Subject: Greater Sage-Grouse Habitat Quantification Tool Scientific Methods document for the Upper Green River Conservation Exchange

Transcription

1 May 23, 2014 Mark Sattelberg Field Supervisor Wyoming Ecological Services Field Office U.S. Fish and Wildlife Service 5353 Yellowstone Road, Suite 308A Cheyenne, WY Subject: Greater Sage-Grouse Habitat Quantification Tool Scientific Methods document for the Upper Green River Conservation Exchange Dear Mark, Please find attached the Greater Sage-Grouse Habitat Quantification Tool (HQT) Scientific Methods document for the Upper Green River Conservation Exchange (Exchange). The HQT is a working draft with outstanding items for development noted within the document. We are requesting initial review by the USFWS and the conservation bank review team. The HQT has also been sent to other experts for simultaneous review. The HQT describes the scientific methods for quantifying impacts (debits) and benefits (credits) to greater sage-grouse generated by participants in the Exchange. The HQT uses a set of metrics, applied at multiple spatial scales, to evaluate vegetation and environmental conditions related to greater sagegrouse habitat quality and quantity. The HQT enables the Exchange to create incentives to generate credits on the most beneficial locations for greater sage-grouse and to minimize impacts to existing high quality habitat. The HQT allows for a transparent accounting of the net benefit to greater sage-grouse provided by the Exchange. The HQT has been developed for broad use in Colorado and Wyoming with input from an independent science team (Science Team), and was field-tested in Sublette County in summer The Science Team includes the following scientists: Dave Anderson, Colorado Natural Heritage Program; Tony Apa, Colorado Parks & Wildlife; Danielle Bilyeu, Colorado Parks & Wildlife; Matt Holloran, Wyoming Wildlife Consultants; and Amy Pocewicz, The Nature Conservancy. Dr. Jeff Beck of University of Wyoming has also reviewed an early version of the HQT. The tool is based on a widely accepted framework for assessing greater sage-grouse habitat (Stiver et al. 2010) 1. The Science Team believes that the HQT reflects the best available science, and has taken care to substantiate the decisions made in the HQT with peer-reviewed literature. Where the science was not available or was inconclusive, best professional judgment was used. This document is still in draft form and the Science Team will continue to review decisions made within the document. The Science Team will carefully consider all comments and questions received from the USFWS, conservation bank review team, and other experts and make improvements to any aspect of the HQT, where appropriate. A number of areas that will require additional discussion within the Science 1 Stiver, S.J., E.T. Rinkes and D.E. Naugle Greater sage-grouse Habitat Assessment Framework. U.S. Bureau of Land Management. Unpublished Report. U.S. Bureau of Land Management, Idaho State Office, Boise, Idaho.

2 Team have already been identified. Those issues are listed in Section 5.0 of the document, and are also highlighted here: The Conservation Value Model is described in Chapter 2, Section 2.2 of the HQT (page 17). The Conservation Value Model will be a landscape-scale prioritization tool for directing habitat conservation and impacts to the most appropriate locations. The model is currently under development by the Science Team for Colorado and should be complete by late June Learning from the Colorado process will be applied to develop a Conservation Value Model for Wyoming, recognizing that the specific targeting approach must be consistent with state priorities, policies, and processes. The 4 th order (or site level) scoring curves, which reflect the potential for a specific vegetation attribute to support greater sage-grouse habitat for a given level of the attribute, will be reviewed to ensure that each reflects conditions specific to Wyoming. Appendix A (page 55) contains the draft scoring curves. The grass height and sagebrush cover scoring curves specifically will be reviewed. The format and scales will be consistent, but the shape of the curve may change. In addition, the scientific literature and expert opinion that was used to develop each curve will be included. The field data collection methods, described in Appendix D, will be revised to include recommendations for timing and frequency of data collection. A User s Guide, as well as a fully functioning calculator, will be created to enable Exchange participants to easily calculate credits and debits for all project sites. Ongoing testing of the HQT will also serve to refine and improve the models in the HQT. A sensitivity-tofindings analysis, which identifies the level of significance associated with each input attribute, has also been completed to identify areas of the model that may be revised. All decisions in the HQT are subject to revision based on new science, comments received, and further discussion amongst the Science Team. While this document is still in draft format, it is near completion (80% complete) and fully reflects the conceptual and scientific basis of the final HQT. As mentioned previously, we are currently seeking broader input on the HQT and expect to incorporate changes when appropriate as we finalize the documents. Thank you for your review. We look forward to engaging with USFWS and the banking review team on finalization of the HQT. Sincerely, Subgroup of the Upper Green River Conservation Exchange on behalf of: Sublette County Conservation District University of Wyoming Wyoming Chapter of the Nature Conservancy Environmental Defense Fund

3 Greater Sage-Grouse Habitat Quantification Tool: A Multi-Scaled Approach for Assessing Impacts and Benefits to Greater Sage-Grouse Habitat Scientific Methods Document, Version 1 Upper Green River Conservation Exchange

4 Acknowledgements This document describes the scientific approach for quantifying impacts and benefits to greater sagegrouse habitat for use in the Upper Green River Conservation Exchange. It includes a description and definition of the attributes measured and scored at multiple scales, methods of measurement for each attribute, and supporting documentation (e.g., peer-reviewed literature, gray literature, expert opinion) illustrating why those specific attributes and methods were chosen. This work is the product of EcoMetrix Solutions Group and Environmental Defense Fund (EDF). It would not have been possible without the input and guidance from a group of highly qualified scientists and technical experts who worked with us to develop the contents of this tool. Science Team Dave Anderson, Colorado Natural Heritage Program Tony Apa, Colorado Parks & Wildlife Danielle Bilyeu, Colorado Parks & Wildlife Matt Holloran, Wyoming Wildlife Consultants Amy Pocewicz, The Nature Conservancy, Wyoming Chapter Photo Credits: Greater sage-grouse image used under Creative Commons from Disnox Other images from Sublette County Conservation District 2

5 Contents Executive Summary Overview of the Habitat Quantification Tool Habitat Quality and Species Performance Framework for Quantifying Habitat Functionality Components of the HQT HQT Development Process Habitat Quantification Methods and Attributes st Order nd Order rd Order th Order Scoring Approach Description of Scoring Approach Map Units Project Examples Pre-Project Condition Credit Project Debit Project Current Gaps in the HQT References Appendix A. Scoring Curves and Tables Appendix B. Forb Species List Appendix C. Field Datasheet Appendix D. Field Data Collection Methods Materials Needed Methods Appendix E. Definitions of Anthropogenic Structures Appendix F. Monitoring and Adaptive Management Tool Evaluation

6 Adaptive Management Appendix G. HQT Development and Review Internal Development and Review External Review Adaptive Management

7 Executive Summary The Greater Sage-Grouse Habitat Quantification Tool (HQT) is the method for quantifying impacts ( debits ) or benefits ( credits ) to greater sage-grouse (Centrocercus urophasianus; hereafter GRSG) habitat characteristics generated by participants in the Upper Green River Conservation Exchange (Exchange). The HQT has been specifically designed for use in the Exchange; however it has broad applicability for use in any GRSG mitigation and conservation effort. It is intended to provide an effective means for targeting credits and debits to the most beneficial locations for GRSG, and tracking the contribution of the Exchange to GRSG habitat and population goals. This is the Scientific Methods Document for the HQT and the contents describe, explain and operationalize the scientific approach to quantifying credits and debits. The methods described in the HQT quantify the quality of GRSG habitat. The methods can determine site conditions relative to GRSG habitat requirements and changes to habitat from activities that generate credits and debits. This document includes a description and definition of the attributes measured, methods of measurement for each attribute, and supporting documentation (e.g., peer-reviewed literature, gray literature, expert opinion) illustrating why those specific attributes and methods were chosen. This document is a draft, and as such, some gaps remain in the development of this quantification approach, as identified in the text. This HQT will undergo further scientific review (described herein) and is subject to revision, where necessary. Framework for Quantifying Habitat Function This document describes the fundamental structure of the HQT, how its components relate to each other, and the approach used for measuring impacts and benefits to breeding, summer, and winter habitat. The HQT uses a set of measurements and methods applied at multiple spatial scales, to evaluate vegetation, anthropogenic, and environmental conditions related to GRSG habitat quality and quantity, over space and time. There are four scales of application that are related to GRSG habitat evaluation (Figure 1): 1 st Order the occupied range for the species in Wyoming; 2 nd Order habitats that have been identified as important for maintaining the species; 3 rd Order habitat surrounding a proposed project site (local scale); 4 th Order habitat conditions at the site of proposed activities (site scale). 5

8 Figure 1. Multiple spatial scales within the HQT 6

9 Habitat Quantification Attributes A summary of the area assessed and the specific attributes measured by the HQT at each scale is provided in Table 1. Table 1. HQT Area Assessed and Attributes Measured, by Scale Scale Area Assessed Attributes Measured or Delineated 1 st Order The occupied range for the species in Wyoming 2 nd Order Habitat important for maintaining the species 3 rd Order 4 mile (6 km) buffer around the perimeter of the site Statewide population conservation goals Upper Green River watershed (expanded or multiple service areas could be used for broader applicability across the state of Wyoming) Density of anthropogenic features through Landscape Disturbance Index Upland tree cover Distance to known lek, for breeding habitat 4 th Order Delineated acreage of development or conservation site Breeding habitat: sagebrush height, cover shape; grass height, cover; forb cover, number of species, species richness Summer habitat: grass height, cover; forb cover, number of species, species richness Winter habitat: sagebrush height, cover Modified by distance to anthropogenic features, exotic annual grass, climatic condition Scoring Approach Six steps are used to score either a debit or a credit project: 1) conduct pre-site visit desktop analysis; 2) conduct site visit; 3) calculate 4 th order scores; 4) apply 3 rd order modifications; 5) calculate total seasonal habitat scores for the site; and 6) apply the mitigation ratio (as described in the Exchange Operations Manual) to determine the number of credits that are necessary to offset the debit (Figure 2). Figure 2. Scoring Steps in the HQT 7

10 Note that the 1 st order assessment does not impact scoring and instead just defines the geographical scope for tracking the benefits of the Exchange. 4 th Order Quantification Approach A conceptual model of the life history requirements of the GRSG was developed to illustrate the conditions being measured and the role they play in providing suitable breeding, summer and winter habitat (Figure 3). These attributes determine scores for a site and the magnitude of habitat change resulting from a debit or a credit project. Habitat condition is expressed in functional acres, which are units of habitat quality ( function ) and quantity ( acres ) relative to optimal conditions. Figure 3. Conceptual Model of GRSG Life History Requirements 8

11 3 rd Order Quantification Approach The functional acre debit or credit score calculated for the 4 th order is adjusted by 3 rd order attributes to account for the indirect effects that the change in site condition is estimated to have on the surrounding landscape s ability to function for the GRSG, as well as the indirect effects of the surrounding landscape on the condition of the site (Figure 4). Adjustments made in relation to the surrounding landscape on the site can increase or decrease the site score. 2 nd Order Quantification Approach Figure 4. 3 rd Order Measurements Capture the Influence of the Surrounding Landscape on the Site, and the Influence of the Site on the Surrounding Landscape The adjusted functional acre score the combination of the site score with the surrounding landscape adjustment determines the total credit or debit amount for the project. The 2 nd order attributes are focused on targeting credits and debits on the landscape based on priority areas within the State and the limited availability of specific seasonal habitat types for each population. The 2 nd order attributes are currently incorporated into the quantification of credits and debits through the Mitigation Ratio defined in the Exchange Operations Manual. Components of the HQT There are three components of the HQT: 1 HQT Scientific Methods document (this document) 2 HQT Calculator (spreadsheet) 3 HQT User s Guide 9

