Integrating Adaptive Capacity Alters Outcomes When Modelling Effects of Warming on a Cold‐Water Fish in a Sub‐Arctic Ecosystem
Bibliographic record
Abstract
ABSTRACT Aim Local species distributions are often geographically restricted to a subset of environmental conditions across a species' full range, complicating forecasting climate warming effects. However, Bayesian species distribution models (SDM) can leverage geographically restricted datasets with broader knowledge of habitat relationships across the species' range to forecast climate vulnerability in data‐limited regions. Location Northern Canada. Methods Principles of niche tracking and niche expansion were explored using an innovative Bayesian SDM approach to refine a climate vulnerability assessment for bull trout ( Salvelinus confluentus ), a cold‐water riverine fish. The SDM was fit to a large, spatially dense fish occurrence and stream temperature dataset to model how climatic and geomorphic factors influence the current and future distribution of bull trout near its northern range extent. To assess niche tracking, wherein modelled relationships were based on observed occurrence patterns, we fitted the SDM with uninformative priors. For niche expansion, which assumes the population can adjust to occupy a warmer niche like southerly populations, we added an informative prior for summer stream water temperature occupancy. Models projected effects of warming on the distribution of suitable habitat using Representative Concentration Pathways 4.5 and 8.5 emissions scenarios for 2061–2080. Results Bull trout distribution was patchy and limited to intermediate thermal and slope conditions in streams with high groundwater contributions. The latter is a key determinant of biogeographic patterns not seen elsewhere across the species' range. Under niche tracking, suitable habitat extent is projected to decline by 36%–46%, while under niche expansion, suitable habitat could increase by 25%–28%. Main Conclusions The large dichotomy between projections illustrates the importance of considering local features and adaptive capacity when forecasting potential responses of cold‐water fishes to climate warming. It also highlights a need for studies to better understand the mechanisms that may prevail as species distributions shift this century.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".