Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti

Planning for conservation success requires identifying effective and efficient monitoring strategies but multiple types of uncertainty affect the accuracy and precision of wildlife abundance estimates. Observation uncertainty, a consequence of sampling effort and design as well as the process of observation, is still understudied, with little attention given to the multiple potential sources of error involved. To establish error minimization priorities and maximize monitoring efficiency, the direction and magnitude of multiple sources of uncertainty must be considered. Using monitoring of two contrasting ungulate species in the Serengeti ecosystem as a case study, we developed a virtual ecologist' framework within which we carried out simulated tests of different monitoring strategies for different types of species. We investigated which components of monitoring should be prioritized to increase survey accuracy and precision and explored the robustness of population estimates under different budgetary scenarios. The relative importance of each process affecting precision and accuracy varied according to the survey technique and biological characteristics of the species. While survey precision was mainly affected by population characteristics and sampling effort, the accuracy of the survey was greatly affected by observer effects, such as juvenile and herd detectability. Synthesis and applications. Monitoring efficiency is of the utmost importance for conservation, especially in the context of limited budgets and other priorities. We provide insights into the likely effect of different types of observation and process error on population estimates for savanna ungulates, and more generally present a framework for evaluating monitoring programmes in a virtual environment. In highly aggregated species, the main focus should be on survey precision; sampling effort should be defined according to wildlife spatial distribution. For random or slightly aggregated species, accuracy is the key factor; this is most sensitive to observer effects which should be minimized by training and calibration by observer.