Introduction
⌅The
Atlantic Forest is an important Brazilian biome, as it is one of the
forests with the greatest biodiversity on the planet and presents a high
level of eudemism (Souza et al., 2021Souza
JL, Bueno ML, Salino, A, 2021. Atlantic Forest: centres of diversity
and endemism for ferns and lycophytes and conservation status. Biodivers
Conserv 30:2207-2222. https://doi.org/10.1007/s10531-021-02194-8
). It stands out for its coverage of 24.3% (32.9 million ha) of primary and secondary forests (MapBiomas, 2022MapBiomas. MapBiomas Brasil. https://brasil.mapbiomas.org/2022/10/19/57-dos-municipios-da-mata-atlantica-tem-menos-de-30-de-vegetacao-natural/. [15 December 2023].
), with forest fragments of up to 50 ha (Scarano & Ceotto, 2015Scarano
FR, Ceotto P, 2015. Brazilian Atlantic Forest: impact, vulnerability,
and adaptation to climate change. Biodiversity Conserv. 24: 2319-2331. https://doi.org/10.1007/s10531-015-0972-y
), surrounded by anthropogenic matrices, pastures and agricultural areas (Joly et al., 2014Joly
CA, Metzger JP, Tabarelli M, 2014. Experiences from the Brazilian
Atlantic Forest: ecological findings and conservation initiatives. New
Phytol. 204(3): 459–473. https://doi.org/10.1111/nph.12989
).
Among
these areas, forests located in urban areas (called urban forests) can
be conceptualized as networks or systems that comprise all forests,
groups of trees and individual trees located in urban and peri-urban
areas (Brun et al., 2017Brun
FGK, Brun EJ, Longhi SJ, Gorenstein MR, Maria TRBC, Rêgo GMS, Higa TT,
2017. Vegetação arbórea em remanescentes florestais urbanos: Bosque do
Lago da Paz, Dois Vizinhos, PR. Pesq. flor. bras. 37(92): 503–512. https://doi.org/10.4336/2017.pfb.37.92.1405
). They may be formed by green areas containing remnants of natural landscapes with different land use histories (Elmqvist et al., 2013Elmqvist
T, Fragkias M, Goodness J, Güneralp B, Marcotullio PJ, McDonald RI,
Parnell S, Schewenius M, Sendstad M, Seto KC, Wilkinson C, 2013.
Urbanization, Biodiversity and Ecosystem Services: Challenges and
Opportunities. Springer Netherlands, Dordrecht. 755pp.
). According to Mapbiomas data (2024)MapBiomas. MapBiomas Brasil. https://brasil.mapbiomas.org/2024/07/31/menos-de-10-das-areas-urbanas-no-brasil-sao-cobertas-por-vegetacao/. [30 August 2024].
,
6.9% of urban areas in Brazil are covered by vegetation, amounting to
283,700 ha. Of this total, 61.5% (174,599 ha), are in the Atlantic
Forest biome. In other Brazilian biomes, approximately one in every five
ha (22%) is in the Cerrado, with 62,533 ha of vegetation. The remaining
16.5% is distributed among the Amazon (18,605 ha – 6.6% of the total),
Caatinga (16,139 ha – 5.7%), Pampa (11,228 ha – 4%), and Pantanal (587
ha – 0.2%). These areas in the Atlantic Forest are essential, as they
are related to the quality of life and health of approximately 120
million people, or 72% of Brazil’s population, according to SOS Mata Atlântica (2020)SOS Mata Atlântica. Atlantic Forest, the forest of the Brazilian population. https://www.sosma.org.br/artigos/mata-atlantica-a-floresta-da-populacao-brasileira/. [15 December 2023].
.
However,
such areas are vulnerable to human actions such as deforestation and
forest degradation, phenomena responsible for greenhouse gas emissions
of approximately 5-10 Gt.CO2eq (Bhatti et al., 2023Bhatti
S, Ahmad SR, Asif M, Farooqi IUH, 2023. Estimation of aboveground
carbon stock using Sentinel-2A data and Random Forest algorithm in scrub
forests of the Salt Range, Pakistan. Forestry 96(1): 104–120. https://doi.org/10.1093/forestry/cpac036
), making this biome one of the hotspots with the biggest threats to biodiversity (Romanelli et al., 2022Romanelli
JP, Meli P, Santos JPB, Jacb IN, Souza LR, Rodrigues AV, Trevisan DP,
Huang C, Almeida DRA, Silva LGM, Assad MLRCL, Cadotte MW, Rodrigues RR,
2022. Biodiversity responses to restoration across the Brazilian
Atlantic Forest. Sci. Total Environ. 821: 153403. https://doi.org/10.1016/j.scitotenv.2022.153403
).
Despite this vulnerability, urban forests provide ecosystem services to
the surrounding population, regulating the microclimate, protecting
biodiversity, and improving quality of life (Alonzo et al., 2016Alonzo
M, McFadden JP, Nowak DJ, Roberts DA, 2016. Mapping urban forest
structure and function using hyperspectral imagery and lidar data. Urban
For Urban Green. 17: 135–147. https://doi.org/10.1016/j.ufug.2016.04.003
). They also contribute to the removal of atmospheric carbon dioxide (CO2),
whose potential has already been recognized and highlighted in
scientific research as the largest and most cost-effective carbon sink (Wang et al., 2021Wang
R, Tan J, Yao S, 2021. Are natural resources a blessing or a curse for
economic development? The importance of energy innovations. Resour.
Policy 72: 102042. https://doi.org/10.1016/j.resourpol.2021.102042
).
In this context, it is essential to quantify the carbon stored in
forest biomass, since biomass and carbon stocks are conditions and
controls of the global carbon cycle, serving as indicators for
constructing scenarios relating to climate change, as well as defining
strategies for mitigating its impacts (Lima et al., 2021Lima
RB, Ferreira RLC, Silva JAA, Alves Jr, FT, Oliveira CP, 2021.
Estimating tree volume of dry tropical forest in the Brazilian semi-arid
region: A comparison between regression and artificial neural networks.
J. Sustain. For. 40(3): 281-299. https://doi.org/10.1080/10549811.2020.1754241
).
