Temporal dynamics and nestedness of an oceanic island bird fauna
Bibliographic record
Abstract
ABSTRACT Aim To examine temporal variation in nestedness and whether nestedness patterns predict colonization, extinction and turnover across islands and species. Location Dahlak Archipelago, Red Sea. Method The distributions of land birds on 17 islands were recorded in two periods 30 years apart. Species and islands were reordered in the Nestedness Temperature Calculator, software for assessing degrees of nestedness in communities. The occupancy probability of each cell, i.e. species–island combinations, was calculated in the nested matrix and an extinction curve (boundary line) was specified. We tested whether historical and current nested ranks of species and islands were correlated, whether there was a relationship between occupancy probability (based on the historical data) and number of extinctions or colonizations (regression analyses) and whether the boundary line could predict extinctions and colonizations (chi‐square analyses). Results Historical and current nested ranks of islands and species were correlated but changes in occupancy patterns were common, particularly among bird species with intermediate incidence. Extinction and turnover of species were higher for small than large islands, and colonization was negatively related to isolation. As expected, colonizations were more frequent above than below the boundary line. Probability of extinction was highest at intermediate occupancy probability, giving a quadratic relationship between extinction and occupancy probability. Species turnover was related to the historical nested ranks of islands. Colonization was related negatively while extinction and occupancy turnover were related quadratically to historical nested ranks of species. Main conclusions Some patterns of the temporal dynamics agreed with expectations from nested patterns. However, the accuracy of the predictions may be confounded by regional dynamics and distributions of idiosyncratic, resource‐limited species. It is therefore necessary to combine nestedness analysis with adequate knowledge of the causal factors and ecology of targeted species to gain insight into the temporal dynamics of assemblages and for nestedness analyses to be helpful in conservation planning.
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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 | 0.001 |
| 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".