By providing an inferential background in likelihood and an introduction to Bayesian inference students will be equipped to analyze the oftentimes complicated experimental and observational data that arise in population ecology. It will develop students' understanding of population ecology and accompanying statistical methodology and provide for student independence through a thorough understanding of the open-source R statistical environment. Finally it will equip students to run Bayesian analyses through an introduction to applied Bayesian inference via WinBUGS
Cohort: 2017, 2018
Ecosystem based fisheries management
Galway Mayo Institute of Technology
An introduction to R
Probability theory of relevance to population dynamics;
Maximum likelihood estimation
Density-independent population growth
Density-dependent population growth
State space analysis
Demonstrate an understanding of the underpinnings of statistical inference
Apply R programming skills
Describe the theory of population dynamics
Develop and apply advanced statistical models to population dynamics data
Draw inference from population dynamics
Describe Bayesian inference as applied to population dynamics
R programming skills
Statistical analysis with particular reference to population dynamics
Bolker, B.M. (2008) Ecological Models and Data in R. Princeton University Press, Princeton, NJ, USA. Clark, J.S. (2007). Models for Ecological Data: An Introduction. Princeton University Press, Princeton, NJ, USA. Hilborn, R. And Mangel, M. (1997). The Ecological Detective: Confronting Models with Data. Princeton University Press, Princeton, NJ, USA.
McCarthy, M.A. (2007). Bayesian Methods for Ecology. Cambridge University Press, Cambridge, UK.
Module resouce material placed on Moodle in GMIt to include:
This module is 100% continuous assessment, all learning outcomes are assessed during the module with no terminal examination. All assessments are formative and summative, they contribute to the overall mark. Assessments are practical assignments.
An average of 3 hours per week to a total of 39 hours