Amazonian tree trait evolution

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Abstract Lineages tend to retain ecological characteristics of their ancestors through time. However, for some traits, selection during evolutionary history may have also played a role in determining trait values. To address the relative importance of these processes requires large-scale quantification of traits and evolutionary relationships among species. The Amazonian tree flora comprises a high diversity of angiosperm lineages and species with widely differing life-history characteristics, providing an excellent system to investigate the combined influences of evolutionary heritage and selection in determining trait variation. We used trait data related to the major axes of life-history variation among tropical trees (e.g. growth and mortality rates) from 577 inventory plots in closed-canopy forest, mapped onto a phylogenetic hypothesis spanning more than 300 genera including all major angiosperm clades to test for evolutionary constraints on traits. We found significant phylogenetic signal (PS) for all traits, consistent with evolutionarily related genera having more similar characteristics than expected by chance. Although there is also evidence for repeated evolution of pioneer and shade tolerant life-history strategies within independent lineages, the existence of significant PS allows clearer predictions of the links between evolutionary diversity, ecosystem function and the response of tropical forests to global change.

Introduction Evolutionary heritage may act as a major constraint on the ecological roles that species in a lineage can occupy. Even under a random model of trait evolution where functional traits drift in state over time (e.g. a Brownian motion model), we would expect closely related species to have similar functional trait values and similar ecologies due to their shared common ancestry [1,2]. However, both divergent selection and convergent evolution lead to weaker relationships between species relatedness and their ecological similarity [1,3,4]. Hence, although it is often assumed that close relatives are more similar because they retain the ecological characteristics of their ancestors, in many clades the ancestral character state may not be conserved. Thus, rather than being simply assumed, the tendency of closely related species to have similar ecological characteristics needs to be tested.

The strength of the link between trait variation and phylogenetic relatedness has a wide range of implications for understanding ecological and evolutionary processes and can be measured by the magnitude of phylogenetic signal (PS) [1,2]. For example, if a selected trait has significant PS, the relatedness of species can help us to understand the underlying mechanisms that drive community structure [5–7]. The presence of significant PS also suggests that the sum of phylogenetic distances among species that occur within a community (i.e. phylogenetic diversity) is a useful proxy for functional diversity and that, in turn, phylogenies of tree taxa may contribute to understanding ecosystem function [8,9]. In addition, if trait values are more similar than expected by chance among closely related lineages, we can predict the trait values for species where trait data are not available.

Understanding the relative importance of evolutionary heritage versus selection in determining trait variation requires large-scale quantification of traits and evolutionary relationships among species. The Amazonian tree flora comprises a high diversity of angiosperm lineages and species with widely differing life-history characteristics, providing an excellent system to investigate these processes. Previous studies of the degree of PS among traits of tropical trees, such as seed mass, leaf structure and chemistry, trunk characteristics and range size, have shown variable results [6,7,10–13]. For example, some studies show significant PS [6,7,13], while for the same traits other studies have failed to detect any PS, with closely related species exhibiting rather different trait values [10,11]. A key limitation of many of these studies is the limited spatial and phylogenetic scale of study, as well as the resolution of the phylogeny that they have used [14]. Here, we explore patterns of PS at large spatial and phylogenetic scales using a sequence-based phylogeny to test whether there are significant levels of PS for four key traits related to the major axes of life-history variation among tropical trees: tree growth and mortality rates, wood density and potential tree size. These traits are related to resource acquisition and allocation, defence and dispersal ability [15,16] and represent important axes of functional variation which drive variation in plant performance and function in many ecosystems [17]. Moreover, those traits are strongly related to differences in carbon fluxes and storage among species [18]. As a result, understanding PS in these traits may help to understand and model ecosystem processes in such highly diverse tropical forests such as Amazonia, which may harbour more than 16 000 tree species [19].

