Skip to main content

sampling techniques

univ-soukahras
Enrollment is Closed

About This Course

The Sampling Techniques module plays a crucial role in training students in ecology and natural resource management. It equips learners with the necessary skills to collect and analyze ecological data with rigor, using both probabilistic (simple random, systematic, stratified) and non-probabilistic sampling methods. These techniques are essential in fields such as biodiversity assessment, resource management, and the study of environmental impacts.

Students will learn to design sampling plans tailored to different ecosystems, overcoming constraints related to sample frequency, size, and specificity, while ensuring representativeness. Key concepts, such as the selection of descriptors, variables, and the scale of observation, will be covered, along with spatial and temporal planning of sampling.

A section of the module focuses on sampling techniques specific to aquatic environments, with an emphasis on vertebrates and invertebrates. Additionally, students will explore scientific and methodological principles that allow them to conduct robust research while considering environmental challenges and sustainable management strategies.

This module is essential for training specialists capable of providing practical solutions for ecosystem management and species conservation, particularly in the face of current environmental pressures.

Prerequisites

The knowledge required to take this course is provided in the Ecotoxicology and Animal Ecology modules taken during the Bachelor's program.

The pedagogical team

Dr. Guerfi Sarra
Lecturer (Class B), Faculty of Natural and Life Sciences, Department of Biology, University of Souk Ahras, Algeria.

Frequently Asked Questions

What web browser should I use?

The Open edX platform works best with current versions of Chrome, Edge, Firefox, or Safari.

See our list of supported browsers for the most up-to-date information.

Question #2

Your answer would be displayed here.

Course Summary

  1. Course Number

    UMS3
  2. Classes Start

  3. Classes End

  4. Estimated Effort

    04:30