Using a social–ecological framework to inform the implementation of conservation plans

One of the key determinants of success in biodiversity conservation is how well conservation planning decisions account for the social system in which actions are to be implemented. Understanding elements of how the social and ecological systems interact can help identify opportunities for implementation. Utilizing data from a large‐scale conservation initiative in southwestern of Australia, we explored how a social–ecological system framework can be applied to identify how social and ecological factors interact to influence the opportunities for conservation. Using data from semistructured interviews, an online survey, and publicly available data, we developed a conceptual model of the social–ecological system associated with the conservation of the Fitz‐Stirling region. We used this model to identify the relevant variables (remnants of vegetation, stakeholder presence, collaboration between stakeholders, and their scale of management) that affect the implementation of conservation actions in the region. We combined measures for these variables to ascertain how areas associated with different levels of ecological importance coincided with areas associated with different levels of stakeholder presence, stakeholder collaboration, and scales of management. We identified areas that could benefit from different implementation strategies, from those suitable for immediate conservation action to areas requiring implementation over the long term to increase on‐the‐ground capacity and identify mechanisms to incentivize implementation. The application of a social–ecological framework can help conservation planners and practitioners facilitate the integration of ecological and social data to inform the translation of priorities for action into implementation strategies that account for the complexities of conservation problems in a focused way.     

Public Willingness to Pay for Recovering and Downlisting Threatened and Endangered Marine Species

Abstract: Nonmarket valuation research has produced economic value estimates for a variety of threatened, endangered, and rare species around the world. Although over 40 value estimates exist, it is often difficult to compare values from different studies due to variations in study design, implementation, and modeling specifications. We conducted a stated‐preference choice experiment to estimate the value of recovering or downlisting 8 threatened and endangered marine species in the United States: loggerhead sea turtle (Caretta caretta), leatherback sea turtle (Dermochelys coriacea), North Atlantic right whale (Eubalaena glacialis), North Pacific right whale (Eubalaena japonica), upper Willamette River Chinook salmon (Oncorhynchus tshawytscha), Puget Sound Chinook salmon (Oncorhynchus tshawytscha), Hawaiian monk seals (Monachus schauinslandi), and smalltooth sawfish (Pristis pectinata). In May 2009, we surveyed a random sample of U.S. households. We collected data from 8476 households and estimated willingness to pay for recovering and downlisting the 8 species from these data. Respondents were willing to pay for recovering and downlisting threatened and endangered marine taxa. Willingness‐to‐pay values ranged from $40/household for recovering Puget Sound Chinook salmon to $73/household for recovering the North Pacific right whale. Statistical comparisons among willingness‐to‐pay values suggest that some taxa are more economically valuable than others, which suggests that the U.S. public's willingness to pay for recovery may vary by species.     

Effects of preference heterogeneity among landowners on spatial conservation prioritization

The participation of private landowners in conservation is crucial to efficient biodiversity conservation. This is especially the case in settings where the share of private ownership is large and the economic costs associated with land acquisition are high. We used probit regression analysis and historical participation data to examine the likelihood of participation of Danish forest owners in a voluntary conservation program. We used the results to spatially predict the likelihood of participation of all forest owners in Denmark. We merged spatial data on the presence of forest, cadastral information on participation contracts, and individual‐level socioeconomic information about the forest owners and their households. We included predicted participation in a probability model for species survival. Uninformed and informed (included land owner characteristics) models were then incorporated into a spatial prioritization for conservation of unmanaged forests. The choice models are based on sociodemographic data on the entire population of Danish forest owners and historical data on their participation in conservation schemes. Inclusion in the model of information on private landowners’ willingness to supply land for conservation yielded at intermediate budget levels up to 30% more expected species coverage than the uninformed prioritization scheme. Our landowner‐choice model provides an example of moving toward more implementable conservation planning.     

Conservation Goals and the Relative Importance of Costs and Benefits in Reserve Selection

Abstract: Including both economic costs and biological benefits of sites in systematic reserve selection greatly increases cost‐efficiency. Nevertheless, limited funding generally forces conservation planners to choose which data to focus the most resources on; therefore, the relative importance of different types of data must be carefully assessed. We investigated the relative importance of including information about costs and benefits for 3 different commonly used conservation goals: 2 in which biological benefits were measured per site (species number and conservation value scores) and 1 in which benefits were measured on the basis of site complementarity (total species number in the reserve network). For each goal, we used site‐selection models with data on benefits only, costs only, and benefits and costs together, and we compared the efficiency of each model. Costs were more important to include than benefits for the goals in which benefits were measured per site. By contrast, for the complementarity‐based goal, benefits were more important to include. To understand this pattern, we compared the variability in benefits and in costs for each goal. By comparing the best and the worst possible selection of sites with regard to costs alone and benefits alone for each conservation goal, we introduced a simple and consistent variability measure that is applicable to all kinds of reserve‐selection situations. In our study, benefit variability depended strongly on how the conservation goal was formulated and was largest for the complementarity‐based conservation goal. We argue that from a cost‐efficiency point of view, most resources should be spent on collecting the most variable type of data for the conservation goal at hand.     