To this end, determining carbon stocks in forests can be conducted in
two ways: direct methods (forest inventory and destructive sampling of
individuals) and/or estimated indirectly (using allometric equations and
data derived from remote sensing technologies) (Romero et al., 2020Romero
MB, Jacovine LAG, Ribeiro SC, Torres CMME, Silva LFD, Gaspar RDO, Rocha
SJSS, Staudhammer CL, Fearnside PM, 2020. Allometric Equations for
Volume, Biomass, and Carbon in Commercial Stems Harvested in a Managed
Forest in the Southwestern Amazon: A Case Study. Forests 11(8): 1-17. https://doi.org/10.3390/f11080874
). In cases where direct determination is possible, some samples (such as 31 trees used by Veres et al., 2019Veres
Q, Watzlawick L, Silva R, 2019. Estimativas de biomassa e carbono em
fragmento de floresta estacional semidecidual no oeste do Paraná. Biofix
Sci. J. 5(1): 23-31. https://doi.org/10.5380/biofix.v5i1.66001
)
are often taken to quantify carbon in such a way that these samples
generate a database for fitting regression models, which enable
estimating biomass and carbon.
Regarding carbon stock estimation, Azevedo et al. (2018)Azevedo
AD, Francelino MR, Camara R, Pereira MG, Leles, PSS, 2018. Estoque de
carbono em áreas de restauração florestal da Mata Atlântica. Floresta
48(2): 183-194. https://doi.org/10.5380/rf.v48i2.54447
conducted a study to quantify aboveground biomass and carbon stock in
reforested areas aged 3, 5, and 7 years in Cachoeiras do Macacu, Rio de
Janeiro, Brazil, using allometric equations (indirect methods). The
results showed that both biomass and carbon increased with forest age.
At 3 years, the biomass was 39.88 t.ha-1 and the carbon was 19.94 t.ha-1. At 5 years, these values increased to 45.78 t.ha-1 and 22.89 t.ha-1, respectively. At 7 years, the biomass reached 71.24 t.ha-1 and the carbon was 35.62 t.ha-1. Similar results were obtained in an area located in Botucatu, São Paulo, Brazil, with biomass ranging from 113.28 t.ha-1 to 130.87 t.ha-1 (Pontes et al., 2019Pontes
DMF, Engel VL, Parrotta JA, 2019. Forest Structure, Wood Standing
Stock, and Tree Biomass in Different Restoration Systems in the
Brazilian Atlantic Forest. Forests 10(7): 588. https://doi.org/10.3390/f10070588
).
Thus,
there is a need and urgency to develop environmental mechanisms to
mitigate the effects of climate change in the Brazilian context, as the
country is part of global agreements to reduce greenhouse gases (GHG)
emissions such as the Paris Agreement (Koh et al., 2021Koh
LP, Zeng Y, Sarira TV, Siman K, 2021. Carbon prospecting in tropical
forests for climate change mitigation. Nat Commun 12: 1271. https://doi.org/10.1038/s41467-021-21560-2
) and has established a National Policy related to Climate Change (Brazil, 2009Brazil.
Law 12.187/2009, of December 29, that establishes the National Policy
on Climate Change (PNMC) and provides other measures. Official Diary of
the Union (Brasília) 30/12/2009.
), main national
legislation on this topic. Therefore, accurate and reliable estimates of
forest biomass and carbon must be generated, especially in a biome of
such relevance as the Atlantic Forest. In view of the above, the
objective of this work was to evaluate the carbon stock and carbon
equivalent dynamics in the biomass of forest species located in a native
Atlantic Forest remnant in southern Brazil.
Material and methods
⌅Study area
⌅The study was conducted in a native forest fragment called “Capão da Engenharia Florestal” (Figure 1), located at the Federal University of Paraná, in the Jardim Botânico, Campus III, Curitiba, Brazil. This fragment has approximately 15.24 ha in extension (Machado et al., 2008Machado
SA, Nascimento RGN, Augustynczik ALD, Silva LCRS, Figura MA, Pereira
EM, Téo SJ, 2008. Comportamento da relação hipsométrica de Araucaria angustifolia no capão da Engenharia Florestal da UFPR. Pesq. flor. bras. 56: 5-5.
),
with 12.96 ha (85%) belonging to the Mixed Ombrophilous Forest (MOF)
and 2.28 ha (15%) formed by other forest formations (such as capoeira,
capoeirão and predominance of bamboo) (Rondon Neto et al., 2002Rondon
Neto RM, Kozera C, Andrade RDR, Cecy AT, Hummes AP, Fritzsons E,
Caldeira MVW, Maciel MDNM, Souza MKF, 2002. Caracterização florística e
estrutural de um fragmento de Floresta Ombrófila Mista, em Curitiba,
PR-Brasil. Floresta 32(1): 3-16.
).
According to the Köppen-Geiger climate classification (Alvares et al., 2013Alvares
CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek, G, 2013. Köppen’s
climate classification map for Brazil. Meteorol. Z. 22(6): 711–728. https://doi.org/10.1127/0941-2948/2013/0507
),
the area has a Cfb climate with temperature and average annual
precipitation of approximately 17℃ and 1,500 mm, respectively (Alvares et al., 2013Alvares
CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek, G, 2013. Köppen’s
climate classification map for Brazil. Meteorol. Z. 22(6): 711–728. https://doi.org/10.1127/0941-2948/2013/0507
).
This mesothermal humid subtropical climate has cool summers and winters
with frequent frosts. The altitude ranges between 893 m and 925 m above
sea level (Machado et al., 2008Machado
SA, Nascimento RGN, Augustynczik ALD, Silva LCRS, Figura MA, Pereira
EM, Téo SJ, 2008. Comportamento da relação hipsométrica de Araucaria angustifolia no capão da Engenharia Florestal da UFPR. Pesq. flor. bras. 56: 5-5.
).
Carbon and biomass dynamics
⌅Field data collection took place in the years 2006, 2009, 2012, 2015, 2018 and 2021 with identification of species existing at the site and the Circumference at Breast Height (CBH) measurement with a millimeter tape. Only individuals with CBH ≥ 31.4 cm were measured, totalling 144 species. Next, CBH measurements were converted into Diameter at Breast Height (DBH) and separated into DBH classes with an interval of 10 cm. The geographic position of each individual was collected using Global Positioning System receivers (GPS). Each year, approximately 12,800 individuals were measured, totalling about 77,000 individuals during the evaluation period.
