comp ecol trans small


richness 4271As biologists we are more and more confronted with problems that are not exactly of biological nature (if such problems ever existed). The sheer amount of data in molecular biology that commonly research teams in this field are facing is paralleled by the growing amount of ecological data. And as the advances in genomics and molecular biology forced the emergence of a new discipline of bio-informatics, ecology still anticipates such a transformation.

This site is intended to provide help for other biologists and interested audience by providing R code to deal with large amount of data in two particular fields of ecology: macroecology and movement ecology.

The code that is presented here is of course one way of approaching the problems and should be viewed as suggestions to how problems could be tackled, rather than strict recipes that will fail if deviated from. Most of this code has been gathered while we were trying to solve our sometimes low level problems, with skills we didn't possess at that time. The codes therefore reflect our progress and achievements and are roughly ordered in an increasing level of complexity. Of course we are not responsible for any good or bad that may come out of the use of the code.

General suggestions and non-specific code:

This part contains mainly generic insight around the use of R, some efficiency issues, as there are some times alternative ways to do the same, and finally bits of useful code that cannot clearly be associated to either of the two other sections.

Movement ecology:

In this section we will post the code that was used in a tutorial/problem based learning session on movement analysis. The movement ecology section is thus organised in problems and the series of possible solutions with clearly increasing complexity from reading to plotting to analysing movement.


Here we deal mostly with range maps from which we derive species richness, extract geographical values such as mean temperature etc. Topics will shift from the currently more data extraction concentrated issues towards analytical i.e. statistical problems solving on a spatial level.

Concluding notes:

The code, as I mentioned, might not be the most elegant way or fastest possible to solve a particular problem, but they worked for me. At least they can give some ideas about how I solved some problems, and inspire others to give it a try. Here you will find only limited information as to how the basics of R work. There is plenty of help out there aimed at getting people going. Instead, here you will find ways of dealing with spatial data in R, more precisely code examples. As more problems will appear and hopefully be solved, new code examples will be posted.ä

Web resources:

R spatial projects site
(official R site)

Roger Bivand's R page
(concise summary and good collection of R resources)

NCEAS wiki page on spatial R
(short and basic, but helpful at the start)

NCEAS geospatial data analysis
(very rich and helpful not only for ecologists)

(helpful general resource for R newbies)