National Park Service U.S. Department of the Interior
Natural Resource Stewardship and Science Landbird Monitoring Protocol and Standard Operating Procedures for the Chihuahuan Desert, Northern Great Plains, Sonoran Desert, and Southern Plains Networks Version 1.00
Natural Resource Report NPS/SOPN/NRR—2013/729 ON THE COVER Upper left: Western Meadowlark (Sturnella neglecta)1, one of the most common species for SOPN. Upper right: Black-throated Sparrow (Amphispiza bilineata)2, one of the most common species for CHDN. Lower left: Grasshopper Sparrow (Ammodramus savannarum)3, one of the most common species for NGPN. Lower right: Gila Woodpecker (Melanerpes uropygialis)2, one of the most common species for SODN.
1Photo © John and Karen Hollingsworth 2Photo © Robert Shantz 3Photographer Dan Licht - NPS. Landbird Monitoring Protocol and Standard Operating Procedures for the Chihuahuan Desert, Northern Great Plains, Sonoran Desert, and Southern Plains Networks Version 1.00
Natural Resource Technical Report NPS/SOPN/NRTR—2013/729 Authors (listed alphabetically) 4National Park Service Kristen Beaupré1 Chihuahuan Desert Network Robert E. Bennetts2 New Mexico State University Jennifer A. Blakesley3 3655 Research Dr., Genesis Building D Kirsten Gallo4 Las Cruces, NM 88003 David Hanni3 Andy Hubbard1 5USGS Southwest Biological Science Center Ross Lock3 Sonoran Desert Research Station Brian F. Powell5 School of Natural Resources Heidi Sosinski2 University of Arizona Patricia Valentine-Darby6 Tucson, Arizona 85721 Chris White3 Marcia Wilson7 6University of West Florida Department of Biology 11000 University Parkway 1National Park Service Pensacola, Florida 32514 Sonoran Desert Network 7660 E. Broadway Blvd., Suite #303 7National Park Service Tucson, Arizona 85710 Northern Great Plains Network 231 East St. Joseph Street 2National Park Service Rapid City, South Dakota 57701-2916 Southern Plains Network Capulin Volcano National Monument Contact (CHDN/SODN/SOPN) PO Box 40 Des Moines, New Mexico 88418 Robert Bennetts2 http://www.nature.nps.gov/im/units/sopn 3Rocky Mountain Bird Observatory 230 Cherry Street, Suite 150 Contact (NGPN) Fort Collins, CO 80521 Marcia Wilson7
November 2013
U.S. Department of the Interior National Park Service Natural Resource Stewardship and Science Fort Collins, Colorado The National Park Service, Natural Resource Stewardship and Science office in Fort Collins, Colorado, publishes a range of reports that address natural resource topics. These reports are of interest and applicability to a broad audience in the National Park Service and others in natural resource management, including scientists, conservation and environmental constituencies, and the public.
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Please cite this publication as:
Beaupré, K., R. E. Bennetts, J. A. Blakesley, K Gallo, D. Hanni, A. Hubbard, R. Lock, B. F. Powell, H. Sosinski, P. Valentine-Darby, C. White and M. Wilson. 2013. Landbird monitoring protocol and standard operating procedures for the Chihuahuan Desert, Northern Great Plains, Sonoran Desert, and Southern Plains Networks: Version 1.00. Natural Resource Report NPS/SOPN/NRR—2013/729. National Park Service, Fort Collins, Colorado.
NPS 960/122806, November 2013 ii Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Contents
Page
Executive Summary...... xiii
Acknowledgments...... xv
Acronyms...... xvii
Revision History Log...... xix
1. Background...... 1
1.1. Chihuahuan Desert Network...... 1
1.2. Northern Great Plains Network...... 1
1.3. Sonoran Desert Network...... 2
1.4. Southern Plains Network...... 3
1.5. Landbirds as a focus for monitoring efforts...... 4
1.6. Key ecological communities and stressors in participating parks...... 5
1.6.1. Chihuahuan Desert Network...... 5
1.6.2. Northern Great Plains Network...... 6
1.6.3. Sonoran Desert Network...... 7
1.6.4. Southern Plains Network...... 8
1.7. Review of existing landbird monitoring programs in the region...... 9
1.7.1. Chihuahuan Desert Network...... 9
1.7.2. Northern Great Plains Network...... 9
1.7.3. Sonoran Desert Network...... 10
1.7.4. Southern Plains Network...... 11
1.7.5. Conclusion...... 11
2. Program Goals and Measurable Objectives...... 13
2.1. Objective 1: Estimate occupancy...... 14
2.2. Objective 2: Estimate bird species richness and composition...... 15
2.3. Objective 3: Estimate density when feasible...... 15
iii Contents (continued)
Page
2.4. Incorporation of vegetation monitoring to augment bird sampling...... 16
3. Sampling ...... 17
3.1. Overview...... 17
3.1.1. Spatial sampling designs...... 17
3.2. Chihuahuan Desert Network...... 18
3.2.1. Grassland sampling sites...... 18
3.2.2. Riparian sampling sites...... 19
3.3. Northern Great Plains Network...... 19
3.3.1. Integrated Monitoring in Bird Conservation Regions Sampling Design...... 19
3.3.2. Systematic Grid Design...... 20
3.4. Sonoran Desert Network ...... 20
3.4.1. Riparian sampling sites...... 20
3.4.2. Upland sampling sites...... 21
3.5. Southern Plains Network...... 21
3.5.1. Grassland sampling sites...... 22
3.5.2. Riparian sampling sites...... 22
3.6. Temporal sampling design...... 23
3.6.1. Annual surveys...... 23
3.6.2. Intra-annual surveys...... 23
3.7. Level of Change Detection...... 24
4. Field Methods...... 25
4.1. Field-season preparation and scheduling...... 25
4.2. Overview of point-transect surveys...... 25
4.3. Seasonal timing of surveys...... 25
4.4. Field protocol...... 27
iv Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Contents (continued)
Page
4.5. Conducting the bird surveys...... 28
4.6. Establishing and marking points...... 28
4.7. Park and network bird lists...... 28
5. Data Management...... 29
5.1. Overview of database design...... 29
5.2. Data entry...... 29
5.3. Data certification process...... 29
5.4. Metadata procedures...... 30
5.5. Data archival procedure...... 30
6. Reporting and Analysis ...... 31
6.1. Reporting...... 31
6.2. Data analysis...... 32
6.2.1. Species Occupancy...... 32
6.2.2. Density...... 33
6.2.3. Species Richness and Composition...... 33
7. Personnel Requirements and Training ...... 35
7.1. Roles and responsibilities...... 35
7.2. Training...... 35
8. Operational Requirements...... 37
8.1. Annual workload and field schedule...... 37
8.2. Facility & equipment needs...... 37
8.3. Budget...... 37
8.4. Collaboration...... 38
9. Procedure for Revising the Protocol and Program Review...... 39
9.1. Revising the protocol...... 39
v Contents (continued)
Page
9.2. Program review...... 39
10. Literature Cited ...... 41
Appendix A. Power Analysis...... 53
Appendix B. Four-Letter Bird Codes for Birds in Chihuhuan Desert, Northern Great Plains, Sonoran Desert, and Southern Plains Networks...... 71
Appendix C. Landbird Sampling Location Maps...... 77
C.1. Chihuahuan Desert Network...... 77
C.2. Northern Great Plains Network...... 87
C.3. Sonoran Desert Network...... 101
C.4. Southern Plains Network...... 114
Appendix D. Landbird species documented in CHDN, NGPN, SODN, and SOPN parks.125
SOP #1. Preparations for the Field Season and Equipment Needed...... 165
SOP #2. Training...... 169
SOP #3. GPS Unit Operation and Navigation and Measuring Techniques...... 173
SOP #4. Field Sampling...... 179
SOP #5. Data Entry Protocol...... 199
SOP #6. Point Transect Quality Assurance/Quality Control...... 207
SOP #7. Revising the Protocol...... 223
vi Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figures
Page
Figure 2.1. Our landbird monitoring program is hierarchical and complementary...... 13
Figure 4.1-1 Major preseason planning and preparation tasks...... 25
Figure 8.1-1. Annual schedule for landbird monitoring...... 37
Figure C.1-1: Amistad NRA (east view) landbird sampling locations...... 78
Figure C.1-2: Amistad NRA (west view) / Rio Grande WSR landbird sampling locations...... 79
Figure C.1-3: Big Bend NP (north view) landbird sampling locations...... 80
Figure C.1-4: Big Bend NP (south view) landbird sampling locations...... 81
Figure C.1-5: Big Bend NP (west view) landbird sampling locations...... 82
Figure C.1-6: Carlsbad Caverns NP landbird sampling locations...... 83
Figure C.1-7: Fort Davis NHS landbird sampling locations...... 84
Figure C.1-8: Guadalupe Mountains NP landbird sampling locations...... 85
Figure C.1-9: White Sands NM landbird sampling locations...... 86
Figure C.2-1: Agate Fossil Beds NM landbird sampling locations...... 88
Figure C.2-2: Badlands NP landbird sampling locations...... 89
Figure C.2-3: Devils Tower NM landbird sampling locations...... 90
Figure C.2-4: Fort Laramie NHS landbird sampling locations...... 91
Figure C.2-5: Fort Union Trading Post NHS landbird sampling locations...... 92
Figure C.2-6: Jewel Cave NM landbird sampling locations...... 93
Figure C.2-7: Knife River Indian Villages NHS landbird sampling locations...... 94
Figure C.2-8: Missouri NRR potential landbird sampling grids...... 95
Figure C.2-9: Mount Rushmore NMEM landbird sampling locations...... 96
Figure C.2-10: Niobrara NSR potential landbird sampling grids...... 97
Figure C.2-11: Scotts Bluff NM landbird sampling locations...... 98
Figure C.2-12: Theodore Roosevelt NP landbird sampling locations...... 99
Figure C.2-13: Wind Cave NP landbird sampling locations...... 100
vii Figures (continued)
Page
Figure C.3-1: Casa Grande Ruins NM landbird sampling locations...... 102
Figure C.3-2: Chiricahua NM landbird sampling locations...... 103
Figure C.3-3: Coronado NMEN landbird sampling locations...... 104
Figure C.3-4: Fort Bowie NHS landbird sampling locations...... 105
Figure C.3-5: Gila Cliff Dwellings NM landbird sampling locations...... 106
Figure C.3-6: Montezuma Castle NM landbird sampling locations...... 107
Figure C.3-7: Organ Pipe Cactus NM landbird sampling locations...... 108
Figure C.3-8: Saguaro NP (Rincon Mountain District) landbird sampling locations...... 109
Figure C.3-9: Saguaro NP (Tucson Mountain District) landbird sampling locations...... 110
Figure C.3-10: Tonto NM landbird sampling locations...... 111
Figure C.3-11: Tumacácori NHP landbird sampling locations...... 112
Figure C.3-12: Tuzigoot NM landbird sampling locations...... 113
Figure C.4-1: Bent’s Old Fort NHS landbird sampling locations...... 115
Figure C.4-2: Capulin Volcano NM landbird sampling locations...... 116
Figure C.4-3: Chickasaw NRA landbird sampling locations...... 117
Figure C.4-4: Fort Larned NHS landbird sampling locations...... 118
Figure C.4-5: Fort Union NM landbird sampling locations...... 119
Figure C.4-6: Lake Meredith NRA landbird sampling locations...... 120
Figure C.4-7: Lyndon B. Johnson NHP landbird sampling locations...... 121
Figure C.4-8: Pecos NHP landbird sampling locations...... 122
Figure C.4-9: Sand Creek Massacre NHS landbird sampling locations...... 123
Figure C.4-10: Washita Battlefield NHS landbird sampling locations...... 124
Figure SOP 4-1. Example transect description sheet...... 181
Figure SOP 4-2. Form used for recording point transect data...... 183
Figure SOP 4-2. Form used for recording point transect data. Continued...... 184
viii Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figures (continued)
Page
Figure SOP 4-3. Example of a completed point information datasheet...... 193
Figure SOP 4-4. Incidental observations field datasheet...... 196
ix
Tables
Page
Table 1.1. Chihuahuan Desert Network parks, from largest to smallest...... 1
Table 1.2. Northern Great Plains Network parks, from largest to smallest. Area data reflect gross acreage (federal and non-federal) from the NPS Land Resource Program except for MNRR. Elevation data were derived from the National Elevation Dataset...... 2
Table 1.3. Sonoran Desert Network parks, from largest to smallest...... 3
Table 1.4. Southern Plains Network parks, from largest to smallest...... 4
Table 3.2-1. Spatial sampling frame and methods used to designate landbird monitoring sites at Chihuahuan Desert Network parks...... 19
Table 3.3-1. Spatial sampling frame and methods used to designate landbird monitoring sites at Northern Great Plains Desert Network parks...... 20
Table 3.4-1. Spatial sampling frame and methods used to designate landbird monitoring sites at Sonoran Desert Network parks...... 22
Table 3.5-1. Spatial sampling frame and methods used to designate landbird monitoring sites at the Southern Plains Network parks...... 23
Table 4.3-1. Annual field schedule for bird monitoring at Chihuahuan Desert Network parks (with some variation from year to year)...... 26
Table 4.3-2. Annual field schedule for bird monitoring at Northern Great Plains Network parks..26
Table 4.3-3. Annual field schedule for bird monitoring at Sonoran Desert Network parks (with some variation from year to year)...... 27
Table 4.3-4. Annual field schedule for bird monitoring at Southern Plains Network parks (with some variation from year to year)...... 27
Table D-1. Bird species known to occur in CHDN parks (through [including] 2012 sampling). Includes species that migrate through or winter in the park...... 125
Table D-2. Bird species listed as present in NGPN parks, including species that migrate through or winter in the parks. All species have a status of “present in park” for the park’s certified species list...... 138
Table D-3. Bird species known to occur in SODN parks (through [and including] 2012 sampling), including species that migrate through or winter in the park...... 147
Table D-4. Bird species known to occur in SOPN parks (through [and including] 2012 sampling), including species that migrate through or winter in the parks...... 157
Table SOP 4-1. Difficulty Rubric...... 182
Table SOP 4-2 Codes (Beaufort scale) used to record wind strength...... 185
xi Tables (continued)
Page
Table SOP 4-3. Sky Codes...... 185
Table SOP 4-4. Subspecies bird codes...... 188
Table SOP 4-5. Unknown Bird Codes...... 189
Table SOP 4-6. Observer Data...... 191
Table SOP 4-7. Breeding behavior codes used to note breeding observations...... 192
Table SOP 4-8. Potential reasons why points were not conducted...... 192
Master Version Table...... 225
xii Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Executive Summary
The NPS initiated the Inventory and Monitoring (I&M) Program to detect long-term changes in biological resources (NPS 1992). Parks with significant natural resources were assigned to one of 32 monitoring networks, each based on ecological similarity and geographic proximity. The Chihuahuan Desert I&M Network (CHDN) includes seven parks in southern New Mexico and western Texas; the Northern Great Plains Network (NGPN) includes 13 park units in Nebraska, North Dakota, South Dakota, and eastern Wyoming; the Sonoran Desert Network (SODN) includes 11 parks in southern Arizona and New Mexico; and the Southern Plains Network (SOPN) includes 11 park units in Colorado, Kansas, New Mexico, Oklahoma, and Texas. Monitoring changes in landbird population and community parameters is an important element of a comprehensive, long-term monitoring program. Together, these four networks developed a landbird monitoring program in collaboration with Rocky Mountain Bird Observatory (RMBO). This protocol describes the monitoring program.
Our landbird monitoring program is intended to be hierarchical and complimentary. The broad goal of the program is to detect biologically significant changes in population parameters over time. Ideally, we would estimate population size or abundance for all species in all parks each year and compare these estimates over time, both within and among parks. Such a goal, however, is seldom realistic as a result of the constraints described herein. We have designed a program that maximizes the strength of our inferences within the context of our finite resources. We have selected three primary areas of focus to monitor that are complementary and together provide a comprehensive assessment of changing bird populations and communities: density, (where feasible), occupancy, and community dynamics. Our intention for monitoring landbirds goes beyond the birds themselves, and includes a broader vision of landbirds as indicators of the ecosystems they inhabit. This dual purpose influences our sampling design, especially in light of our funding and logistic limitations. In some cases tradeoffs have been made to accommodate particular habitat types or park resources that are considered particularly important to a given park. Point surveys are the basis of our sampling design. Points within a given survey can be arranged in a linear transect (e.g., riparian areas along a river) or a grid (surveys in areal landscapes). Field surveys will generally begin in April for CHDN and SODN and in April or early May for NGPN and SOPN.
A thorough analysis of the data from this project should be undertaken after the first 3-5 years and every 10 years thereafter. An initial 3-5-year review is essential and should involve extensive quantitative analyses to evaluate the efficiency of the program design and results, and its ability to meet the stated goals and objectives. A component of the review will involve recommended revisions to the program, with explicit justification. Products from this effort will be trend assessment of population parameters, variance partitioning and power analysis, and an assessment of sampling methods and spatial inferences.
This sampling protocol is divided into a protocol narrative and seven separate Standard Operating Procedures (SOPs). The protocol narrative is a general overview of the protocol that provides the history and justification for the program and an overview of sampling methods. The protocol narrative will only be revised if major changes are made to the protocol. The SOPs, in contrast, are very specific, step-by-step instructions for performing each task. They are expected to be revised more frequently than the protocol narrative. SOPs included in the protocol are: 1) Preparations for the Field Season and Equipment Needed; 2) Training; 3) GPS Unit Operation and Navigation and Measuring Techniques; 4) Field Sampling; 5) Data Entry; 6) Point Transect Quality Assurrance/Quality Control; and 7) Revising the Protocol. There are also several appendices to provide additional information: A) Power Analysis; B) Four-letter Bird Codes; C) Landbird Sampling Location Maps; and D) Landbird Species Documented in CHDN, NGPN, SODN, and SOPN Parks.
xiii
Acknowledgments
Many people contributed to the writing of all aspects of this protocol. Portions of this protocol, including text and figures, were taken from McIntyre et al. (2004), Peitz et al. (2004), and Bennetts et al. (2005). Andy Hubbard has invested resources and provided unending faith in the development of this and other protocols. Cecilia Schmidt provided early technical assistance. Bob Steidl was always available for study-design assistance. Steve Garman provided assistance with power analysis, including providing the R code for power. Ed Debevec (Institute of Arctic Biology, University of Alaska, Fairbanks) generously provided R code for generating bird summary statistics. Emily Yost edited earlier drafts of the document. Finally, Eric W. Albrecht provided early leadership toward making this protocol a reality; we are indebted to his vision and are deeply sad that he will not see its implementation.
xv
Acronyms
AGFO Agate Fossil Beds National Monument AIC Akaike's Information Criteria AMIS Amistad National Recreation Area BADL Badlands National Park BBS Breeding Bird Survey BEOL Bent’s Old Fort National Historic Site BIBE Big Bend National Park CAGR Casa Grande Ruins National Monument CAVE Carlsbad Caverns National Park CAVO Capulin Volcano National Monument CHDN Chihuahuan Desert Inventory & Monitoring Network CHIC Chickasaw National Recreation Area CHIR Chiricahua National Monument CORO Coronado National Memorial DETO Devils Tower National Monument FGDC Federal Geographic Data Committee FOBO Fort Bowie National Historic Site FODA Fort Davis National Historic Site FOLA Fort Laramie National Historic Site FOLS Fort Larned National Historic Site FOUN Fort Union National Monument FOUS Fort Union Trading Post National Historic Site GICL Gila Cliff Dwellings National Monument GPS Global Positioning System GRTS Generalized Random-Tessellation Stratified GUMO Guadalupe Mountains National Park I&M Inventory and Monitoring IRMA Integrated Resource Management Application JECA Jewel Cave National Monument KNRI Knife River Indian Villages National Historic Site LAMR Lake Meredith National Recreation Area LCAS Learning Center of the American Southwest LYJO Lyndon B. Johnson National Historical Park m meter MNRR Missouri National Recreational River MOCA Montezuma Castle National Monument MORU Mount Rushmore National Memorial MVT Master Version Table NGPN Northern Great Plains Network NHP national historical park NHS national historic site NIOB Niobrara National Scenic River NM national monument NMem national memorial NP national park
xvii Acronyms (continued)
NPS National Park Service NRA national recreation area NRTR Natural Resource Technical Report ORPI Organ Pipe Cactus National Monument PECO Pecos National Historical Park QA/QC Quality Assurance / Quality Control RMBO Rocky Mountain Bird Observatory RMD Rincon Mountain District RRQRR Reversed Randomized Quadrant-Recursive Raster RSL Randomized Start Location SAGU Saguaro National Park SAND Sand Creek Massacre National Historic Site SBS Spatially-Balanced Survey SCBL Scotts Bluff National Monument SODN Sonoran Desert Inventory & Monitoring Network SOP Standard Operating Procedure SOPN Southern Plains Inventory & Monitoring Network SPOT Satellite Personal Tracker SQL Search and Query Language SRS Stratified Random Sampling THRO Theodore Roosevelt National Park TMD Tucson Mountain District TONT Tonto National Monument TUMA Tumacácori National Historical Park TUZI Tuzigoot National Monument USGS U.S. Geological Survey UTM Universal Transverse Mercator WABA Washita Battlefield National Historic Site WHSA White Sands National Monument WICA Wind Cave National Park
xviii Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Revision History Log
Previous version Section and Reason for New version Number Revision date Author Changes made paragraph change Number
xix
1. Background
The core mission of the National Park National Monument (WHSA). These units Service (NPS), as outlined in the agency’s range in size from 474 acres (192 hectares) 1916 Organic Act, is the protection and (FODA) to 801,863 acres (324,641 hectares) preservation of natural and cultural (BIBE; Table 1.1). resources for future generations. Responding to criticism that it lacked basic Recently, CHDN staff completed a knowledge of natural resources within monitoring plan that identified “vital signs” parks, the NPS initiated the Inventory or parameters representing a diverse range and Monitoring (I&M) Program to detect of natural resources, including air, water, long-term changes in biological resources climate, soils, plants, invertebrates, and (NPS 1992). Parks with significant vertebrates (NPS 2010). Within each of natural resources were assigned to one these categories, vital signs were chosen of 32 monitoring networks, each based by a workgroup consisting of regional on ecological similarity and geographic and national experts. The workgroup proximity. Each network was charged with evaluated a number of potential parameters developing a monitoring program capable and determined that landbird population of detecting long-term changes in physical parameters, especially breeding birds in and biological resources within each desert grasslands and riparian areas, were network. the most important to the parks and the best vertebrate parameter for long-term The Chihuahuan Desert, Northern Great monitoring (Powell et al. 2007a). Plains, Sonoran Desert, and Southern Plains I&M networks developed a landbird 1.2. Northern Great Plains monitoring program in collaboration Network with Rocky Mountain Bird Observatory The Northern Great Plains Network (RMBO). (NGPN) includes 13 NPS units: Fort Union Trading Post National Historic Site (FOUS), 1.1. Chihuahuan Desert Network Knife River Indian Villages National The Chihuahuan Desert Network (CHDN) Historic Site (KNRI), and Theodore includes seven parks in southern New Roosevelt National Park (THRO) in North Mexico and west Texas: Amistad National Dakota; Badlands National Park (BADL), Recreation Area (AMIS), Big Bend National Wind Cave National Park (WICA), Mount Park (BIBE), Carlsbad Caverns National Rushmore National Memorial (MORU), Park (CAVE), Fort Davis National Historic and Jewel Cave National Monument Site (FODA), Guadalupe Mountains (JECA) in South Dakota; Devils Tower National Park (GUMO), Rio Grande Wild National Monument (DETO) and Fort and Scenic River (RIGR), and White Sands Laramie National Historic Site (FOLA)
Table 1.1. Chihuahuan Desert Network parks, from largest to smallest. Elevation Area Relief Range Park name and code Acres Ha Meters Feet Meters Feet Big Bend National Park (BIBE) 801,863 324,641 1,839 6,033 548-2,387 1,798-7,831 White Sands National Monument (WHSA) 143,733 58,191 105 344 1,185-1,290 3,888-4,232 Guadalupe Mountains National Park (GUMO) 86,416 34,986 1,562 5,125 1,105-2,667 3,625-8,750 Amistad National Recreation Area (AMIS) 57,292 23,195 82 269 282-364 925-1,194 Carlsbad Caverns National Park (CAVE) 46,766 18,934 896 2,939 1,096-1,992 3,596-6,535 Rio Grande Wild and Scenic River (RIGR) 5,164 2,091 256 840 360-616 1,181-2,021 Fort Davis National Historic Site (FODA) 474 192 135 443 1,487-1,622 4,879-5,322
Background 1 Table 1.2. Northern Great Plains Network parks, from largest to smallest. Area data reflect gross acreage (federal and non-federal) from the NPS Land Resource Program except for MNRR. Elevation data were derived from the National Elevation Dataset. Elevation Area Relief Range Park name and code Acres Ha Meters Feet Meters Feet Badlands National Park (BADL) 242,756 98,240 300 985 721-1,021 2,365-3,350 Theodore Roosevelt National Park (THRO) 70,447 28,509 282 925 591-873 1,939-2,864 Missouri National Recreational River (MNRR) 69,011* 27,927* 148 486 327-475 1,072-1,558 Wind Cave National Park (WICA) 33,847 13,697 446 1,463 1,081-1,527 3,547-5,010 Niobrara National Scenic River (NIOB) 29,101 11,777 260 853 547-807 1,795-2,648 Agate Fossil Beds National Monument (AGFO) 3,058 1,238 95 312 1,333-1,428 4,373-4,685 Scotts Bluff National Monument (SCBL) 3,005 1,216 255 837 1,179-1,434 3,868-4,705 Knife River Indian Villages National Historic Site (KNRI) 1,749 708 48 157 508-556 1,667-1,824 Devils Tower National Monument (DETO) 1,347 545 394 1,293 1,168-1,562 3,832-5,125 Mount Rushmore National Memorial (MORU) 1,278 517 398 1,306 1,346-1,744 4,416-5,722 Jewel Cave National Monument (JECA) 1,274 516 240 788 1,550-1,790 5,085-5,873 Fort Laramie National Historical Site (FOLA) 833 337 33 108 1,284-1,317 4,213-4,321 Fort Union Trading Post National Historic Site (FOUS) 444 180 67 219 568-635 1,864-2,083 * MNRR area is based on the MNRR GIS Program, Yankton, SD.
in Wyoming; and Missouri National monitored on a long-term basis (Gitzen Recreational River (MNRR), Niobrara et al. 2010). Given budget realities, only National Scenic River (NIOB), Agate Fossil a few of these priority vital signs will Beds National Monument (AGFO), and be monitored. Landbird population Scotts Bluff National Monument (SCBL) in parameters were identified as one of the Nebraska. vital signs to be monitored and for which the Network would develop a protocol. The 13 NGPN parks vary widely in size, from 444 acres (180 hectares) to more 1.3. Sonoran Desert Network than 240,000 acres (98,240 hectares), The Sonoran Desert Network (SODN) and are located in mixed-grass prairie, includes 11 parks: Casa Grande Ruins mixed-grass/shortgrass transitions, National Monument (CAGR), Chiricahua and Black Hills ponderosa pine (Pinus National Monument (CHIR), Coronado ponderosa) ecoregions (Table 1.2). The National Memorial (CORO), Fort NGPN supports unique natural resources Bowie National Historic Site (FOBO), including large areas of mixed-grass Montezuma Castle National Monument systems at several parks (e.g., AGFO and (MOCA), Organ Pipe Cactus National BADL) and the second largest area of Monument (ORPI), Saguaro National old-growth ponderosa pine in the region Park (SAGU), Tonto National Monument (MORU). Two parks in the network, JECA (TONT), Tumacácori National Historical and WICA, manage two of the four longest Park (TUMA), and Tuzigoot National caves in the world. In addition, several Monument (TUZI) in Arizona; and Gila parks include prairie rivers (Missouri, Cliff Dwellings National Monument Niobrara, and others) of high ecological (GICL) in New Mexico. These units range importance in this semi-arid region. in size from 356 acres (144 hectares) (TUMA) to 330,688 acres (133,882 The 2010 NGPN Monitoring Plan listed hectares) (ORPI) (Table 1.3). Collectively, the “vital signs” identified by Network these parks are representative of most of staff as priority parameters that should be the ecological communities present within
2 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Table 1.3. Sonoran Desert Network parks, from largest to smallest. Elevation Area Relief Range Park name and code Acres Ha Meters Feet Meters Feet Organ Pipe Cactus National Monument (ORPI) 330,688 133,882 1,158 3,800 305–1,463 1,000–4,800 Saguaro National Park (SAGU) 102,011 41,300 2,012 6,600 610–2,621 2,000–8,600 Chiricahua National Monument (CHIR) 11,984 4,852 815 2,675 1,570–2,385 5,150–7,825 Coronado National Memorial (CORO) 4,750 1,923 914 3,000 1,433–2,347 4,700–7,700 Tonto National Monument (TONT) 1,120 453 524 1,720 695–1,219 2,280–4,000 Fort Bowie National Historic Site (FOBO) 1,000 404 183 600 1,417–1,600 4,650–5,250 Montezuma Castle National Monument (MOCA) 858 347 140 460 963–1,103 3,160–3,620 Gila Cliff Dwellings National Monument (GICL) 533 216 52 170 2,027–2,079 6,650–6,820 Casa Grande Ruins National Monument (CAGR) 472 191 5 15 431–436 1,415–1,430 Tuzigoot National Monument (TUZI) 373 149 12 40 1,024–1,036 3,360–3,400 Tumacácori National Historical Park (TUMA) 356 144 104 340 994–1,097 3,260–3,600 the greater Sonoran Desert ecoregion (CHIC) and Washita Battlefield National (Mau-Crimmins et al. 2005). Recently, Historic Site (WABA) in Oklahoma; SODN staff completed a monitoring plan and Alibates Flint Quarries National that identified “vital signs,” or parameters, Monument (ALFL), Lake Meredith representing a diverse range of natural National Recreation Area (LAMR), and resources, including air, water, climate, Lyndon B. Johnson National Historical soils, plants, invertebrates, and vertebrates Park (LYJO) in Texas. (Mau-Crimmins et al. 2005). Within each of these categories, vital signs were The SOPN consists of mostly mixed- and chosen by a workgroup of between four shortgrass ecosystems. It is bordered on and eight regional and national experts. the east by tallgrass prairie, and on the The vertebrate workgroup evaluated 171 west by the forested systems of the Rocky potential parameters and reduced them to Mountains. SOPN parks vary in size from 32 on the basis of ecological significance, 326 acres (132 hectares) to over 46,000 feasibility, and relevance to management acres (18,615 hectares), and contain a wide (Mau-Crimmins et al. 2005). Landbird range of biotic communities and abiotic population parameters were considered conditions (Table 1.4). among the most efficient and feasible vertebrate parameters for long-term Recently, the SOPN held two ecosystem monitoring. workshops that brought together representatives from each park and 1.4. Southern Plains Network subject-matter experts from state and The Southern Plains Inventory and federal agencies, universities, and non- Monitoring Network (SOPN) is composed profit organizations. One of the objectives of 11 National Park Service (NPS) units: of the workshops was to develop and Bent’s Old Fort National Historic Site review the list of potential vital signs and (BEOL) and Sand Creek Massacre National their preliminary justification statements Historic Site (SAND) in Colorado; Fort and monitoring objectives. Grassland Larned National Historic Site (FOLS) birds, particularly in shortgrass habitats, in Kansas; Capulin Volcano National were identified as a high priority issue. Monument (CAVO), Fort Union National In addition, population parameters for Monument (FOUN), and Pecos National landbirds are considered among the most Historical Park (PECO) in New Mexico; efficient and feasible parameters to estimate Chickasaw National Recreation Area
Background 3 Table 1.4. Southern Plains Network parks, from largest to smallest. Elevation Area Relief Range Park name and code Acres Ha Meters Feet Meters Feet Lake Meredith National Recreation Area (LAMR) 46,349 18,757 158 520 853-1,011 2,800-3,320 Chickasaw National Recreation Area (CHIC) 9,889 4,002 116 380 238-354 780-1,160 Pecos National Historical Park (PECO) 6,670 2,699 268 880 2,041-2,309 6,695-7,575 Sand Creek Massacre National Historic Site (SAND) 2,400 971 44 145 1,201-1,245 3,940-4,085 Alibates Flint Quarries National Monument (ALFL) 1,371 555 158 520 853-1,012 2,800-3,320 Bent’s Old Fort National Historic Site (BEOL) 799 323 12 40 1,213-1,225 3,980-4,020 Capulin Volcano National Monument (CAVO) 793 321 363 1,190 2,131-2,493 6,990-8,180 Fort Union National Monument (FOUN) 721 292 46 150 2,038-2,083 6,685-6,835 Fort Larned National Historic Site (FOLS) 718 291 23 75 616-639 2,020-2,095 Lyndon B. Johnson National Historical Park (LYJO) 674 273 114 375 363-477 1,190-1,565 Washita Battlefield National Historic Site (WABA) 326 132 24 80 585-610 1,920-2,000
for long-term monitoring (Powell et al. these situations, relating changes in bird 2007a). populations to environmental features can be complex, especially when confounded 1.5. Landbirds as a focus for by time lags that are characteristic of monitoring efforts site-tenacious bird species. Additional Monitoring changes in landbird population complications occur if birds respond and community parameters can be an more sensitively to environmental change important element of a comprehensive, than we can detect, and when cyclical long-term monitoring program, such as environmental changes result in erratic that being implemented for the CHDN, changes in population size that are NGPN, SODN, and SOPN parks. ultimately inconsequential. However, Landbirds are a conspicuous component the utility of monitoring landbirds is of many ecosystems and have high body strengthened by concurrent monitoring of temperatures, rapid metabolisms, and a broad suite of environmental parameters occupy high trophic levels. As such, (Dale and Beyeler 2001) that may assist changes in landbird populations may with elucidating changes in the bird be indicators of changes in the biotic or community to other environmental factors. abiotic components of the environment Such a broad-based approach is now being upon which they depend (Canterbury undertaken by the CHDN, NGPN, SODN, et al. 2000; Bryce et al. 2002). Relative to and SOPN programs (e.g., NPS 2008) and other vertebrates, landbirds are also highly other broad-based monitoring approaches detectable and can be efficiently surveyed (e.g., Ringold et al. 1996; Stevens and Gold with the use of numerous standardized 2003; Barrows et al. 2005). methods (Bibby et al. 2000; Buckland et al. 2001). Perhaps the most compelling reason to monitor landbird communities in parks Birds select habitat based on behavioral is that birds themselves are inherently cues triggered by the environment valuable. The high aesthetic and spiritual (Hutto 1985a; Alcock 2005). In some values that humans place on native wildlife environments, however, especially those is acknowledged in the agency’s Organic that vary unpredictably, habitat may not Act: “to conserve . . . the wild life therein . be saturated and changes in resources . . unimpaired for the enjoyment of future may not always be tracked by changes generations.” Bird watching, in particular, in animal populations (Wiens 1985). In is a popular, longstanding recreational
4 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN pastime in the U.S., and forms the basis of a extended drought, increased atmospheric large and sustainable industry (Sekercioglu carbon dioxide, fire, redistribution and 2002). heterogeneity of soil resources, and physiological adaptations of plants (Cole 1.6. Key ecological communities and Monger 1994; Fredrickson et al. 2006; and stressors in participating Housman et al. 2006; Peters et al. 2006; parks Peters and Havstad 2006).
1.6.1. Chihuahuan Desert Currently, the majority (50%) of landscapes Network in the Northern Chihuahuan Subregion The seven CHDN park units represent consists of desert shrublands. Desert the most significant natural, cultural, and grasslands, covering approximately 25% recreational values in the U.S. Chihuahuan of the subregion, are often mosaics of Desert. Most of the CHDN park units grass and shrub. Mixed-conifer forests were established for conservation and and woodlands comprise approximately preservation of significant natural and 10% of the subregion. Consequently, the geologic resources (e.g., caverns of CAVE). Chihuahuan Desert is now considered The exceptions are FODA, which was synonymous with shrublands, and established primarily for cultural reasons, its boundaries are determined by the and AMIS, which was established for contiguous distributions of creosote bush recreation. Although not established for (Larrea tridentata) and tarbush (Flourensia their natural resources, both parks contain cernua) (Dick-Peddie 1993). This change significant natural resources embedded has increased the significance of remaining within a framework focused on a human desert grasslands, particularly those once event or activity. dominated by black grama (Bouteloua eriopoda). Six of the CHDN parks (all but AMIS) are located in the Chihuahuan Desert AMIS is located in the Tamaulipan Ecoregion. Conservation organizations Thornscrub (Mezquital) Ecoregion like World Wildlife Fund consider the of southern Texas and northeastern Chihuahuan Desert Ecoregion one of Mexico. The diversity of the Tamaulipan the most diverse deserts in the world Thornscrub is not as high as that of the and a critical reservoir for conserving Chihuahuan Desert, but it still supports biodiversity (Olson and Dinerstein 1998). over six hundred species of plants and At least 1,000 endemic plant taxa occur animals. The region is particularly rich in the Chihuahuan Desert, an astonishing in tree species, including two endemics, richness of biodiversity (Johnson 1974). and birds (Ricketts et al. 1999). In this This high desert area is a center for region, trees such as acacia (Acacia spp.) endemism of yuccas and cacti (Hernández and mesquite (Prosopsis glandulosa) and Bárcenas 1995). Three-hundred and dominate, along with other shrub species fifty of the 1,500 known species of cacti and some grasslands. The most common occur in the Chihuahuan Desert. Four grasses found include curly mesquite grass other plant families (grasses, euphorbs, (Hilaria belangeri), hooded finger grass asters, and legumes) also show high levels (Chloris cucullata), Bouteloua spp., and of endemism across the many basins of Muhlenbergia spp. the desert (Dinerstein et al. 2000). During the last century, woody shrubs have The structure and composition of intruded and expanded into areas of this vegetation communities strongly define ecosystem once dominated by or occupied ecological communities and have by grasses (Peters and Gibbens 2006). significant effects on ecosystem processes. The causes of this shift are equally diverse Invasive plants pose one of the greatest and complex, involving historical fauna threats to natural and cultural resources and land use, human and animal forms of of CHDN parks. Nonnative plant species plant seed dispersal, excessive herbivory, are invading new areas and establishing at
Background 5 unprecedented rates because global trade DETO) are a heterogeneous and dynamic and transportation have allowed these mix of grasslands, savanna, and closed- species to cross biogeographical barriers. canopy pine forests. Potential ecological damage from exotic invasive species includes alteration of Great Plains vegetation communities are natural disturbance regimes and ecosystem shaped by fire, grazing, soil type, landform processes, and subsequent effects on (e.g., badlands and draws), flooding, and native flora and fauna. Specific concerns climate, especially the amount, season, include threatened and endangered species and variability of precipitation (Bachelet sustainability, alteration of density, biomass, et al. 2000; Sims and Risser 2000). Climatic and diversity of native plant communities, variability in the NGPN, which includes species extirpation/extinction due to multi-decade periods drier or wetter than changes in fire regime, and alteration of the century-scale average, has large impacts basic soil processes. Numerous nonnative on vegetation (Albertson and Weaver 1943; plant species have been identified in Clark et al. 2002). For example, Weaver CHDN park units. Saltcedar (Tamarix (1943) reported that the mixed-grass prairie spp.), Russian olive (Elaeagnus angustifolia), biome shifted east a hundred miles during and giant cane (Arundo spp.) threaten the Dust Bowl of the 1930s. Even under the riparian areas along the Rio Grande average conditions, soil moisture often is and other rivers. Grasslands have been low enough to stress native plants of this invaded by Russian thistle (Salsola spp.), region. Marta starthistle (Centaurea melitensis), African rue (Peganum harmala), buffelgrass Historically, frequent fires and grazing (Pennisetum ciliare), and others. were primary disturbances of NGPN terrestrial systems, as in grasslands 1.6.2. Northern Great Plains worldwide (e.g., Anderson 1982; Milchunas Network et al. 1988). Annual flooding and shifting NGPN parks are located in mixed-grass of river channels drove vegetation prairie, mixed-grass/tallgrass and mixed- patterns in riparian areas. Grazing is still grass/shortgrass transitions, and Black Hills a dominant ecological process in the ponderosa pine ecoregions (Küchler 1985; region, but in landscapes adjacent to parks, Omernik 1987; Bailey 1995). Grasslands heterogeneous grazing by native species dominate about 40% of the land area has been replaced by homogeneous grazing of the 13 NGPN parks (U.S. Geological by livestock (Hart and Hart 1997). The Survey [USGS] 2005). Dominant grasses region’s largest native herbivores, bison include western wheatgrass (Pascopyrum and elk, are absent from most parks. In smithii), green needlegrass (Nassella the three parks supporting bison (BADL, viridula), needleandthread (Hesperostipa THRO, and WICA), confinement of herds comata), blue grama (Bouteloua gracilis), within park boundaries produces grazing buffalograss (Buchloe dactyloides), and patterns different from the presettlement big (Andropogon gerardii) and little disturbance pattern. Parks with rivers (Schizachyrium scoparium) bluestem. Only suffer from lack of tree recruitment and a small portion of the grassland landscape degradation of riparian forests resulting is made up of woody draws and patches from flood control, disease, and exotic of green ash (Fraxinus pennslyvanica), plants. Prescribed burning and fuels juniper (Juniperus scopulorum), and shrubs, treatments by the Northern Great Plains but these areas are of high ecological Fire Management Office and park staff importance. Woodlands of cottonwood attempt to mitigate the effects of the (Populus deltoides monilifera), green ash, absence of natural fires, but the extent of and American elm (Ulmus americana) these fires and the conditions under which occur along the larger streams and they occur are different from the regimes rivers. Ponderosa pine (Pinus ponderosa) that shaped the ecosystems. dominates Black Hills forests. The Black Hills foothills (portions of WICA and
6 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Invasion of exotics is a major natural Ecological communities within the SODN resource problem in all NGPN parks are threatened by direct, human-induced (Larson et al. 2001). Smooth brome changes to the landscape and by climate (Bromus inermis) dominates the understory change (Nabhan and Holdsworth 1999). of many riparian areas, and annual brome Among the ecological communities that grasses are common in many upland have experienced much change in recent sites. Kentucky bluegrass (Poa pratensis) times are desert scrub, semi-desert is a naturalized and sometimes dominant grasslands, and mesic riparian woodlands. component in some parks. Infestations of Desert scrub is the most widespread species such as Canada thistle (Cirsium vegetation community in the network, and arvense) and musk thistle (Carduus is well represented at ORPI, SAGU, and nutans) are priorities for treatment at many of the smaller parks. A significant most NGPN parks (NPS 2005). Woody threat to the natural structure and function riparian invaders of high concern include in this community is the invasion of Russian olive, which is present in many nonnative grasses, especially buffelgrass, riparian zones of the region, and saltcedar which is introducing fire into systems with (Tamarix ramosissima), which is present no capacity to withstand its effects, thereby but not established in NGPN parks (NPS altering hydrology, nutrient cycling, and 2007). Exotic invasions have produced native-species composition (Burgess et al. large changes in plant species composition 1991; Franklin et al. 2006; Bowers et al. of many NGPN communities and have 2006). reduced species richness in many sites (e.g., Butler and Cogan 2004). Although fire, Semi-desert grasslands within the network grazing, and other disturbances shaped are represented in small areas of CHIR, the natural vegetation of the Network, CORO, and SAGU. Within the parks, currently exotics often dominate post- semi-desert grasslands are threatened due disturbance communities. to shrub invasion and fire suppression. The spread of velvet mesquite (Prosopis 1.6.3. Sonoran Desert Network velunita), in particular, has increased Sonoran Desert Network parks include significantly in the region’s once-open representative components of a diverse grasslands because of disruption of natural range of ecological communities present fire regimes and overgrazing by domestic in southern Arizona and western New livestock (Bahre and Shelton 1993; Van Mexico. Vegetation communities within the Auken 2000). Conversion of semi-desert network range from lowland desertscrub, grasslands to mesquite-invaded shrubland dominated by creosote and bursage has had important implications for the (Ambrosia spp.) that are characteristic quality and distribution of habitat for of the Sonoran Desert, to highland grassland-obligate birds, many of which mixed conifer forests, dominated by fir have declined precipitously (Bock and (Abies spp.) and pine (Pinus spp.), in the Bock 1988; Knopf 1994; Rappole 1995). Canadian zone. This range of structural and floristic diversity is influenced further by Broadleaf riparian woodlands in the varied biogeographic affinities of species Sonoran Desert region are among the within the network, including those of smallest community in area, yet support the Sonoran, Chihuahuan, and Mojave a higher density and diversity of native deserts, Rocky Mountains, Sierra Madre birds than any other major vegetation Occidental, and Great Plains (McLaughlin communities in the region (Rosenberg et 1986; Brown 1994). Other important al. 1991). Riparian vegetation is preferred factors influencing the diversity in network nesting habitat for a disproportionate parks include a range of topographic, number of landbird species in the region geologic, edaphic, and climatic factors, and (Bock and Bock 1984; Powell and Steidl variable land-use histories (Marshall et al. 2000). Despite its value, however, the 2000). extent of riparian vegetation has declined as a result of water diversion, groundwater
Background 7 pumping, woodcutting, and drought smuggling, and law enforcement are (Rosenberg et al. 1991; Bahre 1991). For pervasive (NPS 2003; Segee and Neeley example, nesting riparian-obligate birds 2006). Other current and future stressors have virtually disappeared in the last few and drivers to regional bird communities years from Rincon Creek, the site of the include global climate change (Brown et al. only stand of broadleaf riparian woodland 1997; Visser et al. 1998; Brown et al. 1999; in SAGU (B. Powell and C. Kirkpatrick, Inkley et al. 2004), diseases such as avian unpublished data). These declines have influenza (Perkins and Swayne 2002; Kou likely resulted from the degradation and et al. 2005) and West Nile virus (Rappole loss of the riparian vegetation community and Hubalek 2003; Caffrey et al. 2005), and from drought and excessive groundwater additional nonnative species (e.g., peach- pumping (e.g., Stromberg et al. 1996). faced lovebird).