Studying PS at large spatial scales is important because the scale of study affects the strength of PS. At small scales, patterns of PS can be obscured because co-occurring species represent just a small fraction of the species richness of clades [20,21]. Small spatial scales encompass limited environmental variation, so the species pool is limited to representatives of different lineages that may have similar ecological traits and environmental requirements: this pattern results in a smaller range in traits and low PS. The strength of this effect depends on how environmental variability changes with spatial scale, on the degree of habitat specialization by species and the proportion of clades that are sampled in small-scale studies [6,7]. However, in general, larger spatial scales incorporate greater environmental heterogeneity and encompass a larger number of lineages with a wider range of trait values. Inferring patterns of PS that are more representative of evolutionary trends therefore typically requires measurement across large spatial scales, including a wide range of environmental conditions and taxa from a broad array of clades [22].

The patterns of PS also depend on traits under investigation and their specific evolutionary history. Some traits may exhibit phylogenetic conservatism where traits in specific lineages are constrained to certain trait values. For example, complex traits, such as growth and mortality, may depend in complex ways on multiple, interacting gene loci [23,24] which impose strong constraints on trait variation. Alternatively, traits may show no PS because they are under strong selective pressure and/or because they show phenotypic plasticity in response to environmental conditions [20,25].

Here, we use a large dataset of several hundred permanent forest plots that occur across a wide range of the environmental conditions from all nine Amazonian countries [26], to quantify key demographic traits of more than 300 lineages of tropical trees and explore the PS of these traits using recently published molecular genus- [13] and species-level phylogenies ([27], KG Dexter & RT Pennington 2013, unpublished data). By exploring how traits are correlated and the strength of PS, our goal is to address the fundamental question of whether repeated convergent and divergent evolution of life-history strategies has erased PS for life-history-related traits in tropical trees, or whether phylogenetic information can be used to understand ecosystem function in the world’s most diverse and ecologically important forest.

Material and methods (a) Plot data This study used inventory data from all trees and palms greater than or equal to 10 cm diameter (DBH) in 577 forest plots from the RAINFOR forest plot network (figure 1; electronic supplementary material, S1) across lowland closed-canopy South American tropical forests. This network is centred on Amazonia and includes plots in forests on the Guiana Shield, in the Choco and northern South America; however, hereafter for simplicity we refer to this sampling region as ‘Amazonia’. Plots are located in old growth, unlogged forests and range in size from 0.04 to 25 ha (most being 1 ha). They span a precipitation gradient from 1300 to 7436 mm yr−1 [28], a broad range of soil types [29], and are found below 500 m in elevation. Data were extracted from the ForestPlots.net database which curates tree-by-tree records from RAINFOR and other plot networks [26,30].

Figure 1. Figure 1. Map of location of 577 selected plots in lowland tropical South America over a backcloth of the precipitation gradient (annual precipitation, from the WorldClim dataset [28]). The map shows plots, with annual precipitation greater than 1300 mm yr−1 and altitude less than 500 m. Yellow circles: single census, plots used exclusively for estimating wood density and potential tree size; red circles: multi censuses, plots used for estimating wood density, potential tree size, growth and mortality rates.

Download figureOpen in new tabDownload PowerPoint For productivity and mortality analyses, we used a subset of 257 repeated census plots with a minimum monitoring period of 2 years from 1962 to 2014. Mean census interval length is 4.4 years and plot mean total monitoring period is 9.9 years. During each census, all surviving trees and palms were measured, dead trees were documented and new trees with greater than or equal to 10 cm DBH were recorded. More detailed measurement methods and plot characteristics have been previously published (e.g. [31,32]). All recorded species and genus names were checked and standardized using the Taxonomic Name Resolution Service [33]. We excluded all trees and palms not identified to genus level (7.9% of stems).

