Addressing uncertainty and limited data in conservation decision-making

Abstract

Biodiversity is declining worldwide at alarming rates, through a range of humaninduced changes. At the same time, there are great uncertainties and biases in our understanding of biodiversity that limit our ability to detect changes. New approaches in estimating and managing uncertainty can inform assessments of the status of biodiversity, and identify what actions might be most beneficial. The thesis examines the applications of these methods in diverse contexts that are of importance to conservation and in which there is limited data available. The potential for Value of Information method to contribute to the prioritisation of conservation action was explored (chapter 2). While its use is increasing, there are currently substantial gaps in its application. Probabilistic graphical models (Bayesian Networks) were built with different Machine Learning algorithms to predict the Red List status of plants, both in the Caatinga region in Brazil (chapter 3) and globally (chapter 4) and to assess why some tiger reserves contain higher tiger numbers than others (chapter 5). Red List status of plants could be predicted reliably by using the number of herbarium specimens of each plant species. The method was used to predict which plants might be threatened globally. The number of poached tigers was a good indicator for the number of tigers in a tiger reserve, but a lack of data at similar spatial scales across the tigers’ range inhibits decision making. Overall, the thesis suggests that we can: a) better predict which species are threatened and prioritise these species for future Red List assessments; b) standardise our research approaches using core outcomes; and c) make better decisions despite uncertainty. We need to make better use of these methods and the currently available data to prevent species from going extinct and to meet global targets aimed to halt the biodiversity crisis.