integrated monitoring in bird conservation regions for playa...

Integrated Monitoring in Bird Conservation

Regions for Playa Lakes Joint Venture (IMBCR for PLJV)

2018 Accomplishment Report for Kansas March 2019

IMBCR for PLJV 2018 Accomplishment Report | of 33

Executive Summary The Integrated Monitoring in Bird Conservation Regions (IMBCR) program provides crucial data about

breeding birds in the Playa Lakes Joint Venture (PLJV) region. The program is unique in that it uses a

statistically rigorous sampling protocol, randomly assigned grids on public and private land, collects data

that can be used for estimating population size and trend of breeding birds, and can answer hypothesis-

driven management questions. In 2016, the PLJV partnership expanded the IMBCR Program to cover the

entire PLJV region (IMBCR for PLJV). The report that follows details the results from the 2018 IMBCR for

PLJV field season for Kansas.

There are 70 one-kilometer square grids assigned throughout Bird Conservation Regions (BCR) 18 and 19

in Kansas. In the 2018 field season, we were able to obtain landowner permission for all grids; thus, they

were all sampled.

In the 2018 field season, 94 species were detected of which 27 are priority species in BCRs 18 and 19 in

Kansas. Split out by BCR, in BCR 18, 62 species were detected of which 21 are priority species, and in

BCR 19, 88 bird species were detected of which 24 are priority species. Of the 94 species detected in

Kansas, 27 species are listed as Species of Greatest Conservation Need (SGCN) in the Kansas State

Wildlife Action Plan (SWAP); these species are the focus of this report with the inclusion of Mourning

Dove, Ring-necked Pheasant, and Wild Turkey. In the Kansas SWAP, there are 87 species listed as SGCN

for BCRs 18 and 19, and we detected 27 of those species during IMBCR for PLJV data collection. Species

that were not detected well generally fall into one of these categories: a) species at the edge of their

range in the western Great Plains (e.g., Greater Prairie-Chicken, Upland Sandpiper), b) habitat specialist

(e.g., Burrowing Owl), c) birds of prey (e.g., Ferruginous Hawk), d) rare species (e.g., Black-capped Vireo,

Snowy Plover), or e) not active during the survey time period (e.g., nocturnal or breed earlier in the

season). For species falling into categories a-d, either additional years of data collection or adding

additional grids will allow us to calculate occupancy or density. For species not active during the time of

the survey, different survey protocols will be needed.

In 2018, the advisory committee reviewed the completed 2017 species distribution and habitat models

and provided input on the 2019 off-year study. Input on the off-year study included, choosing a question

to pursue and providing peer-review on the study design. In Texas, Oklahoma, and New Mexico, the

2019 off-year study will investigate changes in grassland bird community as canopy cover of mesquite

increases. PLJV has been working with biologists at Kansas Department of Parks Wildlife and Tourism to

design an off-year study in 2020 to investigate the effects of eastern redcedar on grassland bird species.


Acknowledgments PLJV is working with many partners, including our management board, to fund the IMBCR program

across our six states. This field season was funded by regional and national partners including Colorado

Parks & Wildlife, Kansas Department of Wildlife, Parks & Tourism, Nebraska Game & Parks Commission,

New Mexico Department of Game & Fish, New Mexico National Audubon Society, Oklahoma

Department of Wildlife Conservation, Texas Parks & Wildlife Department, USDA Farm Service Agency,

USDA Forest Service, and Great Plains Landscape Conservation Cooperative (US Fish & Wildlife Service,

USFWS). The Migratory Bird Program (Southwest Region, USFWS) also provides funding to support

PLJV’s work on this program.

The important work of IMBCR implementation is executed by Bird Conservancy of the Rockies. We give

special thanks to Bird Conservancy staff who helped make this field season a shining success. Bird

Conservancy’s landowner liaison, Jenny Berven, led the effort to compile contact information and reach

out to hundreds of private landowners in order to obtain access to new survey locations. Brittany

Woidersky and Ethan Kistler (crew leader and assistant crew leader, respectively) led and managed the

large field crew across the PLJV. The following field technicians also worked tirelessly to contact

landowners, research survey locations, and collect quality avian and vegetation data: Moez Ali, Kristen

Amicarelle, Charles Britt, Will Jaremko-Wright, Billi Krochuk, Michael McCloy, Dylan Radin, and Ryan

Ubias. Without the efforts of these technicians and the cooperation of private landowners, we would

not have been able to accomplish our goals. We also thank the Wichita Mountains National Wildlife

Refuge, Buffalo Lake National Wildlife Refuge, and the Palo Duro Canyon State Park for providing

training opportunities for the field crew.

Finally, we thank the entire PLJV Management Board and the IMBCR for PLJV Planning and

Implementation Committee, without whom this work would be impossible. This was an incredible effort

to fundraise, develop stratifications, help with landowner access, overcome hurdles and many additional

small but essential tasks. This is a testament to the strength and trust of our partnership.

Background and Need

Within the PLJV region, routes for the annual USGS Breeding Bird Survey are sparsely located and data

are sparsely collected. Regardless of the quantity of data available in the region, Breeding Bird Survey

data have well-documented biases that make modeling habitat associations and density and occupancy

of breeding birds difficult or impossible. These models and statistics form the backbone of PLJV

conservation planning; thus, the access to high quality data is important.

PLJV engages in a conservation planning process called landscape design (Bartuszevige et a. 2016). An

important first step in this process is to establish a quantifiable or measurable conservation goal. To

establish bird conservation goals, PLJV steps down the continental goals outlined in the four bird

conservation plans to the PLJV region then, using known or hypothesized relationships between bird

abundance and habitat type, we can estimate the number of birds that can be “supported” by our

conservation actions. In the past, this has been completed using the HABS (Hierarchical All-Bird System)

Database. Density for birds in different habitat types were gleaned from a search of the scientific


literature; area of available habitat was calculated from the PLJV landcover. IMBCR data will

substantially improve our models of habitat relationships, by using statistically rigorous data collected in

the region our models of will be more accurate.

Another important component of landscape design is, as models are developed to describe current

patterns and processes and project future, to continually estimate how the projections change the

ability of the landscape to support the bird species in question. This allows us to understand the

potential effects of future landscape patterns on bird populations and our ability to recover them. In

addition, understanding how conservation programs, if fully implemented, will benefit targeted species

can help to improve delivery. IMBCR data will form a foundation for answering these types of questions.

In 2007, the North American Bird Conservation Initiative (NABCI) developed standards for monitoring

programs going forward. Bird Conservancy of the Rockies, Colorado Parks and Wildlife, and USDA Forest

Service took up the challenge to develop a monitoring program that met each of the criteria laid out by

NABCI. The result is the Integrated Monitoring in Bird Conservation Regions (IMBCR) protocol.