The hypsometric model (Equation 1) developed by Sanquetta et al. (2001)Sanquetta
CR, Pizatto W, Netto SP, Filho AF, Eisfeld RL, 2001. Estrutura vertical
de um fragmento de floresta Ombrófila Mista no Centro-Sul do Paraná.
Floresta. 32(2): 267-276.
was then used to predict
tree total heights. This model was parameterized in a nearby forest of
similar phytophysiognomy and soil and climate characteristics as the
study area. Table 1 summarizes the coefficients used according to genus based on Sanquetta et al. (2001)Sanquetta
CR, Pizatto W, Netto SP, Filho AF, Eisfeld RL, 2001. Estrutura vertical
de um fragmento de floresta Ombrófila Mista no Centro-Sul do Paraná.
Floresta. 32(2): 267-276.
.
| Genus | β0 | β1 | β2 |
|---|---|---|---|
| Araucaria | -56.2432 | 17.5759 | 0.1127 |
| Others | -79.8530 | 20.0058 | 0.1135 |
Source: Sanquetta et al. (2001)Sanquetta
CR, Pizatto W, Netto SP, Filho AF, Eisfeld RL, 2001. Estrutura vertical
de um fragmento de floresta Ombrófila Mista no Centro-Sul do Paraná.
Floresta. 32(2): 267-276.
.
where: H: total height (m); βn: model coefficients; DBH: Diameter at Breast Height (cm).
Then, the aboveground biomass was determined based on five allometric equations, four of which were developed specifically for certain species and the fifth used for the remaining species (Table 2).
| Species | Equation |
|---|---|
| Araucaria angustifolia | AGB = -0.0270 *(DBH²* H)0.9671 |
| Pinus taeda | AGB = -5.6500 + 2.8385 * DBH - 0.2716 * DBH² + 0.0214 * DBH²* H |
| Annona rugulosa | AGB = -4.8639 + 0.3981 * DBH + 0.2625* DBH² |
| Casearia decandra | AGB = -4.8639 + 0.3981 * DBH + 0.2625* DBH² |
| Machaerium stipitatum | AGB = -4.8639 + 0.3981 * DBH + 0.2625* DBH² |
| Nectandra megapotamica | AGB = -4.8639 + 0.3981 * DBH + 0.2625* DBH² |
| Picramnia parvifolia | AGB = -4.8639 + 0.3981 * DBH + 0.2625* DBH² |
| Prunus brasiliensis | AGB = -4.8639 + 0.3981 * DBH + 0.2625* DBH² |
| Gymnanthes klotzschiana | AGB = 13.3380 -3.7640 * DBH + 0.5270 * DBH² |
| Lithraea brasiliensis | AGB = 13.3380 -3.7640 * DBH + 0.5270 * DBH² |
| Schinus terebinthifolia | AGB = 13.3380 -3.7640 * DBH + 0.5270 * DBH² |
| Xylosma pseudosalzmanii | AGB = 13.3380 -3.7640 * DBH + 0.5270 * DBH² |
| Others | AGB = -3.0250 * DBH + 0.4250 * DBH² + 0.0060 |
Source: Roik et al. (2020)Roik M, Machado SA, Figueiredo Filho A, Sanquetta CR, Ruiz ECZ, 2020. Aboveground Biomass and Organic Carbon of Native Araucaria angustifolia (Bertol.) Kuntze. Floram 27(3): e20180103. https://doi.org/10.1590/2179-8087.010318
; Schikowski et al. (2013)Schikowski
AB, Corte APD, Sanquetta CR, 2013. Modelagem do crescimento e de
biomassa individual de Pinus. Pesq. flor. bras. 33(75): 269-278. https://doi.org/10.4336/2013.pfb.33.75.503
; Veres et al. (2019)Veres
Q, Watzlawick L, Silva R, 2019. Estimativas de biomassa e carbono em
fragmento de floresta estacional semidecidual no oeste do Paraná. Biofix
Sci. J. 5(1): 23-31. https://doi.org/10.5380/biofix.v5i1.66001
and Zanette et al. (2017)Zanette
VH, Kurchaidt SM, Camargo LP, Watzlawick LF, Koehler HS, 2017. Ajuste
de modelos de regressão para a estimativa da biomassa aérea para seis
regiões do estado do Paraná. Enciclopédia Biosfera 14(26): 29-43. https://doi.org/10.18677/EnciBio_2017B3
.
In which: AGB: aboveground biomass (kg); DBH: Diameter at Breast Height (cm); H: total height (m).
The aboveground biomass carbon contents adopted in this study were derived from research conducted by Mognon et al. (2013)Mognon
F, Dallagnol FS, Sanquetta CR, Corte APD, Barreto TG, 2013. Uma década
de dinâmica da fixação de carbono na biomassa arbórea em Floresta
Ombrófila Mista no Sul do Paraná. Floresta 43(1): 153-164.
.
These authors determined the weighted average carbon contents for
groups of different species using data derived from an area with MOF in
the municipality of General Carneiro, State of Paraná, Brazil (Watzlawick et al., 2004Watzlawick
LF, Balbinot R, Sanquetta CR, Caldeira MVW (Ed). 2004. Teores de
carbono em espécies da Floresta Ombrófila Mista. Fixação de carbono:
atualidades, projetos e pesquisas. Curitiba, AM Impressos, 2004.
). Thus, three groups were classified according to their respective carbon contents (Mognon et al., 2013Mognon
F, Dallagnol FS, Sanquetta CR, Corte APD, Barreto TG, 2013. Uma década
de dinâmica da fixação de carbono na biomassa arbórea em Floresta
Ombrófila Mista no Sul do Paraná. Floresta 43(1): 153-164.
), being: i) Araucaria with 426 g.kg-1; ii) Canelas and individuals from the Lauraceae family with 407 g.kg-1; and iii) White wood (remaining tree species) with 411 g.kg-1.
Finally, the carbon stock contained in the aboveground biomass was
determined by multiplying the aboveground biomass (kg) by the average
carbon content of the species group (g.kg-1).
The model developed by Nogueira Júnior et al. (2014)Nogueira
Júnior LR, Engel VL, Parrotta JA, Melo ACG, Ré DS, 2014. Allometric
equations for estimating tree biomass in restored mixed-species Atlantic
Forest stands. Biota Neotrop. 14(2): e20130084. https://doi.org/10.1590/1676-06032013008413
in a forest restoration area belonging to MOF was adopted for underground biomass (Equation 2):
where: BS: dry underground biomass (kg); DBH: Diameter at Breast Height (cm).
