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Spatial Capture Recapture

Spatial Capture Recapture Book
Author : J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
Publisher : Academic Press
Release : 2013-08-27
ISBN : 012407152X
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Greg Distiller
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download A Continuous time Formulation for Spatial Capture recapture Models book written by Greg Distiller, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

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 Book
Author : Neil Faught
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

On the Estimation of Animal Density from Spatial Capture recapture Data

On the Estimation of Animal Density from Spatial Capture recapture Data Book
Author : Callum Kwun Yuen Young
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

On the Topic of Spatial Capture Recapture Modeling

On the Topic of Spatial Capture Recapture Modeling Book
Author : Paul McLaughlin
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Richard Brian Mrozinski
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Estimating Mountain Lion Density Using Unmarked Bayesian Spatial Capture recapture for the Davis Mountains Texas book written by Richard Brian Mrozinski, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

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 Book
Author : Nicholas Forman
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

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 Book
Author : Catherine Sun
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Using Quantitative Approaches to Estimate Space use Population Dynamics Behavior and Climate Change Adaptive Potential for the Red backed Salamander Plethodon Cinereus

Using Quantitative Approaches to Estimate Space use  Population Dynamics  Behavior  and Climate Change Adaptive Potential for the Red backed Salamander Plethodon Cinereus Book
Author : David Munoz
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

The red-backed salamander, Plethodon cinereus, is a common woodland amphibian that is found throughout much of eastern North America. The species is important to forest ecological processes, and changes in their population density are often used to measure the impacts of forest management, pollution, and environmental change. Therefore, consistent methods of density estimation are required. In the first chapter, I review spatial capture-recapture, a modern modeling tool that incorporates spatial information to reliably estimate population density without the need for the ad-hoc methods that render other density estimates incomparable. It can also be used to make inferences on space-use, population dynamics, and connectivity. I then demonstrate the versatility of spatial capture-recapture using P. cinereus mark-recapture data collected from my study sites in central Pennsylvania. For the second chapter of this thesis, I use spatial capture-recapture and other modeling approaches to test hypotheses about P. cinereus climate change adaptive capacity. This salamander is a convenient model for understanding dispersal-limited species, so I tested eight hypotheses to see how behavioral plasticity and fitness were affected by climate variability. Based on previous evidence, I also tested whether a common color polymorphism is a useful visual cue for predicting within-population variation in climate tolerances. Using four years of mark-recapture information from Maryland, I found the color morph is not a useful indicator, but overall, the population did show strong climate preferences, indicating that population persistence could be threatened by warmer and drier conditions predicted in the future.

Analysis of Capture Recapture Data

Analysis of Capture Recapture Data Book
Author : Rachel S. McCrea,Byron J. T. Morgan
Publisher : CRC Press
Release : 2014-08-01
ISBN : 1439836604
Language : En, Es, Fr & De

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Book Description :

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

Canadian Journal of Fisheries and Aquatic Sciences

Canadian Journal of Fisheries and Aquatic Sciences Book
Author : Anonim
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Canadian Journal of Fisheries and Aquatic Sciences book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology Book
Author : J. Andrew Royle,Robert M. Dorazio
Publisher : Elsevier
Release : 2008-10-15
ISBN : 0080559255
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Allan F. O'Connell,James D. Nichols,K. Ullas Karanth
Publisher : Springer Science & Business Media
Release : 2010-10-05
ISBN : 9784431994954
Language : En, Es, Fr & De

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Book Description :

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.

Occupancy Estimation and Modeling

Occupancy Estimation and Modeling Book
Author : Darryl I. MacKenzie,James D. Nichols,J. Andrew Royle,Kenneth H. Pollock,Larissa Bailey,James E. Hines
Publisher : Elsevier
Release : 2017-11-17
ISBN : 0124072453
Language : En, Es, Fr & De

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Book Description :

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

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 Book
Author : Marc Kery,J. Andrew Royle
Publisher : Academic Press
Release : 2020-10-10
ISBN : 0128097272
Language : En, Es, Fr & De

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Book Description :

Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS, Volume Two: Dynamic and Advanced Models provides a synthesis of the state-of-the-art in hierarchical models for plant and animal distribution, also focusing on the complex and more advanced models currently available. The book explains all procedures in the context of hierarchical models that represent a unified approach to ecological research, thus taking the reader from design, through data collection, and into analyses using a very powerful way of synthesizing data. Makes ecological modeling accessible for people who are struggling to use complex or advanced modeling programs Synthesizes current ecological models and explains how they are inter-connected Contains examples throughout the book, walking the reading through scenarios with both real and simulated data Presents an ideal resource for ecologists working in R, an open source version of S known for its exceptional ecology analyses, and in BUGS for more flexible Bayesian analyses

Methods For Monitoring Tiger And Prey Populations

Methods For Monitoring Tiger And Prey Populations Book
Author : K. Ullas Karanth,James D. Nichols
Publisher : Springer
Release : 2017-10-26
ISBN : 9811054363
Language : En, Es, Fr & De

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Book Description :

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.

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 Book
Author : Marc Kery,J. Andrew Royle
Publisher : Academic Press
Release : 2019-11-15
ISBN : 9780128095850
Language : En, Es, Fr & De

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Book Description :

Applied Hierarchical Modeling in Ecology, Volume 2 builds upon the first volume. It continuing to provide a synthesis of the state of the art in hierarchical models for plant and animal distribution, but in this volume focuses on the complex and more advanced models currently available. This book explains all procedures in the context of hierarchical models, which represent a unified approach to ecological research taking the reader from design, through data collection and into the analyses using a very powerful way of synthesizing data. Applied Hierarchical Modeling in Ecology Volume 2 makes ecological modelling accessible for people who are struggling to use current available complex or advanced modelling programs Synthesizes current ecological models and explains how they are inter-connected Specific examples are given throughout the book, walking the reading through scenarios with each real and simulated data for greater comprehension Ideal for ecologists working in R, an open source version of S known for its exceptional ecology analyses and in BUGS for more flexible Bayesian analyses

Introduction to Ecological Sampling

Introduction to Ecological Sampling Book
Author : Bryan F.J. Manly,Jorge A. Navarro Alberto
Publisher : CRC Press
Release : 2014-10-20
ISBN : 1466555149
Language : En, Es, Fr & De

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Book Description :

An Easy-to-Understand Treatment of Ecological Sampling Methods and Data Analysis Including only the necessary mathematical derivations, Introduction to Ecological Sampling shows how to use sampling procedures for ecological and environmental studies. It incorporates both traditional sampling methods and recent developments in environmental and ecological sampling methods. After an introduction, the book presents standard sampling methods and analyses. Subsequent chapters delve into specialized topics written by well-known researchers. These chapters cover adaptive sampling methods, line transect sampling, removal and change-in-ratio methods, plotless sampling, mark-recapture sampling of closed and open populations, occupancy models, sampling designs for environmental modeling, and trend analysis. The book explains the methods as simply as possible, keeping equations and their derivations to a minimum. It provides references to important, more advanced sampling methods and analyses. It also directs readers to computer programs that can be used to perform the analyses. Accessible to biologists, the text only assumes a basic knowledge of statistical methods. It is suitable for an introductory course on methods for collecting and analyzing ecological and environmental data.