Geographical distribution of zooplankton biodiversity in highly polluted running water ecosystems: Validation of fine‐scale species sorting hypothesis

Abstract

Dispersal, rather than species sorting, is widely recognized as the dominant driver for determining meta‐community structure at fine geographical scales in running water ecosystems. However, this view has been challenged by a recently proposed “fine‐scale species sorting hypothesis,” where community structure can be largely determined by an environmental gradient formed by local pollution at fine scales. Here, we tested this hypothesis by studying community composition and geographical distribution of metazoan zooplankton in a heavily polluted river—the North Canal River in the Haihe River Basin, China. Analysis of similarity (ANOSIM) showed that the community composition of metazoan zooplankton differed significantly (p = .001) along the environmental gradient. Ammonium (NH4‐N) was the leading factor responsible for changes in zooplankton community structure and geographical distribution, followed by total dissolved solid (TDS), Na, dissolved oxygen (DO) and temperature (T). Variation partitioning revealed a larger contribution of environmental variables (21.6%) than spatial variables (1.1%) to the total explained variation of zooplankton communities. Our results support that species sorting, rather than dispersal, played a key role in structuring communities. Threshold Indicator Taxa ANalysis (TITAN) also revealed significant change points at both taxon and community levels along the gradient of NH4‐N, providing further support for the influence of environmental variables on zooplankton communities. Collectively, we validate the fine‐scale species sorting hypothesis when an environmental gradient exists in running water ecosystems at fine geographical scales. However, future studies on interactions between pollutants and zooplankton communities are still needed to better understand mechanisms responsible for the meta‐community dynamics.

Anthropogenic pollutant-driven geographical distribution of mesozooplankton communities in estuarine areas of the Bohai Sea, China

“…Whereas inorganic nitrogen was considered as the main driver for observed zooplankton structure in Tangshan Bay 52 . Moreover, total phosphate, NH 4 -N and Mg 2+ were identified as the leading factors in Chaobai river, Beiyun river and Fuyang river of China, respectively 41,43,44 . Our results combined with other findings revealed that different leading anthropogenic pollutants were recognized in different ecosystems, even in different regions of the same ecosystems.…”

Interactome‐based abiotic and biotic impacts on biodiversity of plankton communities in disturbed wetlands

“…Due to the relatively large population size and high dispersal potentials, microscopic organisms have long been considered to be randomly distributed (Lindström & Langenheder, ; Martiny et al, ). However, a large number of recent studies have repudiated this old viewpoint by revealing the non‐random geographical distribution of these communities (Lindström & Langenheder, ; Soininen et al, ; Thompson et al, ), even in running water ecosystems where significant environmental gradients exist (Peng, Xiong, & Zhan, ; Xiong et al, ; Yang et al, ). Similarly, we also detected significant geographical variability of bacterioplankton and zooplankton communities in Sanjiang wetland in this study ( p < 0.05; Figure ).…”

“…Significant variation was also observed at the taxonomic level, such as the reverse relationship between Rotifera and Arthropoda (mostly crustaceans; Figure b). Such a reverse relationship was common in aquatic ecosystems (Xiong et al, ; Yang et al, ) and was mainly related to interspecific interactions, such as competition or predation (Laxson, McPhedran, Makarewicz, Telesh, & MacIsaac, ; Meyer, Hampton, Ozersky, Rusanovskaya, & Woo, ).…”

“…Artificial primers and tags were trimmed with Python scripts. We removed sequences: (a) that contained any undetermined nucleotides (“N”s); (b) with quality score lower than 20 (<Q20); and (c) that had the maximum expected error threshold lower than 0.75 (Yang et al, , ). Filtered sequences were trimmed to the same length of 225 bp based on the recommendation of Xiong et al ().…”

Distribution patterns of dinoflagellate communities along the Songhua River

“…A study at fine geographical scale of a river floodplain showed that less than 17.8% of phytoplankton community variation was explained by environmental and spatial variables, and the authors suggested that random dispersion, ecological drift, and priority effects were important ecological processes responsible for phytoplankton meta-communities (Devercelli et al, 2016). Collectively, the low explanation power of environmental and spatial factors in this study may be attributed to four factors: (I) temporal colonization and extinction due to rainfall; (II) unmeasured environmental factors, such as relative light intensity and disturbance frequency, where the former is essential for the reproduction and growth of dinoflagellates and the latter has been shown to structure phytoplankton communities (Beisner, 2001); (III) higher trophic level predators, such as zooplankton, were not considered in this study; the abundance and taxa of predators can make significant influence on the prey composition and abundance variation (Kozak, Goldyn & Dondajewska, 2015); (IV) although the metabarcoding method shows robust power for diversity assessments of various communities (Zhan et al, 2014; Xiong et al, 2017; Yang et al, 2018), this method still cannot completely quantify the abundance of community composition (Sun et al, 2015), especially for taxonomic groups with large size variation. The poor relationship between sequence abundance based on molecular methods and real species abundance (Godhe et al, 2008) may disturb statistical analyses considerably for the exploration of complex interactions between organisms and environments.…”