RESEARCH ARTICLE Positive effects of forest fragmentation, independent of forest amount, on bat abundance in eastern Ontario,
Bibliographic record
Abstract
Abstract While studies have found that bat abun-dance is positively related to the amount of forest cover in a landscape, the effects of forest fragmen-tation (breaking apart of forest, independent of amount) are less certain, with some indirect evidence for positive effects of fragmentation. However, in most of these studies, the variables used to quantify fragmentation are confounded with forest amount, making it difficult to interpret the results. The purpose of this study was to examine how forest amount and forest fragmentation independently affect bat abundance. We conducted acoustic bat surveys at the centers of 22 landscapes throughout eastern Ontario, Canada, where landscapes were chosen to avoid a correlation between forest amount and forest fragmentation (number of patches) at multiple spatial scales, while simultaneously controlling for other variables that could affect bat activity. We found that the effects of forest amount on bat relative abundance were mixed across species (positive for Lasiurus borealis, negative for Perimyotis subflavus and Lasionycteris noctivagans). When there was evidence for an effect of forest fragmentation, independent of forest amount, on bat relative abundance, the effect was positive (Myotis septentrionalis, Myotis lucifugus and Lasiurus borealis). We suggest that the mecha-nism driving the positive responses to fragmentation is higher landscape complementation in more frag-mented landscapes; that is, increased access to both foraging and roosting sites for these bat species. We conclude that fragmented landscapes that maximize complementation between roosting and foraging sites should support a higher diversity and abundance of bats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".