Biogeographic drivers of community assembly on oceanic islands: The importance of archipelago structure and history
Bibliographic record
Abstract
Abstract Aim Accounting for geo‐environmental dynamics is crucial to understand community assembly across islands. Whittaker et al. ( J Biogeogr , 35:977–994, 2008)’s General Dynamic Model (GDM) aims towards this goal. Yet, it does not explicitly consider that most islands belong to archipelagos. We examined how island biodiversity dynamics are influenced by the interaction of eco‐evolutionary processes acting at the archipelago level with each island's geo‐environmental dynamics. Location Hypothetical archipelagos. Taxon Any. Methods We used an individual‐based model, ecologically neutral within the archipelago. Several islands emerge in succession with a typical volcanic ontogeny. We considered both mainland and inter‐island dispersal. Geographically isolated lineages diverged over time, possibly speciating. Results We found diversity to be at dynamic equilibrium. In an archipelago, islands hosted more diversity and more endemic species, at both island and archipelago levels, than an equivalently‐sized single isolated island. This was due to an ‘archipelago effect’: inter‐island dispersal increased within‐island diversity through species occurrence on multiple islands; species may undergo anagenetic changes on the colonised islands, eventually speciating, thereby increasing archipelago diversity. Biodiversity dynamics of different islands may differ even on islands with identical geo‐environmental dynamics because the archipelago effect varied over time and affected each island differently (‘history effect’). By accounting for these effects, we predicted detectable deviations from the GDM predictions, which are largest for remote archipelagos, with islands located close together and with an intermediate time of island emergence. In linear stepping‐stone archipelagos, we predicted higher diversity on centrally located islands. Main conclusions Our results demonstrate that analyses of insular biodiversity data would greatly benefit from explicitly accounting for both archipelago and history effects. We suggest incorporating variables characterising the spatio‐temporal structure of the whole archipelago. We discuss possible difficulties in distinguishing between the archipelago effect and equilibrium diversity dynamics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".