Achour Hamadi

Vol.:(0123456789)
Journal of Geovisualization and Spatial Analysis (2025) 9:6
https://doi.org/10.1007/s41651-024-00209-w
Selection of Global Climate Models (GCMs) for Climate Change
Analysis Using t‑Distributed Stochastic Neighbour Embedding (t‑SNE):
Implications of Future Bioclimatic Shifts on Forest Trees in Tunisia
and Algeria
Hammadi Achour1 · Imene Habibi2 · Sahar Abidi1 · Lillia Lembrouk3 · Farid Bounaceur4 · Abdelkrim Benaradj2 ·
Touhami Rzigui5
Accepted: 5 December 2024
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024
Abstract
Global climate models (GCMs) are crucial for analysing climate change. With more than 50 models available from the
Coupled Model Intercomparison Project Phase 6 (CMIP6), selecting the most appropriate models for accurate assessments
of climate impacts is a major challenge. The variability of climate projections among these models further complicates this
process. This study addresses this issue by proposing a framework for selecting representative GCMs using t-distributed
stochastic neighbour embedding (t-SNE) in Tunisia and Algeria. To implement this framework, spatial grid data representing
baseline conditions (1970–2000) and future climate scenarios of 20 GCMs under SSP2-4.5 were compiled from the World-
Clim database. Six bioclimatic variables were selected for the analysis. For the temperature-related variables, we calculated
the differences between the future projections (2050 and 2070) and the baseline climate, as well as the relative changes for the
precipitation-related variables. We then performed t-SNE to reduce the dimensionality of the resulting data, testing perplexity
values from 3 to 6 to determine the optimal setting. The k-means clustering algorithm was then applied to the reduced data
to group GCMs with similar climate projections into clusters, and the quality of clustering was assessed using the silhouette
score. From each cluster, we identified the GCM that was closest to the centroid and served as a representative model. Using
these selected GCMs, we calculated the Emberger and modified thermicity indices and assessed the bioclimatic shifts in the
distribution of forest trees. As a result, we identified three clusters of GCMs, each representing different patterns of climate
projections. In particular, the model UKESM1-0-LL, representing cluster 3, systematically projected a more pronounced
shift towards drier conditions in the future for most forest species compared to the other models. This study highlights the
importance of selecting representative GCMs to capture the variability in climate projections, which is essential for reliable
assessments of climate change impacts on forest ecosystems.
Keywords Global climate model · Climate change · T-Distributed stochastic neighbour embedding · Bioclimatic shift ·
Forest trees
* Hammadi Achour
hammadi.achour@gmail.com
1 Laboratoire Des Ressources Sylvo‑Pastorales, Institut Sylvo-
Pastoral de Tabarka, Université de Jendouba, 8110 Tabarka,
Tunisia
2 Laboratory of Sustainable Management of Natural Resources
in Arid and Semi‑Arid Areas, Salhi Ahmed University
Center of Naama, 45000 Naama, Algeria
3 Laboratoire de Production, Sauvegarde Des Espèces
Menacées Et Des Récoltes, Influence Des Variations
Climatiques, Faculté Des Sciences Biologiques Et Des
Sciences Agronomiques, Université Mouloud Mammeri de
Tizi-Ouzou, 15000 Tizi‑Ouzou, Algeria
4 Equipe de Recherche Biologie de La Conservation en
Zones Arides Et Semi‑Arides, Laboratoire Agronomie
Environnement, Faculté Des Sciences Et de Technologie,
Département Des Sciences de La Nature Et de La Vie,
Université de Tissemesilt, 38000 Tissemesilt, Algeria
5 Laboratory of Management and Valorization of Forest
Resources, National Research Institute of Rural Engineering,
Water and Forestry, University of Carthage, Carthage,
Tunisia

Vol.:(0123456789)
Euro-Mediterranean Journal for Environmental Integration
https://doi.org/10.1007/s41207-024-00552-w
ORIGINAL PAPER
A comparative study based on AHP and fuzzy logic approaches
for landslide susceptibility zoning using a GIS‑based multi‑criteria
decision analysis
Faten Ksantini1,2,3 · Ali Sdiri2 · Abdelwaheb Aydi1 · Andrés Felipe Almeida‑Ñauñay3,4 · Hammadi Achour5 ·
Ana M. Tarquis3,4
Received: 27 October 2023 / Accepted: 15 May 2024
© Springer Nature Switzerland AG 2024
Abstract
This work discusses the effectiveness of the Analytical Hierarchy Process (T-AHP) and fuzzy logic approach (FL-AHP)
for landslide susceptibility analysis and zoning based on the Landslide Susceptibility Index (LSI) in the prospective openpit
phosphate mining in Jabbeus Mountain (Tunisia). A third approach was established based on the Synergic fuzzy logic
approach (SFL-AHP). The SFL-AHP expresses the synergy between the main criteria classes of lithology and the main factors
classes, such as distance to faults and slope that can control it through the parametrization of the fuzzy functions. The
results showed that the adopted methodology can effectively integrate various spatial data for landslide susceptibility analysis
and zoning. Furthermore, the LSI from FL-AHP and SFL-AHP showed a clear pattern with the cohesion of sampling points,
showing a smooth relation that creates a more gradual score in LSI. The SFL-AHP method is a promising tool for further
applications, including the integration and interpretation stages of obtaining a decision–supporting layer.
Keywords Landslide · Assessment · Analytical hierarchy process · Multi-criteria approach
Introduction
Landslides are natural hazards that significantly affect the
development of natural landscapes. They relate to a variety
of processes that result in the movement of slope-forming
materials, such as rock earth and/or debris. The dislocated
material can move with different kinematics, according to
the landslide type (Hungr 2011). Landslides are usually
associated with mountainous areas and regions. However,
they can also occur in areas of low relief, or interested by
anthropic activities, such as quarries and open-pit mines
(Varnes 1984). They often cause casualties, property damage,
and other problems environmental assets and infrastructure
projects (Wu et al. 2016; Pirasteh et al. 2018; Sharma
and Mahajan 2019).
Owing to the importance of the issue, individuating the
areas most prone to the landslide occurrence becomes a priority
for Authorities in charge of the land-use management.
Responsible Editor: Settimio Ferlisi.
* Faten Ksantini
faten.ksantini@fsb.ucar.tn
Ali Sdiri
ali.sdiri@enis.tn
Abdelwaheb Aydi
abdelwaheb_2000@yahoo.fr
Andrés Felipe Almeida‑Ñauñay
af.almeida@upm.es
Hammadi Achour
hammadi.achour@gmail.com
Ana M. Tarquis
anamaria.tarquis@upm.es
1 Faculty of Sciences of Bizerte, University of Carthage
(UCAR), 7021 Bizerte, Tunisia
2 RU “Geomaterial, Structures in Civil Engineering
and Environment” (GESTE), National Engineering School,
University of Sfax, Sfax, Tunisia
3 CEIGRAM, ETSIAAB, Universidad Politécnica de Madrid,
28040 Madrid, Spain
4 Grupo de Sistemas Complejos, ETSIAAB, UPM, Ciudad
Universitaria, 28040 Madrid, Spain
5 Laboratory of Silvo‑Pastoral Resources, Silvo‑Pastoral
Institute of Tabarka, University of Jendouba, Tabarka,
Tunisia 345

Plant Ecology & Diversity
ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/tped20
Evaluation of biomass, carbon storage capability,
tree-ring and cork-ring growth of Quercus suber: a
review
Kaouther Mechergui, Wahbi Jaoaudi, Víctor Bello-Rodríguez, Hammadi
Achour & Youssef Ammari
To cite this article: Kaouther Mechergui, Wahbi Jaoaudi, Víctor Bello-Rodríguez, Hammadi
Achour & Youssef Ammari (08 Nov 2024): Evaluation of biomass, carbon storage capability,
tree-ring and cork-ring growth of Quercus suber: a review, Plant Ecology & Diversity, DOI:
10.1080/17550874.2024.2422293
To link to this article: https://doi.org/10.1080/17550874.2024.2422293
Published online: 08 Nov 2024.
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REVIEW
Evaluation of biomass, carbon storage capability, tree-ring and cork-ring
growth of Quercus suber: a review
Kaouther Mecherguia, Wahbi Jaoaudia,b, Víctor Bello-Rodríguezc, Hammadi Achourb and Youssef Ammaria
aLaboratory of Forest Ecology, National Research Institute of Rural Engineering, Water, and Forestry, University of Carthage, Ariana,
Tunisia; bLaboratory of Sylvo-Pastoral Resources, The Silvo-Pastoral Institute of Tabarka, University of Jendouba, Tunisia; cPlant
Conservation and Biogeography Research Group, Departamento de Botanica, Ecología y Fisiología Vegetal, Universidad de La Laguna, La
Laguna, Spain
ABSTRACT
Background: Cork oak (Quercus suber) forests and woodlands cover an area of ca. 2.1 M ha in
the Mediterranean Basin. Cork oak stands are important for biodiversity conservation, and
ecosystem services (carbon storage and the provision of cork, timber, and firewood). Their
sustainable management is essential for their continued multifunctional existence and for
providing ecosystem services.
Aims: The aims of this study were to estimate the biomass production of the species and
quantify its potential carbon storage capacity in the Mediterranean Basin and to make management
recommendations.
Methods: We used a literature survey of allometric equations and cork annual total tree-ring
growth data and applied them to determine the productivity of the species. Estimate biomass
by using regression models and dendrometric characteristics is very important for cork oak in
the Mediterranean region.
Results: The studies reported a wide-ranging above-ground biomass for cork oak: 42 Mg ha−1
for Italy, 64–120 Mg ha−1 for Morocco, 41–50 Mg ha−1 for Portugal, 29–328 Mg ha−1 for Spain,
and 80–312 Mg ha−1 for Tunisia. The radial wood increment ranged from 0.78 to 8.01 mm yr−1.
The annual increment of cork-ring growth was between 0.8 mm yr−1 (northern Algeria) and
5.25 mm yr−1 (Spain), with most values being between 2 and 4.4 mm yr−1. In dry years, major
reductions (10−15%) in the growth of Quercus suber were observed.
Conclusion: Most studies showed stand-specific growth responses to climate variables, with
the radial growth of cork oak being greater in relatively mesic and cool conditions.
