Introduction to R for Terrestrial Ecology

This textbook covers R data analysis related to environmental science, starting with basic examples and proceeding up to advanced applications of the R programming language. The main objective of the textbook is to serve as a guide for undergraduate students, who have no previous experience with R, but part of the textbook is dedicated to advanced R applications, and will also be useful for Masters and PhD students, and professionals.

The textbook deals with solving specific programming tasks in R, and tasks are organized in terms of gradually increasing R proficiency, with examples getting more challenging as the chapters progress. The main competencies students will acquire from this textbook are:

• manipulating and processing data tables

• performing statistical tests

• creating maps in R This textbook will be useful in undergraduate and graduate courses in Advanced LandscapeEcology, Analysis of Ecological and Environmental Data, Ecological Modeling, Analytical Methods for Ecologists, Statistical Inference for Applied Research, Elements of Statistical Methods, Computational Ecology, Landscape Metrics and Spatial Statistics.

Introduces the problems and analysis of terrestrial ecology, and how R can help solve them Comprehensive tutorial begins with installation of the R software, demonstrates how to process data tables, proceeds to processing laboratory results and creating maps, and finally delves into advanced topics such as Supervised Learning Algorithms, Unsupervised Learning Algorithms, and Machine Learning For each chapter, input data that are needed for solving the specific tasks will be provided on Google Drive or similar web-based platform

Auteur
Dr. Milena Lakicevic is an Assistant Professor at University of Novi Sad, Faculty of Agriculture in Novi Sad, Serbia. She holds a PhD in biotechnical sciences and her primary area of expertise is application of R programming, multi-criteria analysis and decision support systems in landscape and natural resources management. Dr. Lakicevic spent six months (January-July 2018) at the Pacific Northwest Research Station (US Department of Agriculture, Forest Service) as a Fulbright Visiting Scholar, working on spatial decision support systems under supervision of Dr. Keith Reynolds. She has published over 60 scientific papers and served as a reviewer for international journals and gave guest lectures at several Universities in Europe.

Dr. Nicholas Povak is a Postdoctoral Fellow with the Oak Ridge Associated Universities and the Pacific Northwest Research Station (US Forest Service). He received his PhD from the University of Washington Seattle, where he explored the application of machine learning algorithms for the prediction of ecological processes such as stream water quality and cavity nesting bird habitat. Dr. Povak worked as a Post-doc with the Institute of Pacific Islands Forestry (Hilo, HI) developing decision support models to help manage invasive species, hydrologic flows, and sediment yields in tropical forested watersheds. Currently, Dr. Povak is applying machine learning algorithms in the R programming environment to predict fire severity patterns and controls on fire spread from satellite data and to better understand biophysical drivers of post-fire conifer regeneration. He also creates web applications using the Shiny R package to provide a portal for stakeholders to interact with decision support models to aid in their decision making process.

Dr. Keith Reynolds is a research forester with the Pacific Northwest Research Station (US Department of Agriculture, Forest Service) and is located at the Corvallis Forestry Sciences Laboratory in Corvallis, OR. Although Dr. Reynolds' advanced degrees are in quantitative plant epidemiology, his primary areas of expertise are in statistics, biomathematics, and knowledge-based systems theory and application. He has been the team leader of the Ecosystem Management Decision Support project at the PNW Station since 1993, designing and implementing new spatially enabled knowledge-based systems technologies for environmental analysis and planning. For the past 20 years, Dr. Reynolds also has been working in a wide variety of specific application areas, including watershed assessment, forest ecosystem sustainability, fish and wildlife habitat suitability, Regional Planning, national forest-fuels management, landscape restoration, environmental effects of atmospheric sulfur deposition, maintenance of national environmental infrastructure for the Army Corps of Engineers, and the national Terrestrial Condition Assessment of the US Forest Service. He has published over 125 refereed articles and book chapters, and has edited five volumes, including Making Transparent Environmental Management Decisions: Applications of the Ecosystem Management Decision Support System (Springer, 2014).

Contenu

1. Types of Data in R.- 2. Numerical Analysis.- 3. Creating Maps.- 4. Basic Statistical Tests.- 5. Predictive Modeling with Machine Learning Applications.- Appendix.