Disturbance reverses classic biodiversity predictions in river-like landscapes
Bibliographic record
Abstract
Global analyses of biodiversity consistently reveal recurrent patterns of species distributions worldwide. However, unveiling the specific mechanisms behind those patterns remains logistically challenging, yet necessary for reliable biodiversity forecasts. Here, we combine theory and experiments to investigate the processes underlying spatial biodiversity patterns in dendritic, river-like landscapes, iconic examples of highly threatened ecosystems. We used geometric scaling properties, common to all rivers, to show that the distribution of biodiversity in these landscapes fundamentally depends on how ecological selection is modulated across space: while uniform ecological selection across the network leads to higher diversity in downstream confluences, this pattern can be inverted by disturbances when population turnover (i.e. local mortality) is higher upstream than downstream. Higher turnover in small headwater patches can slow down ecological selection, increasing local diversity in comparison to large downstream confluences. Our results show that disturbance-mediated slowing down of competitive exclusion can generate a specific transient signature in terms of biodiversity distribution when applied over a spatial gradient of disturbance, which is a common feature of many river landscapes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".