# Linking near-infrared spectroscopy of 25 tropical Gabonese hardwoods to tree ecological temperament

Estelle Nkene Mezui, Loïc Brancheriau, Samuel Ikogou, Rostand Moutou Pitti

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Estelle Nkene Mezui, Loïc Brancheriau, Samuel Ikogou, Rostand Moutou Pitti. Linking near-infrared spectroscopy of 25 tropical Gabonese hardwoods to tree ecological temperament. Bois et Forêts des Tropiques, 2025, 362, 14 p. 10.19182/bft2025.362.a37589. hal-05359792

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Distributed under a Creative Commons Attribution 4.0 International License
# Linking near-infrared spectroscopy of 25 tropical Gabonese hardwoods to tree ecological temperament

Estelle NKENE MEZUI<fnref n="1" />,<fnref n="2" />
Loïc BRANCHERIAU<fnref n="2" />
Samuel IKOGOU<fnref n="3" />
Rostand MOUTOU PITTI<fnref n="1" />,<fnref n="4" />

**Auteur correspondant /**
**Corresponding author:**
Estelle Nkene Mezui –
esthernkenemezui@gmail.com

ID ORCID : https://orcid.org/0000-0001-9525-350X

<figure><img src="image_2.png" /><figcaption>**Photo 1.**
Example of tree studied, *Baillonella toxisperma*, with hemi-heliophilous ecological temperament. Photo Jabea Tongo Etonde, Pixabay (https://pixabay.com/nl/moabi-baillonella-boom-grote-bush-1308588/).</figcaption></figure>

DOI : 10.19182/bft2025.362.a37589 – Droit d’auteur © 2025, Bois et Forêts des Tropiques – © Cirad – Date de soumission : 25 octobre 2024 ; date d’acceptation : 1er mai 2025 ; date de publication : 1er juin 2025.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

cirad

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

Licence Creative Commons : Attribution - 4.0 International. Attribution-4.0 International (CC BY 4.0)

**Citer l'article / To cite the article**

Nkene Mezui E., Brancheriau L., Ikogou S., Moutou Pitti R., 2025. Linking near-infrared spectroscopy of 25 tropical Gabonese hardwoods to tree ecological temperament. Bois et Forêts des Tropiques, 362, 1-14.
DOI : https://doi.org/10.19182/bft2025.362.a37589

<footnote n="1">¹Université Clermont Auvergne, CNRS, Clermont Auvergne INP, IP 63000 Clermont-Ferrand France</footnote>

<footnote n="2">² CIRAD, UPR BioWooEB, Univ Montpellier Montpellier France</footnote>

<footnote n="3">³ École Polytechnique de Masuku (EPM-USTM) Franceville Gabon</footnote>

<footnote n="4">⁴ CENAREST, IRT BP 14070, Libreville Gabon</footnote>
## RÉSUMÉ

### Lien entre la spectroscopie proche infrarouge de 25 bois de feuillus tropicaux du Gabon et le tempérament écologique des arbres

Pour utiliser les essences de bois de manière appropriée et optimale, il est impératif de connaître leur composition chimique. L'application de la spectroscopie dans le proche infrarouge (NIRS) a fourni une approche précieuse pour corréler les propriétés du bois avec les caractéristiques écologiques des arbres et leurs réponses adaptatives. L'objectif de l'étude était de vérifier que des compositions chimiques différentes existent pour une large gamme d'espèces regroupées dans des tempéraments écologiques distincts. Ainsi, 25 bois de feuillus tropicaux du Gabon, principalement des espèces secondaires, ont été étudiés en les divisant en trois tempéraments écologiques : pionnier, hémi-héliophile et tolérant à l'ombre. Les spectres étudiés ont été obtenus à l'aide d'un spectromètre Spectrum Two N (PerkinElmer) sur les faces transversales de 368 échantillons bruts de bois de cœur par réflectance dans la gamme 1000 à 2500 nm. Ces données ont ensuite été soumises à un prétraitement (variable normale standard et dérivée seconde), suivi d'une analyse en composantes principales et d'une analyse discriminante par moindres carrés partiels. Le modèle de discrimination utilisé a montré des performances élevées en validation croisée et en préiction, avec respectivement 93,2 % et 88,3 %. Les résultats indiquent des différences dans les signatures NIRS entre les trois tempéraments écologiques. Les espèces hémi-héliophiles et tolérantes à l'ombre avaient des signatures NIRS similaires mais des concentrations différentes de composés chimiques (dérivées de l'analyse des spectres NIRS). Les pionniers avaient un profil différent des deux autres tempéraments écologiques. Aucune différence significative dans la teneur en cellulose n'a été constatée entre les trois tempéraments écologiques. Les concentrations d'hémicelluloses et de lignine étaient plus élevées dans les espèces d'ombre. Les espèces pionnières présentaient des niveaux significatifs d'ex tractibles.

**Mots-clés** : tempérament écologique, spectrométrie proche infrarouge, composition chimique, bois de feuillus tropicaux du Gabon.

## ABSTRACT

### Linking near-infrared spectroscopy of 25 tropical Gabonese hardwoods to tree ecological temperament

To use wood species appropriately and optimally, it is imperative to know their chemical composition. The application of near-infrared spectroscopy (NIRS) provided a valuable approach to correlate wood properties with tree ecological traits and their adaptive responses. The study aim was to verify that different chemical compositions exist for a wide range of species grouped into distinct ecological temperaments. Thus, 25 tropical Gabonese hardwoods, mainly secondary species, were studied and divided into three ecological temperaments: pioneer, hemi-heliophilous, and shade-tolerant. The spectra studied were obtained using a Spectrum Two N spectrometer (PerkinElmer) on the transverse faces of 368 unground samples of heartwood by reflectance in the range 1000 to 2500 nm. These were then subjected to pre-processing (standard normal variable and second derivative), followed by principal component analysis and partial least squares discriminant analysis. The discrimination model used showed high performance in cross-validation and prediction at 93.2% and 88.3%, respectively. The results indicated differences in NIRS signatures between the three ecological temperaments. The hemi-heliophilous and shade-tolerant species had similar NIRS signatures but different concentrations of chemical compounds (derived from NIRS spectra analysis). The pioneers had a different profile from the other two ecological temperaments. No significant difference in cellulose content was found between the three ecological temperaments. Hemicelluloses and lignin concentrations were higher in the shade species. The pioneer species had significant levels of extractives.

