Strong impacts of lianas on tree allometry lead to overestimation of tropical forest carbon stocks and sink

doi:10.21203/rs.3.rs-2094059/v1

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Strong impacts of lianas on tree allometry lead to overestimation of tropical forest carbon stocks and sink

https://doi.org/10.21203/rs.3.rs-2094059/v1

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Lianas are quintessential components of tropical forests competing strongly with trees for resources. Yet, their role in the structure and functioning of forests is rarely studied. Here, we investigate the impact of lianas on the carbon stocks and sink potential of an intact moist tropical forest in Panama using 3D terrestrial laser scanning. We find that liana-infested trees are significantly shorter with smaller crown areas, thereby resulting in a significant liana-induced reduction in stand-level carbon stocks (5.3%) and coarse woody productivity (24.5%). The widely used pantropical allometric model overestimates the carbon stocks by 10% and underestimates the liana impact on woody productivity by 1.5% at the study site with current infestation level. Increasing liana abundance across Neotropics will further worsen this impact. We show that by ignoring liana impact in carbon estimation, we are currently overestimating the potential of nature-based climate solutions to lock up atmospheric carbon.

Intact tropical forests are a major carbon sink (approximately 1.2 PgC yr^− 1), capturing a substantial proportion of global annual anthropogenic CO₂ emissions [1]. However, there is mounting evidence that the tropical forest carbon sink has been on the decline in the past decade, with such a decline more accelerated in South America than in Africa [1, 2]. Whereas the exact causes of such accelerated decline in the South American forests are not yet clear, some of the hypothesized mechanisms include land use change [2] and climate change induced increased warming [3], more frequent and severe drought events [4] and increased atmospheric CO₂ [5]; all of which result in increased tree turnover rates and shorter carbon residence times. One understudied response of forests to such climate change events is the changing tropical forest plant community composition [5], the most notable of which is increasing liana abundance [6–8].

Lianas are woody climbing plants that use trees as structural support for ascending to the canopy. They strongly compete with trees for both aboveground and belowground resources. Lianas have always been quintessential components of tropical forests. Recent studies highlighting their increasing abundance relative to trees, especially in neotropical forests, have drawn closer attention to these lifeforms [6, 8]. Increasing liana abundance is a cause for concern as lianas have been unequivocally linked to reduced tree growth and survival [9]. Yet, the role of lianas on the carbon stock and carbon accumulation potential of tropical forest is largely underexplored.

Studies documenting the impact of lianas on forest carbon stocks and sink potential have focussed primarily on tree diameter growth and mortality [10]. However, diameter growth and mortality are not the only parameters influencing the carbon dynamics of forest. Tree height and crown structure are critical parameters to estimate aboveground carbon stocks and fluxes of tropical forests [11, 12]. Furthermore, the importance of tree crown damage in the carbon dynamics of tropical forest cannot be overstated [13]. Controlled laboratory experiments on saplings have shown strong evidence for the negative effects of lianas on tree height [30] and crown form [31]. However, most current allometric models relate the aboveground biomass (AGB) only to the diameter and wood specific gravity (WSG) of trees [14]. The representativeness of the destructively harvested trees based on which the most commonly used pantropical allometric models were built is still debated [15]. These allometric models are used to calibrate and validate the biomass estimated from airborne and spaceborne remote sensing platforms for upscaling [16]. By neglecting the negative effects of lianas on tree height and crown structure, current allometric models probably overestimate the tropical forest carbon stocks, with the magnitude of impact varying with liana infestation levels. Tree crowns mediate the interaction of trees with the atmosphere and determine the photosynthesis capacity of trees, thereby playing a major role in their carbon accumulation potential [13, 17]. Therefore, carbon sink decline might be faster than predicted by the current allometric models if the negative effect of liana on the whole tree structure, especially the crown, is not taken into account. Here, we aim to quantify the impact of lianas on carbon stocks and fluxes in an intact tropical forest by taking the impact of lianas on the whole tree structure into account.

To assess the contribution of lianas to the reduced carbon stocks and accelerated decline in the carbon sink potential of an intact tropical forest, we used an unprecedented combination of high-resolution 3D terrestrial laser scanning (TLS) data, coincident field data on trees (diameter and species) and liana infestation levels of trees [18]. We conducted this study on the 50 ha forest dynamic plot on Barro Colorado Island (BCI), Panama (Supplementary section S1 for study site description). We quantified the impact of lianas on individual tree height, crown structure and biomass from the 3D TLS data [19]. We then scaled the individual tree-level observations to site-level to quantify the liana-induced carbon stock and accumulation reduction.

