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Desktop Tools for science:

Lists of journals, impact factors, citation reports

Reference managers,

Submitting manuscripts (cover letters, peer review, rebuttal letters)

Visualizing data: software. graphs, the use of color and shapes.

Marine Biogeography and

Evolution

Part 4. Species distribution modeling

Part 3. Presence records and plot

With a script one tends to run the script by clicking our script away. In this case to fully understand the results you need to consult the package manuals.


R-script for area clustering analysis

BEMO_clust_complete.R


Multidimensional scaling

BEMO_MDS.R

Part 2. Exploratory data session: area cladograms

Students will use CRAN-R to obtain hierarchical (area cladograms) and non-hierarchical (multi-dimensional scaling - MDS) graphs from their data. Even if students do not have the fundamentals of R, we strongly advise them to use it, as it is a widely used tool, with many different applications, and this exercise will allow you an easy introduction to its use.


Atlantic reef fish biogeography and evolution, references:

Floeter SR, Rocha LA, Robertson DR, et al. (2007) Atlantic reef fish biogeography and evolution. Journal of Biogeography, 35 (1),22-47. PDF

Joyeux et al. (2001) Biogeography of tropical reef fishes: the South Atlantic puzzle. Journal of Biogeography, 28 (7):  831-841. PDF



Database:

     Lusitania fish list


Excel functions:

     Excel functions.pdf

Part 1. Exploratory data session: summary of biodiversity and graph building

As a course project each student will work on a presence/absence database of over 900 continental shelf fish species from 13 different regions including the Lusitania Province, Bay of Biscay, Atlantic Iberia, western Mediterranean Sea, eastern Mediterranean Sea, Atlantic Morocco, West Sahara, archipelagoes of the Azores, Madeira and the Canary Islands, the tropical eastern Atlantic, Cape Verde and Sao Tome and Principe). The western tropical Continental Africa and the Boreal Province, are included as an external reference group.

The goal is to understand how geographic regions of the Northeastern Atlantic Ocean relate to each other. Students will curate the database, and then estimate a range of similarity indexes to investigate biogeographical relationships among those areas. A main part of the work will be to summarise information and get data ready to produce the best figures and tables.


The project will span several weeks and consist of analysis of real data regarding a biogeographic approach. In particular, students will explore species richness and level of endemism for the designated area as well as area relationships. Students will work individually to conduct analyses.


A reference list is available on the web side as support for this work, but students are encouraged to go beyond this list and find other relevant information for themselves. Students are strongly encouraged to build figures that best represent their results.


A must for biologists: Brief introductions to R

Starting with R.pdf

Short-R-Intro.pdf

The R Guide.pdf


TP 01-02-03-04

Approach A

1. Get acquainted with GBIF;

2. You should register in GBIF and explore the site (http://www.gbif.org);

3. Next choose a species of your particular interest and get the occurrences as a digital file;

4. Unfortunately, although GBIF names the file as *.csv, the file is indeed tab delimited, and opening in excel requires a few extra steps. Try to find out how it can be done.

5. Open the file in excel;

6. Clean the file of spurious information: you only want two columns (latitude and longitude) and keep only records where coordinates are present.

7. Add a first column in which you add a sequential code to each record, e.g., choose a header designation (#ID), and then fill with 1 to ... N records. (Fill the first two cells of the column with 1, and 2, and then drag and drop, till the end).

8. Save the file as *.xlsx.

9. Open RStudio and load the script GBIF_manual.R

10. You will finish by achieving a plot records on a map.

11. Clean all the obvious mistakes (land records, for instance). This approach is manual, you should identify the points by the number and use that info to eliminate spurious points. [If there are too many points this tasks becomes impossible].

12. Plot final map.


Approach B

Use the GBIF script: GBIF_final.R

Part 5. Species distribution modeling - Final script

TP 05 [all students together]