Investigating the effect of forestry on leaf-litter arthropods (Algonquin Park, Ontario, Canada)
Bibliographic record
Abstract
Arthropods are the most diverse taxonomic group of terrestrial eukaryotes and are sensitive to physical alterations in their environment such as those caused by forestry. With their enormous diversity and physical omnipresence, arthropods could be powerful indicators of the effects of disturbance following forestry. When arthropods have been used to measure the effects of disturbance, the total diversity of some groups is often found to increase following forestry. However, these findings are frequently derived using a coarse taxonomic grain (family or order) to accommodate for various taxonomic impediments (including cryptic diversity and poorly resourced taxonomists). Our intent with this work was to determine the diversity of arthropods in and around Algonquin Park, and how this diversity was influenced by disturbance (in this case, forestry within the past 25 years). We used DNA barcode-derived diversity estimates (Barcode Index Number (BIN) richness) to avoid taxonomic impediments and as a source of genetic information with which we could conduct phylogenetic estimates of diversity (PD). Diversity patterns elucidated with PD are often, but not always congruent with taxonomic estimates-and departures from these expectations can help clarify disturbance effects that are hidden from richness studies alone. We found that BIN richness and PD were greater in disturbed (forested) areas, however when we controlled for the expected relationship between PD and BIN richness, we found that cut sites contained less PD than expected and that this diversity was more phylogenetically clustered than would be predicted by taxonomic richness. While disturbance may cause an evident increase in diversity, this diversity may not reflect the full evolutionary history of the assemblage within that area and thus a subtle effect of disturbance can be found decades following forestry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".