Global change has severely impacted the flow regimes of river systems across the globe, and the species inhabiting those systems now find themselves adapted to hydrographs that no longer exist. This includes many of the flow regimes in California, as streams are experiencing an altered precipitation cycle and a system of dams, culverts, and wastewater treatment plants that have transformed rivers that were once naturally flowing into occasionally concrete-lined channels. In the following chapters, I report the results of several studies aimed at understanding the relationship between flow variability and the persistence of ecological communities in river systems, particularly diatoms and benthic macroinvertebrates. In chapter one, I utilize a set of high-elevation experimental stream channels to subject the stream benthic community to a gradient of drought treatments. In this study, I found that pool habitat serves as a better refuge to benthic macroinvertebrates than riffles, contingent on the presence of hyporheic flow. Additionally, perennial flow–adapted communities appear to resist flow reduction, up to the point when surface flow is lost. My second chapter examines the relationship between benthic macroinvertebrate community composition and flow regimes, spatial connectivity, and environmental variables at large spatial scales and long temporal scales. I found that spatial processes, such as drainage density and upstream area, and flow metrics, such as flow variability and zero-flow days, best explain temporal beta diversity in macroinvertebrate communities at the multi-catchment scale. Chapter three explores the impact of urban flow disturbance, in the form of effluent discharge from wastewater treatment plants and storm runoff, on benthic diatom and macroinvertebrate communities through space and time. I found that macroinvertebrates in such systems are resistant to flow perturbations, compositionally reflected in a relatively disturbance-tolerant community, while the diatoms reflected fast-paced resilience strategies. While erratic flow shutdowns from wastewater treatment plants result in losses of taxa and lower densities of individuals, large storm events further compound flow regime disturbance in the urban stream system. Overall in this dissertation, I demonstrate that flow disturbance is a strong predictor of benthic freshwater communities, and that determining the extent of hydrological disturbance at the correct timescale is critical to the conservation of freshwater biodiversity.

The central theme of my dissertation is the influence of spatial structure and trophic interactions on ecological community dynamics and subsequent patterns of community diversity. I studied this phenomenon in different ecological context. In a synthesis of protist microcosm experiments, I studied how spatial network structure influenced predator-prey persistence (chapter 1). Subsequently, I shifted my focus to Sierra Nevada, CA high elevation aquatic systems. In high elevation lake-stream networks, I investigated whether patterns of macroinvertebrate diversity matched predictions from established stream ecology frameworks and the underlying processes driving these diversity patterns (chapter 2). Lastly, in order to understand how spatial structure and trophic interactions interactively structure communities, I studied how macroinvertebrate and zooplankton communities in Sierra Nevada streams and lakes are structured along spatial gradients and as a function of fish (chapter 3).

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.

Trophic cascades occur when changes in an ecosystem’s top trophic level indirectly drive changes in biomass at lower levels. Cascades of varying magnitudes have now been documented in every ecosystem, but explaining this variation remains a fundamental challenge in ecology; predictions from mathematical models have received equivocal experimental support. This is in part due to the mismatch between the closed-system food chain models that underlie most cascade theory and the complex food webs they aim to abstract. In the following chapters I report the results of field and laboratory experiments, and computational and analytical models that extend cascade theory toward scenarios involving complex, adaptive and open ecological systems. In chapter two I combine models and experiments to demonstrate that feedbacks between animal movement and the foraging behavior of adaptive omnivores in food webs controls the strength and sign of emergent cascades in freshwater habitats. This study reveals that a deeper understanding of how spatial processes modify behavior, and how this scales up to communities, will be required to predict cascades. With my third chapter, I report results of simulations of complex in silico food web networks. By using allometric scaling rules derived from a global species dataset to parameterize models of energy flow, I demonstrate that basic features of network architecture influence the probability that cascades will emerge and the magnitudes of those cascades. In chapter four, I use analytical models to show that bottom-up and top-down cascades can interact to yield non-intuitive patterns in emergent community structure. Namely I demonstrate that under certain conditions, consumption of basal species by top predators (i.e., omnivory) can lead to a greater facilitation of basal species’ biomasses than if predators did not consume producers at all. This result contends with current theory, which expects cascades to be weaker in omnivory systems because of direct consumption of producers by predators. Overall, I demonstrate that cascades are strongly influenced by processes that operate on very different scales: from animal movement at the landscape scale, to community architecture at the network scale, gross diet at the population scale and behavior at the scale of the individual.

It has become increasingly clear that communities in nature are often not isolated systems, but instead interact with other communities via the movement of organisms (`dispersal'), forming metacommunities. The dynamics of these metacommunities can differ dramatically from what is expected from studying individual communities alone, enabling many new mechanisms for the persistence of species within communities. Central to these mechanisms is asynchrony: differences in the timing or quality of dynamics among communities. Asynchrony is often assumed to arise from environmental differences, however it is also possible for asynchrony to emerge from interactions between identical communities, a phenomenon known as pattern formation.

Here we explore the role of pattern formation in metacommunity dynamics by investigating the mechanisms which lead to asynchrony among identical communities. While it is well known in the literature that the number, magnitude, and distribution of dispersal connections within a metacommunity can influence asynchrony, we demonstrate that measures describing the local stability of the synchronized state can vary independently from these properties of dispersal and plays an important role in the emergence of asynchrony in metacommunities. We also demonstrate that not only the frequency of asynchrony but the types and qualities of asynchronous dynamics vary dramatically between spatial structures with the same amount and distribution of dispersal.

These findings illustrate the importance of understanding the effect of dispersal structure on the dynamical quality of asynchrony, however methods for this are limited. Thus we develop and analyze a simplified model which allows prediction of the asynchronous dynamics possible for a given dispersal structure, and the conditions promoting different dynamical regimes.

Finally, we consider how the structure of interactions between species within communities influence asynchrony. We find that communities which cannot persist in the absence of dispersal among communities are the most prone to asynchrony. The net result is a negative feedback between community persistence in isolation and persistence in the presence of dispersal, confounding predicted relationships between community properties and persistence ability derived from the study of isolated communities alone.