Pine forests are found in four parks (CHIR, 1.6.4. Southern Plains Network CORO, GICL, and SAGU). Montane forest Most SOPN parks were established birds of the southwestern “sky islands” primarily for cultural and recreational have evolved in forests that experience reasons; however, all network parks low to moderate burns approximately contain significant natural resources. Many every decade (Swetnam and Baisan 1996; of these resources are embedded within Ganey et al. 1996). Active fire suppression a framework focused on a human event has reduced the frequency of these burns, or activity, and the enabling legislation which have been replaced by high- for many of the parks refers to ecological intensity burns that radically alter forest systems (e.g., requiring that the scene for structure (Allen 1996; Swetnam et al. the period of significance at a historical 1999; Pyne 2001). All four parks with pine park be maintained). SOPN parks are some forests have experienced wildland fires of the only representatives of short- and in recent decades, and all have active fire mixed-grass ecosystems in protected status. management plans. Yet little information is The parks occur in a landscape dominated available on the effects of these fires on bird by agriculture, and act as natural oases that communities. are refugia for endemic, threatened, and endangered species, as well as common Other stressors to bird communities in the species. The SOPN is located primarily in Sonoran Desert are increasing due to rapid the grassland—or Great Plains— biome, human population growth that is increasing considered by some to be the largest at a rate faster than in most regions of the biome in North America (Stubbendieck United States (U.S. Census Bureau, 2000 1988), and among the most productive Census). Because many parks in the SODN ecosystems on Earth (Williams and Diebel are adjacent to expanding urban areas, 1996). However, the North American habitat for some species of concern has prairie is also among the continent’s most been fragmented and degraded by urban endangered resources (Samson and Knopf environments (Germaine et al. 1998; Green 1994; Ricketts et al. 1999). Most ecologists and Baker 2003; Powell 2004; Powell et divide the Great Plains into three types, al. 2005b). For example, prior to the early representing a gradient from tallgrass 1900s, CAGR likely supported a diverse prairie on the eastern plains, to mixed- native bird community. Now, because of grass prairie in the central regions, and vegetation change due to groundwater shortgrass prairie in the west. CHIC, FOLS, withdrawal and urban development, all LYJO, and WABA and are in mixed-grass common species are either nonnative or prairie or savannah. ALFL, BEOL, CAVO, adapted to the agricultural, commercial, or FOUN, LAMR, and SAND are located residential environments found adjacent in shortgrass prairie, and PECO is in the to the monument (Powell et al. 2005a). ecotone between shortgrass prairie and Two SODN parks (CORO and ORPI) are piñon-juniper forest. located along the Mexican border, where impacts associated with illegal immigration,
8 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN The dominant native plant species in rates (i.e., index-to-abundance) of most the western grasslands are blue grama species throughout North America. The and buffalograss in the grasslands, often program has provided evidence for regional interspersed with cottonwood (Populus and national declines of some bird species deltoides) trees along the riparian areas. (Robbins et al. 1989; James et al. 1996) in In the eastern portion of the network, big spite of some methodological challenges and little bluestem, switch grass (Panicum (James et al. 1996; Kendall et al. 1996; Link virgatum), and Indian grass (Sorghastrum and Sauer 1998). nutans) become more dominant in the grasslands, interspersed with American 1.7.1. Chihuahuan Desert elm, sugarberry (Celtis laevigata), bald Network cypress (Taxodium distichum), and green Although a considerable amount of bird ash trees and cottonwoods in riparian work has been conducted in the CHDN, areas. the high-quality data required for effective conservation and management of landbirds Due to alterations in natural fire and is not available for CHDN parks. Although grazing cycles, many SOPN grasslands are numerous studies have been conducted on being invaded by woody species, such as species of interest or concern in a number oneseed juniper (Juniperus monosperma). of parks, few studies were conducted for Exotic plant species, such as smooth long time periods or have sample designs brome, cheatgrass (Bromus tectorum), sufficiently rigorous for monitoring status kochia (Kochia scoparia), and King Ranch and trend. For example, Peregrine Falcon bluestem (Bothriochloa ischaemum), studies were conducted from 1976 through have invaded the grasslands; saltcedar 2003 in BIBE, and some nests are still (Tamarix spp.), scotch thistle (Onopordum checked opportunistically. In GUMO, acanthium), and Russian olive threaten Mexican Spotted Owl studies were riparian areas. conducted from 1994 to 2003. A subset of territories has been monitored since 2005. Many grassland systems have undergone Numerous other studies were conducted significant changes since they were first over a period of one to three years. described by early Europeans. Exotic- species invasions, expanding row- National Audubon Society’s Christmas crop agriculture, overgrazing, mineral Bird Counts are conducted in AMIS (since exploration, and establishment of woodlots the 1970s), BIBE (since 1948), CAVE (since and shelterbelts have all contributed to 1955), and GUMO (since 1963). Three grassland degradation and significant and BBS routes have been conducted in BIBE ongoing loss of genetic diversity in North since 1978. However, these programs American grasslands. Estimates for loss of do not provide either the spatial extent mixed-grass prairie range from 30–99.9%, or statistical rigor to document status and 46–82% for shortgrass, depending on and trend of landbirds in CHDN parks. the region (Samson et al. 1998). Consequently, landbirds were selected as a CHDN vital sign and the network 1.7. Review of existing landbird was tasked with developing a monitoring monitoring programs in the protocol that provides reliable information region on population status and trend at Effective conservation and management meaningful temporal and spatial scales. of landbirds requires high-quality data that currently do not exist for network 1.7.2. Northern Great Plains parks. The Breeding Bird Survey (BBS), Network a volunteer-based program, is the most At the regional scale, partial BBS routes widespread bird monitoring program in run through portions of BADL and WICA. North America (Robbins et al. 1989; Sauer In addition, both parks also conduct 1993). The program, in operation since the National Audubon Society’s Christmas 1960s, seeks to monitor trends in detection Bird Counts. Another regional program
Background 9 called “Integrated Monitoring in Bird Likewise, birds were not inventoried Conservation Regions” was established at AGFO because landbird monitoring and implemented by RMBO and its was originally the responsibility of the partners. The IMBCR design consists of Heartland Network (Peitz et al. 2008). nested strata, with each individual stratum producing its own estimates of species’ WICA was surveyed in 2008 and 2009 densities and occupancy rates, as well as using a modification of the IMBCR survey contributing to estimates for higher order design. Twenty sampling units were strata such as states and Bird Conservation selected using the Generalized Random Regions. The IMBCR design defines the Tessellation Stratified (GRTS) sampling sampling unit as a 1 km2 area, containing methodology (Stevens and Olsen 2004). 16 evenly-spaced sample points (250 meter RMBO conducted point counts in 2008 spacing). This layout allows for estimating and 2009 three times per year on the 20 occupancy rates at the scale of the 1 km2 sampling units (Blakesley et al. 2010). cell as well as the conditional probability This methodology allowed for estimating of occupancy at the scale of the sampling detection probability through the principles point (Pavlacky et al. 2012). Using the of distance sampling and occupancy method of Pavlacky et al. (2012) only one estimation. visit to each sample point is required per sampling season for occupancy modeling; BADL was identified as a large-sized park, removal-in-time is used to estimate and RMBO surveyed the park following detection probability. the IMBCR design in 2011. The park was classified into two strata, targeting With the initiation of the monitoring grasslands and woody/shrubland habitat. phase of the program, NGPN and RMBO In 2011, RMBO technicians conducted developed pilot sampling designs for bird surveys at fifty 1 km2 grids (25 grids in several of the network’s parks. AGFO grasslands and 25 grids in shrubland; Birek has been the most intensively monitored et al., in preparation). network park for birds. Powell (2000) conducted a bird inventory there in 1999. The river parks (MNRR and NIOB) have Two years later, a pilot bird project was had various research projects conducted initiated to monitor avian communities on songbird populations. At MNRR, both at the park. As a result of this pilot work, breeding and migration studies have been Heartland Network completed a bird periodically conducted along parts of the monitoring protocol for AGFO in 2003 59-mile District of the park (Gentry et al. (Peitz et al. 2008). Birds were monitored 2006, Dean 2000, Benson and Dixon 2009, on a systematic grid from 2003 to 2006 Benson 2011). Only a few bird surveys have (Peitz 2007). In 2010, this monitoring been conducted along the NIOB (Brogie responsibility was transferred to the and Mossman 1983, Ducey 1989, Frost and NGPN. That same year, the network had Powell 2011). RMBO conduct repeated bird surveys at the same points established by Heartland 1.7.3. Sonoran Desert Network Network (Stenger et al. 2011). Because only one BBS route exists inside a SODN park (ORPI), the BBS During the inventory phase of the program, is insufficient to provide trend data for the NGPN had RMBO inventory breeding landbirds in SODN parks. Further, because birds at seven of the smaller parks from BBS routes are placed along roadways, 2002 through 2004 (Panjabi 2005). Due adding additional sites in parks would be to budget constraints, landbirds were insufficient for monitoring population not inventoried at the two river parks trends at the network or park scales, (NIOB and MNRR). Birds were also not both because of their limited inference inventoried at BADL, THRO, or SCBL, and because of limited road coverage in because Powell (2000) had conducted network parks. The only other national inventories of birds at these parks in 1999. bird monitoring program implemented
10 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN in SODN parks is the Monitoring Avian Christmas Bird Counts are conducted Productivity and Survivorship program on both the east and west side of Lake (Desante et al. 1995). The program was Meredith near LAMR. A few other SOPN recently discontinued in all parks except park units (e.g., FOLS and WABA) have TUMA, because of its inefficiency and lack counts in the general area (e.g., 20-40 miles of inference (Martinez and Hubbard 2003). away), but not in the immediate vicinity.
At a local scale, there was a comprehensive Current BBS routes pass through only effort to inventory birds at nine of the 11 one SOPN park (PECO). BBS routes are parks in the SODN between 2001 and 2005 located near (e.g., 10 miles away) four other (Albrecht et al. 2005; Powell et al. 2005a, parks— CAVO, CHIC, SAND, and WABA. 2005b, 2006a, 2006b, 2007, and 2008; Additionally, a discontinued route passed Schmidt et al. 2005, and 2007a) and in the through CAVO. Due to the lack of adequate two other parks that were not included in coverage in all SOPN parks and for the the recent effort: ORPI (Groschupf et al. reasons described in the SODN discussion 1988; Schmidt et al. 2007b) and MOCA above, these data are insufficient to provide (Sogge and Johnson 1995). Although trend data for landbirds in SOPN parks. efforts to monitor birds in ORPI have been ongoing, data quality from this 1.7.5. Conclusion effort is limited due to constrained spatial Given the insufficient data for determining extent, erratic sampling effort, and lack of long-term population trends from both standardization of the survey protocol (see national and park-based efforts, there is a Schmidt et al. 2007b). significant need to design a new program with appropriate inference and statistical 1.7.4. Southern Plains Network rigor. Such a program is especially critical Like the other networks, although a given population declines for some species considerable amount of bird work has been at both regional (Rosenberg et al. 1991) conducted in the SOPN, the high-quality and national (Robbins et al. 1989) scales. data required for effective conservation and The challenges in monitoring landbirds in management of landbirds is not available CHDN, NGPN, SODN, and SOPN parks, for SOPN parks. Although baseline then, are (1) to identify parameters that inventory projects were conducted at most provide reliable information on status and of the SOPN parks in the early 2000’s, few trend of populations at meaningful spatial or none of the studies were conducted for and temporal scales, and (2) to establish a long time periods or had sample designs sampling design that is both efficient and sufficiently rigorous for monitoring status informative. and trend. For example, at CAVO, the first thorough survey of breeding birds was conducted in grassland and pinyon- juniper habitats in 2002 by the New Mexico Natural Heritage Program.
Background 11
2. Program Goals and Measurable Objectives
Our monitoring program for landbirds time. Ideally, we would estimate population is intended to be hierarchical and size or abundance for all species in all parks complementary (Figure 2-1). NPS each year and compare these estimates over mandates to preserve native species would time, both within and among parks. Such a imply a need for species population-level goal, however, is seldom realistic as a result assessments, whereas broader goals of of the constraints described above. To meet maintaining ecosystem processes and these challenges, we have tried to design a diversity may require broader assessments program that maximizes the strength of our of communities and their environments. inferences within the context of our finite At the population level, distribution resources. Our program provides a multi- and abundance traditionally have been tiered, flexible framework that will enable mainstays of ecological assessment (e.g., efficient estimation and monitoring of Andrewartha and Birch 1954). Virtually population parameters, periodic evaluation all reliable estimators of abundance are of assumptions, and the opportunity to based on a count statistic divided by an adapt the program to meet additional estimate of detection probability (Seber needs. 1982; Williams et al. 2002). Unfortunately, obtaining sufficient sample sizes to estimate We have selected three parameters to population density can be expensive and monitor that are complementary and time consuming (MacKenzie et al. 2002) together provide a comprehensive due to numerous practical constraints, assessment of changing bird populations including the small size of many parks, and communities: density, occupancy, and diversity of ecological communities, the community dynamics. The parameters in amount of effort required to generate each of these focal areas have different precise estimates for small populations, and properties that vary in terms of the quality the large number of species that occur in of information gained and estimated cost CHDN, NGPN, SODN, and SOPN parks. of measurement. In a previous version of It is also not uncommon that the species this protocol (Powell et al. 2007a), relative of greatest concern are also the most abundance was also considered as a difficult species for which to obtain reliable reasonable parameter only when estimating estimates of abundance. abundance (i.e., density) was not feasible. However, our preliminary sampling, and The broad goal of the landbird monitoring similarly, sampling efforts in other networks program is to detect biologically significant (R. Bennetts, pers. obs.), have indicated changes in population parameters over that it will seldom be feasible to estimate
Figure 2.1. Our landbird monitoring program is hierarchical and complementary.
Program Goals and Measurable Objectives 13 density. A major concern of using relative and species composition. This approach abundance as an index to population also explicitly takes into account detection change is that comparisons over time and/ by treating individual bird species of a or space assume constant detectability community in much the same manner of the species in question. Variation in as mark-recapture treats individuals of a detectability, either among species or across population (Boulinier et al. 1998; Nichols time for the same species, can confound et al. 1998). Estimating community-based comparisons of trend estimates among parameters complements population species or preclude detection of a trend for level assessments and can be a very a single species (Lancia et al. 1996). Thus, important indicator for conservation and after careful consideration, we decided that management (Nichols et al. 1998). Changes concerns about reliability when deriving in species richness and/or composition can inferences about population change over provide indications of broader responses time were sufficiently severe to warrant to management than might be evident dropping the use of relative abundance from a single species. Bird species can also (without estimating detection probability) be assessed as functional groups that can in that context. be considered in the context of specific management concerns. Density is certainly a desirable parameter that facilitates estimates of total population Field methods for estimating all three change (e.g., number of individuals lost parameters will be the same; analyses and or gained) over time. These estimates evaluation procedures used to estimate are directly comparable among species, trends will differ. Our benchmarks for because interspecific variation in effect size, power, and Type I error rate are detectability is explicitly accounted similar to those recently presented in the for during estimation (Buckland et al. literature (Bart et al. 2004). 2001; Rosenstock et al. 2002). However, estimating density in SOPN parks, for As the properties of the parameters we example, is also problematic because of selected vary, so too will their relative the small size of most of the parks and the value for monitoring trends. Estimating number of detections required for reliable and monitoring density is a robust estimates. Thus, density will be estimated technique, yet precise estimates are often where feasible, but this will likely be for costly to generate and sometimes not only a few of the most common species in quantifiable at all, due to limitations in the larger parks. sample size (see Appendix A). Therefore, we will use a multi-tiered approach that Occupancy allows us to monitor changes in emphasizes density estimation where the proportion of sampling units occupied possible. Supplementing this approach with by a species over time (i.e., presence or estimates of occupancy and community absence) and accounts for imperfect dynamics will provide another scale of detectability during estimation (MacKenzie information for monitoring populations et al. 2002). As such, occupancy serves and communities that meet the appropriate as an indicator of changes in distribution assumptions. The rate of change that we and allows for explicit estimates of local will consider to be biologically significant extinction and colonization rates. For all will vary with the quality of the parameters parameters, when samples are drawn from estimated. Below, we briefly review the a larger population of potential samples three parameters and present a framework using a randomized design, inference will for achieving the stated objectives. be at the scale of the population considered for sampling (Thompson 2002). 2.1. Objective 1: Estimate occupancy The third area of focus entails estimating a We will estimate the proportion of sites suite of parameters relating to community occupied for most species in most parks, dynamics, particularly species richness withthe goal of detecting a 3% annual
14 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN decline over a 30-year period. (Note that Our next area of focus is estimating a constant 3% annual decline for 30 years parameters related to community dynamics, equals a 60% population decline.) particularly species richness and species composition. Monitoring the richness and Occupancy is a measure of presence or composition of native communities of absence of a species in space that, when concern and the changes occurring within evaluated across time, indicates changes and among these communities provides a in the distribution of a species. Like those valuable complement to population-based for density, techniques for estimating parameters. Species richness is an essential occupancy explicitly account for variation component of understanding the effects of in detectability, thereby adjusting estimates changing landscapes on native biodiversity. for individuals that are present yet Species composition helps us to understand undetected during surveys, a situation that the effects of management and other can confound the results of most wildlife changes by assessing which species are surveys (Williams et al. 2002). Recent or are not responding to changes in the advancements in occupancy theory and environment. modeling have provided sound justification of its application in monitoring programs 2.3. Objective 3: Estimate (MacKenzie et al. 2003; Field et al. 2005; density when feasible MacKenzie et al. 2006). We will monitor density of the most- common species with goal of detecting a As with any estimator, occupancy has 3% annual decline over a 30-year period. its limitations, including sensitivity (Note that a constant 3% annual decline for to violations of assumptions. 30 years equals a 60% population decline.) Misidentification of the species under consideration can affect the reliability of We will estimate density with the use of the all of our estimators, including occupancy point-transect distance-sampling method (Royle and Link 2006). As a result, we will at fixed points and subsequent analyses use caution when estimating occupancy for using the DISTANCE program (Thomas groups of species that are often confused et al. 2005). Provided that assumptions with others (e.g., some sparrows). Estimates are reasonably met, distance-sampling of occupancy also require an assumption methods allow researchers to model of closure (i.e., no movement in or out) of a detection function that adjusts for the population during the sampling period imperfect detectability. These methods are (MacKenzie et al. 2006). As such, we will robust and widely accepted for estimating constrain the sampling period for species abundance of landbirds (Buckland et al. that arrive early or late on the breeding 2001). With reasonable effort, we will likely grounds (e.g., Yellow-billed Cuckoo). be able to estimate density annually only for the most common species in larger To estimate changes in the proportion of parks (Appendix A). Annual estimates of survey points or sampling units occupied density for a few additional less-common by a species, we will use the same field species, or in parks where detection data methods as those for estimating density and are spatially limited, may be possible by relative abundance, and employ a multi- pooling data across parks and using “park” season model to derive annual estimates as a covariate when fitting the detection of distributional change (MacKenzie et function—an approach that will enable al. 2006). Recent advances in modeling comparisons among parks using a pooled occupancy can also provide the analytical estimate of variance. We may also pool data tools necessary for evaluating relationships across years and use “year” as a covariate between occupancy and abundance. to get annual estimates for species with few detections in any single year. We also will 2.2. Objective 2: Estimate bird explore the influence of other potential species richness and composition covariates on density across the network,
Program Goals and Measurable Objectives 15 such as variation among observers, timing, we will use data collected as part of our and environmental features. vegetation monitoring (NPS 2008). In addition to using vegetation and associated 2.4. Incorporation of vegetation parameters, we will use additional data monitoring to augment bird collected by each network and other sampling organizations (e.g., climate) as covariates It is well known that landbird populations when assessing population trends for birds. are particularly influenced by changes Finally, landbird population parameters, in vegetation structure and composition coupled with detailed environmental (Holmes and Sherry 2001; Krueper et information, can be used to build habitat- al. 2003). Environmental data, such as association models (e.g., Manley et al. vegetation, also allow us to aggregate (i.e., 2004) that can inform conservation efforts to stratify, post-hoc) survey sites that share and scientific inquiry throughout the similar characteristics. For this purpose, region.
16 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 3. Sampling
3.1. Overview Designs may also reflect different Our intention for monitoring landbirds approaches for estimating occupancy, again goes beyond the birds themselves, and depending on the circumstances within includes a broader vision of landbirds as each network. For most parks, especially indicators of the ecosystems they inhabit. the smaller ones, the potential for multiple This dual purpose influences our sampling transects or grids is very limited. Often, a design, especially in light of our funding single grid may cover the entire park. In and logistic limitations. In some cases such cases, occupancy is estimated for tradeoffs have been made to accommodate the proportion of point locations that a particular habitat types or park resources given species may occupy. For these units, that are considered particularly important replication is achieved temporally via to a given park. For example, if grasslands repeated visits. were deemed particularly important to a park, we might forgo sampling in a habitat For several larger parks, particularly those type of lesser concern in order to have that were incorporated into this project stronger inference for those grasslands. later, we can use a newer approach that was more recently developed by Nichols et al. We will survey for landbirds at network (2008) and applied to a hierarchal sampling parks. In general, we will survey areas design for monitoring bird populations that are accessible and present no safety by Pavlacky et al. (2012). This approach problems. The sampling frame for larger is based on spatial, rather than temporal, parks may be more restricted because replication. Thus each transect or grid is of topography and difficulty of access to visited only once. remote areas. References to the sampling frame throughout this document further 3.1.1.1. Primary Spatial Designs clarify the scale of inference. All safe and We use two primary spatial designs, and accessible areas of the target strata will be in some cases we also use non-random included in the sampling frame. sentinel sites when there is a specific site of interest or concern at a given park. The 3.1.1. Spatial sampling designs two primary spatial designs we use are Our overall sampling designs reflect the Stratified Random Sampling and Spatially- needs and situation within each network Balanced Surveys, which are implemented and its parks, and specific elements for each using Generalized Random-Tessellation network are described in more detail below. Stratifiedsampling (GRTS) or Reversed However, the overall approach used by all Randomized Quadrant-Recursive Raster four networks is based primarily on point (RRQRR) (described below). Spatially surveys. Points within a given survey can balanced sampling is a relatively new be arranged in a linear transect or a grid, approach for survey designs that enables depending on the specific circumstances. the spatial sample to reflect the spatial Generally surveys associated with linear patterns of the population, thus leading to features (e.g., riparian areas along a river) increased efficiency in sampling and often are sampled via a linear transect, whereas a lower variance. RRQRR and GRTS are surveys in areal landscapes are typically probability-based sampling schemes that surveyed via a grid. There are however generate survey designs that are spatially exceptions. For example, riparian areas well-balanced (Stevens and Olsen 2004; that are not strictly linear (e.g., a flood Theobald et al. 2007). plain) may be surveyed via a grid. Similarly, many areal surveys in the Sonoran Desert Stratified Random Sampling - In a stratified are surveyed via transect associated with random sample the sampling frame is elevation. divided into mutually exclusive and exhaustive strata, from which n samples are randomly selected from each stratum
Sampling Design 17 (Levy and Lemeshow 1999). There are grasslands and riparian habitats; thus their several reasons for using a stratified approach to birds reflects that designation. sampling design, including increased precision, increased efficiency, and In cases where innately similar sampling greater information about a particular units are considered together as a stratum, subpopulation (Cochran 1977, Lohr 1999). this sampling design increases sampling For increased precision, strata are typically efficiency and precision (Thompson 2002). selected such that the variation among units from the same strata is less than the It is often not recommended to base variation among units from different strata stratification on features that can be (Thompson 2002). dynamic. However, we based our designations on major habitat types (e.g., Generalized Random-Tessellation Stratified grasslands and riparian), which generally (GRTS) – Details of the GRTS design and reflect the underlying soils and ecological how it works to achieve spatial balance sites, rather than the seral stages of those are provided by Stevens and Olsen types which may be more dynamic in (2004). Essentially, the GRTS design uses response to disturbance. Such seral stage a hierarchical randomization process to dynamics are not only unavoidable, but achieve spatial balance across the region they also represent a component of the and the resource. A grid is randomly systems that we are trying to assess in our overlaid on the sample frame and monitoring. subdivided until there is only one sample unit per cell. Cell addresses are assigned via 3.2. Chihuahuan Desert Network a hierarchical random process, and each Chihuahuan Desert Network parks were sample unit is assigned to its corresponding stratified based on two habitat classes: cell address, creating a linear sequence of grassland and riparian. Although additional sample unit cell addresses. By reversing habitat types exist within CHDN parks, we the order of address digits and re-sorting are interested in changes in grassland and this sequence, a systematic sample can riparian bird communities for this protocol. be drawn with a random start point that As mentioned previously, using vegetation maintains the spatial balance of the sample. types as the stratification variable can be problematic because the dynamic nature Reversed Randomized Quadrant-Recursive of vegetation can result in a shifting Raster (RRQRR) – This approach to population for which inference becomes sampling is similar to and partially based nebulous. However, for the purposes of on GRTS. RRQRR is based on hierarchical this protocol, we are interested in changes quadrant-recursive ordering and is in grassland and riparian community implemented in a geographic information classes, which include both the current system (Theobold et al. 2007). While and potential state. As such, our sample GRTS is feature based, RRQRR uses a frame is somewhat analogous to an ecotype raster to approximate a continuous space. based on a combination of habitat types, Additional details on RRQRR are provided soils, and general land form such that it in Theobald et al. (2007). includes existing grassland or riparian habitat types, as well as areas that have a 3.1.1.2. Stratification reasonable potential for such types either Stratification generally mirrored the through management or natural processes. approach to monitoring habitats for Depending on park size, CHDN used a given network. For example, the two approaches to allocate survey point SODN developed protocols to monitor locations (Table 3.2-1). riparian and upland habitats; thus, their stratification for bird monitoring reflects 3.2.1. Grassland sampling sites those same designations. In contrast, The sampling frame is defined as the SOPN developed protocols for monitoring grassland community within each park (all CHDN parks contain grasslands). For most
18 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Table 3.2-1. Spatial sampling frame and methods used to designate landbird monitoring sites at Chihuahuan Desert Network parks. Park Strata Sample frame Method AMIS Grassland All Grasslands SBS (GRTS) Riparian Rio Grande River SBS (GRTS) BIBE Grassland All Grasslands SBS (GRTS) Riparian Rio Grande River, Terlingua Creek SBS (GRTS) CAVE Grassland All Grasslands SBS (GRTS) Riparian Rattlesnake Springs SBS (GRTS) FODA Grassland All Grasslands Systematic grid GUMO Grassland All Grasslands SBS (GRTS) Riparian McKittrick Canyon SBS (GRTS) RIGR Riparian Rio Grande River within BIBE SBS (GRTS) WHSA Grassland All Grasslands SBS (GRTS) See Appendix C for maps of designated sites SBS = Spatialy Balanced Survey GRTS = Generalized Random-Tessellation Stratified
CHDN parks, a 1 km2 grid was overlaid sampling frame, excluding areas that are on the sample frame. The centroid of each unsafe due to steep cliffs or have restricted grid cell was used as the sampling frame access for security reasons. The exception for GRTS, run in R software using the to this is that in BADL, only the North Unit spsurvey library. Within each grid selected will be sampled. by the GRTS algorithm, 16 points spaced 250 m apart were laid out in a grid pattern. RMBO conducted a bird survey at BADL In comparison to the other CHDN parks, (North Unit) in 2011. Two strata were FODA is quite small (474 acres). Therefore, established and were based on alluvial 26 sample points were placed in a grid (shrubland) and non-alluvial (grassland) (spaced 250 m apart) to provide coverage soils. Post analysis of these data indicated of the entire Site. that very few species had robust density or occupancy estimates in only one stratum. 3.2.2. Riparian sampling sites All of these species, however, were detected All CHDN parks except for FODA in the other stratum, just in low numbers. and WHSA have riparian vegetation Given these results and the limited funding, communities. The riparian sampling frame we decided not to stratify the NGPN parks for AMIS consisted of the Rio Grande for future monitoring efforts (Table 3.3-1). River upstream of the Amistad Reservoir. Furthermore, we can also post-stratify the At BIBE, the riparian sampling frame data from a park if it is warranted. consisted of the Rio Grande River and Terlingua Creek. The sampling frames for 3.3.1. Integrated Monitoring CAVE and GUMO were constrained to in Bird Conservation Regions Rattlenake Springs and McKittrick Canyon, Sampling Design respectively. Riparian transects were The NGPN will implement the sampling selected using the GRTS algorithm in the design developed for a regional bird spsurvey library for R software. Sampling monitoring program, “Integrated points within a transect are spaced 250 m Monitoring in Bird Conservation Regions” apart. (IMBCR; White et al. 2012), at 12 of 13 network parks. The multi-scale IMBCR 3.3. Northern Great Plains design consists of nested strata, with each Network individual stratum (i.e., park) producing For all Northern Great Plains Network its own estimates of species densities and parks, the entire area is included in the occupancy rates, as well as contributing
Sampling Design 19 Table 3.3-1. Spatial sampling frame and methods used to designate landbird monitoring sites at Northern Great Plains Desert Network parks. Park Stratification Sample frame Method AGFO none Whole park IMBCR (GRTS) BADL none North unit IMBCR (GRTS) DETO none Whole park IMBCR (GRTS) FOLA none Whole park IMBCR (GRTS) FOUS none Whole park Systematic grid JECA none Whole park IMBCR (GRTS) KNRI none Whole park IMBCR (GRTS) MNRR none Whole park IMBCR (GRTS) MORU none Whole park IMBCR (GRTS) NIOB none Whole park IMBCR (GRTS) SCBL none Whole park IMBCR (GRTS) THRO none Whole park IMBCR (GRTS) WICA none Whole park IMBCR (GRTS) See Appendix C for maps of designated sites IMBCR = Integrated Monitoring in Bird Conservation Regions GRTS = Generalized Random-Tessellation Stratified
to estimates for higher order strata such (Table 3.3-1). Occupancy estimates for as states and Bird Conservation Regions. FOUS will be based on a revisit schedule The IMBCR design defines the sampling of three visits to each point during the unit as a 1-km2 area, containing 16 evenly- breeding season. Occupancy rates in this spaced sample points with 250-meter case apply to the sampling point. spacing between points. This layout allows for estimating occupancy rates at the scale 3.4. Sonoran Desert Network of the 1-km2 cell, as well as the conditional Sonoran Desert Network was one of the probability of occupancy at the scale of the first to initiate bird monitoring, and thus sampling point (Pavlacky et al. 2012). used the more traditional occupancy approach. For most parks, SODN stratified Network parks were classified as either parks by mesic riparian or upland sites. large (> 29,000 acres) or small (< 4,000 They determined that a stratified sampling acres). Depending on the level of funding design was warranted because of the well- available for the landbird vital sign, large known and extreme differences in bird parks will have between 15 and 25 IMBCR communities between riparian and upland grids sampled, whereas the small parks areas. will have between 3 and 6 IMBCR grids sampled. Spatially-balanced samples will Sonoran Desert Network also used a be selected using a Generalized Random- variety of approaches to assign the location Tessellation Stratified (GRTS) sampling of survey points (Table 3.4-1). For many algorithm within each park. The centroid mesic riparian areas and upland sites, they of each grid cell will be used as the cell chose to use sites that were established address for each sampling unit. during the bird inventory work from 2001–2005 (see Introduction) if such an 3.3.2. Systematic Grid Design approach was prudent. Fort Union Trading Post NHS, with its 444 acres, is too small to have the required 3.4.1. Riparian sampling sites number of 1-km2 grid cells fit within its All SODN parks except for CAGR, ORPI, boundary. Thus, FOUS will be sampled and SAGU’s Tucson Mountain District using a uniform grid of points established (SAGU-TMD) have mesic riparian across the park with the points 250 m apart vegetation communities. Because mesic
20 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN communities in the other parks are spatially Sampling Toolbox in ArcGIS 9.0 (http:// restricted, they can be surveyed without www.nrel.colostate.edu/projects/starmap/ the need for sampling. Therefore, given the rrqrr_index.htm). In RMD, the park was limited spatial extent of riparian resources stratified further into three elevation strata and their importance for bird conservation, (<4,000’, 4,000-6,000’, and >6,000’) because we chose a spatial design that would result of significant differences among bird in complete coverage of the most significant communities along elevational gradients riparian resources. In many cases, these there (Powell et al. 2006b). Sites were sites were established during the biological independently allocated to each of these inventories of 2001–2005 (e.g., Powell et al. elevation strata. 2006a, b). Maps in Appendix C depict the sample locations. Organ Pipe Cactus NM was the only large park not inventoried in 2001–2005, 3.4.2. Upland sampling sites and it therefore had no pre-existing Upland sampling points were allocated sampling locations. Accessibility is not using two different approaches. In smaller equal across the monument; some areas parks, where we could achieve complete or are difficult to reach because of complex nearly complete coverage using the design topography or simply because of distance from the biological inventories, those from a trailhead or road. At ORPI, we points were used. In smaller parks where used a spatially-balanced sampling design no inventories were conducted, such as at and employed a continuous surface MOCA, all upland areas were given equal representing accessibility to represent probability of being selected as a sampling inclusion probabilities (i.e., areas closer to location. Samples were randomly allocated trailheads and easier to access were given using the “Generate Random Points” a higher probability of being selected as function in the Hawth’s Tools extension sites than areas far from trailheads and in ArcGIS 9.0 (http://www.spatialecology. requiring a difficult hike). Some portions com/htools/index.php) with the constraint of the monument were excluded from that points be ≥250 m apart. Daily surveys the sampling frame altogether because it consist of a single observer visiting as many would have been unreasonable to expect points as possible during the morning an observer to reach them before dawn, survey window. Points that can be surveyed as bird sampling requires. We will have together in a single morning are considered no inference to areas not included in the a group (but note that this designation is sampling frame. Sampling locations were one of convenience; points within groups generated using the RRQRR algorithm were independently placed, except along (Theobald et al. 2007) in the “spatially riparian areas and in those situations where balanced sample” function in the upland sites were placed non-randomly for STARMAP Spatial Sampling Toolbox in the inventories). ArcGIS 9.0 (http://www.nrel.colostate.edu/ projects/starmap/rrqrr_index.htm). At SAGU, we used the upland sites from the inventory effort (see Powell et al. 2006b) 3.5. Southern Plains Network because they were established using a In the SOPN, we sampled primarily in two stratified random design (Rincon Mountain habitat classes: grassland and riparian, District; RMD) and simple random design which are the dominant vegetation (Tucson Mountain District; TMD). This communities within the network. sampling design provided good dispersion Using vegetation types alone as a basis of sampling locations and broad geographic for defining the sample frame can be coverage. In the RMD, we eliminated two problematic if the intended scope of low-elevation sites because of accessibility inference is broader than the vegetation issues. Therefore, we added two new sites classes themselves. In such cases, the using the RRQRR algorithm (Theobald et dynamic nature of vegetation can result in al. 2007) by using the “spatially balanced a shifting population for which inference sample” function in the STARMAP Spatial becomes nebulous. However, for the
Sampling Design 21 Table 3.4-1. Spatial sampling frame and methods used to designate landbird monitoring sites at Sonoran Desert Network parks. Park Strata Sample frame Method CAGR Upland Entire park (excluding 100 m surrounding SRS cultural features) CHIR Upland Extraction from cost surface; 100 m buffer SRS around trails Riparian Single riparian area Non-random, from biological inventories CORO Upland Eastern portion of park SRS Riparian Single riparian area Non-random, from biological inventories FOBO Upland Remainder of park (excluding circle in eastern SRS* portion) Riparian Single riparian area Non-random, from biological inventories GICL Upland Remainder of park SRS* Riparian Single riparian area Non-random, from biological inventories MOCA (castle unit) Upland Remainder of park SRS Riparian Single riparian area Complete coverage via two transects MOCA (well unit) Upland Remainder of park (excluding well unit) SRS Riparian Single riparian area Complete coverage via one transect ORPI Upland Cost surface calculated for areas within 2 km RRQRR of roads SAGU–RMD Upland Extraction from cost surface; 3 elevation classes RRQRR Riparian Single riparian area Non-random, from biological inventories SAGU–TMD Upland Extraction from cost surface RRQRR TONT Upland Northeastern portion of park SRS Riparian Single riparian area Non-random, from biological inventories TUMA Upland Entire park, split into agricultural and mesquite SRS* bosque Riparian Riparian treated as an area feature SRS* TUZI Upland Remainder of park excluding eastern portions SRS Riparian Single riparian area Non-random, from biological inventories *generated in 2005 See Appendix C for maps of designated sites SRS = Stratified Random Sampling RRQRR = Reversed Randomized Quadrant-Recursive Raster
purposes of this protocol, we are interested to allocate survey point locations (Table in changes in grassland and riparian 3.5-1). community classes, which include both the current and potential state. As such, our 3.5.1. Grassland sampling sites sample frame is somewhat analogous to an Virtually all SOPN parks have grassland ecotype based on a combination of habitat communities, ranging from shortgrass types, soils, and general land form such that to more mixed-grass types. Thus, the it includes existing grassland or riparian sampling frame for this protocol is defined habitat types as well as areas that have a in terms of mapped habitat types using the reasonable potential for such types either criteria described above. A few areas are through management or natural processes. excluded from the sample frame because of For example, some pinyon-juniper the potential for the continuation of high savannahs (CAVO) or honey mesquite disturbance on slopes >35%. shrublands (LAMR, ALFL) are included in the sample frame, even though the current 3.5.2. Riparian sampling sites habitat type may not be strictly considered All SOPN parks except for CAVO a grassland. Two approaches were used and FOUN have riparian vegetation communities that are sampled. Capulin
22 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Volcano has no surface water and no indicator of habitat condition, we decided riparian sites at all, whereas Fort Union has that an impeded ability to distinguish one small riparian area that was too small to environmental variation was not worth effectively sample for bird communities. the limited gains in sampling efficiency from alternative revisit designs. Further, Maps in Appendix C depict the sample USGS staff at Patuxent Wildlife Research locations. Center conducted a small simulation using an occupancy estimator (one of our 3.6. Temporal sampling design primary approaches) that indicated a loss 3.6.1. Annual surveys of precision for most parameters under a Our overall sampling schedule is that sites rotating panel design with alternating year will be sampled every year. We considered revisits, compared to sampling every year alternatives (e.g., rotating and/or split panel (Larissa Bailey and James Nichols, pers. designs), where a subset of sites are visited comm.). every year and another subset is revisited at a longer revisit interval (e.g., every other 3.6.2. Intra-annual surveys year). We decided against this approach When we survey a park, we will visit all sites as a general design, because birds exhibit one to three times during each breeding substantial annual variation which would season. For some large parks, we used a likely result in temporal and spatial effects hierarchical sampling design (Pavlacky et being confounded. Consequently, it could al. 2012), which uses spatial rather than be difficult to tease apart the effects of temporal replication; consequently, only 1 some annual environmental variation of survey is conducted for each grid at those interest. Given that part of our intention parks. For the remainder of the parks in monitoring birds is that they are an (mostly small parks), we tried to balance
Table 3.5-1. Spatial sampling frame and methods used to designate landbird monitoring sites at the Southern Plains Network parks. Park Strata Sample frame Method BEOL Grassland All Grasslands SRS Riparian All Riparian Area (including Cottonwood SRS Bottoms) CAVO Grassland All Grasslands RSL Piñon-Juniper Piñon-Juniper targeted for restoration/ RSL conversion to grasslands CHIC Grassland All Grasslands SRS Riparian All Riparian Area SRS FOLS Grassland All Grasslands SRS Riparian All Riparian Area SRS FOUN Grassland All Grasslands RSL LAMR/ALFL Grassland All Grasslands SRS Riparian All Riparian Area SRS LYJO Grassland All Grasslands RSL Riparian All Riparian Area RSL PECO Grassland All Grasslands SRS Riparian All Riparian Area SRS SAND Grassland All Grasslands SRS Riparian All Riparian Area SRS WABA Grassland All Grasslands RSL Riparian All Riparian Area RSL See Appendix C for maps of designated sites SRS = Stratified Random Sampling RSL = Randomized Start Location for grids that cover the entire sample frame
Sampling Design 23 the number of grids and the number of park) variance estimates and detection visits, and each of those are visited 2-3 functions. However, this was based on times. Replicating either by space or time a goal of detecting a 2% rather than 3% throughout the core breeding season change. Thus, our estimates are likely to ensures a greater chance of detecting be conservative. We would also add that all breeding birds. Visiting sites two or we would gain even more efficiency by more times allows for more detections for pooling among years as they accumulate. estimating density; models used to estimate Pooling data to gain efficiency in estimating density are not biased by counting the same detection probability is a strategy that we bird on consecutive counts (Buckland et al. anticipate for all of our estimates and is not 2001). limited to distance sampling.
3.7. Level of Change Detection For occupancy estimation, the RMBO It is important to recognize that our goals (White et al. 2010) estimated that they for detecting change in bird populations are would be able to meet a goal of detecting quite modest, but probably realistic. It is a 3% change over 30 years for most also important to recognize that our ability species. Since NGPN and CHDN have to achieve these goals is quite variable incorporated this design for most of among locations, designs, and individual their parks, we believe it is reasonable species. For some locations (e.g., in the to assume that we will likewise meet or NGPN and CHDN networks) with larger exceed this expectation. Based on some parks and using hierarchical multi-scale preliminary analyses of occupancy using designs (Pavlakey et al. 2012), these goals the more tradition approach (MacKenzie are likely to be easily met and probably et al. 2003, MacKenzie et al. 2006) we exceeded for most species. However, this believe that will also have the ability to design is based on spatial replicates of a detect modest changes for many species 1 km grid. For our smaller parks (e.g., in at larger parks, but only major changes at SODN and SOPN) a single 1 km grid may smaller parks. We will continue to evaluate cover the entire park; thus, it is not feasible our effectiveness and to explore possible to attain even one replicate, let alone the approaches for improving our estimates. recommendation of 10 (citation). This For example, in smaller parks, we will design was also developed recently well further explore pooling information over after the establishment of transects or grids space and time, stratification for improving in the larger parks of SODN or SOPN. within strata precision, and possible Thus, a decision to adopt this design would even pooling among functional groups require abandoning the existing data (in (e.g., guilds) that may have management some cases several years) for larger parks implications. The results and any changes for what might amount to a small gain given from these efforts will be incorporated into that these larger parks already have higher future revisions of this protocol. sample sizes than the smaller ones. Thus, while we will likely never be able to For distance sampling, a detailed analysis achieve sufficient power in our small parks of statistical power (Appendix A) was to reliably estimate small changes in bird conducted for the SODN parks in their populations over time, our preliminary protocol (Powell et al. 2007a) from assessment is that we should be able to which this one was initially based. They detect modest changes with reasonable concluded that they would be able precision in larger parks. For smaller to estimate density for only the more parks, we will likely be able to detect major common species but that they would be changes for many species, and would rely able to increase the number of species on more regional analyses for inferences to at each park by using pooled (among management.
24 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 4. Field Methods
4.1. Field-season preparation variable circular plot method (Reynolds and scheduling et al. 1980), and is the most widespread Preparation for the field season begins and accepted method for estimating several months before conducting the landbird density (Rosenstock et al. 2002). actual surveys. Confirming sites, preparing The method involves estimating the maps, and contacting the parks all must be linear distance to individual birds while done well in advance of the field season, standing for a predetermined period of the tasks of which are detailed in SOP #1. A time at a fixed point in space. Estimating general annual project schedule is provided the distance to each bird allows density in Chapter 8, and a summary of the major to be approximated via a species-specific preseason planning and preparation tasks detection function that accounts for are shown below in Figure 4.1-1. variation in detectability due to surveyor, environmental, or weather-related factors Prior to each field season, observers will (Buckland et al. 2001; Diefenbach et read the entire protocol narrative and all al. 2003). This method contrasts with standard operating procedures (SOPs). traditional point-count techniques (Ralph Observers should pay special attention et al. 1995) because it does not assume that to the tasks described in SOPs #1, #3, #4, all birds are detected equally within a fixed and #5. Practice and training in identifying radius around survey points (Lancia et al. birds by sight and sound is critical; the 1996). misidentification of a species is the most serious error one can make during counts Our use of distance sampling will provide (Kepler and Scott 1981; Royle and Link data that are comparable both within 2006). All equipment and supplies listed and among parks, and will also facilitate in SOP #1 should be organized and made comparisons with data from other ready for the field season. locations in North America where the same survey methods are used (e.g., Peitz et al. Weather-related interruptions during 2004). We also will be able to compare our scheduled surveys may occur in any of the results to other monitoring efforts such as network parks. Therefore, unpredictable the BBS. Finally, data from our program weather, as well as staff workloads, will be made available to researchers for requires maintaining flexibility in a variety of purposes, such as modeling scheduling the sequence and duration of bird–habitat associations that may be sampling events during the field season. useful for identifying bird communities or environmental features of conservation 4.2. Overview of point-transect or management concern (e.g., Wiens and surveys Rotenberry 1981; Rice et al. 1984; Strong We will use the point-transect survey and Bock 1990). method to estimate and monitor landbird population parameters (Nelson and 4.3. Seasonal timing of surveys Fancy 1999; Buckland et al. 2001). The We will focus our survey efforts during point-transect method is similar to the the breeding season, when increased
Review Prior Season Notes for Any Needed Field Training Changes to Protocol or Sites Prepare Maps Prepare Equipment, etc. 2-3 Months Conduct Surveys Prior to Field Season Provide Parks with Notify Park Contact Preliminary Survey of your speci c Schedules Survey Plans
Figure 4.1-1 Major preseason planning and preparation tasks.
Field Methods 25 territorial behavior by songbirds results sparrows) typically breed after the start in higher detection rates and greater of the summer monsoon season (typically sampling efficiency. We also will count early July), though they usually establish passage migrants (those that breed north territories earlier. of the region; e.g., Wilson’s Warblers) and those species that overwinter but do not Surveys will coincide with the breeding breed in the parks (e.g., in SODN, Brewer’s season for the greatest number of species Sparrow and Green-tailed Towhee) (Rice in each park, recognizing that some species et al. 1983; Hutto 1985b). In contrast to may not be adequately surveyed because parks at more northern latitudes that have of later arrival dates (e.g., the Yellow-billed a fairly synchronous breeding season Cuckoo in SODN). Trend analyses for all (McIntyre et al. 2004), the breeding season species must consider whether a species in the the southern three networks varies is present during the chosen sampling considerably among species (Corman and window, and adjust this window to meet Wise-Gervais 2005), a phenomenon typical program goals. For example, a review of of more southerly latitudes. Resident birds the landbird monitoring program in ORPI at low elevations in the region typically found that the first sampling event of each breed from late February through mid- year was too early for many migrant species June, whereas those at higher elevations to be detected (B. Powell, unpublished breed from early April through June or data). For this monitoring effort, we will early July. Grassland-obligate sparrows attempt to time surveys similarly each year, (Cassin’s, Botteri’s, and Grasshopper because a shift in survey timing could bias
Table 4.3-1. Annual field schedule for bird monitoring at Chihuahuan Desert Network parks (with some variation from year to year). Apr Apr May May June June Park 1–14 15–30 1–14 15–31 1–14 15–30 Amistad National Recreation Area X X Big Bend National Park X X Carlsbad Caverns National Park X X Fort Davis National Historic Site X X Guadalupe Mountains National Park X X White Sands National Monument X
Table 4.3-2. Annual field schedule for bird monitoring at Northern Great Plains Network parks. Apr Apr May May June June Park 1–14 15–30 1–14 15–31 1–14 15–30 Agate Fossil Beds National Monument X X Badlands National Park X Devils Tower National Monument X X Fort Laramie National Historical Site X X Fort Union Trading Post National Historical Site X X Jewel Cave National Monument X X Knife River Indian Villages National Historical Site X X Missouri National Recreational River X X Mount Rushmore National Memorial X X Niobrara National Scenic River X X Scotts Bluff National Monument X X Theodore Roosevelt National Park X X Wind Cave National Park X X
26 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN trend estimates. To ensure that surveys are The daily field schedule for each surveyor clustered around the time of peak breeding will involve visiting 6–19 points each day, activity for each park and community, arranged in transects or grids or groups we will survey annually according to the of individually placed points. The bird general field schedules in Tables 4.3-1 to sampling crew should arrive at the park 4.3-4, which are based on annual timing of or survey area on the afternoon prior to breeding within each park. It is important the first day of sampling to familiarize to recognize that this window may shift in themselves with the area and the birds the coming decades, making it necessary present. If the area being surveyed is too to adjust the protocol (Visser et al. 1998; far from a road, campsite, or trailhead to Brown et al. 1999). be reached in 30 minutes of hiking, every effort should be made to camp near the site 4.4. Field protocol on the night prior to scheduled surveys. Point-transect surveys will be the primary Bird surveys will begin approximately field method and will make up the bulk of 30 minutes before local sunrise. More the effort during the field season.
