# LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data

Vincyane Badouard, Sylvain Schmitt, Guillaume Salzet, Thomas Gaquiere, Margaux Rojat, Caroline Bedeau, Olivier Brunaux, Géraldine Derroire

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## To cite this version:

Vincyane Badouard, Sylvain Schmitt, Guillaume Salzet, Thomas Gaquiere, Margaux Rojat, et al. LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data. Ecological Modelling, 2024, 487, pp.110539. 10.1016/j.ecolmodel.2023.110539. hal-04286637

HAL Id: hal-04286637

https://hal.inrae.fr/hal-04286637v1

Submitted on 18 Feb 2025

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# LoggingLab: an R package to simulate reduced-impact selective logging in tropical forests using forest inventory data

Vincyane Badouard, Sylvain Schmitt, Guillaume Salzet, Thomas Gaquiere, Margaux Rojat, Caroline Bedeau, Olivier Brunaux, Géraldine Derroire

► To cite this version:

Vincyane Badouard, Sylvain Schmitt, Guillaume Salzet, Thomas Gaquiere, Margaux Rojat, et al. LoggingLab: an R package to simulate reduced-impact selective logging in tropical forests using forest inventory data. 2025. hal-04862574

HAL Id: hal-04862574

https://hal.science/hal-04862574v1

Preprint submitted on 3 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
1 Title: LoggingLab: an R package to simulate reduced-impact selective

2 logging in tropical forests using forest inventory data

3 Authors, Affiliations

4 Vincyane Badouard<sup>a,b</sup>, Sylvain Schmitt<sup>c,d</sup>, Guillaume Salzet<sup>e,f</sup>, Thomas Gaquiere<sup>g</sup>, Margaux Rojat<sup>a</sup>,

5 Caroline Bedeau<sup>h</sup>, Olivier Brunaux<sup>h</sup>, Géraldine Derroire<sup>a</sup>.

6

7 <sup>a</sup> Cirad, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de la Guyane),

8 Campus Agronomique, Kourou, French Guiana

9 <sup>b</sup> AMAP, Univ Montpellier, ADEME, CIRAD, CNRS, INRAE, IRD, Montpellier, France

10 <sup>c</sup> CNRS, UMR EcoFoG (Agroparistech, Cirad, INRAE, Université des Antilles, Université de la Guyane),

11 Campus Agronomique, 97310 Kourou, French Guiana

12 <sup>d</sup> CIRAD, Université de Montpellier, UR Forêts & Sociétés, Montferrier-sur-Lez, France

13 <sup>e</sup> UMR BETA, Université de Lorraine, Université de Strasbourg, AgroParisTech, CNRS, INRAE, Nancy,

14 France

15 <sup>f</sup> INRAE, UMR EcoFoG (AgroParisTech, CIRAD, CNRS, INRAE, Université des Antilles), Campus

16 Agronomique, Kourou, French Guiana

17 <sup>g</sup> Université de la Guyane, UMR EcoFoG (AgroParisTech, CIRAD, CNRS, INRAE, Université des Antilles),

18 Campus Agronomique, Kourou, French Guiana

19 <sup>h</sup> Office National des Forêts - RDI - Recherche, développement et innovation (1153317), Cayenne,

20 french Guiana

21

22 <u>E-mail address of each author:</u> Vincyane Badouard: <u>vincyane.badouard@gmail.com</u>

23 Sylvain Schmitt: <u>sylvain.m.schmitt@gmail.com</u>

24 Guillaume Salzet: <u>guillaume.salzet@gmail.com</u>

25 Thomas Gaquiere: <u>gaquiere.thomas@yahoo.com</u>

26 Margaux Rojat: <u>margaux.rojat@gmail.com</u>

27 Caroline Bedeau: <u>caroline.bedeau@onf.fr</u>

28 Olivier Brunaux: <u>olivier.brunaux@onf.fr</u>

29 Géraldine Derroire: <u>geraldine.derroire@cirad.fr</u>

30

31 <u>Orcids:</u>

32 Vincyane Badouard: <u>https://orcid.org/0009-0007-7054-4010</u>
33 Sylvain Schmitt : https://orcid.org/0000-0001-7759-7106

34 Guillaume Salzet : https://orcid.org/0000-0003-4548-5673

35 Thomas Gaquiere : https://orcid.org/0000-0002-8046-5718

36 Margaux Rojat : https://orcid.org/0009-0005-5094-0070

37 Caroline Bedeau : https://orcid.org/0000-0002-6988-6685

38 Olivier Brunaux : https://orcid.org/0000-0002-3392-0423

39 Géraldine Derroire : https://orcid.org/0000-0001-7239-2881

40

41 Highlights (see attached files)

42 Abstract

43 Even where Reduced-Impact Logging (RIL) practices are applied, selective logging causes substantial
damage to tropical forests. To further reduce selective logging damage, the practices that cause the
most damage need to be identified and alternatives tested. To this end, we developed the R package
LoggingLab, a spatially-explicit and individual tree-based selective logging simulator and
demonstrated its functions using data from French Guiana. LoggingLab explicitly simulates damage
during each stage of the selective logging process taking into account topography and hydrography,
which are main constraints on logging. Most LoggingLab parameters can be easily adjusted to a wide
range of local contexts. LoggingLab can also be coupled with forest dynamics models to simulate the
long- term effects of different selective logging scenarios.

