Variation in mainland Northwest Territories late-winter muskox (Ovibos moschatus) density estimations and habitat associations above and below treeline.
Bibliographic record
Abstract
The Arctic and Sub-Arctic ecosystems are seeing accelerated changes in temperature, landcover, and consequently species abundance and distributions. Reliable distributions, and associated population density estimates, are essential for effective conservation and management efforts. Growing concerns from northern communities regarding the relationship between muskox and declining caribou populations strengthens the need for updated information on muskox populations within mainland Northwest Territories (NWT). The first objective for my research was to quantify and map updated winter estimates of abundance, density, and distribution of muskoxen within three recent survey regions located in mainland NWT, using a multiple covariate distance sampling method (MCDS), paired with density surface modelling (DSM). My second objective was to explore spatial and social predictors of muskox habitat associations to help infer the extent and potential causes of their contemporary southward expansion across mainland NWT. I tested two competing hypotheses that drive ungulate distributions generally across large spatial and temporal scales of high habitat heterogeneity, as encompassed by the study regions investigated here; muskox density and distribution may be driven by the nutritional landscape where environmental covariates representing high forage quality best predict muskox occurrence. Alternatively, muskox density and distribution may be driven by a predatory landscape, where environmental covariates that support antipredator grouping behaviours best predict muskox occurrence. Through my analyses I infer muskox populations are stable in northern regions (the Sahtú and Beaufort Delta regions) and growing in southern regions (the East Arm region) of mainland NWT; range expansion of muskoxen appears to be continuing southward beyond their historical boundary. I showed varying support for both hypotheses. Muskox density was best predicted by nutritionally important environmental covariates but muskox distribution did not uphold my nutritional hypothesis, while group size was often correlated with land cover that supports antipredator grouping behaviours. However, weak, and inconsistent results across all regions suggest that unmeasured environmental conditions that occur similarly in all regions may also influence muskox occurrence and grouping behaviours. Snow depth and predator occurrence may be important considerations for future investigation. I suggest continued and expanded aerial survey efforts and additional environmental data collected at finer spatial grains may help to inform future muskox density and distribution analyses across mainland NWT.
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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 | 0.000 | 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 | 0.000 |
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".