Bibliographic record
Abstract
Insects, arachnids, and other terrestrial arthropods account for 90% of the world’s described animals and play a crucial role in maintaining ecosystem stability. Over the past several decades, insect and arachnid richness and abundance have declined. Drivers of insect declines include habitat loss, pesticide use, climate change, and light pollution, among other stressors. Causes of arachnid declines are less well-understood but generally assumed to be similar. Still, these taxa are often overlooked in legal protections for endangered species. In this thesis I first quantify insect and arachnid biodiversity in the continental United States and Canada and the extent to which species’ conservation status has been evaluated. I examine why state policies for protecting endangered insects and arachnids differ in the United States, then determine the role taxonomic bias, extinction risk, and functional traits play in state and federal insect and arachnid protections. I find the conservation status of most insect and arachnid species in the region is unknown and that this reduces their likelihood of receiving legal protections. I identify the percentage of a state’s GDP that comes from oil and gas extraction and mining as determinants of state policy and public perceptions of wildlife as the driver of how many species a state lists. I also find that taxonomic order influences federal and state legal protections, with odonates, lepidopterans, and hemipterans overrepresented in state lists. Given the role data deficiency plays in impeding insect and arachnid conservation, in Chapter 2 I attempt to address a gap in our understanding of spider conservation by examining how spiders respond to light pollution, a proposed cause of insect declines. I test whether the Pennsylvania grass spider, (Agelenopsis pennsylvanica), preferentially build their webs near artificial light in the absence of other stimuli. I then run a choice-conflict experiment in which spiders must choose between placing their web in an artificially lit area without prey or an unlit area with prey. I find spiders preferentially make their webs in artificially lit areas in the absence and presence of prey. While building webs near artificial light may allow them to catch more insect prey in the short-term, the tendency to prioritize light over prey when placing webs may become disadvantageous as insects evolve reduced flight-to-light behavior, creating an ecological trap for spiders. This thesis highlights the ongoing need to assess how insects and arachnids respond to stressors and the need for greater protection for these taxa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".