Liana impact on individual tree structure and allometry

Liana-infested trees were shorter and had smaller crowns (Figs. 1,2,3). In the terrestrial laser scanning dataset, trees with moderate (1–50%) and heavy (> 50%) liana infestation were on average 5% (95% confidence interval: 2.8–7.3%) and 9% (95% CI: 6.7–11.1%) shorter than liana-free trees of similar size, and had crowns that were 7% (95% CI: -7.25-18%) and 43% (95% CI: 33–53%) smaller in area. The difference in tree height between liana-free and liana-infested trees was confirmed using two independent tree height datasets for 3000 + trees at the same site, one measured with ground-based laser rangefinders and the other with drone photogrammetry (see Fig. 2, details and years of data collection in Supplementary). The substantial negative impact of lianas on tree height and crown area has important implications for the estimation of tree biomass from allometric models. Trees with heavy liana infestation have 16.4% (6.8–25%) lower volume than liana-free trees of the same diameter (Fig. 3b), suggesting a corresponding reduction in aboveground carbon stocks. This has a significant impact on the estimated stand-level carbon stocks on BCI as 52% of trees have at least one liana in their crowns and 27% of trees are heavily infested by lianas (more than 50% of the crown covered by lianas).

Impact of lianas on stand-level carbon stocks

To scale up the impact of lianas on carbon stocks from individual tree-level to stand-level, we applied the allometric models derived from our TLS data to all 2278 large trees (≥ 40 cm DBH) from the most recent census (2015) of the 50 ha forest dynamics plot [20–22] (see Methods). TLS-derived allometry reveals that the current levels of liana infestation reduces the stand-level carbon stocks on BCI by an average 5.3% (+/- 3%). TLS-derived biomass estimates are unbiased and more accurate across a variety of forest ecosystems than allometry-derived estimates [19]. TLS data show that pantropical models including only DBH and wood density systematically overestimate tree biomass on BCI, even for liana-free trees, with more severe overestimation in liana-infested trees (Fig. 4). As a result, the pantropical allometric model overestimates the current carbon stocks on BCI by 10% (+/- 4%), with increasing liana abundance having the potential to push this number to as high as 16% (+/- 7%) (See Fig. 4).

Impact of lianas on stand-level carbon accumulation

In an experimental liana removal study, Van der Heijden et al. [10] showed that lianas reduced tree growth and increased tree mortality in regrowing secondary forest stands, and thereby reduced estimated stand-level carbon accumulation. Their stand-level estimates assumed biomass allometries were unaffected; accounting for the liana-induced changes in tree allometry we document here would further magnify this effect. Using the liana-specific tree diameter growth model developed from the diameter growth data on BCI (see Methods and Supplementary section S5) and a liana-specific TLS-derived allometric model for tree biomass, we quantified the impact of lianas on carbon accumulation potential of individual trees (≥ 40 cm DBH) on the 50 ha plot. Our results show that lianas reduce the coarse woody productivity on BCI by 24.5% (+/- 10.5%), which could go up by as much as 49% (+/- 21%) under increasing liana abundance (see Fig. 5). In an old-growth intact tropical forest like BCI, assuming lianas do not alter the tree biomass allometries results in an underestimation of current impact on coarse woody productivity by 1.5% and by up to 3% under increasing liana abundance. We can assume that in regrowing secondary forest like Gigante from the study of van der Heijden et. al. [10], the effect of this assumption of unaltered allometries would be far worse, because these forests grow faster and have net carbon sequestration rates upto 20 times higher than old-growth forests [23]. This leads to far worse impact on biomass accumulation due to liana infestation in regrowing forests compared to old-growth forests.

