Wyoming

Legacy ID: 
55
State Code: 
WY
Country Code: 
USA
Area: 
97 803.20
Latitude: 
43.00
Longitude: 
-107.55

Targeted Research and Monitoring

Code: 
PB00D8K.2
Abstract: 

Statement of Problem

Wyoming supports a diverse flora/fauna (an estimated 297 species identified as having the greatest conservation need). Species of conservation concern include 54 mammals, 60 birds, 26 reptiles, 12 amphibians, 40 fishes, 19 crustaceans, and 68 mollusks. The Green River Basin of Southwest Wyoming is dominated by sagebrush-steppe habitat, which has declined in western North America by nearly 50 percent since Anglo-European settlement. Because the majority of sagebrush steppe in Wyoming occurs on public lands, Wyoming plays an important role in the conservation of sagebrush-dependent and sagebrush-obligate species, including white-tailed prairie dogs (currently under reconsideration for listing under the Federal Endangered Species Act), sage sparrows, pygmy rabbits (considered critically imperiled in Wyoming; currently under reconsideration for federal ESA listing), greater sage-grouse (currently under reconsideration for federal ESA listing), ungulates, and several amphibian and reptile species. Recent and ongoing land-use changes in Southwest Wyoming are transforming the landscape composition, resulting in habitat loss/fragmentation---thus, increasing the threat of harm to wildlife populations. Furthermore, each year, habitat enhancements and vegetation treatments are applied across Southwest Wyoming (during FY07/08, more than 50 conservation enhancement projects were proposed through the WLCI and other government and nongovernmental organizations). In the past, however, little effort has been made to assess the effectiveness of treatments to meet their stated goals and even less to evaluate their collective effectiveness in meeting landscape-level conservation goals like connecting fragmented habitats. Overall, current long-term and effectiveness monitoring in the Green River Basin of Southwest Wyoming is insufficient for assessing cumulative effects or the effectiveness of on-the-ground treatments. When long-term monitoring is coupled with management, it also has the potential to be used for early warning, whereby management interventions are triggered before reaching critical and potentially costly levels of action. Thus, it is crucial to implement project-level effectiveness and long-term monitoring of key indicators to compare management targets, benchmarks, or goals for informing the decision-making process within an adaptive management context.

Objectives

This task entails three subtasks, the overall objectives of which are to monitor Southwest Wyoming ecosystem condition over the long run, monitor the effectiveness of on-the-ground habitat treatments to enhance or maintain wildlife populations, and improve our overall understanding of the mechanisms behind population dynamics of key wildlife groups or species of conservation concern. Objective of Subtask 2.1—Initiate monitoring for landscape-level, long-term trends using key response variables. Objective of Subtask 2.2—Initiate effectiveness monitoring for 07-08 habitat treatments and mitigation using key response variables. Objective of Subtask 2.3—Design and initiate studies to address mechanisms and processes by which key species and populations are affected by energy development activities.

Methods

The overall approach for this task is to conduct landscape-scale, long-term monitoring to ascertain ecosystem changes associated with energy development, other land-use changes, and climate change; monitor the effectiveness of habitat-improvement and mitigation projects; and conduct research to elucidate the mechanisms behind wildlife responses to changes on the landscape. More specifically, the first step will be to stratify characteristics of Southwest Wyoming to ensure appropriate representation of indicator variables and the requisite intensity of sampling to maximize the accuracy of indicator estimates. This will enable us to design and implement the long-term and effectiveness monitoring programs, including monitoring of wildlife (including mule deer, sage-grouse, songbird communities, herptiles, fish communities, invertebrate pollinators, small mammal communities, pygmy rabbits, habitat/vegetation communities (including invasive plants), water, and soils. See each subtask for detailed methods.

Decision Support for Climate Adaptation in the Upper Colorado River Basin: Why Drought Decision Makers Choose to Use Tools (or Not)

Code: 
RB00CME.1
Chris M. Morris, Creative Commons.
Abstract: 

Purpose

Adapting to climate change and variability, and their associated impacts, requires integrating scientific information into complex decision making processes. Recognizing this challenge, there have been calls for federal climate change science to be designed and conducted in a way that ensures the research translates into effective decision support. Despite the existence of many decision support tools, however, the factors that influence which decision makers choose to use which decision support tools remain poorly understood. Using the Upper Colorado River Drought Early Warning System as a case study, this research will 1) examine how managers choose between many available tools and 2) consider how tool creators can better align their offerings to decision maker needs.

