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 operational criteria can lead to very different delineations. For instance, named primate species have more than doubled in number since 1982. Such increases have been partly attributed to a shift away from the “biological species concept” (BSC) in favor of less inclusive species criteria. Critics of recent changes in primate taxonomy have suggested taxonomic splitting may be biased toward certain clades and have unfavorable 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 net 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 older taxonomic methods. We find no cohesive signal of taxonomic splitting leading to higher threat probabilities across primate genera. Thus, our analysis suggests that the threat statuses of primate species are not being overwhelmingly driven 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".