The martelosim R package Quick User guide

Simulating the evolution of a marteloscope following a marking and over a period of several decades with thinning

Introduction

The {martelosim} package is a tool making possible to simulate the evolution of a marteloscope over several decades, a first thinning corresponding to the marking made on the field and later thinning reproducing a strategy deduced from the field marking.

The package uses the model Samsara2 (Lafond et al. (2017), Courbaud et al. (2016), Courbaud et al. (2015)), which is a module of the software CAPSIS. It proposes a specific interface for marteloscopes that facilitates the preparation of all the files describing the marteloscope initial conditions, the loading of field marking information during a marking exercise, the simulation processing, and the production of a standardized output report.

The package is designed to be run by the marteloscope instructor on a computer. During a marteloscope exercise, each marking group enters his marking data on a tablet (we propose an Excel file by default. The EFI I+Trainer can be used also for this step). The marteloscope instructor collects all the marking files and runs the simulations for all the groups on his computer. The package produces a personalized simulation report for each group.

Here are the steps you have to follow:

• Install the {martelosim} package on the marteloscope trainer’s computer:
• Test the {martelosim} package making a virtual workshop on the marteloscope of Prelenfrey
• Set up your marteloscope on the field
• Prepare a marteloscope workshop on your computer and your tablets
• Test the simulator on a random marking
• Carry out the marking on the field
• Simulate the results of a marteloscope exercise

Preparing your marteloscope field data in the Excel template

Specific data describing the initial conditions of your marteloscope are required to run simulations. Organize your data in an Excel file corresponding the templates Data_Marteloscope_template_en.xlsx or Data_Marteloscope_template_fr.xlsx. You can look at Data_Marteloscope_Prelenfrey.xlsx (link to download) for an example and save it with a name corresponding to your marteloscope (ex Data_Marteloscope_MyMarteloscope_en.xlsx).

The page Notice describes the variables to fill and indicates which data is mandatory or optional. Please follow exactly the required data formats otherwise the martelosim package will not be able to read the data correctly. The file contains many pages but several of them are metadata not field data (ex: regeneration size class definitions, price per species, quality and size) and a lot is optional. If you don’t fill optional data, Samsara2 will use default values or simulate virtual objects (ex virtual saplings)

If you have installed your marteloscope following the EFI Integrate+ protocol, you will have some data transformation to make and some additional data to inform but it should not be a big deal. For Samsara2, you have to give only one quality to the whole tree. Samsara2 requires a Z coordinate for every tree in order to calculate light interception correctly. If you have not measured it on the field, you can let this variable empty. An approximate Z will be calculated using the marteloscope average slope and exposure.

If you need to calculate tree X, Y and Z coordinates from field measures of distances, azimuth and vertical angles between reference poles and trees, you can use the additional martelo-main.zip R code by Jean-Matthieu Monnet to convert field measures into X,Y, Z coordinates.

Software installation

Installing Java

Java is usually already installed in most computers. However, for Capsis, you need a version 1.8.xxx of Java. To check the version of Java on your computer,

• Open a MS-DOS command prompt (on Windows)
• type : java –version

If you don’t have a Java 1.8.xxx on your computer, download a “Java SE Runtime Environnement 8” on your machine, https://java.com/en/download/manual.jsp (for Windows choose an offline version) https://www.oracle.com/java/technologies/javase-jre8-downloads.html (subscription is mandatory on this site)

Installing Capsis

Run the Capsis installer.

You can download it at this link : download capsis

• Double click on the installer file : capsis-Samsara2_4.2.7-22961_setup.jar
• Choose a name and location for your Capsis folder (for example /My documents/Capsis).
• Caution, the path to your Capsis folder must not contain names with space, special characters or accents.
• Remember this location and path, because you’ll need it later.
• For more information, see : http://capsis.cirad.fr/capsis/documentation/installation_guide

Be careful : the Capis version must be compatible with the R package version you install. The last stable version of martelosim require version 4.2.7-22961 for Capsis.

To check the version of Capsis on your computer you can go with an explorer into your folder Capsis and open the text file “revision”. Alternatively, when Samsara is running, a message is displayed on the terminal indicating the version.

If you do large simulations, there can be a problem of insufficient RAM (Random Access Memory) allocated to Capsis. If it is the case, you can increase it (within the limit of your machine).

