Guiding conservation and renewable energy

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Biological Conservation 201 (2016) 69–77

Contents lists available at ScienceDirect

Biological Conservation journal homepage: www.elsevier.com/locate/bioc

Guiding conservation and renewable energy development using a paired return-on-investment approach Timothy G. Howard a,⁎, Matthew D. Schlesinger b, Cara Lee c, Gregory Lampman d, Timothy H. Tear e,1 a

State University of New York College of Environmental Science and Forestry, New York Natural Heritage Program, 625 Broadway, Albany, NY 12233-4757, USA State University of New York College of Environmental Science and Forestry, New York Natural Heritage Program, 625 Broadway, Albany, NY 12233-4757, USA The Nature Conservancy, 652 Route 299, Highland, NY 12528, USA d New York State Energy and Research and Development Authority, 17 Columbia Circle, Albany, NY 12203-6399, USA e The Nature Conservancy, 195 New Karner Road, Albany, NY 12205, USA b c

a r t i c l e

i n f o

Article history: Received 2 October 2015 Received in revised form 10 June 2016 Accepted 27 June 2016 Available online xxxx Keywords: Return on investment ROI Wind turbine development Development priorities Conservation priorities

a b s t r a c t Return-on-investment (ROI) can help integrate prioritization efforts for developers and conservation organizations alike. To examine this complementarity and to investigate improving dialogue across these two sectors, we conducted paired ROI assessments from the perspective of wind development and biodiversity conservation in the northeastern United States. Spatially explicit layers defined the three ROI components: benefit, cost, and probability of success. For the wind development ROI, we modeled turbine suitability using the random forest algorithm to develop the benefit surface. We treated biodiversity information related to permitting and development as a cost surface and applied land conservation status towards the probability of success term. The conservation ROI applied biodiversity priorities as the benefit surface, applied a higher environmental cost to areas with high wind turbine development value, and used estimates of ecosystem resilience to define the probability of success. This ROI highlighted conservation potential after applying the constraints of wind energy development. The analysis suggests that New York State, US, may be able to accommodate 16,000 Megawatts of power generation while avoiding conservation priorities, more than sufficient landscape to situate turbines up to the predicted capacity based on grid reliability (6600 MW). Further, the two ROI models taken together are more instructive than results from either model alone. Sensitivity analyses revealed that altering the weightings of the biodiversity input variables rarely changed the relationship among the two ROI models from place to place. We suggest that applying ROI from different perspectives may help form an important communication bridge between conservation and development tradeoffs, and prove valuable in the debate over renewable energy production options in the context of their environmental impacts. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction An essential internal operation for non-profit and for-profit organizations is priority setting. Return-on-investment (ROI) is one approach to prioritization that has its origins in business and recently has received increased use in conservation (Auerbach et al., 2014; Torrubia et al., 2014). Whether to include economic, political, or social constraints in conservation priority setting continues to be debated (Brown et al., 2015; Jenkins et al., 2015), but increased interest in the use of ROI in conservation results partially from the intuitive nature of the concept (getting better return for your investment), and the increased

⁎ Corresponding author. E-mail addresses: [email protected] (T.G. Howard), [email protected] (M.D. Schlesinger), [email protected] (C. Lee), [email protected] (G. Lampman), [email protected] (T.H. Tear). 1 Current address: Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, NY, 10460, USA.

http://dx.doi.org/10.1016/j.biocon.2016.06.029 0006-3207/© 2016 Elsevier Ltd. All rights reserved.

awareness that incorporating multiple criteria and recognizing tradeoffs can improve the prioritization process (e.g., Wilson et al., 2009; Game, 2013). Perhaps most importantly, ROI can provide a common platform on which to compare and balance competing interests. With growing awareness that climate change is a real and significant threat to the planet (e.g., IPCC, 2013), understanding the tradeoffs of developing renewable energy sources is of increasing importance. This challenge is particularly acute in the Northeast United States, where air pollution impacts to people and nature from coal-fired power plants are significant (Lovett et al., 2009), and investment in shale gas development is increasing. More informed dialogue and public debate will result from the development of better methods to balance the tradeoffs of conservation and energy production. As a result, long-term benefits to society (e.g., the conservation of natural resources, carbon emission reductions) will be considered within the context of short-term energy needs. Rather than considering renewable energy and biodiversity conservation priorities independently or as opposing forces, an alternative is to take an ROI approach to both to help improve prioritization. For

