COURSE UNIT TITLE

: MARINE ECOLOGICAL METHODS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CDK 6023 MARINE ECOLOGICAL METHODS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR EYÜP MÜMTAZ TIRAŞIN

Offered to

MARINE LIVING RESOURCES

Course Objective

The aim of the course is to teach oceanographers the modern marine ecological sampling methods and ecological data analysis. Aspects of marine ecological data and appropriate univariate and multivariate statistical tools for analysing these data are covered in detail. In addition to studying various case studies students are also instructed to use their own data sets with personal computer statistical packages.

Learning Outcomes of the Course Unit

1   Recognize the great diversity of marine ecological data.
2   Recognize the need for sampling design and statistical analysis in marine ecological research.
3   Demonstrate an ability to design experiments or research surveys for marine ecological investigations.
4   Demonstrate understanding of a great range of ecological research methods and techniques to estimate abundances of marine plant and animal populations and how they can be used to address particular research questions.
5   Apply univariate and multivariate statistical analysis methods to make inferences about the ecological data and draw meaningful and valid conclusions.
6   Become able to use certain statistical software (R Project for Statistical Computing, PRIMER and CANOCO) to analyse data and interpret results.
7   Demonstrate critical thinking skills in evaluation of the strengths and weaknesses of their own research work and/or that of other researchers.
8   Communicate and discuss results from research projects and their implications with others in written and verbal forms.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Course Course Outline, Textbook, Homeworks and References, General Review of Various Important Topics in Marine Ecology, Assignment of Presentations.
2 Review of Relevant Statistical Theory A Review of Discrete and Continuous Statistical Probability Distributions (Poisson, Binomial, Normal etc), Point and Interval Estimators.
3 Introduction to Statistical Programme Packages Introduction to R, CANACO and PRIMER, Modules in R, PRIMER and CANACO, Data Import and Export, Structure and Collection of Ecological Data.
4 Estimating Abundance in Animal and Plant Populations (I) Mark-Recapture Techniques, Petersen Method, Schnabel Method, Jolly-Seber Method, Statistical Aspects of Mark-Recapture Techniques.
5 Estimating Abundance in Animal and Plant Populations (II) Removal and Resight Methods, Change-in-Ratio Methods, Eberhardt's Removal Method, Catch-Effort Methods, Resight Methods, Boundry Strip Methods, Nested Grids Method.
6 Estimating Abundance in Animal and Plant Populations (III) Quadrat Counts, Wiegert's Method, Hendricks' Method, Test of Independence, Line Intercept Method.
7 Estimating Abundance in Animal and Plant Populations (IV) Line Transects, Hayne Estimator, Fourier Series Estimator, Distance Methods, Byth and Ripley Procedure, T-Square Sampling Procedure, Ordered Distance Methods.
8 Midterm Exam
9 Spatial Pattern and Indices of Dispersion Methods for Spatial Maps, Contiguous Quadrats, Indices of Dispersion for Quadrat Counts, Variance-to-Mean Ratios, k of the Negative Binomial, Green's Coefficient, Morisita's Index of Dispersion.
10 Sample Size Determination and Statistical Power Sample Size for Continuous Variables, Sample Size for Discrete Variables, Sample Size for Specialised Ecological Variables, Statistical Power Analysis.
11 Sampling Designs Simple Random Sampling, Stratified Random Sampling, Adaptive Sampling, Systematic Sampling, Multistage Sampling.
12 Similarity Coefficients and Cluster Analysis Measurements of Similarity, Data Standardization, Cluster Analysis, Single Linkage Clustering, Complete Linkage Clustering, Average Linkage Clustering.
13 Other Multivariate Techniques Multivariate Distances, Idea of Ordination, Principal Component Analysis, Correspondence Analysis, Canonical Correlation Analysis.
14 Species Diversity Measures Species Diversity, Simpson's Index, Shannon-Wiener Function, Brillouin Index.

Recomended or Required Reading

Textbook:

Krebs, Charles J. 1998. Ecological Methodology. Addison-Welsey Educational Publishers Inc., California, USA.

Reference books:

Gerald, J. B. 1990. Quantitative Ecology and Marine Biology. A. A. Balkema Co., Rotterdam, Netherlands.
Manly, B. F. J. 1994. Multivariate Statistical Methods: A Primer (2nd edition). Chapman & Hall, New York, USA.
Legendre, P. and Legendre, L. 1998. Numerical Ecology (2nd edition). Developments in Environmental Modelling, 20. Elsevier, Amsterdam, Netherlands.

Software

Ter Braak, C. J. F. 1988. CANOCO - a FORTRAN program for canonical community ordination by [partial] [detrended] [canonical] correspondence analysis. Agricultural Mathematics Group, Wageningen, Netherlands.
R software environment for statistical computing and graphics (R Project for Statistical Computing / http://www.r-project.org/).
Clarke, K. R. and Warwick, R. M. 2001. Primer. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation. UK.

Planned Learning Activities and Teaching Methods

1. Lectures
Class lectures are carried out in a highly interactive format. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the real world applications, and additional elaboration and illustration will be provided for better comprehension.

2. Class Discussions
In-class assignments and homework assignments are the basis of problems to be solved in classroom discussions. Individual participation by students in classroom discussion will be strongly encouraged.

3. Computer Applications
The R, CANACO and PRIMER will be introduced to perform analyses of data and to produce graphics.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRS PRESENTATION
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + FIN * 0.40
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

1. Regular attendance is essential for satisfactory completion of this course. Statistics is a cumulative subject and each lesson day builds on the previous lessons' material. If you have excessive absences, you cannot develop to your fullest potential in the course.
2. The student is responsible for all homework assignments, changes in assignments or other verbal information given in the class, whether in attendance or not.
3. Homework assignments must be delivered at the beginning of the lesson on the date they are due.

Contact Details for the Lecturer(s)

Dr. E. Mümtaz TIRAŞIN
Dokuz Eylül University, Institute of Marine Sciences and Technology,
Inciraltı 35340, Balçova - Izmir.
Phone:(+90) 232 2785565 /165
Fax: (+90) 232 2785082
E-mail: mumtaz.tirasin@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparation for midterm exam 1 12 12
Preparing assignments 10 4 40
Preparing presentations 1 12 12
Reading 10 3 30
Preparations before/after weekly lectures 11 3 33
Preparation for final exam 1 16 16
Midterm 1 3 3
Final 1 4 4
TOTAL WORKLOAD (hours) 189

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6
LO.1111111
LO.2351111
LO.3451211
LO.4452211
LO.5553211
LO.6251111
LO.7443433
LO.8453444

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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