Andy Royle, Ph.D.
Andy Royle is a Research Statistician at the Eastern Ecological Science Center in Laurel, MD.
Andy Royle is a Senior Scientist at USGS Eastern Ecological Science Center (Patuxent). At EESC, he is engaged in the development of statistical methods and analytic tools for animal demographic modeling, statistical inference and sampling wildlife populations and communities. His current research is focused on hierarchical models of animal abundance and occurrence, and the development of spatial capture-recapture methods and applications. He has authored or coauthored 6 books on quantitative analysis in ecology including the recent book Applied Hierarchical Models Vols. 1 and 2 (2016 and 2021, with Marc Kéry).
Professional Experience
Statistician (1998-2004) for the U.S. FWS in the Migratory Bird Management Office where he worked primarily on waterfowl surveys and monitoring projects
visiting scientist in the Geophysical Statistics Project at the National Center for Atmospheric Research, Boulder, CO.
Education and Certifications
PhD in Statistics (1996) from North Carolina State University
BS in Fisheries and Wildlife (1990) from Michigan State University
Science and Products
Demographic analysis from summaries of an age-structured population
Distribution, abundance, and habitat affinities of the Coastal Plain Swamp Sparrow
Mixture models for estimating the size of a closed population when capture rates vary among individuals
Random effects and shrinkage estimation in capture-recapture models
Statistical mapping of count survey data
Estimating site occupancy rates when detection probabilities are less than one
Science and Products
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Demographic analysis from summaries of an age-structured population
Demographic analyses of age-structured populations typically rely on life history data for individuals, or when individual animals are not identified, on information about the numbers of individuals in each age class through time. While it is usually difficult to determine the age class of a randomly encountered individual, it is often the case that the individual can be readily and reliably assigAuthorsWilliam A. Link, J. Andrew Royle, Jeff S. HatfieldDistribution, abundance, and habitat affinities of the Coastal Plain Swamp Sparrow
We examined the distribution and abundance of the Coastal Plain Swamp Sparrow (Melospiza georgiana nigrescens) at previously occupied sites and points within potential habitat. We found Swamp Sparrows throughout their formerly documented range except in southern Chesapeake Bay. Swamp Sparrows were most common in the Mullica River region of New Jersey where we detected individuals at 78% of systeAuthorsJ. Beadell, R. Greenberg, Sam Droege, J. Andrew RoyleMixture models for estimating the size of a closed population when capture rates vary among individuals
Abstract not supplied at this timeAuthorsR.M. Dorazio, J. Andrew RoyleRandom effects and shrinkage estimation in capture-recapture models
We discuss the analysis of random effects in capture-recapture models, and outline Bayesian and frequentists approaches to their analysis. Under a normal model, random effects estimators derived from Bayesian or frequentist considerations have a common form as shrinkage estimators. We discuss some of the difficulties of analysing random effects using traditional methods, and argue that a BayesiaAuthorsJ. Andrew Royle, W. A. LinkStatistical mapping of count survey data
We apply a Poisson mixed model to the problem of mapping (or predicting) bird relative abundance from counts collected from the North American Breeding Bird Survey (BBS). The model expresses the logarithm of the Poisson mean as a sum of a fixed term (which may depend on habitat variables) and a random effect which accounts for remaining unexplained variation. The random effect is assumed to be sAuthorsJ. Andrew Royle, W. A. Link, J.R. SauerEstimating site occupancy rates when detection probabilities are less than one
Nondetection of a species at a site does not imply that the species is absent unless the probability of detection is 1. We propose a model and likelihood-based method for estimating site occupancy rates when detection probabilities are < 1. The model provides a flexible framework enabling covariate information to be included and allowing for missing observations. Via computer simulation, we foundAuthorsD.I. MacKenzie, J. D. Nichols, G.B. Lachman, Sam Droege, J. Andrew Royle, C.A. Langtimm - Software
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