Habitat Quantification Tool (HQT) Scientific Rationale · 2020-01-23 · This Scientific Rationale...

OREGON SAGE-GROUSEHabitat Quantification Tool (HQT)

Scientific RationaleOctober 2019 Version 2.2

Acknowledgements

This Scientific Rationale was developed for the State of Oregon by the Sage-Grouse Conservation Partnership (SageCon) Quantification Technical Team.

Quantification Technical Team

Molly Anthony Bureau of Land Management Pete Baki Oregon Department of Fish and Wildlife Chad Boyd USDA Agricultural Research Service Brett Brownscombe National Policy Consensus Center, Portland State University Dave Budeau Oregon Department of Fish and Wildlife Theresa Burcsu Institute for Natural Resources/Oregon Department of Administrative Services Matthew Cahill The Nature Conservancy Megan Creutzburg Institute for Natural Resources Jackie Cupples Oregon Department of Fish and Wildlife/US Fish and Wildlife Service Chip Dale Oregon Department of Fish and Wildlife (former member) Lee Foster Oregon Department of Fish and Wildlife Glenn Fredrick Bureau of Land Management Garth Fuller The Nature Conservancy Shauna Ginger US Fish and Wildlife Service Sara Holman Oregon State University Dustin Johnson Oregon State University Jay Kerby The Nature Conservancy Michael Schindel The Nature Conservancy Shonene Scott The Nature Conservancy Nigel Seidel Oregon Department of Fish and Wildlife Angela Sitz US Fish and Wildlife Service Brenda Smith USDA Agricultural Research Service Tony Svejcar USDA Agricultural Research Service (former member) Kellen Tardaewether Oregon Department of Energy Maxwell Woods Oregon Department of Energy Polly Gibson Willamette Partnership (Lead Document- and Tool-Developer) Sara O’Brien Willamette Partnership (Facilitator) Rebecca Kramer Willamette Partnership (Facilitator) June Reyes Willamette Partnership (Facilitator and Tool-Developer)

The format of this document is adapted from the scientific methods documents developed for the Colorado and Nevada Habitat Exchanges. This document also incorporates content from the Oregon Sage-Grouse Action Plan (SageCon 2015) and the Oregon Department of Fish and Wildlife’s (ODFW’s) Greater Sage-Grouse Conservation Assessment and Strategy for Oregon (ODFW 2011). The Oregon Sage-Grouse Habitat Quantification Tool (HQT) draws significantly from the Oregon Sage-Grouse Development Registry, the Sage-Grouse Development Siting Tool, the HQT GIS Toolkit, and the Oregon Decision Support System for Sagebrush Steppe (ORDSS). Special thanks to Michael Schindel, Shonene Scott, Megan Creutzburg, and the teams from The Nature Conservancy and the Institute for Natural Resources who developed those systems and datasets and who have contributed greatly to improving and integrating the tools.

HABITAT QUANTIFICATION TOOL SCIENTIFIC RATIONALE | OCTOBER 2019 | VERSION 2.2 2

This content was created in part through the adaptation of procedures and publications developed by Environmental Incentives, LLC, Environmental Defense Fund, and Willamette Partnership, but is not the responsibility or property of any one of these entities.

Open Content License

The Oregon Sage-Grouse Habitat Quantification Tool Scientific Rationale has been developed with an eye toward transparency and easy extension to address multiple environmental issues and geographic regions. As such, permission to use, copy, modify, and distribute this publication and its referenced documents for any purpose and without fee is hereby granted, provided that the following acknowledgment notice appears in all copies or modified versions: “This content was created in part through the adaptation of procedures and publications developed by Environmental Incentives, LLC, Environmental Defense Fund, and Willamette Partnership, but is not the responsibility or property of any one of these entities.”

Suggested citation: State of Oregon, 2019. Oregon Sage-Grouse Habitat Quantification Tool Scientific Rationale Document. Version 2.2. Portland, OR.

Cover photo courtesy of USDA - NRCS.


Table of Contents 1 Introduction ............................................................................................................................................ 7

1.1 Purpose ............................................................................................................................................ 7 1.2 Users and uses ................................................................................................................................. 8 1.3 Development process ...................................................................................................................... 8

2 Overview of the HQT ............................................................................................................................. 9 2.1 Functional acre approach ................................................................................................................ 9 2.2 Framework for quantifying habitat function ................................................................................... 10

2.2.1 Analysis area and map units ................................................................................................. 11 2.2.2 The habitat quantification algorithm .................................................................................... 11 2.2.3 HQT adjustment overview .................................................................................................... 12

2.3 Quantifying credit acres with the HQT .......................................................................................... 13 2.3.1 Enhancement vs. preservation .............................................................................................. 13 2.3.2 Actions incorporated into the HQT credit calculation .......................................................... 13

2.4 Mesic habitat .................................................................................................................................. 14 2.4.1 Seasonal habitats in the HQT ............................................................................................... 15

2.5 Spatial scale and habitat function .................................................................................................. 15 2.5.1 Range-wide scale .................................................................................................................. 16 2.5.2 Landscape scale .................................................................................................................... 18 2.5.3 Local scale ............................................................................................................................. 19 2.5.4 Site scale ............................................................................................................................... 19

3 Habitat quantification parameters ........................................................................................................ 21 3.1 Management Designations Index .................................................................................................. 21

3.1.1 Description ............................................................................................................................ 21 3.1.2 Scoring .................................................................................................................................. 21

3.2 Ecological state .............................................................................................................................. 23 3.2.1 The threat-based model approach ....................................................................................... 24 3.2.2 Threat-based model for upland sagebrush steppe .............................................................. 24 3.2.3 Threat-based model for mesic areas .................................................................................... 34

3.3 Development impacts .................................................................................................................... 37 3.3.2 Quantification of development impacts ............................................................................... 37 3.3.3 Aggregation and scoring ...................................................................................................... 39

3.4 HQT adjustment factors ................................................................................................................. 42 3.4.1 Impact minimization – development projects only ............................................................... 42 3.4.2 Legal protection credit – credit generation projects only .................................................... 42

4 Adaptive management and limitations of the HQT ............................................................................. 44 4.1 Occupancy and seasonal habitat ................................................................................................... 44 4.2 Threat-based models ..................................................................................................................... 44 4.3 Development impacts .................................................................................................................... 45


4.4 Credit sensitivity ............................................................................................................................. 46 5 References ............................................................................................................................................ 47 Appendix A. Additional resources ....................................................................................................... 51 Appendix B. Field test of ocular assessment methodology ................................................................ 52 Appendix C. Development impact calculation .................................................................................... 55

List of Figures

Figure 2-1. Schematic of the algorithm used to calculate habitat function .............................................. 10 Figure 2-2. Use of multiple spatial scales for quantifying habitat function for sage-grouse. .................... 16 Figure 2-3. Map of mitigation service areas and designated Core and Low Density habitat areas in Oregon. ....................................................................................................................................................... 18 Figure 3-1. Management Designations Index ........................................................................................... 23 Figure 3-2. Resilience and resistance of ecosystem function associated with different ecological threats and climatic conditions, and the relationship between perennial bunchgrass density, native plant resiliency, and ecological states. ................................................................................................................ 26 Figure 3-3. Decision tree for ecological state classification. ..................................................................... 27 Figure 3-4. Photograph examples of each ecological state. ..................................................................... 28 Figure 3-5. Combined development impacts discount surface across the sage-grouse range in Oregon. .................................................................................................................................................................... 41

List of Tables

Table 1-1. Additional supporting documents and tools .............................................................................. 7 Table 3-1. Scoring for the Management Designations Index .................................................................... 22 Table 3-2. Classification levels for ecological states .................................................................................. 25 Table 3-3. Summary of characteristics defining upland ecological states ................................................. 29 Table 3-4. Scoring for upland ecological states ........................................................................................ 33 Table 3-5. Classification and scoring for mesic ecological states .............................................................. 36 Table B-1. Comparison of ocular assessment vs. vegetation transect methods……………………………54

Table C-1. Development impacts table……...…...…………………………………………..…..…………….55

Table C-2. Indirect impact mechanisms and associated minimization measures………......……………...56


List of Acronyms

ARS Agricultural Research Service

BLM Bureau of Land Management

CCA Candidate Conservation Agreement

CCAA Candidate Conservation Agreement with Assurances

DLCD Department of Land Conservation and Development

DST Sage-Grouse Development Siting Tool

GIS Geographic Information System

GNIS Geographic Names Information System

GSG Greater Sage-Grouse

HQT Habitat Quantification Tool

IAG Invasive Annual Grasses

MDI Management Designations Index

NAIP National Agricultural Imagery Program

NRCS Natural Resources Conservation Service

OAR Oregon Administrative Rule

ODFW Oregon Department of Fish and Wildlife

ORDSS Oregon Decision Support System for Sagebrush Steppe

PAA Project Analysis Area

PAC Priority Area for Conservation

PG Perennial Grasses

SSP Site-Specific Plan

TBM Threat-Based Model

UGB Urban Growth Boundary

USDA United States Department of Agriculture

USFWS United States Fish and Wildlife Service

USGS United States Geological Survey

WAFWA Western Association of Fish and Wildlife Agencies


1 INTRODUCTION

1.1 Purpose

Oregon’s Sage-Grouse Habitat Quantification Tool (HQT) is a science-based method for quantifying habitat function and conservation outcomes for greater sage-grouse (Centrocercus urophasianus; hereafter GSG or sage-grouse). This tool supports Oregon’s Sage-Grouse Habitat Mitigation Program (Mitigation Program), a part of Oregon’s broader Greater Sage-Grouse Action Plan (Action Plan; SageCon 2015) to conserve sage-grouse habitat and avoid, minimize, and compensate for development impacts to sage-grouse habitat in Oregon. The HQT was primarily developed to measure projected outcomes of both new development and habitat restoration projects and to help target siting of credit and debit projects in the most beneficial locations for sage-grouse. A standardized quantification of habitat function affected by development impacts and enhancement actions is a key element of the broader Mitigation Program.

The units of habitat function quantified by the HQT will be used to track the contribution of the Mitigation Program to sage-grouse habitat and population goals over time. Additionally, the HQT can be used to quantify habitat function for a range of purposes including evaluating outcomes of conservation projects that occur outside the Mitigation Program.

This Scientific Rationale document (Rationale) describes the development of the HQT algorithm and the scientific basis (including peer-reviewed literature, gray literature, and expert opinion) for its components. This document also explains the parameters that are used to evaluate habitat function, how those parameters are measured and scored for the HQT, and how scores are used to generate a single metric of habitat function at a given site. Specific instructions for using the HQT to calculate credit values associated with a specific project are provided in the separate Oregon Greater Sage-grouse HQT User Guide document (User Guide; Table 1-1). The Greater Sage-Grouse Mitigation Program Manual (Manual) outlines guidelines and processes for understanding and participating in the Mitigation Program. Table 1-1 lists other documents and tools that support operation of the HQT and the Mitigation Program more generally.

Table 1-1. Additional supporting documents and tools

Document or Tool Purpose

1. Oregon GSG Action Plan Outlines Oregon’s comprehensive approach to sage-grouse conservation and recovery.

2. Oregon Administrative Rules

DLCD’s OAR 660-023-0115 outlines avoidance, minimization, and compensatory mitigation requirements for some development projects that require local land-use permitting and affect sage-grouse habitat; ODFW’s OAR 635-140-0025 describes ODFW’s process for implementation of the mitigation hierarchy.

3. Oregon Greater Sage-Grouse Mitigation Program Manual

Defines the processes and information necessary for understanding and participating in Oregon’s Sage-Grouse Mitigation Program.

4. Oregon Greater Sage-Grouse HQT User Guide

Step-by-step instructions for applying the HQT to calculate habitat function at a site.

5. HQT GIS Toolkit A set of base data layers and custom desktop GIS-based tools used to perform the spatial analyses for calculating site-specific scores used in the HQT.


6. HQT Calculator workbook

A spreadsheet that uses scores calculated in GIS and other project-specific information to calculate functional acres, credits, and debits for proposed and implemented conservation and development projects.

7. Oregon Sage-Grouse Data Viewer

Collects and displays current geospatial data sets related to sage-grouse habitat and threats in Oregon, in a publicly available web-based mapping application. Available at: http://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=sagegrouse

8. Sage-Grouse Development Siting Tool (DST)

A web-based mapping application (currently in a Beta Version) designed to help permittees understand mitigation rules in sage-grouse habitat and to minimize both impacts on sage-grouse and mitigation costs. Available at: http://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=sage_grouse_dev_siting.

1.2 Users and uses

The primary audiences of this Scientific Rationale document are the Mitigation Program Administrator and the State of Oregon’s federal agency partners. The Program Administrator will use the Rationale as the basis for implementation and adaptive management of the HQT and will update this document as the HQT is improved over time. Federal agency partners and other stakeholders, including project proponents, may use the Rationale to understand the scientific basis for the HQT. Scientists and other experts may be asked to review the Rationale in order to recommend improvements to the HQT.

The HQT has been specifically designed for use in Oregon’s Sage-Grouse Mitigation Program. However, it could also benefit other sage-grouse conservation efforts in Oregon. For example, the HQT could be used to quantify and track the results of investment of public or non-governmental organization funding for sage-grouse conservation unrelated to mitigation obligations. Use of the HQT requires substantial knowledge of sage-grouse biology, moderate GIS capabilities, and experience with field habitat assessment. This expertise is likely provided by a team rather than a single individual.

1.3 Development process

The HQT is based on a well-established and academically-supported framework. Key sources include the Stiver et al. (2010) Habitat Assessment Framework and the threat-based model approach developed by a collaborative group of rangeland ecologists and sage-grouse biologists and incorporated into Oregon’s Candidate Conservation Agreements (CCAs) beginning in 2014. The SageSHARE project, funded in part by the Natural Resources Conservation Service (NRCS), played a critical role in establishing the scientific foundations of the threat-based modeling approach and in developing tools necessary for model application in this context.

The HQT Calculator and this Rationale were prepared by Willamette Partnership and the SageCon Quantification Technical Team (Technical Team). Following expert review and pilot testing of field and quantification methods in summer 2016, the HQT was substantially revised in 2016-2017. Version 1.0 was released for beta testing application in the Mitigation Program beginning summer 2017. This Version 2.2, released in October 2019, incorporates subsequent developments in the data sources and methods for scoring parameters in the HQT algorithm, as well as decisions by the Technical Team about policies for using outputs from the HQT in the Mitigation Program.


http://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=sagegrouse




http://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=sage_grouse_dev_siting




2 OVERVIEW OF THE HQT

The HQT is a science-based method for assessing habitat function and conservation outcomes for sage-grouse that measures the projected impacts of both credit generation (or credit) and development (or debit) projects. Not all acres of sage-grouse habitat in Oregon are equivalent in the level of function or value that they provide for sage-grouse. Mitigating for the loss of one high-quality acre with an acre of low-quality habitat could result in severe resource loss from the species perspective. Therefore, effective compensatory mitigation and other market-based systems require a common currency that incorporates the quality and size of the habitat being impacted or conserved. This common currency is referred to as a functional acre, and it is calculated by the HQT. One functional acre is equal to one credit but does not necessarily translate to one actual acre of habitat.

