High beta diversity among small islands is due to environmental heterogeneity rather than ecological drift
Bibliographic record
Abstract
Abstract Aims Both ecological drift and environmental heterogeneity can produce high beta diversity among communities, but only the effect of drift is expected to be enhanced in communities of small size. Few studies have explicitly tested the influence of community size on patterns of beta diversity. Here we applied a series of analyses aimed at testing the influence of drift versus environmental heterogeneity on beta diversity among tree communities on islands of variable size. Location Thousand Island Lake, Zhejiang Province, China. Methods We used data on mapped tree communities and environmental conditions for 20 small islands (<1 ha) and nine large islands (>1 ha) created via the construction of a hydroelectric dam in 1959. Beta diversity was calculated using abundance‐based multiple‐site dissimilarity based on the Bray–Curtis index. On the basis of the hypothesis of ecological drift among small islands, we tested for higher beta diversity among small than large islands using: (a) raw data (b) controlling for the number of individual sampled on a given island, and (c) controlling for the contiguous sampling area and thus for intra‐island environmental heterogeneity. We also tested the prediction that the relationship between species composition and environmental variables should be weaker on small islands using canonical correspondence analyses. Results Using raw data and controlling for the number of individuals, community dissimilarity was significantly greater among small islands than among large islands. However, when controlling for contiguous sampling area this difference disappeared. Contrary to the prediction based on ecological drift, the strength of overall composition–environment relationships was not significantly weaker for small islands in any of the analyses, and environmental heterogeneity increased faster with area among small islands than among large islands. Main Conclusions Despite a result using raw data that was consistent with the hypothesis of ecological drift, our full set of results clearly indicated the high beta diversity among small islands was more likely due to environmental heterogeneity rather than ecological drift. This result points to a clear need to control for sampling area among habitats of different size when testing for statistical signatures of drift.
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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 | 0.002 |
| 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".