12 1.0 Overview of the Habitat Quantification Tool The Greater Sage-Grouse Habitat Quantification Tool (HQT) is a multi-scaled approach for assessing vegetation conditions, habitat and conservation outcomes for Greater Sage-grouse (Centrocercus urophasianus; hereafter GRSG). The HQT uses a set of measurements and methods, applied at multiple spatial scales, to evaluate vegetation and environmental conditions related to GRSG habitat quality and quantity, over space and time. The purpose of the HQT is to quantify impacts ( debits ) or benefits ( credits ) to GRSG habitat characteristics through debit and credit projects. The HQT has been specifically designed for use in the Upper Green River Conservation Exchange (Exchange), but with adaptation for specific environmental variability, it has broad applicability for use in any GRSG mitigation and conservation effort. For example, with adaptation the HQT could be used in conjunction with other banking systems or with conservation plans, providing a method for consistent quantification across the state of Wyoming or the range of the GRSG. The methods described in the HQT quantify the quality of GRSG habitat. The methods can determine site conditions relative to GRSG habitat requirements and changes to habitat from activities that generate credits and debits. Changes to habitat quality include the indirect effects those changes have on the ability of the surrounding area to function for the species. Conditions that support each seasonal habitat type for GRSG are measured, including breeding habitat, summer habitat, and winter habitat. Accordingly, the HQT provides scores for each type of seasonal habitat. Habitat condition is expressed in functional acres, which are units of habitat quality ( function ) and quantity ( acres ) relative to optimal conditions. The Exchange Operations Manual (Exchange Manual) defines how these scores are used by the Exchange. To quantify the quality of GRSG habitat, first the pre-project conditions at the site are measured to determine the current ecological performance of the site. The number of functional acres is adjusted to account for the indirect effects of the local area surrounding the site, which can decrease or increase the site score. Next the projected (not actual) post-project condition is evaluated to determine the extent to which the site s ability to support the species is projected to change as a result of the project. The postproject condition is the basis for the credit/debit estimate for the proposed project. Once the project is underway, the actual conditions are verified using the HQT and credits are released according to the actual credit/debit amount and the credit release schedule for the project, as defined in the Exchange Manual. 10

13 1.1 Habitat Quality and Species Performance Habitat represents a particular combination of resources (e.g. food, shelter, and water) and environmental conditions that support a wildlife population s vital rates i.e. survival and reproduction (Morrison et al. 2006). Habitat can vary in quality and therefore its ability to support a population s vital rates over time. Simply put, high quality habitat is more likely to sustain resilient populations than poor quality habitat. Poor habitat quality may lead to low survival and reproduction and eventual extirpation of a population. Marginal habitat may support some amount of occupancy by a species, but may still result in low survival and/or reproduction, which will likely lead to population declines without high levels of immigration. Vegetation is a key component of habitat for many species, including the GRSG, providing shelter, nesting cover, and food resources. Species may range from generalists to specialists in their habitat requirements. The more specialized a species, the greater attention must be paid to managing for very specific features of their habitat, especially vegetation composition and structure. Vegetation composition and structure makes a difference in quantity and quality of habitat for GRSG, and therefore this quantification approach will consider specific vegetation features that are known to be particularly important to GRSG when evaluating habitat quality. GRSG are sagebrush obligates that use a variety of shrub-grassland habitats throughout the life-cycle. The GRSG range has been reduced by 56% since European settlement primarily due to sagebrush habitat loss and fragmentation (Hagen et al. 2007, USFWS 2013). Although individuals may use grasslands or a cultivated habitat type for some part of the annual cycle, it is in combination with native habitats (Schroeder et al. 1999). Different vegetative structure and composition is required for different phases of the breeding cycle. Lek sites are typically sparsely vegetated, while nests are typically placed in thick vegetative cover, with diverse vegetation cover of grasses and shrubs providing maximum vertical and horizontal concealment from predators. After hatching, broods can be found in habitat mosaics including sagebrush, riparian meadows, greasewood bottoms, and croplands, with the common feature being richness of forbs and insects (Schroeder et al. 1999). Sagebrush is important to the species in the winter for cover and forage, and varying heights of sagebrush ensure its availability in different snow depths. Shrub canopy and grass cover are critical for reproductive success, as they provide overhead and side concealment of GRSG nests and young from predators (Hagen et al. 2007, USFWS 2013 and references therein). The most productive nesting areas are typically sagebrush with native grass and forb understory, with vertical and horizontal structure that supports an insect prey base, herbaceous forage for pre-laying and nesting hens, and cover for incubating hens (USFWS 2013 and references therein). GRSG select nest sites with greater sagebrush cover and grass height than random, and for brood rearing, they select sites with less sagebrush cover, taller grasses, greater forb and grass cover than random (Hagen et al. 2007). Breeding GRSG require 15-25% sagebrush cover, > 10% diverse forb cover, > 15% grass canopy 11

14 cover and an herbaceous height of at least 18 cm (Connelly et al. 2000). If sagebrush is tall (>75 cm), then taller herbaceous cover may be required (Hagen et al. 2007, Connelly et al. 2000). GRSG exhibit strong site fidelity to seasonal habitats for breeding, nesting, brood rearing, and wintering (Connelly et al. 2004, 2011a); adults rarely switch from these habitats once they ve selected them (Schroeder et al. 1999). GRSG exhibit limited re-nesting and the average clutch size is 7 (Connelly et al. 2004). Scale As with many ecological processes, habitat selection occurs at multiple spatial scales, with individuals choosing to settle in a location by keying in to different features at different scales (Wiens 1989, Morrison et al. 2006). This applies to vegetation in particular, as birds may perceive physical vegetation structure first over a relatively large, landscape scale, then settle across the landscape according to more fine scale vegetation composition and other factors, such as competitors. While it is logical to conclude that interactions occur among ecological phenomena at different scales for a species, it is a perpetual challenge to precisely define those linkages. Temporal (time) scales also vary among ecological processes and may not be linear (Wiens 1989). The time required for a vegetation community to respond to management practices and for a bird population s vital rates to reflect such changes will vary by ecosystem, geography, area, climate, land use and species. For GRSG, time lags of two to ten years have been observed for population response to infrastructure development (Holloran 2005; Harju et al. 2010; Walker et al. 2007). Despite the uncertainty that will inevitably arise around this topic, temporal scales must be taken into consideration when establishing a mitigation project, and as spatial scales of a project or evaluation area increase, so should temporal scales. Temporal scale for sagebrush projects deserves especially close consideration given that recovery of sagebrush is an especially difficult and slow process due to abiotic variation, short-lived seedbanks, and long generation time of sagebrush; where soils and vegetation are highly disturbed, sagebrush restoration can be challenging if not impossible (USFWS 2013 and references therein). 1.2 Framework for Quantifying Habitat Functionality Species habitat occupancy and population viability respond to conditions and processes at multiple scales (Hilden 1965, Johnson 1980, Weins 1987, Orians and Wittenberger 1991, Morrison et al. 2006, Fuhlendorf and Smeins 1996, Fuhlendorf et al. 2002). The HQT delineates and quantifies the relevant habitat selection criteria corresponding to GRSG survival and reproduction at several appropriate spatial scales. Addressing the multiple spatial scales (Johnson 1980) relevant to a species habitat use and 12

15 performance is essential for effective and efficient conservation and management. The HQT clearly defines the measurement methods at each scale and the interrelationships between the scales. The HQT measures and delineates habitat selection criteria using the following four spatial scales or orders, which are borrowed from Johnson (1980): 1 st order: the occupied range for the species in Wyoming; 2 nd order: habitats that have been identified as important for maintaining the species; 3th order: habitat surrounding a proposed project site (local scale) 4 th order: habitat conditions at the site of proposed activities (site scale). These orders are illustrated in Figure 5. 13

16 Figure 5. Spatial Scales Addressed in the HQT, borrowed from Johnson (1980) The use of multiple scales of measurement enables the HQT to accomplish three essential tasks to program management: 1. Measure impacts (debits) and benefits (credits) for transactions. This is a measurement of the functionality at the 4 th order (site) and how it affects, and is affected by, the 3 rd order (local context). This measure is the basis for calculating debit and credit amounts as defined in the Exchange Manual. 14

17 2. Ensure an effective strategy for targeting transactions to the most beneficial locations for the species. This is a measurement of the mitigation needs and opportunities within respective estimated areas of high levels of use at the landscape scale (2 nd order). This measure is generated using the mitigation ratio defined within the Exchange Manual. 3. Track the contribution of the Exchange to species habitat and population conservation goals in Wyoming over time. This measures the overall performance of the Exchange by evaluating the program s contribution to population conservation goals in Wyoming (1 st order). This measure will be used to adaptively manage Exchange policies and protocols over time. 1.3 Components of the HQT There are three components of the HQT: 1. This HQT Scientific Methods document includes a description and definition of the attributes measured and scored at each of the four scales, methods of measurement for each attribute, and supporting documentation (e.g., peer-reviewed literature, gray literature, expert opinion) illustrating why those specific attributes and methods are used. It also includes the data collection methods (Appendix D) and describes the monitoring and adaptive management process for monitoring and evaluating the accuracy, effectiveness, and efficiency of the HQT and subsequent adaptation of the HQT over time (Appendix F). 2. The HQT Calculator is a Microsoft Excel-based spreadsheet that performs the calculation using field data and the information contained in the HQT Scientific Methods document. It also includes instructions for how to use the Calculator. 3. The HQT User s Guide is a description for how to apply the HQT in the field. The Guide will be developed once the Methods document and Calculator have been finalized. 1.4 HQT Development Process The HQT is based on a well-established and academically-supported framework, which is described above. For the first release of the HQT, Environmental Defense Fund (EDF) hired EcoMetrix Solutions Group to define the important habitat attributes needed to measure habitat functionality for GRSG and identified the methods to measure those attributes. EDF and consultants worked with a group of local biologists and ecologists (the Science Team ) to vet the scoring curves for each attribute to ensure they reflect the best available local and range-wide science. Appendix G describes the overall process for the continued development of the HQT. 15

18 2.0 Habitat Quantification Methods and Attributes The use of multiple spatial scales results in an ecologically comprehensive approach to broadscale siting of anthropogenic features and conservation efforts (2 nd order and 3 rd order) in conjunction with site-based assessments of local environmental suitability conditions (3 rd and 4 th order). The Exchange uses the information provided at the respective scales through either a top-down (1 st order to 4 th order) or a bottom-up (4 th order to 1 st order) manner. For example, using it in a top-down manner provides for conservation planning and targeting; applying the information Figure 6. Top-Down and Bottom-Up Application of Scales in a bottom-up manner allows for understanding cumulative benefits and impacts of landscape integrity over time (Figure 6) st Order Estimated Occupied Range in Wyoming The 1 st order is the current estimated occupied range (EOR) of GRSG in Wyoming. The species distribution is thought to have varied substantially over the species history, and GRSG currently occupy 56% of their potential pre-settlement distribution (Schroeder et al. 2004). The reduction in distribution in North America appears to be a consequence of altered sagebrush habitat quality and quantity (Schroeder et al. 2004). Documented changes to the EOR will be tracked and incorporated into the HQT over time through the adaptive management process described in the Exchange Manual. An important objective at this scale is to estimate the contribution of changed habitat conditions resulting from site level management actions (4 th and 3 rd orders) to regional or statewide habitat and population conservation goals (2 nd and 1 st orders). The ultimate objective of the Exchange is to help contribute to 16

19 conservation of GRSG by providing net habitat benefits. However, these habitat benefits must ultimately lead to larger and more secure GRSG populations. Therefore, the Exchange must have a means of measuring cumulative habitat impacts and benefits, and relating the net contribution of habitat benefits achieved through the Exchange to populations. To make this link, an estimate of population impacts from activities at the 4 th and 3 rd orders is needed. Unfortunately it is not currently possible to make this link directly through published literature and thus site level management actions cannot be quantified for the number of GRSG produced or eliminated. However, as long as debits are offset by credits, and as credits accumulate beyond debits, the Exchange will have contributed to increases in high quality habitat that is more likely to sustain resilient populations over time nd Order Habitat condition surrounding a project site may affect GRSG seasonal habitat use, dispersal, local persistence, and overall population trend. In order for the Exchange to maximize net benefit to the species in high value areas, debits should be guided to those areas that will have the least impact to the species, and credits should be prioritized to areas that would have the largest benefit for species conservation. For example, restoring habitat (e.g. by removing cheatgrass) in a higher quality landscape will have more benefit than if that same action were to occur in a lower quality landscape. Strategies to target conservation actions for GRSG were developed independently by the state of Wyoming. The state has plans in place that identify important areas for GRSG populations that can be used to identify high value areas and to define service areas (see Section 2.2.2) for the Exchange Targeting Projects to the Appropriate Areas To accomplish this, an effective targeting mechanism is necessary. Mitigation ratios are one way that the Exchange can provide the incentive to avoid impacts in high quality areas and motivate credit projects in high quality areas. The HQT does not determine mitigation ratios. Instead, the Conservation Value Model, described here, can inform mitigation ratio decisions. In other words, the Conservation Value Model provides information on the value of a given location based on broader scale conditions. The Model considers two primary factors (Figure 7): 1) Habitat Priority Habitat priority is a combination of an index of landscape disturbance, which measures the degree of anthropogenic disturbance in the landscape, and GRSG habitat suitability, which distinguishes between seasonal habitats. [This assumes that Wyoming has this data available.] 17