**Keywords:** ecological temperament, near-infrared spectrometry, chemical composition, Gabonese tropical hardwoods.

## RESUMEN

### Relación entre la espectroscopía del infrarrojo cercano de 25 maderas duras tropicales de Gabón y el temperamento ecológico de los árboles

Para utilizar de forma adecuada y óptima las especies leñosas, es imprescindible conocer su composición química. La aplicación de la espectroscopía del infrarrojo cercano (NIRS) proporcionó un valioso enfoque para correlacionar las propiedades de la madera con los rasgos ecológicos de los árboles y sus respuestas adaptativas. El objetivo del estudio era verificar que existen diferentes composiciones químicas para una amplia gama de especies agrupadas en distintos temperamentos ecológicos. Así, se estudiaron 25 maderas duras tropicales gabonesas, principalmente especies secundarias, divididas en tres temperamentos ecológicos: pionero, hemiheliófilo y tolerante a la sombra. Los espectros estudiados se obtuvieron utilizando un espectrómetro Spectrum Two N (PerkinElmer) en las caras transversales de 368 muestras de duramen sin triturar mediante reflectancia en el rango de 1000 a 2500 nm. A continuación, se sometieron a un tratamiento previo (variable normal estándar y segunda derivada), seguido de un análisis de componentes principales y un análisis discriminante de mínimos cuadrados parciales. El modelo de discriminación utilizado mostró un alto rendimiento en la validación cruzada y la predicción, con un 93,2% y un 88,3% respectivamente. Los resultados indicaron diferencias en las firmas NIRS entre los tres temperamentos ecológicos. Las especies hemiheliófilas y tolerantes a la sombra tenían firmas NIRS similares pero diferentes concentraciones de compuestos químicos (derivadas del análisis de espectros NIRS). Los pioneros tenían un perfil diferente de los otros dos temperamentos ecológicos. No se encontraron diferencias significativas en el contenido de celulosa entre los tres temperamentos ecológicos. Las concentraciones de hemicelulosas y lignina fueron mayores en las especies de sombra. Las especies pioneras presentaron niveles significativos de extractivos.

**Palabras clave:** temperamento ecológico, spectrometría del infrarrojo cercano, composición química, maderas duras tropicales de Gabón.
Introduction

Wood is a renewable, environmentally sustainable,
and valuable resource with diverse end-use applications.
To ensure its optimal use, rapid and accurate evaluations
are essential for determining the quality of trees, wood, and
wood products. The ideal assessment technology should
offer reliable, cost-effective data while enabling high-speed
analysis for efficient quality control and resource mana-
gement. The use of near-infrared spectroscopy (NIRS) is
a useful complement to time-consuming methods. It can
be used to optimise processes or determine the proper-
ties and characteristics of a large number of samples. It
is a non-destructive analytical method that provides both
quantitative and qualitative information: quantitative when
it allows the prediction of chemical constituent content,
and qualitative when it helps in identifying a product or
component based on its spectral fingerprint. An exhaustive
knowledge of the chemical composition of wood is impor-
tant because it helps to better understand and explain its
physical and mechanical behaviour. According to the lite-
rature, the properties of wood differ according to ecologi-
cal temperament. Compared to species that require light,
those that can withstand shade are denser and stiffer. They
have better mechanical support because of their optimised
mechanical characteristics (Mezui et al. 2024). According to
Read et al. (2011), tall, slender trees had achieved high stif-
fness in part due to low microfibril angles. In other words, a
decrease in microfibril angle led to an increase in stiffness
in fast-growing species. According to the literature, light-
demanding or pioneer species are known to reach the
greatest heights. However, in Nkene Mezui's doctoral the-
sis (2024), she was unable to observe any statistical diffe-
rence between the microfibril angle of light and shade
species. Several authors have confirmed that species that
can withstand shade invariably generate wood with a higher
extractive content than species that require light (Dudt
and Shure 1994; Mäderek et al. 2017; González-Melo 2022;
Mezui et al. 2024). These species have more sophisticated
defence mechanisms. Examples of this investment in the
protective role of shade-tolerant species include their low
levels of herbivory and high levels of tannins and pheno-
lic compounds (Dudt and Shure 1994; Imaji and Seiwa 2010;
Mäderek et al. 2017). This last aspect should be handled
carefully, as research on the extractives from heartwood
has shown different strategies (quantity of extractives
versus quality of extractives, Amusant et al. 2014). The
chemical composition of wood refers to different types
of organic compounds: macromolecular substances such
as cellulose, hemicelluloses and lignin; low molecu-
lar weight substances such as extractives; and mineral
compounds found in ashes (Stevanovic and Perrin 2009;
Campos et al. 2025; Shen et al. 2025). This chemical com-
position was reported to be linked to the structure and
mechanical properties of wood (Stevanovic and Perrin 2009;
Bari et al. 2014; Zhao et al. 2020). There was also growing
interest in the application of near-infrared spectros-

copy to wood. A large number of publications highlighted
the potential of NIR spectroscopy for the characterisa-
tion and identification of wood in a wide range of uses
(Brunner et al. 1996; Schwanninger et al. 2011; He and Hu
2012; Lazarescu et al. 2016; Ma et al. 2018; Lima et al. 2022).
The synthesis of Schwanninger et al. (2011) provided a link
between a large number of wavelengths and the main che-
mical compounds in wood. The NIRS measurements are
influenced by factors such as wood porosity, for example,
which alters the reflection, absorption, and scattering pro-
perties of near-infrared light (Schwanninger et al. 2011).
According to the literature (Barnes et al. 1989;
Schwanninger et al. 2011; Sandak et al. 2016), porosity can
be a factor in spectral variations and thus must be consi-
dered in NIRS spectra pre-treatments. The main compo-
nents of wood are cellulose, lignin and hemicelluloses
(Stevanovic and Perrin 2009). This composition varies signifi-
cantly in terms of the quantity of components if we com-
pare wood from different genera of the same family or even
from different species of the same genus (Stevanovic and
Perrin 2009; Gérard et al. 2019). These differences can be
explained by genetic factors and the conditions and loca-
tions in which the tree grows. Growing conditions may vary
from one tree to another in the same species and might be
identical for different species. In fact, tree species can be
gathered into different groups known as “ecological tempe-
raments”. Ecological temperament can be defined as a set
of strategies adopted by trees to grow, survive and repro-
duce in particular light conditions, depending on their stage
of development (Oldeman and Van Dijk 1991). For some
species, it remains the same throughout growth, while for
others it evolves into a different temperament in response
to the tree’s new needs (Oldeman and Van Dijk 1991). The
notion of ecological temperament was not very common in
the literature and most of the related key words, such as
*shade tolerance, pioneer and heliophilia*, for example, were
the source of some disagreement. These disagreements
concerned mostly the naming of terms and the number of
ecological groups (Swaine and Whitmore 1988; Oldeman
and Van Dijk 1991; Blanc et al. 2003; Meunier et al. 2015).
Moreover, the criteria used to define these groups were not
always the same. However, some authors had succeeded in
proposing classifications by ecological temperament for a
large number of species. This was the case of the book by
Meunier et al. (2015), which provided a classification of more
than 100 tropical hardwood species from Gabon into three
groups, using light requirement as the classification crite-
rion. This classification was supported by Blanc et al. (2003)
earlier. Swaine and Whitmore (1988) proposed a division into
two groups, based on seed and seedling ecology. Tchouto
(2004) classified eight groups for a wide range of species.
Since then, recent studies had also proposed other classi-
fications based on morphology, leaf characteristics, growth
rates, and wood density, for example (Souza et al. 2014;
Shen and Cao 2017; Wen et al. 2023; Jiang et al. 2024).
The ecological temperament of several species was well documented, but the spectral data and chemical composition of many of them have not been studied. Despite the extensive research on the application of NIRS for identifying or characterising wood materials, to our knowledge, there was no study focusing on the chemical characterization of trees based on their ecological temperament. Characterisation by ecological temperament, in addition to by species, would provide a more global chemical picture of species with common ecological strategies (belonging to the same ecological temperament).