Table 4.3-3. Annual field schedule for bird monitoring at Sonoran Desert Network parks (with some variation from year to year). Apr Apr May May June June Park 1–14 15–30 1–14 15–31 1–14 15–30 Casa Grande Ruins National Monument X Chiricahua National Monument X X Coronado National Memorial X X Fort Bowie National Historic Site X X Gila Cliff Dwellings National Monument X X Montezuma Castle National Monument X X Organ Pipe Cactus National Monument X X Saguaro National Park X X X X X Tonto National Monument X X X Tumacácori National Historical Park X X Tuzigoot National Monument X X
Table 4.3-4. Annual field schedule for bird monitoring at Southern Plains Network parks (with some variation from year to year). Apr Apr May May June June Park 1–14 15–30 1–14 15–31 1–14 15–30 Bent’s Old Fort National Historic Site X X Capulin Volcano National Monument X X Chicasaw National Recreation Area X X Fort Larned National Historic Site X Fort Union National Monument X Lake Meredith National Recreation Area X X Lyndon B. Johnson National Historical Park X Pecos National Historical Park X X Sand Creek Massacre National Historic Site X X Washita Battlefield National Historic Site X X
Field Methods 27 information on the bird survey protocol point, allows for a reduction in potential can be found in SOP #4. violations of the assumption of perfect
detectability at the center point (i.e., P0 = For bird surveys, observers should 1), an important assumption of distance review data forms for completeness and sampling (Buckland et al. 2001). The readability prior to leaving the field each most important birds to detect are those day. The project manager is responsible very close to the observer (within 20 m of for the safekeeping and organization of the point), and it is highly desirable that data sheets, and for ensuring that data are estimated distances be within 1 to 2 m entered into the database. of actual distances for all birds detected within this radius. 4.5. Conducting the bird surveys Details on how to conduct point-transect The radial distance from the point to each surveys, and for filling in data forms, are bird detected during a count is measured provided in SOP #4 and summarized with a laser rangefinder. More information here. All birds seen or heard at each point on measuring distances can be found in are recorded during a 6-minute sampling SOP #4. period. We will separate bird observations into minute-long time segments to allow Once a point count is completed, observers comparisons with BBS and other survey locate successive points with the use of efforts, and possibly for use in other a GPS. While walking from one point to analytical applications such as removal the next, observers record species not sampling (e.g., Farnsworth et al. 2002, yet detected on the transect or that are Pavlacky et al. 2012). We will record all not currently on the species list for the birds, regardless of detection distance from park being surveyed. These species are the surveyor. For most species, we will considered “incidental” observations and record each individual bird as a separate should be separately. These data allow for a observation unless groups of birds are more complete record of species at a site. observed at the same distance, in which case the flock or group size will be noted. 4.6. Establishing and marking We will record any bird flying over a point points without showing any signs of using the We will coordinate with the Superintendant surrounding habitat as a “flyover,” with of each park to ensure that the method a designation of “F” in the how detected used to physically mark each point/transect category. is in accordance with park protocol for such activities. When conducting a point-transect survey, observers attempt to obtain an 4.7. Park and network bird lists “instantaneous count” of the birds present. In the spring of each year, each network The count is started as soon as the observer will provide RMBO an updated list of bird arrives at the point. For birds that flush species for each park. In the fall, after the close to a point upon approach by the survey season, RMBO will provide each observer, a note is made of the bird’s network lists of bird species detected in distance from the point before it flushed. If, each park during surveys or incidentally, during the count, a bird that flushed from including species that are documented in the area around a point upon the observers a park for the first time. After receiving arrival is detected, the bird’s original the RMBO survey results, each network distance from the point is recorded. It will update its network and park lists in is assumed that these birds would have coordination with the parks. These updated remained at their original locations were lists will then be provided to RMBO it not for the disturbance created by the again in the spring, prior to the year’s observer. sampling season. This effort will ensure that all parties are operating from the same, Recording birds that are flushed, current park/network species lists. particularly those close to the center
28 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 5. Data Management
5.1. Overview of database QC) mechanisms. RMBO developed a design data entry protocol that accompanies the Data will be stored in a Search and Query database (SOP #5). The project manager Language (SQL) Server database to manage should ensure that data-entry personnel the landbird community and associated have read this user guide and understand environmental data. This database has how to use the database and follow the been developed by RMBO and is used to protocols. house data collected for the I&M networks participating in this protocol. This database Data-entry is done via a web-based front uses a web-based front-end program for end, with Firefox as the recommended data entry and editing. browser. Data can be entered off-line and then uploaded to the database at a The general data model for bird- later time. Location and sampling event community monitoring consists of core information are entered first. Then, tables and two principal groups of field- associated bird or environmental data data tables. One group of tables manages may be entered into the targeted tables. bird detection data; the other, associated Where appropriate, “pick lists” limit values environmental data. Detection and entered into a field to ensure that only valid environmental data are linked in time and names or measures are entered. Bird or space using standardized location and environmental values are selected directly event tables that are shared with other I&M from a “pick list” or by typing the first few protocols. When correctly linked, the bird- characters of the value. The form searches detection and environmental data may be for a similar entry, typically locating the transparently integrated for analysis. desired value after typing only a few characters. The primary table for storing bird- detection data contains observation 5.3. Data certification process information such as species, distance from Data verification is the process of checking observer, how detected, sex, and flock size. the accuracy of the digital data against Supporting tables include count dates, copies of the original paper data sheets, count start and end times, and sampling and it should immediately follow data conditions (temperature, wind, sky). In entry. To minimize transcription errors addition, NPS monitors vegetation and in the final dataset, personnel familiar maintains databases for most of its park with the project’s field and data collection units. These data can be used where methods should verify 100% of the records applicable as supporting data look-up to the source documents. In 2012, RMBO tables to be incorporated as environmental developed a protocol for full proofing all covariates for analyses. Species, contacts, field data collected. An environmental and attribute look-up tables provide characteristics subplot table for IMBCR- standardized values for many data fields, based surveys contains general vegetative and metadata tables track database cover information and serves as the revisions and data edits. focal table to which other environmental characteristics tables, such as dominant 5.2. Data entry overstory and understory cover, are linked Although the primary goal of data entry is (SOP #5). After any required corrections to transcribe all data from paper records have been made in the database, RMBO into the database with 100% accuracy, will perform query proofing to catch errors this target is rarely achieved. To facilitate that are not caught during the full proof data-entry accuracy, and to eliminate processs. This involves running queries as many potential data-entry errors as looking for commonly made errors in three possible, the database has built into it many sections of the database: quality assurance/quality control (QA/
Data Management 29 1. Transect information (start and end spatial data. Both spatial and non-spatial times and sampling conditions), metadata records for non-sensitive data 2. Point data (point start times and rea- will be uploaded to the RMBO Avian sons for not completing points), and Data Center (http://adc.rmbo.org/) and 3. Bird data (distances, cluster IDs, how- the Integrated Resource Management detected codes, invalid minutes, incor- Application (IRMA) (https://irma.nps.gov) rect minute counts, and invalid no-bird where they will be available to the public. records). All metadata records will be updated as needed whenever additional data are Program managers conduct uncommon collected and added to the database. bird proofing by looking for birds misidentified in the field and four-letter 5.5. Data archival procedure species codes entered into the database After all data for a field season have been incorrectly, and to confirm legitimate entered, verified, validated, and certified, detections of rare birds or species not the database will be archived on a secure previously known to occur on a park. server with regularly scheduled back-ups. Original data sheets will be archived with Data certification is the process of ensuring no changes made. All changes made during that the dataset (i.e., portion of the database proofing will be to the digital data only with related to all of the records for that a change log recorded to a spreadsheet year) has been verified and validated for saved to a secure server, and the changes accuracy, is complete, and that metadata/ will be made available to network data documentation for the dataset is finalized. managers. Any editing of archived data Data certification will be completed after will be accomplished jointly by the project each field season for all tabular and spatial manager and data manager. project datasets. The project manager will complete the certification form and notify Additionally, each network data manager the data manager that the data are ready will have a login to access the data for archiving and storage. Once the dataset download though a secure web portal. is certified, it can be used in analyses and Network data managers can be granted reports. access by contacting the project-lead biologist at RMBO. Certified and archived 5.4. Metadata procedures non-sensitive data will be posted to the RMBO is working to develop metadata RMBO Avian Data Center and IRMA, procedures for all data collected. Tabular where they may be downloaded for datasets, and all database objects research and management applications. (e.g., tables, fields) will be defined and Other datasets, including those containing documented in a data dictionary or in sensitive data, may be requested in writing Section 5 of a Federal Geographic Data from the CHDN, NGPN, SODN, or Committee (FGDC)-compliant metadata SOPN data manager. Sensitive data will be file. FGDC-compliant metadata (including released only with a signed confidentiality the NPS profile) will be created for all agreement.
30 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 6. Reporting and Analysis
6.1. Reporting for the specific results from a given Reporting will be hierarchical and project would find it at the project level. intended for multiple audiences and media. I&M monitoring data will contribute Important audiences include park staff, to, and sometimes be the only source of other programs in each I&M network, information for, resource-level products; other bird monitoring efforts in each the monitoring data will also be reported at region, state and federal wildlife agencies, the project level. universities, and the general public. The primary delivery system will be the For the landbird monitoring, we anticipate Internet, via each network’s website (e.g., that NPS will develop products at both http://www.science.nature.nps.gov/im/ the resource and project levels. At the units/NGPN for NGPN). However, the resource level, we expect to produce a individual products available on the web resource brief for landbirds at each park site will also be available in a format (pdf) annually. Each park-specific resource brief that will facilitate easy printing or enable us is a one to two-page document describing to deliver a printed version to appropriate the monitoring purpose, methods, and audiences. results; bird photos and maps of sampling transects are included. At the project level, An additonal reporting location will be we anticipate producing, in coordination the Learning Center of the American with RMBO, a project report (and, possibly, Southwest (LCAS), http://www. a summary) annually and a synthesis report southwestlearning.org. The LCAS is a approximately every five years. partnership between the 48 parks of four monitoring networks (Southern Plains, Annual reports will sumarize the results Sonoran Desert, Southern Colorado of the sampling effort for the year. They Plateau, and Chihuahuan Desert), three are more limited than the synthesis report Cooperative Ecosystem Studies Units (below) in that emphasis is on the number (Desert Southwest, Colorado Plateau, and of sites and frequency of sampling in each Rocky Mountain), and two non-profit of the networks’ parks, as well as relative- organizations (Sonoran Institute and Big abundance summaries. The functions Sky Institute; Folts-Zettner et al. 2008). Its necessary to complete annual reports purpose is to build stronger relationships are to be carried out by existing network between national parks and scientists and staff (and/or network cooperators). In an better communicate science results to effort to disseminate findings in a timely interested park audiences. manner, annual reports will be completed by January 31 following the year of data Information within LCAS is organized collection. Once data are entered and hierarchically, as a series of products within certified (SOPs #5 and #6), we anticipate two major levels, the resource level and that preparation of each annual report will the project level. Resource-level products take approximately one-two weeks. report on the condition of the resource, regardless of the source of information. Annual and synthesis reports will be This is the level that best synthesizes published in the NPS’s Natural Resource the available information regarding Technical Report (NRTR) Series. the status and trend of the resource. In Guidelines for NPS Natural Resource contrast, project-level products report Publications can be found at http://www. the available information from a given nature.nps.gov/publications/NRPM/ project, whether it be monitoring, research, index.cfm. The NRTR Series is used to etc. Thus, someone looking for the most disseminate results of scientific studies in comprehensive information about status the physical, biological, and social sciences and trend of a resource would find it at for both the advancement of science and the resource level, while someone looking the achievement of the NPS mission.
Reporting and Analysis 31 All manuscripts in the series receive the publications (e.g., MacKenzie et al. appropriate level of peer review to ensure 2002 and MacKenzie et al. 2003), and that the information is scientifically synthesized in a book on the topic by credible, technically accurate, appropriately MacKenzie et al. (2006). For this reason, we written for the intended audience, and will not repeat such detailed information designed and published in a professional here. Further, data analysis procedures are manner. very dependent on the specifics of a dataset and sampling design, and will vary through After the first 3-5 years of data collection, time as monitoring data grow. Therefore, we will conduct a complete review of “cookbook” style instructions would likely the program that will include density be of little value to an analyst and could estimation and sample-size evaluation. lead to poor or incorrect analyses. Thus, we This review will determine if the sampling describe here the general approach used design is appropriate for meeting the for analysis of occupancy data and will program’s goal and objectives. If not, the reference more specific applications as part design will be changed. After the design is of our reporting process, as appropriate. finalized, detailed reports (i.e., synthesis reports) will be produced every five years. There are numerous variations within the These reports will include appropriate general theme of estimating occupancy, estimates of density, occupancy, and but the foundation relies on estimation of relative abundance, and trend assessment two parameters: (1) the probability that a in these parameters. Summary reports may species is present at site i (often symbolized
be used in place of annual reports for the by ψi, and (2) the probability that a species year in which the most recent data were will be detected at site i at time t, given collected. presence (often symbolized by p). The data for estimating these parameters are in the 6.2. Data analysis form of an encounter history represented A critical component of any long-term by a matrix of 0s and 1s, depending on monitoring program is consistent, whether a given species was observed systematic data analysis. In addition, it is during the sampling occasion. The basic important that data analysis protocols be model formulation for these parameters flexible to accommodate better approaches is described in detail by MacKenzie et al. as they become available. Here we describe (2002, 2006). Extensions of this basic model our general data analysis approach for the incorporate the dynamics of occupancy, different estimators we use. and include additional parameters for the estimation of local colonization and/or 6.2.1. Species Occupancy extinction rates (MacKenzie et al. 2003), Occupancy allows us to monitor changes in as well as the effects of different covariates the proportion of sampling units occupied on these parameters. Covariates can be in by a species over time (i.e., presence or the form of site-specific covariates, which absence) and accounts for imperfect are constant for a site within a season. detectability during estimation (MacKenzie Examples would be habitat type, patch size, et al. 2002). As such, occupancy serves distance to nearest patch, or generalized as an indicator of changes in distribution weather patterns such as drought or and allows for explicit estimates of local El Niño years. Alternatively, sampling extinction and colonization rates. For all occasion covariates are covariates that may parameters, when samples are drawn from change with each survey of a site, such a larger population of potential samples as local environmental conditions (e.g., using a randomized design, inference will temperature, precipitation, or cloud cover), be at the scale of the population considered time of day, or observer. for sampling (Thompson 2002). We will use an information theoretic Approaches for analysis of occupancy approach for determining the best model have been described in detail in numerous supported by our data. Considerable
32 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN attention has emerged in recent years to Buckland et al. (2001) and Buckland et regarding the use of information theoretic al. (2004) for a more detailed overview. approaches such as Akaike’s Information Criteria (AIC) (Akaike 1973) as a basis We estimate density of birds based on for model selection (e.g., Burnham distance sampling, where the probability and Anderson 2002). AIC treats the of detection is estimated from the decay model selection process as a problem in function; the decay function assumes that optimization of the balance between model all birds are detected at the exact point of fit and precision (Spendelow et al. 1995). the observer, and that detection decreases AIC optimizes the fit of a model balanced with increasing distance from the observer. against the cost of adding excessive The estimation of this detection function parameters. The statistical foundation for enables estimates of the total density taking this approach has been well described into account individuals that were not (e.g., Akaike 1973, Anderson et al. 1994, detected. Thus, the emphasis on analysis is Burnham and Anderson 2002). on determining the appropriate detection function. The means of doing so have been There are currently two software programs summarized in Buckland et al. (2004) and well equipped to handle occupancy described in numerous additional papers. estimation. Program PRESENCE (MacKenzie et al. 2002) was developed 6.2.3. Species Richness and specifically for estimation and analyses of Composition occupancy data and is available at http:// As with our other two objectives, www.mbr-pwrc.usgs.gov/software.html. community level parameters also are estimated while explicitly accounting Estimation of occupancy is also supported for variation in detectability. These by the more general program MARK estimators are based on the principles of (White and Burnham 1999), designed to mark-recapture techniques, whereby an estimate parameters for a variety of models, encounter history is constructed from mostly related to marked individuals that repeated visits to a site and whether or not are re-encountered over time. MARK is a given species was observed at that site also freely available for download at the on a given sampling occasion (Boulinier same address. et al. 1998; Nichols et al. 1998). This encounter history is used to estimate 6.2.2. Density detection probabilities for a species much We will estimate density with the use of like estimating the detection of individually the point-transect distance-sampling marked animals. From these data, a suite method at fixed points and subsequent of estimators can be derived and analyzed analyses using the DISTANCE program using Program COMDYN (Hines et al. (Thomas et al. 2005). As with occupancy, 1999). In addition to species richness and there is a substantial volume of literature composition, other potentially useful already describing the analysis of density parameters (e.g., relative species richness, estimation using distance sampling. As or species present at a given site that are such, we present only a basic summary of or are not present on other sites) can be the data analysis here, and refer the reader estimated or compared among sites or over time.
Reporting and Analysis 33
7. Personnel Requirements and Training
7.1. Roles and responsibilities components for the long-term success Each Network Program Manager or of this program. This point cannot be their designated appointee will work overemphasized. Numerous studies have with the overall Project Manager and shown that bias among observers can RMBO to implement this protocol. Due confound estimates of population trends to the specialization of the work, it will be of songbird populations (Sauer et al. 1994; important for the Program Manager or Kendall et al. 1996; Link and Sauer 1998; designated appointee to be well-versed in Diefenbach et al. 2003). In addition to all aspects of the protocol. The NPS Project being able to identify birds visually, field Manager for the program will coordinate observers must be proficient at identifying with RMBO on implementation. RMBO birds aurally, because many species are will be responsible for hiring and training detected by sound only (Ramsey and Scott observers, conducting the landbird surveys, 1981). Further, the ability to precisely and managing the data. Parks in the four estimate distance to birds that are rarely networks covered by this protocol should seen is an essential skill (Buckland et al. have easy access to the data through the 2004). The importance of having well- RMBO Avian Data Center. RMBO will qualified observers for counting efforts in also prepare summary descriptions of the the four networks is particularly important data for use by each I&M network in their because of high species richness, high preparation of annual monitoring reports. proportion of congeneric, similar species, and presence of species that are not Field personnel (observers) teams will found in other parts of the U.S. Also, bird consist of a full-time RMBO crew leader densities in some areas, such as riparian and one or more full-time temporary areas, are among the highest in the U.S. technician. Crew leaders either report (Carothers et al. 1974). directly to the RMBO project manager or are the project manager themselves. Project RMBO will handle all hiring and training of managers are responsible for hiring their observers. Observers will be trained at the technician(s), planning and conducting beginning of each field season and will be technician training sessions, overseeing the tested periodically in their ability to identify technician(s) during the field season, and and estimate distances to birds. The current coordinating and conducting data entry, observer training program consists of 5-7 proofing, and analysis. Specific protocols full days of training in a classroom setting followed by RMBO for data collection and in the field. Example training topics are are located in SOP#4. Data entry will be provided below; a complete list is provided conducted by field personnel during the in SOP #2. field season and by data entry technicians after completion of the field season as ●● Distance and occupancy sampling necessary. Data management, including methods, data entry and data certification, is ●● Review of point-count protocol, summarized in Chapter 5, and the specific ●● Using a rangefinder, GPS unit, SPOT protocols followed by RMBO for data unit, and timer, management are located in SOP’s #5 and #6. ●● Entering bird data, ●● Bird identification by sight and sound, 7.2. Training and Having qualified, competent, observers ●● Practice (in the field) conducting point to ensure rigor and consistency in data counts and identifying birds. collection is one of the most essential
Personnel Requirements and Training 35
8. Operational Requirements
8.1. Annual workload and field equipment and minimal office space and schedule resources. RMBO will provide equipment Major tasks throughout the year related for the field work (see SOP #1), including to landbird sampling include pre-season GPS units, laser rangefinders, and planning, training and field preparation, stopwatches. RMBO will provide safe, field data collection, completion of data reliable rental vehicles for technicians that entry, and analysis and reporting (Figure do not have their own reliable vehicles. 8.1-1). Observer training will begin in When the opportunity arises, NPS will March, and surveys will begin in April for provide observers housing or camping CHDN and SODN and in April or early facilities when surveying at parks. May for NGPN and SOPN. In most years, surveys in all of the networks will end 8.3. Budget sooner than July 1. Within a given network, As discussed above, via a Cooperative we generally begin surveying at parks with Agreement, RMBO provides their own the lowest elevations located in the most vehicles and equipment. Under the southern areas of the network. Parks with agreement, RMBO hires and trains higher elevations or that are located in observers, conducts the field sampling, more northern areas are surveyed as the manages the data, provides annual report season progresses (see section 4.3 Seasonal tables, and controls and assures the quality Timing of Surveys). of the data. The budget for RMBO’s work in CHDN, SODN, and SOPN 8.2. Facility & equipment needs in 2012 was approximately $118,000. Bird surveys involve low-impact, passive The NGPN anticipates that landbird sampling that requires only limited monitoring by RMBO at its 13 parks will
CHDN/NGPN/SODN/SOPN Landbirds Annual Project Schedule
December r Ja mbe nua ove ry N
F r e Analysis br obe u t a c and r O y Reporting Pre-Season Planning Final QA QC’d data delivered to NPS Annual Report Completed r
Pre-season Planning meeting b e M a
m New season kick-offTraining e r
and Field c p t Analysis h
e SODN and CHDN field seasons starts
S Prep NGPN and S Complete Data Entry OPN field se
QA/QC and GIS
A
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t s u
i a l
s
g u o
Field Data n A s close-out start n Collection o
s
a
a M
y
y
Field se l u
J
e n u
J Majo r project milestones Routine check-in meetings between Figure 8.1-1. Annual schedule for landbird monitoring. project lead and cooperator
Operational Requirements 37 cost approximately $65,000 annually. These Network and RMBO (Bennetts et al. 2005). costs exclude those of network staff to We have described the responsibilities of oversee the project and manage the data. RMBO under the cooperative agreement above and elsewhere in this protocol. Data 8.4. Collaboration management provided by RMBO has been This protocol applies to landbird found to be efficient and effective by other monitoring in CHDN, NGPN, SODN, networks utilzing their services. Storing and and SOPN. Financial and administrative managing data within the same databases efficiencies are gained by partnering with as other networks and organizations multiple networks. Furthermore, the allows for a more comprehensive regional networks are collaborating with RMBO, assessment. Network parks also have similar to an ongoing collaboration access to the data through RMBO’s Avian between the Northern Colorado Plateau Data Center.
38 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 9. Procedure for Revising the Protocol and Program Review
9.1. Revising the protocol protocol narrative should be archived in We have divided this sampling protocol the appropriate folder of each network’s into a protocol narrative and seven separate protocol library. SOPs. The protocol narrative is a general overview of the protocol that provides the 9.2. Program review history and justification for the program We suggest that a thorough analysis of and an overview of sampling methods. The the data from this project be undertaken protocol narrative will only be revised if after the first 3-5 years and every 10 years major changes are made to the protocol. thereafter. An initial 3-5-year review is The SOPs, in contrast, are very specific, essential and should involve extensive step-by-step instructions for performing quantitative analyses to evaluate the each task. They are expected to be efficiency of the program design and revised more frequently than the protocol results, and its ability to meet the stated narrative. goals and objectives. A component of the review will involve recommended revisions Careful documentation of such revisions, to the program, with explicit justification. including a library of previous versions, Products from this effort will be trend is essential for maintaining consistency in assessment of population parameters, data collection, analyses, and reporting. variance partitioning and power analysis To summarize changes, the monitoring (Appendix A), and an assessment of database for each component contains a sampling methods and spatial inferences. field to identify the protocol version used Also, new analytical techniques or tools to gather and analyze data. The steps for may become available that have important changing any aspect of the protocol are application to these data. For example, outlined in SOP #7. Each SOP contains advances in multi-species and multi-year a revision history log that should be occupancy estimation procedures and both completed each time an SOP is revised parametric and Bayesian analytical tools to explain why changes were made, and will certainly become more refined (Darryl to assign a new version number to the MacKenzie, pers. comm.). revised SOP. New versions of SOPs and the
Protocol Revision and Program Review 39
10. Literature Cited
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Allen, L. S. 1996. Ecological role of fire Barrows, C. W., M. B. Swartz, W. L. Hodges, in the Madrean province. Pages M. F. Allen, J. T. Rotenberry, B. L. Li, 5–10 in Effects of fire on Madrean T. A. Scott, and X. W. Chen. 2005. A Province ecosystems. General framework for monitoring multiple- Technical Report RM-GTR-289. U.S. species conservation plans. Journal of Department of Agriculture Forest Wildlife Management 69:1333–1345. Service, Rocky Mountain Forest and Range Experiment Station, Ft. Collins, Bart, J., K. P. Burnham, E. H. Dunn, C. M. Colorado. Francis, and C. J. Ralph. 2004. Goals and strategies for estimating trends Anderson, R. C. 1982. An evolutionary in landbird abundance. Journal of model summarizing the roles of fire, Wildlife Management 68:611–626. climate and grazing animals in the origin and maintenance of grasslands. Bennetts, R. E., A. Schrag, and S. Wolff. Pages 297–308 in J. R. Estes, R. J. Tyrl, 2005. Cooperative bird monitoring and J. N. Brunken, editors. Grasses plan and protocol for the Greater and grasslands: Systematics and Yellowstone Network of parks. ecology. University of Oklahoma Press, Version 1.00. Unpublished protocol Norman. to the Greater Yellowstone Network, National Park Service, Bozeman, Anderson, D. R., K. P. Burnham, G. C. Montana. White. 1994. AIC model selection in overdispersed capture-recapture data. Benson, A. 2011. Effects of forest type Ecology 75: 1780-1793. and age class on songbird populations
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50 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN ecology: Using the past to manage for Society of London Series B: Biological the future. Ecological Applications Sciences 265:1867–1870. 9:1189–1206. Weaver, J. E. 1943. Resurvey of grasses, Theobald, D. M., D. L. Stevens, Jr., D. forbs, and underground plant parts at White, N. S. Urquhart, A. R. Olsen, the end of the great drought. Ecological and J. B. Norman. 2007. Using Monographs 13:63–117. GIS to generate spatially-balanced random survey designs for natural White, G. C., and K. P. Burnham. 1999. resource applications. Environmental Program MARK: Survival estimation Management 40(1): 134-146. from populations of marked animals. Bird Study 46 Supplement, 120-138. Thomas, L., J. L. Laake, S. Strindberg, F. F. C. Marques, S. T. Buckland, D. White, C. M., J. A. Blakesley, J. A. Rehm- L. Borchers, D. R. Anderson, K. P. Lorber, D. C. Pavlacky, Jr., R. A. Sparks Burnham, S. L. Pollard J. H. Hedley, J. and D. J. Hanni. 2010. Monitoring the R. B. Bishop, and T. A. Marques. 2005. Birds of the Badlands and Prairies Bird Distance 5.0. Release Beta 5. Research Conservation Region (BCR 17): 2009 unit for wildlife population assessment, Field Season Report. Tech. Rep. SC- University of St. Andrews, U.K. http:/ BCR1709-01. Rocky Mountain Bird www.ruspa.st-and.ac.uk/distance. Observatory, Brighton, CO. 93
Thompson, S. K. 2002. Sampling. Second White, C. M., N. J. Van Lanen, D. C. edition. New York: John Wiley and Pavlacky Jr., J. A. Blakesley, R. A. Sons. Sparks, J. A. Fogg, M. F. McLaren, J. J. Birek and D. J. Hanni. 2012. Integrated U.S. Geological Survey (USGS). 2005. Monitoring in Bird Conservation USGS-NPS Vegetation Mapping Regions (IMBCR): 2011 annual report. Program. U.S. Department of Interior, Rocky Mountain Bird Observatory, U.S. Geological Survey. Online. (http:// Brighton, Colorado. biology.usgs.gov/npsveg/). Accessed 5 March 2005. Wiens, J. A. 1985. Habitat selection in variable environments: Shrub-steppe Van Auken, O. W. 2000. Shrub invasions of birds. Pages 191–226 in M. L. Cody, North American semiarid grasslands. ed., Habitat selection in birds. Orlando, Annual Review of Ecology and Fla.: Academic Press. Systematics 31:197–215. Wiens, J. A., and J. T. Rotenberry. 1981. Visser, M. E., A. J. Vannoordwijk, J. M. Habitat associations and community Tinbergen, and C. M. Lessells. 1998. structure of birds in shrubsteppe Warmer springs lead to mistimed environments. Ecological Monographs reproduction in Great Tits (Parus 51:21–41. major). Proceedings of the Royal
Literature Cited 51 Williams, B. K., J. D. Nichols, and 19-35 in F. B. Samson and F. L. M. J. Conroy. 2002. Analysis and Knopf, editors. Prairie conservation: management of animal populations. Preserving North America’s most San Diego, Ca.: Academic Press. endangered ecosystem. Island Press, Covelo, California. Williams, J., and P. Diebel. 1996. The economic value of prairie. Pages
52 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 169 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 169 Appendix A. Power AnalysisSection 4
**This section is extracted from the Landbird SupplementsMonitoring Protocol and Standard Operationg Procedures for the Sonoran Desert Network (Powell et al. 2007b). **
Supplement A. Sample Size and Power for Trend in Landbird Density Estimation
Version 1.00
Revision History Log
Previous Revision Section and New Author Changes made Reason for change Version # date paragraph Version #
1 Introduction log-based confidence intervals around density estimates, making it a useful tool for planning Determining the effort required to estimate a monitoring programs (Buckland et al. 2001); population parameter, such as density, is es- the larger the confidence interval, the lower sential when designing a monitoring program our ability to detect trends (i.e., lower statisti- (Gibbs et al. 1998; Bart et al. 2004). Without cal power). Conversely, the smaller the confi- this initial assessment to guide design and in- dence interval, the greater our ability to detect form cost, effort expended on sampling may trends, yet more samples are needed to obtain be inadequate to meet program objectives. The number of samples needed, and the frequency with which they are mea- sured, are often the most important considerations when designing a monitor- ing program, because they greatly influence program cost. In the proposed pro- gram, our primary goal is to derive annual estimates Annual rate of change ( λ ) of density for a subset of Annual rate of change (1- λ ) species, yet the number of samples required depends, Coefficient of variation Coefficient of variation in part, on the desired level of precision of these Figure A.1. (left) The number of years required to monitor so that power to detect a log- estimates (see Figure A.1 linear population trend is 0.9 (assuming two-tailed t-test and α = 0.1). and Thomas et al. 2005). The coefficient of varia- Annual rate of change (1-λ) is a typical range of values that we would expect for most bird tion (CV) is an estimate of populations. See text for description of coefficient of variation. (right) The relative size of the population after the number of years from (left). Note difference in y axis between two figures. precision used to calculate From Thomas et al. (2004:103).
Appendix A - Power Analysis 53 170 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
higher precision. For example, with a target CV cause each type of variance, and its magnitude, of 15%, it would take approximately 19 years to can have important consequences for trend detect a 2% annual change in density (power detection (Urquhart et al. 1998; Kincaid et al. = 0.9 and α = 0.10). If the CV were increased 2004; Sims et al. 2006). to 25%, however, it would take six additional years to detect the same change. To determine the efficacy of a range of potential designs with varying effort to monitor landbirds Prospective power analysis can aid the effi- in Sonoran Desert Network (SODN) parks, we ciency of a monitoring program by providing used pilot data collected at these parks to esti- an understanding of the tradeoffs between ef- mate density at various levels of precision for a fort, cost, and the magnitude and probability subset of species. Using these data, we estimat- of a trend that can be detected. During power ed the number of species in each of several veg- analysis, these design elements can each be var- etation communities for which density can be ied to determine a range of appropriate designs estimated across a range of encounter rates. We for meeting the desired objective (see Gerro- then used data from one park for which mul- dette 1987; Steidl et al. 1997; Gibbs et al. 1998). tiple years of data were available to determine Statistical power is the probability of correctly the magnitude of each component of variance rejecting a null hypothesis (i.e., no trend) that and its influence on statistical power and trend is false. Power analysis has four interrelated detection. Based on these analyses, we deter- components: (1) power, (2) sample size, (3) α, mined that a sufficient number of species can or the Type I error rate (probability of incor- be monitored with the resources available, but rectly concluding a trend has occurred when sampling effort must be partitioned strategical- one has not), and (4) effect size or magnitude of ly to obtain sufficient samples to estimate some the trend of interest (e.g., a 2% annual change). species, especially those with low encounter The expected sampling variance (σ2) or coef- rates. This information will inform both our ini- ficient of variation σ ( /μ) is incorporated into tial sampling effort and suggest ways to analyze power analyses and has an important effect on data after the first 3–5 years after the program power; high variance leads to lower power or is implemented to determine if the program is higher estimates of sample sizes needed to ob- meeting its stated objectives. tain precise estimates. In general, the ability to detect a trend is a function of the magnitude of 2 Methods the trend, variation around the trend line, and the amount of time or leverage over which the 2.1 Study area trend is assessed. The Sonoran Desert Network includes 11 national parks in southern Arizona and New In this supplement, we assess the effects of Mexico (see protocol narrative). The area is variation around a trend line on power. More known for high diversity of plants and animals specifically, we assess whether initial density as a result of varied biogeographic affinities of estimates at a specified level of precision are species in the region, including those of the adequate for monitoring population trends by Sonoran, Chihuahuan, and Mojave deserts, accounting for inter-annual (temporal) varia- Rocky Mountains, Sierra Madre Occidental, tion in estimates that, when high, can obscure and Great Plains (McLaughlin 1986). Other trends that are actually occurring (Type II er- important factors that influence diversity - in ror) and result in lower power. Many planning clude a range of topographic, geologic, edaph- efforts use a single estimate of variance—usu- ic, and climatic factors, and variable land-use ally a combination of spatial and short-term, histories (Marshall et al. 2000). Taken together, within-season temporal variance (process vari- these physical and biological factors make the ation) and sampling error—to estimate power network parks representative of the diverse and calculate sample sizes. Without additional landscape in which they are embedded. information, combining both process and sam- pling variation into one estimate of variance is Five dominant vegetation communities are a reasonable approach when designing a moni- found in SODN parks, separated by elevation toring program if no other information is avail- (see Whittaker and Niering 1965). Sonoran able. However, assessing each component of Desert upland occurs at the lowest elevations variance separately will augment efficiency, -be and is dominated by mixed cactus, paloverde,
54 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 170 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 171
creosote (Larrea tridentata), and bursage (Am- When estimating population parameters, pre- brosia spp.). Sonoran Desert upland transitions cision and sampling efficiency are higher when to semi-desert grasslands dominated by peren- considered within groupings that reflect their nial grasses and, in most parks, by mesquite and inherent organization in nature (Krebs 1999). other short trees and shrubs. Valley-bottom Therefore, we stratified transects by vegetation semi-desert grasslands make up small portions communities as patterns of bird species occur- of only two parks (Chiricahua National Mon- rence varied among communities: (1) upland ument and Coronado National Memorial). and (2) riparian transects in desert scrub/grass- Pine-oak forest and woodland, dominated by land and (3) upland and (4) riparian transects four species of oak (Quercus spp.), juniper, and in woodland/forest. pinyon pine occur above semi-desert grassland and are common in four parks. Mixed conifer The survey design for the landbird monitoring forests dominated by fir (Abies spp.), ponderosa program (see protocol narrative) uses many of and Apache pine (Pinus spp.), and some Gam- the same sampling elements that were used in bel oak (Quercus gambelii) occur in the highest- the pilot effort, most notably the use of survey elevation areas. Broadleaf riparian woodlands, stations arranged in transects and multiple re- dominated by cottonwood and willow (Populus visits to the same transects within each field sea- spp.), sycamore (Platanus wrightii), and dense son. From these pilot data we sought to deter- understory, are located along perennial streams mine the number of station visits (K; the number and rivers and are bordered by Sonoran Desert of stations multiplied by the number of visits) upland and pine-oak forest and woodland in needed to estimate density. First, we calculated many SODN parks. encounter rates as the total number of detec- tions of a species divided by K. We excluded detections of birds that were flying over points 2.2 Data analysis: Initial sample-size and birds detected outside of the eight-minute estimation and allocation of effort count period. While not always appropriate for We used data collected at nine SODN parks be- monitoring population trends because they are tween 2001 and 2005 (see Powell et al. 2006). uncorrected for detectability (Williams et al. Data were collected using the point-transect 2002), encounter rates are useful for assessing survey method, which employs distance sam- sample-size requirements for density estima- pling from fixed survey points (Buckland et al. tion (Buckland et al. 2001). The relationship 2001), each spaced 250 m apart along transects. between encounter rates and the number of Survey effort from 2001–2005 did not include samples required to estimate density is provided Organ Pipe Cactus National Monument or by the following formula (Buckland et al. 2001): Montezuma Castle National Monument, both of which received thorough inventories prior to recent efforts and employed different survey b K methodologies. Because some parks were vis- K = • 0 ited for two consecutive years, we randomly se- ˆ 2 n lected data from a single year within each park {cv()D} 0 to eliminate the possibility that variation in our Equation 1.1. estimates resulted from among-year changes in abundance. The resulting dataset included where cv( ˆ ) is the desired coefficient of varia- 1,413 station visits at 50 point transects, total- D tion or level of precision for the estimate of ing 19,790 bird detections of 190 species. We density, and K /n is the inverse of the encoun- further reduced the dataset by considering 0 0 ter rate determined from pilot data. Note that only those species that were summer breeding b is a variance-inflation parameter equal to the residents within SODN parks (n = 107), leaving number of detections (n) multiplied by the CV a total of 19,479 bird detections. Because the of the density estimate [cv( ˆ )] that can be de- proportion of effort among parks and vegeta- D termined with pilot data. Although the value tion communities during this pilot study was of b is often between 2 and 4 (Eberhardt 1978) similar to that which we propose, these data and often assumed to be 3 for planning point- were adequate to evaluate the efficacy of allo- transect surveys (Buckland et al. 2001), we esti- cating survey effort. mated b to provide more precise estimates of K
Appendix A - Power Analysis 55 172 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
ter rates and K, we selected 28 species with at 90 least 30 detections across a range of variation in numbers of detections and patterns of occur- 80 rence among vegetation communities. Encoun- ter rates varied from 0.032 to 0.960; effort var- ied from 328 to 1,413 point visits within each 70 ) community. We plotted encounter rates of all 28 species versus K (from Equation 1.1) at three 60 levels of precision (10, 15, and 20% CV) and se- 95% CI
+ lected the function that best fit these data as in- ( 50 dicated by the lowest possible residual variation at each level of precision. Because we suspected that detectability could be an important covari-
Density 40 ate in the relationship between encounter rates and K, we assessed this relationship using both 30 quadratic and linear regression. To estimate de- tectability, we determined the relationship be- 20 tween effective detection radius (e.g., distance 10 15 20 25 at which detectability equals 0.5) for the 28 spe- Percent coefficient of variation cies for which density and detectability were es- timated and mean detection distance. Because both variables were highly correlated (r = 0.98, Figure A.2. Effect of coefficient of variation on precision of density estimates for a hypothetical species with density of P < 0.0001), we used mean detection distance 50 birds/km2. for all 107 species as a covariate.
Note that the lower confidence limit is smaller than upper confidence To determine the most efficient allocation of limit; see Equation 1.2. sample effort for our monitoring program, we used these equations to predict K for all 107 and to guide future planning efforts. species. We considered two levels of proposed sampling effort: one with 2,324 station visits We used Distance Version 5 (Thomas et al. (full program) and another with half this effort 2005) to calculate density and cv( Dˆ ) for a sub- (reduced program). As a starting point, we sub- set of species with varying encounter rates. We divided this effort to yield 1,833 station visits in considered detections and effort only within desert/grasslands, 432 in low-elevation ripar- those vegetation types in which each species ian areas, 920 in high-elevation riparian areas, was known to breed, and excluded data from and 488 station visits in woodland/forests. This vegetation types where species occurred as allocation of effort was roughly proportional migrants. This resulted in four groups of spe- to the spatial coverage of vegetation types and cies: (1) those found across all vegetation types similar to that from the pilot surveys; therefore, combined, (2) those found at high elevations it required no adjustment in encounter rates (woodland/forest and high-elevation riparian) due to among-vegetation differences for each or (3) low elevation desert/grassland, and (4) species. We then predicted K using expected those restricted to low-elevation riparian areas. encounter rates and determined the number When using Distance, we fit both uniform and of species within each type for which density half-normal key functions with cosine, simple could be estimated with CV equal to 15%. We polynomial, and hermite polynomial series ex- chose 15% because it presented a reasonable pansions to distance data of each species and tradeoff between the overly restrictive (and selected models with the lowest AIC (Akaike therefore expensive) 10% and 20%, which may information criterion; goodness of fit measure). only be appropriate for detecting large changes We truncated 1–5% of observations farthest in populations. As an example, an increase in from count stations to improve model fit and CV from 15% to 20% results in a 20% increase placed detection data in intervals if histograms in the width of confidence intervals around of detection distances indicated heaping. density estimates (Figure A.2). Using these cal- culations, we were able to estimate the number To estimate the relationship between encoun- of species in each vegetation community and
56 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 172 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 173
reallocate sampling effort according to these wildlife monitoring and includes within-season results. By reallocating effort among vegetation variance, observer bias, data handling, and mis- types, we were able to evaluate the efficacy of cellaneous errors. a number of potential program designs, each with a specified level of effort. These four types of variance can be used to ex- pand Equation 1.2 to:
2.3 Variance partitioning 2 2 σ r We used data from Organ Pipe Cactus National 2 σ e + σ s 2 v Monument (ORPI; NPS 1998) to determine if +σ y + = s s our initial estimates of sample size derived in var(β ) 2 the previous section were appropriate. Spe- ∑(yi − y) cifically, those estimates incorporate only spa- Equation 1.3. tial variance and one component of temporal (within-year) variance. We also estimated two additional sources of temporal variance that where s = number of sites, y = number of years, may affect the precision of trend estimates. and v = number of within-season visits.
In its most simple form, variance about the At ORPI, nine transects were surveyed three trend line (β) is expressed as: times per year from mid- to late February to early May in 2000, 2002, and 2003. Each tran- σ 2 sect had eight points spaced approximately 500 m apart. Detections of each bird were noted var(β ) = 2 ∑ (Yi − Y ) in 50-m distance bands, and all data were col- lected by the same skilled observer. We used Equation 1.2. this dataset because it was the only one that had multiple within-season visits across >2 This model can be expanded to incorporate years. The bird community at ORPI is similar to components of variance from a multi-site, year, that of other parks in the network that have So- and visit model of variance partitioning. In an noran Desert upland vegetation communities analysis of variance frameworks, four types of and were included in the previous analyses. variance can be estimated for landbirds (Lewis 1978; Urquhart et al. 1998; Kincaid et al. 2004). For this analysis, we used nine focal species: 2 black-tailed gnatcatcher, black-throated spar- Population or spatial variance (σ s) represents difference in abundance among sites as the re- row, verdin, northern mockingbird, Gila wood- sult of site-specific factors such as vegetation pecker, curve-billed thrasher, house finch, and elevation. Year-to-year (coherent; Lar- cactus wren, and phainopepla. These species 2 are all common resident breeders, chosen be- son et al.), temporal variance (σ y) is variation among years at all sites combined, and is a re- cause they were present throughout the survey sult of regional phenomena such as productiv- window each year. We used all detection data ity and survivorship and broad-scale climatic from within the first three distance bands (i.e., conditions, such as a wet or dry year. Ephemer- <150 m). We assumed that detectability among 2 transects was not a significant source of error al, or site-x year variance, (σ e) is independent, year-to-year variance, whereby abundance at (which we tested using Analysis of Variance each site tracks local resources independently (ANOVA) and found no statistically significant of other sites and is a result of local factors such differences; B. Powell, unpublished data). Also, as rainfall, changes in land use, fire, or measures we assumed that plot-specific detectability of habitat quality. If multiple measurements are did change among years—a phenomenon that not taken within each year at the same sites, seems unlikely in such a short time period. this type of variance cannot be partitioned and 2 2 To partition variation into its constituent com- it becomes part of σ r. Residual (σ r), the fourth type of variance, is made up of variance that is ponents, we first detrended the data by tak- not explained by other components, including ing the mean encounter rate for each transect both process and sampling variance. Residual across years and subtracting annual estimates variance is an important source of variation in of encounter rates for each year. Detrend- ing ensures that differences among years are
Appendix A - Power Analysis 57 174 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
σ 2 = σ 2 +σ 2 +σ 2 +σ 2 caid et al. 2004), as was the case with our design total s y e r (see protocol narrative). 2 σ r = MSr
2 MSs − MSe 3 Results σ s = Ny × Nv 3.1 Initial sample-size estimation Of the 107 species that occur as breeders within 2 MS y − MSe σ y = the SODN parks we considered, 31 were found Ns × Nv across all vegetation communities, and 33, 25, MS − MS 10, and 8 were restricted to high elevations, low σ 2 = e r e Nv elevations, low-elevation riparian areas, and both low-elevation riparian areas and high el- Equation 1.4. evations, respectively (Table A.1; see end of this SOP). Mean encounter rates did not vary sys- 2 tematically among vegetation communities (F reflected in σ y (Kincaid et al. 2004). Next, we 4, summed the number of detections for each of 101 = 1.34, P = 0.26, ANOVA) yet encounter rates three inter-annual visits to each transect and tended to be higher at low elevations (mean ± transformed these count data using log + 1 to SE = 0.27 ± 0.04) and in low-elevation riparian better meet the assumptions of normality. We areas (0.25 ± 0.07) and lower at high elevations then calculated variance components with (0.14 ± 0.04). Encounter rates among all com- ANOVA using the Variability Chart function in munities averaged 0.20 ± 0.02. Within the veg- the statistical program JMP. As a result, we cal- etation communities in which each occurred, culated the variance components of Equation encounter rates were high for Gambel’s quail 1.3 (next page) as from Lewis (1978), where (0.97), Gila woodpecker (1.04), Bell’s vireo MS = mean squares from Equation 1.3. We par- (0.75), cactus wren (0.83), and Bewick’s wren titioned the sum of squares (SS) from ANOVA (0.74), moderate for summer tanager (0.39), to MS by SS/df. brown-crested flycatcher (0.35), and song spar- row (0.28), and low for Abert’s towhee (0.09), cordilleran flycatcher (0.09), and hermit thrush 2.4 Power analysis (0.09). We used each of the four types of variance out- lined in the previous section to estimate power For the 28 species for which we estimated den- of trend detection for three species at ORPI sity and cv( Dˆ ), density (± CV) varied from 0.003 that exemplified extreme differences in types ± 0.283 to 0.355 ± 0.116 individuals/ha (Table of variance. These species represented a range A.2; see end of this SOP). As expected, there of total variance in relation to their mean en- was a strong linear relationship between en- ˆ counter rate (black-tailed gnatcatcher = 48%; counter rates and cv( D ) (t26 = 4.57, P < 0.0001), black-throated sparrow = 63%; phainopepla = with each one-unit increase in encounter rate 130%). Using the open-source statistical power resulting in a 0.21 ± 0.05 (± SE) decrease in cv( analysis package R (Version 2.2.1; script “pow- Dˆ ). The scaling parameter b averaged 3.23 ± er.fcn” included in this protocol), we varied ef- 0.15 (± SE), with 50% of values ranging from fect size (1–3% annual change in density) and 2.60 to 4.00 (range = 2.04–4.84). The scaling pa- sample size. We maintained α = 0.10 through- rameter increased linearly with both encounter
out to guard against Type II errors. Using these rate and density (t26 ≥ 2.69, P ≤ 0.012); each initial estimates of power, we then simulated one-unit increase in encounter rate resulted in sampling designs that approximated our ef- a 1.54 ± 0.57 increase in b, whereas each one- fort. We simulated power by varying the per- unit increase in density resulted in a 4.14 ± 1.47 cent contributions of each of three sources of increase in b. variance (ephemeral, year, and residual) while holding the other two sources constant at 0%. The relationship between encounter rates and We did not include site variance in this simula- K (i.e., the number of point visits) was best de- tion exercise because it has little or no effect on scribed by a reciprocal model with the form K trend detection when the sampling design in- = 1/a(n0/K0) + b, where a and b are coefficients cludes revisits to the same sites over time (Kin- that are constant (Figure A.3). This relation- ship is simply the reciprocal of a standard
58 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 174 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 175
8,000
) Figure A.3. Relationship between encounter rate and the number of 6,000 point visits required to estimate density at three levels of precision 10% CV 15% CV using distance sampling. 20% CV 4,000 Relationships were determined using detection data and effort from a sample of 28 bird species detected during pilot surveys during 1,413 point counts within nine Sonoran Desert Network parks between 2000 and 2005. Prediction Effort (points*visits 2,000 equations for each curve follow a reciprocal relationship K = 1/a(n0/K0) + b where a and b are coefficients that are constant. Coefficient b equals 2.258 x 10-5, 5.306 x 10-5, or 9.093 x 10-5 and coefficient a equals 0.00269, 0.00662, or 0.0118, at CV 10, 15, and 20% respectively. 0 0.00.2 0.40.6 0.81.0 Encounter rate (detections/point*visits)
least-squares regression equation, where a is 15% for more species that occurred across all the slope and b is the y-intercept, and is simi- vegetation types (65%) and those that occurred b lar to a power function, K = a(n0/K0) , where b exclusively at low elevations (60%) than for spe- is a scaling component equal to the slope from cies at high elevations (12%) or in low-elevation linear regression in log-log space and a is the riparian areas (20%). The minimum number of y-intercept in log space. To determine K re- observations required to estimate density at CV quired to estimate density at cv( Dˆ ) of 10, 15, 15% across all species was 77 (encounter rate = and 20%, we evaluated the model coefficient b 0.04 detections per eight-minute count). as 2.258 x 10-5, 5.306 x 10-5, or 9.093 x 10-5, and the model coefficient a as 0.00269, 0.00662, or The K required to estimate density with CV 10 0.0118, respectively, depending on the desired and 20% varied widely from those at CV 15%. CV. Detectability, as indexed by mean detection For the 31 species that occurred across all veg- distance, did not describe any variation in K (P etation types, for example, 2.26 times more ef- ≥ 0.77), and was therefore not included in the fort was required, on average, to estimate den- model. sity with CV 10% than that for CV 15%, yet only 43% less effort was required to estimate Using our initial scenario of 2,324 station vis- CV 20%. its allocated, as indicated above, we were able to estimate density with CV of 15% for 45 of 3.2 Variance partitioning and power the 107 species (42%) using the full program analysis and 25 species (23%) for the reduced program (Table A.1). These species, however, were not The percent of total variance attributable to equally distributed among species groups. For each of the four sources of variance showed the full program, density was estimable at CV strikingly different patterns among species
Black-tailed gnatcatcher Site Year Black-throated sparrow Interaction Residual Verdin Figure A.4. Percent variance attributed to Northern mockingbird each of the four main Gila woodpecker sources of error from Curve-billed thrasher ANOVA models, Organ Species House finch Pipe Cactus National Monument. Cactus wren Phainopepla
020406080 100 Percent variation explained
Appendix A - Power Analysis 59 176 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
(Figure A.4). For all but two species, year and First, we investigated the sample sizes necessary ephemeral variance accounted for less vari- to obtain initial estimates of density across sev- ance than both site and residual variance. The eral levels of precision for species that occurred percent variance explained by site ranged from in four vegetation community types present in 0% for the black-tailed gnatcatcher to 62% for the parks. Next, we used a long-term dataset to the phainopepla. Conversely, residual variance partition variance components and investigate ranged from 76% for the black-tailed gnat- power to detect trends. catcher to 20% for the phainopepla. Residual variance made up >40% of the total variance 4.1 Program feasibility for seven of the nine species. Most notably, site variance for phainopepla made up 67% of the Efforts to estimate the number of point- vis total variance for the species, and ephemeral its needed to estimate density indicated that a and year variances made up only 14% of the to- reasonable number of species could be moni- tal variance. By contrast, 40% of total variance tored with the effort facilitated by an annual for the black–throated sparrow was made up of program budget of $24,000, but few of these ephemeral and year variances. species were in high-elevation communities (Table A.1). Based on these findings and on Different sources of variance and varying sam- recent clarification of the budget, we suggest pling strategies influenced power to detect reallocating survey effort among the different temporal trends in abundance, as illustrated communities. Our overall suggestion, based on by three of the species we investigated (Figure sampling efficiency, logistical constraints, and A.5). Power to detect trends in abundance was anticipated budget levels, is to reduce sampling greatest for the black-tailed gnatcatcher and at high elevations to once every 3–5 years, un- approximately four times less (averaged among less additional resources can be obtained. One designs) for the black-throated sparrow (Figure reason for this suggestion is that in most SODN A.5). Power was intermediate for the phainope- parks (Chiricahua NM, Coronado NM, Gila pla, despite the fact that the total variance was Cliff Dwellings NM, and Saguaro NP) high-el- >2 times that for the black-throated sparrow. evation areas had much lower encounter rates than other communities, and therefore would Power simulations for a hypothetical species require roughly twice as many point visits to with a mean encounter rate of 1.0 indicated that obtain the same encounter rates as at low eleva- site variance had an overwhelming effect on tions. This lower encounter rate, coupled with power (Figure A.6). When we evaluated each the steep terrain and long travel time among of the three sources of variance separately, so points in most parks (see Supplement B), that each equaled 10% of the mean encounter means that these surveys are inefficient when rate, we found that we could detect a 3% an- compared to those conducted in low-elevation nual change (power = 0.8) after five years with areas. residual variance alone, after eight years with ephemeral variance alone, and after 21 years Despite these challenges, high-elevation com- with year variance alone. Even with only 1% munities are particularly susceptible to change of the encounter rate attributable to year vari- due to global climate change and increases in ance, the ability to detect a 3% annual change in the number and severity of wildland fires. Cli- abundance could be realized after nine years. mate change, in particular, will likely alter the structure of forest communities at highest el- evations in the near term (Allen and Breshears 4 Discussion 1998). If these changes are realized, we expect When designing any monitoring effort, deter- them to occur over longer time periods (15–50 mining the effort needed to estimate popula- years), with corresponding changes to bird spe- tion parameters and detect changes in these cies abundance and community composition. parameters over time is essential. Monitoring Therefore, allocating sufficient effort to- ob programs may not be worth pursuing if the lev- tain precise estimates of abundance every 3–5 el of effort required exceeds available financial years seems prudent to balance the high cost resources. We attempted to address these issues of sampling and the need to detect changes in for the SODN landbird monitoring program by bird parameters. Further, obtaining additional using pilot data recently collected within parks. funds from other sources may facilitate more
60 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 176 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 177
Black-tailed gnatchatcher: 20 sites, 4 visits Black-tailed gnatcatcher: 2% annual change
1.0 1.0
0.8 0.8
0.6 0.6 r er we Po Pow 0.4 0.4 1% 2% 10 sites, 3 visits 3% 10 sites, 4 visits 0.2 0.2 20 sites, 3 visits 20 sites, 4 visits
0.0 0.0 0510 15 20 25 30 0510 15 20 25 30
Years Years
Black-throated sparrow: 20 sites, 4 visits Black-throated sparrow: 2% annual change
1.0 1.0
10 sites, 3 visits 0.8 1% 0.8 10 sites, 4 visits 2% 20 sites, 3 visits 3% 20 sites, 4 visits 0.6 0.6 r r Powe Po we 0.4 0.4
0.2 0.2
0.0 0.0 0510 15 20 25 30 0510 15 20 25 30
Years Years
Phainopepla: 20 sites, 4 visits Phainopepla: 2% annual change
1.0 1.0 10 sites, 3 visits 10 sites, 4 visits 20 sites, 3 visits 0.8 0.8 20 sites, 4 visits
0.6 0.6 r r we Po Po we 0.4 0.4
1% 2% 0.2 3% 0.2
0.0 0.0 0510 15 20 25 30 0510 15 20 25 30
Years Years
Figure A.5. Left: Statistical power curves for 1–3% annual change in abundance for three species from Figure A.4. Right: Effect of varying the number of visits and number of sites on our ability to detect trends.