The RAM allocated to Capsis depends on your machine. It is indicated in Megabytes in the text file “memory” in your Capsis folder. Alternatively it is also written in the MS-DOS terminal when you launch Capsis directly :

You can increase it (within the limit of your machine) typing in the terminal the command “setmem” with the nb of Megabytes you want (too large a number can slow calculations):

Installing R and Rstudio

• Link to install R : https://cran.r-project.org/bin/windows/base/

• Link to install RStudio : https://posit.co/download/rstudio-desktop/

Installing the R package {martelosim}

Open R studio or close any project and paste one of the following commands in the console:

If you are a marteloscope instructor

You should install the last stabilized version of the package, copying and pasting the command line:

install.packages(
    'martelosim', 
    repos = c('https://inrae.r-universe.dev', 'https://cloud.r-project.org')
)

If you are a researcher

You can install the current development version with the command line:

if (!require("remotes", quietly = TRUE)) {install.packages("remotes")}
remotes::install_url(
    'https://forge.inrae.fr/lessem/rpackages/martelosim/-/archive/dev/martelosim-dev.zip'
)

Restart R either in closing and reopening RStudio or by copying and pasting the command .rs.restartR()

If you want to check the version of the package on your computer, type the command: packageVersion("martelosim")

Using the R package {martelosim}

You have to prepare in advance a marteloscope exercise by following the 3 steps in sequence:

1. Testing the {martelosim} package making a virtual workshop on the marteloscope of Prelenfrey
2. Testing the package on your own marteloscope with a random marking
3. Using the package during a marking exercise

Create a directory and an R project to simulate a marteloscope workshop on the marteloscope of your choice

If you don’t have a marteloscope yet, you can use data from the marteloscope of Prelenfrey which are included in the package.

On your windows explorer create a directory where you will store all your marteloscope exercises, and a subdirectory for each marteloscope. Ex: martelosim_Workshops/Prelenfrey

Caution: folder and file names in the path to your files should not include any space or special character. If necessary change the names on your computer.

• Open RStudio

  • File/New project (in the upper left menu bar)

  

  • New Directory

  

  • Martelosim workshop

  

  • Fill out the workshop creation dialog box
    

  

  • Directory name : we suggest that you create a directory named with the marteloscope and the date of your exercise: ex Prelenfrey_2024-11-19. You can create right away a succession of directories to organize your marteloscopes and future workshops. (ex : martelosim_Workshops/Prelenfrey/Prelenfrey_2024-11-19)
  • Create project as subdirectory of : indicate the folder in which you want to store the directory of your exercise : ex martelosim_Workshops/Prelenfrey
  • Marteloscope site name : Repeat the Marteloscope name : ex Prelenfrey
  • Workshop Date : Repeat the workshop date : ex 2026-06-26
  • Capsis path : Click on Browse and develop the pathway up to your Capsis folder and select the file capsis.sh
  • Marteloscope inventory path : Click on Browse and develop the pathway up to your Excel data file. If you are making a test with Prelenfrey, you don’t need to fill this item.(ex: Data_Marteloscope_Prelenfrey_en.xlsx)
  • Create project

The R studio interface opens

martelosim project

• The dialogue box creates a folder for your exercise (ex: Prelenfrey_2026-06-26) In this folder, you find
  • The R project for the exercise (ex: Prelenfrey_2026-06-26.Rproj)
  • Subfolders : scripts, data, martelages, reports
• RStudio opens your project (ex: Prelenfrey_2026-06-26.Rproj).

Run the script 01_start.R

This script prepares the files describing your marteloscope initial conditions. You have to run it once each time you prepare a new exercise.

If R studio has not opened script_01_start.R, go in the RStudio bottom right window. Select the tab Files, open the directory scripts and double click on the file scripts/01_start.R

The script is composed of lines of comments in green starting by a sharp #; and blocks of codes in black with text variables in green surrounded by quotation marks “” and logical variables in blue. You can check the values of text and logical variables that have been set by default or if necessary you can modify them.

To run a block of code, click above the block and select the tab “run”. Alternatively, you can click above or at the beginning of a line of code or above a block and type Ctrl Enter.

• martelosim::martelosim_options() displays the information you entered previously in the dialog box. You can check them and correct them if necessary.