Overall, our study provides strong evidence for the current underestimation of liana-induced impact on an intact tropical forest carbon stocks and fluxes. Lianas are as ubiquitous as trees are in tropical forests constituting ~ 25% of woody stem density and diversity in many forests [24]. Though our study is focussed on a single intact tropical forest site, data from around 31 sites spread across Neotropics show that BCI represents an average forest in terms of liana density and supports less than average proportion of liana-infested trees [see Supplementary section S6], indicating the broader implications of our results. Moreover, lianas are increasing in abundance across Neotropics, likely exacerbated by anthropogenic climate change events [7]. BCI, for instance, has experienced a 30% increase in liana stem density in a decade (2007–2017) [8]. This increasing liana abundance also increases the percentage of trees hosting lianas, indicating the urgent need to account for liana impact in the carbon stock and sink estimates. In addition, there are already forests, where almost all of the trees host at least one liana in their crown. For instance, in the liana forests in Nouragues [25] and Bolivia [26] almost 90% of trees host lianas, similar to the regrowing forest on the Gigante Peninsula close to BCI.

Current allometric models (both pantropical and site-specific) for biomass estimation are likely established from destructively harvested trees of perfect forms that do not include liana-infested trees and/or trees with visibly damaged crowns [15]. Besides, destructively harvesting liana-infested trees is challenging as lianas tend to connect multiple trees in the canopy causing extensive damage [27]. While the current pantropical allometric models are not expected to estimate carbon stocks accurately at individual tree or site-level, multiple recently launched and upcoming spaceborne biomass missions rely on site-level biomass estimates for the calibration and validation of their biomass products [16, 28]. Our study shows that carbon stock estimates from sites with high liana abundance might have a significant systemic bias leading to the overestimation of global carbon stocks. Also, the impact of lianas on global carbon stock estimates is expected to get worse with expected increase in liana abundance. Carbon stock estimates are a crucial parameter in evaluating the effectiveness of policies surrounding nature-based climate change solutions. As a result, overestimating the carbon stocks will essentially undermine the potential of the nature-based climate solution. TLS provides an elegant way to establish new allometric models for estimating tree biomass non-destructively, thereby resulting in unbiased samples to build the allometric model.

Besides carbon stocks, one of the reasons for the declining carbon sink potential of our tropical forests is hypothesized to be the onset of certain counteracting factors suppressing the growth of trees and increasing tree mortality. While there may be several factors at play, our study reveals lianas to be a major factor affecting the carbon sink capacity of intact forests. A 24.5% reduction in coarse woody productivity on BCI with the current liana infestation level translates to a reduction of 0.41 Mg C ha^− 1 yr^− 1, even excluding liana induced tree mortality. This reduction is almost 1.2 times the current pantropical average net carbon uptake (0.34 Mg C ha^− 1 yr^− 1) [29]. Accounting for liana-induced higher tree mortality will further reduce the carbon accumulation, which cannot be compensated by the recruitment and liana growth only [10].

Current pantropical allometric models, while overestimating the current carbon stocks of liana-infested trees, underestimate the impact of lianas on coarse w. This underestimation of impact on biomass growth stems primarily from neglecting the liana impact on crown form. Crown damage from branch fall or branch breakage are frequently occurring disturbances in tropics and is one of the dominant factors related to tree mortality and carbon loss in tropics [13, 17]. While the contribution of lianas to overall carbon loss from crown damage is not quantified in this study, we show strong evidence for significant crown damage across all heavily infested trees. Reduction in the crown area of trees and shadowing of the tree leaves by lianas both result in reduced light interception for the infested trees thereby resulting in reduced photosynthesis and reduced individual carbon accumulation contributing substantially to declining forest carbon sink. As such, it is of top priority to include the liana impact on the whole tree structure while estimating carbon stocks and fluxes to fully understand the carbon dynamics of forest and how it will respond to changing liana abundance under changing climate.

Besides direct impacts of lianas on carbon stocks and accumulation, liana infestation can further affect the carbon accumulation indirectly by changing the species/structural composition of tropical forest. It is evident that some trees are better at tolerating lianas than others, suggesting a possible shift in the species composition or in the evolution of specific structural traits in the light of increasing liana abundance [30]. Any change in species/structural composition will strongly influence the carbon stocks and fluxes, with carbon storage potentially varying by 600% demonstrated by simulating different possible extinction scenarios on BCI [31]. Accelerated liana proliferation may alter the future of tropical forests both in terms of structure and function and push tropical forests towards becoming a net carbon source, faster than previously expected.