Appendix A - Power Analysis 61 178 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
Percent of total variance = 50% Percent of total variance = 20%
1.0 1.0
0.8 0.8
0.6 0.6 Power Power
0.4 0.4
0.2 0.2
0.0 0.0 0510 15 20 25 30 0510 15 20 25 30
Years Years
Percent of total variance = 40% Percent of total variance = 10%
1.0 1.0
0.8 0.8
0.6 0.6 Power Power 0.4 0.4
0.2 0.2
0.0 0.0 0510 15 20 25 30 0510 15 20 25 30
Years Years
Percent of total variance = 30% Percent of total variance = 1%
1.0 1.0
0.8 0.8
0.6 0.6 Power Power
0.4 0.4
0.2 0.2
0.0 0.0 0510 15 20 25 30 0510 15 20 25 30
Years Years
Figure A.6. Simulation of landbird monitoring data to show the relative influence of different types of variance components on statistical power (using code “Power.fcn”).
Dashed line = year variance; dotted line = ephemeral variance; solid line = residual variance. Each simulation involves holding the three other variance components at 0% of total variance. Site variance was not included because the sampling design we chose involves revisits to the same sites; therefore, site variance does not affect our ability to detect trends. For all figures:α = 0.10; trend = 3% annual change; number of sites = 20, number of visits = 3.
62 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 178 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 179
frequent sampling in these areas. Sample occasion (year) Panel The annual budget for bird surveys will likely 1 2 3 4 5 6 7 8 9 10 be fixed at $20–25K/year for the first few years, Figure A.7. Typical 1 x x x x x x x x x x with additional funds needed during years when panel design used in surveying occurs at high elevations. At low el- 2 x x long-term monitoring evations, we will allocate effort that would have 3 x x programs. gone to high-elevation areas to increase num- 4 x x ber of sites and revisits in riparian areas (Table 5 x x A.1). The focus on these areas seems justified 6 x x because of their regional importance and be- cause of conservation concern for many ripar- 7 x x ian-obligate species. Even after reallocating ef- 8 x x fort, it may only be possible to estimate density 9 x x with a CV of 20% or greater for some species. 10 x x Increasing precision of density estimates can 11 x x also be accomplished by further stratifying ri- parian areas to maximize encounters of species of conservation value, particularly those that (but see Harding et al. 2005 for a single-species inhabit mesoriparian areas. We have attempted example). to accomplish this in the spatial sample design. Because variance has such an important influ- ence on trend detection and cost, we chose a 4.2 Other sampling design choices sampling design that will inform this variance We determined that we could estimate biologi- partitioning effort in future years. Specifically, cally meaningful changes in abundance of a suf- we will err on the side of oversampling for ficient number of species in select communities the first few years to capture these sources of to warrant implementation of the monitoring variance and enable a thorough review after protocol. Yet, based on other analyses (Figure 3–5 years. These analyses will help to refine our A.5), it seems our initial estimates of sample sampling design. More specifically, we will use size may be somewhat naïve, particularly for these data to investigate use of panel designs those species that exhibit significant year vari- (e.g., Urquhart et al. 1998; McDonald 2003) ance (Figure A.6). In light of this natural phe- for some communities, such as uplands. There nomenon, which no amount of extra sampling are numerous types of panel designs involving can remedy, our ability to detect trends may be various combinations of either revisiting the compromised. We refer the reader to Larson et same sites in a repeating pattern or visiting all al. (2001) for a detailed discussion of the differ- new sites each year. The most common panel ent types of variation and their consequences design, the augmented serial alternating de- for trend detection. In short, the addition of sign, for example, involves annual visitation to more monitoring sites and/or visits can increase a small number of sites, and annual sampling of trend detection, but only to a point, because the a larger number of sites on an annual repeat- numerator becomes dominated by time. Even ing pattern of 2–10 years (Figure A.7). These more important from a program-design per- designs were not selected because they involve spective is that if year variance is significant, complicated, even uncertain analyses, but are then there is little that can be done except wait expected to be refined in the future. for time to pass. In addition to the variance partitioning and Our analysis was based on data from a single power analyses explored in this effort, we also park. Unfortunately, we do not have data to suggest a more thorough analysis of residual inform us of how many species in our moni- variance that can be controlled by modifying toring region experience the different types of the protocol. For example, if the amount of variance we investigated, and to what degree. In variance attributed to observers is unaccept- fact, to our knowledge, there are no published ably high, then it may be appropriate to inves- manuscripts that assess the effects of different tigate ways to reduce this type of variance by sources of variance on trend detection for birds hiring more-skilled observers. Within-season
Appendix A - Power Analysis 63 180 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
variance may also be an important source of ony attendance of crevice-nesting horned variation (Link et al. 1994) that can be appropri- puffins: Implications for population moni- ately controlled by shifting the survey window. toring. Journal of Wildlife Management Miscellaneous errors include incorrect iden- 69:1279–1296. tification, data handling, and analytical errors, Kincaid, T. M., D. P. Larsen, and N. S. Urqu- which can be controlled through quality-con- hart. 2004. The structure of variation and trol and quality-assurance procedures. its influence on the estimation of status: Finally, it is worth noting that we excluded anal- Indicators of condition of lakes in the ysis of the three other parameters of interest northeast, U.S.A. Environmental Monitor- that can be evaluated using the same sampling ing and Assessment 98:1–21. design and survey methods: occupancy, relative Krebs, C. J. 1999. Ecological methodology. abundance, and species richness (see protocol Second edition. Menlo Park, Ca.: Addison- narrative). Though we recommend analyses of Welsey Educational Publishers. these parameters, we focused our initial effort on density because it is the most desirable pa- Larsen, D. P., T. M. Kincaid, S. E. Jacobs, and rameter. If it was determined that we could de- N. S. Urquhart. 2001. Designs for evaluat- rive only a few density estimates, then we would ing local and regional scale trends. Biosci- have concentrated on other parameters or cho- ence 51:1069–1078. sen not to pursue further development of the Lewis, W. M. 1978. Comparison of temporal landbird-monitoring protocol. and spatial variation in the zooplankton of a lake by means of variance components. 5 Literature Cited Ecology 59:666–671. Allen, C. D., and D. D. Breshears. 1998. Link, W. A., R. J. Barker, J. R. Sauer, and S. Drought-induced shift of a forest-wood- Droege. 1994. Within-site variability in land ecotone: Rapid landscape response to surveys of wildlife populations. Ecology climate variation. Proceedings of the Na- 75:1097–1108. tional Academy of Sciences of the United Marshall, R. M., S. Anderson, M. Batcher, P. States of America 95:14839–14842. Comer, S. Cornelius, R. Cox, A. Gondor, Bart, J., K. P. Burnham, E. H. Dunn, C. M. D. Gori, J. Humke, R. Paredes Aguilar, I. E. Francis, and C. J. Ralph. 2004. Goals and Parra, and S. Schwartz. 2000. An ecological strategies for estimating trends in landbird analysis of conservation priorities in the abundance. Journal of Wildlife Manage- Sonoran Desert Ecoregion. Prepared by ment 68:611–626. The Nature Conservancy Arizona Chapter, Sonoran Institute, and Instituto del Medio Buckland, S. T., D. R. Anderson, K. P. Burnham, Ambiente y el Desarrollo Sustentable del J. L. Laake, D. L. Borchers, and L. Thomas. Estado de Sonora, with support from De- 2001. Introduction to distance sampling: partment of Defense Legacy Program. Estimating abundance of biological popu- lations. Oxford, U.K.: Oxford University McDonald, T. L. 2003. Review of environmen- Press. tal monitoring methods: Survey designs. Environmental Monitoring and Assess- Eberhardt, L. L. 1978. Appraising variability ment 85:277–292. in population studies. Journal of Wildlife Management 42:207–238. McLaughlin, S. P. 1986. A floristic analysis of the southwestern United States. Great Gerrodette, T. 1987. A power analysis for de- Basin Naturalist 46:46–55. tecting trends. Ecology 68:1364–1372. National Park Service (NPS). 1998. Organ Pipe Gibbs, J. P., S. Droege, and P. Eagle. 1998. Mon- Cactus National Monument ecological itoring populations of plants and animals. monitoring program annual report 1994. BioScience 48:935–940. Organ Pipe Cactus National Monument, Harding, A. M. A., J. F. Piatt, G. V. Byrd, S. A. Ajo, Arizona. Hatch, N. B. Konyukhov, E. U. Golubova, Powell, B. F., C. A. Schmidt, and W. L. Halvor- and J. C. Williams. 2005. Variability in col-
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son. 2006. Vascular plant and vertebrate Thomas, L., J. L. Laake, S. Strindberg, F. F. C. inventory of Saguaro National Park, Marques, S. T. Buckland, D. L. Borchers, Rincon Mountain District. USGS OFR D. R. Anderson, K. P. Burnham, S. L. Pol- 2006-1075. USGS, Southwest Biological lard J. H. Hedley, J. R. B. Bishop, and T. A. Science Center, Sonoran Desert Research Marques. 2005. Distance 5.0. Release Beta Station, University of Arizona, Tucson, 5. Research Unit for Wildlife Population Arizona. Assessment, University of St. Andrews, Scotland. http:/www.ruspa.st-and.ac.uk/ Sims, M., S. Wanless, M. P. Harris, P. I. Mitch- distance. ell, and D. A. Elston. 2006. Evaluating the power of monitoring plot designs for Urquhart, N. S., S. G. Paulsen, and D. P. detecting long-term trends in the numbers Larsen. 1998. Monitoring for policy-rel- of common guillemots. Journal of Applied evant regional trends over time. Ecological Ecology 43:537–546. Applications 8:246–257. Steidl, R. J., J. P. Hayes, and E. Schauber. Whittaker, R. H., and W. A. Niering. 1965. Veg- 1997. Statistical power analysis in wildlife etation of the Santa Catalina Mountains, research. Journal of Wildlife Management Arizona: A gradient analysis of the south 61:270–279. slope. Ecology 46:429–452. Thomas, L., K. P. Burnham, and S. T. Buck- Williams, B. K., J. D. Nichols, and M. J. Conroy. land. 2004. Temporal inferences from 2002. Analysis and management of animal distance sampling survey. Pages 71–105 populations. San Diego, Ca.: Academic in S. T. Buckland, D. R. Anderson, K. P. Press. Burnham, J. L. Laake, D. L. Borshers, and L. Thomas, eds. Advanced distance sam- pling. New York: Oxford University Press.
Appendix A - Power Analysis 65 182 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
Table A.1. Number of surveys (point visits) needed for initial estimates of density with 15% confidence intervals on density estimates, Sonoran Desert Network parks.
Point visits for initial Species adequately proposed program monitored by effort Community Species Full Reduced (48K/ (24K/ Full Reduced
year) year) Encounter rate Number of point visits needed All communities Bewick’s wren 2,324 1,162 0.74 201 X X White-winged dove 2,324 1,162 0.73 205 X X Mourning dove 2,324 1,162 0.67 222 X X Ash-throated flycatcher 2,324 1,162 0.61 245 X X Spotted towhee 2,324 1,162 0.36 411 X X Northern mockingbird 2,324 1,162 0.29 514 X X House finch 2,324 1,162 0.28 523 X X Canyon towhee 2,324 1,162 0.26 560 X X Rufous-crowned sparrow 2,324 1,162 0.26 566 X X Canyon wren 2,324 1,162 0.25 586 X X Brown-headed cowbird 2,324 1,162 0.23 637 X X Scott’s oriole 2,324 1,162 0.22 671 X X Cassin’s kingbird 2,324 1,162 0.21 692 X X Ladder-backed woodpecker 2,324 1,162 0.17 846 X X Bridled titmouse 2,324 1,162 0.10 1,365 X Bushtit 2,324 1,162 0.10 1,410 X Rock wren 2,324 1,162 0.10 1,410 X Lesser goldfinch 2,324 1,162 0.10 1,439 X Blue grosbeak 2,324 1,162 0.06 2,086 X Blue-gray gnatcatcher 2,324 1,162 0.06 2,171 X Black-chinned sparrow 2,324 1,162 0.06 2,363 Black-chinned hummingbird 2,324 1,162 0.05 2,502 Common raven 2,324 1,162 0.03 3,598 Say’s phoebe 2,324 1,162 0.03 3,723 Purple martin 2,324 1,162 0.02 4,709 Greater roadrunner 2,324 1,162 0.02 4,815 Anna’s hummingbird 2,324 1,162 0.02 5,876 Cooper’s hawk 2,324 1,162 0.02 6,042 Turkey vulture 2,324 1,162 0.02 6,405 Juniper titmouse 2,324 1,162 0.02 6,405 Red-tailed hawk 2,324 1,162 0.01 7,811 High-elevation Mexican jay 488 244 0.54 276 X forest Black-headed grosbeak 488 244 0.50 299 X Black-throated gray warbler 488 244 0.49 301 X Hutton’s vireo 488 244 0.30 489 X Hepatic tanager 488 244 0.28 519 American robin 488 244 0.25 583 Plumbeous vireo 488 244 0.21 694
66 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 182 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 183
Table A.1. Number of surveys (point visits) needed for initial estimates of density with 15% confidence intervals on density estimates, Sonoran Desert Network parks, cont.
Point visits for initial Species adequately proposed program monitored by effort Community Species Full Reduced (48K/ (24K/ Full Reduced
year) year) Encounter rate Number of point visits needed High-elevation Acorn woodpecker 488 244 0.18 792 forest, cont. Western tanager 488 244 0.16 874 Yellow-eyed junco 488 244 0.16 892 Steller’s jay 488 244 0.16 912 Painted redstart 488 244 0.15 942 Grace’s warbler 488 244 0.13 1,102 House wren 488 244 0.13 1,102 Arizona woodpecker 488 244 0.12 1,147 Virginia’s warbler 488 244 0.10 1,442 Cordilleran flycatcher 488 244 0.09 1,467 Hermit thrush 488 244 0.09 1,519 Mountain chickadee 488 244 0.08 1,814 Brown creeper 488 244 0.06 2,083 Red-faced warbler 488 244 0.06 2,191 Warbling vireo 488 244 0.06 2,249 Yellow-rumped warbler 488 244 0.06 2,376 Broad-tailed hummingbird 488 244 0.05 2,596 Band-tailed pigeon 488 244 0.05 2,678 Hairy woodpecker 488 244 0.04 2,860 Greater pewee 488 244 0.04 3,068 Pygmy nuthatch 488 244 0.04 3,068 Sulphur-bellied flycatcher 488 244 0.03 3,751 Red-breasted nuthatch 488 244 0.03 4,117 Solitary vireo type 488 244 0.03 4,117 Western bluebird 488 244 0.03 4,117 Olive warbler 488 244 0.03 4,329 High-elevation Dusky-capped flycatcher 920 460 0.30 491 X riparian Western wood-pewee 920 460 0.20 716 X Yellow-breasted chat 920 460 0.20 716 X Northern flicker 920 460 0.19 765 X White-breasted nuthatch 920 460 0.14 1,038 Red-winged blackbird 920 460 0.09 1,471 Black phoebe 920 460 0.02 5,557 Common ground-dove 920 460 0.02 6,060
Appendix A - Power Analysis 67 184 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network
Table A.1. Number of surveys (point visits) needed for initial estimates of density with 15% confidence intervals on density estimates, Sonoran Desert Network parks, cont.
Point visits for initial Species adequately proposed program monitored by effort Community Species Full Reduced (48K/ (24K/ Full Reduced
year) year) Encounter rate Number of point visits needed Low-elevation Gila woodpecker 1,833 917 1.04 148 X X desert/grassland Gambel’s quail 1,833 917 0.97 206 X X Cactus wren 1,833 917 0.83 323 X X Black-throated sparrow 1,833 917 0.55 806 X X Low-elevation Verdin 1,833 917 0.55 262 X X desert/grassland, Lucy’s warbler 1,833 917 0.43 166 X X cont. Northern cardinal 1,833 917 0.39 191 X X Curve-billed thrasher 1,833 917 0.36 554 X X Brown-crested flycatcher 1,833 917 0.35 307 X X Phainopepla 1,833 917 0.18 426 X X Black-tailed gnatcatcher 1,833 917 0.17 1,571 X Pyrrhuloxia 1,833 917 0.13 7,488 Gilded flicker 1,833 917 0.10 8,816 Abert’s towhee 1,833 917 0.09 675 X X Rufous-winged sparrow 1,833 917 0.08 1,286 X Western scrub-jay 1,833 917 0.08 2,667 Bullock’s oriole 1,833 917 0.07 1,166 X Great-tailed grackle 1,833 917 0.06 1,866 X House sparrow 1,833 917 0.06 1,866 X Crissal thrasher 1,833 917 0.05 2,195 Western kingbird 1,833 917 0.04 1,798 X Varied bunting 1,833 917 0.03 2,017 Costa’s hummingbird 1,833 917 0.02 5,157 Great horned owl 1,833 917 0.02 7,488 Montezuma quail 1,833 917 0.01 18,847 Low-elevation Bell’s vireo 432 216 0.75 199 X X riparian Summer tanager 432 216 0.39 378 X Song sparrow 432 216 0.28 520 Yellow warbler 432 216 0.24 601 Vermilion flycatcher 432 216 0.22 666 Common yellowthroat 432 216 0.19 781 Broad-billed hummingbird 432 216 0.12 1,139 Gray hawk 432 216 0.11 1,320 Northern beardless-tyrannulet 432 216 0.09 1,622 Hooded oriole 432 216 0.08 1,735 Encounter rate is from pilot survey efforts in each community and represents the average number of detections point visit. See text for formulas for calculating number of surveys needed. Allocation of sampling effort for “Initial proposed program” will change based on the findings of this exercise. See protocol narrative for additional information.
68 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN
184 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network Section 4: Supplements 185 20% 657 860 397 438 266 171 110 681 335 277 173 654 513 474 258 254 169 108 962
2,111 1,149 1,250 2,035 15% 706 778 472 304 196 596 493 308 912 843 459 451 301 191
1,168 ,1528 3,753 1,211 2,043 2,223 1,162 3,617 1,710 %CV 10% Estimate of K at 685 441 694 676 431
2,627 3,439 1,588 1,750 1,063 8,444 2,725 1,341 1,109 4,597 5,001 2,615 2,052 1,896 1,033 1,015 8,139 3,847
b (n*CV2) b Scaling Parameter Parameter Scaling
2.72 3.36 2.86 4.16 3.01 2.69 3.27 4.84 2.34 2.04 2.98 3.31 2.42 4.57 2.66 2.59 3.20 2.48 4.30 3.62 4.13 2.59 2.42 CV (D) CV
0.283 0.156 0.220 0.231 0.180 0.145 0.116 0.389 0.221 0.155 0.141 0.112 0.232 0.242 0.175 0.155 0.149 0.110 0.109 0.089 0.071 0.240 0.165
(no/ha) Density Density
0.003 0.064 0.063 0.146 0.081 0.154 0.355 0.019 0.004 0.018 0.066 0.071 0.023 0.086 0.007 0.016 0.100 0.032 0.221 0.244 0.063 0.007 0.018
per point (n/K) point per Encounter rate rate Encounter
0.104 0.098 0.180 0.238 0.284 0.393 0.741 0.057 0.086 0.152 0.269 0.477 0.053 0.091 0.102 0.126 0.169 0.240 0.424 0.535 0.960 0.032 0.063 Points x visits (K) visits x Points 328 328 328 328 328 328 558 558 558 558 558 854 854 854 854 854 854 854 854 854
1,413 1,413 1,413 Obs (n) Obs
34 59 78 93 32 48 85 45 78 87 45 89
138 129 243 150 266 108 144 205 362 457 820 TUZI
x x x x x x x x x x x x x x x x x x TUMA
x x x x x x x x x x x x x x x x x x TONT
x x x x x x x x x x x x x x x x x x SAGU–TMD
x x x x x x x x x x x x SAGU–RMD
x x x x x x x x x x x x x x x x x x x x x x x GICL x x x x x x x x
Park unit FOBO
x x x x x x x x x x x x x x x x x x CORO
x x x x x x x x x x x x x x x x x CHIR
x x x x x x x x CAGR x x x x x x x x x x x x Grey hawk Grey Common yellowthroat Yellow warbler Yellow Song sparrow Summer tanager Bell’s vireo Bell’s Red-faced warbler Hermit thrush Painted redstart Hepatic tanager Black-headed grosbeak Curve-billed thrasher Abert’s towhee Abert’s Gilded flicker Pyrrhuloxia Black-tailed gnatcatcher Ladder-backed woodpecker Ladder-backed Lucy’s warbler Lucy’s Verdin Gambel’s quail Gambel’s Say’s phoebe Say’s Blue grosbeak Bridled titmouse Table A.2. Sample-size requirements for estimating density of select birds species using distance sampling at three levels of precision. species using distance sampling at three for estimating density of select birds A.2. Sample-size requirements Table Community/Species Low-elevation riparian areas High-elevation Low-elevation uplands All communities and parks
Appendix A - Power Analysis 69
186 Landbird Monitoring Protocol and SOPs for the Sonoran Desert Network 20%
475 319 226 183 163 15% 845 567 402 326 290
%CV 10% Estimate of K at 904 732 653
1,901 1,275
b (n*CV2) b
Scaling Parameter Parameter Scaling
3.12 2.64 3.23 4.32 4.74 CV (D) CV
0.116 0.095 0.080 0.072 0.068
(no/ha) Density Density
0.024 0.018 0.087 0.078 0.308
per point (n/K) point per Encounter rate rate Encounter
0.164 0.207 0.357 0.590 0.725 Points x visits (K) visits x Points
1,413 1,413 1,413 1,413 1,413 Obs (n) Obs
232 292 504 833
1025 TUZI
x x x x x TUMA
x x x x x TONT
x x x x x SAGU–TMD
x x x x x SAGU–RMD
x x x x x GICL x x x x x
Park unit FOBO
x x x x x CORO
x x x x x CHIR
x x x x x CAGR x x x x x Ladder-backed woodpecker Ladder-backed Scott’s oriole Scott’s Spotted towhee Ash-throated flycatcher Ash-throated Bewick’s wren Bewick’s Community/Species Table A.2. Sample-size requirements for estimating density of select birds species using distance sampling at three levels precision, cont.. Table All communities and parks, cont. narrative for park-unit See protocol DISTANCE. computed in program point counts within 10 Sonoran Desert Network park units between 2000 and 2005. Density estimates CV were from Data are acronyms.
70 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Appendix B. Four-Letter Bird Codes for Birds in Chi- huhuan Desert, Northern Great Plains, Sonoran Des- ert, and Southern Plains Networks
Common Name Code Common Name Code Abert's Towhee ABTO Bewick's Wren BEWR Acadian Flycatcher ACFL Black Phoebe BLPH Acorn Woodpecker ACWO Black Rail BLRA Alder Flycatcher ALFL Black Tern BLTE American Avocet AMAV Black Vulture BLVU American Bittern AMBI Black-and-white Warbler BAWW American Black Duck ABDU Black-backed Woodpecker BBWO American Coot AMCO Black-backed Woodpecker BBWO American Crow AMCR Black-bellied Plover BBPL American Dipper AMDI Black-bellied Whistling-Duck BBWD American Golden-Plover AMGP Black-billed Cuckoo BBCU American Goldfinch AMGO Black-billed Magpie BBMA American Kestrel AMKE Blackburnian Warbler BLBW American Pipit AMPI Black-capped Chickadee BCCH American Redstart AMRE Black-capped Gnatcatcher BCGN American Robin AMRO Black-capped Vireo BCVI American Three-toed Woodpecker ATTW Black-chinned Hummingbird BCHU American Tree Sparrow ATSP Black-chinned Sparrow BCSP American White Pelican AWPE Black-crested Titmouse BCTI American Wigeon AMWI Black-crowned Night-Heron BCNH American Woodcock AMWO Black-headed Grosbeak BHGR Anhinga ANHI Black-legged Kittiwake BLKI Anna's Hummingbird ANHU Black-necked Stilt BNST Aplomado Falcon APFA Blackpoll Warbler BLPW Arizona Woodpecker AZWO Black-tailed Gnatcatcher BTGN Ash-throated Flycatcher ATFL Black-throated Blue Warbler BTBW Aztec Thrush AZTH Black-throated Gray Warbler BTYW Baird’s Sandpiper BASA Black-throated Green Warbler BTNW Baird's Sparrow BAIS Black-throated Sparrow BTSP Bald Eagle BAEA Black-vented Oriole BVOR Baltimore Oriole BAOR Blue Grosbeak BLGR Band-tailed Pigeon BTPI Blue Jay BLJA Bank Swallow BANS Blue-gray Gnatcatcher BGGN Barn Owl BNOW Blue-headed Vireo BHVI Barn Swallow BARS Blue-throated Hummingbird BLUH Barred Owl BDOW Blue-winged Teal BWTE Bay-breasted Warbler BBWA Blue-winged Warbler BWWA Bell's Vireo BEVI Boat-tailed Grackle BTGR Belted Kingfisher BEKI Bobolink BOBO Bendire's Thrasher BETH Bohemian Waxwing BOWA Berylline Hummingbird BEHU Bonaparte’s Gull BOGU
Appendix B - Four-letter Bird Codes 71 Common Name Code Common Name Code Botteri's Sparrow BOSP Chuck-will's-widow CWWI Brewer's Blackbird BRBL Chukar CHUK Brewer's Sparrow BRSP Cinnamon Teal CITE Bridled Titmouse BRTI Clapper Rail CLRA Broad-billed Hummingbird BBLH Clark’s Grebe CLGR Broad-tailed Hummingbird BTLH Clark's Nutcracker CLNU Broad-winged Hawk BWHA Clay-colored Sparrow CCSP Bronzed Cowbird BROC Clay-colored Thrush CCTH Brown Creeper BRCR Cliff Swallow CLSW Brown Pelican BRPE Colima Warbler COLW Brown Thrasher BRTH Common Black-Hawk CBHA Brown-crested Flycatcher BCFL Common Gallinule COGA Brown-headed Cowbird BHCO Common Goldeneye COGO Buff-breasted Flycatcher BBFL Common Grackle COGR Buff-breasted Sandpiper BBSA Common Ground-Dove COGD Buff-breasted Sandpiper BBSA Common Loon COLO Buff-collared Nightjar BCNI Common Merganser COME Bufflehead BUFF Common Nighthawk CONI Bullock's Oriole BUOR Common Poorwill COPO Burrowing Owl BUOW Common Raven CORA Bushtit BUSH Common Redpoll CORE Cactus Wren CACW Common Snipe COSN California Gull CAGU Common Tern COTE Calliope Hummingbird CAHU Common Yellowthroat COYE Canada Goose CANG Connecticut Warbler CONW Canada Warbler CAWA Cooper's Hawk COHA Canvasback CANV Cordilleran Flycatcher COFL Canyon Towhee CANT Costa's Hummingbird COHU Canyon Wren CANW Couch's Kingbird COKI Cape May Warbler CMWA Crescent-chested Warbler CCWA Carolina Chickadee CACH Crested Caracara CRCA Carolina Wren CARW Crissal Thrasher CRTH Caspian Tern CATE Curve-billed Thrasher CBTH Cassin's Finch CAFI Dark-eyed Junco DEJU Cassin's Kingbird CAKI Dickcissel DICK Cassin's Sparrow CASP Double-crested Cormorant DCCO Cassin's Vireo CAVI Downy Woodpecker DOWO Cattle Egret CAEG Dunlin DUNL Cave Swallow CASW Dusky Flycatcher DUFL Cedar Waxwing CEDW Dusky-capped Flycatcher DCFL Cerulean Warbler CERW Eared Grebe EAGR Chestnut-collared Longspur CCLO Eastern Bluebird EABL Chestnut-sided Warbler CSWA Eastern Kingbird EAKI Chihuahuan Raven CHRA Eastern Meadowlark EAME Chimney Swift CHSW Eastern Phoebe EAPH Chipping Sparrow CHSP Eastern Screech-Owl EASO
72 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common Name Code Common Name Code Eastern Towhee EATO Greater Roadrunner GRRO Eastern Whip-poor-will EWPW Greater Scaup GRSC Eastern Wood-Pewee EAWP Greater White-fronted Goose GWFG Elegant Trogon ELTR Greater Yellowlegs GRYE Elf Owl ELOW Great-tailed Grackle GTGR Eurasian Collared-Dove EUCD Green Heron GRHE Eurasian Wigeon EUWI Green Kingfisher GKIN European Starling EUST Green-tailed Towhee GTTO Evening Grosbeak EVGR Green-winged Teal AGWT Fan-tailed Warbler FTWA Groove-billed Ani GBAN Ferruginous Hawk FEHA Gull-billed Tern GBTE Ferruginous Pygmy-Owl FEPO Hairy Woodpecker HAWO Field Sparrow FISP Hammond's Flycatcher HAFL Flame-colored Tanager FCTA Harris’s Sparrow HASP Flammulated Owl FLOW Harris's Hawk HRSH Forster’s Tern FOTE Heermann's Gull HEEG Fox Sparrow FOSP Hepatic Tanager HETA Franklin's Gull FRGU Hermit Thrush HETH Gadwall GADW Hermit Warbler HEWA Gambel's Quail GAQU Herring Gull HERG Gila Woodpecker GIWO Hoary Redpoll HORE Gilded Flicker GIFL Hoary Redpoll HORE Glaucous Gull GLGU Hooded Merganser HOME Golden Eagle GOEA Hooded Oriole HOOR Golden-cheeked Warbler GCWA Hooded Warbler HOWA Golden-crowned Kinglet GCKI Horned Grebe HOGR Golden-crowned Sparrow GCSP Horned Lark HOLA Golden-fronted Woodpecker GFWO House Finch HOFI Golden-winged Warbler GWWA House Sparrow HOSP Grace's Warbler GRWA House Wren HOWR Grasshopper Sparrow GRSP Hudsonian Godwit HUGO Gray Catbird GRCA Hutton's Vireo HUVI Gray Flycatcher GRFL Inca Dove INDO Gray Hawk GRHA Indigo Bunting INBU Gray Jay GRAJ Juniper Titmouse JUTI Gray Partridge GRPA Kentucky Warbler KEWA Gray Vireo GRVI Killdeer KILL Gray-cheeked Thrush GCTH King Rail KIRA Gray-crowned Rosy-Finch GCRF Ladder-backed Woodpecker LBWO Great Blue Heron GBHE Lapland Longspur LALO Great Crested Flycatcher GCFL Lark Bunting LARB Great Egret GREG Lark Sparrow LASP Great Horned Owl GHOW Laughing Gull LAGU Great Kiskadee GKIS Lawrence's Goldfinch LAGO Greater Pewee GRPE Lazuli Bunting LAZB Greater Prairie-Chicken GRPC Le Conte’s Sparrow LCSP
Appendix B - Four-letter Bird Codes 73 Common Name Code Common Name Code Le Conte's Thrasher LCTH Northern Goshawk NOGO Least Bittern LEBI Northern Harrier NOHA Least Flycatcher LEFL Northern Mockingbird NOMO Least Grebe LEGR Northern Parula NOPA Least Sandpiper LESA Northern Pintail NOPI Least Tern LETE Northern Pygmy-Owl NOPO Lesser Goldfinch LEGO Northern Rough-winged Swallow NRWS Lesser Nighthawk LENI Northern Saw-whet Owl NSWO Lesser Prairie-Chicken LEPC Northern Shoveler NSHO Lesser Scaup LESC Northern Shrike NSHR Lesser Yellowlegs LEYE Northern Waterthrush NOWA Lewis's Woodpecker LEWO Olive Sparrow OLSP Lincoln's Sparrow LISP Olive Warbler OLWA Little Blue Heron LBHE Olive-sided Flycatcher OSFL Loggerhead Shrike LOSH Orange-crowned Warbler OCWA Long-billed Curlew LBCU Orchard Oriole OROR Long-billed Dowitcher LBDO Osprey OSPR Long-billed Thrasher LBTH Ovenbird OVEN Long-eared Owl LEOW Pacific Loon PALO Louisiana Waterthrush LOWA Pacific-slope Flycatcher PSFL Lucifer Hummingbird LUHU Painted Bunting PABU Lucy's Warbler LUWA Painted Redstart PARE MacGillivray's Warbler MGWA Palm Warbler PAWA Magnificent Hummingbird MAHU Pectoral Sandpiper PESA Magnolia Warbler MAWA Peregrine Falcon PEFA Mallard MALL Phainopepla PHAI Marbled Godwit MAGO Philadelphia Vireo PHVI Marsh Wren MAWR Pied-billed Grebe PBGR McCown's Longspur MCLO Pileated Woodpecker PIWO Merlin MERL Pine Grosbeak PIGR Mexican Chickadee MECH Pine Siskin PISI Mexican Jay MEJA Pine Warbler PIWA Mexican Whip-poor-will MWPW Pinyon Jay PIJA Mississippi Kite MIKI Piping Plover PIPL Montezuma Quail MONQ Piratic Flycatcher PIFL Mountain Bluebird MOBL Plain-capped Starthroat PCST Mountain Chickadee MOCH Plumbeous Vireo PLVI Mountain Plover MOPL Prairie Falcon PRFA Mourning Dove MODO Prairie Warbler PRAW Mourning Warbler MOWA Prothonotary Warbler PROW Nashville Warbler NAWA Purple Finch PUFI Neotropic Cormorant NECO Purple Gallinule PUGA Northern Beardless-Tyrannulet NBTY Purple Martin PUMA Northern Bobwhite NOBO Pygmy Nuthatch PYNU Northern Cardinal NOCA Pyrrhuloxia PYRR Northern Flicker NOFL Red Crossbill RECR
74 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common Name Code Common Name Code Red Phalarope REPH Savannah Sparrow SAVS Red-bellied Woodpecker RBWO Say's Phoebe SAPH Red-breasted Merganser RBME Scaled Quail SCQU Red-breasted Nuthatch RBNU Scarlet Tanager SCTA Red-breasted Sapsucker RBSA Scissor-tailed Flycatcher STFL Reddish Egret REEG Scott's Oriole SCOR Red-eyed Vireo REVI Sedge Wren SEWR Red-faced Warbler RFWA Sedge Wren SEWR Redhead REDH Semipalmated Plover SEPL Red-headed Woodpecker RHWO Semipalmated Sandpiper SESA Red-naped Sapsucker RNSA Sharp-shinned Hawk SSHA Red-necked Grebe RNGR Sharp-tailed Grouse STGR Red-necked Phalarope RNPH Short-eared Owl SEOW Red-necked Phalarope RNPH Short-tailed Hawk STHA Red-shouldered Hawk RSHA Slate-throated Redstart STRE Red-tailed Hawk RTHA Smith's Longspur SMLO Red-winged Blackbird RWBL Snow Bunting SNBU Ring-billed Gull RBGU Snow Goose SNGO Ringed Kingfisher RIKI Snowy Egret SNEG Ring-necked Duck RNDU Snowy Owl SNOW Ring-necked Pheasant RINP Snowy Plover SNPL Rock Pigeon ROPI Solitary Sandpiper SOSA Rock Wren ROWR Song Sparrow SOSP Roseate Spoonbill ROSP Sora SORA Rose-breasted Grosbeak RBGR Spotted Owl SPOW Rose-throated Becard RTBE Spotted Owl SPOW Ross’s Goose ROGO Spotted Sandpiper SPSA Rough-legged Hawk RLHA Spotted Towhee SPTO Royal Tern ROYT Sprague's Pipit SPPI Ruby-crowned Kinglet RCKI Steller's Jay STJA Ruby-throated Hummingbird RTHU Stilt Sandpiper STSA Ruddy Duck RUDU Streak-backed Oriole STRO Ruddy Ground-Dove RUGD Sulphur-bellied Flycatcher SBFL Ruddy Turnstone RUTU Summer Tanager SUTA Ruffed Grouse RUGR Surf Scoter SUSC Rufous Hummingbird RUHU Swainson's Hawk SWHA Rufous-backed Robin RBRO Swainson's Thrush SWTH Rufous-capped Warbler RCWA Swainson's Warbler SWWA Rufous-crowned Sparrow RCSP Swallow-tailed Kite STKI Rufous-winged Sparrow RWSP Swamp Sparrow SWSP Rusty Blackbird RUBL Tennessee Warbler TEWA Sabine’s Gull SAGU Thayer’s Gull THGU Sage Sparrow SAGS Thick-billed Kingbird TBKI Sage Thrasher SATH Townsend's Solitaire TOSO Sanderling SAND Townsend's Warbler TOWA Sandhill Crane SACR Tree Swallow TRES
Appendix B - Four-letter Bird Codes 75 Common Name Code Common Name Code Tricolored Heron TRHE White-winged Scoter WWSC Tropical Kingbird TRKI Whooping Crane WHCR Tropical Parula TRPA Wild Turkey WITU Trumpeter Swan TRUS Willet WILL Tufted Flycatcher TUFL Williamson's Sapsucker WISA Tufted Titmouse TUTI Willow Flycatcher WIFL Tundra Swan TUSW Wilson’s Phalarope WIPH Turkey Vulture TUVU Wilson's Snipe WISN Upland Sandpiper UPSA Wilson's Warbler WIWA Varied Bunting VABU Winter Wren WIWR Varied Thrush VATH Wood Duck WODU Vaux's Swift VASW Wood Stork WOST Veery VEER Wood Thrush WOTH Verdin VERD Worm-eating Warbler WEWA Vermilion Flycatcher VEFL Yellow Grosbeak YEGR Vesper Sparrow VESP Yellow Rail YERA Violet-crowned Hummingbird VCHU Yellow Warbler YEWA Violet-green Swallow VGSW Yellow-bellied Flycatcher YBFL Virginia Rail VIRA Yellow-bellied Sapsucker YBSA Virginia's Warbler VIWA Yellow-billed Cuckoo YBCU Warbling Vireo WAVI Yellow-breasted Chat YBCH Western Bluebird WEBL Yellow-crowned Night-Heron YCNH Western Grebe WEGR Yellow-eyed Junco YEJU Western Kingbird WEKI Yellow-green Vireo YGVI Western Meadowlark WEME Yellow-headed Blackbird YHBL Western Sandpiper WESA Yellow-rumped Warbler YRWA Western Screech-Owl WESO Yellow-throated Vireo YTVI Western Scrub-Jay WESJ Yellow-throated Warbler YTWA Western Tanager WETA Zone-tailed Hawk ZTHA Western Wood-Pewee WEWP Whimbrel WHIM Whiskered Screech-Owl WHSO White Ibis WHIB White-breasted Nuthatch WBNU White-crowned Sparrow WCSP White-eared Hummingbird WEHU White-eyed Vireo WEVI White-faced Ibis WFIB White-rumped Sandpiper WRSA White-tailed Hawk WTHA White-tailed Kite WTKI White-throated Sparrow WTSP White-throated Swift WTSW White-tipped Dove WTDO White-winged Crossbill WWCR White-winged Dove WWDO
76 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Appendix C. Landbird Sampling Location Maps C.1. Chihuahuan Desert Network
Appendix C - Landbird Sampling Location Maps 77 Figure C.1-1: Amistad NRA (east view) landbird sampling locations C.1-1: Amistad NRA (east view) landbird Figure
78 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.1-2: Amistad NRA (west view) / Rio Grande WSR landbird sampling locations C.1-2: Amistad NRA (west view) / Rio Grande WSR landbird Figure
Appendix C - Landbird Sampling Location Maps 79 Figure C.1-3: Big Bend NP (north view) landbird sampling locations C.1-3: Big Bend NP (north view) landbird Figure
80 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.1-4: Big Bend NP (south view) landbird sampling locations C.1-4: Big Bend NP (south view) landbird Figure
Appendix C - Landbird Sampling Location Maps 81 Figure C.1-5: Big Bend NP (west view) landbird sampling locations C.1-5: Big Bend NP (west view) landbird Figure
82 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.1-6: Carlsbad Caverns NP landbird sampling locations C.1-6: Carlsbad Caverns Figure NP landbird
Appendix C - Landbird Sampling Location Maps 83 Figure C.1-7: Fort Davis NHS landbird sampling locations C.1-7: Fort Davis NHS landbird Figure
84 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.1-8: Guadalupe Mountains NP landbird sampling locations C.1-8: Guadalupe Mountains NP landbird Figure
Appendix C - Landbird Sampling Location Maps 85 Figure C.1-9: White Sands NM landbird sampling locations C.1-9: White Sands NM landbird Figure
86 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN C.2. Northern Great Plains Network
Appendix C - Landbird Sampling Location Maps 87 Figure C.2-1: Agate Fossil Beds NM landbird sampling locations C.2-1: Agate Fossil Beds NM landbird Figure
88 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.2-2: Badlands NP landbird sampling locations C.2-2: Badlands NP landbird Figure
Appendix C - Landbird Sampling Location Maps 89 Figure C.2-3: Devils Tower NM landbird sampling locations NM landbird C.2-3: Devils Tower Figure
90 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.2-4: Fort Laramie NHS landbird sampling locations C.2-4: Fort Laramie NHS landbird Figure
Appendix C - Landbird Sampling Location Maps 91 Figure C.2-5: Fort Union Trading Post NHS landbird sampling locations Post NHS landbird C.2-5: Fort Union Trading Figure
92 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.2-6: Jewel Cave NM landbird sampling locations C.2-6: Jewel Cave NM landbird Figure
Appendix C - Landbird Sampling Location Maps 93 Figure C.2-7: Knife River Indian Villages NHS landbird sampling locations
94 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.2-8: Missouri NRR potential landbird sampling grids C.2-8: Missouri NRR potential landbird Figure
Appendix C - Landbird Sampling Location Maps 95 Figure C.2-9: Mount Rushmore NMEM landbird sampling locations NMEM landbird C.2-9: Mount Rushmore Figure
96 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.2-10: Niobrara NSR potential landbird sampling grids C.2-10: Niobrara NSR potential landbird Figure
Appendix C - Landbird Sampling Location Maps 97 Figure C.2-11: Scotts Bluff NM landbird sampling locations NM landbird C.2-11: Scotts Bluff Figure
98 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.2-12: Theodore Roosevelt NP landbird sampling locations Roosevelt NP landbird C.2-12: Theodore Figure
Appendix C - Landbird Sampling Location Maps 99 Figure C.2-13: Wind Cave NP landbird sampling locations C.2-13: Wind Cave NP landbird Figure
100 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN C.3. Sonoran Desert Network
Appendix C - Landbird Sampling Location Maps 101 Figure C.3-1: Casa Grande Ruins NM landbird sampling locations C.3-1: Casa Grande Ruins NM landbird Figure
102 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.3-2: Chiricahua NM landbird sampling locations C.3-2: Chiricahua NM landbird Figure
Appendix C - Landbird Sampling Location Maps 103 Figure C.3-3: Coronado NMEN landbird sampling locations NMEN landbird C.3-3: Coronado Figure
104 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.3-4: Fort Bowie NHS landbird sampling locations C.3-4: Fort Bowie NHS landbird Figure
Appendix C - Landbird Sampling Location Maps 105 Figure C.3-5: Gila Cliff Dwellings NM landbird sampling locations
106 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.3-6: Montezuma Castle NM landbird sampling locations C.3-6: Montezuma Castle NM landbird Figure
Appendix C - Landbird Sampling Location Maps 107 Figure C.3-7: Organ Pipe Cactus NM landbird sampling locations C.3-7: Organ Pipe Cactus NM landbird Figure
108 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.3-8: Saguaro NP (Rincon Mountain District) landbird sampling locations NP (Rincon Mountain District) landbird C.3-8: Saguaro Figure
Appendix C - Landbird Sampling Location Maps 109 Figure C.3-9: Saguaro NP (Tucson Mountain District) landbird sampling locations Mountain District) landbird NP (Tucson C.3-9: Saguaro Figure
110 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.3-10: Tonto NM landbird sampling locations
Appendix C - Landbird Sampling Location Maps 111 Figure C.3-11: Tumacácori NHP landbird sampling locations
112 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.3-12: Tuzigoot NM landbird sampling locations NM landbird C.3-12: Tuzigoot Figure
Appendix C - Landbird Sampling Location Maps 113 C.4. Southern Plains Network
114 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.4-1: Bent’s Old Fort NHS landbird sampling locations
Appendix C - Landbird Sampling Location Maps 115 Figure C.4-2: Capulin Volcano NM landbird sampling locations NM landbird C.4-2: Capulin Volcano Figure
116 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.4-3: Chickasaw NRA landbird sampling locations C.4-3: Chickasaw NRA landbird Figure
Appendix C - Landbird Sampling Location Maps 117 Figure C.4-4: Fort Larned NHS landbird sampling locations C.4-4: Fort Larned NHS landbird Figure
118 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.4-5: Fort Union NM landbird sampling locations C.4-5: Fort Union NM landbird Figure
Appendix C - Landbird Sampling Location Maps 119 Figure C.4-6: Lake Meredith NRA landbird sampling locations NRA landbird C.4-6: Lake Meredith Figure
120 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.4-7: Lyndon B. Johnson NHP landbird sampling locations
Appendix C - Landbird Sampling Location Maps 121 Figure C.4-8: Pecos NHP landbird sampling locations
122 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure C.4-9: Sand Creek Massacre NHS landbird sampling locations
Appendix C - Landbird Sampling Location Maps 123 Figure C.4-10: Washita Battlefield NHS landbird sampling locations Battlefield NHS landbird C.4-10: Washita Figure
124 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Appendix D. Landbird species documented in CHDN, NGPN, SODN, and SOPN parks
Table D-1. Bird species known to occur in CHDN parks (through [including] 2012 sampling). Includes species that migrate through or winter in the park.
Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Acorn Woodpecker Melanerpes formicivorus • • • • • American Avocet Recurvirostra americana • • • • American Bittern Botaurus lentiginosus • • • • American Coot Fulica americana • • • • • American Crow Corvus brachyrhynchos • • American Dipper Cinclus mexicanus • • • American Goldfinch Spinus tristis • • • • • • American Kestrel Falco sparverius • • • • • • American Pipit Anthus rubescens • • • • • American Redstart Setophaga ruticilla • • • • • American Robin Turdus migratorius • • • • • • American Tree Sparrow Spizella arborea • • • American White Pelican Pelecanus erythrorhynchos • • • • American Wigeon Anas americana • • • • • American Woodcock Scolopax minor • • Anhinga Anhinga anhinga • • Anna's Hummingbird Calypte anna • • • Aplomado Falcon Falco femoralis • • Ash-throated Flycatcher Myiarchus cinerascens • • • • • • Aztec Thrush Ridgwayia pinicola • Baird's Sandpiper Calidris bairdii • • • • Baird's Sparrow Ammodramus bairdii • • • Bald Eagle Haliaeetus leucocephalus • • • • Baltimore Oriole Icterus galbula • • • • Band-tailed Pigeon Patagioenas fasciata • • • Bank Swallow Riparia riparia • • • • Barn Owl Tyto alba • • • • Barn Swallow Hirundo rustica • • • • • • Bay-breasted Warbler Dendroica castanea • • Bell's Vireo Vireo bellii • • • • • Belted Kingfisher Megaceryle alcyon • • • • • Berylline Hummingbird Amazilia beryllina • Bewick's Wren Thryomanes bewickii • • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 125 Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Black Phoebe Sayornis nigricans • • • • • • Black Tern Chlidonias niger • • • • Black Vulture Coragyps atratus • • • Black-and-white Warbler Mniotilta varia • • • • • Black-bellied Plover Pluvialis squatarola • • Black-bellied Whistling-Duck Dendrocygna autumnalis • • • Black-billed Cuckoo Coccyzus erythropthalmus • • Black-billed Magpie Pica hudsonia • • Blackburnian Warbler Dendroica fusca • • Black-capped Vireo Vireo atricapilla • • Black-chinned Hummingbird Archilochus alexandri • • • • • • Black-chinned Sparrow Spizella atrogularis • • • • • Black-crested Titmouse Baeolophus atricristatus • • • • Black-crowned Night-Heron Nycticorax nycticorax • • • • Black-headed Grosbeak Pheucticus melanocephalus • • • • • Black-legged Kittiwake Rissa tridactyla • Black-necked Stilt Himantopus mexicanus • • • • Blackpoll Warbler Dendroica striata • • Black-tailed Gnatcatcher Polioptila melanura • • • • • • Black-throated Blue Warbler Dendroica caerulescens • • • • • Black-throated Gray Warbler Dendroica nigrescens • • • • Black-throated Green Warbler Dendroica virens • • Black-throated Sparrow Amphispiza bilineata • • • • • • Black-vented Oriole Icterus wagleri • Blue Grosbeak Passerina caerulea • • • • • • Blue Jay Cyanocitta cristata • • • Blue-gray Gnatcatcher Polioptila caerulea • • • • • • Blue-headed Vireo Vireo solitarius • • • Blue-throated Hummingbird Lampornis clemenciae • • • Blue-winged Teal Anas discors • • • • Blue-winged Warbler Vermivora cyanoptera • • Bobolink Dolichonyx oryzivorus • • Bonaparte's Gull Chroicocephalus philadelphia • • • Brewer's Blackbird Euphagus cyanocephalus • • • • • • Brewer's Sparrow Spizella breweri • • • • • • Broad-billed Hummingbird Cynanthus latirostris • • • Broad-tailed Hummingbird Selasphorus platycercus • • • • •
126 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Broad-winged Hawk Buteo platypterus • • Bronzed Cowbird Molothrus aeneus • • • • • • Brown Creeper Certhia americana • • • • Brown Pelican Pelecanus occidentalis • • • Brown Thrasher Toxostoma rufum • • • • Brown-crested Flycatcher Myiarchus tyrannulus • • • • Brown-headed Cowbird Molothrus ater • • • • • • Bufflehead Bucephala albeola • • • • • Bullock's Oriole Icterus bullockii • • • • • • Burrowing Owl Athene cunicularia • • • • • Bushtit Psaltriparus minimus • • • • • Cactus Wren Campylorhynchus brunneicapillus • • • • • • Calliope Hummingbird Stellula calliope • • • • Canada Goose Branta canadensis • • • Canada Warbler Wilsonia canadensis • • Canvasback Aythya valisineria • • • • Canyon Towhee Melozone fusca • • • • • • Canyon Wren Catherpes mexicanus • • • • • Cape May Warbler Dendroica tigrina • • Carolina Wren Thryothorus ludovicianus • • • • Caspian Tern Hydroprogne caspia • Cassin's Finch Carpodacus cassinii • • • • • Cassin's Kingbird Tyrannus vociferans • • • • • Cassin's Sparrow Peucaea cassinii • • • • • • Cassin's Vireo Vireo cassinii • • • Cattle Egret Bubulcus ibis • • • • • Cave Swallow Petrochelidon fulva • • • • Cedar Waxwing Bombycilla cedrorum • • • • • • Cerulean Warbler Dendroica cerulea • • Chestnut-collared Longspur Calcarius ornatus • • • • • Chestnut-sided Warbler Dendroica pensylvanica • • • • Chihuahuan Raven Corvus cryptoleucus • • • • • • Chimney Swift Chaetura pelagica • • • Chipping Sparrow Spizella passerina • • • • • • Cinnamon Teal Anas cyanoptera • • • • Clark's Grebe Aechmophorus clarkii • Clark's Nutcracker Nucifraga columbiana • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 127 Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Clay-colored Sparrow Spizella pallida • • • • • • Clay-colored Thrush Turdus grayi • Cliff Swallow Petrochelidon pyrrhonota • • • • • • Colima Warbler Oreothlypis crissalis • • Common Black-Hawk Buteogallus anthracinus • • • • Common Gallinule 1 Gallinula chloropus • • Common Goldeneye Bucephala clangula • • Common Grackle Quiscalus quiscula • • • • Common Ground-Dove Columbina passerina • • • • Common Loon Gavia immer • • • Common Merganser Mergus merganser • • • Common Nighthawk Chordeiles minor • • • • • • Common Poorwill Phalaenoptilus nuttallii • • • • • Common Raven Corvus corax • • • • • • Common Yellowthroat Geothlypis trichas • • • • • Connecticut Warbler Oporornis agilis • Cooper's Hawk Accipiter cooperii • • • • • • Cordilleran Flycatcher Empidonax occidentalis • • • • • Costa's Hummingbird Calypte costae • Couch's Kingbird Tyrannus couchii • • Crescent-chested Warbler Oreothlypis superciliosa • Crested Caracara Caracara cheriway • • Crissal Thrasher Toxostoma crissale • • • • Curve-billed Thrasher Toxostoma curvirostre • • • • • • Dark-eyed Junco Junco hyemalis • • • • • Dickcissel Spiza americana • • • • • Double-crested Cormorant Phalacrocorax auritus • • • Downy Woodpecker Picoides pubescens • • Dunlin Calidris alpina • Dusky Flycatcher Empidonax oberholseri • • • • • Dusky-capped Flycatcher Myiarchus tuberculifer • • Eared Grebe Podiceps nigricollis • • • • Eastern Bluebird Sialia sialis • • • Eastern Kingbird Tyrannus tyrannus • • Eastern Meadowlark Sturnella magna • • • • • • Eastern Phoebe Sayornis phoebe • • • • Eastern Screech-Owl Megascops asio • •
128 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Eastern Towhee Pipilo erythrophthalmus • • Eastern Wood-Pewee Contopus virens • • • Elegant Trogon Trogon elegans • Elf Owl Micrathene whitneyi • • • • Eurasian Collared-Dove Streptopelia decaocto • • • • • • Eurasian Wigeon Anas penelope • European Starling Sturnus vulgaris • • • • • • Evening Grosbeak Coccothraustes vespertinus • • • • Fan-tailed Warbler Euthlypis lachrymosa • Ferruginous Hawk Buteo regalis • • • • • Ferruginous Pygmy-Owl Glaucidium brasilianum • Field Sparrow Spizella pusilla • • • • Flame-colored Tanager Piranga bidentata • Flammulated Owl Otus flammeolus • • • Forster's Tern Sterna forsteri • • • Fox Sparrow Passerella iliaca • • • Franklin's Gull Leucophaeus pipixcan • • • Gadwall Anas strepera • • • • Gambel’s Quail Callipepla gambelii • • • Golden Eagle Aquila chrysaetos • • • • • • Golden-cheeked Warbler Dendroica chrysoparia • Golden-crowned Kinglet Regulus satrapa • • • • Golden-crowned Sparrow Zonotrichia atricapilla • • • Golden-fronted Woodpecker Melanerpes aurifrons • • Golden-winged Warbler Vermivora chrysoptera • • Grace's Warbler Dendroica graciae • • • Grasshopper Sparrow Ammodramus savannarum • • • • • Gray Catbird Dumetella carolinensis • • • • Gray Flycatcher Empidonax wrightii • • • • • Gray Hawk Buteo nitidus • • • Gray Vireo Vireo vicinior • • • • Gray-cheeked Thrush Catharus minimus • • • Great Blue Heron Ardea herodias • • • • • • Great Crested Flycatcher Myiarchus crinitus • • Great Egret Ardea alba • • • • • Great Horned Owl Bubo virginianus • • • • • • Great Kiskadee Pitangus sulphuratus • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 129 Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Greater Pewee Contopus pertinax • • • • Greater Roadrunner Geococcyx californianus • • • • • • Greater White-fronted Goose Anser albifrons • • Greater Yellowlegs Tringa melanoleuca • • • • Great-tailed Grackle Quiscalus mexicanus • • • • • Green Heron Butorides virescens • • • • Green Kingfisher Chloroceryle americana • • Green-tailed Towhee Pipilo chlorurus • • • • • • Green-winged Teal Anas crecca • • • • • Groove-billed Ani Crotophaga sulcirostris • • • Gull-billed Tern Gelochelidon nilotica • Hairy Woodpecker Picoides villosus • • Hammond's Flycatcher Empidonax hammondii • • • Harris’s Hawk Parabuteo unicinctus • • • • Harris's Sparrow Zonotrichia querula • • • Hepatic Tanager Piranga flava • • • • • Hermit Thrush Catharus guttatus • • • • • • Hermit Warbler Dendroica occidentalis • • • Herring Gull Larus argentatus • • Hooded Merganser Lophodytes cucullatus • • • Hooded Oriole Icterus cucullatus • • • • • Hooded Warbler Wilsonia citrina • • • • Horned Grebe Podiceps auritus • • Horned Lark Eremophila alpestris • • • • • • House Finch Carpodacus mexicanus • • • • • • House Sparrow Passer domesticus • • • • • • House Wren Troglodytes aedon • • • • • • Hutton's Vireo Vireo huttoni • • • Inca Dove Columbina inca • • • • Indigo Bunting Passerina cyanea • • • • • • Interior Least Tern Sterna antillarum athalassos • Juniper Titmouse Baeolophus ridgwayi • • • Kentucky Warbler Oporornis formosus • • • • Killdeer Charadrius vociferus • • • • • • King Rail Rallus elegans • Ladder-backed Woodpecker Picoides scalaris • • • • • • Lark Bunting Calamospiza melanocorys • • • • • •
130 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Lark Sparrow Chondestes grammacus • • • • • • Laughing Gull Leucophaeus atricilla • • Lawrence's Goldfinch Spinus lawrencei • Lazuli Bunting Passerina amoena • • • • Le Conte’s Sparrow Ammodramus leconteii • • • Least Bittern Ixobrychus exilis • • Least Flycatcher Empidonax minimus • • • Least Grebe Tachybaptus dominicus • Least Sandpiper Calidris minutilla • • • • Least Tern Sterna antillarum • • Lesser Goldfinch Spinus psaltria • • • • • • Lesser Nighthawk Chordeiles acutipennis • • • • • • Lesser Prairie-Chicken Tympanuchus pallidicinctus • Lesser Scaup Aythya affinis • • • • Lesser Yellowlegs Tringa flavipes • • • • Lewis's Woodpecker Melanerpes lewis • • • Lincoln's Sparrow Melospiza lincolnii • • • • • • Little Blue Heron Egretta caerulea • • Loggerhead Shrike Lanius ludovicianus • • • • • • Long-billed Curlew Numenius americanus • • • • Long-billed Dowitcher Limnodromus scolopaceus • • • • Long-billed Thrasher Toxostoma longirostre • • • Long-eared Owl Asio otus • • Louisiana Waterthrush Parkesia motacilla • • Lucifer Hummingbird Calothorax lucifer • • Lucy’s Warbler Oreothlypis luciae • • • MacGillivray's Warbler Oporornis tolmiei • • • • • • Magnificent Hummingbird Eugenes fulgens • • • Magnolia Warbler Dendroica magnolia • • • Mallard Anas platyrhynchos • • • • • Marbled Godwit Limosa fedoa • Marsh Wren Cistothorus palustris • • • • • McCown's Longspur Rhynchophanes mccownii • • • Merlin Falco columbarius • • • • • Mexican Jay Aphelocoma ultramarina • Mississippi Kite Ictinia mississippiensis • • Montezuma Quail Cyrtonyx montezumae • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 131 Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Mountain Bluebird Sialia currucoides • • • • • • Mountain Chickadee Poecile gambeli • • • • Mourning Dove Zenaida macroura • • • • • • Mourning Warbler Oporornis philadelphia • Nashville Warbler Oreothlypis ruficapilla • • • • • Neotropic Cormorant Phalacrocorax brasilianus • • • Northern Beardless-Tyrannulet Camptostoma imberbe • Northern Bobwhite Colinus virginianus • • • Northern Cardinal Cardinalis cardinalis • • • • • Northern Flicker Colaptes auratus • • • • • • Northern Goshawk Accipiter gentilis • • • Northern Harrier Circus cyaneus • • • • • • Northern Mockingbird Mimus polyglottos • • • • • • Northern Parula Parula americana • • • • Northern Pintail Anas acuta • • • • • Northern Pygmy-Owl Glaucidium gnoma • • Northern Rough-winged Swallow Stelgidopteryx serripennis • • • • • Northern Saw-whet Owl Aegolius acadicus • • Northern Shoveler Anas clypeata • • • • Northern Shrike Lanius excubitor • Northern Waterthrush Parkesia noveboracensis • • • • Olive Sparrow Arremonops rufivirgatus • Olive Warbler Peucedramus taeniatus • Olive-sided Flycatcher Contopus cooperi • • • • • Orange-crowned Warbler Oreothlypis celata • • • • • • Orchard Oriole Icterus spurius • • • • • • Osprey Pandion haliaetus • • • • Ovenbird Seiurus aurocapilla • • • Pacific Loon Gavia pacifica • Pacific-slope Flycatcher Empidonax difficilis • Painted Bunting Passerina ciris • • • • • • Painted Redstart Myioborus pictus • • • Palm Warbler Dendroica palmarum • • • Pectoral Sandpiper Calidris melanotos • Peregrine Falcon Falco peregrinus • • • • • Phainopepla Phainopepla nitens • • • • • Philadelphia Vireo Vireo philadelphicus • •
132 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Pied-billed Grebe Podilymbus podiceps • • • • Pine Grosbeak Pinicola enucleator • Pine Siskin Spinus pinus • • • • • Pine Warbler Dendroica pinus • • Pinyon Jay Gymnorhinus cyanocephalus • • • • Piratic Flycatcher Legatus leucophaius • • Plumbeous Vireo Vireo plumbeus • • • • • Prairie Falcon Falco mexicanus • • • • Prairie Warbler Dendroica discolor • • Prothonotary Warbler Protonotaria citrea • • • Purple Finch Carpodacus purpureus • • • Purple Gallinule Porphyrio martinica • Purple Martin Progne subis • • • • • Pygmy Nuthatch Sitta pygmaea • • • Pyrrhuloxia Cardinalis sinuatus • • • • • • Red Crossbill Loxia curvirostra • • • Red-bellied Woodpecker Melanerpes carolinus • Red-breasted Merganser Mergus serrator • • Red-breasted Nuthatch Sitta canadensis • • • • • Reddish Egret Egretta rufescens • • Red-eyed Vireo Vireo olivaceus • • • • Red-faced Warbler Cardellina rubrifrons • • • Redhead Aythya americana • • • • Red-headed Woodpecker Melanerpes erythrocephalus • • • Red-naped Sapsucker Sphyrapicus nuchalis • • • • Red-necked Phalarope Phalaropus lobatus • • Red-shouldered Hawk Buteo lineatus • • • Red-tailed Hawk Buteo jamaicensis • • • • • • Red-winged Blackbird Agelaius phoeniceus • • • • • Ring-billed Gull Larus delawarensis • • • • • Ringed Kingfisher Megaceryle torquata • Ring-necked Duck Aythya collaris • • • • • Ring-necked Pheasant Phasianus colchicus • • Rock Pigeon Columba livia • • • • • Rock Wren Salpinctes obsoletus • • • • • • Roseate Spoonbill Platalea ajaja • • Rose-breasted Grosbeak Pheucticus ludovicianus • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 133 Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Rose-throated Becard Pachyramphus aglaiae • Ross's Goose Chen rossii • Rough-legged Hawk Buteo lagopus • • • • Royal Tern Thalasseus maximus • Ruby-crowned Kinglet Regulus calendula • • • • • • Ruby-throated Hummingbird Archilochus colubris • • • Ruddy Duck Oxyura jamaicensis • • • • Ruddy Ground-Dove Columbina talpacoti • • Rufous Hummingbird Selasphorus rufus • • • • • Rufous-backed Robin Turdus rufopalliatus • Rufous-capped Warbler Basileuterus rufifrons • • Rufous-crowned Sparrow Aimophila ruficeps • • • • • • Rusty Blackbird Euphagus carolinus • • • Sage Sparrow Amphispiza belli • • • • Sage Thrasher Oreoscoptes montanus • • • • • Sanderling Calidris alba • Sandhill Crane Grus canadensis • • • • • Savannah Sparrow Passerculus sandwichensis • • • • • • Say's Phoebe Sayornis saya • • • • • • Scaled Quail Callipepla squamata • • • • • • Scarlet Tanager Piranga olivacea • • • • Scissor-tailed Flycatcher Tyrannus forficatus • • • • Scott's Oriole Icterus parisorum • • • • • • Sedge Wren Cistothorus platensis • • • Semipalmated Plover Charadrius semipalmatus • • Semipalmated Sandpiper Calidris pusilla • • • Sharp-shinned Hawk Accipiter striatus • • • • • • Short-eared Owl Asio flammeus • • • Short-tailed Hawk Buteo brachyurus • • Slate-throated Redstart Myioborus miniatus • Smith's Longspur Calcarius pictus • Snow Bunting Plectrophenax nivalis • Snow Goose Chen caerulescens • • • • Snowy Egret Egretta thula • • • • • Snowy Plover Charadrius alexandrinus • • Solitary Sandpiper Tringa solitaria • • • • Song Sparrow Melospiza melodia • • • • • •
134 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Sora Porzana carolina • • • • Spotted Owl 2 Strix occidentalis •2 •2 Spotted Sandpiper Actitis macularius • • • • • Spotted Towhee Pipilo maculatus • • • • • • Sprague's Pipit Anthus spragueii • • • Steller’s Jay Cyanocitta stelleri • • • • Stilt Sandpiper Calidris himantopus • Sulphur-bellied Flycatcher Myiodynastes luteiventris • Summer Tanager Piranga rubra • • • • • • Swainson's Hawk Buteo swainsoni • • • • • • Swainson’s Thrush Catharus ustulatus • • • • • Swainson's Warbler Limnothlypis swainsonii • • Swallow-tailed Kite Elanoides forficatus • Swamp Sparrow Melospiza georgiana • • • • Tennessee Warbler Oreothlypis peregrina • • Thick-billed Kingbird Tyrannus crassirostris • • Townsend's Solitaire Myadestes townsendi • • • • • Townsend's Warbler Dendroica townsendi • • • • • Tree Swallow Tachycineta bicolor • • • • Tricolored Heron Egretta tricolor • • • Tropical Kingbird Tyrannus melancholicus • Tropical Parula Parula pitiayumi • • Tufted Flycatcher Mitrephanes phaeocercus • Tundra Swan Cygnus columbianus • • Turkey Vulture Cathartes aura • • • • • • Upland Sandpiper Bartramia longicauda • • • Varied Bunting Passerina versicolor • • • • Varied Thrush Ixoreus naevius • • • Veery Catharus fuscescens • Verdin Auriparus flaviceps • • • • • • Vermilion Flycatcher Pyrocephalus rubinus • • • • • • Vesper Sparrow Pooecetes gramineus • • • • • • Violet-crowned Hummingbird Amazilia violiceps • • Violet-green Swallow Tachycineta thalassina • • • • • Virginia Rail Rallus limicola • • • • Virginia's Warbler Oreothlypis virginiae • • • • • Warbling Vireo Vireo gilvus • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 135 Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Western Bluebird Sialia mexicana • • • • • Western Grebe Aechmophorus occidentalis • • • Western Kingbird Tyrannus verticalis • • • • • • Western Meadowlark Sturnella neglecta • • • • • • Western Sandpiper Calidris mauri • • • • Western Screech-Owl Megascops kennicottii • • • • • Western Scrub-Jay Aphelocoma californica • • • • • Western Tanager Piranga ludoviciana • • • • • • Western Wood-Pewee Contopus sordidulus • • • • • Whimbrel Numenius phaeopus • Whip-poor-will 3 Caprimulgus vociferus • • • White Ibis Eudocimus albus • • White-breasted Nuthatch Sitta carolinensis • • • White-crowned Sparrow Zonotrichia leucophrys • • • • • • White-eared Hummingbird Hylocharis leucotis • • White-eyed Vireo Vireo griseus • • • White-faced Ibis Plegadis chihi • • • • White-rumped Sandpiper Calidris fuscicollis • White-tailed Hawk Buteo albicaudatus • White-tailed Kite Elanus leucurus • • White-throated Sparrow Zonotrichia albicollis • • • • • White-throated Swift Aeronautes saxatalis • • • • • • White-tipped Dove Leptotila verreauxi • White-winged Dove Zenaida asiatica • • • • • • Wild Turkey Meleagris gallopavo • • • • Willet Tringa semipalmata • • • • Williamson's Sapsucker Sphyrapicus thyroideus • • • • Willow Flycatcher Empidonax traillii • •2 • • Wilson's Phalarope Phalaropus tricolor • • • • Wilson's Snipe Gallinago delicata • • • • • Wilson's Warbler Wilsonia pusilla • • • • • • Winter Wren Troglodytes hiemalis • • • • Wood Duck Aix sponsa • • • • Wood Thrush Hylocichla mustelina • • Worm-eating Warbler Helmitheros vermivorum • • • • Yellow Grosbeak Pheucticus chrysopeplus • Yellow Rail Coturnicops noveboracensis •
136 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BIBE AMIS CAVE FODA WHSA GUMO
Yellow Warbler Dendroica petechia • • • • • • Yellow-bellied Flycatcher Empidonax flaviventris • Yellow-bellied Sapsucker Sphyrapicus varius • • • Yellow-billed Cuckoo Coccyzus americanus • • • • • Yellow-breasted Chat Icteria virens • • • • • Yellow-crowned Night-Heron Nyctanassa violacea • • • Yellow-eyed Junco Junco phaeonotus • • Yellow-green Vireo Vireo flavoviridis • • Yellow-headed Blackbird Xanthocephalus xanthocephalus • • • • • • Yellow-rumped Warbler Dendroica coronata • • • • • • Yellow-throated Vireo Vireo flavifrons • • • • Yellow-throated Warbler Dendroica dominica • • • Zone-tailed Hawk Buteo albonotatus • • • • •
1 = the AOU common name for this species was Common Moorhen until 2011. 2 = the Spotted Owls in these CHDN parks belong to the Mexican subspecies (Strix occidentalis lucida). 3 = the AOU now considers the Whip-poor-will as two separate species, Eastern Whip-poor-will (Caprimulgus vociferus) and Mexican Whip-poor-will (Caprimulgus arizonae). Those recorded in the CHDN are mostly likely Eastern Whip-poor-wills.
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 137 Table D-2. Bird species listed as present in NGPN parks, including species that migrate through or winter in the parks. All species have a status of “present in park” for the park’s certified species list.
Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Alder Flycatcher Empidonax alnorum • • • • American Avocet Recurvirostra americana • • • • • American Bittern Botaurus lentiginosus • • • American Black Duck Anas rubripes • American Coot Fulica americana • • • • American Crow Corvus brachyrhynchos • • • • • • • • • • • • • American Golden-Plover Pluvialis dominica • American Goldfinch Carduelis tristis • • • • • • • • • • • • American Kestrel Falco sparverius • • • • • • • • • • • • American Pipit Anthus rubescens • • American Redstart Setophaga ruticilla • • • • • • • • • • American Robin Turdus migratorius • • • • • • • • • • • • • American Tree Sparrow Spizella arborea • • • • • • • • • • American White Pelican Pelecanus erythrorhynchos • • • • • • • • American Wigeon Anas americana • • • • • • • • American Woodcock Scolopax minor • • Baird’s Sandpiper Calidris bairdii • • Baird’s Sparrow Ammodramus bairdii • • • Bald Eagle Haliaeetus leucocephalus • • • • • • • • • • • • • Baltimore Oriole Icterus galbula • • • • • • • • • Bank Swallow Riparia riparia • • • • • • • • • Barn Owl Tyto alba • • • Barn Swallow Hirundo rustica • • • • • • • • • • • • Barred Owl Strix varia • Bay-breasted Warbler Dendroica castanea • • Bell’s Vireo Vireo bellii • • • • • Belted Kingfisher Ceryle alcyon • • • • • • • • • • • • Bewick’s Wren Thryomanes bewickii • Black Tern Chlidonias niger • • • • • Black-and-white Warbler Mniotilta varia • • • • • • • • Black-backed Woodpecker Picoides arcticus • • • Black-bellied Plover Pluvialis squatarola • • Black-billed Cuckoo Coccyzus erythropthalmus • • • • • • • Black-billed Magpie Pica hudsonia • • • • • • • • • • Blackburnian Warbler Dendroica fusca • Black-capped Chickadee Poecile atricapillus • • • • • • • • • • • • •
138 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Black-crowned Night-Heron Nycticorax nycticorax • • • Black-headed Grosbeak Pheucticus melanocephalus • • • • • • • • • • • • Black-legged Kittiwake Rissa tridactyla • Black-necked Stilt Himantopus mexicanus • Blackpoll Warbler Dendroica striata • • • • • • Black-throated Blue Warbler Dendroica caerulescens • • Black-throated Green Warbler Dendroica virens • • Blue Grosbeak Passerina caerulea • • • • • • • • Blue Jay Cyanocitta cristata • • • • • • • • • • • • • Blue-gray Gnatcatcher Polioptila caerulea • • • Blue-headed Vireo Vireo solitarius • • Blue-winged Teal Anas discors • • • • • • • • • • • Blue-winged Warbler Vermivora pinus • • Bobolink Dolichonyx oryzivorus • • • • • • • • Bohemian Waxwing Bombycilla garrulus • • • • Bonaparte’s Gull Larus philadelphia • • Brewer’s Blackbird Euphagus cyanocephalus • • • • • • • • Brewer’s Sparrow Spizella breweri • Broad-tailed Hummingbird Selasphorus platycercus • Broad-winged Hawk Buteo platypterus • • • • Brown Creeper Certhia americana • • • • • • • • • • Brown Thrasher Toxostoma rufum • • • • • • • • • • • Brown-headed Cowbird Molothrus ater • • • • • • • • • • • • • Buff-breasted Sandpiper Tryngites subruficollis • Bufflehead Bucephala albeola • • • • • • Bullock’s Oriole Icterus bullockii • • • • • Burrowing Owl Athene cunicularia • • • • California Gull Larus californicus • • • Canada Goose Branta canadensis • • • • • • • • • • • Canada Warbler Wilsonia canadensis • Canvasback Aythya valisineria • • • Canyon Wren Catherpes mexicanus • • • • • Cape May Warbler Dendroica tigrina • Carolina Wren Thryothorus ludovicianus • Caspian Tern Sterna caspia • • Cassin’s Finch Carpodacus cassinii • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 139 Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Cassin’s Kingbird Tyrannus vociferans • • Cattle Egret Bubulcus ibis • Cedar Waxwing Bombycilla cedrorum • • • • • • • • • • • • Chestnut-collared Longspur Calcarius ornatus • • • • • Chestnut-sided Warbler Dendroica pensylvanica • • Chimney Swift Chaetura pelagica • • • • Chipping Sparrow Spizella passerina • • • • • • • • • • • • Cinnamon Teal Anas cyanoptera • • Clark’s Grebe Aechmophorus clarkii • Clark’s Nutcracker Nucifraga columbiana • • Clay-colored Sparrow Spizella pallida • • • • • • • • • • • Cliff Swallow Petrochelidon pyrrhonota • • • • • • • • • • • Common Gallinule 1 Gallinula chloropus • Common Goldeneye Bucephala clangula • • • • Common Grackle Quiscalus quiscula • • • • • • • • • • • Common Loon Gavia immer • Common Merganser Mergus merganser • • • • • • • Common Nighthawk Chordeiles minor • • • • • • • • • • • • • Common Poorwill Phalaenoptilus nuttallii • • • • • • • Common Raven Corvus corax • Common Redpoll Carduelis flammea • • • • • Common Tern Sterna hirundo • • Common Yellowthroat Geothlypis trichas • • • • • • • • • • Connecticut Warbler Oporornis agilis • • Cooper’s Hawk Accipiter cooperii • • • • • • • • Cordilleran Flycatcher Empidonax occidentalis • • • • Dark-eyed Junco Junco hyemalis • • • • • • • • • • • • Dickcissel Spiza americana • • • • • • • Double-crested Cormorant Phalacrocorax auritus • • • • • • • • • • Downy Woodpecker Picoides pubescens • • • • • • • • • • • • Dunlin Calidris alpina • Dusky Flycatcher Empidonax oberholseri • • • • Eared Grebe Podiceps nigricollis • • • • Eastern Bluebird Sialia sialis • • • • • • • • • Eastern Kingbird Tyrannus tyrannus • • • • • • • • • • • Eastern Meadowlark Sturnella magna • • Eastern Phoebe Sayornis phoebe • • • • • •
140 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Eastern Screech-Owl Megascops asio • • • • • • • • Eastern Towhee Pipilo erythrophthalmus • • Eastern Wood-Pewee Contopus virens • • • European Starling Sturnus vulgaris • • • • • • • • • • • Evening Grosbeak Coccothraustes vespertinus • • • • • • Ferruginous Hawk Buteo regalis • • • • • Field Sparrow Spizella pusilla • • • • • • • • • • Forster’s Tern Sterna forsteri • • • • • Fox Sparrow Passerella iliaca • • • Franklin’s Gull Larus pipixcan • • • • • • • • Gadwall Anas strepera • • • • • • • • • Glaucous Gull Larus hyperboreus • Golden Eagle Aquila chrysaetos • • • • • • • • Golden-crowned Kinglet Regulus satrapa • • • • • Golden-winged Warbler Vermivora chrysoptera • • Grasshopper Sparrow Ammodramus savannarum • • • • • • • • Gray Catbird Dumetella carolinensis • • • • • • • • • • • Gray Jay Perisoreus canadensis • • • Gray Partridge Perdix perdix • • • • • • • Gray-cheeked Thrush Catharus minimus • Gray-crowned Rosy-Finch Leucosticte tephrocotis • • • • Great Blue Heron Ardea herodias • • • • • • • • • • • • Great Crested Flycatcher Myiarchus crinitus • • • • • Great Egret Ardea alba • • • Great Horned Owl Bubo virginianus • • • • • • • • • • • • Greater Prairie-Chicken Tympanuchus cupido • Greater Scaup Aythya marila • Greater White-fronted Goose Anser albifrons • • Greater Yellowlegs Tringa melanoleuca • • • • • • Great-tailed Grackle Quiscalus mexicanus • Green Heron Butorides virescens • • • Green-winged Teal Anas crecca • • • • • • • • • Hairy Woodpecker Picoides villosus • • • • • • • • • • • • Harris’s Sparrow Zonotrichia querula • • • • • • Hermit Thrush Catharus guttatus • • • • Herring Gull Larus argentatus • • • • Hoary Redpoll Carduelis hornemanni • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 141 Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Hooded Merganser Lophodytes cucullatus • • • Horned Grebe Podiceps auritus • Horned Lark Eremophila alpestris • • • • • • • • • House Finch Carpodacus mexicanus • • • • • • House Sparrow Passer domesticus • • • • • • • • • House Wren Troglodytes aedon • • • • • • • • • • • • Hudsonian Godwit Limosa haemastica • Indigo Bunting Passerina cyanea • • • • • • • • • • Killdeer Charadrius vociferus • • • • • • • • • • • King Rail Rallus elegans • • Lapland Longspur Calcarius lapponicus • • • • Lark Bunting Calamospiza melanocorys • • • • • • • • Lark Sparrow Chondestes grammacus • • • • • • • • • • • • Lazuli Bunting Passerina amoena • • • • • • • • • • • Le Conte’s Sparrow Ammodramus leconteii • Least Bittern Ixobrychus exilis • • Least Flycatcher Empidonax minimus • • • • • • • • Least Sandpiper Calidris minutilla • • • Least Tern Sterna antillarum • • • • Lesser Goldfinch Carduelis psaltria • Lesser Scaup Aythya affinis • • • • Lesser Yellowlegs Tringa flavipes • • • • • Lewis’ Woodpecker Melanerpes lewis • • • • Lincoln’s Sparrow Melospiza lincolnii • • • • • • Loggerhead Shrike Lanius ludovicianus • • • • • • • • • • Long-billed Curlew Numenius americanus • • • • • Long-billed Dowitcher Limnodromus scolopaceus • • • • Long-eared Owl Asio otus • • • • • • Macgillivray’s Warbler Oporornis tolmiei • • • • • Magnolia Warbler Dendroica magnolia • • Mallard Anas platyrhynchos • • • • • • • • • • • • Marbled Godwit Limosa fedoa • • • • Marsh Wren Cistothorus palustris • • • • Mccown’s Longspur Calcarius mccownii • Merlin Falco columbarius • • • • • • • • Mountain Bluebird Sialia currucoides • • • • • • • • • • Mourning Dove Zenaida macroura • • • • • • • • • • • • •
142 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Mourning Warbler Oporornis philadelphia • • Nashville Warbler Vermivora ruficapilla • • Northern Bobwhite Colinus virginianus • • • • Northern Cardinal Cardinalis cardinalis • • • • Northern Flicker Colaptes auratus • • • • • • • • • • • • • Northern Goshawk Accipiter gentilis • • • • • • • Northern Harrier Circus cyaneus • • • • • • • • • • Northern Mockingbird Mimus polyglottos • • • • • • Northern Parula Parula americana • • • Northern Pintail Anas acuta • • • • • • Northern Rough-winged Swallow Stelgidopteryx serripennis • • • • • • • • • • • • Northern Saw-whet Owl Aegolius acadicus • • • • • Northern Shoveler Anas clypeata • • • • • • • Northern Shrike Lanius excubitor • • • • • • • • Northern Waterthrush Seiurus noveboracensis • • • • • • • Olive-sided Flycatcher Contopus cooperi • • • • • Orange-crowned Warbler Vermivora celata • • • • • • • Orchard Oriole Icterus spurius • • • • • • • • • • Osprey Pandion haliaetus • • • • • • • • Ovenbird Seiurus aurocapilla • • • • • • • • • • Palm Warbler Dendroica palmarum • • • • • Pectoral Sandpiper Calidris melanotos • Peregrine Falcon Falco peregrinus • • • • • • Philadelphia Vireo Vireo philadelphicus • • Pied-billed Grebe Podilymbus podiceps • • • • Pine Grosbeak Pinicola enucleator • • • Pine Siskin Carduelis pinus • • • • • • • • • • • • Pine Warbler Dendroica pinus • • Pinyon Jay Gymnorhinus cyanocephalus • • • • Piping Plover Charadrius melodus • • Plumbeous Vireo Vireo plumbeus • • • • Prairie Falcon Falco mexicanus • • • • • • • • Prairie Warbler Dendroica discolor • • Purple Finch Carpodacus purpureus • • • • • Purple Martin Progne subis • • • Pygmy Nuthatch Sitta pygmaea • Red Crossbill Loxia curvirostra • • • • • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 143 Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Red-bellied Woodpecker Melanerpes carolinus • • Red-breasted Merganser Mergus serrator • Red-breasted Nuthatch Sitta canadensis • • • • • • • • • • Red-eyed Vireo Vireo olivaceus • • • • • • • • • • Redhead Aythya americana • • • • Red-headed Woodpecker Melanerpes erythrocephalus • • • • • • • • • • • Red-naped Sapsucker Sphyrapicus nuchalis • • • Red-necked Grebe Podiceps grisegena • • Red-necked Phalarope Phalaropus lobatus • • • Red-tailed Hawk Buteo jamaicensis • • • • • • • • • • • • • Red-winged Blackbird Agelaius phoeniceus • • • • • • • • • • • • Ring-billed Gull Larus delawarensis • • • • • • • • • Ring-necked Duck Aythya collaris • • • • Ring-necked Pheasant Phasianus colchicus • • • • • • • • • • • Rock Dove Columba livia • • • • • • • • • • • • Rock Wren Salpinctes obsoletus • • • • • • • • • Rose-breasted Grosbeak Pheucticus ludovicianus • • • • • Ross’s Goose Chen rossii • • Rough-legged Hawk Buteo lagopus • • • • • • • Ruby-crowned Kinglet Regulus calendula • • • • • • • • • • • Ruby-throated Hummingbird Archilochus colubris • • • • • Ruddy Duck Oxyura jamaicensis • • • Ruddy Turnstone Arenaria interpres • Ruffed Grouse Bonasa umbellus • Rufous Hummingbird Selasphorus rufus • Rusty Blackbird Euphagus carolinus • • • Sabine’s Gull Xema sabini • Sage Thrasher Oreoscoptes montanus • • Sanderling Calidris alba • Sandhill Crane Grus canadensis • • • • • • • • Savannah Sparrow Passerculus sandwichensis • • • • • • • Say’s Phoebe Sayornis saya • • • • • • • • • • Scarlet Tanager Piranga olivacea • • Scissor-tailed Flycatcher Tyrannus forficatus • Sedge Wren Cistothorus platensis • • Semipalmated Plover Charadrius semipalmatus • • • • • • Semipalmated Sandpiper Calidris pusilla • •
144 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Sharp-shinned Hawk Accipiter striatus • • • • • • • • • • Sharp-tailed Grouse Tympanuchus phasianellus • • • • • • • Short-eared Owl Asio flammeus • • • • • • • Snow Bunting Plectrophenax nivalis • • • • Snow Goose Chen caerulescens • • • • • • • Snowy Owl Bubo scandiacus • • • • Solitary Sandpiper Tringa solitaria • • • • • • Song Sparrow Melospiza melodia • • • • • • • • • • Sora Porzana carolina • • • • • • • Spotted Sandpiper Actitis macularius • • • • • • • • • • • Spotted Towhee Pipilo maculatus • • • • • • • • • • • Sprague’s Pipit Anthus spragueii • • Stilt Sandpiper Calidris himantopus • • • Surf Scoter Melanitta perspicillata • Swainson’s Hawk Buteo swainsoni • • • • • • • • • • Swainson’s Thrush Catharus ustulatus • • • • • • • • • Swamp Sparrow Melospiza georgiana • • Tennessee Warbler Vermivora peregrina • • • • • Thayer’s Gull Larus thayeri • Three-toed Woodpecker Picoides tridactylus • Townsend’s Solitaire Myadestes townsendi • • • • • • • • • • Tree Swallow Tachycineta bicolor • • • • • • • • • Trumpeter Swan Cygnus buccinator • • Tufted Titmouse Baeolophus bicolor • Tundra Swan Cygnus columbianus • Turkey Vulture Cathartes aura • • • • • • • • • • • • • Upland Sandpiper Bartramia longicauda • • • • • • • • Veery Catharus fuscescens • • • Vesper Sparrow Pooecetes gramineus • • • • • • • • • • • Violet-green Swallow Tachycineta thalassina • • • • • • • • Virginia Rail Rallus limicola • • • • Warbling Vireo Vireo gilvus • • • • • • • • • • • Western Bluebird Sialia mexicana • Western Grebe Aechmophorus occidentalis • • • • • Western Kingbird Tyrannus verticalis • • • • • • • • • • • • Western Meadowlark Sturnella neglecta • • • • • • • • • • • • Western Screech-Owl Megascops kennicottii •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 145 Common Name Scientific Name KNRI NIOB SCBL JECA FOLA BADL FOUS DETO THRO WICA AGFO MNRR MORU
Western Tanager Piranga ludoviciana • • • • • • • • Western Wood-Pewee Contopus sordidulus • • • • • • • • • • • Whip-poor-will 2 Caprimulgus vociferus • • White-breasted Nuthatch Sitta carolinensis • • • • • • • • • • • • White-crowned Sparrow Zonotrichia leucophrys • • • • • • • • • • White-rumped Sandpiper Calidris fuscicollis • • White-throated Sparrow Zonotrichia albicollis • • • White-throated Swift Aeronautes saxatalis • • • • • • • White-winged Crossbill Loxia leucoptera • White-winged Scoter Melanitta fusca • Whooping Crane Grus americana • Wild Turkey Meleagris gallopavo • • • • • • • • • • • • • Willet Catoptrophorus semipalmatus • • • • • • Willow Flycatcher Empidonax traillii • • • • • Wilson’s Phalarope Phalaropus tricolor • • • • • • • • Wilson’s snipe Gallinago delicata • • • • • • • Wilson’s Warbler Wilsonia pusilla • • • • • • • Winter Wren Troglodytes troglodytes • • Wood Duck Aix sponsa • • • • • • • • • • • Wood Thrush Hylocichla mustelina • • Yellow Warbler Dendroica petechia • • • • • • • • • • • Yellow-bellied Flycatcher Empidonax flaviventris • Yellow-bellied Sapsucker Sphyrapicus varius • • • Yellow-billed Cuckoo Coccyzus americanus • • • • Yellow-breasted Chat Icteria virens • • • • • • • • • • •
Yellow-headed Blackbird Xanthocephalus xanthocephalus • • • • • • • • Yellow-rumped Warbler Dendroica coronata • • • • • • • • • • • Yellow-throated Vireo Vireo flavifrons • • •
1 = the AOU common name for this species was Common Moorhen until 2011. 2 = the AOU now considers the Whip-poor-will as two separate species, Eastern Whip-poor-will (Caprimulgus vociferus) and Mexican Whip-poor-will (Caprimulgus arizonae). Those recorded in the NGPN are mostly likely Eastern Whip-poor-wills.
146 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Table D-3. Bird species known to occur in SODN parks (through [and including] 2012 sampling), including species that migrate through or winter in the park.
Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Abert's Towhee Melozone aberti • • • • • • Acorn Woodpecker Melanerpes formicivorus • • • • • • American Avocet Recurvirostra americana • American Coot Fulica americana • • • American Crow Corvus brachyrhynchos • American Goldfinch Spinus tristis • • • American Kestrel Falco sparverius • • • • • • • • • • • American Pipit Anthus rubescens • • • • • • American Redstart Setophaga ruticilla • • • American Robin Turdus migratorius • • • • • • • • • • American White Pelican Pelecanus erythrorhynchos • • American Wigeon Anas americana • • • Anna's Hummingbird Calypte anna • • • • • • • • • • Arizona Woodpecker Picoides arizonae • • • Ash-throated Flycatcher Myiarchus cinerascens • • • • • • • • • • • Baird's Sandpiper Calidris bairdii • • Bald Eagle Haliaeetus leucocephalus • • • • • Baltimore Oriole Icterus galbula • Band-tailed Pigeon Patagioenas fasciata • • • • • Bank Swallow Riparia riparia • • • • • • • Barn Owl Tyto alba • • • • • • • • • • Barn Swallow Hirundo rustica • • • • • • • • • • • Bell's Vireo Vireo bellii • • • • • • • • • Belted Kingfisher Megaceryle alcyon • • • • Bendire's Thrasher Toxostoma bendirei • • • • • Bewick's Wren Thryomanes bewickii • • • • • • • • • • • Black Phoebe Sayornis nigricans • • • • • • • • • • • Black Rail Laterallus jamaicensis • Black Tern Chlidonias niger • • Black Vulture Coragyps atratus • • • • • Black-and-white Warbler Mniotilta varia • Black-bellied Whistling-Duck Dendrocygna autumnalis • Black-chinned Hummingbird Archilochus alexandri • • • • • • • • • • • Black-chinned Sparrow Spizella atrogularis • • • • • • • • Black-crowned Night-Heron Nycticorax nycticorax • • • • • Black-headed Grosbeak Pheucticus melanocephalus • • • • • • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 147 Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Black-necked Stilt Himantopus mexicanus • • Blackpoll Warbler Dendroica striata • Black-tailed Gnatcatcher Polioptila melanura • • • • • • • • • Black-throated Blue Warbler Dendroica caerulescens • Black-throated Gray Warbler Dendroica nigrescens • • • • • • • • • • • Black-throated Green Warbler Dendroica virens • Black-throated Sparrow Amphispiza bilineata • • • • • • • • • • Blue Grosbeak Passerina caerulea • • • • • • • • • • Blue-gray Gnatcatcher Polioptila caerulea • • • • • • • • • • • Blue-headed Vireo Vireo solitarius • • Blue-throated Hummingbird Lampornis clemenciae • • Blue-winged Teal Anas discors • • • Bonaparte's Gull Chroicocephalus philadelphia • • Botteri's Sparrow Peucaea botterii • • • Brewer's Blackbird Euphagus cyanocephalus • • • • • • • • • Brewer's Sparrow Spizella breweri • • • • • • • • • • • Bridled Titmouse Baeolophus wollweberi • • • • • • • • Broad-billed Hummingbird Cynanthus latirostris • • • • • • Broad-tailed Hummingbird Selasphorus platycercus • • • • • • • • • • Bronzed Cowbird Molothrus aeneus • • • • • • • • • • Brown Creeper Certhia americana • • • • • • • • • Brown Pelican Pelecanus occidentalis • Brown Thrasher Toxostoma rufum • Brown-crested Flycatcher Myiarchus tyrannulus • • • • • • • • • • Brown-headed Cowbird Molothrus ater • • • • • • • • • • • Buff-breasted Flycatcher Empidonax fulvifrons • • Buff-collared Nightjar Caprimulgus ridgwayi • Bufflehead Bucephala albeola • • • Bullock's Oriole Icterus bullockii • • • • • • • • • • • Burrowing Owl Athene cunicularia • • Bushtit Psaltriparus minimus • • • • • • • • • • Cactus Wren Campylorhynchus brunneicapillus • • • • • • • • • • California Gull Larus californicus • • Calliope Hummingbird Stellula calliope • • • • • • • • • Canada Goose Branta canadensis • • • Canvasback Aythya valisineria • • • Canyon Towhee Melozone fusca • • • • • • • • • • •
148 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Canyon Wren Catherpes mexicanus • • • • • • • • • Cassin's Finch Carpodacus cassinii • • • • • • Cassin's Kingbird Tyrannus vociferans • • • • • • • • • • • Cassin's Sparrow Peucaea cassinii • • • • • Cassin's Vireo Vireo cassinii • • • • • • Cattle Egret Bubulcus ibis • • • Cedar Waxwing Bombycilla cedrorum • • • • • • • • • Chestnut-collared Longspur Calcarius ornatus • Chihuahuan Raven Corvus cryptoleucus • • • • Chipping Sparrow Spizella passerina • • • • • • • • • • • Chukar Alectoris chukar • Cinnamon Teal Anas cyanoptera • • • Clapper Rail Rallus longirostris • Clark's Nutcracker Nucifraga columbiana • • • • Clay-colored Sparrow Spizella pallida • Cliff Swallow Petrochelidon pyrrhonota • • • • • • • • • • Common Black-Hawk Buteogallus anthracinus • • • • • • • • Common Galinule 1 Gallinula chloropus • • Common Goldeneye Bucephala clangula • • Common Ground-Dove Columbina passerina • • • • • • • • Common Loon Gavia immer • • Common Merganser Mergus merganser • • • • Common Nighthawk Chordeiles minor • • • • • • • Common Poorwill Phalaenoptilus nuttallii • • • • • • • • • • • Common Raven Corvus corax • • • • • • • • • • • Common Snipe Gallinago gallinago • • • • Common Tern Sterna hirundo • Common Yellowthroat Geothlypis trichas • • • • • • Cooper's Hawk Accipiter cooperii • • • • • • • • • • • Cordilleran Flycatcher Empidonax occidentalis • • • • • Costa's Hummingbird Calypte costae • • • • • • • • • Crested Caracara Caracara cheriway • • Crissal Thrasher Toxostoma crissale • • • • • • • • • • Curve-billed Thrasher Toxostoma curvirostre • • • • • • • • • • Dark-eyed Junco Junco hyemalis • • • • • • • • • • Dickcissel Spiza americana • Double-crested Cormorant Phalacrocorax auritus • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 149 Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Dusky Flycatcher Empidonax oberholseri • • • • • • • • • Dusky-capped Flycatcher Myiarchus tuberculifer • • • • • • • • Eared Grebe Podiceps nigricollis • • Eastern Bluebird Sialia sialis • • • Eastern Meadowlark Sturnella magna • • • • • • • Eastern Phoebe Sayornis phoebe • • Elegant Trogon Trogon elegans • • • • Elf Owl Micrathene whitneyi • • • • • • • • • • • Eurasian Collared-Dove Streptopelia decaocto • • • • • • • European Starling Sturnus vulgaris • • • • • • • Evening Grosbeak Coccothraustes vespertinus • • • • Ferruginous Hawk Buteo regalis • • • • Ferruginous Pygmy-Owl Glaucidium brasilianum • Flammulated Owl Otus flammeolus • • • • Forster's Tern Sterna forsteri • Fox Sparrow Passerella iliaca • • • Franklin's Gull Larus pipixcan • Gadwall Anas strepera • • • Gambel's Quail Callipepla gambelii • • • • • • • • • • • Gila Woodpecker Melanerpes uropygialis • • • • • • • • • • Gilded Flicker Colaptes chrysoides • • • • • Golden Eagle Aquila chrysaetos • • • • • • • • • • Golden-crowned Kinglet Regulus satrapa • • • Golden-crowned Sparrow Zonotrichia atricapilla • Golden-winged Warbler Vermivora chrysoptera • Grace's Warbler Dendroica graciae • • • • Grasshopper Sparrow Ammodramus savannarum • • • Gray Catbird Dumetella carolinensis • Gray Flycatcher Empidonax wrightii • • • • • • • • • • • Gray Hawk Buteo nitidus • • • • Gray Vireo Vireo vicinior • • • • • • • • Great Blue Heron Ardea herodias • • • • • • • • • Great Egret Ardea alba • • • • • Great Horned Owl Bubo virginianus • • • • • • • • • • • Greater Pewee Contopus pertinax • • • • Greater Roadrunner Geococcyx californianus • • • • • • • • • • • Greater White-fronted Goose Anser albifrons • •
150 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Greater Yellowlegs Tringa melanoleuca • • • Great-tailed Grackle Quiscalus mexicanus • • • • • • • • • Green Heron Butorides virescens • • • Green Kingfisher Chloroceryle americana • Green-tailed Towhee Pipilo chlorurus • • • • • • • • • • • Green-winged Teal Anas crecca • • • Hairy Woodpecker Picoides villosus • • • • Hammond's Flycatcher Empidonax hammondii • • • • • • • • • • • Harris's Hawk Parabuteo unicinctus • • • • • • Heermann's Gull Larus heermanni • Hepatic Tanager Piranga flava • • • • • • • Hermit Thrush Catharus guttatus • • • • • • • • • • Hermit Warbler Dendroica occidentalis • • • • • • Herring Gull Larus argentatus • Hooded Merganser Lophodytes cucullatus • • • Hooded Oriole Icterus cucullatus • • • • • • • • • • Horned Lark Eremophila alpestris • • • • • • • House Finch Carpodacus mexicanus • • • • • • • • • • • House Sparrow Passer domesticus • • • • • • • • House Wren Troglodytes aedon • • • • • • • • • • Hutton's Vireo Vireo huttoni • • • • • • • • • • Inca Dove Columbina inca • • • • • • • Indigo Bunting Passerina cyanea • • • • • • • • Juniper Titmouse Baeolophus ridgwayi • • • • • Killdeer Charadrius vociferus • • • • • • • • • Ladder-backed Woodpecker Picoides scalaris • • • • • • • • • • • Lark Bunting Calamospiza melanocorys • • • • • • • • Lark Sparrow Chondestes grammacus • • • • • • • • • • • Lawrence's Goldfinch Carduelis lawrencei • • Lazuli Bunting Passerina amoena • • • • • • • • • • • Le Conte's Thrasher Toxostoma lecontei • Least Bittern Ixobrychus exilis • • Least Grebe Tachybaptus dominicus • Least Sandpiper Calidris minutilla • • • Least Tern Sterna antillarum • Lesser Goldfinch Carduelis psaltria • • • • • • • • • • • Lesser Nighthawk Chordeiles acutipennis • • • • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 151 Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Lesser Scaup Aythya affinis • • • Lesser Yellowlegs Tringa flavipes • • • Lewis's Woodpecker Melanerpes lewis • • • • • Lincoln's Sparrow Melospiza lincolnii • • • • • • • • • • • Loggerhead Shrike Lanius ludovicianus • • • • • • • • • • Long-billed Curlew Numenius americanus • Long-billed Dowitcher Limnodromus scolopaceus • Long-eared Owl Asio otus • • • • • Louisiana Waterthrush Parkesia motacilla • Lucifer Hummingbird Calothorax lucifer • Lucy's Warbler Oreothlypis luciae • • • • • • • • • • • MacGillivray's Warbler Oporornis tolmiei • • • • • • • • • • • Magnificent Hummingbird Eugenes fulgens • • • Magnolia Warbler Dendroica magnolia • Mallard Anas platyrhynchos • • • • • • • • Marsh Wren Cistothorus palustris • • • • • Merlin Falco columbarius • • • • • • • • • • Mexican Chickadee Poecile sclateri • Mexican Jay Aphelocoma ultramarina • • • • • • Mississippi Kite Ictinia mississippiensis • Montezuma Quail Cyrtonyx montezumae • • • • • Mountain Bluebird Sialia currucoides • • • • • • • Mountain Chickadee Poecile gambeli • • Mourning Dove Zenaida macroura • • • • • • • • • • • Nashville Warbler Oreothlypis ruficapilla • • • • • • • • • • Northern Beardless-Tyrannulet Camptostoma imberbe • • • • • • • Northern Cardinal Cardinalis cardinalis • • • • • • • • • • Northern Flicker Colaptes auratus • • • • • • • • • • • Northern Goshawk Accipiter gentilis • • • • • Northern Harrier Circus cyaneus • • • • • • • • • • Northern Mockingbird Mimus polyglottos • • • • • • • • • • • Northern Parula Parula americana • • Northern Pintail Anas acuta • • • Northern Pygmy-Owl Glaucidium gnoma • • • • • •
Northern Rough-winged Swallow Stelgidopteryx serripennis • • • • • • • • • • Northern Saw-whet Owl Aegolius acadicus • • • • • Northern Shoveler Anas clypeata • • •
152 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Northern Waterthrush Parkesia noveboracensis • • • • • Olive Warbler Peucedramus taeniatus • • • Olive-sided Flycatcher Contopus cooperi • • • • • • • • Orange-crowned Warbler Oreothlypis celata • • • • • • • • • • • Osprey Pandion haliaetus • • • • • • • Ovenbird Seiurus aurocapilla • • • • Pacific-slope Flycatcher Empidonax difficilis • • • • • • • • • Painted Bunting Passerina ciris • • Painted Redstart Myioborus pictus • • • • • • • • Peregrine Falcon Falco peregrinus • • • • • • • • • • • Phainopepla Phainopepla nitens • • • • • • • • • • Pied-billed Grebe Podilymbus podiceps • • • Pine Siskin Spinus pinus • • • • • • • • • • Pinyon Jay Gymnorhinus cyanocephalus • • • • • Plain-capped Starthroat Heliomaster constantii • Plumbeous Vireo Vireo plumbeus • • • • • • • • • Prairie Falcon Falco mexicanus • • • • • • • • • • Purple Finch Carpodacus purpureus • • Purple Martin Progne subis • • • • • • • • Pygmy Nuthatch Sitta pygmaea • • • Pyrrhuloxia Cardinalis sinuatus • • • • • • • • Red Crossbill Loxia curvirostra • • • • • Red Phalarope Phalaropus fulicarius • Red-breasted Merganser Mergus serrator • Red-breasted Nuthatch Sitta canadensis • • • • • • Red-breasted Sapsucker Sphyrapicus ruber • Red-eyed Vireo Vireo olivaceus • Red-faced Warbler Cardellina rubrifrons • • • • Redhead Aythya americana • • • Red-naped Sapsucker Sphyrapicus nuchalis • • • • • • • • • Red-necked Phalarope Phalaropus lobatus • Red-tailed Hawk Buteo jamaicensis • • • • • • • • • • • Red-winged Blackbird Agelaius phoeniceus • • • • • • • Ring-billed Gull Larus delawarensis • • Ring-necked Duck Aythya collaris • • Rock Pigeon Columba livia • • • • • • • Rock Wren Salpinctes obsoletus • • • • • • • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 153 Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Roseate Spoonbill Platalea ajaja • Rose-breasted Grosbeak Pheucticus ludovicianus • • Rose-throated Becard Pachyramphus aglaiae • Rough-legged Hawk Buteo lagopus • • Ruby-crowned Kinglet Regulus calendula • • • • • • • • • • • Ruddy Duck Oxyura jamaicensis • • • Rufous Hummingbird Selasphorus rufus • • • • • • • • • • Rufous-crowned Sparrow Aimophila ruficeps • • • • • • • • • • Rufous-winged Sparrow Aimophila carpalis • • • • • • Sabine's Gull Xema sabini • Sage Sparrow Amphispiza belli • • • Sage Thrasher Oreoscoptes montanus • • • • Sandhill Crane Grus canadensis • • • • Savannah Sparrow Passerculus sandwichensis • • • • • Say's Phoebe Sayornis saya • • • • • • • • • • • Scaled Quail Callipepla squamata • • • • Scarlet Tanager Piranga olivacea • Scissor-tailed Flycatcher Tyrannus forficatus • Scott's Oriole Icterus parisorum • • • • • • • • • Semipalmated Plover Charadrius semipalmatus • Sharp-shinned Hawk Accipiter striatus • • • • • • • • • • • Short-eared Owl Asio flammeus • Short-tailed Hawk Buteo brachyurus • • Snow Goose Chen caerulescens • Snowy Egret Egretta thula • • • • Solitary Sandpiper Tringa solitaria • • • Song Sparrow Melospiza melodia • • • • • • • Sora Porzana carolina • • Spotted Owl Strix occidentalis • • Spotted Sandpiper Actitis macularius • • • • • • • Spotted Towhee Pipilo maculatus • • • • • • • • • • Steller's Jay Cyanocitta stelleri • • • • • • • Stilt Sandpiper Calidris himantopus • Streak-backed Oriole Icterus pustulatus • Sulphur-bellied Flycatcher Myiodynastes luteiventris • • • Summer Tanager Piranga rubra • • • • • • • • • • Swainson's Hawk Buteo swainsoni • • • • • • • • • • •
154 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
Swainson's Thrush Catharus ustulatus • • • • • • • • • Swamp Sparrow Melospiza georgiana • Tennessee Warbler Oreothlypis peregrina • Thick-billed Kingbird Tyrannus crassirostris • • • Townsend's Solitaire Myadestes townsendi • • • • • • • • • • Townsend's Warbler Dendroica townsendi • • • • • • • • • • Tree Swallow Tachycineta bicolor • • • • • • • Tropical Kingbird Tyrannus melancholicus • • Turkey Vulture Cathartes aura • • • • • • • • • • • Varied Bunting Passerina versicolor • • • Varied Thrush Ixoreus naevius • Vaux's Swift Chaetura vauxi • • • • • • Verdin Auriparus flaviceps • • • • • • • • • • Vermilion Flycatcher Pyrocephalus rubinus • • • • • • • • • • • Vesper Sparrow Pooecetes gramineus • • • • • • • • • • Violet-crowned Hummingbird Amazilia violiceps • • Violet-green Swallow Tachycineta thalassina • • • • • • • • • • • Virginia Rail Rallus limicola • Virginia's Warbler Oreothlypis virginiae • • • • • • • • • • • Warbling Vireo Vireo gilvus • • • • • • • • • • Western Bluebird Sialia mexicana • • • • • • • • • Western Grebe Aechmophorus occidentalis • Western Kingbird Tyrannus verticalis • • • • • • • • • • • Western Meadowlark Sturnella neglecta • • • • • • • • • • Western Sandpiper Calidris mauri • • • • Western Screech-Owl Megascops kennicottii • • • • • • • • • • • Western Scrub-Jay Aphelocoma californica • • • • • • • • • Western Tanager Piranga ludoviciana • • • • • • • • • • • Western Wood-Pewee Contopus sordidulus • • • • • • • • • • • Whip-poor-will 2 Caprimulgus vociferus • • • • Whiskered Screech-Owl Megascops trichopsis • • • White-breasted Nuthatch Sitta carolinensis • • • • • • • • White-crowned Sparrow Zonotrichia leucophrys • • • • • • • • • • • White-eared Hummingbird Hylocharis leucotis • • • White-faced Ibis Plegadis chihi • • • • White-tailed Kite Elanus leucurus • • White-throated Sparrow Zonotrichia albicollis • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 155 Common name Scientific name TUZI ORPI GICL CHIR TONT FOBO SAGU CAGR CORO TUMA MOCA
White-throated Swift Aeronautes saxatalis • • • • • • • • • • • White-winged Dove Zenaida asiatica • • • • • • • • • • • Wild Turkey Meleagris gallopavo • • • • Willet Tringa semipalmata • • Williamson's Sapsucker Sphyrapicus thyroideus • • • • • Willow Flycatcher Empidonax traillii • • • Wilson's Phalarope Phalaropus tricolor • • • Wilson's Warbler Wilsonia pusilla • • • • • • • • • • • Wood Duck Aix sponsa • • • Wood Stork Mycteria americana • Worm-eating Warbler Helmitheros vermivorum • Yellow Warbler Dendroica petechia • • • • • • • • • • • Yellow-bellied Sapsucker Sphyrapicus varius • Yellow-billed Cuckoo Coccyzus americanus • • • • • • • • • Yellow-breasted Chat Icteria virens • • • • • • • • • Yellow-eyed Junco Junco phaeonotus • • • Yellow-green Vireo Vireo flavoviridis • Yellow-headed Blackbird Xanthocephalus xanthocephalus • • • • • • • • Yellow-rumped Warbler Dendroica coronata • • • • • • • • • • • Yellow-throated Vireo Vireo flavifrons • • • Zone-tailed Hawk Buteo albonotatus • • • • • • • • • • •
1 = the AOU common name for this species was Common Moorhen until 2011. 2 = the AOU now considers the Whip-poor-will as two separate species, Eastern Whip-poor-will (Caprimulgus vociferus) and Mexican Whip-poor-will (Caprimulgus arizonae). Those recorded in the SODN are mostly likely Mexican Whip-poor-wills.
156 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Table D-4. Bird species known to occur in SOPN parks (through [and including] 2012 sampling), including species that migrate through or winter in the parks.
Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Acadian Flycatcher Empidonax virescens • American Avocet Recurvirostra americana • • American Bittern Botaurus lentiginosus • • American Coot Fulica americana • • • • American Crow Corvus brachyrhynchos • • • • • • • • American Goldfinch Spinus tristis • • • • • • • • American Kestrel Falco sparverius • • • • • • • • American Pipit Anthus rubescens • American Redstart Setophaga ruticilla • American Robin Turdus migratorius • • • • • • • • • American Tree Sparrow Spizella arborea • • American Wigeon Anas americana • Ash-throated Flycatcher Myiarchus cinerascens • • • • • • Bald Eagle Haliaeetus leucocephalus • Baltimore Oriole Icterus galbula • • • Barn Owl Tyto alba • • • Barn Swallow Hirundo rustica • • • • • • • • • • Barred Owl Strix varia • • Bell's Vireo Vireo bellii • • • • • Belted Kingfisher Megaceryle alcyon • • • • • Bewick's Wren Thryomanes bewickii • • • • • • Black Phoebe Sayornis nigricans • Black Rail Laterallus jamaicensis • Black Tern Chlidonias niger • Black Vulture Coragyps atratus • • Black-and-white Warbler Mniotilta varia • • Black-billed Cuckoo Coccyzus erythropthalmus • Black-billed Magpie Pica hudsonia • • • Blackbird Icteridae • • • • Blackburnian Warbler Dendroica fusca • Black-capped Chickadee Poecile atricapillus • • Black-capped Gnatcatcher Polioptila nigriceps • Black-chinned Hummingbird Archilochus alexandri • • • • Black-crested Titmouse Baeolophus atricristatus • Black-crowned Night-Heron Nycticorax nycticorax • • • Black-headed Grosbeak Pheucticus melanocephalus • • • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 157 Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Black-necked Stilt Himantopus mexicanus • Black-throated Gray Warbler Dendroica nigrescens • Black-throated Green Warbler Dendroica virens • Black-throated Sparrow Amphispiza bilineata • Blue Grosbeak Passerina caerulea • • • • • • • • • Blue Jay Cyanocitta cristata • • • • • • • Blue-gray Gnatcatcher Polioptila caerulea • • • • • • Blue-headed Vireo Vireo solitarius • Blue-winged Teal Anas discors • • • • Blue-winged Warbler Vermivora cyanoptera • Boat-tailed Grackle Quiscalus major • Brewer's Blackbird Euphagus cyanocephalus • • • • • Brewer's Sparrow Spizella breweri • • Broad-tailed Hummingbird Selasphorus platycercus • • • Broad-winged Hawk Buteo platypterus • • Bronzed Cowbird Molothrus aeneus • Brown Creeper Certhia americana • • Brown Thrasher Toxostoma rufum • • • • • • • Brown-headed Cowbird Molothrus ater • • • • • • • • • • Bullock's Oriole Icterus bullockii • • • • • • • • Burrowing Owl Athene cunicularia • • Bushtit Psaltriparus minimus • Cactus Wren Campylorhynchus brunneicapillus • Calliope Hummingbird Stellula calliope • Canada Goose Branta canadensis • • • • • • Canyon Towhee Melozone fusca • • • Canyon Wren Catherpes mexicanus • • • • Carolina Chickadee Poecile carolinensis • • • • Carolina Wren Thryothorus ludovicianus • • • • Cassin's Kingbird Tyrannus vociferans • • • • Cassin's Sparrow Peucaea cassinii • • • • • • • Cattle Egret Bubulcus ibis • • Cedar Waxwing Bombycilla cedrorum • • • • Chestnut-sided Warbler Dendroica pensylvanica • • Chihuahuan Raven Corvus cryptoleucus • • • • Chimney Swift Chaetura pelagica • • • • • Chipping Sparrow Spizella passerina • • • • • • • • •
158 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Chuck-will's-widow Caprimulgus carolinensis • • Cinnamon Teal Anas cyanoptera • • • Clark’s Nutcracker Nucifraga columbiana • Clay-colored Sparrow Spizella pallida • • • • Cliff Swallow Petrochelidon pyrrhonota • • • • • • • • • • Common Gallinule 1 Gallinula chloropus • Common Grackle Quiscalus quiscula • • • • • • • • • Common Nighthawk Chordeiles minor • • • • • • • • • • Common Poorwill Phalaenoptilus nuttallii • • • Common Raven Corvus corax • • • Common Snipe Gallinago gallinago • Common Yellowthroat Geothlypis trichas • • • • • • • • • Cooper's Hawk Accipiter cooperii • • • • • • Cordilleran Flycatcher Empidonax occidentalis • • Crested Caracara Caracara cheriway • Curve-billed Thrasher Toxostoma curvirostre • Dark-eyed Junco Junco hyemalis • • • • • Dickcissel Spiza americana • • • • • • • Double-crested Cormorant Phalacrocorax auritus • • Downy Woodpecker Picoides pubescens • • • • • • • • Eastern Bluebird Sialia sialis • • • • • • • Eastern Kingbird Tyrannus tyrannus • • • • • • • • Eastern Meadowlark Sturnella magna • • • • • Eastern Phoebe Sayornis phoebe • • • • • • Eastern Screech-Owl Megascops asio • • • • Eastern Towhee Pipilo erythrophthalmus • Eastern Wood-Pewee Contopus virens • • • Eurasian Collared-Dove Streptopelia decaocto • • • • • • • • • European Starling Sturnus vulgaris • • • • • • • Ferruginous Hawk Buteo regalis • • Field Sparrow Spizella pusilla • • • • Forster's Tern Sterna forsteri • • Fox Sparrow Passerella iliaca • Franklin's Gull Leucophaeus pipixcan • • • Gadwall Anas strepera • • Golden-crowned Kinglet Regulus satrapa • Golden Eagle Aquila chrysaetos •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 159 Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Golden-fronted Woodpecker Melanerpes aurifrons • Grace's Warbler Dendroica graciae • Grasshopper Sparrow Ammodramus savannarum • • • • • • Gray Catbird Dumetella carolinensis • • • • Gray Flycatcher Empidonax wrightii • • • Gray Vireo Vireo vicinior • Gray-cheeked Thrush Catharus minimus • Great Blue Heron Ardea herodias • • • • • • • • • Great Crested Flycatcher Myiarchus crinitus • • • • • Great Egret Ardea alba • • • • • Great Horned Owl Bubo virginianus • • • • • • • • • • Greater Roadrunner Geococcyx californianus • • • • • Greater Yellowlegs Tringa melanoleuca • Great-tailed Grackle Quiscalus mexicanus • • • • • • • Green Heron Butorides virescens • • • • • • Green Kingfisher Chloroceryle americana • Green-tailed Towhee Pipilo chlorurus • • • Green-winged Teal Anas crecca • • Hairy Woodpecker Picoides villosus • • • • • • Harris' Sparrow Zonotrichia querula • • Hepatic Tanager Piranga flava • • Hermit Thrush Catharus guttatus • • • Hooded Warbler Wilsonia citrina • • Horned Lark Eremophila alpestris • • • • House Finch Carpodacus mexicanus • • • • • • • House Sparrow Passer domesticus • • • • • • • House Wren Troglodytes aedon • • • • • • • • • Hudsonian Godwit Limosa haemastica • Inca Dove Columbina inca • Indigo Bunting Passerina cyanea • • • • • • • Juniper Titmouse Baeolophus ridgwayi • • • Killdeer Charadrius vociferus • • • • • • • • • Ladder-backed Woodpecker Picoides scalaris • • • • Lark Bunting Calamospiza melanocorys • • • • • • Lark Sparrow Chondestes grammacus • • • • • • • • • Lazuli Bunting Passerina amoena • Le Conte's Sparrow Ammodramus leconteii •
160 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Least Flycatcher Empidonax minimus • Least Sandpiper Calidris minutilla • • Lesser Goldfinch Spinus psaltria • • • • • • Lesser Nighthawk Chordeiles acutipennis • Lesser Scaup Aythya affinis • Lesser Yellowlegs Tringa flavipes • Lewis's Woodpecker Melanerpes lewis • • Lincoln's Sparrow Melospiza lincolnii • • • • Little Blue Heron Egretta caerulea • • Loggerhead Shrike Lanius ludovicianus • • • • • • Long-billed Dowitcher Limnodromus scolopaceus • Louisiana Waterthrush Parkesia motacilla • • MacGillivray's Warbler Oporornis tolmiei • • • Magnolia Warbler Dendroica magnolia • Mallard Anas platyrhynchos • • • • • • • • Mississippi Kite Ictinia mississippiensis • • • • • Mountain Bluebird Sialia currucoides • • • Mountain Chickadee Poecile gambeli • • Mountain Plover Charadrius montanus • Mourning Dove Zenaida macroura • • • • • • • • • • Northern Bobwhite Colinus virginianus • • • • • • • Northern Cardinal Cardinalis cardinalis • • • • • • Northern Flicker Colaptes auratus • • • • • • • • • • Northern Harrier Circus cyaneus • • • • • • Northern Mockingbird Mimus polyglottos • • • • • • • • • Northern Parula Parula americana • Northern Pintail Anas acuta • Northern Rough-winged Swallow Stelgidopteryx serripennis • • • • • • • Northern Shoveler Anas clypeata • • • Olive-sided Flycatcher Contopus cooperi • • • Orange-crowned Warbler Oreothlypis celata • Orchard Oriole Icterus spurius • • • • • • Osprey Pandion haliaetus • • Painted Bunting Passerina ciris • • • • Peregrine Falcon Falco peregrinus • Pied-billed Grebe Podilymbus podiceps • • Pileated Woodpecker Dryocopus pileatus • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 161 Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Pine Siskin Spinus pinus • • • Pinyon Jay Gymnorhinus cyanocephalus • • • Plumbeous Vireo Vireo plumbeus • • Prairie Falcon Falco mexicanus • • • Prothonotary Warbler Protonotaria citrea • Purple Finch Carpodacus purpureus • Purple Martin Progne subis • • • Red Crossbill Loxia curvirostra • Red-bellied Woodpecker Melanerpes carolinus • • • • • Red-breasted Nuthatch Sitta canadensis • Red-eyed Vireo Vireo olivaceus • • Redhead Aythya americana • Red-headed Woodpecker Melanerpes erythrocephalus • • • • • • • Red-shouldered Hawk Buteo lineatus • • Red-tailed Hawk Buteo jamaicensis • • • • • • • • • • Red-winged Blackbird Agelaius phoeniceus • • • • • • • • • Ringed Kingfisher Megaceryle torquata • Ring-necked Duck Aythya collaris • Ring-necked Pheasant Phasianus colchicus • • • • • • Rock Pigeon Columba livia • • • • Rock Wren Salpinctes obsoletus • • • • • • Rose-breasted Grosbeak Pheucticus ludovicianus • • • Rough-legged Hawk Buteo lagopus • Ruby-crowned Kinglet Regulus calendula • • • Ruby-throated Hummingbird Archilochus colubris • • • Ruddy Duck Oxyura jamaicensis • • Rufous Hummingbird Selasphorus rufus • • Rufous-crowned Sparrow Aimophila ruficeps • • • • Rusty Blackbird Euphagus carolinus • Sandhill Crane Grus canadensis • Savannah Sparrow Passerculus sandwichensis • • Say's Phoebe Sayornis saya • • • • • • • Scaled Quail Callipepla squamata • • • Scissor-tailed Flycatcher Tyrannus forficatus • • • • • Sharp-shinned Hawk Accipiter striatus • • Short-eared Owl Asio flammeus • • Snowy Egret Egretta thula • •
162 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Solitary Sandpiper Tringa solitaria • Song Sparrow Melospiza melodia • • • • • Sora Porzana carolina • Spotted Sandpiper Actitis macularius • • • • • Spotted Towhee Pipilo maculatus • • • • • • • Steller's Jay Cyanocitta stelleri • Stilt Sandpiper Calidris himantopus • Summer Tanager Piranga rubra • • • • • • Swainson's Hawk Buteo swainsoni • • • • • • • Swainson's Thrush Catharus ustulatus • • Tennessee Warbler Oreothlypis peregrina • Tree Swallow Tachycineta bicolor • • Tricolored Heron Egretta tricolor • Tufted Titmouse Baeolophus bicolor • • • Turkey Vulture Cathartes aura • • • • • • • • • • Upland Sandpiper Bartramia longicauda • • Veery Catharus fuscescens • Vermilion Flycatcher Pyrocephalus rubinus • Vesper Sparrow Pooecetes gramineus • • • • • • • Violet-green Swallow Tachycineta thalassina • • • • Virginia Rail Rallus limicola • • Virginia's Warbler Oreothlypis virginiae • • • Warbling Vireo Vireo gilvus • • • • • • Western Bluebird Sialia mexicana • • Western Kingbird Tyrannus verticalis • • • • • • • • • • Western Meadowlark Sturnella neglecta • • • • • • • • • Western Sandpiper Calidris mauri • Western Scrub-Jay Aphelocoma californica • • • Western Tanager Piranga ludoviciana • • • Western Wood-Pewee Contopus sordidulus • • • • • • White-breasted Nuthatch Sitta carolinensis • • • • • • White-crowned Sparrow Zonotrichia leucophrys • • • • • • White-eyed Vireo Vireo griseus • • • White-faced Ibis Plegadis chihi • • White-throated Sparrow Zonotrichia albicollis • • White-throated Swift Aeronautes saxatalis • White-winged Dove Zenaida asiatica • • •
Appendix D - Landbird species documented in CHDN, NGPN, SODN, and SOPN parks 163 Common name Scientific name BEOL CAVO CHIC FOLS FOUN LAMR LYJO PECO SAND WABA
Wild Turkey Meleagris gallopavo • • • • • • • • Willet Tringa semipalmata • Willow Flycatcher Empidonax traillii • • • Wilson's Phalarope Phalaropus tricolor • Wilson's Warbler Wilsonia pusilla • • • Winter Wren Troglodytes hiemalis • Wood Duck Aix sponsa • • • Yellow Warbler Dendroica petechia • • • • • • • • • Yellow-bellied Flycatcher Empidonax flaviventris • Yellow-bellied Sapsucker Sphyrapicus varius • Yellow-billed Cuckoo Coccyzus americanus • • • • • • • Yellow-breasted Chat Icteria virens • • • • Yellow-headed Blackbird Xanthocephalus xanthocephalus • • • Yellow-rumped Warbler Dendroica coronata • • • • • • Yellow-throated Vireo Vireo flavifrons • • Yellow-throated Warbler Dendroica dominica • Note: Unverified observations of additional species in a park are not included. 1 = the AOU common name for this species was Common Moorhen until 2011.
164 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN SOP #1. Preparations for the Field Season and Equipment Needed
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol
Revision History Log Previous Revision Changes Section and Reason for New version version # date Author made paragraph change #
This Standard Operating Procedure (SOP) gives a description of the procedures to follow prior to observers conducting surveys. Pre-season planning, such as creating schedules and organizing equipment, facilitates the completion of the bird monitoring work and allows for budget planning. All of the equipment and supplies listed in Section 1.3 (below) should be organized and made ready for the field season, and copies of the field data forms (shown in SOP #4) should be made. Prior to the field season each year, in early March, all observers should review the entire protocol, including SOPs. Review of bird identification by sight and sound is particularly important and is covered in SOP #2.
SOP 1.1. General preparation and review Rocky Mountain Bird Observatory (RMBO) will be responsible for all aspects of preparations for the field season. Important preparations include:
1. Review protocol narrative and all SOPs. Understand the goals and procedures of the monitoring program and begin preparations for training and field surveys. Discuss with appropriate Inventory & Monitoring (I&M) personnel any proposed or recent changes to the sampling design or survey protocol. If a change has been implemented since the last field season, be sure that all field forms, databases, and appropriate sections in relevant SOPs have been updated. 2. Begin preparations. Discuss the season’s objectives and schedules with I&M personnel. Meet in early December to discuss budget, begin developing a timeline and field schedule, and outline responsibilities of individual observers. 3. Contact parks. Contact appropriate park personnel to discuss any changes in their policy for researchers or specific conditions that may have changed since the last field season (e.g., fires that may have affected sites or areas that may have closed). Contact with park personnel may be especially important in parks with border-security issues (e.g., ORPI and CORO). Also, discuss housing availability and needs with appropriate parks. Fill out web-based permit requests. 4. Review field efforts from previous year. Review efforts from the previous field season to ensure that any issues or problems that arose during the season are addressed before the new field season begins. 5. Review these reports and follow up on any proposed changes, particularly those related to access issues. Suggested changes to the protocol will be collected in each of
SOP #1 - Preparations for the Field Season 165 the first five years, but substantial changes to the protocol should only be instituted after the 3 to 5 year review. 6. Print and review the appropriate network’s species list. Review the species most likely to be encountered in each park. 7. Generate a list of point coordinates for all points. Include GIS-generated points for new sites and a list of the actual marker coordinates for previously sampled sites. 8. Print maps of each site and include with transect/group write-up. Ask the network Database Manager for assistance in printing these maps. 9. Print and copy the field data forms. The field data forms are included in SOP #4. Print approximately 5% of these forms onto Rite-in-the-Rain© paper. 10. Upload waypoints onto the GPS units. Waypoints (the latitude and longitude [UTM] coordinates [NAD83 datum] for each survey point) should be uploaded via computer. Alternatively, they can be manually entered into GPS units before the start of the field season, or entered in the field. Note that manually entering coordinates introduces an extra source of error and should be avoided, if possible. SOP 1.2. Scheduling field work 1. Monitoring efforts at each network’s parks will require a field crew of at least two observers. To ensure safety, contact between crew leaders and field technicians will be maintained via cell phone, email, and a satellite personal tracker (SPOT) unit. A field safety manual prepared by RMBO is provided to field crews and reviewed during training. Observers will survey 6-19 points each day (grouped as a transect or independently placed points; termed sites), and sites will be surveyed one to three times each season. Observers will typically be asked to work a schedule consisting of 10 days on, 4 days off. However, observers will occasionally be asked to survey one or two extra days a week during the peak of the breeding season in a given network. 2. Preliminary field schedules will be drawn up prior to the beginning of surveys and observer training. Surveys at parks will be asynchronous; for example, surveying will begin as early as 1 April in ORPI and as late as early May at GICL. As weather (primarily wind or rain) can be a factor when conducting surveys, field schedules should include some extra time for make-up surveys. 3. Because of the possibility for the need of a ranger escort at some parks (e.g., ORPI, CORO), the park contact person must be notified at least 4 weeks prior to surveys to ensure that rangers are able to accompany observers. Scheduling that far in advance may be difficult because of inevitable changes in field surveys. Therefore, it will be imperative to maintain scheduling flexibility in parks and areas that do not require coordination with law enforcement personnel. 4. If the transect/point has been sampled previously in the season, determine the point sequence used and conduct the survey in the opposite or different direction (if possible). SOP 1.3. Organizing supplies and equipment In preparation for the field season, RMBO will organize and supply observers with all equipment and materials listed below, unless otherwise indicated. To facilitate a timely departure from camp or trailhead to each morning’s survey, observers will prepare food (if necessary) and organize appropriate personal gear, including extra water and a first aid kit, and the following equipment and materials, during each evening of the field season:
A. Timepiece with a countdown timer and a chime; B. Binoculars (provided by the technician); C. Declination-adjustable compass with sighting capability (e.g., a mirror); D. Clipboard (with instruction sheets attached);
166 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN E. Writing utensils (pencil or indelible ink pen) (3 pencils will be provided by your employer at the start of the field season; if you lose them you must provide additional writing utensils); F. GPS unit with grid locations loaded onto it; G. Rangefinder; H. Extra batteries; I. Data forms sufficient for all the points planned that morning; J. Maps and transect locations; K. Master list of four-letter bird codes; and L. Master list of weather, breeding behavior, and habitat codes, attached to the clipboard. 1. The day before conducting a point count survey, familiarize yourself with the park and the habitat, and locate the transects, including access points. Plan an access route to the transect that you will be surveying during the daylight so you will be able to find your way to the first point if you have to hike in the dark the next morning. Determine the point to point route you will take to conduct the survey. If the survey is in a remote area, be sure to make arrangements to camp the previous night near the survey area. 2. Consult weather reports. Canceling surveys during the breeding season is rare in our region, but strong storms can occur. Unless there are extreme conditions predicted for the morning surveys (e.g., strong winds and/or heavy rain), we recommend that observers attempt to conduct a survey. Counts should not be conducted if wind strength on the Beaufort scale is a sustained 5 or greater, or if it is raining (anything greater than a drizzle). If you encounter these conditions, wait until the weather improves or cancel the sampling for that day and try again on another day. On days when surveys are cancelled, consider data entry or scouting other transects on the park that you will be surveying. 3. Sampling will occur in the morning, beginning approximately ½ hour before sunrise (once there is enough light to ID birds by sight) and ending no later than 3 ½ hours after official sunrise. There is considerable variation among sunrise times, and it is advisable to use a table localized for the area being sampled. Attempt to arrive at the first point while it is still dark so that the count can begin as soon as it is light enough to see. Singing rate for most species is usually highest before or near official sunrise and declines slowly over the next few hours. 4. As an added safety measure for RMBO field technicians, SPOT units are provided for each individual. SPOT units are a way for technicians to regularly check in with their field crew leaders to maintain contact when both parties have irregular access to internet and phone service, as well as to send a “help” message in the event of an emergency. RMBO requires field technicians to send their crew leader an “ok” message prior to, and following, the completion of each survey. This assures the field crew leader that technicians are able to safely get into, and out of, their transects on a daily basis. The nature of this form of contact requires the regular and consistent use of the units; otherwise, the field crew leader is left wondering if the technician forgot to check in or if they are in need of assistance. The use of SPOT units will be covered in detail during training, and more information is available in the field safety manual. 5. The location of sample points within groups or transects has been pre-determined and will be provided to the field crew in map form; also, UTM coordinates for each point will be loaded onto GPS units that will be used to navigate to the points. SOPN Only: Despite the fact that the group/transect and its respective bird-survey points have been established, they might not have been visited before. When a point is visited for the first time, theEstablish New Sampling Point datasheet must be filled out for each
SOP #1 - Preparations for the Field Season 167 individual point. That information is also entered into the bird monitoring database. Because the sampling history of each point is tracked, it is extremely important to use the point ID that was pre-assigned, or the ID assigned for new sampling points using the Establish New Sampling Point datasheet. Your supervisor will advise you if and where this datasheet is to be completed.
SOP 1.4. Suggested readings and audio references Baicich, P. J., and C. J. O. Harrison. 1997. A Guide to the nests, eggs, and nestlings of North American birds. Academic Press, San Diego, CA.
Colver, K. J., D. Stokes, and L. Stokes. 1999. Stokes field guide to bird songs. Time Warner, New York, NY.
Epple, A. O., and L. E. Epple. 1995. Plants of Arizona. Falcon Press, Helena, MT.
Keller, G. A. 2001. Bird songs of southeastern Arizona and Sonora, Mexico. Macaulay Library of Natural Sounds, Cornell Laboratory of Ornithology, Ithaca, NY.
Sibley, D. A. 2003. The Sibley field guide to birds of western North America. Alfred A. Knopf, New York, NY.
168 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN SOP #2. Training
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol Revision History Log Previous Revision Changes Section and Reason for New version version # date Author made paragraph change #
This Standard Operating Procedure (SOP) explains the procedures for training bird survey personnel (observers). Accurate and consistent data collection begins with the hiring process, followed by a rigorous training program. Training should ensure that all observers are able to identify, by sight and sound, >90% of bird species expected to be encountered in network parks and >80% of bird species that have reasonable potential to occur in the area. It is also essential that observers are able to accurately and consistently estimate distances to birds and follow standard operating procedures to assure data quality across time. Observers must also be prepared both mentally and physically for the extreme heat and difficult terrain of parks in the four networks.
SOP 2.1. Training Observers In addition to hiring, RMBO will handle all training. Observers will be trained at the beginning of each field season and will be tested periodically in their ability to identify and estimate distances to birds. The training program emphasizes correct identification of species and distance estimation, and the promotion of accuracy and consistency in data collection among observers. In particular, we try to calibrate observers so that they have comparable skills in identifying species, are estimating the numbers seen or heard in the same way, and are recording data in a consistent format. For observers who are already competent at identifying birds by sight and sound, the need for training may be minimal; experienced observers usually recalibrate quickly. If the program is fortunate enough to hire all experienced observers for a season, then the training program that we propose will improve their skills. No observer is exempt from the training program.
The following section outlines the tasks to be completed during the training period. We summarized much of this material from Peitz et al. (2004) and Bennetts et al. (2005). Below this list are details of the training program itself.
1. Prepare for the training program. The instructor must prepare training materials, itineraries, and field gear before the training program begins. Along with the training program, there are a variety of other tasks that will have to be completed in preparation for field work. Thus, it is crucial that instructors prepare well in advance and budget their time appropriately. 2. Issue field gear before training begins. See SOP #1 (“Preparing for the Field Season and Equipment Needed”) for a list of field equipment. Provide a copy of this protocol to all trainees, along with field gear and other training materials, before training begins. Ensure that observers examine the list of necessary personal gear (listed in SOP #1) and that they have all the appropriate equipment by the time field work
SOP #2 - Training 169 begins. Ensure that trainees are informed and prepared for the training and survey events; everyone should bring binoculars, warm clothing (if appropriate), pocket field guides, hats, and other appropriate equipment for each day of training. Have field technicians read Kepler and Scott (1981) for a detailed discussion of training observers, and Chapter 2 of Bibby et al. (2000) for a general discussion of the sources of error in bird surveys. 3. Have all crewmembers participate in a 5-day landbird survey training program. Observers from previous seasons normally do not need as much training in bird identification skills as new observers. However, all observers must attend the entire training course each year. Experienced observers can assist with training less experienced observers. Experienced observers will also continue practicing distance estimation, working on identification of birds by call notes and partial songs, and generally improving their identification skills. 4. Conduct training in areas similar to those encountered during surveys. Conduct the majority of the 5-day training program in areas with similar topography and communities to those encountered during surveys. While emphasizing distance estimation, the initial objective of the training program is to maximize the trainee’s exposure to the species most likely to be encountered during the surveys. Discuss personal techniques for distance estimation, dealing with busy points, flyovers, moving birds, potentially confusing scenarios, and bird species and behaviors that are encountered in similar areas. It is important to recognize that training sessions early in the season will fail to detect all of the species that will be encountered later in the season. This is especially true in the riparian areas, as most of the riparian-obligate species are Neotropical migrants. Therefore, it will be important to discuss common species that are likely to be encountered later in the season. 5. Focus on distance estimation during the first days of training. The 5-day training session occurs immediately before the field season when many landbirds are returning to their nesting grounds. Emphasize distance estimation during the first two to three days of training. Construct distance estimation training courses in a variety of areas and topographical gradients. The importance of accurate distance estimation cannot be overemphasized, and training has been found to improve accuracy considerably (Kepler and Scott 1981). 6. Emphasize field safety procedures and issues. Discuss emergency safety procedures and important safety issues for each network’s region, most notably heat exposure. It is recommended that all field personnel take a one-day CPR/first aid course and/or a field safety course such as that offered through the School of Natural Resources at the University of Arizona. The current observer training program conducted by RMBO consists of 5 full days of training in classroom and field settings. The daily agenda appears below (times spent in each training session are not provided). Training sessions may be changed by RMBO as needed:
Day 1 ●● Introduction and orientation ●● Presentation: RMBO overview and monitoring programs ●● Presentation: Distance and occupancy sampling methods ●● Review of the SOPN point-count protocol ●● Bird identification quiz ●● Equipment checkout, information binders ●● Using a rangefinder, GPS Unit, SPOT unit, and timer ●● Park contacts / communications / policies
170 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN ●● Presentation: Safety measures ●● Bird identification quiz.
Day 2 ●● Practice conducting point counts / bird identification ●● Review datasheets from morning counts ●● Training on entering bird data ●● Completing and submitting reimbursement form and work log ●● Bird identification quiz ●● Uploading points into GPS unit.
Day 3 ●● Practice conducting point counts / bird identification, including using GPS unit to navigate from point to point ●● Review datasheets ●● Scouting transects prior to surveying ●● Practice data entry ●● Training on completing and submitting timesheets ●● Bird identification quiz ●● Individual planning of survey schedules / park survey assignments / crew leader Q&A.
Day 4 ●● Conduct mock point count survey at a complete transect ●● Review datasheets ●● Bird identification quiz ●● Enter data collected from morning survey. Additional data entry or other training as needed.
Day 5 ●● Conduct point count survey at riparian transect ●● Review datasheets ●● Bird quiz and discussion ●● Depart for first transect, or, if needed, conduct another point count survey morning of Day 6 shadowed by crew leader.
Training will count as the first 5 (or 6) days of the 10-day on schedule. Observers will finish out the other 5 (or 4) days meeting with the contact at the first park that will be surveyed, scouting the first transect, and conducting surveys.
SOP 2.2. Miscellaneous Count Issues There are a number of important issues to be aware of while conducting the bird counts (e.g., window species, no pishing, airplane and other noise). Observers should be made aware of these potential issues, which are discussed in SOP #4.
SOP 2.3. Literature Cited Bennetts, R. E., A. Schrag, and S. Wolff. 2005. Cooperative bird monitoring plan and protocol for the Greater Yellowstone Network of parks. Version 1.00. Unpublished protocol.
SOP #2 - Training 171 Bibby, C. J, N. D. Burgess, D. A. Hill, and S. Mustoe. 2000. Bird census techniques. 2nd ed. Academic Press, London, England.
Kepler, C. B., and J. M. Scott. 1981. Reducing bird count variability by training observers. Studies in Avian Biology 6:366-371.
Peitz, D. G., S. G. Fancy, L. P. Thomas, G. A. Rowell, and M. D. Debacker. 2004. Bird monitoring protocol for Agate Fossil Beds National Monument, Nebraska and Tallgrass Prairie National Preserve, Kansas. Unpublished protocol to the Prairie Cluster Prototype Monitoring Program, National Park Service, U.S. Department of the Interior.
172 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN SOP #3. GPS Unit Operation and Navigation and Measuring Techniques
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol Revision History Log Previous Revision Changes Section and Reason for New version version # date Author made paragraph change #
This Standard Operating Procedure (SOP) explains how to use a GPS, as wells as navigational and measurement techniques necessary for landbird monitoring surveys. As field personnel, monitors must be able to use a GPS to navigate over open terrain, determine distances traveled, and plot transect locations on a map. The following sections discuss the correct use of two basic tools used by monitors in the field – a GPS and a compass – as well as some basic mapping techniques for locating and mapping transect locations in the field. Information for this SOP has been taken from the 2003 USDI- National Park Service Fire Monitoring Handbook.
SOP 3.1. Using a GPS All GPS measurements should be set to: ●● UTM coordinates ●● NAD83 datum ●● Meters
All crew members should be familiar with the operation of their GPS unit. RMBO will supply the appropriate Users Guide for the equipment, and the guide should be reviewed by all crew members and carried while on sampling events. Prior to the beginning of the field season, all crew members should participate in field training in the use of their respective GPS units. As field personnel, monitors must be able to use a GPS to navigate to specific monitoring points and mark the beginning/ending points of a transect. This is achieved by marking waypoints. When paper data sheets are used, both the UTM coordinates and the waypoint number should be recorded on the data sheet.
SOP 3.2. Using a Compass The compass is probably the instrument most frequently used by field personnel. Accurate compass bearings are essential for navigating over open terrain and for finding and mapping plot locations.
Parts of a Compass A multitude of compass models exist with different features; however, all compasses have at minimum the following (Figure SOP 3-1): ●● Magnetic needle—The magnetic needle, drawn by the pull of the magnetic north
SOP #3 - GPS Unit Operation and Navigation and Measuring Techniques 173 pole, always points to magnetic north. The north end of the nee- dle is usually marked by an arrow, or painted red. ●● Revolving 360° dial— The dial is marked with the car- dinal points, N, E, S, and W, and is graduated into degrees. Within the dial is a transparent plate with parallel orienting lines and an orienting arrow. ●● Transparent base plate—Has a line of travel arrow and ruled edges.
Obtaining Accurate Compass Bearings Figure SOP 3-1. Parts of a compass. Always hold the compass level, so the magnetic needle can swing freely. Hold the compass away from magnetic objects such as rebar, watches, mechanical pencils, cameras, and belt buckles that can draw the magnetic needle off line.
Setting a Bearing If you know the bearing in degrees from your current position to an object, turn the dial until the degree is aligned with the index point and line of travel arrow. In Figure 1, the bearing is set at 356°.
Taking a Bearing ●● Aim the line of travel arrow on the compass towards the object. ●● Turn the revolving dial until the magnetic needle is aligned within the orienting arrow in the compass dial. ●● Read the bearing on the compass dial at the index point. Facing a Bearing (Direction of Travel) Hold the compass with the base plate level and line of travel arrow pointing forward. Turn your body with the compass until the red north end of the magnetic needle point is aligned within the orienting arrow in the compass dial. You are now facing in the direction of the bearing.
Walking a Bearing Look straight ahead in the direction of travel. Choose a landmark that lies in line with the direction of travel. Walk to the landmark. Continue in this manner until you reach your destination.
SOP 3.3. Using a compass in conjunction with a map You may use a compass in conjunction with a map in either of two ways:
●● Determine a bearing from a map and then travel that direction in the field (map to ter- rain), or ●● Take a bearing in the field and plot that bearing on a map (terrain to map).
Whenever combining compass (field) bearings with map (true) bearings, you must account for declination.
174 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Declination Declination is the degree of difference between true north and magnetic north. True north is where all lines of longitude meet on a map. Magnetic north is the location of the world’s magnetic region (in the upper Hudson Bay region of Canada). Declination is east or west, depending upon where magnetic north lies in relation to your position. Figure SOP 3-2. Magnetic decli- If magnetic north lies to the east of nation map. your position, declination is east. If magnetic north lies to the west of your position, declination is west. In North America, zero declination runs roughly from west of Hudson Bay down along Lake Michigan to the Gulf of Mexico in western Florida.
Declination diagrams are located in the bottom margin of U.S. Geological Survey (USGS) topographic maps. Keep in mind that declination changes slightly over time as magnetic north moves slowly west; therefore, the current declination in your area may be slightly different than the declination at the time the map was printed. Declination maps are re-mapped every five years by the USGS; the most recent declination map is for 1995 (see Figure SOP 3-2). On some maps the annual rate of change may be printed, and thus you can calculate the current declination. USGS also produces a Magnetic Declination Map of the United States. This map shows the rate of change throughout the U.S. so that the current declination in any area can be calculated. You may also obtain the current declination in your area from your county surveyor.
If you need to be more precise, try using a geomagnetic calculator (e.g., National Oceanic and Atmospheric Administration [NOAA] 2000) that will calculate the declination for any year (1900–2005) given the latitude/ longitude or UTM coordinates.
Setting declination on your compass Some compass models have a declination adjustment screw. The set screw key is usually attached to a nylon cord that hangs from the compass. Using the key, turn the set screw to the appropriate declination. Once you have set the proper declination you do not need to change it until you move to a different area. If you move to a new area, remember to reset the declination on your compass. If you do not have a compass with a declination adjustment screw, you must add or subtract declination to determine the correct bearing. Whether you add or subtract declination depends on whether you are working from map to terrain or terrain to map.
Map to terrain If you have determined a bearing between two positions from a map, and you are going to walk the bearing, you must convert that bearing to a magnetic bearing. The rules for converting from map to field bearings are as follows:
●● For declination west, turn dial west (add number of degrees of declination). ●● For declination east, turn dial east (subtract number of degrees of declination).
Terrain to map If you have taken a field bearing and want to plot the position on a map, you must convert the bearing from a magnetic bearing to a map (true) bearing. The rules are simply the reverse of the map to terrain rules:
SOP #3 - GPS Unit Operation and Navigation and Measuring Techniques 175 ●● For declination west, turn dial east (subtract). ●● For declination east, turn dial west (add). SOP 3.4. Determining distances in the field
Distances along the ground can be measured by various means. You can measure distances along a road in a vehicle with an odometer. In the field you can use a meter tape, although over long distances this method is often impractical. Pacing is a common means of measuring distance in the field. By knowing the length of your pace you can measure the distance over ground simply by walking. A pace is defined as the distance between the heel of one foot and the heel of the same foot in the next stride. Therefore, one pace equals two steps—one step of each leg.
Determining Your Pace on Level Ground ●● On level ground, lay out a course of known distance (e.g., 50 m or 1 chain). ●● Walk the length of the course counting each pace (two steps). Take the first step with your left foot, then count each time your right foot touches the ground. ●● Repeat the process several times to obtain an average number of paces per length. ●● Divide the number of paces into the measured distance to arrive at the length of your pace.
To determine the distance you have paced in the field, multiply the number of paces by your distance per pace.
Example: 50 m/32 pace = 1.6 m/pace 66 ft/20 pace = 3.3 ft/pace
Example: The distance between the reference feature and 0P is 30 paces. Your pace distance is 1.6 m. 30 paces × 1.6 m/p = 48 m
Determining Your Pace on Sloping Ground When walking on a slope, either uphill or downhill, your paces will be shorter; consequently, you will take more paces to cover the same distance on a slope compared to level ground. To determine your pace on sloping ground:
●● Lay out a course of the same distance used on level ground with moderately steep slope. ●● Walk upward on this course, counting the number of paces as before. ●● Divide the total distance by the total number of paces. ●● This is the length of one pace on a slope.
