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.     

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.     

Scale Mismatches,Conservation Planning,and the Value of Social‐Network Analyses

Many of the challenges conservation professionals face can be framed as scale mismatches. The problem of scale mismatch occurs when the planning for and implementation of conservation actions is at a scale that does not reflect the scale of the conservation problem. The challenges in conservation planning related to scale mismatch include ecosystem or ecological process transcendence of governance boundaries; limited availability of fine‐resolution data; lack of operational capacity for implementation; lack of understanding of social‐ecological system components; threats to ecological diversity that operate at diverse spatial and temporal scales; mismatch between funding and the long‐term nature of ecological processes; rate of action implementation that does not reflect the rate of change of the ecological system; lack of appropriate indicators for monitoring activities; and occurrence of ecological change at scales smaller or larger than the scale of implementation or monitoring. Not recognizing and accounting for these challenges when planning for conservation can result in actions that do not address the multiscale nature of conservation problems and that do not achieve conservation objectives. Social networks link organizations and individuals across space and time and determine the scale of conservation actions; thus, an understanding of the social networks associated with conservation planning will help determine the potential for implementing conservation actions at the required scales. Social‐network analyses can be used to explore whether these networks constrain or enable key social processes and how multiple scales of action are linked. Results of network analyses can be used to mitigate scale mismatches in assessing, planning, implementing, and monitoring conservation projects. Discordancia de Escalas, Planificación de la Conservación y el Valor del Análisis de Redes Sociales     

Managing Biodiversity in a Forestry Environment

In forest management and other land-use planning, ecological, social, and economical demands often conflict. Forest planning in particular has been biased towards maximizing economic output while disregarding the ecological effects of forestry practices. In this paper we present an approach for taking biological diversity into account in strategic forest management planning. We used the Analytic Hierarchy Process (AHP) method for resource allocation and priority setting. We carried out a case study, whereby a set of management strategies was evaluated in order to identify the strategy which best fulfills the requirement of maintaining biological diversity while at the same time yielding a reasonable income from timber production. We discuss applicability of the approach in solving different forest management and conservation planning problems, as well as in assessment of ecological impacts.     

Reciprocal Model for Meeting Ecological and Human Needs in Restoration Projects

Abstract: Ecological restoration aims to reverse the losses of biodiversity and the degradation of ecosystems that have occurred through time as humans have affected landscapes. To date, however, restoration success has been limited, in spite of increasing ecological knowledge and technical skills. Technical constraints, although significant, are only part of the problem. The reasons for restoration failure, including cost constraints, limitations in land allocation, and insufficient time and labor, often involve underlying human obstacles. Unless such obstacles are overcome, ongoing commitment to restoration projects will likely remain limited. Because restoration involves diverse scientific and social interests and is often laden with conflicting priorities, we view it as a "wicked problem." We examine ways commitment to restoration could be increased by developing beneficial relationships between humans and the natural environment in the context of restoration. We propose a model that shows that the ecological needs of the restoration area have the greatest potential to be met when human contributions are greatest. In turn, humans benefit increasingly as the restored ecosystem recovers. As human needs are addressed over time, their potential contributions to the restoration area increase. The extent to which contributions meet needs is enhanced or constrained by factors ranging from available technology and funding to community support. This ongoing loop of interactions between needs and contributions provides a basis for restoration planning and implementation which potentially reduces both ecological and human roadblocks to success. The model suggests that community-based projects will be most successful when experts train the group in restoration decision making, when expertise and leadership are developed within the group, and when participants experience group cohesiveness and a sense of personal reward.     

What is Good Ecological Restoration?

The rapid rise of ecological restoration is forcing consideration of what good restoration entails. Defining an end point for restoration is as much an ethical matter as a technical one, but scientifically trained restorationists have largely ignored the former issue. I argue that good restoration requires an expanded view that includes historical, social, cultural, political, aesthetic, and moral aspects. This expanded definition is necessary at a practical level to guide practitioners in the pursuit of excellence and at a conceptual level to prevent restoration from being swamped by technological activities and projects that veer away from ecological fidelity. Ecological fidelity is based on three principles: structural/compositional replication, functional success, and durability. These principles produce effective restoration, which is a necessary but not a sufficient condition of good restoration. An examination of characteristic problems that emanate from technological practices—reverse adaptation, an attention to product at the expense of process, and the separation of actions from consequences—leads directly to an expanded, inclusive framework for restoration. The results of an inclusive restoration process set up conditions necessary for restoration to achieve both ecological fidelity and harmonious human relationships within ecosystems.     

