How non-target chironomid communities respond to mosquito control: Integrating DNA metabarcoding and joint species distribution modelling.

The conservation and management of riparian ecosystems rely on understanding the ecological consequences of anthropogenic stressors that impact natural communities. In this context, studies investigating the effects of anthropogenic stressors require reliable methods capable of mapping the relationships between taxa occurrence or abundance and environmental predictors within a spatio-temporal framework. Here, we present an integrative approach using DNA metabarcoding and Hierarchical Modelling of Species Communities (HMSC) to unravel the intricate dynamics and resilience of chironomid communities exposed to Bacillus thuringiensis var. israelensis (Bti). Chironomid emergence was sampled from a total of 12 floodplain pond mesocosms, half of which received Bti treatment, during a 16-week period spanning spring and summer of 2020. Subsequently, we determined the community compositions of chironomids and examined their genus-specific responses to the Bti treatment, considering their phylogenetic affiliations and ecological traits of the larvae. Additionally, we investigated the impact of the Bti treatment on the body size distribution of emerging chironomids. Our study revealed consistent responses to Bti among different chironomid genera, indicating that neither phylogenetic affiliations nor larval feeding strategies significantly contributed to the observed patterns. Both taxonomic and genetic diversity were positively correlated with the number of emerged individuals. Furthermore, our findings demonstrated Bti-related effects on chironomid body size distribution, which could have relevant implications for size-selective terrestrial predators. Hence, our study highlights the value of employing a combination of DNA metabarcoding and HMSC to unravel the complex dynamics of Bti-related non-target effects on chironomid communities. The insights gained from this integrated framework contribute to our understanding of the ecological consequences of anthropogenic stressors and provide a foundation for informed decision-making regarding the conservation and management of riparian ecosystems.

European river typologies fail to capture diatom, fish, and macrophyte community composition.

Typology systems are frequently used in applied and fundamental ecology and are relevant for environmental monitoring and conservation. They aggregate ecosystems into discrete types based on biotic and abiotic variables, assuming that ecosystems of the same type are more alike than ecosystems of different types with regard to a specific property of interest. We evaluated whether this assumption is met by the Broad River Types (BRT), a recently proposed European river typology system, that classifies river segments based on abiotic variables, when it is used to group biological communities. We compiled data on the community composition of diatoms, fishes, and aquatic macrophytes throughout Europe and evaluated whether the composition is more similar in site groups with the same river type than in site groups of different river types using analysis of similarities, classification strength, typical species analysis, and the area under zeta diversity decline curves. We compared the performance of the BRT with those of four region-based typology systems, namely, Illies Freshwater Ecoregions, the Biogeographic Regions, the Freshwater Ecoregions of the World, and the Environmental Zones, as well as spatial autocorrelation (SA) classifications. All typology systems received low scores from most evaluation methods, relative to predefined thresholds and the SA classifications. The BRT often scored lowest of all typology systems. Within each typology system, community composition overlapped considerably between site groups defined by the types of the systems. The overlap tended to be the lowest for fishes and between Illies Freshwater Ecoregions. In conclusion, we found that existing broad-scale river typology systems fail to delineate site groups with distinct and compositionally homogeneous communities of diatoms, fishes, and macrophytes. A way to improve the fit between typology systems and biological communities might be to combine segment-based and region-based typology systems to simultaneously account for local environmental variation and historical distribution patterns, thus potentially improving the utility of broad-scale typology systems for freshwater biota.

Evaluating the biological validity of European river typology systems with least disturbed benthic macroinvertebrate communities.

Humans have severely altered freshwater ecosystems globally, causing a loss of biodiversity. Regulatory frameworks, like the Water Framework Directive, have been developed to support actions that halt and reverse this loss. These frameworks use typology systems that summarize freshwater ecosystems into environmentally delineated types. Within types, ecosystems that are minimally impacted by human activities, i.e., in reference conditions, are expected to be similar concerning physical, chemical, and biological characteristics. This assumption is critical when water quality assessments rely on comparisons to type-specific reference conditions. Lyche Solheim et al. (2019) developed a pan-European river typology system, the Broad River Types, that unifies the national Water Framework Directive typology systems and is gaining traction within the research community. However, it is unknown how similar biological communities are within these individual Broad River Types. We used analysis of similarities and classification strength analysis to examine if the Broad River Types delineate distinct macroinvertebrate communities across Europe and whether they outperform two ecoregional approaches: the European Biogeographical Regions and Illies' Freshwater Ecoregions. We determined indicator and typical taxa for the types of all three typology systems and evaluated their distinctiveness. All three typology systems captured more variation in macroinvertebrate communities than random combinations of sites. The results were similar among typology systems, but the Broad River Types always performed worse than either the Biogeographic Regions or Illies' Freshwater Ecoregions. Despite reaching statistical significance, the statistics of analysis of similarity and classification strength were low in all tests indicating substantial overlap among the macroinvertebrate communities of different types. We conclude that the Broad River Types do not represent an improvement upon existing freshwater typologies when used to delineate macroinvertebrate communities and we propose future avenues for advancement: regionally constrained types, better recognition of intermittent rivers, and consideration of biotic communities.

Should ecologists prefer model- over distance-based multivariate methods?

Ecological data sets often record the abundance of species, together with a set of explanatory variables. Multivariate statistical methods are optimal to analyze such data and are thus frequently used in ecology for exploration, visualization, and inference. Most approaches are based on pairwise distance matrices instead of the sites-by-species matrix, which stands in stark contrast to univariate statistics, where data models, assuming specific distributions, are the norm. However, through advances in statistical theory and computational power, models for multivariate data have gained traction. Systematic simulation-based performance evaluations of these methods are important as guides for practitioners but still lacking. Here, we compare two model-based methods, multivariate generalized linear models (MvGLMs) and constrained quadratic ordination (CQO), with two distance-based methods, distance-based redundancy analysis (dbRDA) and canonical correspondence analysis (CCA). We studied the performance of the methods to discriminate between causal variables and noise variables for 190 simulated data sets covering different sample sizes and data distributions. MvGLM and dbRDA differentiated accurately between causal and noise variables. The former had the lowest false-positive rate (0.008), while the latter had the lowest false-negative rate (0.027). CQO and CCA had the highest false-negative rate (0.291) and false-positive rate (0.256), respectively, where these error rates were typically high for data sets with linear responses. Our study shows that both model- and distance-based methods have their place in the ecologist's statistical toolbox. MvGLM and dbRDA are reliable for analyzing species-environment relations, whereas both CQO and CCA exhibited considerable flaws, especially with linear environmental gradients.