Showing posts with label inference. Show all posts
Showing posts with label inference. Show all posts

Sunday, May 26, 2013

+++ Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation) reviews


Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation)

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Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation)

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"Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation)" Feature

Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.






Saturday, March 16, 2013

%$$$ Best Revews Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach


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Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach

Main Features : Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.