The city of Baltimore, Maryland, used a combination of computer-based tools, primarily the ArcView geographic information system (GIS) and the NED-1 system, to analyze risks to the long-term sustainability of their reservoir lands and to develop and evaluate alternative scenarios for management of the lands. While maintaining water quality was the primary goal, the second and third goals were maintaining and enhancing the forest habitat as a contribution towards regional biodiversity. NED-1 inventories incorporated data needed to evaluate wildlife habitat composition and structure and the quality of habitat along first- and second-order streams. While providing a platform for the management and analysis of data on numerous key abiotic and biotic forest characteristics, the NED-1 decision support software did not provide a mechanism for evaluating the relationships of these landscape elements. The need to understand how landscape context and current ecological processes were shaping the forest required a synthesis of tools and often required stepping outside the decision support mechanism for critical answers to conservation problems.
National forests are required to update their management plans every 10–15 years. The adjacent Boise, Payette, and Sawtooth National Forests in southern Idaho and northern Utah decided to update their plans together in order to better understand larger landscape issues and to address their many common concerns more efficiently. National forest plans do not make specific decisions about timber harvesting or other activities, but rather have been described as more akin to land use zoning in determining overall rules and activities appropriate for certain areas. As part of planning, forests are required to calculate an “Allowable Sale Quantity” (ASQ) of timber, which led the forest to use Spectrum, a linear optimization DSS developed by the Forest Service. The Forests soon realized that the basic forest growth and harvesting model could be expanded to help evaluate other effects of the different possible management alternatives. The model was expanded to include 120 vegetation classes (combinations of vegetation types, successional stages, and canopy closures) that were distributed across seven land allocation zones over 50 years for each of seven broad management alternatives. To get a more detailed view of the feasibility of these alternatives, the RELM DSS was used to take these Spectrum outputs and distribute them further down to 6th field watersheds (about 200 per forest). Because fire is an important influence in the region that was not explicitly modeled by Spectrum and because there was some suspicion of inherent biases in optimization modeling, a parallel modeling exercise using the VDDT DSS was also undertaken near the end of the planning process. (VDDT is a state-transition simulation model that had also been used to model the unforested parts of the planning area).
A decision tree is a classification algorithm that automatically derives a hierarchy of partition rules with respect to a target attribute of a large dataset. However, spatial autocorrelation makes conventional decision trees underperform for geographical datasets as the spatial distribution is not taken into account. The research presented in this paper introduces the concept of a spatial decision tree based on a spatial diversity coefficient that measures the spatial entropy of a geo-referenced dataset. The principle of this solution is to take into account the spatial autocorrelation phenomena in the classification process, within a notion of spatial entropy that extends the conventional notion of entropy. Such a spatial entropy-based decision tree integrates the spatial autocorrelation component and generates a classification process adapted to geographical data. A case study oriented to the classification of an agriculture dataset in China illustrates the potential of the proposed approach.
The aim of this research is to develop and implement a simple spatio-temporal model of population location that might improve risk assessment and damage analysis for decision-making in both the Finnish Fire and Rescue Services and the Finnish Defence Forces. The motivation for the research is that present risk models do not take into account the temporal variation in population location during different times of the day. We use spatio-temporal modeling methods to model the population dynamics, and visualization techniques to represent the model outcomes. In addition, we apply the developed model to a damage-analysis application. The case study site is located in the centre of Helsinki. The model uses a basic population and workplace dataset maintained by the Helsinki Metropolitan Area Council. By means of this model, we intend to advance risk assessment, which considers the consequences of accidents. This model has the potential to help decision-makers evaluate their plans in several application areas - such as achieving better preparedness by having more reliable evacuation plans and resource allocation. In addition to the application-related technological research, a more generic framework about decision-making supported by spatio-temporal knowledge and visualization is presented.
