Professor of Ecology

Department of Biological Applications and Technology
University of Ioannina
Ioannina 45110, Greece


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Department of Biological Application & Technology

University of Ioannina


Some of the exciting courses that I teach or coordinate at the University of Ioannina and elsewhere.

General Ecology
We carry out introduction to ecology with laboratories in theory, data analysis and fieldwork including an excursion to Zagori.


  1. Populations. Sampling and density, population growth, density dependence, life tables, age-structured populations
  2. Ecological interactions. Interactions (++, +- and –), competition, predation, herbivory, parasitism, symbiosis, coevolution.
  3. Communities and biodiversity. Diversity indices, species abundance relations, types of diversity, SARs, island biogeography.


  1. Population dynamics (PC lab)
  2. Measuring plant biodiversity (UOI campus)
  3. Analysis of diversity data (PC lab)
  4. Field trip to Zagori: self-thinning, life tables and succession
  5. Analysis of field data (PC lab)



  1. Molles, Manuel C. (Jr), Ecology: meaning and application, 2009
  2. Lykakis, S., Ecology, Athanasopopulos-Papadamis Publishers, Athens 1996.
  3. J.M. Halley, Notes for General Ecology, UOI Website.
Field course in Ecology
COORDINATORS – J.M.Halley, K.G.Sotiropoulos
LECTURERS – J.M.Halley, K.G.Sotiropoulos & K. Stara (University of Ioannina), R. Tsiakiris (Forestry Authority), M. Argyropoulou & D. Gioulatos (Aristotle University of Thessaloniki),

This is an elective course. It is for students who want to learn more about ecology in the field.

Comprises 60 hours in total and is worth 5 ECTS. This course involves a series of preparation lectures at the university and then, usually in late May, six days in Zagori, a mountainous area, under guidance for the fieldwork. Students (up to 20) stay at the University research station (PALASE) in Ano Pedina. Work includes measurements and data gathering. The students create a database to establish and analyze the data collected.

Nikos shows insects

PRACTICALS (48 hours)

  1. Techniques for observation of animals in the area. Nocturnal detection methods, acoustics & birdsong analysis, invertebrate trapping.
  2. Natural history. Depending on the instructors, natural history of plants, birds, mammals, reptiles and amphibians, butterflies, other invertebrates.
  3. Ecological data analysis. After each field session, students organize their data and carry out statistical analyses
  4. Biodiversity databases. Basic principles of biodiversity database design. Students in teams create their own dB.

LECTURES (12 hours)

  1. Introduction to field ecology
  2. Review of statistics
  3. Introduction to database design
  4. Field safety and practice
  5. The environment of Zagori


  1. W.J. Sutherland, Ecological Census Techniques (2006) Cambridge University Press.
    Mucking amphib
Environmental Data Analysis in R

Where would a scientist be without the power to analyze data? In this course, you use small simple programs to solve problems with big data.

  • Basic Statistics you learned in first year. Now you need to apply them to real data and large datasets?
  • How? – with Simple Programming to do those basic statistics and more.
  • The tools? – the R language is one of the most widely used. And it’s free.
  • Plus: you’ll create impressive maps. Draw not just graphs, but the whole Earth!!

Data analysis has become a major part of every biologist’s job. So, advance your basic programming skills and unleash the power of statistics! Come and join our course!

Week 1.
Calculations : basic mathematical operators (Verzani p4-22)

  • Logical operators and statements
  • Data types (arithmetic, logical, text, special “n.a.” etc)
  • Creating and using vectors
  • Using for() and while() loops (6.2 and 6.5.2 in Verzani, )
  • Creating user-written functions (6.4 in Verzani)

Basic data manipulation in R

  • Inbuilt libraries of R (library(), data() and attach() usage) p23-26,
  • Reading data from a file. Verzani p28,

Week 2.
Data frames (See especially 4.2-4.3 in Verzani)

  • Creating data frames
  • Adding rows or columns to a frame
  • Sampling subsets
  • Removing rows or columns
  • Changing names of columns
  • Reading and writing to a file

Visualizing data (plot() and barplot() commands) see material at link

Week 3.
Basic tools of EDA (exploratory data analysis):

  • Finding mean, median, variance, etc. (2.2.3 and 2.2.4 in Verzani p42-47)
  • Quantiles, 5-number summary (Verzani p49-51)
  • Histograms: using hist() command (2.3.1 in Verzani)
  • Boxplots (2.3.3 in Verzani)

Week 4.
Random variables (5.1-5.2 in Verzani)

  • Types of variable: Discrete and continuous variables
  • The d, p, q and r functions (5.2.1 in Verzani, also my PPT hand-out)
  • Popular random variables: Bernoulli, Binomial, Poisson, Uniform, Normal (5.2.2-5.2.3)

Weeks 5, 6, 7.
Significance tests exploratory data analysis (Ch. 8 in Verzani)

  • Exploratory Data Analysis : purpose (see the my PPT hand-out)
  • Logic of statistical tests: p-value, R2 (see the my PPT hand-out)
  • Using t-test (paired or not), Wilcoxon, ANOVA, Kolmogorov-Smirnoff, Shapiro, Χ2 (chi-sq)
  • Linear Regression (see my PPT hand-out)

Week 8.
Spatial statistics (general)

  • Introduction to spatial statistics
  • Raster and vector graphics
  • Choosing project

Week 9.
Spatial statistics : the Raster package

  • Manipulating raster data
  • Manipulating vector data
  • Start of project

Weeks 10, 11, 12.
Project work

About the Exam: EDA is an open-book exam. Marks awarded for proper answers explaining concepts and solving problems and writing sensible code. Most of the basic R commands are in Verzani Ch. 1, 2 and 4. Students should be familiar with the spatial statistics material highlighted in

Origins and Spread of Infectious Disease

This year’s exam will be held on 12/2/21 at 9am. It is worth 50% of the marks overall (the other 50% is for practicals and presentations) and will be divided into two parts:
1. Theory (closed book) Based on all the material 1-4 below. You will write answers as text on word processer which you will submit before end of exam.
2. Practical (open book) based on the practicals, mainly involving the SIR/SEIR models in 2-3 below. You will present as script and on word-processor.Students should be familiar with the material on the e-course. In particular, you should be familiar with the following:

1. Must know theory from lectures*:
0. Introduction and terminology
1. Historical plagues and pandemics
2. Derivation and application of the SIR and SEIR models*
3. Short-term and long-term behaviour of the SIR and SEIR models
4. Zoonotic diseases.

2. You should have familiarity with basic use of R and carried out the main SIR/SEIR Lab exercises, specifically:
1. COVID Lab A.pdf
2. COVID Lab B.pdf
3. COVID Lab C.pdf
4. COVID Lab C.R (know how to use this code to obtain results).

3. You must be familiar with the papers that we followed in detail in the course, especially the following:
o Snowdon, F.M., 2008. Emerging and reemerging diseases: a historical perspective. Immunological Reviews.
o Daszak, P., Cunningham, A.A. and Hyatt, A.D., 2000. Emerging infectious diseases of wildlife–threats to biodiversity and human health. science.
o Woolhouse, Mark, and Eleanor Gaunt. “Ecological origins of novel human pathogens.” Critical reviews in microbiology 33, no. 4 (2007): 231-242

It is recommended for you to read Vynnycky & White (“Infectious disease modelling”) chapters 2 and 4, but it is not essential for the exam.

* Regarding equations, students are not expected to derive equations nor remember them, apart from those basic ones appearing in a red box. But you should know that they exist, and you should be able to apply the basic formulas when given these.