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J. Andrew Royle

Publications and source records attributed to J. Andrew Royle.

At least 199 records · Page 11Linked to original sources
USGS2005

Modeling anuran detection and site occupancy on North American Amphibian Monitoring Program (NAAMP) routes in Maryland

One of the most fundamental problems in monitoring animal populations is that of imperfect detection. Although imperfect detection can be modeled, studies examining patterns in occurrence often ignore detection and thus fail to properly partition variation in detection from that of occurrence. In this study, we used anuran calling survey data collected on North American Amphibian Monitoring Program routes in eastern Maryland to investigate factors that influence detection probability and site occupancy for 10 anuran species. In 2002, 17 calling survey routes in eastern Maryland were surveyed to collect environmental and species data nine or more times. To analyze these data, we developed models incorporating detection probability and site occupancy. The results suggest that, for more than half of the 10 species, detection probabilities vary most with season (i.e., day-of-year), air temperature, time, and moon illumination, whereas site occupancy may vary by the amount of palustrine forested wetland habitat. Our results suggest anuran calling surveys should document air temperature, time of night, moon illumination, observer skill, and habitat change over time, as these factors can be important to model-adjusted estimates of site occupancy. Our study represents the first formal modeling effort aimed at developing an analytic assessment framework for NAAMP calling survey data.

Maryland↗
USGS2005

Modeling avian abundance from replicated counts using binomial mixture models

Abundance estimation in ecology is usually accomplished by capture–recapture, removal, or distance sampling methods. These may be hard to implement at large spatial scales. In contrast, binomial mixture models enable abundance estimation without individual identification, based simply on temporally and spatially replicated counts. Here, we evaluate mixture models using data from the national breeding bird monitoring program in Switzerland, where some 250 1-km 2 quadrats are surveyed using the territory mapping method three times during each breeding season. We chose eight species with contrasting distribution (wide–narrow), abundance (high–low), and detectability (easy–difficult). Abundance was modeled as a random effect with a Poisson or negative binomial distribution, with mean affected by forest cover, elevation, and route length. Detectability was a logit-linear function of survey date, survey date-by-elevation, and sampling effort (time per transect unit). Resulting covariate effects and parameter estimates were consistent with expectations. Detectability per territory (for three surveys) ranged from 0.66 to 0.94 (mean 0.84) for easy species, and from 0.16 to 0.83 (mean 0.53) for difficult species, depended on survey effort for two easy and all four difficult species, and changed seasonally for three easy and three difficult species. Abundance was positively related to route length in three high-abundance and one low-abundance (one easy and three difficult) species, and increased with forest cover in five forest species, decreased for two nonforest species, and was unaffected for a generalist species. Abundance estimates under the most parsimonious mixture models were between 1.1 and 8.9 (median 1.8) times greater than estimates based on territory mapping; hence, three surveys were insufficient to detect all territories for each species. We conclude that binomial mixture models are an important new approach for estimating abundance corrected for detectability when only repeated-count data are available. Future developments envisioned include estimation of trend, occupancy, and total regional abundance.

Ecological Applications↗
USGS2005

A general class of multinomial mixture models for anuran calling survey data

We propose a general framework for modeling anuran abundance using data collected from commonly used calling surveys. The data generated from calling surveys are indices of calling intensity (vocalization of males) that do not have a precise link to actual population size and are sensitive to factors that influence anuran behavior. We formulate a model for calling-index data in terms of the maximum potential calling index that could be observed at a site (the “latent abundance class”), given its underlying breeding population, and we focus attention on estimating the distribution of this latent abundance class. A critical consideration in estimating the latent structure is imperfect detection, which causes the observed abundance index to be less than or equal to the latent abundance class. We specify a multinomial sampling model for the observed abundance index that is conditional on the latent abundance class. Estimation of the latent abundance class distribution is based on the marginal likelihood of the index data, having integrated over the latent class distribution. We apply the proposed modeling framework to data collected as part of the North American Amphibian Monitoring Program (NAAMP).