The U.S. Forest Service Forestry Support Program (FSP) provides technical assistance, through State forestry agency partners, to nonindustrial private forest owners to encourage and enable active long-term forest management. A primary focus of the program is the development of comprehensive, multi-resource management plans that provide landowners with the information they need to manage their forests for a variety of products and services. Under pressure from the Office of Management and Budget to better demonstrate program effectiveness, the FSP has been developing the Spatial Analysis Program to track and summarize information about properties enrolled in the program. It is providing an online interface that helps create stewardship plans that qualify for the program and stores the information in a central database. It provides a basic set of GIS data which can be used to evaluate impacts of (and possibly prioritize) stewardship activities. States can add their own data layers and weighting systems.
The natural flow regimen of the Green River, one of the most diverse systems in the US, has been negatively affected by four man-made reservoirs in the upper watershed, including Green River Lake. In 1998 The Nature Conservancy and the US Army Engineer District, Louisville prepared a more ecologically compatible water-release schedule for the lake, designed to protect and enhance natural spawning of specific fish and freshwater mussel species. This complex project provides an excellent case study for the Redlands Institute that will integrate engineering models (such as HEC-RAS, HEC-EFM, and HEC-ResSim from the Hydrologic Engineering Center) with spatially-enabled ecosystem condition and decision support tools, such as Ecosystem Management Decision Support. Not only will this demonstrate spatial decision support systems, but it would organize and make available a wide variety of qualitative and quantitative watershed data and information for immediate use by local, state, and federal agencies.
IP developed their Forest Patterns system to help them manage at the landscape level and comply with environmental laws and the sustainable forestry certification standards. The program tracks a hierarchy of land uses beginning with three broad tiers of management: timber production, conservation, and nonforest. It contributes to the conservation of biodiversity via management of landscape units (typically 40,000 to 60,000 acres). Landscape units can be assessed to determine structure and forest cover type gaps or surpluses when compared with regional vertebrate landscape scale models developed by the USDA Forest Service.
Multi-criteria evaluation (MCE) is perhaps the most fundamental of decision support operations in geographical information systems (GIS). This paper reviews two main MCE approaches employed in GIS, namely Boolean and Weighted Linear Combination (WLC), and discusses issues and problems associated with both. To resolve the conceptual differences between the two approaches, this paper proposes the application of fuzzy measures, a concept that is broader but that includes fuzzy set membership, and argues that the standardized factors of MCE belong to a general class of fuzzy measures and the more spefic instance of fuzzy set membership. This perspective provides a strong theoretical basis for the standardization of factors and their subsequent aggregation. In this context, a new aggregation operator that accommodates and extends the Boolean and WLC approaches is discussed: the Ordered Weighted Average. A case study of industrial allocation in Nakuru, Kenya is employed to illustrate the different approaches.
In 2004, Lake County voters approved a public lands referendum by more than 70 percent, which allowed the County to issue $36 million in bonds for the acquisition and improvement of land to protect drinking water, improve water quality of rivers and lakes, protect open space and provide passive recreation areas. Interested Lake County property owners can apply to have their land considered for acquisition or easement under this program. If the evaluation criteria are met, the proposal is referred to the board of county commissioners for approval. The criteria encompass three areas of primary concern: enhancing water resources, protecting environmentally sensitive lands and providing potential recreational lands.
The Lake County Public Lands Program uses the TPL Greenprint for both topdown conservation assessment and bottom-up application evaluation. The Greenprint provides a county-wide perspective on critical areas for conservation. To assist with implementation, TPL staff developed a report identifying the best potential
state funding sources that Lake County can leverage for land protection in critical areas.
In addition, the Greenprint provides tools that automatically generate property profiles specific to the Public Lands Program evaluation criteria. Application screening procedures require that a prospective property satisfy a minimum number of evaluation criteria. The Greenprint provides an instant, objective evaluation (both textual and mapped) for any property with respect to the evaluation criteria.