ARTICLE HISTORY
Received 10 April 2021
Accepted 25 April 2024
KEYWORDS
Allometric equation;
biomass; carbon; cork-ring;
growth–climate relationship;
tree-ring
Introduction
Large increases in anthropogenic emissions of
greenhouse gases have contributed to the observed
changes in global climate over the last decades
(Hansen and Sato 2004). Terrestrial ecosystems
represent a considerable part of global carbon
stocks (IPCC 2013) and of all terrestrial ecosystems,
forests contain around 80% of the above-ground
and 40% of the below-ground terrestrial carbon
(Dixon et al. 1994). Forest ecosystems play
a potential role in climate change mitigation by
acting as carbon sinks (Dixon et al. 1994; Lal 2004;
Mohanraj et al. 2011). Carbon dioxide from the
atmosphere is accumulated in the organic matter
in soil and trees, and it continuously cycles between
forests and the atmosphere through the decomposition
of dead organic matter (Alexandrov 2007). To
estimate forest carbon pools from forest inventories
it is necessary to have reliable models of the biomass
of the bole and apply suitable biomass expansion
factors to estimate the total above-ground biomass
(Ruiz-Peinado et al. 2012). In recent years, the estimation
of forest carbon stocks has gained prominence
due to the role of forests in the mitigation of
global climate change through ecosystem carbon
storage (Ruiz-Peinado et al. 2012).
The quantification of biomass, both above- and
below-ground, and its distribution and dynamics
have been receiving increased attention. This has
been motivated by the need to estimate accurately
the carbon stocks and sequestration (Sousa et al.
2017) as well as woody biomass which might be
used for bioenergy. Biomass equations that relate
different tree biomass components to dendrometric
variables, and biomass expansion factors that relate
biomass to total stand volume are particularly useful
tools in forest biomass estimation (Brown 2002;
Somogyi et al. 2007). Biomass models require treelevel
data, which are usually recorded in forest
inventories, such as diameter and sometimes height
(Teobaldelli et al. 2009). Since biomass expansion
factors depend on species, site (Wirth et al. 2004),
CONTACT Wahbi Jaouadi jaouadiwahbi@gmail.com
PLANT ECOLOGY & DIVERSITY
https://doi.org/10.1080/17550874.2024.2422293
© 2024 Botanical Society of Scotland and Taylor & Francis
age (Lehtonen et al. 2004), and stand timber volume
(Fang et al. 2001), where tree-level data are available,
biomass models are often preferred.
One species that can serve as a model is the cork
oak (Quercus suber L.), a key species in the western
Mediterranean basin. Its biomass, carbon storage
capability, and growth rates have been widely studied
due to its importance in both natural and
human-impacted/planted forest stands (Pereira
2007). The species has been extensively planted for
commercial exploitation of its bark (Gonzalez-
Garcia et al. 2013) making it a species that can
potentially contribute to climate change mitigation,
especially when stands are sustainably managed, as
they can sequester large amounts of CO2 (Pereira
2007; Pereira et al. 2008). In addition, cork oak
stands are important reservoirs of faunal and floral
diversity, play a key role in ecological processes,
such as water retention and soil conservation, and
provide opportunities for development in economically
and socially disadvantaged areas (WWF 2006;
Silva et al. 2009; Bugalho et al. 2011). Cork oak has
made an important economic contribution to the
Mediterranean region (Gonzalez-Garcia et al.
2013); cork production is the most important
source of revenue in cork oak agroforestry systems
(Borges et al. 1997).
Despite the ecological and economic importance
of the species, there is currently no study that compiles
all the existing information on estimates of
biomass, carbon sequestration capacity or growth
rates of cork oak forests in different regions of its
distribution area. Here, we fill this gap by reviewing
the available literature.
The aim of this study was to report the different
allometric equations of productivity of biomass and
cork of Quercus suber, along with values of increment
of wood and cork from different parts of the
Mediterranean Basin.
Materials and methods
This work was carried out using data published
between 1992 and 2020; data were collected from
Clarivate Web of Science® (http://apps.webofknow
ledge.com/), Scopus® and Science Direct®. The terms
used in the literature were ‘Quercus suber’, ‘Cork
oak’, ‘allometric equation’, ‘biomass’, ‘carbon’,
‘cork-ring’, ‘growth-climate relationship’ and ‘treering’.
Additionally, grey literature, including
unpublished doctoral theses was consulted for
unpublished material and information.
Search results were refined based on title and
abstract content and resulted in a final database of
77 references. The selected papers fulfilled the following
criteria: the study was published in English,
and the scope of the study was to examine biomass
and carbon stock models of Quercus suber to evaluate
the tree and cork growth and the relationship
of growth to climate.
The species
Quercus suber (Fagaceae) is an evergreen oak species
that grows to a height of 15–20 m and to
a diameter of 20–60 cm (Pereira 2007). It is
long-lived (200–250 years) and is characterised
by the presence of a conspicuous thick and furrowed
bark with a continuous layer of cork. The
species is endemic to the Mediterranean Basin
and has been present in the western
Mediterranean for more than 60 million years
(Pereira 2007). Its current distribution is restricted
to the western Mediterranean of Europe
(Portugal, Spain, France, and Italy) and North
Africa (Morocco, northern Algeria, and Tunisia)
(Pereira 2007; Rives et al. 2011) (Figures 1 and 2).
The potential distribution range of cork oak has
greatly decreased as a result of historic variations
in climatic conditions, but above all due to
human activities (De Sousa et al. 2008). Today,
some extensive stands of Q. suber are found in
Morocco, Portugal, and Spain, being related to the
industry derived from the exploitation of its bark
(Pereira 2007; Rives et al. 2011). In its distribution
range, the species is frequently accompanied by
other oak species (Q. ilex, Q. faginea), pines
(Pinus pinaster, P. pinea) and shrubs including
species of Cistus or Erica (Ruiz 2001).
Quercus suber is considered a thermophilous
species, being present in areas where the main
temperature in January exceeds 0°C and 18°C in
August. Its stands are mainly found between
elevations of 300 and 500 m a.s.l. and can
reach or exceed 2000 m in the Atlas Mountains
in Morocco. It is usually present in areas with
precipitation more than 400 mm year−1 and its
best stands are located in areas where precipitation
is between 600 and 1000 mm year−1 (Ruiz
2001). Temperature alone is likely to have less
2 K. MECHERGUI ET AL.
effect than it may have when increased evapotranspiration
is considered, or where forecast
precipitation decrease may negatively affect or
limit its local distribution (Ruiz 2001).
Allometric regressions to estimate biomass
The models used to estimate stem and cork volume,
biomass, and carbon stocks of Quercus suber by
different authors in different countries are listed in
Table 1.
Results
Stand biomass
The above-ground biomass at stand level estimated
in Bellif forest (170 Mg ha−1 in the young stands and
312 Mg ha−1 in the mature stands, Zribi et al. 2016)
was higher than in other studies of cork oak forests
reported by Sebei et al. (2001, 2004) in Tunisia (49–
113 Mg ha−1) and Léonardi et al. (1992) in Italy (42
Mg ha−1) (Table 2). The above-ground biomass in
the region of Mora in Portugal was 41 Mg ha−1
Figure 1. Geographical distribution of Quercus suber L. (Caudullo et al. 2017).
Figure 2. Cork oak stands in Tunisia.
PLANT ECOLOGY & DIVERSITY 3
Table 1. Stem and cork volume, biomass and carbon stock model of Quercus suber (d: diameter at breast height in cm; h: tree height in m).
Country
Components
Model
References
Spain
Biomass of Stem
Ws = 0.00525.d2.h + 0.278.d.h (RMSE = 66.87)
Ruiz-Peinado et al. (2012).
Biomass of Thick branches
Wb7 = 0.0135.d2.h (RMSE = 110.76)
Biomass of Medium branches
Wb2–7 = 0.127.d.h (RMSE = 26.47)
Biomass of Thin branches + biomass of leaves
Wb2 + l = 0.0463.d.h (RMSE = 8.55)
Biomass of Root
Wr = 0.0829.d2 (RMSE = 35.39)
Ws : Biomass weight of the stem fraction (kg); Wb7 : Biomass weight of the thick branches fraction (diameter larger than 7 cm) (kg); Wb2–7 : Biomass weight of medium branches fraction (diameter between 2 and 7 cm) (kg); Wb2 + l: Biomass weight of thin branches fraction (diameter smaller than 2 cm) with leaves (kg); Wr : Biomass weight of the belowground fraction (kg); d: diameter at breast height (cm); h: tree height (m).
Morocco
Volume tree
Vtree = 0.6 C2.18 (e = 0.020)
Oubrahim et al. (2016)
Vtree = 0.25 C2.088H0.362 (e = 0.039)
Vtree is the tree volume (in m3), C the circumference at 130 cm (in m) and H the tree height (in m).
Volume cork
Vcork = 0.0151(C130)1.9827
Makhloufi et al. (2008)
Volume stem without bark
Vstem−without−bark =Vtree−Vcork
Vcork is the volume of the cork (in dm3), C130 the circumference at 130 cm (in cm), Vstem-without-bark is the tree volume without bark (m3), and Vtree the tree volume of wood, thick branches and cork (in m3).
Biomass cork
Bcork =Vcork⋅D
Oubrahim et al. (2016)
Biomass stem without bark
Bstem−without−bark =Vstem−without-bark .D
Biomass of medium branches (diameter 2-10 cm) in kg ; Ruiz-Peinado et al. (2012)
Bmedium−branch = 0.127.D130. H
Biomass of small branches (<2 cm diameter) and supported foliage (kg) ; Ruiz-Peinado et al. (2012)
Bsmall−branch− foliage = 0.0463. D130⋅H
Tree biomass (in kg)
Btree −ABG= Bstem−without−bark+ Bcork +Bmedium −branch+ Bsmall−branch− foliage
Carbon stock (in kg) for an individual tree, 10−3 is a conversion factor, Btree-ABG is the aboveground biomass (in kg) for the respective components (i.e. cork, stem without bark, medium-branches, small branches and foliage) and Cconc- ABG is the mean carbon concentration of the respective aboveground tissues (in g kg−1-values from Table 2)
Cst− ABG =Σ 10−3 (Btree−ABG. Cconc−ABG)
Belowground biomass of an individual tree in kg ; Ruiz-Peinado et al. (2012)
Btree – BLG = 0.0829. D2130
Carbon stock in the belowground tissues of an individual tree (in kg), 10−3 is a conversion factor and Cconc-BLG the average carbon concentration in belowground tissues (in g kg−1 values in Table 2).
Cst−BLG = 10−3.Btree−BLG .Cconc−BLG
Total carbon content in the Quercus suber trees of each entire stand (Mg ha−1),
Cst−QUERCUS =Σ (Cst – ABG + Cst−BLG)
Bcork the biomass of cork (kg), Vcork the volume of cork (m3), Bstem-without-bark is the biomass of the stem and coarse branches without bark (kg), Vstem-without bark the volume of the stem without bark (m3) and D the specific tissue density (Table 2, kg m−3). Bmedium-branchis the biomass of medium branches (diameter 2-10 cm) in kg, Bsmall-branch-foliage the biomass of small branches (<2 cm diameter) and supported foliage (kg), D130 the diameter at breast height (in cm) and H the tree height (in m). Cst-ABG is the carbon stock (in kg) for an individual tree, 10−3 is a conversion factor, Btree-ABG is the aboveground biomass (in kg) for the respective components (i.e. cork, stem without bark, medium-branches, small branches and foliage) and Cconc-ABGis the mean carbon concentration of the respective aboveground tissues (in g.kg−1 in Table 2). Btree-BLGis the belowground biomass of an individual tree in kg. Cst-ABG and Cst-BLG are The carbon contents in aboveground and belowground parts of each tree (kg), respectively.