The value of 390 g.kg-1 was adopted for the carbon content in the underground portion, a value obtained by Watzlawick (2003)Watzlawick
LF, 2003. Estimativa de biomassa e carbono em Floresta Ombrófila Mista e
plantações florestais a partir de dados de imagens do Satélite Ikonos
II. Doctoral thesis. Federal University of Paraná, Brazil
considering roots above 1 cm in diameter arranged in trenches measuring
1 m x 1 m and 0.50 m deep. Next, the underground biomass (Equation 2) was multiplied by the underground carbon content, obtaining the carbon stock contained in the root reservoir.
After calculating the carbon present above and below ground, the carbon dioxide equivalent (CO2-eq) was obtained using Equation 3:
In which: CO2eq: carbon dioxide (kg); C: Carbon stock above and below ground (kg).
The variation over time of the three variables of interest (biomass, carbon and CO2eq in t.ha-1) in the monitoring period was based on three phenomena (Hasenauer, 2000Hasenauer, H, 2000. Princípios para a modelagem de ecossistemas florestais. Ciência & Ambiente, 20: 53-69.
),
namely: i) Entry, meaning all trees that entered in a given evaluation
period and that maintained their growth in the following evaluation; ii)
Growth: living trees measured throughout the period; and iii)
Mortality: trunks which were dead at the measurement time whose biomass
was computed only in that year, and was not considered in subsequent
years.
It is possible to calculate the carbon dynamics during the years evaluated from this classification, subtracting the carbon stock between the years of analysis.
Dominance
⌅The absolute dominance (Equation 4) and relative dominance (Equation 5) of the species present in the analyzed fragment were calculated.
where: Da: absolute dominance; BA: basal area of the species (m²); A: area (ha); Dr: relative dominance; BA: basal area of the species (m²); BAt: basal area total (m²);
Results
⌅General dynamics
⌅In general, a total of 92 genera and 144 species were identified during the analyzed period. There was a variation in the number of trees per hectare due to the dynamics of the forest, with differences between the entry and mortality of individuals. The highest values for diameter, height and average basal area were recorded in 2015, while the highest biomass and carbon stock were observed in 2021 (Table 3).
| Variables | 2006 | 2009 | 2012 | 2015 | 2018 | 2021 |
|---|---|---|---|---|---|---|
| Number of trees (N°.ha-1) | 646 | 671 | 684 | 682 | 639 | 641 |
| Mean diameter (cm) | 13.86 | 14.63 | 15.24 | 15.61 | 15.00 | 15.32 |
| Mean height (m) | 12.84 | 13.42 | 13.79 | 13.89 | 13.14 | 13.26 |
| Mean basal area (m².ha-1) | 21.78 | 23.41 | 25.03 | 26.45 | 26.29 | 27.30 |
| Biomass (t.ha-1) | 133.39 | 144.29 | 155.55 | 165.96 | 166.37 | 173.81 |
| Carbon (t.ha-1) | 54.71 | 59.18 | 63.80 | 68.08 | 68.28 | 71.33 |
| Carbon dioxide equivalent (CO2-eq) (t.ha-1) | 200.59 | 217.00 | 233.94 | 249.62 | 250.35 | 261.53 |
Due to the entry of individuals, there was an increase of 64 trees, in total, during the evaluation period (2006-2021), resulting in an increase of around 30% in the carbon stored in this MOF fragment (Table 3). There was a 30% growth in biomass during the period from 2006 to 2021, which denotes a removal of 30% more CO2 compared to the year 2021 (Table 4).
| Dynamic | 2009-2012 | 2012-2015 | 2015-2018 | 2015-2018 | 2006-2021 |
|---|---|---|---|---|---|
| Biomass (t.ha -1 ) | |||||
| Growth | 11.77 | 11.10 | 0.65 | 7.55 | 39.07 |
| Entry | 0.42 | 0.69 | 0.25 | 0.11 | 17.71 |
| Mortality | 5.27 | -0.25 | 5.76 | -4.98 | -28.01 |
| Balance | 6.08 | 10.66 | -5.35 | 12.42 | 49.37 |
| Carbon (t.ha-1) | |||||
| Growth | 4.83 | 4.56 | 0.29 | 3.10 | 16.07 |
| Entry | 0.17 | 0.28 | 0.09 | 0.05 | 5.59 |
| Mortality | 2.16 | -0.11 | 2.35 | -1.63 | -11.41 |
| Balance | 2.50 | 4.39 | -2.15 | 4.68 | 21.89 |
| Carbon dioxide equivalent (CO2eq) (t.ha-1) | |||||
| Growth | 17.70 | 16.27 | 1.08 | 11.36 | 58.94 |
| Entry | 0.62 | 1.04 | 0.35 | 0.17 | 20.49 |
| Mortality | 7.89 | -0.38 | 8.60 | -7.43 | -41.85 |
| Balance | 9.19 | 16.06 | -7.87 | 18.61 | 80.30 |
The variation of the three analysed variables was positive during the evaluated period, except between 2015 and 2018. The highest mortality (896 trees) and the lowest entry (382 trees) were recorded in this interval which had a negative impact on the biomass stock and carbon, registering lower growth than other periods and lower CO2eq removals (0.24 t.ha-1.year-1). Even so, the forest had great potential for biomass production and carbon removal (Figure 2).
The largest amount of CO2 removed from the atmosphere was observed in 2012 (Figure 3), while 2018 showed a marked reduction in carbon fixation. During the analysis period, 64.23 t.ha-1 of carbon were removed, which corresponds to an average of 4.06 t.ha-1.year-1. However, there was a high mortality of individuals between 2015 and 2018, resulting in the fixation of only 0.24 t.ha-1 of carbon in that specific period.
Dynamics by genera
⌅There were 13 predominant genera identified during the evaluation period, namely: Araucaria, Ocotea, Luehea, Casearia, Cedrela, Schinus, Myrcia, Symplocos, Moquiniastrum, Nectandra, Jacaranda, Matayba and Clethra. Among these predominant genera, 4 exhibited the highest carbon stocks: Araucaria, Ocotea, Luehea and Casearia, with 104.15 t.ha-1 (27.03%), 41.20 t.ha-1 (10.69%), 33.85 t.ha-1 (8.78%) and 29.98 t.ha-1 (7.52%), respectively. From 2012 onwards, there were significant entries of individuals of the Gymmanthes, Clethra and Citronella genera in the evaluations (Figure 4).
