Mathematical Methods for Spatially Cohesive Reserve Design

The problem of designing spatially cohesive nature reserve systems that meet biodiversity objectives is formulated as a nonlinear integer programming problem. The multiobjective function minimises a combination of boundary length, area and failed representation of the biological attributes we are trying to conserve. The task is to reserve a subset of sites that best meet this objective. We use data on the distribution of habitats in the Northern Territory, Australia, to show how simulated annealing and a greedy heuristic algorithm can be used to generate good solutions to such large reserve design problems, and to compare the effectiveness of these methods.     

Evaluating surrogates of genetic diversity for conservation planning

Protected-area systems should conserve intraspecific genetic diversity. Because genetic data require resources to obtain, several approaches have been proposed for generating plans for protected-area systems (prioritizations) when genetic data are not available. Yet such surrogate-based approaches remain poorly tested. We evaluated the effectiveness of potential surrogate-based approaches based on microsatellite genetic data collected across the Iberian Peninsula for 7 amphibian and 3 reptilian species. Long-term environmental suitability did not effectively represent sites containing high genetic diversity (allelic richness). Prioritizations based on long-term environmental suitability had similar performance to random prioritizations. Geographic distances and resistance distances based on contemporary environmental suitability were not always effective surrogates for identification of combinations of sites that contain individuals with different genetic compositions. Our results demonstrate that population genetic data based on commonly used neutral markers can inform prioritizations, and we could not find an adequate substitute. Conservation planners need to weigh the potential benefits of genetic data against their acquisition costs.     

Software for prioritizing conservation actions based on probabilistic information

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species’ populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.     

Planning for persistence in marine reserves: a question of catastrophic importance.

Large-scale catastrophic events, although rare, lie generally beyond the control of local management and can prevent marine reserves from achieving biodiversity outcomes. We formulate a new conservation planning problem that aims to minimize the probability of missing conservation targets as a result of catastrophic events. To illustrate this approach we formulate and solve the problem of minimizing the impact of large-scale coral bleaching events on a reserve system for the Great Barrier Reef, Australia. We show that by considering the threat of catastrophic events as part of the reserve design problem it is possible to substantially improve the likely persistence of conservation features within reserve networks for a negligible increase in cost. In the case of the Great Barrier Reef, a 2% increase in overall reserve cost was enough to improve the long-run performance of our reserve network by >60%. Our results also demonstrate that simply aiming to protect the reefs at lowest risk of catastrophic bleaching does not necessarily lead to the best conservation outcomes, and enormous gains in overall persistence can be made by removing the requirement to represent all bioregions in the reserve network. We provide an explicit and well-defined method that allows the probability of catastrophic disturbances to be included in the site selection problem without creating additional conservation targets or imposing arbitrary presence/absence thresholds on existing data. This research has implications for reserve design in a changing climate.     

Protected area planning to conserve biodiversity in an uncertain future

Protected areas are a key instrument for conservation. Despite this, they are vulnerable to risks associated with weak governance, land-use intensification, and climate change. We used a novel hierarchical optimization approach to identify priority areas for expanding the global protected area system that explicitly accounted for such risks while maximizing protection of all known terrestrial vertebrate species. To incorporate risk categories, we built on the minimum set problem, where the objective is to reach species distribution protection targets while accounting for 1 constraint, such as land cost or area. We expanded this approach to include multiple objectives accounting for risk in the problem formulation by treating each risk layer as a separate objective in the problem formulation. Reducing exposure to these risks required expanding the area of the global protected area system by 1.6% while still meeting conservation targets. Incorporating risks from weak governance drove the greatest changes in spatial priorities for protection, and incorporating risks from climate change required the largest increase (2.52%) in global protected area. Conserving wide-ranging species required countries with relatively strong governance to protect more land when they bordered nations with comparatively weak governance. Our results underscore the need for cross-jurisdictional coordination and demonstrate how risk can be efficiently incorporated into conservation planning. Planeación de las áreas protegidas para conservar la biodiversidad en un futuro incierto     

An introduction to decision science for conservation

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.     

Wilderness forms and their implications for global environmental policy and conservation

With the intention of securing industry-free land and seascapes, protecting wilderness entered international policy as a formal target for the first time in the zero draft of the Post-2020 Global Biodiversity Framework under the Convention on Biological Diversity. Given this increased prominence in international policy, it is timely to consider the extent to which the construct of wilderness supports global conservation objectives. We evaluated the construct by overlaying recently updated cumulative human pressure maps that offer a global-scale delineation of industry-free land as wilderness with maps of carbon stock, species richness, and ground travel time from urban centers. Wilderness areas took variable forms in relation to carbon stock, species richness, and proximity to urban centers, where 10% of wilderness areas represented high carbon and species richness, 20% low carbon and species richness, and 3% high levels of remoteness (>48 h), carbon, and species richness. Approximately 35% of all remaining wilderness in 2013 was accessible in <24 h of travel time from urban centers. Although the construct of wilderness can be used to secure benefits in specific contexts, its application in conservation must account for contextual and social implications. The diverse characterization of wilderness under a global environmental conservation lens shows that a nuanced framing and application of the construct is needed to improve understanding, communication, and retention of its variable forms as industry-free places.