To apply the HQT, a user will need to delineate the analysis area for a project and gather relevant spatial data about the area from GIS-based desktop tools and from a field survey. The functional acre calculation is based on data for three parameters: 1) current sage-grouse occupancy (Management Designations Index); 2) current vegetation condition and probable ecological threats (Ecological State); and 3) cumulative impacts of anthropogenic development (Development Impacts).

Scores for these parameters and acreage of a project site are combined to produce a single measure of habitat function at the site, in units of functional acres. By incorporating multiple aspects of sage-grouse habitat quality and threats, the design of the HQT follows Oregon’s “all lands, all threats” approach to sage-grouse conservation. The HQT expresses habitat functionality in a way that supports intact and functioning sagebrush landscapes while also considering the primary threats to sagebrush habitat as well as sustainability of local communities.

2.1 Functional acre approach

The HQT estimates the quantity and quality of sage-grouse habitat at a project site in terms of habitat function, i.e., the value of the area for providing intact, native sagebrush steppe vegetation communities that are most likely to meet the needs of sage-grouse life history requirements (reproduction, recruitment, survival) at multiple spatial scales. Habitat function depends on biotic and abiotic features of the site as well as the direct and indirect effects of anthropogenic development on and surrounding the site. Functional acres, the units used to quantify habitat function, are computed as the area assessed (habitat quantity) discounted by a measure of the quality of that habitat:

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝑎𝑎𝑎𝑎𝑎𝑎 = ℎ𝐹𝐹𝑎𝑎𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑞𝑞𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑞𝑞 (𝐹𝐹𝐹𝐹𝑎𝑎𝑎𝑎𝑎𝑎) 𝑥𝑥 ℎ𝐹𝐹𝑎𝑎𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑞𝑞𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑞𝑞 (2-1)

The functional acre approach has several advantages.

Establishes a common currency. Functional acres serve as the common “currency” for credits and debits. Functional acres account for the quantity and quality of the habitat at multiple spatial scales. The integration of habitat quantity and quality allows for direct comparison of detriments and benefits, which provides a clearer understanding of whether or not conservation goals are being met (McKenney and Kiesecker 2010, Gardner et al. 2013). A common currency allows for standardization in the calculation of credits and debits, which affords the opportunity to conduct mitigation consistently across projects, land ownership types, and jurisdictional boundaries. It also provides a common language and metric for mitigation across agencies and industries, while remaining responsive to new science as it emerges.

Provides full accounting of impacts. Functional acres account for both direct and indirect impacts of anthropogenic development. Accounting for both types of impacts provides a more accurate representation of the full biological effects of development on sage-grouse. It also provides a strong incentive for siting development and credit generation projects at the most appropriate places on the landscape, clustering development where it will have the least detrimental impact on the species, and focusing conservation efforts where they will have the greatest benefit.

Focuses on outcomes. Rather than rewarding the completion of management actions or practices that may or may not succeed, the Mitigation Program focuses the activities of developers, landowners, and


conservationists on what matters most to the sage-grouse – the habitat outcomes that result from management actions or practices. Paying for actual or anticipated outcomes (i.e., effectiveness) rather than for management practices, (i.e., implementation) has been shown to achieve more conservation per dollar spent (Just and Antle 1990, Antle et al. 2003). The outcomes-based functional acre approach of the HQT enables the Mitigation Program to provide strong incentives to achieve habitat benefits relevant to sage-grouse.

Tracks the contribution of the Mitigation Program projects to species habitat and population goals in Oregon over time. The use of functional acres allows for a simple metric to measure the overall performance of the Mitigation Program meeting its net benefit in sage-grouse habitat and population goals.

2.2 Framework for quantifying habitat function

The method used to quantify habitat function in the HQT is designed to be repeatable, accurate, and transparent; based on best available science; practical and straightforward to apply; and capable of assessing projects of different spatial scales. The HQT algorithm is the equation used to calculate functional acres (Figure 2-1; Section 2.2.2). This algorithm relies on three parameters (i.e., inputs): 1) the Management Designations Index, a measure of current sage-grouse occupancy; 2) Ecological State, a classification of current vegetation condition and probable ecological threats; and 3) Development Impacts, a measure of the aggregate impacts of anthropogenic development. The HQT generates a functional score for each parameter based on a combination of field data collection and GIS-based desktop analysis. Parameter scores and site acreage are combined according to the HQT algorithm to calculate a single measure of habitat function at a site, in units of functional acres. This framework is shown in Figure 2-1 and described in more detail below.

Figure 2-1. Schematic of the algorithm used to calculate habitat function

The HQT can be applied in different contexts to serve multiple purposes, including:

• To calculate credits and debits associated with credit generation and development projects that are part of Oregon’s Mitigation Program. In order to calculate the credits or debits generated by a given project, the HQT algorithm is applied twice, first to existing (pre-project) conditions, and again on the expected post-project conditions. The difference in functional acres


between the two results represents the change in habitat function and therefore the estimated gain (credits) or loss (debits) of functional acres that is associated with the project.

• At a point in time to describe the current habitat function of an area for sage-grouse.

• At multiple points in time for a given area to quantify changes in habitat function over time. This application is well-suited to broad-scale monitoring of status and trends in sage-grouse habitat.

In this document, credits are the gains in functional acres at a site as a result of credit generation projects; debits are the losses in functional acres as a result of development projects. The term credit value is used generically to indicate the total change in functional acres associated with a project, whether positive (credits) or negative (debits).

Analysis area and map units

In the HQT, habitat function is quantified for a spatially explicit, discrete area. Thus, in order to calculate functional acres for a specific project, the analysis area for the project must be clearly defined.

• For development projects, the analysis area includes all habitat directly or indirectly affected by the project activities. The area of direct impacts (i.e., the area of proposed surface disturbance) is termed the project footprint or development footprint, while habitat that is only indirectly affected by the new development is termed the area of indirect impact. Impacts associated with anthropogenic development are discussed in Section 3.3.

• For credit generation projects, the analysis area includes all habitat where active restoration will be performed, plus any additional area that is covered by the legal protection and site management plan associated with the project.

To facilitate assessment, the analysis area is divided into map units describing distinct vegetation communities and vegetation structure. Map units are primarily defined based on variation in ecological state; however, map units can also be used to delineate areas with different current or proposed management actions. Mesic features (e.g., playas, wet meadows, riparian areas, springs, and seeps), in particular, are always outlined as separate map units because the habitat function of mesic areas is quantified separately from the habitat function of upland areas (see Section 2.4). Additional guidance for delineating map units is provided in the User Guide.

Parameters in the HQT algorithm are measured and scored separately for each map unit in the analysis area, and functional acres are quantified separately for each map unit. The total number of functional acres for the full project analysis area is obtained by summing the functional acres for all map units within the analysis area.

The habitat quantification algorithm

The algorithm used to calculate functional acres is shown schematically in Figure 2-1. As described in Section 2.1, the algorithm’s output in functional acres represents a measure of the area assessed (habitat quantity) discounted by a measure of the quality of that habitat (Equation 2-1).

The assessment of habitat quality at a site is based on scores for three parameters measured at the site:

1. Management Designations Index (Section 3.1) 2. Ecological State (Section 3.2) 3. Development Impacts (Section 3.3)

All parameters are scored on a scale from 0 to 1, where 0 represents minimal habitat value for sage-grouse and 1 represents optimal conditions. Chapter 3 describes the rationale and scoring method for each parameter.

Parameter scores are combined into two subscores that describe different aspects of habitat quality at a site:


1. Baseline Habitat Function Subscore (blue box in Figure 2-1): a measurement of the existing or pre-project (baseline) conditions.

This component of the algorithm addresses siting of credit generation and development projects at a fine scale. The subscore is calculated as the average of the Management Designations Index score, the pre-project Ecological State score, and the pre-project Development Impacts score.

The Baseline Habitat Function Subscore does not change between the calculation of pre-project functional acres and the calculation of post-project functional acres: regardless of which calculation is being performed, the subscore always reflects the baseline conditions at a project site. Retaining this multiplier for baseline conditions in the calculation of post-project functional acres ensures that a given level of development is costlier when it impacts good condition habitat than when it impacts poor condition habitat. Similarly, a given amount of habitat uplift (in terms of the increase in Ecological State score; see Section 3.2) is more valuable when it restores habitat from fair to good condition than when it restores habitat from poor to fair condition.

2. Habitat Modification Subscore (red box in Figure 2-1): quantifies changes in ecological state or development impacts that are predicted to occur as a result of habitat restoration projects or new development projects.

This component of the algorithm accounts for the change in habitat value as a result of credit generation and development projects. The subscore is calculated as the average of the Ecological State score and the Development Impacts score, using pre-project scores for calculation of pre-project functional acres and post-project scores for calculation of post-project functional acres.

In general, uplift due to habitat enhancement projects is reflected by an increase in the Ecological State score, while degradation due to new development projects is reflected by a reduction in the Development Impacts score. However, habitat enhancement projects that remove existing structures (such as small power lines) to benefit sage-grouse will receive credit through an increase in the Development Impacts score.

Similar to the individual parameter scores, the HQT algorithm subscores (Baseline Habitat Function Subscore Modification Subscore) range in value from 0 to 1, where 0 represents minimal value for sage-grouse and 1 represents optimal conditions.

The area (habitat quantity; yellow box in Figure 2-1) of a given map unit is discounted (multiplied) by both subscores to derive the number of functional acres (orange box in Figure 2-1) provided by that map unit. Thus, while the maximum theoretical possible number of functional acres for a given area is equal to the number of physical acres, functional acres for a given area will virtually always be fewer than physical acres.

HQT adjustment overview

The HQT algorithm described in Section 2.2.2 and Figure 2-1 above calculates the raw habitat credit value associated with a project. This raw value is then modified by one of two project-level adjustment factors to determine the HQT credit value of the project.

HQT adjustment factors:

1. Development projects may earn minimization reductions to account for reduced impacts to sage-grouse as a result of minimization actions by the project developer. The minimization reductions are applied against the calculated indirect impacts for the project.

2. Credit generation projects earn additional legal protection credit to account for the reduced risk of loss of habitat to development as a result of the legal protection required by the Mitigation Program.

Section 3.4 describes the rationale and scoring method for each adjustment factor.


The credit value assessed by the HQT is not the final debit or credit obligation that a project will earn in the Mitigation Program. For development projects, the Program applies additional adjustments to the HQT value in order to ensure that the Program meets its statutory goal of providing net benefit for sage-grouse habitat in the face of uncertainties about mitigation project outcomes and potential temporal loss of habitat. For credit generation projects, the HQT credit value provides an estimate of the maximum credit available to a project, depending on success in meeting agreed-upon project objectives. Mitigation Program adjustments and requirements are described in the Manual.

2.3 Quantifying credit acres with the HQT

Loss of habitat function associated with new development (i.e., permitted development projects) in sage-grouse habitat is directly quantified by the HQT through the pre- and post-project scoring of aggregate development impacts. However, the increase in habitat function and the benefits to sage-grouse that are generated by a credit project are often more indirect, more complex, and more difficult to quantify directly. For this reason, the HQT includes several different mechanisms for quantifying the gains in habitat function generated by credit projects.

Enhancement vs. preservation

The goal of Oregon’s sage-grouse Mitigation Program is to ensure a net benefit for sage-grouse and their habitat. Thus, one emphasis of the Program is to incentivize credit projects that generate habitat enhancement – restoration of degraded habitat, or some other improvement beyond existing conditions. All credit generation projects are required to include some degree of enhancement.

However, effective restoration of sagebrush habitat is slow and difficult, and protection and improved management of existing habitat is essential for any sage-grouse conservation strategy (SageCon 2015). For this reason, the HQT incorporates credit for preservation of existing good-quality habitat, as well as for enhancement of degraded habitat. Specifically, the HQT provides credit for site management and for legal protection, both of which apply to preservation projects as well as to enhancement projects.

Actions incorporated into the HQT credit calculation

The final HQT credit value for a credit generation project incorporates credit for three different types of actions. In practice, habitat enhancement (1) and site management (2) are accounted for in a project’s Ecological State scores (described in detail in Section 3.2), while legal protection (3) is counted in a separate calculation.

Habitat enhancement

Habitat enhancement (restoration) is expected to be an element of all credit generation projects.

For example, credit generators implement an activity such as cutting juniper or restoring wet meadow habitat, in order to convert the habitat in an area from a less-desirable to a more-desirable ecological state. This enhancement activity directly improves the habitat function of the site, and the improvement is reflected in the HQT algorithm through an increase in the Ecological State score.

Site management

In addition to habitat enhancement actions, credit generators must commit to ongoing management and monitoring of the project site. The specific actions required by a site management plan will vary depending on specific circumstances of the site, but typically include elements such as marking fences (to reduce sage-grouse collisions) or installing escape ramps in livestock watering tanks. These smaller management actions do not sum to a change in ecological state, but they still provide benefits for sage-grouse and should be incentivized by the Mitigation Program.


Additionally, credit generators also commit to ongoing monitoring and adaptive management of eligible lands. This could include monitoring for new appearances of invasive annual grasses or juniper incursions, and commitment to treat these threats when they are still small and manageable. In effect, this commitment to monitoring and adaptive management reduces the risk of loss of the habitat to large-scale ecological threats (IAG invasion, conifer encroachment, and wildfire). Although the magnitude of this avoided loss is very difficult to quantify directly, the HQT structure provides a set amount of credit for ongoing management and monitoring within the scoring for ecological states.

In practice, credit for site management is earned for all enrolled lands that are in good condition (ecological state A or B) after any habitat enhancement activities. This applies both to restored and to existing high quality habitat and thus provides some credit for preservation of existing good quality habitat.

Legal protection

Credit generators are required to ensure “permanence and durability” through some form of long-term legal protection for the project site (see Manual). Long-term legal protection can be considered a form of “avoided loss” from potential future development. In order to account for this avoided loss, the final HQT credit value for credit generation projects includes some credit for all lands under legal protection that are in good or fair condition. The amount of credit provided is proportional to the calculated habitat function at the project site and to the estimated risk of loss due to development in the absence of legal protection (see Section 3.4.2).

