Andy Royle, Ph.D.
Biography
Andy Royle has been with Patuxent Wildlife Research Center since 2004. Before that he was a statistician (1998-2004) for the U.S. FWS in the Migratory Bird Management Office where he worked primarily on waterfowl surveys and monitoring projects. Prior to that he was a visiting scientist in the Geophysical Statistics Project at the National Center for Atmospheric Reserach, Boulder, CO. He has a PhD in Statistics (1996) from North Carolina State University and a BS in Fisheries and Wildlife (1990) from Michigan State University.
Science and Products
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...
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...
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...
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...
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...
Spatial 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...
Jiménez, José; Augustine, Ben; Linden, Daniel W.; Chandler, Richard B.; Royle, Andy Optimal 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...
Dupont, Gates; Royle, J. Andrew; Nawaz, Muhammad Ali; Sutherland, Chris Foreword
No abstract available.
Royle, J. Andrew Modeling 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...
Kery, Marc; Royle, Andy Modeling 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...
Kery, Marc; Royle, Andy Modeling population dynamics with multinomial count data
No abstract available.
Royle, Andy; Kery, Marc Spatial 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...
Augustine, Ben C.; Royle, Andy; Linden, Daniel W.; Fuller, Angela K. Migratory 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...
Clark Rushing; Royle, Andy; Ziolkowski, David; Pardieck, Keith L. Movement-assisted localization from acoustic telemetry data
Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires...
Hostetter, Nathan J.; Royle, Andy Consequences of ignoring group association in spatial capture-recapture analysis
Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the...
Bischof, Richard; Dupont, Pierre; Milleret, Cyril; Chipperfield, Joseph; Royle, J. Andrew Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes
There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a...
Rappaport, Danielle I.; Royle, J. Andrew; Morton, Douglas C. Integrating side-scan sonar and acoustic telemetry to estimate the annual spawning run size of Atlantic sturgeon in the Hudson River
There is considerable interest in evaluating the status and trends of sturgeon populations, yet many traditional approaches to estimating the abundance of fishes are intractable due to their biology and rarity. Side-scan sonar has recently emerged as an effective tool for censusing sturgeon in rivers, yet challenges remain for censusing open...
Kazyak, David; Flowers, Amy M; Hostetter, Nathan J.; Madsen, John A; Breece, Matthew W.; Higgs, Amanda; Brown, Lori M.; Royle, Andy; Fox, Dewayne A.
PROGRAM SPACECAP
A Program to Estimate Animal Abundance and Density using Spatially-Explicit Capture-Recapture