title: Integrating multiple data sources in species distribution modeling: a framework for data fusion.
abstract: The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.
authors.affiliation: Department of Forestry and Environmental Resources, Program in Fisheries, Wildlife, and Conservation Biology, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Krishna
authors.initials: K
authors.lastname: Pacifici
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Reich
authors.affiliation: Department of Ecosystem Science and Management, Pennsylvania State University, University Park, Pennsylvania, 16802, USA.
authors.forename: David A W
authors.initials: DA
authors.lastname: Miller
authors.affiliation: School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, 98195, USA.
authors.forename: Beth
authors.initials: B
authors.lastname: Gardner
authors.affiliation: Department of Ecosystem Science and Management, Pennsylvania State University, University Park, Pennsylvania, 16802, USA.
authors.forename: Glenn
authors.initials: G
authors.lastname: Stauffer
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Susheela
authors.initials: S
authors.lastname: Singh
authors.affiliation: U.S. Geological Survey, Core Science Systems, Biodiversity and Spatial Information Center, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Alexa
authors.initials: A
authors.lastname: McKerrow
authors.affiliation: U.S. Geological Survey, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology, North Carolina State University, Raleigh, North Carolina, 27695, USA.
authors.forename: Jaime A
authors.initials: JA
authors.lastname: Collazo
mesh_terms: D004463:Ecology; D016247:Information Storage and Retrieval; D008962:Models, Theoretical; D062206:Spatial Analysis
keywords: Brown-headed nuthatch; data fusion; multivariate conditional autoregressive; species distribution modeling