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
Filter Total Items: 199
Trap configuration and spacing influences parameter estimates in spatial capture-recapture models
An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals th
Authors
Catherine C. Sun, Angela K. Fuller, J. Andrew Royle
A hierarchical nest survival model integrating incomplete temporally varying covariates
Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest surviva
Authors
Sarah J. Converse, J. Andrew Royle, Peter H. Adler, Richard P. Urbanek, Jeb A. Barzan
Spatial capture-recapture
Spatial Capture-Recapture provides a revolutionary extension of traditional capture-recapture methods for studying animal populations using data from live trapping, camera trapping, DNA sampling, acoustic sampling, and related field methods. This book is a conceptual and methodological synthesis of spatial capture-recapture modeling. As a comprehensive how-to manual, this reference contains detai
Authors
J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner
Spatially explicit models for inference about density in unmarked or partially marked populations
Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers a
Authors
Richard B. Chandler, J. Andrew Royle
Integrating resource selection information with spatial capture--recapture
1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR)
Authors
J. Andrew Royle, Richard B. Chandler, Catherine C. Sun, Angela K. Fuller
Presence-only modeling using MAXENT: when can we trust the inferences?
1. Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence
Authors
Charles B. Yackulic, Richard Chandler, Elise F. Zipkin, J. Andrew Royle, James D. Nichols, Evan H. Campbell Grant, Sophie Veran
Spatial capture-recapture models for jointly estimating population density and landscape connectivity
Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have u
Authors
J. Andrew Royle, Richard B. Chandler, Kimberly D. Gazenski, Tabitha A. Graves
Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage
Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the r
Authors
Brady J. Mattsson, Elise F. Zipkin, Beth Gardner, Peter J. Blank, John R. Sauer, J. Andrew Royle
Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach
Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at sca
Authors
Florent Bled, John R. Sauer, Keith L. Pardieck, Paul Doherty, J. Andy Royle
Large-scale monitoring of shorebird populations using count data and N-mixture models: Black Oystercatcher (Haematopus bachmani) surveys by land and sea
Large-scale monitoring of bird populations is often based on count data collected across spatial scales that may include multiple physiographic regions and habitat types. Monitoring at large spatial scales may require multiple survey platforms (e.g., from boats and land when monitoring coastal species) and multiple survey methods. It becomes especially important to explicitly account for detection
Authors
James E. Lyons, Royle J. Andrew, Susan M. Thomas, Elise Elliott-Smith, Joseph R. Evenson, Elizabeth G. Kelly, Ruth L. Milner, David R. Nysewander, Brad A. Andres
Hierarchical distance-sampling models to estimate population size and habitat-specific abundance of an island endemic
Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Ro
Authors
Scott T. Sillett, Richard B. Chandler, J. Andrew Royle, Marc Kéry, Scott A. Morrison
Estimating abundance of mountain lions from unstructured spatial sampling
Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–reca
Authors
Robin E. Russell, J. Andrew Royle, Richard Desimone, Michael K. Schwartz, Victoria L. Edwards, Kristy P. Pilgrim, Kevin S. Mckelvey
Science and Products
Filter Total Items: 199
Trap configuration and spacing influences parameter estimates in spatial capture-recapture models
An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals th
Authors
Catherine C. Sun, Angela K. Fuller, J. Andrew Royle
A hierarchical nest survival model integrating incomplete temporally varying covariates
Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest surviva
Authors
Sarah J. Converse, J. Andrew Royle, Peter H. Adler, Richard P. Urbanek, Jeb A. Barzan
Spatial capture-recapture
Spatial Capture-Recapture provides a revolutionary extension of traditional capture-recapture methods for studying animal populations using data from live trapping, camera trapping, DNA sampling, acoustic sampling, and related field methods. This book is a conceptual and methodological synthesis of spatial capture-recapture modeling. As a comprehensive how-to manual, this reference contains detai
Authors
J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner
Spatially explicit models for inference about density in unmarked or partially marked populations
Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers a
Authors
Richard B. Chandler, J. Andrew Royle
Integrating resource selection information with spatial capture--recapture
1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR)
Authors
J. Andrew Royle, Richard B. Chandler, Catherine C. Sun, Angela K. Fuller
Presence-only modeling using MAXENT: when can we trust the inferences?
1. Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence
Authors
Charles B. Yackulic, Richard Chandler, Elise F. Zipkin, J. Andrew Royle, James D. Nichols, Evan H. Campbell Grant, Sophie Veran
Spatial capture-recapture models for jointly estimating population density and landscape connectivity
Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have u
Authors
J. Andrew Royle, Richard B. Chandler, Kimberly D. Gazenski, Tabitha A. Graves
Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage
Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the r
Authors
Brady J. Mattsson, Elise F. Zipkin, Beth Gardner, Peter J. Blank, John R. Sauer, J. Andrew Royle
Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach
Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at sca
Authors
Florent Bled, John R. Sauer, Keith L. Pardieck, Paul Doherty, J. Andy Royle
Large-scale monitoring of shorebird populations using count data and N-mixture models: Black Oystercatcher (Haematopus bachmani) surveys by land and sea
Large-scale monitoring of bird populations is often based on count data collected across spatial scales that may include multiple physiographic regions and habitat types. Monitoring at large spatial scales may require multiple survey platforms (e.g., from boats and land when monitoring coastal species) and multiple survey methods. It becomes especially important to explicitly account for detection
Authors
James E. Lyons, Royle J. Andrew, Susan M. Thomas, Elise Elliott-Smith, Joseph R. Evenson, Elizabeth G. Kelly, Ruth L. Milner, David R. Nysewander, Brad A. Andres
Hierarchical distance-sampling models to estimate population size and habitat-specific abundance of an island endemic
Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Ro
Authors
Scott T. Sillett, Richard B. Chandler, J. Andrew Royle, Marc Kéry, Scott A. Morrison
Estimating abundance of mountain lions from unstructured spatial sampling
Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–reca
Authors
Robin E. Russell, J. Andrew Royle, Richard Desimone, Michael K. Schwartz, Victoria L. Edwards, Kristy P. Pilgrim, Kevin S. Mckelvey