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UNIVERSITY OF WASHINGTON
Informing Conservation Decisions Based on Ecosystem Services
Prepared for the Regional Open Space Strategy of Central Puget Sound
by John Crawford-‐Gallagher, Melissa Martin, Ayse Nal, Neil Ratliff
3/1/2014
Keystone project for the Environmental Management Certificate administered by the Program on the Environment, University of Washington
Table of Contents Introduction: Regional Open Space Strategy ................................................................................. 1
Scope of Work ................................................................................................................................ 2
Approach 1: InVEST ........................................................................................................................ 4
Models ....................................................................................................................................... 4
Overlap Analysis Model .......................................................................................................... 4
Carbon Storage and Sequestration Model ............................................................................. 6
Biodiversity Model ................................................................................................................. 8
Managed timber production model ....................................................................................... 9
Approach 2: Valuing Ecosystem Services Together (VEST) .......................................................... 11
Using VEST ................................................................................................................................ 11
VEST Results and Feedback ...................................................................................................... 12
Benefits and Limitations .............................................................................................................. 14
Invest Benefits .......................................................................................................................... 14
Invest Limitations ..................................................................................................................... 14
VEST Benefits ........................................................................................................................... 15
VEST Limitations ....................................................................................................................... 15
Conclusion: Applying InVEST and VEST to the ROSS .................................................................... 16
Appendix A: InVEST Models ......................................................................................................... 17
Overlap Analysis Model ............................................................................................................ 17
Carbon Model .......................................................................................................................... 18
Biodiversity Model ................................................................................................................... 20
Managed Timber Production Model ........................................................................................ 23
Appendix B: VEST Instructions ..................................................................................................... 25
Appendix C: Annotated Bibliography ........................................................................................... 27
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Introduction: Regional Open Space Strategy As the Puget Sound region continues to experience rapid growth and development, policy mak-‐ers, developers, advocacy groups and others need a regional strategy to address and balance a disparate set of interests. Central to this strategy is how the region will value, use, and interact with open space, including parks, trails, farmlands, forests, recreation areas, waterways, and green storm water infrastructure, all of which provide essential and valuable benefits and ser-‐vices to all inhabitants of the region. As a project team of graduate students in the University of Washington Environmental Management Certificate program we prepared this report for the Regional Open Space Strategy (ROSS). Our intent was to assist the ROSS in achieving the follow-‐ing two goals:
• Communicate the value of conserving open space, and • Prioritize conservation based on that value.
The Bullitt Foundation, the Green Futures Lab, the Northwest Center for Livable Communities, and the UW College of Built Environments are working in collaboration with regional partners and stakeholders to develop a strategy to “conserve and enhance open space systems that con-‐tribute to ecological, economic, recreational, and aesthetic vitality,” within the Puget Sound area1. Coordinating at a regional level, they hope to identify the most effective projects and then promote and direct resources to them. Our work is part of a pilot project that the ROSS is conducting in the Puyallup-‐White watershed that will be the basis for expanding the strategy throughout the Central Puget Sound region. Evaluating and communicating the value of open spaces and the services and benefits they pro-‐vide is a complex task considering the varied interests of stakeholders making decisions around the acquisition and development of open space. As stakeholders plan development, conserva-‐tion, and resource use in the Puget Sound region they need a process that will allow them to prioritize options that include the consideration of the four key interest areas identified as part of the ROSS:
• Ecosystems; • Recreation and Trails; • Rural and Resource Lands; and • Urban and Community Development.
Ecosystem Services In order to prioritize conservation efforts, the ROSS intends to evaluate the benefits provided by open space and identify areas with overlapping benefits that address the ROSS’s key interest areas. Valuing and quantifying ecosystem services can be thus a valuable approach for ROSS to meet its goals. As described in the Millennium Ecosystem Assessment report prepared by the 1 Preliminary Comprehensive Strategy. Central Puget Sound Regional Open Space Strategy.
http://rossgfl.wordpress.com/anticipated-‐products-‐outcomes/preliminary-‐comprehensive-‐strategy/ 2012
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UNDP, ecosystem services are “benefits people obtain from ecosystems”2. The report catego-‐rizes ecosystem services into four areas: provisioning services such as water and timber, regu-‐lating services such as water quality, cultural services such as recreational areas and supporting services such as soil formation. Other environmental institutes such as TEBB (The Economics of Ecosystems and Biodiversity) have developed similar but slightly different categories for ecosys-‐tem services3. Our work was influenced by a combination of these categorizations. Services can be quantified and then translated to some common measure that allows research-‐ers to compare and evaluate the value of ecosystem services provided by a certain area. Re-‐search resulting from valuation of ecosystem services can be used to influence decision makers and direct policy. The valuation of ecosystem services is a relatively new field that has arisen because of a need to quantify benefits from natural resources that decision makers can use when evaluating land use issues. The frameworks and tools used to guide researchers in this field are still being developed and there is not currently an accepted universal standard. Re-‐searchers must decide on methods to use and which services to quantify. Workers in the field are beginning to propose standards of practice to fulfill this need. One such document outlines principles for researchers to follow that emphasize the need for rationale of methods, stake-‐holder involvement, an interdisciplinary approach, the consideration of resilience of the ser-‐vices in question, and accessibility of the work to stakeholders.4 The ROSS will contribute to the ongoing conversation about ecosystem service valuation, as it will apply valuations to a large region and use that information to promote conservation of priority land areas. The ROSS team and the UW Keystone Project team both believe that natural and built green infrastructure are necessary investments – worthy of public and private resources – which should shape future development decisions. The challenge is to communicate their importance based on sound methods and evidence. We acknowledge and challenge the limitations of a human-‐centered focus when evaluating the environment, but we understand that this method is a necessary communication strategy when appealing to a variety of stakeholders.
Scope of Work The ROSS asked our team to research tools and approaches that could contribute to communi-‐cating the value(s) of ecosystem services to promote public understanding of these values, and prioritizing these services in order to inform investment decisions for the ROSS. We focused on two separate approaches that would inform these goals. First, we tested computer models that quantify and valuate ecosystem services in a spatially explicit way. Second, we designed a tool to elicit expert opinion about how various land conservation types contribute to ecosystem services.