52

53 Graphical abstract (see attached files)

54 Keywords

55 Timber production; Sustainable forest management; Logging damage; Spatially explicit individual-
based simulator; Selective logging; French Guiana.

57 1. Introduction

58 Tropical forests carry the highest terrestrial biodiversity in the world (Barlow et al., 2018) and play an
important role in the mitigation of climate change (Malhi et al., 2008) as well as the maintain of food
security and health for many people (Brandon, 2014). They cover 1,834 million hectares (45% of the
world's forests; FAO, 2021), of which over 400 million hectares (c.a. 20%) are classified as production forests (Asner et al., 2009; Blaser et al., 2011). Timber extraction and logging are identified as the main causes of tropical forest degradation, and account for 53% of its emissions (Hosonuma et al., 2012; Pearson et al., 2017). Beyond the few trees harvested per hectare (Gräfe et al., 2021), selective logging impacts are mostly due to collateral damage (Derroire et al., 2021). Selective logging can lead to a substantial reduction in biomass: usually between 10 and 30%, the biomass loss can reach around 60% under high logging intensity, as found by Pinard & Putz (1996) in Malaysia and Rutishauser et al., (2015) in the Amazon. The recovery of pre-logging biomass can take a few decades to more than a century, that depend on the logging intensity (Derroire et al., 2021; Pinard & Cropper, 2000; Rutishauser et al., 2015).

Practices to reduce selective logging impacts have therefore been proposed under the name of Reduced-Impact Logging (RIL). RIL practices already existed in temperate regions to optimise exploitation (Dykstra, 2003; Sist, 2000), and have been promoted in tropical countries from the 1980s-90s (Esteve & Lepitre, 1980; Krzeszkiewicz, 1959). In contrast to conventional selective logging, RIL relies on pre-harvesting planning to optimise logging operation (Boltz et al., 2003; Johns et al., 1996; Pinard & Putz, 1996). As a result, RIL practices considerably reduce the environmental impacts compared to conventional selective logging (CL) (Ellis et al., 2019; Vidal et al., 2016). Pinard & Putz (1996) showed that 23% less biomass is lost with RIL than with CL, and RIL damages 26% fewer unharvested trees than CL. According to Boltz et al. (2003), RIL results in 90-129% less canopy area loss and up to 4 times less ground disturbance than CL. But RIL practices as currently applied still lead to significant damage with, for example, up to 37% biomass loss with RIL even at a very low intensity of 10.7 m³ per hectare (Bryan et al., 2010). Even with RIL, selective logging also changes forest composition, with an increase in the abundance of smaller light-demanding species (Arets et al., 2003; Huth & Ditzer, 2001). The impact on the volume of commercial species is also important. With typically used logging intensities (~20 m³/ha) and logging cycles (30-40 years), the rate of timber recovery in logged natural forests is not sufficient to recover the amount logged by the end of a logging cycle (Huth & Ditzer, 2001; Piponiot et al., 2019; Putz et al., 2012; Shearman et al., 2012). In their best-case scenario, Sist & Ferreira (2007) observed a recovery of only 50% of the initial commercial volume 30 years after RIL at 21 m³/ha in Indonesia. The composition of communities of large (≥50 cm) and, even more so, very large trees (≥110 cm) is more affected than the composition of communities of smaller trees, by the selective logging of trees from 35 cm DBH. The maximum cutting diameter should therefore be considered and reduced (Bousfield et al., 2023). There is therefore an urgent need to assess the efficiency of RIL practices to reduce logging impacts, in order to further improve logging practices, increase economic performance and preserve the ecosystem and associated services
96 (Hiltner et al., 2017; Putz et al., 2022; Sist & Ferreira, 2007). The disaggregation of the contribution of
each operation to overall damage and the experimentation of different practices can help to identify
the most effective ways to reduce damage.
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98

99
100 The assessment of logging damage has evolved over time to be less costly and more accurate. Initially,
101 and still today, post-harvest diagnostics are carried out in the field in order to quantify logging damage
102 and lost timber volumes (Guitet, 2005). However, these field assessments are relatively expensive and
103 are therefore applied to limited areas (Gond & Guitet, 2009). The development of remote sensing has
104 made it possible to quantify canopy changes on a large scale by identifying tree gaps and trails
105 produced by logging (Asner et al., 2002). Thus, remote sensing can facilitate post-harvest diagnostics
106 by identifying highly impacted areas and reduce human and time costs of post-harvest diagnostics.

