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Spatial Capture-Recapture

Spatial Capture-Recapture
A Book

by J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner

  • Publisher : Academic Press
  • Release : 2013-08-27
  • Pages : 612
  • ISBN : 012407152X
  • Language : En, Es, Fr & De
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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

A Continuous-time Formulation for Spatial Capture-recapture Models

A Continuous-time Formulation for Spatial Capture-recapture Models
A Book

by Greg Distiller

  • Publisher : Unknown Publisher
  • Release : 2017
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Incorporating Animal Movement with Distance Sampling and Spatial Capture-recapture

Incorporating Animal Movement with Distance Sampling and Spatial Capture-recapture
A Book

by Richard Glennie

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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On the Estimation of Animal Density from Spatial Capture-recapture Data

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

by Callum Kwun Yuen Young

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 164
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

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
A Book

by Neil Faught

  • Publisher : Unknown Publisher
  • Release : 2020
  • Pages : 86
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

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

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

by Jason Paul Marshal

  • Publisher : Unknown Publisher
  • Release : 2017
  • Pages : 188
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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On the Topic of Spatial Capture-Recapture Modeling

On the Topic of Spatial Capture-Recapture Modeling
A Book

by Paul McLaughlin

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

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
A Book

by Richard Brian Mrozinski

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 258
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Noninvasive Genetic Sampling with a Bayesian Spatial Capture-recapture Analysis to Estimate Abundance of Roosevelt Elk (Cervus Canadensis Roosevelti)

Noninvasive Genetic Sampling with a Bayesian Spatial Capture-recapture Analysis to Estimate Abundance of Roosevelt Elk (Cervus Canadensis Roosevelti)
A Book

by Makenzie Henk

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 42
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Determining abundance of Roosevelt elk (Cervus canadensis roosevelti) in central Humboldt County, California has presented a unique challenge to wildlife managers due to the dense forest habitat and the animals’ elusive behavior. As the elk population has increased, so has human-wildlife conflict, and wildlife agencies need efficient and repeatable methods for determining abundance to inform management decisions. Traditional monitoring methods such as helicopter surveys are ineffective due to low sighting probability and strong behavioral responses to the aircraft. They also often lead to biased sex ratios when the distribution of males and females varies across the landscape. Non-invasive genetic sampling combined with spatial capture-recapture (SCR) is an alternative approach to monitoring populations that are difficult to observe directly. This study combined a Bayesian SCR with a binomial point process modeling approach and an unstructured single survey search method to estimate elk abundance. We aimed to increase the count of males by using a detection dog to search forested areas, and searched open grassy hillsides for cow-calf groups. Additionally, GPS collar data were used to quantify cohesion of movement among elk through a spatiotemporal analysis of home ranges. Over two seasons, we genotyped 436 unique individuals (326 females, 110 males). For the SCR analysis, we used sex and survey effort as covariates in detection probability, and used a “trap”-level random effect to account for the overdispersion in the count data from the herding behavior of elk. The population estimate in the study area was 618 ± 36.34 individuals (95% BCI 551-693) with a density of 1.09 ± 0.06 elk per km2. This study demonstrated a reliable way to obtain a biological reasonable population estimate for elk in an area that is not conducive to traditional monitoring methods.

Analysis of Capture-Recapture Data

Analysis of Capture-Recapture Data
A Book

by Rachel S. McCrea,Byron J. T. Morgan

  • Publisher : CRC Press
  • Release : 2014-08-01
  • Pages : 314
  • ISBN : 1439836604
  • Language : En, Es, Fr & De
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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

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
The Analysis of Data from Populations, Metapopulations and Communities

by J. Andrew Royle,Robert M. Dorazio

  • Publisher : Elsevier
  • Release : 2008-10-15
  • Pages : 464
  • ISBN : 0080559255
  • Language : En, Es, Fr & De
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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

Estimating Black Bear Population Density in the Southern Black Bear Range of New York with a Non-invasive, Genetic, Spatial Capture-recapture Study

Estimating Black Bear Population Density in the Southern Black Bear Range of New York with a Non-invasive, Genetic, Spatial Capture-recapture Study
A Book

by Catherine Sun

  • Publisher : Unknown Publisher
  • Release : 2014
  • Pages : 228
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Estimating population density and describing spatial patterns are important in conservation and management of wildlife populations. We conducted a non-invasive, genetic, spatial capture-recapture study of black bears (Ursus americanus) in a region of New York in 2011 and 2012 where its range has expanded in order to 1) estimate population density, 2) test for spatial patterns of range expansion related to landcover, and 3) evaluate patterns of genetic diversity. Estimated population density was 9 bears / 100 km2, low compared to other black bear populations in the U.S. We identified patterns in density and detection probability related to landcover types that differed from expected patterns of resource use. Genetic diversity was comparable to that of non-expanding black bear populations, but we also detected a potential signature of population admixture. In addition, we conducted simulations investigating the effects of different sampling designs on population estimation in large mammal studies. Spatially clustered sampling devices resulted in the most accurate and precise estimates, and performance differences between designs diminished as home range size increased.

