What is it?

We develop specialized statistical approaches to harmonize different datasets by correcting for survey methodology and species detectability. 

 

Compiling and analyzing avian data from across North America is challenging because data is collected in a variety of ways. We have conducted research on how various data sets differ and developed specialized statistical approaches to harmonize different datasets by correcting for survey methodology and species detectability. These methods are intended for researchers who have to analyze multiple field observations and who are often confronted with data heterogeneity due to variability in sampling protocols and detection errors. These methods are also suitable for researchers trying to combine ‘legacy’ datasets with new data collected by acoustic recording units or point counts.

R Packages

BAM develops and contributes to R packages for handling and analyzing avian data, including the implementation of our data integration approaches. 

QPAD: Calibrating indices of avian density from non-standardized survey data
The analysis of large heterogeneous data sets of avian point-count surveys compiled across studies is hindered by a lack of analytical approaches that can…

R Package

Phylogeny and species trait effects on detectability
The lhreg R extension package is a supporting material for the manuscript by Solymos et al. (2018).  The package contains the (1) data, (2) analysis code used in the manuscript, and (3) code required to summarize the results and produce tables and figures.

R Package

paired:  example point count data set with paired human and ARU data
Example point count data set with paired human and ARU data

R Package

detect: for analyzing wildlife data with detection error
Models for analyzing site occupancy and count data models with detection error, including single-visit based models, conditional distance sampling and time-removal models.

R Package

bSims – bird point count simulator
A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it.

R Package

Custom reporting for intactness and sector effects.
Custom Reporting for Intactness and Sector Effects

R Package

mefa4: multivariate data handling with S4 classes and sparse matrices
An S4 update of the ‘mefa’ package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.

R Package

Publications

Publications about data integration and detection probability.