Benefits of traditional and local ecological knowledge for species recovery when scientific inference is limited
Bibliographic record
Abstract
Many critically endangered species persist in remnant populations so small that ecological information required to assist recovery, such as species-typical demographic parameters and habitat preferences, can be difficult to acquire based on science alone. Traditional ecological knowledge (TEK) or local ecological knowledge (LEK) can fill information gaps and provide additional understanding, though this expertise is not everlasting and often overlooked. We report on research focused on a species survival plan for mountain bongo (Tragelaphus eurycerus isaaci), a critically endangered antelope endemic to Kenya, persisting in the wild with fewer than 80 individuals in four separated montane forests. In preparation for a potential conservation translocation of captive-bred bongos into one or more forests, extensive camera trapping yielded limited results, suggesting that data were based on the activities of just a few individuals. Moreover, additional information critical to translocations, such as typical group size and sex ratios, could neither be observed nor obtained from the literature. This knowledge gap was largely resolved using expert interviews conducted with eight former Kenyan hunters, along with historical range and browse mapping, enriching understanding of behavioral characteristics rendering bongo particularly vulnerable to exploitation. Consistently similar responses from observations spanning a 50-year period (1950s to 1990s) across four ecosystems added certainty to responses. This study endorses a combination of data sources when dealing with remnant populations, and specifically recommends making use of this documented mountain bongo TEK/LEK to inform decisions about potential bongo reintroductions in Kenya.
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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 | 0.155 | 0.378 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.038 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".