2 Ecosystems and Human Well-‐being: A framework for assessment. Millennium Ecosystem Assessment
United Nations Environment Proramme. http://www.unep.org/maweb/en/Framework.aspx. 2005 3 Ecosystem Services. http://www.teebweb.org/resources/ecosystem-services/ 4 Ervin, D., et al. Principles to Guide Assessments of Ecosystem Service Values. Ecosystem Services Valua-‐
tion Workshop. 2013
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Our work focused only on the Puyallup-‐White watershed to pilot the approaches for incorporat-‐ing ecosystem services into conservation decisions. The Puyallup-‐White is located in Pierce County with small portions in King and Thurston Counties and is bordered by the Green-‐Duwamish Watershed to the North and Nisqually River Watershed to the South. We chose a smaller group of sub-‐basins within the watershed to focus our computer modeling because we believed limiting our geographic area would reduce the difficulty of collecting data needed to run the models. We selected the area based on its potential for future conservation actions. Our first approach, modeling, used the InVEST (Integrated valuation of environmental services and tradeoffs) models, created by the Natural Capital Project, to valuate selected ecosystem services. This group of models is designed to quantify ecosystem services, both biophysical pro-‐cesses and processes with commercial (monetary) value. Many of the models display the results on a map of the geographic area of focus. These programs require spatially explicit, Geographic Information Systems (GIS) data, as well as data describing the biophysical properties of land use/land cover (LULC) types. We researched and performed trials on four of the InVEST mod-‐els: Overlap Analysis, Managed Timber Production, Biodiversity, and Carbon Storage and Se-‐questration. We reached varying degrees of success with each individual model. The modeling approach is time intensive and requires knowledge of lo-‐cal ecology as well as technical skill with geospatial software. These fac-‐tors would make basing decisions about conservation solely on the results of modeling difficult without the appropriate skillsets and fund-‐ing support. Therefore, we also explored a survey approach that would capitalize on the knowledge and expertise of the ROSS participants. The second approach we explored resulted in our team designing a sur-‐vey-‐like tool we call VEST (Valuing Ecosystem Services Together). VEST asks members of the ROSS Ecosystem Service Committee and its various Task Forces to judge the relative contributions of different land invest-‐ment types to ecosystem services. These relative values could be used to determine priority land investment types or strategies. VEST can be completed in a relatively short amount of time, but requires the partici-‐pation of experts. Because the ROSS has already successfully recruited local experts, this approach is more easily achieved. However, VEST is a very coarse tool that does not factor in spatial components and can only reveal relative values of ecosystem services, rather than estimates of actual value. After completing our investigations of these two approaches we have outlined the benefits and limitations of each approach and outlined a process by which the ROSS could combine both approaches and maxim-‐ize their resources.
Figure 1. Complete list of InVEST models with trial models highlighted
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Approach 1: InVEST InVEST is a free software program developed by the Natural Capital Project to model and quan-‐tify ecosystem services. At the suggestion of our faculty mentor we chose to use InVEST as our primary method of quantifying ecosystem services due to its accessibility and familiarity to those who work in the field of ecosystem service valuation. InVEST has sixteen models that val-‐uate ecosystem services listed in Figure 1. Each model requires inputs relevant to the ecosys-‐tem service of interest and LULC data. Most models’ outputs are a series of maps that represent relative values for the aggregate data over the area of interest. We chose a specific focus area for these models within the Puyallup-‐White Watershed that in-‐cludes several sub-‐basins (Figure 2). To select the area we considered the 2011 report by the Pierce County Open Space Task Force that contains recommendations and priorities for the fol-‐lowing 10 years for open space categories, including parks, trails, forests, biodiversity (habitat), freshwater (rivers, lakes and streams), marine shoreline and agricultural land.5 Our focus area
included as many of these open space categories as possible so that the ROSS can eventually use our experience as a test case to model their future sce-‐narios. We chose four InVEST models to focus our initial valuation of ecosystem services based on the rele-‐vance of the outputs to the ROSS priorities and the difficulty of running the model. We used the Biodi-‐versity model to assess threats to biodiversity; the Carbon model to analyze carbon capture from for-‐ests; the Timber model to analyze potential timber production yields; and the Overlap Analysis model to examine connectivity between parks and recrea-‐
tional trails. We used the ROSS geodatabase, a collection of local GIS data relevant to the ROSS, and other publicly available data to complete the models. Specific information about the input requirements and outputs of each model are outlined in Appendix A.
Models Overlap Analysis Model The InVEST model most appropriate for valuing human recreation and trails is the Overlap Ana-‐lysis Model (OAM). This model is designed specifically to evaluate geographic areas based on the weighted importance of human activities that occur within its boundaries. It does this by
5 Report and Recommendations: July 18, 2011. Pierce County Open Space Task Force. http://www.co.pierce.wa.us/DocumentCenter/View/3732. 2011.
Figure 2. Puyallup-White Watershed
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examining areas designated as commons, wherein multiple activities of different weighted im-‐portance occur within the same space and therefore overlap – for example, coastal areas that facilitate recreational swimming, commercial fishing, and commerce from tourism. This InVEST tool is capable of identifying what activities occur in an area and where these activities overlap, therefore helping to prioritize those spaces that enable the most human activities of the most importance. The models compute in two different ways. In the default process, the user designates areas wherein a specific activity occurs – perhaps boating, hiking, or recreational fishing – incorporat-‐ing different activity layers within the area of interest. InVEST then calculates areas of overlap, and scores each point on the map based only on the number of activities it facilitates; the prior example of a coastal beach that allows for multiple commercial and recreational behaviors would therefore by deemed more important, for example, than a landform or vista reachable only by a single hiking trail.
However, the model also allows for more complex analysis wherein the activities themselves are weighted by importance. This is especially helpful for prioritizing zones not only by the level or amount of recreation they make possible, but also by the expected benefits that protection of these zones would produce. For example, an area that harbors only a bike trail would be giv-‐en a default value of 1. But, using the weighted model, this area might be given significantly more status if that bike trail completes a commuter corridor, or connects to a chief transporta-‐tion hub, or leads to a scenic vista for tourists. The model weights inputs by three categories: Intra-‐Activity, wherein activities within a zone are scored (for example, hiking trails indexed by popularity or fishing grounds categorized by catch productions); Inter-‐Activity, wherein each activity itself is assigned a score; and Points of
Figure 3. Overlap analysis map layers
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Human Use Hubs, wherein spaces are assigned value based on their distance from key hubs of activity (bus stations, boat ramps, ranger stations, etc.). This model produces values for areas based on the two principles of frequency and importance. The intra-‐activity values will reveal the number of recreational activities that take place on each point on the map. The inter-‐activity and human-‐use hub inputs allows one to valuate areas based on one's designated importance of those activities. For our project, we analyzed pedestrian trails through the Puyallup-‐White Watershed. These trails were categorized into three types: Regional trails, Sub-‐regional trails, and Connector trails. Using GIS data from Pierce County Parks and Recreation inputed into the Overlap Analysis mo-‐del, we superimposed this trail network onto our area of focus, and weighted the trails by im-‐portance (Regional trails were given a value of three, while Sub-‐regional and Connectors were given values of two point five and two,respectively) (Figure 3). The InVEST output produced a map of the trail network coded by a color gradient, which revealed areas of greater activity (where two types of trails met, for example, or where connectors linked multiple regional trails together). This map clearly and simply articulated these trails by importance, guided by our weighting criteria. Simple actions like this can illuminate a great deal. Hypothetically, the Overlap Analysis model has limitless applications for ecosystem service valuation, and our small-‐scale test certainly proved its usability for analyzing recreational trail networks. If a project wished to identify those areas with the most trails, or the trails of greatest importance (according to stakeholder criteria), or the proximity of trails to nearby public parks or public transportation hubs, for in-‐stance, this model would be perfectly suited to the task. For this model to be used for trail ana-‐lysis, we found input requirements to be minimal and the process relatively straightforward.