Data collected using IMBCR methods are powerful. They can be used to calculate density and

occupancy, create species distribution models and model bird abundance in various habitat associations.

Because the IMBCR grids are randomly sampled, inference can be made to the entire region. The

sampling is flexible enough to increase or decrease effort based on budgetary cycles. And, the data can

be used to answer hypothesis-based questions making these data valuable beyond just establishing

trends and population size.

Fundraising

In 2015, PLJV began a fundraising campaign to establish the IMBCR program in the JV starting in 2016.

Modeled after the tech startup community, initial “angel” investors, the state and federal agencies who

funded this first year of sampling, provide the base of funding to prove the concept. As data are

collected and used to answer management questions, fundraising continues with ‘B’ and ‘C’ investors.

Fundraising targets are any natural resource organization or private industry with an interest in the PLJV

region. The fundraising goal is to have a diverse funding portfolio such that no one partner has to pay

their “full share” (e.g., the amount of money for all sample grids to be completed in their region) and to

secure non-federal matching dollars for federal grant money.

During the fundraising process, PLJV and Bird Conservancy of the Rockies (Bird Conservancy) established

a clear division of labor and working relationship regarding the IMBCR program. Within the PLJV

boundaries, the program is referred to as IMBCR for PLJV; this provides an easy understanding of where

(geographically) money raised by PLJV will be spent and where the data are collected. PLJV does the

fundraising and partner outreach, manages money, contracts and reports to and from the partnership,

and develops models to help answer management questions of the partnership and develop decision

support tools. Bird Conservancy is primarily responsible for private landowner contacts, data collection,

and large-scale, program-wide data analysis.


The best way to fundraise is to demonstrate the value and utility of the data; therefore, PLJV established

an advisory committee composed of biologists from the state wildlife agencies and other interested

partners (Table 1). The purpose of the advisory committee is to 1) help develop management questions

to be answered using IMBCR data based on State Wildlife Action Plans and other conservation planning

documents (Appendix A) and 2) develop hypotheses for testing using overlay studies.

In 2018, the advisory committee provided review on the completed 2017 species distribution and

habitat models and provided input on the 2019 off-year study. Input on the off-year study included

choosing a question to pursue and providing peer-review on the study design. The 2019 off-year study

will investigate changes in the grassland bird community as canopy cover of mesquite increases.

Table 1: IMBCR for PLJV Advisory Committee members.

Committee Member Partner Organization

Corrie Borgman USFWS, R2

Reesa Conrey CPW

Rich Kostecke TNC, TX

Liza Rossi CPW

Kelvin Schoonover ODWC

Rich Schultheis KDWPT

Brad Simpson TPWD

Matt Smith KDWPT

Scott Somershoe USFWS, R6

Matt Steffl NGPC

Kelli Stone USFWS, R2

Austin Teague NMDGF

TJ Walker NGPC

Methods

Sampling Frame & Stratification A key component of the IMBCR design is the ability to infer across spatial scales, from small

management units, such as individual national forests or BLM field offices, to entire states and BCRs.

This is accomplished through hierarchical (nested) stratification, which allows data from smaller-order

strata to be combined to make inferences about higher-order strata. For example, data from each

individual national forest stratum in USFS Region 2 are combined to produce region-wide avian

population estimates; data from each individual stratum in Montana are combined to produce

statewide estimates; data from each individual stratum in BCR 17 are combined to produce BCR-wide

estimates.

The PLJV region is comprised of the following state and BCR intersections: Colorado BCR 18; Kansas BCRs

18 & 19; Nebraska BCR 18; New Mexico BCR 18; Oklahoma BCRs 18 & 19; and Texas BCRs 18 & 19

(Figure 1). Stratification of the region took place over time, beginning with the BCR 18 portion of


Colorado in 2008. It includes strata representing two major rivers (Platte and Arkansas) and the large

swaths of grassland between them, two National Grasslands, and Department of Defense lands. In 2009,

US Forest Service (USFS) lands in Nebraska BCR 18 were stratified, followed by National Park units in

2013. The remaining strata, “All Other Ownership” and the three Biologically Unique Landscapes (BUL),

were added in 2015 as part of the PLJV for IMBCR project. The stratification schemes for Kansas,

Oklahoma, Texas, and New Mexico were developed specifically for the PLJV project and include Playas,

Rivers, All Other Ownership, and national grasslands which had been sampled in the past. All of the

strata are shown in Figure 1.

More details on IMBCR and methods can be found in Appendix B.

Figure 1: Strata and grid locations in the PLJV region.


Working with Landowners In the spring of 2018, Bird Conservancy sent 1,588 letters to private landowners in the PLJV region

requesting permission to access their land. Hundreds of phone calls were made in the following weeks,

primarily by technicians and crew leaders. This initial investment of time and resources is critical. When

landowners have an opportunity to speak to a person and ask questions, they are more likely to grant

permission and continue to allow access in future years.

These 1,588 letters and subsequent phone calls resulted in approximately 326 landowners granting

permission for Bird Conservancy to survey on their property. In total, Bird Conservancy surveyed 330 of

330 planned grids in the PLJV region.

Saying Thank You During the field season, many landowners requested to meet technicians in person. Bird Conservancy

welcomes face to face interactions because they want landowners to feel comfortable about their

contribution to the program. After the field season was completed, all of the landowners who agreed to

participate received a thank-you letter, a list of the bird species detected on their land, and a small gift

of appreciation from Bird Conservancy of the Rockies. In 2018, this was a custom mini calendar,

complete with species profiles for each month and corresponding IMBCR data (Figure 2).

Figure 2: The 2018 landowner participation thank you gift, a custom 2019 mini-calendar featuring

IMBCR data.


Results In the 2018 field season, all necessary landowner permissions were obtained and all grids were sampled.

In Kansas BCR 18, 5,113 birds of 62 species were detected, and in Kansas BCR 19, 4,139 birds of 88

species were detected. Across both BCRs 9,252 individual birds were detected. The raw data were

received by PLJV on October 17, 2018.

Table 2: Kansas strata and number of grids sampled in 2017 and 2018.

Stratum Name

Grids

Assigned

2017

Sampled

2018

Sampled

Kansas BCR 18 All Other Lands 3 3 11

Kansas BCR 18 Playas 3 3 11

Cimarron National Grasslands 5 5 4

Kansas BCR 18 Rivers 3 3 11

Kansas BCR 19 All Other Lands 3 3 11

Kansas BCR 19 Playas 3 3 11

Kansas BCR 19 Rivers 3 3 11

Species Detected Across the entire PLJV region, 61,218 individual birds from over 221 species were recorded (Appendix C

has a complete species list). In Kansas, 94 species were detected of which 27 are priority species (Tables

3-6). Split out by Bird Conservation Region, in BCR 18 62 species were detected of which 21 are priority

species and in BCR 19, 88 bird species were detected of which 24 are priority species. These species are

the focus of this report with the inclusion of Mourning Dove, Ring-necked Pheasant, and Wild Turkey.

