Data from: Landscape complementation is a driver of bumble bee (Bombus sp.) abundance in the Canadian Rocky Mountains
Bibliographic record
Abstract
Context: Land use change is a major factor influencing biodiversity, but the mechanisms that drive species losses require further examination. Habitat loss often reduces biodiversity, but habitat fragmentation can increase biodiversity when examined independently. Processes driving this pattern remain largely unclear. Objectives: We aimed to determine the effects of habitat fragmentation on bumble bee populations after controlling for habitat amount, and to examine possible mechanisms behind observed effects. Methods: We sampled 22 species of bumble bees (Bombus sp.) across 50 unique sites located throughout the Canadian Rockies using a sampling design that minimized correlations between amount and spatial arrangement of land covers that may represent important habitat for bees. We modeled bumble bee abundance, species richness and diversity as a function of land cover metrics. Results: Effects of land cover fragmentation were dependent on both the measure of fragmentation used, and landscape scale. Bumble bee abundance was higher where nesting habitat (forest) and foraging habitat (grassland) were found adjacent to each other within 300 m, suggesting a landscape complementation effect where bees benefit from having access to both land cover types in proximity to one another. Having available habitat split into a greater number of patches was detrimental when considering the immediate area (0-300 m), but beneficial when quantified in more distant areas (300-600 m). Conclusions: Landscape complementation may be an important component behind positive fragmentation effects. Estimates of multiple measures of fragmentation are important when testing the impacts of land cover and landscape changes on species abundance and biodiversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".