Biotic interactions govern the distribution of coexisting ungulates in the Arctic Archipelago – A case for conservation planning
Bibliographic record
Abstract
Climate change and biodiversity loss underscore the need for conservation planning, even in remote areas. Species distribution models (SDMs) can help identify critical habitat for reserve design and selection, and have quickly advanced to the fore of ecological inquiry. Such models are typically dominated by abiotic factors, following the Eltonian Noise Hypothesis (ENH) that physical features set the limits of species distributions. Nevertheless, recent studies challenge this notion and highlight the importance of biotic interactions. Resolving this discrepancy could have significant implications for conservation and ecological understanding. To test these ideas, we built distribution models for two large herbivores, muskoxen (Ovibos muschatus) and Peary caribou (Rangifer tarandus pearyi), systematically observed across a vast spatial extent – 65 islands spanning 800,000 km2 in the Canadian High Arctic. To test the ENH we fit SDMs with two sets of predictors: (1) abiotic only (i.e. topographic, climatic) and (2) abiotic + biotic (i.e. vegetation communities, distance-to-heterospecifics). We evaluated these models and spatially estimated habitat suitability for each species. We found both sets of models had good predictive ability, although biotic variables (i.e. proportion of grass-lichen-moss) improved model performance and substantially narrowed areas of high habitat suitability. Niche overlap between caribou and muskoxen was moderate and highly suitable areas were spatially disjunct between species and largely outside protected areas. These results fail to support the ENH. Our study implies that biotic features, although often overlooked, may be important to the performance of SDMs and vital in identifying priority areas for conservation. For these large herbivores, reflecting trophic interactions in SDMs was essential when estimating areas of conservation value. Our approach helps prepare the way for improved projections regarding the prospects for wildlife while laying the foundation for biologically relevant protected areas.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".