(b) Trait data Trait mean values of potential tree size, mean and maximum growth rates, mortality rates and wood density were calculated at both the genus and species levels. Our main analyses were performed at the genus level and covered genera present in a recently published genus-level phylogeny for Amazonian trees [13]. Species-level trait data for those clades where we had species-level phylogenies with sufficient sampling of species in our dataset (more than 20 species): Burseraceae [27] and Inga (KG Dexter & RT Pennington 2013, unpublished data) were used to investigate whether patterns of PS at the genus level were consistent with species-level patterns. Species-level trait data were also used to account for intrageneric variation in the genus-level analyses of PS: the species-level data were used to calculate the standard error of each trait within each genus, and these values were incorporated into the calculations of PS (described below) [34]. In the methods below, all the details are given for trait values calculated at the genus level; similar calculations and methods were used at the species level.

Potential tree size, mean and maximum growth rates were all calculated in terms of tree diameter, basal area and biomass for each genus with at least 20 individuals across multiple censuses.

Potential tree size was estimated as the 95th percentile of the size distribution of all trees within each genus. For trees with multiple measurements, we selected the maximum size across different censuses to define these distributions. Tree above-ground biomass per stem was calculated using the pan-tropical, three parameter allometric equation (diameter, wood density and E) of Chave, Rejou-Mechain [35], which assumes that tree diameter–height relationships depend linearly on bioclimatic variables (E), where E is a measurement of environmental stress based on measures of temperature seasonality and precipitation seasonality derived from the WorldClim dataset [28] and a measure of Climatic Water Deficit extracted from a global gridded dataset [35]. Palm biomass was estimated using a palm-specific allometric equation based on diameter [36].

For each genus, we computed both mean growth rate and the 95th percentile of growth rates, to represent maximum growth rates within each genus, across all stems. To calculate these parameters, mean stem-level growth rate was first estimated as the mean growth per year across multiple censuses and maximum stem-level growth as the maximum growth rate per year calculated across multiple censuses. Trees with mean negative growth rates (0.9% of stems) were excluded in order to normalize the data (similar to [37]). We also excluded palms, which do not have secondary growth, nine trees exhibiting diameter growth greater than 80 mm yr−1 which may represent recording errors and stems where diameter measurements were not made using a tape measure (0.12% of all stems). If a change in the point of measurement (POM) was made during the measurement record of any given tree, we calculated growth rates using the arithmetic mean of the diameter measured at the original POM and the diameter at the new POM [38].

Mortality rates were estimated for all genera with a minimum of 100 individuals in the plot data, based on the number of individuals found alive in the initial and final censuses of each plot. To estimate average mortality rates within each genus, the survival probability of individual trees within each clade was modelled as an exponentially declining function of the monitoring period while accounting for variation in tree size [39,40].

To account for the wide range of environmental conditions across plots [29], we used mixed models to calculate genus-level values of potential tree size, mean and maximum growth rates and mortality rates while accounting for systematic variation in these parameters among plots [40] (see the electronic supplementary material, S2).

Wood density data were extracted from the Global Wood Density database [41,42] and average values calculated for each genus in the phylogeny [43].

(c) Trait correlations To identify relationships among genus-level traits, we conducted a phylogenetic principal component analysis (PPCA) [44] including genera where we have a complete set of trait data. PPCA incorporates the expected correlation among traits due to their shared evolutionary history into the principal component analyses [45]. We standardized trait values to a mean of zero and unit variance to ensure that each trait contributed equally to the PPCA.

(d) Phylogenetic signal In order to estimate PS for traits, we used Blomberg’s K [1]. This metric quantifies the amount of variance in an observed trait in relation to the expected trait variance under a Brownian motion model of evolution [1,4]. Under this model of evolution, trait values drift randomly over time, with small changes being more likely than large changes within a given unit of time (trait values at t1 are chosen from a normal distribution centred on the trait value at t0). This model generates trait data where the covariance among trait values for taxa is proportional to the duration of their shared evolutionary history [4]. Values of K equal to 0 indicate that there is no PS, while K equal to 1 indicates high PS and is the expected value under a Brownian motion model of evolution. Intermediate values (0 < K < 1) indicate intermediate levels of PS. To assess significance in K, we recalculated K on the tree with randomized tips a thousand times, and compared the simulated values with the observed value of K. If the observed value fell outside the range given by 2.5–97.5 percentiles of the simulated values, this value was considered significant.

