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Spatial Capture-Recapture

Spatial Capture-Recapture
  • Author : J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
  • Publisher :Unknown
  • Release Date :2013-08-27
  • Total pages :612
  • ISBN : 9780124071520
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Summary : Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical – it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package. Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topic Every methodological element has a detailed worked example with a code template, allowing you to learn by example Includes an R package that contains all computer code and data sets on companion website

Spatial Capture-Recapture

Spatial Capture-Recapture
  • Author : J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
  • Publisher :Unknown
  • Release Date :2017-11-13
  • Total pages :612
  • ISBN : 0128100125
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Summary : Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in anR package. Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topic Every methodological element has a detailed worked example with a code template, allowing you to learn by example Includes an R package that contains all computer code and data sets on companion website "

A Continuous-time Formulation for Spatial Capture-recapture Models

A Continuous-time Formulation for Spatial Capture-recapture Models
  • Author : Greg Distiller
  • Publisher :Unknown
  • Release Date :2017
  • Total pages :229
  • ISBN : OCLC:1063706571
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Summary :

Assessing the Performance of an Open Spatial Capture-recapture Method on Grizzly Bear Populations when Age Data is Missing

Assessing the Performance of an Open Spatial Capture-recapture Method on Grizzly Bear Populations when Age Data is Missing
  • Author : Neil Faught
  • Publisher :Unknown
  • Release Date :2020
  • Total pages :86
  • ISBN : OCLC:1157076797
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Summary : It is often difficult in capture-recapture (CR) studies of grizzly bear populations to determine the age of detected bears. As a result, analyses often omit age terms in CR models despite past studies suggesting age influences detection probability. This paper explores how failing to account for age in the detection function of an open, spatially-explicit CR model, introduced in Efford & Schofield (2019), affects estimates of apparent survival, apparent recruitment, population growth, and grizzly bear home-range sizes. Using a simulation study, it was found that estimates of all parameters of interest excluding home-range size were robust to this omission. The effects of using two different types of detectors for data collection (bait sites and rub objects) on bias in estimates of above parameters was also explored via simulation. No evidence was found that one detector type was more prone to producing biased parameter estimates than the other.

Analysis of Capture-Recapture Data

Analysis of Capture-Recapture Data
  • Author : Rachel S. McCrea,Byron J. T. Morgan
  • Publisher :Unknown
  • Release Date :2014-08-01
  • Total pages :314
  • ISBN : 9781439836606
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Summary : An important first step in studying the demography of wild animals is to identify the animals uniquely through applying markings, such as rings, tags, and bands. Once the animals are encountered again, researchers can study different forms of capture-recapture data to estimate features, such as the mortality and size of the populations. Capture-rec

On the Estimation of Animal Density from Spatial Capture-recapture Data

On the Estimation of Animal Density from Spatial Capture-recapture Data
  • Author : Callum Kwun Yuen Young
  • Publisher :Unknown
  • Release Date :2018
  • Total pages :164
  • ISBN : OCLC:1057903656
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Summary : Spatial capture-recapture (SCR) methods can estimate the density of animal populations. SCR contains elements of both capture-recapture, and distance sampling methods. Data are obtained through repeated detections of individuals by detectors at known locations, allowing the incorporation of the detection function in the SCR model. Naturally, individuals whose home ranges are centred nearer to a detector have a greater probability of being detected. Data obtained from SCR surveys are commonly presented as capture histories, which may contain either counts of detections, or binary indications of a detection (or non-detection). As counts can be converted into binary data, either model may be fitted to SCR data. Some advocate fitting models to the binary data, as incorrectly assuming the underlying statistical (count) distribution produces biased estimates; others suggest modelling the full counts, as the magnitudes of the counts provide supplementary information over and above that of the binary capture histories. We introduce the "scr" package for R, and describe its main features. A simulation study is performed to assess the performance of each model fitted to data from various underlying distributions. We show that both models give very similar inferences in all cases, regardless of the model type or true distribution. Additionally, the inference appears to be appropriate, even when the data are significantly overdispersed. Existing methods cannot sufficiently model acoustically detected data without making a number of assumptions that are often violated in practice. We thus present a new model circumventing the issues present in existing methods, whilst improving on them such that there may be a reduction in survey effort and cost. We further extend the application of this new model to situations where clustering of individuals' activity centres creates dependence problems with the data, and describe how our model accounts for this lack of independence.

