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
... 717–718 , 721 Doris , B. , 158–159 Dormann , C. F. , 260 , 272 , 455–456 , 495 , 502 Doutrelant , C. , 500 Draper ... M. R. , 620 Eaton , M. J. , 397 Edwards , H. H. , 123–124 Edwards , M. R. , 583 Edwards , V. L. , 691 Edwards Jr.
... we ignore this moderate lack of fit here. par(mfrow = c(1, 3), mar = c(5,5,3,2), cex = 1.3, cex.lab = 1.5, cex.axis = 1.5) hist(out5$summary[276:542, 1], xlab = "Pearson residuals", col = "grey", breaks = 50, main = "", freq = F, ...
This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works.
Optimal capacity decisions in a developping fisheries. Marine Resource Economics, 1:25–54, 1985. C.W. Clark and M. Mangel. Dynamic State Variable Models in Ecology. Methods and Applications. Oxford Series in Ecology and Evolution.
Darroch, J. N. (1958). The multiple-recapture census. I. Estimation of a closed population. Biometrika 45, 343–359. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm.
However, in year 15, recapture effort was zero, and hence we fix recapture probability at zero in that year. ... occasionsCR-1)){ p[t]
Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,
... Evan Grant, Tabitha Graves, Marc Kéry, Brett McClintock, Leslie New, Allan O'Connell, Krishna Pacifici, Agustín Paviolo, Brian Reich, Robin Russell, Sabrina Servanty, Cat Sun, Yifang Li, Earvin Balderama, and Chris Sutherland.
Calculating Shannon's entropy into GRASS GIS can be done relying onr.li functions such as: r.li.shannon input1⁄4name config1⁄4string output1⁄4name The config parameter is a configuration file basically referring to the grain and the ...
This book shows the lessons learned from teaching this material to several cohorts of graduate students. No other book I've read gives such a good feel for the compromises scientists have to make in searching for good statistical models.