Meredith L. Bastian’s research while affiliated with National Academy of Sciences and other places

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Publications (51)


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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2,946 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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1,430 Reads

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2 Citations

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.


(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.


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

March 2024

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278 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.


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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3,299 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.


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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4,665 Reads

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144 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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3,390 Reads

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17 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.



Distribution of the study data
Distribution of the full study dataset, coded for non-native severity (n = 471,888 plots). The map shows average per cent invasion across a 1-degree hexagonal grid, from non-invaded (0%) pixels in green to completely invaded (100%) pixels in purple. Plots are considered invaded if there is any non-native tree present.
Anthropogenic drivers are more important than native diversity in determining invasion occurrence
a,b, Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on non-native presence (whether a plot is invaded or not) for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, n = 17,640 plots; functional diversity, n = 17,271 plots). All results shown are from random forest models. Note that y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
Native diversity is the most important driver of invasion severity
a,b, Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on invasion severity for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, n = 3,498 plots; functional diversity, n = 3,368 plots). Plots are shown for the severity of invasion measured as non-native species abundance (proportion of basal area with non-native plant species); plots for non-native species richness (proportion of non-native plant species) are shown in Extended Data Fig. 4. All results shown are from random forest models. Note that the y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
Environmental filtering at temperature extremes
a,c, Estimates of overlapping variables included in temperate and tropical GLM models (forest plot) for phylogenetic (a) and functional (c) diversity models (phylogenetic diversity, n = 3,498; functional diversity, n = 3,368). Values to the left of the zero line indicate negative model estimates, and those to the right indicate positive estimates. b,d, Relationship between mean annual temperature and invasion strategy for phylogenetic (b) and functional (d) diversity models, showing that at extreme temperatures invasion occurs through similarity (Supplementary Table 7; phylogenetic diversity: P(1) = 9.69 × 10⁻¹⁴, P(2) = 2.13 × 10⁻¹¹; functional diversity: P(1) < 2 × 10⁻¹⁶, P(2) = 1.07 × 10⁻⁴, where P(1) and P(2) represent each temperature and temperature squared P values, respectively). Note for functional diversity, this pattern only holds at low temperatures. Error bars and bands represent standard error.
Proximity to ports weakens environmental filtering in the temperate bioclimate zone
a,b, In temperate plots far from ports, temperature is positively correlated with an invasion strategy of increasing dissimilarity for phylogenetic (a) and functional (b) diversity (phylogenetic diversity: n = 2,710 plots, P = 6.37 × 10⁻⁶; functional diversity: n = 2,603, P < 2 × 10⁻¹⁶). c,d, This relationship between temperature and invasion strategy weakens for phylogenetic (c) and functional (d) diversity with proximity to ports (Supplementary Table 7; phylogenetic diversity: P = 0.0001; functional diversity: P = 2.71 × 10⁻¹³). Lines and points represent the lowest (c,d) and highest (a,b) 10% of data. Error bands represent standard error.
Native diversity buffers against severity of non-native tree invasions

August 2023

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2,637 Reads

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41 Citations

Nature

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.


Four hypotheses describing how evenness interacts with the relationship between richness and productivity. The four levels of evenness are indicated with different colours from low evenness (yellow) to high evenness (red), and lower colour intensity indicates a lower data density. The dashed black line indicates the average relationship across the system. In all hypotheses, it is assumed that species richness has a positive effect on productivity (Balvanera et al., 2006; Cardinale et al., 2007; Hooper et al., 2005). If community evenness interacts with the effect of species richness on productivity, then (1) the effect of species richness on productivity depends on the evenness of the community, and (2) richness is correlated with evenness across the system (either positively or negatively). If there is no interaction between richness and evenness (Hypothesis A) or if there is no correlation between evenness and richness (Hypothesis B) (Ma, 2005), then the average effect (dashed line) of richness on productivity will neither attenuate nor increase at high richness levels. In such instances, the observed decrease in productivity at high richness levels is more likely a by‐product of other ecological processes (e.g. functional redundancy). If, however, there is a significant interaction between richness and evenness, such that uneven communities have lower productivity at high richness (Hypothesis C), then a negative correlation between richness and evenness (Cook & Graham, 1996; Hanlin et al., 2000; Symonds & Johnson, 2008) would lead to an attenuation in productivity at high richness level: the marginal trend (dashed line) first tracks the high evenness isocline at low richness levels but then bends down towards the low evenness isoclines at high richness. Conversely, if uneven communities exhibit higher productivity at high richness levels (Hypothesis D), then a positive correlation between richness and evenness (Cotgreave & Harvey, 1994; Manier & Hobbs, 2006; Tramer, 1969) would explain this reduction in productivity: the marginal trend first tracks the low evenness isocline at low richness levels but then bends down towards the high evenness isoclines at high richness.
(a) Location of the Global Forest Biodiversity Initiative (GFBI) plots used in this study, where the density of forest plots is indicated from low density (blue) to high density (red). (b) The distribution of evenness and richness in the boreal, temperate and tropical biomes. An evenness value of one resembles either a monospecific stand or an even abundance of species. The tail of richness values of the tropical biomes extends to 380 species (not shown in the graph). The majority of our dataset is composed of secondary forests (mean age is 52 years), and especially the monospecific and relatively species‐poor stands were affected by human activity in some degree.
The relationship between evenness and logged species richness, where the data density is indicated from low density (blue) to high density (red). The Pearson correlation is highly significant for all relationships, we therefore describe the adjusted r² as a measure of effect size. (a) The global relationship between evenness and richness (N = 20,272, r = −0.52, r² = 0.28). (b) The relationship between evenness and richness for the boreal (N = 61,712, r = −0.42, r² = 0.18), temperate (N = 374,142, r = −0.53, r² = 0.28), and tropical (N = 9570, r = −0.48, r² = 0.23) biomes.
(a) Positive (green) and negative (blue) regression coefficients, and (b) variable importance of evenness, richness, the interaction of evenness and richness and climate, soil and human impact variables on biomass and productivity. Only the results for the boreal, temperate, tropical moist forest and all the biomes globally are visualized, and variables causing multicollinearity are taken out (see Section 2). The open circles in (a) indicate non‐significant coefficients, while the filled circles indicate significant coefficients. The adjusted r² values of the linear models are displayed in (a).
The hypothesized effect of different levels of evenness on the relationship between species richness and mean annual biomass accumulation (a–d) or productivity (h–e). The graphs visualize predicted values based on the results of a linear model, with the covariates held constant (see methods), and as a cut‐off point the third quantile of the biomass and NPP values to avoid overfitting. The data are projected on the graph (black line), and the 95% upper and lower confidence intervals are visualized in grey. At the right side of every graph the scaled variable importance, according to a linear model including covariates, of richness (green), evenness (dark blue), and the interaction between richness and evenness (light blue) is visualized. In the graphs at the left side, the global effect is visualized, while at the right side the data are split among boreal, temperate and moist tropical forests. The uncertainty of the biomass calculations and estimated productivity are visualized in Figure S7.
Evenness mediates the global relationship between forest productivity and richness

