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Course, academic year 2019/2020
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Species distribution models in R - MB162P21
Title in English: Species distribution models in R
Guaranteed by: Department of Ecology (31-162)
Faculty: Faculty of Science
Actual: from 2015
Semester: summer
E-Credits: 3
Examination process: summer s.:practical
Hours per week, examination: summer s.:1/0 Ex [weeks/semester]
Capacity: 15
Min. number of students: unlimited
State of the course: not taught
Language: English
Guarantor: Sara Varela Gonzalez, Dr.
Annotation -
Last update: RNDr. Veronika Sacherová, Ph.D. (08.04.2013)
Macroecology deals with large scale biodiversity patterns, and look for the top down processes that regulate those
observed patterns. Biodiversity observed patterns, and specially species richness latitudinal gradient, is one of the
main patterns that macroecologists seek to completely explain. Current climate, energy, historic contrains, biotic
interactions or niche conservatism are some of the factors that have been related to the observed species richness
patterns. The main goal of this course is to give the students theoretical and practical tools to propose and answer
macroecological questions.
Requirements to the exam -
Last update: RNDr. Veronika Sacherová, Ph.D. (16.04.2013)

To end this course, we are going to do a practical lesson. Students should answer a macroecological/biogeographical question by:

1.- Downloading the biological information from an open access data base

2.- Constructing a species distribution model

3.- Interpreting their results

Syllabus -
Last update: RNDr. Veronika Sacherová, Ph.D. (08.04.2013)
1 Introduction to R
This first section is to learn the basics of R. Installing R and RStudio, work

with R Objects, do mathematical operations, learn how to select elements

from an object, learn how to add elements to an object, learn how to import

and save our data, learn how to draw simple plots and save them, learn basic

loops, learn how to work using R libraries and functions.

2 Open access biodiversity data bases
Different open access biodiversity data bases are available on the internet.

These biodiversity data bases contain specifc information about species, in-

cluding geo-referenced occurrences. There are general databases, like GBIF

or IUCN. Palaeontological databases, like Paleobiology database, NOW or

Neotoma. Fish data bases, like FishBase. Plant or vegetation databases, like

SIVIM or ANTHOS, etc. This is the frst time that these data are acces-

sible and ready to use for a single ecologist. Using this global biodiversity

information we could be able to answer large scale ecological questions and,

as a result, macroecological studies are increasing their number exponentially.

Here, we are going to discuss the specific problems of those data bases,

namely; spatial, temporal and taxonomic biases. Besides, we are going to

learn how to download this data from the internet using R.

3 Environmental Data
Variables such as climatic conditions, altitude, pH, land cover, etc. are also

available in GIS format. Combining the biodiversity data from the open

access data bases and those environmental variables, we can investigate and

answer macroecological and biogeographical observed patterns.

Specifically, we are going to discuss the differences between WorldClim

layers and General Circulation Models (GCM) layers. Besides, we are going

to learn how to download and use this information using R libraries.

4 Species distribution models
Species distribution models (SDM) are one of the different kinds of spatial

models that we can construct. SDM are used to predict the geographic

distribution of a species in relation to its climatic requirements. There are

several statistic and mathematic methods to construct those models. Here,

we are going to learn some of them, including Bioclim, Domain, GLM/GAM,

BRT, Maxent and Mahalanobis distances.

5 Develop your own spatial prediction
To end this course, we are going to do a practical lesson. Students should

answer a macroecological/biogeographical question by:

1.- Downloading the biological information from an open access data base

2.- Constructing a species distribution model

3.- Interpreting their results

6 Oral presentations
Oral presentation and discussion of the students exercises.

 
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