Can the richness–climate relationship be explained by systematic variations in how individual species’ ranges relate to climate?
Bibliographic record
Abstract
Abstract Aim At large spatial extents, the species richness of high‐level taxa is generally strongly positively correlated with temperature and precipitation, and consistently so across space and time. Here, we test whether this richness–climate relationship is driven by systematic associations between climate and characteristics of the geographical ranges of individual species. Specifically, we test the hypotheses that spatial variations in richness are driven by variations in species mean range size, climatic niche‐breadth, climatic range filling, frequency distribution of climatic niche position and/or frequency distribution of extant climatic conditions. Location The Americas. Methods We tested hypothetical effects of climatically constrained ranges on species richness using the breeding ranges of 3277 birds and 1659 mammals. We tallied species richness in 104‐km2 quadrats in the Americas as well as summary statistics describing the geographical ranges and climatic niches of the species occurring in each quadrat. We then used regression models to relate species richness to those characteristics. Results We found that species mean range size, climatic niche‐breadth and range filling were generally, but inconsistently, negatively related to species richness. As predicted, species richness per quadrat increased with the number of species having their climatic niches centred in the climatic conditions of the quadrats and with the geographical extent of those conditions, although these relationships were relatively weak. Main conclusion The richness–climate relationship appears to be largely decoupled from systematic variations in the characteristics of species climatic niches. Species generally have larger geographical ranges, wider climatic niches and higher range filling in species‐poor areas, each of which, all else being equal, should generate a richness–climate relationship the inverse of what we generally observe in nature. More species have their ranges centred on warm, wet and common climatic conditions. However, temperature and precipitation variables themselves explain more of the variance in species richness than the measured characteristics of species climatic niches.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".