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renewable energy development in the Northeast US, this translates to incorporating conservation priorities early, as part of the developer's priority setting phase, not later during the site-specific regulatory review phase. Concomitantly, conservation organizations can use the same approach for conservation priority setting by incorporating energy development priorities to help improve the dialogue about the importance of conservation in energy development. In both cases, we define the actions being prioritized as setting aside land for either wind development or protected-area establishment (see Game et al., 2013). The ROI approach for priority setting is ideal for this type of analysis, as it is relatively easy to understand and powered by numbers that makes assessments transparent and accessible. To date, conservation ROI analyses have varied by spatial scale, such as the ecoregion (Murdoch et al., 2007; Underwood et al., 2008), political subdivision (Garnett et al., 2011; McCreless et al., 2013), or finer scales (Auerbach et al., 2014; Murdoch et al., 2010). Working at finer spatial scales not delimited by political or geographic boundaries brings the output to a point that can be used in a variety of different ways by a wide range of stakeholders. Here, we incorporate fine-scale GIS analyses (30-meter raster) into an ROI framework from two potentially antagonistic perspectives: wind turbine development and biodiversity conservation. Our test area is New York State, United States, which is particularly well-suited to this type of analysis. Adequate wind energy development information and a rich, publicly available dataset are both available to improve discussion and debate about natural resource issues (e.g., www.ebd.mapny.info and www.nynhp.org/data). Our goal is to present a case study in which two interconnected ROI analyses can provide a means for spurring conversations between stakeholders with differing land-use interests. Conducted early in the planning process, analyses such as those presented here can help minimize conflict by refining spatial priorities by mapping areas most important for meeting conservation objectives while simultaneously finding sites for renewable energy development. 2. Methods Our objective was to identify sites where biodiversity conservation could be most effective while simultaneously supporting renewable energy production. We followed Tear et al. (2014) with the idea that the magnitude of benefit of a development or conservation decision should be tempered by known uncertainties as well as costs for achieving that benefit. Those two components are incorporated through the following equation: ROI ¼

Benefit  ProbabilityofSuccess Cost

Table 1 Objectives and components for each return on investment scenario. The components for the first scenario (wind turbine development) are depicted spatially in Fig. 1; the benefit component and final output for the second scenario (biodiversity conservation) are depicted in Fig. 3. The range in values for each component are also indicated. Scenario

Objective

Wind

Identify locations with suitable wind development potential and the lowest conflicts with biological conservation Benefit

Cost proxy

Prob. success

Turbine suitability 0–1

Permitting costs 1–41

Conservation lands 0.01–1

Conservation Identify locations with high biological conservation value while simultaneously minimizing conflict with wind development. Benefit

Cost proxy

Prob. success

Biodiversity 0–104

Turbine suitability 1–2

Resilience 0–1

In the following sections we discuss applying each of these components to create a GIS raster surface to support spatial planning and prioritization for each of the two perspectives (Table 1). 2.1. Benefit surface The measure of benefit for an ROI model should be based on the primary goal or outcome of the target organization. For example, if biodiversity conservation is most important then a GIS representation of species richness or diversity may be most appropriate (Underwood et al., 2008). Similarly, if the focus is on connectivity among habitats or landscapes, then a GIS surface representing connectivity areas would be applicable (Howard and Schlesinger, 2013; Theobald et al., 2012). In this context, benefit values have the potential to increase significantly in complexity, and complex weighting schemes (Arponen et al., 2005) or suitability models (Guisan et al., 2013) have been appropriately used for deriving benefit functions in different contexts. Below, we describe two approaches for defining a fine-scale benefit surface. The first employs distribution modeling methods for wind energy development while the second relies on differential weighting to combine existing benefit layers for biodiversity conservation. 2.1.1. Developing the benefit values for wind development using distribution modeling We based this approach on the assumption that existing and proposed wind facilities are good predictors for future facilities. Due to various constraints, turbines have not necessarily been placed at locations with the highest wind speeds. Such constraints are wide ranging, and include factors such as land ownership, distance to existing transmission lines, and amount of forested land in the immediate vicinity. Our goal was to include as many of these other factors as possible to best understand turbine development choices for New York State. Similar analytic approaches have been conducted in other locales, including northern California, US (Rodman and Meentemeyer, 2006); Colorado, US (Janke, 2010); Iowa, US (Petrov and Wessling, 2014); and the United Kingdom (Baban and Parry, 2001). As far as we know, this is the first effort to use the Random Forest classifier and the wide range of environmental variables that we used in this study, although Petrov and Wessling (2014) used other machine-learning algorithms. Our approach had four steps: 1) collecting turbine location information, 2) assembling environmental variables, 3) modeling the relationship between turbine locations and environmental variables, and 4) applying the model to the study area to generate a prediction of localities suitable for turbine development. This is the same modeling approach used to create the species distributions that were important inputs into the benefit value for biodiversity conservation (below). We collected turbine location information for all existing and proposed terrestrial wind turbines from 1990 through 9 January 2013 from the Federal Aviation Administration online database (http:// oeaaa.faa.gov). We removed turbines below 50 m in height and a small set of proposed vertical-bladed turbines that were never installed on Long Island. Duplicate entries were removed by checking for redundant case numbers, redundant location coordinates, and for reported locations within 90 m of each other, which was the minimum distance apart we observed for existing turbines. Our final dataset included 1881 turbine locations. We chose 19 environmental data layers that had potential to be directly or indirectly associated with wind turbine siting. These included wind production capacity, elevation, slope, aspect, distance to the nearest large transmission lines, and variety of land cover, surface relief, and roughness measures (Table 2). We used the random forests algorithm (Breiman, 2001) to model the environmental conditions at each mapped wind turbine. We used the statistical software R (R Development Core Team, 2011), with installed packages randomForest (Liaw and Wiener, 2002), ROCR (Sing et al., 2005), vcd (Meyer et al., 2010), abind (Plate and

