Summary Report: Workshop for Experts on the Genetics of 

the Greater Sage-Grouse

Held at the USGS Fort Collins Science Center in Fort Collins, Colorado, on

October 22 and 23, 2014

Prepared by the Workshop Planning Team

Executive Summary 

The greater sage-grouse (Centrocercus urophasianus) is one of the most intensively studied upland 

game bird in North America. Ongoing research continues to improve our understanding of genetic 

diversity across the species’ range including the degree to which observed differences represent 

distinct or isolated populations. Techniques and metrics used to evaluate and describe genetic 

isolation, divergence, and diversity have also evolved and improved since previous genetic studies 

were published in 2005 and summarized in 2011 (Oyler-McCance et al. 2005; Oyler-McCance and 

Quinn 2011). Since then, new research on the genetics of sage-grouse has been completed while 

other studies are now in progress. 

In 2010, the U.S. Fish and Wildlife Service (Service) determined the greater sage-grouse warranted 

protection under the Endangered Species Act (ESA or Act) but that listing was precluded by higher 

priorities. Habitat loss and fragmentation due to a variety of factors were cited as the primary threats to 

the species. Fragmenting habitats can isolate or reduce the size of populations, which may be more 

vulnerable to population declines, reduced genetic diversity, and potentially extinction. We used this 

workshop to better understand the latest science regarding sage-grouse genetics and any potential 

risks associated with isolation and small populations. 

The Service is currently assessing the status of the greater sage-grouse and is gathering the best 

available scientific and commercial data to inform a status determination by September 30, 2015. While 

a rider to the Fiscal Year 2015 Omnibus Appropriations Bill will prevent the Service from writing or 

publishing a proposed rule, it does not relieve the Service from completing a status review to determine 

whether the species still warrants protection under the Act. 

As the Service gathers the best available information on sage-grouse genetics, the Service partnered 

with the U.S. Geological Survey (USGS) to organize a workshop to elicit information from scientific 

experts about recent developments in the field of sage-grouse genetics. The workshop explored the 

characteristics of gene flow across the range, including potential impediments, or barriers, to gene flow 

and any implications of genetic divergence across the range of the greater sage-grouse. The workshop 

also explored potential interactions between threats and genetic processes, and provided an 

opportunity for scientific experts to discuss ongoing and upcoming research on sage-grouse genetics. 

The workshop was structured to elicit information from the scientific experts that could later help the 

Service identify genetic differentiation across the range, populations with low genetic diversity, or 

populations with unique genetic characteristics. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Executive Summary

Page i
The following is a brief summary of the key issues identified by individual experts during the workshop. 

Group consensus was not asked for or provided during the workshop, so this summary reflects 

individual perspectives shared by experts: 

Currently, the published genetic data is not sufficient to determine the amount of landscapelevel gene flow between large, eastern and western portions of the overall range, but upcoming 

research will investigate further. Some degree of gene flow likely occurs from east-to-west 

across the range. 

Natural and human-caused features that impede the movement of individual birds, such as 

mountains, large rivers, deserts, forests, large reservoirs, agricultural fields, electrical power 

lines, highways, and energy development, may generally act as barriers to gene flow for the 

greater sage-grouse. 

Available genetic data indicates that the Bi-State, Columbia Basin, and Jackson Hole 

populations are isolated from other populations and have low levels of gene flow with other 

sage-grouse populations. The Columbia Basin and Jackson Hole populations are also small, 

isolated, and less genetically diverse than the Bi-State population. The Bi-State population has 

likely been isolated for thousands of years, but it is currently unclear how long the Columbia 

Basin and Jackson Hole populations have been isolated. 

The Missouri River Valley likely acts as a barrier to gene flow between populations located to 

the north and south.

The Strawberry Valley population in Utah has low genetic diversity likely due to predation, but is 

still connected to other nearby populations. 

Small, isolated populations are more vulnerable to extirpation or extinction from demographic 

processes and stochastic events. Threats that fragment sagebrush habitats potentially increase 

the number and magnitude of barriers to gene flow and amplify the risks of small, isolated 

populations.

Populations at the periphery of the range may have low genetic diversity as a result of low 

numbers or isolation, but may also be uniquely adapted to specific environmental conditions. 

These unique adaptations may increase the adaptive potential of sage-grouse so that the 

species has a higher probability of persistence during potential future changes to habitat or 

climate. 

Upcoming research, particularly a range-wide landscape connectivity study, will provide more 

information about the role that barriers play in moderating gene flow across the species’ range. 

Available research has not evaluated the genetic makeup of all sage-grouse populations. 

Upcoming research may also identify additional isolated populations. 

This workshop was one component of the Service’s information gathering process for the status review. 

Information gathered during the workshop will be used by the Service in conjunction with other 

published literature or information submitted by interested parties, to evaluate the status of the species. 

The Service is committed to using the best available scientific and commercial information, and will 

incorporate new information as it becomes available. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Executive Summary

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Introduction

The following report summarizes the workshop’s organization and proceedings, with supporting 

materials attached in appendices. 

We begin with the organization and meeting preparation, then a summary of the workshop exercises 

and discussion. In the Appendices, we have some of the specific materials used in preparation, slides 

shared with the group, and Notes from the meeting.

This workshop was one component of the Service’s information-gathering process for the status review. 

Information gathered during the workshop will be used by the Service in conjunction with other 

published literature or information submitted by interested parties, to evaluate the status of the species. 

The Service is committed to using the best available scientific and commercial information, and will 

incorporate new information as it becomes available. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Introduction

Page iii
Table of Contents

Executive Summary................................................................................................................................ i

Introduction............................................................................................................................................iii

Table of Contents ..................................................................................................................................iv

Organizing the Workshop ...................................................................................................................... 1

Identifying and Inviting Experts ....................................................................................................... 1

Preparing Experts for the Workshop ............................................................................................... 1

Workshop Facilitation ..................................................................................................................... 2

Workshop Agenda .......................................................................................................................... 2

Summary of Workshop Exercises and Discussions ............................................................................... 2

Day 1: Wednesday, October 22, 2015................................................................................................ 2

Part 1: State of the science: Genetics and the greater sage-grouse............................................... 2

Part 2: Barriers to gene flow and genetic divergence in sage-grouse.............................................. 3

Day 2: Thursday, October 23, 2014 8:00 am – 12:00 pm ................................................................. 13

Part 3 and Part 4: Genetic divergence and persistence ................................................................ 13

Part 5: Reflection, new studies, and wrap up ................................................................................ 15

Literature Cited .................................................................................................................................... 17

Appendix 1: Workshop Planning Team and Agenda ............................................................................ 19

Planning Team................................................................................................................................. 19

Workshop Agenda:........................................................................................................................... 21

Wednesday October 22, 2014: 8:00 AM – 5:00 PM and ............................................................... 21

Thursday October 23, 2014: 8:00 am – 12:00pm.......................................................................... 22

Appendix 2: Criteria to Identify and Invite Experts................................................................................ 23

Criteria to ensure that participants are scientific experts................................................................... 23

Criteria to ensure that participants are experts familiar with the workshop topic ............................... 23

Criteria to ensure that the group of invited participants represent the diversity of expert judgment 

within the scientific community ......................................................................................................... 23

Appendix 3: Expert Identification and Invitation List ............................................................................. 24

Appendix 4: Pre-Workshop Webinars and Workshop Ground Rules................................................... 26

Appendix 5: Workshop Bibliography ................................................................................................... 35

Appendix 6: Presentations given by Dr. Sara Oyler-McCance ............................................................. 51

Appendix 7: Workshop Notes............................................................................................................ 102

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Table of Contents

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Purpose of the Workshop Notes..................................................................................................... 102

Day 1: Wednesday, Oct. 22, 2014.................................................................................................. 102

PART 1: INTRODUCTION.......................................................................................................... 102

PART 2: BARRIERS TO GENE FLOW AND GENETIC DIVERGENCE IN SAGE-GROUSE...... 105

PART 3: GENETIC DIVERGENCE............................................................................................. 113

Day 2: Thursday, Oct. 23, 2014..................................................................................................... 115

Thursday Exercise 1: .................................................................................................................. 115

Thursday Exercise 2: .................................................................................................................. 118

Thursday Exercise 3: .................................................................................................................. 119

Thursday Exercise 4: .................................................................................................................. 121

Wrap-up:..................................................................................................................................... 124

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Table of Contents

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Organizing the Workshop

The workshop brought together a group of scientific experts to discuss genetic techniques, 

conservation genetics, and the greater sage-grouse. A workshop planning team with members from 

the Service and USGS: 

Identified, invited, and prepared the experts;

Developed an agenda; and

Facilitated the meeting.

We summarize these organizational steps below. Appendix 1 provides the list of the planning team 

members and the workshop’s agenda. 

Identifying and Inviting Experts

The planning team reviewed publications on sage-grouse genetics to identify experts who had authored 

studies or participated in research relevant to the workshop topic. Then, the planning team used

selection criteria, including a candidate’s professional credentials, position, area of expertise, and 

experience with the greater sage-grouse, to develop a list of potential invitees (Appendix 2). These

criteria helped ensure that invitations to participate were made only to scientific experts familiar with the

topic and that the selections were transparent, unbiased, and captured a broad diversity of expertise 

and professional judgments related to the topic. 

The planning team identified experts based solely on their scientific qualifications, rather than their 

affiliation with a particular organization or interested party. State partners from the Western Association 

of Fish and Wildlife Agencies (WAFWA) reviewed the list and suggested additional experts. The 

workshop planning team then invited experts that met the selection criteria. Appendix 3 provides the 

invitation list. The USGS facilitators emailed the invitations and served as the primary points of contact 

for the experts. If an expert declined the invitation, the facilitators invited a replacement from the 

planning team’s list. 

Invited experts represented a diversity of professional backgrounds and experience, including scientists 

with sage-grouse expertise, expert knowledge of related species, and broad expertise in the field of 

conservation genetics. The facilitators limited the meeting size to nine experts in order to maximize 

open, productive discussion between all participants. Further, in order to maintain an open, intimate

meeting environment, only members of the workshop planning team were invited to observe the 

workshop. 

Preparing Experts for the Workshop

Before the workshop, the planning team hosted two informational webinars attended by all the experts. 

The webinars explained the workshop’s purpose, agenda, and ground rules (Appendix 4). The 

planning team also provided a bibliography of background references (Appendix 5). Before the 

workshop, each expert completed screening forms to identify any potential conflicts of interest. None of 

the invited experts identified any conflict of interest. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Organizing the Workshop

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Workshop Facilitation 

During the workshop, trained facilitators led by USGS used best practices for expert elicitation to 

engage the experts in facilitated discussion (EPA 2011; Drescher 2013). The facilitators used formal 

elicitation techniques on specific technical questions regarding genetics and the greater sage-grouse. 

Throughout the workshop, the facilitators asked experts for their individual, professional knowledge on 

specific topics and did not seek or obtain any group consensus from the participants. The workshop 

obtained facts and information only, and if needed to address uncertainty, professional judgment from 

each individual expert. To address uncertainty, the facilitators asked each expert to explain a 

hypothesis and identify any studies underway or that could be devised to test the hypothesis. 

Workshop Agenda

The workshop’s agenda guided discussion on the following general topics regarding the genetics of the 

greater sage-grouse:

Part 1: The latest techniques and metrics used to evaluate genetic divergence; 

Part 2: Potential barriers to gene flow and the areas with evidence of genetic divergence;

Part 3: Potential implications of genetic divergence;

Part 4: Potential interactions between threats and areas with low genetic diversity; and 

Part 5: Ongoing and upcoming research and research ideas. 

As summarized below, the workshop proceedings generally adhered to the agenda. 

Summary of Workshop Exercises and Discussions 

All exercises were conducted to maximize opportunities for individuals to express their viewpoints. At 

no time did the Service ask for consensus during the workshop. In notes and documentation, the 

conversations are summarized, but this should not be interpreted as a unified perspective from the 

group.

Day 1: Wednesday, October 22, 2015

Part 1: State of the science: Genetics and the greater sage-grouse

To provide background information and context, Dr. Sara Oyler-McCance gave two presentations at the 

beginning of the workshop (Appendix 6). The first presentation provided an overview of the laboratory 

techniques and metrics used by geneticists to measure gene flow and genetic divergence. Background 

information important to later discussion included:

DNA is found in cell nucleus and in mitochondria 

Mitochondrial (mtDNA) and nuclear DNA are two types of DNA that have different properties 

(mode of inheritance, size):

o MtDNA

§ MtDNA is maternally inherited. 

§ MtDNA haplotypes are assigned based on DNA sequence (different sequences 

are different haplotypes)

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§ Haplotypes can differ by only one substitution or many, which determines how 

divergent they are.

§ Comparing the distribution of haplotypes between populations tells you 

something about how much gene flow there is between populations.

§ Reciprocally monophyletic refers to the situation wherein two groups, haplotypes

within the group are more closely related to each other than they are to 

haplotypes outside the group.

§ Some species definitions require two groups to be reciprocally monophyletic in 

order to be considered a good species.

§ Since the haplotypes of Gunnison sage-grouse are not reciprocally monophyletic 

with the haplotypes of greater sage-grouse, these two species would not be 

recognized as different species using this definition.

o Nuclear DNA 

§ Nuclear DNA is bi-parentally (from both parents) inherited.

§ Geneticists usually target nuclear DNA that evolves quickly (e.g. microsatellites).

§ Can compare microsatellite allele frequencies between populations to make 

inferences about levels of gene flow.

§ Single Nucleotide Polymorphisms (SNPs) are another type of nuclear DNA 

marker.

Microsatellites are repetitive regions that do not code for proteins (thus 

we consider them to be neutral markers – not under selection, reflect 

neutral processes like gene flow and changes in population size).

SNPs occur throughout the genome in non-coding as well as coding 

regions so they can potentially be used to measure both neutral 

processes and measure selection and adaptation,

Dr. Oyler-McCance’s second presentation provided a summary of the available literature, papers in 

review, and research in progress on sage-grouse genetics (Appendix 6). 

Part 2: Barriers to gene flow and genetic divergence in sage-grouse 

Exercise 1 - Gene flow between eastern and western portions of the range

Topic: Evaluate landscape-scale, recent gene flow between the eastern and western portions of 

the greater sage-grouse’s range. 

Exercise Purpose: 

The purpose of the first topic of discussion was to evaluate gene flow between the eastern and western 

portions of the greater sage-grouse's range (Figure 1). One or several north-to-south oriented barriers 

could limit gene flow between the eastern and western portions of the range, potentially resulting in

genetic differences from east to west. 

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Summary of Workshop Exercises and Discussions

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Figure 1. Overall range of the greater sage-grouse. The Experts used this blank map in Exercises 1 and 2 to discuss potential barriers. 

Exercise Discussion: 

To begin, the experts were asked to define categories of gene flow. The experts’ discussion led to the 

following operational definitions for categories of gene flow based on migrants per generation (Mills et 

al. 2003; Lowe and Allendorf 2010). For the purposes of this workshop, the experts described the 

following five (5) categories of gene flow:

No gene flow; 

Low gene flow corresponded to greater than 0 to 1 migrants per generation;

Moderate gene flow corresponded to 2 to 10 migrants per generation; 

Substantial gene flow corresponded to greater than 10 migrants per generation; and 

Complete mixing indicative of unrestricted gene flow.

With these operational definitions, the facilitators then asked the experts: 

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Range-wide, does the genetic evidence currently available suggest that there is an east-to-west

barrier to sage-grouse gene flow acting over recent time scales and across a large spatial 

scale? 

The experts debated the term barrier and agreed that a barrier reduces gene flow, but is not 

necessarily impermeable. Then, the facilitators asked each expert to answer the question by 

distributing 100 points across the previously developed five categories of gene flow to score the

likelihood that a category accurately described the barrier, if one exists, to east-to-west gene flow. 

Initially, the experts shared a strong reluctance to speculate or score a numerical response to this 

question for the following reasons:

The available genetic evidence does not specifically address genetic flow or differences

between large, eastern and western portions of the range; 

This question could be answered with greater certainty following a reanalysis of the 2005 data

(Oyler-McCance et al. 2005) for this purpose (see Table 3 below, “An east-to-west split”); and 

Upcoming results from a range-wide genetics assessment expected to be published in the fall of 

2015 (see Table 3 below, “Range-wide landscape connectivity”) should help address this 

question with greater certainty.

Although the experts expressed reluctance to answer and score the question without scientific 

evidence, they participated as a preliminary exercise, but stressed that there was little scientific validity 

to their scores (Figure 2). The resulting discussion regarding the value of quantifying their opinions

prompted the facilitators to rework all of the workshop questions to allow the experts to characterize

information they provided as either information supported by genetic evidence in the available scientific 

literature or a currently untested hypothesis based on professional opinion. 

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Figure 2. Exercise 1 asked each expert to rank a barrier to gene flow between eastern and western portions of the range. The experts 

stressed that there was little scientific validity to their scores due to the lack of scientific evidence in the available literature. 

The experts discussed that they felt that there is some degree of gene flow from east-to-west across 

the overall range of the greater sage-grouse, but there is not complete mixing or a complete barrier

(Figure 2). Gene flow from east-to-west could be best described as a cline, or gradient, with more 

genetic divergence between the easternmost and westernmost extremes of the range, such as a 

pattern of isolation by distance as previously described (Oyler-McCance et al. 2005). Additionally, 

smaller, more isolated populations likely experience more genetic divergence due to genetic drift. 

Reanalyzing data from Oyler-McCance et al. (2005) could provide measures of gene flow between the 

eastern and western portions of the range (Table 3 below, “An east-to-west split”). In addition, the 

upcoming range-wide connectivity study should specifically address this question (Table 3 below, 

“Range-wide landscape connectivity”). 

To summarize Exercise 1, the available genetic data do not specifically address landscape-level gene 

flow between large, eastern and western portions of the range. There is likely a gradient of genetic 

divergence from east to west, with the greatest genetic difference between the eastern and western 

extremes of the range. Existing data will be reanalyzed and new data will soon be available that should 

more specifically investigate genetic divergence between the eastern and western portions of the 

range. The Service is committed to using the best available information. We will consider relevant 

information as it becomes available prior to our deadline for making a status determination.

Responses A B C D E F G H I

A. No gene flow 0 10 0 0 0 0 0 0 5

B. Low gene flow 50 60 33.33333 20 50 50 33 60 65

C. Moderate gene flow 50 30 33.33333 40 50 50 34 30 25

D. Substantial gene flow 0 0 33.33333 40 0 0 33 10 5

E. Complete mixing (no barrier to g 0 0 0 0 0 0 0 0 0

Sum Check 100 100 100 100 100 100 100 100 100

Expert Panel

Looking rangewide, does the genetic evidence suggest an E-W barrier to sage-grouse gene flow over recent time 

scale and over large spatial scale?

0

10

20

30

40

50

60

70

80

90

100

A. No gene

flow

B. Low gene

flow

C.

Moderate

gene flow

D.

Substantial

gene flow

E. Complete

mixing (no

barrier to

gene flow)

Exercise 1: 

GRSG gene flow across E-W barrier

A

B

C

D

E

F

G

H

I

Definition of categories: based on operational

definitions (cf Mills et al 2000 book chapter, Lowe 

and Allendorf) using migrants per generation:

• Low gene flow means: 0-1 migrants per 

generation

• Moderate gene flow means: 2 to 10 migrants per 

generation

• Substanial gene flow means: >10 migrants per 

generation

Expert

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Summary of Workshop Exercises and Discussions

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Exercise 2 - Potential barriers to gene flow across the range

Topic: Identify potential barriers to sage-grouse gene flow, if any. 

Exercise Purpose: 

The purpose of the second topic of discussion was to identify and characterize potential barriers to 

sage-grouse gene flow, if any exist. 

Exercise Discussion: 

The facilitators asked the experts to characterize potential barriers to gene flow across the overall 

range of the greater sage-grouse. Specifically, the facilitators asked:

Are there barriers that may restrict or prevent gene-flow between populations or groups of 

populations of the greater sage-grouse? 

If yes, identify each barrier and describe its location, source, age, mechanism, and magnitude. 

To complete this exercise, the facilitators organized the experts into breakout groups of three. The 

facilitators asked each group to draw lines that represent potential barriers to gene flow. Each group 

characterized a line as either: 

A real barrier supported by existing, data-driven, genetic evidence (Figure 3); or 

A hypothesized barrier based on qualitative information, physical features on the landscape, or 

other data not yet evaluated by the scientific community (Figure 4). 

The experts stressed that the barriers they identified are not definitive (Figure 3); there are likely other 

barriers and areas on the landscape with reduced levels of gene flow that have not been examined. 

Further, ongoing research in Wyoming (Table 3 below, “Landscape genetics in Wyoming”) and the 

range-wide connectivity study (Table 3 below, “Range-wide landscape connectivity”) should specifically 

address the question. The Service will consider relevant information as it becomes available prior to 

our deadline.

