Patterns of functional diversity along latitudinal gradients of species richness in eleven fish families
Bibliographic record
Abstract
Aim: As we enter an era of major biodiversity shifts, understanding large-scale biodiversity patterns has become crucial for ecological and conservation purposes. Often, conservation priorities are based on concepts derived largely from species richness, yet recent works show that different facets of biodiversity are also critical for proper ecosystem continuity, function, and services. One facet of biodiversity increasingly relevant to conservation is functional diversity. Here, we aim to improve our understanding of large-scale patterns of biodiversity by testing the hypothesis that species richness can also accurately estimate functional diversity along the latitudinal gradient of species richness in fish. Location: Marine Environments. Time Period: Contemporary Major taxa studied: 842 species within eleven fish families; Acanthuridae, Blenniidae, Chaetodontidae, Gobiidae, Labridae, Lutjanidae, Pleuronectidae, Pomacanthidae, Pomacentridae, Scombridae, Sparidae. Methods: Using geometric morphometrics to calculate morphological diversity, a proxy for functional diversity, we estimated expected functional diversity for a given number of species and compared it to the observed functional diversity in fish families along latitudes. We then fit a brokenstick regression model with estimates of functional diversity over absolute degree of latitudes to locate latitudes where significant shifts in functional diversity occur. Results: We found that species richness typically over- or under-estimated functional diversity along the latitudinal gradient of species richness in the evaluated fishes. We also show that for most families investigated, there is a pattern of stable functional diversity from the equator through the tropics that shifts with a mean inflection point occurring at absolute latitude 31.7° ± 10.1°. We suggest this pattern may be linked to changes in environmental factors such as global temperature and/or habitat availability beyond tropical latitudes, however, these concepts require more study. Main conclusion: This analysis shows the importance of further considering functional diversity in combination with other biodiversity metrics when developing conservation priorities and policies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".