The remaining species are not included in this report but results can be found in the database at the

Rocky Mountain Avian Data Center (http://rmbo.org/v3/avian/Home.aspx). Unless otherwise specified,

all bird species names listed in this report are from the American Ornithologists’ Union Checklist of

North and Middle American Birds, seventh edition (2007).

PLJV has developed a framework to divide the long list of priority species into easily understandable

categories (Figure 3). The four categories are as follows: 1) breeding species with >8% of their total

population found within BCRs 18 and 19 that also have a declining population trend in BCRs 18 and 19

according to the BBS (Table 3), 2) breeding species with >8% of their total population found within BCRs

18 and 19 that also have an unknown population trend in BCRs 18 and 19 according to the BBS (Table 4),

3) breeding species with >8% of their total population found within BCRs 18 and 19 that do not have a

declining population trend in BCRs 18 and 19 according to the BBS (e.g., stable or increasing, Table 5),

and 4) breeding species with <8% of their total population found within BCRs 18 and 19 (Table 6). We

http://rmbo.org/v3/avian/Home.aspx


selected 8% of the breeding population in BCRs 18 and 19 as a cut-off because it is a population size

large enough that habitat conservation can make a contribution to increasing continental population

size, it also captures all of the short- and mixed-grass prairie birds species that are primarily found in

those grassland types.

We divided the Kansas breeding bird Species of Greatest Conservation Need into the same four

categories and report Kansas BCR-specific density and abundance estimates for those species with

enough detections to calculate those statistics (Tables 3-6). We also include a separate table of

gamebirds (Table 7). Many gamebirds also fall into category one or two above, so it is important to pay

attention to gamebird populations both for harvest management and habitat management.

Our philosophy for dividing the birds in this way is to focus conservation actions and programs on

species for which we think we can make a contribution to increasing population size in a meaningful

way. In other words, given limited time and resources, the species listed in tables 3 and 4 are ones for

which we think conservation actions in the PLJV will make a difference to reducing continent-wide

declining population trends.

Figure 3: Flow chart illustrating the PLJV framework for determining species that should be tracked

and targeted for habitat conservation.

All results, including parameter estimates, distribution maps, raw count data, and effort are available

online. To view interactive maps showing survey and detection locations, species counts, and density

and population and occupancy results using the IMBCR study design, please visit the Rocky Mountain

Avian Data Center at http://rmbo.org/v3/avian/Home.aspx and click on the “Explore the Data” tab.

PLJV updated habitat and species distribution models using the 2018 IMBCR for PLJV data. Several

members of the IMBCR for PLJV advisory committee had expressed that such models, which describe

major habitat associations and depict areas of high and low abundance of each species would be useful

because detailed information of that sort is not available. Figures resulting from these models are made

available to the advisory committee through a link to a Google spreadsheet. Figure 4 is an example of

such a model for Horned Lark.

All Species

Breeding

>8%

Status (SGCN)

Declining

Not DecliningGame

Species

No Status<8%

Winter

Migration

http://rmbo.org/v3/avian/Home.aspx

https://docs.google.com/spreadsheets/d/1-whm6sjhBBR_QBB0gn-lKK-OPSnDuHPqeIDs-SWF7kk/edit#gid=29994937


After three years of data collection, PLJV evaluated how well the IMBCR for PLJV data were detecting

bird species in various vegetation communities in the JV region. We plotted species richness against

number of grids sampled in the PLJV by habitat type. In grasslands, cropland and riparian systems, we

are capturing the bird community well in our data collection (Figure 5). Shrublands and playas have

under-sampled bird communities (Figure 5).

Figure 4: Example species abundance and distribution model for Horned Lark.


Figure 5: Species richness and area sampled of various vegetation types in the PLJV using 2016-2017

IMBCR for PLJV data. Grasslands (purple), croplands (dark blue), and riparian (light blue) areas are

well-sampled; however, shrublands (yellow) and playas (dark green) are under-sampled.


Table 3: Kansas specific mean density and abundance with coefficient of variation (CV) for Kansas

Species of Greatest Conservation Need (SGCN) breeding birds with >8% of their breeding population

found within the PLJV boundaries and a declining population trend in BCRs 18 and 19 according to

BBS. Coefficient of variation (CV) is a ratio of the standard deviation to the mean; a smaller value

indicates less variation and therefore more confidence in the estimate. In general, an estimate with a

CV of <50 is consider to have high confidence.

Species Name KS

SGCN

BCR 18 BCR 19

Density (Birds/km2)

Abundance CV Density

(Birds/km2) Abundance CV

Baltimore Oriole X 8.62 942776 22.75

Bell's Vireo X 0.001 51 329.74 0.250 27294 125.63

Bullock's Oriole X 0.081 3001 37.44 0.022 2443 483

Cassin's Sparrow X 5.74 212395 11.26 0.279 30536 72.30

Chihuahuan Raven X

Common Nighthawk X 0.496 18338 56.90 5.16 564600 42.76

Eastern Kingbird X 0.078 2887 196.47 5.37 586756 45.25

Eastern Meadowlark X 7.82 854843 25.13

Ferruginous Hawk X

Grasshopper Sparrow X 30.80 1139674 9.00 42.66 4664706 9.40

Lark Bunting X 1.02 37672 83.48 0.09 9720 262.62

Lark Sparrow X 2.64 97830 36.96 12.91 1411657 33.96

Least Tern X

Lesser Prairie-Chicken X 0.001 32 224.85 0.027 2936 222.94

Loggerhead Shrike X 0.0003 9 417.30 0.70 76569 57.56

Long-billed Curlew X 0.002 70 100.48

Mississippi Kite X 0.006 224 98.10 0.259 28315 95.87

Mountain Plover X

Mourning Dove 8.28 306368 233.98 24.88 2720871 248.41

Piping Plover X

Ring-necked Pheasant 4.77 176329 7.86 4.82 527246 9.38

Scaled Quail X

Scissor-tailed Flycatcher X 0.062 2287 269.46 0.08 8798 282.17

Snowy Plover X

Swainson's Hawk X 0.076 2816 75.02 0.014 1504 68.12

Western Kingbird X 1.55 57500 67.71 5.37 587553 51.79


Table 4: Kansas specific mean density, and abundance estimates with coefficient of variation (CV) for

Kansas Species of Greatest Conservation Need (SGCN) breeding birds with >8% of their breeding

population found within the JV boundaries and have an unknown population trend in BCRs 18 and 19

according to BBS. SGCN = Species of Greatest Conservation Need. Coefficient of variation (CV) is a ratio

of the standard deviation to the mean; a smaller value indicates less variation and therefore more

confidence in the estimate. In general, an estimate with a CV of <50 is consider to have high

confidence.