• martelosim::new_inventory() creates an inventory for this project, in the subdirectory “data” of your exercise and checks its name :
  • inv_site
    • inv_site = “new” creates an empty inventory to fill.
    • inv_site = “copy” copies an inventory file to the present project. The file path is specified in inv_file.
    • inv_site = Prelenfrey will copy the example file of Prelenfrey stored in the package.
  • The option lang = getOption("mart_lang") : the language (English or French) will correspond to what you have indicated previously in the create marteloscope box.
  • If the option open = TRUE, then the Excel file is open and you can check the Prelenfrey data file. You have to close the Excel file before continuing running the R code.

• martelosim::build_samsa2_files()
  • Creates the files of initial conditions used by the simulator Samsara2 and store them in the subdirectory data. These files will be used by the simulator Samsara2 to make simulations.
  • Creates in the folder markings, the default marking file markings/Prelenfrey_2024-11-19_Marking_Free_Growth.xlsx which can be used on the field to enter marking data for the marking group Free_Growth. Have a look at the results produced on the Console : it will give you indications about the files that were created and potential default values that were used if some information is lacking in the Excel data file.
  • Creates in the folder markings, the default silviculture file markings/Prelenfrey_2024-11-19_Silviculture_Free_Growth.xlsx which is used for the “expert” mode of silviculture parameter. It can be used on the field to detail silvicultural interventions that will be performed during the simulation for the marking group Free_Growth. More details are provided in the dedicated section (see Define the silvicultural interventions performed during the simulation).

• martelosim::prepare_samsara() complements field data if your marteloscope is not rectangular or if you have not measured the regeneration on the field.
  • the option create_periphery = TRUE means that virtual trees will be created in a rectangular plot around the marteloscope to manage border conditions for light calculations
  • The option create_virtual_saplings = TRUE means that virtual saplings in balance with the adult tree stand and ground light conditions are created.

• martelosim::martelo_maps() creates a pdf file with maps to help you locate interesting trees on the field. These maps are stored in the folder reports. (ex: reports/Prelenfrey_Maps.pdf).

• navigateToFile(« script 02_test.R ») opens the script used for the next step (alternatively, you can also open it directly by double clicking on it on the right panel).

Run the script 02_test.R

This script runs marteloscope marking exercise with a random marking in order to test the whole process.

• martelosim::random_marking() creates a virtual marking file in the subdirectory markings where trees have been selected randomly. It makes a marking file similar to a field marking file. In our example: markings/Prelenfrey_2024-11-19_Marking_Random.xlsx
• MarkingGroups <- c("Random") means you are working with marking made by the virtual marking group called Random

• write_samsara_cmd() creates the file data Prelenfrey_CommandFile.txt in the data folder. This command file plans all the simulation steps for Samsara2.

• marking_groups = MarkingGroups means that the simulation will be made for the groups named in the MarkingGroup vector

• thinning_time = 10 means that a thinning will be applied every 10 years. The first one will be the field marking, the newt ones will be made using the Multicriteria_Thinner of Samsara2 mimicking the same kind of marking strategy than the field marking.

• evolution_time = 20 means that the simulation will cover 20 years.

• silviculture = "auto" means that the parameters of the Muticriteria_Thinner will be automatically deduced from the field marking. Alternative is silviculture = "expert", which requires to fill the silviculture file (see Define the silvicultural interventions performed during the simulation).

• run_samsara() runs the simulations for the the random marking and stores result files in the folder data/Output. It takes approximately 1 minutes for a 20 year evolution simulation.

• martelo_report() writes a PDF report for the marking group and stores it in the “reports” folder. In our example: “reports/Prelenfrey_2024-11-19_Random_Report.pdf”. The option:

  • marking_groups = MarkingGroups means that the report will be made for the groups named in the MarkingGroup vector.
  • dendrometry = TRUE, etc… means that the report will include pages for all these topics.
  • multi = TRUE means that you want a multiple report whereas multi = FALSE means you want individual reports for each group

The scripts 03_workshop.R is for a real marking exercise, not needed if you just want to test the package on a virtual marteloscope with a random marking.

Testing the package on your own marteloscope

Objectives:

• Check that the data in your marteloscope is correctly formatted
• Check that the process is running correctly on your marteloscope
• Test the production and storage of a field marking data file on tablets
• Test the transfer of data between tablets and computer
• Test the whole process of simulation based on a field marking

Create a directory and an R project to simulate a marteloscope workshop on the marteloscope of your choice - Before each exercise

A new R project must be created before each exercise. Follow the same instructions as in part 3.1.1. changing Prelenfrey and 2024.11.19 by your own marteloscope name and exercise date.