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Figure 3. Barriers to gene flow supported by existing genetic evidence discussed by the expert groups in Exercises 2 and 3. Blue line is 

river, Brown is mountains/forest, Green is cropland, Orange/Yellow is desert, Purple is high capacity power-lines. Some locations were 

given labels to improve communication during the workshop, but the labels do not necessarily correspond directly to an otherwise 

mapped feature. Lines are not definitive, and the absence of a line does not imply unrestricted gene flow between any areas, but that 

genetic data has not yet been examined. 

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Figure 4. Hypothesized barriers to gene flow discussed by the experts in Exercises 2 and 3. Blue is river, Brown is mountains/forest, Green is 

cropland, Orange/Yellow is desert Black is interstate highway, Gray area is energy development. Some locations were given labels to 

improve communication during the workshop, but the labels do not necessarily correspond directly to an otherwise mapped feature. Lines 

are not definitive, and the absence of a line does not imply unrestricted gene flow between any areas, but that genetic data has not yet been 

examined. 

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While reviewing their maps, the experts discussed the following natural features that may generally act 

as barriers to gene flow for the greater sage-grouse: 

Mountains, such as the Wind River Range in western Wyoming, the Big Horn Mountains in 

northern Wyoming and southern Montana, the Grand Tetons in western Wyoming, and the 

Rocky Mountains in northern Colorado. 

Large rivers and water bodies, such as the Columbia River, the Snake River, and the Missouri 

River.

Low-elevation, hot deserts, such as those in southern Nevada and Utah.

High-elevation coniferous forests, such as those in Colorado, Montana, Idaho, and Wyoming. 

The experts discussed the Snake River Plain in Idaho as a potential barrier to gene flow, with 

agriculture and expanding human populations along the river corridor contributing to the barrier and 

further reducing gene flow. However, sage-grouse may still be able to cross the Snake River Plain, and 

the available genetic data do not indicate that there are significant genetic differences on either side of 

the Snake River Plain. Further, elevation itself may not be a barrier, but the habitat features associated 

with elevation extremes may act as a barrier to gene flow. Populations that were large in the past, but 

have become isolated may be more vulnerable than historically small populations that have been 

isolated. 

The experts also discussed the following anthropogenic (human-caused) features that could act as 

barriers to gene flow for the greater sage-grouse in some circumstances: 

Large reservoirs, such as those along the Missouri River.

Areas with extensive agricultural development.

Electrical transmission lines (e.g., Washington State). 

Interstate highways such as Interstate 80 (I-80) in southern Wyoming. 

Areas of extensive energy development and its associated infrastructure (e.g., NE Wyoming). 

Exercise 3 – Review the evidence and hypotheses for limited gene flow across the range 

The facilitators revised this exercise in order to continue the discussion from Exercise 2. 

Original Topic: Evaluate the relative gene flow across the barriers identified in Exercise 2. 

Revised Topic: Continue discussing the evidence-based barriers and the hypothesized barriers 

to gene flow identified in Exercise 2. 

Exercise Purpose: 

The third topic of discussion reviewed the genetic evidence or supporting hypotheses discussed by 

experts in the workshop for barriers and areas with potentially limited gene flow that the experts

identified in Exercise 2 (Figure 3 and Figure 4). The facilitators asked: 

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Have we captured the places where limited gene flow (no or low gene flow) has been identified 

by data-driven genetic evidence and the places where limited gene flow has been 

hypothesized?

Exercise Discussion: 

The experts again stressed that the list of identified barriers and areas with limited gene flow likely is 

not definitive or comprehensive. Other barriers and places on the landscape that have restricted gene 

flow likely exist, but they have not been identified or analyzed. There may not be distinct breaks, but 

instead a variation or gradient across populations. More research, such as the upcoming range-wide 

connectivity study (Table 3 below, “Range-wide landscape connectivity”, could result in identification of

additional barriers to gene flow. 

As summarized in Table 1, the experts identified four areas with limited gene flow that based on 

available genetic evidence. 

Table 1. Areas with limited gene flow based on the available genetic evidence.

AREA OR 

POPULATION 

WITH LIMITED 

GENE FLOW 

BASED ON THE 

AVAILABLE 

GENETIC 

EVIDENCE

STATE(S) SUMMARY OF DISCUSSION BY THE EXPERTS

Bi-State California 

and Nevada

The Bi-State population has a large proportion of unique 

haplotypes that are found nowhere else. 

The genetic evidence indicates that the Bi-State population may 

have been isolated for thousands to tens of thousands of years. 

During the last glaciation (the Pleistocene), Lake Lahontan may 

have acted as a barrier. 

The Bi-State has haplotypes from both clades and the 

haplotypes in the Bi-state are not reciprocally monophyletic 

compared to haplotypes outside the Bi-state. This means 

that they haven’t been separated long enough to 

experience lineage sorting. 

The haplotype composition of this group is very different 

than all other populations of greater sage-grouse and does 

suggest that it is unique and has been isolated for a long 

time.

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AREA OR 

POPULATION 

WITH LIMITED 

GENE FLOW 

BASED ON THE 

AVAILABLE 

GENETIC 

EVIDENCE

STATE(S) SUMMARY OF DISCUSSION BY THE EXPERTS

Columbia Basin Washington

The Columbia Basin population is small and isolated.

Large areas of non-habitat separate the Columbia Basin 

from other populations, so the lack of gene flow is likely 

due to the isolation by non-habitat. 

The Columbia Basin population shares some common 

haplotypes with the rest of the range, but it has low genetic 

diversity, indicating that it is isolated from the rest of the 

range and experienced a population decline with no or very 

little emigration (migration into the population from outside). 

The Columbia Basin population has likely experienced 

genetic drift. 

The genetic isolation of the Columbia Basin likely occurred 

more recently than the isolation of the Bi-State population, 

because prior to conversion to croplands, sagebrush 

habitats connected Oregon and Washington. 

Additional analysis of the mtDNA data from museum 

specimens collected prior to extensive agricultural 

development would help establish when the Columbia 

Basin population became isolated (Table 3 below,

“Historical connectivity of the Columbia Basin”). 

Two populations in eastern Washington have received 

translocated sage-grouse from Oregon and Nevada, which 

could complicate an analysis of contemporary gene flow. 

Jackson Hole Wyoming

The Jackson Hole population is small, isolated, and declining.

The surrounding forested mountains isolated the population, 

but the time of isolation is unclear. 

Additional analysis of the mtDNA data would help establish 

when the population became isolated. 

Missouri River 

Valley Montana

The Missouri River Valley could inhibit sage-grouse movement, 

which may have resulted in genetic differentiation on either side 

of the river. 

The Missouri River is not necessarily a complete barrier; there 

is likely limited gene flow across the river. 

To summarize Exercises 2 and 3, the experts identified evidence-based and hypothesized barriers to 

gene flow across the range of the greater-sage grouse. Hypothesized barriers not yet considered that 

could reduce gene flow include natural and anthropogenic features, such as forested mountains,

transmission lines, and busy interstate highways. The available genetic evidence indicates that greater 

sage-grouse populations in the Bi-State, Columbia Basin, Jackson Hole, and Missouri River areas 

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experience reduced gene flow as a result of isolation or physical barriers to movement. The Columbia 

Basin and Jackson Hole populations are small and isolated and, as a result, are less genetically 

diverse. Additional research would be needed to evaluate the time of isolation for the Jackson Hole 

and the Columbia Basin populations and the impact of the hypothesized barrier to gene flow caused by 

I-80 in Wyoming and the Missouri River Valley. 

Day 2: Thursday, October 23, 2014 8:00 am – 12:00 pm1

Part 3 and Part 4: Genetic divergence and persistence

Exercise 4 - Areas with low genetic diversity across the range and areas where genetic processes and threats might 

interact to affect species persistence

Topic: Evaluate how the potential loss of a particular area of the range may decrease overall 

genetic diversity. Also identify areas, if any, where low genetic diversity or inbreeding 

depression may affect persistence. 

Exercise Purpose: 

The fourth topic of discussion asked each expert to identify mechanisms that cause low genetic 

diversity. The facilitators then asked the experts to identify areas within the greater sage-grouse’s

overall range where low genetic diversity or inbreeding depression could affect greater sage-grouse 

persistence. The facilitators also asked:

Are there areas within the range of the greater sage-grouse where genetic processes might 

exacerbate the effect of a threat? 

Exercise Discussion: 

The experts identified the following mechanisms that decrease the genetic diversity of a population: 

Isolation and the loss of gene flow.

Reduced population size and genetic drift within the isolated population. 

The potential consequences, or “genetic risks” associated with decreased genetic diversity include: 

Inbreeding depression.

The rapid propagation of deleterious traits due to genetic drift.

Increased loss of genetic diversity. 

Table 2 summarizes the discussion regarding small, isolated populations of greater sage-grouse with

low genetic diversity. The experts stressed that results from the upcoming range-wide connectivity 

study (see Table 3 below, “Range-wide landscape connectivity”) should help evaluate the genetic 

1 This conversation was initiated on Day 1 and resumed on Day 2. For the sake of coherence, this report 

combines the two discussions. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Summary of Workshop Exercises and Discussions

Page 13

 
diversity in these areas, and could reveal more areas of concern with low genetic diversity. 

Additionally, the range-wide connectivity study should help identify important stepping stones, or hubs 

to gene flow, that are crucial to maintaining genetic connectivity across the range. 

The experts stressed that if the overall range becomes increasingly fragmented, populations will 

become more isolated, thereby increasing the risk of losing genetic diversity across the range. Further, 

small populations are more susceptible to genetic drift. Small populations, particularly those at the 

periphery of the range, may also possess unique, adaptive genetic traits that not only provide diversity 

to the species as whole, but may be important in facilitating the species’ adaptation to changing 

environmental conditions. Therefore, from a geneticist’s perspective, threats that fragment habitats and 

isolate small populations would be the most important for the maintenance of genetic diversity. 

Table 2. Areas and populations with low genetic diversity discussed by the experts.

AREA(S) OR 

POPULATION(S) 

THAT MAY BE 

LESS 

GENETICALLY 

DIVERSE AS A 

RESULT OF SMALL 

POPULATION SIZE 

& ISOLATION

STATE(S)

HYPOTHESIZED

OR 

BASED ON 

EXISTING 

GENETIC 

EVIDENCE?

SUMMARY OF DISCUSSION BY THE 

EXPERTS

Columbia Basin Washington Based on 

existing genetic 

evidence

Populations in the Columbia Basin are small 

and isolated, and have low genetic diversity. 

They likely have low genetic diversity 

because they are isolated and populations 

have declined to low numbers.

Additional research is required to evaluate 

the time of isolation (Table 3 below, 

“Historical connectivity of the Columbia 

Basin”). 

The existing data does not provide insight 

into adaptive variation of the Columbia Basin. 

Jackson Hole Wyoming

Based on 

existing genetic 

evidence

The Jackson Hole population is small and 

isolated, with low genetic diversity. 

Strawberry Valley Utah

Based on 

existing genetic 

evidence

The population in the Strawberry Valley has 

reduced genetic diversity, largely the result of 

a high concentration of predators coupled 

with a small sage-grouse population, but the 

population does not necessarily have low 

gene flow. 

Translocations could complicate genetic 

analyses. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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AREA(S) OR 

POPULATION(S) 

THAT MAY BE 

LESS 

GENETICALLY 

DIVERSE AS A 

RESULT OF SMALL 

POPULATION SIZE 

& ISOLATION

STATE(S)

HYPOTHESIZED

OR 

BASED ON 

EXISTING 

GENETIC 

EVIDENCE?

SUMMARY OF DISCUSSION BY THE 

EXPERTS

Klamath area Oregon Hypothesized Isolated pocket of occupied habitat. 

Weiser Idaho Hypothesized Isolated pocket of occupied habitat. 

Belt Mountains Montana Hypothesized Isolated pocket of occupied habitat. 

Populations at the 

periphery of the 

range 

S. Utah

N. 

Colorado

N. Montana

S. Idaho

Hypothesized

Populations at the periphery of the range are 

increasingly fragmented and isolated, and as 

a result are likely more susceptible to 

inbreeding depression and genetic drift and 

loss of genetic diversity. 

However, peripheral populations may be 

uniquely adapted to specific environmental 

conditions, expressed by unique genetic 

adaptive markers. Unique markers provide 

genetic diversity and may be important in the 

future as the species adapts to changing 

environmental conditions. 

Part 5: Reflection, new studies, and wrap up

Ongoing and upcoming genetic studies and results

Topic: Discuss the potential impact of upcoming research on the outcomes of this workshop. 

Exercise Purpose: 

This topic provided an opportunity for the experts to discuss recently completed research, ongoing 

studies, research ideas, and their implications for the current understanding of sage-grouse genetics.

Exercise Discussion: 

As summarized in Table 3, several upcoming or recently completed genetic studies should provide 

additional scientific information regarding gene flow, barriers, and genetic diversity across the range of 

the greater sage-grouse. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Table 3. Recently published studies, ongoing research, and research ideas regarding the genetics of the greater sage-grouse discussed 

by the experts.

PROJECT NAME CITATION OR 

RESEARCHERS LOCATION PROGRESS SUMMARY OF DISCUSSION BY THE EXPERTS

Low neutral 

genetic diversity in 

isolated Greater 

Sage-Grouse 

(Centrocercus 

urophasianus) 

populations in 

northwest 

Wyoming

Schulwitz et al.

2014

Jackson 

Hole, 

Wyoming

Recently 

completed 

and 

published

The Jackson Hole population in 

Wyoming is isolated and has a 

significantly different genetic 

makeup than surrounding 

populations, due largely to the 

surrounding forested 

mountains. 

But it is unclear how long the 

population has been isolated. 

Genetic 

differentiation 

using SNPs

Oyler-McCance Range-wide Currently in 

review

This study will assess

differentiation of the greater 

sage-grouse, the Bi-State

population, and the Gunnison 

sage-grouse using SNPs. 

Landscape 

genetics in 

Wyoming

Row et al. Wyoming Currently in 

review

This study will assess 

landscape genetics of sagegrouse in Wyoming, focusing on 

the importance of seasonal 

habitats to drive landscape 

connectivity. 

Range-wide 

landscape 

connectivity

Knick et al. Range-wide Ongoing

Study investigating range-wide 

landscape connectivity analysis 

using microsatellites and SNPs. 

Previous analyses looked at 44 

populations across the overall 

range using 7 mtDNA and 14 

microsatellite loci. 

This new research uses 14 

microsatellite loci and is 

developing SNPs with over 

8,000 samples from across the 

species range. 

Completion timeline is unclear: 

Ideally available by spring 2015, 

but not likely available before 

fall 2015. SNP panels will not 

be complete before fall 2015. 

Impact of 

translocations in 

Washington 

Schroeder Washington Ongoing

Study investigating the impact of 

translocations on sage-grouse 

genetics in Washington. 

Availability timeframe unknown. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Summary of Workshop Exercises and Discussions

Page 16
PROJECT NAME CITATION OR 

RESEARCHERS LOCATION PROGRESS SUMMARY OF DISCUSSION BY THE EXPERTS

Impact of 

translocations in 

Strawberry Valley

Dunken et al. Utah Ongoing

Study investigating the impact of 

translocations on sage-grouse 

genetics in the Strawberry 

Valley.

Availability timeframe unknown. 

Connectivity 

across the 

Missouri River 

Schwartz et al. Montana Ongoing

Study investigating genetic 

difference between sage-grouse 

populations located to the north 

and south of the Missouri River.

Preliminary results indicate that 

there are genetic differences 

and that the Missouri River is a 

barrier to dispersal. 

Available in a few months. 

An east-to-west 

split Oyler-McCance Range-wide Ongoing

Reevaluate data collected in 

2005 to investigate the cline of 

genetic difference from east-towest across the range. 

Available in a few months. 

Highway I-80 as a 

barrier to gene 

flow

Schroeder and 

Oyler-McCance Wyoming Ongoing 

Investigate I-80 as a potential 

barrier to gene flow using 

available data.

Available in a few months. 

Historical 

connectivity of the 

Columbia Basin

Oyler-McCance

and Fedy Washington

Unfunded 

research 

idea

Use mtDNA from museum 

specimens to evaluate the 

historical connectivity of sagegrouse in the Columbia Basin to 

the rest of the overall range. 

Could complete before fall 2015. 

Time of isolation 

for the Jackson 

Hole population

Unknown Wyoming

Unfunded 

research 

idea

Evaluate the time of isolation for 

the Jackson Hole population. 

Literature Cited

Burgman, M. 2005. Risks and decisions for conservation and environmental management. Cambridge 

University Press, Cambridge, UK.

Drescher, M., A. H. Perera, C. J. Johnson, L. J. Buse, C. A. Drew, and M. A. Burgman. 2013. Toward 

rigorous use of expert knowledge in ecological research. Ecosphere 4 83. 

http://dx.doi.org/10.1890/ES12-00415.1

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Literature Cited

Page 17
Lowe, W. H., and F. W. Allendorf. 2010. What can genetics tell us about population connectivity? 

Molecular Ecology 19:3038-3051.

Mills, L. S., and F. W. Allendorf. 1996. The one-migrant-per-generation rule in conservation and 

management. Conservation Biology 10:1509-1518

Mills, L. S., M. K. Schwartz, D. A. Tallmon, and K. P. Lair. 2003. Measuring and interpreting changes 

in connectivity for mammals in coniferous forests. Pages 587-613 in C. J. Zabel and R. G. 

Anthony, editors. Mammal Community Dynamics: Management and Conservation in the 

Coniferous Forests of Western North America. Cambridge University Press, New York, USA.

Oyler-McCance, S.J., S.E. Taylor, and T.W. Quinn. 2005. A multilocus population genetic survey of the 

greater sage-grouse across their range. Molecular ecology 14(5):1293-1310.

Oyler-McCance, S.J., and T.W. Quinn. 2011. Molecular Insights into the Biology of Greater SageGrouse. Pages 85-94 In S.T. Knick, and J.W. Connelly, eds. Greater Sage-Grouse: ecology and 

conservation of a landscape species and its habitats. Studies in Avian Biology, 38th.

Schulwitz, S., Bedrosian, B., & Johnson, J. A. 2014. Low neutral genetic diversity in isolated Greater 

Sage-Grouse (Centrocercus urophasianus) populations in northwest Wyoming. The Condor

116(4): 560-573.

U.S. Environmental Protection Agency. 2011. Expert elicitation task force white paper. Science and 

Technology Policy Council, USEPA, Washington DC. (http://www.epa.gov/stpc/pdfs/ee-whitepaper-final.pdf)

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Literature Cited

Page 18
Appendix 1: Workshop Planning Team and Agenda

The workshop planning team (Table 1) developed the workshop’s purpose, invitation list, and agenda. 

Twelve of the 19 planning team members observed the meeting. Meeting observers did not participate 

as experts and outside observers were not invited to participate in order to ease facilitation. After the 

meeting, the workshop planning team drafted this summary report. The draft report and workshop 

notes were provided to the expert participants for their review. 

Planning Team

Table 4. Members of the workshop planning team, their organizational affiliations, and primary role on the team.

PLANNING TEAM 

MEMBER ORGANIZATION WORKSHOP ROLE OBSERVED THE WORKSHOP?

Angela Burgess

U.S. Fish and Wildlife Service

Region 6

Planning Yes

Carey Galst

U.S. Fish and Wildlife Service

Headquarters

Planning No

Craig Hansen

U.S. Fish and Wildlife Service

Region 6

Workshop coordinator and 

planning Yes

David Smith U.S. Geological Survey Facilitator Yes

Dawn Davis

U.S. Fish and Wildlife Service

Region 1

Planning No

Genevieve Skora

U.S. Fish and Wildlife Service

Region 8

Planning No

Holly Freifeld

U.S. Fish and Wildlife Service

Region 8

Planning Yes

Jeff Everett

U.S. Fish and Wildlife Service

Region 8

Planning No

Jesse D’Elia

U.S. Fish and Wildlife Service

Region 1

Planning Yes

Jonathan Cummings U.S. Geological Survey Facilitator Yes

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Appendix 1: Workshop Planning Team and Agenda

Page 19
PLANNING TEAM 

MEMBER ORGANIZATION WORKSHOP ROLE OBSERVED THE WORKSHOP?

Kate Norman

U.S. Fish and Wildlife Service

Region 6

Planning Yes

Kris Tita U.S. DOI Office of the 

Solicitor Planning No

Lief Wiechman

U.S. Fish and Wildlife Service

Region 6

Planning Yes

Mary Grim

U.S. Fish and Wildlife Service

Region 8

Planning Yes

Pat Deibert

U.S. Fish and Wildlife Service

Region 6

Planning Yes

Sarah Converse U.S. Geological Survey Planning No

Steve Abele

U.S. Fish and Wildlife Service

Region 8

Planning No

Steve Morey

U.S. Fish and Wildlife Service

Region 1

Facilitator Yes

Theo Stein

U.S. Fish and Wildlife Service

Region 6

External Affairs Yes

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Appendix 1: Workshop Planning Team and Agenda

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Workshop Agenda:

Expert Elicitation Workshop on the Genetics of the Greater Sage-Grouse

Wednesday October 22, 2014: 8:00 AM – 5:00 PM and

Fort Collins, Colorado

Day/time Topic Lead

PART 1: INTRODUCTION

WEDNESDAY

8:00 am Welcome, logistics, and introductions Craig Hansen

Purpose

Description of information packets

8:30 am Methods and metrics used to evaluate greater sagegrouse barriers to gene flow and genetic divergence.

