No abstract

Hunting for bushmeat represents a complex social–ecological system ill‐suited to top‐down management. Community participatory management is an alternative approach with increasing support for both ethical and pragmatic reasons. Key to a community approach is long‐term monitoring: this can both catalyse local ownership of and cohesion around management and is necessary to assess the effects of interventions and make changes as needed through adaptive management. Yet community‐driven methods to monitor hunting remain underdeveloped: they often fail to account for sampling bias and do not incorporate space in a thorough way, and data are not communally analysed to simulate effects of potential management decisions. We created a novel community bushmeat monitoring programme to address these gaps across 20 villages in north‐eastern Gabon. Paraecologists conducted standardised monitoring of bushmeat, and hundreds of hunters conducted GPS self‐follows mapping village hunting catchments. We integrated these data to estimate the proportion of bushmeat sampled and make robust extrapolations of total offtake across space and time, estimating an annual offtake of ~30,000 animals of >56 species across all villages. Here, we present our approach and data—and apply them through a case study of six sympatric duiker species—to inform new directions for social–ecological bushmeat research and management.

Accurate estimations of animal populations are necessary for management, conservation, and policy decisions. However, methods for surveying animal communities disproportionately represent specific groups or guilds. For example, transect surveys can provide robust data for large arboreal species but underestimate cryptic or small‐bodied terrestrial species, whereas camera traps have the inverse tendency. The integration of information from multiple methodologies would provide the most complete inference on population size or responses to putative covariates, yet a simple, robust framework that allows integration and comparison of multiple data sources has been lacking. We use 27,813 counts of 35 species or species groups derived from concurrent visual transects, dung transects, and camera trap surveys in tropical forests and compare them within a generalized joint attribute modeling framework (GJAM) that both compares and integrates field‐collected dung, visual, and camera trap data to quantify the species‐ and trait‐specific differences in detection for each method. The effectiveness of survey method was strongly dependent on species, as well as animal traits. These differences in effectiveness contributed to meaningful differences in the reported strength of a known important covariate for animal communities (distance to nearest village). Data fusion through GJAM allows clear and unambiguous comparisons of the counts provided from each different methodology, the incorporation of trait information, and fusion of all three data streams to generate a more complete estimate of the effects of an anthropogenic disturbance covariate. Research and conservation resources are extremely limited, which often means that field campaigns attempt to maximize the amount of information gathered especially in remote, hard‐to‐access areas. Advances in these understudied areas will be accelerated by analytical methods that can fully leverage the total body of diverse biodiversity field data, even when they are collected using different methods. We demonstrate that survey methods vary in their effectiveness for counting species based on biological traits, but more importantly that generative models like GJAM can integrate data from multiple sources in one cohesive statistical framework to make improved inference in understudied environments.