107 However, remote sensing has difficulty to detect degradation that does not alter the canopy (Asner
108 et al., 2002; Grace et al., 2014; Pearson et al., 2014). Moreover, remote sensing does not provide a
109 detailed analysis of the practices that cause this damage and is therefore of limited interest to identify
110 effective new practices (Gond & Guitet, 2009). The combination of field measurements and remote
111 sensing has however allowed substantial improvements to the assessment of logging damage. This
112 approach, together with the development of standardised indices of carbon emissions caused by
113 logging made it possible, for example, to diagnose and compare the carbon impact of logging
114 practices. This enabled the identification of a subset of RIL practices that promote the reduction of
115 greenhouse gas emissions, an approach called RIL-C (Reduced-Impact Logging for Climate Change
116 Mitigation) (Pearson et al., 2014; Griscom et al., 2014). Assessment of logging damage, either on the
117 ground or by remote sensing, is usually carried out in logging concessions, and is therefore limited to
118 logging practices already implemented. Foresters have set up experimental plots to test a new set of
119 practices. Some of these plots have become permanent inventory plots on which periodical re-census
120 of trees allow to study the logging impact on the forest structure, productivity, biodiversity and carbon
121 fluxes over time (Bertault et al., 1999; Finegan & Camacho, 1999; Gourlet-Fleury et al., 2004; Sist et
122 al., 2015). Experimental plots therefore allow us to study the long-term impacts of logging (Hérault &
123 Piponiot, 2018; Mirabel et al., 2020; Piponiot et al., 2016). However, they usually do not explicitly
124 quantify and spatialize damage, such as the harvested trees, areas affected by skid trails and logging
decks, and volume of collateral damage (i.e. trees killed by trails and secondary windfall).
125
126

127 Logging simulators are developed to address the limitations of field-based and remote-sensing
128 assessment of damage and to help the development of better harvesting practices. At lower human
costs and in less time, logging simulators can test many scenarios (i.e. combination of parameters and
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technical instructions that constitute a particular experiment). They can generate virtual data and perform virtual experiments under conditions beyond those of current field experiments. They also allow to simulate large-scale experiments with a diversity of treatments over a large area and with a large number of replicas (Maréchaux et al., 2021). When coupled to forest dynamics simulators, logging simulators can help predict long term dynamics of forest after logging. Logging simulators have been developed to describe conventional, selective and/or reduced-impact logging, in tropical or temperate contexts. They have different degrees of realism and accuracy, based on the objectives for which they were designed (Table S1). Some simulators do not model individual trees explicitly and may therefore lack (i) the explicit definition of harvesting intensity (FATES Huang et al., 2020), (ii) the choice of harvestable species (FORMIX Bossel & Krieger, 1991; FATES Huang et al., 2020), or (iii) the definition of a minimum cut diameter (GuyaSim Rossi et al., 2015). Yet, these three logging parameters are of primary importance to diagnose the selective logging practices that have the greatest impact. In addition, all the current simulators of tropical logging ignore the topography and the hydrographic network, which are important spatial constraints to the machines that spatially structure the logging operations and the damage they cause. Few tropical logging simulators spatially and explicitly simulate skid trails and felling gaps, the SYMFOR model being the only known example within our knowledge (E. J. M. M. Arets, 2005). In contrast, simulators that do not take into account the spatial structure of trails and felling have to consider damage as an input of the simulator. These logging simulators do not quantify the damage caused by each logging practice and cannot be used to identify, compare and propose ways to limit the consequences of these practices. In summary, to date there is a need for a logging simulator that includes topography and offers enough parameters to allow a fine optimisation of current practices.

Here, we present a new individual-based and spatially-explicit selective logging simulator: LoggingLab. In particular, we are proposing RIL practices in this simulator, as a way to assess and demonstrate the efficiency of these practices to reduce the impact of logging. LoggingLab was developed as an R package because R is a widely used programming language in ecology (Atkins et al., 2022; Lai et al., 2019), which allows the connection with other tools, through the R packages ecosystem. LoggingLab was developed to diagnose and quantify in a spatially explicit manner the impacts of selective logging, so that the reported damage is a consequence of the simulated logging and not an *a priori* choice of the user. It simulates the different selective logging stages taking into account the topography and the hydrographic network, the main spatial constraints for mechanisation. LoggingLab can be used together with any individual-based and spatially explicit forest dynamics simulator like TROLL (Maréchaux & Chave, 2017) or SEIB-DGVM (Sato et al., 2007) to observe the effects of selective
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logging over time on the forest stand and the resilience of each forest to different selective logging
scenarios. It was developed in partnership with the French National Forest Office to realistically
describe selective logging practices implemented in French Guiana, while proposing a large number
of parameters to be a flexible tool that can be adapted to a variety of selective logging practices.

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## 2. Methods

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### 2.1. Reduced-Impact Logging

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RIL is implemented in various ways around the world, in response to economic and environmental
constraints. The most commonly used practices are: (i) pre-harvest inventory of trees of commercial
species and above the minimum exploitable diameter (map and measurement), which allows a
selection of trees to be harvested and the design of an optimised trail network to collect these logs,
with a minimum length of the trail network, (ii) topography-based planning (slopes, hydrographic
network) that allows the optimal location of infrastructure (roads, trails, logging decks) and thus to
reduce the impacted area, (iii) directional tree felling to limit damage due to secondary tree fall,
especially on trees of interest (those to be exploited in the next campaign (Future Crop Trees (FCTs)),
and trees chosen for reproduction (seed trees)) (Dykstra, 2003; Sist, 2000).