Ecology↗
USGS2005

Estimating site occupancy and abundance using indirect detection indices

Knowledge of factors influencing animal distribution and abundance is essential in many areas of ecological research, management, and policy-making. Because common methods for modeling and estimating abundance (e.g., capture–recapture, distance sampling) are sometimes not practical for large areas or elusive species, indices are sometimes used as surrogate measures of abundance. We present an extension of the Royle and Nichols (2003) generalization of the MacKenzie et al. (2002) site-occupancy model that incorporates length of the sampling interval into the model for detection probability. As a result, we obtain a modeling framework that shows how useful information can be extracted from a class of index methods we call indirect detection indices (IDIs). Examples of IDIs include scent station, tracking tube, snow track, tracking plate, and hair snare surveys. Our model is maximum likelihood, and it can be used to estimate site occupancy and model factors influencing patterns of occupancy and abundance in space. Under certain circumstances, it can also be used to estimate abundance. We evaluated model properties using Monte Carlo simulations and illustrate the method with tracking tube and scent station data. We believe this model will be a useful tool for determining factors that influence animal distribution and abundance.

Journal of Wildlife Management↗
USGS2005

Using the North American Breeding Bird Survey as a tool for conservation: A critique of Bart et al. (2004)

Bart et al. (2004) develop methods for predicting needed samples for estimation of long-term trends from Count survey data, and they apply these methods to the North American Breeding Bird Survey (BBS). They recommend adding approximately 40% more survey routes ill the BBS to allow for estimation of long-term (i.e., 20 year) trends for a collection of species. We critique several aspects of their analysis and suggest that their focus on long-term trends and expansion of the present survey design will provide limited benefits for conservation because it fails to either enhance the credibility of the survey or better tie the survey to regional management activities. A primary innovation claimed by Bart et al. (2004) is the incorporation of bias in estimation of study planning. We question the value of this approach, as it requires reliable estimates of range of future bias. We show that estimates of bias used by Bart et al. (2004) are speculative. Failure to obtain better estimates of this bias is likely to compromise the credibility of future analyses of the survey. We also note that the generic analysis of population trends that they provide is of questionable validity and is unlikely to be relevant for regions and species of management concern.

Journal of Wildlife Management↗
USGS2005

Estimating population abundance and mapping distribution of wintering sea ducks in coastal waters of the mid-Atlantic

Survey design for wintering scoters (Melanitta sp.) and other sea ducks that occur in offshore waters is challenging because these species have large ranges, are subject to distributional shifts among years and within a season, and can occur in aggregations. Interest in winter sea duck population abundance surveys has grown in recent years. This interest stems from concern over the population status of some sea ducks, limitations of extant breeding waterfowl survey programs in North America and logistical challenges and costs of conducting surveys in northern breeding regions, high winter area philopatry in some species and potential conservation implications, and increasing concern over offshore development and other threats to sea duck wintering habitats. The efficiency and practicality of statistically-rigorous monitoring strategies for mobile, aggregated wintering sea duck populations have not been sufficiently investigated. This study evaluated a 2-phase adaptive stratified strip transect sampling plan to estimate wintering population size of scoters, long-tailed ducks (Clangua hyemalis), and other sea ducks and provide information on distribution. The sampling plan results in an optimal allocation of a fixed sampling effort among offshore strata in the U.S. mid-Atlantic coast region. Phase I transect selection probabilities were based on historic distribution and abundance data, while Phase 2 selection probabilities were based on observations made during Phase 1 flights. Distance sampling methods were used to estimate detection rates. Environmental variables thought to affect detection rates were recorded during the survey and post-stratification and covariate modeling were investigated to reduce the effect of heterogeneity on detection estimation. We assessed cost-precision tradeoffs under a number of fixed-cost sampling scenarios using Monte Carlo simulation. We discuss advantages and limitations of this sampling design for estimating wintering sea duck abundance and mapping distribution and suggest improvements for future surveys.