Example: On level ground: 50 m = 32 paces = 1.60 m/pace On sloping ground: 50 m = 40 paces = 1.25 m/pace
Walk the course several times both uphill and downhill until you have an average length of a pace on sloping ground. Your upslope pace may be different than your downslope pace.
3.5 Some basic map techniques
Working with Scale
176 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN You will inevitably use maps with many different scales during your monitoring work. If you enlarge or reduce a map, the scale of the map will change and you must determine the new scale. Scale on a map is determined by the formula:
Scale = Map Distance (MD)/Ground Distance (GD)
Map distance equals the distance measured between two points on a map. Ground distance equals the distance on the ground between the same two points.
To determine the new scale of a map that was enlarged or reduced, follow these steps:
●● On the original map of known scale, measure the map distance between 1) two points that are separated horizontally, and 2) two points that are separated vertically. (The reason to measure two distances is that copy machines are not precision instruments and may skew the map.) ●● Compare the distances to the original map scale to determine the four ground distances. ●● On the enlarged (or reduced) map, measure the distances between the same four points. (Although the map distance has changed, the ground distances between the four points are still the same.) ●● Calculate the scale of the enlarged (or reduced) map with the scale formula, using each of the four distances. ●● Average all four scales, and use this for determining ground distances on the enlarged (or reduced) map.
Example: You calculate the scale of a map from an original map at the scale of 1:24,000. On the original map, you measure two separate horizontal distances, 5.6 and 11.1 cm, and two vertical distances 4.15 and 6.5 cm. For a 1:24,000 map, 1 cm = 240 m, so the corresponding ground distances are 1,344, 2,644, 996, and 1,560 m respectively.
Using the enlarged map, now measure the same four distances. Using the first map ground distance combination, we get the following scale:
Scale = MD/GD = 13.2 cm/1344 m = 1 cm/101.82 m
Continuing on for the three remaining distances, the four resulting scales would be 1:10,182; 1:10,129; 1:10,163; and 1:10,163. Taking the average of these four numbers, the scale of the enlarged map is determined to be 1 cm = 101.59 m, or 1:10,159.
Determining the Direction and Distance Between Two Map Points To determine the direction between two points on a map, follow these steps:
●● Draw a line connecting the two points (A B). ●● Place your compass with the edge of the base plate along the line. ●● Orient the compass with the line of travel arrow pointing towards point B. ●● Turn the revolving dial of the compass until the orienting lines within the compass dial are parallel to the north-south meridian lines on the map, and the North (N) arrow points to north on the map.
SOP #3 - GPS Unit Operation and Navigation and Measuring Techniques 177
SOP #4. Field Sampling
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol
Revision History Log Previous Revision Author Changes Section and Reason for New version version # date made paragraph change #
SOP Source: Lock, R.A., and C.M. White. 2012. Landbird Monitoring Protocol for the Chihuahuan Desert Network (CHDN), Sonoran Desert Network (SODN), and Southern Plains Network (SOPN). Unpublished report. Rocky Mountain Bird Observatory, Brighton, CO.
SOP 4.1. Introduction This Field Sampling Standard Operating Procedure (SOP) gives step-by-step instructions for conducting bird counts at parks in the Chihuahuan Desert Network (CHDN), Northern Great Plains Network (NGPN), Sonoran Desert Network (SODN), and Southern Plains Network (SOPN) using the point-transect method (Buckland et al. 2001), including procedures for collecting data and filling in the CHDN/NGPN/SODN/ SOPN “Point-Transect” field data form. Instructions on completing the “Incidental Observations” data form are also included.
SOP 4.2. Navigating to the Survey Location Navigating to randomly selected survey locations can be challenging. Fortunately, there are a number of resources that you can utilize to assist you in finding your way to the most convenient access point for each survey site. You can utilize RMBO’s online transect maps website, view the Google Earth file provided to you by your crew leader, review a previously existing transect description sheet, and/or consult DeLorme and NPS transect/ group maps.
RMBO online maps website (for IMBCR surveys only) RMBO’s online maps website is available at the following link: http://rmbo.org/v2/dataentry/monitoring/transectLocationMaps.aspx You will be required to login using the username and password provided to you for data entry at training. Once logged into the site, please select the appropriate project (e.g., state or park network you are working in) and transect from the drop-down menus. Once the appropriate transect appears, you can use the zoom and scroll features to follow existing roadways to the most convenient access point. You can also toggle between the terrain, satellite, and maps options. The terrain feature shows topography, which is useful for navigating to the transect and between points. The maps feature only shows roads, but it can be useful when determining directions to a particular site. The satellite feature will display satellite photo imagery. We recommend that you take a careful look at steep
SOP #4 - Field Sampling 179 transects using the satellite feature found in the upper left portion of the map. This will give you a better idea of whether steep slopes are vegetated or not.
Google Earth files (for IMBCR surveys only) Prior to training you will receive a Google Earth file with transects that you are expected to complete. To view this KML file you will need to download a free version of Google Earth from the internet. Once Google Earth is installed, you can simply double click on the KML file sent to you and view the transect locations. This file will help you plan the order in which you should conduct your assigned surveys to minimize travel time and distance between survey locations. Additionally, you can zoom-in to get a better idea of existing roadways and the terrain at the survey locations.
Transect Description Sheet You will receive a printed transect description sheet (Figure SOP 4-1) corresponding to each transect that has been assigned to you. If, for some reason, you need a new transect description sheet, notify your crew leader. Most transect description sheets will already have information recorded on them; however, it is possible that you will be assigned a transect that has not been completed before. Please take the time to record or verify all information on the transect description sheet. This is the best opportunity for information obtained “on the ground” to be passed on to crew leaders and future technicians. Be sure that each of the following fields is filled out before leaving the survey location:
1. Observer Initials Record your data entry login here. 2. Date Conducted (MM/DD/YYYY) Record the date you sampled the transect. 3. Transect Accessible to Please record how accessible the transect is (all vehicles, high-clearance, or 4WD). It is important for us to know the accessibility of each transect so that we can assign transects to field technicians with appropriate vehicles. 4. DeLorme Page Do not forget to record the DeLorme page and section that the transect is on. This allows future field technicians to quickly locate the transect on the road map. 5. Access Point UTMs The UTMs and projection zone for the closest spot to the transect where a surveyor can park their vehicle. 6. Access and Transect Difficulty It is helpful to have an idea of what to expect before conducting a transect. Some transects are located on easy terrain and can be conducted relatively quickly, while others are on very difficult terrain and take a long time to complete. It is helpful for surveyors to know if they will be pressed for time to complete all 16 points, so they can ensure that they move quickly between points, etc. Please record the access and transect difficulty using the rubric (Table SOP 4-1), so future field technicians can plan accordingly. 7. Directions to Access Point (VERY IMPORTANT!) You will want to try to locate the most logical and efficient location to access each transect. This location will become the Access Point. This point is the end location for these directions. When recording directions to the Access Point, provide explicit directions from a nearby town, major intersection, or geographical feature readily found on a map to the Access Point. It is extremely helpful to provide mileages from intersections or other landmarks using your odometer. For all sites, take GPS readings and record UTM coordinates for each Access Point. It can be helpful to make the Access Point a recognizable feature on the landscape, like a cattle guard or sign post. You may encounter a situation where a road has been gated, washed
180 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN LANDBIRD MONITORING PROTOCOL
Figure SOP 4-1. Example transectFigure description 1. Example sheet. Transect Description Sheet.
7 SOP #4 - Field Sampling 181 Table SOP 4-1. Difficulty Rubric. Rating Rubric Transect Difficulty Access difficulty 1: Easy 2: Moderate 3: Difficult 4: Inaccessible Terrain 1: Easy 11 12 13 14 2: Moderate 21 22 23 24 3: Difficult 31 32 33 34 4: Inaccessible Terrain 41 42 43 44 Access Difficulty (Measure of the hiking difficulty from the access point to the transect): 1: < 3 km and easy topography. Hike to transect requires less than 45 minutes. 2: 3 km - 6 km with relatively easy topography. Hike to transect requires less than 75 minutes. 3: > 6 km and/or difficult terrain. Transect likely requires backpacking into transect the day before. 4: Transect is inaccessible due to river, cliffs, or other dangerous terrain. Transect Difficulty (Measure of the difficulty traveling between points on a transect): 1: Relatively flat transect. 16 points are easily surveyed in approximately 4 hours. 2: Hilly terrain, areas with dense vegetation, a few stream crossings. Technician might not be able to complete all 16 points during the sampling period. 3: Steep slopes, dense vegetation, or difficult stream crossings throughout the transect. Technician is unlikely to complete 12 or more points during the sampling period. 4: Transect has cliffs, rivers, or other dangerous terrain that do not permit 6 points to be finished.
out, etc. In these instances, it is very important to record appropriate changes to the existing directions. Please do not inconvenience future surveyors by not making these changes.
If necessary, provide the distance and time to hike from the Access Point to the grid, or more specifically, to the first point if it becomes apparent that there is a logical order in which to survey the points. Record recommendations of a survey route through the grid for the subsequent year, if necessary. As some of these grids are miles from the nearest road, explicit details of a good route will help future technicians gre atl y.
Be as clear and accurate as possible when recording directions. Remember, someone will use your directions next year to find these transects. DO NOT FORGET TO RECORD THE UTM’S OF THE ACCESS POINT ON THE TOP OF THE SHEET! 8. Transect description In this section, please record the primary habitat types encountered on the transect. When necessary, please provide between-point accounts, describing paths future technicians may want to follow to travel between points. You can also include useful information about terrain, barbed wire fences encountered, and any other information that would be helpful to know when surveying a transect. 9. Notes, Updates, and Camping Information Please provide directions and a description of camping options in the area in this section. Sometimes, camping is available right at the Access Point. If not, then record directions to where you camped and provide UTMs for that location. It is important for future field technicians to know what their camping options are before arriving at the transect. If camping is unavailable (e.g., the transect is surrounded by private land), then record where you stayed. The nearest library or free internet access you used is often helpful information as well. Also, you can enter information relevant to the site, problems encountered during the transect, cool scenery, or other tidbits that either do not really fit in other places or that future surveyors might find interesting.
SOP 4.3. Location and Event Information Before starting your first point count on each survey day, it is extremely important to fill the blanks at the top and bottom of the field data form (see Figure SOP 4-2). Be sure to fill out this information:
182 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Rocky Mountain Bird Observatory Point Transect Bird Form Proofread by: Entered by: Page_____ of _____ Copied by: Verified by: Location and Event Information Park Code: Transect /Group: Visit Number: Date (MM/DD/YYYY): / / Observer Name: Sampling Conditions Temp (°F): / Wind: / Sky: / Time Start: Time End:
How: V=visual; S=singing; C=calling; F=Flyover; D=drumming; O=other aural detection. Sex: M=male; F=female; U=unknown Between points, point # = 88 Sex: M=male, F=female; U=unknown; J=juvenile If found, please return to: PO Box 1232, Brighton, CO 80601 or call (970) 482-1707 ext. 24
Point Radial Point Radial Species Cluster Species Cluster SEX SEX # Distance HOW # Distance HOW Minute Minute Visual? Visual? Migrating? Migrating? Start Time Start Time Size Code Size Code
Notes:
Figure SOP 4-2. Form used for recording point transect data.
SOP #4 - Field Sampling 183 Rocky Mountain Bird Observatory Point Transect Bird Form Page_____ of _____ How: V=visual; S=singing; C=calling; F=Flyover; D=drumming; O=other aural detection. Sex: M=male; F=female; U=unknown Between points, point # = 88 Sex: M=male, F=female; U=unknown; J=juvenile If found, please return to: PO Box 1232, Brighton, CO 80601 or call (970) 482-1707 ext. 24
Point Radial Point Radial Species Cluster Species Cluster SEX SEX # Distance HOW # Distance HOW Minute Minute Visual? Visual? Migrating? Migrating? Start Time Start Time Size Code Size Code
Notes:
Observer Login Date Visit # Park Code-Transect Name
Figure SOP 4-2. Form used for recording point transect data. Continued.
184 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 1. Park Code: Four-letter park code. See the transect/group maps. 2. Transect/Group (location): Unique identifier for the collection of points (transect or group) on the day’s survey. See transect/group maps. 3. Visit Number: For transect/groups that require multiple visits, record the visit number here. 4. Date: Record the date you are conducting the survey (MM/DD/YYYY). 5. Observer Name: Write in the unique data entry login initials (to be assigned by your supervisor) of the person conducting the counts. 6. Page Number: Fill in the page number associated with the bird data in the upper right corner of the data form. Please count each side of a datasheet as a page.
NOTE: If a field data form does not have this information and it becomes separated from the rest of the data forms, there is no way for us to know what transect/group the data came from. This data would become useless and an entire day’s worth of data collection would be lost. We scan copies of all of our data, so this information needs to be on both sides of the datasheet.
SOP 4.4. Sampling Conditions The following information must be filled in on the field data form at the beginning and end of each survey morning. For each condition there is a “/” on the data form. Conditions at the beginning of each survey are written to the left of the dash, and conditions at the end of the survey are written to the right of the dash. For example, “Temp. (°F) 40/45” indicates the temperature at the beginning of the survey was 40°F and the temperature at the end of the survey was 45°F. To allow for the proper calibration of the weather instrument, place it away from the ground and your body (e.g., hanging from a shrub).
●● Temp(erature) (°F): (start and end) Record the ambient temperature in degrees Fahrenheit, rounded off to the nearest degree. The thermometer should be placed above the ground and allowed to adjust to ambient air temperature. If you do not have a thermometer estimate to the nearest 5 degrees. ●● Wind (0-5): (start and end) Record the one-digit code (0 through 5; see Table SOP 4-2) that applies to the strength of the wind at beginning and end of point count. Record the average wind conditions for each count, not the maximum condition (e.g., periods of gusty winds). ●● Sky: (start and end) Enter one-digit codes (0 through 8; see Table SOP 4-3) at the beginning and end of the point count. ●● Time (hhmm): Write in the time you start surveying and the time you end surveying using military time. All times should be recorded in Mountain Daylight time. Examples: 0630 (6:30 am) and 0802 (8:02 am).
Table SOP 4-2 Codes (Beaufort scale) used to record Table SOP 4-3. Sky Codes. wind strength. Sky Code Explanation Wind Code Explanation 0 0-15% cloud cover 0 Less than 1 mph; smoke rises vertically 1 16-50% cloud cover 1 1-3 mph; smoke drift shows wind direction 2 51-75% cloud cover 2 4-7 mph; leaves rustle, wind is felt on 3 76-100% cloud cover face 5 Fog 3 8-12 mph; leaves, small twigs in constant motion; light flag extended 6 Drizzle 4 13-18 mph; raises dust, leaves, loose paper; small branches in motion 8 Light snow 5 fresh breeze, small trees sway (30-39 You should not survey in any other conditions. km/h) You should not survey when the wind is above 4.
SOP #4 - Field Sampling 185 SOP 4.5. Approaching Points and Beginning the Count 1. Navigate to the first point using the GPS unit. You must be able to get within 25 m of a point to conduct a survey. If you are unable to get within 25 m, most likely because of a physical barrier, then do not survey the point. However, you should try to get as close to each point as possible. Once you arrive at the point, begin the count as soon as possible, but wait at least one minute to calm your heartbeat if hiking to the point was strenuous. If hiking was extremely strenuous, rest away from the point (e.g., 100 m) for a few minutes, then continue to the point. After denoting the point at which you are on the field data form, record the time next to the point number, activate your timepiece, and begin recording the birds you see or hear. The count duration is six minutes. It is extremely important to document the minute of the count at which an individual bird was first detected.
To do this, simply write the number of the minute under the “minutes” column each time the beeper goes off. Stop the count at the end of the sixth minute. DO NOT record any other birds after the six minutes are over, even if there is an interesting bird (you could record this bird in the notes if you so desire). However, if the bird represents a new species for the park being surveyed, then enter “88” in the point number column and record the species, how it was detected, and the sex.
2. Conduct the six minute count without interruption. Occasionally, aircraft noise can be loud and can last for up to 30 seconds. In these instances, stop your stopwatch and wait for the noise to subside. Once the noise is gone, start your stopwatch again and continue the count where you left off. If excessive noise interrupts the count for more than 2 minutes, start the survey again after the disturbance has passed. Include notes about disturbance in the notes on the datasheet.
3. It is important to stay in one place while counting. It is acceptable to take a step or two away from the point in order to identify a bird that you detected from a point but cannot identify from the point, but ALWAYS return ASAP to the point. Do NOT chase birds before or during the count. After the 6 minutes are up, you may chase down a bird that you could not identify on the point in order to get identification, but do not leave the point during the 6 minutes, and do NOT record birds that were only found while chasing another bird after the count. Remember: Consistency of methods and coverage is the key to useful data!
4. Be sure to focus primarily on birds that are close to the point. While we do ask you to record all birds detected, distant birds have little effect on density estimates. However, missing close birds can have a significant effect on density estimates. Also, be sure to look and listen in all directions, including up. It is best to slowly rotate in place while you are counting; making three complete turns in the six minutes is probably adequate.
5. Be aware of what is going on around you and realize that you may hear or see individual birds on multiple points. It is okay to record the same bird on multiple points only if the bird has not moved from the location where you originally detected it. For example, if you see a Western Meadowlark on a powerline, and that same Western Meadowlark is visible from the next two points in the same location, you would record it during all three point counts. However, if you see a Red-tailed Hawk soaring above you, and you still see the hawk soaring on another point, only record this bird once.
6. Pay attention to birds that flush as you approach the survey point, and make a note of the bird’s distance from the point before it flushed. If, during your 6-minute
186 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN count, you detect a bird that flushed from the survey point upon your arrival, record the bird’s original distance from the point. We assume that these birds would have remained at their original locations were it not for the disturbance created by the observer.
7. Point counts are conducted as a “snapshot” in time. The survey results should represent the actual distribution of the birds relative to the point. The underlying theory of distance sampling requires that each point be recorded as close to a “snapshot in time” as possible.
At each point you will record: 1)the number of the point on the transect/group; 2) the time that you begin the count; 3) each minute during the six minute count; 4) the species, using the appropriate four-letter code; 5) the radial distance in meters from you to the bird; 6) how the bird was detected; 7) the sex of the bird, if known; 8) if the bird was visually observed; 9) if you believe the bird is a potential migrant; and 9) the cluster size and cluster ID code for any birds observed as part of a cluster (i.e., non-independent detections).
1. Point #: Enter the number of point on the transect/group you are about to survey. Indicate the start of a new point by leaving a blank line on the data form and recording the next point number. If observations from one point span multiple pages, be sure to include (“cont.”) next to the point number at the top of the next page. NOTE: for birds detected between points that are not currently on the species list for the park being surveyed, enter “88” for the point number (see below for more information).
2. Time: Record the time in military time when you begin the count at each point. Record the time in Mountain Daylight Time.
3. Minute (1-6): Record the minute you are in during the six-minute count. Minute 1 is from 0-60 seconds.
4. Species: Record the four-character code for all birds detected during the six minute count period. Refer to Appendix B of the main protocol for a complete list of bird species codes. Use of the Correct Codes is Crucial, due to data management and data analysis needs. This also assists in the data entry process.
Species that cause particular problems for observers include:
Cackling Goose (CACG not CAGO), Canada Goose (CANG not CAGO), Barn Owl (BNOW not BAOW), Bank Swallow (BANS not BASW), Barn Swallow (BARS not BASW), Barred Owl (BDOW not BAOW), Black-throated Gray Warbler (BTYW not BTGW), Broad-tailed Hummingbird (BTLH not BTHU), Canyon To w h e e (CANT not CATO), Canyon Wren (CANW not CAWR), Cedar Waxwing (CEDW not CEWA), Gray Jay (GRAJ not GRJA), MacGillivray’s Warbler (MGWA not MAWA), Northern Shoveler (NSHO not NOSH), Lark Bunting (LARB not LABU), Lazuli Bunting (LAZB not LABU), Red-winged Blackbird (RWBL not RWBB), Ring-necked Pheasant (RINP not RNPH), Savannah Sparrow (SAVS not SASP), Tree Swallow (TRES not TRSW), and Western Wood-Pewee (WEWP not WWPE).
Some individuals can be identified to subspecies. If you can identify one of the below subspecies, please use the four-letter codes in Table SOP 4-4:
SOP #4 - Field Sampling 187 Table SOP 4-4. Subspecies bird codes. Subspecies Code Northern Flicker (Red-shafted) RSFL Northern Flicker (Yellow-shafted) YSFL Northern Flicker (Intergrade) FLIN Yellow-rumped Warbler (Audubon’s) AUWA Yellow-rumped Warbler (Myrtle’s) MYWA Dark-eyed Junco (Gray-headed) GHJU Dark-eyed Junco (Oregon) ORJU Dark-eyed Junco (Pink-sided) PSJU Dark-eyed Junco (Red-backed) RBJU Dark-eyed Junco (Slate-colored) SCJU Dark-eyed Junco (White-winged) WWJU White-crowned Sparrow (Gambel’s) GWCS White-crowned Sparrow (Mountain) MWCS
If you detect a bird that you are unable to identify, use the appropriate unknown bird code. Never guess on the identity of a bird. This is falsifying data. If you are unsure, we would prefer you to record UNBI rather than incorrectly identify a bird. However, recording a lot of unidentified birds is an indication that you need to study and practice more before performing more point counts. Table SOP 4-5 is a table of unidentified bird codes you can use.
If no birds are detected during a one-minute period, enter NOBI (No Birds) in the space for four-letter bird codes. If no birds are detected during a six-minute count, you should have six time periods recorded, each with NOBI written next to it. This will help you keep track of your minute intervals, and the data will reflect that you did conduct a six-minute count.
5. Distance (m): Using your rangefinder, measure the distance from the point to each and every individual bird detected during the count, and record the distance in meters on the field data form under “Radial Distance”. If you detect a bird beyond 1000 m, enter the distance as “999”. Please note that we record radial distance (horizontal distance), not actual distance. If you detect a bird singing in a tree directly above you, the distance would be 0, not how far the bird is above you.
You should measure all distances to birds using your rangefinder. If you cannot get a direct line of sight to the location of a bird, estimate the distance that the bird is from a visible point and use the rangefinder to measure to that point. Then add or subtract the additional estimated distance between that point and the bird to obtain the best possible distance estimate from the point to the bird. Please estimate the distance from the visible point to the bird BEFORE using the rangefinder to get the distance from you to that point. Distance-sampling relies upon the assumption that you measure all distances accurately, so use your rangefinder to determine distances for every bird detection.
Always measure distances to where you first detected the bird, not to where you first identified it. For birds that are vocalizing but not seen, try to pin-point their locations to a specific tree/bush, then measure the distance to the tree. If you are unable to pin-point its location to a specific tree/bush, then estimate the distance, but do not round distances to the nearest 5 or 10 meter interval. Rounding distances causes heaping at popular values and makes analysis more problematic! If you see or hear a bird that is beyond the range of the rangefinder, estimate the distance the bird is past
188 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Table SOP 4-5. Unknown Bird Codes. Unknown Bird Code Unknown Bird Code Unknown accipiter UNAC Unknown meadowlark UNME Unknown bird UNBI Unknown myiarchus UNMY Unknown blackbird UNBL Unknown nuthatch UNNU Unknown buteo UNBU Unknown oriole UNOR Unknown cardinal UNCA Unknown owl UNOW Unknown chickadee UNCH Unknown pipit UNPI Unknown cormorant UNCT Unknown raptor UNRA Unknown corvid UNCO Unknown sandpiper UNSA Unknown dove UNDO Unknown sparrow UNSP Unknown duck UNDU Unknown swallow UNSW Unknown empidonax UNEM Unknown swift UNSI Unknown falcon UNFA Unknown tanager UNTA Unknown finch UNFI Unknown thrasher UNTR Unknown flicker UNFR Unknown thrush UNTH Unknown flycatcher UNFL Unknown thrasher UNTR Unknown gnatcatcher UNGN Unknown thrush UNTH Unknown grouse UNGR Unknown towhee UNTO Unknown gull UNGU Unknown vireo UNVI Unknown hawk UNHA Unknown warbler UNWA Unknown hummingbird UNHU Unknown woodpecker UNWO Unknown jay UNJA Unknown wren UNWR Unknown kingbird UNKI
a point that is within-range of your rangefinder, and add that distance to what the rangefinder displays. Once again, estimate the distance between the bird and the point within-range BEFORE using the rangefinder to get the distance from you to that point. Add your estimate plus the measured distance, and record the sum as the total distance.
Every bird recorded on point counts must have a radial distance measurement associated with it! This is imperative! Because our monitoring programs rely on Distance-sampling techniques and analyses, bird data recorded without associated distances CANNOT be used in analysis! We will further explain the premises behind Distance-sampling during the training session. But PLEASE, PLEASE do not forget to measure and record radial distances for EACH bird recorded on point counts.
6. How: In the “How” column, record how each bird was detected, i.e., whether the bird was detected by sight or by ear (V=visual, C=calling, S=singing, D=drumming, F=Flyover, or O=other aural, e.g. wing beats). Enter the code for how you first detected each individual in the upper left portion of the box. Remember that how you detect a bird is different from how you identify it.
When birds sing, this is important information for us to know, as it is a strong indicator that the species is holding a breeding territory (and is thus a potentially breeding species in the study area). If you first detect a bird by means other than it singing, and that same individual later sings, neatly write an “S” in the lower right portion of the “How” box.
SOP #4 - Field Sampling 189 Flyovers: A bird observed flying over a point without showing any signs of using the surrounding habitat should be recorded as a “flyover”. However, individuals of species that habitually hunt on the wing (e.g., raptors, swallows, swifts) or appear to be foraging (e.g., crossbills, goldfinches, waxwings) or hunting in the vicinity around the point, should NOT be treated as flyovers. Additionally, individuals that you first detect in flight that are simply flying from perch to perch nearby should NOT be recorded as flyovers. For true flyovers, enter an “F” in the “How” column.
7. Visual: In the “Visual” column enter a checkmark if you were able to visually identify the individual at any time during the survey. Check this box even if you recorded “V” for the detection type. This column is meant to further assure us of proper identification and recorded distances. You may also check this box if you visually identify the individual before or after the point count.
8. Sex: In the “Sex” column, record the sex of the bird only if you visually observe a sexually dimorphic species and can identify the sex of the individual (M, F). If you are unable to visually observe the bird, or if the bird is of a species that does not exhibit sexual dimorphism, record the sex as “U” for unknown. Change the U to an M or F if you later identify the same individual as male or female. Females of many bird species sing at least occasionally, and female singing behavior of many species is poorly understood, so please do not assume that singing birds are males.
If you record a bird and visually identify it as a juvenile, record “J” in the column. The surveys we conduct are for breeding birds, and juveniles do not fall into this category. Marking juvenile birds as such will allow us to factor these birds out of analysis.
Example: On a point count, you detect six birds. You see a male RNSA, you hear a drumming RNSA, you hear a calling WBNU, you see a male AUWA that later sings, you hear a singing CHSP, and you see a brown-plumaged CAFI. You should record the radial distances for all six individuals. In order, the “How” column should be filled in with V, D, C, V/S, S, and V. Fill in the “Sex” column: M, U, U, M, U, and U respectively (male CAFI require two years to achieve adult plumage, thus a brown- plumaged bird cannot be sexed in the field).
9. Migrating?: In the “Migrating?” column enter a checkmark if you have reason to believe the detected individual is not on its breeding territory. Clues that a bird may be migrating through are 1) the bird is in a large flock, 2) the bird is in unusual habitat that differs substantially from where it is typically found during the breeding season (e.g., a Brewer’s Sparrow that is detected in a desert environment with no sagebrush), and/or 3) the bird is outside of its typical breeding range.
10. Clusters: “A cluster is a relatively tight aggregation of objects of interest…” (Buckland et al. 2001). In our point count sampling, clusters are actually our unit of observation, with most cluster sizes = 1. There are generally two cases in which cluster sizes are > 1: single species flocks and paired birds. In either case, we define a cluster as birds of the same species that are observed TOGETHER (foraging, flying, perching, or obviously interacting with each other). Distances between members of a cluster should be very short (within 20 m). Two males of the same species singing within 20 m do NOT constitute a cluster. Please record the two types of clusters as follows.
Flocks: When two or more individuals of the same species are obviously in a flock and cannot be readily sexed (e.g., Cliff Swallow or Pine Siskin), record the distance to the center of the flock and record the number of individuals in the “Cluster Size”
190 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN column of your data form. You do not need to enter a Cluster Code. When you can determine sex, enter the number of males on one line, and the number of females on the next line, with the appropriate number of each sex in the corresponding “Cluster Size” boxes. Then enter the same letter on both lines for the “Cluster Code” (a, b, c …). The Cluster Code is only used to link clusters that take up multiple lines on the datasheet.
Pairs: Often, you may hear a bird singing or calling, look up, and see that it is a male bird with a female perched or foraging nearby. Or you may see one individual moving about, raise your binoculars to identify it, and observe that there are actually two individuals of the same species but of the opposite sex in that location. In these cases, enter the male and female on separate lines of your datasheet, with the appropriate codes for “HOW” detected and “Sex”. In the first scenario, the male “HOW” = S(inging) and the female “HOW” = V(isual). In the second scenario, “HOW” = V(isual) for both the male and female. In both cases enter the same letter for the “Cluster Code” of each member of the pair (a, b, c …).
Example: After recording a Western Tanager (WETA) and an American Robin (AMRO) on a point count, the observer hears a Black-headed Grosbeak (BHGR) give its distinctive squeaky call note. The observer turns to see the bird and notes that the calling bird is a male BHGR 27 m away AND also notes that there is a female BHGR in the same tree, but about 29 m away. Next, the observer hears 5 Pine Siskins (PISI), looks up, and measures that they are 36-38 m away. Finally, the observer hears a Mountain Chickadee (MOCH) calling, looks up and sees that MOCH as well as a second MOCH in the same tree, both at 17 m away. The sex of both individuals is unknown, but the method of detection differs, so record them on separate lines with a common Cluster Code. The observer’s data looks like Table SOP 4-6.
11. Notes: Record any comments that seem appropriate and that might affect the quality of the data in the notes section at the bottom of the page (e.g., noise from nearby stream or vehicles on road). Clearly indicate which point you are referring to in the notes.
Record any breeding behavior that may be observed using the standard breeding bird atlas codes (see Table SOP 4-7). If you locate a nest, record the UTM, indicate what plant species it is in or near, and note the number of eggs and the number of brown-headed cowbird eggs, if appropriate or feasible. After completing a survey, record your notes on any observations of breeding behavior or nests on an Incidental Observation form.
Table SOP 4-6. Observer Data. Cluster Time Point # Minute Species Radial How Sex Visual Migrant? Distance Size Code 0552 03 1 WETA 46 S M X 1 AMRO 103 S U 1 2 BHGR 27 C M X 1 A BHGR 29 V F X 1 A 3 PISI 37 C U X 5 4 NOBI 5 MOCH 17 C U X 1 B MOCH 17 V U X 1 B 6 NOBI
SOP #4 - Field Sampling 191 Table SOP 4-7. Breeding behavior codes used to note breeding observations. Code Explanation CN Carrying nesting material (e.g., stick, grass, mud, cobwebs). This applies for all species except some species of wrens (cactus, Bewick’s, house, marsh) and verdins. NB Nest building seen at actual nest site, excluding some species of wrens (see above), woodpeckers, and verdins. DD Distraction displays. Defense of unknown nest or young or injury feigning. Used if adult bird is seen trying to lead people away from nest or young (e.g., killdeer broken-wing act, Cooper’s hawk diving at you). Does not include agitated behavior. UN Used nest or eggshells found. Use only when identification is unmistakable. Do not use for species that build multiple nests in a breeding season, such as cactus wrens and verdins. FL Recently fledged young of altricial species incapable of sustained flight or downy young of precocial species restricted to the natal area by dependence on adults or limited mobility. Note – barely fledged blackbirds and swallows may fly considerable distances. ON Occupied nest indicated by adult entering or leaving nest in circumstances indicating an occupied nest, including those in high trees, cliffs, cavities, and burrows where the contents of the nest and incubating brood cannot be seen. CF Adults seen carrying food, excluding raptors, corvids, roadrunners, and shrikes. FY Adults feeding recently fledged young. Young cowbirds begging for food- confirm both the cowbird and the host. FS Adult carrying fecal sac. NE Nest with eggs found. Be careful with identification unless you see adult. Cowbird eggs- confirm both the cowbird and the host. NY Nest with young seen or heard. Use when you see or hear the young. Cowbird chick in the nest- confirm both the cowbird and the host.
Make notes about rare or unusual birds, or species not known to occur on the park, that you detect. After the field season, RMBO staff will review your data looking for any detections that seem odd or out of place. If you positively identify a species that you believe might be questioned later, write notes to affirm your identification. Useful notes regarding a rare species should include information on key field marks (both visual and/or auditory), the age and sex of the bird, how you differentiated the rare species from other similar species, and any relevant information regarding behavior and/or weather conditions. Also, note whether you were able to obtain a photo or Table SOP 4-8. Potential audio recording of the species. reasons why points were not conducted. This is also the location to record problems encountered during the survey, or other Code Reason information that either does not fit in other places or that future surveyors might find P: Private Property - Denied Permission interesting. N: Private Property - No contact with When entering data into the database, do not forget to look through the notes landowner sections on your field data form. U: Terrain Unsafe (could not safely approach to within 25 m of point) SOP 4.6. Point Information Datasheet There is a separate datasheet with 16 lines on it; one line is for each point on the survey R: Cannot cross River (Figure SOP 4-3). If you are conducting a survey with fewer than 16 points, simply S: Snow pack impassible cross out the additional points on the datasheet. If you are unable to survey a point on H: Running water near point - unable to Hear a transect, record the reason why you were unable to survey on this datasheet. Possible reasons points were not conducted are listed in Table SOP 4-8. W: Weather (rain or wind) G: No GPS reception, cannot find point These are just a few reasons; you may run into other unexpected issues in the field. For these instances, record “O” for “Other” and be sure to take detailed notes on why points T: Ran out of Time (5 hours past sunrise or were not conducted. We need to report this information to our funders after the field noticeably decreased season, so the more information you provide us, the less we will have to contact you with bird activity) questions after the field season. O: Other - explain SOP 4.7. Other important information Check over your point-count data before leaving each count station to make sure you have recorded all the required information (e.g. distances, how, sex info, etc.). All
192 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Figure SOP 4-3. Example of a completed point information datasheet.
SOP #4 - Field Sampling 193 individual birds on a particular point should be bunched together on the field data form, followed by a blank line before starting entries for the next point.
Once you finished the transect/group and before leaving the site, do not forget to:
A. Check to make sure you entered all of the information at the bottom of each page of the field data form, and you filled in the “Page __ of __” section in the upper right- hand corner of each field data form. B. Record the sampling conditions data (time, temp, sky, and wind) immediately upon completing the transect/group. C. Go through your field data forms carefully to make sure you have not forgotten to record any data.
Upon return to your camp or vehicle, use your list of four-letter species codes to verify any codes that you were unsure of when recording in the field, and use sources of known bird calls and songs to identify any unknown vocalizations detected during the survey. Your work is not done until you have reviewed your data from the morning!
SOP 4.8. Potential issues when conducting point counts A. Window species--This is “listening through” (not detecting) a particular common species because you are habituated to it (Mourning Dove is a common window species). B. Looking/listening everywhere--Be sure to look up regularly, particularly if you are wearing a hat. Do not wear sunglasses or hats that can affect your hearing while counting birds! This includes caps that pull down over your ears as well as full- brimmed hats that can deflect sound away from your ears. Be sure to look AND listen in all directions (try to look and listen in all directions equally). Avoid wearing bright colors that may attract species to you (hummingbirds, etc.) or frighten birds away from you. C. Stand at points--Do not sit or kneel as this can reduce the number of individuals recorded, by decreasing visibility, audibility, and dexterity. If you are tired, take a short break after the point count. As long as you start early, you should have plenty of time for short rests along the way. D. Recording data--Unless specifically instructed, do not use a second person as a recorder; this can enable the observer to record more birds (or fewer, if the recorder detracts from the job at hand or creates more disturbances). E. NO Pishing--Do not attract birds to you during the counts. Pishing is permissible after the count in an attempt to identify an individual that was not identifiable on the count, but do not add other individuals after the count that were not first detected during the count period. Never pish or attract birds toward you when you are near a point that has not been completed! F. Airplane (and other) noise--If audibility of birds is reduced by mechanical noise, interrupt the count (i.e., stop your timer), and restart when the noise abates so that the total time still equals a 6-minute count. G. Guessing--Never guess on the identity of a bird. Instead, use an unknown code (e.g., unidentified sparrow - UNSP) for those individuals about which you are unsure. However, recording a lot of unidentified birds is an indication that you need to learn/practice more before performing point counts. If you are unsure of the correct unknown code, make a note in the comments section so you can write in the correct code later.
H. Practice--Practice identifying birds in a habitat before counting in that area. Become familiar with species found in that habitat and their songs and calls. Be familiar with the songs and calls of all species found in an area before conducting point counts.
194 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Use BCR-, NPS-, or habitat-specific bird data queried from the RMBO Avian Data Center website (http://rmbo.org/v3/avian/ExploretheData.aspx) along with audio recordings to practice before (and during) the field season. Consult the park bird checklists that will be provided to you.
I. Weather--Weather can always be a factor when conducting point counts. Never conduct a point count when it is raining, as birds will not be very active and visibility may be poor. Also, do not conduct counts if the wind is strong enough to hinder your ability to hear bird calls and songs, as this will affect the number of birds you are able to detect. If you are unsure that the weather is impacting your ability to detect birds or bird activity, conduct the count and review the data afterwards. If you detected very few birds, or almost all of your detections were visual, it is likely that your ability to hear, and/or bird activity, was impacted by the weather. In these instances, make a note that the data should not be included in the analyses.
SOP 4.9. Recording incidental observations If you are within a park but not conducting a point count and you observe a species not known to occur on a park, an unusual or rare bird, or a bird in an unusual location or exhibiting breeding behavior, you are encouraged to record “Incidental” observations. These observations are recorded on the Incidental Observations Field Datasheet (Figure SOP 4-4). Unique observations of other taxa are encouraged if the observer is confident of his/her identification skills.
The following information is recorded on each Incidental Observations datasheet:
Location Information: to be recorded at the top of each data form only once.
1. Park Code: Four digit code for the park. 2. Observer Name: full name of the observer. 3. Year: yyyy format.
Individual Observations: all fields should be filled out to the best of the observer’s ability.
1. Date: mm/dd format. 2. Time: record as 24-hour time (e.g., 8:05am = 0805; 3:17pm = 1517). 3. Ta xo n : taxonomic class (bird, plant, fish, herp, mammal). 4. Species: use four-letter species codes for birds or write out entire common names for other taxa groups. 5. Number of individuals. 6. UTM (X, Y) coordinates: using NAD83 datum. 7. Comments: any comments unique or relevant to the detection. Include breeding behavior codes for birds.
SOP 4.10. Park and network bird lists In the spring of each year, each network will provide RMBO an updated list of birds for each park. In the fall, after the survey season, RMBO will provide each network a list of birds detected in each park during surveys and any incidental observation forms that may include species not known to occur on a park. After receiving the RMBO survey results and the incidental observation forms, each network will update its network and park lists in coordination with the parks. These updated lists will then again be provided to RMBO in the spring, prior to the year’s sampling season. This effort will ensure that new park species are included on park/network bird lists.
SOP #4 - Field Sampling 195 National Park Service Sonoran Desert Network U.S. Department of the Interior Inventory and Monitoring Program Page ____ of ____
Landbird Monitoring Protocol Proofread by: ______Field Data Sheet: Incidental Observations Copied by: ______Entered by: ______Version 1.03 Verified by: ______
Location Information Park Code: Observer Name: Year: Protocol Name: Birds Protocol Version: 1.03
Number of Date Time Taxon Species Individuals X coord / Y coord Comments / / / / / / / / / / / / / / / / / / / / / / / / / / /
Figure SOP 4-4. Incidental observations field datasheet.
Birds_Incidental_Data_Sheet_v103 If you find this data sheet completed, please call (520) 731-3420 ext. 1#.
196 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN See Appendix D of the main protocol for tables for each network that show bird species occurrence by park. The tables were adapted from those occurring in each network’s 2012 landbird monitoring report (e.g., Lock et al. 2012).
SOP 4.11. Literature Cited and References Ali, M., K. Beaupré, P. Valentine-Darby, and C. White. 2013. Landbird monitoring in the Sonoran Desert Network: 2012 annual report. Natural Resource Technical Report. NPS/SODN/NRTR—2013/xxx. National Park Service. Fort Collins, Colorado.
Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001. Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, Oxford, England.
Lock, R., P. Valentine-Darby, H. Sosinksi, and R. E. Bennetts. 2012. Landbird monitoring in the Southern Plains Network: 2012 annual report. Natural Resource Technical Report. NPS/SOPN/NRTR—2012/656. National Park Service. Fort Collins, Colorado.
White, C., and P. Valentine-Darby. 2013. Landbird monitoring in the Chihuahuan Desert Network. 2012 annual report. Natural Resource Technical Report. NPS/CHDN/ NRTR—2013/702. National Park Service. Fort Collins, Colorado.
197
SOP #5. Data Entry Protocol
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol
Revision History Log Previous Revision Changes Section and Reason for New version version # date Author made paragraph change #
Standard Operating Procedure (SOP) Source: Van Lanen, N.J., C.M. White, J.A. Fogg, and M.F. McLaren. 2012. Integrated Monitoring In Bird Conservation Regions (IMBCR): Data Entry Protocol. Unpublished report. Rocky Mountain Bird Observatory, Brighton, CO, USA.
SOP 5.1. Accessing the Databases You will need to visit up to three separate web pages in order to enter all of the data associated with a given transect.
Private Landowner Contact Information (for IMBCR surveys only) You can record information regarding private landowner contacts by visiting https:// fc.rmbo.org/Default.aspx. Once there, you will need to log in and click on the link entitled “Landowner Database”.
Transect Description Information To change or enter information on how to get to a transect, open the transect.htm file that your crew leader provided to you.
Survey Information In order to enter data collected during a survey, you will need to open the index.htm file in the Data Entry Program that your crew leader provided to you.
SOP 5.2. Data Categories The data entry can be broken up into six major sections (sections A and E for IMBCR surveys only): A. Landowner Contact Information Recording all forms of communication with private landowners. B. Transect Description Directions to the transect, notes about the transect, camping, landowner contact information, etc. C. Transect Visit Data Observer, transect name, date, start and end times, sky, wind, temperature, etc. D. Point Visit Data The points visited during the survey, start times, GPS accuracy, reasons points were not surveyed, etc.
SOP #5 - Data Entry Protocol 199 E. Vegetation Data Vegetation and other point information such as the presence/absence of prairie dogs. F. Bird Data Species recorded at the point and information related to the observation, such as detection type, sex, distance from observer, etc. SOP 5.3. Entering Data A. Landowner Contact Information To access the Landowner Database go to: https://fc.rmbo.org/Default.aspx and log in. Next, click on the link for the Landowner Database. To find a landowner you can either use the search box in the upper left corner of the screen or select the transect associated with the landowner using the drop down menu in the upper right corner. To view information or update the contact log for a particular landowner, click on the landowner’s name. To add contact information, click on the button at the top of the screen labeled “View Contact Log”; type a description of the contact/contact attempt in the text box and click on the “Add Note” button using your mouse (pressing the “Enter” button will not add your comment to the log). Please be specific when recording contact information. Try to include whether or not you spoke with someone or if you reached their voicemail, whether or not you left a message, and what the outcome of the correspondence was. For example, “Called and left message on voicemail asking Frank to call me back” is more informative than “Called, no answer”. Each contact note will be “tagged” with a date and time stamp, as well as your name. If you are unable to add the note to the contact log immediately after the correspondence, please include the date and time when the contact was made/attempted in the note since the time stamp will be incorrect.
B. Transect Description To record or edit transect description information, open the transect.htm file provided by your crew leader. Select “Load a Transect” from the top of the page, select the transect you want to edit, and hit “Load”. Once you have entered all of the information on this page, hit “Save Transect” to save this information to the database. If the transect has been surveyed in past years, this page should already be filled in. For a full description of the information that should be entered or edited (and checked for accuracy), see SOP #4, Section 4.2, under Transect Description Sheet. C. Survey Data Transect Visit Data, Point Data, Vegetation Data, and Bird Data should all be entered by double clicking on the browser file entitled “index.htm,” which will be provided to you by your crew leader. This data entry system works best in Firefox (free downloads are readily available). If Firefox is not your default browser, right click on the index. htm file, select “Open With”, and then select Firefox, or open a blank tab in Firefox and drag and drop the “Index” file into the open browser window. These data can be entered offline and then subsequently uploaded into RMBO’s database. At this time, there is no way to easily edit or complete data entry on a different computer than that which the data were initially entered on. This means that once you begin to enter data for a transect on one computer, you should ensure that you can finish entering the data for that transect on the same computer. Also BE SURE to upload any data that you enter; otherwise, your data entry efforts will be lost (since, without your computer, no one else can upload the information you have entered).
Occasions may arise in which the data recorded on the data sheet seem incorrect or are missing. Under no circumstances should you enter any values other than those recorded on the data sheet or those values which are designated below to indicate
200 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN “missing values”. If you run into a problem where you must enter a value and do not know what to enter, please refer to the data entry protocol; it will tell you what to enter for that value. If you still cannot figure out what to enter after reviewing the data entry protocol, then contact your crew leader or Chris White (chris.white@rmbo. org).
When entering data, we recommend that you use the “Tab” button to move from one field to the next across rows. To return to the previous cell you can use “Shift + Tab”.
1. Transect Visit This is the start-up page upon opening index.htm. a. View Options - The very first thing you should do before entering any data is to make sure you are using the correct version of the program according to your study area. Since we conduct surveys using a variety of study designs, we customized different data entry pages for each study design. When you select “View Options”, you will get a pop-up window that says “Data Entry Options” at the top. You will be given a list of options to choose from: IMBCR, Habitat, Parks, or Jalisco. At training, your crew leader will instruct you on which version you should be using. b. Observer - Enter the observer’s username login using the drop down menu. (Required). c. Date - Enter the date on which the survey was conducted, as mm/dd/yyyy, by typing in the appropriate digits or by selecting the date from the pop-up calendar (e.g., 07/15/2005). (Required). d. GPS Unit - Enter the four digit number located on the silver property tag of the GPS unit that you used to complete the transect. If you are using a personal GPS unit, please write “personal” in this box. If you are using a GPS that was provided by your employer, and it does not have a silver property tag on it, please enter “none”. e. Transect ID - Select a Transect ID from the drop-down list. (Note: if you click in the box and then type “c” the list will jump to the Colorado transects, or type “w” and it will go to the Wyoming transects). (Required). f. Who Collected? - Select the name of your employer from the drop down menu. (Required). g. Data Entry - Type in your first and last name. Please be sure to enter this even if you were the observer for this transect visit. h. Time - Enter the start and end time of the survey using the format hhmm (e.g., 0545) (Note: there is no colon and the time must be 4 digits. Enter a “0” in the first place holder if the time is between 1 AM and 9 AM). If no time was recorded on the data sheet, enter “0000”. i. Sky - Enter the Sky information recorded at the start and end of the transect. If no Sky information was recorded, enter “-1”. j. Wind - Enter the Wind information recorded at the start and end of the transect. If no Wind information was recorded, enter “-1”. k. Temp - Enter the temperature in degrees Fahrenheit recorded at the start and end of the transect. If no Temp was recorded, enter “-99”. l. Notes - Record any notes written in the “Notes” section of the data sheet. 2. Point Data To enter Point Data, click on the button labeled “Point Data” located at the bottom of the screen. Add a row for each point on the survey, regardless of whether you surveyed all points. For IMBCR surveys, each transect has a total of 16 points, so in
SOP #5 - Data Entry Protocol 201 this case all 16 points should be added. Please enter the points in numerical order on this page. This will make it easier for us to proof the data after the field season. a. Start Time - Enter the start time for each point from the Bird Data sheet in the format hhmm (e.g., 0545) (Note: there is no colon and time must be four digits. Enter a “0” in the first place holder if the time is between 1 AM and 9 AM). If no time was recorded on the data sheet for a given point, enter “0000”. b. Accuracy - Enter the accuracy of the GPS location that was recorded on the Vegetation data sheet in meters. If no accuracy was recorded on the data sheet, enter “-1”. c. Completed - Select a reason why the point was not completed from the drop down menu if you did not survey the point. This information can be found on the back of the Vegetation Data sheet. By default each point is set as “completed”. d. Notes -Type any notes associated with reasons why points were not completed. 3. Vegetation Data Those of you that are not required to collect vegetation data for your study design should have chosen a data entry option that does not have a “Vegetation Data” tab, and you should skip this section. For the rest of you, click on the button labeled “Vegetation Data” located at the bottom of the screen. You can quickly create rows for all the points that were completed on the survey by clicking on the “Add Rows From Point Data” button located below the vegetation data table. Alternatively, you can create empty rows in which to enter the Vegetation Data by clicking on the “Add Row” or “Add Five Rows” buttons. Enter all the vegetation information for each point following the directions below. a. Private property - click the box for each point that is on private property. Otherwise, leave blank. b. Primary habitat - Enter the 2 letter code or select the code from the drop-down list that corresponds to the primary habitat that was recorded. If this field was left blank on the data sheet, please select “XX - Not recorded”. c. Canopy Cover (%) - Enter the percentage of overstory cover, rounded to the nearest integer. Do not include decimals. If there is no overstory, enter “0”. d. Canopy height (m) - Enter the estimated mean height of the canopy, rounded to the nearest meter. If there is no overstory, leave this blank. e. Overstory species and abundance - Enter the two letter code of each species, followed by its percentage abundance as a whole number. You can verify that the species percentages for each point add up to 100% by looking at the “Total %” column located to the right of Species 5 Abundance. If the Total % does not add up to 100, check that you have entered all of the percentages correctly. If you have entered all of the percentages to reflect the data sheet and the Total % column does not equal 100, do not worry about it and move on. Do NOT change the values to sum to 100% if the values on the data sheet do not sum to 100%. Leave this section blank if there is no overstory. If you enter OT for “other tree”, please record the species in the notes section of the transect information tab if it is known. Also, if a two letter code was recorded on the data sheet that is not available in the drop-down menu for Species 1 -5, enter “XX-Not listed” and record the discrepancy in the Notes section of the Transect Information tab. If a transect has more than one species recorded as OT, please enter a note that includes the Species and point(s) where it occurred (e.g., overstory species 1 - OT = Western Red Cedar for points 1,5,14; overstory species 3 - OT = Western Larch for points 3 and 9). f. Midstory - check this box if it was checked on the data sheet. g. Cliff/Rock - check this box if it was checked on the data sheet. h. P. dog Town - check this box if it was checked on the data sheet.
