Integrated Monitoring in Bird Conservation Regions David J. Hanni & Jennifer A. Blakesley Rocky Mountain Bird Observatory

Guidelines for Avian Monitoring Goal 1: Integrate monitoring into bird management and conservation practices. Goal 2: Coordinate monitoring programs among organizations and integrate them across spatial scales. Goal 3: Increase the value of monitoring information by improving statistical design. Goal 4: Maintain bird population monitoring data in modern data management systems. In 2007 … http://www.nabci-us.org/aboutnabci/monitoringreportfinal0307.pdf 2

Our Objectives Provide a design framework to integrate bird monitoring efforts Precisely estimate distribution, density, site occupancy, population trends and species richness Provide habitat association data relevant to landscape changes Maintain a high-quality database, accessible online Create decision support tools to help guide conservation efforts 3

Bird Conservation Regions

Current Monitoring Programs All lands in BCR 17 All lands in CO, WY and MT Portions of 9 additional states All BLM lands in: CO, WY, MT, ND, SD All USFS lands in Regions 1 & 2 (CO, WY, NE, KS, SD, ND, ID, MT) 3 National Forests in Region 3 (Kaibab, Coconino, Prescott) 5

2010 Landbird Monitoring

Current Partnerships Colorado Division of Wildlife Wyoming Game and Fish Department Montana Fish, Wildlife and Parks South Dakota Game, Fish and Parks USFS: 27 National Forests, 9 National Grasslands, 4 regions, 12 states BLM in 5 states National Park Service …(continued)

Current Partnerships (cont.) Northern Great Plains Joint Venture Audubon Wyoming Wyoming National Diversity Database Montana Natural Heritage Program Idaho Bird Observatory Great Plains Landscape Conservation Cooperative

Sampling Design and Methods Sampling Frame Stratification Sampling Units Sample Selection Sample Allocation (effort) Sampling Methods Analytical Methods Example Results

Sampling Frame: BCRs Spatial scale should correspond to ecological process of interest.

Stratification Stratification should be defined by areas to which we want to make inferences Strata are based on fixed attributes Federal/state land ownership Elevation, latitude, soil type, ecoregion All vegetation types available for sampling Flexible: Each state within the BCR and each BCR within a state can be stratified differently (depending on local needs) 11

Colorado BCR 16 12

Colorado BCR 18 13

Montana BCR 11, 10 and 17

Wyoming BCRs 16, 17, 18

Wyoming BCR 9 and 10

Sampling Units 1 km2 cell Step 1: Overlay grid on sampling frame Step 2: Attribute each cell with pertinent data (unique ID, spatial location, land ownership, elevation, soil type, …)

Sample Selection Spatially Balanced: Generalized Random Tessellation Stratification (GRTS) Ensures a spatially balanced distribution of samples within each stratum ~ Random Analyses can incorporate spatial information in estimation of sampling variance 18

Sample Allocation Minimum of 2 samples required/stratum Recommend 10 Determined by funding partners May vary annually

Base Sample

Additional Samples in Colorado

Additional Samples - Comanche NG

Sampling Effort example: BCR 17 All of BCR 17: 264 samples MT: 84 samples WY: 46 samples ND: 37 samples SD: 90 samples NE: 6 samples

Sampling Effort example: BCR 17

Sampling methods 16 points per cell 5 minute point count 250 m spacing 125 m from edge 5 minute point count 1 minute intervals Record distance to each bird seen or heard Record species and sex for each observation 1 minute intervals allow for Removal modeling Distances are required for Distance sampling 25

Analytical methods Estimate detection probability and density Distance sampling (Buckland et al. 2001) Removal sampling (Farnsworth et al. 2002) Estimate detection probability and occupancy rate Combined Removal and Occupancy modeling (Pavlacky et al. in review) Habitat modeling Multi-scale occupancy estimation 26

Scalable Design Aggregation of stratum-wide estimates to BCR- or state-wide estimates is built into the design Example: Black Hills National Forest in WY Contributes to estimates for: BHNF Wyoming BCR 17

Results: Density and Abundance Example: Townsend’s Solitaire D N %CV n Black Hills NF 3.74 24,533 19 89 Wyoming BCR 17 0.36 23,406 77 7 BCR 17 0.24 84,817 40 109

Results: Site Occupancy Example: Brown Creeper Psi %CV n Tran Black Hills NF 0.287 34 10 BCR 17 0.073 63 15

Results of Habitat Modeling Example: Brewer’s Sparrow CO, BCR 16, BLM Large scale (1 km2 sampling cell)

Results of Habitat Modeling Example: Brewer’s Sparrow CO, BCR 16, BLM Large scale (1 km2 sampling cell) Occupancy rates predicted using sagebrush area.

Results of Habitat Modeling Example: Brewer’s Sparrow CO, BCR 16, BLM Small scale (sampling point)

Benefits of IMBCR design Collaboration Shared costs among partners Handles fluctuating funding Ability to compare bird trend to habitat trend Ability to compare local to regional results Flexibility in stratification All vegetation types available for sampling Can be (is) used for other taxa Aggregation of strata-wide estimates to BCR- or state-wide estimates is built into the design Because each stratum has its own spatially-balanced, ordered sample, sampling effort can vary among strata and among years and still provide statistically valid estimates

Acknowledgements Original Collaborators in Project Design Robert Skorkowsky, USFS David Klute, CDOW Paul Lukacs, CDOW Greg Hayward, USFS Christina Hargas, USFS 34

GPLCC Introduction Landscape Conservation Cooperatives (LCCs) Great Plains LCC Landscape Conservation Cooperatives (LCCs) Science-based Partnerships Help guide and coordinate conservation efforts at regional levels Great Plains LCC (GPLCC): BCR 18 and 19

GPLCC Project Objectives Create a standardized grid USNG Develop strata for GPLCC Grid attributes Select samples Spatially balanced sampling grts function in R

US National Grid (USNG) USNG identified as a potential standard for monitoring populations at regional levels Developed by FGDC for emergency response coordination 1-km square grid cells Contains key elements for a proper monitoring grid (national coverage, commonly used datum/projection, scalable)

Benefits of a standardized grid Same starting point Spatial continuity across different projects decrease project costs, necessary labor, and duplicate sampling efforts No need to create multiple grids every time a project is funded A sampling grid provides an organized spatial extent, partitioned into sampling units needed in spatially explicit models, and is integral to research and monitoring.

Standardized grid Will improve partnership coordination at the landscape and local level to accomplish shared conservation goals Can also be used for other monitoring programs Adaptive management

GPLCC Stratification Strata based on grid attributes Primarily NRCS ecoregions, federal land ownership, and strahler order

For more details… RMBO Avian Data Center: http://www.rmbo.org/public/monitoring e.g., Monitoring the Birds of the Badlands and Prairies Bird Conservation Region (BCR 17): 2009 Field Season Report