Kuswata Kartawinata’s research while affiliated with Block Center for Integrative Cancer Treatment and other places

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Publications (78)


Observed wood densities across the global forest inventory plots and within gymnosperms, angiosperms, forest types and biomes
a–c, Wood density distribution of gymnosperm (a) and angiosperm (c) species and influence of the proportion of angiosperms on CWD (b). The wood density distribution in gymnospermous species is narrower and has a smaller mean (~20% lower) than in angiospermous species. b, CWD increases with increasing proportion of angiospermous species in forest communities. We included 8,249 taxa with information on angiosperms and gymnosperms comprising 8,036 angiosperms and 213 gymnosperms, each with wood density information available at the species or genus level. d, Map of CWD observations for the ~1.1 million plots from the GFBi database. e,f, Box plots of observed CWD at the forest type (e) or biome level (f). Box plot shows the median, interquartile range and whiskers for data spread, excluding outliers.
Phylogenetic tree and wood density information of global tree species
The phylogenetic tree was constructed using the R package V.PhyloMaker, with wood density information available for 4,298 species (189 families from 55 orders). Wood density exhibits a strong phylogenetic signal (Pagel’s lambda = 0.92, P < 0.01, Blomberg’s K = 0.01, P < 0.01). The colours of the branches and the grey bars at the tips represent the wood density of each species. To identify orders that have significantly different wood densities compared to all other tree species, we conducted a two-tailed significance test by comparing the order-level wood density with 999 randomized wood density values from the entire phylogenetic tree. The coloured circle surrounding the phylogeny represents different orders. The filled blue/red circles inside the phylogeny indicate orders that show significantly (P < 0.05) lower (blue) or higher (red) wood densities relative to all the species. Numbers inside the circles represent the average wood density of the respective order.
Global maps of wood density
a,c,e, Wood density maps for all species (a), angiosperms-only (c) and gymnosperms-only (e). a, The community-level wood density map was derived from an ensemble approach, averaging the global predictions from the 200 best random-forest models. c,e, Angiosperm-only (c) and gymnosperm-only (e) wood density maps were derived from ensemble averaging of the global predictions from the 100 best random-forest models, respectively. b,d,f, Corresponding latitudinal trends in wood density aggregated for each 0.1 arc degree latitude: all species (b), angiosperms (d) and gymnosperms (f). Error ranges represent 1 s.d. either side of the mean. Maps are projected at 30 arcsec (~1 km²) resolution. Non-forested areas are displayed in grey. In the wood density maps for angiosperms (c) and gymnosperms (e), we correspondingly excluded pixels where angiosperms and gymnosperms constituted <5% of the entire community.
Variable importance of the selected environmental metrics
a–f, The environmental metrics are based on random-forest models (a,c,e) and linear partial regression models (b,d,f). a,b, Variable importance of the selected covariates across global forests, including angiosperm ratio to control for wood density differences between angiosperms and gymnosperms. c,d, Variable importance within angiosperm-only communities. e,f, Variable importance within gymnosperm-only communities. Mean decrease in accuracy values in a, c and e represents the relative contribution of each variable to CWD variation, whereby we averaged the values of 100 bootstrapped random-forest models. Bootstrapped partial regression coefficients for each variable (b,d,f) were calculated by averaging the partial regression coefficients from 100 multivariate linear models. All variables were standardized to allow for direct effect size comparison. In addition, we quantified the absolute effects of these covariates using partial regression analysis, as detailed in Supplementary Table 5.
Comparison of global living tree biomass distribution using spatially explicit wood density data versus a universal wood density value
a, The global distribution of living tree biomass (in tonnes per hectare), derived by integrating our wood density map with spatially explicit data on living tree volume, root mass fraction and biomass expansion factors. b, Percentage difference in estimated living tree biomass when comparing results derived using the global wood density map (from a) with estimates using a single, universal wood density value. The difference is calculated as the percentage change by subtracting the spatially explicit estimate from the universal estimate and then dividing by the spatially explicit estimate. Blue areas show regions where the universal estimate is higher, and red/orange areas indicate where the spatial estimate is higher. c, Percentage difference between the two biomass estimation methods across biomes. Box plots show the median, interquartile range and whiskers for data spread, excluding outliers.
The global distribution and drivers of wood density and their impact on forest carbon stocks
  • Article
  • Full-text available