T.G. Howard et al. / Biological Conservation 201 (2016) 69–77 Table 2 Environmental data layers used to model the locations of existing wind turbines. A total of 19 layers were input into the model to evaluate their relationship with the presence of existing turbines. Some rows represent multiple layers at the roving-window distances specified. Group

Layer

Wind Elevation Elevation Elevation Elevation Elevation Elevation Power Land Cover Land Cover Land Cover

Wind production capacity at 50 m height Elevation Slope Aspect Surface relief ratio at 90, 270, 810 m Roughness at 90, 270, 810 m Terrain wetness indicator Distance to transmission % developed at 30, 300, 990 m Proportion of open cover at 300, 990 m Proportion of forest cover at 300, 990 m

Heiberger, 2011), RODBC (Ripley and Lapsley, 2010), foreign (R Development Core Team et al., 2011), and raster (Hijmans et al., 2014). The random forest algorithm has been shown to be quite robust and accurate for this type of spatial modeling (Bisrat et al., 2012; Iverson et al., 2004; Lawler et al., 2006). We tested the accuracy of the model by applying leave-one-out cross-validation for all turbines grouped into 100 groups based on their location (Fielding, 2002). Once the relationship among environmental variables and turbine locations was encapsulated in the random forests model, we used that model to evaluate the potential suitability throughout the state for turbine siting. We created a GIS surface (raster layer with 30-meter pixel size) with each cell containing a value representing the probability that the location is similar to other wind turbine locations. For visualization, we removed areas predicted as not suitable for turbine development (Fig. 1A) using as a threshold the point closest to the upper left corner of the receiver operating characteristics (ROC) curve of the final model (Liu et al., 2005). 2.1.2. Developing benefit values for conservation using a weighted approach In order to develop a benefit surface tuned for setting conservation priorities, we combined species distribution models, large unfragmented forest areas, and connectivity metrics (Table 4). Following the distribution modeling approach as described in Howard and Schlesinger (2013), we created distribution models for 379 rare species (plants and animals) occurring in New York. Forty of these species did not pass validation (we considered models with TSS N 0.50 to pass validation [Allouche et al., 2006]), leaving 339 models that were used in this analysis. For each model, we created a binary representation of suitable/not suitable habitat using the F-measure (Sing et al., 2005; Van Rijsbergen, 1979) with alpha = 0.01 to find a balance of precision and recall weighted conservatively towards higher recall (more land area represented as suitable). We combined these binary models into different ‘stacks’ by summing them across all species in a specified group. The weighted approach described here uses two of these stacks: one stack of all 339 species that resulted in a raster with cells with a range of values from 0 to 32 (where 32 species were modeled to have suitable habitat) and a second stack of only state-listed species (232 species) resulted in a raster with a range of values from 0 to 24. Due to the importance of bat conservation in New York to wind energy development, we also included a stack of summer distributions for three bat species (Eastern Red Bat, Lasiurus borealis; Hoary Bat, L. cinereus; Little Brown Bat, Myotis lucifugus), modeled using slightly different inputs and approach to the other distribution models (Perkins et al. unpubl. Data). Indiana Bat was included separately as a single layer depicting known hibernacula, known summer roosting locations, and flight zones between these winter and summer residences.