Carbon Storage and Sequestration Model This model measures the amount of carbon stored by various types of land cover. It has the ability to measure carbon storage at a given point in time and can measure a change in car-‐bon storage given past or future scenarios. The model can also monetize stored carbon based on prices in carbon markets. The model requires LULC GIS data as well as a list of measurements of the amount of carbon stored in each LULC type in the dataset. For ex-‐ample, if the GIS data contains Douglas Fir forest the carbon pool list must indicate the amount of carbon that Douglas Fir forests typically store in
Figure 4. Carbon model output
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megagrams per hectare (Mg ha-‐1) a common measurement of the volume of carbon. Running the model produces several outputs that may be useful to the ROSS. It can provide to-‐tals of the number of tons of carbon currently stored and the number of tons to be stored in a future scenario. It can also provide maps to visually demonstrate the differences between cur-‐rent and future scenarios, as well as carbon storage totals for each of the individual carbon pools. As a result of our trials we were able to produce a map of our focus area within the Puyallup-‐White watershed (Figure 4). The map displays the relative volume of stored carbon using a col-‐or gradient scale. Dark shades of green represent higher levels of carbon storage, while light green and white represent low levels of carbon storage. We used carbon pool values from an ecosystem services valuation of Joint Base Lewis McChord.6 While these values (Table 1) can be used for modeling in the Pacific Northwest we recommend expanding the list to include the ex-‐act LULC types in a given focus area. The values we used for the purpose of our trial were ap-‐proximate matches and yielded inaccurate results.
Table 1. Volume of carbon stored by LULC types.
The Carbon model is one of the simplest InVEST models and, with the appropriate data, can be very useful to the ROSS. The maps it produces, the ability to compare scenarios, and the ability to monetize stored carbon can help decision-‐makers understand the value of preserving partic-‐ular pieces of land.
6 Ma, S., Duggan, J., Eichelberger, B., McNally, B., Foster, J., Pepi, E., Conte, M., Daily, G., Ziv, G. (prepub-‐lication) Valuation of Ecosystem Services to Inform Military Base Management: The Case of Joint Base Lewis McChord.
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Biodiversity Model The Biodiversity model assesses relative habitat degradation, habitat quality, and habitat rarity within the defined geography. It can be used to evaluate how different scenarios of changes in land cover or habitat threats might affect the availability of quality habitat, and consequently biodiversity. Specific requirements of the model are outlined in appendix A. The model com-‐piles data over the geography of interest and results in maps showing the relative measures of habitat degradation, habitat quality, and habitat rarity as predicted by the model. Running the model requires an in-‐depth understanding of the local ecology of the region of in-‐terest. The model includes the threats to habitat in the area of interest and the distance those threats would have an effect. Figure 5 provides a simplistic representation of the model inputs. The size of the circles around the threats represents their distance of influence on habitat quali-‐ty. Some examples of potential threats are roads, invasive species, or mines. One must also in-‐clude information about the sensitivity of each land cover type to the threat, represented in figure 5 by the color of the threat cir-‐cle. The accuracy and relevance of the information included in the model will affect its usefulness in predicting habi-‐tat quality. In addition, the model requires geo-‐spatial data. First, habitats are defined by different land cover types, so basic land cover data is needed. Second, spatial distribution of the threats is needed, so one would need spatial data for all of the threats included in the model. For example, if there were an invasive species that was considered a threat to a habitat, a GIS data layer would need to exist with the distribution of that species. It has the ability to consider legal land protections as well as physical barriers that may affect how much an impact threats to habitat may have. If including these considerations, one would need GIS maps of these protected areas and infor-‐mation about their relative protective effects. The model can be used for different purposes. It could be designed to look at the habitat of a specific species, including only land covers and threats that are relevant to the species of inter-‐est. Alternatively, the model can be used more generically to estimate how common threats affect all types of viable habitat in the defined area. Because this model does not provide quan-‐tification for biodiversity, it is not directly useful for the ROSS goal of communicating a specific value of preserving an area. However, it would be useful for prioritizing conservation. If future scenarios for development or other threats to habitat were developed, predictions could be made using the model of how those changes in threats in the future would affect habitat. Those habitats most threatened in future scenarios might be priorities for conservation.
Figure 5. Representation of Biodiversity model inputs
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In the context of the ROSS, we attempted to use the model to assess general habitat rarity and quality within our focus area. All types of land covers that were open space were classified as habitat. Threats considered in the model were roads, highway, trails, and developed land. The relative sensitivities of land cover to these threats used in the model were placeholders since conclusive data for these values could not be found. Ultimately, we could not run the model, even as a trial, because of technical issues. The InVEST software displayed an error that the GIS data used did not cover the same geographic space. While this was not the case, our team did not resolve the issue in time for this report. Managed timber production model The InVEST timber model has been developed to measure the amount and volume of the tim-‐ber produced over a time period and to calculate the net present value of that. The amount of timber harvests from both natural forests and managed plantations can be estimated by using this model. The model requires vector GIS data, information about harvest levels, frequency of harvest, costs of harvesting and management practices for each timber harvest parcel. The model can make two types of calculations in terms of the selected time period: the timber par-‐cel map can be related either to a current map or to a future scenario map. The timber model can be especially useful for one of the ROSS’ key areas: “Rural and Resource Lands”. Since the model gives as output the amount and volume of the timber produced over a period of time and that harvest’s net present value, it can be beneficial in terms of calculating the opportunity costs of preserving a forestland or opening it up for development. The Washington State Department of Natural Resources has GIS spatial data sets about forest practices where the timber harvest areas can be seen in polygons. The information about the volume of timber produced is available too. However, in order to be able to run the model oth-‐er data needs (such as frequency of harvesting, percentage of harvesting, maintenance cost, and harvesting cost) need to be collected from the timber parcel owners. While running trial of this model we discovered that in order to find the necessary data men-‐tioned above to run the model we would need to conduct a field study and collect the infor-‐mation from each parcel owner. As our time to complete the study was limited, we could not conduct a field study. It may be possible in the future to use sustainable forest practices infor-‐mation to estimate for example the frequency of timber harvesting in Pierce County. However, we learned that the definition of sustainable forest practices may vary from one landowner to another and that we cannot generalize one model for each timber harvest. Thus, as a result we could not run the model. Figure 6 provides an example for how the model output can be used in visualization of different scenarios. The last column in the figure entitled “market value of commodity production” in-‐cludes the value of the timber produced in that area. The greenest color represents the highest production of ecosystem services and the pinkest color represents the lowest value of them. For example, in the conservation scenario it can be seen that the market value of the commodi-‐ty produced is lowest whereas carbon sequestration has the highest value in that scenario. By
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using these kinds of visual maps, InVEST models can be used to communicate the outputs of different scenarios in a clear and comprehensive way.