Book chapter↗
USGS2005

Estimation of stream salamander (Plethodontidae, Desmognathinae and Plethodontinae) populations in Shenandoah National Park, Virginia, USA

Stream salamanders in the family Plethodontidae constitute a large biomass in and near headwater streams in the eastern United States and are promising indicators of stream ecosystem health. Many studies of stream salamanders have relied on population indices based on counts rather than population estimates based on techniques such as capture-recapture and removal. Application of estimation procedures allows the calculation of detection probabilities (the proportion of total animals present that are detected during a survey) and their associated sampling error, and may be essential for determining salamander population sizes and trends. In 1999, we conducted capture-recapture and removal population estimation methods for Desmognathus salamanders at six streams in Shenandoah National Park, Virginia, USA. Removal sampling appeared more efficient and detection probabilities from removal data were higher than those from capture-recapture. During 2001-2004, we used removal estimation at eight streams in the park to assess the usefulness of this technique for long-term monitoring of stream salamanders. Removal detection probabilities ranged from 0.39 to 0.96 for Desmognathus, 0.27 to 0.89 for Eurycea and 0.27 to 0.75 for northern spring (Gyrinophilus porphyriticus) and northern red (Pseudotriton ruber) salamanders across stream transects. Detection probabilities did not differ across years for Desmognathus and Eurycea, but did differ among streams for Desmognathus. Population estimates of Desmognathus decreased between 2001-2002 and 2003-2004 which may be related to changes in stream flow conditions. Removal-based procedures may be a feasible approach for population estimation of salamanders, but field methods should be designed to meet the assumptions of the sampling procedures. New approaches to estimating stream salamander populations are discussed.

Alytes↗
USGS2004

Dispersal and individual quality in a long lived species

The idea of differences in individual quality has been put forward in numerous long-term studies in long-lived species to explain differences in lifetime production among individuals. Despite the important role of individual heterogeneity in vital rates in demography, population dynamics and life history theory, the idea of 'individual quality' is elusive. It is sometimes assumed to be a static or dynamic individual characteristic. When considered as a dynamic trait, it is sometimes assumed to vary deterministically or stochastically, or to be confounded with the characteristics of the habitat. We addressed heterogeneity in reproductive performance among individuals established in higher-quality habitat in a long-lived seabird species. We used approaches to statistical inference based on individual random effects permitting quantification of heterogeneity in populations and assessment of individual variation from the population mean. We found evidence of heterogeneity in breeding probability, not success probability. We assessed the influence of dispersal on individual reproductive potential. Dispersal is likely to be destabilizing in species with high site and mate fidelity. We detected heterogeneity after dispersal, not before. Individuals may perform well regardless of quality before destabilization, including those that recruited in higher-quality habitat by chance, but only higher-quality individuals may be able to overcome the consequences of dispersal. Importantly, results differed when accounting for individual heterogeneity (an increase in mean breeding probability when individuals dispersed), or not (a decrease in mean breeding probability). In the latter case, the decrease in mean breeding probability may result from a substantial decrease in breeding probability in a few individuals and a slight increase in others. In other words, the pattern observed at the population mean level may not reflect what happens in the majority of individuals.

Oikos↗
USGS2004

N-mixture models for estimating population size from spatially replicated counts

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, I describe a class of models (n-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, n, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for n. Carroll and Lombard (1985, Journal of American Statistical Association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on n that is exploited by the n-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the n-mixture estimator compared to the Caroll and Lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.

Biometrics↗
USGS2004

Estimating population trends with a linear model: Technical comments

Controversy has sometimes arisen over whether there is a need to accommodate the limitations of survey design in estimating population change from the count data collected in bird surveys. Analyses of surveys such as the North American Breeding Bird Survey (BBS) can be quite complex; it is natural to ask if the complexity is necessary, or whether the statisticians have run amok. Bart et al. (2003) propose a very simple analysis involving nothing more complicated than simple linear regression, and contrast their approach with model-based procedures. We review the assumptions implicit to their proposed method, and document that these assumptions are unlikely to be valid for surveys such as the BBS. One fundamental limitation of a purely design-based approach is the absence of controls for factors that influence detection of birds at survey sites. We show that failure to model observer effects in survey data leads to substantial bias in estimation of population trends from BBS data for the 20 species that Bart et al. (2003) used as the basis of their simulations. Finally, we note that the simulations presented in Bart et al. (2003) do not provide a useful evaluation of their proposed method, nor do they provide a valid comparison to the estimating- equations alternative they consider.