October 2024

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2,824 Reads

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1 Citation

Nature Ecology & Evolution

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Constantin M. Zohner

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

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Positive feedbacks and alternative stable states in forest leaf types

May 2024

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1,399 Reads

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1 Citation

The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4–43% higher growth rates, 14–17% higher survival rates and 4–7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.


From earthquakes to island area: multi-scale effects upon local diversity

March 2024

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275 Reads

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1 Citation

Ecography

Tropical forests occupy small coral atolls to the vast Amazon basin. They occur across bioregions with different geological and climatic history. Differences in area and bioregional history shape species immigration, extinction and diversification. How this effects local diversity is unclear. The Indonesian archipelago hosts thousands of tree species whose coexistence should depend upon these factors. Using a novel dataset of 215 Indonesian forest plots, across fifteen islands ranging in area from 120 to 785 000 km 2 , we apply Gaussian mixed effects models to examine the simultaneous effects of environment, earthquake proximity, island area and bioregion upon tree diversity for trees ≥ 10 cm diameter at breast height. We find that tree diversity declines with precipitation seasonality and increases with island area. Accounting for the effects of environment and island area we show that the westernmost bioregion Sunda has greater local diversity than Wallacea, which in turn has greater local diversity than easternmost Sahul. However, when the model includes geological activity (here proximity to major earthquakes), bio-region differences are reduced. Overall, results indicate that multi-scale, current and historic effects dictate tree diversity. These multi-scale drivers should not be ignored when studying biodiversity gradients and their impacts upon ecosystem function.


(a) Forests across Indonesia's three bioregions. Top left wet lowland Central Kalimantan. Top middle wet lowland Halmahera. Top right wet lowland Waigeo. Bottom left Mount Salak, Java. Bottom middle seasonally dry forest West Sumbawa. Bottom right Arfak mountains, Indonesian New Guinea. (b) Map of Indonesian rarefied forest plot tree diversity and their global position (inset). (c) Rarefied tree diversity per island. Islands with single plots not shown. Boxplots represent island mean and SD diversity. (d) +/− 1 SE (horizontal lines) for effects (points) of environmental variables, earthquake proximity and island area on local tree diversity in Indonesia. Both models either with precipitation seasonality or earthquake proximity parameters are shown, hence single effects shown for these two parameters. Inset shows the random effect intercepts for bioregion across our two models. Point colour shows model prediction error as measured by the Aikake Information Criterion (AIC). (e) Mixed effect model predictions for local tree diversity in plots along the precipitation seasonality gradient, on islands of varying area, across three bioregions. Point opaqueness shows overlapping points. Photographers for (a): top left Nanang Sujana, CIFOR; top middle JS; top right JS; bottom left Mokhamad Edliadi, CIFOR; bottom middle LAT; bottom right LAT.
From earthquakes to island area: multi‐scale effects upon local diversity

March 2024

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182 Reads

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2 Citations

Tropical forests occupy small coral atolls to the vast Amazon basin. They occur across bioregions with different geological and climatic history. Differences in area and bioregional history shape species immigration, extinction and diversification. How this effects local diversity is unclear. The Indonesian archipelago hosts thousands of tree species whose coexistence should depend upon these factors. Using a novel dataset of 215 Indonesian forest plots, across fifteen islands ranging in area from 120 to 785 000 km², we apply Gaussian mixed effects models to examine the simultaneous effects of environment, earthquake proximity, island area and bioregion upon tree diversity for trees ≥ 10 cm diameter at breast height. We find that tree diversity declines with precipitation seasonality and increases with island area. Accounting for the effects of environment and island area we show that the westernmost bioregion Sunda has greater local diversity than Wallacea, which in turn has greater local diversity than easternmost Sahul. However, when the model includes geological activity (here proximity to major earthquakes), bioregion differences are reduced. Overall, results indicate that multi‐scale, current and historic effects dictate tree diversity. These multi‐scale drivers should not be ignored when studying biodiversity gradients and their impacts upon ecosystem function.


