Using Camera Traps to estimate Habitat Preferences and Occupancy Patterns of Vertebrates in Boreal Wetlands
Bibliographic record
Abstract
Abstract Wetlands are a critical habitat for boreal mammals and birds that rely on them for breeding, foraging, and resting. However, wetlands in boreal regions are under increasing natural and human pressure, leading to a reduction in habitat availability for these species. To inform management and conservation of wildlife, camera traps can help investigate habitat preferences. We aimed to evaluate the effect of habitat features on the occupancy of mammals and birds in boreal wetlands, namely beaver ponds and peatland ponds. We used a multispecies occupancy model to estimate the habitat associations of 11 mammals and 45 avian species detected at 50 ponds during the summers of 2018 and 2019 in Northern Quebec. The patterns of habitat response do not lend support to the hypothesis that beaver ponds host higher levels of occupancy of birds and mammals than peatland ponds. Our results suggest that certain mammals, such as Red Fox (Vulpes vulpes) and River Otters (Lontra canadensis), and birds including the American Pipit (Anthus rubescens), Common Raven (Corvus corax), Hooded Merganser (Lophodytes cucullatus), and Greater Yellowlegs (Tringa melanoleuca) preferred peatland ponds, whereas the Common Grackle (Quiscalus quiscula) preferred beaver ponds. We found a few effects of distance to roads, but no effect of amount of forest cover on species occupancy. The occupancy of 27% of mammals and 24% of birds decreased with increasing latitude. These findings offer valuable insights for the preservation of different wetland types and their associated wildlife communities.
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 | 0.001 | 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".