Forest landscape disturbance and succession models have become practical tools for large-scale, long-term analyses of the cumulative effects of forest management on real landscapes. They can provide essential information in a spatial context to address management and policy issues related to forest planning, wildlife habitat quality, timber harvesting, fire effects, and land use change. Widespread application of landscape disturbance and succession models is hampered by the difficulty of mapping the initial landscape layers needed for model implementation and by the complexity of calibrating forest landscape models for new geographic regions. Applications are complicated by issues of scale related to the size of the landscape of interest (bigger is better), the resolution at which the landscape is modeled and analyzed (finer is better), and the cost or complexity of applying a landscape model (cheaper and easier is better). These issues spill over to associated analyses that build on model outputs or become integrated as auxiliary model capabilities. Continued development and application of forest landscape disturbance and simulation models can be facilitated by (1) cooperative efforts to initialize more and larger landscapes for model applications, (2) partnerships of practitioners and scientists to address current management issues, (3) developing permanent mechanisms for user support, (4) adding new capabilities to models, either directly or as compatible auxiliary models, (5) increasing efforts to evaluate model performance and compare multiple models running on the same landscape, and (6) developing methods to choose among complex, multi-resource alternatives with outputs that vary over space and time.
Recent trends indicate increasing use of computer modeling in support of local environmental policy making. The ability of such models to improve local environmental decision making will depend not only on the characteristics of the models but also on those who will draw on them in making local policy: local government officials. In this study we examine the views of town officials concerned about nitrogen levels in local estuaries about computer models developed to inform their understandings and decisions regarding nitrogen loading. We also compare the views of the town officials with a sample of modelers. We find that town officials are supportive of models and the scientists who build them. However, town officials seek more information about the impacts of changes at small spatial scales (e.g., house building lots) than current models provide or than modelers believe that they can accurately provide, while recognizing the inability of current models to support such analysis. Town officials are also interested in more distant endpoints in the causal chain (e.g., effects on fish populations) than the modelers feel comfortable providing. Finally, our findings suggest that town officials are not supportive of broad public use of the models.
The goals of the Aquatic and Riparian Effectiveness Monitoring Program (AREMP) are to determine present watershed condition, track trends in watershed condition over time, and report on the Northwest Forest Plan's effectiveness across the region. To account for biophysical differences across the region, the AREMP team divided the Plan area into seven biophysical provinces and held a series of expert workshops to develop a model for each. Models are being developed using the Ecosystem Management Decision Support (EMDS) system and are run on data summarized to 6th field hydrologic units (~10,000 � 40,000 ac).
Geographers are increasingly adopting visualization methods for exploring spatial data and generating hypotheses. Data exploration is not limited to the examination of original attribute data, but can be applied at different stages of geographic information processing. Multi-criteria evaluation is a simple yet powerful data processing technique for decision support using geographic information systems. This paper discusses the benefits of a geographic visualization approach to multi-criteria decision-making. In particular, an interactive, spatially enabled implementation of the analytic hierarchy process will be described. The effectiveness of this method will be demonstrated using a case study of assessing population health status for health regions in Ontario.
The crisis in the early 1990s over conservation of biodiversity in the forests of the Pacific Northwest caused an upheaval in forest policies for public and private landowners. These events led to the development of the Coastal Landscape Assessment and Modeling Study (CLAMS) for the Coast Range Physiographic Province of Oregon, a province containing over two million hectares of forest with a complex mixture of public and private ownership. Over a decade, CLAMS scientists developed regional data bases and tools to enable assessments of the implications of current policies for biodiversity and have begun using these data and tools to test ideas for solving policy problems. We summarize here four main lessons from our work: (1) Regional ecosystem perspectives, while rewarding, are difficult to achieve. Helping policy makers and the public understand biodiversity policies for an entire province can assist in developing more reasoned policies. However, this result is difficult to achieve because needed scientific building blocks generally do not exist, few policy institutions address regional cross-ownership issues, people can find it difficult to take a regional view, and the appropriate region for analysis changes with the policy problem. (2) Interest in environmental policy analysis may come as much from a pursuit of power as a pursuit of understanding. Biodiversity policy analyses are often viewed as weapons in an ongoing political battle. Also, results that might destabilize existing policies generally will not be well received by those in power. (3) The relationship of regional analyses to civic processes remains challenging and unsettled. Communication between citizens and scientists takes real effort. Also, collaborative processes both inspire and constrain regional policy analysis, and scientific work often proceeds at a different pace than these processes. In the end, CLAMS’s most important effect on the civic dialogue may be to change how people think about the Coast Range. (4) An important role exists for anticipatory assessments done independently by scientists. Independent review will be especially important as policy analyses shift to management of nonfederal forests. Our future efforts in CLAMS will focus on evaluating ideas for fundamental changes in forest management.
