Movements and Resource Selection of the Northern Long-Eared Myotis (<i>Myotis septentrionalis</i>) in a Forest—Agriculture Landscape
Bibliographic record
Abstract
Abstract The fragmentation of forests by the expansion of agriculture is recognized as an important factor influencing worldwide declines of forest-dependent species. Species that are forest dependent may be especially vulnerable to fragmentation because they have specialized resource requirements and may exhibit lower mobility in an agricultural matrix. We investigated movement patterns and resource selection of forest-dependent northern long-eared myotis (Myotis septentrionalis) in a forest–agricultural landscape on Prince Edward Island, Canada. Radiotelemetry was used to locate day-roosts and to estimate locations of female bats during nightly foraging bouts. Day-roost locations and foraging areas were mapped using a geographic information system to generally characterize the forest cover of foraging and roosting areas. Vegetative structure and insect prey availability were measured in the field and compared between foraging and roosting areas to describe resource selection at these sites. Movements of female northern long-eared myotis were constrained to forest features and foraging areas were concentrated along forest-covered creeks with bats roosting predominantly in deciduous trees within the same forest fragment, although bats at 1 site seemed to exclusively use a barn during late pregnancy and lactation. Differences in prey availability did not explain the spatial segregation of roosting and foraging areas. Relative to roost areas foraging sites were more likely to be close to forested creeks and densely forested areas, whereas roost sites were characterized by the availability of potentially suitable roosts. This study demonstrates the importance of investigating movements and resource selection of individuals in fragmented landscapes because a specialization on forest resources can highly restrict the vagility of forest-dependent species to a local environment.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".