How are tree species distributed in climatic space? A simple and general pattern
Bibliographic record
Abstract
ABSTRACT Aim Although many factors undoubtedly affect species geographic distributions, can a single, simple model nonetheless capture most of the spatial variation in the probability of presence/absence in a large set of species? For 482 North American tree species that occur east of the Rocky Mountains, we investigated the shape(s) of the relationship between the probability of occupancy of a given location and macroclimate, and its consistency among species and regions. Location North America. Methods Using Little's tree range maps, we tested four hypothetical shapes of response relating occupancy to climate: (1) high occupancy of all suitable climates; (2) threshold response (i.e. unsuitable climates exclude species, but within the thresholds, species presence is independent of climate); (3) occupancy is a bivariate normal function of annual temperature and precipitation; and (4) asymmetric limitation (i.e. abiotic factors set abrupt range limits in stressful climates only). Finally, we compared observed climatic niches with the occupancy of similar climates on off‐shore islands as well as west of the Rockies. Results (a) Species' distributions in climatic space do not have strong thresholds, nor are they systematically skewed towards less stressful climates. (b) Occupancy can generally be described by a bivariate normal function of temperature and precipitation, with little or no interaction between the two variables. This model, averaged over all species, accounts for 82% of the spatial variation in the probability of occupancy of a given area. (c) Occupied geographic ranges are typically ringed by unoccupied, but climatically suitable areas. (d) Observed climatic niche positions are largely conserved between regions. Main conclusions We conclude that, despite the complexities of species histories and biologies, to a first approximation most of the variation in their geographic distributions relates to climate, in similar ways for nearly all species.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".