To provide decision support to the Board of Forestry for its 2001 revision of the strategic long-term plans for state forest management, the Oregon Department of Forestry (ODF) modeled different alternatives on timber production and complex stand structure development. A public planning process, begun in the mid-1990's, had identified a range of management options, from industrial forestry to conservation-focused approaches. A compromise active-management approach, referred to as "structure-based management," was identified as the preferred alternative. The first analytical effort in 1999 compared outputs from structure-based management to five other scenarios from the identified range of alternatives. Originally scheduled to be completed in five months, the effort took nearly a year longer because of the extensive work needed to prepare all the necessary data. A variety of growth and thinning options were generated with the ORGANON growth-and-yield program. These were fed into a custom-programmed, spatially explicit harvest scheduling model created by a professor at Oregon State University. The model projected the alternatives for a 200-year planning horizon in 10-year intervals. The primary indicators used to describe the results of the alternatives were harvest volume, net present value and area of land in the oldest two (of five) structure classes. The Board of Forestry approved the structure-based management plan in early 2001.
As with most modeling efforts, both available time and data were major constraints. Stand-level inventories, road access data, and information on the growth-retarding effects of an emerging disease problem (Swiss needle cast) were all weak or not available in formats that could be easily incorporated into the model. Further, there was little time to involve the various district and field foresters in refining the results. Because of these shortcomings, the results from these initial modeled alternatives were portrayed as relative, not absolute. Nevertheless, when operational estimates the districts produced (after the plan had been adopted) came in at only about half the model-predicted harvest, it became a major political issue with the counties and forest industry that depended heavily on revenues from these forests.
In 2003, the counties and the state agreed to a formal three-year, $2 million dollar project to enhance the modeling process in order to provide decision support for a potential revision to the management plan, to support a decision on whether to pursue a habitat conservation plan, and to help set harvest levels. This Harvest and Habitat Model Project (H&H) utilized a new stand-level inventory, improved growth projections (including updated impacts from Swiss needle cast and the use of the Forest Vegetation Simulator program), and incorporated the costs and constraints of silvicultural options and operational harvest units, including associated transportation systems. District foresters were involved at every stage in the development of model inputs and in a feedback loop with the modelers to help check and refine the feasibility of model operations. A separate GIS-based tool was developed to help facilitate this checking. Four alternatives were modeled: the current management plan, using both a proposed habitat conservation plan and an endangered species “take avoidance” strategy; the current management plan with only “take avoidance”; and timber and conservation-oriented alternatives (elaborated in conjunction with these separate stakeholder groups). The final results of this second phase were presented to the Board in early 2006. Although Board members seemed to understand the model results, they were not clear on their “decision space”, i.e., how much legal latitude they have to adjust the plan and what are the specific features they can adjust. Timber interests questioned the validity of the plan (not the model) because the new model estimates are considerably below earlier estimates and what they consider sustainable. Although the H&H project is now officially completed, the tools created will continue to be refined and used in the state's forest planning processes. In August 2006, a peer review of the model was conducted, providing considerable information on the strengths and weaknesses of this effort (available at the website cited below).
The red-cockaded woodpecker (RCW, Picoides borealis) is one of the longest recognized federally endangered species. It lives only in open, mature and old-growth pine ecosystems in the southeastern United States, a habitat that has declined rapidly due to fire suppression and short-rotation forestry. Its current abundance is estimated at less than 3% of its abundance at the time of European settlement.
In 2003, the U.S. Fish and Wildlife Service published a major new revision of the RCW recovery plan that includes updated management guidelines for both federal and nonfederal lands. Applying these guidelines on the ground can be complex because breeding groups often occupy a cluster of nesting trees, and multiple groups may be found adjacent to one another. To encourage compliance with the new regulations, the FWS has developed an extension to the popular ArcGIS software that can assist managers in meeting the new guidelines. The software is referred to as the “RCW Foraging Habitat Matrix Application” and can be downloaded free from the internet (http://www.fws.gov/rcwrecovery/matrix_info.htm ). It builds on previous work by Fort Bragg on automating habitat evaluations based on digital forest inventories. The GIS software company (ESRI) and U.S. Army Environmental Center also contributed significant resources to the effort. One important difference from the past effort is that the new guidelines require habitat details not normally present in forest inventories, including ground cover and midstory hardwoods.
