A meta-analysis of home range studies in the context of trophic levels: Implications for policy-based conservation
Bibliographic record
Abstract
Home ranges have been widely-used as ecological tools, though using home range estimates in decision-support for conservation biology is a relatively new idea. However, trophic levels are rarely taken into consideration when estimating home range. This lapse could present issues when interpreting past studies, especially in policy-based conservation. The objectives of this study were to survey the current literature, to critically analyse published articles with home range analyses, and to compare home range size by species' trophic level. We predicted that animals residing in higher trophic levels would have significantly larger home ranges than animals occupying lower trophic levels. We found that terrestrial carnivores had larger home ranges than terrestrial herbivores, though terrestrial mesocarnivores had the largest home ranges. We also found that aquatic herbivores had larger home ranges than both aquatic carnivores and aquatic mesocarnivores. Our results are important to consider for planning and management sectors, to avoid the implementation of ineffective conservation policies.
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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 | 0.030 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".