Figure 6. Source: Nelson et al., 20097
7 Nelson, Erik, et al. "Modeling multiple ecosystem services, biodiversity conservation, commodity pro-duction, and tradeoffs at landscape scales."Frontiers in Ecology and the Environment 7.1 (2009): 4-11.
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Approach 2: Valuing Ecosystem Services Together (VEST) Our second approach focuses on gathering expert opinion to collect general information about ecosystem services. It may be used to consider general effects of conservation types on ecosys-‐tem services or to assess specific projects in specific geographic areas. We developed a tool for the ROSS Ecosystem Services Committee to use in making conservation decisions and prioritiz-‐ing further research. VEST is a survey-‐like tool that the ROSS can distribute as a spreadsheet to subject matter experts. The experts assign points to indicate the relative effects of conservation actions on ecosystem services allowing the Ecosystem Services Committee to identify which conservation activities may have greater impacts on ecosystem services. Using VEST VEST is a matrix with columns that represent types of potential conservation activities and rows that represent various ecosystem services. We chose the conservation types based on the Pierce County Ten Year Priorities and ecosystem services from the Millennial Ecosystem As-‐sessment.8 Respondents complete one row at a time, allotting points to each land investment type based on its relative contribution to the corresponding ecosystem service. More points indicate a greater contribution. Respondents may allot points as they see fit, including allotting unique values for each land investment or allotting all points to only one land investment type. We originally intended for respondents to allot 36 across each row, which would allow each cell to receive a unique value. Some respondents in our initial trial reported difficulty with this number of points. To address this problem the ROSS could adjust the instructions to request a more us-‐er-‐friendly 100 percentage points. Figure 7 shows a sample distribution of points for four of the twenty ecosystem services included in the tool. The full VEST tool can be found in Figure 8.
Figure 6. Example data with selected ecosystem services
Our instructions (Appendix B) include the following assumptions that respondents should use as they assign values:
1. All investments will be made by public or not-‐for-‐profit organizations with the intention of public benefit. Private benefits may result from the investment, but the analysis is based on the intention of public benefit.
8 Ecosystems and Human Well-‐being: A framework for assessment. Millennium Ecosystem Assessment United Nations Environment Proramme. http://www.unep.org/maweb/en/Framework.aspx. 2005
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2. Agencies will follow best management practices in perpetuity for their land investments. While this may not be realistic, it will allow for easier comparisons.
3. Only consider effects on ecosystem services that are generated directly from the ac-‐quired land type, absent of additional investment. For example, forest acquisition pro-‐vides carbon sequestration and timber production, but would not necessarily provide aesthetic value or recreational opportunities without further investments in trail build-‐ing or campgrounds.
4. Do not consider opportunity costs that may be forgone by making one type of invest-‐ment over another.
We recognize that some of the ecosystem services, such as aesthetics, require subjective judg-‐ment. Others call on expert opinions about the function of ecosystems. For this reason, we al-‐low respondents to opt out of individual rows if they are not comfortable assigning points for a particular ecosystem service. For reference we included the definitions of each ecosystem ser-‐vice with the VEST survey. Based on the ROSS’s structure that includes committees of experts we intend for VEST to be used by a team. Each expert will complete their own VEST survey and an analyst will aggregate the results. The combined spreadsheet should show “hotspots” where experts think certain in-‐vestment areas would have larger relative effects on ecosystem services, both in the number of ecosystem services each conservation type provides and the extent to which they provide eco-‐system services. Alternatively, a team could complete one survey together as a way to discuss and debate potential effects on ecosystem services. Another approach would be to assign the task to an individual researcher or small research team who would complete a more rigorous analysis of each conservation type and assign values in the VEST tool accordingly. The ROSS may choose their preferred completion method based on the number and type of available experts as well as their need for general or exact results. An important feature of VEST that should be useful for the ROSS is that it can be customized depending on the ROSS’s needs. Our team chose conservation types and ecosystem services based on existing projects and literature. The ROSS could change the conservation types to as-‐sess particular projects that may be under consideration. It could also add or subtract ecosys-‐tem services to align with the values of the ROSS or particular stakeholders. VEST Results and Feedback In order to test this approach, we distributed the tool to members of the ROSS Ecosystem Ser-‐vices Committee. We received three completed surveys, one partially complete survey, and several comments on the tool’s usability. While we were not able to get a large sample size of responses that would allow us to assess the range of opinions within the ROSS participants, we were able to make some recommendations for how the tool could be most useful, and how it might be adjusted for future use. The point distributions of the three sample responses were very similar in their distribution of points. An average of these responses can be viewed in Appendix C. In addition to our intended
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benefits of the tool, examining the different responses highlighted other potential uses. We be-‐lieve the tool could be useful in revealing areas of disagreement about the degree of benefits provided by a certain type of land investment, and also identify substitute land investments that provide similar ecosystem services. For example, participants in different specialty areas might account for ecosystem service benefits of a land type differently. Going through the pro-‐cess of combining opinions in a tool like VEST would facilitate a conversation about those dif-‐ferences. Also, if a committee has a certain ecosystem priority in mind, the tool could be useful in identifying potential substitute land investments to achieve the same goal. The recreation and ecotourism service is a good example of a service that can be achieved through multiple types of land investments. While trail building and parks are easily identifiable land invest-‐ments that provide recreation, farmland tours and biodiversity attractions could also achieve those aims while providing more of other types of ecosystem services than trail or park invest-‐ments could.
Figure 7. Combined responses from VEST trial
Based on the feedback from participants, we can also make the following suggestions for how to improve the tool for future use:
• The distribution of 36 points was confusing for some. We recommend distributed 100 percentage points instead. Percentages are more familiar to people and would allow a more accurate allocation of points.
• The ecosystem services considered should be refined for the ROSS. For example, the “recreation and ecotourism” service might be separated into two rows so that the
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health benefits of recreation could be accounted for separately from the entertainment and educational value of ecotourism.
• The ROSS could request that participants assign relative weights to ecosystem services to account for which ecosystem services have a greater value to the team.