Tunisia
Trunk wood Biomass (Y in kg) ; X is DHB in cm
Y = 0.2387X1.9555
Sebei et al. (2001) Sebei et al. (2004)
Trunk cork biomass (Y in kg) ; X is DHB in cm
Y = 0.1949X1.6027
Branch wood biomass (Y in kg) ;X is DHB in cm
Y = 0.1275X2 − 0.6083X + 3.3126
Branch cork biomass (Y in kg) ;X is DHB in cm
Y = 0.0743X2 − 0.7362X + 6.5845
Twig biomass (Y in kg) ;X is DHB in cm (DBH <7 cm)
Y = 0.2543X
Twig biomass (Y in kg) ;X is DHB in cm (DBH ≥7)
Y = 0.502X– 1.7336
Leaves and small twig biomass (Y in kg) ; X is DHB in cm (DBH <7 cm)
Y = 0.2536X
Leaves and small twig biomass (Y in kg) X is DHB in cm (DBH ≥7)
Y = 0.4912X − 1.6631
Root biomass (Y in kg) ; X is DHB in cm
Y = 0.119X1.9625
Reproduction cork in kg (female)
RC = 0.0098 DBH1.4756
(Continued)
4 K. MECHERGUI ET AL.
Table 1. (Continued).
Country
Components
Model
References
Italy
Wood biomass (Y biomass in g) ;X is circumference at DBH in cm
Y = 4.078 X2.304
Léonardi et al. (1992)
Cork biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 1.218 X2.359
Branche biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 0.474 X2.683
Total wood biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 19.223 X2.062
Leave biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 0.266 X2.385
Above-ground carbon stock (AGC, expressed in Mg ha−1
AICORK = −217.5 + 11.34 ·√ NPT + 50.36·√ TH (SEE = 49.3)
Corona et al. (2018)
Annual increment of cork mass (AICORK, expressed in kg ha−1yr−1)
AGC = 9.712 + 3.100·√NT - (287.2/TH) (SEE = 14.6)
(NPT): number of cork productive trees ; (NT): number of trees (expressed per hectare) ; (TH) : stand top height (expressed in m)
Portugal
Wood (ww)
ww = 0.164185.d2.01100
Gonçalves et al. (2019)
Bark (wb)
wb = 0.600169d1.355957
crown (wc)
wc = 1.909152.d1.200354
AGB (Forest inventory)
AGB=ww + wb + wc
AGB Satellite Images
AGB = 1.2545.CC (RMSE = 40.5)
D is the diameter at breast height (in cm), h the total height (in m); CC the crown cover calculated with satellite data (in %) and AGB the above ground biomass per hectare (t ha−1)
Inventory data : AGB : Above-ground biomass evaluated with forest inventory in kg ; CHP : Crown horizontal projection in m2
AGB = 14.8889 × CHP
Sousa et al. (2017)
Satellite image data : AGB : Above-ground biomass evaluated with satellite data in kg ; CHP : Crown horizontal projection in m2
AGB = 9.2671 × CHP
Wood biomass (Y in kg) ; DBH in cm
Y = 0.164185. DBH2.011002
Pereira (2007)
Bark biomass (Y in kg) ; DBH in cm
Y = 0.600169. DBH1.355957
Crown biomass (Yin kg) ; DBH in cm
Y = 1.909152. DBH1.200354
Above‐ground biomass(AGB) = Wood biomass + Bark biomass + Crown biomass (in kg)
AGB = 0.164185. DBH2.011002 +0.600169. DBH1.355957 +1.909152. DBH1.200354
Tunisia
Biomass of stem wood in kg
ySW=e−3.2433DBH2.4134 (RMSE = 27.4581)
Zribi et al. (2016)
ySW=e−4.1886DBH1.6962H1.3323 (RMSE = 16.3393)
Biomass of branch wood in kg
yBW=e−7.5788DBH3.5021 (RMSE = 33.0006)
yBW=e−8.3637(DBH2LCL)1.4059 (RMSE = 29.3477)
Biomass of stem cork in kg
ySC=e−7.5788DBH3.5021 (RMSE = 11.0113)
ySC=e−4.5075DBH1.4698H1.3296 (RMSE = 8.4275)
Biomass of branch cork in kg
yBC=e−4.4855DBH2.2495 (RMSE = 5.8765)
yBC=e−3.5835 (DBH2CR)1.0458 (RMSE = 5.1144)
Biomass of leaves in kg
yF =e−4.2891DBH1.9694 (RMSE = 3.5465)
yF =e−3.8257 (DBH2CR)0.9651 (RMSE = 3.0165)
Biomass of belowground biomass in Kg (root)
yB=e−1.1182DBH1.7559 (RMSE = 18.0774)
yB=e−1.2074DBH1.4976LCL0.4420 (RMSE = 16.3462)
DBH, the Diameter at Breast Height (cm), H, the Total Tree Height (m), LCL, Life Crown Length (m) and CR, Crown Ratio (=LCL/H)
RMSE: root mean square error; e = The mean relative error of cubing; SEE = standard error of the estimate.
PLANT ECOLOGY & DIVERSITY 5
(Sousa et al. 2017) and according to 2010, p. 50 (Mg ha−1) (Table 2). According to Oubrahim et al. (2016), in degraded (overexploited) sites, the measured stand values ranged from 38 to 104 (Mg ha−1) for the lowest density class, 51–98 (Mg ha−1) for the medium stand density class and 58–170 (Mg ha−1) for the highest stand density class (Table 2). Estimates of above-ground biomass in the forest of Bellif were comparable with biomass estimates for the same kind of forest in Spain (Zribi et al. 2016), with values ranging between 159 Mg ha−1 and 328 Mg ha−1 (Robert et al. 1996) and 29 Mg ha−1 to 196 Mg ha−1 for only wood and cork biomass of cork oak trees (38–158 years) (Cañellas et al. 2008) (Table 2).
Carbon content in cork oak ecosystems
Zribi et al. (2016) have shown that the carbon stock as a percentage of the total tree biomass at stand level averaged 47% (total tree biomass: 241 Mg ha−1; carbon 114 Mg C ha−1) in the young stands and 47% (total tree biomass: 411 Mg ha−1; 194 Mg C ha−1) in the mature forest (Table 3). According to Oubrahim et al. (2016), in Morocco, the above-ground biomass carbon ranges from 36 to 67 Mg C ha−1 and the below- ground biomass carbon ranges from 5 to 11 Mg C ha−1. In Spain, the above-ground biomass carbon ranged from 4 to 5 Mg C ha−1 in northern Spain (Caritat et al. 1996) to 6 Mg C ha−1 in south-western Spain (Andivia et al. 2010) (Table 3). In Tunisia, Brahim et al. (2010) have estimated organic carbon stock in soils to range from 1 to 200 Mg C ha−1 down to 30 cm depth and from 10 to 449 Mg ha−1 down to 1 m depth (Table 3).
Factors that affect biomass accumulation
The carbon sequestration capacity of trees is related to climate and soil, species (variety), age, and management practices. Optimum growth of Q. suber is achieved in non-restrictive climatic conditions for tree growth with deep soil and a high water storage capacity (Zribi 2016). Its biomass increment is mainly controlled by the amount of water stored before the growing season and depends less on the spring and summer rainfall (Yan et al. 2015). However, stands that grow in favourable climatic and edaphic conditions also experience large soil CO2 fluxes which balance the high carbon assimilation by trees. In addition to climate and soil type, tree age is an important factor that affects carbon sequestration and storage (Alexandrov 2007; Terakunpisut 2007; Taylor et al. 2007; Ali et al. 2014). The rate of carbon sequestration by forests in tree biomass and soil compartments varies with stand/tree age (Paul et al. 2002; Lal 2005). Young trees grow vigorously and via their relative growth rates capture more CO2 than mature trees. In a study by Zribi (2016) mature forests had larger above- and below-ground biomass but a lower potential for accumulation of carbon than young forests which had both greater growth and carbon storage potential. These results highlight the effect of age on carbon stocks and therefore on climate change
Table 2. Above-ground biomass and dendrometric characteristic of cork oak forest in the Mediterranean region.
Country
Site
Age
DBH (cm)
Tree density (tree ha−1)
Above-ground biomass
References
Italy
-
-
-
540
156.7 cork mass (kg ha−1yr−1)
Federici et al. (2008).
Morocco
Mamora forest
-
-
D < 100
64.27 (Mg ha−1)
Oubrahim et al. (2016)
Mamora forest
-
-
100≤D ≤ 200
68.70 (Mg ha−1)
Mamora forest
-
-
D > 200
120.12 (Mg ha−1)
Spain
Los Alcornocales Natural Park and Sierra de San Pedro
-
10.5-83
33 (number of samples)
11-2412 (kg) (Mean = 512 kg)
Ruiz-Peinado et al. (2012)
Portugal
-
-
-
-
49.5 (Mg ha−1)
2010
Region of Mora
-
≥6 cm
88
40.7 (Mg ha−1) ± 11.9
Sousa et al. (2017)
Tunisia
Bellif (young stand)
35
27.8
603
169.68 (Mg ha−1) ± 15.23
Zribi et al. (2016)
Bellif (Aged stand)
71- 102
40.6
475
312.49 (Mg ha−1) ± 21.90
Beni Métir
81
14
723
80 (Mg ha−1)
Sebei et al. (2001, 2004)
Ain Debba
92
16.2
322
48.90 (Mg ha−1)
Col des Ruines
113
20.2
528
113 (Mg ha−1)
Italy
Sicile
7-79
16.5
345
42.20 (Mg ha−1)
Léonardi et al. (1992)
Spain
-
38
14.6
500
28.82 (Mg ha−1)
* Cañellas et al. (2008)
-
117
51.6
70
195.50 (Mg ha−1)
-
158
77.9
40
153.66 (Mg ha−1)
Quart
-
9.3-29
-
159 (Mg ha−1)
Robert et al. (1996)
St Hilari
-
24-57
-
328 (Mg ha−1)
(*wood and cork biomass).
6 K. MECHERGUI ET AL.
mitigation (Zribi 2016). Hence, the importance of preserving these ecosystems, which integrate a set of interactions, is modified either directly or indirectly by the consequences of climate change (Zribi 2016). A study of the relationship between carbon stocks and stand age has led to the conclusion that there is a strong correlation and that carbon stocks increase with the age of the cork oak stands (Zribi 2016). Seely et al. (2010) have suggested that forests are capable of restoring in 50 years their initial state of the carbon stock after clear-cutting, which will then increase
Table 3. Carbon stocks in Quercus suber forest.
