Geology topics

J. Andrew Royle

Publications and source records attributed to J. Andrew Royle.

At least 145 records · Page 8Linked to original sources

USGS2011

Spatial capture-recapture models for search-encounter data

1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km 2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.

Methods in Ecology and Evolution↗

USGS2011

Density estimation in a wolverine population using spatial capture-recapture models

Classical closed-population capture-recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture-recapture models that accommodate the spatial attribute inherent in capture-recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km 2 area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000-km 2 (95% Bayesian CI: 5.9-15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies.

Alaska↗

USGS2011

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost‐effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees ( Trichechus manatus latirostris ) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non‐independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non‐independent detection of individuals. We simulated correlated behaviour using beta‐binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta‐binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta‐binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness‐of‐fit procedure to evaluate the fit of beta‐binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta‐binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta‐binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness‐of‐fit when analysing ecological data with N‐mixture models. Indeed, both the simulations and the goodness‐of‐fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non‐independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N‐mixture models to ecological data.

Methods in Ecology and Evolution↗

USGS2011

Bayesian analysis of multi-state data with individual covariates for estimating genetic effects on demography

Inbreeding depression is frequently a concern of managers interested in restoring endangered species. Decisions to reduce the potential for inbreeding depression by balancing genotypic contributions to reintroduced populations may exact a cost on long-term demographic performance of the population if those decisions result in reduced numbers of animals released and/or restriction of particularly successful genotypes (i.e., heritable traits of particular family lines). As part of an effort to restore a migratory flock of Whooping Cranes (Grus americana) to eastern North America using the offspring of captive breeders, we obtained a unique dataset which includes post-release mark-recapture data, as well as the pedigree of each released individual. We developed a Bayesian formulation of a multi-state model to analyze radio-telemetry, band-resight, and dead recovery data on reintroduced individuals, in order to track survival and breeding state transitions. We used studbook-based individual covariates to examine the comparative evidence for and degree of effects of inbreeding, genotype, and genotype quality on post-release survival of reintroduced individuals. We demonstrate implementation of the Bayesian multi-state model, which allows for the integration of imperfect detection, multiple data types, random effects, and individual- and time-dependent covariates. Our results provide only weak evidence for an effect of the quality of an individual's genotype in captivity on post-release survival as well as for an effect of inbreeding on post-release survival. We plan to integrate our results into a decision-analytic modeling framework that can explicitly examine tradeoffs between the effects of inbreeding and the effects of genotype and demographic stochasticity on population establishment.

Journal of Ornithology↗

USGS2011

Hierarchical modeling of an invasive spread: The eurasian collared-dove streptopelia decaocto in the United States

Invasive species are regularly claimed as the second threat to biodiversity. To apply a relevant response to the potential consequences associated with invasions (e.g., emphasize management efforts to prevent new colonization or to eradicate the species in places where it has already settled), it is essential to understand invasion mechanisms and dynamics. Quantifying and understanding what influences rates of spatial spread is a key research area for invasion theory. In this paper, we develop a model to account for occupancy dynamics of an invasive species. Our model extends existing models to accommodate several elements of invasive processes; we chose the framework of hierarchical modeling to assess site occupancy status during an invasion. First, we explicitly accounted for spatial structure and how distance among sites and position relative to one another affect the invasion spread. In particular, we accounted for the possibility of directional propagation and provided a way of estimating the direction of this possible spread. Second, we considered the influence of local density on site occupancy. Third, we decided to split the colonization process into two subprocesses, initial colonization and recolonization, which may be ground-breaking because these subprocesses may exhibit different relationships with environmental variations (such as density variation) or colonization history (e.g., initial colonization might facilitate further colonization events). Finally, our model incorporates imperfection in detection, which might be a source of substantial bias in estimating population parameters. We focused on the case of the Eurasian Collared-Dove (Streptopelia decaocto) and its invasion of the United States since its introduction in the early 1980s, using data from the North American BBS (Breeding Bird Survey). The Eurasian Collared-Dove is one of the most successful invasive species, at least among terrestrial vertebrates. Our model provided estimation of the spread direction consistent with empirical observations. Site persistence probability exhibits a quadratic response to density. We also succeeded at detecting differences in the relationship between density and initial colonization vs. recolonization probabilities. We provide a map of sites that may be colonized in the future as an example of possible practical application of our work. ?? 2011 by the Ecological Society of America.