May 2023

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2,018 Reads

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18 Citations

1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive. 4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions.


Citations (36)


... 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

... These areas are essential for storing carbon and protecting biodiversity hotspots worldwide. The highest carbon stored in forests worldwide is in tropical forests, followed by temperate, boreal, and dryland zones (Mo et al., 2023). However, deforestation, land-use changes, and forest degradation pose a severe threat to these ecosystems because they alter carbon cycles and increase carbon emissions (Kumar et al., 2022;Olorunfemi et al., 2022). ...

Integrated global assessment of the natural forest carbon potential

Nature

... In contrast, the leaves of evergreen woody plants are durable and remain throughout the year, so more stable nutrient concentrations with no significant seasonal trends are helpful to maintain the function of the evergreen leaves (Givnish, 2002;Reich & Oleksyn, 2004;Wang et al., 2023). Moreover, evergreen broadleaf plants generally grow in warmer climate conditions with weaker seasonality and this may also benefit the weaker seasonality of foliar nutrients in comparison with deciduous broadleaf woody plants (Axelrod, 1966;Givnish, 2002;Ma et al., 2023;Reich & Walters, 1992). In addition, the seasonality of foliar N concentration in evergreen conifers was found to be significantly higher ECOLOGY than in evergreen broadleaf woody plants. ...

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

... Additionally, canopy height plays a pivotal role in characterizing habitat structural heterogeneity as an important factor in explaining biodiversity spatial patterns Marselis et al., 2022;Torresani et al., 2023). Endemic forests represent one of the global biodiversity hotspots and must-preserved ecosystems (Delavaux et al., 2023), but climate change and human pressure are jeopardizing the capability of species to adapt fast enough to resist disturbances due to stand replacement or prolonged heat waves (Anderegg et al., 2015;Hartmann et al., 2018). In the Mediterranean basin, the landscape is undergoing transformations driven by droughts, extreme heat episodes and increasingly recurrent wildfires, impacting carbon fluxes and threatening the habitats of endemic species (Grünig et al., 2023;Moreira et al., 2011;Ruffault et al., 2020). ...

Native diversity buffers against severity of non-native tree invasions

Nature

... 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

... However, there is very little data available for the weight of unflanged male orangutans, since in captivity males nearly always develop flanges (Pradhan, van Noordwijk and van Schaik, 2012). Therefore, as Bornean and Sumatran orangutans are considered to be approximately the same size (Smith and Jungers, 1997), the comparative data includes a range of different sources, including captive (Fooden and Izor, 1983) and wild-born (Markham and Groves, 1990;Smith and Jungers, 1997) Sumatran orangutans, wild-born Bornean orangutans living in rehabilitation centres with no flanges or developing flanges (Prasetyo, 2019), and unflanged orangutan skeletal data (species unknown) (Kralick et al., 2023). ...

Beyond Dimorphism: Body Size Variation Among Adult Orangutans Is Not Dichotomous by Sex

Integrative and Comparative Biology

... In the Quebec region, which spans each forest type, a combination of climatic, geographic, and soil variables may help capture the environmental constraints specific to each forest type. Additionally, the inclusion of geographical variables such as latitude in the Quebec region likely captures the trend of decreasing species richness with increasing latitude across this large area (Liang et al. 2022). In the deciduous forest climatic variables are particularly important because temperature is a limiting factor, as trees in these regions begin to suffer cell damage when temperatures drop below −40 C (Goldblum and Rigg 2010). ...

Co-limitation towards lower latitudes shapes global forest diversity gradients

Nature Ecology & Evolution

... This debate is particularly important in the context of the tree flora of the wet tropics, as up to 53,000 of the global total of ca. 73,000 tree species occur in old-growth, closed-canopy wet tropical forests [7][8][9] . ...

The number of tree species on Earth

Proceedings of the National Academy of Sciences