Run the script 01_start.R - Before each exercise

Before the exercise, run 01_start.R to prepares the files describing your marteloscope initial conditions. Follow the same instructions as in part 3.1.2. Note that :

• martelosim::new_inventory() copies the Excel file of field data in the subdirectory data of your exercise and checks its name :
  • inv_site = getOption(“mart_site”) imports the data file indicated previously
  • The option lang = getOption("mart_lang") : the language (English or French) will correspond to what you have indicated previously in the create marteloscope box.
  • If the option open = TRUE, then the Excel file is open and you can check the Prelenfrey data file. You have to close the Excel file before continuing running the R code.

Be careful that the format of the Excel data file is correct.

Run the script02_test.R to test a random marking

To run a simulation with a random marking, follow the instructions in Run the script 02_test.R.

Prepare the field tablets - Before going to the field

• To enter on the field the marking made by a marking group, you have to prepare on each tablet either the simple Excel file provided by in the package or the I+Trainer software. It is a good precaution to prepare the Excel file in any case.
• If you intend to use the mode silviculture = "expert", you have to transfer the silviculture file on tablets. This file has been prepared when running the project script_01.R. For example: YourMarteloscopeName_YYYY-MM-DD_Silviculture_Free_Growth.xlsx. Change the name of the file with the correct tablet name or marking group name (ex: YourMarteloscopeName_YYYY-MM-DD_Silviculture_A.xlsx for group A)
• To transfer files between your computer and your tablets, you can use the cable of your tablet charger. Authorize the transfer. The tablet becomes an extension of your computer. Use the windows explorer on your computer to create folders and copy files on the tablet.

Case 1: You intend to use the simple Excel file provided with the package

• Create a folder for your marteloscopes on each tablet. (ex: Internal storage/Documents/Marteloscopes/MyMarteloscope/)
• To speed up file transfers when you carry out a real exercise, we recommend that you prepare shortcuts on your tablet’s desktop to the Marteloscopes directory.
• Transfer from your computer to the tablet the Excel marking file that has been prepared on your computer when running the project script_01.R. For example: YourMarteloscopeName_YYYY-MM-DD_Marking_Free_Growth.xlsx. Change the name of the file with the correct tablet name or marking group name (ex: YourMarteloscopeName_ YYYY-MM-DD _Marking_A.xlsx for group A)
• Transfer also on the tablet the map of the marteloscope: it will be helpful to navigate on the field.

Case 2: You intend to use the I+Trainer software

• If the marteloscope is registered in the European Forest Institute marteloscope database, you can use the I+Trainer instead of the Excel file to enter the field marking data. To discover this software see: http://iplus.efi.int/
• In that case, when you finish the field marking exercise on the I+Trainer, you will have to save the result file on the tablet directory Internal storage/iplus/ which has been created automatically when installing the I+Trainer on your tablet.
• To speed up file transfers when you carry out a real exercise, we recommend that you prepare shortcuts on your tablet’s desktop to the I+Trainer software and to the Internal storage/iplus/directory.

Make a virtual field marking on your tablet – On the field

Case 1: You use the default Excel marking file provided with the package

• The marking group opens the Excel file. A table presents all the trees of the marteloscope with their species and diameter. By default, the Action column has been filled with the keyword Wait which mean that you don’t do anything on this tree. If you click on the box, you can choose among the actions: Cut, Wait, Promote or Retain. You can select a reason for your choice in the column Reason.
• Transfer the marking file on the marteloscope instructor’s computer in the markings folder for simulation. Check that the name of the file is correct. it should follow the rule: YourMarteloscopeName_ YYYY-MM-DD _Marking_YourGroup.xlsx.