20 2) Landscape Permeability Landscape permeability combines ecological data on the probability of movement between seasonal habitat types by an individual bird, with other data on topography, disturbance, and other barriers to movement. Figure 7. Factors Used to Determine Landscape Conservation Value The mitigation ratio provides appropriate incentives and disincentives for both credit and debit projects. These broad scale targeting strategies are applied at the 2 nd order and described in greater detail below. Landscape Disturbance Index (LDI) [The GIS layers and the distance decay curves that go into the LDI will be made available in an Appendix in a future version of the HQT.] The Landscape Disturbance Index (LDI) accounts for the direct and indirect effects of anthropogenic disturbance on habitat value. The LDI is based on mapped anthropogenic disturbances and peer reviewed data on the sensitivities of GRSG to those disturbances. The LDI is generated through spatial analysis of mapped anthropogenic activities that have a known or estimated impact on the quality of GRSG habitats. In Wyoming, base data layers that are available to be used to develop a LDI specific to GRSG include roads, housing development, surface mines, oil and gas wells, agriculture, pipelines, transmission lines, and renewable energy development. [Need to confirm these base data layers.] These impacts are combined within the LDI to portray the total human footprint on the landscape the direct impacts. In addition, the effects of anthropogenic changes to the landscape often cause indirect impacts, extending some distance into the surrounding environment beyond the actual footprint of the disturbance. The effect generally decreases, or decays, with increasing distance. 18

21 Hence, distance-decay functions for each type of anthropogenic disturbance are included in the LDI to capture the full scope of the impact of disturbance on GRSG. 1 Habitat Suitability Research suggests that GRSG generally move between nesting, summer and winter ranges as resource requirements differ during these seasons (Fedy et al. 2012). This means GRSG are typically found within a landscape context that includes the seasonal habitats within a distance that GRSG are capable of moving. The location and juxtaposition of seasonal habitat can be assessed through predictions of GRSG occurrence as it varies throughout potential suitable nesting, summer, and winter habitat in relation to habitat characteristics. Rice et al. (2013) used Colorado Parks & Wildlife datasets from 11 radio-telemetry studies completed over 12 years across northwestern Colorado to predict GRSG location counts by vegetation type during breeding and summer seasons. The resulting seasonal distribution maps are a useful method for identifying large-scale high use areas throughout the annual lifecycle of GRSG in Colorado. A similar dataset for studies conducted in Wyoming could be used to identify high use areas in Wyoming. Habitat Priority The seasonal distribution maps applied in combination with the LDI results provide a valuation of a given site as potential habitat for GRSG. This is a relatively straight-forward spatial analysis in which the habitat probability model and LDI values are weighted and combined to generate a composite spatial layer of habitat suitability that incorporates anthropogenic impacts. The resulting Habitat Priority factors provide an accurate estimation of the extent, juxtaposition, and quality of potential GRSG habitat. Landscape Permeability Landscape permeability is a measure of how easy it is for GRSG to move between suitable habitat patches. Many factors may affect the distances individual GRSG move between key life stages to access required habitat. For example, Fedy et al. (2013) found that landscape composition topography, slope, physical features like rivers and streams was one variable that affected variation in inter-seasonal movement distances among study sites that were adjacent to each other and shared similar geography. Information about the likelihood of GRSG movement across particular landscape features is combined 1 For factors such as noise, human activity, and various types of management, we have data for GRSG that can be used to develop distance-decay functions for mappable anthropogenic disturbances that are specific to their known impacts on habitat quality. The Science Team is currently developing response curves based on these data that can be used as the starting point for developing the distance-decay functions in the LDI. 19

22 with spatial representation of such features or barriers to movement to produce a model where each cell has an associated degree of difficulty for GRSG movement. Inverting this permeability surface into a cost surface allows the calculation of a cost-distance for any particular path between suitable patches. This cost can then be incorporated into the conservation value score of a particular habitat area. Combining Habitat Priority with Landscape Permeability thus provides a valuation for high quality habitat from many perspectives, including anthropogenic disturbance, habitat, and landscape composition. Determining the specific methodology for how these factors will be combined is the primary task in the development of the Conservation Value model. The result is that for any given location within a landscape there will be a Conservation Value score. The Conservation Value score is a reflection of the quality of the landscape for GRSG. This score can then be used to inform mitigation ratios that are meant to provide incentives to avoid impacts in high quality sites and to target benefits to high quality landscapes Service Area A service area is the mapped geographic region where credits and debits are tracked, exchanged and reported. Credits and debits must occur within the same service area. The Upper Green River Conservation Exchange s service area is shown in Figure 8. Expanded or multiple service areas could be used for broader applicability across the state of Wyoming. Figure 8. Service Area in Upper Green River 20

23 2.3 3 rd Order The purpose of the 3 rd order measurement is to understand how the site s habitat value is affected by its local surroundings, and is intended to evaluate conditions that may affect GRSG performance in an area more localized in the service area to provide a more comprehensive understanding of the value of that site for GRSG. Research suggests that in terms of distance effects, as measured by the number of males attending leks, impacts are discernable out to 6 km (Holloran 2005, Walker et al. 2007, Johnson et al. 2011). In other words, infrastructure located beyond the 6km distance will not have a direct effect on the project area. Accordingly, the 3 rd order is defined as a 6 km (4 mile) buffer including and around the impact or benefit site. Surrounding conditions related to GRSG performance include the extent of suitable seasonal habitat, developed land cover, and other features. For example, habitat occupancy probabilities decrease as the amount of sagebrush within 18 km of a location decreases, the probability of an active lek decreases as the linear distance of highways within 5 km increases, and nesting habitat suitability decreases in habitats within 1 km of anthropogenic infrastructure, etc. (Holloran et al. 2010, Wisdom et al. 2011, Knick et al. 2013). This suggests that this scale is ecologically important to GRSG and is therefore useful for evaluating conditions relating to GRSG habitat suitability and quality. The HQT quantifies both the extent to which site changes affect the ability of the surrounding area to fulfill its function for the species, and the extent to which the surrounding area affects the site s ability to perform up to its full potential (Figure 9). In other words, impacts and benefits to neighboring/adjacent areas are accounted for at this order. At the project scale, simple distance-to-features may be a good indicator of avoidance behavior in GRSG (Holloran 2005). However at the 3 rd order (local surroundings), patterns of habitat use relative to density of features may be more informative. The HQT measures the following characteristics surrounding the site: Density of anthropogenic features (LDI) Upland tree cover Distance to a known lek, for breeding habitat The LDI (described in the 2 nd order description in Section 2.2.1) is used at the 2 nd and the 3 rd orders. However, it should be noted that at the 2 nd order, the LDI is applied to target credits to the most beneficial locations, while at the Figure 9. 3 rd Order Measurements Capture the Influence of the Surrounding Landscape on the Site, and the Influence of the Site on the Surrounding Landscape 21

24 3 rd order the LDI is applied to account for indirect effects of disturbance on local habitat values Density of Anthropogenic Features The presence of anthropogenic features surrounding a site can reduce the potential for GRSG use of that site during any season even if the site is otherwise fully functional. For example, male and female GRSG may abandon leks if repeatedly disturbed by vehicle traffic on nearby roads (Lyon and Anderson 2003), or by noise and human activity associated with energy development during the breeding season (Connelly et al. 2004; Holloran 2005; Kaiser 2006). Collisions with power lines and vehicles may increase mortality of GRSG at leks (Connelly et al. 2000). The density of anthropogenic features is determined using the output of the LDI. The relative level of anthropogenic influence (e.g., highly impacted, moderately impacted, lightly impacted) at the 3 rd order can be computed using GIS to generate a composite 3 rd order anthropogenic disturbance score (Table 2). Table 2. Landscape Disturbance Index Values Landscape Disturbance Index Impact Level LDI* Highly Impacted X Moderately Impacted Y Lightly Impacted Z No Impact 0 * The LDI values in this column are illustrative so that the scoring approach can be demonstrated below. Each 4 th order seasonal habitat score (breeding, summer, and winter) is reduced by the LDI value. For example, if the breeding score is 75% and LDI = 0.8*, or lightly impacted, the calculation is: 75% * 0.8 = 60% * The LDI value in this example is illustrative so that the scoring approach can be demonstrated Upland Tree Cover [This section may need to be revised to reflect conditions in WY. GRSG habitat is not compromised by conifer encroachment (with the exception of mountain big sagebrush habitat in the Bighorn Basin and areas in southwest WY closet to UT and CO).] Upland trees (of any height), especially Utah Juniper (Juniperus osteosperma) and piñon (Pinus edulis) can impair habitat quality for GRSG. Encroachment of trees has significant potential to influence processes within sagebrush communities once suitable for GRSG, transforming them to a less suitable state (Patten et al. 2005). Ongoing Colorado Parks & Wildlife research in Colorado has found that GRSG 22

25 use intact sagebrush habitats more frequently than similar areas that have encroaching piñon and juniper trees (Walker 2013). The same study found that mechanical removal of trees increased GRSG use in 2 of 3 cases (Walker 2013). The degree of tree encroachment required to preclude GRSG seems to be very small. A study in Oregon found a linear decline in the probability of lek use with increasing conifer cover, with a 0% probability of use with only 6% conifer cover (Baruch-Mordo et al 2013). Because lek use and habitat use may not be synonymous, and because precise data on how conifer cover affects late brood-rearing and winter use is lacking, the Science Team recommends a somewhat higher threshold for conifer cover. Therefore, when conifer cover reaches 10%, then the breeding, summer, and winter habitat functionality scores are reduced to 0. Scores are decreased according to a linear relationship for conifer cover values less than 10% (Table 3). The scale of avoidance for nesting includes a 1 km radius (Baruch-Mordo et al 2013). Therefore, conifer cover is calculated for the project area plus a 1-km buffer. [Given this scale, it may be more appropriate to include this modifier at the 4 th order rather than at the 3 rd. This is currently under consideration.] Table 3. 3 rd Order Modifications for Conifer Cover Conifer Cover within 1km Percent Adjustment radius of project area 0 1% 100% 1 2% 85% 2 3% 75% 3 4% 65% 4 7% 40% 7 10% 20% >10% 0 A modifier that reduces GRSG habitat functionality according to conifer cover may provide incentive for piñon-juniper removal projects. Removal of the piñon-juniper cover can restore the productivity of shrubs and herbaceous vegetation in the understory, which is important for GRSG. However, not all piñonjuniper stands are suitable for this type of treatment. Miller et al. (2005) found that as sagebrush declines, the ability of the understory to respond positively to tree removal is decreased, with a threshold occurring at approximately 20% juniper cover (Miller et. al 2005). Therefore, a piñon-juniper project should only be eligible for credit generation if the pre-treatment piñon - juniper cover is 20% or lower. This criterion will help protect older piñon-juniper stands, and may aid in preventing unintended negative consequences of tree removal such as expansion of non-natives, such as cheatgrass (Bromus tectorum), which has been reported in several studies of piñon-juniper removal (Owen et al 2009, Ross et al 2012, Huffman et al 2013). 23

26 2.3.3 Distance to Lek, During the Breeding Season This modification only applies to the breeding season habitat score. Research has found that 70-80% of GRSG females nest within 6 km of their capture lek (Holloran and Anderson 2005, Colorado Greater Sage-grouse Steering Committee, 2008). This research suggests that as the distance from a lek increases, the probability of nesting decreases. In other words, if a project site is located within 6 km of a lek, it is located in breeding habitat; and if a project site is not located within 6 km of a lek, then it is not located in viable breeding habitat. Accordingly, the suitability score is based on a decay curve in Figure 10 that decreases the breeding score as the distance from a known lek increases. Figure 10. Distance to Lek Scoring Curve and Table, for Breeding Season Habitat Other Considerations Exotic Annual Grass The Science Team also considered including the presence of exotic annual grass such as cheatgrass within the 3 rd order. Big sagebrush ecosystems of the Intermountain West are especially vulnerable to invasions by cheatgrass as it can dominate the herbaceous understory community (Miller et al. 2011). However, currently there is no adequate mapping for cheatgrass that is sufficiently detailed for use in the Exchange for the 3 rd order. Accordingly, invasive annual grass will be measured at the 4 th order (project site) (see details in section 2.4.2). Exchange participants may have limited influence on conditions outside of their control. However, the significance of the effect of surrounding context conditions on the quality of any given area is an important consideration (Stiver et al. 2010; Connelly et al. 2011). As such, the HQT is designed to balance conditions outside of the control of participants by including these conditions within the scoring system to provide incentives and disincentives to guide credit projects to areas where they will be most effective and debit projects to where they will be least impactful. 24

27 2.4 4 th Order The HQT measures baseline conditions and change at the 4 th order, which is defined as the area of the debit project (i.e., the project footprint) or the area of the credit project (i.e., the area that has been delineated for credit generation within a participant s contract). Measurements include attributes that are indicative of habitat suitability and quality for GRSG, including conditions that support breeding, summer, and winter habitats. The measurements focus on vegetation composition and structure and the presence and extent of anthropogenic features. The concept model below illustrates the conditions being measured and the role they play in providing suitable breeding, summer, and winter habitat (Figure 11). Figure 11. Conceptual Model of GRSG Life History Requirements 25