Sara Oyler-McCance

Overview of greater sage-grouse genetics: state of 

the science

10:00 am Break

PART 2: BARRIERS TO GENE FLOW AND GENETIC DIVERGENCE IN SAGE-GROUSE

10:15 am Barriers to Gene Flow (Exercise 1): EVALUATE GENE 

FLOW BETWEEN THE EASTERN AND WESTERN PORTIONS 

OF THE GREATER SAGE-GROUSE'S RANGE. 

Dave Smith

Steve Morey

noon Lunch Jonathan Cummings

1:00 pm Barriers to Gene Flow (Exercise 2): IDENTIFY 

POTENTIAL BARRIERS TO SAGE-GROUSE GENE FLOW, IF 

ANY

Barriers to Gene Flow (Exercise 3): EVALUATE THE 

RELATIVE GENE FLOW ACROSS THE POTENTIAL BARRIERS

PART 3: GENETIC DIVERGENCE

4:00-5:00 pm Genetic Divergence (Exercise 4): EVALUATE HOW THE 

LOSS OF PARTICULAR AREAS OF THE RANGE RESULT IN 

LOSS OF THE GENETIC DIVERSITY OF THE SPECIES? (E-W

PORTIONS)

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Appendix 1: Workshop Planning Team and Agenda

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Thursday October 23, 2014: 8:00 am – 12:00pm

Day/time Topic Lead

THURSDAY

8:00 am Logistics and check in Craig Hansen

Genetic Divergence (continuation of Exercise 4, if 

needed)

Dave Smith

Steve Morey

Jonathan Cummings

PART 4: GENETIC DIVERGENCE AND PERSISTENCE

10:00 am Identify areas within greater sage-grouse range, if any, 

where low genetic diversity or inbreeding depression is 

likely to affect greater sage-grouse persistence. 

PART 5: REFLECTION AND NEW STUDIES AND WRAP UP

11:15 am DISCUSS THE POTENTIAL IMPACT OF UPCOMING RESEARCH

RESULT ON THE OUTCOMES OF THIS WORKSHOP.

11:50 am CLOSING Craig Hansen

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Appendix 2: Criteria to Identify and Invite Experts 

The workshop planning team followed accepted best practices for eliciting information from experts to 

identify and invite experts (Burgman 2005). To ensure a transparent, unbiased captured a broad 

diversity of expertise and judgments related to the topic selection process, the workshop planning team 

used the following criteria to identify and select experts: 

Criteria to ensure that participants are scientific experts 

Potential participants must meet all of the following criteria 1-3: 

1. Participant holds a graduate degree in wildlife biology, zoology, genetics, or a related field.

2. Participant holds a research position in government, academia, or in the nonprofit research 

sector; or participant holds a governmental management agency position with responsibility for 

sage-grouse.

3. Participant has expertise in wildlife genetics or ecology of sage-grouse or lekking grouse 

species, demonstrated by recent (within the past 10 years) peer-reviewed publications and 

related types of professional scientific expression.

The science and techniques of genetics have advanced rapidly within the last decade, 

so we define recent as the past 10 years to ensure that participants are qualified and 

proficient in the latest genetic research and applications. 

Criteria to ensure that participants are experts familiar with the workshop topic 

Each participant must also meet one or more of the following criteria 4-6: 

4. Participant is directly engaged in analysis of connectivity of sage-grouse populations or sage 

grouse habitat.

5. Participant is directly engaged in the study of sage-grouse population genetic structure.

6. Participant is a government or academic research scientist with expertise in conservation, 

population or landscape genetics, demonstrated by recent (within the past 10 years) peerreviewed publications and related types of professional scientific expression.

Criteria to ensure that the group of invited participants represent the diversity of expert judgment 

within the scientific community

Consistent with best practices for an effective expert elicitation workshop, we recommend selecting 

nine or fewer participants. The basis for selection would be to ensure diverse and representative 

scientific judgment. We recommend expert selection be structured by stratifying on affiliation type 

(academia, government, NGO), specialty (population genetics, conservation or landscape genetics, 

evolutionary genetics), and taxa experience (GSG, related species, non-GSG). Then select 

participants to represent each strata.

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Appendix 2: Criteria to Identify and Invite Experts

Page 23
Appendix 3: Expert Identification and Invitation List

Table of experts, invitees, and their responses to the Genetics Expert Elicitation workshop. 

GREEN = RSVP Yes RED = RSVP No WHITE = No RSVP or Invitation

EXPERT 

NAME

ORGANIZATION

CATEGORY

1 = GREATER SAGEGROUSE EXPERTS

2 =RELATED SPECIES 

EXPERTS

3 = NON-GRSG EXPERTS 

IN CONSERVATION 

GENETICS

RSVP

(Yes or N0)

Mevin Hooten Colorado Cooperative Fish and 

Wildlife Research Unit 1 Yes

Michael Schwartz USFS 1 Yes

Sara Oyler-McCance USGS 1 Yes

Mike Schroeder Washington State Department

of Fish and Wildlife 1 Yes

Christin Pruett Florida Institute of Technology 2 Yes

Jesus Maldonado 

Smithsonian Center for 

Conservation and Evolutionary 

Genetics

3 Yes

Chris Funk Colorado State University 3 Yes

Robert Gibson University of Nebraska –

Lincoln 2 Yes

Jeffrey Olsen USFWS Conservation Genetics 

Laboratory Alaska 3 Yes

Robert Zink University of Minnesota 3

Initially agreed to 

participate, but later 

withdrew due to 

prior commitments

Steve Knick USGS 1 No

Brad Fedy Univ. of Waterloo 1 No

Lisette Waits Univ. of Idaho 3 No

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Appendix 3: Expert Identification and Invitation List

Page 24
EXPERT 

NAME

ORGANIZATION

CATEGORY

1 = GREATER SAGEGROUSE EXPERTS

2 =RELATED SPECIES 

EXPERTS

3 = NON-GRSG EXPERTS 

IN CONSERVATION 

GENETICS

RSVP

(Yes or N0)

J.L. Bouzat University of Illinois at UrbanaChampaign 2 No

Andrew Martin University of Colorado –

Boulder 3 No

Rob Fleischer

Smithsonian Center for 

Conservation and Evolutionary 

Genetics 

3 No

Melanie Murphy University of Wyoming 2 or 3

Ephraim Hanks Penn State 3

Todd Cross University of Montana 1

Sergei Drovetski University of Alaska 3

Tom Quinn University of Denver 1

Krissy Bird (Bush) University of Alberta 1

Christian Hagen Oregon State University 2

Sue Haig Oregon State University 3

Ron Van Den 

Bussche Oklahoma State University 3

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Page 25
Appendix 4: Pre-Workshop Webinars and Workshop Ground Rules

The following slides were used during webinars to prepare the experts before the workshop. 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Ground Rules for the

Genetics Expert Elicitation Workshop Conducted as

Part of the Greater Sage-Grouse Status Review

U.S. Fish and Wildlife Service

October 9, 2014

Introduction

The U.S. Fish and Wildlife Service (Service) and the U.S. Geological Survey (USGS) 

invite you to participate in a workshop for scientific experts to address the genetics of the greater sagegrouse. This meeting, scheduled for October 22 and 23, 2014, at the Fort Collins Science Center in

Fort Collins, Colorado, is part of the Service’s process for collecting the best available scientific

information for its status review of the greater sage-grouse across its range.

1 This status review

will inform the Service’s decision on whether to propose the species for listing under the Endangered 

Species Act of 1973 (ESA), as amended (16 U.S.C. 1531 et seq.).

An important aspect of this meeting is ensuring that it complies with the Federal 

Advisory Committee Act (FACA) and the Administrative Procedure Act (APA). The ground rules

stated below are intended to serve this purpose. These ground rules are also intended to ensure

that the meeting focuses on providing the Service with the best available scientific information for its 

status review and listing decision under the ESA.

Principles Underlying These Ground Rules

ESA: Under the ESA, the Service is required to determine whether to list a species based 

consideration of the five factors set out in the statute, based solely on the best scientific and 

commercial data available to it. As described above, this meeting is part of the Service’s effort to

collect the best available scientific information for this listing determination2

. Thus, the 

Service is not asking for input from participants on the determinations that are the Service’s 

responsibility under the ESA. Instead, the Service is only seeking expert, unbiased input on the 

questions the Service has provided you in order to collect scientific information that Service biologists

will use in assessing the species’ status and formulating recommended determinations under the ESA. 

The information the Service receives through the expert meeting will be one source of information,

among other sources, that it Service will consider its listing determination under the ESA.

FACA: The purpose of the Federal Advisory Committee Act (FACA) is to ensure that 

advice provided to the Executive Branch of the Federal government by advisory committees is 

2 This status review does not include the Bi-State Distinct Population of the Greater Sage-Grouse, which is the 

subject of a separate status review and listing process. See generally 78 FR 64357.

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

Appendix 4: Pre-Workshop Webinars and Workshop Ground Rules

Page 32

 
transparent and accessible to the public. To achieve this purpose, FACA mandates a process for 

establishing, operating, overseeing, and terminating advisory bodies that are covered by the Act Groups

that are assembled to provide individual advice to a Federal agency, rather than advice from the group

as a whole, are not covered by FACA and hence are not required to comply with FACA’s

procedural and other requirements. The same is true of groups assembled to exchange facts and

information with a Federal agency. The meeting you will be attending will be structured and conducted

to meet both of these criteria: The primary purpose of the meeting will be to exchange facts and

information and, to the extent any advice may be sought on biological or other questions relevant to the

Service’s status review, that advice will only be sought on an individual basis, not from the group as

a whole. Maintaining these principles is critical to ensuring that the meeting does not violate FACA’s

requirements for advisory committees.

APA: The APA requires Federal agencies to maintain and make available to the public for

review all information used by them in developing a proposed rule, including a proposed rule to list a

species under the ESA. The APA also mandates that judicial review of any final Federal decision be

based on the administrative record that includes all records presented to or used by the Federal

agency in making the challenged decision. As a result of both of these APA requirements, the 

Service must maintain open, public records to document its decision-making process regarding the

greater sage-grouse. Therefore, information gained via this meeting will be documented and will

be subject to public disclosure through the processes just described. This documentation will also 

be subject to release upon request under the Freedom of Information Act (FOIA).

Ground Rules

1. The Service is only seeking the best available scientific information from the experts 

participating in this meeting.

2. The Service has developed a meeting agenda that, among other things, identifies the scientific 

questions on which the Service seeks the scientific expertise of the meeting participants. The

meeting participants should focus their participation in the meeting to providing information 

that is responsive to these questions.

3. The Service invited the individuals participating in this expert meetings based solely on their

scientific qualifications, rather than as a representative of a particular organization or

interested party. Accordingly, the participants should share their scientific expertise during

the meeting and not seek to represent any particular position of an agency, their employer, or

other interested party.

4. Participants should fully disclose to the Service and all other participants any potential 

conflict of interest (such as a commercial stake) in the Service’s listing determination 

regarding the greater sage-grouse. The Service may also ask participants to complete a 

questionnaire to evaluate and document any potential conflicts of interest.

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5. The Service is not asking for and will not accept input on the determinations it is charged with

making under the ESA. The Service retains full control over the ESA determination and no 

pre-decisional assumptions of those determinations will be discussed.

6. Participants should follow standard norms of behavior for effective meetings so that the 

Service can receive the information it seeks from all participants. The Service 

encourages an open, inclusive, and science-based discussion characterized by respectful, 

orderly dialogue.

7. Any opinion from any participants in this meeting will be provided on an individual basis and

not from the group as a whole. Group debate is encouraged, but the Service does not seek

consensus or group advice on issues to be discussed, and such consensus or group advice

should not be provided.

8. Information provided by meeting participants through the course of expert meetings will be

documented in meeting notes and a written summary report by the Service. The notes and

corresponding report will become part of the administrative record for use in agency decisionmaking. The meeting documentation will summarize the biological and other scientific

information gained during the meeting. Consistent with the preceding ground rule limiting 

advice provided to individual advice only, this report will document any advice provided by a

meeting participant on an individual basis.

9. Conference calls or other communications between the Service and meeting participants may 

occur after the meeting to clarify, for documentation purposes, the information discussed at the

meeting. Any such communications will also be subject to and follow these ground rules.

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Appendix 5: Workshop Bibliography

Background References – Greater Sage-grouse Genetics Workshop

October 22 and 23, 2014

USGS Fort Collins Science Center

Fort Collins, Colorado

UPDATED OCTOBER 14, 2014

Baumgardt, J.A., C.S. Goldberg, K.P. Reese, J.W. Connelly, D.D. Musil, E.O. Garton, and L.P. 

Watts. 2013. A method for estimating population sex ratio for sage-grouse using noninvasive 

genetic samples. Molecular Ecology Resources 13(3):393-402. 

http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12069/pdf

Abstract

Population sex ratio is an important metric for wildlife management and conservation, but 

estimates can be difficult to obtain, particularly for sexually monomorphic species or for species 

that differ in detection probability between the sexes. Noninvasive genetic sampling (NGS) 

using polymerase chain reaction (PCR) has become a common method for identifying sex from 

sources such as hair, feathers or faeces, and is a potential source for estimating sex ratio. If, 

however, PCR success is sex-biased, naively using NGS could lead to a biased sex ratio 

estimator. We measured PCR success rates and error rates for amplifying the W and Z 

chromosomes from greater sage-grouse (Centrocercus urophasianus) faecal samples, 

examined how success and error rates for sex identification changed in response to faecal 

sample exposure time, and used simulation models to evaluate precision and bias of three sex 

assignment criteria for estimating population sex ratio with variable sample sizes and levels of 

PCR replication. We found PCR success rates were higher for females than males and that 

choice of sex assignment criteria influenced the bias and precision of corresponding sex ratio 

estimates. Our simulations demonstrate the importance of considering the interplay between the 

sex bias of PCR success, number of genotyping replicates, sample size, true population sex 

ratio and accuracy of assignment rules for designing future studies. Our results suggest that 

using faecal DNA for estimating the sex ratio of sage-grouse populations has great potential 

and, with minor adaptations and similar marker evaluations, should be applicable to numerous 

species.

Benedict, N.G., S. Oyler-McCance, S.E. Taylor, C.E. Braun, and T.W. Quinn. 2003. Evaluation 

of the eastern (Centrocercus urophasianus urophasianus) and western (Centrocercus 

urophasianus phaios) subspecies of sage-grouse using mitochondrial control-region sequence 

data. Conservation Genetics 4(3):301-310. 

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http://download.springer.com/static/pdf/769/art%253A10.1023%252FA%253A1024089618546.p

df?auth66=1412710440_a44d491b1e23c11af156ee0d066f3a9b&ext=.pdf

Abstract

The status of Sage-grouse (Centrocercus urophasianus) is of increasing concern, as 

populations throughout its range have contracted as a result of habitat loss and degradation. 

Historically, Sage-grouse were classified into two subspecies: eastern (C. u. urophasianus) and 

western Sage-grouse (C. u. phaios) based on slight differences in coloration noted among eight 

individuals sampled from Washington, Oregon, and California. We sequenced a rapidly evolving 

portion of the mitochondrial control region in 332 birds from 16 populations. Although our 

sampling area covers the proposed boundary between the eastern and western subspecies, 

no genetic evidence to support the delineation of these subspecies was found. However, a 

population straddling southwestern Nevada and eastern California was found to contain an 

unusually high proportion of unique haplotypes, consistent with its genetic isolation from other 

Sage-grouse populations. Of additional interest was the lack of diversity in the two populations 

sampled from Washington, one of which contained only a single haplotype. We suggest that 

multiple lines of evidence are valuable for the formulation of conservation strategies and 

hence the southwestern Nevada/eastern California population merits further morphological, 

behavioral, and molecular investigation.

Bird, K.L., C.L. Aldridge, J.E. Carpenter, C.A. Paszkowski, M.S. Boyce, and D.W. Coltman. 

2013. The secret sex lives of sage-grouse: multiple paternity and intraspecific nest parasitism 

revealed through genetic analysis. Behavioral Ecology 24(1):29-38. 

http://beheco.oxfordjournals.org/content/24/1/29.full.pdf+html

Abstract

In lek-based mating systems only a few males are expected to obtain the majority of matings in 

a single breeding season and multiple mating is believed to be rare. We used 13 microsatellites 

to genotype greater sage-grouse (Centrocercus urophasianus) samples from 604 adults and 

1206 offspring from 191 clutches (1999–2006) from Alberta, Canada, to determine paternity and 

polygamy (males and females mating with multiple individuals). We found that most clutches 

had a single father and mother, but there was evidence of multiple paternity and intraspecific 

nest parasitism. Annually, most males fathered only one brood, very few males fathered multiple 

broods, and the proportion of all sampled males in the population fathering offspring averaged 

45.9%, suggesting that more males breed in Alberta than previously reported for the species. 

Twenty-six eggs (2.2%) could be traced to intraspecific nest parasitism and 15 of 191 clutches 

(7.9%) had multiple fathers. These new insights have important implications on what we know 

about sexual selection and the mating structure of lekking species.

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Breidinger, L.S., K.E. Mock, and T.A. Messmer. 2013. Greater Sage-Grouse and natural gas 

development in Utah: using population genetic data for conservation efforts. Western North 

American Naturalist 73(2):177-183.

http://utahcbcp.org/files/uploads/publications/BreidingerEtAl2013LeahSmith.pdf

Abstract

Population declines of Greater Sage-Grouse (Centrocercus urophasianus) throughout the 

western United States have been attributed to the loss, degradation, and fragmentation of 

sagebrush (Artemisia spp.) habitats. Increased energy development may further fragment 

sagebrush habitat, isolating sage-grouse populations and resulting in genetic drift, inbreeding, 

local extinction, or rapid divergence. We conducted a genetic survey of 3 remote sagegrouse 

populations in northeastern Utah to assess mitochondrial diversity relative to other portions of 

the species’ range. We did not detect any unusual haplotype compositions in these populations. 

However, haplotype composition of the Anthro Mountain and Strawberry Valley reference 

populations differed from haplotype compositions of other northeastern Utah populations. These 

populations are spatially separated by Desolation Canyon of the Green River. This canyon may 

constitute a geographic barrier to gene flow in this area, given low population densities and 

reduced dispersal potentials. This potential barrier will be an important consideration in future 

conservation efforts such as translocations. The halotype composition of the Anthro Mountain 

and Strawberry Valley reference populations may be altered by translocations subsequent to 

our sampling effort. The effect of these translocations on the reference halotypes and population 

vital rates is currently under study.

Bush, K. L., C. K. Dyte, B. J. Moynahan, C. L. Aldridge, H. S. Sauls, A. M. Battazzo, B. L. 

Walker, K. E. Doherty, J. Tack, J. Carlson, D. Eslinger, J. Nicholson, M. S. Boyce, D. E. 

Naugle, C. A. Paszkowski, and D. W. Coltman. 2011. Population structure and genetic 

diversity of Greater Sage-grouse (Centrocercus urophasianus) in fragmented landscapes at the 

northern edge of their range. Conservation Genetics 12: 527-542.

http://link.springer.com/article/10.1007/s10592-010-0159-8#

Abstract

Range-edge dynamics and anthropogenic fragmentation are expected to impact patterns of 

genetic diversity, and understanding the influence of both factors is important for effective 

conservation of threatened wildlife species. To examine these factors, we sampled greater 

sage-grouse (Centrocercus urophasianus) from a declining, fragmented region at the northern 

periphery of the species’ range and from a stable, contiguous core region. We genotyped 2,519 

individuals at 13 microsatellite loci from 104 leks in Alberta, Saskatchewan, Montana, and 

Wyoming. Birds from northern Montana, Alberta, and Saskatchewan were identified as a single 

population that exhibited significant isolation by distance, with the Milk River demarcating two 

subpopulations. Both subpopulations exhibited high genetic diversity with no evidence that 

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peripheral regions were genetically depauperate or highly structured. However, river valleys and 

a large agricultural region were significant barriers to dispersal. Leks were also composed 

primarily of non-kin, rejecting the idea that leks form because of male kin association. Northern 

Montana sage-grouse are maintaining genetic connectivity in fragmented and northern 

peripheral habitats via dispersal through and around various forms of fragmentation.