Species Name KS

SGCN

BCR 18 BCR 19

Density (Birds/km2)



Burrowing Owl X 0.03 1102 131.95 0.028 3065 197.74

Greater Prairie-Chicken X 0.0003 36 388.70

Northern Bobwhite X 2.54 93919 14.65 8.97 981367 11.33

Painted Bunting X

Red-headed Woodpecker

X 0.587 64142 50.77

Wild Turkey 0.0001 3 464.16 0.129 14143 136.21

Table 5: Kansas specific mean density and abundance estimates, with coefficient of variation (CV) for

Kansas Species of Greatest Conservation Need (SGCN) breeding birds with >8% of their breeding

population found within the JV boundaries, and do not have a declining population trend (e.g., stable

or increasing trend) in BCRs 18 and 19 according to BBS. SGCN = Species of Greatest Conservation

Need. Coefficient of variation (CV) is a ratio of the standard deviation to the mean; a smaller value

indicates less variation and therefore more confidence in the estimate. In general, an estimate with a

CV of <50 is consider to have high confidence.

Species Name KS

SGCN

BCR 18 BCR 19

Density (Birds/km2)



Dickcissel X 16.00 592202 22.79 46.67 5102807 19.08

McCown's Longspur X



Kansas Species of Greatest Conservation Need (SGCN) breeding birds with <8% of their breeding

population found within the JV boundaries. SGCN = Species of Greatest Conservation Need. Coefficient

of variation (CV) is a ratio of the standard deviation to the mean; a smaller value indicates less

variation and therefore more confidence in the estimate. In general, an estimate with a CV of <50 is

consider to have high confidence.

Species Name KS

SGCN

BCR 18 BCR 19

Density (Birds/km2)



American Avocet X 0.001 33 269.52

American White Pelican X

Bald Eagle X

Barn Owl X

Black Rail X

Black Tern X

Black-billed Cuckoo X

Bobolink X

Canvasback X

Chestnut-collared Longspur

X

Chuck-will's-widow X

Common Poorwill X

Curve-billed Thrasher X 0.0002 8 530.13

Eared Grebe X

Eastern Whip-poor-will X

Eastern Wood-Pewee X 0.201 22014 26.19

Forster's Tern X

Golden Eagle X

Henslow's Sparrow X

Kentucky Warbler X

Ladder-backed Woodpecker

X

Least Bittern X

Northern Pintail X

Peregrine Falcon X

Prothonotary Warbler X

Short-eared Owl X

Spotted Towhee X 0.054 5942 73.81

Upland Sandpiper X 0.006 215 382.00 1.00 109446 36.52

Western Grebe X

Wilson's Phalarope X



gamebird species in Kansas. SGCN = Species of Greatest Conservation Need. Coefficient of variation

(CV) is a ratio of the standard deviation to the mean; a smaller value indicates less variation and

therefore more confidence in the estimate. In general, an estimate with a CV of <50 is consider to have

high confidence.

Species Name KS

SGCN

BCR 18 BCR 19

Density (Birds/km2)



Mourning Dove 8.28 306368 233.98 24.88 2720871 248.41

Ring-necked Pheasant 4.77 176329 7.86 4.82 527246 9.38

Scaled Quail X

Northern Bobwhite X 2.54 93919 14.65 8.97 981367 11.33

Wild Turkey 0.0001 3 464.16 0.129 14143 136.21

Discussion In 2018, we completed the third year of baseline data collection. Baseline data are the data collected

from the randomly allocated grids in the defined strata. This nomenclature has been adopted by PLJV to

distinguish these data from the data that will be collected during off-year studies. In 2019, we will begin

data collection on our first off-year study. This study has three objectives, 1) document changes in the

bird community through a gradient of mesquite invasion, 2) collect data to better inform models of

shrubland bird response to habitat conditions, addressing the under-sampling issue raised in the results

section, and 3) provide guidance to partners about mesquite management.

We will temporarily re-allocate 100 grids in New Mexico, Oklahoma, and Texas to areas with varying

levels of mesquite canopy cover. Bird and vegetation data will be collected using the IMBCR protocol

(Appendix B). The benefit to this sample design is that we can leverage the entire baseline dataset to

bolster the data collected through the off-year study. Data collection for this study will begin in May

2019. PLJV has been working with biologists at Kansas Department of Parks Wildlife and Tourism to

design an off-year study in 2020 to investigate the effects of eastern redcedar on grassland bird species.

In 2018 we were also able to evaluate how well we have been able to sample bird species in the region,

e.g., do we detect all or most of the species that are found in a particular habitat type? We learned that

we are adequately sampling grasslands, croplands, and riparian areas. However, shrublands and playas

are undersampled. The off-year study will address shrublands.

The playa stratum was included because we think that the bird community on the western Great Plains

responds to playas differently than upland habitats. Given our current under-sampling of the bird

community in the strata, we are unable to determine if this is indeed true. We are working with Bird

Conservancy of the Rockies to address some of the sampling issues (e.g., grid points where data are

collected are far away from the playa). These corrections are being implemented in the 2019 field

season.


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Stevens, D. L., Jr., and A. R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the

American Statistical Association 99:262-278.

Thomas, L., S. T. Buckland, E. A. Rexstad, J. L. Laake, S. Strindberg, S. L. Hedley, J. R. B. Bishop, T. A.

Marques, and K. P. Burnham. 2010. Distance software: design and analysis of distance sampling surveys

for estimating population size. Journal of Applied Ecology 47:5-14.

White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked

animals. Bird Study 46:120-139.

http://www.r-project.org/


Appendix A

IMBCR Management Questions Compiled by the IMBCR for PLJV Advisory Committee

Partner Management Question

Texas Parks and Wildlife (TPWD)

Is there a measurable difference in native grassland bird abundance associated with CP1 vs CP2 CRP grasslands?

Is there a significant difference in native grassland bird population abundance when mid-contract management is applied to CP1 fields?

Can we develop some targeting tools for landowner outreach for grassland easements in Texas?

Can we develop targeting tools for shortgrass prairie conservation in the Texas portion of BCR18, particularly the HP region?

What influence does energy development (e.g., oil & gas, wind, solar) have on grassland bird populations?

PLJV (Staff) What characteristics of playas or playa complexes attract high densities of bird use?