The hypothesis of the current study stated that the chemical composition of trees varied according to ecological temperament. In other words, whether or not trees were tolerant of shade influenced their chemical structure. Our work aimed to demonstrate this hypothesis by creating an initial database highlighting the chemical composition of 25 tropical hardwoods from Gabon belonging to three different ecological temperaments, namely pioneer, hemiheliophilous and shade-tolerant. To do this, NIRS was used to characterise 368 samples of wood cubes taken from the heartwood by analysing their spectra and the associated

<caption>Table I.</caption>

Distribution of species according to ecological temperament. Temp: ecological temperament; N<sub>T</sub>: number of trees by species; N<sub>ST</sub>: average number of samples per tree; N<sub>SS</sub>: total number of samples by species.

<table><thead><tr><th>Temp</th><th>Species</th><th>Local or pilot names</th><th>Botanical family</th><th>N<sub>T</sub></th><th>N<sub>ST</sub></th><th>N<sub>SS</sub></th></tr></thead><tbody><tr><td rowspan="6">Pioneer (P)</td><td><sup>1</sup>Anthocleista nobilis G.Don</td><td>Ahinebe</td><td>Gentianaceae</td><td>3</td><td>5</td><td>16</td></tr><tr><td><sup>1</sup>Cleistopholis glauca Pierre</td><td>Ovok / Avom</td><td>Annonaceae</td><td>3</td><td>4</td><td>11</td></tr><tr><td><sup>1</sup>Croton oligandrus Pierre</td><td>Ngueul / Obamba</td><td>Euphorbiaceae</td><td>6</td><td>6</td><td>37</td></tr><tr><td><sup>1</sup>Discoglypremna caloneura Prain</td><td>Atieghe</td><td>Euphorbiaceae</td><td>4</td><td>7</td><td>26</td></tr><tr><td><sup>2</sup>Fillaeopsis discophora Harms</td><td>Nieuk</td><td>Mimosaceae</td><td>1</td><td>10</td><td>10</td></tr><tr><td><sup>2</sup>Klainedoxa gabonensis Pierre</td><td>Eveuss</td><td>Irvingiaceae</td><td>2</td><td>15</td><td>29</td></tr><tr><td colspan="2">Total</td><td></td><td></td><td>19</td><td></td><td>129</td></tr><tr><td rowspan="13">Hemi-heliophilous (HH)</td><td><sup>1</sup>Baillonella toxisperma Pierre</td><td>Moabi</td><td>Sapotaceae</td><td>1</td><td>6</td><td>6</td></tr><tr><td><sup>1</sup>Brachystegia laurentii Louis</td><td>Bomanga</td><td>Caesalpiniaceae</td><td>3</td><td>5</td><td>15</td></tr><tr><td><sup>2</sup>Celtis tessmannii Rendle</td><td>Diana</td><td>Ulmaceae</td><td>1</td><td>4</td><td>4</td></tr><tr><td><sup>2</sup>Dacryodes buettneri H.J.Lam</td><td>Ozigo</td><td>Burseraceae</td><td>1</td><td>4</td><td>4</td></tr><tr><td><sup>2</sup>Dacryodes normandii Aubrev. et Pellegr.</td><td>Ossabel</td><td>Burseraceae</td><td>2</td><td>10</td><td>19</td></tr><tr><td><sup>1</sup>Dialium dinklagei Harms</td><td>Eyoum</td><td>Caesalpiniaceae</td><td>1</td><td>10</td><td>10</td></tr><tr><td><sup>2</sup>Erismadelphus exsul Mildbr.</td><td>Angoa</td><td>Vochysiaceae</td><td>2</td><td>14</td><td>27</td></tr><tr><td><sup>1</sup>Gilbertiodendron brachystegioides J.Leon.</td><td>Limbali / Abeum</td><td>Caesalpiniaceae</td><td>1</td><td>8</td><td>8</td></tr><tr><td><sup>1</sup>Gossweilerodendron balsamiferum Harms</td><td>Tola</td><td>Caesalpiniaceae</td><td>1</td><td>18</td><td>18</td></tr><tr><td><sup>1</sup>Parkia bicolor A.Chev.</td><td>Essang</td><td>Mimosaceae</td><td>1</td><td>15</td><td>15</td></tr><tr><td><sup>2</sup>Poga oleosa Pierre</td><td>Ovoga</td><td>Anisophylleaceae</td><td>1</td><td>6</td><td>6</td></tr><tr><td><sup>1</sup>Sacoglottis gabonensis Urb.</td><td>Ozouga</td><td>Humiriaceae</td><td>2</td><td>5</td><td>10</td></tr><tr><td><sup>2</sup>Scyphocephalium mannii Warb.</td><td>Sorro</td><td>Myristicaceae</td><td>2</td><td>6</td><td>11</td></tr><tr><td colspan="2">Total</td><td></td><td></td><td>19</td><td></td><td>153</td></tr><tr><td rowspan="7">Shade-tolerant (ST)</td><td><sup>2</sup>Beilschmiedia spp. Nees</td><td>Nkonengui / Kanda</td><td>Lauraceae</td><td>3</td><td>8</td><td>25</td></tr><tr><td><sup>2</sup>Dacryodes igaganga Aubrev. et Pellegr.</td><td>Igaganga</td><td>Burseraceae</td><td>1</td><td>5</td><td>5</td></tr><tr><td><sup>1</sup>Daniellia soyauxii Rolfe</td><td>Faro</td><td>Caesalpiniaceae</td><td>1</td><td>17</td><td>17</td></tr><tr><td><sup>2</sup>Desbordesia glaucescens Van Tiegh.</td><td>Alep</td><td>Irvingiaceae</td><td>1</td><td>12</td><td>12</td></tr><tr><td><sup>2</sup>Dialium pachyphyllum Harms</td><td>Eyoum / Omvong</td><td>Caesalpiniaceae</td><td>1</td><td>10</td><td>10</td></tr><tr><td><sup>1</sup>Scytopetalum klaineanum Pierre</td><td>Andong</td><td>Scytopetalaceae</td><td>4</td><td>4</td><td>17</td></tr><tr><td colspan="2">Total</td><td></td><td></td><td>11</td><td></td><td>86</td></tr></tbody><tfoot><tr><td colspan="7"><sup>1</sup>Bénédet et al. (2019); <sup>2</sup>Meunier et al. (2015).</td></tr></tfoot></table>
wavelengths. For most of the species studied, so-called
secondary species, this NIRS profile acquisition had never
been carried out before. This study was part of a wider pro-
ject focusing on the links between ecological strategies and
associated functional traits, with a view to gaining a better
understanding not only of biodiversity but also of the deve-
lopment of wood quality in terms of technological proper-
ties useful for human activities (Mezui et al. 2024).

