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J. Andrew Royle

Publications and source records attributed to J. Andrew Royle.

At least 181 records · Page 10Linked to original sources
USGS2007

Evaluation of the status of anurans on a refuge in suburban Maryland

Because many anurans have well-defined breeding seasons and male anurans produce loud advertisement calls, surveys of these breeding choruses are believed to provide a dependable means of monitoring population trends. The Patuxent Research Refuge initiated such a calling survey in the spring of 1997, which uses volunteers to collect anuran (frog and toad) calling survey data. The primary goal of initiating the calling surveys at the Patuxent Refuge was to obtain baseline information on anuran populations, such as species occurrence, frequency of occurrence, and relative abundance over time. In this paper, we used the calling survey data to develop models for the "proportion of area occupied" by individual anuran species, a method in which analysis is focused on the proportion of sites that are occupied by a species, instead of the number of individuals present in the population. This type of analysis is ideal for use in large-scale monitoring programs focused on species that are difficult to count, such as anurans or birds. We considered models for proportion of area occupied that allow for imperfect detection (that is, a species may be present but go undetected during sampling) by incorporating parameters that describe detection probability and the response of detection probability to various environmental and sampling covariates. Our results indicate that anuran populations on the Patuxent Research Refuge have high rates of occupancy compared to areas nearby and that extinction and colonization rates are stable. The potential uses for "proportion of area occupied" analyses are far-reaching and will allow for more accurate quantification of data and better-informed management decisions for calling surveys on a larger scale.

Maryland↗
USGS2007

Making great leaps forward: Accounting for detectability in herpetological field studies

Detecting individuals of amphibian and reptile species can be a daunting task. Detection can be hindered by various factors such as cryptic behavior, color patterns, or observer experience. These factors complicate the estimation of state variables of interest (e.g., abundance, occupancy, species richness) as well as the vital rates that induce changes in these state variables (e.g., survival probabilities for abundance; extinction probabilities for occupancy). Although ad hoc methods (e.g., counts uncorrected for detection, return rates) typically perform poorly in the face of no detection, they continue to be used extensively in various fields, including herpetology. However, formal approaches that estimate and account for the probability of detection, such as capture-mark-recapture (CMR) methods and distance sampling, are available. In this paper, we present classical approaches and recent advances in methods accounting for detectability that are particularly pertinent for herpetological data sets. Through examples, we illustrate the use of several methods, discuss their performance compared to that of ad hoc methods, and we suggest available software to perform these analyses. The methods we discuss control for imperfect detection and reduce bias in estimates of demographic parameters such as population size, survival, or, at other levels of biological organization, species occurrence. Among these methods, recently developed approaches that no longer require marked or resighted individuals should be particularly of interest to field herpetologists. We hope that our effort will encourage practitioners to implement some of the estimation methods presented herein instead of relying on ad hoc methods that make more limiting assumptions.

Journal of Herpetology↗
USGS2007

A Bayesian state-space formulation of dynamic occupancy models

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.

Ecology↗
USGS2007

Hierarchical spatial models of abundance and occurrence from imperfect survey data

Many estimation and inference problems arising from large-scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate ('census') all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the Population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non-detection is necessary to resolve important spatial inference problems based on animal survey data. In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size, N(s). We augment the observation model with a spatial process model for N(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model-based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence. We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

Ecological Monographs↗
USGS2007

Analysis of multinomial models with unknown index using data augmentation

Multinomial models with unknown index ('sample size') arise in many practical settings. In practice, Bayesian analysis of such models has proved difficult because the dimension of the parameter space is not fixed, being in some cases a function of the unknown index. We describe a data augmentation approach to the analysis of this class of models that provides for a generic and efficient Bayesian implementation. Under this approach, the data are augmented with all-zero detection histories. The resulting augmented dataset is modeled as a zero-inflated version of the complete-data model where an estimable zero-inflation parameter takes the place of the unknown multinomial index. Interestingly, data augmentation can be justified as being equivalent to imposing a discrete uniform prior on the multinomial index. We provide three examples involving estimating the size of an animal population, estimating the number of diabetes cases in a population using the Rasch model, and the motivating example of estimating the number of species in an animal community with latent probabilities of species occurrence and detection.

