Woodland caribou calf recruitment in relation to calving/post-calving landscape composition
Bibliographic record
Abstract
Since the 1990s, Newfoundland’s woodland caribou (Rangifer tarandus caribou) population has declined by an estimated 66%. Low calf recruitment has been associated to the decline, possibly triggered by increasing calf predation and/or decreasing resources. To investigate the role of landscape composition in this system, we studied the yearly (2005-2008) calving/post-calving range (CPCR) of 104 satellite-collared females belonging to six herds. We mapped nine disturbance factors (e.g. roads, logging, etc), as well as vegetation cover types (e.g. coniferous, deciduous forests, etc), and determined the total area they occupied within CPCRs yearly for each herd. Using an information theoretic approach, we assessed the model that best explained variation in recruitment using these components. Based on corrected Akaike Information Criterion, the model that best explained variation in calf recruitment included total disturbance and deciduous forest area, both showing the expected negative relationship with calf recruitment. Other landscape variables among the models with ΔAICc < 2 were mixed forest, also with a suggested negative relationship, and barrens and wetlands with a significant positive trend. This study highlights the need to minimize total disturbance footprint and account for resulting changes in forest composition within CPCRs during land use planning. Expanding forestry operations and road infrastructure in critical woodland caribou habitat across Canada may additionally contribute to habitat loss via fragmentation. This in turn, may lead to range recession beyond the initial local avoidance footprint. We see the possibility of using calf recruitment models based on landscape parameters, among others, to predict the impact of new industrial developments on calf recruitment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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 teacher head, 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".