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### 2.2. Simulations of the different steps of selective logging in LoggingLab

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LoggingLab simulates timber harvesting according to Reduced-Impact Logging (RIL) practices in a forest plot, that require a spatialised census as well as the plot's geographic features. LoggingLab also includes the possibility to harvest fuel wood as a by-product of timber production (detailed in S4). It covers the different logging steps, from the definition of harvestable zones to the tree felling, and returns a quantification of timber volume harvested and logging collateral damage (i.e. trees killed by trails and secondary windfall). It proposes a function to simulate the whole selective logging process: `loggingsimulation()` but is also intended to be modular as each function that simulates a given stage of the selective logging process (described below) can be used independently (Figure 1).
<figure><img src="image_3.png" /></figure>
Figure 1: Flowchart of the LoggingLab R package. In green the Reduced-Impact

Logging steps realised in reality, in black the added steps in the simulator and in blue
the names of the functions that perform these steps.

2.2.1. Layout of the main skid trails

LoggingLab does not aim to model the main skid trails and the damage they cause, as these trails are located outside the few hectare forest inventory plots that provide the data for simulation. In real conditions, the main trails would be located between these plots. These trails however need to be simulated as they provide access to the zones to harvest. In the absence of information on the position of the main trail, the simulator randomly draws a single entry point per harvestable zone, on the edge of the plot adjacent to an harvestable zone (maintrailextract()) (Figure 6). However, the user can provide a shapefile with the main trail network, that LoggingLab will use to get the entry point.

2.2.2. Definition of the harvestable zone

The harvestable zone is defined under certain conditions linked to the technical constraints of the machines used. Topography is the main limitation. It is only possible to log on a slope inferior to a certain threshold (See S3: for the default values of slopes and distances thresholds). The bottomlands, the hydrographic network and the riparian buffer zones must be avoided (See S3: for the default values taken into account to define riparian buffer zones). The function *harvestableareadefinition* defines the harvestable zones and machine-accessible zones within the plot. The harvestable zone may be larger than the machine-accessible zone if a skid cable is used: the machine will only circulate on the machine zone but the cable may collect a tree in a buffer zone around the machine zone, thus extending the harvestable zone beyond the machine zone (Figure 6).

2.2.3. Tree selection

The tree selection consists to identify the harvestable trees (harvestable()), trees that will be
harvested in the next campaigns: Future Crop Trees (FCTs) (future trees), and trees that should be
protected from harvest as they were chosen as seed trees (i.e. trees kept for reproduction) (reserve
trees) (futurereserve()). The function treeselection implements the selection process. A tree is
harvestable if it meets all the following criteria: it (1) is in the harvestable zone, (2) belongs to a
commercial species, (3) has a diameter above the harvestable diameter, and (4) has no visible defect
(holes, broken crowns, deformed trunks). In addition, if the considered tree belongs to an aggregative
species (i.e. forming clusters of conspecific individuals), the tree must be less than a maximum distance
from other conspecific individuals to be considered harvestable (IsolateTreeMinDistance), to keep
single individuals of this species as seed trees (See S3: for the default value of the *IsolateTreeMinDistance* and other advanced logging parameters). The trees that will actually be harvested are selected among the harvestable trees (*selected*()), in ascending order of their commercial interest (1st, 2nd or more economic ranks entered in the *speciescriteria* argument, see section 2.3.), and in descending order of harvestable volume of the tree until the target volume (*objective*) is reached. If the total harvestable volume of the plot is higher than the target volume (*objective*), the Minimum Felling Diameter (*MinFD*) is increased (*UpMinFD*) in order to harvest the largest trees preferentially. If the harvestable volume is too low, the user can choose between: (1) to increase the commercial species list (diversification) to reach the objective, (2) to harvest without having reached its target volume, or (3) do not harvest the plot. A proportion of the designated trees will be found to be hollow when surveyed by the logger. If the simulated scenario (Table 1) only includes timber production, hollow trees are not harvested and additional healthy harvestable trees are chosen to meet the target volume. If the simulated scenario (Table 1) only includes timber production, hollow trees are not harvested and additional healthy harvestable trees are chosen to meet the target volume. The Future Crop Trees (FCTs) (*future trees*) belong only to species of the first economic rank (i.e. strongest economic interest), with a diameter between the minimum diameter threshold chosen for the FCTs (*FutureTreesMinDiameter*), and the minimum harvestable diameter of the species (*MinFD* or *UpMinFD*) (See S3: for the default value of the *FutureTreesMinDiameter* and other advanced logging parameters). The FCTs (*future trees*) are preserved if this practice is selected by the user and if possible (alternative orientation of the treefall, alternative passage for the trail) during harvesting. Among the FCTs, a number of *reserve trees* equal to the number of harvested trees is randomly selected. By default, protection of *reserve trees* is favoured over the FCTs when skid trails are laid out (Figure 2) (See S3: for default values of the advanced logging parameters).
<figure><img src="image_4.png" /><figcaption>Figure 2: The tree selection in Reduced-Impact Logging (RIL). Black circles: all trees in the plot; Light blue: the harvestable trees; Dark blue: the harvestable trees with raised minimum DBH (MinDBH). Tree selection: Red area: selected trees for logging. Orange circles: Future Crop Trees (FCTs) (future trees); Purple: reserve trees, both chosen among 1st economic rank species only. Dotted lines: all species; Solid lines: commercial species.</figcaption></figure>