Social preferences for the design of biodiversity offsets for shorebirds in Australia

Understanding the social acceptability of biodiversity offsets is important to the design of offset policy. We used a discrete choice experiment to quantify preferences of Australians for a migratory shorebird offset in the context of an oil and gas development project. We surveyed a nationally representative sample of 1371 respondents on their preferences for current and prospective offset‐policy characteristics via an online questionnaire to inform policy design of the social dimensions related to offset acceptability. The majority of respondents accepted offsetting as a means to allow economic development; the option to reject development (and an offset) was selected in 13% of possible offset scenarios. Substituting protection of a species affected by the development with protection of a more endangered species was a desirable policy characteristic, as was having the offset implemented by a third party or the government rather than the company responsible for the development. Direct offset activities (e.g., improving degraded habitat) were preferred over indirect activities (e.g., a research program), and respondents were strongly against locating the offset at a site other than where the impact occurred. Positive and negative characteristics of offsets could be traded off by changing the number of birds protected by the offset. Our results show that Australians are likely to support increased flexibility in biodiversity‐offset policies, particularly when undesirable policy characteristics are compensated for.     

Understanding the effects of different social data on selecting priority conservation areas

Conservation success is contingent on assessing social and environmental factors so that cost‐effective implementation of strategies and actions can be placed in a broad social–ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land‐use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial‐prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land‐use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2–51% different from those based on biological data alone. The inclusion of conservation‐compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.     

Expanding Kenya's protected areas under the Convention on Biological Diversity to maximize coverage of plant diversity

Biodiversity is highly valuable and critically threatened by anthropogenic degradation of the natural environment. In response, governments have pledged enhanced protected‐area coverage, which requires scarce biological data to identify conservation priorities. To assist this effort, we mapped conservation priorities in Kenya based on maximizing alpha (species richness) and beta diversity (species turnover) of plant communities while minimizing economic costs. We used plant‐cover percentages from vegetation surveys of over 2000 plots to build separate models for each type of diversity. Opportunity and management costs were based on literature data and interviews with conservation organizations. Species richness was predicted to be highest in a belt from Lake Turkana through Mount Kenya and in a belt parallel to the coast, and species turnover was predicted to be highest in western Kenya and along the coast. Our results suggest the expanding reserve network should focus on the coast and northeastern provinces of Kenya, where new biological surveys would also fill biological data gaps. Meeting the Convention on Biological Diversity target of 17% terrestrial coverage by 2020 would increase representation of Kenya's plant communities by 75%. However, this would require about 50 times more funds than Kenya has received thus far from the Global Environment Facility.     

The need for spatially explicit quantification of benefits in invasive‐species management

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive‐species control, and planning processes are needed to identify cost‐effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive‐species management. There is a need to improve understanding of how such assets are considered in invasive‐species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty‐four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty‐five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision‐making processes that guide invasive‐species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge.     

Application of the Anthropogenic Allee Effect Model to Trophy Hunting as a Conservation Tool

Trophy hunting can provide economic incentives to conserve wild species, but it can also involve risk when rare species are hunted. The anthropogenic Allee effect (AAE) is a conceptual model that seeks to explain how rarity may spread the seeds of further endangerment. The AAE model has increasingly been invoked in the context of trophy hunting, increasing concerns that such hunting may undermine rather than enhance conservation efforts. We question the appropriateness of uncritically applying the AAE model to trophy hunting for 4 reasons. First, the AAE assumes an open‐access resource, which is a poor characterization of most trophy‐hunting programs and obscures the potential for state, communal, or private‐property use rights to generate positive incentives for conservation. Second, study results that show the price of hunting increases as the rarity of the animal increases are insufficient to indicate the presence of AAE. Third, AAE ignores the existence of biological and behavioral factors operating in most trophy‐hunting contexts that tend to regulate the effect of hunting. We argue that site‐specific data, rather than aggregated hunting statistics, are required to demonstrate that patterns of unsustainable exploitation can be well explained by an AAE model. Instead, we suggest that conservation managers seeking to investigate and identify constraints that limit the potential conservation role of trophy hunting, should focus on the critical governance characteristics that shape the potential conservation role of trophy hunting, such as corruption, insecure property rights, and inadequate sharing of benefits with local people. Aplicación del Modelo Antropogénico del Efecto Allee sobre la Caza de Trofeos como una Herramienta de Conservación     