How are ‘fallow’ fields used by native species of grassland birds in the plains?

Colorado Parks and Wildlife (CPW)

How might future changes in CRP enrollment in the southern high plains influence native shortgrass bird populations?

Can we describe how shortgrass prairie birds use landscapes with an agricultural component?

Kansas Department of Wildlife Parks and Tourism (KDWPT)

How do grassland birds respond to the playa restoration work we are doing with the Playa Conservation Initiative?

How can we target efforts surrounding existing conservation easement in the Smoky Hills to expand the conservation impact beyond the easement boundaries?

Oklahoma Department of Wildlife Conservation (ODWC)

Do wind farms or infrastructure associated with wind farms influence populations of native species of grassland birds in the plains?

KDWPT & ODWC Where can we get the most bang for our buck treating woody invasion at lower canopy levels for priority species of grassland birds?


New Mexico Game and Fish Department (NMDGF)

What are the optimal characteristics for riparian habitat roost sites selected by wild turkeys? Where are there good roost sites that are not currently inhabited by wild turkeys?

Would targeted shrub planting benefit Scaled Quail in the shortgrass prairie? Where?

Would shrub reduction in eastern New Mexico be beneficial to Northern Bobwhite? Where?

National Wild Turkey Federation

Can we distinguish differences in abundance for wild turkeys associated with riparian areas with eastern cottonwood vs. Russian olive? Can we demonstrate a population response for wild turkeys associated with Russian olive removal?

Pheasants Forever Is CRP a driver of ring-necked pheasant population abundance? How might populations of pheasants respond to forecasted changes in CRP enrollment in the future?

The Nature Conservancy (TNC)

What are the relationships between energy development and grassland birds in the panhandle? Can we meaningfully measure species response?

There are demographic and economic changes driving agricultural land use changes in the panhandle (e.g., 'land abandonment'). Can we model these types of landscape change and demonstrate their potential effect on people and biodiversity?


Appendix B

Description of IMBCR History and Methods

ABOUT IMBCR The Integrated Monitoring in Bird Conservation Regions (IMBCR) program is one of the largest breeding

bird monitoring programs in North America; its reach is depicted in figure A-1. The program was

developed in 2007 to address the need for a collaborative avian monitoring program that produces

scientifically defensible estimates of bird distribution and abundance across various spatial scales. The

program’s objectives were established following guidelines published by the North American Bird

Conservation Initiative’s (NABCI) Monitoring Subcommittee for improving avian monitoring in North

America. NABCI outlined four recommendations: (1) fully integrate monitoring into bird management

and conservation practices; (2) coordinate monitoring programs among organizations and integrate

them across spatial scales; (3) increase the value of monitoring information by improving statistical

design; and (4) maintain bird population monitoring data in modern data management systems (North

American Bird Conservation Initiative 2007).

The IMBCR program’s objectives are to: provide a framework to integrate bird monitoring efforts across

Bird Conservation Regions (BCR); provide robust population density and occupancy estimates that

account for incomplete detection and are comparable at different geographic extents; use annual

population estimates to monitor population trends and evaluate causes of population change; provide

basic habitat association data for most landbird species to address habitat management issues; maintain

a high-quality database that is accessible to all of our collaborators, as well as to the public over the

Internet, in the form of raw and summarized data; and generate decision support tools that help guide

conservation efforts and provide a quantitative measure of conservation success.

IMBCR utilizes a robust spatially balanced sampling design, which allows inferences to avian species

occurrence and population sizes at various spatial scales, from local management units to entire BCRs or

states, facilitating conservation at local and national levels. Collaboration across organizations and

spatial scales increases sample sizes and improves the accuracy and precision of population estimates.

Analyzing the data collectively allows for the estimation of detection probabilities for species that would

otherwise have insufficient numbers of detections at local scales.

More information about IMBCR, including a detailed history of the program, can be found online in the

“Integrated Monitoring in Bird Conservation Regions (IMBCR): 2015 Field Season Report” at the

following location: http://rmbo.org/v3/Portals/5/Reports/2015_IMBCR_Report.pdf.

http://rmbo.org/v3/Portals/5/Reports/2015_IMBCR_Report.pdf


Figure A-1. Areas sampled by IMBCR protocols.

METHODS

Sample Frame and Stratification

Sample frame and stratification for the PLJV region is described in the methods in the main text above.

Sampling Units

The IMBCR design defines sampling units as 1 km² cells, each containing 16 evenly-spaced sample

points, 250 meters apart (Figure A-2). We define potential sampling units by superimposing a uniform

grid of cells over each state in the study area, then we assign each cell to a stratum using ArcGIS version

10.X and higher (Environmental Systems Research Institute 2006).


Figure A-2: Example 1 km2 grid using the IMBCR design.

Sample (Grid) Selection

Within each stratum, the IMBCR design used generalized random-tessellation stratification (GRTS), a

spatially-balanced sampling algorithm, to select grids (Stevens and Olsen 2004). A minimum of two grids

were required within each stratum to estimate the variances of population parameters.

Sampling Methods

IMBCR surveyors with excellent aural and visual bird-identification skills conducted fieldwork in 2015.

Prior to conducting surveys, technicians completed an intensive training program to ensure full

understanding of the field protocol, review bird and plant identification, and practice distance

estimation in a variety of habitats. Many field technicians attended a second, shorter mid-season

training to review protocol and practice bird and plant identification at high-elevation sites that were

inaccessible earlier in the season.

Field technicians (also referred to as technician, or observer in this report) conducted point counts

(Buckland et al. 2001) following protocols established by IMBCR partners (Hanni et al. 2014). Observers

conducted surveys in the morning, beginning one-half hour before sunrise and concluding no later than

five hours after sunrise. Technicians recorded the start time for every point count conducted. For every

bird detected during the six-minute period, observers recorded species; sex; horizontal distance from

the observer; minute; type of detection (e.g., call, song, visual); whether the bird was thought to be a

migrant; and whether or not the observer was able to visually identify each record.

Observers measured distances to each bird using laser rangefinders, when possible. When it was not

possible to measure the distance to a bird, observers estimated the distance by measuring to some


object near the bird. In addition to recording all bird species detected in the area during point counts,

observers recorded birds flying over but not using the immediate surrounding landscape. Observers also

recorded American red squirrels and in other parts of the larger IMBCR study area, Abert’s squirrels and

American pika. While observers traveled between points within a sampling unit they recorded the

presence of any species not recorded during a point count. The opportunistic detections of these species

are used for distribution mapping purposes only.

Technicians considered all non-independent detections of birds (i.e., flocks or pairs of conspecific birds

together in close proximity) as part of a “cluster” rather than as independent observations. Observers

recorded the number of birds detected within each cluster along with a letter code to distinguish

between multiple clusters.

