Spatial species‐richness gradients across scales: a meta‐analysis
Bibliographic record
Abstract
Abstract Aim We surveyed the empirical literature to determine how well six diversity hypotheses account for spatial patterns in species richness across varying scales of grain and extent. Location Worldwide. Methods We identified 393 analyses (‘cases’) in 297 publications meeting our criteria. These criteria included the requirement that more than one diversity hypothesis was tested for its relationship with species richness. We grouped variables representing the hypotheses into the following ‘correlate types’: climate/productivity, environmental heterogeneity, edaphics/nutrients, area, biotic interactions and dispersal/history (colonization limitation or other historical or evolutionary effect). For each case we determined the ‘primary’ variable: the one most strongly correlated with taxon richness. We defined ‘primacy’ as the proportion of cases in which each correlate type was represented by the primary variable, relative to the number of times it was studied. We tested for differences in both primacy and mean coefficient of determination of the primary variable between the hypotheses and between categories of five grouping variables: grain, extent, taxon (animal vs. plant), habitat medium (land vs. water) and insularity (insular vs. connected). Results Climate/productivity had the highest overall primacy, and environmental heterogeneity and dispersal/history had the lowest. Primacy of climate/productivity was much higher in large‐grain and large‐extent studies than at smaller scales. It was also higher on land than in water, and much higher in connected systems than in insular ones. For other hypotheses, differences were less pronounced. Throughout, studies on plants and animals showed similar patterns. Coefficients of determination of the primary variables differed little between hypotheses and across the grouping variables, the strongest effects being low means in the smallest grain class and for edaphics/nutrients variables, and a higher mean for water than for land in connected systems but vice versa in insular systems. We highlight areas of data deficiency. Main conclusions Our results support the notion that climate and productivity play an important role in determining species richness at large scales, particularly for non‐insular, terrestrial habitats. At smaller extents and grain sizes, the primacy of the different types of correlates appears to differ little from null expectation. In our analysis, dispersal/history is rarely the best correlate of species richness, but this may reflect the difficulty of incorporating historical factors into regression models, and the collinearity between past and current climates. Our findings are consistent with the view that climate determines the capacity for species richness. However, its influence is less evident at smaller spatial scales, probably because (1) studies small in extent tend to sample little climatic range, and (2) at large grains some other influences on richness tend to vary mainly within the sampling unit.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".