Marissa F. McBride

Prioritizing global conservation efforts

One of the most pressing issues facing the global conservation community is how to distribute limited resources between regions identified as priorities for biodiversity conservation. Approaches such as biodiversity hotspots, endemic bird areas and ecoregions are used by international organizations to prioritize conservation efforts globally. Although identifying priority regions is an important first step in solving this problem, it does not indicate how limited resources should be allocated between regions. Here we formulate how to allocate optimally conservation resources between regions identified as priorities for conservation—the ‘conservation resource allocation problem’. Stochastic dynamic programming is used to find the optimal schedule of resource allocation for small problems but is intractable for large problems owing to the “curse of dimensionality”. We identify two easy-to-use and easy-to-interpret heuristics that closely approximate the optimal solution. We also show the importance of both correctly formulating the problem and using information on how investment returns change through time. Our conservation resource allocation approach can be applied at any spatial scale. We demonstrate the approach with an example of optimal resource allocation among five priority regions in Wallacea and Sundaland, the transition zone between Asia and Australasia.

Eliciting Expert Knowledge in Conservation Science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.

Conserving biodiversity efficiently: What to Do, Where, and When

Conservation priority-setting schemes have not yet combined geographic priorities with a framework that can guide the allocation of funds among alternate conservation actions that address specific threats. We develop such a framework, and apply it to 17 of the worlds 39 Mediterranean ecoregions. This framework offers an improvement over approaches that only focus on land purchase or species richness and do not account for threats. We discover that one could protect many more plant and vertebrate species by investing in a sequence of conservation actions targeted towards specific threats, such as invasive species control, land acquisition, and off-reserve management, than by relying solely on acquiring land for protected areas. Applying this new framework will ensure investment in actions that provide the most cost-effective outcomes for biodiversity conservation. This will help to minimise the misallocation of scarce conservation resources.

Reducing overconfidence in the interval judgments of experts.

Elicitation of expert opinion is important for risk analysis when only limited data are available. Expert opinion is often elicited in the form of subjective confidence intervals; however, these are prone to substantial overconfidence. We investigated the influence of elicitation question format, in particular the number of steps in the elicitation procedure. In a 3-point elicitation procedure, an expert is asked for a lower limit, upper limit, and best guess, the two limits creating an interval of some assigned confidence level (e.g., 80%). In our 4-step interval elicitation procedure, experts were also asked for a realistic lower limit, upper limit, and best guess, but no confidence level was assigned; the fourth step was to rate their anticipated confidence in the interval produced. In our three studies, experts made interval predictions of rates of infectious diseases (Study 1, n = 21 and Study 2, n = 24: epidemiologists and public health experts), or marine invertebrate populations (Study 3, n = 34: ecologists and biologists). We combined the results from our studies using meta-analysis, which found average overconfidence of 11.9%, 95% CI [3.5, 20.3] (a hit rate of 68.1% for 80% intervals)-a substantial decrease in overconfidence compared with previous studies. Studies 2 and 3 suggest that the 4-step procedure is more likely to reduce overconfidence than the 3-point procedure (Cohens d = 0.61, [0.04, 1.18]).

Cost-effective global conservation spending is robust to taxonomic group

Priorities for conservation investment at a global scale that are based on a single taxon have been criticized because geographic richness patterns vary taxonomically. However, these concerns focused only on biodiversity patterns and did not consider the importance of socioeconomic factors, which must also be included if conservation funding is to be allocated efficiently. In this article, we create efficient global funding schedules that use information about conservation costs, predicted habitat loss rates, and the endemicity of seven different taxonomic groups. We discover that these funding allocation schedules are less sensitive to variation in taxon assessed than to variation in cost and threat. Two-thirds of funding is allocated to the same regions regardless of the taxon, compared with only one-fifth if threat and cost are not included in allocation decisions. Hence, if socioeconomic factors are considered, we can be more confident about global-scale decisions guided by single taxonomic groups.

Expert status and performance.

Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience [1], [2]. The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback.

Protecting Biodiversity when Money Matters: Maximizing Return on Investment

Background Conventional wisdom identifies biodiversity hotspots as priorities for conservation investment because they capture dense concentrations of species. However, density of species does not necessarily imply conservation ‘efficiency’. Here we explicitly consider conservation efficiency in terms of species protected per dollar invested. Methodology/Principal Findings We apply a dynamic return on investment approach to a global biome and compare it with three alternate priority setting approaches and a random allocation of funding. After twenty years of acquiring habitat, the return on investment approach protects between 32% and 69% more species compared to the other priority setting approaches. To correct for potential inefficiencies of protecting the same species multiple times we account for the complementarity of species, protecting up to three times more distinct vertebrate species than alternate approaches. Conclusions/Significance Incorporating costs in a return on investment framework expands priorities to include areas not traditionally highlighted as priorities based on conventional irreplaceability and vulnerability approaches.

Incorporating the effects of socioeconomic uncertainty into priority setting for conservation investment.

Uncertainty in the implementation and outcomes of conservation actions that is not accounted for leaves conservation plans vulnerable to potential changes in future conditions. We used a decision-theoretic approach to investigate the effects of two types of investment uncertainty on the optimal allocation of global conservation resources for land acquisition in the Mediterranean Basin. We considered uncertainty about (1) whether investment will continue and (2) whether the acquired biodiversity assets are secure, which we termed transaction uncertainty and performance uncertainty, respectively. We also developed and tested the robustness of different rules of thumb for guiding the allocation of conservation resources when these sources of uncertainty exist. In the presence of uncertainty in future investment ability (transaction uncertainty), the optimal strategy was opportunistic, meaning the investment priority should be to act where uncertainty is highest while investment remains possible. When there was a probability that investments would fail (performance uncertainty), the optimal solution became a complex trade-off between the immediate biodiversity benefits of acting in a region and the perceived longevity of the investment. In general, regions were prioritized for investment when they had the greatest performance certainty, even if an alternative region was highly threatened or had higher biodiversity value. The improved performance of rules of thumb when accounting for uncertainty highlights the importance of explicitly incorporating sources of investment uncertainty and evaluating potential conservation investments in the context of their likely long-term success.

What Is Expert Knowledge, How Is Such Knowledge Gathered, and How Do We Use It to Address Questions in Landscape Ecology?

Expert knowledge plays an integral role in applied ecology and conservation (Burgman 2005). Environmental systems are characterized by complex dynamics, multiple drivers, and a paucity of data (Carpenter 2002). Action is often required before uncertainties can be resolved. Where empirical data are scarce or unavailable, expert knowledge is often regarded as the best or only source of information (Sutherland 2006; Kuhnert et al. 2010). Experts may be called upon to provide input for all stages of the modeling and management process, and specifically to inform the definition and structuring of the problem (Cowling and Pressey 2003; Sutherland et al. 2008), to inform the selection of data or variables, model structures, and assumptions about functional relationships between variables (Pearce et al. 2001; Czembor and Vesk 2009), and to inform the analysis of data, estimation of parameters, interpretation of results, and the characterization of uncertainty (Alho and Kangas 1997; Martin et al. 2005).

Optimal Dynamic Allocation of Conservation Funding Among Priority Regions

The optimal allocation of conservation resources between biodiverse conservation regions has generally been calculated using stochastic dynamic programming, or using myopic heuristics. These solutions are hard to interpret and may not be optimal. To overcome these two limitations, this paper approaches the optimal conservation resource allocation problem using optimal control theory. A solution using Pontryagin’s maximum principle provides novel insight into the general properties of efficient conservation resource allocation strategies, and allows more extensive testing of the performance of myopic heuristics. We confirmed that a proposed heuristic (minimize short-term loss) yields near-optimal results in complex allocation situations, and found that a qualitative allocation feature observed in previous analyses (bang-bang allocation) is a general property of the optimal allocation strategy.