Improving risk assessments in conservation ecology.

Conservation efforts and management decisions on the living environment of our planet often rely on the results from statistical models. Yet, these models are imperfect and quantification of risk associated with the estimate of management-relevant quantities becomes crucial in providing robust advice. Here we demonstrate that estimates of risk themselves could be substantially biased but by combining data fitting with an extensive simulation-estimation procedure, one can back-calculate the correct values. We apply the method to 627 time series of population abundance across four taxa using the Gompertz state-space model as an example. We find that the risk of large bias in population status estimate increases with the species' growth rate, population variability, weaker density dependence, and shorter time series, across taxa. We urge scientists dealing with conservation and management to adopt a similar approach to ensure a more accurate estimate of risk measures and contribute towards a precautionary approach to management.