202 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN i. P. dogs present? - check this box if it was checked on the data sheet. j. # of Snags - Enter the number of snags that were recorded at each point. If there were no snags present or if the field was left blank, enter “0”. k. Shrub layer cover (%) - Enter the percentage cover of the shrub layer to the nearest integer. Do not include decimals. If this field was left blank, enter “0”. l. Mean height of the shrub layer (m) - Enter the estimated mean height of the shrub layer to the nearest 0.25 meter (e.g., 0.25, 0.50, 1.50, etc.). If this field was left blank, enter “0”. m. Shrub species and abundance - Enter the two letter code of each species followed by its percent abundance as a whole number. You can verify that the species percentages for each point add up to 100% by looking at the “Total %” column located to the right of Species 5 Abundance. If the Total % does not add up to 100, check that you have entered all of the percentages correctly. If you have entered all of the percentages to reflect the data sheet and the Total % column does not equal 100, do not worry about it and move on. Do NOT change the values to sum to 100% if the values on the data sheet do not sum to 100%. Leave this section blank if there are no shrubs present. If you enter OT for “other”, please record the species in the notes section of the transect information tab if it is known. Also, if a two letter code was recorded on the data sheet that is not available in the drop-down menu for Species 1 -5, enter “XX-Not listed” and record the discrepancy in the Notes section of the Transect Information tab. If a transect has more than one species recorded as OT, please enter a note that includes the Species and point(s) where it occurred (e.g., shrub species 1 - point 1 OT = False Azalea; shrub species 2 OT = Snowbrush). n. Ground Cover - Enter the percentages to the nearest integer of each ground cover category: 1) Snow, 2) Water, 3) Woody, 4) Dead and Down, 5) Herbaceous, 6) Bare/litter, 7) Residual Grass, and 8) Live Grass. You can verify that the ground cover percentages for each point add up to 100% by looking at the “Total %” column located to the right of Live Grass %. If the Total % does not add up to 100, check that you have entered all of the percentages correctly. If you have entered all of the percentages to reflect the data sheet and the Total % column does not equal 100, do not worry about it and move on. Do NOT change the values to sum to 100% if the values on the data sheet do not sum to 100%. If any category is not present, enter “0”. o. Grass and Herbaceous height (cm) - Enter the average height in cm of Residual Grass and Live grass and herbs in the appropriate columns. If there is no grass or herbaceous cover present, enter “0”. 4. Bird Data To enter Bird Data, click on the button labeled “Bird Data” located at the bottom of the screen. You can add blank rows in which you can enter the bird data by clicking “Add a row”, “Add five rows”, or “Add fifty rows”. Often, adding 50 rows is very slow, so we recommend that you add five or fewer rows at a time. Use the tab button to move from left to right through the fields. Note: when entering “88” bird detections, enter the point that the observer was coming from in the “Point” column and record the “Minute” as “88”. a. Point - Select the point number that corresponds to the bird records you are entering. (Required). b. Minute - Enter the minute that the bird was detected. For birds detected during a point count you should enter a value of 1 to 6. If the bird was recorded between points, select “88” for minute. If no minute was recorded for the bird detection enter “0”. Make sure you have entered all of the bird detections for the current point before moving on to the next point. c. Species -Enter the four letter bird code for the bird detection. To quickly find the
SOP #5 - Data Entry Protocol 203 species you want, while the drop-down list is selected, type the four letter code of the species to automatically select its name. Verify you have selected the correct species, because mistakes are commonly made here (e.g., HOWR not HOWA). Press enter or tab to save your entry and move to the next field. When entering “NOBI” records, the data entry system will automatically fill in the Distance, How, Sex, and Cluster Size fields. If the bird code was left blank on the data sheet, enter “UNBI”. (Required). d. Distance -Enter the distance from the observer to the bird in meters (whole numbers only). If no distance was recorded on the data sheet, enter “-1”. (Required). e. How - Select the code for how the bird was initially detected from the drop-down menu. Note: If the bird was detected by any means other than singing or flying over, and then was heard singing, there should be a two-letter code to enter (i.e., VS, CS, OS). If the bird was first detected singing, then you should only have one code to enter (S). If a bird is detected flying overhead and is then heard singing, it is not a flyover. Just enter the detection as “S”. If the how code was not recorded on the data sheet, enter “U”. (Required). f. Visual - Check this box if the Visual box was checked on the data sheet, either by left-clicking on the box using your mouse or by tabbing to this field and pressing the space bar. g. Sex - Enter the sex as it was recorded on the data sheet. If Sex was not recorded on the data sheet, enter “U”. (Required). h. Migrating - Check this box if the Migrating box was checked on the data sheet, either by left-clicking on the box using your mouse, or by tabbing to this field and pressing the space bar. i. Cluster Size - Enter the number recorded on the data sheet. If Cluster Size was not recorded on the data sheet, enter “1”. (Required) j. Cluster Code - Enter the letter recorded on the data sheet. If Cluster Code was left blank on the data sheet, leave this field blank.
Before moving on to the next point, make sure you have entered the same number of birds as are recorded on your data sheet, and that the Point, Minute, Species, Distance, How, Visual, Sex, Migrating, Cluster Size, and Cluster Code are all filled in correctly. Verify that you have entered all time periods, including NOBIs for minutes without birds.
Because many of the fields on the bird data tab are required, you will not be able to move to a different tab if you have any blank rows. To quickly delete all blank rows once you are done entering bird data, click the “Remove Empty Rows” button at the bottom of the screen. Hint: Remember to use the tab button to move from one cell to the next.
5. Saving your entered survey data You can save your data entry progress by selecting the Transect Information tab and clicking on the “Save Transect” button. Please keep in mind that you will not be able to navigate away from the Point, Vegetation, or Bird tabs if required data are missing. In these instances, you should delete empty rows or enter data in an acceptable format in order to navigate to a different tab. 6. Editing entered data/ Completing a partially entered transect To edit or modify a transect for which you have begun to enter data, you must open the index.htm file on the same computer on which you began data entry. From the Transect Information tab click on the button labeled “Load Transect”. A drop-down menu will appear with the dates, transect names, and observer(s) for any transects that
204 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN you have entered on the computer you are using. Use the mouse to select the transect that you would like to modify. You can make changes just like you would enter data for the first time. Please remember to save your work. 7. Uploading entered data to the database Once you have entered all of the Survey data using the index.htm browser, and you have obtained internet access, you can upload your data by 1) Loading the transect, and 2) pressing the “Submit transect” button. You can submit a transect multiple times. In the event that you submit a transect and then realize that you need to make edits to the uploaded data, you can simply edit the transect as described above (#6) and re-submit the transect. A warning message will ask if you want to overwrite an existing transect. Be sure you know what you are overwriting before you press “Yes”! If you receive a warning message that the submission failed, relax; the information you entered will have already been saved and can be recovered at any time by clicking the “Load Transect” button. If you see an error message when submitting a transect, check all of the tabs to make sure that you have entered all required data. This is the most likely reason that you would receive an error message when submitting a transect.
SOP #5 - Data Entry Protocol 205
SOP #6. Point Transect Quality Assurance/Quality Control
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol
Revision History Log Previous Revision Changes Section and Reason for New version version # date Author made paragraph change #
Standard Operating Procedure (SOP) Source: Van Lanen, N.J., C.M. White, and M.F. McLaren. 2012. Integrated Monitoring in Bird Conservation Regions (IMBCR): Point transect quality assurance/quality control protocol.
SOP 6.1. Introduction In order to provide data quality and assurance, Integrated Monitoring in Bird Conservation Regions (IMBCR) partners proof the data collected and entered into the database after each field season. To efficiently catch as many errors as possible, the data undergoes both a full proof and a proof using data queries. The full proof is designed to catch data entry errors that cannot be detected using the data queries. The proofing queries will identify records that are unusual in some way to facilitate further inspection. This process forms the second line of defense in eliminating erroneous records that may be missed during the full proof. Every technician and project is different; so proofers will need to actively consider the data proofing process for their area of interest. Do not ignore any incomplete or incorrect data that may become evident while following this protocol. If at any point you have concerns about data accuracy, do not wait to address them because you may not remember to correct these issues later.
You will need to run several queries in order to complete the data proofing process. We have set up a website you can go to where you can run queries specific to your study area (https://fc.rmbo.org/queryproof.aspx). Before you begin proofing, log onto this website. There you will find filter options and a “Download” button. Before you download a query, add the “Study Design” filter and select “IMBCR”. Next, select the state in which you are interested, followed by the BCR. Note that you can select multiple BCRs if you want (e.g., Avian Science Center can select IMBCR, MT, BCR10, BCR17) to get data specific to your area, or you can leave out the BCR altogether and just select a state. Lastly, you will want to add the “Year” filter and select “2012”. The more specific your filters are the faster the query will run, so if you have a large project you may want to break it down into smaller pieces. There are more query filters as well, such as stratum, county, and management entity, that should allow you to make the query as specific as you need.
SOP #6 - Point Transect Quality Assurance/Quality Control 207 Once you have set your filters, hit the “Download” button to download the queries for your study area. This will generate a single Excel workbook for you with multiple tabs; one for each query. Save this workbook on your computer for the proofing process. The first two tabs in this workbook contain the raw bird and raw vegetation data for your project and will be a useful reference tool. The third tab, labeled “Usable Data”, will be used to track usable and unusable vegetation data as described in the Full Proof section. The remaining tabs should appear in the order in which they are discussed in this document.
You will record changes made to the database and notes on those changes on the different query tabs in this workbook. Once the proofing process is complete, you will send your saved Excel Workbook with notes to Chris White at [email protected].
Note that some errors may show up on multiple query tabs. Once you have done a full proof of the data, you may want to rerun the queries to eliminate erroneous data that has already been fixed. You can rerun queries as often as you like as long as you keep a master Excel workbook that tracks all changes made in the database.
SOP 6.2. Physical Data 1. Data Entry We cannot begin proofing data on a project until 1) we have hard copies or scanned copies of all the datasheets, and 2) all of the data have been entered into the database. To view a list of transects entered into the database, go to https://fc.rmbo.org/ Default.aspx and enter your username and password. Click on the “Data Entry Status (CSV Download)” option at the top of the page. A data entry status query will automatically run and generate an excel workbook for you. This workbook contains a list of transects entered into the database, the date the survey was conducted, the number of points checked as completed, the number of points with bird data entered (Bird Points), the number of points with vegetation data entered (Veg Points), the observer, the data enterer, and the date and time the transect was submitted to the database. Additionally, the workbook contains a column entitled “Point Difference”, which subtracts the number of points with bird data from the number of points with vegetation data.
2. Scanning Data Electronic copies of all bird monitoring datasheets serve as backups in case physical datasheets are lost. Field technicians should scan all of their data at the end of each 10-day work period and email it to their crew leaders. Crew leaders will be responsible for keeping this data organized until they return to the office.
If, for some reason, you do not have a scanned copy of a transect, you will need to create one. Before scanning, compile the datasheets for each transect in the following order: a) transect description page, b) transect map, c) Landowner Contact Page, d) Landowner Contact Log, e) vegetation datasheet, f) reasons points not completed, and g) bird datasheet(s). Be sure to scan each transect in this order.
After receiving scanned data from your technicians or scanning your data, open each individual file and verify that the scanned data is legible and complete. Make sure each file is saved with the exact same name as the transect, followed by the year in which the survey was conducted (i.e., “CO-BCR16-BL21_2012”). If you are working from the RMBO office in Fort Collins, please save all scanned copies of your data on the server at \\RMBONAS1\Science\Datasheets. If you do not work in the Fort Collins office you will need to upload your scanned data onto RMBO’s ftp site (ftp:// fc.rmbo.org/public_uploads). To do this, open a Windows Explorer window on your computer, and paste this ftp link into the address bar. This should take you to the
208 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN upload folder. Once you are there, copy all of the scanned data and paste it into the uploads folder. Once you have done this, you will need to email Chris White at chris. [email protected], and notify him that datasheets have been uploaded. RMBO staff will then transfer the data to the proper location on our server. You should always retain a copy of the scanned data on your own computer for future reference.
3. Datasheet Organization Compile and staple together datasheets for each transect in the order that they were scanned. Organize transects by alphanumeric order according to project, BCR, state, or some other logical grouping. File these datasheets into the appropriate, pre-existing binders or create new binders as necessary.
4. Datasheet Check Once you receive all of the data from your field technicians, check through the hard copies of the data to verify that you have a copy of each transect completed. Check against the list of completed transects you put together during the field season for this. It is essential to have hard copies of datasheets for each transect, because during proofing we compare the data entered into the database with the data on the physical datasheets. You may need to contact field technicians to track down any missing data.
SOP 6.3. Transect Description Pages Refer to the “Transect Descriptions” worksheet in the query workbook. This query will only display transect description information for surveys that were conducted this season, and it will contain all of the information listed on the Transect Description Page for each transect in the database. Look through this worksheet and verify that technicians have entered their information correctly and completely. If you find errors in this table, you will need to make corrections using the Transect Description portion of the data entry application.
Note that it may be necessary to shorten long transect directions, descriptions, or notes, as they may not print properly when generating transect description pages. If you are concerned that one of these fields may contain too many characters, go to https://fc.rmbo. org/TransectDescriptionSheets.aspx, and generate the transect description page to see if looks okay.
1. Access Point UTMs It is very useful to have Access Point (AP) UTMs associated with each transect. Make sure there are AP UTMs associated with every completed transect. If you cannot find UTMs on any of the datasheets or the technician’s GPS, then make a note in the notes section telling future observers to record that information.
2. DeLorme Page Check to make sure every active transect has a DeLorme Page number and location code entered on the transect description page (i.e., 24 B3). This information makes it much easier to locate the transect on a map. If an active transect is missing this information, look up the transect map (http://rmbo.org/v2/dataentry/monitoring/ transectLocationMaps.aspx) and find the location on a DeLorme Atlas.
3. Vehicle Accessibility Verify that every survey completed has vehicle accessibility associated with it. The column for this on the transect table will have number values on it. These number values correspond with the vehicle accessibility options seen on the transect description page on the data entry site: 0 = Blank, 1 = All vehicles, 2 = High-clearance, and 3 = 4WD only. If there are any 0’s for completed surveys, then there is no
SOP #6 - Point Transect Quality Assurance/Quality Control 209 accessibility associated with that survey and the information needs to be filled in. Contact technicians about any missing vehicle accessibility information because you cannot always determine vehicle accessibility without physically visiting a site.
4. Transect Directions Accurate and detailed directions to each transect are essential to finding transects on the ground (refer to the field protocol for more information on transect directions). Check to make sure every completed transect has directions. If a transect is missing directions, first check the hard copy of the transect description page to see if the field technician recorded directions there. If not, contact the field technician responsible for this transect and ask them to enter the directions. If they no longer have this information or cannot remember how to get there, record directions to the transect as best you can using a map. If you have to resort to this last option, make a note that the directions are approximate and that they should be updated by the next surveyor.
5. Transect Description The transect description should contain information about 1) which points are accessible, 2) which points are private or public, 3) what habitat types are present on the transect, and 4) in what order the points should be conducted (if applicable). Fill in any missing information as described in the above section.
6. Notes/Camping Info The notes section should contain directions to the nearest or best camping spot in the area. If no camping is available, then there should be notes about where to stay in town, or other options. Eliminate any notes listing rare or unusual species detected on the transect. These notes may influence future observers and the birds they detect. Notes about interesting bird species at camp or somewhere outside of the survey grid are acceptable and may be retained.
7. Access and Transect Difficulty This information should be filled out for every completed survey using the difficulty rubric found in the field protocol. There are two separate columns, one for access difficulty and one for transect difficulty. If any completed surveys are missing this information, refer to the corresponding transect description page and see if the technician recorded this information. If he or she did not, contact the technician and ask him/her for this information.
SOP 6.4. Full Proof Starting in 2012, we will be conducting a full proof of all IMBCR data collected to catch and fix all data entry errors made. The main goal of a full proof is to find data entry errors and locate information which was not recorded correctly in the field. This is a long and tedious process requiring considerable concentration. Proofers are therefore encouraged to work through the full proof process by dedicating shorter intervals of time each day to the proofing process so they may maintain a sufficient level of focus (and sanity). Following the full proof, crew leaders will run some proofing queries to catch errors unrelated to typos (e.g., ground cover percentages not adding up to 100%).
To conduct a full proof, you will need to compare the data on the datasheet with the data entered into the database for every survey conducted in your study area this season. If the data do not match up, then you will need to correct the data in the database to match the datasheet. In some cases you may find erroneous or missing data on the actual datasheet.
To make this process easier and more efficient, we have figured out a way to have the computer read aloud to you the data associated with a transect. This will allow you to keep
210 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN your eyes on the physical datasheet and listen for discrepancies between the database and the physical datasheet. Follow these steps to enable your computer to read the data aloud to you. You will also need to make sure you are using the most recent version of the data entry application.
1. Open Mozilla Firefox. 2. Visit: http://addons.mozilla.org/en-us/firefox/addon/foxvox/. 3. Follow the steps to install the FoxVox addon. 4. Restart Firefox to complete the installation. 5. Open the data entry application.
To view the data in the database, open up the index.htm file within the data entry system, click the “View Options” button, and select “Enable Remote Transect Editing”. You will be prompted to enter a password which can be obtained from your supervisor. Next, click the “Load Remote Transect” button, and select the transect and visit (year) of interest from the drop-down menu. Once you have loaded the transect, click on the “Save Transect” button. This will generate readable text below all of the data entry forms. Select the text that you want FoxVox to read aloud. You will probably want to select one point of bird data at a time, or a few points of vegetation data at a time, to make it easier to keep your place. Once you have selected the text you want read, right click on the text and select the FoxVox menu, and go to “Speak Selected Text”. Follow along on the hard copy or pdf scan of the datasheet while the computer reads the data aloud.
The following sections will give you a detailed breakdown of what to look for while conducting a full proof, how to document errors found in the database, and how to handle erroneous or missing data on the datasheet. The first section covers the transect information listed at the top of every vegetation datasheet, the second deals with point data, the third with vegetation data, and the fourth covers bird data.
1. Transect Information Check the following information to verify that it was entered correctly. a. Observer Verify the observer ID on the datasheet matches that in the database. b. Date Verify the date on the datasheet matches the database and the work log for each technician. If the date in the database does not match the date on the datasheet and work log, correct it. If the date on the datasheet and the date in the database do not match the work log, email the crew leader who will verify with the technician when the survey was actually conducted. If no date was recorded, consult the technician’s work log to determine when the survey was conducted and enter that date. c. GPS Unit # Verify the correct GPS Unit # is listed. If a personal GPS was used, then the GPS Unit # should be “0000”. d. Who Collected Make sure the survey is associated with the organization that conducted the survey. e. Time Verify that correct times were entered into the database. If no time is recorded on the datasheet, then the time should have been entered as “0000”. f. Sky Verify the sky codes on the datasheet match those in the database. If no code
SOP #6 - Point Transect Quality Assurance/Quality Control 211 was recorded on the datasheet, the code should have been entered as “-1” in the database. g. Wind Verify the wind codes on the datasheet match those in the database. If no code was recorded on the datasheet, the code should have been entered as “-1” in the database. h. Te m p Verify the temperatures on the datasheet match the temperatures in the database. If no temperature was recorded on the datasheet, then the temperature should have been entered as “-99”.
2. Point Data This page contains information on point count start times, GPS Accuracy, and reasons why points were not completed. Before going through the following categories line-by-line, check the hard copy or scan of the datasheet to make sure there are bird data for each point listed as “completed”. Also check to make sure there are no bird data recorded for a point on the hard copy of the datasheet that is listed as “not completed” on the point data page. If a point is listed as “completed” on the point data page in the database, but there are no corresponding bird data on the hard copy of the data, change the status of that point to “O: Other” and enter “Reason unknown” in the notes section. If a point is listed as “not completed” but you find bird data for that point, first make sure there are no notes on the datasheets explaining why that point was not entered. If there are no notes and the data are legitimate, change the status to “Completed”, enter the start time and accuracy for that point, and enter the Veg and Bird data in the corresponding tabs for that point. Once this is complete, check each of the following categories for accuracy: a. Start Times Verify that the start time for each point on the datasheet matches the start time in the database. If no start time was recorded then “0000” should have been entered. b. GPS Accuracy Verify that the GPS accuracy on the datasheet matches that in the database. If no GPS accuracy was recorded on the datasheet, then “-1” should have been entered in the database. c. Completed Verify that each point listed as completed was actually completed. To do this, look at the bird datasheets and find the data for each point listed as completed. You should also verify there are not additional points of data on the hard copy that are not listed as completed in the database. Lastly, for points that were not completed, verify that the reasons points were not completed entered in the database match the reasons on the datasheet. If the reason is “O = Other”, make sure that the notes section contains notes about why the point was not completed. Let your supervisor know about any missing information.
3. Bird Data Check the following information to verify it was entered correctly.
A. Point Make sure that the point number associated with each bird record matches the datasheet.
B. Minute Make sure that the Minute associated with each bird record matches the datasheet. There is one scenario where the Point and Minute number in the
212 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN database will not match the Point and Minute number on the datasheet. If a technician recorded an “88” bird between points, on the datasheet the bird will have “88” recorded in the Point column and no Minute recorded in the Minute column. However, in the database, that 88 bird should have a Point number (the Point the observer completed before recording the 88 bird). The Minute associated with this 88 bird should be “88”.
If no Minutes were recorded on the datasheet for a Point, then each Minute for that Point should be entered as “0”.
C. Species Codes Verify that the four-letter Species Codes on the datasheet match the four-letter Species Codes in the database. If a four-letter Species Code on the datasheet does not appear in the database, consult the Crew Leader for that project. If you are the Crew Leader, see if you can decipher the incorrect Species Code to figure out what the species should be. If you cannot decipher the correct Species Code, contact the field technician to see if they can figure out what the species should be. If you still cannot figure it out, enter “UNBI” for the Species Code.
Note that for “NOBI” (No Bird) detections, the Distance, How, Sex, Visual, Migrating, Cluster Size, and Cluster Code information will be grayed out. These fields do not need to be verified. You should check that all “NOBI” detections have a Point and Minute associated with them. Make any necessary corrections to the database.
D. Distance Verify that the correct Distance was entered for each bird record. If no Distance was recorded on the datasheet, then the Distance should be entered in the database as “-1”. If a Distance over 999 m was recorded on the datasheet, that Distance should be entered as “999”. 88 birds should not have Distances associated with them, so if a distance was entered for an 88 bird, change the Distance in the database to “-1”.
E. How Verify that the correct How code was entered for each bird record. If no How code was recorded on the datasheet, then “U” should be entered.
F. Sex Verify that the correct Sex code was entered for each bird record. If no Sex code was recorded on the datasheet then “U” should be entered.
G. Visual? If the Visual checkbox is checked on the datasheet, make sure that box is checked in the database.
H. Migrating? If the Migrating checkbox is checked on the datasheet, make sure that box is checked in the database.
I. Cluster Size Verify that the Cluster Size on the datasheet matches the Cluster Size in the database. If the Cluster Size is blank on the datasheet, it should be entered as “1” in the database.
SOP #6 - Point Transect Quality Assurance/Quality Control 213 J. Cluster Code If a Cluster Code is recorded on the datasheet, make sure the Cluster Code was entered into the database.
K. 88 Birds Field technicians tend to make mistakes when recording and entering 88 birds. Check to make sure all 88 birds are associated with the correct point (the point the technician surveyed immediately prior to detecting the 88 bird). If a field technician records an 88 bird before the first point or after the last point on a transect, remove this data from the database. You should note these bird detections in the notes section on the transect visit page (the page where you enter wind, sky, time, etc.). Make sure the distance to each 88 bird was entered as “-1” in the database, since we do not record distances for 88 birds. Make any necessary changes in the database and record notes for any changes you make.
This concludes the full proof portion of data proofing. Be sure to fully proof all data within a study area before moving on to the following sections.
SOP 6.5. Uncommon Bird Proofing This check should be completed by the crew leader to look for possible field identification errors made by field technicians. If a species was recorded infrequently, it could either be because a four-letter Species Code was entered into the database incorrectly, the bird was misidentified and does not occur in the study area, or it was identified correctly and is either a rare detection or an infrequently surveyed habitat type. The idea behind looking at uncommon Species Codes is to correct any mistakes in the first two scenarios, and to confirm rare detections in the third scenario. Data entry errors should have been corrected during the Full Proof process, so you will mostly be looking for misidentifications and confirming legitimate rare detections.
Determining whether a rare detection is legitimate or not typically requires input from the technician that conducted the survey. Because technicians may take some time to respond to your inquiry, we suggest that you work on the Uncommon Bird proofing before starting the query proofing process.
There are two steps to Uncommon Bird Proofing. The first step is to identify the Uncommon Birds you want to proof, and the second step is to proof each of these uncommon records.
1. Identifying Uncommon Birds For the first step, you will need to re-run the online proofing queries. Once you have generated a new workbook, delete all worksheets except for the first worksheet labeled “Raw Bird Data”. Save this workbook as “Uncommon Birds”. You will use this raw data to create a pivot table displaying the number of each species recorded on the project. You should create two pivot tables, one showing the number of species detected by BCR and one showing the number of species by Primary Habitat (the BCR and Primary Habitat columns will be included in the raw bird data query). Separating your data by BCR and by Primary Habitat will better identify rare detections. For example, you create Uncommon Birds by Primary Habitat pivot table and discover that one of the 98 WEME detections that occurred in one BCR in your study area occurred in Spruce-Fir habitat. This record should be investigated. Had you not looked at the data this way, you would not have investigated any of your 98 detections, since Western Meadowlarks are generally not considered uncommon.
214 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN To create a pivot table by BCR, select the “Insert” tab at the top of your excel document, then select “PivotTable” and hit “Okay”. From the Pivot Table Field List on the right, drag and drop “Common Name” into the Row Labels box at the bottom. Next, drag and drop BCR into the Column Labels box. Then, drag and drop the Cluster Count (CL_Count) in the values box at the bottom. You should see a table on the left side of the screen summarizing how many of each species were detected in each BCR. At the top of the table you should see “Sum of CL_Count”. Right-click on this, select “Summarize Values by”, and then select “Count”. This will give you the number of detections for each species instead of the number of individuals recorded (that way one record of a cluster of 40 WTSW will show up as one, not 40). Label this sheet “BCR Pivot”.
Copy the BCR Pivot table and special paste “values” in a new worksheet. Delete all unknown Species Codes and “NOBI” records. Copy and paste this table into a separate worksheet for each BCR within your study area. In each worksheet, isolate one BCR by deleting all other BCR columns, then name the worksheet after the BCR to avoid confusion. Sort each table by the “count” column. Go through all Species Codes that have greater than 10 detections and delete any that do not look odd to you. You will proof the remaining Species Codes on this list, as well as any Species Codes with 10 or fewer detections. Once you have a list of Species Codes to proof for each BCR within your study area, use the “Filters” option in Excel to isolate the bird detections of interest, and paste them into an excel worksheet labeled “Uncommon Birds by BCR”. Repeat this entire process using Primary Habitat instead of BCR to get a list of Species Codes by Primary Habitat.
2. Proofing Uncommon Birds Proofing for uncommon birds is more complicated than just comparing the data in the database to the data on the datasheets. However, you should check the datasheets first to make sure the uncommon bird in the database was entered correctly. If a field technician entered the wrong Species Code, simply change the record to the correct Species Code in the database. If the Species Code was entered correctly, then you will need to verify that the Species Code recorded is legitimate. Using a field guide, Breeding Bird Atlas, and any other relevant resources, check the Primary Habitat where the field technician recorded the species to make sure the species can be found there. Use the transect maps website to locate the area where the detection occurred, and compare this location against the range map for the species to determine if it normally occurs there. Check historic RMBO data to see if the species has been detected on the same project in the past. Some records will be obviously right or wrong. For those that are obviously wrong, navigate to the transect through the data entry application and make the appropriate correction. Other detections may be questionable and will require contacting the field technician to obtain specifics regarding the observation. You will need to make judgment calls on many of these records, so please do your best to make an informed decision. Occasionally, technicians fail to respond or provide pertinent information regarding these uncommon bird sightings. In these cases you need to decide whether the record should be left in the database as is, or whether the Species Code should be changed to “UNBI” (Unknown Bird).
Ultimately, you will need to make a final decision about each questionable bird record in the database. If you are at all unsure about a record, change the Species Code to “UNBI”. Please keep detailed notes on your actions, whatever your decision may be.
SOP #6 - Point Transect Quality Assurance/Quality Control 215 SOP 6.6. Queried Data Proofing After the full proof is complete, and before the query proofing begins, run the data entry status query found at https://fc.rmbo.org/Default.aspx. The numbers in the “PointVisits”, “BirdPoints”, and “VegPoints” columns should be identical for each transect. Unequal values across these columns indicate there is a problem. You will need to figure out what the problem is and fix it. Once you complete this step you are ready to move on to the query proofing process described below.
There are certain errors that are not caught during the Full Proof process. In order to eliminate as many errors as possible, we will run several queries in the database to look for commonly made errors. There are four query proofing sections, one for each tab on the data entry application – 1) Transect info, 2) Point Data, 3) Vegetation Data, and 4) Bird Data.
The query workbook you generated will contain a separate worksheet for each query listed below. You will probably want to re-run the query once you have completed the Full Proof and the Uncommon Bird proof to eliminate data entry errors you have already fixed. Be sure to merge the old and new query documents together so that any changes you have tracked are all contained within a single workbook.
Once you have re-run the queries, you will need to go through the data line by line on each worksheet, looking for errors as detailed in the following sections. If a line of data is correct, highlight it in blue. If a line of data contains an error, highlight it in red, and enter notes in the corresponding Notes column to describe what was wrong with the data and how it was fixed. To correct the error in the database you will need to remotely load the transect with the error, edit the data, and resubmit the data to the database. Proofers should also correct the entry on the datasheet using a red pen, and re-scan the data.
Note that blank query worksheets mean that no errors were found by that query, and you can simply move on to the next query worksheet. Because the new data entry application was designed to cut down on the number of errors in the data, you may find quite a few queries with no data.
1. Transect Info
A. Time
1) Transect Start Times Refer to the worksheet labeled “Transect Start and End Times” in the query workbook. Sort by Transect Start Time ascending and check all Transect Start Times that are entered as “0000”. If there is a Transect Start Time recorded on the vegetation datasheet, then enter the Transect Start Time on the Transect Info page. If the Transect Start Time was not recorded on the vegetation datasheet, then enter the Point Start Time recorded on the bird datasheet for the first point count conducted. If no Transect Start Time was recorded on the vegetation datasheet, and no Point Start Time was recorded on the bird datasheet for the first point count conducted, leave the Transect Start Time as “0000”. Next, check all Transect Start Times that are more than 1 hour earlier than sunrise in your study area (these times are 1/2 hour earlier than a technician should start surveying). Finally, scroll down to check all Transect Start Times more than 1 hour after sunrise in your study area. If you find either of these situations, and the data on the datasheet matches that in the database, email the corresponding technician for more information. If they cannot explain or verify that the Transect Start Time recorded is correct, change the
216 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Transect Start Time to “0000”. Record any notes from your correspondence with the technician on the worksheet. If the Transect Start Time was entered incorrectly, change it to match the Transect Start Time recorded on the datasheet.
2) Transect End Times Next, sort the “Transect Start and End Times” worksheet by Transect End Time ascending. This will display all transects from earliest to latest Transect End Times. Check all Transect End Times that are entered as “0000”. If there is a Transect End Time recorded on the vegetation datasheet, then enter the Transect End Time in the database. If the Transect End Time was not recorded on the vegetation datasheet, then add six minutes to the Point Start Time recorded on the bird datasheet for the last point count conducted, and enter that time into the database. If no Point Start Time was recorded on the bird datasheet for the last point count conducted, and the Transect End Time was not recorded on the vegetation datasheet, leave the Transect End Time as “0000”. Next, sort the sheet by Transect End Time descending. This will list transects from the latest Transect End Time to earliest Transect End Time. Check all Transect End Times that are 5 hours later than sunrise in your area. If you find either of these situations and the data on the datasheet matches that in the database, email the corresponding technician for more information. If they cannot explain or verify that the Transect End Time recorded is correct, change the Transect End Time to “0000”. If the Transect End Time was entered incorrectly, change it to match the datasheet.
3) Transect Start Time Later Than Transect End Time Refer to the “Start – End > 0” worksheet. Any transects on this list have a Transect Start Time that is later than the Transect End Time. Check the vegetation datasheet to see if the times were entered correctly. If consulting the vegetation datasheet does not provide you with a reasonable Transect Start or Transect End Time, you should consult the bird datasheets. The first point conducted in the morning should represent the Transect Start Time for the survey. Similarly, if a Transect End Time appears to be incorrectly entered, add six minutes to the Point Start Time recorded on the bird datasheet for the final point surveyed. This should represent the end of the survey. If the Transect Start or Transect End Times were entered incorrectly, then fix the error. If you are unable to determine the Transect Start and/or Transect End Times using the vegetation and bird datasheets, then enter “0000” for the missing time.
B. Sky Refer to the “Unusual Sky Values” worksheet. This worksheet will contain a list of all Sky Codes that do not match accepted codes for Sky Conditions (the acceptable Sky Codes are -1, 0, 1, 2, 3, 5, 6 or 8). Compare all Sky Codes on this worksheet to the vegetation datasheet. If a Sky Code was entered incorrectly, change the code to match the datasheet. If a Sky Code was recorded incorrectly on the vegetation datasheet, then change the Sky Code in the database to “-1”.
C. Wind Refer to the “Unusual Wind Values” worksheet. This worksheet will contain a list of all Wind Codes that do not match accepted codes for wind conditions (the acceptable Wind Codes are 0 – 4). Compare all Wind Codes on this worksheet to the datasheet. If a Wind Code was entered incorrectly, change the code to match the datasheet. If a Wind Code greater than 4 was recorded on the datasheet and one or more points were not completed because of weather, then change the
SOP #6 - Point Transect Quality Assurance/Quality Control 217 end wind to “4”. If an incorrect Wind Code was recorded on the datasheet but weather was not a reason for any points being skipped, change the Wind Code to “-1”.
2. Point Data
A. Point Start Time Refer to the “Empty Point Start Time” worksheet. This query will pull out any Point Start Times entered as “0000”. This means a Point Start Time was not recorded for that point. If it is the first point conducted for that survey, see if a Transect Start Time was recorded on the vegetation datasheet. If so, use that as the Point Start Time for the first point. If not, leave the Point Start Time as “0000”. If it is the last point, see if there is a Transect End Time recorded on the vegetation datasheet. If so, subtract six minutes from the Transect End Time and use it as the Point Start Time for the last point. If not, leave the Point Start Time as “0000”. If a point is missing a Point Start Time, but the point before and the point after have Point Start Times, then determine the midpoint between those two Point Start Times and use that as the Point Start Time for the point with missing data. If consecutive points are missing Point Start Times, then leave the Point Start Times as “0000”.
Note that if you change a Point Start Time from “0000” to an actual time, the Point Start Time should fall between the Transect Start and End Times found on the vegetation datasheet. If it does not, refer to the following section for how to handle this situation.
B. Point Start Times outside of Transect Start and End Times Refer to the “Point Start Not Btw Start & End” worksheet. This query will pull out any Point Start Times that do not fall between the Transect Start and End Times. For each record, check the Point Start Time recorded on the bird datasheet to make sure it was entered correctly. If the Point Start Time was entered correctly but does not fall between the Transect Start and End Times, and it is the first point conducted, use the Transect Start Time listed on the vegetation datasheet. If it is the last point, subtract six minutes from the Transect End Time listed on the vegetation datasheet and use it as the Point Start Time for the last point. If any other Point Start Time falls outside of the Transect Start and End Times for a survey but the point before and the point after have Point Start Times, then determine the midpoint between those two Point Start Times and use that as the Point Start Time for the point with missing data. If consecutive points are missing Point Start Times, then leave the Point Start Times as “0000”.
C. Points Not Completed Refer to the “Points Skipped with No Code” worksheet. It is important to know why points were not completed during the season. You will need to check that all points not conducted on completed surveys within your study area have associated reasons why they were not conducted. This query will contain points that do not have reasons associated with them. Look at the hard copies or scan of the data to see if the codes were recorded on the datasheet but not entered in the database. If so, enter the recorded codes in the database. If no reason codes were recorded, you will need to contact the field technician responsible for that transect to ask him/her why he/she did not complete those points. Once you have tracked this information down, enter it into the database. If you are unable to obtain this information, record the code as “O: Other” and enter “Reason unknown” in the notes section.
218 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN 3. Bird Data
A. Bird Distances ≤ 0 The “Bird Distances ≤0” worksheet displays all bird records, except for NOBI and 88 records, with distances equal to or less than 0. Check all of the records to make sure the data were entered correctly. Make any necessary changes in the database, and record notes for any changes you make.
B. Birds with Cluster IDs The “Birds with Cluster IDs” worksheet displays all bird records with an alpha code in the Cluster ID field. We need to verify that these are in fact clusters. Check each record to make sure they meet the definition of a cluster. A cluster linked by an alpha code must meet the following requirements:
1) Must be the same species; 2) Must occur on the same point and same minute within a transect; 3) Must be within 20 m of one another; 4) Can NOT be two singing males.
Look for errors like clusters across species or minutes, a single bird detection missing its partner, or non-alpha codes entered into this column. These are incorrect entries and should be fixed. Make any necessary changes in the database, and record notes for any changes you make.
C. Birds with F, U, or Invalid How Codes Refer to the “Birds with Invalid How or F U” worksheet. This worksheet displays all bird records with a “How” code entered as “U” or “F”, as well as any codes not found on the drop-down list of accepted codes.
1) “U” Code Check all codes equal to “U” against the physical datasheets to make sure these were entered correctly. If there was no “How” recorded on the datasheet, then leave the code in the database as “U”. If there is an accepted “How” code recorded, then enter the correct code in the database.
2) Flyovers For all “F” (flyover) detections, it is a good idea to make sure technicians were correctly recording birds as flyovers. In the past, this has been a difficult concept for technicians to understand; often, birds that are flying get recorded as flyovers even if they are actually using the surrounding habitat (e.g., Red Crossbill, swallows/swifts, raptors). If a technician repeatedly records a bird like Red Crossbill as a “flyover”, and you believe the bird was using the habitat, you should change the “F” code to “U”. We do not include “flyover” birds in analysis, so if a technician consistently recorded a species as a “flyover” when it was not, the data analyses will be significantly impacted.
3) Unacceptable Codes If there are bird detections with a how code letter that does not match any of the accepted how codes, look at the datasheet to see if someone entered the code into the database incorrectly, and fix it in the database if you can. If a field technician actually recorded the code on the datasheet incorrectly, then change the code in the database to “U” for unknown.
SOP #6 - Point Transect Quality Assurance/Quality Control 219 D. Birds with Invalid Minutes The “Birds with Invalid Minute” worksheet will show all bird records where the Minute field is equal to “0”, greater than “6”, or missing altogether.
1) Minute equals 0 For bird records where the Minute field is equal to “0”, check the datasheet to see if a valid Minute was recorded, and if so, enter that into the database. If no Minute was recorded and the Minute can be deduced by looking at the bird data (e.g., there is one bird recorded followed by five NOBIs and none of the records have a Minute recorded), enter the Minutes as they should have been recorded. If the Minute associated with the bird record cannot be deduced (e.g., six bird records on a point with no Minutes recorded), then set all Minutes equal to “0”.
2) Minute Greater Than 6 We record bird data for six minutes on point counts using the IMBCR protocol, so no bird record should have a Minute greater than “6”. For any records with a Minute greater than “6”, contact the technician to see if they can remember what they did. If the technician confirms recording too many minutes, delete bird records associated with any Minutes above six. If the technician does not respond or does not know, count the number of Minutes recorded for the timesheet. If only six Minutes were recorded, see if you can adjust the Minute numbers to be 1 – 6. If more than six Minutes were recorded, delete the data for any Minutes above 6 and change all Minutes to “0”.
3) Minute Null If no Minute was entered for a bird record, refer to the datasheet to see if a Minute was recorded and, if so, enter that number into the database. If no Minute was recorded on the datasheet, follow the instructions in the “Minute equals 0” section above.
E. Incorrect Minute Count Each point count conducted using the IMBCR design should have six Minutes of data recorded. The “Incorrect Minute Count” worksheet will show the number of Minutes recorded for each point on each survey conducted when the number of Minutes does not equal six. Investigate why these points do not have six Minutes recorded. If errors were made during data entry, make any necessary changes in the database and record notes for any changes you make. If errors were made in the field, there are two ways of handling the problem:
1) No 6th Minute If a technician recorded Minutes sequentially (e.g., 1, 2, 3, 4, 5), but forgot to record the sixth minute, you can assume that no birds were detected in the last minute. Enter a “NOBI” record for minute six in this case.
2) Too many or too few Minutes recorded If a field technician recorded too many Minutes on the physical datasheet, contact the technician to see if they can remember what they did. If the technician confirms recording too many minutes, delete bird records associated with Minutes above six. If the technician does not respond or does not know, delete the data for any Minutes above six and change all Minutes to “0”. If a technician recorded too few Minutes and they were not recorded sequentially (e.g., 1, 2, 4, 5, 6), then change all of the Minutes for these points to “0”.
220 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN F. Invalid NOBIs The “Invalid NOBI” worksheet will display any “NOBI” records that do not match the auto-fill requirements in the data entry application (Distance = -1, How = U, Sex = U, Visual and Migrant boxes not checked, Cluster Size = 0, and no Cluster Code recorded). Check each of these records to confirm that each “NOBI” on the list is a legitimate “NOBI”. If the record was entered incorrectly, then change the “NOBI” to the appropriate four-letter Species Code and ensure the rest of the data are correct for that record. If the record is a legitimate “NOBI”, note that on the worksheet and do not worry about fixing the values in the database. We will run a query to automatically update this information once proofing is complete.
G. NOBIs with Birds The “NOBI with Birds” worksheet will display all Transects, Points, and Minutes where there are both a NOBI and a non-NOBI Bird Code. Each of these lines represents an error since these two codes should never be found in the same minute. Consult the bird datasheet and make necessary changes to the database.
H. Migrant Birds The “Migrant Birds” worksheet will display any bird records that have the “Migrating?” checkbox selected in the database. Review these records and make sure that the “Migrating?” box is checked on the datasheet too. If the bird record on the datasheet does NOT have the “Migrating?” box checked, please uncheck the “Migrating?” box in the database.
SOP 6.7. Final Steps (for the Monitoring Coordinator only) After all proofing is complete, the Monitoring Coordinator should request the database manager to do the following:
●● Change NOBI distance = -1, How = U, Sex = U, Visual and Migrant boxes not checked, Cluster Size = 0, and no Cluster Code recorded ●● Change 88 bird distances to “-1” ●● Change all recorder to “none” ●● Clear all cluster codes that do not meet the criteria ●● Check for Missing UTMs/zones for on point and pointvisit tables ●● Check for points missing bird data and reasons skipped that were entered into the database ●● Check for points missing reasons skipped ●● Check for points with bird data and a reason skipped ●● Checked for points marked as completed with no bird data ●● Make data available on ADC.
SOP #6 - Point Transect Quality Assurance/Quality Control 221
SOP #7. Revising the Protocol
Version 1.00 (November 2013)
Supercedes all previous versions of SODN protocol
Revision History Log Previous Revision Changes Section and Reason for New version version # date Author made paragraph change #
This Standard Operating Procedure (SOP) explains how to make and track changes to the Landbird Monitoring Protocol narrative and associated SOPs for the CHDN, NGPN, SODN, and SOPN units of the National Park Service (NPS). The Landbird Protocol narrative and SOPs have attempted to incorporate the soundest methodologies for collecting landbird data. However, all protocols, regardless of how sound they are, will require editing as new and different information becomes available. Changes should first be evaluated in terms of cost and benefit, then subjected to appropriate review, and, if approved, implemented in a timely manner.
These procedures must be followed when making changes to ensure that previous data collection and processing procedures are clearly understood when using and interpreting historical data sets. Similarly, clearly articulating new methods is crucial to credible interpretation of data acquired since the implementation of changes. Personnel making changes must be familiar with this SOP to ensure that proper reviews are conducted and that documentation standards are followed.
SOP 7.1. Procedures Protocol Review and Revision 1. A thorough review of the entire protocol will be conducted approximately every 10 years. In addition, protocol implementation will be evaluated at the end of each field season to 1) identify any problems that may have been encountered, and 2) determine whether changes to any methods or procedures are necessary. If so, revisions will be made according to the guidelines presented in this SOP. Substantial problems or concerns disclosed during an annual review may trigger earlier initiation of a complete review. 2. Discuss proposed modifications with project staff prior to making any changes. Consult with the Data Manager before making changes to ensure that dataset integrity will be maintained. Advance notice of changes is also important because they may require revisions to the database structure or functions that could interfere with project operations. Project and data management staff should agree on who will make the changes and a timeline for completion. 3. Make the agreed-upon revisions in the appropriate protocol document (narrative or SOP). Be aware that a change in one document may require changes in other documents; for example, renumbering an SOP may require changing references to the SOP in other parts of the protocol. 4. Use the Revision History Log to document edits only in the documents that were
SOP #11 - Revising the Protocol 223 modified. Record the previous version number, date of revision, author of the revision, paragraphs and pages where changes were made, the reason(s) for the changes, and the new version number. For minor changes, version numbers increase incrementally by hundredths (e.g., version 1.01, 1.02, 1.03). Designate major revisions with the next whole number followed by a decimal point and two zeroes (e.g., version 2.00, 3.00, 4.00). 5. Review all modifications for clarity and technical soundness. Small changes or additions to existing methods are reviewed in-house by the Project Manager, network program managers or designated I&M staff, and cooperators. An outside review may be required for substantive changes, such as those made to the sampling design, data collection methods, or analysis techniques. NPS Regional and National staff, and perhaps outside experts, with familiarity in avian research and statistical methodologies for data analysis will review major changes. Document Completion 1. Change the “Version” in the narrative or SOP header and title sections, and change the “Effective Date.” 2. Rename the digital narrative or SOP file according to the standards in the Digital File Management guidance document, and update the file name and path in the document footer. 3. Ensure that version numbers, file names, etc., are updated appropriately throughout the protocol documents. 4. Enter all narrative and SOP version changes in the Master Version Table (MVT), which is located after this SOP. Any time a narrative or an SOP version change occurs, create and record a new Version Key number (VK#) in the MVT along with the date of change and the version of the current narrative and each SOP. The VK# increases incrementally by integers (e.g., 1, 2, 3, 4, 5; no zeroes). NOTE: The Version Key number is essential for project information to be properly analyzed and interpreted. Do not distribute the protocol narrative, SOPs, and data independently of this table. 5. Ensure that the VK# is updated on the title page of the protocol. 6. Submit all revised sections of the protocol (e.g., narrative, SOPs, title page) to the appropriate Science Writer-Editor, who will prepare the protocol for publication in the Natural Resource Technical Report (NRTR) series. Long-term Storage and Distribution The individual files that make up the current version of the protocol (narrative, SOPs, and all other associated files) and the final pdf of the NRTR version are stored in the Protocols folder on both the working and archive servers. Previous versions of documents are stored only on the archive server.
1. Place a copy of each revised document file in the Protocols folder on the working drive and notify the Data Manager that the protocol revisions are complete. 2. The Data Manager transfers the previous versions of the protocol documents and pdf to the project Protocols folder on the archive drive. 3. The Data Manager places the pdf of the current protocol in the project folder on both the working and archive servers. 4. The Data Manager catalogs the NRTR in the network Library and places a printed copy in the network Library files. 5. The Data Manager posts the revised version and updates associated records in the appropriate I&M databases and repositories, including but not limited to NatureBib, NPS Data Store, Protocol Database, the LCAS web site, and each network’s Internet and Intranet web sites.
224 Landbird Monitoring Protocol and Standard Operating Procedures for CHDN, NGPN, SODN, and SOPN Master Version Table Version Date of Narrative SOP #1 SOP #2 SOP #3 SOP #4 SOP #5 SOP #6 SOP #7 Key # Change
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