Bush, K. L., C. L. Aldridge, J. E. Carpenter, C. A. Paszkowski, M. S. Boyce, and 

D.W. Coltman. 2010. Birds of a feather do not always lek together: Genetic diversity and 

kinship structure of Greater Sage-grouse (Centrocercus urophasianus) in Alberta. Auk 127: 

343-353.

http://aoucospubs.org/doi/pdf/10.1525/auk.2009.09035

Abstract

Endangered species are sensitive to the genetic effects of fragmentation, small population size, 

and inbreeding, so effective management requires a thorough understanding of their breeding 

systems and genetic diversity. The Greater Sage-Grouse (Centrocercus urophasianus) is a 

lekking species that has declined by 66–92% during the past 35 years in Alberta. Our goals 

were to assess the genetic diversity of Greater Sage-Grouse in Alberta and to determine the 

degree of sex-specific relatedness within and among leks. Six hundred and four individuals 

sampled in 1998–2007 were genotyped at 13 microsatellite loci. Levels of genetic diversity were 

high, with the exception of one recently founded lek, and did not change over time. Overall, we 

did not observe isolation-by-distance among leks, and most leks were not differentiated from 

one another, which suggests that gene flow occurs across the study area. Males and females 

exhibited similar patterns of isolation-by-distance, so dispersal was not sex-specific. Overall 

relatedness was close to zero for both sexes at the level of the province, lek, and year, which 

suggests that neither sex forms strong kin associations. However, we found relatedness within 

leks at the year level to be greater than zero, which indicates interannual variation. We also 

found no evidence that Greater Sage-Grouse follow the typical avian pattern of male philopatry. 

Although the species is endangered in Alberta and occurs in fragmented habitat, it has 

maintained genetic diversity and connectivity.

Bush, K.L., M.D. Vinsky, C.L. Aldridge, and C.A. Paszkowski. 2005. A comparison of sample 

types varying in invasiveness for use in DNA sex determination in an endangered population of 

greater Sage-Grouse (Centrocercus uropihasianus). Conservation Genetics 6(5):867-870. 

http://www.aviangenetics.com/Sage-Grouse/downloads/bush_et_al_2005_dna_extract.pdf

[No abstract]

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Davis, D. M. 2012. Population structure of Greater Sage-grouse in northeastern California: 

Implications for conservation in a declining peripheral population. Ph.D. dissertation, University 

of Idaho, Moscow.

[Contact author for a .pdf: dawn_davis@fws.gov]

Abstract (excerpt)

To evaluate the population genetic structure of greater sage-grouse a total of 167 blood 

samples collected from birds captured on 13 known, active leks and 20 blood samples from 

birds captured off-lek during the autumn were genotyped at 19 microsatellite loci. Although 

greater sage-grouse populations are declining and have lost a significant portion of their range 

in northeastern California, the species in my study exhibited high genetic diversity. I observed 

no population structure and despite population declines and habitat loss, leks in northeastern 

California were not differentiated. My results showed significant isolation-by-distance among 

males, suggesting that male greater sage-grouse are the more philopatric sex and females have 

a greater predisposition to disperse. Furthermore, spatial autocorrelation analysis revealed 

stronger spatial structuring for males than for females. The results from the corrected 

Assignment Index (AIc) also confirmed female-biased dispersal, although difference between 

genders was not significant. Although greater sage-grouse in northeastern California have 

maintained gene flow across the sampled region and have tolerated some degree of habitat 

fragmentation and deterioration without losing genetic diversity, continued habitat loss could 

result in small, isolated greater sage-grouse populations at risk of losing genetic variation. Thus, 

improving habitat quality and connectivity of greater sage-grouse habitats in northeastern 

California is critical for maintaining gene flow and is important for the long-term persistence of 

greater sage-grouse occurring on the geographic periphery of the species’ distribution. 

Gibson, R. M., D. Pires, K. S. Delaney, and R. K. Wayne. 2005. Microsatellite DNA analysis 

shows that greater sage grouse leks are not kin groups. Molecular Ecology 14: 4453–4459.

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2005.02746.x/pdf

Abstract

The spectacular social courtship displays of lekking birds are thought to evolve via sexual 

selection, but this view does not easily explain the participation of many males that apparently 

fail to mate. One of several proposed solutions to this ‘lek skew paradox’ is that kin selection 

favours low-ranking males joining leks to increase the fitness of closely related breeders. We 

investigated the potential for kin selection to operate in leks of the greater sage grouse, 

Centrocercus urophasianus, by estimating relatedness between lekking males using 

microsatellite DNA markers. We also calibrated these estimates using data from known families. 

Mean relatedness within leks was statistically indistinguishable from zero. We also found no 

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Page 39
evidence for local clustering of kin during lek display, although males tended to range closer to 

kin when off the lek. These results make kin selection an unlikely solution to the lek skew 

paradox in sage grouse. Together with other recent studies, they also raise the question of why 

kin selection apparently promotes social courtship in some lekking species, but not in others. 

Oyler-McCance, S.J., M.L. Casazza, J.A. Fike, and P.S. Coates. 2014. Hierarchical spatial 

genetic structure in a distinct population segment of greater sage-grouse. Conservation 

Genetics . 

http://link.springer.com/article/10.1007/s10592-014-0618-8/fulltext.html

Abstract

Greater sage-grouse (Centrocercus urophasianus) within the Bi-State Management Zone (area 

along the border between Nevada and California) are geographically isolated on the 

southwestern edge of the species’ range. Previous research demonstrated that this population 

is genetically unique, with a high proportion of unique mitochondrial DNA (mtDNA) haplotypes 

and with significant differences in microsatellite allele frequencies compared to populations 

across the species’ range. As a result, this population was considered a distinct population 

segment (DPS) and was recently proposed for listing as threatened under the U.S. Endangered 

Species Act. A more comprehensive understanding of the boundaries of this genetically unique 

population (where the Bi-State population begins) and an examination of genetic structure within 

the Bi-State is needed to help guide effective management decisions. We collected DNA from 

eight sampling locales within the Bi-State (N = 181) and compared those samples to previously 

collected DNA from the two most proximal populations outside of the Bi-State DPS, generating 

mtDNA sequence data and amplifying 15 nuclear microsatellites. Both mtDNA and microsatellite

analyses support the idea that the Bi-State DPS represents a genetically unique population, 

which has likely been separated for thousands of years. Seven mtDNA haplotypes were found 

exclusively in the Bi-State population and represented 73 % of individuals, while three 

haplotypes were shared with neighboring populations. In the microsatellite analyses both 

STRUCTURE and FCA separate the Bi-State from the neighboring populations. We also found 

genetic structure within the Bi-State as both types of data revealed differences between the 

northern and southern part of the Bi-State and there was evidence of isolation-by-distance. 

STRUCTURE revealed three subpopulations within the Bi-State consisting of the northern Pine 

Nut Mountains (PNa), mid Bi-State, and White Mountains (WM) following a north–south 

gradient. This genetic subdivision within the Bi-State is likely the result of habitat loss and 

fragmentation that has been exacerbated by recent human activities and the encroachment of 

singleleaf pinyon (Pinus monophylla) and juniper (Juniperus spp.) trees. While genetic concerns 

may be only one of many priorities for the conservation and management of the Bi-State greater 

sage-grouse, we believe that they warrant attention along with other issues (e.g., quality of

sagebrush habitat, preventing future loss of habitat). Management actions that promote genetic 

connectivity, especially with respect to WM and PNa, may be critical to the long-term viability of 

the Bi-State DPS.

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Oyler-McCance, S.J., and T.W. Quinn. 2011. Molecular Insights into the Biology of Greater 

Sage-Grouse. Pages 85-94 In S.T. Knick, and J.W. Connelly, eds. Greater Sage-Grouse: 

ecology and conservation of a landscape species and its habitats. Studies in Avian Biology, 

38th. 

https://www.fort.usgs.gov/sites/default/files/products/publications/22121/22121.pdf

Abstract

Recent research on Greater Sage-Grouse (Centrocercus urophasianus) genetics has revealed 

some important findings. First, multiple paternity in broods is more prevalent than previously 

thought, and leks do not comprise kin groups. Second, the Greater Sage-Grouse is genetically 

distinct from the congeneric Gunnison Sage Grouse (C. minimus). Third, the Lyon-Mono 

population in the Mono Basin, spanning the border between Nevada and California, has unique 

genetic characteristics. Fourth, the previous delineation of western (C. u. phaios) and eastern 

Greater Sage-Grouse (C. u. urophasianus) is not supported genetically. Fifth, two isolated 

populations in Washington show indications that genetic diversity has been lost due to 

population declines and isolation.

Oyler-McCance, S.J., S.E. Taylor, and T.W. Quinn. 2005. A multilocus population genetic 

survey of the greater sage-grouse across their range. Molecular ecology 14(5):1293-1310. 

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2005.02491.x/pdf

Abstract

The distribution and abundance of the greater sage-grouse (Centrocercus urophasianus) have 

declined dramatically, and as a result the species has become the focus of conservation efforts. 

We conducted a range-wide genetic survey of the species which included 46 populations and 

over 1000 individuals using both mitochondrial sequence data and data from seven nuclear 

microsatellites. Nested clade and analyses revealed that, in general, the greater sage-grouse 

populations follow an isolation-by-distance model of restricted gene flow. This suggests that 

movements of the greater sage-grouse are typically among neighbouring populations and not 

across the species, range. This may have important implications if management is considering 

translocations as they should involve neighbouring rather than distant populations to preserve 

any effects of local adaptation. We identified two populations in Washington with low levels of 

genetic variation that reflect severe habitat loss and dramatic population decline. Managers of 

these populations may consider augmentation from geographically close populations. One 

population (Lyon/Mono) on the southwestern edge of the species’ range appears to have been 

isolated from all other greater sage-grouse populations. This population is sufficiently genetically 

distinct that it warrants protection and management as a separate unit. The genetic data 

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presented here, in conjunction with large-scale demographic and habitat data, will provide an 

integrated approach to conservation efforts for the greater sage-grouse.

Oyler-McCance, S., J. St. John, S.E. Taylor, A.D. Apa, and T.W. Quinn. 2005. Population 

Genetics of Gunnison Sage-Grouse: Implications for Management. The Journal of Wildlife 

Management 69(2):630-637. 

http://www.jstor.org/stable/3803733

Abstract

The newly described Gunnison sage-grouse (Centrocercus minimus) is a species of concern for 

management because of marked declines in distribution and abundance due to the loss and 

fragmentation of sagebrush habitat. This has caused remaining populations to be unusually 

small and isolated. We utilized mitochondrial DNA sequence data and data from 8 nuclear 

microsatellites to assess the extent of population subdivision among Gunnison sage-grouse 

populations in southwestern Colorado and southeastern Utah, USA. We found a high degree of 

population structure and low amounts of gene flow among all pairs of populations except the 

geographically adjacent Gunnison and Curecanti populations. Population structure for Gunnison 

sage-grouse was significantly higher than has been reported for greater sage-grouse (C. 

urophasianus). Further, we documented low levels of genetic diversity in some populations 

(particularly Dove Creek/Monticello and Pifion Mesa with an average of only 3.00 and 2.13 

alleles per locus respectively) indicating that translocations from larger, more genetically diverse 

populations may be warranted. Bayesian analysis identified 3 potential migrants (involving San 

Miguel, Dove Creek/Monticello, Crawford, and Curecanti). Further, this analysis showed that 4 

individuals from Cerro/Cimarron were more closely related to birds from San Miguel than to its 

geographically closer neighbors Gunnison and Curecanti. This suggests the Cerro/Cimarron 

area may act as a stepping stone for gene flow between San Miguel and Gunnison and that 

habitat restoration and protection in areas between these 2 basins should be a priority in an 

attempt to facilitate natural movement among these populations. Conservation plans should 

include monitoring and maintaining genetic diversity, preventing future habitat loss and 

fragmentation, enhancing existing habitat, and restoring converted sagebrush communities.

Quinn, T.W., N.W. Kahn, J.R. Young, N.G. Benedict, S. Wood, D. Mata, and C.E. Braun. 1997. 

Probing the evolutionary history of sage grouse Centrocercus urophasianus populations using 

mitochondrial DNA sequence. Wildlife Biology 3(3-4):291. 

Abstract only:

Sage grouse Centrocercus urophasianus are a classic example of a lek-mating species, and are 

becoming yet another example of a species encountering dramatic human-induced changes to 

its environment. A study of the patterns of genetic variation across the range of both subspecies 

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Page 42
and including the unique small-bodied birds in the Gunnison Basin, Colorado was done by 

sequencing 141 bp or more within region I of the mitochondrial control region. Within Colorado, 

the same haplotype was present in 31/32 (97%) of the small-bodied birds surveyed in the 

Gunnison Basin whereas a wider variety of haplotypes was found at each of the five surveyed 

locales within the range of the large-bodied birds (132 individuals). The predominant haplotype 

within the Gunnison Basin is at a frequency of 97%, but elsewhere that haplotype is at a 

frequency of less than 20%. This, and the observation that other haplotypes which predominate 

in large-bodied birds are not present in the Gunnison Basin provides evidence there has been 

virtually no recent gene flow into the Gunnison population from large-bodied sage grouse found 

elsewhere in Colorado. Preliminary sampling from across the western USA revealed similarities 

across the range among large-bodied birds in that three haplotypes are shared throughout, but 

there was also a subset of haplotypes that was more localized in distribution and, hence, 

potentially more informative for defining population subdivision.

Schroeder, M.A. 2008. Variation in greater sage-grouse morphology by region and population. 

Washington Department of Fish and Wildlife, Bridgeport, WA. 

http://wdfw.wa.gov/publications/01305/wdfw01305.pdf

Abstract

The greater sage-grouse (Centrocercus urophasianus) is wildlife species with important 

conservation and management considerations in western North America. Research on sagegrouse has established their historical and current distribution (Schroeder et al. 2004) as well as 

providing an assessment of their populations and habitats (Connelly et al. 2004). This research 

helped define the presence of 41 distinct populations (Fig. 1) based on the Berryman’s (2002) 

definition of a population; “as a group of individuals of the same species that live together in an 

area of sufficient size to permit normal dispersal and/or migration behavior and in which 

numerical changes are largely determined by birth and death processes.

In addition to defining populations, research has provided an evaluation of the rangewide 

genetics of sage-grouse with an assessment of potential issues associated with population size, 

population connectivity, and sub-specific variation (Young 1994; Young et al. 1994; OylerMcCance et al. 1999, 2005). Although research helped establish the Gunnison sage-grouse as 

a behaviorally and morphologically distinct species (Young et al. 1994), similar research within 

the remaining populations of greater sage-grouse has been somewhat limited (Oyler-McCance 

et al. 2005, Taylor and Young 2006). Oyler-McCance et al. (2005) showed that sage-grouse 

displayed relatively integrated genetics across their range with notable exceptions being the 

Moses Coulee, N Mono Basin, and S Mono Basin populations.

The initial objective of this research was to examine the availability of previously collected

morphological and behavioral data from both published and unpublished sources. Although the 

collection of additional data would certainly aid in this process, this was not an objective for this 

initial phase of research. The overall goal of the research was to acquire and examine data with 

reference to variation associated with region, population, and/or previously established genetic 

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characteristics. Because of the nature of this type of data (rarely published), it was believed that 

this initial report effort would be ‘preliminary’ and that analysis would continue as additional data 

was collected and/or acquired.

Taylor, R.L., D.E. Naugle, and L.S. Mills. 2012. Viability analyses for conservation of sagegrouse populations: Buffalo Field Office, Wyoming. Bureau of Land Management, Buffalo Field 

Office, Buffalo, Wyoming. 

http://www.eenews.net/assets/2012/03/28/document_gw_01.pdf

Executive Summary 

Impacts from energy development to sage-grouse (Centrocercus urophasianus) present a 

challenge to public land managers tasked with maintaining large and intact landscapes that 

support viable populations. We provide decision support to Bureau of Land Management (BLM) 

managers tasked with maintaining sage-grouse populations in the oil and gas (energy) fields of 

northeast Wyoming by assessing four aspects of energy development as they relate to 

sagegrouse. Findings reflect the status of a small remaining sage-grouse population that has 

already experienced an 82% decline within the expansive energy fields (Walker et al. 2007a), a 

level of impact that has severely reduced options for delineating core areas that are large 

enough and in high enough quality habitats to sustain populations.

1) We identified the spatial scale at which energy development most influences sage-grouse 

populations, as indexed by counts of males at leks. Ignoring state boundaries to include counts 

from unimpacted leks in Montana was critical to identifying the far reaching impacts of 

development on grouse in Wyoming. Using a statistical technique (AIC, Burnham and Anderson 

1998) that is akin to using a dial to tune a radio to pick up the strongest signal, we detected that 

development had the greatest influence on male counts within 12.4 mi (20 km) surrounding a 

lek. The signal is much stronger at the 12.4-mi radius than any of the smaller radii we tested, 

encompassing a large spatial scale that covers an area of 483 1,257 km2

).

2) We evaluated the current viability of sage-grouse populations. We linked lek count data to 

energy development and West Nile virus (WNv) by associating the density of producing wells 

within the 12.4-mi (20-km) radius to each lek count, and the occurrence of widespread WNv 

outbreaks in the year preceding the count. Predictions of resulting male lek counts were 

consistently within 0 to 1% of the observed counts, underscoring the success of our approach 

(Table 2).

Declines in number of active leks and number of attending males indicate that both energy 

development and WNv outbreaks reduce sage-grouse populations. At current well spacing (328 

ac spacing, 0.75 wells/km2) and without accounting for WNv outbreaks, our predicted number of 

males on leks is 3,648 (95% CIs = 3,147, 4,204, Table 3). Absent an outbreak year, the lower 

95% confidence limit on the count is 3,147 males, suggesting that immediate extirpation of the 

northeast Wyoming population is unlikely if all environmental conditions for sage-grouse other 

than energy development remain favorable.

Wyoming’s core area policy will be most effective where implemented in advance of extensive 

energy development, and in southwest portions of the state where high elevation populations 

are less susceptible to WNv impacts. But in northeast Wyoming, WNv outbreak years are the 

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Page 44
wild card in core area management, and predictions made without accounting for WNv are 

optimistically high. The effect of a WNv outbreak year alone can more than cut a population in 

half, which is similar to drilling an undeveloped landscape at 4-8 wells/mi2 (1.5-3.1 wells/km2

, 

Table 3). When we include impacts of an outbreak year at all leks, we predict 1,473 males given 

current well densities (Table 3). With outbreak years as part of the equation, the lower 

confidence limit on our prediction is 65 males, which, if reached, would indicate functional 

extinction. Incorporating outbreaks into analyses suggests that even with no additional energy 

development many local populations may be one bad WNv year away from extirpation.

3) We formulated and simulated potential, realistic future management scenarios for sagegrouse populations, using the models we developed above to evaluate viability. Our results 

suggest that if development continues, future viability of the already small sage-grouse 

populations in northeast Wyoming will be compromised. Small populations are vulnerable to 

extirpation by chance events (Soule and Mills 1998), and WNv outbreaks are an excellent 

example of this type of catastrophic event. Despite impacts, the potential may still exist to 

maintain a population inside core areas, but further drilling in and around cores will compromise 

their remaining value. Notably, core areas in northeast Wyoming were delineated after 

widespread development had already occurred, leaving few options for conserving populations. 

Our findings do not negate the benefits of core areas, in general. However, to achieve maximum 

effectiveness, core areas must be constructed proactively by conserving high quality habitat, not 

reactively by drawing borders around planned and existing development.

4) We provide recommendations for evaluating the future viability of sage-grouse populations if 

restoration efforts begin as the energy play subsides. First and foremost we recommend that 

BLM commit to monitoring outcomes of restoration as measured by the distribution and number 

of sage-grouse in northeast Wyoming. Focusing restoration where plugged and abandoned 

wells are clustered would increase the size of habitats available to birds, thus enhancing the 

chance of increasing their abundance and distribution. Leaving energy infrastructure such as 

roads, power lines, and water impoundments on the landscape for other purposes is an 

unintended impact of development that will compromise restoration success. Appropriate 

monitoring of leks as wells are removed from production is imperative to allow for a rigorous 

analysis of restoration success, which cannot be supported by currently available data. Lek 

counting needs to be conducted at least across the area of northeast Wyoming, and preferably 

across the entire study region including eastern Montana (Fig 1).

Genetic connectivity is the glue that holds populations together, and remaining core areas, 

though impacted, may help maintain connectivity among populations further south in Wyoming 

and those in Montana. Until genetic studies currently underway delineate the degree to which 

sage-grouse populations are connected, we recommend maintaining the potential areas of 

connectivity outlined in the Wyoming Governor’s Executive Order as undeveloped, contiguous 

habitat.

Taylor, S.E., S. Oyler-McCance, and T.W. Quinn. 2003. Isolation and characterization of 

microsatellite loci in Greater Sage-Grouse (Centrocercus urophasianus). Molecular Ecology 

Notes 3(2):262-264. 

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http://onlinelibrary.wiley.com/doi/10.1046/j.1471-8286.2003.00424.x/pdf

Abstract

Primers for five polymorphic microsatellite loci were developed for Greater Sage-Grouse 

(Centrocercus urophasianus) using an enrichment/detection protocol. The high level of 

polymorphism (nine to 33 alleles) suggests that these loci will be applicable for investigating 

mating systems and paternity analysis as well as population genetics. Cross-species 

amplification was successful for each locus in at least two other galliform species.