Data acquisition and processing

Liana infestation data acquisition

Between January and March 2019, we evaluated liana infestation status of all trees ≥ 20 cm DBH in 25 plots of 40 by 40 m each (covering a total area of four ha), distributed across the 50 ha plot on BCI (see supplementary section S2). We estimated the crown occupancy index (COI) for trees following the protocol in [18]. Liana infestation for a tree was initially scored on a five-point scale based on the fraction of the tree crown covered by lianas: 0 - no lianas, 1–1 to 25%, 2–26 to 50%, 3–51 to 75% and 4–76–100% of the crown infested. We then converted the five-point scale measurements to three-point scale, with 0 being no lianas and 2 being 51 to 100% of the tree’s crown covered by lianas, to compensate for the lack of sufficient data in class 2 and 3 of the original scale. These liana crown infestation measurements were made from the forest floor by Antonio Aguilar (a trained personnel) using binoculars. These 25 subplots included 593 living trees (≥ 20 cm DBH) belonging to 92 species. Of the 593 trees, 52% had at least one liana in their crown and 27% of the trees had a heavy liana infestation (more than 50% of the crown covered by lianas).

TLS data acquisition

We collected TLS data from the same 25 subplots of size 40 by 40 m between January and March 2019. For every subplot, TLS data was collected from 25 scan locations spaced 15 m apart covering a total of 60 by 60 m with the 40 by 40 m area of interest at the center. We randomly distributed retro-reflective targets in the field to co-register the data from all the scan locations to get a single high resolution 3D point cloud with minimal occlusion [32]. All the data were collected using a RIEGL VZ-400, a multiple return time-of-flight based scanner using a narrow infrared laser beam of wavelength 1550 nm and a beam divergence of 0.35 mrad. The registration of data from all scan positions for each plot was done using the RISCAN Pro software (version 2.5.3, RIEGL Laser Measurement Systems GmbH, Horn, Austria).

TLS data processing

We manually extracted all trees (≥ 40 cm DBH) from co-registered 3D TLS data using the CloudCompare software [33]. This resulted in a total of 182 trees from this four ha area. We also carefully manually removed the liana stems and leaves (whenever possible) from the liana-laden trees (point clouds of all 182 trees are given in Supplementary section S4). Once we had the trees extracted from the point cloud, we derived the tree height, crown area and tree volume (including both trunk and crown) for every tree from the point cloud. We estimated the crown area by fitting a convex hull to the 2D projection in the xy-plane of the tree point cloud. The convex hull was fitted using alpha shape [34]. The total wood volume of the tree was estimated by fitting Quantitative Structure Models (QSMs) using the recent version of the TreeQSM algorithm [35] after removing the leaves [36]. For buttressed trees, we estimated the volume of the trunk from the base up to the first branching point using the triangulation function implemented in the TreeQSM algorithm [37]. Aboveground biomass estimated from this algorithm has been successfully validated against ground-truth biomass data derived from multiple destructive harvesting experiments across different tropical forest sites [19]. Figures of tree point clouds and QSMs can be found in the Supplementary section S4.

2011 and 2015 height datasets from BCI

In addition to the TLS data collected in 2019 from BCI, we used two additional tree height datasets from BCI. One dataset was collected using a laser range finder using the sine or the tangent method in 2011 [38]. Another height dataset collected in 2015, was obtained from the high-resolution drone aerial photogrammetry (details can be found in [39]). The trees from the 2015 dataset were all in the canopy chosen implicitly based on their height resulting in a biased subset of trees whereas the trees from 2011 and 2019 were chosen based only on DBH. Both 2011 and 2015 height datasets include co-incident liana infestation information for 1117 and 1999 trees (≥ 20 cm DBH) respectively. Owing to the datasets coming from three different sources, we used these three datasets separately and developed three different models to test the impact of liana on tree height.

Liana-specific allometric models for tree height, crown area and volume

To assess the impact of lianas on 3D tree structure, we focused primarily on large trees (≥ 40 cm DBH) because these trees contribute roughly half of the aboveground live biomass (AGB) and play a disproportionate role in forest carbon cycling [40]. Further, trees in this size class have their crowns in the canopy more than 80% of the time [41], and it is canopy trees that are disproportionately affected by lianas, which tend to deploy their leaves at the top of the forest canopy [24].

