Exceptions to the rule: Relative roles of time, diversification rates and regional energy in shaping the inverse latitudinal diversity gradient
Bibliographic record
Abstract
Abstract Aim Inverse latitudinal diversity gradients (i‐LDGs), whereby regional richness peaks outside the tropics, have rarely been investigated, and their causes remain unclear. Here, we investigate three prominent explanations, postulating that species‐rich regions have had: (1) longer time to accumulate species; (2) faster diversification; and (3) more energy to support species‐rich communities. These mechanisms have been shown to explain the tropical megadiversity, and we examine whether they can also explain i‐LDG. Location Global. Time period Contemporary. Major taxa studied Amphibians, birds and mammals. Methods We estimated the time for species accumulation, regional diversification rates and regional energy for six tetrapod taxa ( c . 800 species). We quantified the relative effects and interactions among these three classes of variables, using variance partitioning, and confirmed the results across alternative metrics for time (community phylometrics and BioGeoBEARS ), diversification rates (BAMM and DR) and regional energy (past and current temperature, and productivity). Results Although regional richness across each of the six taxa peaked in the temperate region, it varied markedly across hemispheres and continents. The effects of time, diversification rates and regional energy varied greatly from one taxon to another, but high diversification rates generally emerged as the best predictor of high regional richness. The effects of time and regional energy were limited, with the exception of salamanders and cetaceans. Main conclusions Together, our results indicate that the causes of i‐LDG are highly taxon specific. Consequently, large‐scale richness gradients might not have a universal explanation, and different causal pathways might converge on similar gradients. Moreover, regional diversification rates might vary dramatically between similar environments and, depending on the taxon, regional richness might or might not depend on the time for species accumulation. Collectively, these results underscore the complexity behind the formation of richness gradients, which might involve a symphony of variations on the interplay of time, diversification rates and regional energy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".