Revolution V: quantitative ecology and spatial prediction of species distribution

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General information

Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. This course provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity. The course’s practical value is enhanced by extensive use of biological examples and the computer language R for graphics, programming and data analysis.

This course is divided into 10 theoretical-practical sessions of 4 hours long, including 4 assignments through which you can practice your mastery under supervision. The sessions will be held between 2:00 pm and 6:00 pm.

Sessions

aRound
Description: installation and management of R packages. Review of packages. Input and output of data.

useR
Description: R session. Elements of the language.

gRaphics
Description: plotting functions and parameters.

autoR
Description: basic R programming and automatize tasks.

wRite
Description: preparing statistical reports using R.

Rspatial
Description: analysis of vector and raster cartography, also connecting with GRASS and QGIS.

aRtistics
Description: experimental design. Hypothesis contrast, ANOVA. Basic multi-variate statistics.

multivaR
Description: diversity and multivariate analysis: ordination and gradient analyses, ENFA (Ecological Niche Factor Analysis), habitat suitability maps, metapopulation simulations.

lineaR
Description: construction, optimization and evaluation of linear models. Representation and spatial interpretation.

modelaRt
Description: construction, optimization and evaluation of non-linear models. Representation and spatial interpretation.

geneRate
Description: importation of genetic data, basic diversity estimations, genetic distances, boundaries detection, polyploidy, spatial autocorrelation, Bayesian clustering. Representation and spatial interpretation.

Learning outcome:

Participants will be introduced to the R environment and how to manage their R working space and data. Participants will learn how to write, comment and save scripts, how to prepare the results in a variety of formats that can easily be embedded in papers and presentations and how to prepare statistical reports into R. Participants will learn how to create standard statistical graphs such as bar plots, histograms, box-plots, scatter-plots and time series plots. They will also learn how to enhance these plots through the addition of titles,
labels, legends, text annotations, colours and symbols. Next, participants will learn how to create multi-panel and 3D plots.

Contact Person: (ccmar@ualg.pt)

Content

The highlighted icons, represent the fields of education (in compliance with ISCED Classification) engaged during this course/programme.

Venue

Venue: University of Algarve
Faro, Portugal

Centre of Marine Sciences
Campus de Gambelas, 805-139 Faro

Application


Cost:

Students: 250€, CCMAR Research Fellows are also included in this fee.

Other participants: 350€


Prerequisites:

This course requires some prior experience in statistics and elemental mathematics. Knowing object-oriented programming is not needed. We will provide students with a selection of data sets with which to work, however participants are encouraged to bring their own data.


Application Procedure:

To register please follow the online registration form

Important Dates

  • End call for grants: October 9th
  • Grant Results: October 10th
  • Start registration to all: October 10th

Grant Opportunities:

We offer 4 grants for students. The grant will cover the half of the expenses for the course. If you are interested, you should submit a short CV, a justification of your situation and a letter of motivation, explaining why and how do you think this course will improve you and your professional development, and how the grant will help you. The decision won't be subjected to appeal.

Qualification

Academic level: Master, PhD, Lifelong Learning
Occupations (not validated):
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