Publication

No single driver of biodiversity: Divergent responses of multiple taxa across land use types

Date
2017-11-17
Type
Journal Article
Abstract
Understanding the responses of biodiversity to different land use regimes is critical for managing biodiversity in the face of future land use change. However, there is still significant uncertainty around how consistent the responses of different taxonomic groups to land use change are. Here, we use a combination of high‐throughput environmental DNA sequencing and traditional field‐based survey methods to examine how patterns of richness and community composition correlate among four domains/kingdoms (bacteria, fungi, plants, and metazoans) and the four most‐abundant animal taxonomic groups (arachnids, Collembola, insects, and nematodes) across five different land use types (natural forest, planted forest, unimproved grassland, improved grassland, and vineyards). Richness for each taxonomic group varied between land use types, yet different taxa showed inconsistent responses to land use, and their richness was rarely correlated. This contrasted with community composition of taxonomic groups, for which there was relatively good discrimination of land use types and there was strong correlation between group responses. We found little evidence for consistent drivers of taxonomic richness, yet identified several significant drivers of community composition that were shared across many groups. Drivers of composition were not the same as the drivers of diversity, suggesting diversity and composition are independently controlled. While land use intensification has been viewed as having generally negative effects on biodiversity, our results provide evidence that different taxa respond divergently across different land uses. Further, our study demonstrates the power of high‐throughput sequencing of environmental DNA as a tool for addressing broad ecological patterns relating to landscape biodiversity.
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© 2017 Wood et al. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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