Figure 1. Observed wood densities across the global forest inventory plots (a), and forest
Figure 2. Phylogenetic tree and wood density information of global tree species. The
Consistent climatic controls of global wood density among angiosperms and gymnosperms

February 2024

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3,273 Reads

The density of wood is a key indicator of trees’ carbon investment strategies, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here, we analyze information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially-explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical dry forests being up to twice as dense as that in boreal forests. In both angiosperms and gymnosperms, temperature and water availability emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.


Figure 2. Diameter class distribution of palm tree species in Altingia excelsa planted forest in Bodogol Resort, GGPNP, West Java, Indonesia
List of species, Basal Area (BA), Density (De) and Frequency (F) of palm tree species in Altingia excelsa planted forest area of Bodogol Resort, GGPNP, West Java, Indonesia
Distribution pattern of palm tree species on the one- hectare plot in the Altingia excelsa planted forest in Bodogol Resort, GGPNP, West Java, Indonesia
Structure and distribution of palm tree species in Altingia excelsa planted forest in Gunung Gede Pangrango National Park, West Java, Indonesia

February 2024

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96 Reads

Biodiversitas Journal of Biological Diversity

Sadili A, Salamah A, Mirmanto E, Kartawinata K. 2024. Structure and distribution of palm tree species in Altingia excelsa planted forest in Gunung Gede Pangrango National Park, West Java, Indonesia. Biodiversitas 25: 249-256. Gunung Gede Pangrango National Park (GGPNP) is a conservation area consisted of natural forest and planted forest, including Altingia excelsa Noronha stands which were planted in 1925. As a result of secondary succession, various plants grow under Altingia excelsa stands, including palm tree species (Arecaceae). This research was conducted to determine the structure, population, and distribution patterns of palm trees in Altingia excelsa planted forest in Bodogol Resort, GGPNP. The data were collected by establishing a one-hectare plot divided into 100 subplots of 10 m × 10 m. Palm tree species with a Diameter at Breast Height (DBH) ?3 cm were measured, and the tree heights were estimated. Data analysis included Relative Dominance (RDo), Relative Density (RDen), Relative Frequency (RF), Importance Value Index (IVI), Shannon Wiener Diversity Index (H'), Evenness Index (E), Index of Dispersion (ID), analysis of variance values (S2) and Chi-square analysis (Q). The study results recorded four species from three genera of Arecaceae which consisted of 171 trees with a total Basal Area (BA) of 0.63 m2/ha. Pinanga coronata (Blume ex Mart.) Blume was the most dominant palm tree species with IVI of 156.89%), followed by Pinanga sp. (IVI=72.04%), Caryota rumphiana Mart. (IVI=55.77%), and Arenga pinnata (Wurmb) Merr. (IVI=15.38%). In term of diameter class distribution, Pinanga coronata and Pinanga sp. were regenerating well. Spatial distribution patterns of Arenga pinnata, Caryota rumphiana, and Pinanga sp. were regular, while Pinanga coronata was clustered. Periodical studies in the future to determine the mortality and natality of the palm trees are recommended to understand the dynamics of vegetation succession.


Consistent patterns of common species across tropical tree communities

January 2024

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1,970 Reads

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26 Citations

Nature

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1–6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories⁷, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.