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To represent stream quality, we included a model of freshwater mussel richness and a model of aquatic macroinvertebrate richness. Both of these were developed by White et al. (2011) and then converted to raster by simply intersecting the line data with raster cells. In order to estimate important locations for migratory bird habitat we included migratory bird data modeled by the Cornell Laboratory of Ornithology (Fink et al., 2010). Each of 28 species was modeled for spring and fall migrations and those models (probabilities scaled from 0 to 1) were combined to create overall summed probabilities for each migration season. Relatively intact floodplain systems were mapped as floodplain complexes (White et al., 2011) and included as an input to represent the conservation benefits of the system. Finally, large forested areas have been important conservation targets for capturing variability and maintaining resilience (Anderson et al., 2012) and connectivity among forest blocks are important for maintaining gene flow, population persistence, and allowing range shifts (Goetz et al., 2009; e.g., Minor and Lookingbill, 2010). We included unfragmented forest blocks as targeted by The Nature Conservancy (e.g., Anderson et al., 2006) and modeled connections between these blocks via least-cost paths (LCP, Howard and Schlesinger, 2013) and built connectivity zones using a Conditional Minimum Transit Cost (Pinto and Keitt, 2009) set at the LCP cost plus 20% for each connection. In contrast to the first scenario where a single layer defined the benefit input, in this scenario we faced a different challenge to incorporate the many biodiversity metrics into a consolidated analysis. We chose to use weighting in order to maximize information use and yet be transparent in its valuation. This allows for flexibility and potential for variability in outputs depending on the modeler's priorities, following methods used by Tear et al. (2014). For illustrative purposes, we combined layers with a focus on biodiversity conservation, giving higher weights to those layers more specifically related to conservation benefits for species. We did this because in New York, as in all of the United States, particular species (e.g., Federally listed species like the Indiana Bat) and some habitats (like wetlands) are protected by law. For example, distribution models for rare species were given extra weight by allowing the metric to scale to the full number of species predicted to occur on the landscape. We also included another layer of only state-listed species, effectively giving two points to each of these species (Table 4). Layers representing more common or single species were scaled from 0 to 5 and those representing habitat quality were scaled from 0 to 10 (Table 4). We evaluated the sensitivity of final ROI comparisons to changes in these weights, below. More graphical representation and detailed justification of these products and the methods are available at www.ebd.mapny.info. 2.2. Cost surface Obtaining relevant and accurate cost information is one of the most challenging parts of applying ROI methods in a conservation context (Naidoo et al., 2006). For example, the relative ‘cost’ of an action varies from place to place depending on potential factors such as local resistance from a community (Kempton et al., 2005), regulatory policies (Bird et al., 2005), land costs (Naidoo et al., 2006), and other political, environmental, or social issues (e.g., Adams et al., 2010). Costs may be absolute and tallied through expected monetary expenditures (e.g. acquisition, or management costs) or they may be relative and more abstractly based on the magnitude of regulatory or siting difficulty expected at a site as non-monetary proxies (Naidoo et al., 2006). In this assessment, we rely on different types of cost estimates based on their applicability, using direct cost proxies for wind energy development and indirect cost proxies for biodiversity conservation. 2.2.1. Developing cost proxies for wind development When considering potential costs to wind development in the context of biodiversity conservation, we focused on the factors most likely

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Fig. 1. A. Model output for wind turbine suitability in New York State. The small panel depicts a portion of the state with input turbine locations as an overlay. B. GAP Status for protected areas. Darker shades represent assumed higher protection status; the probability of success term is based on these codes. C. Biodiversity costs for wind turbine development, based on adding together four biodiversity layers. Darker shades represent places that may have higher regulatory burden for wind development activity. Indiana Bat roosting areas and hibernacula are evident as dark circles with travel corridors between them. D. Return on investment (ROI) results for wind development priorities while considering biodiversity constraints and land protection status. The version 1 cutoff for suitability is shown here. Jenks natural breaks were applied for the color ramps above the cutoffs for the turbine and ROI models and for the entire range of the biodiversity costs layer.