Journal of Computational and Graphical Statistics↗
USGS2006

Occupancy Estimation and Modeling : Inferring Patterns and Dynamics of Species Occurrence

This is the first book to examine the latest methods in analyzing presence/absence data surveys. Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models. It provides authoritative insights into the latest in estimation modeling; discusses multiple models which lay the groundwork for future study designs; addresses critical issues of imperfect detectibility and its effects on estimation; and explores the role of probability in estimating in detail.

Book↗
USGS2006

Migratory connectivity of a widely distributed songbird, the American redstart (Setophaga ruticilla)

Determining the degree of connectivity between breeding and wintering populations is critical for understanding the ecology and evolution of migratory systems. We analyzed stable hydrogen isotopic compositions in tail feathers ($Dw) collected from 26 sites in 11 countries throughout the wintering range of the American Redstart (Setophaga ruticilla), a Nearctic- Neotropical migratory passerine bird. Feathers were assumed to have molted on the breeding grounds, and $Dw was used to estimate breeding origin. Values of $Dw were highly correlated with longitude of sampling location, indicating that breeding populations were generally distributed along the east-west axis of the wintering grounds. Within the Caribbean region, Florida, and Bahamas, $Dw values were negatively correlated with winter latitude, which suggests that American Redstarts exhibit a pattern of chain migration in which individuals wintering at northern latitudes are also the most northern breeders. To identify the most probable breeding regions, we used a likelihood-assignment test incorporated with a prior probability of breeding abundance using Bayes?s rule. Expected $D values of feathers from five breeding regions were based on interpolated $D values from a model of continent-wide growing-season $D values in precipitation ($Dp) and were adjusted to account for a discrimination factor between precipitation and feathers. At most wintering locations, breeding assignments were significantly different from expected frequencies based on relative breeding abundance. Birds wintering in eastern and western Mexico had a high probability of breeding in northwest and midwest North America, whereas birds in the Greater and Lesser Antilles were likely to have originated from breeding regions in the northeast and southeast, respectively. Migratory connectivity, such as we report here, implies that the dynamics of breeding and nonbreeding populations may be linked at a regional scale. These results provide a key opportunity for studying the year-round ecology and evolution of spatially connected populations in a migratory species.

Book chapter↗
USGS2006

Site occupancy models with heterogeneous detection probabilities

Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these ?site occupancy? models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability (p). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p. For any mixing distribution, the likelihood has the general form of a zero-inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link (2003, Biometrics 59, 1123?1130) demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs.

Biometrics↗
USGS2006

Generalized site occupancy models allowing for false positive and false negative errors

Site occupancy models have been developed that allow for imperfect species detection or ?false negative? observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that ?false positive? errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.

Ecology↗
USGS2006

Estimating species richness and accumulation by modeling species occurrence and detectability

A statistical model is developed for estimating species richness and accumulation by formulating these community-level attributes as functions of model-based estimators of species occurrence while accounting for imperfect detection of individual species. The model requires a sampling protocol wherein repeated observations are made at a collection of sample locations selected to be representative of the community. This temporal replication provides the data needed to resolve the ambiguity between species absence and nondetection when species are unobserved at sample locations. Estimates of species richness and accumulation are computed for two communities, an avian community and a butterfly community. Our model-based estimates suggest that detection failures in many bird species were attributed to low rates of occurrence, as opposed to simply low rates of detection. We estimate that the avian community contains a substantial number of uncommon species and that species richness greatly exceeds the number of species actually observed in the sample. In fact, predictions of species accumulation suggest that even doubling the number of sample locations would not have revealed all of the species in the community. In contrast, our analysis of the butterfly community suggests that many species are relatively common and that the estimated richness of species in the community is nearly equal to the number of species actually detected in the sample. Our predictions of species accumulation suggest that the number of sample locations actually used in the butterfly survey could have been cut in half and the asymptotic richness of species still would have been attained. Our approach of developing occurrence-based summaries of communities while allowing for imperfect detection of species is broadly applicable and should prove useful in the design and analysis of surveys of biodiversity.