We estimated the height (m), crown area (m²) and wood volume (m³) for every individual tree from the 3D point cloud (182 trees ≥ 40 cm DBH). We fit allometric models for height, crown area and wood volume of trees as a function of DBH and liana infestation class. Models that included DBH and liana infestation class consistently outperformed those including tree DBH only (Supplementary section S5).

We adopted a hierarchical Bayesian (HB) approach to analyze the allometric relationships of tree height across varying levels of liana infestation with tree DBH [42]. One of the most important advantages of the HB approach is that it can easily handle nonlinear models, allowing us to extend the analysis beyond those non-linear functions that can be transformed into linear regression by data transformation. We used the BRMS package of R for fitting the height and crown area allometric models [43].

For exploring the relationship between crown area, volume and tree size across different levels of liana infestation, we used a simple linear mixed-model approach using the ‘lmertest’ package in R [44].

Height allometric model

We used a generalized Michaelis–Menten (gMM) model for height-diameter relationship, which is nonlinear in the logarithmic scale and asymptotic (Eq. 1). This model best represents the height-diameter relationship for trees on BCI when compared to the more conventional power law model as it allows for the height to saturate with trunk diameter consistent with theory [42].

\(ln\left(H\right) = a+ \left(b * ln\right(DBH\left)\right) - ln(k + {DBH}^{b})\) ------------ (1)

where H (height) is in m and DBH is in cm. This allometric model has three parameters: \(a (asymptote\)), \(b \left(exponent\right)\) and \(k (half-saturation parameter)\). We investigated the impact of lianas on height by assessing the eight different model formulations: the default model with no effect of liana on model parameters and seven models with the effect of lianas on either one, two or all three model parameters. All eight model formulations included tree species as a random effect on \(a\), \(b\) and \(k\) to account for the species-specific differences in height. The best model was chosen as the one with the lowest value for the Watanabe information criterion (WAIC) [45]. All eight models for the three different height datasets, associated results and their WAIC are all given in the Supplementary section S5.

Crown area allometric model

We used the linear transformation of the power model for exploring the allometric relationship between the crown area and the tree DBH across different levels of liana infestation (Eq. 3).

\(ln\left(Crown area\right) =m * ln\left(DBH\right) + b\) ----------------------------------------------(3)

where the crown area is in m² and DBH is in cm. This linear allometric model has two parameters: \(m (slope\)) and\(b \left(intercept\right).\)

We had a total of five different model formulations: default model with no liana effect and four other models with liana infestation level as fixed effect. All five model formulations had tree species as a random effect (on intercept and/or slope) to account for the species-specific differences in crown structure. The model selection was based on the lowest Akaike Information Criterion (AIC) value [46]. The models and the associated results are presented in the Supplementary section S5.

Volume and AGB allometric model

The allometric relationship between tree volume and DBH along different levels of liana infestation was represented in a similar fashion as the crown area. We used the linear regression form of the power law function by log-transforming the volume and DBH data of trees (Eq. 5).

\(ln\left(Volume\right) =m * ln\left(DBH\right) + b\) ----------------------------------------------(5)

where Volume is in m³ and DBH is in cm. This linear allometric model has two parameters: \(m (slope\)) and\(b \left(intercept\right).\)

Here again we tested the default model with no liana effect against the model that included liana infestation level of tree as the fixed effect. All the model formulations had tree species as random effect (on intercept and/or slope) to account for the species-specific differences in volume. The model selection was based on the lowest AIC value. All the model formulations and the associated results can be found in the Supplementary section S5.

We estimated AGB from volume by multiplying the volume by species-specific wood densities available for BCI [47].

Upscaling carbon stock and carbon accumulation using bootstrapping approach

We scaled up the impact of lianas on carbon stock and carbon accumulation from individual tree-level to stand-level on BCI using the 2015 inventory data from the 50 ha plot [20, 21, 48]. We simulated the liana impact on stand-level carbon stock and accumulation by randomly assigning different proportions of trees to be infested with lianas, ranging from 10 to 100% in steps of 10 with 1000 iterations for each proportion. Within each of these iterations, we simulated the liana infested trees to have varying levels of liana infestation. For instance, within each of the 1000 iterations for 10% liana infestation level, we ran another 1000 iterations for varying proportions of trees with moderate and heavy liana infestation, ranging from 0% trees with heavy load and 100% trees with moderate load to 100% trees with heavy load and 0% trees with moderate load in steps of 10.