Incorporating consumer–resource spatial interactions in reserve design

The persistence of species in reserves depends in large part on the persistence of functional ecological interactions. Despite their importance, however, ecological interactions have not yet been explicitly incorporated into conservation prioritization methods. We develop here a general method for incorporating consumer–resource interactions into spatial reserve design. This method protects spatial consumer–resource interactions by protecting areas that maintain the connectivity between the distribution of consumers and resources. We illustrate our method with a conservation planning case study of a mammalian predator, American marten (Martes americana), and its two primary prey species, Red-backed vole (Clethrionomys rutilus) and Deer mouse (Peromyscus maniculatus). The conservation goal was to identify a reserve for marten that comprised 12% of a forest management unit in the boreal forest in Québec, Canada. We compared reserves developed using analysis variants that utilized different levels of information about predator and prey habitat distributions, species-specific connectivity requirements, and interaction connectivity requirements. The inclusion of consumer–resource interactions in reserve-selection resulted in spatially aggregated reserves that maintained local habitat quality for the species. This spatial aggregation was not induced by applying a qualitative penalty for the boundary length of the reserve, but rather was a direct consequence of modelling the spatial needs of the interacting consumer and resources. Our method for maintaining connectivity between consumers and their resources within reserves can be applied even under the most extreme cases of either complete spatial overlap or complete spatial segregation of consumer–resource distributions. The method has been made available via public software.     

A Markov model for assessing ecological stability properties

Ecological systems are frequently modeled as dynamic systems. It is natural then to use the techniques of dynamic systems theory to analyze such models. However, the methods and results that are produced by dynamic systems theory do not always capture the aspects of ecological systems that are of greatest interest to managers and decision-makers. We identify some of the challenges of using dynamic systems theory to explore ecological systems and propose an alternative approach that emphasizes the understanding of transient effects in light of uncertainty and variability. We illustrate this method by an examination of a model for phosphorus levels in fresh water lakes.     

Crafting Effective Solutions to the Large Carnivore Conservation Problem

How problems are defined and redefined largely dictates how they will (or if they can) be solved, especially complex problems such as the challenge of conserving large carnivores in North America. We describe a practical and comprehensive method for analyzing problems within specific human and ecological contexts and for inventing, evaluating, and selecting solutions. We conducted a problem definition exercise for the large carnivore conservation problem and arrived at five key variables that must be addressed in order to protect these threatened species: cultural history, valuation, ecology, management systems, and the policy process. Our analysis maps the broad dimensions of the problem and identifies areas to be targeted by any successful solution strategy. The method we present and our proposed initial definition of the carnivore conservation problem can serve as a springboard for further analysis and policy making at specific sites and at appropriate scales for on-the-ground solutions to this and other natural resource problems.     

State-of-the-art of ecological modelling with emphasis on development of structural dynamic models

The paper deals with two major problems in ecological modelling today, namely how to get reliable parameters? and how to build ecosystem properties into our models? The use of new mathematical tools to answer these questions is mentioned briefly, but the main focus of the paper is on development of structural dynamic models which are models using goal functions to reflect a current change of the properties of the biological components in the models. These changes of the properties are due to the enormous adaptability of the biological components to the prevailing conditions. All species in an ecosystem attempt to obtain most biomass, i.e. to move as far away as possible from thermodynamic equilibrium which can be measured by the thermodynamic concept exergy. Consequently, exergy has been proposed as a goal function in ecological models with dynamic structure, meaning currently changed properties of the biological components and in model language currently changed parameters. An equation to compute an exergy index of a model is presented. The theoretical considerations leading to this equation are not presented here but references to literature where the basis theory can be found are given. Two case studies of structural dynamic modelling are presented: a shallow lake where the structural dynamic changes have been determined before the model was developed, and the application of biomanipulation in lake management, where the structural dynamic changes are generally known. Moreover. it is also discussed how the same idea of using exergy as a goal function in ecological modelling may be applied to facilitate the estimation of parameters.     

