Rediscovery of the lost skink Proscelotes aenea and implications for conservation
Bibliographic record
Abstract
Biodiversity loss is recognized as a grand challenge of the twenty-first century but ascertaining when a species is "lost" can be incredibly difficult-since the absence of evidence is not evidence of absence. This may be a relatively easy task for large and conspicuous animals, but extremely difficult for those living hidden lives or at low population sizes. We showcase this challenge by focusing on Africa's montane skink, Proscelotes aenea (Barbour & Loveridge 1928). In this study, we embarked on a year-long intensive survey to find this fossorial species in Lumbo, Northern Mozambique, the only remaining location where it may still occur but was recorded for the last time over 100 years ago. We located the species already after 20 days of intensive and targeted searching by five members of our team. The finding allowed us to describe, for the first time, details on the biology and ecology of the species, alongside photos and videos of live specimens (including a pregnant female), and to sequence DNA from the species, which we used to infer the phylogenetic placement. Our combined 12S and 16S phylogenetic analysis weakly suggest that the genus Proscelotes may not be monophyletic and therefore requires further phylogenetic work and potentially taxonomic revision. We also gathered evidence of a possible decrease in population abundance and, based on the species' ecology, we identified urbanization as a potential key threat, which could lead to the local or global extirpation of the species. We call for urgent conservation actions that help protect the future of the montane skink, and additional surveys to map its full distribution. As countries now work towards implementing the goals and targets of the Kunming-Montreal Global Biodiversity Framework, our study demonstrates the need for proper investments in biodiversity inventories and monitoring in order to halt species extinctions by 2030.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".