At the start and end of each survey, observers recorded time, ambient temperature, cloud cover,

precipitation, and wind speed. Technicians navigated to each point using hand-held Global Positioning

System units. Before beginning each six-minute count, surveyors recorded vegetation data (within a 50

m radius of the point). Vegetation data included the dominant habitat type and relative abundance;

percent cover and mean height of trees and shrubs by species; as well as grass height and ground cover

types. Technicians recorded vegetation data quietly to allow birds time to return to their normal habits

prior to beginning each count.

For more detailed information about survey methods and vegetation data collection protocols, refer to

Bird Conservancy’s “Field Protocol for Spatially Balanced Sampling of Landbird Populations” on our

Avian Data Center website at http://rmbo.org/v3/avian/DataCollection.aspx. There you will find links to

current protocols and data sheets.

Seasonal Timing of Surveys

In order to complete the work in the field, we hired and trained 7 technicians. We assigned each

technician 35-40 grids in the PLJV area. Point counts should be performed after all migratory species

have returned to their breeding areas and as early in the season as possible. Capturing the optimal

survey time is important to mitigate counting too many transient birds that are still migrating through;

however, we will never be able to fully avoid counting migrant individuals. In general, birds arrive at

their breeding grounds both latitudinally (settling in southern latitudes first, then moving north) and

elevationally.

The seasonal timing for many previously established IMBCR study areas is largely driven by elevation. In

Colorado, for example, Lark Buntings and Western Meadowlarks begin breeding on the plains in early

May, whereas alpine tundra dwelling species like American Pipit and Horned Lark may not begin

breeding at those elevations until later in the spring when the snow has melted and their food sources

are more readily available. Because changes in elevation are relatively insignificant in the Southern

Great Plains, we decided to focus on latitude to determine optimal survey dates: start surveying in the

southern portion of the study area and end in the north.

http://rmbo.org/v3/avian/DataCollection.aspx


Data Analysis

Distance Analysis

Distance sampling theory was developed to account for the decreasing probability of detecting an object

of interest (e.g., a bird) with increasing distance from the observer to the object (Buckland et al. 2001).

The detection probability is used to adjust the count of birds to account for birds that were present but

undetected. Application of distance theory requires that five critical assumptions be met: 1) all birds at

and near the sampling location (distance = 0) are detected; 2) distances to birds are measured

accurately; 3) birds do not move in response to the observer’s presence (Buckland et al. 2001, Thomas

et al. 2010); 4) cluster sizes are recorded without error; and 5) the sampling units are representative of

the entire survey region (Buckland et al. 2008).

Analysis of distance data includes fitting a detection function to the distribution of recorded distances

(Buckland et al. 2001). The distribution of distances can be a function of characteristics of the object

(e.g., for birds, size and color, movement, volume of song or call and frequency of call), the surrounding

environment (e.g., density of vegetation), and observer ability. Because detectability varies among

species, we analyzed these data separately for each species. The development of robust density

estimates typically requires 80 or more independent detections (n ≥ 80) within the entire sampling area.

We excluded birds flying over, but not using the immediate surrounding landscape, birds detected while

migrating (not breeding), juvenile birds, and birds detected between points from analyses.

We estimated density for each species using a sequential framework where 1) year specific detection

functions were applied to species with greater than or equal to 80 detections per year (n ≥ 80), 2) global

detection functions were applied to species with less than 80 detections per year (n < 80) and greater

than or equal to 80 detections over the life of the project (n ≥ 80), and 3) remedial measures were used

for species with moderate departures from the assumptions of distance sampling (Buckland et al. 2001).

In 2014, we streamlined the analysis by fitting models with no series expansions to all species using the

recommended 10% truncation for point grids. For the year-specific detection functions, we fit

Conventional Distance Sampling models using the half-normal and hazard-rate key functions with no

series expansions (Thomas et al. 2010). For the global detection functions, in addition to the above

models, we fit Multiple-Covariate Distance Sampling models using half-normal and hazard-rate key

function models with a categorical year covariate and no series expansions (Thomas et al. 2010). We

selected the most parsimonious detection function for each species using Akaike’s Information Criterion

adjusted for sample size (AICc; Burnham & Anderson 2002; Thomas et al. 2010), and considered the

most parsimonious model as the estimation model. We estimated population size ("N")̂ for each stratum

as "N" ̂"= " "D" ̂"*A" , where "D" ̂ was the estimated population density and A was the number of 1 km²

grids in each stratum. We calculated Satterthwaite 90% Confidence Intervals (CI) for the estimates of

density and population size for each stratum (Buckland et al. 2001). In addition, we combined the

stratum-level density estimates at various spatial scales, such as management entity, State and BCR,

using an area-weighted mean. For the combined density estimates, we estimated the variance for

detection and cluster size using the delta method (Powell 2007, Thomas et al. 2010) and the variance for

the encounter rate using the design-based estimator of Fewster et al. (2009).


We reviewed the highest ranking detection function for each species to check the shape criteria,

evaluate the fit of the model and identify species with moderate departure from the assumptions of

distance sampling (Buckland et al. 2001). First, we checked the shape criteria of the histogram to make

sure the detection data exhibited a “shoulder” that fell away at increasing distances from the point.

Second, we evaluated the fit of the model using the Kolmogorov-Smirnov goodness-of-fit test. Finally,

we visually inspected the detection histograms to identify species that demonstrated evasive movement

and/ or measurement errors. We looked for a type of measurement error involving the heaping of

detections at certain distances that occurs when observers round detection distances. We also looked

for histograms with detections that were highly skewed to the right, which may indicate a pattern of

evasive movement (Buckland et al. 2001).

For species with moderate departures from the assumptions and shape criteria, we used two sequential

remedial measures. First, we truncated the data to the point where detection probability was

approximately 0.1 [g(w) ~ 0.1] and included key functions with second order cosine series-expansion

terms in the candidate set of models (Buckland et al. 2001). We did not include detection function

models with a single cosine expansion term because the half-normal and hazard-rate models require the

order of the terms are > 1 (Buckland et al. 2001). Second, when the goodness-of-fit test and/ or

inspection of the detection histogram continued to suggest evasive movement and/or measurement

errors, we grouped the distance data into four to eight bins, and applied custom truncation and second

order expansion terms. These remedial measures can ameliorate problems associated with moderate

levels of evasive movement and/ or distance measurement errors (Buckland et al. 2001).

Occupancy Analysis

Occupancy estimation is most commonly used to quantify the proportion of sample units (i.e., 1 km²

cells) occupied by an organism (MacKenzie et al. 2002). The application of occupancy modeling requires

multiple surveys of the sample unit in space or time to estimate a detection probability (MacKenzie et al.

