Abteilung Aquatische Ökologie

Intermittent rivers and ephemeral streams (IRES) – waterways in which flow ceases periodically or that dry completely – are found worldwide, and their frequency and extent are expected to increase in the future in response to global climate change and growing anthropogenic demand for fresh water. Repeated wet–dry cycles generate highly dynamic settings within river networks composed of aquatic and terrestrial habitats, which act as evolutionary triggers for aquatic and terrestrial biota. Drying also alters functions and processes within river networks, with consequences for ecosystem services. Despite the emergence of promising conceptual and methodological developments, our understanding of the occurrence and diversity of organisms in these ecosystems is limited primarily due to their coupled aquatic–terrestrial characteristics. Novel genomic tools based on high-throughput sequencing have the potential to tackle unanswered questions of pivotal importance to predict future change in IRES. Here, we outline why genomic tools are needed to assess these dynamic ecosystems from the population to the metacommunity scale, and their potential role in bridging ecological–evolutionary dynamics.

Anthropogenic activities are changing the state of ecosystems worldwide, affecting community composition and often resulting in loss of biodiversity. Rivers are among the most impacted ecosystems. Recording their current state with regular biomonitoring is important to assess the future trajectory of biodiversity. Traditional monitoring methods for ecological assessments are costly and time-intensive. Here, we compared monitoring of macroinvertebrates based on environmental DNA (eDNA) sampling with monitoring based on traditional kick-net sampling to assess biodiversity patterns at 92 river sites covering all major Swiss river catchments. From the kick-net community data, a biotic index (IBCH) based on 145 indicator taxa had been established. The index was matched by the taxonomically annotated eDNA data by using a machine learning approach. Our comparison of diversity patterns only uses the zero-radius Operational Taxonomic Units assigned to the indicator taxa. Overall, we found a strong congruence between both methods for the assessment of the total indicator community composition (gamma diversity). However, when assessing biodiversity at the site level (alpha diversity), the methods were less consistent and gave complementary data on composition. Specifically, environmental DNA retrieved significantly fewer indicator taxa per site than the kick-net approach. Importantly, however, the subsequent ecological classification of rivers based on the detected indicators resulted in similar biotic index scores for the kick-net and the eDNA data that was classified using a random forest approach. The majority of the predictions (72%) from the random forest classification resulted in the same river status categories as the kick-net approach. Thus, environmental DNA validly detected indicator communities and, combined with machine learning, provided reliable classifications of the ecological state of rivers. Overall, while environmental DNA gives complementary data on the macroinvertebrate community composition compared to the kick-net approach, the subsequently calculated indices for the ecological classification of river sites are nevertheless directly comparable and consistent.