Maximizing nonhuman primate fecal sampling in the Republic of Congo.

Techniques for detection of pathogens in wildlife feces allow disease surveillance of species that are difficult to locate and capture (e.g., great apes). However, optimal strategies for detection of feces in logistically challenging environments, such as the forests of Central Africa, have not been developed. We modeled fecal gorilla sampling in the Republic of Congo with computer simulations to explore the performance of different fecal sampling designs in large tropical landscapes. We simulated directed reconnaissance walk (recce) and line-transect distance-sampling survey designs and combinations thereof to maximize the number of fecal samples collected, while also estimating relative ape density on a virtual landscape. We analyzed the performance of different sampling designs across different densities and distributions of ape populations, assessing each for accuracy as well as cost and time efficiencies. Past ape density surveys and fecal deposition rates were used to parameterize the simulated fecal sampling designs. Our results showed that a mixed sampling design that combines traditional transect and a directed reconnaissance sampling design maximized the number of fecal samples collected and estimates of species density. Targeted sampling produced strongly biased estimates of population abundance but maximized efficiency. This research will help design the fecal sampling component of a larger study relating great ape density to Ebola fecal antibody prevalence.