The Effectiveness of Surrogate Taxa for the Representation of Biodiversity

Abstract: Biodiversity is too complex to measure directly, so conservation planning must rely on surrogates to estimate the biodiversity of sites. The species richness of selected taxa is often used as a surrogate for the richness of other taxa. Surrogacy values of taxa have been evaluated in diverse contexts, yet broad trends in their effectiveness remain unclear. We reviewed published studies testing the ability of species richness of surrogate taxa to capture the richness of other (target) taxa. We stratified studies into two groups based on whether a complementarity approach (surrogates used to select a combination of sites that together maximize total species richness for the taxon) or a richness‐hotspot approach (surrogates used to select sites containing the highest species richness for the taxon) was used. For each comparison of one surrogate taxon with one target, we used the following predictor variables: biome, spatial extent of study area, surrogate taxon, and target taxon. We developed a binary response variable based on whether the surrogate taxon provided better than random representation of the target taxon. For studies that used an evaluation approach that was not based on better than random representation of target taxa, we based the response variable on the interpretation of results in the original study. We performed a categorical regression to elucidate trends in the effectiveness of surrogate taxa with regard to each of the predictor variables. A surrogate was 25% more likely to be effective with a complementarity approach than with a hotspot approach. For hotspot‐based approaches, biome, extent of study, surrogate taxon, and target taxon significantly influenced effectiveness of the surrogate. For complementarity‐based approaches, biome, extent, and surrogate taxon significantly influenced effectiveness of the surrogate. For all surrogate evaluations, biome explained the greatest amount of variation in surrogate effectiveness. From most to least, extent, surrogate taxon, and target taxon explained the most variation after biome. Surrogate taxa were most effective in grasslands and in some cases boreal zones, deserts, and tropical forests; surrogate taxa also were more effective in studies examining larger areas. Herpetofauna were the most effective taxon as both surrogate and target when a richness‐hotspot approach was used; however, herpetofauna were analyzed in fewer studies, so this result is tentative. For complementarity approaches, taxa that are easy to measure and tend to have a large number of habitat specialists distributed collectively across broad environmental gradients (e.g., plants, birds, and mammals) were the most effective surrogates.     

Exposing ecological and economic costs of the research‐implementation gap and compromises in decision making

The frequently discussed gap between conservation science and practice is manifest in the gap between spatial conservation prioritization plans and their implementation. We analyzed the research‐implementation gap of one zoning case by comparing results of a spatial prioritization analysis aimed at avoiding ecological impact of peat mining in a regional zoning process with the final zoning plan. We examined the relatively complex planning process to determine the gaps among research, zoning, and decision making. We quantified the ecological costs of the differing trade‐offs between ecological and socioeconomic factors included in the different zoning suggestions by comparing the landscape‐level loss of ecological features (species occurrences, habitat area, etc.) between the different solutions for spatial allocation of peat mining. We also discussed with the scientists and planners the reasons for differing zoning suggestions. The implemented plan differed from the scientists suggestion in that its focus was individual ecological features rather than all the ecological features for which there were data; planners and decision makers considered effects of peat mining on areas not included in the prioritization analysis; zoning was not truly seen as a resource‐allocation process and not emphasized in general minimizing ecological losses while satisfying economic needs (peat‐mining potential); and decision makers based their prioritization of sites on site‐level information showing high ecological value and on single legislative factors instead of finding a cost‐effective landscape‐level solution. We believe that if the zoning and decision‐making processes are very complex, then the usefulness of science‐based prioritization tools is likely to be reduced. Nevertheless, we found that high‐end tools were useful in clearly exposing trade‐offs between conservation and resource utilization.     

A habitat‐based approach to predict impacts of marine protected areas on fishers

Although marine protected areas can simultaneously contribute to biodiversity conservation and fisheries management, the global network is biased toward particular ecosystem types because they have been established primarily in an ad hoc fashion. The optimization of trade‐offs between biodiversity benefits and socioeconomic values increases success of protected areas and minimizes enforcement costs in the long run, but it is often neglected in marine spatial planning (MSP). Although the acquisition of spatially explicit socioeconomic data is perceived as a costly or secondary step in MSP, it is critical to account for lost opportunities by people whose activities will be restricted, especially fishers. We developed an easily reproduced habitat‐based approach to estimate the spatial distribution of opportunity cost to fishers in data‐poor regions. We assumed the most accessible areas have higher economic and conservation values than less accessible areas and their designation as no‐take zones represents a loss of fishing opportunities. We estimated potential distribution of fishing resources from bathymetric ranges and benthic habitat distribution and the relative importance of the different resources for each port of total catches, revenues, and stakeholder perception. In our model, we combined different cost layers to produce a comprehensive cost layer so that we could evaluate of trade‐offs. Our approach directly supports conservation planning, can be applied generally, and is expected to facilitate stakeholder input and community acceptance of conservation.