The global distribution of tree carbon observations and the impact of human disturbances
a, Map of ground-sourced aboveground tree carbon observations (GFBI data; aggregated to 30-arcsec (1-km²) resolution). b, Satellite-derived ESA-CCI map of current aboveground tree carbon stocks (1-km resolution). c,f, Observed biome-level tree carbon densities in existing forests based on ground-sourced (c) and satellite-derived (f) data. d,g, Principal component analysis (top two principal components shown) of the eight human-activity variables either directly or indirectly reflecting human-caused forest disturbances or the lack thereof, such as land-use change, human modification, cultivated and managed vegetation and wilderness area, to detect the effect of human disturbance on tree carbon densities for the ground-sourced (d) and satellite-derived data (g). e,h, Partial regression of the global variation in forest carbon density along the human-disturbance gradient (represented by the first principal component of the eight human-activity variables; see panels d and g) for the ground-sourced (e) and satellite-derived data (h), controlling for 40 environmental covariates. Relative carbon density is the observed carbon density divided by the global average.
The natural tree carbon potential under current climate conditions in the absence of humans
a,b, The total living tree carbon potential of 600 Gt C within the natural canopy cover area of 4.4 billion ha². c,d, The differences between current and potential tree carbon stocks, totalling 217 Gt C. e,f, The difference of tree carbon potential between the GS and SD models, subtracting the mean values of the six SD models from the mean values of the four GS models. Blue colours indicate that the GS models predict higher potential than the SD models, whereas red colours indicate the opposite. b,d,f, Latitudinal distributions (mean ± standard deviation) of the total tree carbon potential for the GS1, GS2, SD1 and SD2 models (b), the difference between current and potential tree carbon (d) and the difference of tree carbon potential between the GS and SD models (f). Maps represent the average estimates across all GS and SD models and are projected at 30-arcsec (about 1-km²) resolution. We show dryland and savannah biomes with stripes to denote that many of these areas are not appropriate for forest restoration. Where trees would naturally exist, they often exist far below 100% canopy cover, and restoration of forest cover should be limited to natural conditions.
The living tree carbon potential estimated from the ground-sourced (GS1 and GS2) and satellite-derived (SD1 and SD2) models
a, Total estimated living tree biomass potential of the GS1, GS2, SD1 and SD2 models. Error bars represent the lower and upper boundaries based on the 5% and 95% quantiles from a bootstrapping procedure. Colours represent the different input datasets, that is, upper or lower canopy cover boundaries (GS models) and ESA-CCI, Walker et al.² or harmonized (SD models). Light colours above white lines indicate the difference between current and potential tree carbon stocks. b, Meta-analysis showing literature estimates of living tree carbon potential based on ensemble models4,53,54, inventory data19,55–61 and mechanistic62–67 or data-driven² models. The horizontal dashed line represents the average existing living tree carbon of 443 Gt C estimated in these publications. c, Differences between current and potential tree carbon stocks. d, Literature estimates for the difference between current and potential tree carbon stocks from ref. ⁴ (ensemble models), refs. 1,53,58,61 (inventory data), refs. 63,64 (mechanistic models) and ref. ² (data-driven models).
Sources of uncertainty in forest carbon potential for the GS and SD models
a,b, Relative contribution of individual uncertainty sources to the overall uncertainty in carbon potential for the GS (a) and SD (b) models: (1) model approach (type 1 versus type 2 models); (2) input data (current aboveground tree carbon input, that is, upper and lower canopy cover boundaries for GS models and ESA-CCI, Walker et al.² and harmonized for SD models); (3) aboveground biomass potential estimates (bootstrapping); (4) belowground biomass (accounting for uncertainties in both root mass fraction and aboveground biomass); (5) dead wood and litter (accounting for uncertainties in both dead wood and litter-to-tree biomass ratios and tree biomass); and (6) soil organic carbon potential²³. The maps show the top uncertainty source within each pixel. The pie charts show the relative contribution of uncertainties worldwide.
Contribution of land-use types, forest types, carbon pools and countries to the difference between current and potential ecosystem-level carbon stocks
a, Of the 328 Gt C discrepancy between current and potential carbon stocks, 226 Gt C is found outside urban and agricultural (cropland and pasture) areas, with 61% in forested regions in which the recovery of degraded ecosystems can promote carbon capture (conservation potential) and 39% in regions in which forests have been removed (restoration potential). b, Relative contribution of forest degradation (conservation potential; blue area) and land-cover change (orange colours) to the difference between current and potential ecosystem-level carbon stocks. The darker blue area represents the conservation potential of 10.5 Gt C in forest plantation regions. c, Relative contribution of tropical, temperate, boreal and dryland forests to the total forest conservation potential. d, Relative contribution of the three main carbon pools (living biomass, dead wood and litter, and soil) to the difference between current and potential carbon stocks. e, The nine countries contributing more than 50% to the difference between current and potential carbon stocks.
Integrated global assessment of the natural forest carbon potential

