Modelling the spatial distribution of selected North American woodland mammals under future climate scenarios
Bibliographic record
Abstract
Abstract North America has a diverse array of mammalian species. Model projections indicate significant variations in future climate conditions of North America, and the habitats of woodland mammals of this continent may be particularly sensitive to changes in climate. We report on the potential spatial distributions of 13 wide‐ranging, relatively common species of North American woodland mammals under future climate scenarios. We examined the potential influence of the mean and seasonal climate variables on the distribution of species. Presence‐only occurrence records of species, four predictor variables, two future climate scenarios (Representative Concentration Pathways 4.5 and 8.5), and two time steps (current and 2070) were used to build species’ distribution models using a maximum entropy algorithm (MaxEnt). Our results suggested that overall, 11 of the 13 species are likely to gain climatically suitable space (regions where climate conditions will be similar to those of area currently occupied) at the continental scale, but American marten Martes americana and ‘woodland’ caribou Rangifer tarandus are likely to lose suitable climate range by 2070. Furthermore, climate space is likely to be expanding northwards under future climate scenarios for most of the mammals, and many jurisdictions in the border region between Canada and the USA are likely to lose iconic species, such as moose Alces alces. We identified regions as potential in situ and ex situ climate change refugia, which are increasingly considered to be important for biodiversity conservation. The model results suggest significant implications for conservation planning for the 13 mammalian species under global climate change, especially at fine spatial scales. Numerous species that are presently common at their southern range edge will be functionally or completely extirpated in 50 years. The potential in situ and ex situ climate change refugia could provide an effective support for adaptive strategies aimed at species conservation planning.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".