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
A Book

by Nicholas Forman

  • Publisher : Unknown Publisher
  • Release : 2015
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

Capture-Recapture: Parameter Estimation for Open Animal Populations

Capture-Recapture: Parameter Estimation for Open Animal Populations
A Book

by George A. F. Seber,Matthew R. Schofield

  • Publisher : Springer
  • Release : 2019-08-13
  • Pages : 663
  • ISBN : 3030181871
  • Language : En, Es, Fr & De
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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.

Methods For Monitoring Tiger And Prey Populations

Methods For Monitoring Tiger And Prey Populations
A Book

by K. Ullas Karanth,James D. Nichols

  • Publisher : Springer
  • Release : 2017-10-26
  • Pages : 303
  • ISBN : 9811054363
  • Language : En, Es, Fr & De
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This book addresses issues of monitoring populations of tigers, ungulate prey species and habitat occupancy, with relevance to similar assessments of large mammal species and general biodiversity. It covers issues of rigorous sampling, modeling, estimation and adaptive management of animal populations using cutting-edge tools, such as camera-traps, genetic identification and Geographic Information Systems (GIS), applied under the modern statistical approach of Bayesian and likelihood-based inference. Of special focus here are animal survey data derived for use under spatial capture-recapture, occupancy, distance sampling, mixture-modeling and connectivity analysees. Because tigers are an icons of global conservation, in last five decades,enormous amounts of commitment and resources have been invested by tiger range countries and the conservation community for saving wild tigers. However, status of the big cat remains precarious. Rigorous monitoring of surviving wild tiger populations continues to be essential for both understanding and recovering wild tigers. However, many tiger monitoring programs lack the necessary rigor to generate the reliable results. While the deployment of technologies, analyses, computing power and human-resource investments in tiger monitoring have greatly progressed in the last couple of decades, a full comprehension of their correct deployment has not kept pace in practice. In this volume, Dr. Ullas Karanth and Dr. James Nichols, world leaders in tiger biology and quantitative ecology, respectively, address this key challenge. The have collaborated with an extraordinary array of 30 scientists with expertise in a range of necessary disciplines - biology and ecology of tigers, prey and habitats; advanced statistical theory and practice; computation and programming; practical field-sampling methods that employ technologies as varied as camera traps, genetic analyses and geographic information systems. The book is a 'tour de force' of cutting-edge methodologies for assessing not just tigers but also other predators and their prey. The 14 chapters here are lucidly presented in a coherent sequence to provide tiger-specific answers to fundamental questions in animal population assessment: why monitor, what to monitor and how to monitor. While highlighting robust methods, the authors also clearly point out those that are in use, but unreliable. The managerial dimension of tiger conservation described here, the task of matching monitoring objectives with skills and resources to integrate tiger conservation under an adaptive framework, also renders this volume useful to wildlife scientists as well as conservationists.

Cheetahs: Biology and Conservation

Cheetahs: Biology and Conservation
Biodiversity of the World: Conservation from Genes to Landscapes