Case 2: You use the field the I+Trainer software

• Alternatively, make a virtual field marking using the I+Trainer. When you finish the exercise, be careful to save the marking on your tablet: Save results on the device -> Save final results on the SD card, create PDF and restart the application. A csv result file is automatically saved in the directory Internal storage/iplus/ with a long name given by Iplus (ex Benoit_Prelenfrey_France_2024-10-02_1557.csv). You will have to change this name by a Samsara2 compatible name (YourMarteloscopeName_YYYY-MM-DD_Marking_YourGroup.csv) either now or later on your computer.
• Transfer the marking file on the marteloscope instructor’s computer in the markings folder for simulation. Check that the name of the file is correct. it should follow the rule: YourMarteloscopeName_ YYYY-MM-DD _Marking_YourGroup.csv

Run the script 03_workshop.R to simulate the field marking - After the marking

This script runs a marteloscope marking exercise in order to make the whole process.

• find_marking() reads the making file template. You can check that it identifies all of your marking files. If the option open = TRUE, the Excel sheet is opened to be checked. You have to close the Excel sheet before going on.
• ‘MarkingGroups’ allows you to choose the marking groups for which you want to run the simulation (ex: MarkingGroups <- c("A","B","C") if your groups are named A, B and C).

• write_samsara_cmd() (as in Run the script 02_test.R) creates the command file planing all the simulation steps for Samsara2.

- `marking_groups = MarkingGroups` means that the simulation will be made for the groups named in the MarkingGroup vector.
- `thinning_time = 10` means that a thinning will be applied every 10 years. The first one will be the field marking, the newt ones will be made using the Multicriteria_Thinner of Samsara2 mimicking the same kind of marking strategy than the field marking.
- `evolution_time = 20` means that the simulation will cover 20 years.
- `silviculture = "auto"` means that the parameters of the Muticriteria_Thinner will be automatically deduced from the field marking. Alternative is `silviculture = "expert"`, which requires to fill the silviculture file (see `Define the silvicultural interventions performed during the simulation`).

• run_samsara() runs the simulations for the marking groups you have chosen in the creation of the CommandFile and stores result files in the folder data.

• martelo_report() writes a PDF report for the marking group and stores it in the “reports” folder.

  • marking_groups = MarkingGroups, means that reports will be made for all the groups in MarkingGroups.
  • dendrometry = TRUE, etc… makes you able to select topics you want to have in the reports (1 page / topic).
  • multi = TRUE means that you want a multiple report whereas multi = FALSE means you want individual reports for each group

Define the silvicultural interventions performed during the simulation

In write_samsara_cmd(), the option silviculture = “expert” allows to each marking group to define the silvicultural interventions performed during the simulation. To this end, the characteristics of the interventions must be provided in the silviculture file (Silviculture sheet):

• Year corresponds to the year of intervention. This parameter does not require a year expressed as a calendar year, but as the time between the exercise and the intervention. For example, a marking exercise performed in 2026 corresponds to year 0. An intervention performed in 2036 therefore corresponds to year 10.
• Intervention_Type corresponds to the type of cutting performed during the intervention. The user can select one scenario among five depending on the stand structure. These choices are detailed in the Infos_Scenarios sheet of the silviculture file.
• Nb_Retained_Trees_ha corresponds to the number of habitat trees per hectare.
• Nb_Promoted_Trees_ha corresponds to the number of trees per hectare that will be selected as elite trees.
• Target_BasalArea_AfterCutting_m2ha corresponds to the basal area per hectare that should reach the stand after cutting.
• Percentage_of_Thinning_In_Cut_BasalArea corresponds to the proportion of the cut basal area dedicated to thinning. Expressed as a percentage, from 0 to 100 %.
• Favor_Rare_Species is a binary answer, yes if the rarest species should be favoured.
• Species_Group_To_Promote corresponds to a group of species as defined in Species_Group variable (Species sheet of Data_Marteloscope_MyMarteloscope) that should be promoted. This parameter is optional, leave blank if no group should be favoured.
• Species_Group_To_Restrict corresponds to a group of species as defined in Species_Group variable (Species sheet of Data_Marteloscope_MyMarteloscope) whose presence must be controlled. This parameter is optional, leave blank if no group should be controlled.

Up to 20 interventions can be defined, however, make sure that Year never exceeds evolution_ time.

The silviculture file is generated in the folder markings in the format YourMarteloscopeName_YYYY-MM-DD_Silviculture_Free_Growth.xlsx. To complete the silviculture file, you should duplicate it and rename the file following the rule: YourMarteloscopeName_YYYY.MM.DD_Silviculture_YourGroup.csv. After its completion, transfer the silviculture file on the marteloscope instructor’s computer in the markings folder for simulation.

