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
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.
Mammals
Our science portfolio includes the study and management of mammal populations, which can require the use of methods and analysis that incorporate the difficulty in detecting them – picture how hard it is to count and identify bats at dusk or estimate the number of mountain lions in an area.
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...
Estimating spatially and temporally complex range dynamics when detection is imperfect
Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we...
Rushing, Clark S.; Royle, J. Andrew; Ziolkowski, David; Pardieck, Keith L. Genetic tagging in the Anthropocene: Scaling ecology from alleles to ecosystems
The Anthropocene is an era of marked human impact on the world. Quantifying these impacts 51 has become central to understanding the dynamics of coupled human-natural systems, resource52 dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to 53 quantify the size, distribution, and trajectory of wild...
Clayton T Lamb; Adam T Ford; Michael Proctor; Royle, Andy; Garth Mowat oSCR: A spatial capture–recapture R package for inference about spatial ecological processes
Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly...
Chris Sutherland; Royle, J. Andrew; Dan Linden Integrated modeling reveals shifts in waterfowl population dynamics under climate change
1. Climate change has been identified as one of the most important drivers of wildlife populations. The development of appropriate conservation strategies relies on reliable predictions of population responses to climate change, which require in-depth understanding of the complex relationships between climate and population dynamics through...
Qing Zhao; Scott Boomer; Royle, Andy Dynamic N-mixture models with temporal variability in detection probability
In theory parameters of dynamic N-mixture models can be estimated with multiple years of data without the robust design under the assumption of constant detection probability. However, such an assumption can rarely be met in long-term studies, and the consequences of violating this assumption in the inferences of dynamic N-mixture models have not...
Zhao, Qing; Royle, J. Andrew Importance of riparian forest corridors for the ocelot in agricultural landscapes
Worldwide, private lands have attracted increased attention from conservationists, not only because most of the globe is privately owned, but also because private lands can be an asset to the protected area conservation strategy. In Brazil, the riverine Areas of Permanent Protection (APPs) is a key instrument of the Forest Code to protect native...
Roberta Paolino; Royle, Andy; Natalia Versiani; Thiago F. Rodrigues; Nielson Pasqualotto; Victor Krepschi; Adriano Chiarello Observer-free experimental evaluation of habitat and distance effects on the detection of anuran and bird vocalizations
Acoustic surveys of vocalizing animals are conducted to determine density, distribution, and diversity. Acoustic surveys are traditionally performed by human listeners, but automated recording devices (ARD) are becoming increasingly popular. Signal strength decays, or attenuates, with increasing distance between source and receiver and some...
MacLaren, Andrew R.; Crump, Paul S.; Royle, J. Andrew; Forstner, Michael R. J. Eco‐evolutionary rescue promotes host–pathogen coexistence
Emerging infectious pathogens are responsible for some of the most severe host mass mortality events in wild populations. Yet, effective pathogen control strategies are notoriously difficult to identify, in part because quantifying and forecasting pathogen spread and disease dynamics is challenging. Following an outbreak, hosts must cope with the...
DiRenzo, Graziella V.; Zipkin, Elise F.; Campbell Grant, Evan H.; Royle, J. Andrew; Longo, Ana V.; Zamudio, Kelly R.; Lips, Karen R. Modelling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations
Acoustic sampling methods are becoming increasingly important in biological monitoring. Sound attenuation is one of the most important dynamics affecting the utility of acoustic data as it directly affects the probability of detection of individuals by acoustic sensor arrays and especially the localization of acoustic signals necessary in...
Royle, J. Andrew Using partial aggregation in spatial capture recapture
Spatial capture–recapture (SCR) models are commonly used for analysing data collected using noninvasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g., centre of grid cells) is an option to...
Milleret, Cyril; Dupont, Pierre; Broseth, Henrik; Kindberg, Jonas; Royle, J. Andrew; Bischof, Richard Occupancy in community-level studies
Another type of multi-species studies, are those focused on community-level metrics such as species richness. In this chapter we detail how some of the single-species occupancy models described in earlier chapters have been applied, or extended, for use in such studies, while accounting for imperfect detection. We highlight how Bayesian methods...
MacKenzie, Darryl I.; Nichols, James D.; Royle, Andy; Pollock, Kenneth H.; Bailey, Larissa L.; Hines, James E. Spatial capture–recapture with partial identity: An application to camera traps
Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and, while not previously recognized, introducing bias. Here, we...
Augustine, Ben C.; Royle, J. Andrew; Kelly, Marcella J.; Satter, Christopher B.; Alonso, Robert S.; Boydston, Erin E.; Crooks, Kevin R.