Efficient and equitable design of marine protected areas in Fiji through inclusion of stakeholder‐specific objectives in conservation planning

The efficacy of protected areas varies, partly because socioeconomic factors are not sufficiently considered in planning and management. Although integrating socioeconomic factors into systematic conservation planning is increasingly advocated, research is needed to progress from recognition of these factors to incorporating them effectively in spatial prioritization of protected areas. We evaluated 2 key aspects of incorporating socioeconomic factors into spatial prioritization: treatment of socioeconomic factors as costs or objectives and treatment of stakeholders as a single group or multiple groups. Using as a case study the design of a system of no‐take marine protected areas (MPAs) in Kubulau, Fiji, we assessed how these aspects affected the configuration of no‐take MPAs in terms of trade‐offs between biodiversity objectives, fisheries objectives, and equity in catch losses among fisher stakeholder groups. The achievement of fisheries objectives and equity tended to trade‐off concavely with increasing biodiversity objectives, indicating that it is possible to achieve low to mid‐range biodiversity objectives with relatively small losses to fisheries and equity. Importantly, the extent of trade‐offs depended on the method used to incorporate socioeconomic data and was least severe when objectives were set for each fisher stakeholder group explicitly. We found that using different methods to incorporate socioeconomic factors that require similar data and expertise can result in plans with very different impacts on local stakeholders.     

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.     

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.     

Use of Inverse Spatial Conservation Prioritization to Avoid Biological Diversity Loss Outside Protected Areas

Globally expanding human land use sets constantly increasing pressure for maintenance of biological diversity and functioning ecosystems. To fight the decline of biological diversity, conservation science has broken ground with methods such as the operational model of systematic conservation planning (SCP), which focuses on design and on‐the‐ground implementation of conservation areas. The most commonly used method in SCP is reserve selection that focuses on the spatial design of reserve networks and their expansion. We expanded these methods by introducing another form of spatial allocation of conservation effort relevant for land‐use zoning at the landscape scale that avoids negative ecological effects of human land use outside protected areas. We call our method inverse spatial conservation prioritization. It can be used to identify areas suitable for economic development while simultaneously limiting total ecological and environmental effects of that development at the landscape level by identifying areas with highest economic but lowest ecological value. Our method is not based on a priori targets, and as such it is applicable to cases where the effects of land use on, for example, individual species or ecosystem types are relatively small and would not lead to violation of regional or national conservation targets. We applied our method to land‐use allocation to peat mining. Our method identified a combination of profitable production areas that provides the needed area for peat production while retaining most of the landscape‐level ecological value of the ecosystem. The results of this inverse spatial conservation prioritization are being used in land‐use zoning in the province of Central Finland.     

Selecting cost‐effective areas for restoration of ecosystem services

Selection of areas for restoration should be based on cost‐effectiveness analysis to attain the maximum benefit with a limited budget and overcome the traditional ad hoc allocation of funds for restoration projects. Restoration projects need to be planned on the basis of ecological knowledge and economic and social constraints. We devised a novel approach for selecting cost‐effective areas for restoration on the basis of biodiversity and potential provision of 3 ecosystem services: carbon storage, water depuration, and coastal protection. We used Marxan, a spatial prioritization tool, to balance the provision of ecosystem services against the cost of restoration. We tested this approach in a mangrove ecosystem in the Caribbean. Our approach efficiently selected restoration areas that at low cost were compatible with biodiversity targets and that maximized the provision of one or more ecosystem services. Choosing areas for restoration of mangroves on the basis carbon storage potential, largely guaranteed the restoration of biodiversity and other ecosystem services. Selección de Áreas Rentables para la Restauración de Servicios Ambientales     

Evaluating approaches for scaling-up community-based marine-protected areas into socially equitable and ecologically representative networks

