The Long and Short of Biodiversity: Cumulative Diversity and Its Drivers
Bibliographic record
Abstract
Long-term or cumulative diversity is the biodiversity that accumulates at a site over many generations of community members. Cumulative diversity is likely important to the intrinsic and functional value of ecosystems given the legacies left behind by many species. While its components—average short-term diversity (alpha) and temporal turnover (beta)—have been extensively studied, cumulative diversity itself has not. We therefore examined the environmental and community drivers of cumulative diversity with a novel hierarchical diversity partition. This partition breaks cumulative diversity into short-term, turnover, richness, and evenness components. We applied this framework to 49 tropical rock pool communities, censused over tens to hundreds of organism generations. Results uncovered two environmental regimes that differentially impacted the richness and evenness components of cumulative diversity: Occasional drying events mainly limited richness and reset communities, while less severe physicochemical variations reduced the evenness of communities. These causal pathways amount to differential controls on cumulative diversity; controls that can oppose each other to buffer diversity against change as well as create unexpected trade-offs for managers. We conclude that maintaining diversity at longer timescales requires new analytical tools and an expanded view that can account for its complexity.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.005 |
| 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".