### 2.2.4. Layout of secondary skid trails

Secondary trails join the main trails and the selected trees through zones accessible to the machines (secondtrailsopening()). The trails do not necessarily have to reach every tree, as the machines have a synthetic cable to pull them (Figure 3). The trails are therefore designed to pass between the trees, approaching them at the maximum cable or grapple length. For one of the parameters proposed in LoggingLab (winching = "2"), the grapple is preferred to the cable, however, when the slope does not allow the grapple to access the tree, the cable is used (See S3: for functions arguments description) (See S3 Figure 1). This simulation choice is aimed to test situation when grapple is favoured over cable as it is less costly and time consuming, but this do not mean that we recommend the use of grapple over cable, as the grapple cause more damage because it requires the machine to go closer to the cutted tree to harvest it (Putz et al., 2022). The design of secondary trails layout uses a Dijkstra algorithm with directed spatial weights (Sales et al., 2019) in order to minimise their length, according to RIL principles, while respecting longitudinal and transverse slope thresholds for reasons of human and mechanical safety. The allocation of a width to the trails allows to account for the mortality of all the trees situated on the trails. If a weight is given to them, large trees can be avoided to reduce the impacted biomass and because they would push over and leave holes on the trail. The secondary trail
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layout is computed sequentially so as to integrate, at each step of the algorithm, already designed
trails and reuse them preferentially. This specific approach aims to harvest several trees with the same
trail and thus allow a reduced trail area compared to a unique minimization step (Picard et al., 2006).

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<figure><img src="image_5.png" /><figcaption>Figure 3: Trail design when winching is done with a cable.</figcaption></figure>

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2.2.5. Tree felling

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Three felling orientation rules are proposed to the user: (i) the tree is felled foot towards the nearest trail (main or secondary), (ii) in addition to this condition, tree felling avoids FCTs (future trees) and reserve trees, (iii) in addition to these two previous conditions, the tree is angled towards the trail, to optimise the skidding of the tree. When the angle to optimise skidding is chosen between 30° and 45°, it produces a branched structure that allows easier skidding, and thus reduces skidding damage (Hendrison, 1990). The orientation of tree felling can fail and in this case the angle of fall depends on the natural orientation of the tree, its crown and the topography. In LoggingLab, the failure of the felling direction is simulated with a probability chosen by the user, and results in a random orientation. In the simulator, treefelling() simulates all these felling possibilities according to the user's choice
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(See S3: for the description of the functions arguments and of the advanced logging parameters)

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(Figure 6).

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2.2.6. Quantify harvested timber

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When harvest planning is complete, the simulator quantifies the production. The timber harvested
volume (*timberharvestedvolume()*) includes the log of healthy trees harvested for timber, and the
healthy part of the log of hollow trees (⅔ of the tree trunk) if they are also harvested (Figure 4).

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<figure><img src="image_6.png" /></figure>

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<caption>Figure 4: Part of the tree valuable for timber (brown) and tree parts not harvested (grey). The unused part (i.e. defects, decay and buttresses) of the trunk of the healthy harvested trees (named "purge" within the simulator) (default value: 0.14 m³/m³ of volume of timber harvested).</caption>

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2.3. Data requirement and input

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The main input data is a tree census (inventory) for which trees are (i) measured (Diameter at Breast Height, DBH), (ii) spatialised and (iii) botanically identified (in the case of logging inventory using vernacular/commercial name, a correspondence with botanical name will have to be made). The data requirements for the input inventory can be adjusted to the data availability and to the expectation of the user. The more comprehensive the input data are, the more detailed and informative the output will be. As the main objective of LoggingLab is to assess logging damage, we recommend that the whole tree community is provided in the inventory. If the inventory covers only a subset of the whole tree community, the user will have to take this into account to interpret the outcomes. In particular,

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307 (i) for the DBH cut-off, we recommend to include inventory with a minimum DBH as little as possible.
308 10 or 20 cm DBH is a common threshold used in forest inventory, and inventory with a similar
309 minimum DBH will allow comparison with other contexts; (ii) the spatial coordinates (x, y) of all trees
310 that are considered in the inventory need to be provided for LoggingLab to be able to simulate the
311 fate of the tree after logging. (iii) the botanical identification of the commercial species needs to be
312 provided, at least for trees big enough to be harvested, as the botanical identification is a criterion
313 used to choose harvestable trees. The botanical identification of the other trees is optional, but not
314 providing it would restrict the outputs to indices that do not need botanical identification. To simulate
315 a realistic logging operation, the topography is taken into account from a Digital Terrain Model (DTM)
316 of the inventoried plot (topography). The hydrographic network also represents a major constraint for
317 logging, so the definition of the zone to harvest takes into account the relative vertical and horizontal
318 distances from the nearest channel network (creekdistances). In case the user does not already have
319 a map of these hydrological features, we propose a script to generate them using a raster of the Digital
320 Elevation Model (https://vincyanebadouard.github.io/LoggingLab/articles/CreekDistancesRmd.html).
321 In order to ensure the correct delimitation of the plot, a mask (i.e. spatialised polygon) of the plot
322 (plotmask) is required. The table of species criteria (speciescriteria) allows the user to specify the
323 commercial species, the species commercial interest (1st, 2nd or more economic ranks) and species
324 harvestable diameters (MinFD, UpMinFD, MaxFD). A table for volume parameters
325 (volumeparameters) allows to compute the harvestable volume of each tree. Different parameter
326 values can be given for different geographic zones when the volume allometry differs from one study
327 site to another (See S3: for the description of the functions arguments).

