Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania
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 li...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | People and Nature |
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| Online Access: | https://doi.org/10.1002/pan3.70085 |
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| author | Justin Raycraft Reilly Becchina Danielle Bettermann Stephen Koester Elana Kriegel Kiana Lindsay Edwin Maingo Ole Emily Ramirez Bryan Spizuco Christian Kiffner |
| author_facet | Justin Raycraft Reilly Becchina Danielle Bettermann Stephen Koester Elana Kriegel Kiana Lindsay Edwin Maingo Ole Emily Ramirez Bryan Spizuco Christian Kiffner |
| author_sort | Justin Raycraft |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6bbb2c180bef4c009609f902a520b9ef |
| institution | Kabale University |
| issn | 2575-8314 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | People and Nature |
| spelling | doaj-art-6bbb2c180bef4c009609f902a520b9ef2025-08-20T04:02:09ZengWileyPeople and Nature2575-83142025-08-01782002201810.1002/pan3.70085Socio‐ecological correlates of wildlife species identification across rural communities in northern TanzaniaJustin Raycraft0Reilly Becchina1Danielle Bettermann2Stephen Koester3Elana Kriegel4Kiana Lindsay5Edwin Maingo Ole6Emily Ramirez7Bryan Spizuco8Christian Kiffner9Department of Anthropology University of Lethbridge Lethbridge Alberta CanadaDepartment of Anthropology University of Maryland College Park Maryland USADepartment of Environmental Initiative Lehigh University Bethlehem Pennsylvania USAMuhlenberg College Allentown Pennsylvania USADepartment of Animal Science Cornell University Ithaca New York USAUniversity of San Diego San Diego California USADepartment of Agricultural Extension and Community Development Sokoine University of Agriculture Morogoro TanzaniaLafayette College Easton Pennsylvania USAThe Rochester Institute of Technology Rochester New York USADivision of Land Use Systems, Albrecht Daniel Thaer‐Institute of Agricultural and Horticultural Sciences Humboldt‐Universität zu Berlin Berlin GermanyAbstract 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.https://doi.org/10.1002/pan3.70085citizen sciencehuman–wildlife coexistencehuman–wildlife conflicthuman–wildlife interactionslarge carnivoreslarge herbivores |
| spellingShingle | Justin Raycraft Reilly Becchina Danielle Bettermann Stephen Koester Elana Kriegel Kiana Lindsay Edwin Maingo Ole Emily Ramirez Bryan Spizuco Christian Kiffner Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania People and Nature citizen science human–wildlife coexistence human–wildlife conflict human–wildlife interactions large carnivores large herbivores |
| title | Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania |
| title_full | Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania |
| title_fullStr | Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania |
| title_full_unstemmed | Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania |
| title_short | Socio‐ecological correlates of wildlife species identification across rural communities in northern Tanzania |
| title_sort | socio ecological correlates of wildlife species identification across rural communities in northern tanzania |
| topic | citizen science human–wildlife coexistence human–wildlife conflict human–wildlife interactions large carnivores large herbivores |
| url | https://doi.org/10.1002/pan3.70085 |
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