Wed, Aug 04, 2021:On Demand
Background/Question/Methods
A major goal of community ecology is understanding the processes responsible for generating biodiversity patterns along spatial and environmental gradients. In stream ecosystems, system specific conceptual frameworks such as the river continuum concept (RCC), the mighty headwaters hypothesis (MHH), and serial discontinuity concept (SDC) have dominated research describing how biodiversity patterns change longitudinally along the spatial and environmental gradients of river networks. However, support for these conceptual frameworks has been mixed, mainly applicable to the specific stream ecosystems and biomes in which they were developed, and these frameworks have placed less emphasis on the general mechanisms driving biodiversity patterns. A focus on the general mechanisms structuring biodiversity patterns will provide a more holistic understanding of why biodiversity patterns conform or vary from established frameworks. Here, we apply the Theory of Ecological Communities (TEC) conceptual framework to stream ecosystems to focus explicitly on the core ecological processes structuring communities: speciation, dispersal, niche selection, and drift. Using a unique case study from high elevation networks of connected lakes and streams, we sampled stream invertebrate communities in the Sierra Nevada, CA to test the generality of stream frameworks and compared them to the TEC framework.
Results/Conclusions Overall, we found support that stream biodiversity in lake-stream networks is structured along the river network, similar to well-studied streams, but lakes modify aspects of community structure. Local diversity increases and β-diversity decreases moving downstream from the headwaters, consistent with the RCC and the “small but mighty” concept of mountain stream biodiversity. Local diversity is also structured by distance from upstream lakes, where community diversity increases with distance from upstream lakes, in support of the SDC. Despite support for the predicted biodiversity patterns, the mechanistic reasons hypothesized to structure biodiversity were only partially supported in our case study. Locally, species diversity was structured by niche selection and ecological drift, where local diversity was highest in environmentally favorable sites close to lakes and downstream from the headwaters, and sites with small community size, which are found further downstream from lakes. Higher β-diversity in the headwaters is influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites support higher community variation. By combining the RCC, MHH, and SDC with the TEC, we were able to mechanistically determine why data did and did not conform to predictions from established stream ecology frameworks.
Results/Conclusions Overall, we found support that stream biodiversity in lake-stream networks is structured along the river network, similar to well-studied streams, but lakes modify aspects of community structure. Local diversity increases and β-diversity decreases moving downstream from the headwaters, consistent with the RCC and the “small but mighty” concept of mountain stream biodiversity. Local diversity is also structured by distance from upstream lakes, where community diversity increases with distance from upstream lakes, in support of the SDC. Despite support for the predicted biodiversity patterns, the mechanistic reasons hypothesized to structure biodiversity were only partially supported in our case study. Locally, species diversity was structured by niche selection and ecological drift, where local diversity was highest in environmentally favorable sites close to lakes and downstream from the headwaters, and sites with small community size, which are found further downstream from lakes. Higher β-diversity in the headwaters is influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites support higher community variation. By combining the RCC, MHH, and SDC with the TEC, we were able to mechanistically determine why data did and did not conform to predictions from established stream ecology frameworks.