Summary
The choice of appropriate methods to detect species is crucial for biodiversity monitoring. Camera trapping is currently one of the most widely used methods for characterizing mammal communities, although it requires substantial investment in equipment and personnel. In contrast, questionnaires administered to local populations provide a faster and more cost-effective alternative for assessing community composition, but may be influenced by respondent-related biases that compromise data reliability. This study evaluates the concordance between these approaches for characterizing the carnivore community in the Sierra de Segura (Jaen, southern Spain), using Cohen s kappa coefficient, while also examining the individual and social factors shaping Local Ecological Knowledge (LEK). We deployed 24 camera-trap stations (144 trap nights) across a 25 km2 area to record carnivore presence. In parallel, we conducted two types of surveys with local residents (n = 103): (i) free-listing and (ii) image-based species recognition, while recording individual and social characteristics of respondents. Free-listing surveys tended to underreport species, whereas image-based surveys showed higher agreement with camera-trap data, although occasionally overestimating species presence. Higher concordance was associated with social factors indicative of closer and prolonged contact with the environment, such as permanent residence and ownership of agricultural land. Mammal communities differed between methods; however, agreement improved when respondents had higher LEK, while species-specific behavioral traits could also influence perception. Our findings demonstrate that image-based questionnaires can provide results comparable to camera trapping when respondents have strong connections to their natural surroundings. These results highlight the importance of both survey design and respondent selection in improving the accuracy of biodiversity monitoring, offering a transferable framework for integrating LEK into conservation protocols across diverse ecosystems.
Outcomes reported
The choice of appropriate methods to detect species is crucial for biodiversity monitoring. Camera trapping is currently one of the most widely used methods for characterizing mammal communities, although it requires substantial investment in equipment and personnel. In contrast, questionnaires administered to local populations provide a faster and more cost-effective alternative for assessing community composition, but may be influenced by respondent-related biases that compromise data reliability. This study evaluates the concordance between these approaches for characterizing the carnivore community in the Sierra de Segura (Jaen, southern Spain), using Cohen s kappa coefficient, while also examining the individual and social factors shaping Local Ecological Knowledge (LEK). We deployed 24 camera-trap stations (144 trap nights) across a 25 km2 area to record carnivore presence. In parallel, we conducted two types of surveys with local residents (n = 103): (i) free-listing and (ii) image-based species recognition, while recording individual and social characteristics of respondents. Free-listing surveys tended to underreport species, whereas image-based surveys showed higher agreement with camera-trap data, although occasionally overestimating species presence. Higher concordance was associated with social factors indicative of closer and prolonged contact with the environment, such as permanent residence and ownership of agricultural land. Mammal communities differed between methods; however, agreement improved when respondents had higher LEK, while species-specific behavioral traits could also influence perception. Our findings demonstrate that image-based questionnaires can provide results comparable to camera trapping when respondents have strong connections to their natural surroundings. These results highlight the importance of both survey design and respondent selection in improving the accuracy of biodiversity monitoring, offering a transferable framework for integrating LEK into conservation protocols across diverse ecosystems.
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