to have direct costs related to construction or operation. Direct monetary costs are associated with extra permits, wildlife or wetland mitigation, or added construction such as forest clearing. Other costs (such as potential biodiversity loss or degradation of ecosystem services) are more difficult to quantify and so we deliberately chose to focus on the biodiversity factors most related to those more direct costs. In that category are layers relating to state and federally listed species (permitting), water quality (mitigation and permitting), and the amount of forest in the landscape (land clearing). In addition to choosing which GIS layers represent appropriate proxies for wind development costs, we were explicit about how each layer related to the other. Transparency in the application of weighting is important, as it had the potential to change the ROI output depending on the weighting scheme (Tear et al., 2014). Here, we weighted each layer equally, with each continuous surface varying from zero to ten. We evaluated the sensitivity to model outputs of variation in these weights in a separate analysis, below. The only layer with non-continuous data was for the Indiana Bat (Myotis sodalis), a federally listed species whose presence requires considerable survey effort and possibly

mitigation. In addition, there is a significant conservation concern regarding the potential impact of wind turbines on bat populations (Hayes, 2013), which has been exacerbated by the dramatic decline in bat population size in New York due to an introduced disease called “white nose syndrome” (Thogmartin et al., 2013). Consequently, we modified the weighting scheme for data on Indiana bats because we considered any habitat important to Indiana bats has unusual importance at this time (due to disease) and in this context (due to turbine impacts) and therefore restricted the only spatial information available related to have high cost values. Indiana bat travel zones and bat locations were allocated values of seven and ten, respectively, representing our interpretation of their relative cost in comparison to the other biodiversity information with maximum scores of ten (Table 3). All cost surface GIS data layers were added together into a final cost proxy surface (Fig. 1B). 2.2.2. Developing cost proxies for conservation A primary assumption of this analysis is based on the study area's global position as a place impacted significantly by the atmospheric

T.G. Howard et al. / Biological Conservation 201 (2016) 69–77 Table 3 Variables, justification, a relative scaling value to develop the cost surface for the wind turbine development ROI analysis. Variable

Justification

Scaling

1. Distribution model: State listed species

More state-listed species are likely to increase permit related costs

0–10

2. Indiana Bat hibernacula, summer roosting areas, travel zones

This federally listed species will trigger additional permitting. (0 = not in any categories, 7 = travels zones, 10 =

0,7,10

hibernacula or roosting areas). 3. Stream quality estimate

Stream crossings require additional permits; higher quality streams like potential trout streams have special requirements; wetlands (more permits) are often associated with stream systems.

0–10

4. Amount of forest and forest conservation priorities

Building roads and timber harvesting increases development costs in forests; trees increase turbulence and require higher hubs.

0–10

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probability. While GAP status codes 1–3 ostensibly designate permanent protection from conversion the levels of protection and management for conservation do lessen with higher status values and altering the probability of success in this way allows us to reflect those changes. Similarly, a true 100% probability of turbine development success is not realistic but applicable here simply to illustrate the lack of a conservation impediment to energy development in a mathematical setting. 2.3.2. Developing the probability of success values for conservation The probability of success term in the ROI formula should reflect how successful any action would be for conserving the conservation priorities for a particular site. While many factors would contribute to true conservation success, one contributing factor is the ability of the site to withstand disturbance and other stressors – such as climate change. Anderson et al. (2012) developed a site resilience scoring to address this issue based on the assumption that sites with a higher diversity of landforms and geology types are likely to have higher resilience. We used this layer scaled from zero to one to represent the probability of successful conservation in the long-term. 2.4. Sensitivity of ROI components on final outcomes

deposition of coal fired power plant emissions (Lovett et al., 2009). This supports a local conservation position that traditional or fossil fuel energy production negatively impacts ecosystems and biological diversity in this area. If the lack of renewable energy or sustainable development is detrimental, then setting aside places with high potential for wind development would be a cost for conservation. Or, said another way, we would want to prioritize locations for conservation that do not conflict with wind development. Operationally, this translates to using the turbine distribution model (Fig. 1A) as the cost proxy in our ROI formula. 2.3. Probability of success surface Many factors have the potential to influence the probability that a planned action will be successful. These generally fall into the socio-political realm, such as the magnitude of civil society involvement (McCreless et al., 2013) or political corruption (Garnett et al., 2011). Obtaining these metrics is also challenging and researchers must be cognizant of the components missing from any particular analysis or the bias that specific variables may introduce into the assessment. We selected variables that represented the probability of success for either energy development or biodiversity conservation that were relevant to both sectors to minimize the number of factors used in the analysis. This not only has important societal benefits by improving the chance that the results of each model can be understood by each sector, it has mathematical benefits by reducing the number of variables used in the modeling process. 2.3.1. Developing probability of success values for wind development Many factors influence the probability of successful wind energy development at any given site. Some of these factors are very hard to predict, such as the level of support or resistance among the local community or changes in real estate or residential development patterns. We selected the level or degree of conservation land ownership as it influences regulatory procedures for development. We used a GIS layer depicting protected lands (www.nypad.org) and the protection status attributed to each property, as defined by GAP status, a set of nationally consistent codes depicting conservation status that supports IUCN rankings (gapanalysis.usgs.gov/blog/iucn-definitions). Properties designated as having very high protection (such as state wilderness areas) which explicitly restrict any development received an extremely low value for probability of success (i.e., GAP Status 1 = 0.1% probability). However, properties with lower protection status received higher probability values (i.e., GAP Status 2, 3, 4 = 30, 50, and 70%, respectively) and lands not assigned any GAP status were considered to have no restrictions based on known protection status and assigned 100%

