riin tamme, lars götzenberger , martin zobel , james m. bullock, danny a. p. hooftman ,
DESCRIPTION
Predicting seed dispersal distances from simple plant traits. Riin Tamme, Lars Götzenberger , Martin Zobel , James M. Bullock, Danny A. P. Hooftman , Ants Kaasik , Meelis Pärtel. We collected available maximum dispersal distance data for plant species. - PowerPoint PPT PresentationTRANSCRIPT
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RIIN TAMME, LARS GÖTZENBERGER, MARTIN ZOBEL,
JAMES M. BULLOCK, DANNY A. P. HOOFTMAN, ANTS KAASIK, MEELIS PÄRTEL
Predicting seed dispersal distances from simple plant traits
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We collected available maximum dispersal distance data for plant species
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576 plant species are currently represented in our database
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We collected plant trait data from original studies or databases
dispersal syndromegrowth formseed mass
seed releasing heightterminal velocity
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We related these plant traits to maximum dispersal distances
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Average maximum dispersal distance
increases from species with no special mechanisms for dispersal to ballistic, ant,
wind, and animal dispersal
1.1 m
3.6 m
6.5 m
47.6 m
196 m
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Average maximum dispersal distance
also increases from herbs to shrubs and
trees5.2 m
24.4 m
295 m
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We then built models to predict plant species’ maximum dispersal distances from simple plant traits
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OBSERVED MAXIMUM DISPERSAL DISTANCE (LOG; M)
PR
ED
ICT
ED
MA
XIM
UM
DIS
PE
RS
AL
DIS
TA
NC
E
(LO
G;
M)
Simple plant traits explained up to 60%
of variation in maximum dispersal
distances
We used 2/3 of the data to build the
models and 1/3 of the data as a test set to test the predictions
For the test set we predicted dispersal
distances using parameters from the
models and related these to observed values
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We provide a function dispeRsal to predict maximum dispersal distances for users’ own datasets
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How to predict plant species’ dispersal distances using
dispeRsal function in ?
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Download and install http://www.r-project.org
R is a free software environment for
statistical computing and graphics
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Download and load dispeRsal functionhttp://www.botany.ut.ee/dispersal
Simply download the dispeRsal file
Double-clicking the file automatically
opens R and loads the dispeRsal
function
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
You can use Excel or similar software to
prepare the dataset
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
Enter the species names without
authorship
It is also possible to only include genus
level
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
Enter either tree, shrub, or herb
Species growth form
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
Enter either animal, ant, ballistic, wind.none, or wind.special
Species dispersal syndrome
You can use different online databases to
obtain data on species’ dispersal
syndrome
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
Seed mass
Enter the value in log10 transformed format (using mg)
If no data is available, you can
leave the cell empty
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
If no data is available, you can
leave the cell empty
Enter the value in log10 transformed format (using m/s)
Seed terminal velocity
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Prepare the data fileYour data file has to follow a specific format
Species GF DS SM TV RH
If no data is available, you can
leave the cell empty
Seed releasing height (or plant
height)
Enter the data in log10 transformed format (using m)
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For example…
Species GF DS SM TV RH
Acer platanoides tree wind.special 2.14 0.01 1.35
Abies alba tree wind.special 1.90 1.46
Viola montana herb ballistic
Viola arvensis herb ballistic -0.29 0.48 -0.79
Viola arvensis herb ant -0.29 0.48 -0.79
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For example…
Save your file in a comma separated file
format (.csv)
Make sure your data file is in the same directory as the dispeRsal file
Note that you can enter a species multiple times to predict dispersal
distance for different syndromes
Species GF DS SM TV RH
Acer platanoides tree wind.special 2.14 0.01 1.35
Abies alba tree wind.special 1.90 1.46
Viola montana herb ballistic
Viola arvensis herb ballistic -0.29 0.48 -0.79
Viola arvensis herb ant -0.29 0.48 -0.79
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4
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your.data <- read.table(“YourFileName.csv”, header=TRUE, sep=“;”, dec=“.”)
You may need to modify the values for separator (sep) and
decimal (dec) depending on your
file format
Read in your data to
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5
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dispeRsal(your.data, model=5)
Use dispeRsal function
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dispeRsal(your.data, model=5)
The value for model can be either 1, 2, 3,
4, or 5
Use dispeRsal function
1 uses DS, GF, TV2 uses DS, GF, SM,
RH3 uses DS, GF, RH4 uses DS, GF, SM
5 uses DS, GF
Choose the model depending on the data available (you can run the function several times using different
models)
Note that the simplest model (5) only uses DS and GF data even for
species that have more data available
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The output…
Species Order Family DS log10MDD_Family
log10MDD_Order
log10MDD_measured
Acer platanoides Sapindales Sapindaceae wind.special 2.36 2.32 2.68
Abies alba Pinales Pinaceae wind.special 2.57 2.32 3.85
Viola montana Malphigiales Violacea ballistic 0.61 0.47 NA
Viola arvensis Malphigiales Violacea ballistic 0.61 0.47 0.38
Viola arvensis Malphigiales Violacea ant 0.82 0.69 NA
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The output…
The function automatically assignes your
species to a family and an order
Species Order Family DS log10MDD_Family
log10MDD_Order
log10MDD_measured
Acer platanoides Sapindales Sapindaceae wind.special 2.36 2.32 2.68
Abies alba Pinales Pinaceae wind.special 2.57 2.32 3.85
Viola montana Malphigiales Violacea ballistic 0.61 0.47 NA
Viola arvensis Malphigiales Violacea ballistic 0.61 0.47 0.38
Viola arvensis Malphigiales Violacea ant 0.82 0.69 NA
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The output…
The function predicts dispersal distances taking account the taxonomy of the
species (family or order)
Species Order Family DS log10MDD_Family
log10MDD_Order
log10MDD_measured
Acer platanoides Sapindales Sapindaceae wind.special 2.36 2.32 2.68
Abies alba Pinales Pinaceae wind.special 2.57 2.32 3.85
Viola montana Malphigiales Violacea ballistic 0.61 0.47 NA
Viola arvensis Malphigiales Violacea ballistic 0.61 0.47 0.38
Viola arvensis Malphigiales Violacea ant 0.82 0.69 NA
Note that the maximum dispersal distance values are
log10 transformed (in m)
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The output…
If possible, also the measured maximum dispersal distance
from the original data source is given
Species Order Family DS log10MDD_Family
log10MDD_Order
log10MDD_measured
Acer platanoides Sapindales Sapindaceae wind.special 2.36 2.32 2.68
Abies alba Pinales Pinaceae wind.special 2.57 2.32 3.85
Viola montana Malphigiales Violacea ballistic 0.61 0.47 NA
Viola arvensis Malphigiales Violacea ballistic 0.61 0.47 0.38
Viola arvensis Malphigiales Violacea ant 0.82 0.69 NA
Note that the maximum dispersal distance values are
log10 transformed (in m)
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For more information…http://www.botany.ut.ee/dispersal
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dispeRsal is being presented by a research article in Ecology, which we kindly ask you to cite in case you use the tool and its output for your own publications
Riin Tamme, Lars Götzenberger, Martin Zobel, James M. Bullock, Danny A. P. Hooftman, Ants Kaasik, and Meelis Partel (In press). Predicting species' maximum
dispersal distances from simple plant traits. Ecology. http://dx.doi.org/10.1890/13-1000.1