28 2.4.1 Measuring Vegetation Conditions The 4 th order quantification includes assessment of the extent to which the site is providing conditions suitable for breeding, summer and winter habitat. The height, structure, density, and cover of vegetation are important for providing cover from predators, thermal refugia, food resources and adequate nesting substrate and cover at nest sites and for early brood-rearing (Connelly et al. 2011). The following attributes of site vegetation are measured (Tables 4-6): Table 4. Site Vegetation Attributes for Breeding Habitat Cover / Refugia Foraging Sagebrush height Sagebrush canopy cover Sagebrush shape Perennial grass height Grass canopy cover Forb canopy cover Forb species richness Specific forb species BREEDING GRSG populations are allied closely with sagebrush. This measures the average height of shrubs above 7 inches (18 cm). It is collected along line transects within a Daubenmire frame. This serves as nesting horizontal overstory substrate. This is estimated with line intercept or line-point intercept. This measures the growth-form of sagebrush. A columnar structure (Stiver et al. 2010) provides horizontal cover at the ground level that provides the security and visual obstruction needed by a nesting female. For scoring purposes, if more than 50% of the sagebrush is columnar, the value of the height measure is reduced by 50%. [This will include description of measurement method.] Grass can provide nesting substrate for sage-grouse and have been found to be important in nest site selection. This measures the average height of grass. It is collected along line transects within a Daubenmire frame. Grass canopy provide concealment for nests and chicks and can be critical for reproductive success. Percent canopy cover is estimated with line intercept or line-point intercept. Forbs are an important food resource. This is estimated with line intercept or point-line intercept. This is a measure of the variety of forbs available across the early brood-rearing period. Because the number of species found in an area increases with the size of area sampled (Rosenzweig 1995), sampling for this attribute should be done over a standard-sized area of 10 square meters. Species are tallied using a 1m 2 quadrat. This is a measure of the quality of the forage available during the early brood-rearing period. See Appendix B for list of forb species. 26

29 Table 5. Site Vegetation Attributes for Summer Habitat Cover / Refugia Foraging Grass height Grass canopy cover Forb canopy cover Forb species richness Specific forb species SUMMER Grass can provide nesting substrate for sage-grouse and have been found to be important in nest site selection. This measures the average height of grass. It is collected along line transects within a Daubenmire frame. Grass canopy provide concealment for nests and chicks and can be critical for reproductive success. Percent canopy cover is estimated with line intercept or line-point intercept. Forbs are an important food resource. This is estimated with line intercept or line-point intercept. This is a measure of the variety of forbs available across the early brood-rearing period. Because the number of species found in an area increases with the size of area sampled (Rosenzweig 1995), sampling for this attribute should be done over a standard-sized area of 10 square meters. Species are tallied using a 1m 2 quadrat. This is a measure of the quality of the forage available during the early brood-rearing period. See Appendix B for list of forb species. Table 6. Site Vegetation Attributes for Winter Habitat Cover/Refugia & Foraging Sagebrush height Sagebrush canopy cover WINTER Food availability is the primary variable in winter. This is a measure of the access to the resource in winter conditions. This measures the average height of sagebrush. It is collected along line transects within a Daubenmire frame. This serves as nesting horizontal overstory substrate. This is estimated with line intercept or line-point intercept. A set of scoring curves has been developed with the Science Team for each of the attributes to reflect the potential for supporting GRSG for a given level of the attribute, representing how a site s functional performance changes as the attribute values change. The scoring curves, which are in the form of scoring tables, and the conceptual models they feed into, are the key to the functional performance scoring. More detailed information on how the scoring curves are used to calculate scores is available in Section Modification of Vegetation Conditions At the 4 th order, the presence of anthropogenic structures and the presence of invasive annual grass are two examples of conditions that can limit or reduce habitat use or quality. Table 7 shows the conditions or modifiers that are applied to the 4 th order functional habitat scores: 27

30 Table 7. Modifiers Applied to 4 th Order Functional Habitat Scores Breeding Summer Winter Anthropogenic structures Hydrologic condition Sagebrush cover Distance to sagebrush Exotic annual grass Modification for Anthropogenic Structures Research suggests that the noise and activity associated with anthropogenic structures may cause a disturbance to GRSG, reducing the overall quality of the site for breeding and winter habitat and influencing nesting habitat selection of females breeding on leks influenced by that activity (Manier et al. 2013). Several authors have reported a distance-effect associated with the infrastructure of energy fields whereby GRSG are negatively influenced to a greater extent near infrastructure with the response diminishing as distances from infrastructure increase (Manier et al. 2013). Additionally, the distanceeffect of infrastructure with higher levels of human activity may be larger than that of infrastructure with lower levels of activity. For example: Walker et al. (2007) found a strong negative effect of infrastructure within 0.8 and 3.2 km of leks on lek persistence, but impacts to lek persistent were apparent to 6.4 km; Holloran (2005) reported that impacts of development to the number of males occupying leks were greatest when infrastructure was located near the lek, but that impacts were discernable to 3 km for lower activity sites (well pads) and 6 km for higher activity sites (drilling rigs); Dzialak et al. (2011) reported that the closer a nest was to a natural gas well that existed or was installed in the previous year, the more likely that nest was to fail; they also documented GRSG during the winter avoiding the infrastructure of a gas field during the day, but not at night suggesting that avoidance was of human activity rather than the infrastructure itself; Remington and Braun (1991) reported that the upgrade of a haul road accessing a coal mine was correlated with a 94% decline in the number of GRSG on leks <2 km from the road over a 5-year period although traffic levels were not measured the potential for increased traffic was inferred from upgraded road surface; Holloran (2005) reported that declines in lek counts on leks within 3 km of roads were positively correlated with increased traffic volumes, and that vehicle activity on roads within 3 km of leks during the time of day GRSG were present on leks influenced the number of males on leks more negatively than leks where roads within 3 km had no vehicle activity during the strutting period; 28

31 Lyon and Anderson (2003) reported that traffic disturbance (1 to 12 vehicles/day) within 3 km of leks during the breeding season reduced nest-initiation rates and increased distances moved from leks during nest site selection of female sage-grouse breeding on those leks. It should be noted sightings of individual GRSG near energy development may be a result of site fidelity or the presence of remnant habitat (Manier et al. 2013), but fitness of such individuals could be compromised and anecdotal sightings of individuals should not be confounded with their ability to contribute to local and regional populations (Holloran 2005). The HQT has two categories of anthropogenic features based their level of disturbance: medium activity disturbance structures and high activity disturbance structures. High activity disturbance structures are those that have consistently high levels of human activity, and medium activity disturbance structures are those that have intermittently high levels of human activity or have consistently low levels of human activity. The anthropogenic structures listed in Table 7 are potential sources of disturbance to GRSG, and are categorized by their level of activity. However, for many of these structures there is very little research on the type and magnitude of disturbance. Given this uncertainty, there are some structures that are placed in an Inconclusive category. For these structures, future research is needed in order to further refine the categorizations applied by the HQT. Definitions for the structures in Table 8 are in Appendix F. Table 8. Anthropogenic Structures Categorized by Type of Activity Anthropogenic Structure Medium High Inconclusive Transmission lines 1 Power lines Non-oil and gas related two lane paved road Non-oil and gas related improved gravel road Interstate highway and four lane paved road Non-wind power related vertical structures 2 Infrastructure associated with oil and gas development: Two lane paved road Improved gravel road Well pad Active drilling site Infrastructure associated with wind energy 3 : Tall structures Access roads Transmission lines Infrastructure associated with in situ uranium Infrastructure associated with oil shale and tar sands production Infrastructure associated with solar energy production Solar array field Access roads Transmission lines Infrastructure associate with mining activity 29

32 1 The literature regarding effects of transmission lines on GRSG suggests a distance effect with some decay (Walker et al. 2007; Ellis, 1985). The Science Team recommends that transmission lines be included in the inconclusive category because the current research is inconsistent with respect to their disturbance effect to GRSG. However, there is stakeholder interest in applying the HQT to projects associated with transmission line development. If transmission line projects become imminent prior to the development of new, more conclusive science, and there is a need to apply the HQT for those projects, then EDF recommends that these structures be placed in the medium category. This recommendation is being made independent of the Science Team. EDF highlights the effects of transmission lines and also wind turbines on GRSG as a knowledge gaps, and notes that when new research becomes available, the placement of transmission lines and wind turbines in the inclusive category should be reconsidered by the Exchange s Science Advisory Committee. The rationale for placement as a medium level disturbance is as follows: The 3 km distance effects of the medium disturbance category approximate the mean of the distance effects shown in the literature. For example, Walker et al. (2007) found that power lines have a negative effect on lek persistence up to 6.4 km while also noting that power line models were weakly supported ; and Ellis (1985) recommends that transmission line routes should avoid leks and other day use areas by at least 1.2 km and preferably 1.5 km if breeding activity is to remain undisturbed. 2 Non-wind power related vertical structures: Research suggests that these structures (primarily communication towers) negatively influence lek counts (Wisdom et al. 2011). However, researchers have not isolated the effects of non-wind power related vertical structures from those of high activity sites such as interstate highways and urban centers. As such, the correlation between the location of these vertical structures and high activity sites such as interstate highways and urban centers is not clear. In any case, the 3 rd order is likely to include the effect of these structures through the LDI. 3 Wind turbines: Research in Wyoming indicates that the risk of nest or brood failure declined with every 1-km increase in distance from the nearest turbine (Le Beau et al. 2014). The percent adjustment to the breeding, summer and winter habitat functionality scores is based on the distance of type of anthropogenic features to a sample point within the project area. Research suggests that the effect on GRSG is greater the closer the anthropogenic structure; as the distance from the structure increases, the effect on GRSG decreases (Manier et al. 2013). However, the literature is inconclusive on a specific magnitude of effect over distance for specific anthropogenic structures. The main conclusion that can be drawn from the research is there is a significant effect near the source, and the effect fades gradually as distance from the source increases. 30

33 Given this distance effect and the varying degrees of influence over distance in the literature, the Science Team developed conservative estimates for the linear rate of decrease for the two categories of disturbance noted above (Figure 12). Figure 12. Decay Curves for Medium and High Activity Disturbance Anthropogenic Structures [The Science Team is considering developing seasonally-specific decay curves for anthropogenic structures. This means there would be a medium and high activity curves for breeding, medium and high activity curves for summer, and medium and high activity curves for winter. These six additional curves would be included in the next version of this document.] Accounting for Temporal Variation in Disturbance Levels The Science Team recognizes the temporal variations in the level of disturbance from some anthropogenic structures. However, there is a wide range of variation associated with the timing of activities among individual companies. In addition, there is no conclusive research that suggests impact to GRSG changes if the level of activity of a structure changes. As such, the Science Team is not able to provide a science-based recommendation for accounting for temporal variation in disturbance levels. Please refer to the description of verification in Chapter 2 of the Exchange Manual for accounting for temporal variation in disturbance levels. 31

34 Accounting for Disturbance to Leks Because leks sites are used traditionally year after year by female GRSG (Wiley 1978, Schroeder et al. 1999, Connelly et al. 2011) and represent selection for optimal breeding and nesting habitat, it is important to protect the area surrounding lek sites from impacts. The Science Team s expert opinion and the research cited above suggest that noise and activity associated with anthropogenic structures negatively influences GRSG on leks. As such, activities that take place near a lek will incur more debits than if those same activities take place in an area with no known leks (Figure 13). Figure 13. Modification for Disturbance to a Lek Modifications for Hydrologic Conditions The wide range of the GRSG results in different vegetation potentials in different regions in Wyoming. This may be due to variation in factors such as topography and soil characteristics. Encouraging the identification of suitable and high quality habitat within each region of the state requires some flexibility in how attributes are scored. For example, vegetation in lower precipitation areas may not attain the same heights as vegetation in wetter areas, even though the former area may otherwise be high quality habitat for GRSG. The HQT addresses this potential for variability by using different scoring curves and table for sites in mesic and arid conditions. The relative scoring structure may evolve over time as local conditions and habitat availability change. As the relative quality of conditions changes over time, it may be useful to reevaluate the standard for these scoring curves given improved understanding or changes in climate. Modifications for Sagebrush Cover GRSG require a minimum amount of sagebrush during the breeding, summer, and winter seasons. Accordingly, the 4th order adjustment is as follows: If sagebrush < 5%, the score is reduced to 0 32

