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
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...
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 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. 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 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. 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 Examining the occupancy–density relationship for a low-density carnivore
The challenges associated with monitoring low-density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non-invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially...
Linden, Daniel W.; Fuller, Angela K.; Royle, J. Andrew; Hare, Matthew P. Concepts: Assessing tiger population dynamics using capture–recapture sampling
Capture-recapture can be viewed as an animal survey method in which the count statistic is the total number of animals caught, and the associated detection probability is the probability of capture.
Royle, J. Andrew; Gopalaswamy, Arjun M.; Dorazio, Robert; Nichols, James D.; Jathanna, Devcharan; Parameshwaran, Ravishankar