328 2.3.1. *Parameterisation of LoggingLab*

329 LoggingLab proposes predefined Reduced-Impact Logging scenarios (Table 1) based on practices
330 applied in French Guiana, or a manual mode to choose between all possible combinations of practices.
331 The practices are the volume to extract (objective), the economic rank of the species to harvest
332 (diversification), the maximum distance at which trees can be retrieved from the trail (winching), the
333 type of directional felling (different objectives of directional felling that condition the angle of felling,
334 directionalfelling) (See S3 for functions arguments description). Note that the LoggingLab includes a
335 possibility to harvest fuel wood as a by-product of logging. As this practice may not be very common
336 outside of our study site, we only present its implementation in detail in S4.
<table><caption>Table 1: Description of the default parameters of the Reduced-Impact Logging (RIL) predefined scenarios proposed in the LoggingLab R package (See S3: for details and S4 for additional scenarios that include use of logging by-products for fuel wood).</caption><thead><tr><th>Scenario</th><th>RIL2 experimental</th><th>RIL2</th><th>RIL3</th></tr></thead><tbody><tr><td>Objective (m³/ha)</td><td>25</td><td>25</td><td>30</td></tr><tr><td>Diversification</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Winching</td><td></td><td></td><td></td></tr><tr><td>Grapple (6m)</td><td>No, skid trail to tree foot</td><td>No</td><td>Yes</td></tr><tr><td>Cable (40m)</td><td></td><td>Yes</td><td>Yes</td></tr><tr><td>Direction felling</td><td></td><td></td><td></td></tr><tr><td>Orientation angle to the trail</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Avoid future crop trees and reserve trees</td><td>No</td><td>No</td><td>Yes</td></tr></tbody></table>

It is possible to use a predefined scenario and change only one or a few parameters, by specifying only the parameters that differ from those of the predefined scenario. The user can also access and modify all the numerical values (e.g. slopes limits, trail width, cable length) and models (eg. volume, crown allometries, tree defects probability, probability to die under a felled tree) to define a custom selective logging operation (advancedloggingparameters) (See S3: for the default values of the advancedloggingparameters). LoggingLab allows the user to integrate repetitions over several iterations (iter) to explore the uncertainties of the different selective logging steps, whose computing time can be reduced with the built-in parallelisation of the simulations (cores).
### 2.3.2. Data preparation

Before the simulation, the data format is checked (`inventorycheckformat()`) and "cleaned" (`cleaninventory()`): only alive trees larger than the minimum inventory diameter (`MinDBHValue`), within the inventoried plot are kept. The dimensions of each tree (tree, trunk and crown heights, crown diameter, harvestable volume, wood density, and AboveGround Biomass (AGB)) are computed from their Diameter at Breast Height (DBH) using the `addtreedim()` function (See S3: for the description of the allometries and their sources) (Figure 5).

<figure><img src="image_7.png" /><figcaption>Figure 5: Tree dimensions computed in *LoggingLab* from the input DBH (Diameter at Breast Height).</figcaption></figure>

## 3. Example application of LoggingLab

### 3.1. Command line

In the following example 30 iterations of a manual scenario (equivalent to scenario **RIL3 with fuelwood & hollow trees** in Table 1) are run on the example data of Plot 6 in Paracou, with other parameters set to default values.

```
Rslt <- loggingsimulation(
  inventory = Paracou6_2016,
  plotmask = PlotMask, topography = DTMParacou,
  creekverticaldistance = CreekDistances$distvert,
  creekhorizontaldistance = CreekDistances$disthorz,
  speciescriteria = SpeciesCriteria,
  volumeparameters = ForestZoneVolumeParametersTable,
  scenario = "manual", objective = 30,
```
373 fuel = "2", winching = "2", directionalfelling = "2",
374 diversification = TRUE, specieslax = FALSE,
375 objectivelax = TRUE,
376 crowndiameterparameters = ParamCrownDiameterAllometry,
377 advancedloggingparameters = loggingparameters(),
378 iter = 30, cores = 30)

379 3.2. Package outputs

380 `loggingsimulation()` returns the forest inventory with new columns that include the tree
381 dimensions, the logging status ("harvestable", "non-harvestable", "future", "reserve", or "selected"),
382 the death cause (trails opening, under a felled tree, harvested), and a column with the coordinates of
383 the felled trees silhouettes (Figure 6) for each iteration (See Table S2: for the metadata of the
384 LoggingLab output inventory). Each simulation produces spatial objects such as the harvestable zones
385 (*HarvestablePolygons*) and the trail layout (Figure 6). The simulator also returns the collected timber,
386 as well as several values related to logging operation and short-term damage (harvestable area,
387 volume of collateral damage, trails density etc...). The numeric outputs of each simulation can then be
388 summarised with `loggingsummary()`, which provides summary statistics of volume production and
389 damage across iterations (Table 2).