35 Modifications for Distance to Sagebrush for Summer Habitat This modifier only applies to the summer habitat score and highlights the type of meadow system selected by GRSG during the summer season. The interface between the sagebrush and meadow edge produces the most forbs for GRSG, and provides nearby escape cover (Connelly et al. 2000). Based on the expert opinion of the Science Team, during this season GRSG may use habitat that does not have sagebrush present, as long as sagebrush is accessible to them. Meadows, riparian areas, or other moist areas adjacent to sagebrush habitat can provide foraging areas during this season. Given the range of distances presented in the literature across which GRSG will travel between meadows and similar areas to sagebrush cover, the Science Team chose a conservative estimate. Thus as long as sagebrush is located with 300-m of each sample point, it is considered viable summer (late brood-rearing) habitat and there is no effect to the score. If sagebrush is located beyond 300-m of the sample point, the score is reduced to zero. Modifications for Exotic Annual Grass The 4 th order adjustment for exotic annual grass is a multiplier on each of the breeding, summer, and winter scores, as shown in Table 9: Table 9. 4 th Order Modification for Exotic Annual Grass Percent Cover of Exotic Percent Adjustment Annual Grass <5% 100% 5 10% 80% 10 15% 65% 15 20% 50% 20 25% 40% 25 30% 30% 30 35% 20% 35 40% 15% 40 45% 10% 45 50% 5% >50% 0 Triggers A trigger indicates that functions associated with specific habitats will only be scored when appropriate conditions are present. In the concept model, there are two triggers: 1) the presence of facultative species in summer habitat; and 2) slope and aspect in winter habitat. Presence of Facultative Species for Summer Habitat During nesting and early brood-rearing, GRSG use uplands for raising chicks or nesting (Connelly et al. 2011). As the vegetation loses moisture, GRSG move into summer habitat with abundant mesic forbs. GRSG will preferentially select sites that are closer to sagebrush, but seek the areas where moisture 33

36 allows forbs to grow throughout the summer (Connelly et al. 2011). Accordingly, the HQT classifies summer habitat based on the presence of specific species that indicate that the vegetation at the site will remain green over the course of the summer. If there are facultative species present, then the summer score is calculated. If there are no facultative species present, the project site scores 0 for summer habitat. Topography and Aspect for Winter Habitat GRSG generally prefer relatively open sagebrush flats or open rolling sagebrush hills (Connelly et al. 2011, Hupp 1987, Hupp and Braun 1998). However, in winter GRSG inhabit areas with moderate to dense black and low sagebrush and are also found on ridge tops with a south to west aspect (Hupp and Braun 1989, Doherty et al. 2008). Based on the expert opinion of the Science Team, there is no minimum height requirement for black and low sagebrush on ridge tops as there is in open sagebrush flats, and sagebrush height greater than 18cm is an ideal condition during the winter. Accordingly, the 4 th order winter score is based on the topography (percent slope) and aspect of the project site. There are two scoring curves and tables that correspond to the topography where the sample point is taken: one scoring curve and table is used for slope greater than 5%, and a different scoring curve and table is used for slope less than 5%. 34

37 3.0 Scoring Approach 3.1 Description of Scoring Approach This section describes how a credit or debit score is calculated (Figure 14). All figures included here are hypothetical and for illustrative purposes only. As previously described in the overview section of this document, site scores are first calculated (4 th order) and then modified by conditions within the surrounding context (3 rd order). This functional performance output is multiplied by the area of the map unit (see Section 3.2 on map units) to provide a functional acre score. Finally, the net mitigation ratio is applied to the functional acre score to determine the final score (2 nd order), which is the basis for a credit or debit. Each credit or debit project has a breeding, summer, and winter functional acre score. Figure 14. Steps in the scoring calculation Once the field attribute information has been collected on site, the scores for pre-project conditions can be calculated (4 th order). To convert field measurements to functional performance scores, a set of 35

38 scoring curves and scoring tables are referenced within the Calculator spreadsheet. The scoring tables were developed with the Science Team for each attribute to reflect the potential for supporting GRSG habitat for a given level of the attribute, or the percent of potential optimal performance (see Appendix A for scoring curves). In the example below in Figure 15, the attribute is measured at 10% cover. Within the Calculator spreadsheet, the field measurement is referenced or looked up in the scoring table, which corresponds with a percent performance value for that field measurement. In this case, 10% cover corresponds to 0.8 or 80% functional performance. Figure 15. Example of Scoring Table for Converting Field Data to Functional Performance Output The performance scores for all of the attributes are combined in weighted scoring algorithms based on the relationships identified in the concept model (Section describes scoring steps for each seasonal habitat) th Order Calculation Descriptions SCORING FOR BREEDING HABITAT The Breeding score combines the cover/refugia and foraging scores in a weighted additive process. Cover/refugia is a combination of the sagebrush and grass calculations (Figure 16). The foraging score combines species richness, forb cover, and presence of specific forb species calculations. Figure 16. Scoring Algorithms for Calculating Breeding Habitat Score 36

39 Cover/Refugia and Foraging are calculated separately. The scoring step for Foraging is described here (Figure 17) and shows how within the Calculator, the scoring tables are referenced to convert field measurements to percent performance outputs. Figure 17. Scoring Tables Used to Convert Field Measurements to Percent Performance Outputs Foraging = Forb cover + Species richness + Specific forb species Forb cover = 5-7% 0.8 or 80% performance Species richness = 4 species 0.75 or 75% performance Specific forb species = No noxious weeds 1.0 or 100% performance Each attribute contributes equally to Foraging, so they are combined in a weighted average: Foraging = (80% + 75% + 100%) 3 = 85% To calculate the Cover/Refugia score, the same process of referring to the scoring tables to convert field measurements (for sagebrush cover & height and grass cover & height) to percent performance outputs and then combining them in a weighted average is repeated. To calculate the Breeding score, the Cover/Refugia and Foraging scores are combined in a weighted average (as shown in Figure 16 above). In this example, if Cover/Refugia = 55%, then the Calculator combines this with the Foraging in this way: Cover/Refugia = 55% Foraging = 85% Breeding = (55% + 85%) 2 = 70% Apply 4 th Order Modifiers The Calculator applies the 4 th order modifications for sagebrush cover, invasive annual grass, anthropogenic disturbance and hydrologic condition to the preliminary 4 th order breeding score. For hydrologic condition, unique scoring tables were developed for sagebrush, forb, and grass cover and 37

40 height in mesic and arid conditions. The remaining modifiers are applied to the preliminary 4 th order score as illustrated below: Prelim 4 th order score Modifier: Sagebrush cover BREEDING HABITAT Modifier: Annual grass Modifier: Anthropogenic disturbance Modified 4 th Order Score 70% Multiply by 1.0 Multiply by 0.8 Multiply by % * All values in this table are ILLUSTRATIVE only to demonstrate scoring steps. In this example, the modifier values are based on the following: Sagebrush cover: the modifier is based on sagebrush cover at the project site. If sagebrush cover <5%, then the habitat score is reduced to zero. In this example, sagebrush cover > 5%, so there is no impact to the score and the modifier is 1.0. Exotic annual grass: the modifier references the scoring table for cheatgrass. For example, cheatgrass = 10%, which corresponds to 0.8 or 80% performance, as shown below. Percent Cover of Exotic Percent Adjustment Annual Grass <5% 100% 5 10% 80% 10 15% 65% 15 20% 50% 20 25% 40% 25 30% 30% 30 35% 20% 35 40% 15% 40 45% 10% 45 50% 5% >50% 0 Anthropogenic disturbance: this modifier is based on the distance-decay curve developed for the type of human activity. If there are no anthropogenic structures present, there is no effect to the score, so the modifier value is 1.0. SCORING FOR SUMMER HABITAT The Summer score is triggered based on the presence of facultative species. If no facultative species are present, the summer score is 0. If facultative species are present, the summer score combines the cover/refugia and foraging. However, the foraging score is weighted more heavily (70%) than the cover/refugia score (30%) because GRSG are seeking suitable forage when making movements onto summer range (Connelly et al. 2011). Cover/refugia and foraging combine respective attributes also in a weighted additive process (Figure 16). 38

41 Figure 18. Scoring Algorithms for Calculating Summer Habitat Score Apply 4 th Order Modifiers The Calculator applies the 4 th order modifications for distance to sagebrush, exotic annual grass, anthropogenic disturbance and hydrologic condition to the preliminary 4 th order summer score. For hydrologic condition, unique scoring tables were developed for forb cover and grass cover in mesic and arid sites. The remaining modifiers are applied to the preliminary 4 th order score as illustrated below: Prelim 4 th order score Modifier: Distance to Sagebrush SUMMER HABITAT Modifier: Annual grass Modifier: Anthropogenic disturbance Modified 4 th Order Score 65% Multiply by 1 Multiply by 0.8 Multiply by 1 52% * All values in this table are ILLUSTRATIVE only to demonstrate scoring steps. In this example, the modifier values are based on the following: Distance to sagebrush: this modifier is only relevant for summer habitat, and is based on the distance to sagebrush from a sample point within the map unit. For example, if sagebrush is 300- m away it is accessible to GRSG, so there is no effect on the summer. If sagebrush is greater than 300-m from the sample point, the score is reduced to zero. Exotic annual grass: the modifier references the scoring table for cheatgrass. For example, cheatgrass = 10%, which corresponds to 0.8 or 80% performance. Percent Cover of Exotic Annual Grass Percent Adjustment 39

42 <5% 100% 5 10% 80% 10 15% 65% 15 20% 50% 20 25% 40% 25 30% 30% 30 35% 20% 35 40% 15% 40 45% 10% 45 50% 5% >50% 0 Anthropogenic disturbance: this modifier is based on the distance-decay curve developed for the type of human activity. If there are no anthropogenic structures present, there is no effect to the score, so the modifier value is 1.0. SCORING FOR WINTER HABITAT The Winter score is based on the sagebrush calculation of sagebrush height and canopy cover (Figure 17). Figure 19. Scoring Algorithms for Calculating Winter Habitat Score Apply 4 th Order Modifiers The Calculator applies the 4 th order modifications for sagebrush cover and anthropogenic disturbance to the preliminary 4 th order winter score. The modifiers are applied to the preliminary 4 th order score as illustrated below: 40

43 Prelim 4 th order score WINTER HABITAT Modifier: Sagebrush Cover Modifier: Anthropogenic disturbance Modified 4 th Order Score 75% Multiply by 1 Multiply by 1 75% * All values in this table are ILLUSTRATIVE only to demonstrate scoring steps. In this example, the modifier values are based on the following: Sagebrush cover: for winter habitat, if sagebrush < 10%, the score is reduced to zero. For example, sagebrush = 30%, so there is no effect on the late brood-rearing score. Anthropogenic disturbance: this modifier is based on the distance-decay curve developed for the type of human activity. If there are no anthropogenic structures present, there is no effect to the score, so the modifier value is rd Order Calculation Description There are three 3 rd order adjustments that can adjust the amount of credits and debits: 1. The breeding, summer and winter 4 th order scores are each adjusted based on density of anthropogenic features (LDI); 2. The breeding, summer and winter 4 th order scores are each adjusted based on the percent cover of upland trees (conifer cover); and 3. The breeding score is adjusted based on the project site s distance to a known lek. The following table shows hypothetical 4 th order scores: 4 th Order Scores Breeding Summer Winter 56% 52% 75% The 3 rd order modifications are applied to each seasonal habitat score, as shown below: 3 rd Order Modifications Seasonal Habitat 4 th Order Score Modifier: LDI Modifier: conifer cover Modifier: distance to lek MODIFIED 3 rd Order Score Breeding 56% Multiply by Multiply by 0.85 Multiply by % 0.8 Summer 52% Multiply by Multiply by 0.85 N/A 35% 0.8 Winter 75% Multiply by 0.8 Multiply by 0.85 N/A 51% * All values in this table are ILLUSTRATIVE only to demonstrate scoring steps. 41

44 The 3 rd order modifier values are based on the following: Density of anthropogenic structures: LDI = 0.8 (lightly impacted). This is an illustrative example only; the LDI values for disturbance levels are currently under development. Conifer cover modifier: this modifier is based on the percent conifer cover scoring table: Conifer Cover within 1km Percent Adjustment radius of project area 0 1% 100% 1 2% 85% 2 3% 75% 3 4% 65% 4 7% 40% 7 10% 20% >10% 0 After the 3 rd order seasonal habitat scores have been calculated, each score is multiplied by the acreage of the map unit to determine the functional acre score (see Section 3.2 on description of map units): Functional Acre Scores Seasonal Habitat 3 rd Order Score Acres Functional Acre Score Breeding 38% functional acres Summer 35% functional acres Winter 51% functional acres nd Order Calculation Description The Landscape Permeability Model determines the mitigation ratio that is applied to both credit and debit projects. The application of the mitigation ratio is described in detail in the Exchange Manual. Once the Mitigation Ratio is determined, it is applied to the functional acres scores: Credit / Debit Scores Seasonal Habitat Functional Acre Net Mitigation Credits / Debits Score Ratio Breeding 57 functional acres Summer 53 functional acres Winter 77 functional acres * All values in this table are ILLUSTRATIVE only to demonstrate scoring steps. 3.2 Map Units Before attributes are measured, the credit or debit project site is divided into map units. A map unit is a relatively homogeneous area within a project site that is scored individually based on attributes unique to 42

