The influence of spatial processes on fish community structure: using a metacommunity framework for freshwater bioassessment.

The use of biological indicators in a bioassessment approach is important for inferences of anthropogenic stress in routine monitoring programs. One of the primary assumptions of bioassessment is that biological indicators observed at specific sampling sites will allow for inferences of local environmental quality; however, this assumption requires a reliable understanding of dispersal processes, which is particularly relevant in river systems due to their dendritic network. Inter-stream dispersal between different points of the river network could bias bioassessment, especially for highly mobile organisms like fish. Here, we examine standard biological metrics used in routine biomonitoring to determine how spatial variables, including dispersal, influence inferences between fish populations and environmental gradients. Using redundancy analysis (RDA) and variation partitioning, we tested the relative influence of both environmental and spatial variables on fish community structure and related community metrics. Fish were collected from 99 sampling sites distributed across 44 rivers and streams of the Great Morava River Basin, Serbia. Electroconductivity, the percentage of agricultural areas, dissolved oxygen, ammonia, and nitrate-nitrogen were found to be significant environmental variables, while ten spatial predictors from broad- to small-scales were found to influence fish community structure and community metrics. Our results suggest that contemporary dispersal among streams influences fish community composition, but that trait-based metrics are less sensitive than basic measures of diversity to spatial processes. Our results highlight the need for spatially independent sampling, as well as the need to consider dispersal-based processes in routine biomonitoring.

Implications of local niche- and dispersal-based factors that may influence chironomid assemblages in bioassessment.

Local environmental factors and dispersal-based processes can both influence the structure of metacommunities in freshwater ecosystems. Describing these patterns is especially important for biomonitoring approaches that are based on inferences made from benthic macroinvertebrate assemblages. Here, we examine the metacommunity structure of chironomid assemblages collected from 28 sampling stations along the Southern Morava River, Serbia. We examined the extent of dispersal-based processes along a temporal scale. We obtained 8 models for the different sampling seasons that determined the spatial variables that best explained variability in chironomid assemblages. Spatial processes were found to be a significant predictor of variation for chironomids during the late winter/spring (March and May) and autumn (October and November), concordant with the known phenology of common taxa. Species sorting and mass effects were found to be significant processes that structured the chironomid metacommunity. In addition, biological interactions, inferred from fish biomass, and habitat traits, demonstrated by macrophyte and riparian vegetation, were found to influence species sorting. A high variability of chironomid metacommunity structure across sampling seasons suggests that monitoring programs that include macroinvertebrates in bioassessment should avoid months with pronounced spatial processes, and consequently maximize a correlation between community structure and local environmental factors.

The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology.

The analysis of community structure in studies of freshwater ecology often requires the application of dimensionality reduction to process multivariate data. A high number of dimensions (number of taxa/environmental parameters × number of samples), nonlinear relationships, outliers, and high variability usually hinder the visualization and interpretation of multivariate datasets. Here, we proposed a new statistical design using Uniform Manifold Approximation and Projection (UMAP), and community partitioning using Louvain algorithms, to ordinate and classify the structure of aquatic biota in two-dimensional space. We present this approach with a demonstration of five previously published datasets for diatoms, macrophytes, chironomids (larval and subfossil), and fish. Principal Component Analysis (PCA) and Ward's clustering were also used to assess the comparability of the UMAP approach compared to traditional approaches for ordination and classification. The ordination of sampling sites in 2-dimensional space showed a much denser, and easier to interpret, grouping using the UMAP approach in comparison to PCA. The classification of community structure using the Louvain algorithm in UMAP ordinal space showed a high classification strength for data with a high number of dimensions than the cluster patterns obtained with the use of a Ward's algorithm in PCA. Environmental gradients, presented via heat maps, were overlayed with the ordination patterns of aquatic communities, confirming that the ordinations obtained by UMAP were ecologically meaningful. This is the first study that has applied a UMAP approach with classification using Louvain algorithms on ecological datasets. We show that the performance of local and global structures, as well as the number of clusters determined by the algorithm, make this approach more powerful than traditional approaches.