Ecological land classification as a surrogate for epigaeic arthropod biodiversity and conservation in boreal forest landscapes
Bibliographic record
Abstract
The use of biodiversity surrogates offers a scientifically sound, robust, and cost-efficient approach for supporting conservation strategies over large landscapes. We examined the habitat associations of ground beetles, rove beetles, and spiders collected with pitfall traps from 12 undisturbed forest types that conform to different ecosites and ecophases using the ecosite classification system for the boreal mixedwood subregion of Alberta, Canada. A total of 79 578 epigaeic arthropods, representing 370 species, were collected during the summer of 2018. Catches of ground and rove beetles were highest in mesic deciduous forest stands, whereas spider catch was highest in subxeric, open canopy jack pine stands. Species richness and diversity differed among taxa and ecophases; however, evenness for the beetle assemblages increased along the moisture gradient, whereas, for spiders evenness was highest at both ends of the moisture gradient. Geographic location accounted for less variation among arthropod assemblages than ecosite and ecophase. In general, epigaeic arthropod assemblages reflected the environmental variation across the ecosite edatopic grid. Hence, the forest ecosite classification system can be used as a surrogate for epigaeic arthropod assemblage structure across large boreal forest mixedwood landscapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".