For biomass estimation, we compared the liana-specific biomass derived from the TLS-derived allometric model with the commonly used the DBH-based pantropical allometry when height is not available from [14]:

\(AGB = {exp}^{(-2.024 - 0.896 * E + 0.92 * ln(WSG) + 2.795 * ln(DBH) - 0.0461 *ln({DBH}^{2})}\) ----------(7)

where WSG (Wood Specific Gravity) is in g cm^− 3 and DBH is in cm. E stands for environmental stress and is estimated to be 0.05645985 for BCI using the raster layer and R script available at http://chave.ups-tlse.fr/pantropical_allometry.htm. For WSG, we used the species-specific WSG available from the dataset published in [47].

For coarse woody productivity estimation, we used the diameter growth model that accounted for liana infestation level of trees. We built this diameter growth model using the tree mortality, growth and liana infestation dataset on Barro Colorado Island from visser et. al. [49, 50]. We used a linear model (linking growth rate with tree diameter and liana infestation level) to quantify the liana impact on the diameter growth rate of trees in the BCI 50 ha plot(see Supplementary section S5 for results). We further quantified the effect of lianas-induced change in height and crown structure on annual biomass accumulation by applying the TLS-derived biomass allometric model to the liana-specific annual DBH growth model. We compared this effect to the effect of lianas on coarse woody productivity derived solely based on the impact on DBH using the equation in [7].

DATA AVAILABILITY

All the codes and the corresponding datasets used to build the models can be downloaded from the following link: https://github.com/sruthimoorthy/TLS-Liana_specific_allometry_models. All the tree point clouds and the corresponding QSMs can be downloaded from the following link: https://doi.org/10.5281/zenodo.6981485.

ACKNOWLEDGEMENTS

This project and SMKM were funded by the European Research Council Starting Grant 637643 (TREECLIMBERS). TLS data collection from the 50 ha Forest Dynamics plot of BCI, Panama was funded by the Forest Global Earth Observatory Research Grants Program of 2018. The authors would like to thank the Smithsonian Tropical Research Institute (STRI) for their support. FM was funded by the FWO as a junior/senior postdoc and is thankful to this organization for its financial support (FWO grant no. 1214720N/1214723N)

AUTHOR CONTRIBUTIONS

SMKM, FM, KC, SAS, MV, HML and HV conceptualized the study. SMKM and FM performed formal analysis, and contributed to methodology and visualization. SMKM, AA, NP and HML contributed to data curation and investigation. SMKM and HV contributed to funding acquisition. SMKM wrote the original draft. All authors wrote, reviewed and edited the manuscript.