Objectives

1. Improve understanding of:

  •  The factors that influence decision makers’ choices to use decision support tools or not, and how they choose between available tools
  •  How scientists creating decision support tools currently interface with decision makers and how their outreach efforts do or do not match information channels preferred by managers
  •  The role that decision support tools play in drought decision making

2. Provide useful information to the National Integrated Drought Information System about the current use of the Upper Colorado River Basin Drought Early Warning System

Methods

Study Area and Scope

The Upper Colorado River Basin (UCRB) was one of the first pilot areas, beginning in 2008, for implementation of a regional drought early warning system (DEWS) under the National Integrated Drought Information System (NIDIS), which now supports ten regional DEWS. The selection of the UCRB for a DEWS reflects the regional importance of drought monitoring for managing water supply for agriculture and other uses, and the need for effective decision support related to drought. New drought-information tools have been developed specifically for the UCRB DEWS, and a number of others have been created since 2008, adding to the pre-existing toolkit for drought decision making. The various tools that are now available in the UCRB region can be expected to be more or less suitable for different decision makers’ needs. As a result, the broad decision context of this case study (managing drought) is fixed, but information needs vary. Thus decision makers will make varied choices about which of the available tools to use or not use.

Data Collection

The overall aim is to juxtapose understanding of the tool development process of tool creators with understanding of the choices made by prospective tool users to incorporate (or not) given decision support tools into their drought decision making. Document analysis will provide context and an official view of tool development or agency decision making. Conversations with scientists creating tools and drought decision makers will be used to understand motivations, priorities, concerns, and tacit influences on behavior.

Evaluation of Genetic, Behavioral and Morphological Distinctness of Greater Sage-grouse in the Bi-State Planning Area

Code: 
RB00CNJ
A sage brush habitat. USGS photo.
A sage brush habitat. USGS photo.
Abstract: 

The goal of this study was to obtain a more comprehensive understanding of the boundaries of this genetically unique population (where the Bi-State population begins) and to examine the genetic structure within the Bi-State, which is needed to help guide effective management decisions. Our genetic data supports the idea that the Bi-State population represents a genetically unique population and identified the Pine Nut Mountains to be the northern boundary of the Bi-State population.  We also found three distinct subpopulations (southern Pine Nut Mountains, mid Bi-State, and White Mountains) within the Bi-State that would benefit from conservation and management actions.

Landscape Genetic Analysis of Greater Sage-grouse in Wyoming

Code: 
RB00CNJ
Flying Greater Sage-grouse. Photo by T. Gettleman, USGS.
Flying Greater Sage-grouse. Photo by T. Gettleman, USGS.
Abstract: 

This study compared the genetic differences between Greater Sage-grouse breeding areas with seasonal habitat distributions or combinations of landscape factors – such as amount of sagebrush habitat, agriculture fields or roads – to understand how each factor or combination of factors influence effective dispersal of sage-grouse across the state. The study revealed that the juxtaposition and quality of nesting and winter seasonal habitats were the greatest predictors of gene flow for Greater Sage-grouse in Wyoming. Furthermore, the combinations of high levels of forest cover and highly rugged (steep and uneven) terrain or low levels of sagebrush cover and highly rugged terrain were correlated with low levels of gene flow among sage-grouse populations. This research is in collaboration with the University of Waterloo, supported by Wyoming Game and Fish Department and BLM.

Re-examining Patterns of Genetic Variation in Sage-grouse Using Genomic Techniques

Code: 
RB00CNJ
A Gunnison Sage-grouse. Photo by Doug Ouren, USGS.
A Gunnison Sage-grouse. Photo by Doug Ouren, USGS.
Abstract: 

The goal of this study was to use new comprehensive genomic markers to re-examine patterns of genetic variation in sage-grouse focusing on differences between Gunnison Sage-grouse, the Bi-State population of Greater Sage-grouse, and the rest of the range of Greater Sage-grouse. We found that by using genomic methods we were able to reveal that Gunnison Sage-grouse are much more diverged from Greater Sage-grouse than the Bi-State population of Greater Sage-grouse is from Greater Sage-grouse. This study confirms definitively that Gunnison Sage-grouse represent a distinct species and that the Bi-State is a distinct population of Greater Sage-grouse.  This study also confirms that Gunnison Sage-grouse have much lower genomic diversity than Greater Sage-grouse. This research was in collaboration with the University of Colorado, Denver.