Dynamics by diameter class
⌅Regarding diameter classes, individuals of Araucaria with diameters between 60 and 70 cm exhibited higher carbon fixation (34.62 t.ha-1, corresponding to 33.28%) (Figure 5). For other genera, the classes >20 cm and 20 to 30 cm were responsible for storing carbon in greater quantities (173.21 t.ha-1), representing 61.04% (Figure 6). For the genus Araucaria, the carbon stock by diameter class during the monitoring period is illustrated in Figure 7. Figure 8, in turn, shows the carbon variation in the other genera, considering the diameter classes and the evaluation years.
Dominance
⌅The dominance analysis (Table 5) revealed that Araucaria angustifolia (Bertol.) Kuntze exhibited the highest dominance percentage (24%). In second place was Luehea divaricata Martius et Zucarini with 8%, followed by Casearia sylvestris Swartz with 7% and Ocotea puberula (Rich.) Nees with 6%. Cedrela fissilis Vellozo and Schinus terebinthifolia Raddi both had an equal proportion (4%), while Myrcia hatschbachii D. Legrand and Jacaranda puberula Cham. each had 3%. Other species from the 13 predominant genera had a dominance percentage of 2%. The remaining species had lower representation, with 1% and 0%.
| Species | DoA | DoR | Species | DoA | DoR |
|---|---|---|---|---|---|
| Aegiphila brachiata | 0.08 | 0% | Lonchocarpus nitidus | 0.02 | 0% |
| Allophylus edulis | 2.18 | 1% | Luehea divaricata | 12.42 | 8% |
| Allophylus semidentatus | 0.38 | 0% | Machaerium brasiliense | 0.49 | 0% |
| Annona rugulosa | 0.66 | 0% | Machaerium paraguariense | 1.04 | 1% |
| Araucaria angustifolia | 36.46 | 24% | Machaerium stipitatum | 0.83 | 1% |
| Baccharis dracunculifolia | 0.00 | 0% | Magnolia champaca | 0.02 | 0% |
| Banara parvifolia | 0.09 | 0% | Matayba elaeagnoides | 2.96 | 2% |
| Banara tomentosa | 0.11 | 0% | Maytenus alaternoides | 0.08 | 0% |
| Blepharocalyx salicifolius | 0.74 | 0% | Maytenus aquifolia | 0.00 | 0% |
| Bougainvillea glabra | 0.05 | 0% | Maytenus evonymoides | 0.05 | 0% |
| Calyptranthes concinna | 0.07 | 0% | Mimosa scabrella | 0.04 | 0% |
| Campomanesia guaviroba | 0.61 | 0% | Mollinedia clavigera | 0.03 | 0% |
| Campomanesia guazumifolia | 0.07 | 0% | Monteverdia aquifolia | 0.01 | 0% |
| Campomanesia xanthocarpa | 0.68 | 0% | Monteverdia evonymoides | 0.15 | 0% |
| Casearia decandra | 0.18 | 0% | Moquiniastrum polymorphum | 3.54 | 2% |
| Casearia lasiophylla | 0.07 | 0% | Myrceugenia acutiflora | 0.01 | 0% |
| Casearia obliqua | 3.01 | 2% | Myrceugenia miersiana | 0.04 | 0% |
| Casearia sylvestris | 10.26 | 7% | Myrceugenia myrcioides | 0.00 | 0% |
| Cedrela fissilis | 5.65 | 4% | Myrcia hatschbachii | 3.85 | 3% |
| Ceiba speciosa | 0.14 | 0% | Myrcia laruotteana | 0.01 | 0% |
| Celtis iguanaea | 0.04 | 0% | Myrcia palustris | 0.09 | 0% |
| Cestrum | 0.00 | 0% | Myrcia splendens | 1.27 | 1% |
| Chionanthus filiformis | 0.01 | 0% | Myrsine coriacea | 0.33 | 0% |
| Cinnamodendron dinisii | 2.11 | 1% | Myrsine gardneriana | 0.43 | 0% |
| Cinnamomum amoenum | 0.03 | 0% | Myrsine umbellata | 0.01 | 0% |
| Cinnamomum glaziovii | 0.07 | 0% | Myrtaceae | 0.05 | 0% |
| Cinnamomum sellowianum | 0.02 | 0% | Nectandra lanceolata | 3.12 | 2% |
| Cinnamomum vesiculosum | 0.13 | 0% | Nectandra megapotamica | 0.08 | 0% |
| Citharexylum solanaceum | 0.03 | 0% | NI | 0.19 | 0% |
| Citronella gongonha | 0.84 | 1% | Ocotea bicolor | 2.45 | 2% |
| Citronella paniculata | 0.60 | 0% | Ocotea diospyrifolia | 0.12 | 0% |
| Clethra scabra | 2.85 | 2% | Ocotea nutans | 3.33 | 2% |
| Coutarea hexandra | 0.94 | 1% | Ocotea puberula | 9.53 | 6% |
| Croton celtidifolius | 0.03 | 0% | Ocotea pulchella | 0.04 | 0% |
| Cryptocarya aschersoniana | 0.08 | 0% | Ocotea sp | 0.00 | 0% |
| Cupania vernalis | 1.20 | 1% | Oreopanax fulvus | 0.45 | 0% |
| Cybistax antisyphilitica | 0.04 | 0% | Picramnia excelsa | 0.01 | 0% |
| Dahlstedtia floribunda | 0.79 | 1% | Picramnia parvifolia | 0.02 | 0% |
| Dalbergia | 0.00 | 0% | Picrasma crenata | 0.23 | 0% |
| Dalbergia brasiliensis | 0.42 | 0% | Pimenta pseudocaryophyllus | 0.06 | 0% |
| Dalbergia frutescens | 0.00 | 0% | Pinus taeda | 0.36 | 0% |
| Dasyphyllum tomentosum | 0.54 | 0% | Piptocarpha angustifolia | 0.02 | 0% |
| Drimys brasiliensis | 0.10 | 0% | Piptocarpha axillaris | 0.96 | 1% |
| Duranta vestita | 0.12 | 0% | Pittosporum undulatum | 0.01 | 0% |
| Dyospiros kaki | 0.00 | 0% | Podocarpus lambertii | 0.01 | 0% |
| Eriobotrya japonica | 0.03 | 0% | Prunus brasiliensis | 0.90 | 1% |
| Erythrina falcata | 0.50 | 0% | Psidium cattleyanum | 0.00 | 0% |
| Erythrina speciosa | 0.00 | 0% | Randia ferox | 0.01 | 0% |
| Erythroxylum deciduum | 0.23 | 0% | Roupala montana | 0.68 | 0% |
| Escallonia bifida | 0.06 | 0% | Sapium glandulosum | 0.38 | 0% |
| Escallonia montevidensis | 0.04 | 0% | Schinus terebinthifolia | 5.32 | 4% |
| Eugenia chlorophylla | 0.89 | 1% | Scutia buxifolia | 0.24 | 0% |
| Eugenia involucrata | 0.03 | 0% | Sebastiania brasiliensis | 0.01 | 0% |
| Eugenia sp, | 0.00 | 0% | Senna macranthera | 0.00 | 0% |
| Eugenia uniflora | 0.61 | 0% | Senna multijuga | 0.03 | 0% |
| Guettarda uruguensis | 0.00 | 0% | Sloanea lasiocoma | 1.19 | 1% |
| Gymnanthes klotzschiana | 2.48 | 2% | Sloanea monosperma | 0.40 | 0% |
| Handroanthus albus | 0.02 | 0% | Solanum | 0.01 | 0% |
| Hovenia dulcis | 0.55 | 0% | Solanum pseudoquina | 0.45 | 0% |
| Ilex brevicuspis | 0.15 | 0% | Solanum sanctaecatharinae | 0.51 | 0% |
| Ilex dumosa | 0.08 | 0% | Solanum swartzianum | 0.00 | 0% |
| Ilex paraguariensis | 0.25 | 0% | Styrax leprosus | 0.69 | 0% |
| Ilex theezans | 0.01 | 0% | Syagrus romanzoffiana | 0.06 | 0% |
| Inga marginata | 0.07 | 0% | Symplocos tenuifolia | 0.03 | 0% |
| Inga sessilis | 0.02 | 0% | Symplocos tetrandra | 3.74 | 2% |
| Jacaranda puberula | 3.84 | 3% | Symplocos uniflora | 0.24 | 0% |
| Lafoensia pacari | 0.12 | 0% | Vitex megapotamica | 0.07 | 0% |
| Lamanonia ternata | 0.91 | 1% | Xylosma ciliatifolia | 0.02 | 0% |
| Lantana brasiliensis | 0.03 | 0% | Xylosma pseudosalzmanii | 0.16 | 0% |
| Laplacea fruticosa | 0.01 | 0% | Zanthoxylum kleinii | 0.74 | 0% |
| Ligustrum lucidum | 0.75 | 0% | Zanthoxylum petiolare | 0.30 | 0% |
| Lithraea brasiliensis | 0.79 | 1% | Zanthoxylum rhoifolium | 0.24 | 0% |
In which: DoA: absolute dominance; DoR: relative dominance.
Discussion
⌅According to the results presented, there is vast potential for carbon fixation (64.23 t.ha-1)
in the biomass of forest species existing in the native fragment
located in the MOF area, since only the biomass contained in the
aboveground compartment (and consequently the carbon) was considered,
excluding biomass from other compartments such as necromass or soil.
This fact is related to species diversity, a hypothesis supported by the
identification of 144 species and 92 genera during the 15 years
collection (2006 to 2021). This factor significantly contributed to the
increase in carbon stock due to the optimized photosynthesis of the
different species present at the site (Catovsky et al., 2002Catovsky
S, Bradford MA, Hector A, 2002. Biodiversity and ecosystem
productivity: Implications for carbon storage. Oikos 97(3): 443–448. https://doi.org/10.1034/j.1600-0706.2002.970315.x
).
Furthermore, the area presents a dominance of early and late secondary species belonging to the following genera: Allophylus, Araucaria, Casearia, Citronella, Clethra, Gymmanthes, Jacaranda, Luehea, Moquiniastrum, Myrcia, Ocotea, Schinus and Symplocos.
The species succession stage ha a direct interaction with carbon
storage, as the initial and medium stages, which are in the growth
phase, accumulate greater amounts of biomass and carbon (approximately
42.11 t.ha-1 of biomass) (Veres et al., 2019Veres
Q, Watzlawick L, Silva R, 2019. Estimativas de biomassa e carbono em
fragmento de floresta estacional semidecidual no oeste do Paraná. Biofix
Sci. J. 5(1): 23-31. https://doi.org/10.5380/biofix.v5i1.66001
).
Over
the 15-year monitoring period, there was a 30% increase in the biomass
of the analyzed fragment, suggesting that species richness played an
important role, particularly due to the different growth rates of each
species and the consequent accumulation of biomass. Species diversity
was also an important factor in the research by Pontes et al. (2019)Pontes
DMF, Engel VL, Parrotta JA, 2019. Forest Structure, Wood Standing
Stock, and Tree Biomass in Different Restoration Systems in the
Brazilian Atlantic Forest. Forests 10(7): 588. https://doi.org/10.3390/f10070588
and Capellesso et al. (2021)Capellesso
ES, Cequinel A, Marques R, Sausen TL, Bayer C, Marques MCM, 2021.
Co-benefits in biodiversity conservation and carbon stock during forest
regeneration in a preserved tropical landscape. For. Ecol. Manag. 492:
119222. https://doi.org/10.1016/j.foreco.2021.119222
.
These authors highlighted that the structural characteristics of trees,
the density of the forest, the forest's resilience and stability, as
well as different successional stages and life cycles, impacted the
composition of the plant community and the capacity for carbon
absorption.
This pattern was also evident in an area located in MOF in General Carneiro, Paraná, Brazil, with an increase of 0.75 t.ha-1.year-1 for biomass and 0.31 t.ha-1.year-1 for biomass carbon (Mognon et al., 2013Mognon
F, Dallagnol FS, Sanquetta CR, Corte APD, Barreto TG, 2013. Uma década
de dinâmica da fixação de carbono na biomassa arbórea em Floresta
Ombrófila Mista no Sul do Paraná. Floresta 43(1): 153-164.
).
