Planning for ex situ conservation in the face of uncertainty

Abstract

Ex situ conservation strategies for threatened species often require long-term commitment and financial investment to achieve management objectives. We present a framework that considers the decision to adopt ex situ management for a target species as the end point of several linked decisions. We used a decision tree to intuitively represent the logical sequence of decision making. The first decision is to identify the specific management actions most likely to achieve the fundamental objectives of the recovery plan, with or without the use of ex-situ populations. Once this decision has been made, one decides whether to establish an ex situ population, accounting for the probability of success in the initial phase of the recovery plan, for example, the probability of successful breeding in captivity. Approaching these decisions in the reverse order (attempting to establish an ex situ population before its purpose is clearly defined) can lead to a poor allocation of resources, because it may restrict the range of available decisions in the second stage. We applied our decision framework to the recovery program for the threatened spotted tree frog (Litoria spenceri) of southeastern Australia. Across a range of possible management actions, only those including ex situ management were expected to provide >50% probability of the species' persistence, but these actions cost more than use of in situ alternatives only. The expected benefits of ex situ actions were predicted to be offset by additional uncertainty and stochasticity associated with establishing and maintaining ex situ populations. Naïvely implementing ex situ conservation strategies can lead to inefficient management. Our framework may help managers explicitly evaluate objectives, management options, and the probability of success prior to establishing a captive colony of any given species.

Making structured decisions for reintroduced populations in the face of uncertainty

“…In our decision analysis, we combined three types of uncertainty: the uncertainty in parameter estimations, uncertainty in model selection, and demographic stochasticity. Given first order dominance there is no reason to further investigate the general risk tolerance of the group members (Canessa et al, 2016). As a result, uncertainty in the outcomes of management alternatives made choosing among them difficult, because the stakeholders had to consider the distributions of these outcomes.…”

“…Uncertainty permeates most components of reintroduction decisions; linguistic uncertainty compromises our meaning at all decision steps causing confusion within management teams, whereas aleatory and epistemic uncertainties mean our knowledge of systems and how they will respond to management are never known with certainty. For example, Canessa et al (2016) advocated such an approach for deciding whether to initiate a captive breeding program for threatened species management. The latter implies that the parameters are estimated without error, and the optimal decision is clear as long as the different objectives are weighted within a representative utility function.…”

“…In fact, many reintroduction decisions are made based on poorly defined objectives (Ewen, Soorae, 2014;Ewen, Walker, 2014) and simple deterministic predictions of the consequences of management alternatives that do not incorporate uncertainty. Representing uncertainty through a decision tree allowed a more nuanced representation of how captive breeding could assist in species recovery and had the benefit of clearly linking uncertain outcomes directly on stated objectives (Canessa et al, 2016). Incorporating uncertainty in the estimations of management outcomes may change the perception of the utility of alternatives and certainly makes decisions more transparent (Gregory et al, 2012).…”

A Framework for Modeling Emerging Diseases to Inform Management

“…Multiple methods are available for assessing the extent of the uncertainty associated with various parameters, including variance-based methods, global uncertainty and sensitivity analyses, and Bayesian belief networks, which can help identify the uncertainties that are most likely to affect the management decision ( 47 ). These uncertainties can then become the focus of future research and monitoring efforts ( 48 , 49 ). Decision models that can evaluate trade-offs among multiple objectives (such as multicriteria decision analysis and portfolio decision analysis) ( 49 ) under uncertainty and evaluate different optimal policies over time (stochastic dynamic programming and Markov decision process models) can be integrated with probabilistic disease predictive models to provide insights about optimal disease management strategies under deep uncertainty.…”

“…These uncertainties can then become the focus of future research and monitoring efforts ( 48 , 49 ). Decision models that can evaluate trade-offs among multiple objectives (such as multicriteria decision analysis and portfolio decision analysis) ( 49 ) under uncertainty and evaluate different optimal policies over time (stochastic dynamic programming and Markov decision process models) can be integrated with probabilistic disease predictive models to provide insights about optimal disease management strategies under deep uncertainty.…”