Abstract
AbstractConservationists need to present biological monitoring data to decision makers in a way which clearly represents uncertainty. Providing results in terms of the probability of a hypothesis being true may have greater utility for decision‐making than traditionally used frequentist statistical approaches. Here, we demonstrate such an approach with regard to assessing the suitability of the Cardamom Rainforest Landscape, Cambodia for Panthera tigris (tiger) reintroduction. We estimated the density of tiger prey in the core of the landscape using the Random Encounter Model from camera‐trap data and used Monte Carlo simulation to prorogate uncertainty around our model parameter estimates. This suggests there is currently a low probability that the core area of the landscape supports sufficient prey for a population of 25 adult tigers and that significant prey recovery is thus required prior to any reintroduction into the landscape. The Random Encounter Model contains a number of assumptions and we stress our main purpose is to illustrate an approach to incorporating uncertainty into conservation decision‐making rather than providing robust estimation of current tiger prey densities in the Cardamom Rainforest Landscape. Our approach has wide utility for conveying species monitoring information to conservation planners in a simple to understand fashion.
Highlights
Conservation managers often need to make important decisions based on uncertain and imperfect information
Biological monitoring data are inherently uncertain with observational uncertainty often obscuring the “true” status of conservation targets
Non-frequentist approaches to presenting data variability may make it simpler for conservation decision makers to interpret the uncertainty surrounding species monitoring data and, as such, more assess the risks and rewards associated with their
Summary
Conservation managers often need to make important decisions based on uncertain and imperfect information. Biological monitoring data, which form the basis of planning and operational decision-making for many protected area managers, are inherently uncertain with observational uncertainty impacting most estimates of species' abundance (Milner-Gulland & Shea, 2017). Bayesian results are given in terms of the probability of a hypothesis (e.g., prey densities are sufficient to support a certain number of tigers) being true, and may have much greater utility for decision-making than the more traditionally used frequentist statistical approaches and associated 95% confidence intervals (Gray, Nguyen, & Nguyen, 2014). We demonstrate the use of such a non-frequentist analytical framework for conservation decision-making by presenting uncertainty around estimates of the density of tiger prey in the Cardamom Rainforest Landscape. We subsequently estimate the probability that prey density is sufficient to support various sized tiger populations—critical information for planning, implementing, and reintroduction in the landscape
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