Effect of habitat type and pitfall trap installation on captures of epigaeic arthropod assemblages in the boreal forest
Bibliographic record
Abstract
Abstract It is unrealistic to achieve high-resolution biodiversity inventories required to support local conservation strategies over large areas; however, benchmark associations between arthropods and ecosystem classification can support landscape scale biomonitoring. We investigated habitat associations of ground-dwelling spiders (Araneae), staphylinid beetles (Coleoptera: Staphylinidae), and carabid beetles (Coleoptera: Carabidae) in three forest ecosystems in northwestern Alberta, Canada and also studied the effect of variation in depth of pitfall trap installation on catch. Composition and diversity of all three taxa were correlated with the ecosystem classification map, and 20 species were strong indicators of particular habitats. The black spruce (Picea mariana(Miller) Britton, Sterns, and Poggenburg; Pinaceae) bog supported fewer species and individuals of beetles but this trend was not observed for spiders because of natural history traits associated with their performance in this environment. Pitfall trapping biases were constant among habitats enabling proper comparison of ground-dwelling invertebrate assemblages. Three species of beetles (Agonum retractumLeConte (Coleoptera: Carabidae),Pterostichus brevicornis(Kirby) (Coleoptera: Carabidae), andQuedius veloxSmetana (Coleoptera: Staphylinidae)) were disproportionally active beneath the soil surface, as catches were greater in pitfall traps with the lip situated 15–25 cm below the soil surface. Thus, even highly standardised trap placement will influence the concept of biodiversity achieved through pitfall trapping, because some target organisms are disproportionately active in subterranean zones.
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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 | 0.000 | 0.001 |
| 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".