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The biogeography of lower Mesoamerican freshwater fishes

2005· article· en· W2103236862 on OpenAlexafffund
Scott A. Smith, Eldredge Bermingham

Bibliographic record

VenueJournal of Biogeography · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaSmithsonian Tropical Research Institute
KeywordsBiogeographyBiological dispersalEndemismEcologyRange (aeronautics)MesoamericaFreshwater fishBiodiversitySpecies richnessGeographyBiodiversity hotspotBiologyFish <Actinopterygii>FisheryPopulation

Abstract

fetched live from OpenAlex

Abstract Aim This paper examines the importance of regional processes in determining the patterns of distribution and diversity of lower Mesoamerican freshwater fishes. Location We focused our analyses on the lower Mesoamerican region, which we define to include all the rivers of Panama and Costa Rica. The geographic boundaries are the Colombian Choco to the south and Lake Nicaragua to the north. Methods We described the biogeographical provinces of lower Mesoamerica (LMA) using presence/absence data of primary and secondary LMA freshwater fishes. We conducted subsequent analyses at the spatial resolution of the biogeographical provinces and described patterns of community composition, species richness, endemism, range size, and the permeability of dispersal barriers between biogeographical provinces. Results This study represents the first attempt since that of W. A. Bussing in 1976 to investigate the biogeographical regions of Mesoamerica, and our analyses demonstrate increased regional complexity in biodiversity patterns relative to previous studies. Changes in community composition across LMA clearly highlight the importance of both extrinsic geological processes and intrinsic biological differences among freshwater fish species in shaping the dispersal and diversification histories of the LMA freshwater fish fauna. The influence of biology and geology is also exemplified by patterns of endemism and turnover between biogeographical provinces, which suggests that the relative importance of regional speciation and dispersal varies spatially across the LMA landscape. Finally, it would seem to follow that secondary freshwater fishes will have larger range sizes than primary fishes as a result of the increased salinity tolerance posited for the former group, and thus the increased probability of dispersal along coastlines. We did not, however, find a significant difference between the average range size of primary and secondary freshwater fishes, indicating that the putative differences in physiological tolerance to seawater between the two groups are not reflected in their distribution patterns at the scale of LMA. The geometric distribution of range size of LMA freshwater fishes suggests that dispersal of both primary and secondary freshwater fishes along coastlines must be infrequent. Main conclusion The observation that regional processes exerted a strong influence on the assembly and maintenance of LMA freshwater fish communities has important consequences for both theory and conservation. We suggest that large‐scale biogeographical analyses are required to illuminate the backdrop upon which local interactions play themselves out, supporting a top‐down approach to the study of biological diversity. Our results also identify areas of high conservation priority, providing a baseline for informing conservation strategies for freshwater fishes in LMA. We conclude by calling for conservation planning and action that acknowledges the importance that regional processes play in determining patterns of organismal diversity, and that incorporates these processes in strategies to conserve remnant biological diversity.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.222
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations124
Published2005
Admission routes2
Has abstractyes

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