The software was released in April 2006, so it is too early to gauge its impact. The authors expect considerable feedback and refinement of the tool, and a central design goal was to build in as much flexibility as possible.
A few RCW modeling efforts simulate how populations of the bird will fluctuate over time given environmental influences. These models are considerably more complex in that they simulate individual birds over time in a spatially explicit manner. Designers of one of these models run it on their mainframe computer for clients on a contractual basis. They have also recently (2006) received a contract from the Department of Defense to create a desktop computer version for managers, expected to be completed in 2009.
The Sustainable Forestry Initiative Standard (SFIS) is a form of self-regulation initiated by the forest industry. All companies belonging to the American Pulp and Paper Association are required to undergo SFIS certification. As seen in the IP case above, DSS can contribute significantly towards the certification process. In contrast to the previous case, here we look at DSS use from the perspective of those doing the certification (regulators), rather than the landowners. Under SFIS objective 4, companies are required to have “programs to promote biological diversity at stand and landscape levels.” A review of several certification summary reports and interviews with two certification specialists revealed that there is no one standardized procedure for these biodiversity analyses. It is up to each company to devise methods and each certifier to judge their acceptability.
The most visible emerging trends are collaboration with and borrowing techniques from The Nature Conservancy’s (TNC) ecoregional analyses and the use of a global species ranking system devised by NatureServe and its network of Natural Heritage programs. Companies are required to have plans to conserve native biological diversity (ecological communities and individual species) in general, and to locate and protect known sites associated with viable occurrences of critically imperiled (NatureServe rank G1) and imperiled (rank G2) species and communities. These requirements are most often met by developing customized GIS analyses that combine the company’s forest inventory data with other biophysical layers (e.g., slope, soils) to identify important biodiversity areas. These GIS “screens” are used to identify both ecological communities (coarse filter) and individual species (fine filter) habitat needs. NatureServe recently came out with a new DSS named “Vista,” which could help companies integrate economic and biodiversity values to prioritize conservation areas. The SFIS 2005–2009 standards now require expertise in “forest modeling” on the certification teams, which may begin to raise the bar on expectations for habitat analysis (especially simulations into the future).
The convening purpose of this project was to bring all the entities (federal, state, local governments, watershed council, NGOs) together and develop a basin-wide watershed restoration strategy for the Sandy River Basin in northwest Oregon. The process was structured to focus on aquatic habitat and produce a collaborative stakeholder vision across all ownerships. This first phase of the project identified anchor habitats. These are distinct stream and river reaches that harbor specific life-history stages of four species of salmon and steelhead to a greater extent than the river system at large, are critical for the creation and maintenance of high quality habitat, or both. Three data sources were used: empirical data from existing stream surveys, habitat modeling data generated by the Ecosystem Diagnosis and Treatment model, and professional judgment from three local experts. Anchor habitat stream segments were identified for the four species, and these priority areas can now be used to help guide habitat restoration planning activities.
The Upper San Pedro River Basin in southeastern Arizona is well known for its avian diversity; however, water use by Sierra Vista, Fort Huachuca, and agriculture in the basin threatens to lower its water table. This, in turn, could alter vegetation in the basin in a way that would negatively impact habitat currently supporting nesting of the endangered Southwestern Willow Flycatcher (Empidonax trailii extimus) and foraging for a large number of resident and neotropical migratory birds during the breeding season and migration. We determined the range of potential Alternative Future growth patterns for the basin (Alternative Futures) and compared them for their relative impacts on a suite of environmental parameters including hydrology, biodiversity, and landscape vegetation pattern. The intent is to inform decision makers of which potential Alternative Future would have the greatest and least impacts on those parameters.