We accounted for intrageneric trait variation in the calculation of K by measuring the standard error for each genus, treating individual genera as species and intrageneric variation as intraspecific variation sensu [34]. For genera where the standard error could not be computed, we assigned the mean value of the standard error for all genera with estimates for multiple species [34]. Including this within-genus variation allows us to account for uncertainty in trait estimation (e.g. population variation and measurement error), improve parameter estimation and reduce bias in the calculation of PS [1,34].

We also calculated PS using Pagel’s λ [46] in order to explore whether our results were dependent on the particular method used to calculate PS (see the electronic supplementary material, S3).

(e) Sensitivity analysis To investigate whether our results were affected by the spatial scale of our study, we repeated our analyses using 26 plots within 55 km of each other near Manaus. Similarly, to verify whether our results were affected by our use of genus-level data, we conducted the same analyses at the species level for the genus Inga and the Protieae (Burseraceae). Likewise, to investigate whether the number of lineages included in the analyses affected the extent of PS, we repeated the calculations of PS with just the genera with a complete set of trait values (214).

Statistical analyses were performed in the R v. 3.1.1 program [47], using ape [48], phytools [44] and data.table [49] packages.

Results (a) Trait data All traits measured varied substantially among genera (table 1 and figure 2): wood density varied eightfold, potential size in tree diameter 12-fold, potential size in biomass 814-fold, maximum growth rates in tree diameter 23-fold, mean diameter growth rates 35-fold and mortality rates 275-fold. Overall, the trait values after correcting for environmental variation and those estimated directly from the database without accounting for variation among plots were highly correlated with each other (p < 0.001 in all cases and τ ranging from 0.59 to 0.79).

Figure 2. Figure 2. Phylogeny (based on rbcL and matK plastid gene) of 497 Amazonian tree and palm genera. Number of genera varied in the different phylogenies according to the selection criterion for each trait (see Material and methods). Branches are coloured according to (a) wood density (wd g.cm3), (b) potential tree size in diameter (Max.D cm), (c) maximum tree growth in diameter (Max.gr cm yr−1) and (d) mortality rates (% yr−1). Continuous traits were coloured using a continuous colour gradient, with colour codes indicate the wide range of trait values, from blue to red, indicating higher and lower trait values, respectively. Phylogenies for each trait with all tips labelled are available in the electronic supplementary material (S6).

Download figureOpen in new tabDownload PowerPoint Table 1. Summary of trait data, including number of genera per trait, number of species and number of individuals used for selection criterion, minimum, maximum and mean trait values per genera. In addition, PS for absolute trait values, accounting for intrageneric variation, environmental variation and both environmental and intrageneric variation. PS measured using Blomberg’s K. Statistical significance in probability tests are indicated by asterisks.

View inlineView popup ***p < 0.001, **p < 0.05, *p < 0.1.

(b) Trait relationships Trait associations among lineages were analysed with a PPCA: 83% of the variation in the four-dimensional space was accounted for by the first two axes (figure 3). The first axis (PPCA1) explained 52.8% of the variation and shows strong positive loadings for mortality and maximum growth rates, while wood density was negatively associated with this axis (electronic supplementary material, S4). PPCA1 thus represents a continuum from pioneer and light demanding lineages with low wood density and fast demographic traits (e.g. high mortality and growth rates) to non-pioneer lineages with high wood density and slow demographic rates. The second axis (PPCA2) explained 30.5% of the variation and was associated more closely with potential tree size, and reflects the variation from individuals of understory genera, to individuals of canopy and emergent lineages (figure 3).

Figure 3. Figure 3. PPCAs for the first two principal components with PC loadings for the four traits studied here: wood density, potential tree size in terms of diameter (Max. diameter), potential growth rates in terms of diameter (Maxgr. growth rate) and annual mortality rates (Mortality rate). Points represent 221 genera of trees; position of 22 key genera marked in bold and named.