Ecological Applications↗

USGS2011

Assessing hypotheses about nesting site occupancy dynamics

Hypotheses about habitat selection developed in the evolutionary ecology framework assume that individuals, under some conditions, select breeding habitat based on expected fitness in different habitat. The relationship between habitat quality and fitness may be reflected by breeding success of individuals, which may in turn be used to assess habitat quality. Habitat quality may also be assessed via local density: if high‐quality sites are preferentially used, high density may reflect high‐quality habitat. Here we assessed whether site occupancy dynamics vary with site surrogates for habitat quality. We modeled nest site use probability in a seabird subcolony (the Black‐legged Kittiwake, Rissa tridactyla ) over a 20‐year period. We estimated site persistence (an occupied site remains occupied from time t to t + 1) and colonization through two subprocesses: first colonization (site creation at the timescale of the study) and recolonization (a site is colonized again after being deserted). Our model explicitly incorporated site‐specific and neighboring breeding success and conspecific density in the neighborhood. Our results provided evidence that reproductively “successful” sites have a higher persistence probability than “unsuccessful” ones. Analyses of site fidelity in marked birds and of survival probability showed that high site persistence predominantly reflects site fidelity, not immediate colonization by new owners after emigration or death of previous owners. There is a negative quadratic relationship between local density and persistence probability. First colonization probability decreases with density, whereas recolonization probability is constant. This highlights the importance of distinguishing initial colonization and recolonization to understand site occupancy. All dynamics varied positively with neighboring breeding success. We found evidence of a positive interaction between site‐specific and neighboring breeding success. We addressed local population dynamics using a site occupancy approach integrating hypotheses developed in behavioral ecology to account for individual decisions. This allows development of models of population and metapopulation dynamics that explicitly incorporate ecological and evolutionary processes.

Brittany↗

USGS2011

Inference about density and temporary emigration in unmarked populations

Few species are distributed uniformly in space, and populations of mobile organisms are rarely closed with respect to movement, yet many models of density rely upon these assumptions. We present a hierarchical model allowing inference about the density of unmarked populations subject to temporary emigration and imperfect detection. The model can be fit to data collected using a variety of standard survey methods such as repeated point counts in which removal sampling, double‐observer sampling, or distance sampling is used during each count. Simulation studies demonstrated that parameter estimators are unbiased when temporary emigration is either “completely random” or is determined by the size and location of home ranges relative to survey points. We also applied the model to repeated removal sampling data collected on Chestnut‐sided Warblers ( Dendroica pensylvancia ) in the White Mountain National Forest, USA. The density estimate from our model, 1.09 birds/ha, was similar to an estimate of 1.11 birds/ha produced by an intensive spot‐mapping effort. Our model is also applicable when processes other than temporary emigration affect the probability of being available for detection, such as in studies using cue counts. Functions to implement the model have been added to the R package unmarked .

New Hampshire↗

USGS2010

Modelling community dynamics based on species-level abundance models from detection/nondetection data

1. In large‐scale field surveys, a binary recording of each species’ detection or nondetection has been increasingly adopted for its simplicity and low cost. Because of the importance of abundance in many studies, it is desirable to obtain inferences about abundance at species‐, functional group‐, and community‐levels from such binary data. 2. We developed a novel hierarchical multi‐species abundance model based on species‐level detection/nondetection data. The model accounts for the existence of undetected species, and variability in abundance and detectability among species. Species‐level detection/nondetection is linked to species‐level abundance via a detection model that accommodates the expectation that probability of detection (at least one individuals is detected) increases with local abundance of the species. We applied this model to a 9‐year dataset composed of the detection/nondetection of forest birds, at a single post‐fire site (from 7 to 15 years after fire) in a montane area of central Japan. The model allocated undetected species into one of the predefined functional groups by assuming a prior distribution on individual group membership. 3. The results suggest that 15–20 species were missed in each year, and that species richness of communities and functional groups did not change with post‐fire forest succession. Overall abundance of birds and abundance of functional groups tended to increase over time, although only in the winter, while decreases in detectabilities were observed in several species. 4. Synthesis and applications . Understanding and prediction of large‐scale biodiversity dynamics partly hinge on how we can use data effectively. Our hierarchical model for detection/nondetection data estimates abundance in space/time at species‐, functional group‐, and community‐levels while accounting for undetected individuals and species. It also permits comparison of multiple communities by many types of abundance‐based diversity and similarity measures under imperfect detection.

Journal of Applied Ecology↗

USGS2010

Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals

Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat ( Felis silvestris ) from cheek-rub lure sticks, extracted DNA from the samples, and identified each animals' genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture-recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km 2 (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home-range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap- and individual-level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture-recapture models will improve population assessments, especially for rare and elusive animals.

Conservation Biology↗

USGS2010

Estimating black bear density using DNA data from hair snares

DNA-based mark-recapture has become a methodological cornerstone of research focused on bear species. The objective of such studies is often to estimate population size; however, doing so is frequently complicated by movement of individual bears. Movement affects the probability of detection and the assumption of closure of the population required in most models. To mitigate the bias caused by movement of individuals, population size and density estimates are often adjusted using ad hoc methods, including buffering the minimum polygon of the trapping array. We used a hierarchical, spatial capturerecapture model that contains explicit components for the spatial-point process that governs the distribution of individuals and their exposure to (via movement), and detection by, traps. We modeled detection probability as a function of each individual's distance to the trap and an indicator variable for previous capture to account for possible behavioral responses. We applied our model to a 2006 hair-snare study of a black bear (Ursus americanus) population in northern New York, USA. Based on the microsatellite marker analysis of collected hair samples, 47 individuals were identified. We estimated mean density at 0.20 bears/km 2 . A positive estimate of the indicator variable suggests that bears are attracted to baited sites; therefore, including a trap-dependence covariate is important when using bait to attract individuals. Bayesian analysis of the model was implemented in WinBUGS, and we provide the model specification. The model can be applied to any spatially organized trapping array (hair snares, camera traps, mist nests, etc.) to estimate density and can also account for heterogeneity and covariate information at the trap or individual level. ?? The Wildlife Society.