2.4 Mesic habitat

In the HQT, habitat function is quantified separately for upland (sagebrush steppe) and mesic (wetland) habitat, directly reflecting the two credit types of the Mitigation Program.

Mesic areas – including wet meadows, isolated spring wetlands, playas, seeps, and riparian areas along streams – constitute a relatively rare habitat type that provides essential resources for sage-grouse. These wetland sites maintain saturated soils for most or all of the growing season, and thus they can support communities of perennial forbs and associated insects. Sage-grouse typically move between different seasonal habitat types in order to meet resource requirements during different phases of their life cycle (breeding, late brood-rearing, winter). In June and July, as sagebrush habitats desiccate and herbaceous plants mature, female sage-grouse usually move their broods to mesic sites where succulent vegetation is available for forage. In effect, mesic areas help to provide a protein-rich diet of forbs and associated insects, which is especially important for chick survival, before the grouse change to a diet of sagebrush leaves during winter.

Mesic areas are relatively rare in occurrence throughout the sagebrush ecosystem landscape in Oregon (Donnelly et al. 2016). Yet, mesic habitat is crucial for sage-grouse to fulfill their late brood-rearing life cycle requirements, and thus the absence of mesic areas across a greater landscape can make the surrounding upland habitats unsuitable for sage-grouse. Availability of mesic areas (i.e., late brood-rearing habitat) is thought to be a limiting factor for sage-grouse in Oregon (Atamian et al. 2010, Donnelly et al. 2016).

Given the unique importance of mesic areas to the life cycle of sage-grouse, loss of mesic habitat function cannot be replaced by improvements to upland habitat function. Mesic functional acres are therefore quantified separately from upland functional acres in the HQT, and in-kind replacement of mesic functional acres is required by the Mitigation Program. It should be noted that mesic functional acres and upland functional acres are not equivalent units.


Seasonal habitats in the HQT

Beyond the distinction between upland and mesic habitats, the Oregon HQT does not separately quantify site-level function in terms of seasonal habitat needs (breeding, late brood-rearing, and winter). This decision was based on practical and conceptual concerns about site-level measurement of seasonal habitat attributes:

• Separate quantification of seasonal habitat types would require multiple (2-3) field visits for data collection during different seasons, significantly increasing the cost and potential delay associated with applying the method.

• Some key attributes of seasonal habitat types, such as the availability, diversity, and abundance of forb species, are subject to significant interannual variability and may, in many cases, be more responsive to climate factors than to management interventions. Assessing habitat function based on these attributes could result in skewed results, depending on whether the baseline and post-project assessments were conducted in unusually wet or dry years, for example, and it would decrease the sensitivity of the HQT for capturing the results of on-the-ground management actions.

The HQT recognizes the importance of access to seasonal habitat for sage-grouse, and incorporates assessment of seasonal habitat in three ways.

First, as described above, mesic areas (which provide late summer/brood-rearing habitat) are quantified separately from upland areas, and impacts to mesic features require in-kind mitigation.

Second, ODFW’s habitat designations (Core, Low Density, and General habitat; see Section 2.5.2) were delineated based on seasonal habitat use: the designations primarily reflect kernel density of known lek locations (i.e., breeding habitat), and then the initial delineations based solely on leks were expanded to also include important areas of winter habitat and brood-rearing habitat, as identified from telemetry data (ODFW 2011). ODFW habitat designations are incorporated both in the design of the Mitigation Program (Section 2.5.2), and in the Management Designations Index parameter of the HQT algorithm (Section 3.1).

Finally, the Management Designations Index also incorporates proximity to leks, a measure of seasonal (breeding) habitat availability at a relatively fine scale.

As better information about seasonal habitat use by sage-grouse in Oregon becomes available, this could be incorporated into the HQT as part of the adaptive management cycle (Chapter 4).

2.5 Spatial scale and habitat function

The sage-grouse is a wide-ranging species that requires a variety of plant community types within the broader sagebrush landscape to meet the needs of its annual life cycle: lekking habitat, abundant perennial grass for nesting habitat, forb-rich communities for brood rearing, and relatively dense stands of sagebrush for winter months. The HQT was designed to account for habitat characteristics or attributes, both natural and anthropogenic, that influence sage-grouse habitat across multiple scales.

The HQT and the broader Mitigation Program address spatial scale at four levels, which are roughly analogous to the four orders of selection identified by Johnson (1980) (Figure 2-2): Range-wide scale, Landscape scale, Local scale, and Site scale. These levels also correspond to scales at which sage-grouse policy and management are typically implemented (Stiver et al. 2010). In general, the broader range-wide and landscape scales are addressed by the design of Oregon’s Mitigation Program, while parameters in the HQT algorithm measure attributes at the finer local and site scales.


Figure 2-2. Use of multiple spatial scales for quantifying habitat function for sage-grouse. Blue type indicates parameters in the HQT algorithm.

Range-wide scale

Range-wide scale describes the geographic range of sage-grouse in Oregon. An important objective at this scale is to evaluate the contribution of net change in habitat conditions resulting from site-level management actions to regional or statewide habitat and population conservation goals. This scale can be compared to 1st order selection; policy for conservation at the range-wide scale is typically set by high levels of government (Johnson 1980, Stiver et al. 2015).

Oregon’s Mitigation Program addresses the range-wide scale through project eligibility requirements laid out in the Manual and through tracking and adaptive management of the results of the Mitigation Program. The scope of potential application of the HQT and limitations on service area under the Mitigation Program are briefly described below.

Geographic Scope. The HQT’s geographic scope is the extent of current and potential sage-grouse habitat in Oregon, as described in the Action Plan and the Manual. Documented changes to the range

1st Order:Range-Wide

Scale

2nd Order: Landscape

Scale

4th Order: Site Scale

3rd Order:Local Scale

Evaluatecontributions to overall species habitat and population goals

Target actions to the landscape

Inform actions by assessing local contextEncourage appropriate siting of actionsMeasure habitat conditions resulting from actions

Functional Acres

Project Eligibility RequirementsSpatial Tracking of credit and debit sites in mitigation service areas (GSG population areas)

Habitat Designations (Core, Low Density, General) Avoidance and Minimization Criteria

Management Designations IndexDevelopment Impacts: indirect impacts

Ecological State: sagebrush cover; juniper presence; perennial bunchgrass cover; invasive annual grass coverDevelopment Impacts: direct impacts


will be addressed through the adaptive management cycles of those documents and incorporated into future versions of the HQT as applicable.

Service Areas. Service areas describe geographic subdivisions for tracking debits and credits to sage-grouse habitat. A service area also describes the geographic region within which compensatory mitigation for an impact must occur, unless ODFW determines that a greater benefit to the species can be provided by going outside of the service area (see the Manual for more detail). The Mitigation Program aims to meet its net conservation benefit goal within each applicable service area, and the state will track development impacts and conservation outcomes within each service area as part of its adaptive management cycle.

The three service areas for the Mitigation Program are defined by sage-grouse population areas (Figure 2-1): Oregon sage-grouse have been classified into four populations based on probable natural and anthropogenic habitat barriers: Baker, Central, Northern Great Basin, and Western Great Basin (Schroeder et al. 2004, Stiver et al. 2006). A fifth population, the Klamath, is considered extirpated in Oregon. For purposes of the Mitigation Program, the small Baker population area is combined with the Northern Great Basin population area as a single Baker/Norther Great Basin mitigation service area.


Figure 2-3. Map of mitigation service areas and designated Core and Low Density habitat areas in Oregon. The mitigation service areas are equivalent to sage-grouse population areas in Oregon, except that the small Baker population has been combined with the Northern Great Basin population to form a single Northern Great Basin/Baker service area.

Landscape scale

Landscape scale is analogous to the home range of a sage-grouse population or subpopulation. Assessment of the landscape scale is relevant for delineating the best areas for conservation and identifying where credit projects should be targeted and where development should be avoided. This scale can be compared to 2nd order selection (Johnson 1980, Stiver et al. 2015). In Oregon’s Mitigation Program, the landscape scale is addressed through identification of high-priority areas for investing in conservation, and through avoidance and minimization criteria outlined in Oregon’s policies (OAR 635-140-0025) and described in the Action Plan and the Manual.

The HQT incorporates the relative importance of different landscape-scale habitat designations developed by ODFW. Following a landscape approach to sage-grouse habitat protection that has been developed across the Western states, the ODFW 2011 Strategy (ODFW 2011) identified a set of priority habitats based on density of sage-grouse breeding bird populations.

Broadly, the core area approach identifies the most productive habitat for sage-grouse and directs the highest level of conservation effort there (SageCon 2015). Similarly, new development should be steered away from these highly productive areas.


Specifically, the sage-grouse range in Oregon was split into three classification levels (Figure 2-3):

• Core habitat (i.e., Core areas, or Priority Areas for Conservation [PACs]): Core areas are the highest-priority habitat for conservation. These areas make up just 38% of the species’ current range statewide, but they protect approximately 90% of the state’s breeding population of sage-grouse (based on counts at known lek locations)1. Core habitat designations also include additional areas identified as important for habitat connectivity or winter seasonal habitat (according to telemetry data) (ODFW 2011).

• Low Density habitat: Low Density areas delineate similar features (protection of leks and known seasonal habitats) around the remaining, less densely-populated leks that were not included in Core.

• General habitat: General habitat is defined as all areas within 3.1 miles of a lek that are not mapped as Core or Low Density habitat but that maintain habitat attributes which could lead to occupancy or use by sage-grouse.

These habitat designations are incorporated into the HQT algorithm in the Management Designations Index parameter (Section 3.1).

Local scale

Local scale describes the habitat conditions within and surrounding a project site that may affect sage-grouse seasonal habitat use, dispersal, local persistence, and overall population trend (Connelly et al. 2011a). In general, this scale describes the local context of a specific project site. The local scale can be compared to 3rd order selection (Johnson 1980, Stiver et al. 2015).

Local-scale features at a project site are measured in two of the parameters in the HQT algorithm (Figure 2-1, Figure 2-2). The Management Designations Index (Section 3.1) describes the location of the project site relative to documented sage-grouse occupancy and important areas for breeding habitat. The Development Impacts score describes the location of the project site relative to development in the local area. This development score incorporates the effects of anthropogenic features that are located outside the analysis area for a project, but that still have effects on habitat function within the analysis area. Both local-scale parameters in the HQT incentivize appropriate siting of projects and management actions.

Site scale

At the site scale, sage-grouse select for vegetation structure and composition that provide for their daily needs, including forage and cover2. Site-scale features at a project site are measured in one of the parameters in the HQT algorithm (Figure 2-1, Figure 2-2): Ecological State characterizes the ecological threats and current condition of the vegetation at a site. Vegetation condition is measured either through field observations or through remotely sensed data, using vegetation functional groups that indicate the primary ecological threats and presence and resilience of native plant communities important to sage-grouse. These functional groups have been previously identified as important components in sage-grouse habitat selection (Connelly et al. 2000, 2003, Hagen et al. 2009). Vegetation functional groups

1OAR 635-140-0025(2)(d)(A) also includes an additional avoidance requirement for areas of high population richness within core. 2 The HQT’s analysis of habitat function at the site scale is not closely analogous to the 4th order habitat selection described in Johnson 1980 and Stiver et al. 2015 in that it does not specifically point to feeding, nesting, and other sage-grouse-specific life history needs within the site. Due to the practical difficulties associated with collecting data on indicators of seasonal use at the site level, as well as the high level of natural interannual variability associated with indicators of seasonal use such as forb composition, the HQT addresses factors such as the availability of summer habitat and the likelihood of habitat use through local-scale data and focuses site-level assessment on ecological states as relatively broad categories of plant communities that vary both in terms of the resiliency of native plant communities and functional value as sage-grouse habitat.


are used to classify the ecological state in each map unit according to a threat-based model (see Section 3.2).

Delineating the development footprint of anthropogenic features (i.e., non-habitat areas) is also a site-scale measure of habitat function.

Site-scale parameters in the HQT incentivize appropriate placement of project actions relative to specific on-the-ground conditions within the general project site.

Changes in site function due to development or credit generation projects are captured in both local-scale (Development Impacts score) and site-scale (Ecological State score) parameters.


3 HABITAT QUANTIFICATION PARAMETERS

The following sections describe the three parameters that are used as inputs into the HQT algorithm, as well as adjustments to the raw habitat credit value calculated by the algorithm. The sections detail how attributes defining each parameter are measured, and how measurements are translated into the scores that are used to quantify habitat function. For a complete, step-by-step description of the calculation performed by the HQT Calculator, please see the User Guide.

3.1 Management Designations Index

Description

The Management Designations Index (MDI) provides a metric of current sage-grouse occupancy for the HQT. Protection of areas that are currently used by sage-grouse is an important component of Oregon’s sage-grouse conservation strategy. Sage-grouse exhibit strong fidelity to their seasonal ranges and especially to their breeding areas, which include leks, nesting, and early-brood rearing habitats (Connelly et al. 2011b). Thus, sage-grouse may be present in areas of poor condition habitat, or, conversely, absent from sites with habitat in good condition. The HQT assigns a higher level of habitat function to areas currently used by sage-grouse than to otherwise equivalent habitat where sage-grouse are not currently present.

The MDI is a wall-to-wall data layer that uses ODFW’s sage-grouse habitat designations and proximity to known leks to classify all areas of the sage-grouse range in Oregon.

ODFW Habitat Designations The ODFW 2011 Strategy (ODFW 2011) identified a set of priority habitats based on breeding bird density of sage-grouse populations. Specifically, the sage-grouse range in Oregon was divided into three classification levels: Core habitat (i.e., Priority Areas for Conservation [PACs]), Low Density habitat, and General habitat. See Section 2.5.2 for details.

Proximity to Leks Sage-grouse breeding behavior is known to be sensitive to the effects of human development, and maintaining a protected buffer of undeveloped habitat around lek locations is a standard sage-grouse conservation strategy (Manier et al. 2014). The MDI accounts for habitat that is located within a four-mile buffer distance from any known leks with conservation status of ‘Occupied’ or ‘Pending’. Lek locations are based on ODFW annual lek survey data. ‘Occupied’ describes regularly surveyed leks where at least one male was counted present in at least one of the past seven years. At ‘Pending’ leks, sage-grouse have been counted present at some time in the past, but these leks have not been regularly surveyed in the past seven years. The Pending category includes both Occupied Pending (birds were present during the most recent visit) and Unoccupied Pending (birds were not observed during the most recent visit) leks (ODFW 2011). The four mile buffer matches the standard buffer distance used in the Action Plan (SageCon 2015).

