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HERO ID
8042257
Reference Type
Journal Article
Title
Context dependency in biodiversity patterns of central German stream metacommunities
Author(s)
Tonkin, JD; Heino, J; Sundermann, A; Haase, P; Jaehnig, SC
Year
2016
Is Peer Reviewed?
Yes
Journal
Freshwater Biology
ISSN:
0046-5070
EISSN:
1365-2427
Volume
61
Issue
5
Page Numbers
607-620
DOI
10.1111/fwb.12728
Web of Science Id
WOS:000373833400004
Abstract
Context dependency is an emerging topic that is challenging our understanding of the factors shaping biodiversity in metacommunities. River networks and other dendritic systems provide unique systems for examining variation in the processes shaping biodiversity between different metacommunities. We examined biodiversity patterns in five benthic invertebrate data sets, from two catchments in central Germany, with the aim of exploring context dependency in these systems. We used variance partitioning to disentangle the variation explained in three biodiversity metrics: taxonomic richness, Simpson's diversity and local contribution to beta diversity (LCBD; a measure of the uniqueness of a site). As explanatory variables, we used proxies of network position (i.e. catchment size and altitude) and habitat conditions. Contrary to our expectation, we found no evidence of a decline in LCBD downstream in our study. Local habitat conditions and catchment land use played a much stronger role than catchment size and altitude in explaining variation in the three biodiversity metrics. Observed patterns were highly variable between different data sets in our study. These findings suggest that factors shaping biodiversity patterns in these systems are highly context dependent and less related to their position along the river network than local habitat conditions. Given the clear context dependency between data sets, we urge researchers to focus on disentangling the factors driving the high levels of variability between individual systems through the study of a number of replicate, rather than single, metacommunities.
Keywords
benthic invertebrates; beta diversity; local contribution to beta diversity; river network; variance partitioning
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