Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».