Modeling species' realized climatic niche space and predicting their response to global warming for several western forest species with small geographic distributions.
Bibliographic record
Abstract
The Random Forests multiple regression tree was used to develop an empirically based bioclimatic model of the presence-absence of species occupying small geographic distributions in western North America. The species assessed were subalpine larch (Larix lyallii), smooth Arizona cypress (Cupressus arizonica ssp. glabra), Paiute cypress (syn. Piute cypress) (Cupressus arizonica ssp. nevadensis), and Macfarlane’s four-o’clock (Mirabilis macfarlanei). Independent variables included 33 simple expressions of temperature and precipitation and their interactions. These climate variables were derived from a spline climate model for the Western United States that provides point estimates (latitude, longitude, and altitude). Analyses used presence-absence data largely from the Forest Inventory and Analysis, USDA Forest Service database. Overall errors of classification ranged from 1.39 percent for Macfarlane’s four-o’clock to 3.55 percent for smooth Arizona cypress. The mapped predictions of species occurrence using the estimated realized climatic niche space were more accurate than published range maps. The Hadley and Canadian general circulation models (scenario IS92a for 1 percent increase GGa/year) were then used to illustrate the potential response of the species’ contemporary realized climatic niche space to climate change. Predictions were mapped at a 1-km2 resolution. Concurrence between species’ geographic distribution and their contemporary realized climatic niche rapidly disassociates through the century. These models demonstrate the heightened risk for species occupying small geographic ranges of displacement into climatic disequilibrium from rapid climate change and provide tools to assist decisionmakers in mitigating the threat.
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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) | 0.000 | 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".