Capture-Recapture: Parameter Estimation for Open Animal Populations

Capture-Recapture: Parameter Estimation for Open Animal Populations
  • Author : George A. F. Seber,Matthew R. Schofield
  • Publisher :Unknown
  • Release Date :2019-08-13
  • Total pages :663
  • ISBN : 9783030181871
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Summary : This comprehensive book, rich with applications, offers a quantitative framework for the analysis of the various capture-recapture models for open animal populations, while also addressing associated computational methods. The state of our wildlife populations provides a litmus test for the state of our environment, especially in light of global warming and the increasing pollution of our land, seas, and air. In addition to monitoring our food resources such as fisheries, we need to protect endangered species from the effects of human activities (e.g. rhinos, whales, or encroachments on the habitat of orangutans). Pests must be be controlled, whether insects or viruses, and we need to cope with growing feral populations such as opossums, rabbits, and pigs. Accordingly, we need to obtain information about a given population’s dynamics, concerning e.g. mortality, birth, growth, breeding, sex, and migration, and determine whether the respective population is increasing , static, or declining. There are many methods for obtaining population information, but the most useful (and most work-intensive) is generically known as “capture-recapture,” where we mark or tag a representative sample of individuals from the population and follow that sample over time using recaptures, resightings, or dead recoveries. Marks can be natural, such as stripes, fin profiles, and even DNA; or artificial, such as spots on insects. Attached tags can, for example, be simple bands or streamers, or more sophisticated variants such as radio and sonic transmitters. To estimate population parameters, sophisticated and complex mathematical models have been devised on the basis of recapture information and computer packages. This book addresses the analysis of such models. It is primarily intended for ecologists and wildlife managers who wish to apply the methods to the types of problems discussed above, though it will also benefit researchers and graduate students in ecology. Familiarity with basic statistical concepts is essential.

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
  • Author : J. Andrew Royle,Robert M. Dorazio
  • Publisher :Unknown
  • Release Date :2008-10-15
  • Total pages :464
  • ISBN : 9780080559254
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Summary : A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site

Camera Traps in Animal Ecology

Camera Traps in Animal Ecology
  • Author : Allan F. O'Connell,James D. Nichols,K. Ullas Karanth
  • Publisher :Unknown
  • Release Date :2010-10-05
  • Total pages :271
  • ISBN : 4431994955
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Summary : Remote photography and infrared sensors are widely used in the sampling of wildlife populations worldwide, especially for cryptic or elusive species. Guiding the practitioner through the entire process of using camera traps, this book is the first to compile state-of-the-art sampling techniques for the purpose of conducting high-quality science or effective management. Chapters on the evaluation of equipment, field sampling designs, and data analysis methods provide a coherent framework for making inferences about the abundance, species richness, and occupancy of sampled animals. The volume introduces new models that will revolutionize use of camera data to estimate population density, such as the newly developed spatial capture–recapture models. It also includes richly detailed case studies of camera trap work on some of the world’s most charismatic, elusive, and endangered wildlife species. Indispensible to wildlife conservationists, ecologists, biologists, and conservation agencies around the world, the text provides a thorough review of the subject as well as a forecast for the use of remote photography in natural resource conservation over the next few decades.

On the Topic of Spatial Capture-Recapture Modeling

On the Topic of Spatial Capture-Recapture Modeling
  • Author : Paul McLaughlin
  • Publisher :Unknown
  • Release Date :2019
  • Total pages :229
  • ISBN : OCLC:1196364456
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Summary : Over the past two decades there have been many advancements in modeling capture-recapture (CR) data to account for emerging data collection technology and techniques. Spatial capture-recapture (SCR) models have been introduced to estimate population size and numerous other demographic parameters from spatially explicit CR data. Here we offer a comprehensive review of the development of CR modeling up to and including SCR models. We then introduce a new SCR model which allows for attractions between individuals via their daily movements. A simulation study is used to demonstrate accounting for these attractions can improve population size estimation. Additionally, we apply our model to an iconic SCR dataset to estimate the population size and attraction parameters of a Bengal tiger (\textit{Panthera tigris tigris}) population. To conclude we present a reversible-jump Markov chain Monte Carlo (RJMCMC) approach for parameter estimation which has not previously been extended to SCR models. Simulation studies are presented to show the superior computational efficiency of this proposed approach. We also demonstrate the application of this RJMCMC method to SCR data by estimating the size of an American black bear (Ursus americanus) population.

