Species turnover in vertebrate communities along elevational gradients is idiosyncratic and unrelated to species richness
Bibliographic record
Abstract
Abstract Aim Studies of species turnover commonly assume that turnover is a critical determinant of species richness patterns. But the concordance in patterns of turnover and species richness along gradients is poorly known. Here we characterize elevational patterns of species turnover and test whether turnover and species richness are strongly related. Location Sixty‐two elevation gradients world‐wide, from 17° S to 43° N. Methods We used elevational range data for six terrestrial vertebrate groups to characterize species turnover between neighbouring elevational bands. We measured turnover as Simpson's dissimilarity, a metric that is unaffected by measured differences in species richness among recorded samples. To assess differences from random patterns, elevational turnover was compared with three null models (hard, soft and no boundaries). Lastly, elevational turnover was compared with the combined species richness of neighbouring elevational bands. Analyses were conducted at three grain sizes (200, 400 and 800 m elevation). Results We found no consistent, repeated patterns in elevational turnover. Variability among gradients was very high, with most datasets displaying multiple but inconsistently located peaks. Concordance between null predictions and empirical turnover was poor (average r2 for 200, 400 and 800m grains were: hard boundaries 0.06, 0.12 and 0.15; soft boundaries 0.06, 0.11 and 0.14; unbounded 0.03, 0.07 and 0.10; respectively), although many empirical values fell within the confidence intervals of the null model. Correlations of turnover and species richness were generally poor, but increased with analysis grain (average r2 = 0.19, 0.33 and 0.54, respectively). Main conclusions Turnover cannot serve as a general explanation for richness patterns within elevational gradients. Elevational turnover patterns are highly idiosyncratic, change with scale, and are often indistinguishable from random patterns. Despite the common assertion that the highest species richness occurs where distinct, dominant communities turn over on mountains (e.g. low‐ and high‐elevation communities at a middle ecotone), we found no strong support for such Clementsian‐structured patterns.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".