**Materials and Methods**

Sampling

The study covered 25 species of tropical hardwood
trees from Gabon with diffuse pores. These were divided
into three distinct ecological temperaments (table I). These
temperaments were taken from the book "Les arbres utiles
du Gabon" by Meunier et al. (2015) for 13 species and from
CIRAD's database of information on the characteristics of
African plants (Bénédet et al. 2019) for 12 species for which
the temperament was unknown in Meunier et al. (2015).
Tree species were selected according to three criteria: (i)
certain species had to be of economic interest to Gabon
(even fewer known species); (ii) species had to be in CIRAD's
physical-mechanical database and also available in CIRAD's
wood collection (in order to carry out the trials); (iii) spe-
cies had to be equally distributed in number (as far as
possible) among the three ecological temperaments. The
vernacular names and botanical families were taken from
the "Atlas des Bois Tropicaux" by Gérard et al. (2016) and
"La forêt du Gabon" by De Saint Aubin (1963). For this work,
the criterion chosen to define ecological temperament
was the light requirement. Among the 25 species, there
were 6 pioneer species (P), 13 hemi-heliophilous species
(HH) and 6 shade-tolerant species (ST). Unlike the other
two groups, the hemi-heliophilous group was defined for
the purposes of this study. Indeed, after comparing the

semi-heliophilous of Meunier et al. (2015) and the non-
pioneer heliophilous of Bénédet et al. (2019), we came to
the conclusion that they were the same ecological group
and it was only the naming that differed. Therefore, it was
decided to group them and name them as hemi-heliphi-
lous species. Pioneers are species that require full sun from
the beginning to the end of their life cycle and were capable
of rapidly colonising disturbed areas and producing many
seeds (Swaine and Whitmore 1988; Macieira et al. 2020).
Hemi-heliophilous have a strong need for light throughout
their cycle but could tolerate temporary shade (Fourdrigniez
and Meyer 2008). Finally, shade-tolerant species are non-
pioneer species capable of establishing themselves in
the canopy even with low light levels (Canham 1989;
Gravel et al. 2010; Macieira et al. 2020). Once the heartwood
trays (rectangular parallelepipeds 60 cm long and at least
3 cm thick) had been taken from the CIRAD wood collection
in Montpellier (France), they were cut into rectangular bars
and then into small heartwood cubes (figure 1) measuring
20 mm x 20 mm x 10 mm (radial R × tangential T × longitu-
dinal L). Later on, the cubes were machined and used for
the near-infrared spectroscopy (NIRS) measurements. The
specimens were first stabilised in a climate chamber at a
temperature of 20 °C ± 2 °C and a relative humidity (RH) of
65 % ± 5 %. In total, there were 129 specimens for the pioneer
group, 153 for the hemi-heliophilous and 86 for the shade
tolerant. The surfaces of the cubes were sanded to ensure
optimum quality (smooth, even and free of roughness).

Near-infrared spectrometry measurement

One measurement per wood sample was taken
and the spectra were acquired on the transverse face
or RT plane (figure 1) of the cubes (Brunner et al. 1996;
Razafimahatratra et al. 2018; Ma et al. 2019) using the
Spectrum Two N spectrometer (PerkinElmer) by reflectance
in the range [10,000 – 4,000] cm⁻¹. The spectrometer has a
resolution of 16 cm⁻¹ with an average of 32 acquisitions for
each scan.

<figure><img src="image_7.png" /><figcaption>**Figure 1.**
Experimental set-up and dimensions of specimens tested.</figcaption></figure>
## Statistical analysis

The NIRS spectra obtained were analysed in various stages using the R-4.3.3 software (2024) via the RStudio interface (2023). Initially, a pre-processing step was performed, including a Standard Normal Variate correction (SNV, R library prospectr), the aim of which was to eliminate the effect of granulometry and thus reduce intra-spectral variability. Also, as part of the pre-processing, the absorption spectra were derived twice (with a window width of 11 wavelengths) in order to resolve the problems of overlapping peaks and large baseline variations (R library prospectr, Savitzky and Golay 1964). Principal component analysis (PCA, R libraries FactoMineR and factoextra) was used to highlight the differences between the ecological groups. This analysis revealed that 30 principal components gathered 95% of the total variance (component selection based on the eigenvalues criterion), but it was decided to show and to discuss the results for the three first components (58% of the variance) in this article. Partial Least Squares Discriminant Analysis (PLS-DA, R library mdatools) allowed to identify the wavelengths of interest and to develop species classification models based on the ecological temperament of the trees, by selecting an optimal number of 10 latent variables. Leave-one-out cross-validation (with a random calibration

set of 236 samples) and prediction (random validation set of 132 samples) methods were used to verify and determine the performance of the PLS-DA model. Kruskal-Wallis and Dunn's multiple comparison tests were carried out in order to observe better the differences in concentrations given by the second derivative spectra between the three ecological temperaments.