• Information about how to equate the land investments would be useful. This would re-‐quire research about what certain monetary investments might yield in a particular land investment type. For example, one might research the number of miles of trail that could be built with $100,000 and the number of acres of forest that could be acquired with the same amount of money. As a result users would better understand the effi-‐ciency of an investment type for the ecosystem services yielded.
Benefits and Limitations Invest Benefits The InVEST model enables users to monetize and/or quantify many of the ecosystem services such as carbon sequestration, timber production or the value of the pollinators to agricultural production. Secondly, through the maps created by using GIS and InVEST models, different land use and land cover scenarios can be visualized and compared in terms of their provision of cer-‐tain ecosystem services. Thirdly, InVEST uses scientific data to relate human actions to envi-‐ronmental outcomes. Invest Limitations However, there are significant limitations of the InVEST model. First, the resources required to run the models with high quality data may not be feasible for the ROSS and, as with all models, the quality of the data determine the quality of the outputs. Using computer modeling to valu-‐ate ecosystem services can be challenging because a project of this nature requires a cross-‐section of experts and professionals who are proficient in a variety of disciplines, in addition to the significant data requirements. Accurate service valuation requires expertise in spatially ex-‐plicit information, such as GIS mapping, as well as biophysical expertise (knowledge of terrain, flora, fauna, climate, etc.). The information needed for some models is not readily available and field research would need to be conducted which requires time and labor. The technical profi-‐ciency and infrastructure needed for a project of this kind is also substantial: actors must be competent in programs such as GIS and InVEST, and require access to data that can be expen-‐sive, generated from multiple sources, or even nonexistent. A second limitation of InVEST is that it has a limited number of models and those models may not be able to address the full scope of the ROSS. For example, ecosystem services provided in urban areas cannot be measured with the current models available. Among the most obvious shortcomings of the InVEST tool that we have identified is considera-‐tion of urban areas and social equity. We have identified the following tools that may assist the ROSS in considering social equity:
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• STAR Community Rating System: STAR measures livability and sustainability of commu-‐nities by providing “a framework for sustainability encompassing the social, economic and environmental dimensions of community.”9
• Kirwan Institute for the Study of Race and Ethnicity Opportunity Maps: Opportunity maps allow researchers to determine the effect of place on opportunities for education, health, and other indicators of social equity. There are currently maps available for King County, WA, the Puget Sound, and several other jurisdictions throughout the U.S. 10
• Genuine Progress Indicator: The State of Maryland developed a tool to measure progress beyond the traditional economic indicators (i.e Gross Domestic/State Products). It measures the effects of development activities on long-‐term prosperity indicators.11
• LEED-‐ND: LEED for Neighborhood Development is the U.S. Green Building Council’s rating system for environmental and social conditions. It seeks to promote green building practices with urban neighborhood development best practices that consider social equity, parks access, and community building.12
Finally, some consider it controversial to assign values to ecosystem services. Some conserva-‐tion proponents argue that existence value of open space is beyond measurement. So, the tool itself could alienate potential partners of the ROSS. This may also be a limitation of the VEST tool. VEST Benefits The VEST tool condenses many opinions or sources in one table. It requires a lower cost and time commitment overall while considering a large range of ecosystem services. Moreover, it is customizable. The ecosystem services can be added or removed from the table according to the priority areas of the projects. The ecosystem services can be also weighted differently depend-‐ing on project needs. VEST Limitations However, the VEST model has also some limitations. Unlike InVEST, which quantifies absolute values, the VEST tool quantifies relative values. It also relies on participation of different experts and/or stakeholders who may or may not base their responses on expertise. Finally, the criteria to fill in the form must be very clear in order to reduce ambiguities.
9 STAR Communities. (2013). The Rating System. Retrieved from
http://www.starcommunities.org/rating-‐system. 10 Kirwan Institute for the Study of Race and Ethnicity. (2013). Opportunity Mapping Initiative and Pro-‐
ject Listing. Retrieved from http://kirwaninstitute.osu.edu/opportunity-‐communities/mapping/. 11 State of Maryland. (2013). Maryland Genuine Progress Indicator. Retrieved from
http://www.dnr.maryland.gov/mdgpi/index.asp. 12 U.S. Green Building Council. (2013). Neighborhood Development. Retrieved from
http://www.usgbc.org/neighborhoods.
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In sum, the InVEST model requires more time, money, technical expertise and infrastructure, whereas the VEST tool can condense opinions in a shorter time period and merge common pri-‐ority areas of different stakeholders. However, the process of filling out of the VEST tool and compiling the information can be subjective or unclear.
Conclusion: Applying InVEST and VEST to the ROSS The ROSS can use the two approaches we explored to inform the goals of valuing ecosystem services produced by conserved land and prioritizing conservation. Computer modeling (In-‐VEST) and surveying (VEST) are complementary approaches that work best in tandem in order to prioritize land acquisition and valuate ecosystem services. VEST requires relatively small investments of time, labor, and money, and the results can quick-‐ly reveal information that can better mold project objectives. Obstacles encountered with this approach (clarity of directions, delayed responses, omissions/additions) can be rapidly ad-‐dressed. The VEST tool can also help highlight possible challenges very early on into a project. Computer modeling for ecosystem service valuation, because of its significant time, labor, and capital requirements, should be used only after information is collected via survey. By prioritiz-‐ing lands for acquisition or prioritizing ecosystem services during the initial stages of a project, resources for modeling can be utilized more effectively (by identifying optimal models and re-‐vealing what type of data will be needed. Based on our research and trials of computer modeling and the survey tool we recommend that the ROSS use the VEST surveying approach at the beginning of a project to help identify useful InVEST models. This will allow the ROSS to set priorities before engaging in time and resource intensive computer modeling.
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Appendix A: InVEST Models
Overlap Analysis Model The InVEST model most appropriate for valuing human recreation and trails is the Overlap Ana-‐lysis Model (OAM). This model is designed specifically to evaluate geographic areas based on the weighted importance of human activities that occur within its boundaries. It does this by examining areas designated as commons, wherein multiple activities of different weighted im-‐portance occur within the same space and therefore overlap – for example, coastal areas that facilitate recreational swimming, commercial fishing, and commerce from tourism. This InVEST tool is capable of identifying what activities occur in an area and where these activities overlap, therefore helping to prioritize those spaces that enable the most human activities of the most importance. Input Needs The OAM model computes in two different ways. In the default process, the user designates areas wherein a specific activity occurs – perhaps boating, hiking, or recreational fishing – in-‐corporating different activity layers within the area of interest. InVEST then calculates areas of overlap, and scores each point on the map based only on the number of activities it facilitates; the prior example of a coastal beach that allows for multiple commercial and recreational be-‐haviors would therefore by deemed more important than, say, a landform or vista reachable only by a single hiking trail. However, the model also allows for more complex analysis wherein the activities themselves are weighted by importance. This is especially helpful for prioritizing zones not only by the level or amount of recreation they make possible, but also by the expected benefits that protection of these zones would produce. For example, an area that harbors only a bike trail would be giv-‐en a default value of 1. But, using the weighted model, this area might be given significantly more status if that bike trail completes a commuter corridor, or connects to a chief transporta-‐tion hub, or leads to a scenic vista for tourists. The model weights inputs by three categories: Intra-‐Activity, wherein activities within a zone are scored (for example, hiking trails indexed by popularity or fishing grounds categorized by catch productions); Inter-‐Activity, wherein each activity itself is assigned a score; and Points of Human Use Hubs, wherein spaces are assigned value based on their distance from key hubs of activity (bus stations, boat ramps, ranger stations, etc.). Outputs This model will produce values for areas based on the two principles of frequency and im-‐portance. The intra-‐activity values will reveal the number of recreational activities that take place on each point on the map. The inter-‐activity and human-‐use hub inputs allows one to val-‐uate areas based on one's designated importance of those activities.
