Partitioning the colonization and extinction components of beta diversity: Spatiotemporal species turnover across disturbance gradients
Bibliographic record
Abstract
ABSTRACT Changes in species diversity often result from species losses and gains. The dynamic nature of beta diversity (i.e., spatial variation in species composition) that derives from such temporal species turnover, however, has been largely overlooked. Here, we disentangled extinction and colonization components of beta diversity by using the sets of species that went locally extinct and that newly colonized the given sites. We applied this concept of extinction and colonization beta diversity to plant communities that have been repeatedly measured in experimentally disturbed forests. We first found no difference in beta diversity across disturbance gradients when it was analyzed for communities at a single point in time. From this result, we might conclude that disturbance caused no impact on how species assemble across space. However, when we analyzed the extinction and colonization beta diversity, both measures were found to be significantly lower in disturbed sites compared to undisturbed sites. These results indicate that disturbance removed similar subsets of species across space, making communities differentiate, but at the same time induced spatially uniform colonization of new species, causing communities to homogenize. Consequently, the effects of these two processes canceled each other out. The relative importance of extinction and colonization components per se also changed temporally after disturbance. Analyses using extinction and colonization beta diversity allowed us to detect nonrandom dis- and re-assembly dynamics in plant communities. Our results suggest that common practices of analyzing beta diversity at one point in time can mask significant variation driven by disturbance. Acknowledging the extinction–colonization dynamics behind beta diversity is essential for understanding the spatiotemporal organization of biodiversity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".