Ecology↗
USGS2006

Hierarchical models of animal abundance and occurrence

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

Journal of Agricultural, Biological, and Environme↗
USGS2006

Lead poisoning in captive Andean condors (Vultur gryphus)

Elevated lead in the tissues of raptors, especially those that scavenge, is a common occurrence, and lead poisoning appears to be a significant problem in the ongoing recovery effort for California condors (Gymnogyps californianus). Elevated blood lead levels have been found in released birds, and a number of birds have died of lead poisoning. In earlier work, we dosed turkey vultures (Cathartes aura) with lead shot but found them to be a poor model for lead poisoning. In this study, we dosed four Andean condors (Vultur gryphus) with lead shot and found them to be quite sensitive, as two of the birds died and the other two exhibit signs of lead poisoning within 50 days. All lead-responsive parameters were affected, and regurgitation of dosed shot occurred only once. The response of the Andean condors appeared to mimic California condors, suggesting that once exposed to lead, the possibility of survival is poor. This is consistent with observations in the wild, where otherwise healthy birds exposed to metallic lead quickly succumb. At the very least, the release program has to maintain constant surveillance and an active lead monitoring program.

Journal of Wildlife Diseases↗
USGS2005

Designing occupancy studies: General advice and allocating survey effort

1. The fraction of sampling units in a landscape where a target species is present (occu pancy) is an extensively used concept in ecology. Yet in many applications the species will not always be detected in a sampling unit even when present, resulting in biased estimates of occupancy. Given that sampling units are surveyed repeatedly within a r elatively short timeframe, a number of similar methods have now been developed to provide unbiased occupancy estimates. However, practical guidance on the efficient design of occupancy studies has been lacking. 2. In this paper we comment on a number of general issues related to designing occu pancy studies, including the need for clear objectives that are explicitly linked to science or management, selection of sampling units, timing of repeat surveys and allocation of survey effort. Advice on the number of repeat surveys per sampling unit is considered in terms of the variance of the occupancy estimator, for three possible study designs. 3. We r ecommend that sampling units should be surveyed a minimum of three times w hen detection probability is high (> 0·5 survey − 1 ), unless a removal design is used. 4. We f ound that an optimal removal design will generally be the most efficient, but we suggest it may be less robust to assumption violations than a standard design. 5. Our results suggest that for a rare species it is more efficient to survey more sampling units less intensively, while for a common species fewer sampling units should be surveyed more intensively. 6. Synthesis and applications . Reliable inferences can only result from quality data. To ma ke the best use of logistical resources, study objectives must be clearly defined; sampling units must be selected, and repeated surveys timed appropriately; and a sufficient n umber of repeated surveys must be conducted. Failure to do so may compromise the integrity of the study. The guidance given here on study design issues is particularly a pplicable to studies of species occurrence and distribution, habitat selection and modelling, metapopulation studies and monitoring programmes.

Journal of Applied Ecology↗
USGS2005

Dynamic design of ecological monitoring networks for non-Gaussian spatio-temporal data

Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatio-temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

Environmetrics↗
USGS2005

Modelling occurrence and abundance of species when detection is imperfect

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.

Oikos↗
USGS2005

Efficient statistical mapping of avian count data

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Environmental and Ecological Statistics↗
USGS2005

Estimating size and composition of biological communities by modeling the occurrence of species

We develop a model that uses repeated observations of a biological community to estimate the number and composition of species in the community. Estimators of community-level attributes are constructed from model-based estimators of occurrence of individual species that incorporate imperfect detection of individuals. Data from the North American Breeding Bird Survey are analyzed to illustrate the variety of ecologically important quantities that are easily constructed and estimated using our model-based estimators of species occurrence. In particular, we compute site-specific estimates of species richness that honor classical notions of species-area relationships. We suggest extensions of our model to estimate maps of occurrence of individual species and to compute inferences related to the temporal and spatial dynamics of biological communities.

Journal of the American Statistical Association↗