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To counteract global species decline, modern biodiversity conservation engages in large projects, spends billions of dollars, and includes many organizations working simultaneously within regions. To add to this complexity, the conservation sector has hierarchical structure, where conservation actions are often outsourced by funders (foundations, government, etc.) to local organizations that work on-the-ground. In contrast, conservation science usually assumes that a single organization makes resource allocation decisions. This discrepancy calls for theory to understand how the expected biodiversity outcomes change when interactions between organizations are accounted for. Here, we used a game theoretic model to explore how biodiversity outcomes are affected by vertical and horizontal interactions between 3 conservation organizations: a funder that outsourced its actions and 2 local conservation organizations that work on-the-ground. Interactions between the organizations changed the spending decisions made by individual organizations, and thereby the magnitude and direction of the conservation benefits. We showed that funders would struggle to incentivize recipient organizations with set priorities to perform desired actions, even when they control substantial amounts of the funding and employ common contracting approaches to enhance outcomes. Instead, biodiversity outcomes depended on priority alignment across the organizations. Conservation outcomes for the funder were improved by strategic interactions when organizational priorities were well aligned, but decreased when priorities were misaligned. Meanwhile, local organizations had improved outcomes regardless of alignment due to additional funding in the system. Given that conservation often involves the aggregate actions of multiple organizations with different objectives, strategic interactions between organizations need to be considered if we are to predict possible outcomes of conservation programs or costs of achieving conservation targets., (© 2016 Society for Conservation Biology.)

Protected area networks are designed to restrict anthropogenic pressures in areas of high biodiversity. Resource users respond by seeking to replace some or all of the lost resources from locations elsewhere in the landscape. Protected area networks thereby perturb the pattern of human pressures by displacing extractive effort from within protected areas into the broader landscape, a process known as leakage. The negative effects of leakage on conservation outcomes have been empirically documented and modeled using homogeneous descriptions of conservation landscapes. Human resource use and biodiversity vary greatly in space, however, and a theory of leakage must describe how this heterogeneity affects the magnitude, pattern, and biodiversity impacts of leakage. We combined models of household utility, adaptive human foraging, and biodiversity conservation to provide a bioeconomic model of leakage that accounts for spatial heterogeneity. Leakage had strong and divergent impacts on the performance of protected area networks, undermining biodiversity benefits but mitigating the negative impacts on local resource users. When leakage was present, our model showed that poorly designed protected area networks resulted in a substantial net loss of biodiversity. However, the effects of leakage can be mitigated if they are incorporated ex-ante into the conservation planning process. If protected areas are coupled with nonreserve policy instruments such as market subsidies, our model shows that the trade-offs between biodiversity and human well-being can be further and more directly reduced., (© 2014 Society for Conservation Biology.)

Global declines in biodiversity and the widespread degradation of ecosystem services have led to urgent calls to safeguard both. Responses to this urgency include calls to integrate the needs of ecosystem services and biodiversity into the design of conservation interventions. The benefits of such integration are purported to include improvements in the justification and resources available for these interventions. Nevertheless, additional costs and potential trade-offs remain poorly understood in the design of interventions that seek to conserve biodiversity and ecosystem services. We sought to investigate the synergies and trade-offs in safeguarding ecosystem services and biodiversity in South Africa's Little Karoo. We used data on three ecosystem services--carbon storage, water recharge, and fodder provision--and data on biodiversity to examine several conservation planning scenarios. First, we investigated the amount of each ecosystem service captured incidentally by a conservation plan to meet targets for biodiversity only while minimizing opportunity costs. We then examined the costs of adding targets for ecosystem services into this conservation plan. Finally, we explored trade-offs between biodiversity and ecosystem service targets at a fixed cost. At least 30% of each ecosystem service was captured incidentally when all of biodiversity targets were met. By including data on ecosystem services, we increased the amount of services captured by at least 20% for all three services without additional costs. When biodiversity targets were reduced by 8%, an extra 40% of fodder provision and water recharge were obtained and 58% of carbon could be captured for the same cost. The opportunity cost (in terms of forgone production) of safeguarding 100% of the biodiversity targets was about US$500 million. Our results showed that with a small decrease in biodiversity target achievement, substantial gains for the conservation of ecosystem services can be achieved within our biodiversity priority areas for no extra cost.

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.

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 world's 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.

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.

Addressing the vulnerability of areas to habitat loss remains a challenge for conservation planners. Different areas are often assumed equally vulnerable to habitat loss or, worse, conservation attention focuses on remote, unproductive areas contributing little to minimizing biodiversity loss. Understanding vulnerability is crucial to planning but gathering the required information can be time consuming and expensive; and any data on vulnerability will be uncertain. We investigated the circumstances in which including vulnerability data produces better conservation decisions. We found that it is best to use existing information on vulnerability only when uncertainty is less than 20%-30%. With higher uncertainty and large spatial variance in vulnerability, it is best to improve vulnerability data before making conservation decisions. Otherwise, it is best to ignore vulnerability and consider only biodiversity value. Other important factors are whether reservation displaces or inhibits habitat loss and the correlation between biodiversity value and vulnerability. [ABSTRACT FROM AUTHOR]

Agencies charged with nature conservation and protecting built-assets from fire face a policy dilemma because management that protects assets can have adverse impacts on biodiversity. Although conservation is often a policy goal, protecting built-assets usually takes precedence in fire management implementation. To make decisions that can better achieve both objectives, existing trade-offs must first be recognized, and then policies implemented to manage multiple objectives explicitly. We briefly review fire management actions that can conflict with biodiversity conservation. Through this review, we find that common management practices might not appreciably reduce the threat to built-assets but could have a large negative impact on biodiversity. We develop a framework based on decision theory that could be applied to minimize these conflicts. Critical to this approach is (1) the identification of the full range of management options and (2) obtaining data for evaluating the effectiveness of those options for achieving asset protection and conservation goals. This information can be used to compare explicitly the effectiveness of different management choices for conserving species and for protecting assets, given budget constraints. The challenge now is to gather data to quantify these trade-offs so that fire policy and practices can be better aligned with multiple objectives. [ABSTRACT FROM AUTHOR]

Land acquisition is a common approach to biodiversity conservation but is typically subject to property availability on the public market. Consequently, conservation plans are often unable to be implemented as intended. When properties come on the market, conservation agencies must make a choice: purchase immediately, often without a detailed knowledge of its biodiversity value; survey the parcel and accept the risk that it may be removed from the market during this process; or not purchase and hope a better parcel comes on the market at a later date. We describe both an optimal method, using stochastic dynamic programming, and a simple rule of thumb for making such decisions. The solutions to this problem illustrate how optimal conservation is necessarily dynamic and requires explicit consideration of both the time period allowed for implementation and the availability of properties. [ABSTRACT FROM AUTHOR]