Latitudinal biodiversity gradients at three levels: Linking species richness, population richness and genetic diversity
Bibliographic record
Abstract
Abstract Motivation Theory describing biodiversity gradients has focused on species richness with less conceptual synthesis outlining expectations for intraspecific diversity gradients, that is, broad‐scale population richness and genetic diversity. Consequently, there is a need for a diversity–gradient synthesis that complements species richness with population richness and genetic diversity. Review methods Species and population richness are the number of different species or populations in an area, respectively. Population richness can be totalled across species, within a species or averaged across species. Genetic diversity within populations can be summed or averaged across all species in an area or be averaged across an individual species. Using these definitions, we apply historical, ecological and evolutionary frameworks of species richness gradients to formulate predictions for intraspecific diversity gradients. Review conclusions All frameworks suggest higher average population richness at high latitudes, but similar total population richness across latitudes. Predictions for genetic diversity patterns across species are not consistent across frameworks and latitudes. New analysis methods Species range size tends to increase with latitude, so we used empirical data from c. 900 vertebrate species to test hypotheses relating species range size and richness to population richness and genetic diversity. New analysis conclusions Species range size was positively associated with its population richness but not with species‐specific genetic diversity. Furthermore, a positive linear relationship was supported between species richness and total population richness, but only weakly for average population richness. Overall conclusion Through the lens of species richness theories, our synthesis identifies an uncoupling between species richness, population richness and genetic diversity in many instances due to historical and contemporary factors. Range size and taxonomic differences appear to play a large role in moderating intraspecific diversity gradients. We encourage further analyses to jointly assess diversity–gradient theory at species, population and genetic levels towards better understanding Earth’s biodiversity distribution and refining biodiversity conservation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".