Water Resources Research Prioritization and Management-Problems and Perspectives1

ABSTRACT: At a time when productive water resources research is essential to protecting the quality of and wisely using our limited water resources, pervasive trends toward consumerism, fiscal restraint, centralized management, and using research for other social objectives threaten research productivity. The research prioritization and management structure that these trends have created was examined in the microcosm of its application at the Utah Water Research Laboratory. The result indicated that ephemeral prioritization and the failure of research users to target researchers to their own particular needs is diluting productivity. Incremental research prioritization and greater use of advisory councils in facilitating user-researcher interaction are suggested as corrective approaches, but the only firm conclusion at this point in time must be that empirical studies of the performance of alternatives in research management structure are solely needed.     

Combining the Species-Area-Habitat Relationship and Environmental Cluster Analysis to Set Conservation Priorities: a Study in the Zhoushan Archipelago, China

Abstract: Identification of priority areas is a fundamental goal in conservation biology. Because of a lack of detailed information about species distributions, conservation targets in the Zhoushan Archipelago (China) were established on the basis of a species–area–habitat relationship (choros model) combined with an environmental cluster analysis (ECA). An environmental‐distinctness index was introduced to rank areas in the dendrogram obtained with the ECA. To reduce the effects of spatial autocorrelation, the ECA was performed considering spatial constraints. To test the validity of the proposed index, a principal component analysis–based environmental diversity approach was also performed. The priority set of islands obtained from the spatially constrained cluster analysis coupled with the environmental‐distinctness index had high congruence with that from the traditional environmental‐diversity approach. Nevertheless, the environmental‐distinctness index offered the advantage of giving hotspot rankings that could be readily integrated with those obtained from the choros model. Although the Wilcoxon matched‐pairs test showed no significant difference among the rankings from constrained and unconstrained clustering process, as indicated by cophenetic correlation, spatially constrained cluster analysis performed better than the unconstrained cluster analysis, which suggests the importance of incorporating spatial autocorrelation into ECA. Overall, the integration of the choros model and the ECA showed that the islands Liuheng, Mayi, Zhoushan, Fodu, and Huaniao may be good candidates on which to focus future efforts to conserve regional biodiversity. The 4 types of priority areas, generated from the combination of the 2 approaches, were explained in descending order on the basis of their conservation importance: hotspots with distinct environmental conditions, hotspots with general environmental conditions, areas that are not hotspots with distinct environmental conditions, and areas that are not hotspots with general environmental conditions.     

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.     

Protecting biodiversity and economic returns in resource-rich tropical forests

In pursuit of socioeconomic development, many countries are expanding oil and mineral extraction into tropical forests. These activities seed access to remote, biologically rich areas, thereby endangering global biodiversity. We examined how protection of biodiversity and economic revenues can be balanced in biologically valuable regions. Using spatial data on oil profits and predicted species and ecosystem extents, we optimized the protection of 741 terrestrial species and 20 ecosystems of the Ecuadorian Amazon across a range of opportunity costs (i.e., sacrifices of extractive profit). We also applied spatial statistics to remotely sensed, historic deforestation data to focus the optimization on areas most threatened by imminent forest loss. Giving up 5% of a year's oil profits (US$221 million) allowed for a protected area network that retained an average of 65% of the extent of each species and ecosystem. This performance far exceeded that of the network produced by simple optimization for land area (which required a sacrifice of approximately 40% of annual oil profits [US$1.7 billion]) and used only marginally less land to achieve equivalent levels of ecological protection. We identified what we call emergency conservation targets: regions that are essential components of a cost-effective conservation reserve network but at imminent risk of destruction, thus requiring urgent and effective protection. Governments can use our methods when evaluating extractive-led development options to responsibly manage the associated ecological and economic trade-offs and protect natural capital.     