Modifying any component of the ROI inputs will alter the final values for ROI simply due to algebraic constraints. Our interest in a sensitivity analysis, instead, is how much a model component influences the direction of effect between our two ROI products. If, for example, there is consistently higher ROI for biodiversity than wind development from one location to the next, how often does that consistency break when model component weightings are changed? Thus, we focused on the relative relationship between the two ROI scenarios in various localities. We assessed the effects of changing ROI terms by extracting each component for both models at 124,000 spatially balanced random locations throughout the study area. We then calculated the difference in the relationship between the two ROI models (model pairs) at two randomly chosen locations and compared that difference (slope) to a case where a model component was modified at the second location. We also used variance-based sensitivity analysis in the R package sensitivity (Pujol et al., 2015) to estimate the effect of changing each ROI component on the variance of the same ROI model pair comparisons. We were most interested in the effect of the biodiversity weighting factors used in the cost term of the wind ROI and the benefit term of the biodiversity conservation ROI so we modified these components in two separate model comparisons. 3. Results 3.1. Results for wind development prioritization scenario The ROI output consisted of a GIS surface that adjusted the turbine distribution map based on the added constraints of existing land conservation actions and biodiversity cost proxies. In this scenario, we determined a threshold of acceptable benefit by assessing the probability surface of the turbine distribution model. The lowest probability value from the distribution model intersecting with any existing input turbine location was designated as the threshold. Thus, ROI values ranged from 0.185 to 0.996 and locations with these ROI values are present in various parts of the state (Fig. 1C). Understanding how the different components of the ROI model interact to provide the final output can help demonstrate how this model works. Areas in northern New York, for example, showed a large decrease from the amount of suitable landscape for turbine development to the amount of landscape within a favorable ROI (circled area in Fig. 2). The primary drivers in decreasing the ROI for this area were the rare species components of the biodiversity layers. Specifically, the high values for state-listed species and the presence of Indiana bat reduced the very high wind turbine similarity values to convert much of

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Fig. 2. A comparison of the turbine suitability surface to the final ROI surface, showing the constraints applied based on biodiversity costs and probability of success. The circles highlight an area with high wind suitability but low ROI for wind development with respect to the model specifics as applied here. Color ramps determined as in Fig. 1.

the landscape to less than suitable conditions. Other areas changed very little in their representation of suitable wind turbine sites with and without ROI incorporated. Many scenarios are possible, each with unique outcomes depending on how each component of the ROI is formulated. In this specific formulation the total amount of land suitable for wind turbine development, based solely on the turbine distribution model (cutoff 0.185), is 11,930 km2. Using ROI as described here, this area is reduced by approximately 45% and we are left with 5430 km2 (1.3 million acres), the total area shaded in (Fig. 1C). If we relax our requirements for turbine suitability (by lowering the cutoff for turbine distribution), the total amount of appropriate land based solely on the turbine distribution model

becomes 15,252 km2, with ROI restrictions reducing that number in half to 7409 km2. Using an estimate of total-area capacity density of 3.0 ± 1.7 MW/Km2 (Denholm et al., 2009), our estimates suggest New York could provide about 16,300 MW (with a range from 7060 to 25,525 MW) of total capacity in the model described here and 22,000 MW of total capacity in the relaxed version. Total capacity, however, may be restricted by factors other than those considered in this analysis. Grid reliability, for example, may limit New York's terrestrial production to 6600 MW (Mosenthal et al., 2014). Consequently, the results of our analysis support the position that there is sufficient land to meet the maximum estimated wind energy production while factoring in biodiversity conservation as an explicit constraint.

Fig. 3. A. Biodiversity conservation benefit GIS surface with a focus on sensitivity towards wind development, based on adding together ten separate biodiversity layers (Table 4). B. A return on investment scenario for setting conservation priorities while accommodating wind development. C. and D. show a comparison of these two layers in northern New York. The circles highlight areas with (a) high biodiversity conservation benefit but low conservation ROI, (b) mixed conservation benefit but patches of high ROI, and (c) mixed conservation benefit but mostly low ROI. Color ramps use Jenks natural breaks.