Hubau W, Lewis SL, Phillips OL, Affum-Baffoe K, Beeckman H, Cuní-Sanchez A, et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature. 2020;579: 80–87.
Gatti LV, Basso LS, Miller JB, Gloor M, Gatti Domingues L, Cassol HLG, et al. Amazonia as a carbon source linked to deforestation and climate change. Nature. 2021;595: 388–393.
Sullivan MJP, Lewis SL, Affum-Baffoe K, Castilho C, Costa F, Sanchez AC, et al. Long-term thermal sensitivity of Earth’s tropical forests. Science. 2020;368: 869–874.
Yang Y, Saatchi SS, Xu L, Yu Y, Choi S, Phillips N, et al. Post-drought decline of the Amazon carbon sink. Nat Commun. 2018;9: 3172.
ForestPlots.net, Blundo C, Carilla J, Grau R, Malizia A, Malizia L, et al. Taking the pulse of Earth’s tropical forests using networks of highly distributed plots. Biol Conserv. 2021; 108849.
Phillips OL, Martínez RV, Arroyo L, Baker TR, Killeen T, Lewis SL, et al. Increasing dominance of large lianas in Amazonian forests. Nature. 2002;418: 770–774.
Schnitzer SA, Bongers F. Increasing liana abundance and biomass in tropical forests: emerging patterns and putative mechanisms. Ecol Lett. 2011;14: 397–406.
Schnitzer SA, DeFilippis DM, Visser M, Estrada-Villegas S, Rivera‐Camaña R, Bernal B, et al. Local canopy disturbance as an explanation for long‐term increases in liana abundance. Ecol Lett. 2021. doi:10.1111/ele.13881
Estrada-Villegas S, Pedraza Narvaez SS, Sanchez A, Schnitzer SA. Lianas significantly reduce tree performance and biomass accumulation across tropical forests: A global meta-analysis. Front For Glob Chang. 2022;4. doi:10.3389/ffgc.2021.812066
Van Der Heijden GMF, Powers JS. Lianas reduce carbon accumulation and storage in tropical forests. Proceedings of the. 2015. Available: https://www.pnas.org/content/112/43/13267.short
Feldpausch TR, Lloyd J, Lewis SL, Brienen RJW, Gloor M, Monteagudo Mendoza A, et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences. 2012;9: 3381–3403.
Jucker T, Caspersen J, Chave J, Antin C, Barbier N, Bongers F, et al. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Chang Biol. 2017;23: 177–190.
Needham JF, Arellano G, Davies SJ, Fisher RA, Hammer V, Knox R, et al. Tree crown damage and its effects on forest carbon cycling in a tropical forest. Glob Chang Biol. 2022. doi:10.1111/gcb.16318
Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 2014;20: 3177–3190.
Clark DB, Kellner JR. Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci. 2012;23: 1191–1196.
Duncanson L, Armston J, Disney M, Avitabile V, Barbier N, Calders K, et al. The Importance of Consistent Global Forest Aboveground Biomass Product Validation. Surv Geophys. 2019;40: 979–999.
Zuleta D, Arellano G, Muller-Landau HC, McMahon SM, Aguilar S, Bunyavejchewin S, et al. Individual tree damage dominates mortality risk factors across six tropical forests. New Phytol. 2022;233: 705–721.
Clark DB, Clark DA. Distribution and effects on tree growth of lianas and woody hemiepiphytes in a Costa Rican tropical wet forest. J Trop Ecol. 1990;6: 321–331.
Demol M, Verbeeck H, Gielen B, Armston J, Burt A, Disney M, et al. Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions. Methods Ecol Evol. 2022. doi:10.1111/2041-210x.13906
Condit R, Pérez R, Aguilar S, Lao S, Foster R, Hubbell S. Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years. Dryad; 2019. doi:10.15146/5xcp-0d46
Condit RS, Aguilar S, Perez R, Lao S, Hubbell SP, Foster RB. [Dataset:] Barro Colorado 50-ha Plot Taxonomy 2017. 2017. Available: https://repository.si.edu/handle/10088/32990
Condit R. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama and a Comparison with Other Plots. Springer Science & Business Media; 1998.
Bongers F, Chazdon R, Poorter L, Peña-Claros M. The potential of secondary forests. Science. 2015;348: 642–643.
Schnitzer SA, Bongers F. The ecology of lianas and their role in forests. Trends Ecol Evol. 2002;17: 223–230.
Tymen B, Réjou-Méchain M, Dalling JW, Fauset S, Feldpausch TR, Norden N, et al. Evidence for arrested succession in a liana-infested Amazonian forest. J Ecol. 2016;104: 149–159.
Perez-Salicrup DR, Sork VL, Putz FE. Lianas and Trees in a Liana Forest of Amazonian Bolivia. Biotropica. 2001;33: 34–47.
Vidal E, Johns J, Gerwing JJ, Barreto P, Uhl C. Vine management for reduced-impact logging in eastern Amazonia. For Ecol Manage. 1997;98: 105–114.
Chave J, Davies SJ, Phillips OL, Lewis SL, Sist P, Schepaschenko D, et al. Ground Data are Essential for Biomass Remote Sensing Missions. Surv Geophys. 2019;40: 863–880.
Rutishauser E, Wright SJ, Condit R, Hubbell SP, Davies SJ, Muller-Landau HC. Testing for changes in biomass dynamics in large-scale forest datasets. Glob Chang Biol. 2020;26: 1485–1498.
Muller-Landau HC, Visser MD. How do lianas and vines influence competitive differences and niche differences among tree species? Concepts and a case study in a tropical forest. J Ecol. 2019. Available: https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2745.13119