Scoring

The MDI assigns ordinal index values to the Core and Low Density ODFW habitat designation levels, corresponding to the relative population density of sage-grouse in each level: areas designated as Low Density habitat are assigned an index value of 2, and Core habitat is assigned a value of 3 (Table 3-1). Additionally, the index value is increased by 1 for all areas within four miles of a lek. For example, an area that is within Core habitat and within four miles of lek has an index value of 4, while an area that is in Low Density habitat but the nearest lek is six miles away has an index value of 2. An area that is two miles from the nearest lek but not within either Core or Low Density habitat has an index value of 1. The index values (1, 2, 3, and 4) are normalized to range from 0-1 (i.e., 0.25, 0.5, 0.75, and 1.0, respectively) as scores to be used in the HQT algorithm. Figure 3-1 shows the value of the MDI across the range of sage-grouse in Oregon.


Unlike the other parameters in the HQT algorithm (Ecological State and Development Impacts), which are scored separately for each map unit, the MDI score is calculated as a single average value for the full project analysis area. This same project-level value is then used in the calculation of functional acres for each map unit. Calculating a single MDI score for the full analysis area reflects the relatively coarse scale of the attributes that are being measured.

Table 3-1. Scoring for the Management Designations Index

Habitat Designation Index Value Score

Core 3 0.75

Low Density 2 0.50

within 4 miles of a lek + 1 + 0.25


Figure 3-1. Management Designations Index

3.2 Ecological state

Ecological states, as used in the Mitigation Program, characterize the current vegetation condition of a site in terms of the primary threats facing sagebrush systems. Ecological states are relatively wide bins with straightforward classification, derived from a set of threat-based models (TBMs) of rangeland


ecosystems. This section introduces the TBM approach, defines the criteria differentiating ecological states, and discusses how ecological states are applied by the Mitigation Program.

The threat-based model approach

The TBMs used by the Mitigation Program use simplified state-and-transition models, which are non-linear frameworks that characterize ecological dynamics. State and transition models recognize that ecosystems develop alternate stable states or multiple successional pathways at different times, in contrast to traditional linear climax models of succession (Westoby et al. 1989). The HQT uses one TBM for upland sagebrush steppe habitat (Section 3.2.2) and a separate TBM for mesic areas (i.e., playas, wet meadows, springs, and seeps; see Section 3.2.3). These TBMs focus on the threats that can lead to transitions to less desirable states.

An ecological state is a stable, persistent vegetation community (Westoby et al. 1989). Transitions between ecological states may occur quickly or gradually over time and are often triggered by natural disturbances (e.g., climatic events or fire) and/or management actions (e.g., grazing, farming, burning, etc.) (Stringham et al. 2003). These successional processes, ecological disturbances, and management prescriptions may work alone or in interaction to cause a community to cross threshold to an alternative community on the same site (Briske et al. 2005).

The TBM approach is more simplified than many state-and-transition models and focuses on how primary threats alter ecosystem function. These threats are termed ‘primary’ because they can and often do completely compromise desirable ecological functions. For upland systems, these threats include introduced annual grass invasion, juniper encroachment, and wildfire. The TBM approach recognizes that primary threats must be addressed to ensure successful management of finer-scale ecological processes.

This approach uses letter grades to represent comparative levels of ecosystem function. Highly-functioning sites are scored as A or B, intermediate function is score as C, and poorly-functioning sites degraded by primary threats are scored as D or E. This simplified and readily understandable system helps land managers to determine appropriate treatments and potential outcomes, based on primary threats, including the risks associated with taking no management action (Briske et al. 2005).

Threat-based model for upland sagebrush steppe

The ecological states of the Upland TBM describe a gradient of sagebrush ecosystem function, from largely intact conditions to those compromised by primary threats: introduced annual grass invasion, juniper encroachment, and wildfire. States are determined by the composition of vegetation functional groups. Intact states – States A and B – possess desirable vegetation functional groups and lack functional groups indicative of primary threats. Transitioning states – State C – have increasing levels of primary threats. Compromised states – States D and E – are characterized principally by primary threat functional groups.

Although the TBM approach is an ecosystem model, it supports sage-grouse conservation by prioritizing and managing for the foundational components of viable sage-grouse habitat. While even the best condition ecological states may not provide all the habitat requirements for sage-grouse, they retain the potential to do so. Ecological states with primary threats, on the other hand, reduce and ultimately remove the potential to provide any sage-grouse habitat value. Because the available and effective habitat management interventions for sage-grouse, such as juniper removal and perennial grass seeding, are broadly limited to addressing primary threats, the TBM approach is consistent with the conservation and restoration options land managers use both to maintain sagebrush ecosystem function and to promote sage-grouse habitat. For instance, preferred food forbs are an important feature of sage-grouse nesting and brood-rearing habitats (Casazza et al. 2011), but the abundance and diversity of desirable forbs are subject to significant interannual and spatial variability (Chambers et al. 2014). Additionally, there are few practical management actions that directly influence forb abundance (e.g., planting). Therefore, food forbs are not included in the TBM, as the focus remains on features of the landscape


that affect broader ecosystem function and that also respond to management intervention. However, site-specific plans for individual projects may monitor and manage forbs as a management goal where appropriate.

The Upland TBM used in the HQT is adapted from the approach outlined in “Threat-Based Land Management in the Northern Great Basin: A Manager’s Guide” (Johnson et al., 2019), used in Candidate Conservation Agreements (CCAs) in Oregon. The TBM framework was initially developed by US Department of Agriculture (USDA) Agricultural Research Service (ARS), Oregon State University, The Nature Conservancy, the US Fish and Wildlife Service (USFWS), and others as a framework for assessing ecological resiliency and habitat suitability at the site scale. This original framework included three separate models, later combined into a single model for greater consistency and simplicity. The resulting qualitative model provides an effective communication tool for discussing rangeland issues among diverse stakeholders, and for determining appropriate management actions on a single site or property. By incorporating an existing model framework, the HQT and the Mitigation Program take advantage of the extensive scientific and practical evaluation that went into developing the model. Additionally, using this existing program facilitates coordination of the Mitigation Program with other sage-grouse management and monitoring programs throughout the state of Oregon.

Important features of the Upland TBM that are relevant to sage-grouse and the HQT are summarized in the following sections. Users should refer to the Manager’s Guide (Johnson et al., 2019) for additional context and details.

Ecological states

For the HQT, ecological states are classified into five grade levels – A, B, C, D, and E – that describe ecosystem function and, relatedly, the potential to provide sage-grouse habitat (Table 3-2). States A and B both represent high-functioning ecosystem conditions with limited threats, though State B has lower current potential habitat value for sage-grouse due to a lack of sagebrush. State C has lower ecosystem function and lower current potential habitat value because significant threats are present, while States D and E represent compromised ecosystem function due to expressed threats and provide no current potential habitat value. Expression of threats has progressed farther in State E than in State D, but both stats are effectively non-habitat from the sage-grouse perspective, and therefore States D and E both receive the same score in the HQT.

The Upland TBM also incorporates the tenets of resistance and resilience ecology, where sites are considered for both their resilience following disturbance and their resistance to changing states because of those disturbances (Chambers et al. 2014). Juniper encroachment largely occurs at higher elevations associated with cooler growing conditions, while introduced annual grass invasion occurs largely at lower elevations associated with warmer growing conditions. A broad profile of overlap, where both threats occur, is termed the dual-threat (Figure 3-2). Although State C uniformly describes a similar level of current ecosystem function, the resilience and resistance of the site inform how easily the lost native plant community (i.e., State A or State B) can be recovered (Figure 3-2). For example, State C with only an annual grass threat is typically less resilient and more difficult to recover than State C with only a juniper threat, largely due to associated climatic conditions, and available management interventions.

Table 3-2. Classification levels for ecological states

A. High ecosystem function / High potential habitat value / Limited threats

B. High ecosystem function / Moderate potential habitat value / Limited threats

C. Impaired ecosystem function / Limited or no current potential habitat value / Significant threats

D and E. Compromised ecosystem function / No current potential habitat value / Threats fully expressed


To incorporate resilience and resistance into the HQT, ecological states are further subdivided based on the types of threats expressed. States are placed in one of three different ecological threat categories: invasive annual grass threat, juniper threat, or dual threat. There is only one State A and one State B because by definition minimal threats are expressed in these states. State C and State E both have two subdivisions, and State D has three, differentiated based on the primary threats that are present (Figure 3-3, Table 3-3). The name of each ecological state consists of the letter name and the threat expression. For example, State D is denoted as D-IAG, D-Juniper, or D-Dual, depending on whether it is within the Invasive annual grass, Juniper, or Dual threat models, respectively.

Figure 3-2. Resilience and resistance of ecosystem function associated with different ecological threats and climatic conditions, and the relationship between perennial bunchgrass density, native plant resiliency, and ecological states. Dual threat refers to both invasive annual grass (IAG) and juniper threats expressed on a site. Figure from Johnson et al., 2019.


Figure 3-3. Decision tree for ecological state classification. Each state is color-coded by threat category in the decision tree: the invasive annual grass threat with orange, the juniper expansion threat with teal, the dual threat with purple, and minimal threats expressed with all three colors. Ecological states are indicated by letters A-E (see Table 3-2). States A and B (minimal threats expressed) are color-coded with all three colors because they are shared across the threat categories. Similarly, state C is shared across the juniper and dual threat categories, thus it is color-coded with both teal and purple. Quantitative metrics should be considered guidelines and adjusted relative to specific site conditions. Defining features of each ecological state are also summarized in Table 3-3. Figure from Johnson et al., 2019.


Figure 3-4. Photograph examples of each ecological state. While the photographs represent clear examples of ecological states, some states will look different depending on site potential and time of year. See Figure 3-3 for guidance on ecological state classification. Defining features of each ecological state are also summarized in Table 3-3. Figure from Johnson et al., 2019.


Table 3-3. Summary of characteristics defining upland ecological states

Threat category State name Description

Is juniper threat expressed?

(juniper present at site)

Is IAG threat expressed?

(IAG cover greater than a 1:1 ratio with

PG cover)

Is sagebrush well

represented? (generally >10%

cover)

Is juniper canopy cover

well-developed? (generally >10%

cover)

Is perennial understory3

largely intact? (generally >5% cover and/or more than 5

plants per m2)

Minimal threats

expressed

A Intact native shrubland No No Good sagebrush cover No juniper cover Intact understory

B Intact native grassland No No Sparse sagebrush cover

No juniper cover Intact understory

IAG threat

C-IAG Shrubland with annual grass understory No Yes Good

sagebrush cover No juniper cover Degraded understory

D-IAG Annual grassland No Yes Sparse sagebrush cover

No juniper cover Degraded understory

Dual/ Juniper C-Dual/Juniper

Shrubland with encroaching juniper Yes No

Good sagebrush cover

Limited juniper cover

Degraded understory

Dual threat

D-Dual Shrubland or grassland with encroaching juniper and annual grass understory

Yes Yes Good sagebrush cover

Limited juniper cover

Degraded understory

E-Dual Juniper woodland with annual grass understory

Yes Yes Sparse sagebrush cover

Dense juniper cover

Degraded understory

Juniper threat

D-Juniper Juniper woodland with native perennial understory Yes No Sparse

sagebrush cover Dense

juniper cover Intact understory

E-Juniper Juniper woodland with denuded understory

Yes No Sparse sagebrush cover

Dense juniper cover

Degraded understory

3Perennial understory for this question includes both perennial bunchgrasses and native perennial forbs.


Functional groups

Ecological states are defined by the relative abundances of four major vegetation functional groups:

• Juniper

• Invasive annual grasses (IAG)

• Sagebrush

• Native perennial bunchgrasses (PG).

A decision tree (Figure 3-3) is used to classify a site into an ecological state based on the representation of each functional group. The first decision points in the tree depend on which primary threats are expressed at the site (no threats expressed, IAG threat, juniper threat, or both threats). Additional decision points depend on the condition of the other functional groups.

Defining appropriate thresholds for classifying the relative abundance of each functional group is complicated by high variability among sites (space) and across years (time). Sage-grouse habitat in Oregon extends across a gradient associated with elevation. Low elevation sites are typically more arid and less productive (warm-dry), while higher elevation sites are typically cooler, wetter, and more productive (cold-moist) (Chambers et al. 2014). Temporally, interannual fluctuations in climate conditions (temperature, rainfall, snowfall) can produce wide variability in observed conditions at a given site over time, especially in terms of grass and forb cover. In recognition of this variability, thresholds between categories in the decision tree are expressly qualitative; quantitative values for these thresholds are provided as guidelines only, and should be interpreted using information on both current site characteristics and ultimate site potential (Johnson et al., 2019).

The following sections describe the rationale for including each functional group in the decision tree.

• Juniper Encroachment of conifers (primarily western juniper, Juniperus occidentalis) into the historically treeless sagebrush steppe is one of the primary threats facing sagebrush ecosystems. At higher elevations, especially, the extent fire-intolerant juniper has increased dramatically due to reduced fire frequency and other factors (Baruch-Mordo et al. 2013). Increased juniper can reduce desired perennial vegetation and negatively impact sensitive wildlife, including sage-grouse.

Sage-grouse are sensitive to the presence of juniper and other conifers: birds avoid areas with juniper encroachment, and as little as 4% juniper canopy cover may be enough to deter presence of leks (Baruch-Mordo et al. 2013, Severson et al. 2016, 2017). Presence of juniper within a site sharply reduces the value of the habitat for sage-grouse, and it also indicates the potential for further expansion of juniper beyond its current extent. Therefore, the ecological states used in the HQT account for the presence and extent of juniper encroachment.

Ecological succession from sagebrush steppe to juniper woodland follows a well-defined trajectory. In early stages of juniper encroachment, typically young trees are present but sparse or scattered, and sagebrush and herbaceous vegetation still dominate the site. In later stages, approaching a well-developed juniper woodland, trees dominate and the shrub and herb understory declines rapidly (Miller et al. 2005, JFSP 2008). The potential for restoring a site with some level of juniper encroachment to good-quality sagebrush steppe depends on how far along this trajectory the site has progressed. Sites in the early stages of juniper encroachment retain a significant shrub and understory layer, and removal of juniper from these sites can prevent loss of key understory plants and produce immediate habitat benefits for sage-grouse. At sites with a well-developed juniper woodland, however, treatment takes significantly more resources and time to recover the shrub and understory vegetation required to support sage-grouse (Miller et al. 2005). These sites are often considered not restorable.