A novel application of Sensitivity Analysis is presented. Useful applications of Global SA (GSA) already exist in the field of numerical modelling. In this paper, we explore the joint use of GSA, Geographical Information Systems (GIS) and Multi-Criteria Evaluation. In this preliminary case study, 11 factors have been used to find the best place to locate a hazardous waste landfill. Two variance-based methods (Sobol’ and E-FAST) are used to compute sensitivity indices in order to identify the factors that determine the variance of the model output. The results show that only three factors jointly account for 97% of the output variance. This information is employed to make a simplification of the original model, retaining only these three influential factors. In addition, if the SA is carried out in a pilot area where the spatial properties are similar to those of the whole region, we can infer the results to the whole area. This procedure achieves the goal of the study with an optimized allocation of resources for GIS data acquisition.
Much of the responsibility for land use planning in the U.S. falls to county-level government. There are, however, few written examples of DSS use related to biodiversity issues at the county level. One exception is the work of Tom Hobbs and David Theobold at Colorado State University. One of their examples is a collaboration with Summit County, Colorado, which is located about 60 miles west of Denver and is the home of the mountain resorts of Breckenridge, Vail, and Keystone. In terms of population, it has been one of the fastest growing counties in the nation (99.5% increase from 1990–2000). The White River National Forest occupies over 80% of the total land area in the county, and considerable development has occurred in forested areas or on private urbanized lands that are forested and adjacent to federal lands (i.e., the wildland/urban interface). As is common in many U.S. counties, a citizen committee updates a “master plan” for the county every few years. These plans do not directly set regulations, but rather provide guidance in setting legal standards, such as zoning regulations. In Summit County, the comprehensive plan is further subdivided into four subbasins, of which the Lower Blue subbasin is the least developed to date.
Dr. Hobbs championed the need to better integrate biodiversity information into county-level planning and received funding in 1994 to develop such a system from the Great Outdoors Colorado fund (state lottery money) and the Colorado Division of Wildlife. County commissioners from Summit and Larimer expressed interest and provided support for implementation in their counties. In addition to the computer programmers and scientists, Hobbs assembled a collaborative design group consisting of a county commissioner, a planner, a developer, a land owner, a wildlife manager, and some environmental advocates. The system was built using an iterative process of collecting ideas from the design group, constructing prototypes, and obtaining feedback. Theobald et al. (2000) describe one of the lessons learned in the collaborative design process:
Scientists on our design team originally advocated development of generalized population viability models as a way to analyze the consequences of development of a patch of habitat. However, the citizen participants found this approach to be obtuse and excessively technical, requiring them to take ‘on faith’ the validity of models produced by experts. There was a strongly expressed sentiment among these nontechnical members of our design team that they must be able to explain any analysis we used in a reasonable way to their fellow citizens, without relying on ‘outside’ technical expertise to establish the credibility of the analysis.
Additionally, they found a gap between the generalized theories that scientists work with and the more specific information needed in local land-use planning. Bridging this gap required experts willing to make difficult judgments or assumptions. Many of the assumptions and parameters also involved value judgments, which were most appropriately derived from the stakeholders. They also encountered difficulties in bridging the differences in time and space as they relate to ecological processes (long times and large areas) versus county planning processes (shorter times and smaller areas). Most biodiversity data is collected at the state level, so the level of detail is often less than ideal for local planning.
The resulting maps were used by the county in the update of their Lower Blue Master Plan. By the end of the process, the SCoP tool had become too complex to be easily transferable to other counties, and there was less political support at the state level for such planning related to private lands. Some of the ideas were incorporated into a statewide service operated by the Division of Wildlife called the Natural Diversity Information Source (NDIS). NDIS provides basic county-level statistics, species status lists, and internet maps of historical land use development trends. It does not, however, provide the type of species distribution and future build-out analyses that formed the core of the Blue Subbasin analysis.