Condor↗
USGS2004

Modeling abundance effects in distance sampling

Distance-sampling methods are commonly used in studies of animal populations to estimate population density. A common objective of such studies is to evaluate the relationship between abundance or density and covariates that describe animal habitat or other environmental influences. However, little attention has been focused on methods of modeling abundance covariate effects in conventional distance-sampling models. In this paper we propose a distance-sampling model that accommodates covariate effects on abundance. The model is based on specification of the distance-sampling likelihood at the level of the sample unit in terms of local abundance (for each sampling unit). This model is augmented with a Poisson regression model for local abundance that is parameterized in terms of available covariates. Maximum-likelihood estimation of detection and density parameters is based on the integrated likelihood, wherein local abundance is removed from the likelihood by integration. We provide an example using avian point-transect data of Ovenbirds ( Seiurus aurocapillus ) collected using a distance-sampling protocol and two measures of habitat structure (understory cover and basal area of overstory trees). The model yields a sensible description (positive effect of understory cover, negative effect on basal area) of the relationship between habitat and Ovenbird density that can be used to evaluate the effects of habitat management on Ovenbird populations.

Maryland↗
USGS2004

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895—1993 on a 0.5° latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers.

Climate Research↗
USGS2004

Generalized estimators of avian abundance from count survey data

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture?recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.

Animal Biodiversity and Conservation↗
USGS2003

Demographic analysis from summaries of an age-structured population

Demographic analyses of age-structured populations typically rely on life history data for individuals, or when individual animals are not identified, on information about the numbers of individuals in each age class through time. While it is usually difficult to determine the age class of a randomly encountered individual, it is often the case that the individual can be readily and reliably assigned to one of a set of age classes. For example, it is often possible to distinguish first-year from older birds. In such cases, the population age structure can be regarded as a latent variable governed by a process prior, and the data as summaries of this latent structure. In this article, we consider the problem of uncovering the latent structure and estimating process parameters from summaries of age class information. We present a demographic analysis for the critically endangered migratory population of whooping cranes (Grus americana), based only on counts of first-year birds and of older birds. We estimate age and year-specific survival rates. We address the controversial issue of whether management action on the breeding grounds has influenced recruitment, relating recruitment rates to the number of seventh-year and older birds, and examining the pattern of variation through time in this rate.

Biometrics↗
USGS2003

Mixture models for estimating the size of a closed population when capture rates vary among individuals

We develop a parameterization of the beta-binomial mixture that provides sensible inferences about the size of a closed population when probabilities of capture or detection vary among individuals. Three classes of mixture models (beta-binomial, logistic-normal, and latent-class) are fitted to recaptures of snowshoe hares for estimating abundance and to counts of bird species for estimating species richness. In both sets of data, rates of detection appear to vary more among individuals (animals or species) than among sampling occasions or locations. The estimates of population size and species richness are sensitive to model-specific assumptions about the latent distribution of individual rates of detection. We demonstrate using simulation experiments that conventional diagnostics for assessing model adequacy, such as deviance, cannot be relied on for selecting classes of mixture models that produce valid inferences about population size. Prior knowledge about sources of individual heterogeneity in detection rates, if available, should be used to help select among classes of mixture models that are to be used for inference.

Biometrics↗
USGS2003

Sexual selection affects local extinction and turnover in bird communities

Predicting extinction risks has become a central goal for conservation and evolutionary biologists interested in population and community dynamics. Several factors have been put forward to explain risks of extinction, including ecological and life history characteristics of individuals. For instance, factors that affect the balance between natality and mortality can have profound effects on population persistence. Sexual selection has been identified as one such factor. Populations under strong sexual selection experience a number of costs ranging from increased predation and parasitism to enhanced sensitivity to environmental and demographic stochasticity. These findings have led to the prediction that local extinction rates should be higher for species/populations with intense sexual selection. We tested this prediction by analyzing the dynamics of natural bird communities at a continental scale over a period of 21 years (1975-1996), using relevant statistical tools. In agreement with the theoretical prediction, we found that sexual selection increased risks of local extinction (dichromatic birds had on average a 23% higher local extinction rate than monochromatic species). However, despite higher local extinction probabilities, the number of dichromatic species did not decrease over the period considered in this study. This pattern was caused by higher local turnover rates of dichromatic species, resulting in relatively stable communities for both groups of species. Our results suggest that these communities function as metacommunities, with frequent local extinctions followed by colonization. Anthropogenic factors impeding dispersal might therefore have a significant impact on the global persistence of sexually selected species.

Proceedings of the National Academy of Sciences↗