# Numerical Methods in Community Ecology

群聚生態學分析方法

EEB 5083, spring semester 110-2

The course focused on the analysis of community ecology data in R, organized by Institute of Ecology and Evolutionary Biology, College of Life Science, National Taiwan University

**When:** every Thursday 9:10-12:10 (classes will be recorded and recording made available to enrolled students and auditors)

**Where:** 3A Life Science building (生科3A), National Taiwan University

**Instructor:** David Zelený (澤大衛), Vegetation Ecology Lab

**TA:** Ching-Lin Huang (Andy) 黃敬麟, r09b44010@ntu.edu.tw

**Language of the course:** English

**Course content:** analysis of community ecology data, including ordination and cluster analysis, diversity analysis and analysis of species attributes. We will use real community ecology data (mostly vegetation and zoological datasets) and practice the analysis using the R program.

**Link to NTU COOL (NTU study system):**https://cool.ntu.edu.tw/courses/13065 (note that we do not use CEIBA anymore)

**Target audience:** senior undergraduate and graduate ecology students focused on botany and zoology, who are planning to do a study at the community level (i.e. not on a single species, but on the multiple species occurring at multiple localities). The class may be useful also for other disciplines handling multivariate data (e.g. microbiology), but the main focus is on ecological data. If you study PhD and want to join, you are welcome, but expect that some of the teaching materials will cover rather basic things.

**Teaching strategy:** The class includes an introduction to the theory, application of methods using the R program, solving individual exercises during the class, homework assignments, test of knowledge (analogy of midterm and final test) and preparation of individual projects and their presentation (in English).

**Disclaimer:** this is rather an intensive course, focused on a theory of community data analysis, and practical exercise using R on real community ecology datasets. In addition to lectures taught in the classroom, you need to also complete homework assignments, midterm quiz, final test and prepare (and present in English) the final project focused on the analysis of community data. This is not an R course for advanced use of the R program - I expect that you have a basic knowledge of R before you enter the class, but the main focus is on learning the theory behind the methods and their use, not advanced R programming. If you want to learn the basics of R, consider taking the other class I teach Introduction to R for Ecologists (regular 3 credit class taught every winter semester).

## Requirements:

**Basic knowledge of the R program**: the course is not focused on advanced use of R, but we will use R as a tool to do all the analysis, so the basic skill in operating R is necessary. You can learn R by yourself in advance or participate in some of the R courses. At the beginning of the class, we will have a sequence of simple mini-assignments to make sure you have basic R capability.

The minimum what you need to do is to- install R and RStudio and get familiar with it (we will use RStudio in the class);
- know how to assign the value to the object, what are the difference between object types (vector, matrix, data frame, list), and how to subset them (choose only part of the object for use);
- know how to upload external data into R (from a txt file, online file and Excel);
- know how to install and upload an external R package;
- be able to plot simple data (scatterplot and boxplot).

**Basic knowledge of statistics is expected**(correlation, regression, ANOVA, testing the significance, confidence intervals, the meaning of P-values etc.)- You need to bring your own computer with installed R and RStudio, and access to the Internet (if you are a student from outside NTU, you can use EDUROAM connection available in the classroom, or make sure you have access to your own portable mobile hotspot).

## References

- Borcard, D., Gillet, F. & Legendre, P. 2018.
*Numerical Ecology with R. Second Edition.*Springer. - Legendre, P. & Legendre, L. 2012.
*Numerical Ecology. Third English edition.*Elsevier Science BV, Amsterdam. - McCune, B. & Grace, J.B. 2002.
*Analysis of Ecological Communities.*MjM Software Design, Oregon. - Šmilauer, P. & Lepš, J. 2014.
*Multivariate Analysis of Ecological Data using Canoco 5. Second Edition.*Cambridge University Press, Cambridge, UK.