Countries
Components
Carbon stocks
References
Locality
Tunisia
Above-ground biomass (YF) (603 trees ha−1 : age = 35 years)
82.52 Mg C ha−1 ±7.40
Zribi et al. (2016)
Bellif, north- western Tunisia
Below-ground biomass (YF) (603 trees ha−1 : age = 35 years)
31.25 Mg C ha−1 ±1.78
Total tree biomass (YF) (603 trees ha−1 : age = 35 years)
113.61 Mg C ha−1 ±9.11
Litter biomass (YF) (603 trees ha−1 : age = 35 years)
3.55 Mg C ha−1 ±0.64
Total forest biomass (trees and litter) (YF) (603 trees ha−1 : age = 35 years)
117.16 Mg C ha−1
Total soil organic carbon 0-150 cm (YF) (603 trees ha−1 : age = 35 years)
302.30 Mg C ha−1
Total ecosystem (YF) (603 trees ha−1 : age = 35 years)
419.46 Mg C ha−1
Above-ground biomass (AF) (475 trees ha−1 : age = 71 at 102 years)
151.96 Mg C ha−1 ±10.65
Below-ground biomass (AF) (475 trees ha−1 : age = 71 at 102 years)
43.55 Mg C ha−1 ±5.74
Total tree biomass (AF) (475 trees ha−1 : age = 71 at 102 years)
194.08 Mg C ha−1 ±12.54
Litter (AF) (475 trees ha−1 : age = 71 at 102 years)
5.73 Mg C ha−1 ±0.91
Total forest (trees and litter) (AF) (475 trees ha−1 : age = 71 at 102 years)
199.81 Mg C ha−1
Total soil organic carbon 0-150 cm (AF) (475 trees ha−1 : age = 71 at 102 years)
458.28 Mg C ha−1
Total ecosystem (AF) (475 trees ha−1 : age = 71 at 102 years)
658.09 Mg C ha−1
Carbon stock in the litter of the cork oak forest (AS)
13.00 Mg C ha−1
Nouri (2009)
Bellif, north- western Tunisia
10.12 Mg C ha−1
Khroufa, north- western Tunisia
3.37 Mg C ha−1
Zouaraa, north- western Tunisia
7.06 Mg C ha−1
Jouza, north- western Tunisia
Total soil organic carbon at 30 cm depth
1.2 to 199.8 Mg C ha−1
Brahim et al. (2010)
North of Tunisia
Total soil organic carbon at 1 m cm depth
10.30 to 449.2 Mg C ha−1
Morocco
Above-ground biomass (D < 100 stem ha−1)
35.80 Mg C ha−1
Oubrahim et al. (2016)
Mamora north- west of Morocco
Below-ground biomass (D < 100 stem ha−1)
5.40 Mg C ha−1
Total (D < 100 stem ha−1)
41.20 Mg C ha−1
Above-ground biomass (100≤D ≤ 200 stem ha−1)
38.30 Mg C ha−1
Below-ground biomass (100≤D ≤ 200 stem ha−1)
5.90 Mg C ha−1
Total (100≤D ≤ 200 stem ha−1)
44.20 Mg C ha−1
Above-ground biomass (D > 200 stem ha−1)
66.90 Mg C ha−1
Above-ground biomass (D > 200 stem ha−1)
10.80 Mg C ha−1
Total (D > 200 stem ha−1)
77.70 Mg C ha−1
Plant component
mean carbon concentration of the tissues (g kg−1)
Wood of the trunk and thick branches (Ø > 10 cm)
560 g kg−1
Medium branches (2 ≤ Ø ≤ 10 cm)
540 g kg−1
Small branches (Ø < 2 cm)
530 g kg−1
Foliage
520 g kg−1
Cork
560 g kg−1
Roots
540 g kg−1
Spain
Carbon stock in the litter of the cork oak forest (99.6 trees ha−1; basal area : 8.1 m2 ha−1)
6.35 Mg C ha−1
Andivia et al. (2010)
Huelva, south- western Spain
Carbon stock in the litter of the cork oak forest (575 at 875 trees ha−1 ; basal area : 28 at 58 m2 ha−1)
3.96 to 4.62 Mg C ha−1
Caritat et al. (1996)
north-eastern Spain
YF : Young forest; AF : Aged forest, D : Density class (stem ha−1)
PLANT ECOLOGY & DIVERSITY 7
with the age of the stand age. Disturbances that
reduce above-ground standing biomass can result
in negative stand level carbon balance (Zribi
2016). Climate scenarios predict that the
Mediterranean zone will be much more vulnerable
over the coming decades, with a decrease in
precipitation and an increase in temperature,
which will have a negative impact on carbon
sequestration mechanisms (Zribi 2016).
Luyssaert et al. (2008) have proposed that
old-growth forests are generally carbon sinks
because they accumulate carbon over centuries
without interruption and thus, they contain
large quantities of it. Forests can continue to
act as carbon sinks even when they reach the
old-growth stage (Luyssaert et al. 2008), constituting
important carbon reservoirs to be conserved
(Matsuzaki et al. 2013). Luyssaert et al.
(2008) have shown that old-growth forests are
generally carbon sinks. As these forests have
been steadily accumulating carbon for centuries,
they store large quantities of it. They will lose
a large proportion of this carbon to the atmosphere
if disturbed, making carbon accounting
a difficult task, which is why carbon accounting
rules for forests should give credit for leaving
old-growth forests intact. Luyssaert et al. (2008)
have shown that the decline in biometry-based
net primary production could be partly attributed
to the presence or absence of management.
However, we expect that this decline is not
strictly a management effect, but a reflection of
differences in disturbance history between managed
and unmanaged forests.
Tree-ring growth of cork oak: climate – growth
relationships
Most studies carried out on declining oak forests
have shown stand-specific growth responses
to climate variables (Corcuera et al. 2004). The
few investigations that have compared climate –
growth relationships among trees of different
extents of decline, reported slight differences
between non-declining and declining trees (Di
Filippo et al. 2010; Camarero et al. 2016;
Colangelo et al. 2017. Radial growth in
Mediterranean oak species is higher in relatively
mesic and cool conditions during spring and
summer in the north of the western
Mediterranean region (Tessier et al. 1994). Oak
species in general are evergreen in areas where
growth depends on summer water availability
(Corcuera et al. 2004; Montserrat-Martí et al.
2009). However, Q. suber shows a particular
sensitivity to spring precipitation (Corcuera
et al. 2004; Gutiérrez et al. 2011; Besson et al.
2014). Tree-ring growth in young Q. suber trees
is strongly controlled by the precipitation during
the autumn and winter months prior to the
growing season (October–February). It is the
soil water available at the beginning of the
growing season, accumulated and stored during
the autumn and winter months, that determines
the growth in a given year (Leal et al. 2008).
Rainfall exclusion experiments in semi-arid conditions
have shown a high resilience of Q. suber
to inter-annual precipitation variability, with
a capacity for remarkable recovery after severe
drought events (Besson et al. 2014). Moreover,
recent observations in north-eastern Spain
(Banqué Casanovas et al. 2013) have indicated
that Q. suber trees were vulnerable to droughtinduced
damage, but they were also able to
recover rapidly. Vicente-Serrano et al. (2020)
have assessed the links between tree-ring width
indices (TRWi), the Normalised Difference
Vegetation Index (NDVI) and climatic variables
(e.g. air temperature, precipitation, evapotranspiration
and water balance). They showed
a generally positive and significant relationship
between the inter-annual variability of the
NDVI and tree-ring growth. In a statistical analysis
of stem growth of Mediterranean Q. suber
using a time series spanning 1918–2008, Zribi
et al. (2016) have shown a major role of autumnal
rainfall before the growing season on annual
tree growth. The authors concluded that analysis
of recent climate changes in the region illustrated
that an increase in temperatures that
enhanced the evaporative demand and the
onset of growth and a decline in rainfall in
autumn, were the two main factors driving
stem growth. Zribi et al. (2016) have obtained
a negative correlation between mean annual
total tree-ring growth and precipitation in the
current summer for a mature stand. Q. ilex and
Q. suber show a particular sensitivity to spring
precipitation (Corcuera et al. 2004; Gutiérrez
et al. 2011; Besson et al. 2014). Gentilesca
et al. (2017) have found that declining Quercus
(dead trees) responded more negatively to warmer
spring conditions (higher mean maximum
temperatures) than non-declining trees, whereas
correlations with precipitation or with water
balance did not differ between the two vigour
8 K. MECHERGUI ET AL.
classes. These authors showed that the investigation of oak decline associated with extreme climatic events such as dry spells, showed how either exceptionally wide or narrow rings can be formed prior to tree death, albeit sudden growth reductions are often associated with decline (Wyckoff and Bowers 2010). The most influential climatic variables on Q. suber growth were late spring and early summer precipitation, which enhanced growth, and high temperatures in the previous August and current July, which negatively affected growth (Gea-Izquierdo et al. 2009). Cork oak in Portugal usually ceases radial growth between December and February with diameter (xylem and bark) growth occurring all through the rest of the year (Costa et al. 2001, 2003). It has also been observed that cork oak in Portugal reaches maximum growth (wood and cork) in June–July and growth is negatively correlated with August temperature, similar to the relationships observed by Gea- Izquierdo et al. (2009). Data in the average tree-ring growth of Q. suber ranged from 1.13 to 8.01 mm yr−1 for young trees and from 0.78 to 4 mm yr−1 for mature trees (Table 4).
Cork-ring growth and management recommendations
The average cork-ring growth of Q. suber ranges from 5.25 to 5.85 mm yr−1 in Spain, from 1.6 to 4.8 mm yr−1 in Portugal, and 1.8 to 4.4 mm yr−1 in Algeria. The average cork-ring widths ranged from 2.6 to 3.4 mm yr−1 in Italy, 2.8 to 4.4 mm yr−1 in France and 2.89 to 3.15 mm yr−1 in Tunisia and Morocco (Table 5).
The cork produced by the trees is removed periodically, with intervals of 9 to 12 years. The management practices of cork oak stands aim to maintain the existing stand density and encourage natural regeneration by protecting the seedlings during cork harvesting (Sánchez-González 2006).
In North Africa, most cork oak forests have management planning and use a regular rotation. The area to be regenerated is estimated based on an exploitable age of 100 years and a management period of 24 years. The method of natural regeneration of Q. suber by progressive cuts was chosen to renew the cork oak forests (Aloui et al. 2007). Harvesting the cork is every 9–12 years (Aloui et al. 2007). Thinning in cork oak stands and natural regeneration of cork oak acorns are widely practiced in the Mediterranean zone (Hasnaoui 1998)..
Table 4. Tree-ring growth of Quercus suber.