Publication: Genomic single-nucleotide polymorphisms confirm that Gunnison and Greater sage-grouse are genetically well differentiated and that the Bi-State population is distinct

Preble’s Meadow Jumping Mouse Genetic Diversity and Population Connectivity

Code: 
RB00CNJ.24
A Preble's meadow jumping mouse in a scientist's hand. Fish and Wildlife Service photo.
A Preble's meadow jumping mouse in a scientist's hand. Fish and Wildlife Service photo
Abstract: 

The goal of this study is to assess the level of Preble’s Meadow Jumping Mouse exchange between proximate recovery populations and among drainages in the same recovery population, and use this information to direct management actions, aid recovery plan development, and evaluate conservation strategies outlined in the draft recovery plan. This research is in collaboration with the Colorado Natural Heritage Program.

Rangewide Connectivity and Landscape Genetic Assessment for Greater Sage-grouse

Code: 
RB00CNJ.22.3
Greater Sage-grouse chicks. USGS photo.
Greater Sage-grouse chicks. USGS photo.
Abstract: 

Greater Sage-grouse consist primarily of a few large core populations surrounded by numerous small populations. The viability of these small populations is sustained by dispersing individuals from neighboring populations.  Development that causes habitat loss or creates barriers to dispersal between core areas restricts movements important to maintain genetic diversity, augment small populations, or recolonize extirpated populations. The goal of this study is to assess connectivity among core areas, and to identify features that may act as barriers to movement.  In addition to defining populations and assessing connectivity, this study uses genetic approaches to address many other relevant questions including the conservation of genetic diversity, the impacts of inbreeding, and the association among landscape and geographic characteristics, habitats, and genetics. This research is in collaboration with USGS, USFS, and the University of Montana and is supported by Natural Resources Conservation Service, USFWS, and 11 US state fish and wildlife agencies.

Latent Spatial Models and Sampling Design for Landscape Genetics

Code: 
RB00CNJ.22.2
A male Greater Sage-grouse. USGS photo.
A male Sage-grouse. USGS photo.
Abstract: 

The goal of this study is to develop a spatially-explicit approach for modeling genetic variation across space and to illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We are using a multinomial data model for categorical microsatellite allele data and introduced a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for Greater Sage-grouse. This research is in collaboration with Pennsylvania State University, USGS, Colorado State University, USFS, and the University of Montana.

Developing Best Practices for Linear Mixed Modelling in Landscape Genetics Through Landscape-directed Dispersal Simulations

Code: 
RB00CNJ.21
Flying Sage-grouse in Wyoming. USGS photo.
Flying Sage-grouse in Wyoming. USGS photo.
Abstract: 

Mixed models that account for the error structure of pairwise datasets are being utilized to compare models relating genetic differentiation with pairwise measures of landscape resistance.  A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing spatial genetic structure, yet there are currently no tests of the error rates of this approach, or a consensus for the best protocols for minimizing them. The goal of this project is to develop and test a landscape-directed dispersal model to simulate a series of replicates that emulate independent empirical datasets of two species with vastly different life history and habitat use characteristics (Greater Sage-grouse and eastern fox snakes). This study develops best practices for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes. This research is in collaboration with University of Waterloo and is supported by Wyoming Game and Fish Department and BLM.

Contrasting Evolutionary Histories of MHC Class I and Class II Loci in Grouse - Effects of Selection and Gene Conversion

Code: 
RB00CNJ.22.1
A sage-grouse chick wearing a GPS locator "backpack." USGS photo.
A sage-grouse chick wearing a GPS locator "backpack." USGS photo.
Abstract: 

This project examines the evolutionary history of two different classes of MHC genes in five closely related species of grouse.  We are investigating the roles of selection and gene conversion on class I and class II MHC genes and are comparing diversity among the five different prairie grouse. We are finding differences in the strength of balancing selection acting on MHC class I and class II genes. We are also identifying much stronger gene conversion shaping the evolution of MHC class II genes than class I genes. Overall, the combination of strong positive (balancing) selection and frequent gene conversion may be maintaining higher diversity of MHC class II than class I in prairie grouse. This research is in collaboration with University of Łódź, University of Wisconsin-Milwaukee, University of North Texas.

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