Capturing northern biodiversity: diversity of arctic, subarctic and north boreal beetles and spiders are affected by trap type and habitat
Bibliographic record
Abstract
Abstract Rapid environmental changes in arctic, subarctic and boreal biomes are fuelling the need for effective biodiversity monitoring programs in these regions. Terrestrial arthropods are ideal focal taxa for monitoring, but quantitative comparisons of the efficacy and outcomes of different sampling protocols are limited. Here, the influence of trap type (yellow pan trap or traditional pitfall) and habitat (wet or mesic) on the abundance and diversity of ground‐dwelling arthropods is determined for samples collected in the three northernmost ecoclimatic zones of Canada, using over 32 000 specimens of beetles and spiders. Trap and habitat both influence the abundance, richness, and assemblage composition of arthropods collected, but these effects differ between ecoclimatic zones and depend on taxonomic resolution. Sampling in different habitats yields greater diversity than sampling with different traps in the high arctic, while the inverse is true in the north boreal zone, and neither factor appears to have a significant effect on the diversity of arthropods collected in the subarctic. In all zones, the addition of recessed yellow pan traps to a traditional pitfall trap‐based sampling regime results in the capture of many additional unique species, suggesting that colour is an attractant for at least some ground‐dwelling taxa. These findings have significant implications for large‐scale terrestrial diversity monitoring programs being established or implemented in northern systems. It is recommended that sampling regimes be designed to maximize the diversity of arthropods collected, by including a minimum of two distinct habitats and by using yellow pitfall traps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".