DATA HARMONIZATION METHODS AND ANALYSIS

 

Compiling and analyzing avian data from across North America is challenging because data is collected in a variety of ways. We have developed specialized statistical approaches to harmonize different datasets by correcting for survey methodology and species detectability. 

Explore these methods below:

PUBLICATIONS

Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity. 2018. Condor 120, 765–786. https://doi.org/10.1650/CONDOR-18-32.1

Phylogeny and species traits predict bird detectability. 2018. Ecography 41, 1595–1603. https://doi.org/10.1111/ecog.03415

Ecological monitoring through harmonizing existing data: Lessons from the boreal avian modelling project. 2015. Wildlife Soc B 39, 480–487. https://doi.org/10.1002/wsb.567

Reviving common standards in point-count surveys for broad inference across studies. 2014. Condor 116, 599–608. https://doi.org/10.1650/CONDOR-14-108.1

Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. 2013. Methods Ecol Evol 4, 1047–1058. https://doi.org/10.1111/2041-210X.12106

Using binomial distance-sampling models to estimate the effective detection radius of point-count surveys across boreal Canada. 2012. Auk 129, 268–282. https://doi.org/10.1525/auk.2012.11190

ADDITIONAL RESOURCES

R Packages: detect, QPAD, lhreg, paired, bSims, and more...

QPAD Book: provides material for the workshop “Analysis of point-count data in the presence of variable survey methodologies and detection error”.