Lowering the rate of timber harvesting to mitigate impacts of climate change on boreal caribou habitat quality in eastern Canada
Bibliographic record
Abstract
Many boreal populations of woodland caribou (Rangifer tarandus caribou) have declined in Canada, a trend essentially driven by the increasing footprint of anthropogenic disturbances and the resulting habitat-mediated apparent competition that increases predation pressure. However, the influence of climate change on these ecological processes remains poorly understood. We evaluated how climate change will affect boreal caribou habitat over the 2030-2100 horizon and in a 9.94 Mha study area, using a climate-sensitive simulation ensemble that integrates climate-induced changes in stand dynamics, fire regime, and different levels of commercial timber harvesting. We assessed the relative importance of these three drivers under projections made using different radiative forcing scenarios (RCP 2.6, 4.5, 8.5). Habitat quality was estimated from resource selection functions built with telemetry data collected from 121 caribou between 2004 and 2011 in 7 local populations. At the beginning of our simulations, caribou habitat was already structured along a south-to-north increasing quality gradient. Simulations revealed changes in forest cover that are driven by climate-induced variations in fire regime and scenarios of harvesting levels, resulting in the loss of older coniferous forests and an increase in deciduous stands. These changes induced a generalized decrease in the average habitat quality and in the percentage of high-quality habitat for caribou, and in a northward recession of suitable habitat. Timber harvesting was the most important agent of change for the 2030-2050 horizon, although it was slowly replaced by changes in fire regime until 2100. Our results clearly showed that it is possible to maintain the current average habitat quality for caribou in future scenarios that consider a reduction in harvested volumes, the only lever under our control. This suggests that we still have the capacity to conciliate socioeconomic development and caribou conservation imperatives in the face of climate change, an important issue debated throughout the species distribution range.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".