Incorporating geographical and evolutionary rarity into conservation prioritization

Key goals of conservation are to protect both species and the functional and genetic diversity they represent. A strictly species-based approach may underrepresent rare, threatened, or genetically distinct species and overrepresent widespread species. Although reserves are created for a number of reasons, including economic, cultural, and ecological reasons, their efficacy has been measured primarily in terms of how well species richness is protected, and it is useful to compare how well they protect other measures of diversity. We used Proteaceae species-occurrence data in the Cape Floristic Region of South Africa to illustrate differences in the spatial distribution of species and evolutionary diversity estimated from a new maximum-likelihood molecular phylogeny. We calculated species richness, phylogenetic diversity (i.e., summed phylogenetic branch lengths in a site), and a site-aggregated measure of biogeographically weighted evolutionary distinctiveness (i.e., an abundance weighted measure that captures the unique proportion of the phylogenetic tree a species represents) for sites throughout the Cape Floristic Region. Species richness and phylogenetic diversity values were highly correlated for sites in the region, but species richness was concentrated at a few sites that underrepresented the much more spatially extensive distribution of phylogenetic diversity. Biogeographically weighted evolutionary diversity produced a scheme of prioritization distinct from the other 2 metrics and highlighted southern sites as conservation priorities. In these sites, the high values of biogeographically weighted evolutionary distinctiveness were the result of a nonrandom relation between evolutionary distinctiveness and geographical rarity, where rare species also tended to have high levels of evolutionary distinctiveness. Such distinct and rare species are of particular concern, but are not captured by conservation schemes that focus on species richness or phylogenetic diversity alone.     

A method for mapping fire hazard and risk across multiple scales and its application in fire management

This paper presents modeling methods for mapping fire hazard and fire risk using a research model called FIREHARM (FIRE Hazard and Risk Model) that computes common measures of fire behavior, fire danger, and fire effects to spatially portray fire hazard over space. FIREHARM can compute a measure of risk associated with the distribution of these measures over time using 18 years of gridded DAYMET daily weather data used to simulate fuel moistures to compute fire variables. We detail the background, structure, and application of FIREHARM and then present validation results of six of the FIREHARM output variables that revealed accuracy rates ranging from 20 to 80% correct depending on the quality of input data and the behavior of the fire behavior simulation framework. Overall accuracies appeared acceptable for prioritization analysis and large scale assessments because precision was high. We discuss advantages and disadvantages of the fire hazard and risk approaches and a possible agenda for future development of comprehensive fire hazard and risk mapping is presented.     

Ecological Risk Assessment and Problem Formulation for Lake Uluabat,a Ramsar State in Turkey

Unlike the United States and the European Union, developing countries do not have sufficiently structured legal and institutional systems to apply certain environmental management tools such as ecological risk assessment. However, it is important for countries with valuable environmental and ecological resources to have appropriate tools and to strengthen their environmental management capabilities and capacities for the sake of those resources. The case study described in this paper attempts to be a case study towards developing environmental management plans, especially in developing countries. The problem formulation step of Ecological Risk Assessment applied in this study contributed to the basic elements of an environmental management plan including the following: the partnership-building process, prioritization of the problems and issues of the ecosystem, and development of the action plan. Based on the information provided by participants from a series of workshops held to develop an environmental management plan for Uluabat Lake, ecosystem risks were ranked and an action plan was formed. The results obtained with the aid of fuzzy set theory provided a base for identification of the action steps by allowing scientific information to be included in the process. The degree to which Uluabat Lakes problem formulation fits into the existing legal framework of Turkey is also analyzed in this paper.     

Integrating larval connectivity into the marine conservation decision-making process across spatial scales

Larval dispersal connectivity is typically integrated into spatial conservation decisions at regional or national scales, but implementing agencies struggle with translating these methods to local scales. We used larval dispersal connectivity at regional (hundreds of kilometers) and local (tens of kilometers) scales to aid in design of networks of no-take reserves in Southeast Sulawesi, Indonesia. We used Marxan with Connectivity informed by biophysical larval dispersal models and remotely sensed coral reef habitat data to design marine reserve networks for 4 commercially important reef species across the region. We complemented regional spatial prioritization with decision trees that combined network-based connectivity metrics and habitat quality to design reserve boundaries locally. Decision trees were used in consensus-based workshops with stakeholders to qualitatively assess site desirability, and Marxan was used to identify areas for subsequent network expansion. Priority areas for protection and expected benefits differed among species, with little overlap in reserve network solutions. Because reef quality varied considerably across reefs, we suggest reef degradation must inform the interpretation of larval dispersal patterns and the conservation benefits achievable from protecting reefs. Our methods can be readily applied by conservation practitioners, in this region and elsewhere, to integrate connectivity data across multiple spatial scales.     

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.