Macroinvertebrates are affected by anthropogenic stressors, resulting in adverse effects on individual, population or community level. However, the effect of a stressor might be altered by preceding exposures and interactions with concurrent stressors. The mechanisms by which such legacy effects and stressor interactions arise and affect not only community degradation but also recovery after stressor removal are still poorly understood. In Phase I, Project A08 aimed to experimentally disentangle the effects of three globally important stressors (reduced flow, salinisation and warming) on macroinvertebrates, and the processes of community recovery after stressor release. We conducted three ExStream experiments at the Emscher and Kinzig catchments, representing a restored ecosystem with a strong stressor history and a near-natural community, respectively. Our results from the two Emscher catchment experiments indicate resistance of the local macroinvertebrate community to salinisation, probably due to legacy effects from previous salt pollution. In contrast, reduced flow velocity and warming had a more substantial impact on macroinvertebrate assemblages, highlighting the key role of flow regimes and water temperature in structuring biotic communities. This is particularly concerning as European streams are expected to face increased intermittency and prolonged droughts due to climate change. Therefore, in Phase II we focus on three key stressors associated with drought, i.e. warming, surface-flow cessation and salinisation, to investigate their impacts across different levels of ecological complexity (individuals, populations, communities and ecosystem functions). First, we will investigate degradation of macroinvertebrate communities in ExStream/SIGMA an adapted version of the ExStream mesocosms, designed in Phase I, to simulate subsurface flows. The stressors surface-flow cessation and warming will be applied across a temporal gradient. We expect that the effects of surface-flow cessation will increase non-linearly with time and that the size of the invertebrate taxa will determine their sensitivity to both stressors. Second, we will assess recovery of macroinvertebrate communities from the same set of stressors with a second ExStream/SIGMA experiment in which drought is followed by surface flow resumption. By comparing the results from both experiments, we aim to test key components of the Asymmetric Response Concept (ARC). We anticipate that recovery after drought differs substantially based on stressor severity, i.e. drought duration, and the dispersal capacity and size of taxa. Third, based on key findings in Phase I, we will study the role of evolutionary adaptations of Gammarus sp., a key shredder, under multiple-stressor scenarios. A small-scale indoor experiment will be conducted with a salt-exposed and saltnaïve population to determine, based on transcriptomic data, whether the low response to salinisation could be a result of evolutionary adaptation. Furthermore, we will investigate with that experiment if eco-evolutionary trade-offs lead to higher susceptibility of Gammarus sp. when an additional stressor with a different mode of action (warming) is applied. As a key innovation, this project will apply combinations of increasingly relevant riverine stressors in gradient design experiments to unveil critical thresholds of these and thereby support mechanistic explanations to the ARC.

Iris Madge Pimentel (University of Duisburg-Essen)

Individual and combined stressor effects on freshwater invertebrate communities and an associated ecosystem function

Streams are increasingly exposed to multiple human-induced stressors which reduce biodiversity and alter the composition of species communities. An understanding of the effects that these stressors have on species communities and how the communities recover after removal of the stressors is crucial to pave the way for the success of stream restoration projects. Focusing on invertebrate communities in streams, this project studies both, the effects of and the recovery from three common human stressors: temperature increase, salinization and flow velocity reduction. In addition, the project explores the combination of two complementary approaches for community analyses: DNA metabarcoding and automated image-based species identification with deep-learning methods.

Freshwater invertebrates substantially contribute to leaflitter decomposition and thereby to the provisioning of nutrients within the food web of rivers. Unfortunately, stream restoration actions often entail only slow and incomplete reassembly of the natural invertebrate communities. This makes evident that processes of community degradation and recovery are not symmetric, as the recolonization of restored ecosystems depends on factors that are hard to predict. These factors include differential dispersal abilities of species, potential dispersal barriers, and the persistence of populations which may compete with or facilitate the establishment of former community members. So, how are stream invertebrate communities and their associated ecosystem functions such as leaflitter decomposition altered by common human stressors? And in how far does their recovery differ depending on the stressor(s) they were exposed to?

The ExStream system is exceptionally suitable to answer these questions. Water is redirected from the stream through multiple mesocosms. This yields near-natural conditions in the mesocosms, enables sufficient replication for statistic inferences and allows for migration of invertebrates. We manipulate three variables of interest, increased temperature, salinization and reduced flow velocity. This allows us to analyze the degradation process of invertebrate communities by single and multiple stressors. In addition, we can explore how this affects ecosystem functioning, exemplified on leaflitter decomposition. To allow for recovery, we reset all environmental variables to natural conditions and study community reassembly upon stressor release.

Using morphological identification of invertebrates for community analysis is time-consuming and prone to misidentification of morphologically similar species. In contrast, DNA metabarcoding is a much faster approach and can even differentiate between such cryptic species by taking advantage of differences in DNA sequences in a specific gene region. However, it does not give data on abundances, although this is a crucial information for diversity measures. Here, the project explores the combination of DNA metabarcoding with automated image-based species identification to retrieve both comprehensive species lists and abundance data. First, convolutional neural networks which are trained on freshwater macroinvertebrates identified by experts are used to identify specimens to the lowest taxonomic level possible. This generates abundance data for species that can be recognized by the algorithm. Second, DNA metabarcoding is applied to retrieve a comprehensive taxa list including also morphologically similar species.

Contact: iris.madge-pimentel@uni-due.de

First Supervisor: Prof. Dr. Florian Leese (University of Duisburg-Essen, Aquatic Ecosystem Research)
Second Supervisor: Dr. Jeremy Piggott (Trinity College Dublin (Ireland), Zoology)
Mentor: Dr. Thomas Ehlert (Bundesamt für Naturschutz)