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Here on the Texas coastal plain, we exploit North America’s sharpest precipitation gradient not associated with elevation change to determine how differences in annual rainfall affect stream community composition and trophic structure in this region, and ultimately, how stream and river communities (lotic communities) in other regions might respond to future climate change-related shifts in global precipitation patterns. Over an area of less than 300 km, annual rainfall nearly triples (Fig. 1 below), transitioning from semi-arid to sub-humid climates—making this region a prime setting for studying the effects of changing precipitation regimes on biological communities.

Much of my focus in this project centers on fishes—the assembly processes that structure their communities, their diversity and functional roles within each stream, how they utilize resources from both within and outside the streams, their diets, and how they fit into the larger food webs present in these systems. Ultimately, my work seeks to understand how these things change between streams that have historically received, on average, more or less rainfall. Some specific research avenues I’m currently addressing are:

1. ‘Environmental Filtering and Competition

These concepts are well-described mechanism for how biological communities assemble. In regard to environmental filtering, very simply put, if a species can’t tolerate a certain environmental condition (think temperature, for example), it’s “filtered” out and can’t persist in a given ecosystem. This is why there are no iguanas in the tundra. On the Texas coastal plain, streams near the dry end of the rainfall gradient present more of these filters; for example, it rains less frequently here, so long periods of drought mean dissolved oxygen in the water dips to levels too low for some fish species. These communities are therefore likely to be less species-rich, and we predict that environmental filtering plays the dominant role in shaping their structure. On the other hand, moving toward the wetter of the rainfall gradient, streams are likely less heavily filtered and more species rich, leaving more “room” for interactions between species to shape community structure (namely, competition for resources). Testing these predictions quantitatively can be relatively straight forward, if we utilize functional traits—essentially measures of what species “do” rather than how they are classified taxonomically (see Jon Lefcheck’s excellent description and application of functional diversity here: https://jonlefcheck.net/2014/10/20/what-is-functional-diversity-and-why-do-we-care-2) These traits can come in a myriad of categories with different ‘states’ in each (for example diet: piscivore, invertivore, planktivore, etc.; reproductive ecology: broadcast spawner, simple nester, nest guarder, etc.), and each species’ combination of traits gives it a unique functional value, which, among other things, better relates it to the functioning of the ecosystem it inhabits than understanding it in terms of taxonomy alone. Functional traits analysis can be used to address a number of questions—including how particular traits vary between communities across an environmental gradient (for example, see my poster presented at the 2020 Texas A&M University Marine Biology Retreat). To estimate the degrees of environmental filtering versus interspecific competition in shaping communities across space or time (like fishes in Texas streams spanning a spatial rainfall gradient), we can plot species richness against functional richness (how many unique functions exist in a community) for our collected data and compare that to a random or “expected” model community created by resampling our own data thousands of times (see Petchy et al. 2007 for an example). If our collected community data fall out below the model community data, functional richness is lower than what would be expected by random chance—in other words, there functions being performed by our species are quite similar, indicating that the environment played a substantial role in filtering out species with more diverse functional roles (see Fig. 2 below). Conversely, if our data fall out above the expected data, the community is more functionally rich than we would expect; the environment hasn’t filtered out many unique functions, and competition between species is likely a stronger driver of the species assemblage we observe.

In addition to delineating environmental filtering and competition as community assembly mechanisms, we can also use the functional richness/species richness relationship to understand how sensitive or robust communities may be in the face of environmental change (e.g., rainfall) across space or time. By plotting the change in functional richness against the corresponding change in species richness (essentially, the second derivatives of these variables) between locations or time points, we can visualize and measure how say, a loss in species may result in a loss (or lack thereof) in ecological function. If for example, a community loses a given number or species, or if species richness declines across a region, how many unique functions (if any) are lost? If only a small number of species are lost, but this corresponds to a substantial loss in function, a community can be said to be exhibiting ‘functional sensitivity’; i.e. it is sensitive to changes in species richness, because it results in a large change in functional richness. On the other hand, if many species are lost, but we see only a small change in functional richness, a community shows ‘functional redundancy; the species lost were likely not very functionally unique, and their functions “backed up” by other species. Ultimately, these concepts have major implications in regard to the effects of biodiversity loss due to habitat destruction, climate change, among other factors at play in the anthropocene—namely, in asking ‘how do differences in the numbers or types of species lost affect ecosystem function?’

Mapping out the strength of competition itself, and how it differs between communities across the rainfall gradient, poses a much greater challenge (take a look at Leibold and Chase’s excellent—and very readable— synthesis of the state of inquiry in this field, Ecological Niches: Linking Classical and Contemporary Approaches). My approach involves one basic question, utilizing two key pieces of information: How strong is competition between species likely to be based on 1) functional similarity between species and 2) proportional abundance of each species within a community? Interspecific competition is expected to be strongest in communities where species both are more similar to one another in terms of ecological function and when they exist in more similar relative abundances. To address the first point, we can use a clustering or ‘dendrogram’ analysis of species x functional trait data (similar to a phylogenetic tree, but based on functional similarity rather than phylogeny) for each community, and measure the distance between each combination of species in ‘functional trait space’ (how functionally dissimilar they are). The inverses of these values provide a metric for how functionally similar they are; essentially, how strong competition is likely to be between each pair of species. The sum of these values then, gives a single value for competition strength based on functional similarity. We can then multiply this by the sum of the proportional abundances for each pair of species in a given community, and divide by the total number of possible species combinations. Finally, these single values for each community (which now incorporate functional similarity and proportional abundances between species) can be plotted in a regression against mean annual rainfall to determine how our estimated ‘community competition strength’ varies with changing precipitation regimes. Ultimately, the prediction is that it increases non-linearly, with the sharpest increases between the driest sites and the middle of the rainfall gradient. More on this to come…

2. Trophic Controls and the Influence of Predators

The factors shaping lotic community structure that have historically received most of the attention in the scientific literature are hydrologic regimes and resource bases driving things through bottom-up control. The effect of predators and top-down control has received much less focus, but as it relates to climate, there are a couple key pieces of evidence to suggest this may be an important process well worth looking into—but we’ll get to that in a moment. Relating these community-structuring processes to our south Texas rainfall gradient, in terms of hydrology, streams under more arid climates receiving less rainfall are generally more stochastic, or “flashy”, and become more stable with increasing annual rainfall. More stochastic flows mean higher disturbance stress on taxa, leading to communities more heavily ‘filtered’ by these environmental constraints (see above); whereas streams under wetter climates typically feature more stable flow regimes (less filtering, more complex interactions—like competition—between species), leading to more diverse biological communities and longer/more complex food chains. In terms of basal food resource dynamics and bottom-up control, stream systems that receive less rainfall

Link between precipitation, hydrology, and predators—’environmentally-mediated top-down control’ 2022 Joint Aquatic Sciences Meeting (JASM) presentation