Journal of Wildlife Management↗

USGS2010

Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models

Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture-recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding "observations" to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture-recapture models, we add a set of "all zero" encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated "black boxes" and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture-recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model's assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models ( M 0, M h , distance sampling, and spatial capture-recapture models) and open-population models (Jolly-Seber) with individual effects.

Journal of Ornithology↗

USGS2010

Distribution patterns of wintering sea ducks in relation to the North Atlantic Oscillation and local environmental characteristics

Twelve species of North American sea ducks (Tribe Mergini) winter off the eastern coast of the United States and Canada. Yet, despite their seasonal proximity to urbanized areas in this region, there is limited information on patterns of wintering sea duck habitat use. It is difficult to gather information on sea ducks because of the relative inaccessibility of their offshore locations, their high degree of mobility, and their aggregated distributions. To characterize environmental conditions that affect wintering distributions, as well as their geographic ranges, we analyzed count data on five species of sea ducks (black scoters Melanitta nigra americana, surf scoters M. perspicillata, white-winged scoters M. fusca, common eiders Somateria mollissima, and long-tailed ducks Clangula hyemalis) that were collected during the Atlantic Flyway Sea Duck Survey for ten years starting in the early 1990s. We modeled count data for each species within ten-nautical-mile linear survey segments using a zero-inflated negative binomial model that included four local-scale habitat covariates (sea surface temperature, mean bottom depth, maximum bottom slope, and a variable to indicate if the segment was in a bay or not), one broad-scale covariate (the North Atlantic Oscillation), and a temporal correlation component. Our results indicate that species distributions have strong latitudinal gradients and consistency in local habitat use. The North Atlantic Oscillation was the only environmental covariate that had a significant (but variable) effect on the expected count for all five species, suggesting that broad-scale climatic conditions may be directly or indirectly important to the distributions of wintering sea ducks. Our results provide critical information on species-habitat associations, elucidate the complicated relationship between the North Atlantic Oscillation, sea surface temperature, and local sea duck abundances, and should be useful in assessing the impacts of climate change on seabirds.

Oecologia↗

USGS2010

Traffic effects on bird counts on North American Breeding Bird Survey routes

The North American Breeding Bird Survey (BBS) is an annual roadside survey used to estimate population change in >420 species of birds that breed in North America. Roadside sampling has been criticized, in part because traffic noise can interfere with bird counts. Since 1997, data have been collected on the numbers of vehicles that pass during counts at each stop. We assessed the effect of traffic by modeling total vehicles as a covariate of counts in hierarchical Poisson regression models used to estimate population change. We selected species for analysis that represent birds detected at low and high abundance and birds with songs of low and high frequencies. Increases in vehicle counts were associated with decreases in bird counts in most of the species examined. The size and direction of these effects remained relatively constant between two alternative models that we analyzed. Although this analysis indicated only a small effect of incorporating traffic effects when modeling roadside counts of birds, we suggest that continued evaluation of changes in traffic at BBS stops should be a component of future BBS analyses.

The Auk↗

USGS2010

Modeling spatial variation in avian survival and residency probabilities

The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both phi and pi among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush phi and pi that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.

Ecology↗

USGS2010

Spatially explicit inference for open populations: Estimating demographic parameters from camera-trap studies

We develop a hierarchical capture–recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture–recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual‐based formulation of a Jolly‐Seber type model with recently developed spatially explicit capture–recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.

Ecology↗

USGS2010

Models for inference in dynamic metacommunity systems

A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species‐ and location‐specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species‐specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

Ecology↗

USGS2009

Trend estimation in populations with imperfect detection

1. Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme. 2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1–10 (mean 3) replicate surveys during each spring 1994–2005. 3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35–151). 5. Synthesis and applications . Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.

Journal of Applied Ecology↗

USGS2009

Analysis of capture–recapture models with individual covariates using data augmentation

I consider the analysis of capture–recapture models with individual covariates that influence detection probability. Bayesian analysis of the joint likelihood is carried out using a flexible data augmentation scheme that facilitates analysis by Markov chain Monte Carlo methods, and a simple and straightforward implementation in freely available software. This approach is applied to a study of meadow voles ( Microtus pennsylvanicus ) in which auxiliary data on a continuous covariate (body mass) are recorded, and it is thought that detection probability is related to body mass. In a second example, the model is applied to an aerial waterfowl survey in which a double‐observer protocol is used. The fundamental unit of observation is the cluster of individual birds, and the size of the cluster (a discrete covariate) is used as a covariate on detection probability.

Biometrics↗