References.bib

@article{Miller2019,
author = {Miller, David A. W. and Pacifici, Krishna and Sanderlin, Jamie S. and Reich, Brian J.},
title = {The recent past and promising future for data integration methods to estimate species’ distributions},
journal = {Methods in Ecology and Evolution},
volume = {10},
number = {1},
pages = {22-37},
keywords = {data fusion, integrated distribution model, joint likelihood, spatial point process, species distribution modelling},
doi = {10.1111/2041-210X.13110},
url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13110},
eprint = {https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13110},
abstract = {Abstract With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. Key to recent advances has been using point-process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.},
year = {2019}
}

@Manual{Spocc,
    title = {spocc: Interface to Species Occurrence Data Sources},
    author = {Scott Chamberlain},
    year = {2018},
    note = {R package version 0.9.0},
    url = {https://CRAN.R-project.org/package=spocc}
}
    
@Manual{NABBS,
    title = {North American Breeding Bird Survey Dataset 1966 - 2017, version 2017.0.},
    author = {Pardieck, K.L., D.J. Ziolkowski Jr., M. Lutmerding and M.-A.R. Hudson. },
    year = {2018},
    institution = {U.S. Geological Survey, Patuxent Wildlife Research Center},
    url = {https://doi.org/10.5066/F76972V8}
}

@Manual{elevatr,
    author = {Jeffrey Hollister and Tarak Shah},
    title = {elevatr: Access Elevation Data from Various APIs},
    year = {2017},
    note = {R package version 0.1.3, doi:10.5281/zenodo.400259},
    url = {http://github.com/usepa/elevatr},
}

@Manual{FedData,
    title = {FedData: Functions to Automate Downloading Geospatial Data Available from
Several Federated Data Sources},
    author = {R. Kyle Bocinsky},
    year = {2019},
    note = {R package version 2.5.7},
    url = {https://CRAN.R-project.org/package=FedData},
}

@Manual{censusapi,
    title = {censusapi: Retrieve Data from the Census APIs},
    author = {Hannah Recht},
    year = {2019},
    note = {R package version 0.6.0},
    url = {https://CRAN.R-project.org/package=censusapi},
}