<figure><img src="image_8.png" /><figcaption>Paracou plot 6 - RIL3 with fuelwood & hollow trees</figcaption></figure>
391 Figure 6: Summary plot of *LoggingLab* outputs with the simulation presented in example simulation,

392 for one iteration.
<table><thead><tr><th></th><th>Unit</th><th>Mean</th><th>Sd</th><th>Q1 (25%)</th><th>Median</th><th>Q3 (75%)</th></tr></thead><tbody><tr><td>Harvestable area</td><td>ha</td><td>3.7</td><td>0.0</td><td>4.0</td><td>3.7</td><td>4.0</td></tr><tr><td>Objective volume</td><td>m³</td><td>112.4</td><td>0.0</td><td>112.0</td><td>112.4</td><td>112.0</td></tr><tr><td>Initial harvestable volume</td><td>m³</td><td>182.2</td><td>8.7</td><td>178.0</td><td>181.8</td><td>187.0</td></tr><tr><td></td><td>m³/ha</td><td>49.2</td><td>0.0</td><td>44.5</td><td>49.1</td><td>46.8</td></tr><tr><td>Timber logged volume</td><td>m³</td><td>106.6</td><td>2.8</td><td>105.0</td><td>107.3</td><td>109.0</td></tr><tr><td></td><td>m³/ha</td><td>28.8</td><td>0.0</td><td>26.3</td><td>29.0</td><td>27.3</td></tr><tr><td>No hollow timber logged volume</td><td>m³</td><td>96.6</td><td>8.4</td><td>92.0</td><td>98.3</td><td>103.0</td></tr><tr><td></td><td>m³/ha</td><td>26.1</td><td>0.0</td><td>23.0</td><td>26.6</td><td>25.8</td></tr><tr><td>Timber extracted volume (timber volume after purge)</td><td>m³</td><td>93.1</td><td>1.6</td><td>92.0</td><td>93.6</td><td>94.0</td></tr><tr><td></td><td>m³/ha</td><td>25.2</td><td>0.0</td><td>23.0</td><td>25.3</td><td>23.5</td></tr><tr><td>Fuel wood biomass</td><td>ton</td><td>151.5</td><td>8.8</td><td>148.0</td><td>151.3</td><td>155.0</td></tr><tr><td></td><td>ton/ha</td><td>40.9</td><td>0.0</td><td>37.0</td><td>40.9</td><td>38.8</td></tr><tr><td>Logging residual biomass</td><td>ton</td><td>71.6</td><td>6.1</td><td>68.0</td><td>71.2</td><td>75.0</td></tr><tr><td></td><td>ton/ha</td><td>19.4</td><td>0.0</td><td>17.0</td><td>19.2</td><td>18.8</td></tr><tr><td>Total forest biomass lost</td><td>ton</td><td>296.5</td><td>14.7</td><td>290.0</td><td>296.6</td><td>304.0</td></tr></tbody></table>
<table><tr><td>Trails density</td><td>m/ha</td><td>104.7</td><td>10.0</td><td>101.0</td><td>104.2</td><td>111.0</td></tr><tr><td>Adjusted trails density (for fuel wood recovery)</td><td>m/ha</td><td>107.2</td><td>9.3</td><td>105.0</td><td>109.1</td><td>112.0</td></tr></table>

Table 2: Summary of the numeric outputs of iterations of the simulation example (see section 3.1.) computed with `loggingsummary()`, which provides summary statistics of volume production and damage across 30 iterations. Please note that all volumes in m³/ha and biomass in ton/ha are per exploitable hectare, not per plot hectare. “Total forest biomass lost” is the total forest biomass lost due to logging (the sum of the AGB of all dead trees, whatever the death cause. For “Adjusted trails density” definition see S4: for details.

# 4. LoggingLab evaluation

## 4.1. Validation of LoggingLab outputs

Practitioners from the National Forestry Office of French Guiana validated the realism of the simulations on the basis of numerical outputs (timber, and collateral damage volumes, trail density, proportion of dead trees by category) and graphical outputs (spatialisation of exploitable areas and trails). In addition, following a RIL2 scenario at 20 m³/ha with data from French Guiana, LoggingLab determined a collateral damage volume (i.e. trees killed by trails and secondary windfall) of 47.67 m³/ha [28.33,62.47] in agreement with values obtained by statistical model using data from the same region: 41.54 m³/ha [13.55,90.25] (Derroire et al., 2021).

## 4.2. Limitations and perspectives of development

Several further developments could be implemented in a future version of LoggingLab. (1) The current version of LoggingLab requires an important computational time to perform several iterations. Despite the parallelisation of the code, the informatic resources remains important (e.g. 22 minutes of computing time for the 30 iterations of the simulation presented in the example simulation (see section 3.1.), for 30 cores) and a computing cluster may be required. It would be interesting in a future version of LoggingLab to translate the most time-consuming part of the code (the spatialised part) into C++ to reduce computation time, and integrate it into the main R code. (2) LoggingLab has been developed to test different RIL practices and therefore does not allow to simulate a non-optimised trail design to describe Conventional Logging (CL). As there is no planning phase in CL, the trees to harvest are not mapped before logging. The logger therefore collects trees from one to the next,
without optimised trail design, that result in a higher trail density. Conventional secondary trails can be provided by the user because LoggingLab is modular (each function simulate a given stage of the selective logging process can be used independently). An algorithm to simulate tree to tree collection could be implemented in a future version of LoggingLab. Other CL practises (non-directional felling, to collect trees at their base, no particular intention to avoid FCTs and reserve trees) are already implemented in LoggingLab. (3) LoggingLab does not simulate main trails and logging decks. The reason for this is that the simulator has been designed to work on inventory plots that include all species and a large range of size classes, to be able to assess the impacts on the whole tree community. Such plots generally only cover a few hectares. Large-scale structures as main trails and logging decks would therefore be located outside the plot. In case the user would like to use LoggingLab on large logging plots (several hundred hectares), the user could provide LoggingLab with the main trails and logging decks generated by another tool, and LoggingLab would calculate the collateral damage (mortalityunderpolygon()). The algorithms to create main trails and logging decks may be implemented in a future version of LoggingLab.