Average radial wood increment
References
1.30 mm yr −1 YT
Costa et al. (2002)
3 mm y−1 MT
Leal et al. (2006)
2.61 mm y−1 YT
Leal et al. (2008)
1.63 mm y−1 MT
3.90 mm y−1 MT
Knapic et al. (2007)
4.20 mm yr−1 YT
2 mm yr−1 YT
Nunes (1996)
from 1 to 4 mm yr−1 MT
Gonzalez-Adrados and Gourlay (1998); Gourlay and Pereira (1998)
from 1.13 to 8.01 mm yr−1 YT
Zribi et al. (2016)
from 0.78 to 3.30 mm yr−1 MT
YT : young tree ; MT : mature tree.
Table 5. Cork-ring growth of Quercus suber.
Country
Average cork-ring widths
Locality
References
Spain
3.80 mm yr−1
-
Costa et al. (2002)
2.2 to 4.8 mm yr−1
-
Caritat et al. (2000)
1.85 to 5.25 mm yr−1
-
Costa et al. (2016)
2.43 mm yr−1
-
CORKASSESS (2001)
2 to 4.8 mm yr−1
Andalusia and Catalonia
Chorana et al. (2019)
Italy
2.6 to 3.4 mm yr−1
Sardinia
France
2.8 to 4.4 mm yr−1
Corsica
Portugal
3.8 to 4.3 mm yr−1
-
1.6 to 4.6 mm yr−1
-
Lauw et al. (2018).
2.2 to 4.8 mm yr−1
South-western Portugal
Ferreira et al. (2000)
3.50 mm yr−1
-
Pereira (2007)
3.30 mm yr−1
-
Leite et al. (2019)
3.30 mm yr−1
-
Oliveira et al. (2016)
Algeria
1.8 to 4.4 mm yr−1
Oran
Chorana et al. (2019)
3.28 mm yr−1
North-west Algeria
Dehane and Ghefar (2017)
3.22 mm yr−1
Eastern Algeria
0.8 to 3.2 mm yr−1
-
Dehane (2012)
Tunisia
2.89 mm yr−1
-
Aloui et al. (2006)
Morocco
3.15 mm yr−1
-
PLANT ECOLOGY & DIVERSITY 9
Conclusions
The study of the biomass, carbon storage, and corkring
growth of Quercus suber can be key for forest
management in the Mediterranean region and also to
prevent the expected negative effects of climate variable.
Given their importance, according to the consulted
literature, they can and should continue having
that role in the future. The production of cork oak
forests could be an important element in ensuring
this continuing role. Under drought, drastic changes
were observed in the growth of Quercus suber. The
studies documented a wide range of above-ground
biomass for cork oak: 42 Mg ha−1 in Italy, 64–120 Mg
ha−1 in Morocco, 41–50 Mg ha−1 in Portugal, 29–328
Mg ha−1 in Spain, and 80–312 Mg ha−1 in Tunisia.
The radial wood increment varied from 0.78 to
8.01 mm yr−1, while the annual increment of corkring
growth ranged from 0.8 mm yr−1 in northern
Algeria to 5.25 mm yr−1 in Spain, with most values
falling between 2 and 4.4 mm yr−1. The biometry of
the stands and the cork increment is highly variable,
the precise causes of this related to site.
This study should assist forest managers in the
Mediterranean area to manage the production of
cork oak forests and to create a sustainable strategy
for cork oak stands. Finally, forest managers should
implement silvicultural practices aimed at increasing
the stand density and avoid continued overexploitation
in cork oak ecosystems.
Acknowledgments
This project is carried out under the MOBIDOC scheme,
funded by The Ministry of Higher Education and Scientific
Research of Tunisia through the PromESsE project and
managed by the ANPR.
The authors sincerely thank R. Touchan (Laboratory of
Tree-Ring Research, University of Arizona, Tucson, AZ,
USA) for language editing.
Disclosure statement
No potential conflict of interest was reported by the
author(s).
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PLANT ECOLOGY & DIVERSITY 13Plant Ecology & Diversity
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Evaluation of biomass, carbon storage capability,
tree-ring and cork-ring growth of Quercus suber: a
review
Kaouther Mechergui, Wahbi Jaoaudi, Víctor Bello-Rodríguez, Hammadi
Achour & Youssef Ammari
To cite this article: Kaouther Mechergui, Wahbi Jaoaudi, Víctor Bello-Rodríguez, Hammadi
Achour & Youssef Ammari (08 Nov 2024): Evaluation of biomass, carbon storage capability,
tree-ring and cork-ring growth of Quercus suber: a review, Plant Ecology & Diversity, DOI:
10.1080/17550874.2024.2422293
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REVIEW
Evaluation of biomass, carbon storage capability, tree-ring and cork-ring
growth of Quercus suber: a review
Kaouther Mecherguia, Wahbi Jaoaudia,b, Víctor Bello-Rodríguezc, Hammadi Achourb and Youssef Ammaria
aLaboratory of Forest Ecology, National Research Institute of Rural Engineering, Water, and Forestry, University of Carthage, Ariana,
Tunisia; bLaboratory of Sylvo-Pastoral Resources, The Silvo-Pastoral Institute of Tabarka, University of Jendouba, Tunisia; cPlant
Conservation and Biogeography Research Group, Departamento de Botanica, Ecología y Fisiología Vegetal, Universidad de La Laguna, La
Laguna, Spain
ABSTRACT
Background: Cork oak (Quercus suber) forests and woodlands cover an area of ca. 2.1 M ha in
the Mediterranean Basin. Cork oak stands are important for biodiversity conservation, and
ecosystem services (carbon storage and the provision of cork, timber, and firewood). Their
sustainable management is essential for their continued multifunctional existence and for
providing ecosystem services.
Aims: The aims of this study were to estimate the biomass production of the species and
quantify its potential carbon storage capacity in the Mediterranean Basin and to make management
recommendations.
Methods: We used a literature survey of allometric equations and cork annual total tree-ring
growth data and applied them to determine the productivity of the species. Estimate biomass
by using regression models and dendrometric characteristics is very important for cork oak in
the Mediterranean region.
Results: The studies reported a wide-ranging above-ground biomass for cork oak: 42 Mg ha−1
for Italy, 64–120 Mg ha−1 for Morocco, 41–50 Mg ha−1 for Portugal, 29–328 Mg ha−1 for Spain,
and 80–312 Mg ha−1 for Tunisia. The radial wood increment ranged from 0.78 to 8.01 mm yr−1.
The annual increment of cork-ring growth was between 0.8 mm yr−1 (northern Algeria) and
5.25 mm yr−1 (Spain), with most values being between 2 and 4.4 mm yr−1. In dry years, major
reductions (10−15%) in the growth of Quercus suber were observed.
Conclusion: Most studies showed stand-specific growth responses to climate variables, with
the radial growth of cork oak being greater in relatively mesic and cool conditions.
ARTICLE HISTORY
Received 10 April 2021
Accepted 25 April 2024
KEYWORDS
Allometric equation;
biomass; carbon; cork-ring;
growth–climate relationship;
tree-ring
Introduction
Large increases in anthropogenic emissions of
greenhouse gases have contributed to the observed
changes in global climate over the last decades
(Hansen and Sato 2004). Terrestrial ecosystems
represent a considerable part of global carbon
stocks (IPCC 2013) and of all terrestrial ecosystems,
forests contain around 80% of the above-ground
and 40% of the below-ground terrestrial carbon
(Dixon et al. 1994). Forest ecosystems play
a potential role in climate change mitigation by
acting as carbon sinks (Dixon et al. 1994; Lal 2004;
Mohanraj et al. 2011). Carbon dioxide from the
atmosphere is accumulated in the organic matter
in soil and trees, and it continuously cycles between
forests and the atmosphere through the decomposition
of dead organic matter (Alexandrov 2007). To
estimate forest carbon pools from forest inventories
it is necessary to have reliable models of the biomass
of the bole and apply suitable biomass expansion
factors to estimate the total above-ground biomass
(Ruiz-Peinado et al. 2012). In recent years, the estimation
of forest carbon stocks has gained prominence
due to the role of forests in the mitigation of
global climate change through ecosystem carbon
storage (Ruiz-Peinado et al. 2012).
The quantification of biomass, both above- and
below-ground, and its distribution and dynamics
have been receiving increased attention. This has
been motivated by the need to estimate accurately
the carbon stocks and sequestration (Sousa et al.
2017) as well as woody biomass which might be
used for bioenergy. Biomass equations that relate
different tree biomass components to dendrometric
variables, and biomass expansion factors that relate
biomass to total stand volume are particularly useful
tools in forest biomass estimation (Brown 2002;
Somogyi et al. 2007). Biomass models require treelevel
data, which are usually recorded in forest
inventories, such as diameter and sometimes height
(Teobaldelli et al. 2009). Since biomass expansion
factors depend on species, site (Wirth et al. 2004),
CONTACT Wahbi Jaouadi jaouadiwahbi@gmail.com
PLANT ECOLOGY & DIVERSITY
https://doi.org/10.1080/17550874.2024.2422293
© 2024 Botanical Society of Scotland and Taylor & Francis
age (Lehtonen et al. 2004), and stand timber volume
(Fang et al. 2001), where tree-level data are available,
biomass models are often preferred.
One species that can serve as a model is the cork
oak (Quercus suber L.), a key species in the western
Mediterranean basin. Its biomass, carbon storage
capability, and growth rates have been widely studied
due to its importance in both natural and
human-impacted/planted forest stands (Pereira
2007). The species has been extensively planted for
commercial exploitation of its bark (Gonzalez-
Garcia et al. 2013) making it a species that can
potentially contribute to climate change mitigation,
especially when stands are sustainably managed, as
they can sequester large amounts of CO2 (Pereira
2007; Pereira et al. 2008). In addition, cork oak
stands are important reservoirs of faunal and floral
diversity, play a key role in ecological processes,
such as water retention and soil conservation, and
provide opportunities for development in economically
and socially disadvantaged areas (WWF 2006;
Silva et al. 2009; Bugalho et al. 2011). Cork oak has
made an important economic contribution to the
Mediterranean region (Gonzalez-Garcia et al.
2013); cork production is the most important
source of revenue in cork oak agroforestry systems
(Borges et al. 1997).
Despite the ecological and economic importance
of the species, there is currently no study that compiles
all the existing information on estimates of
biomass, carbon sequestration capacity or growth
rates of cork oak forests in different regions of its
distribution area. Here, we fill this gap by reviewing
the available literature.
The aim of this study was to report the different
allometric equations of productivity of biomass and
cork of Quercus suber, along with values of increment
of wood and cork from different parts of the
Mediterranean Basin.
Materials and methods
This work was carried out using data published
between 1992 and 2020; data were collected from
Clarivate Web of Science® (http://apps.webofknow
ledge.com/), Scopus® and Science Direct®. The terms
used in the literature were ‘Quercus suber’, ‘Cork
oak’, ‘allometric equation’, ‘biomass’, ‘carbon’,
‘cork-ring’, ‘growth-climate relationship’ and ‘treering’.
Additionally, grey literature, including
unpublished doctoral theses was consulted for
unpublished material and information.
