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Note: In the spring semester 2019 (107-2), I will teach this class in three weekend blocks: 2/23-24 (Saturday+Sunday), 3/23-24 (Saturday+Sunday) and 5/25 (Saturday).

Numerical Methods in Community Ecology


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: 2/23-24 (Saturday+Sunday), 3/23-24 (Saturday+Sunday) and 5/25 (Saturday). Every day we start at 9:00! (detailed schedule is in Calendar)

Where: 4A Life Science building, National Taiwan University

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

TA: Jia-Ang “Will” Ou

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 project.

Link to CEIBA (NTU study system):

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 presentation of theory, application of methods using R program, solving individual exercise during the class, homework assignments, test of knowledge (analogy of midterm and final test) and preparation of individual projects and their presentation (in English).

Schedule: 5 days during 3 weekends. In selected weekend days, the course will start morning at 9 am and end evening around 6 pm. The first two weekends are focused on lectures and practice, the third weekend (including only Saturday) will be the final exam and presentations of individual projects.

Location: Life Science Building, National Taiwan University, Taipei.

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. Along to the 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 analysis of community data. This is not an R course for advanced use of R program - I expect 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 basics of R, consider taking my other class Introduction to R for Ecologists (regular 3 credit class taught every winter semester).


  • If you are NTU (or NTNU, NTUST) student, please enroll for this course under the code EEB5083 (B44 U1950) using standard way how you would enroll for any other course in NTU.
  • If you are from other university than NTU, NTNU or NTUST, you can register for the course as registered student outside NTU/NTNU/NTUST, or as an auditor. If you participate as registered student and you meet all criteria for final evaluation (homework assignments, midterm quiz, final test and final presentation), you will receive an unofficial certificate (issued by me, with your final grade, by with no credit value) about the participation in the course (you cannot get a credit from this course unless you are from NTU, NTNU or NTUST; theoretically, students from University of Taipei and Taipei National University of the Arts can also apply for credit, since these universities have contract with NTU about that). If you pre-registered for the course (before 31 December 2018), you will receive instructions how to register for the course by email, and you need to send the registration information before the registration deadline (January 17).


  • Basic knowledge of 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. Before the class begins, 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
    1. install R and RStudio and get familiar with it (we will use RStudio in the class);
    2. 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);
    3. know how to upload external data into R (from txt file, online file and Excel);
    4. know how to install and upload external R package;
    5. be able to plot simple data (scatterplot and boxplot).
  • Basic knowledge of statistics is expected (correlation, regression, ANOVA, testing the significance, confidence intervals, meaning of P-values etc.)
  • You need to bring your own computer with installed R and RStudio, and the 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).

Information for students outside of Taipei

  • The course is taught during the weekend to make it convenient for students outside of Taipei to attend. Please, find an accomodation near the NTU's campus, and make sure you can be in the class on Saturday morning 9 a.m. (if you are coming from farer distance, consider arriving to Taipei already on Friday night). If you do not wish to get certificate, you do not need to attend the third weekend (only one day focused on final test, presentations and discussion), even if I would suggest you to do so. If you have any question related to the participation in the course, please feel free to contact me ([email protected])!


  • 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.

numecol/start.txt · Last modified: 2019/02/23 06:30 by david