LSM3257
QUANTITATIVE METHODS FOR ECOLOGICAL RESEARCH (2013/2014, Semester 2) 

 MODULE OUTLINE Created: 13-Nov-2013, Updated: 13-Nov-2013
 
Module Code LSM3257
Module Title QUANTITATIVE METHODS FOR ECOLOGICAL RESEARCH
Semester Semester 2, 2013/2014
Modular Credits 4
Faculty Science
Department Biological Sciences
Timetable Timetable/Teaching Staff
Module Facilitators
ASSOC PROF Carrasco Torrecilla, Luis Roma Lecturer
Weblinks
Tags data analysis,ecological statistics,Ecology,environmental biology,spatial statistics


Learning Outcomes | Prerequisites | Teaching Modes | Schedule | Synopsis | Syllabus | Assessment | Workload


 LEARNING OUTCOMES Top
Overall objectives
Learn how to design ecological experiments and data collection and identify the most appropriate technique to analyze the data, as well as critically evaluate the results of the model. 
 
Specific objectives
 
  • Be aware of the importance of quantitative methods in ecology and conservation research.
  • Design ecological experiments.
  • Obtain an introductory knowledge of R programming language for data exploration and analysis.
  • Interpret data exploration plots.
  • Select the appropriate statistical approach for a specific problem.
  • Interpret model results and evaluate the model.
  • Simplify the model.
  • Learn to deal with non-independence from nested designs.
Be aware of the main difficulties in the design and analysis of ecological experiments: e.g. spatial and temporal correlation.


 PREREQUISITES Top
ST1232 Statistics for Life Sciences AND
LSM2251 Ecology and Environment


 TEACHING MODES Top
Lectures, practicals in computer lab, problem-based learning and project work involving analysis of data from your own case studies.


 SCHEDULE Top
  LECTURES PRACTICALS
  MONDAYS THURSDAYS
TIME: 1200-1400 hrs 1200-1400 hrs
VENUE: S2-0410 LS Lab 5 S2 Lvl 4

WK

MTH

LECTURES
MONDAYS
PRACTICALS
THURSDAYS
  1.  
Jan 13 The importance of quantitative methods in ecology. 16 Practical 1. Introduction to R I.
  1.  
  20 Review of basic concepts. 23 Practical 2. Introduction to R II.
  1.  
  27 Experimental design in ecology. 30 Practical 3. Experimental design.
  1.  
Feb 3 Univariate tests. 6 Practical 4. Univariate tests.
  1.  
  10 Linear and multiple regression. 13 Practical 5. Regression.
  1.  
  17 ANOVA and ANCOVA. 20 Practical 6. ANOVA and ANCOVA.
    Recess week: Sat 22 Feb - Sun 2 Mar 2014 (1 week)
  1.  
Mar 3 Revision of questions. 6 Midterm practical exam.
  1.  
  10 Generalized Linear Models. 13 Practical 7. GLMs.
  1.  
  17 Difficulties of ecological data: spatial and temporal correlation. 20 Practical 8. Spatial and temporal correlation.
  1.  
  24 Analysis of ecological communities. 27 Practical 9. Analysis of ecological communities.
  1.  
Mar/Apr 31 Mixed-effects models I. 3 Practical 10. Mixed-effects models.
  1.  
Apr 7 Mixed-effects models II. 10 Clinic: analyse your own data.
  1.  
  14 Project presentation. 17 Revision of questions for exam.
  READING PERIOD: Sat 19 Apr to Fri 25 Apr 2014 (1 week)
  EXAMINATION: Wed 7 May 2014 5:00 PM
  VACATION: Sun 11 May – Sun 3 Aug 2014 (12 weeks)


 SYNOPSIS Top

It is important that authentic ecological problems, are used as working examples to enhance student significant learning. This is one of the key features of the problem-based approach this module adopts. Another important aspect covered by the module is the familiarization with the R statistical environment. This statistical environment has become essential in ecological research, as demonstrated by the increasing number of available libraries for the analyses of very specialized ecological problems. Using a practical, hands-on approach, the students will gain expertise in the use of this software.
 



 SYLLABUS Top
  1. The importance of quantitative methods in ecology.
  2. Review of basic concepts.
  3. Experimental design in ecology.
  4. Introduction to R.
  5. Univariate tests.
  6. Linear and multiple regression.
  7. ANOVA and ANCOVA.
  8. Generalized Linear Models.
  9. Difficulties of ecological data: spatial and temporal correlation.
  10. Analysis of ecological communities.
  11. Mixed-effects models.
  12. Clinic: analyze your own data.


 ASSESSMENT Top
CA components:
(i)     Tutorials/Seminars: 0%
(ii)    Laboratories: 0%
(iii)   Tests: practical mid-term exam using the computer. 30%
(iv)    Essays: 0%
(v)    Others (eg. fieldwork, projects): project of analysis of own or assigned data to be presented to the class. 20%
Total for CA: 50%
Total for Final Examinations: 50%
Total Assessment: 100%


 WORKLOAD Top
2-0-2-2-4

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week