45 that homogeneous area. All data are collected at the map unit level and each map unit is scored individually for each seasonal habitat type, both in terms of percent performance and functional area. Though map units are somewhat open to interpretation, in general a map unit encompasses a relative homogenous area of habitat. Major changes across the landscape, such as change from water to terrestrial, woodland to pasture, and roads to undeveloped areas, are relatively straight-forward to delineate via GIS. However, other distinctions such as changes in topography, changes in vegetation community, slope and aspect, or density of trees or shrubs should also be considered. 43

46 4.0 Project Examples The hypothetical attribute measurements for a single map unit that is 175 acres are shown below. There are no anthropogenic structures in the area. Sagebrush Grass Forbs Height: 114cm Height: 20cm Forb Cover: 7% Canopy Cover: 10% Canopy Cover: 10% Species Richness: 6 Shape: spreading Exotic annual grass: 10% Specific Forb Species: Yes Climate condition: mesic Slope < 5% Aspect: N/A, because slope< 5% Conifer cover: Pre-Project Condition Using the scoring tables to determine the percent performance for the measured attributes above, the Calculator computes the following preliminary 4 th order scores. All values in this example are illustrative only. Breeding habitat: 55% Summer habitat: 42% Winter habitat: 65% The Calculator applies the 4 th order modifications directly, and the description here (Table 10) describes the calculation that takes place within the Calculator to modify the preliminary scores above (hydrologic condition is not shown below because the use of the mesic scoring tables for forb cover and perennial grass cover applies the necessary modifications): Cheatgrass modifier = 0.65, based on the scoring table Sagebrush cover modifier = 1.0, because there is more than 5% sagebrush present Distance to sagebrush modifier = 1.0, because there is sagebrush within 300-m of the sample point (applied to summer habitat score). Anthropogenic disturbance modifier = 1.0, because there are no anthropogenic structures present 44

47 Table th Order Modifications Applied to Preliminary Scores Prelim 4 th order score Breeding 55% Multiply by PRE-PROJECT CONDITION Modifier: Modifier: Modifier: Cheatgrass Sagebrush Distance to cover sagebrush Multiply by Multiply by Multiply by Summer 42% Multiply by Winter 65% N/A Multiply by 1.0 Modifier: Anthropogenic disturbance Modified 4 th Order Score N/A Multiply by % Multiply by % 1.0 N/A Multiply by % The 3 rd order modifiers are applied to the 4 th order (site) scores above to adjust for the context of the surrounding area (Table 11). Density of anthropogenic structures (LDI) modifier = 0.9, because there is little to no human activity or anthropogenic structures in the surrounding area. This is an illustrative example only; the LDI values for disturbance levels are currently under development. Conifer cover modifier = 1.0, because conifer cover is less than 10%. Distance to lek modifier = 1.0, because there are no known leks in the area. Table rd Order Modifications Applied to 4 th Order Scores PRE-PROJECT CONDITION 3 rd Order Modifications Seasonal Habitat 4 th Order Score Modifier: LDI* Modifier: conifer cover Modifier: distance to lek MODIFIED 3 rd Order Score Breeding 36% Multiply by Multiply by 1.0 Multiply by 1 32% 0.9 Summer 27% Multiply by Multiply by 1.0 N/A 24% 0.9 Winter 65% Multiply by 0.9 Multiply by 1.0 N/A 59% *The LDI value in in this table is ILLUSTRATIVE only to demonstrate the scoring steps. These scores are multiplied by the number of acres to determine the functional acre score. PRE-PROJECT CONDITION Pre-Project Functional Acre Scores Seasonal Habitat 3 rd Order Score Acres Functional Acre Score Breeding 32% functional acres Summer 24% functional acres Winter 59% functional acres The three seasonal habitat scores in the table above represent the pre-project condition. The examples that follow illustrate a credit project and a debit project using this pre-project condition as a starting point. 45

48 4.2 Credit Project The landowner plans to carry out the following activities to enhance the existing habitat: Seed perennial grass capable of competing with annual species; also seed native grass species Remove cheatgrass through application of herbicide Manage livestock grazing to protect seeded areas, residual grass areas, and areas around water sources and wet meadows It is expected that these activities will change the forb cover, forb species, and cheatgrass values. The projected post-project condition for preliminary 4 th order (site) scores are: Nesting habitat: 58% Late Brood-Rearing habitat: 52% Winter habitat: 65% Among the 4 rd order modifiers, only the cheatgrass modifier value changes. Based on the cheatgrass removal activity, cheatgrass is projected to decrease from 15% pre-project to 5% post-project. Based on the cheatgrass scoring table, this changes the modifier value from 0.65 to 0.8, as highlighted below. CREDIT PROJECT: PROJECTED POST-PROJECT CONDITION Prelim 4 th order score Modifier: Cheatgrass Modifier: Sagebrush cover Multiply by 1.0 Multiply by Breeding 55% Multiply by 0.8 Summer 42% Multiply by Winter 65% N/A Multiply by 1.0 Modifier: Distance to sagebrush Modifier: Anthropogenic disturbance Modified 4 th Order Score N/A Multiply by % Multiply by Multiply by % 1.0 N/A Multiply by % The 3 rd order modifiers do not change from pre-project to post-project. CREDIT PROJECT: PROJECTED POST-PROJECT CONDITION 3 rd Order Modifications Seasonal Habitat 4 th Order Score Modifier: LDI* Modifier: conifer cover Modifier: distance to lek MODIFIED 3 rd Order Score Breeding 44% Multiply by Multiply by 1.0 Multiply by 1 40% 0.9 Summer 34% Multiply by Multiply by 1.0 N/A 30% 0.9 Winter 65% Multiply by 0.9 Multiply by 1.0 N/A 59% *The LDI value in in this table is ILLUSTRATIVE only to demonstrate the scoring steps. 46

49 Functional acres are calculated for the post-project condition: CREDIT PROJECT: PROJECTED POST-PROJECT CONDITION Projected Post-Project Functional Acre Scores Seasonal Habitat 3 rd Order Score Acres Functional Acre Score Breeding 40% functional acres Summer 30% functional acres Winter 59% functional acres Comparing the pre-project and projected post-project condition: CREDIT PROJECT: PRE- AND PROJECTED POST- COMPARISON Pre-Project and Projected Post-Project Functional Acre Scores Seasonal Habitat Pre-Project Projected Post-Project Difference Breeding 56 functional acres 69 functional acres + 13 functional acres Summer 42 functional acres 53 functional acres + 11 functional acres Winter 102 functional acres 102 functional acres No change 4.3 Debit Project A two-lane access road is planned for the project area: A two-lane access road is considered a medium level disturbance activity (see Table 7 Anthropogenic Structures by Level of Human Activity) The road is located 2-km from the sample point It is expected that these activities will change the 4 th order modifiers for anthropogenic disturbance, and 3 rd order LDI modifier. The projected post-project condition for preliminary 4 th order (site) scores do not change, because vegetation attributes did not change: Nesting habitat: 55% Late Brood-Rearing habitat: 42% Winter habitat: 65% As noted, among the 4 rd order modifiers, only the anthropogenic disturbance modifier may change. Based on the 2-km distance from the sample point to the road, the anthropogenic disturbance modifier changes from pre-project 1.0 value to post-project 0.75 value. [The determination of this modifier value is under development.] 47

50 DEBIT PROJECT: PROJECTED POST-PROJECT CONDITION Prelim 4 th order score Modifier: Cheatgrass Modifier: Sagebrush cover Multiply by 1.0 Multiply by Breeding 55% Multiply by 0.65 Summer 42% Multiply by Winter 65% N/A Multiply by 1.0 Modifier: Distance to sagebrush N/A Multiply by 1.0 N/A Modifier: Anthropogenic disturbance Multiply by 0.75 Multiply by 0.75 Multiply by 0.75 Modified 4 th Order Score 27% 20% 49% Among the 3 rd order modifiers, only the LDI changes in value from pre-project 0.9 to post-project 0.7. DEBIT PROJECT: PROJECTED POST-PROJECT CONDITION 3 rd Order Modifications Seasonal Habitat 4 th Order Score Modifier: LDI* Breeding 27% Multiply by 0.7 Summer 20% Multiply by 0.7 Winter 49% Multiply by 0.7 Modifier: conifer Modifier: MODIFIED 3 rd cover distance to lek Order Score Multiply by 1.0 Multiply by 1 19% Multiply by 1.0 N/A 14% Multiply by 1.0 N/A 34% *The LDI value in in this table is ILLUSTRATIVE only to demonstrate the scoring steps. Functional acres are calculated for the projected post-project condition: DEBIT PROJECT: PROJECTED POST-PROJECT CONDITION Projected Post-Project Functional Acre Scores Seasonal Habitat 3 rd Order Score Acres Functional Acre Score Breeding 19% functional acres Summer 14% functional acres Winter 34% functional acres Comparing the pre-project and post-project condition: DEBIT PROJECT: PRE- AND PROJECTED POST-PROJECT COMPARISON Pre-Project and Projected Post-Project Functional Acre Scores Seasonal Habitat Pre-Project Projected Post-Project Difference Breeding 56 functional acres 33 functional acres -23 functional acres Summer 42 functional acres 25 functional acres -17 functional acres Winter 102 functional acres 60 functional acres -42 functional acres 48

51 5.0 Current Gaps in the HQT This is an initial draft of the HQT. All of the content is subject to change upon further review. The purpose of this section of the document is to describe some of the larger remaining gaps to be filled in subsequent drafts. 2 nd Order: Conservation Value Model; 4 th Order: the scoring curves and tables in appendix A will be re-visited to ensure they reflect conditions in Wyoming, and additional detail with respect to the use of literature and best professional judgment will be included; Field Data Collection Methods (Appendix D): these will be revised to include recommendations for timing and frequency of data collection; User s Guide: description for how to apply the HQT in the field 49

52 6.0 References Baruch-Mordo, S., J.S. Evans, J.P. Severson, D.E. Naugle, J.D. Maestas, J.M. Kiesecker, M.J. Falkowski, C.A. Hagen, and K.P. Reese Saving sage-grouse from the trees: A proactive solution to reducing a key threat to candidate species. Biological Conservation 167: Benson, L.A., C.E. Braun, and W.C. Leininger Sage-grouse response to burning in the big sagebrush type. Proceedings, issues and technology in the management of impacted western wildlife. Thorne Ecological Institute 5: Colorado Greater Sage-grouse Steering Committee Colorado greater sage-grouse conservation plan. Colorado Division of Wildlife, Denver, Colorado, USA. Connelly, J.W., E. T. Rinkes, and C.E. Braun Characteristics of greater sage-grouse habitats: a landscape species at micro- and macroscales. pp in S.T. Knick and J.W. Connelly (editors). Greater sage-grouse: ecology and conservation of a landscape species and its habitats. Studies in Avian Biology (vol. 38). University of California Press, Berkeley, CA. Connelly, J.W and S.T. Knick, M.A. Schroeder and S.J. Stiver Conservation Assessment of Greater Sage-Grouse and Sagebrush Habitat. Western Association of Fish and Wildlife Agencies, Cheyenne, WY. Available at Connelly, J.W., A. D. Apa, R. B. Smith, and K. P. Reese Effects of predation and hunting on adult sage-grouse Centrocercus urophasianus in Idaho. Wildlife Biology 6: Doherty et al Doherty, K.E., D.E. Naugle, B.L. Walker and J.M. Graham Greater sage-grouse winter habitat selection and energy development. Journal of Wildlife Management 72(1): Dzialak, M.R., Olson, C.V., Harju, S.M., Webb, S.L., Mudd, J.P., Winstead, J.B., Hayden-Wing, L.D Identifying and Prioritizing Greater Sage-Grouse Nesting and Brood-Rearing Habitat for Conservation in Human-Modified Landscapes. PLoS ONE 6(10): e doi: /journal.pone Ellis, K.L Behavior of lekking sage-grouse in response to a perched Golden Eagle. Western Birds 15: Fedy, B. C., C. L. Aldridge, K. E. Doherty, M. O'Donnell, J. L. Beck, B. Bedrosian, M. J. Holloran, G. D. Johnson, N. W. Kaczor, C. P. Kirol, C. A. Mandich, D. Marshall, G. McKee, C. Olson, C. C. Swanson, and B. L. Walker Interseasonal movements of greater sage-grouse, migratory 50

