Trait similarity in reef fish faunas across the world’s oceans
Bibliographic record
Abstract
Species' traits, rather than taxonomic identities, determine community assembly and ecosystem functioning, yet biogeographic patterns have been far less studied for traits. While both environmental conditions and evolutionary history shape trait biogeography, their relative contributions are largely unknown for most organisms. Here, we explore the global biogeography of reef fish traits for 2,786 species from 89 ecoregions spanning eight marine realms with contrasting environmental conditions and evolutionary histories. Across realms, we found a common structure in the distribution of species traits despite a 10-fold gradient in species richness, with a defined "backbone" of 21 trait combinations shared by all realms globally, both temperate and tropical. Across ecoregions, assemblages under similar environmental conditions had similar trait compositions despite hosting drastically different species pools from separate evolutionary lineages. Thus, despite being separated by thousands of kilometers and millions of years of evolution, similar environments host similar trait compositions in reef fish assemblages worldwide. Our findings suggest that similar trait-based management strategies can be applied among regions with distinct species pools, potentially improving conservation outcomes across diverse jurisdictions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".