The distribution of lotic insect traits in relation to reference conditions and projected climate change in the western United States - Pyne, Matthew Ivern, author; Poff, N. LeRoy, advisor; Bledsoe, Brian P., committee member; Hoeting, Jennifer A., committee member; Webb, Colleen T., committee member (Colorado State University. Libraries, 2014)
The use of species traits (e.g., life history, morphological, physiological, or ecological characteristics of an organism) to describe community responses to environmental change has become a common practice in stream ecosystems, with over 900 papers describing macroinvertebrate trait-environment relationships in streams. The use of traits provides some advantages over traditional taxonomic metrics, such as providing a mechanistic link between an organism and its environment, but also presents some challenges, such as many traits being correlated with other traits and multiple environmental variables. Various methods have been recommended to address these challenges, such as using multiple traits, posing a priori hypotheses, and evaluating streams across large-spatial scales. The vast majority of studies have not incorporated these recommendations, however, particularly in North America. My research had two general objectives: 1) describe the dominant trait-environmental relationships in natural streams in the western United States and 2) use two distinct traits-based methods to evaluate how stream aquatic insect communities are currently distributed in terms of multiple environmental variables and how species and communities may respond to climate change. Traits are often used to evaluate the ecological integrity of streams and a baseline understanding of aquatic insect trait-environment relationships is needed for the western United States. I used logistic regression, multinomial regression, and redundancy analysis to explore the relationships between 20 trait distributions and 83 environmental variables in 253 least-disturbed streams across 12 western states. Traits had the strongest relationships with regional climate and local stream habitat conditions (e.g., air temperature, conductivity, mean annual runoff) rather than elevation, land use, or measures of extreme hydrological events. Traits such as thermal tolerance, size, swimming strength, rheophily, voltinism, and armoring exhibited strong relationships with the environmental data and would be ideal for large-scale stream assessments. Aquatic insect communities contain many taxa that are sensitive to temperature increases and changes to runoff. Two traits, cold water preference and erosional obligate (i.e., needs to live in fast-water habitat) have been used in the past to estimate the effect of climate change on stream insect communities, but no study has accounted for both climatic and non-climatic effects on these two traits. I developed a Bayesian path analysis describing how the distributions of these two traits respond to multiple environmental gradients, not just temperature, and discovered that the distribution of cold-adapted taxa was strongly correlated with changes in air temperature in the wet, cool ecoregions, but was correlated with thermal buffers and refuges in most dry, warm ecoregions, indicating that temperature-sensitive taxa are likely on the brink of their thermal tolerance in those ecoregions. A second approach to assess community sensitivity to climate change is to determine the specific thermal tolerance of each taxon individually. I computed the thermal and stream runoff thresholds of common stream taxa and compared the World Climate Research Programme's climate model predictions to these thresholds. I found that the stream communities most at risk to climate change were found in some dry ecoregions, concurring with the previous results, and in wet, warm ecoregions with a high proportion of spatially restricted and endemic taxa, such as northern California. These two approaches describe possible mechanisms of climate change resistance and identify sensitive ecoregions.
Keywords: aquatic insects; climate change; streams; traits; Bayesian regression