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Journal ArticleDOI

Program MARK: survival estimation from populations of marked animals

01 Jan 1999-Bird Study (Taylor & Francis Group)-Vol. 46
TL;DR: Mark as discussed by the authors provides parameter estimates from marked animals when they are re-encountered at a later time as dead recoveries, or live recaptures or re-sightings.
Abstract: MARK provides parameter estimates from marked animals when they are re-encountered at a later time as dead recoveries, or live recaptures or re-sightings. The time intervals between re-encounters do not have to be equal. More than one attribute group of animals can be modelled. The basic input to MARK is the encounter history for each animal. MARK can also estimate the size of closed populations. Parameters can be constrained to be the same across re-encounter occasions, or by age, or group, using the parameter index matrix. A set of common models for initial screening of data are provided. Time effects, group effects, time x group effects and a null model of none of the above, are provided for each parameter. Besides the logit function to link the design matrix to the parameters of the model, other link functions include the log—log, complimentary log—log, sine, log, and identity. The estimates of model parameters are computed via numerical maximum likelihood techniques. The number of parameters that are...
Citations
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Journal ArticleDOI
TL;DR: The steps of model selection are outlined and several ways that it is now being implemented are highlighted, so that researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.
Abstract: Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and mark-recapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.

3,489 citations

Journal ArticleDOI
TL;DR: Models with uninformative parameters are frequently presented as being competitive in the Journal of Wildlife Management, including 72% of all AIC-based papers in 2008, and authors and readers need to be more aware of this problem and take appropriate steps to eliminate misinterpretation.
Abstract: As use of Akaike's Information Criterion (AIC) for model selection has become increasingly common, so has a mistake involving interpretation of models that are within 2 AIC units (ΔAIC ≤ 2) of the top-supported model. Such models are <2 ΔAIC units because the penalty for one additional parameter is +2 AIC units, but model deviance is not reduced by an amount sufficient to overcome the 2-unit penalty and, hence, the additional parameter provides no net reduction in AIC. Simply put, the uninformative parameter does not explain enough variation to justify its inclusion in the model and it should not be interpreted as having any ecological effect. Models with uninformative parameters are frequently presented as being competitive in the Journal of Wildlife Management, including 72% of all AIC-based papers in 2008, and authors and readers need to be more aware of this problem and take appropriate steps to eliminate misinterpretation. I reviewed 5 potential solutions to this problem: 1) report all model...

2,700 citations

Journal ArticleDOI
TL;DR: It is found that null hypothesis testing is uninformative when no estimates of means or effect size and their precision are given, and an alternative paradigm of data analysis based on Kullback-Leibler information is described.
Abstract: This paper presents a review and critique of statistical null hypothesis testing in ecological studies in general, and wildlife studies in particular, and describes an alternative. Our review of Ecology and the Journal of Wildlife Management found the use of null hypothesis testing to be pervasive. The estimated number of P-values appearing within articles of Ecology exceeded 8,000 in 1991 and has exceeded 3,000 in each year since 1984, whereas the estimated number of P-values in the Journal of Wildlife Management exceeded 8,000 in 1997 and has exceeded 3,000 in each year since 1994. We estimated that 47% (SE = 3.9%) of the P-values in the Journal of Wildlife Management lacked estimates of means or effect sizes or even the sign of the difference in means or other parameters. We find that null hypothesis testing is uninformative when no estimates of means or effect size and their precision are given. Contrary to common dogma, tests of statistical null hypotheses have relatively little utility in science and are not a fundamental aspect of the scientific method. We recommend their use be reduced in favor of more informative approaches. Towards this objective, we describe a relatively new paradigm of data analysis based on Kullback-Leibler information. This paradigm is an extension of likelihood theory and, when used correctly, avoids many of the fundamental limitations and common misuses of null hypothesis testing. Information-theoretic methods focus on providing a strength of evidence for an a priori set of alternative hypotheses, rather than a statistical test of a null hypothesis. This paradigm allows the following types of evidence for the alternative hypotheses: the rank of each hypothesis, expressed as a model; an estimate of the formal likelihood of each model, given the data; a measure of precision that incorporates model selection uncertainty; and simple methods to allow the use of the set of alternative models in making, formal inference. We provide an example of the information-theoretic approach using data on the effect of lead on survival in spectacled eider ducks (Somateria fischeri). Regardless of the analysis paradigm used, we strongly recommend inferences based on a priori considerations be clearly separated from those resulting from some form of data dredging.