53 behavior, and an assessment of the core regions concept in Wyoming. Journal of Wildlife Management 76: Fuhlendorf, Samuel D., Alan J.W. Woodard, David M. Leslie Jr, and John S. Shackford Multi-scale effects of habitat loss and fragmentation on lesser prairie-chicken populations of the US Southern Great Plains.Landscape Ecology 17: Fuhlendorf, S. D. and F. E. Smeins Spatial scale influence on longterm temporal patterns of a semi-arid grassland. Landscape Ecology 11: Hagen, C. A., Connelly, J.W., Schroeder, M.A A meta-analysis of greater sage-grouse Centrocercus urophasianus nesting and brood-rearing habitats. Wildlife Biology 13(Suppl. 1): Harju S.M., Dzialak M.R., Taylor R.C., Hayden-Wing L.D., Winstead J.B. (2010) Thresholds and time lags in effects of energy development on Greater Sage-Grouse Populations. Journal of Wildlife Management 73: Hilden, O Habitat selection in birds: a review. Annales Zoologici Fennici 2: Holloran, M.J., R.C. Kaiser, and W.A. Hubert Yearling greater sage-grouse response to energy development in Wyoming. Journal of Wildlife Management 74: Holloran, M. J Greater sage-grouse (Centrocercus urophasianus) population response to natural gas field development in western Wyoming. Dissertation, University of Wyoming, Laramie, WY. Huffman, D. W., M. T. Stoddard, J. D. Springer, J. E. Crouse, and W. W. Chancellor Understory plant community responses to hazardous fuels reduction treatments in pinyon-juniper woodlands of Arizona, USA. For. Ecol. Manage. 289: Hupp J.W Sage grouse resource exploitation and endogenous reserves in Colorado. Dissertation, Colorado State University, Fort Collins, CO. Hupp, J.W. and C.E. Braun Topographic Distribution of Sage Grouse Foraging in Winter. The Journal of Wildlife Management. 53 (3): Johnson, D.H., M.J. Holloran, J.W. Connelly, S.E. Hanser, C.L. Amundson, and S.T. Knick Influences of of environmental and anthropogenic features on greater sage-grouse populations. Pages in S.T. Knick and J.W. Connelly, editors. Greater sage-grouse: ecology and conservation of a landscape species and its habitats. Studies in Avian Biology 38. University of California Press, Berkeley, CA, USA. 51

54 Johnson, D. H The comparison of usage and availability measurements for evaluating resource preference. Ecology 61: Kaiser R.C Recruitment by Greater Sage-Grouse in Association with Natural Gas Development in Western Wyoming. Masters thesis, Department of Zoology and Physiology, University of Wyoming, Laramie. Knick, S.T., S.E. Hanser, and K.L. Preston Modeling ecological minimum requirements for distribution of greater sage-grouse leks: implications for population connectivity across their western range, U.S.A. Ecology and Evolution 3(6): LeBeau, C.W., J.L. Beck, G.D. Johnson and M.J. Holloran Short-term impacts of wind energy development on greater sage-grouse fitness. Journal of Wildlife Management 78: Lyon, A.G. and S.H. Anderson Potential gas development impacts on sage-grouse nest initiation and movement. Wildlife Society Bulletin 31: D.J. Manier, D.J.A. Wood, Z.H. Bowen, R.M. Donovan, M.J. Holloran, L.M. Juliusson, K.S. Mayne, S.J. Oyler-McCance, F.R. Quamen, D.J. Saher, and A.J. Titolo Summary of science, activities, programs, and policies that influence the rangewide conservation of Greater Sage-Grouse (Centrocercus urophasianus): U.S. Geological Survey Open-File Report , 170 p., Miller, R.F, S.T. Knick, D.A. Pyke, C.W. Meinke, S.E. Hanser, M.J. Wisdom and A.L. Hild Characteristics of sagebrush habitat and limitations to long-term conservation. Greater Sage- Grouse: Ecology and Conservation of a Landscape Species and its Habitats. Studies in Avian Biology, Volume 38 (eds. S.T. Knick and J.W. Connelly), pp , University of California Press, Berkeley, CA Miller, R. F., J. D. Bates, T. J. Svejcar, F. B. Pierson, and L. E. Eddleman Biology, ecology, and management of western juniper (Juniperus occidentalis): Oregon State University Agricultural Experiment Station.Miller, R. F., T. J. Svejcar, and J. A. Rose Impacts of western juniper on plant community composition and structure. J. Range Manage. 53: Morrison, M.L., B.G. Marcot and R.W. Mannan Wildlife-Habitat Relationships: Concepts and Applications (3rd ed.). University of Wisconsin Press, Madison, WI. Orians, G. H. and J. F. Wittenberger Spatial and temporal scales in habitat selection. American Naturalist 137:

55 Owen, S. M., C. H. Sieg, C. A. Gehring, and M. A. Bowker Above- and belowground responses to tree thinning depend on the treatment of tree debris. For. Ecol. Manage. 259: Patten, Michael A., Donald H. Wolfe, Eyal Shocat, and Steve K. Sherrod. 2005a. Habitat fragmentation, rapid evolution and population persistence. Evolutionary Ecology Research 7: Patterson, R.L The sage grouse in Wyoming. The Wyoming Game and Fish Commission and Sage Books. Denver, Colorado. Rice, M.B, A.D. Apa, M.L Phillips, J.H. Gammonley, B.B. Petch, and K. Eichhoff Analysis of Regional Species Distribution Models Based on Radio-Telemetry Datasets From Multiple Small- Scale Studies. Journal of Wildlife Management 77(4): Romme, W. H., C. D. Allen, J. D. Balley, W. L. Baker, B. T. Bestelmeyer, P. M. Brown, K. S. Eisenhart, M. L. Floyd, D. W. Huffman, B. F. Jacobs, R. F. Miller, E. H. Muldavin, T. W. Swetnam, R. J. Tausch, and P. J. Weisberg Historical and Modern Disturbance Regimes, Stand Structures, and Landscape Dynamics in Pinon-Juniper Vegetation of the Western United States. Rangeland Ecology & Management 62: Rosenzweig, M. L Species Diversity in Space and Time Cambridge: Cambridge University Press. Ross, M. R., S. C. Castle, and N. N. Barger Effects of fuels reductions on plant communities and soils in a Pinon-juniper woodland. Journal of Arid Environments 79: Schroeder, M.A., C.L. Aldridge, A.D. Apa, J. R. Bohne, C.E. Braun, S. D. Bunnell, J.W. Connelly, P.A. Deibert, S.C. Gardner, M.A. Hilliard, G.D. Kobriger, S.M. McAdam, C.W. McCarthy, J.J. McCarthy, D.L. Mitchell, E.V. Rickerson, and S. J. Stiver Distribution of Sage-Grouse in North America. The Condor 106 (2): Schroeder, M.A., J.R. Young, and C.E. Braun Sage Grouse (Centrocercus urophasianus). In The Birds of North America, No. 425 (A. Poole and F. Gill, eds.) The Birds of North America, Inc., Philadelphia, PA. Stiver, S.J., E.T Rinkes, and D.E. Naugle Sage-grouse Habitat Assessment Framework. U.S. Bureau of Land Management. Unpublished Report. U.S. Bureau of Land Management, Idaho State Office, Boise, Idaho. U.S. Fish and Wildlife Service Greater Sage-grouse (Centrocercus urophasianus) Conservation Objectives: Final Report. U.S. Fish and Wildlife Service, Denver, CO. February

56 Walker, B.L., D.E. Naugle, and K.E. Doherty Greater sage-grouse population response to energy development and habitat loss. Journal of Wildlife Management 71: Weins, J. A., J. T. Rotenberry, and B. V. Horne Habitat occupancy patterns of North American shrubsteppe birds: the effects of spatial scale. Oikos 48: Wiens, J.A Spatial scaling in ecology. Functional Ecology 3(4): Wiley, Jr., R.H The lek mating system of the sage grouse. Scientific American 238: Wisdom, M. J., C. W. Meinke, S. T. Knick, and M. A. Schroeder Factors associated with extirpation of sage-grouse. pp in S. T. Knick and J. W. Connelly (editors). Greater Sage-Grouse: ecology and conservation of a landscape species and its habitats. Studies in Avian Biology (vol. 38), University of California Press, Berkeley, CA, USA. 54

57 Appendix A. Scoring Curves and Tables The specific shape of these curves is tentative and subject to additional scientific review. [These curves are being reviewed to ensure they meet conditions for WY. Additional detail on the literature and expert opinion used to develop these curves will also be included.] Breeding: Sagebrush Height 55

58 Breeding: Sagebrush Canopy Cover Breeding: Grass Height 56

59 Breeding: Grass Canopy Cover 57

60 Breeding: Forb Cover 58

61 Breeding: Forb Species Richness 59

62 Breeding: Presence of Specific Forb Species Summer: Forb Cover 60

63 Summer: Forb Species Richness 61

64 Summer: Presence of Specific Forb Species Summer: Grass Height 62

65 Summer: Grass Canopy Cover Winter: Sagebrush Height The scoring curve and table applied depends on the slope at the project site. 63

66 WINTER Sagebrush HEIGHT (cm), Topography & Aspect Topography <5%, Aspect not important < > ` WINTER Sagebrush HEIGHT (cm), Topography & Aspect Topography >5%, Aspect: SW to SE < > Winter: Sagebrush Percent Cover 64

67 WINTER Sagebrush % COVER < > Invasive Annual Grass 65

68 Appendix B. Forb Species List Information compiled by Alan Sands and Kerry Reese. Modified from a list compiled by Scott Lambert for the northern Great Basin with additions to reflect conditions across the species range. Species Name Common Name Species Name Common Name Achillea lanulosa Yarrow Lithophragma parvifolia Woodland star Agoseris glauca False dandelion L. bulbifera Woodland star Allium spp. Wild onion Lithospermum ruderale Western stoneseed Androsace septentrionalis Northern rock jasmine Lomatium dissectum Fernleaf biscuitroot Antennaria spp. Pussytoes L. triternatum Nineleaf biscuitroot Arabis cobrensis Sagebrush rockcress L. nevadense Nevada biscuitroot Arnica spp. Arnica Lupinus spp. Lupine Aster occidentalis Western aster Medicago sativa Alfalfa Astragalus spp. Milkvetch (native aridland ecotypes) Melilotus officinalis Yellow sweetclover Balsamorhiza sagittata Arrowleaf balsamroot Mertensia oblongifolia Oblongleaf bluebells B. hookeri Rock balsamroot Microseris nutans Nodding microceris Calochortus macrocarpus Sagebrush mariposa lily Oenothera spp. Evening-primrose C. nuttallii Sego lily Onobrychis viciifolia Sainfoin C. gunnisoni Gunnison s mariposa lily Penstemon strictus Rocky mountain penstemon Castilleja spp. Indian paintbrush Phlox hoodii Hoods phlox Cleome lutea Yellow spikeflower P. gracilis Slender phlox C. serrulata Rocky mountain beeplant P. longfolia Longleaf phlox Collinsia parviflora Maiden blue eyed Mary Polygonum spp. Knotweed Crepis acuminata Tapertip hawksbeard Sanguisorba minor Small burnet C. occidentalis Largeflower hawksbeard Sphaeralcea munroana Globemallow C. intermedia Limestone hawksbeard Taraxicum officinale Dandelion Erigeron spp. Fleabane Townsendia hookeri Hooker s townsend daisy Eriogonum umbellatum Sulphur-flower buckwheat Tragopogon dubius Yellow salsify Grindelia squarrosa Curlcup gumweed Trifolium macrocephalum Largehead clover Hydrophyllum capitatum Ballhead waterleaf T. variegatum Whitetip clover Lactuca serriola Prickly lettuce T. gymnocarpon Hollyleaf clover Lepidium densiflorum Common pepperweed Vicia spp. Vetch Linum lewisii Prairie flax L. perenne Blue flax 66

69 Appendix C. Field Datasheet EcoMetrix Revised (JKo) SAGE GROUSE Attribute Measurements 1 of 2 Site Name: Site is: ARID or MESIC (circle one) Date: Observers: Map Unit #: Transect #: Transect UTM E: Transect UTM N: % Slope: Aspect (N, E, SE, S, SW, W, NW): Camera / Photo #'s: GRASS and FORB COVER (Small Plots) Quad # Presence of Facultative Species* (Yes/No) % Native Perennial GRASS Cover % Nonnative PerennailG RASS Cover Grass Perennial Height % BROTEC Cover # of FORB Species in Quad 10 *Facultative species = Species that remain green over the course of the summer SAGEBRUSH COVER (30m x 30m) % Estimated Sagebrush Canopy Cover for Plot: % Native or Desireable FORB Cover % Invasive FORB Cover % Invasive Plant Cover (GRASS and FORB) Distance to Suitable Cover (sage brush) # Sage Height Columnar / Spreading # Sage Height Columnar / Spreading # Sage Height Columnar / Spreading Mountain Big Sage Dominant? (Yes/No) 67