## Results

Figure 2 showed the absorbance spectra of the 368 samples tested (figure 2-a) and the mean spectra as a function of the ecological temperament of the species (figure 2-a'). All the spectra obtained had the characteristic appearance of wood sample spectra (Brunner et al. 1996; Chi Leung et al. 2004; Tsuchikawa and Kobori 2015; Yu et al. 2019; Santos et al. 2020; Tsuchikawa et al. 2023; Ali et al. 2024; Mazega et al. 2024) and there were no outlier spectra in the batch tested. Figures 2-b and 2-b' showed firstly the second derivative spectra of the absorbance of the 368 samples and secondly the mean second derivative spectra as a function of ecological temperament. The most important wavelengths associated with peaks 1, 2 and 3 of the second derivatives

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

**Figure 2.** Absorption spectrum and second derivatives. (a) and (a') absorption spectra of all samples and spectra as a function of ecological temperament respectively. (b) and (b') second derivative of absorbance spectra of all samples and average spectra of second derivatives as a function of ecological temperament. P: pioneer species; HH: hemi-heliophilous species; ST: shade-tolerant species.
<figure><img src="image_12.png" /><figcaption>**Figure 3.**
Principal Component Analysis (plot of individuals). Dim1: dimension 1 (35.1% of the total variance); Dim2: dimension 2 (14.1%), Dim 3: dimension 3 (8.8%). P: pioneer species; HH: hemi-heliophilous species; ST: shade-tolerant species.</figcaption></figure>

were 1,423 nm, 1,912 nm and 2,269 nm. These peaks corresponded to concentrations of cellulose or water (peak 1), water (peak 2), and cellulose or hemicelluloses (peak 3), according to the literature (Schwanninger et al. 2011; Sandak et al. 2016) for the three groups.

A principal component analysis (PCA) was used to highlight the differences between the species according to the three temperaments. Figure 3 showed these differences according to the first three dimensions (Dim 1, Dim 2 and Dim 3) of the PCA, which explained 58% of the total variability. Dimension 1 was mainly driven by cellulose, followed by lignin, hemicelluloses and water molecules in smaller proportions (PCA loadings analysis). Dimension 2 was driven by relatively equivalent proportions of cellulose, hemicellulose and lignin. Dimension 3 was mainly carried by cellulose. Dimension 2 seemed to distinguish the pioneer group (P). Samples belonging to this group were distributed along dimension 1 and mainly towards the negative part of dimension 2. The hemi-heliophilous (HH) species were not discriminated by the two dimensions because their samples were rather concentrated at the level of the centre of gravity of the PCA. Finally, the samples from the shade-tolerant (ST) group appeared to be dispersed on both sides of the two dimensions, so they were not discriminated by either of them, nor by the third dimension, which was not represented here. The distribution of the pioneers was found to be very different from the distribution of the other two temperaments.

In order to test the hypotheses of our study and to understand what the differences between the groups were, a partial least squares discriminant analysis (PLS-DA) was carried out (figure 4). Table II showed the NIRS absorption wavelengths normally associated with the main chemical components in wood samples. Figure 4-a showed the mean spectra of the differences in second derivatives between ecological temperaments two by two. For each ecological

temperament, a mean spectrum (average of the spectra of all samples belonging to the ecological group) was calculated. The averaged spectra were then subtracted in pairs to obtain the differences shown in figure 4-a. Calculating the differences between the mean spectra was a very effective method for detecting differences and changes between two spectra (Ozaki et al. 2001). Figure 4-b showed the PLS-DA regression coefficients for each ecological temperament. Together with table II, it helped to establish a chemical portrait of each temperament. A group of samples was considered to belong to a specific temperament if its regression coefficients were very different from zero. In this case, wavelength (and chemical bonding) played an important role in discrimination. We also noted that the spectrum of pioneers was opposed to the spectra of hemi-heliophilous and shade-tolerant. This confirmed the differences observed earlier in figure 3 in that the pioneer group was very different from the other two groups. In addition, the spectra were characterised by

more peaks of cellulose and hemicelluloses than that of lignin and extractives (table II).

Figure 4-a and table II showed that the differences in NIRS spectra between the three temperaments depended mainly on the concentrations of *j*₁, *j*₂, *j*₃, and *j*₄. There were other differences, but they were less significant in terms of intensity. Overall, it was the P-ST difference that was best dis-

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 4.**
(a) Mean spectra of differences in second derivatives between ecological temperaments in pairs. (b) Regression coefficients of the first component from PLS-DA analyses of the three ecological temperaments P (pioneer), HH (hemi-heliophilous) and ST (shade-tolerant).</figcaption></figure>
<caption>**Table II.**</caption>

Wavelength of NIRS absorption waves associated with the main chemical components contained in wood samples. OT: overtone; str: stretching vibration; def: deformation vibration; asym: antisymmetric; All: all components.

<table><caption>**Table III.**</caption><thead><tr><th>Index</th><th>Wavelength (nm)</th><th>Component</th><th>Bond vibration</th><th>References</th></tr></thead><tbody><tr><td>i<sub>1</sub></td><td>1144-1195</td><td>Lignin</td><td>2<sup>nd</sup> OT C-H str./2<sup>nd</sup> OT C-H str. of CH3/2<sup>nd</sup> OT asym. str. C-H, HC=CH</td><td>(Kelley et al. 2004); (Heim et al. 2022b); (Schwanninger et al. 2011)</td></tr><tr><td>i<sub>2</sub></td><td>1200-1250</td><td>Cellulose</td><td>2<sup>nd</sup> OT C-H str.</td><td>(Schwanninger et al. 2011); (Heim et al. 2022b)</td></tr><tr><td>i<sub>3</sub></td><td>1369</td><td>Hemicellulose/All Cellulose</td><td>1<sup>st</sup> OT C-H str. + C-H def.</td><td>(Schwanninger et al. 2011); (Sandak et al. 2016)</td></tr><tr><td>i<sub>4</sub></td><td>1393-1417</td><td>Cellulose</td><td>1<sup>st</sup> OT O-H str.</td><td>(Sandak et al. 2016); (Heim et al. 2022b)</td></tr><tr><td>i<sub>5</sub></td><td>1447-1450</td><td>Lignin/Extractives</td><td>1<sup>st</sup> OT O-H str.</td><td>(Schwanninger et al. 2011); (Heim et al. 2022a)</td></tr><tr><td>i<sub>6</sub></td><td>1621-1639</td><td>Cellulose</td><td>1<sup>st</sup> OT O-H str.</td><td>(Schwanninger et al. 2011)</td></tr><tr><td>i<sub>7</sub></td><td>1666</td><td>Hemicellulose Lignin</td><td>1<sup>st</sup> OT C-H str.</td><td>(Schwanninger et al. 2011); (Sandak et al. 2016)</td></tr><tr><td>j<sub>0</sub></td><td><strong>1669</strong></td><td><strong>Extractives</strong></td><td></td><td>(Schwanninger et al. 2011); (Sandak et al. 2016)</td></tr><tr><td>i<sub>8</sub></td><td>1696</td><td>Lignin</td><td>1<sup>st</sup> OT C-H str.</td><td>(Schwanninger et al. 2011)</td></tr><tr><td>i<sub>9</sub></td><td>1711-1732</td><td>Lignin/Hemicellulose/ (Cellulose)</td><td>1<sup>st</sup> OT C-H str.</td><td>(Schwanninger et al. 2011); (Fujimoto et al. 2008); (Fujimoto et al. 2007)</td></tr><tr><td>i<sub>10</sub></td><td>1738</td><td>Cellulose</td><td>1<sup>st</sup> OT C-H str.</td><td>(Schwanninger et al. 2011); (Sandak et al. 2016)</td></tr><tr><td>i<sub>11</sub>/j<sub>1</sub></td><td>1891</td><td>Water</td><td>2<sup>nd</sup> OT C=O str.</td><td>(Sandak et al. 2016)</td></tr><tr><td>i<sub>12</sub></td><td>1915</td><td>Water</td><td>2<sup>nd</sup> OT C=O str.</td><td>(Sandak et al. 2016); (Schwanninger et al. 2011)</td></tr><tr><td>i<sub>13</sub></td><td>1999-2002</td><td>Water</td><td>OH combinations</td><td>(Sandak et al. 2016)</td></tr><tr><td>i<sub>14</sub></td><td>2152</td><td>All</td><td></td><td>(Sandak et al. 2016)</td></tr><tr><td>i<sub>15</sub>/j<sub>2</sub></td><td>2248-2251</td><td>Cellulose/Hemicellulose</td><td>O-H str. + C-O str.</td><td>(Sandak et al. 2016); (Schwanninger et al. 2011)</td></tr><tr><td>i<sub>16</sub></td><td>2269-2272</td><td><strong>Cellulose</strong><br>Cellulose/Hemicellulose</td><td>O-H str. + C-O str.</td><td>(Schwanninger et al. 2011); (Sandak et al. 2016)</td></tr><tr><td>i<sub>17</sub>/j<sub>3</sub></td><td>2317-2338</td><td>Cellulose/Hemicellulose<br><strong>Hemicellulose</strong></td><td>C-H str. + C-H def.</td><td>(Sandak et al. 2016); (Schwanninger et al. 2011)</td></tr><tr><td>i<sub>18</sub>/j<sub>4</sub></td><td>2365-2368</td><td>Cellulose/Hemicellulose<br>Cellulose</td><td>C-H str. + C-H def.</td><td>(Sandak et al. 2016); (Schwanninger et al. 2011)</td></tr><tr><td>j<sub>5</sub></td><td>2485</td><td><strong>Lignin</strong><br>Cellulose/Hemicellulose</td><td>C-H str. + C-C str.</td><td>(Schwanninger et al. 2011); (Sandak et al. 2016)</td></tr></tbody></table>