Tebbenkamp, J.M. 2014. Greater Sage-Grouse in the Bi-State Distinct Population Segment: An 

evaluation of genetic structure, connectivity, and vital rate in Mono County, California. Master of 

Science. University of Idaho. 

[This link likely not available outside of DOI]

https://fishnet.fws.doi.net/projects/home/NSGC/Literature/Tebbenkamp,%20JM.%202014,%20G

reater%20sage-grouse%20in%20the%20bi-state%20dps-%20an%20evaluation.pdf

Abstract

Because isolation may imperil populations, maintaining demographic and genetic connectivity is 

a high priority for conservation and management. One population of conservation concern due 

to its high degree of isolation is the Bi-State Distinct Population Segment (DPS) of greater sagegrouse (Centrocercus urophasianus). Located on the periphery of their range, along the 

California-Nevada border, the Bi-State DPS is genetically isolated from other populations. 

Further, telemetry data suggest there is additional subdivision within the Bi-State DPS. Here, we 

combine telemetry and genetic data to investigate both demographic and genetic structuring 

within the Mono County, California portion of the Bi-State DPS. From 2007-2012, we radiomarked and monitored 122 greater sage-grouse and collected genetic data at 17 microsatellite 

loci for 334 individuals. Pairwise FST estimates (mean = 0.146, range = 0.090-0.205) along with 

2 Bayesian clustering methods provided evidence for 5 genetic populations. We did not 

document any dispersal events between populations using radio-, however, using 4 genetic 

assignment methods found 10 individuals were likely recent dispersers. Combined, these data 

show that there is both demographic and genetic subdivision within the Bi-State DPS, and while 

demographic support between populations is unlikely due to the low number of dispersers, 

these infrequent dispersal events are capable of preventing genetic isolation. Thus, effective 

conservation of the Bi-State DPS will require maintaining genetic connectivity while also 

attending to demographic processes of each population.

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Thompson, T. R. 2012. Dispersal ecology of Greater Sage-grouse in northwestern Colorado: 

Evidence from demographic and genetic methods. Ph.D. dissertation, University of Idaho, 

Moscow.

http://cpw.state.co.us/Documents/Research/Birds/Thompson_dissertation_2012FINAL.pdf

ABSTRACT 

The greater sage-grouse (Centrocercus urophasianus; here after sage-grouse) has undergone 

dramatic population declines over the last 25 years as a result of loss, fragmentation, and 

degradation of sagebrush (Artemisia tridentata spp.) habitats on which it depends. Because of 

these declines and the subsequent loss of habitat, knowledge concerning the juvenile ecology 

of sage-grouse, including natal dispersal patterns and abilities and its influences on population 

persistence, colonization, and connectivity are critical for the conservation planning and 

management of this species. The focus of this dissertation was two-fold: first, to assess the 

feasibility of actively collecting and hatching sage-grouse eggs from wild radiomarked sagegrouse and rearing subsequent domestically-hatched (DH) chicks from 1-10 days of age before 

augmenting wild sage-grouse broods (Chapter 2), and second to investigate natal dispersal in 

greater sage-grouse through both demographic (radio telemetry) and genetic methods. In 

Chapter 3, I monitored survival and causes of mortality in wild-hatched chicks (n = 431) in wild 

broods (n = 115) from hatch to 16 weeks of age in the Axial Basin and Cold Springs Mountain 

study areas in northwestern Colorado, 2005-2007 and evaluated potentially important predictors 

of brood and chick survival. In addition, I monitored survival from hatch to 16 weeks of age for a 

cohort of DH chicks raised to 1-10 days of age in captivity (n = 116) and introduced into a 

subset of wild broods during this same time period. Model averaged estimates of brood and 

chick survival indicated that survival varied both temporally and spatially. In Chapter 4, I 

captured, radiomarked, and monitored survival and recruitment of 183 transmitter-equipped 

juveniles (from Chapter 3) from 1 September – 31 March. Survival from September through 

March was similar for all juveniles, but varied by month, study area, and gender. Median 

dispersal distance was greater for juvenile males compared to females (M: 3.84 + 1.26 km; F: 

2.68 + 0.30 km), as well as the proportion dispersing > 5 km (M: 31.6%; F: 15.5%). In Chapter 

5, I examined the patterns of dispersal, gene flow, and genetic structure at 15 leks in 6 

population management zones (PMZs). Genetic analyses were largely congruent and 

suggested that gene flow followed an isolation-by-distance pattern, and supported male-biased 

dispersal findings based on demographic data (Chapter 4). Finally, in Chapter 6, I investigated 

how coarse-grained landscape characteristics influenced dispersal and settlement patterns. 

Landscape metrics primarily differed between study areas rather than genders, and among predispersal, winter, and post-dispersal landscapes. Effect of extent upon analyses depended upon 

the specific metric and landscape. 

Zink, R.M. 2014. Comparison of Patterns of Genetic Variation and Demographic History in the 

Greater Sage-Grouse (Centrocercus urophasianus): Relevance for Conservation. The Open 

Ornithology Journal 7:11. 

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http://www.miningamerica.org/wp-content/uploads/Zink-Report.pdf

Abstract

The greater sage-grouse (Centrocercus urophasianus) was once widespread in western North 

America but its range has contracted by an uncertain degree owing to anthropogenic and 

natural causes. Concern over population declines has led to its proposed listing as threatened 

under the U.S. Endangered Species Act. Detailed genetic and demographic analyses of this 

species throughout its range are available but heretofore have not been compared. Reduced 

genetic variability is often taken as a proxy for declining populations, but rarely are there 

quantitative population estimates with which to compare. I compared published mitochondrial 

DNA (mtDNA) control region sequences, microsatellite allele frequencies at seven loci, and 

estimates of numbers of males per lek, number of active leks, percent decline in the 

best population models, and the probability (P) of Ne < 50 in 30 years and P(Ne < 500) in 100 

years, at two spatial scales, 45 local population samples and 16 larger aggregates of samples. 

When excluding the populations from the Columbia Basin, which show little genetic diversity 

and are statistical outliers, there were no consistent relationships between estimates of genetic 

variation and demographic trends across the remainder of the range at either spatial scale. A 

measure of inbreeding derived from microsatellite data was also not related to population 

trends. Thus, despite habitat reduction and range fragmentation, the greater sage-grouse does 

not exhibit expected genetic signatures of declining populations. Possibly, the mtDNA and 

microsatellite data are insufficiently sensitive to detect population declines that have occurred 

over the span of a half century. Alternatively, only when populations are reduced to the levels 

seen in the Columbia Basin will genetic effects be seen, suggesting that the bulk of the range of 

the greater sage-grouse is not currently in genetic peril.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Additions October 14, 2014

Stiver, J. R., A. D. Apa, T. E. Remington, and R. M. Gibson. 2008. Polygyny and female 

breeding failure reduce effective population size in the lekking Gunnison sage-grouse. 

Biological Conservation 141:472-481.

http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1224&context=bioscifacpub&seiredir=1&referer=http%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3DStive

r%2B2008%2BPolygyny%2Band%2Bfemale%2Bbreeding%2Bfailure%2Breduce%2Beffective%

2Bpopulation%2Bsize%2Bin%2Bthe%2Blekking%2BGunnison%2Bsagegrouse%26btnG%3D%26as_sdt%3D1%252C5%26as_sdtp%3D#search=%22Stiver%202008%

20Polygyny%20female%20breeding%20failure%20reduce%20effective%20population%20size

%20lekking%20Gunnison%20sage-grouse%22

Abstract

Populations with small effective sizes are at risk for inbreeding depression and loss of adaptive 

potential. Variance in reproductive success is one of several factors reducing effective 

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Page 48
population size (Ne) below the actual population size (N). Here, we investigate the effects of 

polygynous (skewed) mating and variation in female breeding success on the effective size of a 

small population of the Gunnison sage-grouse (Centrocercus minimus), a ground nesting bird 

with a lek mating system. During a two-year field study, we recorded attendance of marked 

birds at leks, male mating success, the reproductive success of radio-tagged females, and 

annual survival. We developed simulations to estimate the distribution of male reproductive 

success. Using these data, we estimated population size (N ̂ ) and effective population size Ne 

for the study population. We also simulated the effects of population size, skewed vs. random 

mating, and female breeding failure on Ne. In our study population, the standardized variance in 

seasonal reproductive success was almost as high in females as in males, primarily due to a 

high rate of nest failure (73%). Estimated Ne (42) was 19% of N ̂ in our population, below the 

level at which inbreeding depression is observed in captive breeding studies. A high hatching 

failure rate (28%) was also consistent with ongoing inbreeding depression. In the simulations, 

Ne was reduced by skewed male mating success, especially at larger population sizes, and by 

female breeding failure. Extrapolation of our results suggests that six of the seven extant 

populations of this species may have effective sizes low enough to induce inbreeding 

depression and hence that translocations may be needed to supplement genetic diversity. 

Young, J. R. 1994. The influence of sexual selection on phenotypic and genetic divergence 

among sage-grouse populations. Dissertation. Purdue University, West Lafayette, Indiana, 

USA.

http://search.proquest.com/docview/304119371

Abstract

I studied the mating behavior, ecology, and genetics of an isolated population of sage grouse 

(Centrocercus urophasianus) in the Gunnison Basin, Colorado. Sage grouse have a lek mating 

system in which only a small percentage of males mate. Sexual selection in such a mating 

system can lead to rapid evolution of sexual dimorphism in size, plumage characteristics and 

mating behavior as well as associated female preferences for such male traits. As a result, I 

predicted that sexual selection could increase population divergence as well as reduce 

population persistence. Field observations indicated that traits important to male mating 

success, such as mating vocalizations, had diverged in the Gunnison population relative to 

other sage grouse populations. To determine the effect of this divergence in male mating 

vocalizations on female behavior, I conducted reciprocal field playback experiments at two leks, 

one in Gunnison and the other in a nearby, but allopatric, northern Colorado population. 

Females in each population avoided male vocalizations from the other population, suggesting 

the existence of a pre-mating barrier.^ While female mating behavior differed between 

populations, my investigation of female nesting ecology and summer habitat use showed that 

Gunnison females are ecologically similar to females in other sage grouse populations. Females 

in Gunnison (a) chose nest sites with more sagebrush density and structure than random sites, 

(b) had higher nesting success in areas with greater shrub density and forb and grass cover, 

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and (c) used flat, mesic areas with extensive grass and forb components when rearing broods. 

Genetic analyses of four sage grouse populations revealed moderately higher band-sharing and 

Fst values compared to non-lekking bird species. In addition, some genetic differentiation exists 

among the four populations and between leks in Gunnison.^ My results demonstrate the 

Gunnison population is distinct in secondary sexual traits, but not female ecology. These results 

are consistent with the view that sexual selection can have a direct role in the initial stages of 

population divergence leading to speciation. My results also suggest that lek mating species 

may have reduced genetic variation relative to non-lekking species and subsequently are more 

vulnerable to environmental changes.

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Appendix 6: Presentations given by Dr. Sara Oyler-McCance 

Presentation 1: 

Slide 1

INTRODUCTION TO GENETICS: THE

BASICS

Sara Oyler-McCance

USGS Fort Collins Science Center

Slide 2

DNA

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Slide 3

THE BASICS

DNA (the genetic 

material) is coiled and 

folded into chromosomes 

which are located in the 

nucleus of the cell

Slide 4

THE BASICS

• Gene – sequence of DNA that codes for some 

gene product (protein or RNA)

• Not all DNA codes for gene products 

• The location where a gene or non-coding 

sequence is found in the genome is called a locus

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Slide 5

GENOTYPES

• Pairs of genes or noncoding sequences at a 

locus

• Homozygous – both 

sequences are the same

• Heterozygous – the two 

sequences are different

Slide 6

SOURCES OF DNA

Any biological tissue or fluid:

Blood, saliva, urine, semen, feces (scat)

Muscle, liver, tongue, skin, etc.

Hair, feather, scale, claw, nail

Bone, tooth, ivory

Study skin, taxidermy mount

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Slide 7

POLYMERASE CHAIN REACTION

Amplification of specific regions (markers) of 

DNA

Slide 8

MOLECULAR MARKERS

• Some molecular markers are coding 

sequences (non neutral)

– Proteins

– RNA

• Others are non-coding sequences (neutral)

– Introns 

– Microsatellites

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Slide 9

Slide 10

MITOCHONDRIAL DNA

• Maternally Inherited

• Small, circular genome (~16,000 bp in birds)

• Evolves more quickly than nuclear genome

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Slide 11

MITOCHONDRIAL DNA

Slide 12

MTDNA SEQUENCING

...CTGAAAGGGT...Haplotype A

...CTGTAAGGGT... Haplotype B

Base substitution

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Slide 13

A

B

C

A

B

C

D

E

F

G

Droup 3

Slide 14

....AATCGCTATTGCACAAGTCAACTGAG...... Haplotype A

....AATCGCTATTGCACATGTCAACTGAG...... Haplotype B

1 substitution

....AATCGCTATTGCACAAGTCAACTGAG...... Haplotype A

....AATGGATATACCACATGTCATGAGAG...... Haplotype C

8 substitutions

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Slide 15

Phylogenetic Relationship Among 

Haplotypes

Haplotype A

Haplotype B

Haplotype C

Slide 16

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Slide 17

Slide 18

NUCLEAR DNA

• Large 

• Typically evolves slowly

• Some regions evolve fast

• Inherited from both parents

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Slide 19

MICROSATELLITES

..ATAGATAGATAGATAGATAG.................. 

..ATAGATAGATAGATAGATAGATAG.......... 

..ATAGATAGATAGATAGATAGATAGATAG.. 

one repeat deleted

one repeat added

Allele A

Allele B

Allele C

(ATAG)6

(ATAG) 5

(ATAG)7

Slide 20

SINGLE NUCLEOTIDE POLYMORPHISMS

• A region in the genome 

where individuals vary by a 

single nucleotide

• Most common type of 

genetic variant

• SNPs can occur in coding 

regions or in non coding 

regions

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Slide 21

SINGLE NUCLEOTIDE POLYMORPHISMS

Individual 1 (A) ATGCGGCGATTGCCATGGGTA

Individual 2 (A) ATGCGGCGATTGCCATGGGTA

Individual 3 (A) ATGCGGCGATTGCCATGGGTA

Individual 4 (B) ATGCGGCCATTGCCATGGGTA

Individual 5 (B) ATGCGGCCATTGCCATGGGTA

Individual 6 (B) ATGCGGCCATTGCCATGGGTA

SNP

Slide 22

Individual 1

A: 159/163

B: 181/185

C: 224/226

Individual 3

A: 159/163

B: 181/185

C: 224/226

Individual 2

A: 159/159

B: 185/185

C: 224/226

MICROSATELLITE GENOTYPING

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Slide 23

SNP GENOTYPING

TT TC

CC

Slide 24

Individual Pop CA38 CA19 CA47 M23 CA11

6_11_05 1 175 179 118 118 123 139 182 192 170 176

6_11_07 1 177 181 124 130 119 139 156 178 170 174

6_11_10 1 179 183 130 132 129 131 168 186 168 174

6_11_11 2 175 187 112 118 129 133 178 206 168 176

6_11_12 2 159 173 114 120 121 129 174 178 172 172

6_11_14 2 153 167 114 124 121 127 178 182 172 176

6_11_15 3 179 181 128 136 129 133 158 164 172 174

6_11_16 3 179 189 112 132 123 123 210 218 168 170

6_11_17 3 179 183 122 128 125 125 170 178 170 176

Individual Pop SNP1 SNP2 SNP3 SNP4 SNP5 SNP6

BH32A A 1 1 1 4 1 2 1 2 1 3 1 4

LV18A A 1 4 1 4 1 2 2 2 3 3 4 4

NV91B B 1 1 4 4 1 2 1 2 1 3 4 4

EG9B B 1 4 4 4 2 2 2 2 1 3 4 4

CU29C C 4 4 1 4 1 2 1 2 3 3 1 4

DVCC C 4 4 1 4 2 2 2 2 3 3 1 4

Microsatellite data

SNP data

Alleles represent fragment sizes

Alleles are different nucleotides (1 = A, 2 = G, 3 = C, 4 = T)

Greater Sage-Grouse 2015 Status Review, Genetics Workshop Summary Report

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Slide 25

Individual SG1 SG2 SG3 SG4 SG5

Chick 1 173/181 152/154 156/160 157/161 156/156

Chick 2 173/181 154/172 156/160 155/161 152/154

Hen 173/175 154/166 160/160 157/161 152/158

Assessment of Parentage 

Which chick does not belong to the hen?

Slide 26

Individual SG1 SG2 SG3 SG4 SG5

Chick 1 173/181 152/154 156/160 157/161 156/156

Chick 2 173/181 154/172 156/160 155/161 152/154

Hen 173/175 154/166 160/160 157/161 152/158

Assessment of Parentage

Which chick does not belong to the hen?

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Slide 27

HOW CONNECTED ARE POPULATIONS?

Slide 28

.

.

. . . .

. .

.

Cold Springs Eagle

North Park

Middle Park

Dove Creek Crawford Gunnison Basin

Dry Creek

Blue Mountain

Microsatellite Primer 8

143

137

157

143

137

157

143

137

143 143

143

143

137

157

163

143

137

157

143

137

157

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Slide 29

GENETIC DISTANCES BETWEEN POPULATIONS

Gunnison Curecanti Crawford Cimmaron

San Miguel 

Basin

Dove Creek/

Monticello

Gunnison Basin

Curecanti 0.01976

Crawford 0.16961 0.18145

Cimmaron 0.20025 0.21331 0.16078

San Miguel Basin 0.09165 0.11175 0.14317 0.08954

Dove Creek/Monticello 0.27581 0.32011 0.38536 0.27149 0.19017

Pinon Mesa 0.45559 0.44904 0.44846 0.21331 0.29601 0.43082

Slide 30

STRUCTURE ANALYSIS – INDIVIDUAL BASED

METHODS

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Slide 31

GENETIC DIVERSITY

Population Mean sample 

size (SD)

Allelic Richness 

per population 

(SD)

% of 

polymorphic 

loci

Gunnison 83.13 (4.45) 5.00 (3.85) 100

Curecanti 25.00 (1.46) 3.88 (1.25) 88

Crawford 22.50 (0.76) 3.00 (1.41) 88

San Miguel 56.75 (2.55) 3.25 (1.98) 75

Dove 

Creek/Monticello

42.38 (2.26) 3.00 (1.77) 75

Piñon Mesa 19.50 (0.93) 2.13 (1.55) 50

Slide 32

LANDSCAPE GENETICS

Population Genetics

Landscape Ecology

Spatial Statistics

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Slide 33

LANDSCAPE GENETICS

Moving from “population” 

genetics....

• Populations defined ahead 

of time

• Genetic distance calculated 

between all pairs of 

“populations”

• Coarse scale

Slide 34

to “landscape” genetics. 

• Much finer scale

• Many more individuals spread 

more evenly across the 

landscape

• Calculate genetic distance 

between pairs of individuals

• Let the data describe 

biologically meaningful 

populations

• Assess levels of connectivity 

and identify barriers to gene 

flow

LANDSCAPE GENETICS

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Slide 35

LANDSCAPE GENETICS

• Shift from gene flow in 

theoretical space 

characterized by geographical 

distances only

• To gene flow in fragmented 

landscapes providing 

estimates of functional 

connectivity

Slide 36

Random Isolation by distance

Isolation by distance

with a barrier Isolation by barrier Isolation by resistance

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Slide 37

QUESTIONS?

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Presentation 2: 

Slide 1

OVERVIEW OF GREATER SAGE-GROUSE

GENETICS

Sara Oyler-McCance

USGS Fort Collins Science Center

Slide 2

OVERVIEW OF GREATER SAGE-GROUSE

GENETICS

• BACKGROUND

• PUBLISHED STUDIES

– Investigating the lek mating system

– Reassessing taxonomic boundaries

– Population genetics

– Miscellaneous studies

• PAPERS IN REVIEW

– Taxonomy

– Population/landscape genetic studies

• ONGOING RESEARCH

– Impacts of translocations

– Population/landscape genetic studies

– Rangewide Connectivity

– Development of genomic markers 

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Slide 3

Figure from Connelly et al., 2004, adapted from Schroeder et al. 2004.

Slide 4

• Regional population 

declines in some areas (17% 

– 47%)

• Likely linked to loss, 

fragmentation, and 

degradation of sagebrush 

habitat

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Slide 5

MOVEMENTS

• Seasonal movements are 

variable

• Some populations are 

described as migratory 

moving over 80 km

• Movements and 

connectivity among 

populations remain 

largely unknown

Slide 6

MATING SYSTEM

• Poylgynous

• Males defend territories 

on leks and conduct an 

elaborate mating display

• Females are attracted to 

dominant males

• Not clear what 

proportion of males 

actually breed

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Slide 7

Western

C. u. phaios

Eastern

C. u. urophasianus

Slide 8

Western

C. u. phaios

Eastern

C. u. urophasianus

Gunnison

C. minimus

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Slide 9

MOLECULAR GENETIC METHODS

• Parentage/Mating 

System

• Taxonomy

• Population structure 

– Gene Flow

– Connectivity

– Genetic Diversity

Slide 10

PARENTAGE

Objective: Investigate the mating system by 

comparing behavioral data and genetic data

Markers: Microsatellites

Location: California

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Slide 11

PARENTAGE

• 20% of broods had multiple fathers

• In all cases where they had data, the genetic 

data matched the observational data

• Multiple paternity may be more prevalent that 

was once thought

Slide 12

PARENTAGE

Objective: Determine paternity and polygamy 

Markers: Microsatellites

Location: Alberta

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Slide 13

PARENTAGE

• Most clutches had a single mother and father but 

there was evidence of multiple paternity and 

intraspecific nest parasitism

• Nest parasitism occurred in 2.2% of eggs 

• Multiple paternity occurred 7.9% of clutches

• The proportion of all sampled males who fathered 

offspring was 45.9% (from 1999- 2006) 

Slide 14

LEK FORMATION

Objective: Test whether lekking behavior evolved in 

GRSG because kin selection favored subordinate 

males joining leks of close relatives in order to 

increase their inclusive fitness 

Marker: Microsatellites

Location: California

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Slide 15

LEK FORMATION

• Mean relatedness values on leks were not different from 

zero 

• Males on leks are not closely related

• Mean relatedness values were significantly lower than 

relatedness values calculated among known family 

members

• No evidence for local clustering of related individuals 

while on leks, yet they did find that related individuals 

tended to cluster together when off the lek

• Leks consist of groups of largely unrelated males with little 

if any spatial association among kin either within or 

between leks

Slide 16

LEK FORMATION

Objectives: Assess the genetic diversity of GRSG in 

Alberta and to determine the degree of sex-specific 

relatedness within and among leks

Marker: Microsatellites

Location: Alberta

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Slide 17

LEK FORMATION

• Levels of genetic diversity were high and did not 

change over the 9 years of the study

• Did not observe isolation-by-distance among leks

• Leks were not differentiated from each other 

suggesting substantial gene flow across the study 

area

• Overall relatedness was ~ 0 for both sexes at the 

level of province, lek and year suggesting neither 

sex forms strong kin associations

• No evidence of male philopatry

Slide 18

TAXONOMY

Large-bodied

Small-bodied

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Slide 19

TAXONOMY

Objectives: Determine whether genetic differences 

exist between the large and small-bodied forms of 

Sage Grouse and to estimate a crude time frame 

involved in the morphological and behavioral 

differences between the forms

Marker: mtDNA (control region)

Location: Colorado

Slide 20

TAXONOMY

• Haplotypes fell into 2 divergent clades 

likely representing an ancestral isolation 

into two distinct populations, that have 

since extensively intermixed

• Estimated that the two clades began 

diverging at least 850,000 years ago

• Haplotype frequencies in the smallbodied population were different and less 

diverse than the large-bodied populations 

• Absence of gene flow between the two 

groups

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Slide 21

Slide 22

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Slide 23

TAXONOMY

• Gunnison Sage-grouse recognized as a new species 

(Centrocercus minimus) 

• All other Sage-grouse renamed 

Greater Sage-grouse (C. urophasianus)

Slide 24

TAXONOMY

Objectives: Evaluate the validity of the eastern and 

western subspecies of Sage-grouse

Marker: mtDNA control region

Location: Western portion of the range

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Slide 25

TAXONOMY

• No genetic evidence was found to support the 

delineation of these subspecies

• Population straddling the border between California 

and Nevada (Bi-State) had an unusually high proportion 

of unique haplotypes, consistent with its genetic 

isolation from other populations 

• This population may have been isolated for thousands 

or tens of thousands of years

• Also found low genetic diversity in the two populations 

from Washington

Slide 26

• Objective: Assess the 

extent of population 

subdivision among 

Gunnison Sage-grouse 

populations

• Markers: mtDNA control 

region, microsatellites

• Location: Colorado, Utah

POPULATION GENETICS

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Slide 27

• High degree of population structure and low amounts 

of gene flow among populations (except between 

Gunnison Basin and Curecanti)

• Population structure for GUSG was higher than had 

been reported for GRSG

• Low levels of diversity in Dove Creek/Monticello and 

Pinion Mesa

• Identified 3 potential migrants 

• Cerro/Cimarron/Sims may act as a stepping stone for 

gene flow between San Miguel Basin and Gunnison

POPULATION GENETICS

Slide 28

POPULATION GENETICS

Objectives: To examine the distribution of genetic 

variation across the entire range 

Markers: mtDNA control 

region, microsatellites

Location: Rangewide

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Slide 29

• Populations follow an isolation-by-distance model 

of restricted gene flow

• Identified two populations in Washington with low 

levels of genetic variation that reflect severe habitat 

loss and dramatic population decline 

• Another population in Strawberry Valley, UT also 

had low diversity

• The Lyon/Mono (Bi-state) population is isolated and 

genetically distinct

POPULATION GENETICS

Slide 30

• Objectives: Examining 

range-edge dynamics 

and impacts of 

fragmentation 

• Marker: Microsatellites

• Location: Northeast 

portion of the range

POPULATION GENETICS

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Slide 31

• Birds from northern MT, Alberta and Saskatchewan 

were identified as a single population that exhibited 

isolation-by-distance with the Milk River 

demarcating two subpopulations

• Both subpopulations had high genetic diversity

• River valleys and a large agricultural region were 

significant barriers to dispersal

• Leks were primarily made up of non-kin 

• Northern Montana sage-grouse are maintaining 

genetic connectivity in fragmented habitats 

POPULATION GENETICS

Slide 32

Objective: To assess mtDNA

diversity of 3 remote populations 

relative to other portions of the 

species range

Marker: mtDNA control region

Location: Utah

POPULATION GENETICS

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Slide 33

• No unusual haplotype compositions in the three 

remote populations 

• Haplotype composition of the Anthro Mountain 

and Strawberry Valley reference populations 

differed from other northeastern Utah 

populations

• Desolation Canyon of Green River may constitute 

a barrier to gene flow

• Anthro Mountain and Strawberry Valley may be 

altered by translocations subsequent to this study

POPULATION GENETICS

Slide 34

Objective: Compare published genetic and demographic 

data to determine whether populations estimated to be 

on downward trajectories also showed reductions in 

levels of genetic diversity

Markers: mtDNA control region,

microsatellites 

Location: Rangewide

POPULATION GENETICS

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Slide 35

• With the exception of the Washington populations, no 

consistent relationships were found between 

estimates of genetic variation and demographic 

trends across the range at either of two spatial scales

• A measure of inbreeding derived from microsatellite 

data was not related to population trends

• GRSG does not exhibit expected genetic signatures of 

declining populations

POPULATION GENETICS

Slide 36

Objectives: Gaining a better 

understanding of the 

boundaries of the Bi-State 

population and examining 

genetic structure within it

Markers: mtDNA control 

region, microsatellites

Location: Bi-State (NV and CA)

POPULATION GENETICS

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Slide 37

• Both mtDNA and microsatellite analyses support 

the idea that the Bi-State represents a genetically

unique population

• There was genetic structure within the Bi-State 

with differences between the northern and 

southern part of the Bi-State

• Evidence for 3 subpopulations following a northsouth gradient

POPULATION GENETICS

Slide 38

Objective: To assess genetic 

structure among isolated 

populations (Jackson Hole 

and Gros Ventre) in parts of 

Wyoming and Southeast 

Montana

Marker: Microsatellites

Location: Parts of WY, MT 

POPULATION GENETICS

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Slide 39

• Found four genetic clusters with Pinedale and Casper 

forming one cluster, Powder River Basin and southeast 

MT forming a second cluster, and both Jackson Hole and 

Gros Ventre forming their own clusters

• All samples outside of JH and GV showed isolation-by- distance

• JH and GV were separate but had asymmetric dispersal 

into GV

• Both JH and GV had reduced genetic diversity and low 

effective number of breeders

• Only a few alleles were unique to JH and GV suggesting 

some historical gene flow

POPULATION GENETICS

Slide 40

POPULATION GENETICS - DISSERTATION

Chapter 5 – Dispersal, gene flow, and population genetic structure 

in the greater sage-grouse: implications for connectivity and 

natural colonization 

Thomas R. Thompson, Kerry P. Reese, Anthony D. Apa, and Lisette P. 

Waits

Submitted to Condor: Ornithological Applications

Objective: Examine patterns of dispersal, gene flow, and 

genetic structure in northwest Colorado

Markers: Microsatellites

Location: Northwestern Colorado

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Slide 41

POPULATION GENETICS - DISSERTATION

• All leks had high genetic diversity 

• Moderate-high gene flow between neighboring PMZ 

• PMZs showed isolation-by-distance with gene flow 

following a directional or two-dimensional stepping-stone 

pattern that was local and between neighboring leks, but 

also between neighboring PMZs 

• Evidence of male-biased dispersal in both direct (radiotelemetry) and indirect (genetic) methods 

• Higher levels of genetic structure in females 

• Significant fine scale structuring for both males and 

females at distances < 15 km 

• Dispersal was rare at distances > 40 – 60 km for both sexes 

Slide 42

Chapter 5 – Integrating molecular data to detect patterns of 

sex-biased dispersal and barriers to gene flow in Greater Sage- Grouse

Dawn M. Davis, University of Idaho (Kerry Reese Advisor)

Submitted to Journal of Wildlife Management

Objective: To assess patterns of dispersal among 

greater sage-grouse in northeastern California

Marker: Microsatellites

Location: Northeastern California

POPULATION GENETICS - DISSERTATION

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Slide 43

• Significant isolation-by-distance among males, 

suggesting that males are more philopatric sex and 

that females have a greater predisposition to 

disperse

• Stronger spatial structuring for males than females

• Assignment Index confirmed female-biased 

dispersal, although differences between genders 

were not significant

POPULATION GENETICS - DISSERTATION

Slide 44

POPULATION GENETICS - THESIS

Chapter 2 – A multi-method evaluation of connectivity in a 

threatened DPS of Greater Sage-grouse

Joel M. Tebbenkamp, University of Idaho (Kerry Reese and Lisette 

Waits, Co-Advisors)

Submitted to Conservation Genetics

Objectives: To evaluate population 

genetic substructure and dispersal

Marker: Microsatellites

Location: Bi-State

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Slide 45

POPULATION GENETICS - THESIS

• Pairwise FST estimates and Bayesian clustering 

methods provided evidence for 5 genetic populations 

within the Bi-State

• No documented dispersal events between 

populations using radio-telemetry, however, using 4 

genetic assignment methods found 10 individuals 

were likely recent dispersers among populations

Slide 46

MISCELLANEOUS – SEX DETERMINATION

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Slide 47

MISCELLANEOUS – PRIMER NOTES

Slide 48

MISCELLANEOUS – GENOMIC APPLICATIONS

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Slide 49

PAPERS IN REVIEW

Re-examining genetic variation in Sage-grouse using 

genomic methods

S. J. Oyler-McCance, R. S. Cornman, K. L. Jones, and J. A. Fike

Submitted to AUK: Ornithological Advances

Objectives: Examine differentiation between GRSG, GUSG and 

Bi-State using genomic approaches

Marker: Single Nucleotide 

Polymorphisms 

Location: GUSG, Bi-State, 

Southern part of GRSG range

Slide 50

PAPERS IN REVIEW

Fast divergence of Z-linked nucleotide polymorphisms in a genus of 

lekking birds

S.J. Oyler-McCance, R. S. Cornman, K. L. Jones, and J. A. Fike

Submitted to Heredity

Objectives: Investigate the chromosomal location of nuclear genomic 

divergence between GUSG and GRSG

Marker: Single nucleotide polymorphisms

Location: GUSG and southern GRSG 

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Slide 51

Latent spatial models and sampling design for landscape 

genetics

E.M. Hanks, M.B. Hooten, S.T. Knick, S.J. Oyler-McCance, J.A. Fike, T.B. Cross, and M.K. Schwartz

Submitted to: Biometrics

Objective: Develop a spatially explicit approach for 

modelling genetic variation across space and illustrate how 

this approach can be used to optimize spatial prediction and 

sampling design for landscape genetic data

Marker: Microsatellites

Location: Rangewide

PAPERS IN REVIEW

Slide 52

PAPERS IN REVIEW

Integration of genetic and demographic data to assess 

relative population risk

B.C. Fedy, J. Row, and S.J. Oyler-McCance

Submitted to Ecological Applications

Objectives: To assess population structure and use those 

defined population boundaries to assess population trends

Marker: Microsatellites

Location: Wyoming 

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Slide 53

Landscape characteristics influencing the genetic structure of 

greater sage-grouse within the stronghold of their range: A 

holistic modeling approach

J.R. Row, S.J. Oyler-McCance, J.A. Fike, M. O’Donnell, K. Doherty, 

C.L. Aldridge, Z. Bowen, and B.C. Fedy

Submitted to Ecology and Evolution

Objective: Establish and compare the importance of 

seasonal habitat distribution and landscape components 

in driving functional connectivity

Marker: Microsatellites

Location: Wyoming 

PAPERS IN REVIEW

Slide 54

RESEARCH EFFORTS IN PROGRESS

• Assessing impacts of translocations in Washington

• Assessing impacts of translocations into Strawberry, 

Valley, UT

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Slide 55

LANDSCAPE GENETICS

RESEARCH EFFORTS IN PROGRESS

Slide 56

LANDSCAPE GENETICS

Intensive landscape 

genetic efforts in 

Wyoming and 

Montana and for 

Gunnison Sagegrouse in Colorado

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Slide 57

RANGEWIDE CONNECTIVITY ASSESSMENT

RESEARCH EFFORTS IN PROGRESS

Slide 58

OBJECTIVES

• Delineate the genetic population 

structure of Greater Sage-grouse 

across their range

• Quantify genetic connectivity 

among populations

• Identify the spatial scale and 

relative importance of landscape 

features that influence gene flow

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Slide 59

METHODS

• Collect genetic data 

across the range

– Collect feathers at leks

in all states and 

provinces

– Process feathers in lab 

to obtain genetic 

information

• Use genetic data in 

combination with 

spatial analysis of 

landscape 

components

Slide 60

COLLABORATIVE EFFORT

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Slide 61

DEVELOPMENT OF A HIGH RESOLUTION

GENOME-WIDE SNP ARRAY

RESEARCH EFFORTS IN PROGRESS

Slide 62

Marker Panel Design Marker Panel 

Testing

Gallus 600k SNP 

Assay

Greater sage-grouse 

DNA Sequences

Adaptive SNPs

Candidate 

SNPs

Neutral SNPs Microsatellites

Bioinformatics

Tests for Target 

Accuracy & 

Selection/Neutrality

GENOME WIDE MARKER DEVELOPMENT

Microsatellites

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Slide 63

IDENTIFY CONSERVATION UNITS INFERRED FROM

GENETIC VARIATION ACROSS THE LANDSCAPE

• Do CUs designated using microsatellites differ 

from CUs delineated using neutral SNPs? 

• Is local adaptation found within or 

overlapping CUs? 

Slide created by: Todd Cross 

(tbcross@fs.fed.us)

Slide 64

QUESTIONS?

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Appendix 7: Workshop Notes

Purpose of the Workshop Notes

The Workshop Notes presented here were collected for the purpose of drafting the formal 

genetics expert elicitation report (Report). This information is provided here in the interest of 

transparency. Notes to clarify the record are provided in brackets.

These notes are not exhaustive. They capture the highlights of conversations and the general 

flow of the meeting. These notes do not represent a complete transcript; statements are not 

attributed to individuals. We have tried to indicate which statements were provided by U.S. Fish 

and Wildlife Service (FWS), U.S. Geological Survey (USGS), or Experts. 

These notes should not be used for citations. 

Day 1: Wednesday, Oct. 22, 2014

PART 1: INTRODUCTION

FWS: Welcome, logistics, and introduction

Purpose: Gather information, published literature. One step to gather information to inform the 

Service’s decision. This meeting is strictly to gather information. All issues related to policy are

the Service’s decision. 

Civil, orderly, respectful discussion requested at the workshop. No group consensus. Individual 

judgments and opinions only. 

Introductions: 

Dave Smith: USGS facilitating discussion. Appreciates expert participation in workshop.

Jonathan Cummings: Post-doc with USGS, primarily providing computer support for 

workshop

Steve Morey: USGS facilitating discussion. 

Jesse D’Elia: FWS, R1

Kate Norman: FWS, R6

Mary Grim: FWS, R1

Theo Stein: FWS, R6 EA

Holly Freifeld: FWS, R1

Pat Diebert: FWS, R6

Angela Burgess: FWS, R6

Lief Wiechman: FWS, R6

Jesus Maldonado- Smithsonian Center for Conservation and Evolutionary Genetics 

Jeff Olsen- FWS, Conservation Genetics Laboratory, Anchorage, Alaska

Chris Funk – Colorado State University

Michael Schroeder – Washington Dept. of Fish and Wildlife

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Christin Pruett – Florida Institute of Technology

Robert Gibson – University of Nebraska - Lincoln

Mike Schwartz – University of Montana and USFS

Sara Oyler-McCance – USGS Fort Collins Science Center

Mevin Hooten – USGS Colorado Cooperative Fish and Wildlife Research Unit

FWS- Information packets – various maps provided, also hanging on the wall. Agenda 

also included. Sara will provide short discussion on genetics, followed by a break and 

then into Exercise 1.

USGS- genetics presentation primarily to assist everyone to be on the same page and 

help provide additional information for FWS and USGS facilitators. Any questions? No 

questions.

FWS- meeting notes being taken, will be collated and combined and passed along to 

experts for review prior to finalization. 

Sara Oyler-McCance: Introduction to Genetics - The Basics

Dr. Oyler-McCance provided this presentation; it can be found above in Appendix 6.

Questions: 

1) USGS: Reciprocal Monophyly: One set of cluster looked different from rest, is that what 

the “mono” in monophyly comes from?

Those that share same node on tree would be reciprocally monophyletic. 

If they are reciprocally monophyletic, they may be considered by some to be 

separate species

Some may argue that’s not a good way to define a species because there may 

not have been enough time for speciation to occur.

2) Expert : SNPs - looking at microsatellite data, there are typically thousands, correct?

Yes, typically thousands of microsatellites are compared. 

Would you look at the same part of the genome to look at SNPs that you look at 

microsatellite?

Expert A: Typically different regions, though SNPs may be adjacent to 

microsatellite.

3) USGS: Metrics of SNPs? 

used to measure genetic diversity – heterozygosity; not allelic richness like in 

microsatellite metrics

Comparing microsatellites and SNPs: More heterozygosity in SNPs, though they 

would be correlated with each other. A single microsatellite loci has a lot of 

power per locus, but when you look at tens of thousands of SNPs, it could 

potentially be more powerful than 15 microsatellites.

Recent studies have compared how powerful SNPs are compared to 

microsatellites. Need about 300 SNPs to do that. 

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4) Expert: Primers used for microsatellites, same markers still being used? 

Yes, some are still being used, in addition to some new ones

5) Expert: just because there is a lot of new information and excitement about that does 

not mean that microsatellite data is inferior. 

Sarah Oyler-McCance: Overview of Greater Sage-Grouse Genetics

Dr. Oyler-McCance provided this presentation; it can be found above in Appendix 6.

Questions/Discussion:

Expert: Currently don’t have good study for paternity – we know that other things happen 

outside of the lek that we don’t know about. Feathers collected in the Bush study on 

paternity from males were collected from leks, which may not be the best method to 

collect genetic information for paternity studies. 

Expert: a male in some leks can mate a lot of times in a morning - fertility may drop 

throughout the morning

Expert: is lek the unit of sample? 

o Expert: Not all studies use lek information. For example, some studies use wings 

collected at hunter stations. Each lek is not considered its own population.

o Expert: More of a hierarchical structure: individual, lek, etc. [Note: individuals are 

found on leks, multiple leks may be part of a population.]

o Expert: Birds use different parts of landscape at different times of the year. In spring, 

males go to leks and females look for somewhere to nest. Leks tend to be near 

where females are nesting. Once mating is over, birds congregate in completely 

different areas based on food sources, so distribution on landscape is completely 

different. Therefore, notion of lek as population is likely not accurate. Leks are 

useful for sampling because breeding birds go there. 

Expert: non-migratory population – does not think there is really this type of population, 

unless it is so isolated they have nowhere else to go. More likely that all birds move 

around to different habitats depending on time of the year.

Expert: What’s known about female decisions on where to nest in comparison to lek? 

o Some females move from one failed nest to re-nest 20 km away . We don’t know 

how a female response to an area will affect where they are and if fidelity to an area 

would outweigh moving to a new location.

o Females will first visit area where they will eventually nest, and then go to lek, and go 

back to nest. Most females will go to peripheral areas around the lek to nest

Expert: Across the range of the GRSG, there is probably a 25% variation in body size. 

Display behavior is pretty comparable. Gunnison SG, however, is recognizably different. 

Sometimes body size is a plastic characteristic, as can be seen in GRSG – birds in the 

northern part of the range are larger than birds farther south. May have implications to 

translocation. Body size alone is not enough to distinguish them as different species. 

