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
A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity
Leveraging community science data for population assessments during a pandemic
Evaluation of a two-season banding program to estimate and model migratory bird survival
Spatial capture–recapture with random thinning for unidentified encounters
Optimal sampling design for spatial capture‐recapture
Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring
Modeling population dynamics with count data
Modeling false positives
Modeling population dynamics with multinomial count data
Spatial proximity moderates genotype uncertainty in genetic tagging studies
Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change
Science and Products
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Filter Total Items: 198
A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity
Poaching is a global driver of wildlife population decline, including inside protected areas (PAs). Reducing poaching requires an understanding of its cryptic drivers and accurately quantifying poaching scales and intensity. There is little quantification of how poaching is affected by law enforcement intensity (e.g., ranger stations) versus economic factors (e.g., unemployment), while simultaneouAuthorsMahmood Soofi, Ali T. Qashqaei, Jan-Niklas Trei, Shirko Shokri, Javad Selyari, Benjamin Ghasemi, Poorya Sepahvand, Lukas Egli, Bagher Nezami, Navid Zamani, Gholam Hosein Yusefi, Bahram H. Kiabi, Niko Balkenhol, Andy Royle, Chris R. Pavey, Steve M. Redpath, Matthias WaltertLeveraging community science data for population assessments during a pandemic
The COVID-19 pandemic has disrupted field research programs, making conservation and management decision-making more challenging. However, it may be possible to conduct population assessments using integrated models that combine community science data with existing data from structured surveys. We developed a space-time integrated model to characterize spatial and temporal variability in populatioAuthorsPaige Howell, Patrick Devers, Orin J. Robinson, Andy RoyleEvaluation of a two-season banding program to estimate and model migratory bird survival
The management of North American waterfowl is predicated on long-term, continental scale banding implemented prior to the hunting season (i.e., July–September) and subsequent reporting of bands recovered by hunters. However, single-season banding and encounter operations have a number of characteristics that limit their application to estimating demographic rates and evaluating hypothesized limitiAuthorsPatrick K. Devers, Robert L. Emmet, G. Scott Boomer, Guthrie S. Zimmerman, J. Andrew RoyleSpatial capture–recapture with random thinning for unidentified encounters
Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual idAuthorsJosé Jiménez, Ben Augustine, Daniel W. Linden, Richard B. Chandler, Andy RoyleOptimal sampling design for spatial capture‐recapture
Spatial capture‐recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remAuthorsGates Dupont, J. Andrew Royle, Muhammad Ali Nawaz, Chris SutherlandEstimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring
The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injurAuthorsRichard Bischof, Cyril Milleret, Pierre Dupont, Joseph Chipperfield, Mahdieh Tourani, Andres Ordiz, Perry de Valpine, Daniel Turek, Andy Royle, Olivier Gemenez, Oystein Flagstad, Mikael Akesson, Linn Svensson, Henrik Broseth, Jonas KindbergModeling population dynamics with count data
In this chapter, we describe models of open populations that are subject to change over time due to additions and subtractions. Additions may be in the form of recruitment and immigration, and subtractions may be in the form of mortality, emigration, or both. Conceptually, these models are described by the Birth-Immigration-Death-Emigration (BIDE) model of population dynamics (Conroy and Carroll,AuthorsMarc Kery, Andy RoyleModeling false positives
Many of the models we are concerned with included explicit descriptions of false negative errors. However, false positive errors can also be commin in practice, especially in citizen science applications where observer skill is highly variable. In addition, new methods which determine detection based on statistical classification or machine learning methods are also prone to false positive errorsAuthorsMarc Kery, Andy RoyleModeling population dynamics with multinomial count data
No abstract available.AuthorsAndy Royle, Marc KerySpatial proximity moderates genotype uncertainty in genetic tagging studies
Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified withouAuthorsBen C. Augustine, Andy Royle, Daniel W. Linden, Angela K. FullerMigratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change
Over the past half century, migratory birds in North America have shown divergent population trends relative to resident species, with the former declining rapidly and the latter increasing. The role that climate change has played in these observed trends is not well understood, despite significant warming over this period. We used 43 y of monitoring data to fit dynamic species distribution modelsAuthorsClark Rushing, Andy Royle, David Ziolkowski, Keith L. Pardieck - Software
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