In the ecological state decision tree, the primary assessment metric for juniper is simply presence/absence: if juniper or other conifers are present at a site, then the juniper threat is


expressed at that site. Additionally, for states D and E of the Juniper and Dual threat categories, the model distinguishes between early stages and later stages of juniper encroachment. In the Dual threat category, the distinction between state D and state E focuses on the extent of juniper canopy cover, while in the Juniper threat category the distinction focuses on the condition of the perennial understory (perennial bunchgrasses and native perennial forbs), but both metrics are related to the trajectory of ecological succession toward juniper woodland.

• Invasive annual grasses (IAG) Invasion by non-native annual grasses is considered another primary threat to sage-grouse and sagebrush habitat in Oregon (ODFW 2011). Invasive annual grasses (primarily cheatgrass, Bromus tectorum; medusahead, Taeniatherum caput-medusae; and ventenata, Ventenata dubia) alter habitat suitability for sage-grouse by reducing or eliminating native forbs and grasses essential for food and cover. Establishment of plant communities that do not provide suitable habitat (e.g., introductions and monocultures of non-native, invasive plants) is reducing sage-grouse habitat quality and quantity in Oregon. IAG and other weeds continue to expand from borders of large infestations. Many sagebrush-steppe plant communities have crossed a threshold, beyond which they are no longer recoverable using currently available control methods. IAG also increase fire frequency, which directly threatens sage-grouse habitat and promotes further establishment and dominance of IAG (Balch et al. 2013). This IAG-fire feedback loop can result in conversion of sagebrush-steppe communities to annual grasslands (Miller et al. 2011, Davies et al. 2011).

Using IAG as an indicator of habitat condition presents some conceptual and practical challenges. First, IAG cover tends to vary widely among years in response to rainfall and other conditions (Bradley and Mustard 2005). Second, although the other indicators can be distinguished with reasonable consistency in remote sensing data, IAG cover remains difficult to map with high accuracy from these data sets (Boyd et al. 2017). Ongoing research seeks to develop better techniques for identifying IAG remotely. Despite these challenges, IAG was retained as an indicator in the HQT because accounting for the risk and extent of IAG expansion is essential to evaluating the status of sage-grouse habitat in Oregon.

In the ecological state decision tree, the assessment metric for IAG compares the relative abundance of IAG vs. perennial bunchgrasses (PG). As a general guideline, the IAG threat is considered to be expressed at a site when IAG is more abundant than PG at that site. Using a threshold based on a ratio rather than on simple percent cover of IAG normalizes to some extent for the effects of annual rainfall and site potential and provides some robustness to these inherent sources of variation.

• Sagebrush Sage-grouse require sagebrush cover during every phase of their lifecycle (Connelly et al. 2011a). Sagebrush provides nesting and hiding cover, and forage at various times of the year; high canopy cover of sagebrush is strongly associated with breeding, nesting, and overwintering habitat. A threshold of at least 10% sagebrush canopy cover is often used as a threshold to identify sites with enough sagebrush to provide good habitat for sage-grouse (Connelly et al. 2000, Hagen et al. 2009).

In the ecological state decision tree, the presence or absence of adequate sagebrush cover (generally, at least 10% cover) is an important characteristic defining most ecological states (Figure 3-3, Figure 3-4, Table 3-3). In the dual threat category, sagebrush cover is not directly assessed and the combination of IAG presence and juniper woodland development are the determining factors. However, even for these states understanding sagebrush cover will inform restoration needs and potential at a site.

Although healthy sagebrush cover is an essential feature of most sage-grouse habitat, it is difficult to create through management intervention. Rehabilitation of sagebrush is a slow process and not always successful (Pyke 2011). Therefore, existing good-quality sagebrush habitat is valued highly in the scoring of ecological states for the HQT.


• Perennial bunchgrasses (PG)

Perennial bunchgrasses are a key feature of high-quality breeding habitat for sage-grouse. These tall-stature bunchgrasses provide cover and nesting sites (Crawford et al. 2004). Sites with healthy perennial bunchgrasses also tend to support forb-rich communities and are more drought resistant than rhizomatous grasses. The relatively deep roots of PG are more resistant to invasion by annual grasses, and the deep roots also help PG survive and recover after a fire or other disturbance (Miller et al. 2013). The presence and vigor of PG is thus an indication of native plant resiliency, the likelihood that a plant community will recover to a native-plant dominated state following disturbance (Figure 3-2).

The expected cover of PG for a site in good condition varies widely with site potential, primarily along the warm-dry/cold-moist elevational gradient. In the ecological state decision tree, PG is assessed in terms of its abundance relative to IAG (see above). Additionally, states D and E in the Juniper threat category are distinguished by the presence or absence of an intact perennial understory consisting of PG and native perennial forbs.

Classification method

In order to calculate functional acres for a project site, each map unit in the project analysis area must be assigned to the appropriate ecological state. First, the relative abundances of the four major vegetation functional groups (juniper, IAG, sagebrush, and PG) are assessed in each map unit. Then each map unit is classified into the appropriate ecological state according to the decision tree shown in Figure 3-3. There are two primary methods for assessing the vegetation functional groups at a project site: remote sensing data and field data collection in a site visit.

• Remote sensing data A wall-to-wall data layer for estimated ecological state across the sage-grouse range in Oregon has been developed based on remote sensing data. In this method, remote sensing data is used to estimate percent cover of each vegetation indicators in each pixel across the landscape; percent cover values are then used to classify each pixel into an ecological state, according to a modified, quantitative version of the TBM decision tree. This coarse-scale remote estimate of ecological state is not appropriate for making final mitigation credit or debit determinations. Rather, it is primarily used to assess habitat condition at a landscape scale, including estimating the likely condition across a relatively large project site and aiding in the delineation of provisional map units to help guide the field assessment (see User Guide). The estimated ecological state data layer and details of the calculation methods are available on the Sage-Grouse Data Viewer (Table 1-1).

• Site visit Final determination of ecological state for use in the HQT requires a site visit and field data collection, in order to assess current ecological state at a finer scale and with more precision than can be obtained from remote sensing data. In the field, ocular assessment (overall visual estimate) by experienced field personnel is used to assign ecological states.

Ocular assessment methodology represents a compromise between the competing demands for detail and precision vs. practicality in applying the HQT. The most objective and repeatable method for determining ecological state according to the decision tree of the TBM would be to use vegetation transect sampling data to measure percent cover of functional groups. However, the level of effort required to achieve adequate sampling density in every map unit of an analysis area would be prohibitive, especially for large projects. Further, the level of precision provided by sampling transects is considerably greater than what is required to determine ecological state. The ecological states used in the HQT are a set of relatively coarse bins with simple thresholds. Although the accuracy and repeatability of visual estimates for percent cover are generally low, estimates of whether percent cover is above or below a given threshold (e.g., 10% cover for sagebrush) should be much more consistent, at least when the site is not too close to the threshold. For sites that are



close the threshold, field crews have the option to assign transitional or intermediate states. Additionally, example transects can be used to help field crews “calibrate” their visual estimates. Finally, the more flexible and holistic determination of ecological state using the ocular assessment approach allows field personnel to account for the full range of specific features observed at a site. Detailed field methods are provided in the User Guide.

In order to test the assumption that ocular assessment provides an adequate method for determining ecological state, a sub-group of the Technical Team conducted a pilot study that compared the ecological states assigned using ocular assessment to the states that would be obtained from using vegetation sampling transect data at a set of pilot test sites. Percent cover estimates from the vegetation sampling were used classify transect sites into ecological states according to the ecological state decision tree in an earlier version of the TBM. Results showed a high degree of consistency between the two methods: ocular assessment assigned the same ecological state as transect data for 28 out of 47 transects (59%), and differed by only a transitional state (e.g., transect data indicate State C, but the field crew’s ocular assessment assigned the transitional State A-C, intermediate between State A and State C) for 14 of the 47 transects (30%). The two methods assigned a different state for only 5 transects (11%). Additionally, assignment of transitional states by ocular assessment correlated well with measured percent cover of relevant indicators (crews were more likely to assign a transitional state when the percent cover of an indicator was nearer its threshold level), which provides further evidence that ocular assessment is capable of distinguishing between sites that are above, below, and near threshold cover levels. Further details of pilot study methods and results are provided in Appendix B.

Scoring

Each basic ecological state is associated with a score between 0 and 1, where 0 represents minimal habitat value for sage-grouse and 1 represents optimal conditions (Table 3-4). Scores primarily reflect the current value of the ecological state as sage-grouse habitat, following Table 3-2; thus, state A receives a maximum score of 1, with scores decreasing for less desirable states. Areas that provide no current or potential habitat for sage-grouse, including developed areas (e.g., roads or agricultural fields) and non-sagebrush habitat (e.g., steep canyons or open water) receive a score of 0.

In addition to current sage-grouse habitat value, scores also reflect the correlated dimensions of resistance and resilience to landscape-scale threats, and potential for restoration to high-value states. High functioning ecological states (A and B) are by definition more resistant or resilient to major threats than are the states with impaired ecological function (C, D, and E) (see “Functional groups” section above). These lower value states indicate the expression of one or more major threats. For example, habitat in State C-IAG is degraded by presence of IAG, and these grasses also increase the risk of wildfire. If a site is successfully restored from State C-IAG to State A, then the value of the habitat for sage-grouse is increased, and the risks of wildfire and further spread of IAG have been reduced. Similarly, cutting juniper from a C-

Table 3-4. Scoring for upland ecological states Category Ecological state Score

Prime A* 1.2

Prime/Transitional A-B* 1.1

Basic A 1.0

Prime B* 1.0

Transitional AB 0.9

Basic B 0.8

Transitional AC 0.65

Transitional BC 0.55

Transitional BD 0.45

Basic C-IAG 0.3

Basic C-Dual/Jun. 0.3

Transitional CD 0.2

Basic D-IAG 0.1

Basic D-Dual 0.1

Basic D-Juniper 0.1

Basic E-Dual 0.1

Basic E-Juniper

Nonhabitat Nonhabitat 0

Nonhabitat Developed 0


Dual/Juniper site reduces the risk of further juniper encroachment. Thus, the large increase in score between State C (0.3) and State A (1.0) provides credit both for the improvement in current sage-grouse habitat function and also for the avoided loss (i.e., reduced risk) due to primary ecological threats. In general, C states have fewer and/or less severe threats than do D and E states. Similarly, C states retain good potential for restoration to high-value A or B habitat, whereas restoration of D and E sites is likely to be slower, more difficult, and less successful.

• Transitional states Although the TBM formally defines A, B, C, D, and E-level states, the HQT allows field crews to assign transitional or intermediate states for conditions that fall near percent cover thresholds or where field observations indicate that the apparent trend for the site is in transition between two established states. Scores for transitional states are intermediate between the relevant basic scores. For example, a map unit classified as A-C transitional would be assigned a score of 0.65, i.e., the average of the score for State A (1.0) and the score for State C (0.3).

• Prime states Scoring of ecological states for the HQT includes two state classifications that are not included in the basic TBM: State A-prime (A*) and State B-prime (B*), with scores of 1.2 and 1.0, respectively. These “prime” states (symbolized by an asterisk) were added to the scoring rubric in order to incentivize protection of existing good-quality habitat and to provide some credit for active site management that is not associated with a change in ecological state. For credit generation projects, prime scores are awarded to all areas in State A or State B that are covered by active site management and performance standards. Thus, for a map unit that is successfully restored from State C to State A, with ongoing management for the resulting A habitat, the Ecological State score will increase from 0.3 (pre-project State C) to 1.2 (post-project State A*). Similarly, the score for a map unit that is initially State A habitat and that is anticipated to remain in State A with protection and active site management will increase from 1.0 (pre-project State A) to 1.2 (post-project State A*).

The “extra credit” provided by prime state scores reflects several considerations:

- Effective restoration of sagebrush habitat is slow and difficult; protection and enhancement of existing habitat is essential for sage-grouse conservation (SageCon 2015). For this reason, the HQT provides some incentive for protection of existing good-quality habitat as well as for restoration of degraded habitat.

- Throughout the sage-grouse range, existing habitat is at risk of loss (to varying degrees) to the landscape-scale ecological threats of fire, IAG, and conifer encroachment. Active site management includes monitoring and management designed to reduce risks from juniper (identifying and cutting early encroachment of juniper) and annual grass invasion (preventing establishment of invasive species). The prime state scores provide some credit for this “avoided loss” to ecological threats.

- In addition to actions aimed at reducing landscape-scale ecological threats, as described above, active site management as required by the Mitigation Program usually also include enhancement measures that are not associated with a change in ecological state, but that still provide benefits for sage-grouse. Such enhancement measures might include marking fences to prevent sage-grouse collisions or installing escape ramps in livestock watering tanks. The prime state scores provide some credit for these beneficial actions.

Threat-based model for mesic areas

Conservation, restoration, and management of rare mesic habitats is an important component of Oregon’s sage-grouse conservation strategy (see Section 2.4). Mesic areas tend to be disproportionately in private rather than public ownership (SageCon 2015, Donnelly et al. 2016), which highlights the need for private landowner participation in conservation efforts. A very simple threat-based model was


developed to describe the conditions, threats, and management actions appropriate to mesic resources within the sage-grouse range in Oregon.

The primary indicators of habitat value in mesic areas are the timing and duration of hydroperiod (i.e., green period) and the presence of preferred forbs. Both of these indicators are highly variable according to interannual climate factors (rainfall, snowfall, temperature) and differences in site potential. Further, hydroperiod and forb community are likely to meet the maximum site potential in the absence of external threats. Therefore, the TBM for mesic areas assesses presence and extent of common, observable threats to mesic areas, emphasizing threats that are amenable to management actions.

Threats to mesic habitats

The most common threats to mesic habitats across the sage-grouse range in Oregon include grazing pressure, hydrologic modification, invasive plants, and woody species encroachment (USFWS 2015). Mesic habitats also face significant landscape threats from drought, climate change, and groundwater pumping (Patten et al. 2008). However, because these landscape-scale threats cannot be directly observed at individual sites nor directly addressed by site-scale management actions they are not directly considered in the mesic TBM.

• Grazing pressure Excessive or improperly managed grazing is a primary threat to mesic habitats, and it is the threat that is most directly under the control of land managers. Heavy use of mesic areas by livestock reduces abundance and diversity of native forbs by grazing and trampling (Beever and Brussard 2000). These sites are particularly vulnerable to overuse during late summer and during drought conditions, when they are also most essential for sage-grouse (Crawford et al. 2004, ODFW 2011). As secondary effects, the loss of native vegetation associated with overgrazing may also increase the risks of hydrologic modification and invasive species (Chambers 2004). Overgrazing in riparian areas may also reduce bank stability and increase channel incision, see below (Chambers 2004, Poff et al. 2011, SageCon 2015)

Management actions to reduce impacts associated with grazing pressure include construction of alternate watering sites and seasonal or total exclusion of livestock from sensitive areas (SageCon 2015).

