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Temperature drives local contributions to beta diversity in mountain streams: Stochastic and deterministic processes
by
Soininen, Janne
, Zhou, Jizhong
, Shen, Ji
, Wang, Jianjun
, Graham, Emily
, Baselga, ed., Andres
, Stegen, James
, Legendre, Pierre
, Yeh, Chih‐Fu
, Casamayor, Emilio O.
, Pan, Feiyan
2019
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Temperature drives local contributions to beta diversity in mountain streams: Stochastic and deterministic processes
by
Soininen, Janne
, Zhou, Jizhong
, Shen, Ji
, Wang, Jianjun
, Graham, Emily
, Baselga, ed., Andres
, Stegen, James
, Legendre, Pierre
, Yeh, Chih‐Fu
, Casamayor, Emilio O.
, Pan, Feiyan
in
2019
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Do you wish to request the book?
Temperature drives local contributions to beta diversity in mountain streams: Stochastic and deterministic processes
by
Soininen, Janne
, Zhou, Jizhong
, Shen, Ji
, Wang, Jianjun
, Graham, Emily
, Baselga, ed., Andres
, Stegen, James
, Legendre, Pierre
, Yeh, Chih‐Fu
, Casamayor, Emilio O.
, Pan, Feiyan
2019
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Temperature drives local contributions to beta diversity in mountain streams: Stochastic and deterministic processes
Journal Article
Temperature drives local contributions to beta diversity in mountain streams: Stochastic and deterministic processes
2019
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Overview
Abstract
Aim
Community variation (i.e. beta diversity) along geographical gradients is of substantial interest in ecology and biodiversity reserves in the face of global changes. However, the generality in beta diversity patterns and underlying processes remains less studied across trophic levels and geographical regions. We documented beta diversity patterns and underlying ecological processes of stream bacteria, diatoms and macroinvertebrates along six elevational gradients.
Locations
Asia and Europe.
Methods
We examined stream communities using molecular and morphological methods. We characterised community uniqueness with local contributions to beta diversity (LCBD), and investigated the drivers of its geographic patterns using Mid‐Domain Effect (MDE), coenocline simulation, Raup‐Crick null model approach, and through comparisons to environmental factors. MDE is a stochastic model by considering species elevational range, while coenocline simulation is a deterministic model by considering species niche optima and tolerance. The null model provides possible underlying mechanisms of community assembly with the degree to which deterministic processes create communities deviating from those of null expectations.
Results
Across all taxa, we revealed a general U‐shaped LCBD‐elevation relationship, suggesting higher uniqueness of community composition at both elevational ends. This pattern was confirmed and could be explained by both stochastic and deterministic models, that is, MDE and coenocline simulation, respectively, and was supported by the dominance of species replacement. Temperature was the main environmental factor underlying elevational patterns in LCBD. The generalists with broad niche breadths were key in maintaining community uniqueness, and the higher relative importance of deterministic processes resulted in stronger U‐shaped patterns regardless of taxonomic group.
Conclusions
Our synthesis across both mountains and taxonomic groups clearly shows that there are consistent elevational patterns in LCBD among taxonomic groups, and that these patterns are explained by similar ecological mechanisms, producing a more complete picture for understanding and bridging the spatial variation in biodiversity under changing climate.
Publisher
Wiley-Blackwell
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