Climate change mitigation through woodland caribou (<i>Rangifer tarandus)</i> habitat restoration in British Columbia
Bibliographic record
Abstract
Abstract Climate change poses a significant global threat, requiring rapid and effective mitigation strategies to limit future warming. Tree planting is a commonly proposed and readily implementable natural climate solution. It is also a vital component of habitat restoration for the threatened woodland caribou (Rangifer tarandus). There is potential for the goals of caribou conservation and carbon sequestration to be combined for co-benefits. We examine this opportunity by estimating the carbon sequestration impacts of tree planting in woodland caribou range in British Columbia (BC), Canada. To do so, we couple Landsat-derived datasets with Physiological Processes Predicting Growth, a process-based model of forest growth. We compare the sequestration impacts of planting informed by woodland caribou habitat needs to planting for maximum carbon sequestration under multiple future climate scenarios including shared socio‐economic pathways (SSP) 2, representing ∼2.7 °C warming, and SSP5, representing ∼4.4 °C warming. Trees were modelled as planted in 2025. Province-wide by 2100, planting for maximum-carbon sequestration averaged 1062 Mg CO2 · ha−1 planted, while planting for caribou habitat resulted in an average of 930 Mg CO2 · ha−1 planted, a reduction of 12%. We found that relative sequestration between herds remained similar across warming scenarios and that, for most ecotypes, sequestration increased from 5% to 7% between the coldest (∼2.7 °C warming) and warmest (∼4.4 °C warming) scenario. Variability in the relative sequestration impacts of planting strategies was observed between herds, highlighting the importance of spatially-explicit, herd-level analysis of future forest growth when planning restoration activities. Our findings indicate a large potential for co-benefits between carbon sequestration and woodland caribou habitat restoration across BC in all warming scenarios modelled. They also underscore the value of process-based forest growth models in evaluating the carbon implications of tree planting and habitat restoration across large areas under a changing climate.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".