Ecology in R
4.4 (81 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
364 students enrolled

Ecology in R

Ecological data-mining, map making and GIS, SDM, movement models/home range analyses, phylogenetics & tree building
4.4 (81 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
364 students enrolled
Created by Russell J Gray
Last updated 7/2020
English [Auto]
Current price: $12.99 Original price: $19.99 Discount: 35% off
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This course includes
  • 4.5 hours on-demand video
  • 1 article
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Mining ecological and environmental data
  • Species range map making
  • Species Distribution Modelling (SDM)
  • Movement data analysis
  • Trapping data analysis
  • Phylogenetic tree building
  • Download R and R Studios
  • Internet connection

Learn a wide variety of ecological data analyses by mining your own species occurrences and environmental data from various online sources. R code provided in each lesson is reproducible and easy to modify for your own projects and research. Upon completion of this course, you should have the knowledge to perform these analyses and GIS techniques with your own data, with an improved knowledge and understanding of the packages, functions, and R language.

Who this course is for:
  • Ecologists
  • Researchers
  • Wildlife conservationists
  • Naturalists
  • Environmental data scientists
Course content
Expand all 20 lectures 04:29:26
+ Lesson 1: Introduction to R
5 lectures 38:34

File download

Ecology in R file package download

Lesson 1 Overview

Introduction to R

•Packages: dplyr, lubridate

•Basic calculations

•Vectors and strings

•Data frames

•Data manipulation

•Time formatting

Importing and Exporting Data

•Simple importing and exporting of .csv files

Visualization Basics

•Packages: ggplot2

•Base r


Preview 01:16

An introductory lesson to the R language, with definitions of vectors, data frames, and objects

Preview 21:15

Simple examples of importing and exporting data into and out of R

Importing and Exporting Data

Basic concepts of data visualizations in base R and ggplot

Visualization Basics
+ Lesson 2: Species Occurrences & Density/Distance Analysis
3 lectures 57:44

Lesson 2 Overview

Occurrence Data

•Packages: rgbif, mapview, scrubr, sp, dplyr

•Data mining species occurrence data from GBIF

•Creating spatial points data objects

•Exploratory visualization techniques

Species Density Maps

•Packages: sp, raster, usdm, mapview, rgbif, scrubr, GISTools, maps, ggplot2, RColorBrewer, ggspatial

•Spatial data cleaning techniques

•Querying country polygon outlines

•Clustering techniques

•Basic species density and spatial distance analyses

•Multiple data visualization techniques

Lesson 2 overview

A walk-through of Lesson 2, exercise 1

Species Occurrence Data Mining

Density and distance analysis using species occurrence data queried from GBIF, various mapping techniques, and simple statistical analyses.

Species Density and Distance Analysis
+ Lesson 3: Field Guide Maps & SDM
4 lectures 42:28

Lesson 3 Overview

Environmental Data

•Packages: raster, mapview, ggplot2, dplyr, rgbif, maptools, scrubr

•Querying environmental data (elevation, temperature) from various online sources

•Forming datasets with species occurrence records and extracted environmental data

•Raster manipulation techniques

Field Guide Maps

•Packages: rnaturalearth, rnaturalearthdata, ggspatial, sf, dismo, jsonlite, mapdata, raster, mapview, ggplot2, dplyr, rgbif, maptools, rgdal

•Use environmental data to project current climate suitability models

•Raster to polygon manipulation for range map generation

Species Distribution Models (SDM)

•Packages: dismo, jsonlite, mapdata, raster, mapview, ggplot2, dplyr, rgbif, maptools, rgdal

•Using climate data to create current and future projections of climate suitability models

Lesson 3 Overview

Mining environmental data using the raster package, basic raster manipulation techniques, extracting data from species occurrences, and building species data frames.

Gathering Environmental Data

Basic techniques to create species distribution maps  based on climate suitability

Correction: In the video I say the highest AUC value indicates the best fit model. I accidentally misspoke in this regard. The best fit model is indicated by the LOWEST AUC value. I must have been still thinking of AIC at the time, in which the best fit model is the highest value. Sorry for the confusion everyone!

Field Guide Maps

A basic introduction to MaxEnt modelling in R using current and future climate data from WorldClim and species occurence data from GBIF.

Note: The models generated in this exercise and the last exercise are simply to show you the basic functionality of MaxEnt in R. SDMs should incorporate multiple parameters and as much data as possible to make robust models when used for scientific publication.

Species Distribution Modelling (SDM)
+ Lesson 4: Movement Ecology
3 lectures 01:00:55

Lesson 4 Overview

Movement Dynamics

•Packages: adehabitatHR, sp, ggplot2, dplyr, lubridate, mapview

•Analysis of time series, tracking data

•Tabulating movement summaries and saving to .csv

Home Ranges

•Packages: move, adehabitatHR, caTools, spatialEco, reshape2, tibble, sp, ggplot2, dplyr, lubridate, mapview, cowplot, ggspatial

•Testing several utilization density (UD) estimators

•Visual and mathematical methods of examining Type I (overfitting) and Type II (under-fitting) errors in UD estimators

Lesson 4 Overview

A tutorial on basic exploratory analyses on movement data

Movement Dynamics

An in-depth lecture on movement home range models and the limitations of Utilization Density (UD) estimators regarding Type I (over-fitting) and Type II (under-fitting) errors.

Home Range Analyses
+ Lesson 5: Trapping Data
2 lectures 36:23

Lesson 5 Overview

Trapping Analysis

•Packages: rgbif, mapview, sp, dplyr, ggplot2, Viridis, scatterpie, raster, reshape2, tibble, Vegan, BiodiversityR, ggspatial

•Query trapping study datasets from GBIF

•Data wrangling techniques to transpose data columns to long-form

•Visualizations of species richness

•Map richness based pie-charts for each study site

•Analysis of species richness, density, and abundance

•Species richness indices

•Rarefaction curves, and rank abundance plots

Lesson 5 Overview

A simple walk-through on querying datasets from GBIF, exploring trapping data, and using data visualizations and statistical indexes to analyze the data.

Note: When discussing the best-fit model of the radlattice() function call on line 274, I incorrectly state the NULL model would be the best-fit model in this case, therefore disqualifying the others. The best-fit model is in fact the Mandelbrot test with an AIC of 350.17.

Sorry for my mistake and any confusion it may have caused!

Trapping Analyses
+ Lesson 6: Phylogenetics in R
3 lectures 33:22

Lesson 6 Overview

Trait Analysis

•Packages: ape, taxise, rentrez, phytools, Select, treeio, ggtree, data.tree, tidytree, ggplot2, dplyr, traits, stringr, cowplot

•Create phylogenetic trees using a species list and taxonomic data from NCBI

•Examine various branch distance simulation options

•Combine trait data with tree data

•Subset and visualize trait data in trees for comparative analysis

Sequence Analysis

•Packages: sequin, adegenet, ape, ggtree, DECIPHER, viridis, ggplot2

•Query raw Internal Transcribed Spacer (ITS) sequences from NCBI

•Read fasta data into R

•Form alignments and distance matrices

•Visualize tree data using baseR

•Modify and customize tree plots in ggtree

Lesson 6 Overview

An end-to-end tutorial on gathering sequence data from NCBI in fasta format, importing/exporting, aligning, and building customized trees using ggtree.

Sequence Analysis

Basic guide to tree building and including trait data in R for comparative analysis

Trait Analysis