Incorporating Animal Movement with Distance Sampling and Spatial Capture-recapture

Incorporating Animal Movement with Distance Sampling and Spatial Capture-recapture
  • Author : Richard Glennie
  • Publisher :Unknown
  • Release Date :2018
  • Total pages :229
  • ISBN : OCLC:1079244198
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Summary :

Population Estimation in African Elephants with Hierarchical Bayesian Spatial Capture-recapture Models

Population Estimation in African Elephants with Hierarchical Bayesian Spatial Capture-recapture Models
  • Author : Jason Paul Marshal
  • Publisher :Unknown
  • Release Date :2017
  • Total pages :188
  • ISBN : OCLC:1027961367
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Summary :

Handbook of Capture-Recapture Analysis

Handbook of Capture-Recapture Analysis
  • Author : Steven C. Amstrup,Trent L. McDonald,Bryan F. J. Manly
  • Publisher :Unknown
  • Release Date :2010-12-16
  • Total pages :336
  • ISBN : 1400837715
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Summary : Every day, biologists in parkas, raincoats, and rubber boots go into the field to capture and mark a variety of animal species. Back in the office, statisticians create analytical models for the field biologists' data. But many times, representatives of the two professions do not fully understand one another's roles. This book bridges this gap by helping biologists understand state-of-the-art statistical methods for analyzing capture-recapture data. In so doing, statisticians will also become more familiar with the design of field studies and with the real-life issues facing biologists. Reliable outcomes of capture-recapture studies are vital to answering key ecological questions. Is the population increasing or decreasing? Do more or fewer animals have a particular characteristic? In answering these questions, biologists cannot hope to capture and mark entire populations. And frequently, the populations change unpredictably during a study. Thus, increasingly sophisticated models have been employed to convert data into answers to ecological questions. This book, by experts in capture-recapture analysis, introduces the most up-to-date methods for data analysis while explaining the theory behind those methods. Thorough, concise, and portable, it will be immensely useful to biologists, biometricians, and statisticians, students in both fields, and anyone else engaged in the capture-recapture process.

Estimating Animal Abundance

Estimating Animal Abundance
  • Author : D.L. Borchers,Stephen T. Buckland,Walter Zucchini
  • Publisher :Unknown
  • Release Date :2013-03-09
  • Total pages :314
  • ISBN : 9781447137085
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Summary : The first accessible introduction to the many various wildlife assessment methods! This book uses a new approach that makes the full range of methods accessible in a way that has not previously been possible. Accompanied by free, user-friendly software to get some "hands-on" experience with the methods and how they perform in different contexts.

Estimating Mountain Lion Density Using Unmarked Bayesian Spatial Capture-recapture for the Davis Mountains, Texas

Estimating Mountain Lion Density Using Unmarked Bayesian Spatial Capture-recapture for the Davis Mountains, Texas
  • Author : Richard Brian Mrozinski
  • Publisher :Unknown
  • Release Date :2018
  • Total pages :258
  • ISBN : OCLC:1083265858
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Summary :

Occupancy Estimation and Modeling

Occupancy Estimation and Modeling
  • Author : Darryl I. MacKenzie,James D. Nichols,J. Andrew Royle,Kenneth H. Pollock,Larissa Bailey,James E. Hines
  • Publisher :Unknown
  • Release Date :2017-11-17
  • Total pages :648
  • ISBN : 9780124072459
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Summary : Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling. Provides authoritative insights into the latest in occupancy modeling Examines the latest methods in analyzing detection/no detection data surveys Addresses critical issues of imperfect detectability and its effects on species occurrence estimation Discusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation

Modeling Demographic Processes in Marked Populations

Modeling Demographic Processes in Marked Populations
  • Author : David L. Thomson,Evan G. Cooch,Michael J. Conroy
  • Publisher :Unknown
  • Release Date :2008-12-11
  • Total pages :1132
  • ISBN : 038778151X
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Summary : Here, biologists and statisticians come together in an interdisciplinary synthesis with the aim of developing new methods to overcome the most significant challenges and constraints faced by quantitative biologists seeking to model demographic rates.

