Module: Combining Ecological Models and Data (3201-590)

Persons:

• Prof. Dr. Frank Schurr (verantwortlich)
• Dr. Jörn Pagel (verantwortlich)

Degree Program:

• Landscape Ecology (Master, since 01.10.2014)
  2. Semester, semi-elective
• Environmental Science - Soil, Water, and Biodiversity (Regulations 2014) (Master, since 01.10.2014)
  2. Semester, semi-elective
• Environmental Science - Soil, Water, and Biodiversity (Regulations 2019) (Master, since 01.04.2019)
  2. Semester, compulsory

Relation to other Modules:

This module prepares for modelling-based projects in the module Project in Landscape Ecology (3201-610).

Prerequisites for Attendance:

Participants need a basic understanding of population dynamics and experience with the programming language R as acquired in the module Landscape Ecology (3201-560) or equivalent courses.

Sprache:

English

ECTS:

7,5 credits

Frequency:

every summer semester

Length of the Module:

4 weeks (block 1)

Workload:

70 h presence + 130 h preparation at home + exam = 200 h workload

Professional competences:

The module provides an overview of modelling approaches in the environmental and ecological sciences and introduces modern statistical methods that can be used to parameterize and evaluate such models with empirical data. A range of examples deepens the understanding of theoretical concepts in landscape and population ecology. Hands-on exercises familiarize the participants with the practical basics of scientific programming.

Key competences:

Participants gain insights into the iterative process of formulating quantitative models and confronting them with empirical data as an integral part of the scientific method. They learn that the awareness and communication of uncertainties, which are attached to any study results and derived predictions, are crucial in both scientific and professional projects. Course projects improve their skills in work organization, data management and (in particular graphical) presentation of own findings.

Comments:

Participants are required to register for this module on ILIAS before 30th March 2020.

eLearning:

Course in ILIAS

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