• Hydrologic modification. Hydrologic modification refers to the alteration of the flow or distribution of water by human activities. Stream channels and wetlands throughout the sage-grouse range have been degraded, channelized, diverted, dredged, and filled, resulting in the loss of connectivity between the stream channels and the floodplains. This de-watering has led to site desiccation and a loss of associated riparian/wetland plant communities (ODFW 2011, Lev et al. 2012). For systems without a defined channel, such as wetlands, springs, and playas, hydrologic modification typically consists of sediment removal or fill, most often in the form of diversions created for livestock watering (Lev et al. 2012, Sada and Lutz 2016). For example, concentrating water for livestock use may prevent natural dispersion of water to meadows (SageCon 2015). In riparian sites with a defined channel, hydrologic modification is typically in the form of modifications to stream channels (such as canals, ditches, dams, culverts) or, indirectly, floodplain disconnection resulting from stream channel incision (downcutting). Stream channel incision leads to a lowered water table and reduction or elimination of riparian vegetation.

Management actions to address hydrologic modification include removal of existing diversions or spring developments; managing grazing; construction of bioengineered, low-tech structures such as beaver dam analogues; and intensive mechanical restoration of channel or wetland morphology (USFWS 2015, NRCS 2017).


• Invasive plants Invasion by annual grasses poses a similar threat to mesic areas as it does to upland sagebrush habitats. Invasive species in mesic areas typically form dense, monotypic stands that crowd out native forbs, reduce forb diversity, and reduce habitat value for sage-grouse. Additionally, large woody invasive plants like tamarisk (Tamarix spp.) may lower water tables and thus reduce water availability and habitat value of invaded wetlands (Zedler and Kercher 2004). Invasive species of concern in Oregon wetlands include reed canary grass (Phalaris arundinacea), perennial pepperweed (Lepidium latifolium), meadow foxtail (Alopecurus pratensis), and Canada thistle (Cirsium arvense) (Lev et al. 2012, SageCon 2015).

The primary management actions to address invasive plants are removal of invasive plants and replanting with native species.

• Woody plants Conifer encroachment poses a similar threat to mesic areas as it does to upland sagebrush habitats (ODFW 2011). In addition to direct loss of habitat from conifer encroachment into mesic areas, presence of conifers near or surrounding the mesic sites may prevent sage-grouse from accessing this valuable habitat (NRCS 2017). Conifer encroachment into riparian zones is a high priority for removal effort (SageCon 2015).

The primary management actions to address woody plants are conifer removal and post-removal fuel treatments.

States and scoring

Ecological states used in the mesic TBM characterize the number and extent of threats present in an area of mesic habitat, which generally correlates with the relative value of the habitat for sage-grouse. Scoring a mesic site requires a field visit in order to directly observe the presence or absence of threats. For threats that are present, field crews assess the level of degradation as high, medium, or low. Table 3-5 describes the ecological states associated with different combinations of threat levels. The mesic TBM uses the same general classification levels (A, B, C, D) and associated scoring that are used for the upland TBM (see Table 3-2), ranging from high habitat value and limited threats (State A) to no current habitat value and threats fully expressed (State D). As in the upland TBM, states of A* and B* are assigned to A and B habitat, respectively, that are covered by a site-specific management plan and performance standards. The B* category recognizes that some threats (e.g., invasive species) may be very difficult to eliminate entirely, and active management to prevent low-level threats from intensifying in valuable mesic resources still provides substantial benefit for sage-grouse.

Table 3-5. Classification and scoring for mesic ecological states

State Score Threat criteria

A* 1.2 No threats present; applies to sites with ongoing monitoring and active site management

A 1.0 No threats present

B* 1.0 Threats are present at a low level only; with ongoing monitoring and active site management

B 0.8 Threats are present at a low level only

C 0.3 One threat present at a medium level; additional threats are present at low level or absent

D 0.1 One or more threats present at high level, or multiple medium-level threats


3.3 Development impacts

Sage-grouse are very sensitive to the effects of development within their habitat. Research consistently indicates that sage-grouse select habitat based on the presence or absence of anthropogenic features such as roads, powerlines, and buildings, and that key demographic changes may be influenced by proximity to these features (e.g., decreased nesting success due to change in predator community in proximity to powerlines).

Effects of development on sage-grouse include both direct and indirect impacts.

Direct Impacts

The conversion of land from sagebrush habitat to non-habitat such as pavement or buildings is a direct impact; this area of surface disturbance (the area of direct impact) is termed a development footprint (also project footprint, in the context of a specific development project). For new development projects, the project footprint represents total loss of habitat function for sage-grouse within that area.

Indirect Impacts

The presence of anthropogenic features surrounding an area can reduce the suitability of that area as habitat—even the area itself has characteristics that are beneficial to sage-grouse. This is known as an indirect impact or indirect effect. Research suggests that the indirect effects of development on sage-grouse depend on proximity to the feature: impacts to sage-grouse are highest immediately adjacent to the feature and decline with increasing distance (Manier et al. 2014).

Development categories

Anthropogenic features tracked by the HQT are classified into development categories, such as roads, power lines, or solar energy facilities, which vary in their mechanism and magnitude of impacts to sage-grouse. These standard development categories are listed in Appendix C, Table C-1. Several development categories (powerlines, roads, and railroads) are further classified into subcategories, depending on the size or activity level of the feature. For example, subcategories of roads include interstate highways, major roads, and surface streets.

Quantification of development impacts

Indirect impact magnitude and distance

The HQT accounts for the indirect effects associated with development by applying empirically-based distance-decay curves to all mapped anthropogenic features near a site. In order to quantify these indirect effects, each development category and subcategory is assigned a relative indirect impact magnitude and an indirect impact distance. Table C-1 (Appendix C) lists the indirect impact magnitude and distance for each development category and subcategory.

Impact magnitudes characterize the degree of disturbance relative to the highest level of disturbance possible, on a scale of 0 (no impact) to 1 (maximum disturbance). These magnitude values are set by expert opinion and evaluation of available scientific literature.

An indirect impact distance describes the maximum distance from a feature at which there is some measurable impact of the feature on sage-grouse. For many development categories, impacts to sage-grouse have been shown to extend several kilometers beyond the area of direct impact (Manier et al. 2014). Indirect impacts may include habitat avoidance, increased predation, or decreased recruitment. Several primary mechanisms, summarized in Appendix C, Table C-2, are believed to account for these indirect impacts of development on sage-grouse. Indirect impact distances used in the HQT are based on empirical studies that evaluate the influence of anthropogenic activities and infrastructure on sage-grouse. The primary source for this information is a US Geological Survey (USGS) report summarizing conservation buffer distance estimates for sage-grouse (Manier et al. 2014). When data for specific


development categories were not available, impact distances were assigned based on the distance associated with the presumed mechanism(s) of impact.

While the distance effect of anthropogenic features is generally well established (Manier et al. 2014), the literature is inconclusive on the magnitude of the effect at specific distances for specific development categories. The main conclusion that can be drawn from the research is that there is a significant effect near the source, and the effect fades as distance from the source increases. Therefore, in the HQT most development impacts are modeled spatially as simple linear decay: the full impact magnitude at the feature itself decreases linearly with distance out to an impact magnitude of zero at the specified indirect impact distance.

The quantification of indirect impacts is somewhat different for power lines than for other development categories. Power lines are classified by voltage to account for the size of the line. Indirect impacts for power lines are computed by applying two separate linear decay curves, each with its own impact magnitude and impact distance. First, a curve with high magnitude and short distance is applied to represent the highly impacted area near the power line where sage-grouse exhibit behavioral avoidance of the feature. Second, a more gradual linear decay curve with a longer impact distance and a much lower impact magnitude is applied. This wide zone of low impacts reflects research showing reduced sage-grouse demographic rates associated with power lines at distances up to 10 km from the lines, likely an effect of increased predator (raven) abundance associated with the powerlines (Gibson et al. 2018).

Data sets

The basis for the development impacts calculation is a spatial data set, the Development Footprints Layer, which collects the development footprints of existing anthropogenic features across the range of sage-grouse in Oregon. The Development Footprints Layer compiles numerous data sets for the various development categories, including the following:

A. Major roads

The roads data depict major roads (defined as those which might alter sage-grouse behavior because of noise, collision risk, etc.) across the occupied range of sage-grouse in Oregon. Beginning with the dataset of major roads from within PAC boundaries, agreed upon by Counties and the Bureau of Land Management (BLM) to be included in the PAC "baseline" calculations, additional road arcs were selected that:

• Depicted the continuation of a major road from within core to areas beyond PAC boundaries; or

• Depicted a state, federal or county highway, completely beyond PAC boundaries; or

• Depicted primary connectors between highways outside of PACs, and major roads within PACs; or

• Roads determined by ODFW staff to be impactful that may not meet any of the above criteria.

Esri basemap imagery was used to confirm the location of the roads, and that they met the criteria of inclusion.

B. Communication Towers (FCC)

Communication towers data consist of towers that the Federal Communications Commission (FCC) licenses, including cellular, paging, microwave, AM, and FM towers. All communication towers from Oregon were extracted for the analysis. The source data was last updated in June 2012.

C. Mines (DOGAMI, ODOT)


The mines data depict surface mines and aggregate extraction sites. Base data for mine locations come from the Oregon Department of Geology and Mineral Industries (DOGAMI) Mineral Information Layer for Oregon, from the Oregon Department of Transportation (ODOT) Aggregate Source Database, and from BLM data for mine locations within PACs. Polygons were delineated at these locations using Esri National Agriculture Imagery Program (NAIP) imagery. Additional mines were identified from visual inspection of the imagery.

D. Transmission Lines

Base data for transmission line locations come from a public domain dataset provided by the State of Oregon. These data were updated within the range of sage-grouse in Oregon, using NAIP and Esri basemap imagery to:

• ensure that the major transmission network was complete;

• differentiate major transmission lines from distribution lines; and

• estimate the buffer width to be associated with each major line, assuming previous data attributes included in the source information were correct.

Attributes were added to differentiate features that were added during the update, and to separate distribution lines from the broader transmission system. Transmission lines that were not present in the original State of Oregon dataset and that are not discernible in aerial imagery do not appear in the data set.

E. Power Plants

Base data for power plant locations come from the original transmission line data set described above. Polygons were delineated at these locations using Esri NAIP imagery. Additional attributes of facility operator, megawatts (power plants), and maximum voltage capacity (substations) were populated based on information that is publicly available online.

F. Railways (FRA)

The railways data come from the Federal Railroad Association (FRA) Rail Network data set, which includes both freight and passenger train lines.

G. Wind Turbines (FAA)

Base data for wind turbine locations come from the Federal Aviation Association (FAA) Digital Obstacle File. Additional wind turbine locations were identified from visual inspection of Esri imagery.

H. Airports/Heliports

Base data to identify airport locations come from the BLM’s Structures point data layer and from the Geographic Names Information System (GNIS) point data layer. Polygons were digitized at these locations using Esri NAIP imagery. Additional airports and heliports were identified from visual inspection of the imagery, and all polygons are general representations of those areas.

I. Developed Areas

Base data identifying developed areas come from a 2016 dataset of Urban Growth Boundaries (UGBs) in the state of Oregon, available from the Oregon Spatial Library, and from the GNIS point data layer. Polygons were digitized using Esri NAIP imagery; UGBs from the base data were modified to capture only the current developed footprint visible in the imagery. Additional developed areas were identified from visual inspection of the imagery.

Aggregation and scoring

In the HQT, a measure of aggregate development impacts at a project site is estimated with a spatial analysis of digital GIS data. This analysis is a multi-step process to model the indirect impacts of each


development category and then combining these impacts into a single raster surface representing the aggregate impacts of development throughout a project site. A summary description of the calculation process is given below; instructions for using the HQT GIS Toolkit to calculate development scores are provided in the User Guide, and detailed information on the mechanics of the calculation is available in the HQT GIS Toolkit documentation.

1. Existing anthropogenic features on and near the project site are digitized in a GIS and classified by development category and subcategory. These data come primarily from the Development Footprints Layer. Additional small features (e.g., small distribution power lines and minor roads) observed during a field survey may be delineated and added to the existing development footprints data set when calculating functional acres for a project site.

2. Distance to the nearest anthropogenic feature for each development subcategory is calculated to create a continuous raster surface representing the distance from each cell to the nearest feature of that development subcategory.

3. For each raster surface, distances are translated into impact magnitudes according to a pattern of linear decay, using the appropriate indirect impact magnitude and distance values defined in Table C-1. The inverse of the impact magnitude represents the functional score, also termed a discount, for development impacts: for example, an impact magnitude of 0.8 translates to a functional score of 0.2, where 0 represents minimal habitat value for sage-grouse and 1 represents optimal conditions. A raster surface representing this functional score for development impacts is termed a discount surface.

4. Each discount raster is multiplied together to produce a final discount raster, where values range from 0 (fully developed; minimal habitat value for sage-grouse) to 1 (no development impacts; optimal conditions for sage-grouse): Figure 3-5 depicts this combined development impacts discount surface, representing the aggregate direct and indirect impacts of development, across the sage-grouse range in Oregon.

5. The average value of the combined development impacts discount surface is calculated for each map unit in the project analysis area to obtain the Development Impacts score for each map unit.

6. For a development project, the combined development impacts discount surface will be calculated first using only the existing features (i.e., pre-project conditions), and then a second time with the new project footprint included (i.e., post-project conditions). The impacts associated with a new development project will thus be reflected in reduced post-project scores for Development Impacts. The same process in reverse applies to credit generation projects that propose to remove or reduce existing anthropogenic features in order to earn mitigation credit.

The process used to model indirect impacts allows the HQT to track specific, site-scale increases in impacts associated with new development projects. Quantifying the development impacts associated with individual structures is consistent with many of the scientific studies that provide the basis for the indirect impact distance values (e.g., Gillan et al. 2013, Coates et al. 2014). Aggregating the impacts from multiple development categories accounts for density effects of anthropogenic features on habitat function (e.g., Doherty et al. 2010, Harju et al. 2010). It also recognizes that different development categories are believed to have different mechanisms of impact on sage-grouse.

Mathematically, the method for aggregating development impacts ensures that new development in a pristine area results in a high mitigation cost (i.e., substantial decrease in functional acres). In contrast, locating the same new development in an area that is already developed results in a lower mitigation cost. This is consistent with sage-grouse conservation objectives to encourage co-location of impacts and to maintain contiguous areas of pristine, undeveloped habitat.

For development categories with multiple subcategories (e.g., roads), the magnitude of impact varies according to subcategory (Table C-1), but the mechanism of impact to sage-grouse is expected to be consistent across subcategories. Therefore, only a single development impacts surface, representing the maximum impact across all subcategories, is calculated for each development category.


Because of the large indirect impact distances associated with most development categories, features located well outside of the project analysis area can still impact habitat function within the analysis area. These effects are incorporated in the Development Impact scores calculated for a project site.

Figure 3-5. Combined development impacts discount surface across the sage-grouse range in Oregon. Red colors (low functional scores) indicate areas with extensive development and thus poor conditions for sage-grouse; blue colors (high functional scores) indicate relatively pristine areas with minimal development impacts on sage-grouse.


3.4 HQT adjustment factors

The parameters described in Sections 3.1 - 3.3 above provide the input for the basic quantification of change in habitat function associated with a development or credit generation project. The raw credit value is then modified by one of two project-level adjustment factors, depending on whether it is a development project or a credit generation project, to determine the final HQT credit value of the project. These adjustment factors are applied to account for (a) important habitat features that are difficult to quantify directly through the HQT algorithm; and (b) Mitigation Program policy decisions about how HQT outputs are applied.

Impact minimization – development projects only

New development projects may be able to implement minimization measures which, when incorporated, are intended to reduce the negative effects of the new development on sage-grouse and their habitat. For example, seasonal closure of operations at an active mine during sage-grouse breeding season may reduce the negative effects of the mine on sage-grouse. The effects of minimization measures can in some cases be incorporated in the HQT to reduce the calculated mitigation burden associated with new development projects.

Minimization measures reduce the indirect impacts associated with a given development category. In practice, the adjustment factor is applied as a percentage reduction of the total functional acre loss (i.e., debit acres) due to the indirect impacts of the new development. Direct impacts, however (total habitat loss within the project footprint) are not affected by standard minimization measures.

Potential minimization measures for an individual project are identified during consultation with the Program Administrator. The impact reductions calculated for minimization measures are based on the presumed mechanism(s) of indirect impacts to sage-grouse. A set of standard minimization measures for each impact mechanism are presented in Table C-2. This information is provided in order to allow general estimates of the reduction in mitigation cost that may be possible with minimization measures, but the list is neither comprehensive nor appropriate for every site. Final minimization actions and associated debit reductions will be determined in consultation with the Program Administrator. Additionally, ODFW staff will evaluate other minimization measures on a case-by-case basis.

Legal protection credit – credit generation projects only

In order participate in the Mitigation Program, credit generation projects must provide long-term legal protection for all enrolled acres (see the Manual for details). Legal protections are put in place to eliminate the risk that the habitat will be lost to development during the term of the protection. This avoided loss to development (distinct from avoided loss to ecological threats), is accounted for by applying supplemental legal protection credit to the HQT’s raw credit value.

Oregon’s sage-grouse Action Plan (SageCon 2015) notes several types of development with potential to impact sage-grouse: urban and exurban development, energy development, transmission lines for electricity and natural gas, mining activity, and roads and other infrastructure such as communications towers. In particular, renewable energy development (i.e., wind, solar, and geothermal) has been identified as a potentially significant threat to occupied sage-grouse habitat in Oregon (SageCon 2015, ODFW 2011). Even without major new developments, small projects such as construction of connector roads and power lines have the potential to diminish and fragment sage-grouse habitat. Legal protection for credit generation projects eliminates this risk at the project site.

Conceptually, the supplemental credit for legal protection that is applied to credit generation project should be proportional to the actual risk of loss of the habitat without legal protection. However, this risk of loss is difficult to quantify directly, especially at the scale of an individual site or property. The HQT attempts to address this difficulty by basing the supplemental credit on the economic value of the legal protection mechanism, such as a conservation easement. For projects where a property appraisal is


conducted to determine the economic value of the easement, the HQT uses the percent change in appraised value before and after legal protection as a proxy for the avoided risk of habitat loss to development. This percent change value is termed the legal protection multiplier. In cases where a property appraisal is not available, a default value of 5% is used for the legal protection multiplier.

The multiplier is applied to the total number of post-project functional acres at a project site, for map units that are in good or fair condition (i.e., ecological state A or B); no credit is provided for protecting areas that provide little or no sage-grouse habitat value (i.e., map units in C, D, or E states). For example, if a credit project provides 1000 functional acres of sage-grouse state A and state B habitat in its post-project condition, and the legal protection multiplier is 20%, then the project earns 200 functional acres of legal protection credit in addition to any habitat credit for restoration or enhancement actions.

The Program Administrator may determine that legal protection mechanisms other than conservation easements (permanent and term) provide a lower level of protection from development and therefore receive a lower default legal protection multiplier.


4 ADAPTIVE MANAGEMENT AND LIMITATIONS OF THE HQT

The credibility and effectiveness of both the HQT and the Mitigation Program rest on the quality of the science that underlie the tool and the integrity with which it is applied. Each version of the HQT has been developed using the best available science and best professional judgment at the time of revision. Additionally, the HQT has been subjected to pilot testing and expert review to evaluate the accuracy of the tool and the sensitivity of its output to the parameters in the algorithm, and revised in light of findings.

Ongoing evaluation and updating of the HQT will be incorporated into the annual adaptive management cycle, as described in the Action Plan (SageCon 2015). As part of that cycle, the HQT will be revised to reflect new scientific information and understanding of the ecology of sage-grouse and sagebrush ecosystems. In particular, new information about the response of sage-grouse to anthropogenic development is rapidly evolving, and the impact magnitudes and impact distances associated with anthropogenic features should be revised as needed to reflect new research.

Despite the best efforts of the Technical Team to develop an HQT that accurately and reliably reflects the functional value of sage-grouse habitat, there are elements of the tool that should be improved as it is adaptively managed over time. These limitations, described below, should be considered when applying the HQT.

Finally, the HQT is a generalized tool whose purpose is to consistently, defensibly, and efficiently quantify habitat function for credit and debit projects over a wide range of sites and situations. The HQT is not expected to capture all features of sage-grouse habitat, nor to provide all the information that will be required for appropriate decisions about siting and management of restoration and conservation projects.

4.1 Occupancy and seasonal habitat

Accurate, current information about where sage-grouse are present is important for making decisions about mitigation and conservation projects. It is expected that lek locations and habitat designations (Core, Low Density, and General habitat) and their applications in the HQT will be regularly updated as additional data become available and as sage-grouse occupancy shifts over time.

A better understanding and mapping of seasonal habitat use by sage-grouse in Oregon could substantially improve the precision of the HQT. During development of the HQT Version 1.0, the probability of seasonal occupancy (modeled probability) was initially considered as a potential parameter in the algorithm. However, this probability model, which was based primarily on a relatively small telemetry dataset, was judged to be incomplete and not reliable enough for use in mitigation decisions. In its place, the Technical Team opted to use ODFW’s existing, relatively coarse-scale habitat designations. These designations are objective, well-established, and well-documented, and thus they provide a solid foundation for use in the HQT. As mapping of seasonal habitat use improves, however, it should be considered again for incorporation into future iterations of the HQT.

Even without full seasonal habitat mapping, better mapping of existing mesic resources across the sage-grouse range in Oregon could identify the most important mesic areas for specific protection and help guide siting decisions. This kind information could also be incorporated in the HQT as a parameter measuring local density of mesic habitat or distance to nearest mesic feature.

4.2 Threat-based models

The threat-based model approach used for a variety of sagebrush ecosystem applications throughout Oregon will continue to be adapted and updated over time. As the HQT is also adapted over time, it is expected that HQT developers will continue to engage with SageSHARE partners to keep the TBMs and the ecological states used in the HQT updated and integrated with other TBM applications. In particular,


several aspects of the TBMs are expected to be revised as part of the adaptive management cycle of the HQT.

Defining upland ecological states

The decision tree for upland ecological states and, especially, its guideline quantitative metrics for distinguishing between states (Figure 3-3), were established primarily based on ecologists’ best professional judgment. Additionally, crews conducting pilot testing of HQT field methods (Appendix B) agreed that the thresholds (as used in an earlier but similar version of the upland TBM) provide a useful and appropriate level of guidance for determining ecological state, corresponding reasonably well with crews’ overall assessment of condition and trend. However, it is expected that these metrics and thresholds will be refined over time to better capture ecologically significant differences among sites, as the TBM is applied in practice across a broader range of sites.

Methods for determining upland ecological state based on remote sensing data, in particular, should be refined as more information becomes available. Technology for collecting remote sensing data and techniques for determining percent cover of vegetation functional groups from that data are steadily being improved (e.g., Boyd et al. 2017). Additionally, studies comparing remote sensing information with field observations of ecological state should be used to continue to improve classification thresholds for the remote sensing data and to improve guidelines for ecological state classification based on field data collection. Currently, the HQT does not make use of remote sensing data to determine ecological state (other than to help establish preliminary map units); however, as remote sensing methodology and classification models are improved, the HQT may eventually be able to incorporate some of this methodology to make the field assessment faster, more efficient, and more accurate, or even to replace the field assessment altogether.

Mesic threat-based model

The TBM for mesic areas is proposed as a simple and easily-applied approach to provide credit for protection and management of important mesic habitats in a mitigation context. However, refinement or replacement of this approach with a more complete model should be considered if available. A detailed and well-documented state-and-transition model for closed-basin wetlands is currently under development by The Wetlands Conservancy and the USDA ARS. When complete, this may provide a better classification of mesic ecological states for use in the HQT.

4.3 Development impacts

As described above, the HQT includes a robust methodology for identifying anthropogenic features and quantifying their direct and indirect effects on sage-grouse. Nonetheless, there are several potential limitations to the data sources currently in use and the methodology for assessing indirect impacts that should be updated or closely examined in adaptive management of the tool:

• The data sets defining existing anthropogenic features across the sage-grouse range in Oregon for the Development Footprints Layer should be updated as better data sets become available. Additionally, the list of which development categories are considered by the HQT may need to be reviewed in future iterations of the HQT.

• Indirect impact magnitudes and distances should be updated as needed to reflect new research results on indirect impacts. Impact magnitudes, in particular, rely heavily on expert opinion and should be closely examined and revised as needed both in pilot testing and ongoing adaptive management.

• Further, additional research could better define the shape of the distance decay curves used to model indirect effects of development. The specific relationship of declining impacts relative to increasing distances from the source is not fully understood. The linear decay curves currently


used by the HQT were adopted as a reasonable approximation of this relationship, given the current empirical evidence. Recently, BLM and USFWS staff working on assessing indirect effects of individual transmission line projects have developed a methodology that defines bands of disturbance associated with specific mechanisms (avoidance, increased predation, and decreased productivity), each associated with an estimated level of loss of function. The HQT applies this method of assessing impacts for power lines; future iterations of the HQT could consider a similar approach for other development categories, where sufficient scientific evidence is available to support decisions about the distance and magnitude of specific kinds of indirect effects. Incorporating modeled effects of noise (see next bullet) could also improve the accuracy of impact distance-decay curves.

• The effects of noise on sage-grouse behavior are not well accounted for in the current version of the HQT. The effects of noise from active surface mining and geothermal energy production may be particularly significant. The Oregon Department of Transportation (ODOT) is currently working with a noise attenuation model, relevant to their gravel pit mining operations, that accounts for the effects of topography on noise propagation. Incorporating such a noise attenuation model in the estimation of indirect impacts of development for the HQT could both better account for impacts of noise in general, and more accurately model the spatially explicit impacts of specific anthropogenic features and activities on the landscape.

4.4 Credit sensitivity

Of the three parameters used in the HQT algorithm, only the Ecological State score is expected to change as the direct result of most crediting actions under the Mitigation Program3. Further, the current HQT methodology requires that crediting projects produce a transition between ecological states in order to receive credit for management interventions related to vegetation management. Ecological transitions at this scale may take years, or even decades, to accomplish, and the results may be unpredictable. As the HQT begins to be applied in a mitigation context, future iterations of the tool should evaluate whether it is sufficiently sensitive to capture meaningful differences in habitat function both across time and between sites.

3However, credit generation projects that remove or reduce existing anthropogenic impacts earn credit through increases in the Development Impacts score.


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Appendix A. ADDITIONAL RESOURCES

Oregon Sage-Grouse Action Plan https://oregonexplorer.info/content/oregon-sage-grouse-action-plan?topic=203&ptopic=179

Oregon Sage-Grouse Data Viewer http://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=sagegrouse

ODFW Sage-Grouse Program https://www.dfw.state.or.us/wildlife/sagegrouse/

Sage-Grouse Development Registry https://oregonexplorer.info/content/sage-grouse-development-registry

Sage-Grouse Development Siting Tool http://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=sage_grouse_dev_siting

SageSHARE Partnership http://sageshare.org/

Threat-Based Land Management in the Northern Great Basin: A Field Guide http://sageshare.org/the-field-guide/


https://oregonexplorer.info/content/oregon-sage-grouse-action-plan?topic=203&ptopic=179


https://www.dfw.state.or.us/wildlife/sagegrouse/

https://oregonexplorer.info/content/sage-grouse-development-registry


http://sageshare.org/

http://sageshare.org/the-field-guide/

Appendix B. FIELD TEST OF OCULAR ASSESSMENT METHODOLOGY

Review and revision of the HQT prior to its release as Version 1.0 included pilot testing of the field methods. Field crews conducted both vegetation transect sampling and ocular assessment of ecological state at a set of pilot sites. This information was used to assess the adequacy of ocular assessment as a standard field method, as described in Section 3.2.2. The following sections describe the methods and the results of this pilot field study.

B.1 Methods

In September 2016, field crews from the Institute for Natural Resources collected baseline information from 14 pilot sites. The crews had spent the prior several months collecting data under the Bureau of Land Management’s Assessment, Inventory, and Monitoring (AIM) program and were knowledgeable in plant identification and ecology of Great Basin sagebrush steppe ecosystems. At the beginning of the two-week field period, they spent a half day training on the threat-based model ocular assessment methodology (using threat-based TBMs) with experts at the USDA ARS’s Research Range near Burns, Oregon. The crews then spent the first day of their field work collecting data with ODFW staff to finalize their training and address any questions with the methodology.

