Habitat loss decreases predator–prey ratios in a pine‐bark beetle system
Bibliographic record
Abstract
Increasing intensity of land use by humans has led to loss of natural habitats, resulting in isolation of remaining habitat fragments. Using a pine‐bark beetle ecosystem as a model, we tested the hypothesis that the ratio of abundance of predators to prey should decrease with increasing habitat loss at the landscape scale. We selected ten red pine ( Pinus resinosa ) sites, representing extremes of available habitat within a 2 km radius surrounding each stand. The bark beetle, Ips pini , and its coleopteran predators were sampled using baited multiple funnel traps. Effects of stand isolation were considerable; ratios of predators to prey (mean number of predators/number of prey±SE) were significantly reduced in isolated stands (0.38±0.09) as compared to those with large amounts of surrounding conifer habitat (1.63±0.41). The decline in ratio occurred both because there was: a) a lower abundance of predators (ca 0.5–0.8×) captured in isolated stands; and b) a significantly higher number of prey (ca 2.2×) captured in isolated stands. Isolation or loss of habitat, therefore, differentially affected the two trophic levels, supporting theoretical predictions. Reductions in predator abundance and, presumably, enemy‐caused mortality may lead to changes in the population dynamics of their prey species, possibly leading to increased outbreaks as habitat becomes increasingly isolated.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".