November 2023

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4,610 Reads

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137 Citations

Nature

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system¹. Remote-sensing estimates to quantify carbon losses from global forests2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced⁶ and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.


Global coverage of forest inventory locations (GFBi data) and plot-level leaf-type proportions
a, A total of 9,781 forest inventory plots (green points) were used for geospatial modelling of forest leaf types. b, Number of plots in relation to their proportion of evergreen vs deciduous and broadleaved vs needle-leaved individuals.
The global distribution of forest leaf types
a, The global distribution of tree leaf type as predicted by a random forest model built from area-based leaf-type data (see Methods). Pixels are coloured in the red, green and blue spectrum according to the percentage of total tree basal area occupied by broadleaved evergreen, broadleaved deciduous and needle-leaved tree types, as indicated by the ternary plot. Needle-leaved evergreen and needle-leaved deciduous forests were combined due to the low global coverage of needle-leaved deciduous trees. b–e, Predicted relative coverage of each leaf type from random forest models. Ref. ⁸¹ was used to mask non-forest areas. b, Broadleaved evergreen coverage. c, Broadleaved deciduous coverage. d, Needle-leaved evergreen coverage. e, Needle-leaved deciduous coverage.
Variable importance of environmental covariates on forest leaf-type variation
a,b, Cumulative importance of the first six principal components of climate, soil, topographic and vegetation covariates in the variation of leaf habit (a) and leaf form (b). c,d, Variable importance of selected environmental features on variation in leaf habit (c) and leaf form (d). Bars in c and d represent the mean ± 95% CI; relative importance based on the 10 best random forest models (n = 10; see Methods). Area-based leaf-type proportions were used to represent forest (plot-level) leaf-type variation.
The global proportion of evergreen broadleaved, deciduous broadleaved, needle-leaved evergreen and needle-leaved deciduous trees
The relative proportions of trees that occur within tropical, temperate, boreal and arid regions are shown as separate pie charts for each leaf type.
Forested areas where future climates may no longer support prevailing leaf types
If a pixel’s forest area was predominantly (>60%) covered by one leaf type, it was classified as that specific leaf type. Pixels where no leaf type exceeded 60% coverage were classified as mixed forest. To determine the relative proportion of each leaf type per plot, we considered the basal area of individual trees (area-based leaf type). Coloured pixels on the map indicate areas that, by the end of the century (2071–2100), will face climate conditions that currently support a different forest type. The future climate conditions were represented using three climate change scenarios: low-emission (SSP1–RCP2.6; a,b), business-as-usual (SSP3–RCP7; c,d) and high-emission (SSP5–RCP8.5; e,f) for the period 2071–2100. Panels a, c and e show the present forest types. In contrast, panels b, d and f show the type of forest expected under the projected future climate of each respective pixel.
The global biogeography of tree leaf form and habit

October 2023

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3,359 Reads

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13 Citations

Nature Plants

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.



Citations (61)


... zenodo.7989963 (Trethowan et al. 2024). ...

Reference:

From earthquakes to island area: multi‐scale effects upon local diversity
From earthquakes to island area: multi-scale effects upon local diversity

Ecography

... In contrast, the Indonesian New Guinea is part of the most floristic island in the world; some of the vegetation has not been described taxonomically (Cámara-Leret et al. 2020), and part of Melanesia is the world's most diverse amphibian fauna (Oliver et al. 2022). Some studies conducted as well in this area to describe the vegetation ranging from lowlands to highlands have been conducted to share useful information (Cámara-Leret and Dennehy 2019; Trethowan et al. 2022Trethowan et al. , 2024. Hence, efforts to restore and conserve the lowland forest are crucial in order to mitigate the environmental impact and preserve the natural habitats from a conservation perspective. ...