1,848 citations

Journal ArticleDOI
TL;DR: The R package unmarked provides a unified modeling framework for ecological research, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.
Abstract: Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

1,675 citations

BookDOI
TL;DR: The field of birds in urban environments has been a hot topic in the last few decades as discussed by the authors, with a large body of work focusing on the effects of urbanization on birds.
Abstract: Preface. Section 1: Introduction to the Study of Birds in Urban Environments. 1. A historical perspective on urban bird research: trends, terms, and approaches J.M. Marzluff, R. Bowman, R. Donnelly. 2. Worldwide urbanization and its effects on birds J.M. Marzluff. 3. Synanthropic birds of North America R.F. Johnston. 4. Human perception and appreciation of birds: A motivation for wildlife conservation in urban environments of France P. Clergeau, G. Mennechez, A. Sauvage, A. Lemoine. 5. Quantifying the urban gradient: linking urban planning and ecology M. Alberti, E. Botsford, A. Cohen. 6. Urbanization, avian communities, and landscape ecology J.R. Miller, J.M. Fraterrigo, N. Thompson Hobbs, D.M. Theobald, J.A. Wiens. 7. The importance of multi-scale analyses in avian habitat selection studies in urban environments M. Hostetler. Section 2: Processes Affecting Birds in Urban Environments. 8. Urban birds: Population, community, and landscape approaches D. Bolger. 9. Interactions among non-native plants and birds S. Hayden Reichard, L. Chalker-Scott, S. Buchanan. 10. Urban sprawl and juniper encroachment effects on abundance of wintering passerines in Oklahoma B.R. Coppedge, D.M. Engle, S.D. Fuhlendorf, R.E. Masters, M.S. Gregory. 11. Nest predator abundance and urbanization D.G. Haskell, A.M. Knupp, M.C. Schneider. 12. Bird tolerance to human disturbance in urban parks of Madrid (Spain): Management implications E. Fernandez-Juricic, M.D. Jiminez, E. Lucas. 13. Settlement of breeding European Starlings in urban areas: importance oflawns vs. anthropogenic wastes G. Mennechez, P. Clergeau. 14. Variation in the timing of breeding between suburban and wildland Florida Scrub-Jays: Do physiologic measures reflect different environments? S.J. Schoech, R. Bowman. Section 3: Bird Populations in Urban Environments. 15. The ecology of Western Gulls in habitats varying in degree of urban influence R. Pierotti, C. Annett. 16. Causes and consequences of expanding American Crow populations J.M. Marzluff, K.J. McGowan, R. Donnelly, R.L. Knight. 17. Demographic and behavioral comparisons of suburban and rural American Crows K. McGowan. 18. Nest success and the timing of nest failure of Florida Scrub-Jays in suburban and wildland habitats R. Bowman, G.E. Woolfenden. 19. Synurbanization of the Magpie in the Palearctic L. Jerzak. 20. Maccaw abundance in relation to human population density in the western Amazon basin D.M. Brooks, A.J. Begazo. 21. Waterbird production in an urban center in Alaska M.R. North. Section 4: Bird Communities in Urban Environments. 22. Creating a homogeneous avifauna R.B. Blair. 23. Avian community characteristics of urban greenspaces in St. Louis, Missouri J.M. Azerrad, C.H. Nilon. 24. The importance of the Chicago region and the 'Chicago Wilderness' initiative for avian conservation J.D. Brawn, D.F. Stotz. 25. Do temporal trends in Christmas Bird Counts reflect the spatial trends of urbanization in southwestern Ohio? N.A. Crosby, R.B. Blair. 26. Survey techniques and habitat relationships of breeding birds in residential areas of Toronto, Canad

931 citations

References
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Book
01 Jan 1983
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Abstract: The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log- likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components).

