Andy Royle, Ph.D.
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
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 occur --and imperfect detection – species or individuals might go undetected in the sample. Principles of hierarchical modeling can be applied directly to accommodate both features of ecological data. Prior to the development of hierarchical models at PWRC, studies of unmarked populations focused on simplistic descriptions of distribution patterns and temporal trends. Hierarchical models have advanced the field of population ecology by enabling the estimation of demographic and movement parameters that previously could only be obtained using costly field methods. Ecologists can now make inferences about population dynamics at broad spatial and temporal scales using models designed specifically for this task.
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 landscape within which populations exist. Furthermore, capture-recapture does not invoke any spatially explicit biological processes and thus is distinctly non-spatial, accounting neither for the inherent spatial nature of the sampling nor of the spatial distribution of individual encounters. Linking observed encounter histories of individuals to mechanisms of spatial population ecology will enable ecologists to study these processes using new technologies such as noninvasive genetics, remote cameras and bioacoustic sampling (Figure 1 under the Science Tab).
Concepts and practices: Estimating abundance of prey species using hierarchical model-based approaches
Tigers predominantly prey on large ungulate species, such as sambar (Cervus unicolor), red deer (Cervus elaphus), gaur (Bos gaurus), banteng (Bos javanicus), chital (Axis axis), muntjac (Muntiacus muntjak), wild pig (Sus scrofa), and bearded pig (Sus barbatus). The density of a tiger population is strongly correlated with the density of such prey...Dorazio, Robert; Kumar, N. Samba; Royle, Andy; Gopalaswamy, Arjun M.
Living on the edge: Opportunities for Amur tiger recovery in China
Sporadic sightings of the endangered Amur tiger Panthera tigris altaica along the China-Russia border during the late 1990s sparked efforts to expand this subspecies distribution and abundance by restoring potentially suitable habitats in the Changbai Mountains. To guide science-based recovery efforts and provide a baseline for future monitoring...Wang, Tianming; Royle, Andy; Smith, J.L.D.; Zou, Liang; Lu, Xinyue; Li, Tong; Yang, Haitao; Li, Zhilin; Feng, Rongna; Bian, Yajing; Feng, Limin; Ge, Jianping
Spatially explicit dynamic N-mixture models
Knowledge of demographic parameters such as survival, reproduction, emigration, and immigration is essential to understand metapopulation dynamics. Traditionally the estimation of these demographic parameters requires intensive data from marked animals. The development of dynamic N-mixture models makes it possible to estimate demographic...Zhao, Qing; Royle, Andy; Boomer, G. Scott
Model-based approaches to deal with detectability: a comment on Hutto (2016)
In a recent paper, Hutto (2016a) challenges the need to account for detectability when interpreting data from point counts. A number of issues with model-based approaches to deal with detectability are presented, and an alternative suggested: surveying an area around each point over which detectability is assumed certain. The article contains a...Marques, Tiago A.; Thomas, Len; Kéry, Marc; Buckland, Steve T.; Borchers, David L.; Rexstad, Eric; Fewster, Rachel M.; MacKenzie, Darryl I.; Royle, Andy; Guillera-Arroita, Gurutzeta; Handel, Colleen M.; Pavlacky, David C. ; Camp, Richard J.
Model-based estimators of density and connectivity to inform conservation of spatially structured populations
Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter...Morin, Dana J.; Fuller, Angela K.; Royle, J. Andrew; Sutherland, Chris
Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors
Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for...Steenweg, Robin; Hebblewhite, Mark; Kays, Roland; Ahumada, Jorge A.; Fisher, Jason T.; Burton, Cole; Townsend, Susan E.; Carbone, Chris; Rowcliffe, J. Marcus; Whittington, Jesse; Brodie, Jedediah; Royle, Andy; Switalski, Adam; Clevenger, Anthony P.; Heim, Nicole; Rich, Lindsey N.
Incorporating imperfect detection into joint models of communites: A response to Warton et al.
Warton et al.  advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of...Beissinger, Steven R.; Iknayan, Kelly J.; Guillera-Arroita, Gurutzeta; Zipkin, Elise; Dorazio, Robert; Royle, Andy; Kery, Marc
Integrating occurrence and detectability patterns based on interview data: a case study for threatened mammals in Equatorial Guinea
Occurrence models that account for imperfect detection of species are increasingly used for estimating geographical range, for determining species-landscape relations and to prioritize conservation actions worldwide. In 2010, we conducted a large-scale survey in Río Muni, the mainland territory of Equatorial Guinea, which aimed to estimate the...Martínez-Martí, Chele; Jiménez-Franco, María V.; Royle, J. Andrew; Palazón, José A.; Calvo, José F.
Southeast regional and state trends in anuran occupancy from calling survey data (2001-2013) from the North American Amphibian Monitoring Program
We present the first regional trends in anuran occupancy for eight states of the southeastern United States, based on 13 y (2001–2013) of North American Amphibian Monitoring Program (NAAMP) data. The NAAMP is a longterm monitoring program in which observers collect anuran calling observation data at fixed locations along random roadside routes. We...Villena Carpio, Oswaldo; Royle, J. Andrew; Weir, Linda; Foreman, Tasha M.; Gazenski, Kimberly D.; Campbell Grant, Evan H.
Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling
Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical...Yamaura, Yuichi; Connor, Edward F.; Royle, Andy; Itoh, Katsuo; Sato, Kiyoshi; Taki, Hisatomo; Mishima, Yoshio
Management decision making for fisher populations informed by occupancy modeling
Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season,...Fuller, Angela K.; Linden, Daniel W.; Royle, J. Andrew
Estimating population density and connectivity of American mink using spatial capture-recapture
Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model...Fuller, Angela K.; Sutherland, Christopher S.; Royle, Andy; Hare, Matthew P.