## 5. Discussion

LoggingLab was designed on the basis of past, current and future Reduced-Impact Logging practices in French Guiana. However, the simulator has been designed to be as accessible and flexible as possible, in order to simulate local selective logging practices in other contexts. Flexibility was made possible by offering the possibility to change all default numerical parameters and allometries, and by proposing an open tool, with code accessible online. LoggingLab can be widely used for ecological studies of the short-term impacts of RIL on the forest ecosystem, such as impacts on floristic diversity or carbon stock. LoggingLab can also be coupled with a Dynamic Vegetation Model (DVM) to study the long-term effects of selective logging, and the resilience of the forest ecosystem to this disturbance in interaction with other ones such as climate change. As the simulation of selective logging implemented in LoggingLab is much more detailed than the one implemented in most forest dynamics simulators, this coupled use would improve long-term predictions and widen the range of tested logging practices. This coupling can be done by using the forest inventory returned by LoggingLab with the life status of each spatialised tree after logging as an input of individual-based and spatially explicit forest dynamics simulators. LoggingLab can also be used as a planning tool for selective logging operations by forest managers, to define the harvestable zone, identify the trees to harvest according to the logging objectives, and design the optimal trail layout to collect these trees (but see Limitations part).
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In conclusion, the ambition of LoggingLab is to provide a simple method to test a wide range of selective logging practices and better assess the quantity and sources of logging damage in a wide range of ecological and wood production contexts in tropical forests. Its in silico approach allows the assessment of selective logging at no cost beyond the cost of forest inventories, and is therefore particularly useful for the design of less destructive logging practices.

457

## 6. Software availability

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LoggingLab is an R package for all operating systems (Windows, Linux, Mac OS). It is a free and open source tool (version 1.0.0 with GPL-3 licence, requiring R >= 4.1) available on GitHub (https://github.com/VincyaneBadouard/LoggingLab), and can be installed within the R environment using the command `devtools::install_github("VincyaneBadouard/LoggingLab")`. The code is available at https://github.com/VincyaneBadouard/LoggingLab, and a vignette (i.e. a tutorial) is available for this package (https://vincyanebadouard.github.io/LoggingLab/index.html). LoggingLab is automatically tested and controlled under CRAN criteria for all operating systems. To facilitate its portability, we also integrated LoggingLab into a singularity container: https://github.com/sylvainschmitt/singularity-LoggingLab.

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We encourage users to report any issues and/or desired extensions on our active issues page (https://github.com/VincyaneBadouard/LoggingLab/issues).

471

## Author contributions

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G.D., S.S. and V.B. conceptualised the study. O.B. and C.B. provided information on logging practices in French Guiana and checked the realism of the algorithm and its outputs. V.B. designed the architecture of the package, created the R package and managed its collaborative development, with inputs from GD., S.S., G.S. and T.G. V.B., G.S. and T.G. wrote the code. V.B. and M.R developed some of the models implemented in the package. V.B. wrote the package documentation and the unit tests. S.S. supervised the development of the package and checked the code. G.D. checked the package documentation. M.R. beta tested the package under development. V.B. led the writing of the manuscript, with inputs from G.D. and S.S.. All authors contributed critically to the manuscript and gave final approval for publications.
481 Acknowledgements

482 This work has benefited from a grant [ManagForRes project] from Office Français de la Biodiversité
483 (OFB) and 'Centre Méditerranéen de l'Environnement et de la Biodiversité', an 'Investissements
484 d'Avenir' grant managed by Agence Nationale de la Recherche [CeMEB, ref. ANR-10-LABX-04-01], and
485 from an "Investissement d'Avenir" grant managed by Agence Nationale de la Recherche [CEBA, ref.
486 ANR-10-LABX-25-01]. We thank Eric Marcon for checking the good practices in the development of
487 the R package, and for his extensive and precious advice, Stéphane Traissac for its participation in
488 discussions, for its opinions and advice guided by its forestry expertise, Mélaine Aubry-Kientz for the
489 development of a crown diameter allometry, and Nino Page, Bruno Hérault and Stéphane Traissac for
490 previous work on a selective logging simulator that led to the development of LoggingLab.

491 Declaration of competing interest

492 The authors declare that they have no known competing financial interests or personal relationships
493 that could have appeared to influence the work reported in this paper.

494 Appendix A. Supplementary data (see attached files)

495 References

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602 https://www.science.org

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