TY - JOUR
T1 - Local environmental, geo-climatic and spatial factors interact to drive community distributions and diversity patterns of stream benthic algae, macroinvertebrates and fishes in a large basin, Northeast China
AU - Zhou, Shuchan
AU - Wu, Naicheng
AU - Zhang, Min
AU - Peng, Wenqi
AU - He, Fengzhi
AU - Guo, Kun
AU - Yan, Shiyuan
AU - Zou, Yi
AU - Qu, Xiaodong
N1 - Funding Information:
This study was supported financially by the National Natural Science Foundation of China (No. 51779275 and 41671048), the National Key R & D Program of China, grant number 2017YFC0404506, and the IWHR Research and Development Support Program, grant number WE0145B532017. We thank Dr. Xuwang Yin, Dr. Weijing Kong, Dr. Dr. Yuan Zhang and other colleagues for their supports during the field campaigns. Two anonymous reviewers gave constructive comments. All data have been uploaded as online supporting information.
Funding Information:
This study was supported financially by the National Natural Science Foundation of China (No. 51779275 and 41671048 ), the National Key R & D Program of China, grant number 2017YFC0404506, and the IWHR Research and Development Support Program, grant number WE0145B532017. We thank Dr. Xuwang Yin, Dr. Weijing Kong, Dr. Dr. Yuan Zhang and other colleagues for their supports during the field campaigns. Two anonymous reviewers gave constructive comments.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/10
Y1 - 2020/10
N2 - Understanding processes and mechanisms driving patterns of species distribution and diversity is a vital theme in community ecology and conservation biology. There has been a continuous increase in studies focusing on diversity patterns in freshwater ecosystems during the last few decades. However, comparative studies of species distributions, diversity patterns and underlying processes across trophic levels remain limited. The unique characteristics of the study area (i.e. Hun-Tai River in Northeast China) generates a wide range of environmental conditions to advance our understanding of what drives community assembly and diversity pattern of three stream organism groups. We applied variance partitioning (VPA) to correlate community compositions with environmental and spatial factors to infer stochastic and deterministic assembly processes, respectively. Linear regression (LR) models were used to identify the main drivers of species richness and local contributions to beta diversity (LCBD) as a function of different factors, including local environmental (e.g., in situ parameters, hydrology, nutrients), geo-climatic variables (e.g., land use, topography, climate) and spatial factors. Results indicated that species compositions of stream biota showed significant correlations with local environmental, geo-climatic and spatial factors. VPA demonstrated that both paradigms (i.e. deterministic and stochastic processes) interact to influence the biota distributions with stochastic process contributing more than deterministic process. In addition, the strongest stochastic process was found in fishes (10%), followed by benthic algae and macroinvertebrates with the same effects (8%). Notably, geo-climatic factors explained a substantial fraction of species composition, richness and beta diversity, although their effects were partially manifested via local and spatial variables. We demonstrate the relative importance of both stochastic and deterministic processes in shaping community composition and biodiversity of three stream organism groups in a large basin. This emphasizes the need to move beyond observed patterns and consider metacommunity theory into river management and conservation practices.
AB - Understanding processes and mechanisms driving patterns of species distribution and diversity is a vital theme in community ecology and conservation biology. There has been a continuous increase in studies focusing on diversity patterns in freshwater ecosystems during the last few decades. However, comparative studies of species distributions, diversity patterns and underlying processes across trophic levels remain limited. The unique characteristics of the study area (i.e. Hun-Tai River in Northeast China) generates a wide range of environmental conditions to advance our understanding of what drives community assembly and diversity pattern of three stream organism groups. We applied variance partitioning (VPA) to correlate community compositions with environmental and spatial factors to infer stochastic and deterministic assembly processes, respectively. Linear regression (LR) models were used to identify the main drivers of species richness and local contributions to beta diversity (LCBD) as a function of different factors, including local environmental (e.g., in situ parameters, hydrology, nutrients), geo-climatic variables (e.g., land use, topography, climate) and spatial factors. Results indicated that species compositions of stream biota showed significant correlations with local environmental, geo-climatic and spatial factors. VPA demonstrated that both paradigms (i.e. deterministic and stochastic processes) interact to influence the biota distributions with stochastic process contributing more than deterministic process. In addition, the strongest stochastic process was found in fishes (10%), followed by benthic algae and macroinvertebrates with the same effects (8%). Notably, geo-climatic factors explained a substantial fraction of species composition, richness and beta diversity, although their effects were partially manifested via local and spatial variables. We demonstrate the relative importance of both stochastic and deterministic processes in shaping community composition and biodiversity of three stream organism groups in a large basin. This emphasizes the need to move beyond observed patterns and consider metacommunity theory into river management and conservation practices.
KW - Beta diversity
KW - Metacommunity
KW - Stochastic and deterministic processes
KW - Variance partitioning
UR - http://www.scopus.com/inward/record.url?scp=85087919434&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2020.106673
DO - 10.1016/j.ecolind.2020.106673
M3 - Article
AN - SCOPUS:85087919434
SN - 1470-160X
VL - 117
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 106673
ER -