Predictors of taxonomic splitting and its role in primate conservation
Bibliographic record
Abstract
ABSTRACT Species are the main unit used to measure biodiversity, but different preferred diagnostic criteria can lead to very different delineations. For instance, named primate species have more than doubled in number since 1982. Such increases have been attributed to a shift away from the ‘biological species concept’ (BSC) in favour of less inclusive species criteria. Critics of recent changes in primate taxonomy have suggested taxonomic splitting may be biased toward certain clades and have unfavourable consequences for conservation. Here, we explore predictors of taxonomic splitting across primate taxa since the initial shift away from the BSC nearly 40 years ago. We do not find evidence that diversification rate, the rate of lineage formation over evolutionary time, is significantly linked to splitting, contrary to expectations if new species concepts and taxonomic methods identify incipient species. We also do not find evidence that research effort in fields where work has been suggested to motivate splitting is associated with increases in species numbers among genera. To test the suggestion that splitting groups is likely to increase their perceived risk of extinction, we test whether genera that have undergone more splitting have also observed a greater increase in their proportion of threatened species since the initial shift away from traditional taxonomic methods. We find no cohesive signal of taxonomic splitting leading to higher threat probabilities across primate genera. Overall, this analysis sends a positive message: threat statuses of primate species are not being overwhelmingly affected by splitting. Regardless, we echo warnings that it is unwise for conservation to be reliant on taxonomic stability. Species (however defined) are not independent from one another, thus, monitoring and managing them as such may not meet the overarching goal of conserving biodiversity.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".