Hierarchical spatial structuring of stream insect diversity through DNA barcoding
Bibliographic record
Abstract
Biodiversity is often studied in the context of species distributions across spatial scales. Diversity components analysis—the partitioning of total diversity into local diversity and distributional heterogeneity measures—assesses the spatial structure of biodiversity. While previous works have relied on morphological specimen identifications, here, DNA barcoding is coupled with additive diversity partitioning to assess stream larval Trichoptera (caddisfly) species diversity across spatial scales ranging from m2 to Canadian sub-arctic vs. temperate USA regions, and is used in conjunction with checkerboard analyses at a small spatial extent to investigate the importance of biotic interactions. I found that taxonomic resolution influenced the interpretation of results. In addition, Trichoptera diversity was similarly structured at two disparate regions, suggesting similar underlying mechanisms govern how regional diversity is distributed. Interspecific competition was important at small spatial scales. My thesis illustrates the utility of DNA-based species identification coupled with diversity partitioning in the study of biodiversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 teacher head, 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".