Results of the Kruskal-Wallis test and Dunn's multiple comparison test, showing differences in concentrations between the ecological temperaments of the trees studied. ns: not significant; significance of p-values: 0 '***', 0.001 '**', 0.01 '**', 0.05", 0.1", 1; P: pioneer; HH: hemi-heliophilous; ST: shade-tolerant.

<table><thead><tr><th rowspan="2">Chemical properties</th><th rowspan="2">Kruskal-Wallis test<br>p-values</th><th colspan="3">Dunn test</th></tr><tr><th>P-HH</th><th>HH-ST</th><th>ST-P</th></tr></thead><tbody><tr><td>Extractives</td><td>7.61E-04 "***"</td><td>***</td><td>ns</td><td>ns</td></tr><tr><td>Cellulose</td><td>3.58E-01 "ns"</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>Hemicellulose</td><td>1.09E-08 "***"</td><td>**</td><td>***</td><td>***</td></tr><tr><td>Lignin</td><td>8.07E-04 "***"</td><td>*</td><td>.</td><td>***</td></tr></tbody></table>

criminated, followed by HH-ST and P-HH. Using figure 2-b', we were able to compare the concentrations of j₁, j₂, j₃, j₄ and j₅ and the other smaller differences between the different groups because the height of the second derivative peaks was proportional to the concentration. Taking into account the bibliographical references (Schwanninger et al. 2011; Sandak et al. 2016) on the attribution of chemical bonds characteristic of wood molecules at certain wavelengths, we decided to compare (table III) the cellulose, hemicellulose, lignin and extractives concentrations of our three temperaments at the peak j₀ for extractives (table II), i₁₆ for cellulose (j₂ and i₁₆ corresponding to the same molecule but with a higher concentration of i₁₆), j₃ for hemicellulose, and j₅ for lignin. We were not comparing the concentrations of j₁, as this was the water molecule. We considered only the attributions of Schwanninger et al. (2011).
The results of the Kruskal-Wallis and Dunn tests were given in table III. The results of the Kruskal-Wallis test showed that only the differences in hemicelluloses, lignin and extractives were significant and not the differences in the amount of cellulose.

Figure 5 showed the differences observed using Dunn's test. There were more extractable compounds in pioneer species than in hemi-heliophilous. However, there were no significant differences between the extractives content of hemi-heliophilous and shade-tolerant species, and between pioneer and shade-tolerant species (figure 5-a). The amounts of lignin in the shade-tolerant and hemi-heliophilous trees were greater than the amount of lignin in the pioneers. No significant difference was observed between the shade-tolerant and the hemi-heliophilous trees in terms of lignin quantities (figure 5-b). The quantities of hemicelluloses were highest in shade-tolerant species, followed by hemi-heliophilous and pioneer species (figure 5-c).

As shown in figure 6, the prediction model showed high performance (Equation 1) in both validation and prediction. In addition, the percentage of correct classifications for P, HH and ST (Equations 2 to 4) was high, especially for P and HH, with 94.3% and 84.7%, respectively, in cross-validation and 85.1% and 83.9%, respectively, in prediction. The error rate (Equation 5) was less than 12% (6.8% for cross-validation and 11.7% for prediction). In addition, only less than 15% of the species were not classified (Equation 6), which was acceptable overall. The results obtained using the PLS-DA model showed that there were differences in chemical composition and concentration between the pioneer, hemi-heliophilous and shade-tolerant temperaments. These differences in composition were sufficient to distinguish species according to ecological temperament. This was exactly what the PCA suggested.

$$MP = \frac{\text{Total well classified P, HH and ST trees}}{\text{Total number of classified trees}} \times 100 \quad (\text{equation 1})$$

$$MP(P) = \frac{\text{Total number of well classified P trees}}{\text{Total number of trees P}} \times 100 \quad (\text{equation 2})$$

$$MP(HH) = \frac{\text{Total number of well classified HH trees}}{\text{Total number of trees HH}} \times 100 \quad (\text{equation 3})$$

$$MP(ST) = \frac{\text{Total number of well classified ST trees}}{\text{Total number of trees ST}} \times 100 \quad (\text{equation 4})$$

$$E_r = \frac{\text{Total misclassified trees P, HH and ST}}{\text{Total number of classified trees}} \times 100 \quad (\text{equation 5})$$

$$None = \frac{\text{Total number of trees not determined}}{\text{Total number of trees}} \quad (\text{equation 6})$$

With MP being the model performance and $E_r$ the error rate.

