Historical Field Records Reveal Habitat as an Ecological Correlate of Locomotor Phenotypic Diversity in the Radiation of Neotropical Geophagini Fishes
Bibliographic record
Abstract
AbstractPhenotypic macroevolutionary studies provide insight into how ecological processes shape biodiversity. However, the complexity of phenotype-ecology relationships underscores the importance of also validating phenotype-based ecological inference with direct evidence of resource use. Unfortunately, macroevolutionary-scale ecological studies are often hindered by the challenges of acquiring taxonomically and spatially representative ecological data for large and widely distributed clades. The South American cichlid fish tribe Geophagini represents a continentally distributed radiation whose early locomotor morphological divergence suggests habitat as one ecological correlate of diversification, but an association between locomotor traits and habitat preference has not been corroborated. Field notes accumulated over decades of collecting across South America provide firsthand environmental records that can be mined for habitat data in support of macroevolutionary ecological research. In this study, we applied a newly developed method to transform descriptive field note information into quantitative habitat data and used it to assess habitat preference and its relationship to locomotor morphology in Geophagini. Field note-derived data shed light on geophagine habitat use patterns and reinforced habitat as an ecological correlate of locomotor morphological diversity. Our work emphasizes the rich data potential of museum collections, including often-overlooked material such as field notes, for evolutionary and ecological research.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".