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  • Quantitative Ecology - NOT AVAILABLE IN 2024-2025
  • Quantitative Ecology - NOT AVAILABLE IN 2024-2025








    (1) General



    School:Of the Environment
    Academic Unit:Department of Marine Sciences
    Level of studies:Undergraduate
    Course Code:191ΕΩ5ΕSemester:H
    Course Title:Quantitative Ecology - NOT AVAILABLE IN 2024-2025
    Independent Teaching ActivitiesWeekly Teaching HoursCredits
    Total credits5
    Course Type:
    Skills development
    Prerequisite Courses:
    Statistics
    Language of Instruction and Examinations:
    Greek
    Is the course offered to Erasmus students:
    No
    Course Website (Url):https://www.mar.aegean.gr/?lang=en&pg=3.1.1&lesson=1091

    (2) Learning Outcomes

    Learning Outcomes


    After the successful completion of the course the student should be able to:

    Use the principles of Quantitative Ecology and Biological Diversity science to explain patterns of population and community distribution

    Use the principles of Quantitative Ecology to estimate species richness in biotic communities

    Estimate the distributions of species abundances in communities.

    General Competences


    • Search for, analysis and synthesis of data and information, with the use of the necessary technology
    • Decision-making
    • Working independently
    • Team work
    • Working in an international environment
    • Working in an interdisciplinary environment
    • Production of new research ideas
    • Respect for the natural environment
    • Production of free, creative and inductive thinking

    (3) Syllabus


    • Introduction to Biological Diversity
    • Databases,
    • Community Species Richness
    • Estimation Methods (Rarefaction, Jackknife, Bootstrap),
    • Diversity Indices, Evenness Indices, Alpha - Beta - Gama Diversity,
    • Species - Area Relationship.
    • Species Richness and Abundance Relationship in Communities,
    • Statistical Models, Log Series, Lognormal Series,
    • Niche Apportionment Models, Geometric Series, Broken Stick Model, Dominance preemption - Random Fraction - Dominance decay

    (4) Teaching and Learning Methods - Evaluation


    Delivery:

    Face-to-face

    Use of Information and Communication Technology:

    Use of statistical language (R) in teaching and in Labs. Use of platform open eclass, a complete Course Management System that supports Asynchronous eLearning Services. Instructor notes, homework.

    Teaching Methods:
    ActivitySemester workload
    Lectures26
    Laboratory exercises13
    Lectures60
    Lectures23
    Final exam3
    Course total125
    Student Performance Evaluation:

    Language of evaluation:

    Greek.

    Method of evaluation:

    Final project: problem solving through statistical software (20%)

    End of semester exam: written problem solving (80%)




    (5) Attached Bibliography


    • Καρανδεινός Μ.Γ. 2007. Ποσοτικές Οικολογικές Μέθοδοι. Πανεπιστημιακές Εκδόσεις Κρήτης
    • Στάμου Γ. 2009. Οικολογία. Ζήτη

    - Related academic journals: