Detecting abundance trends under uncertainty: the influence of budget, observation error and environmental change

Population monitoring must robustly detect trends over time in a cost-effective manner. However, several underlying ecological changes driving population trends may interact differently with observation uncertainty to produce abundance trends that are more or less detectable for a given budget and over a given time period. Errors in detecting these trends include failing to detect declines when they exist (type II), detecting them when they do not exist (type I), detecting trends in one direction when they are actually in another direction (type III) and incorrectly estimating the shape of the trend. Robust monitoring should be able to avoid each of these error types. Using monitoring of two contrasting ungulate species and multiple scenarios of population change (poaching, climate change and road development) in the Serengeti ecosystem as a case study, we used a ‘virtual ecologist’ approach to investigate monitoring effectiveness under uncertainty. We explored how the prevalence of different types of error varies depending on budgetary, observational and environmental conditions. Higher observation error and conducting surveys less frequently increased the likelihood of not detecting trends and misclassifying the shape of the trend. As monitoring period and frequency increased, observation uncertainty was more important in explaining effectiveness. Types I and III errors had low prevalence for both ungulate species. Greater investment in monitoring considerably decreased the likelihood of failing to detect significant trends (type II errors). Our results suggest that it is important to understand the effects of monitoring conditions on perceived trends before making inferences about underlying processes. The impacts of specific threats on population abundance and structure feed through into monitoring effectiveness; hence, monitoring programmes must be designed with the underlying processes to be detected in mind. Here we provide an integrated modelling framework that can produce advice on robust monitoring strategies under uncertainty.