The increase of approximately 30% in biomass also occurred in an area
with different successional regeneration stages in the Northwest Region
of the State of Rio Grande do Sul, Brazil (Erthal et al., 2023Erthal
DA, Balbinot R, Breunig FM, Rosa PAD, 2023. Dinâmica espacial do
estoque de biomassa e carbono em remanescentes florestais no Rio Grande
do Sul – Brasil. Biofix Sci. J. 8(1): 01-09. https://doi.org/10.5380/biofix.v8i1.86271
), reaffirming the carbon fixation potential of native forest fragments of the South of the country (Machado et al., 2019Machado
A, Martins APM, Sanquetta CR, Corte APD, Wojciechowski, J, Machado SA,
Santos R, Landim, IAM, 2019. Dinâmica do volume, biomassa e carbono na
mata atlântica por ferramenta de detecção de mudanças. Nativa 7(4):
437–444. https://doi.org/10.31413/nativa.v7i4.6935
).
Growth
and entry rates of individuals were higher than mortality rates during
the monitored period, except for 2015-2018. The entries directly
contributed to floristic enrichment and species diversity in the area,
and growth was influenced by the satisfactory climatic and environmental
conditions of the site, as evidenced by Batista et al. (2020)Batista
DB, Dácol FV, Dalla Corte AP, Martine A, Reis ARN, 2020. Aporte de
serapilheira e teor de carbono orgânico em um fragmento florestal
urbano. Nat. Conserv. 13(4): 22-30. https://doi.org/10.6008/CBPC2318-2881.2020.004.0003
.
Furthermore, extreme climatic and meteorological conditions, such as
prolonged droughts, storms, heatwaves, and frosts, can increase
mortality and reduce tree growth, as can changes in competition for
resources (water, nutrients, light, among others).
Entries over time significantly contributed to biomass accumulation, as according to Caron et al. (2015)Caron
BO, Eloy E, Souza VQ, Schmidt D, Balbinot R, Behling A, Monteiro GC,
2015. Quantificação da biomassa florestal em plantios de curta rotação
com diferentes espaçamentos. Comunicata Scientiae 6(1): 106-112.
,
the greater the density of individuals in an area, especially with
advanced successional stages, the greater the biomass production per
unit area.
The carbon removal potential in the study area during the 15 years of monitoring (2006 to 2021) was 64.23 t.ha-1, with an average of 4.06 t.ha-1.year-1, being similar to other studies conducted in similar regions, such as that by Souza et al. (2023)Souza
CR, Mariano RF, Maia VA, Pompeu PV, Santos RM, Fontes MAL, 2023. Carbon
stock and uptake in the high-elevation tropical montane forests of the
threatened Atlantic Forest hotspot: Ecosystem function and effects of
elevation variation. Sci Total Environ 882: 163503. https://doi.org/10.1016/j.scitotenv.2023.163503
,
conducted in a hotspot of the Atlantic Forest, in the Mantiqueira
Range, Southeastern region of Brazil. In this study, the authors
assessed the carbon stock and uptake patterns of the sampled forests
along a high-altitude gradient (1500–2100 m a.s.l.) and monitored in two
inventories (2011 and 2016). The results indicated variations in carbon
accumulation over the period, with a carbon gain of 3.82–5.14
t.ha⁻¹.year⁻¹.
The study conducted by Reis et al. (2019)Reis
AN, Biondi D, Ivasko Junior S, Viezzer J, Maria, TRBM, Zamproni K,
2019. Estoques de carbono e dióxido de carbono equivalente em árvores de
rua de cidades brasileiras. Rev. Soc. Bras. Arborização Urbana 14(4):
36-35. https://doi.org/10.5380/revsbau.v14i4.68565
quantified the carbon stock of 600 trees of five species (Tipuana tipu (Benth.) Kuntze, Acer negundo L., Ficus benjamina L., Terminalia catappa Linn, and Licania tomentosa (Benth.) Fritsch planted in three Brazilian cities (Curitiba, Paraná;
Itanhaém, São Paulo; and Bonito, Mato Grosso do Sul, Brazil) using the
allometric equations of Brown (1997)Brown S (ed),1997. Estimating biomass and biomass change of tropical forests: a primer. Rome, FAO. 57 pp.
and Brianezi et al. (2013)Brianezi
D, Jacovine LAG, Soares CPB, Castro RVO, Basso VM, 2013. Equações
alométricas para estimativa de carbono em árvores de uma área urbana em
Viçosa-MG. Árvore 37(6): 1073–1081. https://doi.org/10.1590/S0100-67622013000600009
. According to the Brown (1997)Brown S (ed),1997. Estimating biomass and biomass change of tropical forests: a primer. Rome, FAO. 57 pp.
equation, the species with the highest occurrence in Curitiba, Paraná (A. negundo. and T. tipu) had, respectively, 20.61 tons of carbon and 75.64 tCO₂eq, and 95.17 tons of carbon and 349.27 tCO₂eq. Using the Brianezi et al. (2013)Brianezi
D, Jacovine LAG, Soares CPB, Castro RVO, Basso VM, 2013. Equações
alométricas para estimativa de carbono em árvores de uma área urbana em
Viçosa-MG. Árvore 37(6): 1073–1081. https://doi.org/10.1590/S0100-67622013000600009
equation, 21.58 tons of carbon and 79.21 tCO₂eq were found for A. negundo, and 88.51 tons of carbon and 324.83 tCO₂eq for T.a tipu.
According to the obtained results, individuals of A. angustifolia with a diameter greater than 60 cm exhibited higher carbon allocation. This is due to the characteristics of the analyzed fragment, which shows a higher occurrence of individuals of this species compared to trees of other species with smaller diameters and in earlier stages.
This behavior was also observed by Lipinski et al. (2017)Lipinski
ET, Corte APD, Sanquetta CR, Rodrigues A, Mognon F, Behling A, 2017.
Tree biomass and carbon dynamics between 1995-2012 in a Mountain
Araucaria Forest. Floresta 47(2): 197–206. https://doi.org/10.5380/rf.v47i2.40024
in São João do Triunfo, State of Paraná, Brazil, in the analysis of the
temporal and spatial dynamics of biomass and carbon between 1995 and
2012. Araucaria angustifolia was predominant throughout the period, with biomass stocks of 102.5 t.ha-1 in 1995; 126.8 t.ha-1 in 2012; 43 t.ha-1 of carbon in 1995; and 54 t.ha-1 in 2012, revealing an increase in 1.17 t.ha-1.year-1.