T.G. Howard et al. / Biological Conservation 201 (2016) 69–77 Table 4 Variable weighting schemes in the context of biodiversity conservation. Variables, justification, a relative scaling value to develop the cost surface for the biodiversity priorities development ROI analysis are defined. The first group (rows 1–3) scale the maximum value of the original layer; the second group (rows 4–8) scales to five; the final group (rows eight and nine) scales to ten. Variable

Justification

Scaling

1. Distribution model: Rare species

Higher intersections of rare species 0–32 habitats increases site conservation benefit

2. Distribution model: State-listed species

Among rarities, state-listed species receive 0–24 extra points with the inclusion of this layer

3. Bat summer distributions

An independent estimate of summer bat distributions highlights these important species

0–3

4. Mussel richness

An estimate of stream quality

0–5

5. EPT richness

A second, independent, estimate of stream quality

0–5

6. Migratory birds (spring)

An estimate of important habitats for migratory birds

0–5

7. Migratory birds (fall)

An estimate of important habitats for migratory birds

0–5

8. Indiana Bat hibernacula, summer roosting areas, travel zones

A single species layer, but an important, Federally listed species

0,3,5

9. Floodplain complexes

The few remaining floodplains in the state offer quality ecosystem services and other conservation benefits

5–10

10. Forested areas

Large forested areas and connectivity 0–6, zones offer myriad of conservation benefits 6–8, (inside matrix forest blocks, vary by forest 8–10 percent cover to create scores ranging from 8 to 10; inside connectivity zones between forest blocks, vary by forest cover to create scores ranging from 6 to 8; remaining areas in state vary by forest cover to create scores 0–6)

3.2. Results for biodiversity conservation in a wind energy development context The ROI output for this scenario focused conservation priorities toward places with higher resilience and least conflict with wind energy development (Fig. 3B). Conservation targets in the benefits surface include elements of rare species, bird migration habitats, and higher quality stream and unfragmented forest ecosystems. Understanding how each conservation target plays a role in setting the ROI priority requires delving into the details of the individual layers that formed the biodiversity benefits data set. The weighting applied to these layers varied considerably, with the potential for rare species predicted habitat to score the highest (Table 4), but the moderate scores for large forested areas and connectivity zones had a substantial impact because of the large extent of these features. Not all the locations with high biodiversity benefit ratings became locations with high return on investment values. For example, one area in northern New York had very high biodiversity benefit but scored low in ROI (Location a, Fig. 3) while another had variable biodiversity benefit scores but scored high in ROI (Location b, Fig. 3).

3.3. Sensitivity analysis results After we randomly assigned higher biodiversity weights for the benefit term in the conservation ROI at one location and compared the relationship between the wind development and conservation ROI outputs

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at a second location, 93.2% (115,648 of 124,073) of the cases trended in the same direction as the same comparison with unmodified ROI pairs. The components contributing most to the variability of the output included the probability of success terms of both models, and the benefit term, overall, for the biodiversity ROI. The modified biodiversity components showed little difference in their individual effects on model output variability. Our second test of modifying weightings showed similar results. After we randomly assigned higher biodiversity weights for the different factors in the cost term in the wind development ROI at one location and compared the relationship between ROI outputs to a second location, 98.6% (122,329) of the cases trended in the same direction as the unmodified location to location ROI relationship. Thus, if ROI for wind development is higher than the ROI for biodiversity at one locality and the inverse is the case at a second locality, it is very unlikely that modifying the biodiversity weightings in either model will change that pattern between those two localities. Of the biodiversity cost components, modifying the forest and statelisted species variables had greater effects on the output than modifying the Indiana Bat and stream condition measures. The probability of success term for the second ROI pair again had large effects on the output of the final model. 4. Discussion In the conservation community we are challenged by large and looming threats, particularly climate change and habitat loss. Mitigation of these threats can often be at odds with one another. The expansion of utility-scale, renewable energy resources (e.g., wind turbines, photovoltaic arrays) to mitigate carbon emissions associated with fossil fuel combustion will result in additional habitat loss. While habitat loss might be viewed as a more direct and immediate stressor than the indirect effects of power generation from fossil fuels (via atmospheric deposition and climate change), both are very real threats to biodiversity (Pimm, 2008), compounded by interacting effects (Oliver and Morecroft, 2014). The full analysis described here strives to address both stressors by identifying sites where habitat loss could be minimized while simultaneously supporting renewable energy production (biodiversity ROI) and also by identifying sites best suited to renewable energy production where the impact on conservation might be lowest (wind development ROI). Identifying and addressing these land-use tradeoffs in tandem, early in the planning cycle rather than as an afterthought, would improve the dialogue and potentially lead to more mutually beneficial decisions. Typically, potential conservation constraints to a renewable energy project are considered during the site review phase. At this point in the process, a developer would have a set of priority sites and the primary goal of overcoming the various hurdles to site development, such as acquiring permits and mitigating any potential effects the development may have on biodiversity. High enough hurdles may cause the development plans to be abandoned, but not before considerable funds are expended in the process. Incorporating potential constraints during the earlier site prioritization phase provides an opportunity to avoid, or at least minimize, these “soft” costs of development that emerge later during the site review phase. By incorporating constraints early, developers are able to avoid the costs of pursuing sites with highest conservation value that pose expensive permitting hurdles. Conversely, from the perspective of setting conservation priorities, incorporating renewable energy as a desirable feature provides a broader, landscape-level perspective on the needs of renewable energy. Supported by education and outreach, it has the potential to reduce the “not-in-mybackyard” reactions common with some renewable energy developments. Thus, the cost proxies included in the assessments provide a linkage between perspectives and a prompt for dialogue between parties. Perhaps most importantly, this approach provides analyses derived from similar data, using similar methods, that may improve the capacity

