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).
Science and Products
READI-Net: Transitioning eDNA aquatic invasive species surveillance from research to actionable science
USGS researchers are working with the Monterey Bay Aquarium Research Institute to optimize autonomous, robotic samplers for detection of DNA fragments shed by biological threats (BT; invasive species, parasites, pathogens) in our nation’s waters. Finding DNA fragments (a method known as environmental DNA sampling) produced by an emerging BT in water is akin to finding a needle in a haystack—many...
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Ecosystems Mission Area, Biological Threats and Invasive Species Research Program, Columbia Environmental Research Center, Eastern Ecological Science Center, Forest and Rangeland Ecosystem Science Center, New York Water Science Center, Northern Rocky Mountain Science Center, Upper Midwest Environmental Sciences Center, Wetland and Aquatic Research Center , Wyoming-Montana Water Science Center, Pacific Northwest Environmental DNA Laboratory
Quantitative Turtle Analysis Project: Machine learning with turtles
The Quantitative Turtle Analysis Project (QTAP) was created in 2019 with the goal of investigating how machine learning can be used to study wildlife populations using capture-recapture methods. QTAP has specifically been researching how digital images of the eastern box turtle (Terrapene carolina carolina) can be used by automated programs to recognize unique individual turtles, in place of a...
Enabling AI for citizen science in fish ecology
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This proje
Terrestrial wildlife and legacy oil mining on National Wildlife Refuges
Amphibian surveys are being conducted on select National Wildlife Refuges with active and/or legacy oil mining to determine species relative distribution and their risk to short- and long-term effects from exposure to crude oil and its byproducts.
Capture-recapture meets big data: integrating statistical classification with ecological models of species abundance and occurrence
Advances in new technologies such as remote cameras, noninvasive genetics and bioacoustics provide massive quantities of electronic data. Much work has been done on automated (“machine learning”) methods of classification which produce “sample class designations” (e.g., identification of species or individuals) that are regarded as observed data in ecological models. However, these “data” are actu...
Spatio-Temporal Statistical Models for Forecasting Climate Change Effects on Bird Distribution
Ecological indicators of climate change are needed to measure concurrent changes in ecological systems, inform management decisions, and forecast the consequences of climate change. We seek to develop robust bird-based, climate-change indicators using North American Breeding Bird Survey data.
Hierarchical Models of Animal Abundance and Occurrence
The Challenge: Research goals of this project are to develop models, statistical methods, sampling strategies and tools for inference about animal population status from survey data. Survey data are always subject to a number of observation processes that induce bias and error. In particular, inferences are based on spatial sampling – we can only ever sample a subset of locations where species...
Spatial Capture-Recapture Models to Estimate Abundance and Density of Animal Populations
The Challenge: For decades, capture-recapture methods have been the cornerstone of ecological statistics as applied to population biology. While capture-recapture has become the standard sampling and analytical framework for the study of population processes (Williams, Nichols & Conroy 2002) it has advanced independent of and remained unconnected to the spatial structure of the population or the...
Modeling species response to environmental change: development of integrated, scalable Bayesian models of population persistence
Estimating species response to environmental change is a key challenge for ecologists and a core mission of the USGS. Effective forecasting of species response requires models that are detailed enough to capture critical processes and at the same time general enough to allow broad application. This tradeoff is difficult to reconcile with most existing methods. We propose to extend and combine exis
SERAP: Assessment of Climate and Land Use Change Impacts on Terrestrial Species
Researchers from North Carolina State University and the USGS integrated models of urbanization and vegetation dynamics with the regional climate models to predict vegetation dynamics and assess how landscape change could impact priority species, including North American land birds. This integrated ensemble of models can be used to predict locations where responses to climate change are most lik
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READI-Net: Transitioning eDNA aquatic invasive species surveillance from research to actionable science
USGS researchers are working with the Monterey Bay Aquarium Research Institute to optimize autonomous, robotic samplers for detection of DNA fragments shed by biological threats (BT; invasive species, parasites, pathogens) in our nation’s waters. Finding DNA fragments (a method known as environmental DNA sampling) produced by an emerging BT in water is akin to finding a needle in a haystack—many...ByEcosystems Mission Area, Biological Threats and Invasive Species Research Program, Columbia Environmental Research Center, Eastern Ecological Science Center, Forest and Rangeland Ecosystem Science Center, New York Water Science Center, Northern Rocky Mountain Science Center, Upper Midwest Environmental Sciences Center, Wetland and Aquatic Research Center , Wyoming-Montana Water Science Center, Pacific Northwest Environmental DNA LaboratoryQuantitative Turtle Analysis Project: Machine learning with turtles
The Quantitative Turtle Analysis Project (QTAP) was created in 2019 with the goal of investigating how machine learning can be used to study wildlife populations using capture-recapture methods. QTAP has specifically been researching how digital images of the eastern box turtle (Terrapene carolina carolina) can be used by automated programs to recognize unique individual turtles, in place of a...Enabling AI for citizen science in fish ecology
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This projeTerrestrial wildlife and legacy oil mining on National Wildlife Refuges
Amphibian surveys are being conducted on select National Wildlife Refuges with active and/or legacy oil mining to determine species relative distribution and their risk to short- and long-term effects from exposure to crude oil and its byproducts.Capture-recapture meets big data: integrating statistical classification with ecological models of species abundance and occurrence
Advances in new technologies such as remote cameras, noninvasive genetics and bioacoustics provide massive quantities of electronic data. Much work has been done on automated (“machine learning”) methods of classification which produce “sample class designations” (e.g., identification of species or individuals) that are regarded as observed data in ecological models. However, these “data” are actu...Spatio-Temporal Statistical Models for Forecasting Climate Change Effects on Bird Distribution
Ecological indicators of climate change are needed to measure concurrent changes in ecological systems, inform management decisions, and forecast the consequences of climate change. We seek to develop robust bird-based, climate-change indicators using North American Breeding Bird Survey data.Hierarchical Models of Animal Abundance and Occurrence
The Challenge: Research goals of this project are to develop models, statistical methods, sampling strategies and tools for inference about animal population status from survey data. Survey data are always subject to a number of observation processes that induce bias and error. In particular, inferences are based on spatial sampling – we can only ever sample a subset of locations where species...Spatial Capture-Recapture Models to Estimate Abundance and Density of Animal Populations
The Challenge: For decades, capture-recapture methods have been the cornerstone of ecological statistics as applied to population biology. While capture-recapture has become the standard sampling and analytical framework for the study of population processes (Williams, Nichols & Conroy 2002) it has advanced independent of and remained unconnected to the spatial structure of the population or the...Modeling species response to environmental change: development of integrated, scalable Bayesian models of population persistence
Estimating species response to environmental change is a key challenge for ecologists and a core mission of the USGS. Effective forecasting of species response requires models that are detailed enough to capture critical processes and at the same time general enough to allow broad application. This tradeoff is difficult to reconcile with most existing methods. We propose to extend and combine exisSERAP: Assessment of Climate and Land Use Change Impacts on Terrestrial Species
Researchers from North Carolina State University and the USGS integrated models of urbanization and vegetation dynamics with the regional climate models to predict vegetation dynamics and assess how landscape change could impact priority species, including North American land birds. This integrated ensemble of models can be used to predict locations where responses to climate change are most lik - Data
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