70 EcoMetrix Revised (JKo) SAGE GROUSE Attribute Measurements 2 of 2 Date: Observers: Site Name: Map Unit #: Transect #: Distance to nearest lek (km) HUMAN-GENERATED DISTURBANCE REGIME (check all that are present) Type Drilling Producing WP w/ LGS Producing WP w/o LGS Transmission Lines Distribution Lines Quantity Distance (ft) Activity Level Fences 4-strand 5-strand Hog wire Chain link Other Height (ft) Distance (ft) Linear ft/ sq mi Wind Turbines Buildings Surface Mines Other: Other: Roads Interstate Distance (ft) LOS State Highway County Road Dirt Road Two Track Other 68

71 Appendix D. Field Data Collection Methods The methods outlined below are for field data collection of attributes associated with the GRSG functional habitat assessments, developed for the Upper Green River Conservation Exchange. The tool is part of a larger methodology and habitat model that scores habitat areas on a system of functional acres. Materials Needed Hand-held GPS unit, preloaded with sample points Field maps, using aerial photos as background Tripod-mounted laser pointer (or pin flag if laser not available) 50-meter tape PVC or wooden 1 m 2 quadrat Wooden 1 meter ruler Plant field guides for the area 1 meter tape Methods Map Unit Delineation In order to properly assess functional acreage, the site and its map units must be clearly defined. Because the currency of this evaluation, function acres, is tied to a unit area, this step is essential. The site is the overall area that is being evaluated, and includes all habitats and landscapes within the exterior boundaries of what is delineated as the site. The map units are the individual parcels of land that are evaluated either in the field or through GIS. Though map units are somewhat open to interpretation, in general a map unit will encompass a relative homogenous area of habitat. Major breaks such as water vs. terrestrial, woodland vs. pasture, and roads vs. undeveloped area, are relatively straight-forward to delineate via GIS. However, other distinctions such as changes in topography, surface vegetation, slope and aspect, or density of trees or shrubs should also be considered. This often depends on the parameters of interest and breaks in map units will often correspond to significant thresholds that are important for a species of interest. Defining map units requires a good working knowledge of the habitats important to GRSG, the local habitats, and the questions being addressed by the assessment. 69

72 Map units are best delineated over a layout of recent aerial photography. Once map units are defined (including all roads and developed areas) a sampling intensity should be developed. This will depend on several factors, including the accuracy and precision required by the study, and occasionally on budget or personnel resources available. Ideally, at least one sample point should be placed in each map unit, with more in larger map units. More sample points will always lead to a higher level of confidence in the characterization of a map unit. The table below is a starting point for discussion in determining some limits on number of samples needed. Select the number of sample points that fits within logistical and budget constraints with the understanding that the level of confidence is increased with more samples per acre (i.e. low, medium, high). Larger areas may be sampled with some rigor and used to characterize similar map units although a minimum of 1 sample point should be collected in every map unit regardless of size. The primary sampling scheme will based on 50-meter transects, which provide multiple measurements sampled along the transect that are averages for their single respective values. A 100-meter transect may be used to reduce the number of transects while boosting the number of samples per transect, and thus increase confidence. A transect equates to a single sample point in this methodology. # Acres # Transects # Sample points for grass, forb, and shrub measurements on a 50m transect # Sample points for grass, forb, and shrub measurements on a 100 m transect Level of certainty Low Medium High Sample points should be randomly chosen (within the map unit) and loaded into hand-held GPS units. 70

73 Map Unit Quality Check / Site Reconnaissance Upon arrival at the site, field crews should walk the site together to quality check the map unit delineations. Map units, especially in agricultural areas, often change from year to year, and may not reflect even relatively recent aerial photography. Crews should be prepared to modify map unit boundaries in the field based on observations, and should also come to a common understanding of the plants present, and the protocol that follows. Transect Layout and Initial Measurements The core layout for the measurements that follow will be a 50-meter transect. Field crews should navigate to a sample point via hand-held GPS. Select a random direction by blindly spinning a compass wheel, flipping a pen in the air, or other commonly used method. Insert a stake in the ground, and lay out a 50- meter transect along the direction chosen, taking care to lay it over any shrubs along the transect. Fill in all fields at the top of the data sheet, with the date, observer initials, site name (a unique identifier assigned by the workers), the site UTMs (including UTM Zone and Datum), and whether the site is arid or mesic. Be sure to record all photo numbers and the camera used throughout data collection. At the midpoint of the transect (25 meters) record the slope and aspect of the map unit. NOTE: During the course of sampling, a 1 m 2 quadrat and tripod-mounted laser will be used to sample cover, vegetation composition, and other attributes. (If a laser is not available, crews should use standard point intercept methods, e.g. with a pin flag. It should be decided a-priori the side of the transect the sampling will occur (e.g. the right side as seen from 0 to 50 meters), and all unnecessary foot traffic should occur on the other (e.g. left) side of the transect line so as not to trample vegetation. Percent Cover with Laser Using the tripod-mounted laser, record a maximum of three hits of the laser at every 0.5 meters from 0.5 to 50. Record a top story, and understory. Record the following categories: The species of plant hit, if known If species is not known or cannot be determined, record as Native Forb (NF), Introduced Forb (IF), Native Grass (NG), Introduced Grass (IG). Grass, Forb, and Shrub Measurements with Quadrat Using a 1m 2 quadrat, take the following measurements on the right side of the transect line, by placing the quadrat every 5 meters from 5 to 50 (ten measurements total). 71

74 Grass height. Height is the estimate of the height above the ground where the preponderance of grass biomass is present (this is important for both forage and hiding cover for GRSG). It is NOT necessarily the total height of the grass. Grasses with different morphologies will have different bulk heights. For example, grasses with large and diffuse seed heads (such as Indian ricegrass) will likely have the bulk height near the top of the plant; bunch grasses with small seed heads will likely have it near the bottom. Measure this in centimeters with a wooden ruler. Number of Forb Species Present. Tally the number of different annual and perennial forb species present inside the quadrat. Height of Sagebrush. Measure the height of the nearest sagebrush plant within one meter of the meter mark where the quadrat is located (5, 10, 15, etc.). If there is NO sagebrush plant within one meter of the mark, put a dash in this field. Then, measure the distance to the nearest sagebrush plant. Height of Other Shrub. If another non-sage shrub is within one meter of the meter mark, note the species and the height of the plant. Sagebrush and Forb Cover via Line-Intercept Using a one-meter tape or stick, record the lengths of sagebrush and forb cover at all places where sagebrush and forbs intersect the transect. Consider a length of sagebrush and forbs unbroken if there is no more than a 5.0 cm. gap. The total of all the lengths over the 50 meters will enable an easy calculation of percent canopy cover of sagebrush and forbs. (If you do not have a one-meter tape, record the start of stop points of the sage along the transect, but the above method is much more rapid). Anthropogenic Disturbance Measurements Most of the measurements in the data Sheet related to anthropogenic disturbance will be accomplished with GIS. Greater Sage-Grouse Food and Cover Abundance On the data sheet, record the abundance of the plants listed. A pre-study analysis of the study area will determine if all of these species listed in Appendix B are applicable. Without taking too much time, record a qualitative estimate of the relative abundance of the plants over the entire map unit, not just what may or may not have fallen in the quads or along the transect. 72

75 Appendix E. Definitions of Anthropogenic Structures The definitions here correspond to the structures in Table 7 (page 31). They were developed from multiple sources, including the Colorado Oil and Gas Conservation Commission. Rules and Regulations Definitions (100 Series). Transmission lines: Conductor or conductors designed to carry electricity or an electrical signal over large distances with minimum losses and distortion. Power lines: A cable carrying electrical power. Non-oil and gas related two lane paved road: Any two-lane road covered with a semi-permanent surface placed on it, such as asphalt or concrete. Non-oil and gas related improved gravel road: Any road covered with a mix of stone, sand and finesized particles used as sub-base, base or surfacing. Interstate highway: One of a system of expressways covering the 48 contiguous states. Nonwind-energy related vertical structures: Any upright anthropogenic structure that is not used for wind power generation, such as a communication tower. Infrastructure associated with oil and gas development: Two lane paved road: Any two-lane road covered with a semi-permanent surface placed on it, such as asphalt or concrete that is used to access infrastructure associated with oil and gas development. Improved gravel road: Any road covered with a mix of stone, sand and fine-sized particles used as sub-base, base or surfacing that is used to access infrastructure associated with oil and gas development. Well or drill pad: Area from which multiple wells may be drilled to various bottomhole locations. Active drilling site or well site: An area of land that is directly disturbed during the drilling and subsequent operation of, or affected by production facilities directly associated with any oil or gas well. Wind energy developments : Tall structures: Wind turbine, a device that converts kinetic energy from the wind into electrical power. 73

76 Access roads: Any road that is used to access infrastructure associated with wind energy development. Transmission lines: Conductor or conductors designed to carry wind powered electricity or an electrical signal over large distances with minimum losses and distortion. Infrastructure associate with in situ uranium: Infrastructure associated with the underground processing of uranium ore. Infrastructure associated with oil shale and tar sands energy: Production and distribution facilities directly associated with oil shale and tar sands energy development. Infrastructure associated with solar energy: Solar array field: Area that contains solar arrays, which are solar panels arranged in a group. Access roads: Any road that is used to access infrastructure associated with solar energy development. Transmission lines: Conductor or conductors designed to carry solar powered electricity or an electrical signal over large distances with minimum losses and distortion. Infrastructure associated with mining activity: Infrastructure associated with the process of obtaining or extracting minerals from a mine. 74

77 Appendix F. Monitoring and Adaptive Management This section is divided into two subsections: Tool Evaluation and Adaptive Management. The descriptions provided here represent only guidelines for monitoring and adaptive management and not a plan for carrying out these activities. Biological monitoring is an essential element of any conservation and management plan, and is referenced in the Exchange Manual. Tool Evaluation Tool evaluation is defined and collection and analysis of data that pertains to the functionality and performance of the HQT. In particular, tool evaluation is concerned with: 1) Internal functioning (e.g. scoring tools, GIS applications, etc.), 2) Accuracy of the scores in measuring real and expected outcomes, 3) Utility (ease of use, efficiency, and cost) for a variety of users, 4) Repeatability of scores from one user to the next, and 5) Reliability of scores over time. Adaptive Management Adaptive management is a structured dynamic process that helps embrace and address uncertainty in management by incorporating structural components and procedures that seek to periodically revise and update tools, strategies and approaches using new information. Adaptive management strategies allow for changes to the overall conservation strategy to occur in response to changing conditions or new information, including those identified during monitoring. Adaptive approaches to management recognize that the answers to all management questions are not known and that the information necessary to formulate answers is often unavailable. Adaptive management also includes, by definition, a commitment to change management approaches when determined appropriate for attaining the biological goals and objectives of the conservation strategy. The goal of adaptive management for the HQT is to make periodic changes that keep it up to date with the current state of ecological knowledge. Adaptive management will be used here to refine and update the HQT over time. In other words, none of the content or components of the HQT are meant to be static in time, rather the HQT is intended to evolve over time as needed according to new monitoring. As specified in the Exchange Manual, an advisory committee structure will be in place through which the adaptive management decision-making process will occur. A Science Advisory Team will meet periodically to review and evaluate new information including new research on the species biology or ecology, new or changing threats to the species, recent substantial gains or losses of habitat for the species, and the establishment of new protected areas. Such review and evaluation will be used to assess the overall status of the covered species, plan implementation and progress, and whether any 75

78 adjustments are needed to further ensure conservation benefits to the species. The Science Committee will decide whether any modifications are necessary according to the best available science, (and where such modifications should be incorporated) and make a recommendation regarding such modifications to the Oversight Committee. The Oversight Committee will confer about the Science Committee s findings and recommendations. The principles of Adaptive Management will be used to inform any modifications deemed necessary to the HQT. Modifications to the HQT will not apply retroactively. Any entity that would like to participate in the Exchange will be required to adhere to the biological standards and monitoring requirements outlined in the Exchange Manual and the HQT. This will allow the Exchange Administrator and Oversight Committee to assess progress, overall changes in conservation value on credited properties, and conservation status of covered species. Figure 20. Steps in the Conservation Exchange Adaptive Management Process 76