Bridging Local Ecological Knowledge and Remote Sensing for Wildlife Assessment and Conservation: Insights from Sebitoli, Kibale National Park, Uganda

Effective conservation of tropical forests under sustained anthropogenic pressure requires reliable, up-to-date knowledge of species presence, yet local ecological knowledge (LEK), camera trapping (CT), and passive acoustic recording (PAR) are rarely compared and used jointly against the same species list at the same site, simultaneously. Kibale National Park (KNP) in Uganda is a well-known biodiversity hotspot and the local culture is deeply shaped by wildlife, with social life and cultural identity rooted in a system of clans and totems. Sebitoli, in the northern sector of KNP, was logged in the 1970s and was part of a park-wide census in 2005. We used the three approaches for 54 vertebrate taxa (32 mammals, 15 birds, 7 reptiles) in this regenerating forest patch twenty years after. Over six months, from 1 February to 30 July 2025, 20 paired camera-trap/acoustic-recorder stations using automated deep-learning classifiers to detect species from camera-trap footage and audio recordings were combined with a structured LEK survey of 34 local research and conservation staff. The 2005 park-wide census provides a historical reference for interpreting present-day detections, although differences in survey design and metrics preclude direct inference about changes in density or abundance. All seven taxa for which non-zero density estimates were reported in Sebitoli in 2005 were detected by at least one method in the present study, the endangered elephants and chimpanzees being among the most frequently detected by the three methods. Camera trapping also detected African golden cat Caracal aurata and African Buffalo Syncerus caffer, which were not recorded during the 2005 transects. No single method captured the full community: CT and PAR combined detected 61% of taxa within their joint taxonomic scope, against 50% for the best single sensor, while LEK alone returned a non-zero score for all 54 species, including every reptile and most taxa currently outside classifier coverage. LEK-sensor correlation weakened substantially once classifier scope was accounted for (ρ = 0.29–0.30), and naming consensus among respondents tracked visual familiarity rather than totemic or cultural salience. Our findings show the detections of species rare or absent twenty years before and highlight how integrating LEK, CT, and PAR can provide a broader and more complementary assessment of biodiversity than any single monitoring approach.