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Riin Tamme, Lars Götzenberger , Martin Zobel , James M. Bullock, Danny A. P. Hooftman ,

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.

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Riin Tamme, Lars Götzenberger , Martin Zobel , James M. Bullock, Danny A. P. Hooftman ,

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  1. Predicting seed dispersal distances from simple plant traits Riin Tamme, Lars Götzenberger, Martin Zobel, James M. Bullock, Danny A. P. Hooftman, Ants Kaasik, MeelisPärtel

  2. We collected available maximum dispersal distance data for plant species

  3. 576 plant species are currently represented in our database

  4. We collected plant trait data from original studies or databases dispersal syndrome growth form seed mass seed releasing height terminal velocity

  5. We related these plant traits to maximum dispersal distances

  6. 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

  7. Average maximum dispersal distance also increases from herbs to shrubs and trees 5.2 m 24.4 m 295 m

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

  9. 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 Predicted maximum dispersal distance (Log; m) Simple plant traits explained up to 60% of variation in maximum dispersal distances Observed maximum dispersal distance (log; m)

  10. We provide a function dispeRsalto predict maximum dispersal distances for users’ own datasets

  11. How to predict plant species’ dispersal distances using dispeRsalfunction in ?

  12. 1

  13. Download and install http://www.r-project.org R is a free software environment for statistical computing and graphics

  14. 2

  15. Double-clicking the file automatically opens R and loads the dispeRsal function Download and load dispeRsal function http://www.botany.ut.ee/dispersal Simply download the dispeRsal file

  16. 3

  17. You can use Excel or similar software to prepare the dataset Prepare the data file Your data file has to follow a specific format

  18. Prepare the data file Your data file has to follow a specific format It is also possible to only include genus level Enter the species names without authorship

  19. Species growth form Enter either tree, shrub, or herb Prepare the data file Your data file has to follow a specific format

  20. You can use different online databases to obtain data on species’ dispersal syndrome Prepare the data file Your data file has to follow a specific format Enter either animal, ant, ballistic, wind.none, or wind.special Species dispersal syndrome

  21. Seed mass Prepare the data file Your data file has to follow a specific format If no data is available, you can leave the cell empty Enter the value in log10 transformed format (using mg)

  22. Seed terminal velocity Enter the value in log10 transformed format (using m/s) Prepare the data file Your data file has to follow a specific format If no data is available, you can leave the cell empty

  23. Enter the data in log10 transformed format (using m) If no data is available, you can leave the cell empty Prepare the data file Your data file has to follow a specific format Seed releasing height (or plant height)

  24. For example…

  25. Make sure your data file is in the same directory as the dispeRsal file For example… Save your file in a comma separated file format (.csv) Note that you can enter a species multiple times to predict dispersal distance for different syndromes

  26. 4

  27. You may need to modify the values for separator (sep) and decimal (dec) depending on your file format Read in your data to your.data <- read.table(“YourFileName.csv”, header=TRUE, sep=“;”, dec=“.”)

  28. 5

  29. Use dispeRsal function dispeRsal(your.data, model=5)

  30. 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 Use dispeRsal function dispeRsal(your.data, model=5) 1 uses DS, GF, TV 2 uses DS, GF, SM, RH 3 uses DS, GF, RH 4 uses DS, GF, SM 5 uses DS, GF The value for model can be either 1, 2, 3, 4, or 5

  31. The output…

  32. The function automatically assignes your species to a family and an order The output…

  33. Note that the maximum dispersal distance values are log10 transformed (in m) The function predicts dispersal distances taking account the taxonomy of the species (family or order) The output…

  34. Note that the maximum dispersal distance values are log10 transformed (in m) If possible, also the measured maximum dispersal distance from the original data source is given The output…

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

  36. 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

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