Voting Systems for Environmental Decisions

Voting systems aggregate preferences efficiently and are often used for deciding conservation priorities. Desirable characteristics of voting systems include transitivity, completeness, and Pareto optimality, among others. Voting systems that are common and potentially useful for environmental decision making include simple majority, approval, and preferential voting. Unfortunately, no voting system can guarantee an outcome, while also satisfying a range of very reasonable performance criteria. Furthermore, voting methods may be manipulated by decision makers and strategic voters if they have knowledge of the voting patterns and alliances of others in the voting populations. The difficult properties of voting systems arise in routine decision making when there are multiple criteria and management alternatives. Because each method has flaws, we do not endorse one method. Instead, we urge organizers to be transparent about the properties of proposed voting systems and to offer participants the opportunity to approve the voting system as part of the ground rules for operation of a group. Sistemas de Votación para Decisiones Ambientales     

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.     

Understanding why consumers in China switch between wild,farmed, and synthetic bear bile products

An important rationale for legally farmed and synthetic wildlife products is that they reduce illegal, wild-sourced trade by supplying markets with sustainable alternatives. For this to work, more established illegal-product consumers must switch to legal alternatives than new legal-product consumers switch to illegal wild products. Despite the widespread debate on the magnitude and direction of switching, studies among actual consumers are lacking. We used an anonymous online survey of 1421 traditional Chinese medicine consumers in China to investigate switching among legal farmed, synthetic, and illegal wild bear bile. We examined the past consumption behavior, applied a discrete choice experiment framed within worsening hypothetical disease scenarios, and used latent class models to investigate groups with shared preferences. Bear bile consumers (86% respondents) were wealthier, more likely to have family who consumed bile, and less knowledgeable about bile treatments than nonconsumers. Consumer preferences were heterogenous, but most consumer preferences switched between bile types as disease worsened. We identified five distinct latent classes within our sample: law-abiding consumers (34% respondents), who prefer legal products and were unlikely to switch; two all-natural consumer groups (53%), who dislike synthetics but may switch between farmed and wild; and two nonconsumer groups (12%), who prefer not to buy bile. People with past experience of bile consumption had different preferences than those without. Willingness to switch to wild products was related to believing they were legal, although the likelihood of switching was mediated by preferences for cheaper products sold in legal, familiar places. We found that consumers of wild bile may switch to legal alternatives, given the availability of a range of products, whereas legal-product consumers may switch to illegal products if the barriers to doing so are small. Understanding preferences that promote or impede switching should be a key consideration when attempting to predict consumer behavior in complex wildlife markets.     

Exploring human–nature interactions in national parks with social media photographs and computer vision

Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human–nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human–nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.     

Trophy hunters’ willingness to pay for wildlife conservation and community benefits

In the face of fundamental land‐use changes, the potential for trophy hunting to contribute to conservation is increasingly recognized. Trophy hunting can, for example, provide economic incentives to protect wildlife populations and their habitat, but empirical studies on these relationships are few and tend to focus on the effects of benefit‐sharing schemes from an ex post perspective. We investigated the conditions under which trophy hunting could facilitate wildlife conservation in Ethiopia ex ante. We used a choice experiment approach to survey international trophy hunters’ (n = 224) preferences for trips to Ethiopia, here operationalized as trade‐offs between different attributes of a hunting package, as expressed through choices with an associated willingness to pay. Participants expressed strong preferences and, consequently, were willing to pay substantial premiums for hunting trips to areas with abundant nontarget wildlife where domestic livestock was absent and for arrangements that offered benefit sharing with local communities. For example, within the range of percentages considered in the survey, respondents were on average willing to pay an additional $3900 for every 10 percentage points of the revenue being given to local communities. By contrast, respondents were less supportive of hunting revenue being retained by governmental bodies: Willingness to pay decreased by $1900 for every 10 percentage points of the revenue given to government. Hunters’ preferences for such attributes of hunting trips differed depending on the degree to which they declared an interest in Ethiopian culture, nature conservation, or believed Ethiopia to be politically unstable. Overall, respondents thus expressly valued the outcomes of nature conservation activities—the presence of wildlife in hunting areas—and they were willing to pay for them. Our findings highlight the usefulness of insights from choice modeling for the design of wildlife management and conservation policies and suggest that trophy hunting in Ethiopia could generate substantially more financial support for conservation and be more in line with conservation objectives than is currently the case.     

Integrating Biological and Social Values When Prioritizing Places for Biodiversity Conservation

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species’ distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes. Integración de Valores Biológicos y Sociales al Priorizar Sitios para la Conservación de la Biodiversidad     

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.