R Coding for Ecology

This book is devoted to ecological data analysis with R, with examples created by the developers of the described packages. R software is now an important reality in ecological informatics. Specific packages have been developed to manage, analyse and visualize ecological data. This book is a comprehensive catalogue of ecological R packages with running examples! This step-by-step procedure book is aimed at students and researchers with previous experience in R with a passion for coding their own ecological analyses.

The first book in ecology with running examples made by the authors of the presented packages Main branches of ecology are included, e.g., community ecology, spatial ecology, ecological remote sensing Leads young and experienced researchers to build their own code starting from the presented examples

Autorentext

Duccio Rocchini [born in Siena in 1975] is Full Professor at Alma Mater Studiorum University of Bologna since December 2019, after having been Associate Professor in Biology and Ecology at the University of Trento, Italy. He attained his PhD in 2005, dealing with remote sensing applied to the study of plant communities. His main research interests are related to plant community ecology, biodiversity analysis at multiple spatial scales, species distribution modelling, spatial and computational ecology and ecological remote sensing.

Over the years he promoted the use of remote sensing for the study of biodiversity change in space and time, publishing more than 200 indexed papers on this topic. He is continuing this activity in projects like the H2020 SHOWCASE or the Horizon Europe B3 and EarthBridge projects.

Inhalt

Part I: Introduction.- 1. Introduction to the book.- . Part II: Species distribution modelling and Community ecology.- 2. The sdm R package for species distribution modelling .- 3. The ecospat R package: a collection of pre-, core- and post-modeling tools to investigate species niches and distributions .- 4. The ecodist package for dissimilarity-based analysis of ecological data .- 5. Sampling ecologically meaningful pseudo-absences with the USE R package .- 6. Rarefaction curves with Rarefy package .- . Part III: Ecological remote sensing.- 7. A plant trait-based approach to map forest biodiversity using imaging spectroscopy and physical modeling .- 8. Bridging remote sensing and movement ecology .- 9. The rasterdiv package for measuring diversity from space .- 10. The imageRy package for teaching remote sensing in a straightforward way .- . Part IV: Spatial ecology.- 11. The landscapemetrics and motif packages for measuring landscape patterns and processes.- 12. The CAST package for training and assessment of spatial prediction models .- 13. The cblindplot R package: Building new bridges to visually inclusive science for colourblind people.- 14. The cartogram package for mapping ecological patterns.