A Trait-Based Predictive Framework for Community Assembly in River Networks
- Conway, Ryan Matthew
- Advisor(s): Anderson, Kurt E
Abstract
Climate change and human-driven landscape alterations pose critical threats to riverine organisms. In California, aquatic taxa have evolved to cope with stream intermittency, but novel flow regimes including extreme disturbances can cause profound shifts in these ecosystems. My dissertation presents the findings of three studies on aquatic insect communities, based on large biomonitoring datasets, and a field study on community changes over time in a Southern California intermittent headwater stream. This research aimed to understand how dispersal connectivity, variable flow regimes, and environmental factors influence taxonomic and functional biodiversity. In the first chapter, I utilized over 8,000 benthic community samples to build machine learning models associating taxa, traits, and trait clusters with flow, climate, and landscape variables. These models were then tested for their effectiveness in predicting similar relationships across ecoregions. The findings revealed that certain trait-based groups, linked to flow conditions, surpassed traditional bioindicator groups in predictive performance. Additionally, a subset of specialist trait groups and taxonomic orders showed consistent environmental factor associations, regardless of ecoregion. My second chapter involved analyzing aquatic insect communities through repeated temporal observations. I found that downstream communities in river networks were more influenced by dispersal mechanisms, while upstream communities were primarily shaped by environmental processes over time. My third chapter extended these analyses, evaluating whether network theory-based connectivity measures could surpass traditional stream order metrics as proxies for dispersal. These indices proved effective, demonstrating that more connected communities were more resilient to variable flow regimes compared to isolated ones. Finally, my fourth chapter details a field study in an intermittent montane headwater stream, focusing on fine-scale temporal and spatial biodiversity shifts. Here I found that remnant pools served as biodiverse refuges during drying periods. Notably, functional diversity showed resistance to reductions in water volume until streams shrank to very low pools. Overall, my dissertation highlights the interplay of dispersal, environmental conditions, and flow regimes on aquatic biodiversity, providing insights into protecting these ecosystems in the face of climate change.