Abstract

Abstract Facing the bushmeat crisis, tropical forests require effective monitoring for sustainable wildlife management. To gain credibility with local people and conservation officials, the monitoring needs indicators that comply with local knowledge and predict the available faunal resources. This study explores predictive indicators for bushmeat biomass—the total biomass of five main hunted mammals—in a Cameroonian rainforest. We employed camera trapping and the Random Encounter and Staying Time (REST) model to estimate the spatial variation in each species' population density and bushmeat biomass at three sites. We then calculated six indicators from camera‐trap capture rate estimates and assessed their predictive performance for the total wild meat amount. Duikers generally increased with distance from the public road, but two red duiker species were more markedly affected by the distance than blue duikers. Spatial density patterns of brush‐tailed porcupines and Emin's pouched rats differed between sites. Consequently, bushmeat biomass displayed exponential growth away from the road with varying degrees among the sites. Of the six indicators, the R/B ratio (red‐to‐blue duiker ratio) and the D/R ratio (duiker‐to‐rodent ratio) exhibited positive linear‐like correlations to bushmeat biomass at all sites. The correlation lines were moderately similar across sites in the R/B ratio but largely different in the D/R ratio, suggesting that the latter is unsuitable for sharing information between neighbouring communities. Synthesis and applications. The two indicators based on captured animal composition may effectively predict the total biomass of the main target species for bushmeat hunting, given a reasonably large sample size. The R/B ratio (red duikers/blue duikers) is recommended as a first choice; the D/R ratio (duikers/rodents) can be a good alternative when information sharing is not essential. Because local hunters are aware of depletion‐related changes in species composition of caught animals, these indices may be effectively incorporated into community‐based wildlife monitoring.

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