1. Identifying and characterizing stressor interactions is central to multiple-stressor research. Such interactions refer to stronger (synergism) or weaker (antagonism) joint effects of co-occurring stressors on biological entities, when compared to the predictions of a theoretical null model. Various null models have been developed and selection of the most appropriate null model for a specific research question is ideally based on assumptions on co-tolerance patterns in communities, and mechanisms of stressor effects. 2. Statistical models are commonly used to evaluate the statistical significance of interaction terms. However, they introduce constraints by imposing a specific null hypothesis on stressor combinations that cannot be flexibly changed. This can introduce a mismatch between the null model that the analyst wants to test, and the one imposed by the statistical model. 3. Here, we show under which conditions the statistical null hypothesis for interaction terms misaligns with a multiple-stressor null model and propose to resolve such misalignments using post-estimation inference. Null-model specific interaction estimates can be calculated from adjusted predictions of a fitted regression model, and associated standard errors are derived using the delta method, posterior simulations or bootstrapping. We illustrate the suggested approach with three case studies and validate statistical conclusions through data simulations. 4. Post-estimation inference has the potential to advance hypothesis-driven research on stressor interactions by flexibly testing any a priori defined null model independent from regression model structure.

Environmental DNA (eDNA) extracted from water is routinely used in river biodiversity research, and via metabarcoding eDNA can provide comprehensive taxa lists with little effort and cost. However, eDNA-based species detection in streams and rivers may be influenced by sampling season and other key factors such as water temperature and discharge. Research linking these factors and also informing on the potential of eDNA metabarcoding to detect shifts in ecological signatures, such as species phenology and functional feeding groups across seasons, is missing. To address this gap, we collected water samples every two weeks for 15 months at a long-term ecological research (LTER) site and at three different positions in the river’s cross section, specifically the water surface, riverbed, and riverbank. For these 102 samples, we analyzed macroinvertebrate species and molecular Operational Taxonomic Unit (OTU) richness and temporal community turnover across seasons based on COI metabarcoding data. Using Generalized Additive Models, we found a significant influence of sampling season on species richness. Community turnover followed a cyclic pattern, reflecting the continuous change of the macroinvertebrate community throughout the year (‘seasonal clock’). Although water temperature had no influence on the inferred species richness, higher discharge reduced the number of Annelida and Ephemeroptera species detectable with eDNA. Most macroinvertebrate taxa showed the highest species richness in spring, in particular merolimnic species with univoltine life cycles. Further, we detected an increase in proportion of shredders in winter and of parasites in summer. Our results show the usefulness of highly resolved eDNA metabarcoding time series data for ecological research and biodiversity monitoring in streams and rivers.

Environmental DNA (eDNA) extracted from water is routinely used in river biodiversity research, and via metabarcoding eDNA can provide comprehensive taxa lists with little effort and cost. However, eDNA-based species detection in streams and rivers may be influenced by sampling season, location, and other key factors such as water temperature and discharge. Research linking these factors and also informing on the potential of eDNA metabarcoding to detect shifts in ecological signatures, such as species phenology and functional feeding groups across seasons, is missing. To address this gap, we collected 102 water samples every two weeks for 15 months at a long-term ecological research (LTER) site and at three different positions in the river’s cross section, specifically the water surface, riverbed, and riverbank. We analyzed macroinvertebrate species and molecular Operational Taxonomic Unit (OTU) richness and temporal community turnover across seasons and sampling positions based on COI metabarcoding data. Using Generalized Additive Models, we found a significant influence of sampling season but not sampling position on community composition. Community turnover followed a cyclic pattern, reflecting the continuous change of the macroinvertebrate community throughout the year (‘seasonal clock’). Although water temperature had no influence on the inferred community composition, higher discharge reduced the number of Annelida and Ephemeroptera species detectable with eDNA. Most macroinvertebrate taxa showed the highest detection rates in spring, in particular merolimnic species with univoltine life cycles. Further, we detected an increase in proportion of shredders in winter and of parasites in summer. Our results show the usefulness of highly resolved eDNA metabarcoding time series data for ecological research and biodiversity monitoring in streams and rivers.