River Otter Population Monitoring in Northeastern Pennsylvania Using Non-invasive Genetic Sampling and Spatial Capture-recapture Models

River Otter Population Monitoring in Northeastern Pennsylvania Using Non-invasive Genetic Sampling and Spatial Capture-recapture Models
  • Author : Nicholas Forman
  • Publisher :Unknown
  • Release Date :2015
  • Total pages :229
  • ISBN : OCLC:943065318
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Summary : River otter (Lontra canadensis) populations in Pennsylvania experienced a range reduction and subsequent expansion of the remnant population, as well as re-colonization of parts of the state through reintroduction efforts and expansion of neighboring populations. There are currently no estimates of population size or densities for river otter populations in Pennsylvania, and large-scale monitoring efforts are hampered by the elusive behavior of river otter. Non-invasive genetic sampling has been used to survey river otter populations, but given the river otter's unique distribution across the landscape, estimation of population size and densities has been limited to linear habitats in river systems or along coastlines. Spatial capture-recapture models incorporate spatial information from captures into the estimation process, and estimates are more explicitly linked to the area in which observations occur. I analyzed the efficacy of non-invasive genetic sampling to identify individual river otter and I used spatial capture-recapture models to estimate river otter population size and density, and in northeastern Pennsylvania.I surveyed nine counties in northeastern Pennsylvania, opportunistically collecting samples from latrine sites on public and private land. Latrines were visited on three to four occasions at 6--14 day intervals, clearing latrines after each visit, in a capture-recapture framework. I amplified DNA extracted from the samples at ten microsatellite markers, to generate a genotype for each sample. I matched genotypes using program CERVUS to identify individuals. My first analysis compared amplification success rates and error rates for samples of different type and time of environmental exposure or freshness, and compared my amplification success rates to other studies. Previous studies on river otter had lower genotyping success rates than those for other otter species, and did not follow a common sampling protocol despite laboratory studies for the river otter and recommendations from field studies on other otter species. My amplification success rates were most comparable to those from studies on otter species conducted in the winter with samples collected in a storage buffer. I observed similar patterns of success rates as other studies for different sample types and samples classified for different categories related to lengths of environmental exposure, but had higher success rates for every category. Amplification error rates for the different sample types and environmental exposure categories were not reported in the literature, but I included them in the study as another measure of sample quality and to better inform future studies. The importance of comparing success rates and error rates is to better inform future studies on the preferred sampling protocol, and give measures for the amount of effort necessary for studies looking to use non-invasive genetic sampling to identify individual river otter for population analyses.To estimate population size and density in spatial capture-recapture models, I compiled spatial encounter histories given the location and occasion of collection of each sample assigned to an individual. I also used full likelihood models in program MARK to test for differences in capture and recapture probabilities. I reported the first density estimates for a river otter population in northeastern Pennsylvania (2.1 otter/100 km2, 1.4--5.0 otter/100 km2 95% Asymptotic Wald-type CI). The estimates of capture and recapture probabilities in the MARK model with those parameters estimated separately indicated that capture and recapture probabilities were not different, but that the probability of capturing an individual did vary by occasion. I observed a difference in density estimates for my SCR and MARK models. I would recommend using SCR models because of the spatial justification for density estimates, and the ability to include landscape covariates to build more informed models, which may prove to be useful for river otter given their unique space use.Future studies conducting non-invasive genetic sampling for river otter should conduct their studies in winter and use a storage buffer for samples. Sample type and length of environmental exposure should be considered when considering the amount of sampling effort to derive a genotype for identification of individual otter. NGS and SCR can be used to generate reliable population or density estimates, but as I documented from my MARK estimates of capture probability, numerous sampling occasions are desirable because of the variation in capture probability between occasions. Spatial capture-recapture models are preferable for river otter in Pennsylvania because the area for which density is being estimated is directly tied into the model, which is ideal given the diversity of linear and non-linear habitat types in northeastern Pennsylvania.

Handbook of Environmental and Ecological Statistics

Handbook of Environmental and Ecological Statistics
  • Author : Alan E. Gelfand,Montserrat Fuentes,Jennifer A. Hoeting,Richard Lyttleton Smith
  • Publisher :Unknown
  • Release Date :2017-09-15
  • Total pages :854
  • ISBN : 9781351648547
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Summary : This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science.

Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS

Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS
  • Author : Marc Kery,J. Andrew Royle
  • Publisher :Unknown
  • Release Date :2015-11-14
  • Total pages :808
  • ISBN : 9780128014868
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Summary : Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection Presents models and methods for identifying unmarked individuals and species Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses Includes companion website containing data sets, code, solutions to exercises, and further information

The Geometry of Ecological Interactions

The Geometry of Ecological Interactions
  • Author : Ulf Dieckmann,Richard Law,Johan A. J. Metz,Metz, Johan Anton Jacob Metz
  • Publisher :Unknown
  • Release Date :2000-05-04
  • Total pages :564
  • ISBN : 0521642949
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Summary : Spatial ecology, space.