## Discussion

The characteristics of 25 tropical Gabonese species belonging to three ecological temperaments (pioneer, hemi-heliophilous and shade-tolerant) were determined using near-infrared spectroscopy. The results showed that there were chemical differences between the three ecological profiles only for lignin, hemicelluloses and extractives. The amount of cellulose was statistically the same between the three groups. Shade-tolerant and hemi-heliophilous species had higher lignin concentrations than pioneer species. The quantity of hemicelluloses was highest in shade-tolerant species. Studies by Novaes et al. (2010) and Liu et al. (2019) corroborated these observations.

<figure><img src="image_15.png" /><figcaption>**Figure 5.** Distribution of statistically significant chemical compounds according to ecological temperament. P: pioneer; HH: hemi-heliophilous; ST: shade tolerant.</figcaption></figure>
<table><thead><tr><th rowspan="2">70% of obs.</th><th colspan="4">Confusion matrix</th></tr><tr><th>P</th><th>HH</th><th>ST</th><th>None</th></tr></thead><tbody><tr><th>P</th><td>82</td><td>1</td><td>0</td><td>5</td></tr><tr><th>HH</th><td>3</td><td>72</td><td>3</td><td>16</td></tr><tr><th>ST</th><td>1</td><td>6</td><td>37</td><td>10</td></tr></tbody></table>

In cross-validation:

86.86% of trees classified: 236 – none = 205 trees
13.14% of trees classified as "none"

Global model performance: **93.2%**
Global error rate: **6.8%**

Model performance of P: **94.25%**
Model performance of HH: **84.7%**
Model performance of ST: **78.7%**

<table><thead><tr><th rowspan="2">30% of obs.</th><th colspan="4">Confusion matrix</th></tr><tr><th>P</th><th>HH</th><th>ST</th><th>None</th></tr></thead><tbody><tr><th>P</th><td>40</td><td>0</td><td>0</td><td>1</td></tr><tr><th>HH</th><td>2</td><td>47</td><td>4</td><td>6</td></tr><tr><th>ST</th><td>5</td><td>3</td><td>19</td><td>5</td></tr></tbody></table>

In prediction:

90.9% of trees classified: 132 – none = 120 trees
9.1% of trees classified as "none"

Global model performance: **88.3%**
Global error rate: **11.7%**

Model performance of P: **85.1%**
Model performance of HH: **83.9%**
Model performance of ST: **61.3%**

<caption>**Figure 6.**</caption>

Results of PLS-DA with 10 principal components. P: pioneer species; HH: hemi-heliophilous species; ST: shade-tolerant species.

In the first study, lignin was reduced by 10% in fast-growing trees compared with slow-growing trees. In most studies, fast growth has always been associated with pioneer species (King, 1991; Morel et al. 2018; Schmitt et al. 2022). The negative correlation between growth and lignin content observed by Novaes et al. (2010) was considered as a competition for carbon allocation to lignin compared to carbon allocation to cellulosic compounds (cellulose and hemicellulose). In the second study, the shade-tolerant variety of the soybean plant had a higher lignin content than the other less tolerant varieties. According to Ranade et al. (2022), light stimulated lignin synthesis while shade reduced it in most angiosperms. However, the author found that Scots pine, a shade-tolerant species, showed an increase in lignin synthesis. The high exposure of shade-tolerant species to the risk of pathogen attacks could encourage them to increase lignin synthesis. This would justify the results observed in these previous studies and in our own. In a study by Hussain et al. (2019), it was proven that trees subjected to shading stress had reduced lignin and cellulose contents. In fact, according to Hussain et al. (2019), the low light exposure of certain trees could lead to a reduction in sugars, which could affect the substrates responsible for lignin synthesis. This was linked to the reduction in photosynthesis during low light exposure (Hussain et al. 2019). The study by Cuzzuol et al. (2021) also showed that pressure and temperature deficits (0.4 KPa and 19.8 °C, for example) reduced cellulose and lignin concentrations in shade-tolerant trees and increased hemicellulose concentrations in light-seeking trees. For Cuzzuol et al. (2021), environmental conditions (pressure and temperature) had a greater influence on shade-tolerant trees and on their hemicelluloses in particular. In addition, studies by Niinemets and Kull (1998) and Cuzzuol et al. (2020) reported that exposure to ultraviolet B (UV-B) rays increased lignin levels to the detriment of hemicelluloses in light trees. However, UV-B had no effect on the cellulose content of these same trees or on the chemical compounds in shade trees. The results of these various studies were in apparent

disagreement with our results. It would appear from the results of the previously mentioned studies that there was an effect of growth or environmental conditions (exposure to UV, shading stress, temperature and pressure, for example) on the increase or reduction of wood polymers, particularly in shade species. However, the species in our study were not subjected to any environmental stress during testing, which made comparison with this work difficult. The higher levels of lignin in the shade and hemi-heliophilous species in our study gave their cell walls a greater modulus of elasticity in longitudinal direction and an increased resistance to biological attack, particularly by micro-organisms (Stevanovic and Perrin 2009; Trouy 2015; Candelier et al. 2023). On the other hand, pioneer species appeared to have higher quantities of extractives than hemi-heliophilous species, but no different from shade species. These results were somewhat surprising, especially the non-significant difference between the extractives of pioneer and shade-tolerant species. This could be due to the sampling protocol, but other factors can explain this observation. Extractives influenced lignin (Aloui et al. 2020) wood colour and odour (Imamura 1989; Stevanovic and Perrin 2009). We observed that samples from shade species (ST) were darker than those from pioneer species (P). The expected result would have been a lower quantity of extracts in the pioneer species. However, between species, the quantity of extracts is much less important than the quality (Stevanovic and Perrin 2009; Mecca et al. 2019). The study by Aloui et al. (2020) showed that the woods most affected by solar radiation (produced by four lamps) had a low quantity of extracts. Pioneer species were familiar with very bright environments. In addition, Gianoli et al. (2023) noted that the damage caused by herbivore attacks was greater in light species. In other words, light-demanding or pioneer species had weaker defence mechanisms. However, extractives content depended on several factors, such as position in the tree, tree growth rate, geographical origin, humidity, temperature, light, genetics and seasonal variations (Stevanovic and Perrin 2009; Aloui et al. 2020;
Ouaar et al. 2022). Their variability as a function of ecological temperament was therefore not as obvious. The role of extractives in protecting wood both in the living tree and in wood products against attack by fungi, insects and rot was of great importance (Hillis 1971; Imamura 1989). Considering only the results for extractives, we could deduce that the pioneer (P) and shade-tolerant (ST) species in our study had better defences against the various attacks on their wood. However, all the results obtained in this study, in particular the non-significant difference between the extractives of pioneer species and shade-tolerant species, must be considered with the influence of natural ageing of wood on its properties and therefore on the NIRS spectra. It is known that ageing is governed by chemical changes such as a decrease in extract and hemicellulose content and is accompanied by a change in wood colour and a change in physical and mechanical properties, for example (Kránitz 2014). These changes in wood properties have an effect on the NIRS spectra. In a study by Tsuchikawa et al. (2005), by comparing the NIRS results of a sample of archaeological wood and a sample of modern wood, the NIRS spectra revealed a reduction in the macromolecules of the wood except for the crystalline region of cellulose. A recent study by Han et al. (2023) confirmed these observations. Finally, the presence of water in non-negligible proportions in the spectra obtained was discussed. In the bibliography, several studies (Tsuchikawa and Tsutsumi 1998; Ozaki et al. 2001; Workman and Weyer 2007; Schwanninger et al. 2011; Yang et al. 2014) had highlighted the presence of water in the spectra. According to Schwanninger et al. (2011), for example, free water and adsorbed water frequently contributed to wood spectra and could hardly be avoided. Tsuchikawa and Tsutsumi (1998) highlighted the presence of adsorbed water and water condensed by capillary action in the spectra analysed.