Evaluate the simulations on your marteloscope

Now that simulations are running on your computer, look in more detail at simulation reports to check that their predictions are realistic. Do you think the order of magnitude of production is correct? Do you think the regeneration flux is correct? Do you think the natural mortality is correct? Do you think the tree microhabitat evolution is correct?

A model is a strong simplification of reality. Predictions depend on the equations chosen to represent the different processes, on the parameter values in these equations and on the description of the initial state. Processes have been calibrated on large datasets averaging many different conditions and it is inevitable that the specific conditions of your marteloscope are represented quite poorly. If there are big inconsistencies, please let us know.

When analyzing the results of a simulation, you should not concentrate too much on quantitative predictions. It is more interesting to try and understand how the different variables evolve in relation to each other. Are some variables moving in similar directions ? Are other variables moving in opposite directions ? Is a change in one variable the consequence of an earlier change in another ?

The D-Day : Performing a real marteloscope field and simulation workshop

A few days before the field workshop, you have to prepare a new project on your computer and your tablets and test everything. The D day you have to be able to do all the manipulations quickly and smoothly.

Preparing your computer and your tablets before the workshop

On your computer:

• Create a directory for your field exercise (ex: martelosim_Workshops/MyMarteloscope/MyMarteloscope_YYYY-MM-DD)
• Run the script 01_start.R to prepare the initial state files required for the simulations.
• Run the script 02_test.R to check that everything is working with a random marking

On your tablets: Transfer from your computer to the tablets the empty marking file YourMarteloscopeName_YYYY-MM-DD_Marking_Free_Growth.xlsx and change the names replacing Free_Growth by the name of each tablet Alternatively, prepare your tablets with the I+Trainer software.

Import marking files, make the simulations and produce the reports

At the beginning try not to have too many groups in the field: the more groups there are, the more manipulations there are to do.

• Be very careful that every group save correctly their marking file on their tablet. If you use the I+ trainer, be especially cautious that the csv result file is saved correctly using : Save results on the device -> Save final results on the SD card, create PDF and restart the application
• Transfer the marking files from the tablets to your computer.
• Run the script 03_workshop.R to make the simulations and produce the pdf reports.

Be careful that file names are correct and that you have written all the group names in the vector MarkingGroups.

• Visualize the pdf simulation report of each group and transfer it back to the group’s tablet.
• To share and discuss the results, if you can gather in a room, the best is to project simulation reports using a video-projector. Alternatively, you can print the reports using a portable printer or simply transfer them back to the tablets.
• After the exercise, you can transfer the reports of all groups to all the participants by email.

The transfer of files between tablets and computer is a bit of a pain but is doable. If you are well prepared, the whole process should take about 30 minutes.

Other simulation projects with Samsara2

You can use your marteloscope data to make simulations with Samsara2 for other purposes than field marking workshops. Marteloscope simulation has been standardized to be efficient and fast, but the drawback is that the simulation scenario is not very flexible. In other contexts, you can imagine more complex simulation scenarios using Samsara2 in two ways:

• You can use the CAPSIS interactive interface to make simulations step by step. See the corresponding Samsara2 tutorial and exercise for students.
• You can use the script mode to make a simulation experiment. You have to prepare a command file describing all the scenarios you want to run together. A documentation is in preparation.

References

Courbaud, Benoit, Thierry Sardin, François De Coligny, et al. 2016. “Utilisation Du Modèle Individu-Centré Samsara2 Pour Analyser Les Sylvicultures En Peuplement Irrégulier.” Rendez-Vous Techniques de l’ONF 53: 11–23. https://capsis.cirad.fr/capsis/_media/help_en/courbaudb_al.-rdvtech2016.pdf.

Courbaud, B., V. Lafond, G. Lagarrigues, et al. 2015. “Applying Ecological Model Evaludation: Lessons Learned with the Forest Dynamics Model Samsara2.” Ecological Modelling 314: 1–14. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2015.06.039.

Lafond, Valentine, Thomas Cordonnier, Zhun Mao, and Benoit Courbaud. 2017. “Trade-Offs and Synergies Between Ecosystem Services in Uneven-Aged Mountain Forests: Evidences Using Pareto Fronts.” European Journal of Forest Research 136 (5): 997–1012. https://doi.org/10.1007/s10342-016-1022-3.