2006). The detection probability adjusts the proportion of sites occupied to account for species that

were present but undetected (MacKenzie et al. 2002). We used a removal design (MacKenzie et al.

2006), to estimate a detection probability for each species, in which we binned minutes one and two,

minutes three and four and minutes five and six to meet the assumption of a monotonic decline in the

detection rates through time. After the target species was detected at a point, we set all subsequent

sampling intervals at that point to “missing data” (MacKenzie et al. 2006).

The 16 points in each sampling unit served as spatial replicates for estimating the proportion of points

occupied within the sampled grids. We used a multi-scale occupancy model to estimate 1) the

probability of detecting a species given presence (p), 2) the proportion of points occupied by a species

given presence within sampled grids (θ, Theta) and 3) the proportion of grids occupied by a species (ψ,

Psi).

We truncated the data, using only detections less than 125 m from the sample points. Truncating the

data at less than 125 m allowed us to use bird detections over a consistent plot size and ensured that

the points were independent (points were spread 250 m apart), which in turn allowed us to estimate

Theta (the proportion of points occupied within each sampling unit) (Pavlacky et al. 2012). We expected


that regional differences in the behavior, habitat use, and local abundance of species would correspond

to regional variation in detection and the fraction of occupied points. Therefore, we estimated the

proportion of grids occupied (Psi) for each stratum by evaluating four models with different structure for

detection (p) and the proportion of points occupied (Theta). Within these models, p and Theta were

held constant across the BCRs and/or allowed to vary by BCR. Models are defined as follows:

Model 1: Held p and Theta constant;

Model 2: Held p constant, but allowed Theta to vary across BCRs;

Model 3: Allowed p to vary across BCRs, but held Theta constant;

Model 4: Allowed both p and Theta to vary across BCRs.

We ran model 1 for species with less than 10 point detections in each BCR or less than 10 point

detections in all but one BCR. We ran models 1 through 4 for species with greater than 10 point

detections in more than one BCR. For the purpose of estimating regional variation in detection (p) and

availability (Theta), we pooled data for BCRs with fewer than 10 point detections into adjacent BCRs

with sufficient numbers of detections. We used model selection and AIC corrected for small sample size

(AICc) to weight models from which estimates of Psi were derived for each species (Burnham and

Anderson 2002). We model averaged the estimates of Psi from models 1 through 4 and calculated

unconditional standard errors and 90% CIs (Burnham and Anderson 2002). We combined stratum-level

estimates of Psi using an area-weighted mean. The variances and standard errors for the combined

estimates of Psi were estimated using the delta method (Powell 2007).

Our application of the multi-scale model was analogous to a within-season robust design (Pollock 1982)

where the two-minute intervals at each point were the secondary samples for estimating p and the

points were the primary samples for estimating Theta (Nichols et al. 2008, Pavlacky et al. 2012). We

considered both p and Theta to be nuisance variables that were important for generating unbiased

estimates of Psi. Theta can be considered an availability parameter or the probability a species was

present and available for sampling at the points (Nichols et al. 2008, Pavlacky et al. 2012).

Automated Analysis

We estimated population density using point grid distance sampling and site occupancy using the multi-

scale occupancy model within a modified version of the RIMBCR package (R Core Team 2014; Paul

Lukacs, University of Montana, Missoula). The RIMBCR package streamlined the analyses by calling the

raw data from the IMBCR Structured Query Language (SQL) server database and incorporated the R

code created in previous years. We allowed the input of all data collected in a manner consistent with

the IMBCR design to increase the number of detections available for estimating global detection rates

for population density and site occupancy. The RIMBCR package used package mrds (Thomas et al. 2010,

R Core Team 2014) to fit the point grid distance sampling model, and program MARK (White and

Burnham 1999) and package RMark (Laake 2013, R Core Team 2014) to fit the multi-scale occupancy

model. The RIMBCR package provided an automated framework for combining strata-level estimates of

population density and site occupancy at multiple spatial scales, as well as approximating the standard

errors and CIs for the combined estimates.


In October 2014, we revised the RIMBCR distance sampling code to accommodate updates to package

mrds 2.18. However, because we were unable to troubleshoot the complex structure of the RIMBCR

code, we completely rewrote the distance sampling code between October 2014 and April 2015. The

new distance sampling code retained the “roll-up” code for combining the strata-level estimates from

the previous version of RIMBCR. In March 2015, we discovered a delta method (Powell 2007) error in

the RIMBCR “roll-up” code (Powell 2007). We estimated the proportion of sampling units occupied (Psi)

for all species that estimates the standard errors and CIs for the combined occupancy estimates. In April

2015, we revised RIMBCR to fix the error, but we were unable to troubleshoot the complex structure of

the RIMBCR code. We plan to rewrite the RIMBCR occupancy code in way that allows testing, but in the

meantime we developed an R “roll-up” patch that correctly estimates the standard errors and CIs for the

combined occupancy estimates. We reran the “roll-up” patch for 2012-2014 to retroactively correct the

standard errors and CIs for the previous combined (superstrata) occupancy estimates. We currently

maintain version control of the automated analysis code in the Bird Conservancy repository (Atlassian

Stash, version 3.6.1).

Results for these analyses are found at

http://rmbo.org/v3/avian/Projects/IntegratedMonitoringinBirdConservationRegions.aspx

http://rmbo.org/v3/avian/Projects/IntegratedMonitoringinBirdConservationRegions.aspx


