Temporal changes in spatial variation: partitioning the extinction and colonisation components of beta diversity
Bibliographic record
Abstract
The last two decades have witnessed unprecedented changes in beta diversity, the spatial variation in species composition, from local to global scales. However, analytical challenges have hampered empirical ecologists from quantifying the extinction and colonisation processes behind these changing beta diversity patterns. Here, we develop a novel numerical method to additively partition the temporal changes in beta diversity into components that reflect local extinctions and colonisations. By applying this method to empirical datasets, we revealed spatiotemporal community dynamics that were otherwise undetectable. In mature forests, we found that local extinctions resulted in tree communities becoming more spatially heterogeneous, while colonisations simultaneously caused them to homogenise. In coral communities, we detected non-random community disassembly and reassembly following an environmental perturbation, with a temporally varying balance between extinctions and colonisations. Partitioning the dynamic processes that underlie beta diversity can provide more mechanistic insights into the spatiotemporal organisation of biodiversity.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".