explained significantly more variance in all 39 biodiversity metrics tested. Regression models revealed significant interactions between geo-climatic descriptors and land use for a third of the models, with interactions accounting for up to 17% of the model's deviance. However, no consistent patterns were observed related to the type of biodiversity metric and organism group considered. Subdividing data according to the strongest geo-climatic gradient in each dataset aimed to reduce the strength of natural descriptors relative to land use. Although data sub-setting can highlight land use effects on freshwater biodiversity, sub-setting our data often failed to produce stronger land use effects. There was no increase in spatial congruence in the subsets, suggesting that the observed land use effects were not dependent on the spatial extent of the subsets. Our results confirm significant joint effects of, and interactions between, land use and natural environmental descriptors on freshwater biodiversity, across ecosystem types and organism groups. This has implications for biodiversity monitoring. First, the combined analysis of anthropogenic and natural descriptors is a prerequisite for the analysis of human threats to biodiversity. Second, geo-climatically, but not necessarily geographically more homogeneous datasets can help unmask the role of anthropogenic descriptors. And third, whole community-based biodiversity metrics (including taxon richness) are not ideal indicators of anthropogenic effects on biodiversity at broad scales.