Appendix C

All Species Detected

Species CO KS NE NM OK TX

18 18 19 18 18 18 19 18 19

American Avocet X X X X X X

American Coot X X X X X

American Crow X X X X X X X X X

American Goldfinch X X X X X

American Kestrel X X X X X X X X

American Redstart X

American Robin X X X X X X X X X

American Three-toed Woodpecker X

American White Pelican X

American Wigeon X X

Ash-throated Flycatcher X X X X X X X

Bald Eagle X

Baltimore Oriole X X X X X

Band-tailed Pigeon X

Bank Swallow X X X X

Barn Swallow X X X X X X X X X

Barred Owl X X

Bell's Vireo X X X X X X

Belted Kingfisher X X X X X X

Bewick's Wren X X X X X X

Black-and-white Warbler X

Black-billed Cuckoo X

Black-billed Magpie X X X

Black-capped Chickadee X X X X

Black-chinned Hummingbird X X X X

Black-crested Titmouse X X

Black-headed Grosbeak X X X

Black-throated Gray Warbler X

Black-throated Sparrow X X X X

Blue Grosbeak X X X X X X X X X

Blue Jay X X X X X X X X X

Blue-gray Gnatcatcher X X X X X X X

Blue-winged Teal X X X X X X X X

Bobolink X X

Brewer's Blackbird X X X


Brewer's Sparrow X X X

Broad-tailed Hummingbird X X

Brown Creeper X

Brown Thrasher X X X X X X X X

Brown-headed Cowbird X X X X X X X X X

Bullock's Oriole X X X X X X X X X

Burrowing Owl X X X X X X X X

Bushtit X X X

Cactus Wren X X X

California Gull X

Canada Goose X X X X X X

Canyon Towhee X X X X X

Canyon Wren X X X X X

Carolina Wren X X X X

Cassin's Kingbird X X X X

Cassin's Sparrow X X X X X X X X X

Cave Swallow X X

Cedar Waxwing X X

Chestnut-collared Longspur X

Chihuahuan Raven X X X X X X

Chimney Swift X X X X X X X

Chipping Sparrow X X X X

Clay-colored Sparrow X

Cliff Swallow X X X X X X X X X

Common Grackle X X X X X X X X X

Common Nighthawk X X X X X X X X X

Common Poorwill X X X

Common Raven X X X X X

Common Yellowthroat X X X X X X

Cooper's Hawk X X X X X X

Cordilleran Flycatcher X X

Crissal Thrasher X

Curve-billed Thrasher X X X X X X

Dark-eyed Junco X X

Dickcissel X X X X X X X X X

Double-crested Cormorant X X

Downy Woodpecker X X X X X X

Dusky Flycatcher X

Eastern Bluebird X X X X X X X X

Eastern Kingbird X X X X X X X X X

Eastern Meadowlark X X X X X X X X


Eastern Phoebe X X X X X X X X

Eastern Wood-Pewee X

Eurasian Collared-Dove X X X X X X X X X

European Starling X X X X X X X X X

Evening Grosbeak X

Ferruginous Hawk X X

Field Sparrow X X X X X X

Gadwall X

Golden Eagle X X X X

Golden-fronted Woodpecker X X X

Grasshopper Sparrow X X X X X X X X X

Gray Catbird X X X

Gray Flycatcher X

Gray Vireo X X

Great Blue Heron X X X X X X X X X

Great Crested Flycatcher X X X X X X X

Great Horned Owl X X X X X X X X X

Great-tailed Grackle X X X X X X X X X

Greater Prairie-Chicken X X X

Greater Roadrunner X X X X X X

Green-tailed Towhee X

Green-winged Teal X X

Hairy Woodpecker X X X

Hepatic Tanager X

Horned Lark X X X X X X X X X

House Finch X X X X X X X X X

House Sparrow X X X X X X X X X

House Wren X X X X X X X X

Indigo Bunting X X X X X

Juniper Titmouse X X

Killdeer X X X X X X X X X

Ladder-backed Woodpecker X X X X X X

Lark Bunting X X X X X X X X X

Lark Sparrow X X X X X X X X X

Lazuli Bunting X X X

Least Flycatcher X

Lesser Goldfinch X X X

Lesser Nighthawk X X

Lesser Scaup X

Lesser Prairie-Chicken X X X X

Lewis’s Woodpecker X X


Lincoln's Sparrow X X

Loggerhead Shrike X X X X X X X X X

Long-billed Curlew X X X X X X

MacGillivray’s Warbler X

Mallard X X X X X X X X X

Marbled Godwit X

Marsh Wren X

McCown's Longspur X

Mississippi Kite X X X X X X X X

Mountain Bluebird X X X

Mountain Chickadee X

Mountain Plover X

Mourning Dove X X X X X X X X X

Northern Bobwhite X X X X X X X X X

Northern Cardinal X X X X X X X X

Northern Flicker X X X X X X X X X

Northern Harrier X X X X X X X X

Northern Mockingbird X X X X X X X X X

Northern Pintail X

Northern Rough-winged Swallow X X X X X X X X

Northern Shoveler X X X X

Olive-sided Flycatcher X

Orange-crowned Warbler X

Orchard Oriole X X X X X X X X

Osprey X

Ovenbird X

Painted Bunting X X X X X

Peregrine Falcon X X

Pied-billed Grebe X X X X X

Pine Siskin X X

Pinyon Jay X

Plumbeous Vireo X X

Prairie Falcon X X

Purple Martin X

Pygmy Nuthatch X

Pyrrhuloxia X X X

Red Crossbill X X

Red-bellied Woodpecker X X X X X X X

Red-breasted Nuthatch X X

Red-eyed Vireo X X X X

Red-headed Woodpecker X X X X X X X X


Red-naped Sapsucker X X

Red-tailed Hawk X X X X X X X X X

Red-winged Blackbird X X X X X X X X X

Ring-billed Gull X X

Ring-necked Pheasant X X X X X X X X X

Rock Pigeon X X X X X X X X X

Rock Wren X X X X X X

Rose-breasted Grosbeak X X X

Ruby-crowned Kinglet X

Ruddy Duck X X X

Rufous-crowned Sparrow X X X X X

Savannah Sparrow X X

Say's Phoebe X X X X X X X X X

Scaled Quail X X X X X X

Scissor-tailed Flycatcher X X X X X X X

Scott’s Oriole X

Sharp-shinned Hawk X X X

Sharp-tailed Grouse X

Song Sparrow X X

Sora X X

Spotted Sandpiper X X

Spotted Towhee X X X X

Summer Tanager X X

Swainson's Hawk X X X X X X X X X

Swainson's Thrush X X

Townsend’s Solitaire X

Tree Swallow X X X X

Tufted Titmouse X X X X X

Turkey Vulture X X X X X X X X X

Upland Sandpiper X X X X X X

Veery X

Verdin X

Vermilion Flycatcher X

Vesper Sparrow X X X

Violet-green Swallow X X X

Virginia’s Warbler X X

Warbling Vireo X X X X X

Western Bluebird X X

Western Flycatcher X X

Western Grebe X X X

Western Kingbird X X X X X X X X X


Western Meadowlark X X X X X X X X X

Western Tanager X X X

Western Wood-Pewee X X X

White-breasted Nuthatch X X X X

White-crowned Sparrow X X X

White-faced Ibis X X

White-throated Swift X X

White-winged Dove X X X X X X X

Wild Turkey X X X X X X X X X

Willow Flycatcher X X X

Wilson's Phalarope X

Wilson's Snipe X

Wilson's Warbler X

Wood Duck X X X

Woodhouse’s Scrub-Jay X X X

Yellow Warbler X X X X X X X X

Yellow-billed Cuckoo X X X X X X X

Yellow-breasted Chat X X X X X X

Yellow-headed Blackbird X X X X X X

Yellow-rumped Warbler X X

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