Search results were refined based on title and
abstract content and resulted in a final database of
77 references. The selected papers fulfilled the following
criteria: the study was published in English,
and the scope of the study was to examine biomass
and carbon stock models of Quercus suber to evaluate
the tree and cork growth and the relationship
of growth to climate.
The species
Quercus suber (Fagaceae) is an evergreen oak species
that grows to a height of 15–20 m and to
a diameter of 20–60 cm (Pereira 2007). It is
long-lived (200–250 years) and is characterised
by the presence of a conspicuous thick and furrowed
bark with a continuous layer of cork. The
species is endemic to the Mediterranean Basin
and has been present in the western
Mediterranean for more than 60 million years
(Pereira 2007). Its current distribution is restricted
to the western Mediterranean of Europe
(Portugal, Spain, France, and Italy) and North
Africa (Morocco, northern Algeria, and Tunisia)
(Pereira 2007; Rives et al. 2011) (Figures 1 and 2).
The potential distribution range of cork oak has
greatly decreased as a result of historic variations
in climatic conditions, but above all due to
human activities (De Sousa et al. 2008). Today,
some extensive stands of Q. suber are found in
Morocco, Portugal, and Spain, being related to the
industry derived from the exploitation of its bark
(Pereira 2007; Rives et al. 2011). In its distribution
range, the species is frequently accompanied by
other oak species (Q. ilex, Q. faginea), pines
(Pinus pinaster, P. pinea) and shrubs including
species of Cistus or Erica (Ruiz 2001).
Quercus suber is considered a thermophilous
species, being present in areas where the main
temperature in January exceeds 0°C and 18°C in
August. Its stands are mainly found between
elevations of 300 and 500 m a.s.l. and can
reach or exceed 2000 m in the Atlas Mountains
in Morocco. It is usually present in areas with
precipitation more than 400 mm year−1 and its
best stands are located in areas where precipitation
is between 600 and 1000 mm year−1 (Ruiz
2001). Temperature alone is likely to have less
2 K. MECHERGUI ET AL.
effect than it may have when increased evapotranspiration
is considered, or where forecast
precipitation decrease may negatively affect or
limit its local distribution (Ruiz 2001).
Allometric regressions to estimate biomass
The models used to estimate stem and cork volume,
biomass, and carbon stocks of Quercus suber by
different authors in different countries are listed in
Table 1.
Results
Stand biomass
The above-ground biomass at stand level estimated
in Bellif forest (170 Mg ha−1 in the young stands and
312 Mg ha−1 in the mature stands, Zribi et al. 2016)
was higher than in other studies of cork oak forests
reported by Sebei et al. (2001, 2004) in Tunisia (49–
113 Mg ha−1) and Léonardi et al. (1992) in Italy (42
Mg ha−1) (Table 2). The above-ground biomass in
the region of Mora in Portugal was 41 Mg ha−1
Figure 1. Geographical distribution of Quercus suber L. (Caudullo et al. 2017).
Figure 2. Cork oak stands in Tunisia.
PLANT ECOLOGY & DIVERSITY 3
Table 1. Stem and cork volume, biomass and carbon stock model of Quercus suber (d: diameter at breast height in cm; h: tree height in m).
Country
Components
Model
References
Spain
Biomass of Stem
Ws = 0.00525.d2.h + 0.278.d.h (RMSE = 66.87)
Ruiz-Peinado et al. (2012).
Biomass of Thick branches
Wb7 = 0.0135.d2.h (RMSE = 110.76)
Biomass of Medium branches
Wb2–7 = 0.127.d.h (RMSE = 26.47)
Biomass of Thin branches + biomass of leaves
Wb2 + l = 0.0463.d.h (RMSE = 8.55)
Biomass of Root
Wr = 0.0829.d2 (RMSE = 35.39)
Ws : Biomass weight of the stem fraction (kg); Wb7 : Biomass weight of the thick branches fraction (diameter larger than 7 cm) (kg); Wb2–7 : Biomass weight of medium branches fraction (diameter between 2 and 7 cm) (kg); Wb2 + l: Biomass weight of thin branches fraction (diameter smaller than 2 cm) with leaves (kg); Wr : Biomass weight of the belowground fraction (kg); d: diameter at breast height (cm); h: tree height (m).
Morocco
Volume tree
Vtree = 0.6 C2.18 (e = 0.020)
Oubrahim et al. (2016)
Vtree = 0.25 C2.088H0.362 (e = 0.039)
Vtree is the tree volume (in m3), C the circumference at 130 cm (in m) and H the tree height (in m).
Volume cork
Vcork = 0.0151(C130)1.9827
Makhloufi et al. (2008)
Volume stem without bark
Vstem−without−bark =Vtree−Vcork
Vcork is the volume of the cork (in dm3), C130 the circumference at 130 cm (in cm), Vstem-without-bark is the tree volume without bark (m3), and Vtree the tree volume of wood, thick branches and cork (in m3).
Biomass cork
Bcork =Vcork⋅D
Oubrahim et al. (2016)
Biomass stem without bark
Bstem−without−bark =Vstem−without-bark .D
Biomass of medium branches (diameter 2-10 cm) in kg ; Ruiz-Peinado et al. (2012)
Bmedium−branch = 0.127.D130. H
Biomass of small branches (<2 cm diameter) and supported foliage (kg) ; Ruiz-Peinado et al. (2012)
Bsmall−branch− foliage = 0.0463. D130⋅H
Tree biomass (in kg)
Btree −ABG= Bstem−without−bark+ Bcork +Bmedium −branch+ Bsmall−branch− foliage
Carbon stock (in kg) for an individual tree, 10−3 is a conversion factor, Btree-ABG is the aboveground biomass (in kg) for the respective components (i.e. cork, stem without bark, medium-branches, small branches and foliage) and Cconc- ABG is the mean carbon concentration of the respective aboveground tissues (in g kg−1-values from Table 2)
Cst− ABG =Σ 10−3 (Btree−ABG. Cconc−ABG)
Belowground biomass of an individual tree in kg ; Ruiz-Peinado et al. (2012)
Btree – BLG = 0.0829. D2130
Carbon stock in the belowground tissues of an individual tree (in kg), 10−3 is a conversion factor and Cconc-BLG the average carbon concentration in belowground tissues (in g kg−1 values in Table 2).
Cst−BLG = 10−3.Btree−BLG .Cconc−BLG
Total carbon content in the Quercus suber trees of each entire stand (Mg ha−1),
Cst−QUERCUS =Σ (Cst – ABG + Cst−BLG)
Bcork the biomass of cork (kg), Vcork the volume of cork (m3), Bstem-without-bark is the biomass of the stem and coarse branches without bark (kg), Vstem-without bark the volume of the stem without bark (m3) and D the specific tissue density (Table 2, kg m−3). Bmedium-branchis the biomass of medium branches (diameter 2-10 cm) in kg, Bsmall-branch-foliage the biomass of small branches (<2 cm diameter) and supported foliage (kg), D130 the diameter at breast height (in cm) and H the tree height (in m). Cst-ABG is the carbon stock (in kg) for an individual tree, 10−3 is a conversion factor, Btree-ABG is the aboveground biomass (in kg) for the respective components (i.e. cork, stem without bark, medium-branches, small branches and foliage) and Cconc-ABGis the mean carbon concentration of the respective aboveground tissues (in g.kg−1 in Table 2). Btree-BLGis the belowground biomass of an individual tree in kg. Cst-ABG and Cst-BLG are The carbon contents in aboveground and belowground parts of each tree (kg), respectively.
Tunisia
Trunk wood Biomass (Y in kg) ; X is DHB in cm
Y = 0.2387X1.9555
Sebei et al. (2001) Sebei et al. (2004)
Trunk cork biomass (Y in kg) ; X is DHB in cm
Y = 0.1949X1.6027
Branch wood biomass (Y in kg) ;X is DHB in cm
Y = 0.1275X2 − 0.6083X + 3.3126
Branch cork biomass (Y in kg) ;X is DHB in cm
Y = 0.0743X2 − 0.7362X + 6.5845
Twig biomass (Y in kg) ;X is DHB in cm (DBH <7 cm)
Y = 0.2543X
Twig biomass (Y in kg) ;X is DHB in cm (DBH ≥7)
Y = 0.502X– 1.7336
Leaves and small twig biomass (Y in kg) ; X is DHB in cm (DBH <7 cm)
Y = 0.2536X
Leaves and small twig biomass (Y in kg) X is DHB in cm (DBH ≥7)
Y = 0.4912X − 1.6631
Root biomass (Y in kg) ; X is DHB in cm
Y = 0.119X1.9625
Reproduction cork in kg (female)
RC = 0.0098 DBH1.4756
(Continued)
4 K. MECHERGUI ET AL.
Table 1. (Continued).
Country
Components
Model
References
Italy
Wood biomass (Y biomass in g) ;X is circumference at DBH in cm
Y = 4.078 X2.304
Léonardi et al. (1992)
Cork biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 1.218 X2.359
Branche biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 0.474 X2.683
Total wood biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 19.223 X2.062
Leave biomass (Y biomass in g) ; X is circumference at DBH in cm
Y = 0.266 X2.385
Above-ground carbon stock (AGC, expressed in Mg ha−1
AICORK = −217.5 + 11.34 ·√ NPT + 50.36·√ TH (SEE = 49.3)
Corona et al. (2018)
Annual increment of cork mass (AICORK, expressed in kg ha−1yr−1)
AGC = 9.712 + 3.100·√NT - (287.2/TH) (SEE = 14.6)
(NPT): number of cork productive trees ; (NT): number of trees (expressed per hectare) ; (TH) : stand top height (expressed in m)
Portugal
Wood (ww)
ww = 0.164185.d2.01100
Gonçalves et al. (2019)
Bark (wb)
wb = 0.600169d1.355957
crown (wc)
wc = 1.909152.d1.200354
AGB (Forest inventory)
AGB=ww + wb + wc
AGB Satellite Images
AGB = 1.2545.CC (RMSE = 40.5)
D is the diameter at breast height (in cm), h the total height (in m); CC the crown cover calculated with satellite data (in %) and AGB the above ground biomass per hectare (t ha−1)
Inventory data : AGB : Above-ground biomass evaluated with forest inventory in kg ; CHP : Crown horizontal projection in m2
AGB = 14.8889 × CHP
Sousa et al. (2017)
Satellite image data : AGB : Above-ground biomass evaluated with satellite data in kg ; CHP : Crown horizontal projection in m2
AGB = 9.2671 × CHP
Wood biomass (Y in kg) ; DBH in cm
Y = 0.164185. DBH2.011002
Pereira (2007)
Bark biomass (Y in kg) ; DBH in cm
Y = 0.600169. DBH1.355957
Crown biomass (Yin kg) ; DBH in cm
Y = 1.909152. DBH1.200354
Above‐ground biomass(AGB) = Wood biomass + Bark biomass + Crown biomass (in kg)
AGB = 0.164185. DBH2.011002 +0.600169. DBH1.355957 +1.909152. DBH1.200354
Tunisia
Biomass of stem wood in kg
ySW=e−3.2433DBH2.4134 (RMSE = 27.4581)
Zribi et al. (2016)
ySW=e−4.1886DBH1.6962H1.3323 (RMSE = 16.3393)
Biomass of branch wood in kg
yBW=e−7.5788DBH3.5021 (RMSE = 33.0006)
yBW=e−8.3637(DBH2LCL)1.4059 (RMSE = 29.3477)
Biomass of stem cork in kg
ySC=e−7.5788DBH3.5021 (RMSE = 11.0113)
ySC=e−4.5075DBH1.4698H1.3296 (RMSE = 8.4275)
Biomass of branch cork in kg
yBC=e−4.4855DBH2.2495 (RMSE = 5.8765)
yBC=e−3.5835 (DBH2CR)1.0458 (RMSE = 5.1144)
Biomass of leaves in kg
yF =e−4.2891DBH1.9694 (RMSE = 3.5465)
yF =e−3.8257 (DBH2CR)0.9651 (RMSE = 3.0165)
Biomass of belowground biomass in Kg (root)
yB=e−1.1182DBH1.7559 (RMSE = 18.0774)
yB=e−1.2074DBH1.4976LCL0.4420 (RMSE = 16.3462)
DBH, the Diameter at Breast Height (cm), H, the Total Tree Height (m), LCL, Life Crown Length (m) and CR, Crown Ratio (=LCL/H)
RMSE: root mean square error; e = The mean relative error of cubing; SEE = standard error of the estimate.
