Factors shaping the range‐size frequency distribution of the endemic fish fauna of the Tropical Eastern Pacific
Bibliographic record
Abstract
Abstract Aim To assess the effect of habitat fragmentation and isolation in determining the range‐size frequency distribution (RFD) of the shorefish fauna endemic to a discrete biogeographical region. Location The Tropical Eastern Pacific (TEP). Methods Habitat isolation represents the separation between oceanic islands and the continental shore of the TEP and habitat fragmentation the degree of spatial continuity of habitats (i.e. reefs, soft bottom, nearshore waters) along the continental coast of the TEP. The effects of habitat isolation and fragmentation were quantified by comparing the RFDs of (1) the species found on oceanic islands vs. the continental shore, and (2) species on the continental shore that use different habitat types. Results The RFD of the entire TEP fauna was bimodal, with peaks at both small‐ and large‐range ends of the spectrum. The small‐range peak was due almost entirely to island species and the large‐range peak due mainly to species found in both the continental shore and oceanic islands. RFDs varied among species using different habitats on the continental shore: reef‐fishes had a right‐skewed RFD, soft‐bottom species a flat RFD, and coastal‐pelagic fishes a left‐skewed RFD. Main conclusions Variation in dispersal capabilities associated with habitat isolation and fragmentation in the TEP appears to be the main mechanism contributing to differences among RFD structure, although variation in tolerances arising from the dynamic regional environment may contribute to some patterns. Because diversity patterns are strongly affected by RFD structure, it is now evident that the insular and continental components of a fauna should be treated separately when analysing such patterns. Furthermore, contrasts in RFD structure among species using different habitats demonstrate that a full understanding of the causes of diversity patterns requires analyses of complete regional faunas in relation to regional geography.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".