R IIN T AMME, L ARS G ÖTZENBERGER, M ARTIN Z OBEL, J AMES M. B ULLOCK, D ANNY A. P. H OOFTMAN, A NTS K AASIK, M EELIS P ÄRTEL Predicting seed dispersal

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

We collected available maximum dispersal distance data for plant species

576 plant species are currently represented in our database

We collected plant trait data from original studies or databases

dispersal syndromegrowth formseed mass

seed releasing heightterminal velocity

We related these plant traits to maximum dispersal distances

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

Average maximum dispersal distance

also increases from herbs to shrubs and

trees5.2 m

24.4 m

295 m

We then built models to predict plant species’ maximum dispersal distances from simple plant traits

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

We provide a function dispeRsal to predict maximum dispersal distances for users’ own datasets

How to predict plant species’ dispersal distances using

dispeRsal function in ?

1

Download and install http://www.r-project.org

R is a free software environment for

statistical computing and graphics

2

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

3

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



Enter the species names without

authorship

It is also possible to only include genus

level



Enter either tree, shrub, or herb

Species growth form



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



Seed mass

Enter the value in log10 transformed format (using mg)

If no data is available, you can

leave the cell empty





Enter the value in log10 transformed format (using m/s)

Seed terminal velocity





Seed releasing height (or plant

height)

Enter the data in log10 transformed format (using m)

For example…


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

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


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

4

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

5

dispeRsal(your.data, model=5)

Use dispeRsal function

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

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

The output…

The function automatically assignes your

species to a family and an order


log10MDD_Order

log10MDD_measured






The output…

The function predicts dispersal distances taking account the taxonomy of the

species (family or order)


log10MDD_Order

log10MDD_measured






Note that the maximum dispersal distance values are

log10 transformed (in m)

The output…

If possible, also the measured maximum dispersal distance

from the original data source is given


log10MDD_Order

log10MDD_measured






Note that the maximum dispersal distance values are

log10 transformed (in m)

For more information…http://www.botany.ut.ee/dispersal

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

http://dx.doi.org/10.1890/13-1000.1

Documents

R IIN T AMME, L ARS G ÖTZENBERGER, M ARTIN Z OBEL, J AMES M. B ULLOCK, D ANNY A. P. H OOFTMAN, A NTS K AASIK, M EELIS P ÄRTEL Predicting seed dispersal