James S. Clark – författare

New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.

New Statistical tools are changing the wau in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide constant framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirment for a clear exposition of the methodology through to application for a range of environmental challenges.

The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.* Consistent treatment from classical to modern Bayes * Underlying distribution theory to algorithm development * Many examples and applications * Does not assume statistical background * Extensive supporting appendixes * Accompanying lab manual in R

The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.* Consistent treatment from classical to modern Bayes * Underlying distribution theory to algorithm development * Many examples and applications * Does not assume statistical background * Extensive supporting appendixes * Accompanying lab manual in R

Biomass burning profoundly affects atmospheric chemistry, the carbon cycle, and climate and may have done so for millions of years. Bringing together renowned experts from paleoecology, fire ecology, atmospheric chemistry, and organic chemistry, the volume elucidates the role of fire during global changes of the past and future. Topics covered include: the characterization of combustion products that occur in sediments, including char, soot/fly ash, and polycyclic aromatic hydrocarbons; the calibration of these constituents against atmospheric measurements from wildland and prescribed fire emissions; spatial and temporal patterns in combustion emissions at scales of individual burns to the globe.

Biomass burning profoundly affects atmospheric chemistry, the carbon cycle, and climate and may have done so for millions of years. Bringing together renowned experts from paleoecology, fire ecology, atmospheric chemistry, and organic chemistry, the volume elucidates the role of fire during global changes of the past and future. Topics covered include: the characterization of combustion products that occur in sediments, including char, soot/fly ash, and polycyclic aromatic hydrocarbons; the calibration of these constituents against atmospheric measurements from wildland and prescribed fire emissions; spatial and temporal patterns in combustion emissions at scales of individual burns to the globe.