Structure of small tropical islands freshwater fish and crustacean communities: a niche- or dispersal-based process?
Bibliographic record
Abstract
Determining the relative importance of niche- and dispersal-based processes in the structuring of animal communities is central in ecology. Freshwater fish and crustacean communities of small tropical islands can bring new insights for understanding these processes as all their species present a pelagic larval stage which gives them important dispersal capacities. Consequently, we hypothesized that dispersal-based process maybe preponderant for structuring these communities from the regional- to the local, i.e. survey site, scales. Gradient forest analyses allowed us to model the effect of 12 environmental variables on freshwater taxa abundances in two southwestern Indian Ocean islands: Mayotte (26 taxa) and Reunion (21). A total of 153 surveys in Mayotte and 266 in Reunion were used for building the models. Despite the strong heterogeneity of environmental conditions between the two islands, the main factors structuring freshwater fish and crustacean communities in both islands were the elevation and the slope of the sites. The observed structure appeared more pronounced for predatory species than for primary consumers and omnivores. As predators generally have limited locomotor capacities, it is concluded that dispersal-based process structures these communities not only at large geographical scales but also at the intra-watershed scale, by limiting the inland penetration (or dispersion) of species in relation to their locomotor capacities. However, more knowledge concerning ecological traits and taxonomic status of many species is needed to confirm this assumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".