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Society for Conservation Biology

Society for Conservation Biology

The 5th Bio-logging Science Symposium - Strasbourg 22-26 Sep 2014 - Important dates - 30 Sep 2013 : Workshop submission Closed- 31 Mar 2014 : Abstract submission Closed - 20 Apr 2014: Early registration Soon! 21 Apr 2014 - : Late registration 22-27 Sep 2014 : Symposium Abstract submission GBIF : Global Biodiversity Information Facility Try out the new GBIF portal! Why not try out the new GBIF portal at, which has many more features and includes lots of information about the GBIF community, including great examples of data uses in research and interesting applications? The old GBIF data portal which you are viewing now will continue to be supported until we are satisfied it can be taken down without causing major inconvenience. Be aware that the content here is static and has not been updated since the launch of the new portal on 9 October 2013.

Home - Ploteus PLOTEUS aims to help students, job seekers, workers, parents, guidance counsellors and teachers to find out information about studying in Europe. On this portal you will find information on learning opportunities and training possibilities available throughout the European Union. The website contains links to web sites of universities and higher education institutions, databases of schools and vocational training and adult education courses. To help making informed choices this portal also contains links to websites where you find everything you need to know when moving to another European country. You will find links to: Websites with descriptions of and explanations about European education and training systems.

Species Distribution Modelling - pecies Distribution Model (SDM) can be defined as a statistical/analytical algorithm that predicts either actual or potential distribution of a species, given field observations and auxiliary maps, as well as expert knowledge. A special group of Species Distribution Models (SDMs) focuses on the so-called occurrence-only records --- pure records of locations where a species occurred (Engler at al. 2004; Tsoar et al. 2007). This article describes a computational framework to map species' distributions using occurrence-only data and environmental predictors. For this purpose, we will use the dataset "bei", distributed together with the spatstat package, and used in school books on point pattern analysis by Baddeley (2008) and many other authors. To run this script, you will need to obtain some of the following packages. For more details about this topic consider obtaining the original article:

The Biodiversity Hotspots Page Content Life on Earth faces a crisis of historical and planetary proportions. Unsustainable consumption in many northern countries and crushing poverty in the tropics are destroying wild nature. Biodiversity is besieged. Extinction is the gravest aspect of the biodiversity crisis: it is irreversible. While extinction is a natural process, human impacts have elevated the rate of extinction by at least a thousand, possibly several thousand, times the natural rate.

High Resolution Figures in R As I was recently preparing a manuscript for PLOS ONE, I realized the default resolution of R and RStudio images are insufficient for publication. PLOS ONE requires 300 ppi images in TIFF or EPS (encapsulated postscript) format. In R plots are exported at 72 ppi by default. I love RStudio but was disappointed to find that there was no options for exporting figures at high resolution.

distance {argosfilter Great circle distance between geographical coordinates Description Function distance calculates the distance, in km, between two geographical locations following the great circle route. Function distanceTrack calculates the distance, in km, between a sequence of locations. Usage distance(lat1, lat2, lon1, lon2) distanceTrack(lat,lon) Graphical Parameters You can customize many features of your graphs (fonts, colors, axes, titles) through graphic options. One way is to specify these options in through the par( ) function. If you set parameter values here, the changes will be in effect for the rest of the session or until you change them again. The format is par(optionname=value, optionname=value, ...) # Set a graphical parameter using par() par() # view current settings opar <- par() # make a copy of current settings par(col.lab="red") # red x and y labels hist(mtcars$mpg) # create a plot with these new settings par(opar) # restore original settings