A stochastic modelling framework to accommodate the inter-annual variability of habitat conditions for Peary caribou (Rangifer tarandus pearyi) populations
Bibliographic record
Abstract
Climate variability and a wide range of anthropogenic disturbances in the Canadian Arctic Archipelago (CAA) have a negative impact on Peary caribou (Rangifer tarandus pearyi) populations by encumbering seasonal migration patterns, forage accessibility, and calving processes. Increasing Arctic temperatures and precipitation along with the higher frequency of extreme weather events (winter cyclones, rain-on-snow) are responsible for spatiotemporal shifts in floral and faunal phenology, changes in species overlap, and functional alterations of trophic relationships. Building upon empirical estimates of the Peary caribou population rates of change on the Canadian Arctic Archipelago, we introduce a two-pronged approach aiming to characterize year-to-year variability of habitat conditions across the Canadian Arctic Archipelago from 2000 to 2013. Our explanatory variables are based on meteorological (surface snow melt, precipitation, temperature, wind speed) variables, landscape features (fraction of rockland), and resource competition with muskoxen (Ovibos moschatus). These habitat suitability estimates form the basis of a stochastic algorithm to recreate the population growth rates and identify locations, where Peary caribou could experience >10%, 25%, or 50% decrease relative to the population levels at the beginning of our study period. Our analysis identifies the Boothia island complex as a high-risk area, where the low Peary caribou population size is suggestive of their increased susceptibility to extirpation after episodic weather-related events, disease outbreaks, or even elevated incidental predation. In Banks island complex, we provide evidence that the volunteered curtailment of hunting, achieved through co-management between Indigenous communities and regional biologists, moderated (and possibly reversed) the declining trends as established during the pre-2000 period. Our random-walk search also suggests that the prevailing habitat conditions in Melville and Bathurst island complexes were generally favorable and could partly explain the recent Peary caribou population increase. We conclude by identifying unaccounted factors that are critical for building an empirical modelling framework with quasi-mechanistic foundation in order to evaluate the impact of climate change on the integrity of Peary caribou populations within any location of the Canadian Arctic.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".