23,215 citations

Journal ArticleDOI
TL;DR: A class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis, of particular interest for statisticians in medicine, biology, agriculture, social science, and engineering.
Abstract: Addresses a class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis. Incorporates numerous exercises, both theoretical and data-analytic Discusses quasi-likelihood functions and estimating equations, models for dispersion effect, components of dispersion, and conditional likelihoods Holds particular interest for statisticians in medicine, biology, agriculture, social science, and engineering

5,678 citations

Journal ArticleDOI
TL;DR: Generalized linear models, 2nd edn By P McCullagh and J A Nelder as mentioned in this paper, 2nd edition, New York: Manning and Hall, 1989 xx + 512 pp £30
Abstract: Generalized Linear Models, 2nd edn By P McCullagh and J A Nelder ISBN 0 412 31760 5 Chapman and Hall, London, 1989 xx + 512 pp £30

5,146 citations

Journal ArticleDOI
TL;DR: A recent survey of capture-recapture models can be found in this article, with an emphasis on flexibility in modeling, model selection, and the analysis of multiple data sets.
Abstract: The understanding of the dynamics of animal populations and of related ecological and evolutionary issues frequently depends on a direct analysis of life history parameters. For instance, examination of trade-offs between reproduction and survival usually rely on individually marked animals, for which the exact time of death is most often unknown, because marked individuals cannot be followed closely through time. Thus, the quantitative analysis of survival studies and experiments must be based on capture- recapture (or resighting) models which consider, besides the parameters of primary interest, recapture or resighting rates that are nuisance parameters. Capture-recapture models oriented to estimation of survival rates are the result of a recent change in emphasis from earlier approaches in which population size was the most important parameter, survival rates having been first introduced as nuisance parameters. This emphasis on survival rates in capture-recapture models developed rapidly in the 1980s and used as a basic structure the Cormack-Jolly-Seber survival model applied to an homogeneous group of animals, with various kinds of constraints on the model parameters. These approaches are conditional on first captures; hence they do not attempt to model the initial capture of unmarked animals as functions of population abundance in addition to survival and capture probabilities. This paper synthesizes, using a common framework, these recent developments together with new ones, with an emphasis on flexibility in modeling, model selection, and the analysis of multiple data sets. The effects on survival and capture rates of time, age, and categorical variables characterizing the individuals (e.g., sex) can be considered, as well as interactions between such effects. This "analysis of variance" philosophy emphasizes the structure of the survival and capture process rather than the technical characteristics of any particular model. The flexible array of models encompassed in this synthesis uses a common notation. As a result of the great level of flexibility and relevance achieved, the focus is changed from fitting a particular model to model building and model selection. The following procedure is recommended: (1) start from a global model compatible with the biology of the species studied and with the design of the study, and assess its fit; (2) select a more parsimonious model using Akaike's Information Criterion to limit the number of formal tests; (3) test for the most important biological questions by comparing this model with neighboring ones using likelihood ratio tests; and (4) obtain maximum likelihood estimates of model parameters with estimates of precision. Computer software is critical, as few of the models now available have parameter estimators that are in closed form. A comprehensive table of existing computer software is provided. We used RELEASE for data summary and goodness-of-fit tests and SURGE for iterative model fitting and the computation of likelihood ratio tests. Five increasingly complex examples are given to illustrate the theory. The first, using two data sets on the European Dipper (Cinclus cinclus), tests for sex-specific parameters,

4,038 citations