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General data needs Outline of focus area, Land Use Land Cover Model-‐specific data needs Shape/layer files that identify areas of activity (location of bicycle trails, location of parks, etc.).For weighting, a spreadsheet designating the assigned values to the assigned activity. Recommendations Contacting local government agencies for GIS data is highly recommended. In many cases, the government professionals are more than willing to provide (public and free) resources when asked. In addition, research for activity weighting is helpful. Have similar agencies weighted the activities you are modeling? Chances are good that they have, and can provide background and analysis on how and why they generated their activity rankings. For example, our team used the OAM to analyze bicycle trails that intersected our area of focus. With a little research, we discovered that state agencies in Florida had similarly prioritized trails within their state based on criteria that were carefully produced, and (better yet) were fully applicable to our modeling.
Carbon Model Model description The carbon model measures the amount of carbon stored by various types of land cover. It has the ability to measure carbon storage at a given point in time as well as a change in carbon storage given past or future scenarios. The model can monetize stored carbon based on prices in carbon markets, but the Natural Capital Project recommends that users focus on the social value of stored carbon. According to the model’s user manual,
[t]he social value of a sequestered ton of carbon is equal to the so-‐cial damage avoided by not releasing the ton of carbon into the atmosphere. Calculations of social cost are complicated and con-‐troversial, but have resulted in value estimates that range from USD $9.55 to $84.55 per metric ton of CO2 released into the at-‐mosphere.13
Our research avoided monetization and focused instead on measuring the amount of carbon under present and potential future scenarios based on the proposed Pierce County Ten Year Priorities and in order to discover which conservation investments have the highest potential for carbon sequestration.
13 Natural Capital Project. (2012) Carbon Storage and Sequestration User Manual. Retrieved from
http://ncp-‐dev.stanford.edu/~dataportal/invest-‐releases/documentation/current_release/carbonstorage.html.
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Preparing the Workspace The user should create a folder that will comprise the workspace. Within the workspace create a folder called, “Input.” InVEST will create folders for outputs when it completes the model. The Input folder should contain the files described below. Input Needs The model requires the following inputs in order to measure carbon sequestration. • Current land use/land cover (LULC): This is the foundation of the model. InVEST requires
a raster file which identifies each type of land cover with a numeric code. The codes may be arbitrarily assigned numbers, but should correspond with the codes in the car-‐bon pool file described next. Each analysis cell must contain a code. The user should al-‐so identify the year (if running future scenarios) and spatial resolution of the data.
• Carbon pools: The model measures carbon stored in four carbon pools (with an optional fifth) using a table of LULC classes and carbon storage measurements. Possible sources of LULC classification include the Intergovernmental Panel on Climate Change, which provides generic values, or more local data, which would provide classifications specific to the Pacific Northwest. The model considers the following carbon pools: o Aboveground biomass; o Belowground biomass; o Soil; o Dead organic matter; o (Optional) Parcels with harvested wood products (represents amount of carbon
saved by a product). The user must create a table indicating the amount of carbon stored (in Mg ha-‐1) by each of the land cover types. Rows consist of land cover types; columns consist of car-‐bon pools. If measurements for a carbon pool are not available, the user should assign a zero value, as in the example table below. Values in the column labeled “lucode” should correspond with the land cover codes in the LULC raster. The table below may be used as a model. The carbon pool values in the table were taken from an ecosystem services valuation of Joint Base Lewis McChord and can be used for modeling in the Pacific Northwest.14
14 Ma, S., Duggan, J., Eichelberger, B., McNally, B., Foster, J., Pepi, E., Conte, M., Daily, G., Ziv, G. (prepub-‐lication) Valuation of Ecosystem Services to Inform Military Base Management: The Case of Joint Base Lewis McChord.
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• Future scenario(s): The model analyzes proposed changes to LULC in order to measure a
change in carbon storage and sequestration. The user must obtain a second LULC raster or change the original raster to depict the future scenario.
Outputs To date, our team has only run the model to measure the present amount of carbon stored in our focus area. Running the model this way produces two outputs, a summary file with the to-‐tal amount of carbon stored, and a .tif file (viewable in ArcMap) to which the user may assign a graduated color scale to visually depict carbon storage by pixel. When using a future scenario the model will provide totals of the number of tons of carbon cur-‐rently stored and the number of tons to be stored in a future scenario. It will also provide a map that shows the differences between current and future scenarios, as well as carbon storage to-‐tals for each of the individual carbon pools. Recommendations The Carbon model is relatively simple compared to other InVEST models. However, the user should take care if it is necessary to convert the LULC file to a raster. In our work the conversion tool in changed the order and values of the LULC types in the attributes table so it no longer corresponded with our carbon pool table. This can be easily resolved by adjusting the carbon pool table to match the attributes table of the converted LULC raster.