The pilot sites were among a set of 35 potential sites selected through a GIS exercise aimed at identifying a small set of locations representing as much variability as possible across major ecological types of sagebrush steppe habitat. The sites were selected through a stratification of elevation, soil temperature, soil moisture, burn severity, and sage-grouse seasonal habitat use variables. To facilitate access, all sites were located on public lands within the Trout Creek, Baker, or Cow Lakes Core areas. Where possible, sites were located where BLM AIM data had already been collected during the 2016 field season. A ‘site’ was defined as a 1 km-radius circle around a selected point. Only 14 sites were visited because of time limitations, and crews aimed to split their time roughly equally among the three Core areas.

Based on remotely sensed and modeled data, ODFW and TNC staff estimated map unit boundaries and ecological states for each site. INR field crews then traveled to the site and:

1. Verified or corrected the map unit boundaries based on the ocular assessment methodology outlined by the USDA ARS team and used for sage-grouse candidate conservation agreements.

2. Performed an ocular assessment of ecological state within each map unit, noting additional information such as dominant species, potential threats to sage-grouse and sage-grouse habitat in the area, and observed trend in habitat quality.

3. Conducted vegetation sampling transects at three or four randomly selected locations within each site, using a simplified version of the AIM methodology.

Teams were asked to make note of any problems, concerns, or suggestions in data collection methods.

Transect vegetation sampling data were used to calculate percent cover of the vegetation indicators:

• Percent cover of juniper (for this exercise, mountain mahogany was also treated as juniper).

• Percent cover of sagebrush (excluding other shrubs)

• Percent cover of native perennial bunchgrasses (PG)

• Percent cover of invasive annual grasses (IAG)

Percent cover for each functional type at a transect was calculated simply as the number of points recording an occurrence of that functional type relative to the total number of sample points for the transect (usually n=150). These percent cover values were used to assign an ecological state to each transect, according to the TBMs and classification thresholds defined in version 0.99 of the HQT. At this


time there were three separate upland TBMs, one for each primary threat (IAG threat model, juniper threat model, and mixed threat model), closely analogous to the threat categories within the combined TBM used by the present HQT. No juniper was present at any of the pilot sites, and therefore the IAG threat model was used for all sites4.

Finally, the ecological state calculated from transect data was compared to the ecological state determined by ocular assessment for each transect (according to the map units designated by field crews). Because transect locations within a site were selected randomly, transects did not necessarily sample the most characteristic conditions for their map units, and transect lines may have crossed map unit boundaries in some cases.

B.2 Results

Ecological states calculated from vegetation transect data were largely consistent with the field crews’ ocular assessment of ecological state, despite the randomized locations for transects. Of the 47 transects examined, the ocular- and transect-based state had an exact match in 28 transects (green cells in Table B-1), were within a single intermediate state (e.g., A vs AB or AC; yellow cells) for 14 transects, and differed by more than one intermediate state for only 5 transects (red cells). Further, results indicate that ocular assessment was generally effective at distinguishing percent cover thresholds at a coarse scale. Percent sagebrush cover (according to transect data) was always higher in plots assessed as ‘A’ than in those flagged as transitional ‘AB’. The ratio of IAG cover to PG cover was always higher in plots assessed as ‘C’ than in those flagged as transitional ‘AC’. These results support the assumption that ocular assessment is capable of distinguishing between sites that are above, below, and near threshold cover levels.

Discrepancies between the ocular assessment and the transect data could reflect the different area and scale assessed by the two methods, rather than failure to accurately distinguish thresholds in ocular assessment. Ocular assessment is supposed to identify the most appropriate ecological state for an entire map unit, while transects sampled only a small piece of any given map unit, and the vegetation in that small piece might not match the most characteristic vegetation for the map unit.

Only one of the 14 pilot sites had any juniper functional group present (in this case, mountain mahogany rather than western juniper), so this exercise does not assess the match between ocular assessment and transect data for juniper states. Given the typical patchy distribution of juniper, the limited vegetation sampling transects used here are likely to be ineffective at characterizing juniper percent cover at the scale of a full map unit. However, remote identification juniper percent cover is currently much more accurate than identification of IAG cover (Boyd et al. 2017), so remote sensing of juniper percent cover may be able to substitute for a field-based estimate.

4With one exception for a site where mountain mahogany (part of the juniper functional group) was present; the ‘Mixed’ threat model was used for this site.


Table B-1. Comparison of ocular assessment vs. vegetation transect methods for determining ecological state. For example: of the 9 plots that were ocularly assessed as State A, 8 were classified as State A using vegetation transect data (exact match), while the one remaining plot was classified as state ‘AC’ using vegetation transect data (partial match). Green color highlights exact matches, yellow color highlights partial matches, and red color highlights non-matches.

Ocular Assessment Ecological State According to Veg. Transects Observed state Count A B AC C BD D E Comments

A 9 8 1 The plot assigned to AC had very little of either IAG or PG. This could be a situation where best professional judgment overrides official percent cover thresholds.

AB 4 3 1 Sage cover in all plots ocularly assessed as AB is lower than cover in all plots identified assessed as A, indicating ability of field crews to identify conditions near the threshold (10% sage cover).

AC (IAG) 3 1 1 1 Location of the plot assigned to BD is shown near the boundary with a BD map unit in the site map.

AC (Mixed) 2 1 1 Officially there is no state ‘AC’ in the Mixed threat model. State A is distinguished from C by presence of juniper cover.

B 6 4 1 1 For the plot assigned to AC, field crew notes indicate that the transect was located “on the edge of a B/C”.

BD 7 1 2 4 B, BD, and D are distinguished by the IAG:PG ratio. Measured IAG ratios at these plots range from 0.7 – 5.5.

C 7 7 Measured IAG ratios at these sites range from 9.7 to ‘Infinite” (all IAG, no PG).

D 9 1 1 8 For the plot assigned to BD, field crew notes indicate that the plot was “on the BD end” of D habitat.


Appendix C. DEVELOPMENT IMPACT CALCULATION

Methods used for calculating development impacts (as described in Section 3.3.2) will be updated as new science becomes available. Below are the approaches in use at the time of publication of this document. Please contact the program administrator for updated tables currently in use.

Table C- 1. Development impacts table

1Listed are potential indirect impact mechanisms that would be expected during the operation period; it is assumed that impacts during the construction period would be minimized with timing restrictions.

Description for Direct Footprint

(Complete Habitat Loss)

Footprint Buffer

Distance (ft)

Impact Model

Method

Indirect Impact

Distance (km)

Indirect Impact

Magnitude

Indirect Impact

Distance 2 (km)

Indirect Impact

Magnitude 2Indirect Impact Mechanism1

Energy (Coal Mine) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, low structures, CORA nest subsidies, CORA food subsidiesEnergy (Geothermal) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, tall structures, low structures, CORA nest subsidies, CORA food subsidiesEnergy (Hydroelectric) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, tall structures, low structures, CORA nest subsidies, CORA food subsidiesEnergy (Nuclear) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, tall structures, low structures, Cora nest subsidies, CORA food subsidiesEnergy (Oil and Gas) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, low structures, CORA nest subsidies, CORA food subsidiesEnergy (Oil Shale Ex-Situ) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, tall structures, low structures, CORA nest subsidies, CORA food subsidies Energy (Oil Shale In-Situ) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, tall structures, low structures, CORA nest subsidies, CORA food subsidies Energy (Solar) All Digitize footprint or perimeter 0.0 Linear 3.3 0.8 0.0 0.0 Low structures, CORA nest subsidies, CORA food subsidiesEnergy (Tar Sands) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, tall structures, low structures, CORA nest subsidies, CORA food subsidies Energy (Wind) All Digitize footprint or perimeter 0.0 Linear 5.0 0.8 0.0 0.0 Noise, tall structures, CORA food subsidiesFarm/Forest Resource All Digitize footprint or perimeter 0.0 Linear 3.3 0.5 0.0 0.0 Low structures, CORA nest subsidies, CORA food subsidiesInfrastructure (Airports) All Digitize footprint or perimeter 0.0 Linear 5.0 0.8 0.0 0.0 Noise, low structuresInfrastructure (Military) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, low structures, CORA nest subsidies, CORA food subsidiesInfrastructure (Pipeline) All Digitize footprint or perimeter 0.0 Linear 0.8 0.3 0.0 0.0 If buried - none; if above ground - low structuresInfrastructure (Powerline) Above Ground Power Line1-199kV Lines Buffer line to 15 ft 15.0 Step 0.4 0.8 3.0 0.03 Tall structures, CORA nest subsidies, raptor perch subsidiesInfrastructure (Powerline) Above Ground Power Line200-399kV LinesBuffer line to 15 ft 15.0 Step 0.6 0.8 10.0 0.03 Tall structures, CORA nest subsidies, raptor perch subsidiesInfrastructure (Powerline) Above Ground Power Line400-699kV LinesBuffer line to 15 ft 15.0 Step 0.6 0.8 10.0 0.03 Tall structures, CORA nest subsidies, raptor perch subsidiesInfrastructure (Powerline) Above Ground Power Line700+kV Lines Buffer line to 15 ft 15.0 Step 0.6 0.8 10.0 0.03 Tall structures, CORA nest subsidies, raptor perch subsidiesInfrastructure (Powerline) Ancillary Facility Digitize footprint or perimeter 0.0 Linear 3.3 0.8 0.0 0.0 Tall structures, CORA nest subsidies, raptor perch subsidiesInfrastructure (Powerline) Buried Power Line Digitize surface disturbance 0.0 Linear 0.0 0.0 0.0 0.0 noneInfrastructure (Powerline) Substation Digitize footprint or perimeter 0.0 Linear 3.3 0.8 0.0 0.0 Tall structures, CORA nest subsidies, raptor perch subsidiesInfrastructure (Powerline) Construction Impacts Digitize surface disturbance 0.0 Linear 5.0 0.3 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Railroad) Mainline Buffer center line to 30.8 ft* 30.8 Linear 5.0 1.0 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Railroad) Spur Buffer center line to 30.8 ft* 30.8 Linear 5.0 1.0 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Railroad) Construction Impacts Digitize surface disturbance 0.0 Linear 5.0 0.3 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Recreation) All Digitize footprint or perimeter 0.0 Linear 5.0 0.5 0.0 0.0 Noise, low structures, CORA food subsidiesInfrastructure (Road) Temporary Road Buffer center line to 12 ft 12.0 Linear 5.0 0.3 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Road) Access Road Buffer center line to 12 ft 12.0 Linear 5.0 0.3 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Road) Surface Street Buffer center line to 40.7 ft* 40.7 Linear 5.0 0.5 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Road) Major Road Buffer center line to 84.0 ft* 84.0 Linear 5.0 1.0 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Road) Interstate Highway Buffer center line to 240.2 ft* 240.2 Linear 5.0 1.0 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Road) Construction Impacts Digitize footprint or perimeter 0.0 Linear 5.0 0.3 0.0 0.0 Noise, CORA food subsidiesInfrastructure (Tower) All Digitize footprint or perimeter 0.0 Step 0.6 0.8 10.0 0.03 Tall structures, CORA nest subsidies, raptor perch subsidiesMining (Non-Coal) All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, low structures, CORA nest subsidies, CORA food subsidiesNatural Resource All Digitize footprint or perimeter 0.0 Linear 3.3 0.5 0.0 0.0 CORA food subsidies, WN subsidiesOther All Digitize footprint or perimeter 0.0 Linear 0.0 0.0 0.0 0.0 variableOutdoor Gathering All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, CORA food subsidiesPark/Public/Quasi-Public All Digitize footprint or perimeter 0.0 Linear 5.0 1.0 0.0 0.0 Noise, low structures, CORA nest subsidies, CORA food subsidiesCommercial All Digitize footprint or perimeter 0.0 Linear 3.3 0.5 0.0 0.0 Low structures, CORA nest subsidies, CORA food subsidiesResidential All Digitize footprint or perimeter 0.0 Linear 3.3 0.5 0.0 0.0 Low structures, CORA nest subsidies, CORA food subsidiesUpzoning All Digitize footprint or perimeter 0.0 Linear 0.0 0.0 0.0 0.0 noneUtility/Solid Waste Disposal F All Digitize footprint or perimeter 0.0 Linear 3.3 0.8 0.0 0.0 Low structures, CORA nest subsidies, CORA food subsidies, WN subsidiesSmall Structures All Digitize footprint or perimeter 0.0 Linear 0.8 1.0 0.0 0.0 Low structures (avoidance only)* buffer widths are based on BLM RMP Appendix I, Table I-2, and are consistent with development baseline dataset

Mitigation Calculations

Voltage (if applicable)

Development CategoryDevelopment Subcategory


Table C- 2. Indirect impact mechanisms and associated minimization measures

Source of impact

Mechanism of impact

Distance (km)

Explanation Potential minimization measures1

References

Noise Noise (NS) 5.0

Increased noise levels interfere with sage-grouse breeding behavior, and may increase stress on individuals throughout the year. A distance of 5 km should allow noise to drop to less than 10 decibels above ambient for relatively loud impacts (oil and gas wells), and to below ambient for relatively quiet impacts (infrastructure construction).

Closure during breeding season (March 1 - June 30) (Patricelli et al. 2013,

Manier et al. 2014) Seasonal closures beyond breeding season

Structures

Avoidance (AV) 0.8 Sage-grouse behaviorally avoid vertical features, including both tall structures such as transmission lines or communications towers and low structures or features such as rural buildings or juniper trees.

No potential minimization (Manier et al. 2014, Howe et al. 2014, Severson et al. 2016)

Avian perch subsidies (AP) 3.3

Vertical features such as powerlines provide perch sites for raptors and ravens, which can measurably increase predator foraging behavior in proximity to such features.

Installation of perch deterrents

(Slater and Smith 2010, Manier et al. 2014)

Corvid2 nest subsidies (CN)

3.3 Ravens use structures such as buildings and transmission line poles for nesting sites, which may lead to locally increased density of raven populations.

Unoccupied corvid nest destruction (Dwyer and Leiker 2012,

Manier et al. 2014, Dwyer et al. 2015) Installation of corvid nest

diverters

Provision of food and water

Corvid food and water subsidies (CF) 3.3

Human development may provide subsidies in the form of food and water to ravens and crows, which increases raven population density and thus predation on sage-grouse.

Water abatement programs

(Manier et al. 2014, Howe et al. 2014) Trash abatement programs

Carcass collection

West Nile Virus subsidies (WN) 2.0 Human development that creates standing water during mosquito

breeding seasons can increase the risk of transmission of West Nile virus. Draining of water sources during summer months

(Hamer et al. 2014, Ferraguti et al. 2016)

1 Minimization measures and impact reduction are explained in Section 3.4.1. 2 Corvids include crows and ravens.


Habitat Quantification Tool (HQT) Scientific Rationale · 2020-01-23 · This Scientific Rationale...

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