by Anonim

  • Publisher : Academic Press
  • Release : 2017-11-28
  • Pages : 596
  • ISBN : 012804120X
  • Language : En, Es, Fr & De
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Cheetahs: Biology and Conservation reports on the science and conservation of the cheetah. This volume demonstrates the interdisciplinary nature of research and conservation efforts to study and protect the cheetah. The book begins with chapters on the evolution, genetics, physiology, ecology and behavior of the species, as well as distribution reports from range countries. These introductory chapters lead into discussions of the challenges facing cheetah survival, including habitat loss, declining prey base, human-wildlife conflict, illegal trade, and newly-emerging threats, notably climate change. This book also focuses on conservation strategies and solutions, including environmental education and alternative livelihoods. Chapters on the role of captive cheetahs to conservation and the long-term research of the species are included, as are a brief discussion of the methods and analyses used to study the cheetah. The book concludes with the conservation status and future outlook of the species. Cheetahs: Biology and Conservation is a valuable resource for the regional and global communities of cheetah conservationists, researchers, and academics. Although cheetah focussed the book provides information relevant to the study of broader topics such as wildlife conservation, captive breeding, habitat management, conservation biology and animal behaviour. Cover photograph by Angela Scott Includes chapters by the world’s leading cheetah researchers and practitioners, who have focused their efforts on this high-profile species of conservation concern Provides findings as a combination of scientific detail and basic explanations so that they can be available not only to cheetah researchers and conservationists, but also to policy makers, business leaders, zoo managers, academics, students, and people interested in the cheetah and its future Presents the current knowledge of the species, helping lay the foundations and best practices for cheetah conservation and research worldwide Additional protocols and forms (which were provided by authors) can be found at the Cheetahs: Biology and Conservation companion site: https://www.elsevier.com/books-and-journals/book-companion/9780128040881

Handbook of Environmental and Ecological Statistics

Handbook of Environmental and Ecological Statistics
A Book

by Alan E. Gelfand,Montserrat Fuentes,Jennifer A. Hoeting,Richard Lyttleton Smith

  • Publisher : CRC Press
  • Release : 2019-01-15
  • Pages : 854
  • ISBN : 1498752128
  • Language : En, Es, Fr & De
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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.

Occupancy Estimation and Modeling

Occupancy Estimation and Modeling
Inferring Patterns and Dynamics of Species Occurrence

by Darryl I. MacKenzie,James D. Nichols,J. Andrew Royle,Kenneth H. Pollock,Larissa Bailey,James E. Hines

  • Publisher : Elsevier
  • Release : 2017-11-17
  • Pages : 648
  • ISBN : 0124072453
  • Language : En, Es, Fr & De
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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

Reptile Ecology and Conservation

Reptile Ecology and Conservation
A Handbook of Techniques

by Dodd Jr.

  • Publisher : Oxford University Press
  • Release : 2016-05-05
  • Pages : 512
  • ISBN : 0191039071
  • Language : En, Es, Fr & De
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This practical handbook of reptile field ecology and conservation brings together a distinguished, international group of reptile researchers to provide a state-of-the-art review of the many new and exciting techniques used to study reptiles. The authors describe ecological sampling techniques and how they are implemented to monitor the conservation status and population trends of snakes, lizards, tuatara, turtles, and crocodilians throughout the world. Emphasis is placed on the extent of statistical inference and the biases associated with different techniques and analyses. The chapters focus on the application of field research and data analysis for achieving an understanding of reptile life history, population dynamics, movement patterns, thermal ecology, conservation status, and the relationship between reptiles and their environment. The book emphasises the need for thorough planning, and demonstrates how a multi-dimensional approach incorporates information related to morphology, genetics, molecular biology, epidemiology, statistical modelling, animal welfare, and biosecurity. Although accentuating field sampling, sections on experimental applications in laboratories and zoos, thermal ecology, genetics, landscape ecology, disease and biosecurity, and management options are included. Much of this information is scattered in the scientific literature or not readily available, and the intention is to provide an affordable, comprehensive synthesis for use by graduate students, researchers, and practising conservationists worldwide.

Encyclopedia of Ecology

Encyclopedia of Ecology
A Book

by Brian D. Fath

  • Publisher : Elsevier
  • Release : 2018-08-23
  • Pages : 2780
  • ISBN : 0444641300
  • Language : En, Es, Fr & De
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Encyclopedia of Ecology, Second Edition continues the acclaimed work of the previous edition published in 2008. It covers all scales of biological organization, from organisms, to populations, to communities and ecosystems. Laboratory, field, simulation modelling, and theoretical approaches are presented to show how living systems sustain structure and function in space and time. New areas of focus include micro- and macro scales, molecular and genetic ecology, and global ecology (e.g., climate change, earth transformations, ecosystem services, and the food-water-energy nexus) are included. In addition, new, international experts in ecology contribute on a variety of topics. Offers the most broad-ranging and comprehensive resource available in the field of ecology Provides foundational content and suggests further reading Incorporates the expertise of over 500 outstanding investigators in the field of ecology, including top young scientists with both research and teaching experience Includes multimedia resources, such as an Interactive Map Viewer and links to a CSDMS (Community Surface Dynamics Modeling System), an open-source platform for modelers to share and link models dealing with earth system processes