COS 154-5 - NA-POPS: A collaborative effort to synthesize detection probabilities for North American landbirds

Wednesday, August 17, 2022

11:00 AM – 11:15 AM EDT

Location: 516A

Presenting Author(s)

BE

Brandon P. Edwards

Department of Biology, Carleton University, Ottawa, ON Canada, Canada

Coauthor(s)

AS

Adam C. Smith

Environment and Climate Change Canada
TD

Teegan D. Docherty

Department of Renewable Resources, University of Alberta, Edmonton, Canada
MG

Marcel A. Gahbauer

Environment and Climate Change Canada, Canada
CG

Caitlyn R. Gillespie

Klamath Bird Observatory, Ashland, OR, USA, United States
AG

Alexis R. Grinde

University of Minnesota, Duluth, MN, USA, United States
TH

Taylor Harmer

Environment and Climate Change Canada, Ottawa, ON, Canada, Canada
DI

David T. Iles

Environment and Climate Change Canada, Ottawa, ON, Canada, Canada
SM

Steven M. Matsuoka

Alaska Science Center, U.S. Geological Survey, Anchorage, AK, USA, United States
NM

Nicole L. Michel

Director Quantitative Science
National Audubon Society, New York City, NY, USA, United States
AM

Andrew Murray

Environment and Climate Change Canada, Ottawa, ON, Canada, Canada
GN

Gerald J. Niemi

University of Minnesota, Duluth, MN, USA, United States
JP

Jon Pasher

Environment and Climate Change Canada, Ottawa, ON, Canada, Canada
DP

David C. Pavlacky Jr

Bird Conservancy of the Rockies, Brighton, CO, USA, United States
BR

Barry G. Robinson

Environment and Climate Change Canada, Edmonton, AB, Canada, Canada
TR

T. Brandt Ryder

Bird Conservancy of the Rockies, Brighton, CO, USA, United States
PS

Péter Sólymos

University of Alberta, Department of Biology, Edmonton, AB, Canada, Canada
DS

Diana Stralberg

Research Scientist
Canadian Forest Service, Natural Resources Canada
Edmonton, Alberta, Canada
EZ

Edmund J. Zlonis

Minnesota Department of Natural Resources, Bemidji, MN, USA, United States

Background/Question/Methods

Bird monitoring in North America over several decades has generated millions of bird observations. Despite this, North America has still lost close to 2.9 billion birds since the 1970s. In a world where biodiversity monitoring data is increasing year after year, but biodiversity itself continues to decrease, methods and expertise from the world of Big Data are now needed to properly synthesize these data into one cohesive story. These datasets can be integrated to estimate bird densities, but variation in factors such as underlying field methods, timing, land cover, proximity to roads, and uneven spatial coverage must first be accounted for. The QPAD methodology was developed to specifically account for differences in bird detectability among varying survey protocols, while also allowing to account for any number of external temporal or spatial covariates. For any bird with sufficient data, the QPAD approach creates detectability offsets by considering the probability of detection (q) and availability (p) of birds in relation to area (a) and density (d). To facilitate integration across databases, we introduce NA-POPS: Point Count Offsets for Population Sizes of North American Landbirds. NA-POPS is a large-scale, multi-agency project providing an open-source database of detectability functions for all North American landbirds.

Results/Conclusions

To date, NA-POPS has compiled over 7.1 million point count observations across North America, spanning 292 individual projects comprising over 700,000 point count events. By applying the QPAD methodology to this collection of data, NA-POPS has derived detectability estimates for 338 species of North American landbirds, which accounts for nearly 75% of all landbirds that breed in North America. For each species, detectability estimates were derived by considering time of survey and day of survey, which affects a bird’s availability, and by considering forest coverage and presence of roads, which affects a bird’s probability of detection by an observer. All the detectability estimates produced by NA-POPS are freely available for use through the R package “napops”, which will allow for other researchers across North America to use these open-access estimates in their own projects to correct for detectability. This broad-scale database of detectability estimates is the first of its kind in the field of ornithology, and will pave the way for future data integration exercises to improve distribution and trend models of North American birds.