This paper proposes to use principles of geographic visualization in conjunction with multi-criteria evaluation methods to support expert-level spatial decision-making. Interactive maps can be combined with analytical tools to explore various settings of multi-criteria evaluation parameters that define different decision-making strategies. In a case study, the analytic hierarchy process (AHP) is used to calculate composite measures of urban quality of life (QoL) for neighbourhoods in Toronto. The AHP allows for an interactive exploration of decision-making strategies, while offering a view on spatial patterns in the evaluation results. In particular, an interactive blending between a classical and a contemporary QoL model is supported. This feature is used in a pilot study to assess the usefulness of geographic visualization in urban QoL evaluation. Three user interviews provide positive feedback on the utility and usability of the tool that was operated by the investigator.
In the “Heart of Texas,” Travis County is divided by the Colorado River running west to east. One of the fastest growing counties in the state, Travis County is surrounded by hills on the west and blackland prairie on the east.Many of the 850,000 residents work for the government, or in high tech industry, research, and education.
With local partners, TPL used an interactive, stakeholder-driven Geographic Information System (GIS) model to develop a Greenprint for the county. During TPL-facilitated sessions, stakeholders identified four goals: protect water quality and quantity; provide recreational opportunities; enhance cultural resources; and preserve sensitive environmental features.
A technical team, led by TPL, incorporated relevant data into the Greenprint model. For example, to identify the best locations for new parks, they included a park equity component. The analysis examined “gaps” in park availability (with variables such as travel distance, park amenities and carrying capacity) with assessment of need (based on demographic variables such as percentage of children under 18, population density, percentage of minorities, and percentage of low income families).
The resulting Greenprint provides a composite picture of the lands that, if protected, would best provide equitable investments across the county, and help to meet all four of the goals.
NED is one of the forestry decision support systems most oriented towards small landowners. In an interview with the software developer, he noted that few small landowners appear to use it themselves; rather, the main users seem to be consulting foresters. Three foresters were contacted and interviewed about their use of NED with small landowners. The NED system contains a wildlife module that uses a forest inventory to estimate habitat types and qualitative likelihood of wildlife presence/absence (based on DeGraaf and Yamasaki 2001). None of the consultants interviewed has used the NED wildlife module with clients, however; instead they simply use their own knowledge to advise landowners on wildlife issues.
To guide biodiversity conservation and land use planning across Washington State, the Washington Departments of Fish and Wildlife (WDFW) and Natural Resources (WDNR) joined with The Nature Conservancy (TNC) in a partnership to do an ecoregional assessment for each of Washington’s nine ecoregions. Each assessment attempts to identify and prioritize places for the conservation of all biodiversity in an ecoregion. The relative priorities are based on such factors as species rarity, species richness, species representation, site suitability, and overall efficiency. Statistical models for suitability are typically not available, so therefore much of the index is based on expert opinion. Expert opinion was incorporated by using an abbreviated version of the analytic hierarchy process. The analysis utilizes an optimization program known as Sites to find the most efficient set of conservation units.
The Washington Forest Practice rules require different riparian buffer widths to fishbearing and nonfishbearing streams (making this distinction is referred to as “water typing”). The regulatory maps in force in the mid-1990s were found to significantly underestimate fish habitat, so the multi-stakeholder group negotiating the new regulations agreed to develop a new scientific, model-based approach. The state Board of Forestry adopted a regulation supporting the model-based approach, with the stipulations that the model achieve 95% accuracy and that a precautionary interim rule, which overestimates fish presence, would be followed during model development. A multistakeholder science group has been working on the model since 2000, but their modeling has not been able to meet the 95% accuracy threshold in all areas of the state because of geomorphic variability and the limited resolution of the topographic data. Debate on the further development and potential use of the water-typing model continues, and the interim rule remains in force.
Willamette Basin Alternative Futures Analysis was designed to help diverse stakeholders understand the ecological consequences of possible societal decisions related to changes in human populations and ecosystems in the Pacific Northwest and to develop transferable tools to support management of ecosystems at multiple spatial scales. It involved simulating the effects of three possible development scenarios on eight regional measures of biodiversity over the next 50 years (2000-2050). The process included a four-tiered stakeholder involvement and outreach plan and multiple biodiversity modeling efforts. Elements of ecosystem management it addressed included cutting across ownerships and integrating biophysical and socio-economic information.