A global assessment of nested patterns in insular mammal assemblages
Bibliographic record
Abstract
Abstract Aim A nested pattern (nestedness) in species composition is a frequent signature of insular communities. However, it remains unclear whether the drivers of nestedness are consistent across multiple island systems. Here, we investigated the pattern and drivers of taxonomic, functional and phylogenetic nestedness in terrestrial mammal assemblages from 10 distinct island systems (archipelagos). Location Global. Time period Contemporary. Major taxa studied Terrestrial mammals. Methods We compiled occurrence data and species traits of terrestrial mammals from 228 islands in 10 distinct island assemblages. We assembled a dataset of island biogeographic characteristics for each of these islands, including island area, isolation index and maximum elevation. For all 10 assemblages, we first tested for significant patterns of taxonomic, functional and phylogenetic nestedness. We then examined the associations between nestedness, island biogeographic characteristics and species traits. Results We detected significant patterns of taxonomic, functional or phylogenetic nestedness in mammal assemblages from all 10 archipelagos. Biogeographic characteristics of islands affecting the rate of extinction in island species, namely, island area and elevation, were significantly associated with the degree of nestedness in these assemblages. Traits associated with the extinction probability of a species, such as litter size, further drove the nested pattern in some assemblages. Main conclusions All analyses pointed to selective extinction as a main mechanism shaping the observed nested patterns in island mammal assemblages. From a conservation point of view, different management strategies should be implemented for mammal assemblages in these island systems by identifying the drivers of species extinction rates specific to each island system and species occurring on these islands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".