Download figureOpen in new tabDownload PowerPoint (c) Phylogenetic signal All traits and the first two PPCA axes exhibited significant PS, with closely related genera being more similar than expected by chance, using either Bloomberg’s K (table 1) or Pagel’s λ (electronic supplementary material, S3). Because estimates of Pagel’s λ and Blomberg’s K are strongly correlated and most studies of PS in tropical trees have focused on the K metric rather than λ, we focus our results and discussion on the calculations using Blomberg’s K-value.

Traits showed significant and similar values for K, varying from 0.25 to 0.39 and from 0.17 to 0.27, with and without accounting for intrageneric variation, respectively. These K-values indicate that evolutionarily related genera tend to be more similar to each other, but less than expected under a BM model of evolution (table 1). Finally, removing the environmental contribution to trait variation did not substantially alter the magnitude of PS (table 1).

(d) Sensitivity analyses Although using just the Manaus plot data significantly reduced the number of genera, species and individual trees included in the analyses, PS at smaller spatial scales showed similar patterns to PS calculated using the whole dataset (electronic supplementary material, S5). Similarly, reducing the number of lineages to genera we had all trait values showed congruent patterns of PS (electronic supplementary material, S5). In addition, all traits showed similar or slightly higher Blomberg’s K values for just Inga or Protieae than for all taxa together (electronic supplementary material, S5).

Discussion This is the first study, to our knowledge, to investigate the extent of PS for traits that quantify the main axes of life-history variation in survival and growth of trees at such a large phylogenetic and spatial scale. Our results demonstrate that for Amazonian forests, closely related genera have similar life-history strategies, with all traits showing similar levels of PS (table 1 and figure 2; electronic supplementary material, S6). The similar level of PS found across all the different, correlated traits suggests that the main axes of life-history variation among lineages of Amazonian trees may represent the result of repeated evolution of a suite of coordinated functional characteristics.

(a) Relationships among traits Strong correlations among traits were represented by two major axes of variation, which are likely to be associated with adaptations to horizontal and vertical light gradients. Ecological differences among species adapted to gaps versus the shaded understory or to the understory versus the canopy are well established as the principal axes of functional variation among tropical forest tree species [50,51]. The first axis runs from pioneer and light demanding genera with low wood density and fast demographic traits (e.g. high mortality and high growth rates) to shade tolerant genera with dense wood and slow demographic traits. The second axis represents variation in tree size and contrasts understorey genera, from lineages of canopy trees. For example, these axes distinguish Cecropia and Croton, classic pioneers with low wood density and fast demographic traits, from Hirtella—a typically dense-wooded and slow-growing understory genus of trees. Lineages of emergent trees which all achieve very large potential tree sizes (e.g. Bertholletia, Ceiba, Hura, Dipteryx), are also distinguished in this analysis by their different wood densities and growth rates (figure 3).

(b) Phylogenetic signal Our results demonstrate significant levels of PS among demographic and structural traits of tropical trees, with Blomberg’s K ranging from 0.25 to 0.39. This pattern suggests that evolutionary relationships provide useful information about the ecological similarity of these lineages. However, while our analyses of PS shows that evolutionarily related lineages have more similar traits than expected by chance, their values are lower than expected under a pure BM model of evolution (table 1 and figure 2) under which K-values would be close to 1. PS can be lower than expected under BM if there is convergent evolution across distantly related lineages and/or divergent selection among closely related groups [3,4]. This result therefore suggests that there has been repeated convergent evolution and/or divergent selection, along the two main axes of variation identified by the PPCA analysis (figure 3). This finding suggests that adaptations to light gaps, or understorey and canopy light environments, have repeatedly evolved within multiple lineages of tropical trees as shown by the different pioneer and shade tolerant genera within a series of unrelated families (e.g. Cecropia versus Brosimum (Urticaceae/Moraceae), Vismia versus Calophyllum (Clusiaceae) and Inga versus Dipteryx/Parkia (Fabaceae); figure 2).

