Forces Structuring Caddisfly Communities: Biogeographical to Anthropogenic Effects
Bibliographic record
Abstract
Historical processes, patterns of diversification, and landscape characteristics have resulted in the distribution of biodiversity that forms the context in which local community interactions occur. These local dynamics include relationships among different species and interactions between species and the environment. I studied the influence of regional and historical processes on local communities of caddisflies, aquatic insects in the order Trichoptera, within the Nearctic bioregion. I focused primarily on communities in running-water (lotic) systems in the United States and Canada. Much of my research on Trichoptera specimens and freshwater habitat was generated from publicly available data. First, I examined the biogeography of caddisflies in the US and Canada by quantifying overall Trichoptera species richness in different geographical regions and analyzing the relationships of richness of five focal caddisfly families with geographical and climatic predictors. I found that longitude was the most important predictor of richness, though the relationship had different directionality depending on family. Paleoclimate variables were also important predictors of richness. Focusing on adult caddisfly size, the most important predictors were longitude, contemporary temperature, and latitude. Next, I studied lotic caddisfly community structure across geographic regions in the contiguous US and evaluated whether taxa-environment relationships could be explained by functional traits. Community structure changed with geography, as did the most important landscape- and local-level environmental predictors of taxon-specific abundance. The traits I analyzed were not reliably related to environmental parameters. I then focused more specifically on caddisfly communities in the context of urbanization. Community scientists contributed samples from across the US and Canada, from which the effects of geography, seasonality, and urbanization were determined on family-level richness, abundance, diversity, and presence-absence of taxa. Though I expected the effects of urbanization to be mediated by geography, this was not the case. Finally, with acknowledgement of the influence of taxonomy on community ecology data and predictions, I present a molecular phylogenetic assessment of the relationships between established subfamilies in the microcaddisfly family Hydroptilidae. As a whole, my thesis examines the role of different types of contexts that can shape the community structure of an ecologically important aquatic insect group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".