Biodiversity Model The Biodiversity model measures habitat degradation, habitat quality, and habitat rarity within the defined geography. It can be used to evaluate how different scenarios can lead to condi-‐tions that threaten habitat and therefore threaten biodiversity. It has the ability to consider le-‐gal land protections as well as physical barriers that may affect how much an impact threats to habitat may have. Input Needs
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In order to determine a relative measure of these variables across the area, several inputs are needed. The model requires the habitat types in the area and their relative sensitivity to threats. In addition, the threats to those habitats are defined and the distance those threats affect habi-‐tats. Users should determine priority species and their habitats in order to identify the land co-‐vers of interest and the threats to those habitats. The numbers used in the example data spreadsheets below are estimates and should not be used for analysis. For the Puyallup-‐White watershed, we have focused on using the model to add rarity and quali-‐ty ratings to areas already identified for biodiversity conservation. The model will only consider roads, trails and urban development as threats to the habitat. Roads provide human access to habitat and introduce potentially harmful runoff. Trails introduce a threat of spread of invasive species along the trail. Human activities such as production of waste and disturbance of land in developed areas also threaten habitat. The different land covers in our geography will be affected differently by the threats, which will be accounted for in the model by weighting. The differences in a threat’s distance of influence are also included in the model. For example, roads will affect habitat only in close proximity, where developed areas will have a wider ranging overall effect. Also incorporated into the model are legal and physical barriers to threats such as parks or preserves GIS Data Needs Outline of focus area Land Use Land Cover with habitats of species of interest Pierce County Biodiversity Corridors and Connectors 10 year priorities (or other the ROSS priori-‐ty boundary areas) Separate maps for the threats to the habitats of species of interest -‐High, medium, and low density developed areas -‐Roads -‐Trails Protected areas -‐preserves/parks Model Specific Data Needs: For each threat:
-‐“The maximum distance over which each threat affects habitat quality (measured in km). “15
-‐“The impact of each threat on habitat quality, relative to other threats… Weights can range from 1 at the highest, to 0 at the lowest.”16 The distance of the threat of road and
15 InVEST Online User’s Guide. Retrieved from: http://ncp-‐dev.stanford.edu/~dataportal/invest-‐releases/documentation/current_release/. Accessed March 2014. 16 Ibid. 12
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highway are based on a study of indicators of threats to biodiversity.17 The distance of the threat of development was assumed to be greater than highway.
rd=roads, hwy=highway, trl=trail, dvp=developed land For each habitat:
-‐”The relative sensitivity of each habitat type to each threat. Values range from 0 to 1, where 1 represents high sensitivity to a threat and 0 represents no sensitivity.” Land cover that is potential habitat is indicated with a “1” in the habitat column. Sensitivities in the table below were based loosely on the findings of Alkemade et al. 18
LULC NAME HABITAT L_rd L_hwy L_trl L_dvp 11 Open Water 0 0 0 0 0 21 Developed, Open Space 0 0 0 0 0 22 Developed, Low Intensity 0 0 0 0 0 23 Developed, Medium Intensi-‐
ty 0 0 0 0 0
24 Developed, High Intensity 0 0 0 0 0 31 Barren Land (rock/sand/clay) 0 0 0 0 0 41 Deciduous Forest 1 0.3 0.4 0.3 0.5 42 Evergreen Forest 1 0.3 0.4 0.2 0.4 43 Mixed Forest 1 0.3 0.4 0.3 0.5 52 Shrub/Scrub 1 0.3 0.4 0.5 0.7 71 Grassland/Herbaceous 1 0.3 0.4 0.5 0.7 81 Pasture/Hay 0 0 0 0 0 82 Cultivated Crops 0 0 0 0 0 90 Woody Wetlands 1 0.5 0.6 0.4 0.6 95 Emergent Herbaceous Wet-‐
lands 1 0.5 0.6 0.4 0.6
For each type of protected area:
-‐The relative accessibility of the area to degradation. Areas with the no restrictions on accessibility are given a value of 1. Areas that are protected or have limited accessibility
17 Stoms, David M. "GAP management status and regional indicators of threats to biodiversity." Land-‐scape Ecology 15.1 (2000): 21-‐33. 18 Alkemade, Rob, et al. "Applying GLOBIO at different geographical levels." Ch 8 (2011): 150-‐170.
MAX_DIST WEIGHT THREAT 100 0.5 rd 250 0.7 hwy 30 0.3 trl 500 0.7 dvp
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are given a value less than 1 relative to the amount of protection. Protected areas were not incorporated into this model.
Outputs The model will compile this data over the geography of interest and result in maps showing the relative measures of habitat degradation, habitat quality, and habitat rarity as predicted by the model. After other future scenarios are determined and run in the model, the different out-‐comes should be compared to determine better and worse results for biodiversity.
Limitations and Recommendations The model in this study was designed specifically to provide a coarse measure of habitat rarity and quality to prioritize the conservation of land already designated to preserve. However, this model could be used to assess the overall habitat rarity for specific species of interest through-‐out the entire watershed. This would require more specific research and knowledge of the spe-‐cies and habitat in question.
Managed Timber Production Model This InVEST model has been developed to measure the amount and volume of the timber pro-‐duced over a time period and to calculate the timber’s net present value. The amount of timber harvests from both natural forests and plantations that are managed can be estimated by using this model. On the other hand, the model can only be applied to legally harvested timber by the government, local groups like tribes, private companies and communities. Timber harvested without legal rights cannot be calculated with this model, but the Open Access Timber and Non-‐Timber Products InVEST models that will be released soon can also be applied in our project in order to deal with this type of timber harvesting. One of the key areas of the the ROSS project is “Rural and Resource Lands”. The managed tim-‐ber production model can be beneficial in this project in terms of calculating the opportunity costs of preserving a forestland or opening it up for development. General Data Needs GIS polygon file (a vector database) where the forest practices (timber harvesting parcels) can be seen. Model-‐specific Data Needs The model first of all requires a vector GIS dataset that shows on the map the parcels where timber is harvested or planned for future harvest. Secondly, the information about harvest lev-‐els, frequency of harvest and harvest and management costs for each timber harvest parcel is needed. Thirdly, the period of time for which the calculation will be made should be selected. The model can make two types of calculations in this regard: either the timber parcel map can be related to the current map or to a future scenario map. For calculating the volume of the harvested wood an expansion factor (BCEF) is used in the model that transforms the mass of harvested wood into volume.
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In order to calculate the value of the timber produced the marketplace value of the wood should be given into the table of the model. In addition to that, market discount rate is being used while making calculations about the value of timber produced. Example spreadsheet of the data input
Par-‐cel_ID
Parcl_area
Perc_harv
Freq_harv
Harv_mass
Price
Maint_cost
Harv_cost
T Immed_harv
BCEF
1 1000 2.22 1 80 300 190 50 50
Y 1
2 1000 2.22 1 70 200 260 124 50
Y 1
3 1000 25 20 70 200 310 225 50
N 1
4 500 100 1 95 350 180 45 1 Y 1 5 500 20 2 95 400 190 105 1
0 Y 1
Timber model outputs can be viewed in this power point presentation in more detail: http://ncp-‐dev.stanford.edu/~dataportal/training_feb2012_stanford/Timber%20model_ShanMa_12feb9.pdfWebsites: Washington Department of Natural Resources has a special section about Forest Practices: http://www.dnr.wa.gov/ There are GIS Spatial Data Sets available about the Forest Practices where the timber harvest areas can be seen in polygons. The information about the volume of timber produced is availa-‐ble too, however for other data needs (such as frequency of harvesting, percentage of harvest-‐ing, maintenance cost, and harvesting cost) information from landowners is needed. Outputs This model gives as an output the amount and volume of the timber produced over a time peri-‐od and the timber’s net present value. Recommendations The model runs based on estimated values, such as the percentage of the harvested forest, the mass of harvested timber, the frequency of harvesting. The harvesting and maintenance costs are thought to stay unchanged throughout the time period selected. However, these numbers can change due to several factors such as changes in the mixture of tree species in harvest are-‐as. Thus, it can be argued that the length of time period selected for the calculations can play a role in determining the accuracy of the estimations made by the model.
