Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania
Bibliographic record
Abstract
Abstract Citizen or community science has the potential to inform wildlife management by including the general public in research and generating datasets on human perceptions of wildlife population dynamics and human–wildlife interactions. These contributions are especially valuable in areas with limited formal capacity for wildlife monitoring. However, people's perceptions are not always reliable and hinge on the accurate classification of species. In the absence of artificial intelligence‐supported automatic identification tools or wildlife experts, effectively incorporating people's reports of wildlife sightings into conservation management plans depends on the abilities of people to accurately identify animals (i.e. species literacy). These skills likely vary across human populations in accordance with a range of demographic, geographic and species‐specific factors. We carried out 680 semi‐structured interviews with rural citizens, randomly selected along transects in 25 villages across northern Tanzania. We showed photographs of 17 mammal species to participants and assessed species identification ability. Using a generalized linear mixed model within a Bayesian framework that accommodated the hierarchical data structure and non‐independence of the data, we tested specific hypotheses regarding the correlations of species identification accuracy with human demographic (ethnicity, education, age, wealth, gender), geographic (Human Footprint Index [HFI], distance to protected areas, district) and species‐specific (conservation status, activity patterns, body mass, diet) variables. Most respondents accurately identified key wildlife species commonly involved in human–wildlife interactions. Gender strongly influenced species identification accuracy, with men three times more likely to correctly identify species as compared to women. Formal education was negatively correlated with species identification accuracy. Respondents identified large species more accurately than smaller ones, whereas other species traits were not markedly correlated with identification accuracy. Distance to the nearest protected area, district and the HFI score in the area surrounding the household of the respondent were not markedly associated with species identification accuracy. Our results show that rural residents in northern Tanzania can reliably identify key wildlife species implicated in consequential human–wildlife interactions, though identification accuracy was affected by a combination of demographic and species‐specific factors that must be appropriately contextualized. This finding validates studies of local perceptions of wildlife populations and community reports of human–wildlife interactions. Finally, we discuss how local perspectives on wildlife can be applied to improve human–wildlife coexistence. Read the free Plain Language Summary for this article on the Journal blog.
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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".