PLANT ECOLOGY & DIVERSITY 5
(Sousa et al. 2017) and according to 2010, p. 50 (Mg ha−1) (Table 2). According to Oubrahim et al. (2016), in degraded (overexploited) sites, the measured stand values ranged from 38 to 104 (Mg ha−1) for the lowest density class, 51–98 (Mg ha−1) for the medium stand density class and 58–170 (Mg ha−1) for the highest stand density class (Table 2). Estimates of above-ground biomass in the forest of Bellif were comparable with biomass estimates for the same kind of forest in Spain (Zribi et al. 2016), with values ranging between 159 Mg ha−1 and 328 Mg ha−1 (Robert et al. 1996) and 29 Mg ha−1 to 196 Mg ha−1 for only wood and cork biomass of cork oak trees (38–158 years) (Cañellas et al. 2008) (Table 2).
Carbon content in cork oak ecosystems
Zribi et al. (2016) have shown that the carbon stock as a percentage of the total tree biomass at stand level averaged 47% (total tree biomass: 241 Mg ha−1; carbon 114 Mg C ha−1) in the young stands and 47% (total tree biomass: 411 Mg ha−1; 194 Mg C ha−1) in the mature forest (Table 3). According to Oubrahim et al. (2016), in Morocco, the above-ground biomass carbon ranges from 36 to 67 Mg C ha−1 and the below- ground biomass carbon ranges from 5 to 11 Mg C ha−1. In Spain, the above-ground biomass carbon ranged from 4 to 5 Mg C ha−1 in northern Spain (Caritat et al. 1996) to 6 Mg C ha−1 in south-western Spain (Andivia et al. 2010) (Table 3). In Tunisia, Brahim et al. (2010) have estimated organic carbon stock in soils to range from 1 to 200 Mg C ha−1 down to 30 cm depth and from 10 to 449 Mg ha−1 down to 1 m depth (Table 3).
Factors that affect biomass accumulation
The carbon sequestration capacity of trees is related to climate and soil, species (variety), age, and management practices. Optimum growth of Q. suber is achieved in non-restrictive climatic conditions for tree growth with deep soil and a high water storage capacity (Zribi 2016). Its biomass increment is mainly controlled by the amount of water stored before the growing season and depends less on the spring and summer rainfall (Yan et al. 2015). However, stands that grow in favourable climatic and edaphic conditions also experience large soil CO2 fluxes which balance the high carbon assimilation by trees. In addition to climate and soil type, tree age is an important factor that affects carbon sequestration and storage (Alexandrov 2007; Terakunpisut 2007; Taylor et al. 2007; Ali et al. 2014). The rate of carbon sequestration by forests in tree biomass and soil compartments varies with stand/tree age (Paul et al. 2002; Lal 2005). Young trees grow vigorously and via their relative growth rates capture more CO2 than mature trees. In a study by Zribi (2016) mature forests had larger above- and below-ground biomass but a lower potential for accumulation of carbon than young forests which had both greater growth and carbon storage potential. These results highlight the effect of age on carbon stocks and therefore on climate change
Table 2. Above-ground biomass and dendrometric characteristic of cork oak forest in the Mediterranean region.
Country
Site
Age
DBH (cm)
Tree density (tree ha−1)
Above-ground biomass
References
Italy
-
-
-
540
156.7 cork mass (kg ha−1yr−1)
Federici et al. (2008).
Morocco
Mamora forest
-
-
D < 100
64.27 (Mg ha−1)
Oubrahim et al. (2016)
Mamora forest
-
-
100≤D ≤ 200
68.70 (Mg ha−1)
Mamora forest
-
-
D > 200
120.12 (Mg ha−1)
Spain
Los Alcornocales Natural Park and Sierra de San Pedro
-
10.5-83
33 (number of samples)
11-2412 (kg) (Mean = 512 kg)
Ruiz-Peinado et al. (2012)
Portugal
-
-
-
-
49.5 (Mg ha−1)
2010
Region of Mora
-
≥6 cm
88
40.7 (Mg ha−1) ± 11.9
Sousa et al. (2017)
Tunisia
Bellif (young stand)
35
27.8
603
169.68 (Mg ha−1) ± 15.23
Zribi et al. (2016)
Bellif (Aged stand)
71- 102
40.6
475
312.49 (Mg ha−1) ± 21.90
Beni Métir
81
14
723
80 (Mg ha−1)
Sebei et al. (2001, 2004)
Ain Debba
92
16.2
322
48.90 (Mg ha−1)
Col des Ruines
113
20.2
528
113 (Mg ha−1)
Italy
Sicile
7-79
16.5
345
42.20 (Mg ha−1)
Léonardi et al. (1992)
Spain
-
38
14.6
500
28.82 (Mg ha−1)
* Cañellas et al. (2008)
-
117
51.6
70
195.50 (Mg ha−1)
-
158
77.9
40
153.66 (Mg ha−1)
Quart
-
9.3-29
-
159 (Mg ha−1)
Robert et al. (1996)
St Hilari
-
24-57
-
328 (Mg ha−1)
(*wood and cork biomass).
6 K. MECHERGUI ET AL.
mitigation (Zribi 2016). Hence, the importance of preserving these ecosystems, which integrate a set of interactions, is modified either directly or indirectly by the consequences of climate change (Zribi 2016). A study of the relationship between carbon stocks and stand age has led to the conclusion that there is a strong correlation and that carbon stocks increase with the age of the cork oak stands (Zribi 2016). Seely et al. (2010) have suggested that forests are capable of restoring in 50 years their initial state of the carbon stock after clear-cutting, which will then increase
Table 3. Carbon stocks in Quercus suber forest.
Countries
Components
Carbon stocks
References
Locality
Tunisia
Above-ground biomass (YF) (603 trees ha−1 : age = 35 years)
82.52 Mg C ha−1 ±7.40
Zribi et al. (2016)
Bellif, north- western Tunisia
Below-ground biomass (YF) (603 trees ha−1 : age = 35 years)
31.25 Mg C ha−1 ±1.78
Total tree biomass (YF) (603 trees ha−1 : age = 35 years)
113.61 Mg C ha−1 ±9.11
Litter biomass (YF) (603 trees ha−1 : age = 35 years)
3.55 Mg C ha−1 ±0.64
Total forest biomass (trees and litter) (YF) (603 trees ha−1 : age = 35 years)
117.16 Mg C ha−1
Total soil organic carbon 0-150 cm (YF) (603 trees ha−1 : age = 35 years)
302.30 Mg C ha−1
Total ecosystem (YF) (603 trees ha−1 : age = 35 years)
419.46 Mg C ha−1
Above-ground biomass (AF) (475 trees ha−1 : age = 71 at 102 years)
151.96 Mg C ha−1 ±10.65
Below-ground biomass (AF) (475 trees ha−1 : age = 71 at 102 years)
43.55 Mg C ha−1 ±5.74
Total tree biomass (AF) (475 trees ha−1 : age = 71 at 102 years)
194.08 Mg C ha−1 ±12.54
Litter (AF) (475 trees ha−1 : age = 71 at 102 years)
5.73 Mg C ha−1 ±0.91
Total forest (trees and litter) (AF) (475 trees ha−1 : age = 71 at 102 years)
199.81 Mg C ha−1
Total soil organic carbon 0-150 cm (AF) (475 trees ha−1 : age = 71 at 102 years)
458.28 Mg C ha−1
Total ecosystem (AF) (475 trees ha−1 : age = 71 at 102 years)
658.09 Mg C ha−1
Carbon stock in the litter of the cork oak forest (AS)
13.00 Mg C ha−1
Nouri (2009)
Bellif, north- western Tunisia
10.12 Mg C ha−1
Khroufa, north- western Tunisia
3.37 Mg C ha−1
Zouaraa, north- western Tunisia
7.06 Mg C ha−1
Jouza, north- western Tunisia
Total soil organic carbon at 30 cm depth
1.2 to 199.8 Mg C ha−1
Brahim et al. (2010)
North of Tunisia
Total soil organic carbon at 1 m cm depth
10.30 to 449.2 Mg C ha−1
Morocco
Above-ground biomass (D < 100 stem ha−1)
35.80 Mg C ha−1
Oubrahim et al. (2016)
Mamora north- west of Morocco
Below-ground biomass (D < 100 stem ha−1)
5.40 Mg C ha−1
Total (D < 100 stem ha−1)
41.20 Mg C ha−1
Above-ground biomass (100≤D ≤ 200 stem ha−1)
38.30 Mg C ha−1
Below-ground biomass (100≤D ≤ 200 stem ha−1)
5.90 Mg C ha−1
Total (100≤D ≤ 200 stem ha−1)
44.20 Mg C ha−1
Above-ground biomass (D > 200 stem ha−1)
66.90 Mg C ha−1
Above-ground biomass (D > 200 stem ha−1)
10.80 Mg C ha−1
Total (D > 200 stem ha−1)
77.70 Mg C ha−1
Plant component
mean carbon concentration of the tissues (g kg−1)
Wood of the trunk and thick branches (Ø > 10 cm)
560 g kg−1
Medium branches (2 ≤ Ø ≤ 10 cm)
540 g kg−1
Small branches (Ø < 2 cm)
530 g kg−1
Foliage
520 g kg−1
Cork
560 g kg−1
Roots
540 g kg−1
Spain
Carbon stock in the litter of the cork oak forest (99.6 trees ha−1; basal area : 8.1 m2 ha−1)
6.35 Mg C ha−1
Andivia et al. (2010)
Huelva, south- western Spain
Carbon stock in the litter of the cork oak forest (575 at 875 trees ha−1 ; basal area : 28 at 58 m2 ha−1)
3.96 to 4.62 Mg C ha−1
Caritat et al. (1996)
north-eastern Spain
YF : Young forest; AF : Aged forest, D : Density class (stem ha−1)
PLANT ECOLOGY & DIVERSITY 7
with the age of the stand age. Disturbances that
reduce above-ground standing biomass can result
in negative stand level carbon balance (Zribi
2016). Climate scenarios predict that the
Mediterranean zone will be much more vulnerable
over the coming decades, with a decrease in
precipitation and an increase in temperature,
which will have a negative impact on carbon
sequestration mechanisms (Zribi 2016).