It is important to highlight those factors such as tree density, size
of individuals, species composition, and canopy architecture (denser and
symmetrically shaped canopies), as pointed out by Brianezi et al. (2013)Brianezi
D, Jacovine LAG, Soares CPB, Castro RVO, Basso VM, 2013. Equações
alométricas para estimativa de carbono em árvores de uma área urbana em
Viçosa-MG. Árvore 37(6): 1073–1081. https://doi.org/10.1590/S0100-67622013000600009
and Nowak et al. (2013)Nowak
DJ, Greenfield EJ, Hoehn RE, Lapoint E, 2013. Carbon storage and
sequestration by trees in urban and community areas of the United
States. Environ Pollut 178: 229–236. https://doi.org/10.1016/j.envpol.2013.03.019
, can influence the carbon absorption capacity of forests.
In
addition to the factors mentioned, it is important to highlight the
differences between urban forests and natural forests, which can
influence the dominance of A. angustifolia. In urban
environments, forests are susceptible to anthropogenic actions and land
management practices, which can alter forest structure and species
diversity, favouring the establishment of species with greater tolerance
to urban stresses, while native or dominant species in natural forests
may be less prevalent or replaced by other species more adapted to the
urban environment (Olgun et al., 2024Olgun
R., Cheng C, Coseo P, 2024. Desert urban ecology: urban forest,
climate, and ecosystem services. Environ Dev Sustain 1-21. https://doi.org/10.1007/s10668-024-05751-7
). The species A. angustifolia,
native to the Atlantic Forest in southern Brazil, may not exhibit the
same dominance in urban forests as it does in natural forests because,
in urban forests, Araucaria is subject to competition with exotic
species and may have a heterogeneous distribution (Olgun et al., 2024Olgun
R., Cheng C, Coseo P, 2024. Desert urban ecology: urban forest,
climate, and ecosystem services. Environ Dev Sustain 1-21. https://doi.org/10.1007/s10668-024-05751-7
). Moreover, A. angustifolia is a large, long-lived species that contributes significantly to carbon
storage, and over time, due to natural forest dynamics, it may be
replaced by other species with different growth characteristics and life
cycles, potentially altering the carbon storage potential of the
forest. Thus, the diameter at breast height distribution may suggest the
presence of many small trees and few large trees, reflecting a
successional stage where carbon stocks might be lower due to the reduced
biomass of large trees (Chazdon, 2008Chazdon
RL, 2008. Beyond deforestation: Restoring forests and ecosystem
services on degraded lands. Science, 320(5882): 1458-1460. https://doi.org/10.1126/science.1155365
).
Furthermore, there is a gap related to the development and adjustment
of allometric equations for urban forests to estimate the variables
analyzed in this study. This occurs due to the impossibility of directly
determining biomass, which leads to the use of adjusted equations
developed for established native forests, which can cause
overestimations in trees located on urban roads.
The evaluated remnant located in an area of MOF in the Atlantic Forest biome revealed that an average of 4.06 t.ha-1.year-1 of carbon were removed, with Curitiba emitting 1.85 tCO2eq per inhabitant, according to the Inventory of Greenhouse Gas Emissions in the city of Curitiba, base year 2016 (Curitiba, 2019Curitiba. 2019. Greenhouse Gas Emission Inventory for the City of Curitiba. Base year 2016. https://mid.curitiba.pr.gov.br/2019/00284780.pdf. [24 April 2024].
).
Therefore, it is noted that the evaluated fragment positively
contributes to combating climate change, being capable of removing twice
the amount of carbon emitted.
Finally, it is recommended to
investigate the role of urban forests in the face of climate change,
especially in reducing GHG emissions and obtaining information about the
carbon stock contained in the biomass of these areas. Furthermore, it
is essential to assess the conservation status of forest fragments
located in urban areas to conserve existing biodiversity, and if
necessary, to restore the site (Batista et al., 2020Batista
DB, Dácol FV, Dalla Corte AP, Martine A, Reis ARN, 2020. Aporte de
serapilheira e teor de carbono orgânico em um fragmento florestal
urbano. Nat. Conserv. 13(4): 22-30. https://doi.org/10.6008/CBPC2318-2881.2020.004.0003
).
Conclusion
⌅Based on the assessment of the potential for removing CO2 from the atmosphere by a native urban forest fragment located in the Atlantic Forest biome between 2006 and 2021, the following conclusions can be drawn regarding i) overall dynamics, i.e., the entire assessment period (2006-2021); ii) dynamics by genus (considering that there are different species in different successional stages) and by diameter classes (to illustrate differences in carbon accumulation).
General: the 92 genera and 144 species identified had a positive biomass, carbon and equivalent carbon dioxide balance during the evaluation period. The fragment removed an average of 4.06 t.ha-1.year-1, and produced 156.56 t.ha-1 of biomass, accumulating 64.23 t.ha-1 of carbon and promoting the removal of 235.51 t.ha-1 of CO2eq from the atmosphere, acting to reduce greenhouse gas emissions.
Genus: The 13 predominant genera during the evaluation period were responsible for storing 301.20 t.ha⁻¹ of carbon, or 78.16%, highlighting the largest contribution to carbon sequestration by the following genera: Araucaria, Ocotea, Luehea, and Casearia, with 104.15 t.ha⁻¹ (27.03%), 41.20 t.ha⁻¹ (10.69%), 33.85 t.ha⁻¹ (8.78%), and 29.98 t.ha⁻¹ (7.52%), respectively.
Diametric class: Araucaria individuals with diameters ranging between 60 and 70 cm showed higher carbon fixation (34.62 t.ha-1, equivalent to 33.28%). For other genera, the diameter classes greater than 20 cm and from 20 to 30 cm were responsible for the highest carbon storage (173.21 t.ha-1), representing 61.04%.
Competing interests
⌅This manuscript has not been published or presented elsewhere in part or in entirety, and is not under consideration by another journal. All study participants provided informed consent. All the authors have approved the manuscript and agree with submission to your esteemed journal. There are no conflicts of interest to declare.
Authors’ contributions
⌅Carla T. Pertille: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft. Ernandes da Cunha-Neto: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft. Carlos R. Sanquetta: Conceptualization, Project administration, Resources, Software, Supervision, Validation, Writing – review & editing. Alexandre Behling: Conceptualization, Visualization, Writing – review & editing. Ana P. Dalla-Corte: Conceptualization, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing.
Funding
⌅The authors received no specific funding for this work.