Abteilung Aquatische Ökologie

Introduced species can alter the dynamics and structure of a native community. Network analysis provides a tool to study host–parasite interactions that can help to predict the possible impact of biological invasions or other disturbances. In this study, we used weighted bipartite networks to assess differences in the interaction patterns between hosts and helminth parasites of native (Sea of Japan) and invasive (Black Sea and Sea of Azov) populations of Planiliza haematocheilus (Teleostei: Mugilidae). We employed three quantitative network descriptors, connectance, weighted nestedness and modularity, to gain insight into the structure of the host–parasite networks in the native and invaded areas. The role of parasite species in the networks was assessed using the betweenness centrality index. We analyzed networks encompassing the whole helminth community and subsets of species classified by their transmission strategy. The analyses were downscaled to host individual-level to consider intraspecific variation in parasite communities. We found significant differences between networks in the native and invaded areas. The latter presented a higher value of nestedness, which may indicate a co-occurrence between parasite species with many connections in the network and species with fewer interactions within the same individual-host. In addition, modularity was higher in the native area’s networks than those of the invaded area, with subgroups of host individuals that interact more frequently with certain parasite species than with others. Only the networks composed of actively transmitted parasites and ectoparasites did not show significant differences in modularity between the Sea of Azov and the Sea of Japan, which could be due to the introduction of a part of the native community into the invaded environment, with a lower diversity and abundance of species. We show that network analysis provides a valuable tool to illuminate the changes that occur in host–parasite interactions when an invasive species and its parasite community are introduced into a new area.

Food webs are often simulated dynamically to explore how trophic interactions influence resource and consumer abundances. As large trophic networks cannot be simulated in their original size – it would be too computationally expansive – they are shrunk by aggregating species together. However, key species may get lumped during this process, masking their unique role in their ecosystem. Therefore, a more systematic understanding of the aggregation effects on key positions is needed. Here, we study how six aggregation methods change 24 importance indices used to find key species in food webs. Our work was carried out on 76 aquatic food webs from the Ecopath with Ecosim database (EcoBase). The aggregation methods we considered were: 1) hierarchical clustering with the Jaccard index; 2) hierarchical clustering with the REGE index; 3) clustering within classic food web modules, which we refer to as 'density-based' modules; 4) clustering within 'predator-based modules' in which species fed on the same preys; 5) clustering within 'prey-based modules' in which species are fed upon by the same predators; and 6) clustering within 'groups' in which species share the same probability to interact with other groups. Hierarchical clustering with the REGE index produced the best results. Therefore, we recommend using it if we were interested in maintaining the identity of key species. The other algorithms could also be used to study specific network processes. However, we need to consider the bias they produce when masking important species.

Populations that expand their range can undergo rapid evolutionary adaptation of life-history traits, dispersal behaviour and adaptation to the local environment. Such adaptation may be aided or hindered by sexual reproduction, depending on the context. However, few empirical and experimental studies have investigated the genetic basis of adaptive evolution during range expansions. Even less attention has been given to the question how sexual reproduction may modulate such adaptive evolution during range expansions. We here studied genomic adaptation during experimental range expansions of the protist Tetrahymena thermophila in landscapes with a uniform environment or a pH gradient. Specifically, we investigated two aspects of genomic adaptation during range expansion. First, we investigated adaptive genetic change in terms of the underlying numbers of allele frequency changes from standing genetic variation and de novo variants. We focused on how sexual reproduction may alter this adaptive genetic change. Second, we identified genes subject to selection caused by the expanding range itself, and directional selection due to the presence or absence of the pH gradient. We focused this analysis on alleles with large frequency changes that occurred in parallel in more than one population to identify the most likely candidate targets of selection. We found that sexual reproduction altered adaptive genetic change both in terms of de novo variants and standing genetic variation. However, sexual reproduction affected allele frequency changes in standing genetic variation only in the absence of long-distance gene flow. Adaptation to the range expansion affected genes involved in cell divisions and DNA repair, whereas adaptation to the pH gradient additionally affected genes involved in ion balance and oxidoreductase reactions. These genetic changes may result from selection on growth and adaptation to low pH. In the absence of gene flow, sexual reproduction may have aided genetic adaptation. Gene flow may have swamped expanding populations with maladapted alleles, thus reducing the extent of evolutionary adaptation during range expansion. Sexual reproduction also altered the genetic basis of adaptation in our evolving populations via de novo variants, possibly by purging deleterious mutations or by revealing fitness benefits of rare genetic variants.