Luyssaert et al. (2008) have proposed that
old-growth forests are generally carbon sinks
because they accumulate carbon over centuries
without interruption and thus, they contain
large quantities of it. Forests can continue to
act as carbon sinks even when they reach the
old-growth stage (Luyssaert et al. 2008), constituting
important carbon reservoirs to be conserved
(Matsuzaki et al. 2013). Luyssaert et al.
(2008) have shown that old-growth forests are
generally carbon sinks. As these forests have
been steadily accumulating carbon for centuries,
they store large quantities of it. They will lose
a large proportion of this carbon to the atmosphere
if disturbed, making carbon accounting
a difficult task, which is why carbon accounting
rules for forests should give credit for leaving
old-growth forests intact. Luyssaert et al. (2008)
have shown that the decline in biometry-based
net primary production could be partly attributed
to the presence or absence of management.
However, we expect that this decline is not
strictly a management effect, but a reflection of
differences in disturbance history between managed
and unmanaged forests.
Tree-ring growth of cork oak: climate – growth
relationships
Most studies carried out on declining oak forests
have shown stand-specific growth responses
to climate variables (Corcuera et al. 2004). The
few investigations that have compared climate –
growth relationships among trees of different
extents of decline, reported slight differences
between non-declining and declining trees (Di
Filippo et al. 2010; Camarero et al. 2016;
Colangelo et al. 2017. Radial growth in
Mediterranean oak species is higher in relatively
mesic and cool conditions during spring and
summer in the north of the western
Mediterranean region (Tessier et al. 1994). Oak
species in general are evergreen in areas where
growth depends on summer water availability
(Corcuera et al. 2004; Montserrat-Martí et al.
2009). However, Q. suber shows a particular
sensitivity to spring precipitation (Corcuera
et al. 2004; Gutiérrez et al. 2011; Besson et al.
2014). Tree-ring growth in young Q. suber trees
is strongly controlled by the precipitation during
the autumn and winter months prior to the
growing season (October–February). It is the
soil water available at the beginning of the
growing season, accumulated and stored during
the autumn and winter months, that determines
the growth in a given year (Leal et al. 2008).
Rainfall exclusion experiments in semi-arid conditions
have shown a high resilience of Q. suber
to inter-annual precipitation variability, with
a capacity for remarkable recovery after severe
drought events (Besson et al. 2014). Moreover,
recent observations in north-eastern Spain
(Banqué Casanovas et al. 2013) have indicated
that Q. suber trees were vulnerable to droughtinduced
damage, but they were also able to
recover rapidly. Vicente-Serrano et al. (2020)
have assessed the links between tree-ring width
indices (TRWi), the Normalised Difference
Vegetation Index (NDVI) and climatic variables
(e.g. air temperature, precipitation, evapotranspiration
and water balance). They showed
a generally positive and significant relationship
between the inter-annual variability of the
NDVI and tree-ring growth. In a statistical analysis
of stem growth of Mediterranean Q. suber
using a time series spanning 1918–2008, Zribi
et al. (2016) have shown a major role of autumnal
rainfall before the growing season on annual
tree growth. The authors concluded that analysis
of recent climate changes in the region illustrated
that an increase in temperatures that
enhanced the evaporative demand and the
onset of growth and a decline in rainfall in
autumn, were the two main factors driving
stem growth. Zribi et al. (2016) have obtained
a negative correlation between mean annual
total tree-ring growth and precipitation in the
current summer for a mature stand. Q. ilex and
Q. suber show a particular sensitivity to spring
precipitation (Corcuera et al. 2004; Gutiérrez
et al. 2011; Besson et al. 2014). Gentilesca
et al. (2017) have found that declining Quercus
(dead trees) responded more negatively to warmer
spring conditions (higher mean maximum
temperatures) than non-declining trees, whereas
correlations with precipitation or with water
balance did not differ between the two vigour
8 K. MECHERGUI ET AL.
classes. These authors showed that the investigation of oak decline associated with extreme climatic events such as dry spells, showed how either exceptionally wide or narrow rings can be formed prior to tree death, albeit sudden growth reductions are often associated with decline (Wyckoff and Bowers 2010). The most influential climatic variables on Q. suber growth were late spring and early summer precipitation, which enhanced growth, and high temperatures in the previous August and current July, which negatively affected growth (Gea-Izquierdo et al. 2009). Cork oak in Portugal usually ceases radial growth between December and February with diameter (xylem and bark) growth occurring all through the rest of the year (Costa et al. 2001, 2003). It has also been observed that cork oak in Portugal reaches maximum growth (wood and cork) in June–July and growth is negatively correlated with August temperature, similar to the relationships observed by Gea- Izquierdo et al. (2009). Data in the average tree-ring growth of Q. suber ranged from 1.13 to 8.01 mm yr−1 for young trees and from 0.78 to 4 mm yr−1 for mature trees (Table 4).
Cork-ring growth and management recommendations
The average cork-ring growth of Q. suber ranges from 5.25 to 5.85 mm yr−1 in Spain, from 1.6 to 4.8 mm yr−1 in Portugal, and 1.8 to 4.4 mm yr−1 in Algeria. The average cork-ring widths ranged from 2.6 to 3.4 mm yr−1 in Italy, 2.8 to 4.4 mm yr−1 in France and 2.89 to 3.15 mm yr−1 in Tunisia and Morocco (Table 5).
The cork produced by the trees is removed periodically, with intervals of 9 to 12 years. The management practices of cork oak stands aim to maintain the existing stand density and encourage natural regeneration by protecting the seedlings during cork harvesting (Sánchez-González 2006).
In North Africa, most cork oak forests have management planning and use a regular rotation. The area to be regenerated is estimated based on an exploitable age of 100 years and a management period of 24 years. The method of natural regeneration of Q. suber by progressive cuts was chosen to renew the cork oak forests (Aloui et al. 2007). Harvesting the cork is every 9–12 years (Aloui et al. 2007). Thinning in cork oak stands and natural regeneration of cork oak acorns are widely practiced in the Mediterranean zone (Hasnaoui 1998)..
Table 4. Tree-ring growth of Quercus suber.
Average radial wood increment
References
1.30 mm yr −1 YT
Costa et al. (2002)
3 mm y−1 MT
Leal et al. (2006)
2.61 mm y−1 YT
Leal et al. (2008)
1.63 mm y−1 MT
3.90 mm y−1 MT
Knapic et al. (2007)
4.20 mm yr−1 YT
2 mm yr−1 YT
Nunes (1996)
from 1 to 4 mm yr−1 MT
Gonzalez-Adrados and Gourlay (1998); Gourlay and Pereira (1998)
from 1.13 to 8.01 mm yr−1 YT
Zribi et al. (2016)
from 0.78 to 3.30 mm yr−1 MT
YT : young tree ; MT : mature tree.
Table 5. Cork-ring growth of Quercus suber.
Country
Average cork-ring widths
Locality
References
Spain
3.80 mm yr−1
-
Costa et al. (2002)
2.2 to 4.8 mm yr−1
-
Caritat et al. (2000)
1.85 to 5.25 mm yr−1
-
Costa et al. (2016)
2.43 mm yr−1
-
CORKASSESS (2001)
2 to 4.8 mm yr−1
Andalusia and Catalonia
Chorana et al. (2019)
Italy
2.6 to 3.4 mm yr−1
Sardinia
France
2.8 to 4.4 mm yr−1
Corsica
Portugal
3.8 to 4.3 mm yr−1
-
1.6 to 4.6 mm yr−1
-
Lauw et al. (2018).
2.2 to 4.8 mm yr−1
South-western Portugal
Ferreira et al. (2000)
3.50 mm yr−1
-
Pereira (2007)
3.30 mm yr−1
-
Leite et al. (2019)
3.30 mm yr−1
-
Oliveira et al. (2016)
Algeria
1.8 to 4.4 mm yr−1
Oran
Chorana et al. (2019)
3.28 mm yr−1
North-west Algeria
Dehane and Ghefar (2017)
3.22 mm yr−1
Eastern Algeria
0.8 to 3.2 mm yr−1
-
Dehane (2012)
Tunisia
2.89 mm yr−1
-
Aloui et al. (2006)
Morocco
3.15 mm yr−1
-
PLANT ECOLOGY & DIVERSITY 9
Conclusions
The study of the biomass, carbon storage, and corkring
growth of Quercus suber can be key for forest
management in the Mediterranean region and also to
prevent the expected negative effects of climate variable.
Given their importance, according to the consulted
literature, they can and should continue having
that role in the future. The production of cork oak
forests could be an important element in ensuring
this continuing role. Under drought, drastic changes
were observed in the growth of Quercus suber. The
studies documented a wide range of above-ground
biomass for cork oak: 42 Mg ha−1 in Italy, 64–120 Mg
ha−1 in Morocco, 41–50 Mg ha−1 in Portugal, 29–328
Mg ha−1 in Spain, and 80–312 Mg ha−1 in Tunisia.
The radial wood increment varied from 0.78 to
8.01 mm yr−1, while the annual increment of corkring
growth ranged from 0.8 mm yr−1 in northern
Algeria to 5.25 mm yr−1 in Spain, with most values
falling between 2 and 4.4 mm yr−1. The biometry of
the stands and the cork increment is highly variable,
the precise causes of this related to site.
This study should assist forest managers in the
Mediterranean area to manage the production of
cork oak forests and to create a sustainable strategy
for cork oak stands. Finally, forest managers should
implement silvicultural practices aimed at increasing
the stand density and avoid continued overexploitation
in cork oak ecosystems.
Acknowledgments
This project is carried out under the MOBIDOC scheme,
funded by The Ministry of Higher Education and Scientific
Research of Tunisia through the PromESsE project and
managed by the ANPR.
The authors sincerely thank R. Touchan (Laboratory of
Tree-Ring Research, University of Arizona, Tucson, AZ,
USA) for language editing.
Disclosure statement
No potential conflict of interest was reported by the
author(s).
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