For these authors, the presence of condensed water was linked to the distribution of pore size in the cell wall. According to them, this water could exist in pores of diameter ≈ 3.5 nm. Whatever the ecological temperament of the species studied in this thesis, they presented vessel diameters far in excess of 3.5 nm (of the order of 160 to 300 µm). The samples were also stabilised in a climate chamber at a theoretical humidity of 12% (RH = 65% and T = 20 °C) for 2 to 8 weeks. However, experience has shown, and this has been confirmed in the literature (Ella et al. 2023), that at this theoretical stabilisation humidity, each species has its own actual stabilisation humidity, which largely explains the presence of the water peak in the spectra.

## Conclusion

Near-infrared spectroscopy was used to characterise 25 tropical Gabonese hardwoods belonging to three different ecological temperaments. To our knowledge, no study was focused on the near-infrared spectroscopy of trees based on their ecological temperament, and this highlights the pioneering nature of this study. The results showed that there were significant differences in NIRS signatures between pioneer, hemi-heliophilous and shade-tolerant species. These differences depended mainly on the concentrations of hemicelluloses, lignin and extractives. Shade-tolerant (ST) and hemi-heliophilous (HH) species had higher lignin concentrations than pioneer (P) species. Pioneer species (P) had a higher concentration of extractives than hemi-heliophilous and the lowest concentration of hemicelluloses. The higher quantities of lignin and hemicelluloses in the shade species, on one hand, and lignin in the hemi-

Shade tolerant (ST)

<figure><img src="image_16.png" /><figcaption>Desbordesia glaucescens</figcaption></figure>

Hemi-heliophilous (HH)

<figure><img src="image_17.png" /><figcaption>Dacryodes buettneri</figcaption></figure>

Pioneer (P)

<figure><img src="image_18.png" /><figcaption>Fillaeopsis discophora</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Dialium pachyphyllum</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Baillonella toxisperma</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Klainedoxa gabonensis</figcaption></figure>

**Photo 2.**

Wood samples according to the three ecological temperaments studied, shade tolerant, hemi-heliophilous, pioneer.
Photos Gérard et al. 2016, Quæ Publishing (https://www.quae.com/produit/1477/9782759227716/tropical-timber-atlas).
heliophilous species, on the other, gave them high cell wall
stiffness and resistance to pathogens. The high level of
extractives in pioneer species (P) provided them with good
defences against various attacks of pathogens. There was
no difference in cellulose concentration between the eco-
logical groups. A significant peak corresponding to water
molecules was observed in the spectra of the species and
was thought to be linked to the size of the pores in the cell
wall and internal humidity of the samples. The PLS-DA pre-
diction model used to distinguish species on the basis of
ecological temperament provided good results. According
to the literature, the differences in NIRS spectra observed
between light and shade species were linked to a reduction
in photosynthesis, the growth-defence trade-off and spe-
cific environmental conditions. It would be interesting to
supplement and compare the results of this work with fibre
analyses and extractions in order to go into greater detail
in understanding the chemical composition of the ecologi-
cal groups, for example, the types of extractables present in
each spectrum. In addition, an extension of our work would
focus on the species of Meunier et al. (2015) that have not
been studied here, using chemical composition data extrac-
ted from Gérard et al. (2019) and capturing NIRS spectra on
samples from the CIRAD xylotheque (Langbour et al. 2019)
matched to trees measured in chemistry.

Acknowledgements

The authors would like to thank Nabila Boutahar and Alban
Guyot from the BioWooEB unit at CIRAD for their various
contributions to this work.

Funding

This work benefited from the financial support of the
National Scholarship Agency of Gabon (ANBG) as part of the
thesis.

Access to data

The data are available on the Zenodo digital repository
by requesting permission from the corresponding author
via the following Internet link: https://doi.org/10.5281/
zenodo.15838848

Researchers using these data have to cite these data and the
related article.

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## Nkene Mezui et al. - Credit authorship contribution statement

<table><thead><tr><td>Contributor role</td><td>Contributor names</td></tr></thead><tbody><tr><td>Conceptualisation</td><td>L. Brancheriau</td></tr><tr><td>Data Curation</td><td>E. Nkene Mezui, L. Brancheriau</td></tr><tr><td>Formal Analysis</td><td>E. Nkene Mezui, L. Brancheriau</td></tr><tr><td>Investigation</td><td>E. Nkene Mezui</td></tr><tr><td>Methodology</td><td>L. Brancheriau, E. Nkene Mezui</td></tr><tr><td>Project Administration</td><td>R. Moutou Pitti, L. Brancheriau</td></tr><tr><td>Resources</td><td>L. Brancheriau</td></tr><tr><td>Software</td><td>L. Brancheriau, E. Nkene Mezui</td></tr><tr><td>Supervision</td><td>R. Moutou Pitti, L. Brancheriau</td></tr><tr><td>Validation</td><td>L. Brancheriau, R. Moutou Pitti, S. Ikogou</td></tr><tr><td>Visualisation</td><td>E. Nkene Mezui</td></tr><tr><td>Writing - Original Draft Preparation</td><td>E. Nkene Mezui</td></tr><tr><td>Writing - Review & Editing</td><td>L. Brancheriau, R. Moutou Pitti, S. Ikogou</td></tr></tbody></table>

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