Statistical analysis in population ecology

Semester 1

Semester 2

Semester 3

Semester 4

ECTS
5

MANAGEMENT

Ecosystem based fisheries management
Galway Mayo Institute of Technology

Synopsis

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

Content

An introduction to R
Probability theory of relevance to population dynamics;
Likelihood-based inference
Maximum likelihood estimation
Bayesian inference;
Density-independent population growth
Density-dependent population growth
Trophic interactions
Stochasticity
Environmental drivers
Population harvesting
State space analysis

Aims

Through practical instruction and case studies this course aims to provide learners with the theoretical and applied basis for statistical analyses of population dynamics.

Objectives

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

Key skills acquired

R programming skills
Statistical analysis with particular reference to population dynamics

Bibliography

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:
Lecture notes
Data
Case studies
Reading

Assessment

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.

Involved teachers

Dr Cóilín Minto

Contact hours

lectures

practicals

seminars

computerclass
An average of 3 hours per week to a total of 39 hours

fieldwork

other