USGS: East/West (E/W) split – more info?

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Expert: Subspecies delineation – original paper was based on a very small number 

of individuals in museum specimens. Genetic study showed that there was no 

genetic delineation between east and west.

Expert: Rangewide study of genetics could be re-examined with east/west split 

question in mind

FWS: We were petitioned for E and W – nothing behaviorally or movement-wise that 

suggested the delineation. 2010 finding determined that FWS did not believe 

subspecies delineation was necessary.

Expert: There could be a different east/west split than was previously analyzed –

Green River – WY and UT

Expert: Data in MT may show a split in W Montana – concern about ensuring that 

there are enough samples that could show potential connectivity.

Expert: Something to keep in mind - in the 1940s and 50s, translocations related to 

crop-related to damage were extensive and birds were released all over WY and MT 

– thousands of birds. A lot of movements were not successful – moving birds from 

WA to NM – birds were not in great shape after a few days.

Expert: What’s going on with landscape and habitat?

Expert: Possibly expanding based on historic levels of sagebrush pollen – prior to 

latest contraction; could make the case that they expanded into Canada – don’t show 

up in literature until 1930s. Lewis and Clark did not see GRSG in E Montana – “Prior 

to latest contraction”- the population recently has been hammered in the last 30 

years or so, but prior to 1930s, we just don’t know.

BREAK

PART 2: BARRIERS TO GENE FLOW AND GENETIC DIVERGENCE IN SAGE-GROUSE 

Barriers to Gene Flow (Exercise 1): Evaluate gene flow between the eastern and western portions of the 

GRSG range

USGS: Goal is to have a much richer response having a discussion on genetics with a group of 

geneticists in the room (compared to just asking one geneticist). Different frames of reference 

based on background. Ask that everyone participate even if they don’t feel it’s their specific 

realm of expertise in an attempt to bring out further constructive discussion. No right or wrong 

answer, no pressure to conform.

Format based on information needs from FWS – 4 or 5 topics/questions that have been 

identified. Introduce topic, discuss/clarify any issues, ask formal question (usually) – will allow 

us to take narrative discussion and make it more specific and allow us to capture uncertainty in 

terms of judgment. It will also allow us to capture uncertainty between experts. 

Expert: Seems like there’s a lot of upcoming research coming out – how will FWS 

use the information that comes out subsequent to the meeting?

USGS: FWS has deadline of Sept 2015 to make a specific decision, but they will look 

at all available information. We want to know uncertainties and rationale for thoughts 

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(i.e., I’m uncertain about XYZ, but I think it will be clarified by X upcoming 

research...)

USGS: First question: Evaluating gene flow between the E and W portions of the GRSG range. 

Low/High diversity, Low/moderate gene flow across a barrier previously discussed. We want to 

get a common understanding of what that means to each expert. We’ll use those definitions 

and standards in later exercises. 

USGS: East/west split – just looking at the map from a naïve point of view, one could see within 

the map larger areas in the W and E with various blank areas in between. We’d like you to look 

at the whole range of the species and consider any types and amounts of gene flow. 

Spread 100 points across categories of A) No Gene Flow; B) Low Gene Flow; C) Moderate 

Gene Flow; D) Substantial Gene Flow; E) Complete Mixing (No barrier to gene flow)

Discuss how you’re thinking about gene flow so we can build definitions for the above 

categories (Round 1), and then think about how to answer the questions again based on the 

definitions (Round 2).

Expert: Barrier is a tough thing to define, are we stuck in that category?

USGS: Yes, for now, we’d like to see where it takes us.

Expert: Very uncomfortable with how the exercise is being laid out...to give opinions 

instead of data driven process 

USGS: If there’s a paper or data that answers the question, that can feed into your 

opinion; but sometimes decisions have to be made that don’t fit into the exact 

category, which is where expert opinion comes into play. If you know upcoming 

research will reduce uncertainty, we would like to know that as well. If you are 

completely uncertain on this issue, you can add 20 points to each of these. 

Regulatory processes have to march on regardless of timing of research.

Expert: What do you define as E and W portions of the range? Seems like we have 

to have some idea of genetic groupings as assess flow between those groupings?

USGS: What makes sense from your point of view on east/west barrier? 

Expert: Seems like it would have to be based on Oyler-McCance 2005 paper. Not 

super-distinct genetic groups.

Expert: Point of the paper was not to specifically look at east/west gene flow, but 

there was a lot more information from that study that could be looked to attempt to 

look at that east/west issue

Expert: Gene flow is tough to define- for example, what scale, temporally, are we 

looking at? Are we looking at short-term scale or long-term scale?

USGS: We would like your opinion on what the best way to look at this...

Expert: Important to define if it’s historical or current

USGS: Would contemporary gene flow be a good starting point?

Expert: Gene flow now is a complete guess [Note: Expert is responding to the 

question of analyzing gene flow happening at this point in time (currently) 

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Gene flow is analyzed based upon changes over time therefore this expert is

stating that quantifying current levels of gene flow would not be reasonable. 

Experts went on to describe timeframes that would be reasonable]

Expert: What’s the shortest timeframe to look at?

Expert: Haplotype would be long term timescale, Microsatellite would potentially 

more contemporary

Expert: Simulations and empirical analyses can potentially look at genetic differences 

based on barriers, but just because you see a genetic difference, you can’t say 

exactly how long ago those differences formed. Effective population size - # of 

individuals contributing their genes to the populations – 80 generations for patterns 

to form? 

Expert: Could vary based on what’s happening on the landscape, hard to get at...

USGS: Genetic evidence that has something significant to say about barriers from 

east/west, could be a wide ranging period

Expert: Nuclear vs. Mitochondrial – Mitochondrial DNA doesn’t pick up any male 

patterns of dispersal. 

Expert: Male vs. Female dispersal – no consistent patterns in sage grouse

FWS: may be helpful to look at other data sources other than genetics. 

Expert: Note, there is typically a conflict between behavioral analyses of dispersal 

vs. genetic analysis. Genetic analysis is much more reliable because of bias related 

to behavioral research.

FWS: first question – line of east/west boundary – potentially based on lek location?

Expert: Hypothesis – evidence does not support gene flow between east/west?

Exercise – use sticky notes to write your estimate. 

Results of Exercise:

USGS: Some folks saw symmetric distribution, and some distributions showed a tail (rather 

than a normal bell-shape) near lower levels of gene flow. 

Discussion:

Expert: In order to answer this question, we have to consider spatial scale- if there’s 10 

years apart, maybe a ton of gene flow. If we’re talking about long-term, there may not 

be quite as much.

Expert: These birds move around frequently to make it throughout the year. If we were 

to take question about how it varies across distance X, it would vary significantly –

seems to be an empirical question rather than professional estimate.

USGS: How much gene flow there is between these 2 large east/west patches?

Expert: Snake River, Idaho- higher human population, ag., becomes non-habitat for 

most purposes. However, that doesn’t necessarily mean that they don’t cross it – a bird 

travelling in Nov. may not have any issues crossing the river. Even so, that’s only 

representing changes in the last century and not historically across the whole range. 

Birds on the W. side of the Tetons connect to the rest of Idaho. Do the birds in Jackson 

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Hole get to the birds on the other side of the Tetons – not very far distance, but have the 

Tetons in the middle. Lean towards low gene flow from E to W. Thinking that “low gene 

flow” as a certain percent below where you would have unrestricted movement. 

Behaviorally, small populations show that movement is related to population size- area 

of occupancy. Density dependence may play a role along with a genetic component.

USGS: Low vs. moderate gene flow - how are you thinking about it?

Expert: Number of migrants from A to B per generation. 1 migrant per generation would 

cause pretty substantial difference in FST value for populations.

Expert: Are we having enough gene flow to have X% gene flow? Mills et al. 2000? 

Mammal communities in pacific NW – Lowe and Allendorf - 2012 – give numbers of 

migrants per generation. Low 0-1, med 1-10, high >10. Meaningful level to get at –

gene flow between 2 populations. 

Expert: Isn’t that a function of what size of pop. you’re going to or coming from?

Expert: If you have a huge pop, you’re not getting much genetic drift, if you have a tiny 

population, genetic drift could be an issue.

Expert: You can only really ask these questions if you know where we’re discussing 

these limitations for gene flow.

Expert: We don’t even really know where the east/west split is – may be helpful for an 

expert who works on the ground would draw the line. To clarify, we are trying to think 

about if there’s any disconnect between the rest of the range of the GRSG?

USGS: Yes

Expert: Is another goal to assess if there’s DPS within the range?

USGS: We’re just talking about evidence of genetic barriers, not to make policy 

decisions.

Expert: Historical evidence (bones in caves) could give us more genetic 

information/evidence of location of GRSG and Gunnison – no evidence of a split for east 

side of the Rockies and west side of the Rockies that we’re aware of. 

Expert: If we focus on the barrier itself, it would be a completely different question. 

There’s not a single N-S oriented barrier – the question would be where and how many 

reproductive birds are going across.

FWS: How are you looking at Oyler-McCance et al. 2005 when thinking about your 

answers to questions? 

Expert: Data could probably be used in different ways to try to get to better answer to 

the question.

Expert: What was the thought process behind others previous answers – an attempt to 

better understand the question.

Expert: Thinking of it in terms of a hypothetical barrier –

Expert: We all need to be on same page with question, so how about we define the 

question: How much gene flow is there between east/west - divide into scales – ranking 

between large-scale gap between E and W 

USGS: Next exercise will help us define these questions

Expert: Need to all be in agreement on how we’re discussing space and time – small, 

large scale; recent, historic time

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USGS: Need is for large scale, recent time.

Expert: In the grand scheme of things, there haven’t been a lot of things changing in the 

dividing area (Yellowstone, Tetons, etc.)

Expert: Definition of recent and historic: Recent – now; historic – before Lewis and 

Clark?

Round 2 of Exercise 1: Re-evaluate your answers based on previous discussion.

Question: Large-scale, recent barrier (north-south) separating E and W – what is the likelihood 

of individuals to cross (as in what is the degree of gene flow) an imaginary north-south barrier 

between east and west sections of the range?

LUNCH BREAK

Barriers to Gene Flow (Exercise 2): Identify potential barriers to sage-grouse gene flow across potential 

barriers

Identify potential barriers to GRSG gene flow, if any.

Break out into 3 groups (30 minutes), use distribution map to identify no more than 5 barriers to

GRSG gene flow (if any) in terms of location, source (why they’re barriers), how long they’re 

been barriers, and to what degree they’re a barrier – in the scale of gene flow defined in last 

discussion.

Indicate the permeability and the spatial and temporal scale considered.

Discussion pre-exercise:

Expert: You don’t want us to put hypothetical barriers, but rather actual barriers 

(assuming we know enough about it).

USGS: Yes. 

Expert: these may not be impermeable barriers, but rather something that may be

passable, but add difficulty, correct?

USGS: Yes – something that might reduce natural levels of dispersal.

Expert: Ongoing research in WY about potential barriers – current study in review?

USGS: When groups come back in, we can get summaries and may discuss any 

thoughts. Opportunity tomorrow morning to discuss if there are analyses that can be 

done to inform genetics perspective

Discussion post-exercise:

Group 1 - 3 natural features and 3 anthropogenic features

Natural Features

o Mountains:

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§ Wind River

§ Big Horn

§ Tetons

§ N. CO Mountains

o Large Rivers:

§ Columbia River – wide and deep valley

§ Missouri River – anthropogenic factor – large reservoirs

o Desert:

§ Portion of S. Nevada (Shrub-Steppe habitat on ridge tops, 

have to cross desert to get to other habitat.)

Anthropogenic Features

o Cropland:

§ E. Washington

o Powerlines

§ Washington – connectivity issues in Columbia plateau (pub 

in review) related to transmission lines – Huge kV lines, 4 

right next to each other

o Highways

§ Interstate 80 – 24 hour roads/365 days a year

Discussion:

Expert: Tree impacts on GRSG?

Expert: Trees may impact ability to fly. While they typically walk rather than fly, though 

can and will fly ridge to ridge

Expert: Avoidance of areas with trees is likely from raptor/predator perches

FWS: If you were strictly looking at genetic data, how different would the lines be?

Expert: Clearly Bi-State area. Washington – primarily because of cropland issues. Rest 

of the range is a matter of degree- less movement associated with some feature, but 

does not completely limit movement – it would not suddenly show up but rather be a 

gradual effect. A barrier of 10-20 km may be detectible now; something around 5 km 

may be hard to detect.

Expert: Website available that shows Washington info: waconnected.org

USGS: How did highways impact those in WA?

Expert: Powerlines were more important than highways

Group 2 –

Barriers:

Low, hot desert

High elevation, coniferous forests

Large habitat gaps with anthropogenic shifts in habitat

Roads?

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Ag Fields?

Divisions:

Bi-State

Washington – division between Washington and Oregon (more recent)

Eastern Montana/Western Montana – Jackson Hole unique

Snake River- Looking at genetic data, it didn’t look like big differences on either side of 

the river

Discussion:

Expert: Why is WA/OR separation considered more recent?

Expert: Compared to Bi-State, considered more recent because there was sagebrush 

habitat there until recently

Expert: Potential for a lake near Bi-State may have also been a reason for separation 

during Ice Age

Expert: Barrier around Jackson Hole population drawn due to forested mountains

FWS: Will new genetic information potentially available provide information related to 

barriers (SNPs)?

Expert: May be able to compare birds on one side of highway compared to other side (I80)

Expert: Because highways are in some parts of the range are in locations with large 

rivers (valleys through mountainous regions), can we tell which feature would cause the 

barrier?

Expert: That is not the case with I-80, which goes straight through GRSG habitat. 

Group 3

Divisions - Looked at genetics to look for strong evidence of breaks:

Bi-State

Jackson Hole

Strawberry Valley

Anthro Mountain

Barriers:

Trees

Agriculture

High Mountain Ranges

Large Water Bodies

Discussion:

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Expert: Energy Development discussed, but it seemed like a small island of 

development rather than a large, unpassable barrier.

USGS: Mechanism vs. Barrier, what’s the difference?

Expert: Differences between mechanism on landscape – models were for sampling not 

for separations of populations

Expert: Agreed, viewed more of a hypothesis that then you would test with data

Expert: Jackson Hole population naturally isolated – is there a difference between how 

the long-term fragmented populations behave compared to those that have secondary 

impacts?

Expert: SNP information can give us info about that duration of the bottleneck – areas 

that have been isolated and population size at isolation event

Expert:Populations that have been historically large and become isolated may be more 

vulnerable than historically small populations that have been isolated

Expert: Management context- for translocation, is it better to get birds from areas that 

are already isolated and used to isolation?

Expert: Generally a bad idea – tends to be a failed strategy and safer to go with large 

populations

Expert: Strawberry Valley – found in study that it has low genetic diversity – afterwards 

UT stated that there’s a large predator population

FWS: Clarification – Are there any places in the range where energy development is so 

extensive that it could provide genetic barriers?

Expert: Yes, when that development is put over a huge swath of area, that type of 

energy development would be lumped into similar effects of agricultural development. 

So much habitat is turned into non-habitat. Should have added some lines in WY with 

barriers, perhaps a “zone”

Expert: What happens when you have a high density of leks on landscape, do you 

remove stepping stones with major effects on the landscape? What happens if we lose 

some of the leks due to energy development?

Expert: Bird dispersal related to translocation- single biggest determining factor of if they 

stay or come back is related to if they find other birds. Social component to dispersal. 

Plays a bigger role in declining densities.

FWS: Evidence for genetic barrier in MO River Valley? 

Expert: Yes, still in process of being analyzed and completed.

USGS: Discussion of “stepping stones” – what would a discussion about that look like?

Expert: Distance as a mechanism of separating genetics over space – Think of the core 

area – don’t want them completely separate, it would likely be worth having stepping 

stones. At what distance between areas do they not become stepping stones or allow 

dispersal/gene flow.

USGS: As discussions progress, hopeful that dialogue continues for all exercises and increase 

comfort level on all sides to provide useful information. 

BREAK

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FWS: Uncertainty is something we’re used to dealing with. This is just one piece of 

information for our listing decision. Any policy decisions will be made at a later time – we are

not asking you to be involved in that decision. After we collect more information, we feel we will 

be more able to evaluate additional genetic information and as it relates to the species’ status.

Expert: Decisions should be data-based as opposed to opinion-based, which is where 

some of the discomfort from this morning.

FWS: The conversation from these exercises is key and provides a good foundation of 

information and background that will be used as a part of our status review - helpful to 

those of us in information analysis and decision-making positions.

Expert: What’s uncomfortable about it [Note: expert elicitation activity] is that they

[Note: experts] feel like they could have the data and information in a couple of years 

but may not have the exact data at this time.

Expert: Putting lines on maps made me uncomfortable at first because I did not know 

what it was to be used for. As long as the maps are used in described ways [Note: 

experts described the hypothetical lines as possible areas for study, not 

necessarily boundaries or barriers], feel much better about this.

Expert: Good to get FWS’s perspective and appreciate the work being done – level of 

discomfort comes from the level of controversy related to research done. We have 

much of the data that is being discussed and that information may be able to be 

provided. 

Expert: Interesting to look at map and look at the lines, thinking about potential 

hypotheses and research that could be conducted. 

FWS: 2 maps- one of genetic barriers and one of hypotheses

Expert: That would be very beneficial, especially with further review following the 

meeting

PART 3: GENETIC DIVERGENCE

Genetic Divergence (Exercise 4): What analyses could be done to assess the loss of genetic diversity for the 

GRSG if any portion of the range was lost?

What metric(s) would be useful, e.g., percent of haplotypes lost? Are there others?

What can be done with existing data?

What analyses/studies are ongoing related to this question?

Discussion:

Expert: Pattern of loss is important, isolating the portion of the range may matter a lot.

Expert: Mechanism may matter as well- if you have a lot of movement, effect is smaller 

pop size and greater risk for genetic drift – if there is genetic variation between 

subpopulation and there is a decrease in subpopulation, you’re not only losing numbers 

in the population, but also whatever is unique about that subpopulation.

Expert: What are your units on the landscape? Tricky for GRSG because they are 

somewhat continuously distributed on landscape.

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Expert: Worked really hard to keep local genes on the landscape, even if they’re maladapted because of genetic drift. Looking at what they’ve done with Attwater’s prairie 

chicken – Pygmy rabbit and Atwater’s prairie chicken are good examples of this. 

Expert: Suggestion to table gene underselection. Neutral genes- want to maintain 

genetic variation. One way to allow genetic variation is to maintain gene flow. Long 

term splits, you probably don’t want gene flow [Note: in some instances, it is 

preferable not to mix materials from genetically isolated populations. In other 

cases, outside genetic material may be beneficial to the population], whereas areas 

with very recent loss of flow you would want to increase connectivity. Need to be very 

careful about genetic variation – sink populations could have genetic variation from 

genes coming in. Loss of genetic variation compared to surrounding areas, it’s a red 

flag [Note: “red flag” is used here to describe areas that could benefit from

additional research into the cause of lost genetic variation] to try to determine what 

is going on there.

USGS: Don’t want to lose any genetic diversity due to linkage of persistence over time.

Expert: Red flags – some areas with lower genetic variation. Other component is 

temporal variation- data to see how things have changed over time. Need monitoring 

over time to determine temporal variation. 

Expert: Strawberry Valley and Washington – both showed red flags. [Note: “red flag” in 

this discussion refers to a difference in genetic diversity between these 

populations and surrounding populations]

Expert: Phenotype issues – weight variation across range, but other things show up in 

certain populations – 1) Number of tail feathers (WA- fixed at 20), almost everywhere 

else it’s not fixed- either 18 or 20. Why is it mixed across the range except for WA? 2) 

Clutch size – 9 in WA, average across range is 7.5; 3) High rate of nesting and renesting in WA- not sure why it doesn’t show up in other areas. Probably other things as 

well, but we may not be paying attention, may not be measuring all birds when trapped. 

Curious how important those things are. Important in translocations because of unique 

characteristics.

Expert: Phenotype data wouldn’t be picked up through genetic study

Expert: Take into account phenotypic variation as well as the adaptive information. In 

terms of determining vulnerability of those phenotypes would be hard to determine.

Expert: Wing measurement variation? Likely proportional to body size.

Expert: In order to assess these issues you need more localized studies that focus on 

fitness and how those phenotypes affect local fitness.

Expert: Translocation of birds to new areas can show if it’s a plastic trait or not. 

Expert: Don’t know if you can use a phenotypic trait to show if there’s a loss of genetic 

diversity.

Expert: Temporal variation can be used infers to differences genetically or potentially 

phenotypes

USGS: Has genetics been able to identify GRSG populations?

Expert: Hard to do because of methods of collection

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Expert: Genetic modeling can lead to inferences of genetic variation over time – gene 

flow based using data we’ve been collecting. 

Expert: How do you separate any recent anthropogenic stuff that may be happening? 

What sort of data would that take to statistically show that information? Probably need 

to have temporally spaced data.

Expert: Always an issue but if we know population size we can probably get some sort 

of inference.