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of both sectors to work together. The process and its results acknowledge the tradeoffs, and provide the opportunity to minimize conflict by creating purposefully linked yet equally credible assessments. We suggest that developing complementary ROI models may encourage more collaboration and emphasize the benefits of defining sector-specific outcomes, defining them in a shared approach, and then comparing the results in search of a workable solution. An illustrative example from this assessment can be seen from the analysis of the two ROI models in northern New York (i.e., Figs. 2, 3). One area (Location a, Fig. 3) had high initial benefit values (Figs. 2A, 3A) but low ROI values for both wind turbine development and biodiversity conservation (Figs. 2B, 3D). This was due, in part, to the fact that this site resulted in having both high development and conservation cost proxies. This result emphasizes the importance of working together to come up with mutually agreeable solutions. Conversely, trade-offs in other locations were evident where high ROI values for conservation and low ROI values for wind development were modeled in some areas (e.g., Location b, Fig. 3), and the inverse in other areas (e.g., Location c, Fig. 3). The northern New York examples also emphasize that although it may seem the numerator and denominator are simply flipped in the two ROI analyses, the results are not the inverse of each other. Different inputs to the probability of success terms and different groupings and weighting schemes incorporated into the biodiversity priorities layer act together to alter the final model output. In this way results from both models are more instructive than results from either model taken alone, suggesting there is sufficient value in going through the exercise from each perspective. The ability to modify the inputs into the ROI model using different weighting schemes provides the flexibility to make this a tool applicable for a wide variety of contexts and organizations. This does not, however, imply that any answer can be derived from this approach simply to suit the needs of the tool user. Responsibility falls on the model developers to clearly state what inputs were used and how they were weighted, placing the model output within the context of the input priorities. Sensitivity analysis of modifications of the weighting schemes indicate the relative changes in ROI scores will most often remain consistent from place to place. The generality of this finding emphasizes suitable locations for both wind development and biodiversity conservation will remain so no matter how weightings are applied. These analyses suggest that ROI-identified priority areas can help decision-makers and energy resource planners meet stated energy goals, as in our case study which demonstrates that considerably more suitable lands exist for wind development to support the New York State's energy policies (i.e., the Clean Energy Standard) and this development can be accomplished while minimizing impacts on environmental conservation priorities. At the same time, we find high potential for continued habitat conservation while simultaneously reducing impacts of fossil fuel emissions on biodiversity. In an ideal world, the unrestricted benefit surfaces depicted in Figs. 1A and 3A would be equivalent to wind development and biodiversity conservation priorities, respectively. Where these priorities intersect with contradictory land-use objectives, conflict arises in the land-use planning process. One way to move forward is to incorporate the priorities of each sector as early in the decision-making process as possible. ROI provides a way to bring potentially conflicting interests together in a transparent dialogue that is more informed, more productive, and more likely to succeed. Acknowledgments Thank you to K. Perkins for developing the bat models and to C. Herzog of NYSDEC for supplying bat survey information. A. Chaloux, K. Perkins, J. Corser, E. White, R. Ring, and S. Young reviewed species locations for other distribution models. R. Rohrbaugh, A. Farnsworth, F. La Sorte, and D. Fink developed the migratory bird models. B. Stratton, K. France, R. Shirer, L. Wright, P. McGlew, E. Spencer, and G. Edinger helped

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