Bunker DE, DeClerck F, Bradford JC, Colwell RK, Perfecto I, Phillips OL, et al. Species Loss and Aboveground Carbon Storage in a Tropical Forest. Science. 2005;310: 1029–1031.
Wilkes P, Lau A, Disney M, Calders K, Burt A. Data acquisition considerations for terrestrial laser scanning of forest plots. Remote Sens Environ. 2017. Available: https://www.sciencedirect.com/science/article/pii/S003442571730189X
Girardeau-Montaut D. Cloudcompare-Open Source Project; 2018. URL: http://www danielgm net/cc.
Edelsbrunner H, Kirkpatrick D, Seidel R. On the shape of a set of points in the plane. IEEE Trans Inf Theory. 1983;29: 551–559.
Raumonen P, Kaasalainen M, Åkerblom M. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing. 2013. Available: https://www.mdpi.com/2072-4292/5/2/491
Moorthy SMK, Calders K, Vicari MB. Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests. IEEE Transactions on. 2019. Available: https://ieeexplore.ieee.org/abstract/document/8889474/
Zuleta D, Krishna Moorthy SM, Arellano G, Verbeeck H, Davies SJ. Vertical distribution of trunk and crown volume in tropical trees. For Ecol Manage. 2022;508: 120056.
Larjavaara M, Muller-Landau HC. Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest. Methods Ecol Evol. 2013;4: 793–801.
Araujo RF, Grubinger S, Celes CHS, Negrón-Juárez RI, Garcia M, Dandois JP, et al. Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot. Biogeosciences. 2021. pp. 1–19. doi:10.5194/bg-2021-102
Bastin J-F, Barbier N, Réjou-Méchain M, Fayolle A, Gourlet-Fleury S, Maniatis D, et al. Seeing Central African forests through their largest trees. Sci Rep. 2015;5: 13156.
Bohlman S, Pacala S. A forest structure model that determines crown layers and partitions growth and mortality rates for landscape-scale applications of tropical forests. J Ecol. 2012;100: 508–518.
Martínez Cano I, Muller-Landau HC, Wright SJ, Bohlman SA, Pacala SW. Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: a comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits. Biogeosciences. 2019;16: 847–862.
Bürkner P-C. brms: brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan. Github; Available: https://github.com/paul-buerkner/brms
Kuznetsova A, Brockhoff PB, Christensen RHB, Others. Package “lmertest.” R package version. 2015;2: 734.
Watanabe S. A widely applicable Bayesian information criterion. J Mach Learn Res. 2013. Available: https://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf
Bozdogan H. Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika. 1987;52: 345–370.
Wright SJ, Kitajima K, Kraft NJB, Reich PB, Wright IJ, Bunker DE, et al. Functional traits and the growth–mortality trade-off in tropical trees. Ecology. 2010;91: 3664–3674.
Condit R, Pérez R, Lao S, Aguilar S, Hubbell SP. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For Ecosyst. 2017;4. doi:10.1186/s40663-017-0103-1
Visser MD, Schnitzer SA, Muller-Landau HC, Jongejans E, Kroon H, Comita LS, et al. Tree species vary widely in their tolerance for liana infestation: A case study of differential host response to generalist parasites. J Ecol. 2018;106: 781–794.
Tree mortality, growth and liana infestation on Barro Colorado. https://repository.si.edu › handlehttps://repository.si.edu › handle. Available: https://repository.si.edu/handle/10088/32270

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Strong impacts of lianas on tree allometry lead to overestimation of tropical forest carbon stocks and sink

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Strong impacts of lianas on tree allometry lead to overestimation of tropical forest carbon stocks and sink

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Abstract

Figures

Main

Results

Liana impact on individual tree structure and allometry

Impact of lianas on stand-level carbon stocks

Impact of lianas on stand-level carbon accumulation

Discussion

Methods

Data acquisition and processing

Liana infestation data acquisition

TLS data acquisition

TLS data processing

2011 and 2015 height datasets from BCI

Liana-specific allometric models for tree height, crown area and volume

Height allometric model

\(ln\left(H\right) = a+ \left(b * ln\right(DBH\left)\right) - ln(k + {DBH}^{b})\) ------------ (1)

Crown area allometric model

\(ln\left(Crown area\right) =m * ln\left(DBH\right) + b\) ----------------------------------------------(3)

Volume and AGB allometric model

\(ln\left(Volume\right) =m * ln\left(DBH\right) + b\) ----------------------------------------------(5)

Upscaling carbon stock and carbon accumulation using bootstrapping approach

Declarations

References

Additional Declarations

Supplementary Files

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