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Appendix B: VEST Instructions
Valuing Ecosystem Service Together (VEST) We are a group of UW graduate students working to identify methods and tools that could aid the Regional Open Space Strategy (the ROSS) in the prioritization of land conservation. The as-‐signment of the value of ecosystem services provided by different land types is very difficult and often not clearly defined in literature. In order to help the ROSS ecosystem services com-‐mittee identify where resources might be best used to investigate ecosystem valuation more specifically in the watersheds, we call on the ROSS team to contribute their expertise to this col-‐laborative effort. We appreciate your help in exploring how this new tool could assist the ROSS. Directions Please fill in the table and information about you in the attached excel document. When you are finished, save the file as “[your last name_ESframework]” and attach it to an email sent to Jenny Duggan ([email protected]). Please respond by March 3rd. Types of land investments are listed by column and ecosystem services are listed by row in the excel document. For each row, please allot points to each land investment type based on its relative contribution to the corresponding ecosystem service. More points indicate a greater contribution. There are 36 points to allot for each row. You can allot each land investment dif-‐ferently (ex. rank 1-‐8), give all the points to only one land investment type, or distribute points in any other way you like. Some of the ecosystem services such as “aesthetics” require more subjective judgments. Others call on your expert opinion about the function of ecosystems. If you do not feel comfortable assigning points for a particular ecosystem service, you may leave that row blank. However, if you choose to assign points to one land investment in a particular row, you must distribute the rest of the 36 points in the row. The definitions of each ecosystem service can be found in the second tab of the excel document. We used the Millennium Ecosystem Assessment framework to develop this tool. Assumptions 1. All investments will be made by public or not-‐for-‐profit organizations with the intention of public benefit. Private benefits may result from the investment, but the analysis is based on the intention of public benefit. 2. Agencies would follow best management practices in perpetuity for their land investments. While this may not be realistic, it will allow for easier comparisons. 3. Only consider effects on ecosystem services that are generated directly from the acquired land type, absent of additional investment. For example, forest acquisition provides carbon se-‐questration and timber production, but would not necessarily provide aesthetic value or recrea-‐tional opportunities without further investments in trail building or campgrounds.
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4. Do not consider opportunity costs that may be forgone by making one type of investment over another.
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Appendix C: Annotated Bibliography Alcamo, Joseph, et al. "Ecosystems and human well-‐being: a framework for assessment." (2003).
This paper outlines a framework that divides services into provisioning, regulating, cultural, and supporting. It also includes a discussion of robustness of ecosystems and different stabi-‐lization states as well as ways to account for substitution services.
Batker, D., Schmidt, R., Harrison-‐Cox, J., Christin, Z. The Puyallup River Watershed: An Ecological Economic Characterization. Earth Economics. 2011.
This is an overview of land cover types and biodiversity in the watershed. It proposes a gen-‐eral plan for approaching the valuation of ecosystem services in the area.
Chan, Kai MA, et al. "Conservation planning for ecosystem services." PLoS biology 4.11 (2006): e379.
This study looks at how targeting conservation by different prioritizations (i.e. biodiversity or pollination) will result in overall ecosystem services benefits. They used MARXAN as a valua-‐tion tool.
Díaz, Sandra, et al. "Linking functional diversity and social actor strategies in a framework for interdisciplinary analysis of nature's benefits to society." Proceedings of the National Academy of Sciences 108.3 (2011): 895-‐902.
This is a framework for evaluating ES using social actor priorities as a guide. They argue that biodiversity by itself doesn't mean much to people and doesn't always have quantifiable re-‐sults. They propose having different kinds of social actors describe how different compo-‐nents of the ecosystem are important to them and using those to prioritize ES. This approach allows them to integrate social information and ecological information and also potentially conflicting land use strategies.
Fürst, Christine, Katrin Pietzsch, and Franz Makeschin. "Pimp your landscape." Sustainable De-‐velopment 2005 (2008): 2014.
A generic approach for integrating regional stakeholder needs into land use planning. Goldstein, J. H., et al. “Integrating ecosystem-‐service tradeoffs into land-‐use decisions.” Pro-‐ceedings of the National Academy of Sciences of the United States of America 109. 19 (2012): 7565-‐70.
This study quantifies ecosystem-‐service values for a land-‐use development plan in Hawaii by using InVEST tools. The financial and environmental impacts of seven planning scenarios are evaluated that include different land use types such as biofuel feedstock, food crops, forest-‐ry, livestock and residential development. Three metrics that are being used for comparison are: carbon storage, water-‐quality improvement, and financial return. The researchers found
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out that there are trade-‐offs between carbon storage and water quality and between finan-‐cial gain and environmental improvement. In the end it was decided to implement a plan that will improve carbon storage (0.5% increase) but that will have negative effects on water quality (15.4% increase in nitrogen export). There are also some plans to reduce this nitro-‐gen increase by using vegetation buffers that will reduce the increase to a 4.9% level.
Halpern, Benjamin S., et al. “Achieving the triple bottom line in the face of inherent tradeoffs among social equity, economic return, and conservation.” Proceedings of the National Academy of Sciences 110.15 (2013): 6229-‐6234.
This study uses three case studies to develop a theory of how to formally include equity in conservation planning and prioritization.
Machado, E.A., et al. “Prioritizing farmland preservation cost-‐effectively for multiple objec-‐tives.” Journal of Soil and Water Conservation 61.5 (2006): 250-‐258.
In this study the researchers present a framework they have created in order to prioritize farmland conservation projects. The framework includes the social benefits of farmlands and a ranking system based on the following criteria is used: objectives, priorities, farmland val-‐ue expected to be lost to development, secured farmland value and the cost of farmland conservation.
Menzel, Susanne; Teng, Jack. “Ecosystem services as a stakeholder-‐driven concept for conserva-‐tion science.
The authors contend that ES valuation as used today introduces conflict rather than facilitat-‐ing communication because it strengthens the power of those (experts and scientists) who already hold it. They argue that true sustainable solutions will have to incorporate local user values and preferences.
Naidoo, Robin, et al. "Global mapping of ecosystem services and conservation priorities." Pro-‐ceedings of the National Academy of Sciences 105.28 (2008): 9495-‐9500.
This study mapped four basic ecosystem services: Carbon sequestration, Carbon storage, Grassland production of livestock, Water provision. They found that choosing conservation based on biodiversity produced no more ecosystem services than random plots. However, there is value in choosing win-‐win sections that have both.