Expert: One approach to answer posed question is modeling- but that relies on 

assumption after assumption. Building uncertainty in it, it would likely to be reliable?

[Note: discussion of modeling identifies the limitations of models. Models relying 

upon multiple assumptions with large uncertainties may be less useful]

Expert: Looking at variation over time would be a way to do it. 

Expert: Developing a set of markers that could be a tool to allow for temporal 

comparison- how have things changed over time? 

Expert: SNPs should be able to be translated to whatever is used in the future

Expert: Is the possibility of going back and looking at a very coarse scale- looking at 

wings from 2000 and looking at temporal variation in that amount of time to those being 

collected now. 

Expert: General test to estimate change over time and inference to some of the 

landscape metrics. 

USGS: Red flags- are there any others – any surprises that might come out for GRSG, 

or do you feel you have a good handle on it?

Expert: I would be surprised if there are more surprises. Need to check on sensitivity 

and details in areas where there are currently no red flags.

Expert: Seems like Steve Knick would be a good person to talk to about this last issue

Quick preview of tomorrow: Areas of low diversity that could have an effect on persistence? 

Revisit issue of things that could or will be done to address issues of genetics and importance of 

status review- what is coming down, are there things that could be done to help address some 

of these questions about loss of diversity and genetic persistence of sage grouse. 

Day 2: Thursday, Oct. 23, 2014

Logistics and check in – USGS

Thursday Exercise 1:

Question 1: Have we captured the places where limited gene flow (no or low gene flow) 

have been found from data-driven genetic evidence and the places where limited gene 

flow have been hypothesized?

USGS: Places where there is low or no gene flow- not just limited, but “no” or “low” gene flow –

there were a few identified yesterday – we want to check in – did we capture them all? 

Washington, Jackson Hole, Strawberry Valley,Missouri River in Montana.

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Expert: Not sure Strawberry Valley has low gene flow. May have low genetic diversity 

due to predator problem, but not convinced that it has low gene flow. Bi-State also does 

not actually have lower genetic diversity – it is different genetics.

Expert: Some of these are likely to have greater than “low” gene flow – it’s all 

gradations.

FWS: When everyone discusses “low” gene flow, what do you mean? How big are the 

“holes in the colander”?

Expert: Using adaptive markers to identify Conservation Units and Evolutionarily 

Significant Units [NOTE: Conservation Units and Evolutionarily Significant Units

(ESUs) do not equate to the Service’s use of these terms or the term “Distinct 

Population Segment”] – akin to “subspecies” that have been isolated for long periods 

of time and may have adaptive differences between them and can be identified using 

various genetic methods. Use all loci – neutral loci that show historical and adaptive loci 

that show adaptation. Some ESUs identified show no emigration/immigration. So far we 

don’t have that data yet to identify ESUs. ESUs are units that have very little gene flow 

between them – may have evolutionary differences or adaptive differences –

management units within ESU are demographic populations that exchange some level 

of gene flow- maybe moderate – don’t have evolutionary or adaptive differences. 

USGS: When would that information potentially be available

Expert: Early spring?

Expert: Management units are usually watershed based...fisheries are managed to 

achieve a certain number of spawning adults- ESUs are typically on a broader scale.

Expert: Some populations may exhibit distinct trait that may limit gene flow temporarily 

or spatially.

Expert: ESUs sometimes considered using microsatellites, but may also include 

behavior or habitat-based reasoning

Expert: Reciprocal monophyly – previously defined for use in ESUs as NO gene flow in 

many generations, now considered too strict. Now it’s been accepted to look at adaptive 

genes 

Expert: Debate in the literature to confirm that something is actually adaptive

Expert: Good examples of status revision based on adaptive genes in North Sea stocks, 

such as herring, cod, or Atlantic salmon – markers under selection were similar – looking 

at different classes of markers to determine if they’re actually adaptive or not.

Expert: Our lab is developing tool to look at array of tools to look at adaptation

Expert: There are likely to be more boundaries as more data – not safe to assume that 

just because we haven’t identified any additional boundaries, doesn’t mean they won’t 

be found in the future. 

Expert: If we had to identify where ESUs were now – other than the Bi-State, are there 

any other ESUs on the landscape 

Expert: Interesting to look at haplotypes for Jackson Hole- could compare to what we 

have on Bi-State. When WA analysis was done, there were some really common 

haplotypes but really low diversity – probably not going to have a bird move naturally 

from WA to OR. 

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Expert: What does historical data look like related to WA pop?

Expert: We have samples, but haven’t had the opportunity to do it yet. Samples came 

from N-Central WA. 

Expert: What is the recent translocation info in WA?

Expert: Translocations in Yakima – tribe – also in the Yakima training center- haven’t 

analyzed the data to see if the birds are now part OR/part WA birds now. Weren’t 

overwhelmingly optimistic that we have gene flow – needs more research

Expert: What we’ve looked at so far – the break shows up pretty strongly in SE Montana 

across Continental Divide – doesn’t go away even when you consider if it’s a sampling 

effect.

Expert: You might hypothesize that here’s the range that has very different 

environments – that would make me have a hypothesis that those may constitute distinct 

ESUs and I would test for that. Most people wouldn’t consider something an ESU 

because it’s been divided by anthropogenic forces, as ESUs are considered large, longterm evolutionary divisions.

Expert: In context of adaptive variation – in E there’s a lot of summer rainfall and ground 

cover, the farther W you go, the less ground cover you have – appears to be a totally 

different situation – no breaks but yet there is an incline in variation, how does that affect 

adaptive variation

Expert: Maybe there aren’t distinct breaks or ESUs, but it may be important for 

managers to recognize that there is adaptive variation within the species through the 

gradient and maintaining it through the population. 

Expert: Difference between genetic change from drift (like WA) – short term, small 

population dynamics that’s causing the population to spread apart – not necessarily 

significant from the adaptive side (like Bi-State) – the data needs to be looked at in WA 

to compare historic connectivity. Could handle 10 samples in a very short period of time. 

Have to work with labs on historic DNA analysis. Pretty important to know historic 

genetic analysis to determine gene flow, as we might find out something different from 

what we expect currently.

Expert: If you were going to do that – useful to compare to OR and MT. Could probably 

get something going very soon. 

USGS: Anything to add about Jackson Hole

Expert: I would be really interested to get mitochondrial data from Jackson Hole- hard to 

say without collecting/analyzing additional data.

Expert: Agreed, looking at mitochondrial DNA would be really important to confirm.

Expert: Bi-State populations – most have haplotypes that don’t occur anywhere else – if 

you actually had all the haplotypes in the pie graph map, it would tell you a different 

story. Map shows that common haplotypes shift as you go across the range, but there’s 

more that could be looked at.

USGS: Hypothesized barriers with low or no gene flow- are there any of the places 

identified yesterday (mountains, highways, deserts, rivers) – is there any existing data 

that can be analyzed to test these hypothesized barriers.

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Expert: Definitely I-80 – currently being looked at –current analysis of “paved roads” did 

not pop out as a barrier in existing data – but you could look specifically at SW WY data 

or I-80 to look more closely at that. 

Expert: Look at 50 genotypes N of I-80 and compare to 50 genotypes S of I-80. Model 

“100 highways” and compare those breaks to what’s happening in I-80. 

Expert: If it doesn’t show up in I-80, it likely wouldn’t show up in any other road in N. 

America. With the sample sizes available, the amount of traffic, the amount/type of 

habitat there – if it doesn’t show up there, it would probably suggest that there’s not too 

much to worry about. Isolation by distance model – Null Model.

Expert: Would not want to bias samples by only comparing samples that also have 

east/west affects (some by Montana, some by Cheyenne)- would need variation across 

I-80 to have comparable samples.

Expert: Yes, there is data available for that – quick look at where the samples are show 

that there’s a good variety of data points at leks around I-80

USGS: Are there any other barriers (other than I-80) hypothesized that have data that 

could be tested?

Expert: Yes, everything in the range-wide analysis could be looked at – a lot of the 

hypothesized barriers could be looked at.

FWS: Threats appear very different on E and W side of the range – when looking at 

Management Units, would it make sense that there’s any disconnection between the 

east/west of the line?

Expert: Would like to look at the data that went into previous genetic papers – don’t 

know how much of it is a gradient, might be able to tell you something in a couple of 

weeks. Have data to resolve it right now – Could be figured out...what’s the FWS 

timeframe for questions to be answered?

FWS: Current timeline for recommendation is April – of course the FWS will use the 

best available science, so if new information comes available, we would incorporate that 

to the best of our abilities.

Expert: Important to look at in broader population scale- becomes more of a 

management/long-term conservation question.

Thursday Exercise 2:

Identify areas within the GRSG range where low genetic diversity or inbreeding 

depression is likely to affect GRSG persistence?

Question 1: What is the significance of these, or any/all combination of these areas to 

the persistence of GRSG? So what (for each/any/all combination) if we lost these areas?

Expert: A lot of literature of importance of peripheral populations- WA and Bi-State are 

margins of the species range and adapted to specific environmental conditions that may 

be “pre-adapted” to what the environment of the species as a whole would be in the 

future (climate change). These populations could be disproportionally important 

because they might be adapted to these marginal conditions that may be important in 

the future for the species as a whole.

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Expert: Study has shown that birds in Alberta are in a sink population maintained by 

birds in MT – not sure that these birds in Canada would be locally adapted – if they’re 

really MT birds. If there are peripheral parts of the range that are in fact sinks, perhaps 

those may not be locally adapted. May just be for that particular situation.

Expert: Like thinking of it as a “red flag” – not a specific metric that can test for 

everything –still need ecology and people on the ground – don’t expect us to deliver a 

silver bullet.

Expert: Example- Strawberry Valley – genetics looked like a red flag, but on the ground 

learned that it was related to predators.

Question 2: How much can be lost before we go from a minor “so what” to “oh darn it”?

USGS: Is there a point where a loss of areas – from a genetic perspective- that changes 

the genetic integrity of the species at large –

Expert: Exactly the network analysis- how connected are leks across a landscape, if you 

lose this one, what is the significance for the range as a whole- planned for analysis in 

the rangewide study. Stay tuned.

Thursday Exercise 3:

Question 1: Channeling a decision-makers perspective, are there areas where genetics 

exacerbate the effect of threats on species persistence?

Real-world manager objective – conceptual feedback, response

Expert: Statistical comment – Is the question really - is there an interaction between 

genetic and non-genetic threats? 

Expert: The way we’re using genetics – looking at the genome to characterize what’s 

occurring on the landscape. Similar to taking DBH of trees – couldn’t ask if DBH is 

exacerbating the threats 

Expert: May make more sense to say “Do the effects of other threats exacerbate 

genetic issues related to the species”

Expert: Could say that inbreeding depression could exacerbate the threats – 2 reasons 

why genetics are important for conservation – a tool to measure things AND they can 

affect things like genetic fitness – are there populations where genetic variation is so low 

or have so few individuals that it is exacerbating the issue. What I heard was that WA 

may be a candidate where I would try to assess the fitness.

Expert: Could estimate effective population size of all the isolated areas – 50 rule- when 

genetics would potentially be a problem for inbreeding depression – population size less 

than 50 – could be entering into depression vortex – would potentially be a red flag.

Expert: This type of issue has been studied with Gunnison SG – you could look at that 

information. One of the other layers of information should be population size, 

reproductive success, mortality rates

Expert: Strawberry Valley population may be a good one to think about- if the manager 

is thinking of instances where genetic information is influenced by what’s going on “on 

the ground”

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Expert: Look at juvenile survival, or probably better - hatching success – which is a 

common place where you see inbreeding effects – might be a way to look at areas to 

alert geneticists about potential issues.

Expert: Prairie chicken where it became genetically a problem- located it in the 

hatchability (hatching success) of eggs- normally 9.5 out of 10 would hatch successfully; 

what happened was that 4 out of 10 eggs weren’t hatching because they were infertile or 

not viable- brought in birds from outside areas and were able to bring it up. No 

examples known where anything has looked at the chick survival –

Expert: Effective population size tends to be much smaller than the actual demographic 

population size is much smaller, so don’t wait until you have demographic size of 50 to 

determine effective population size. Has that been done for GRSG?

Expert: Gunnison sage-grouse was looked at...female breeding success was 23% 

Expert: WA calculated it too but not with genetics – also have to make assumptions of 

how many males are actually breeding, which is hard information to get.

Expert: You can estimate that information with some assumptions if you have markers. 

Expert: Reference – Maples/Waples 2013/2014 

Expert: History of population as well as all the other things that play into the sex ratio- If 

you do it demographically, you’d get “this is what’s happening right now” 

Expert: Demographic vs. genetic estimates would be different – demographic would 

likely be smaller because they are influence by current things happening on the ground.

Expert: Because it’s a lekking species it would probably have a smaller effective 

population size than a species that’s monogamous

Expert: In some cases it’s difficult to make these estimates in areas with contiguous 

populations (compared to distinct populations like WA)

Expert: Still not sure exactly the way to respond to this type of question related to other 

threats- we know there are other threats but not the extent or how they interact with 

these issues. How do you tie genetic information into modelling threats? 

Expert: Example – could include genetic as a covariant in models- additive effect of 

inbreeding coefficient on a fitness measure. Theory and empirical data suggest that 

inbreeding can really be a problem in harsh years or with other threats/issues – drought, 

ex. Models can see if in those years there’s really a threat from inbreeding effects.

Expert: Survival (population or individual) = threat + inbreeding + threat*inbreeding; 

examine the correlation between demography and inbreeding coarsely.

Expert: Long-term genetic research needed for future monitoring.

USGS: If a manager were to say – I need to know if there’s a place to focus my 

resources related to genetics, in addition to WA, which we’ve discussed

Expert: Jackson Hole has been declining and has also had decreased genetic diversity, 

so that would likely be an area to explore further. 

Expert: There likely will be areas, but don’t want to identify that yet

Expert: Weiser, Klamath area (down to 7 males a few years ago). Areas experiencing 

fragmentation that always catch my eye are the isolated areas in Utah, which have been 

declining for years. South-Central UT, areas in NE interior of UT are also isolated. 

Piceance also a naturally fragmented area but also really heavy in energy development 

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currently. This is speaking more on a demographic level vs. a genetic level – currently 

unknown at a genetic level. Belt Mountain, Wisdom, Red Rocks in E. Montana.

Catch up on topics – anything thought of that didn’t get said yesterday?

Expert: Non-genetic issue about birds that find other birds stay and those that don’t go 

back – literature does exist about territorial dispersal – males looking for territory will look 

for other males and then stay on the peripheral – wide spread documented phenomenon 

that leads to clustering rather than widespread, even distribution that some might expect.

Expert: 25 historic specimens – Puget Sound, Museum at Washington State- Pullman –

also some potential from OR, which would make sense to also analyze for comparison. 

A lot of translocations to N OR in an attempt to re-establish populations, which didn’t 

work

Expert: Mitochondrial samples from those specimens would be very beneficial

Expert: Good to compare S. OR to those in WA to see if the genetic diversity differences 

are relatively recent.

Thursday Exercise 4:

Question: Opportunity for the F&W Service members to ask unaddressed questions

FWS: Central Idaho has a lot of threats that we get questions about – will the new data be able 

to tell us anything about this?

Expert: New data definitely will. Currently working on it – hope to have it available by 

April.

FWS: Drift in WA – question previously discussed about is it actually drift or exacerbated by 

isolation – tying threat occurrence and intensity of threats and how they influence the threat of 

bottlenecking 

Expert: If your threats get to the point where you’re isolating a population and it’s 

becoming small, genetics will become an issue.

Expert: The greatest risk of threats seem like they would occur in the large portion of the 

range as opposed to the outer areas – don’t forget about those.

Expert: Could be related to PACs and creating individual pockets of isolated 

populations. Reason why some area is not a PAC is because energy developers are 

really interested in developing in those areas.

FWS: Background of PACs – Developed map of PACs – states indicated that they could 

manage for long term conservation if they had PACs. Over half of GRSG habitat is on Federal 

lands, which has multiple-use management. Question: We are asked – are there places where 

we can potentially allow further development in these PACs without affecting long-term 

conservation? Is there anything related to genetics (solely related to genetics) that would be 

related to this question?

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Expert: Cumulative impacts analysis must be done to help with this. Every little thing 

needs to be evaluated against the whole.

FWS: We’re interested in the genetic component that can be put into that cumulative 

analysis. Are there key areas where genetics may be an integral part of cumulative 

analysis.

Expert: Yes, we are asking that type of a question in the rangewide study. What are the 

key areas that are acting as stepping stones or within the main areas? Looking for leks 

that are disproportionately affecting gene flows- looking for “hubs” for gene flow.

Expert: Specific scenario that would be interesting to look at: NE WY where there are 

only a few small areas identified as PACs – Can those areas persist if all other areas 

were developed? Same for Montana. 

Expert: Some states tried to determine the smallest amount of areas that had the most 

number of birds.

FWS: Some states also took into account what areas had already been leased and 

therefore focused on areas where conservation actions would benefit the most number 

of birds

Expert: I would love to have a map that shows “If you remove this area, this is what 

happens to the rest of the areas and overall connectivity”, but we don’t have that yet.

Expert: Has been produced in WA as centrality map- which leks are the most important 

– also ranks connections – Andrew Shirk – on WA website.

Expert: Any effort to think about corridors between PACs so you don’t isolate areas?

FWS: That was the recommendation, but typically outside the PACs, regulatory 

mechanisms are more relaxed. Connectivity map shows links.

FWS: On west side, other threats (such as fire and invasive) may relate to genetic 

implications. These may work together such that genetic implications are greater for 

those PACs than may otherwise be apparent when viewing genetic issues alone. 

Expert: PAC map would appear very depressing if you pictured it as a range mapdoesn’t look good. On the other hand, there are birds that live in naturally fragmented 

areas on the W side.

Expert: What is the risk of invasives?

FWS: Cheat grass and other vegetation encroachment 

Expert: This issue is also being looked at in genetic rangewide study.

Expert: What about predation from Strawberry?

FWS: Related to recreation and human population in Strawberry Valley area – fox in 

areas that had not been seen before and capitalized on the predation situation.

Expert: Did they get rid of predators in Strawberry?

FWS: yes, they maintain very intensive predator control

Expert: Good to consider- if that changed, it could become a genetic issue after all.

FWS: Maintaining peripheral populations related to adaptation - Any information that would 

suggest that some areas are problematic (genetically) in terms of maintaining rangewide 

integrity?

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Expert: For one specific peripheral population, it’s likely it wouldn’t be a huge impact 

on entire population given the entire size – could look to see if any alleles are lost.

Expert: Spatial analysis of diversity vs. individuals.

Expert: See if genetic diversity is different if you took out some of the central 

individuals vs. peripheral populations and see if there is a difference in the inflection 

point. Could also compare current distribution vs. PAC. 

FWS: What does the inflection point actually mean? 

Expert: Inflection point is where rate of change changes. If you get to the point where 

there’s no gene flow over many generations, it would be too late.

Expert: This does not take into account the point at which populations become 

isolated, really has to be spatial.

FWS: We’d probably get asked, “So what if you lose the Columbian Basin, what does 

that mean for genetic diversity for GRSG? How much emphasis should be placed on 

protecting that genetic variation?” [The purpose of this question was to understand 

the biological importance of the Columbia Basin population to assist the Service 

with future decisions.]

Expert: In non-genetic terms, you lose that organism on the landscape and the impact 

of the species on the ecosystem. 

Expert: For the neutral markers analysis, you could find something that’s completely 

unique compared to the rest of the range and change your thinking on how important 

the area is genetically.

Expert: Depends on if it’s determined that it’s already in the “extinction vortex” and if 

it’s worth going to the extra effort to save it 

Expert: Could look at data and say “If we lose WA, what would happen?” Current data 

just indicates information about losing neutral variation.

Expert: Amount of total genetic variation may not be related to the amount of area the 

species occurs in. 

Expert: Curious how the PAC map could change related to climate change, would 

some areas not even be habitat anymore, even with conservation?

Expert: Sagebrush plants would presumably move N – are populations going to track 

that, or would those populations just disappear? 

FWS: Work underway on climate change modeling. 

Expert: Related to genetics, given that we predict shifting demographics and habitat, 

genetic diversity is important related to connectivity to allow birds to adapt and follow 

where sagebrush occurs. Warm-adapted populations may be very important for 

potentially warmer areas in the future.

Expert: West Nile Virus (WNv), are there any individuals that have been seen to be 

more fit genetically to survive?

Individuals that do get WNv die, the birds are susceptible to West Nile virus, but some 

survive. Difficult to study because you have to get the carcasses before they decay. 

There are mortalities every year; sometime more when conditions are right for the 

virus, - transmission issues and collection of mortalities – potential die offs that are 

easy to miss. Biggest die-offs from WNv are in areas with coal-bed methane and 

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surface water, such as the Powder River Basin, especially in late summer when hens 

bring broods to mesic habitats, which promotes transmission.

Wrap-up: 

On behalf of everyone in FWS, THANKS!

We will circulate notes and appreciate any review possible to make sure we captured everything 

correctly.

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