Marine-protected areas (MPAs) are vital to marine conservation, but their coverage and distribution is insufficient to address declines in global biodiversity and fisheries. In response, many countries have committed through the Aichi Target 11 of the Convention on Biological Diversity to conserve 10% of the marine environment through ecologically representative and equitably managed MPAs by 2020. The rush to fulfill this commitment has raised concerns on how increasing MPA coverage will affect other elements of Target 11, including representation and equity. We examined a Philippines case study to assess and compare 3 MPA planning approaches for biodiversity representation and equitable distribution of costs to small-scale fishers. In the opportunistic approach, MPAs were identified and supported by coastal communities. The donor-assisted approach used local knowledge to select MPAs through a national-scale and donor-assisted conservation project. The systematic conservation planning approach identified MPA locations with the spatial prioritization software Marxan with Zones to achieve biodiversity objectives with minimal costs to fishers. We collected spatial data on biodiversity and fisheries features and performed a gap analysis to evaluate MPAs derived from different approaches. We assessed representation based on the proportion of biodiversity features conserved in MPAs and distribution equity by the distribution of opportunity costs (fishing areas lost in MPAs) among fisher stakeholder groups. The opportunistic approach did not ineffectively represent biodiversity and resulted in inequitable costs to fishers. The donor-assisted approach affected fishers disproportionately but provided near-optimal regional representation. Only the systematic approach achieved all representation targets with minimal and equitable costs to fishers. Our results demonstrate the utility of systematic conservation planning to address key elements of Target 11 and highlight opportunities (e.g., integration of local and scientific knowledge can address representation and equity concerns) and pitfalls (e.g., insufficient stakeholder considerations can exacerbate social inequalities) for planning MPAs in similar contexts.     

Quantifying the sustainability of water use systems: Calculating the balance between network efficiency and resilience

In ecological network theory, network efficiency and resilience are two essential but complementary attributes of the network structure, and a balance between these factors is critical for an ecosystem's long-term sustainability. Our paper introduces this method and related concepts into water use systems to provide a new angle for sustainability quantification. In this paper, we investigate the meanings of network efficiency and resilience in the context of sustainable development of water use systems, and define sustainable systems based on the optimal balance between network efficiency and resilience. With the consideration of complex artificial characteristics of water use, we propose an optimal water use network and quantify its flows. By ascendency calculation, the balanced network structure can be determined. We then use the four sub-basins of China's Haihe River as a case study to illustrate how the optimal network can be constructed and how the optimal balance for each scenario can be calculated. The results show that the optimal balance for the sub-basins has ascendency values ranging from 0.5970 to 0.7161. By analyzing the contribution of each water use activity to network's balance structure, the location of the optimal balance in water use systems can be better understood. This research represents the first attempt to explore the balance between a network structure's efficiency and resilience as a way to quantify the sustainability of water use systems, and builds a foundation for future studies on the assessment, regulation, and management of water resources.     

A state-space model approach to optimum spatial sampling design based on entropy

We consider the spatial sampling design problem for a random field X. This random field is in general assumed not to be directly observable, but sample information from a related variable Y is available. Our purpose in this paper is to present a state-space model approach to network design based on Shannon's definition of entropy, and describe its main points with regard to some of the most common practical problems in spatial sampling design. For applications, an adaptation of Ko et al.'s (1995) algorithm for maximum entropy sampling in this context is provided. We illustrate the methodology using piezometric data from the Velez aquifer (Malaga, Spain). © Rapid Science 1998     

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.     

Mapping Human and Social Dimensions of Conservation Opportunity for the Scheduling of Conservation Action on Private Land

Abstract Spatial prioritization techniques are applied in conservation‐planning initiatives to allocate conservation resources. Although typically they are based on ecological data (e.g., species, habitats, ecological processes), increasingly they also include nonecological data, mostly on the vulnerability of valued features and economic costs of implementation. Nevertheless, the effectiveness of conservation actions implemented through conservation‐planning initiatives is a function of the human and social dimensions of social‐ecological systems, such as stakeholders’ willingness and capacity to participate. We assessed human and social factors hypothesized to define opportunities for implementing effective conservation action by individual land managers (those responsible for making day‐to‐day decisions on land use) and mapped these to schedule implementation of a private land conservation program. We surveyed 48 land managers who owned 301 land parcels in the Makana Municipality of the Eastern Cape province in South Africa. Psychometric statistical and cluster analyses were applied to the interview data so as to map human and social factors of conservation opportunity across a landscape of regional conservation importance. Four groups of landowners were identified, in rank order, for a phased implementation process. Furthermore, using psychometric statistical techniques, we reduced the number of interview questions from 165 to 45, which is a preliminary step toward developing surrogates for human and social factors that can be developed rapidly and complemented with measures of conservation value, vulnerability, and economic cost to more‐effectively schedule conservation actions. This work provides conservation and land management professionals direction on where and how implementation of local‐scale conservation should be undertaken to ensure it is feasible.