From earthquakes to island area: multi‐scale effects upon local diversity

... Exploring the relationship between patterns of adaptive and neutral genetic structuring may further clarify the application of genetic markers in in natural areas management. By focusing restoration genetic studies on highly abundant and ecological significant species that are common targets in environmental restoration practices 45 , it is possible to largely eliminate the need for interpretative proxies and generalisations 46 . ...

Consistent patterns of common species across tropical tree communities

Nature

... Forests may be one of the largest drivers of 'carbon sink' effects around the world (Mo et al., 2023), with forest expansion contributing to nearly 44% of the terrestrial carbon sinks between 1980 and 2019 (Yu et al., 2022). In this study, we detected a slight increase in forest areas, mainly in northern China, which may reflect reforestation efforts since 1978 under the Three-North Shelterbelt Forest Program. ...

Integrated global assessment of the natural forest carbon potential

Nature

... We further extracted the FVC within the Yangtze River Basin using data from MODIS (resolution 500 m, MCD12Q1), and the results showed that the area of evergreen trees was 3 348 885.88 m 2 and that of deciduous trees was 27 131 922.99 m 2 , with a mixed forest area of 96 731 654.07 m 2 . In addition, at the global scale, Ma et al. (2023) estimated that of the approximately 3 trillion adult trees presently existing, 29.1% are broadleaf evergreen and 27.1% are broadleaf deciduous. The findings also imply that future climates may no longer support prevailing leaf types; approximately 7-20% of broadleaf evergreen forests are expected to undergo changes as conditions shift to supporting deciduous species. ...

The global biogeography of tree leaf form and habit

Nature Plants

... Consequently, random forest algorithms have been widely used in regression prediction problems [66] and feature classification [67] in the ecological field. In addition, the random forest model can effectively assess and rank the importance of each variable [68]. Therefore, it is also possible to further determine the degree of importance of each factor to the RSEI of mining cities, and this method has been applied in the study of ecological quality changes in mainland China [69]. ...

Author Correction: Native diversity buffers against severity of non-native tree invasions

Nature

... The lowest level of plant invasion was associated with greater native canopy cover and this is consistent with previous studies that focussed on the role of tree richness or density on non-native richness (Delavaux et al. 2023;Ibanez et al. 2019;Rossignaud et al. 2022). These results support the importance of native canopy cover in the resistance of forest habitats to plant invasions. ...

Native diversity buffers against severity of non-native tree invasions

Nature

... Establishing M. eminii in nature reserves has become widespread (Padmanaba et al. 2017). Some studies have reported anecdotal observations regarding the role of this invasive plant as a food source for important animal species that reside in nature reserves (Sadili et al. 2023;Allasselcida et al. 2024). However, a study that elaborates on the role of M. eminii as a resource for some important animal species has not been conducted. ...

VARIATION IN THE COMPOSITION AND STRUCTURE OF NATURAL LOW- LAND FORESTS AT BODOGOL, GUNUNG GEDE PANGRANGO NATIONAL PARK, WEST JAVA, INDONESIA

REINWARDTIA

... In this study, we focused on species richness as a main proxy for species diversity. Yet, it is worth mentioning that other facets of diversity may be impactful on DPRs, such as species evenness (Hordijk et al., 2023). We also added a 'country' effect in the statistical model to take into account differences in sampling design (Table A). ...

Evenness mediates the global relationship between forest productivity and richness

... This plant can grow on less fertile soil and is resistant to shade (Sujarwo and Keim, 2017). This plant is commonly found in lowland forests (Kartawinata et al., 2022). Streblus asper Lour. is a small tree in the Moraceae family which is found in many tropical countries (Sivamaruthi et al., 2022) and is native to Southeast Asia (Oraon and Mondal, 2022; Prasansuklab et al., 2018). ...

Natural Vegetation and Ethnobotany of Bali