Using strategic foresight to assess conservation opportunity.

The nature of conservation challenges can foster a reactive, rather than proactive approach to decision making. Failure to anticipate problems before they escalate results in the need for more costly and time-consuming solutions. Proactive conservation requires forward-looking approaches to decision making that consider possible futures without being overly constrained by the past. Strategic foresight provides a structured process for considering the most desirable future and for mapping the most efficient and effective approaches to promoting that future with tools that facilitate creative thinking. The process involves 6 steps: setting the scope, collecting inputs, analyzing signals, interpreting the information, determining how to act, and implementing the outcomes. Strategic foresight is ideal for seeking, recognizing, and realizing conservation opportunities because it explicitly encourages a broad-minded, forward-looking perspective on an issue. Despite its potential value, the foresight process is rarely used to address conservation issues, and previous attempts have generally failed to influence policy. We present the strategic foresight process as it can be used for proactive conservation planning, describing some of the key tools in the foresight tool kit and how they can be used to identify and exploit different types of conservation opportunities. Scanning is an important tool for collecting and organizing diverse streams of information and can be used to recognize new opportunities and those that could be created. Scenario planning explores how current trends, drivers of change, and key uncertainties might influence the future and can be used to identify barriers to opportunities. Backcasting is used to map out a path to a goal and can determine how to remove barriers to opportunities. We highlight how the foresight process was used to identify conservation opportunities during the development of a strategic plan to address climate change in New York State. The plan identified solutions that should be effective across a range of possible futures. Illustrating the application of strategic foresight to identify conservation opportunities should provide the impetus for decision makers to explore strategic foresight as a way to support more proactive conservation policy, planning, and management.

Ecology of zoonoses: natural and unnatural histories.

More than 60% of human infectious diseases are caused by pathogens shared with wild or domestic animals. Zoonotic disease organisms include those that are endemic in human populations or enzootic in animal populations with frequent cross-species transmission to people. Some of these diseases have only emerged recently. Together, these organisms are responsible for a substantial burden of disease, with endemic and enzootic zoonoses causing about a billion cases of illness in people and millions of deaths every year. Emerging zoonoses are a growing threat to global health and have caused hundreds of billions of US dollars of economic damage in the past 20 years. We aimed to review how zoonotic diseases result from natural pathogen ecology, and how other circumstances, such as animal production, extraction of natural resources, and antimicrobial application change the dynamics of disease exposure to human beings. In view of present anthropogenic trends, a more effective approach to zoonotic disease prevention and control will require a broad view of medicine that emphasises evidence-based decision making and integrates ecological and evolutionary principles of animal, human, and environmental factors. This broad view is essential for the successful development of policies and practices that reduce probability of future zoonotic emergence, targeted surveillance and strategic prevention, and engagement of partners outside the medical community to help improve health outcomes and reduce disease threats.

Agronomic or contentious land change? A longitudinal analysis from the Eastern Brazilian Amazon.

Since 1984, nearly 1,000 people have been killed in the Brazilian Amazon due to land conflicts stemming from unequal distribution of land, land tenure insecurity, and lawlessness. During this same period, the region experienced almost complete deforestation (< 8% forest cover by 2010). Land conflict exacts a human toll, but it also affects agents' decisions about land use, the subject of this article. Using a property-level panel dataset covering the period of redemocratization in Brazil (1984) until the privatization of long-term leases in the Eastern Amazon (2010), we show that deforestation is affected by land conflict, particularly in cases of expropriation of property for agrarian reform settlement formation and when that conflict involves fatalities. Deforestation on agrarian reform settlements is much greater when soils are poor for agriculture and when the land has been the object of past conflict. Deforestation and conflict are episodic, and both agronomic drivers and contentious drivers of land change are active in the region. Ultimately, the outcome of these processes of contentious and agronomic land change is substantial deforestation, regardless of who was in possession and control of the land.

Integrating Scenario Planning and Cost-Benefit Methods.

By their nature, the most vexing social problems reflect collisions between social and economic interests of parties with highly divergent views and perspectives on the cause and character of what is at issue and the consequences that flow from it. Conflicts around biotechnology applications are good examples of these problems. When considering the potential consequences of proposed biotechnology applications, an enormous range of perspectives arise reflecting the breadth of different and often competing interests with a stake in life's future. This essay starts from an assumption that the traditional tool of cost-benefit analysis is not adequate for adjudicating competing claims around the introduction of new biotechnology applications. It tends to require implicit simplifying assumptions that reduce or mask true underlying levels of complexity and uncertainty, and the results it produces deliver a definitive and singular answer, as opposed to a multiplicity of outcomes. In this essay, I describe some of the key elements of formal scenario planning to show how CBA could be redeployed as a supporting tool within the broader decision support methodology of formal scenario planning.

A Presence-Only Model of Suitable Roosting Habitat for the Endangered Indiana Bat in the Southern Appalachians.

We know little about how forest bats, which are cryptic and mobile, use roosts on a landscape scale. For widely distributed species like the endangered Indiana bat Myotis sodalis, identifying landscape-scale roost habitat associations will be important for managing the species in different regions where it occurs. For example, in the southern Appalachian Mountains, USA, M. sodalis roosts are scattered across a heavily forested landscape, which makes protecting individual roosts impractical during large-scale management activities. We created a predictive spatial model of summer roosting habitat to identify important predictors using the presence-only modeling program MaxEnt and an information theoretic approach for model comparison. Two of 26 candidate models together accounted for >0.93 of AICc weights. Elevation and forest type were top predictors of presence; aspect north/south and distance-to-ridge were also important. The final average best model indicated that 5% of the study area was suitable habitat and 0.5% was optimal. This model matched our field observations that, in the southern Appalachian Mountains, optimal roosting habitat for M. sodalis is near the ridge top in south-facing mixed pine-hardwood forests at elevations from 260-575 m. Our findings, coupled with data from other studies, suggest M. sodalis is flexible in roost habitat selection across different ecoregions with varying topography and land use patterns. We caution that, while mature pine-hardwood forests are important now, specific areas of suitable and optimal habitat will change over time. Combining the information theoretic approach with presence-only models makes it possible to develop landscape-scale habitat suitability maps for forest bats.