This is an extremely well-composed book, offering an interdisciplinary exposure to the concepts and methods that are very valuable to perform environmental and ecological data analysis. The contributors are recognized experts in the topics of their writing. The readers need to have a good background in statistical theory to read, comprehend, and appreciate the contents of this book. There are 35 well-written chapters covering modeling for environmental and ecological processes, time series methodology, dynamic modelling, geostatistical modelling, spatial-temporal point processes, data assimilation, univariate and multivariate extremes, sampling design, accommodating so many zeros, gradient analysis, species distribution, capture–recapture with distance sampling, animal movement, population demography, modelling traits, modelling vegetation fires, stream networks, exposure assessment, air pollution exposure, preferential sampling, monitoring networks, source apportionment, environmental epidemiology, connecting exposure to outcome, epidemiologic study designs, climatic trends and modelling, climatology, assimilating data to models, spatial extremes, statistics in oceanography, paleoclimate reconstruction, climate change detection and attribution, health risks of climate variability among others. The bibliography in every chapter provides up-to-date and thorough, well-selected and commented references for the readers.
Noteworthy features in this book are introducing uncertainty, anisotropy and non-stationarity, threshold exceedance, coenospace, stochasticity, tail-down models, entropy-based design among others.
I enjoyed a lot reading this book. I highly recommend this book to environmental, climate, statistics and computing researchers and practicing professionals.