(c) Sensitivity analyses The PS found here for trees across lowland closed-canopy South American forests is generally stronger than previously reported in the literature for tropical forests in smaller scale analyses (electronic supplementary material, S7). In previous studies, some traits showed low but significant PS [6,7,13], while others have even found that traits are randomly dispersed over the phylogeny [10,11]. However, although K-values are standardized to allow comparison between traits and phylogenetic trees [1,4], direct comparisons of PS are affected by differences in the spatial and taxonomic scale of the studies, the number of lineages and the use of different kinds of phylogenies.

A first issue for comparing the extent of PS among studies is variation in spatial scales. However, here we show that the higher PS in this study is unlikely to be an artefact of our larger spatial scale: restricting our analyses to 26 plots around Manaus shows consistent patterns, with similar levels of PS for all traits compared to analyses for the whole Amazon (electronic supplementary material, S5).

Secondly, different numbers of lineages in different studies may play a role in determining variation in the extent of PS. Although Blomberg’s K is efficient at detecting the strength of similarity among closely related lineages for sample sizes greater than 20 [1], the ability to detect different levels of PS may increase with larger sample sizes [52]. To address this issue, we conducted a set of analyses restricted to genera for which we had all trait values (214 genera). As estimates of K are highly consistent when we include fewer genera (electronic supplementary material, S5), it appears that the number of lineages is unlikely to have caused the observed trends of high levels of PS for our traits.

Thirdly, most of previous studies [6,7,10–12] were conducted at the species level, and taxonomic scale can also affect the degree of PS. PS in any trait may vary at different taxonomic scales; a single trait can have high similarity at one level (e.g. genus level) but this pattern can break down at higher or lower taxonomic levels [52]. Here, the PS of these traits at the species level within the Protieae and Inga were similar or slightly greater than for the genus-level results (electronic supplementary material, S5), suggesting that our results are consistent at finer taxonomic levels. However, as our analyses at low taxonomic levels were limited to two lineages it remains to be fully tested whether the result indeed holds within all clades of Neotropical trees.

Finally, the use of different kinds of phylogenies is likely to affect the extent of similarity among related species that is reported in different studies (electronic supplementary material, S7). Much previous work was carried out using community-level phylogenies, restricted to locally co-occurring species [6,12] and in many cases using unresolved phylogenies with relationships represented as polytomies [11]. Such community-level phylogenies may lack sister lineages for many clades that may be critical to effectively measure PS. In addition, the use of trees with many polytomies, e.g. those which add genera and species as polytomies onto backbone family-level trees [53], leads to uncertainty in PS estimates [14]. More importantly, phylogenetic sampling may play a major role in determining the extent of PS. Although the genus-level phylogeny used here is far from complete, our analyses do encompass a far wider range of lineages than previous studies, including the major angiosperm lineages present in the Amazon basin.

Our results demonstrate that there is significant PS for key demographic and structural traits in tropical forests. This finding opens the way for clearer predictions of how evolutionary diversity relates to ecosystem structure and function, and how different drivers will, in turn, affect the evolutionary diversity of Amazonian forests. For example, this study suggests that community-level measures of evolutionary relatedness among species are likely to be good predictors of the structure and functioning of these ecosystems [8,9]. These results also indicate that changes in environmental conditions or disturbance regimes that favour particular life-history strategies will ultimately erode evolutionary diversity [54,55], although the presence of some convergent evolution across lineages may prevent significant loss of phylogenetic diversity over some scales of anthropogenic disturbance [56]. Our results may therefore help to resolve why different studies of the effect of disturbance on phylogenetic diversity have obtained contrasting results [54–56]: in particular, this study suggests that investigating the PS of traits that influence species ability to persist after disturbance within the species pool of interest will be critical to understand how disturbance will alter phylogenetic diversity. Finally, our results also suggest that any long-term changes in the evolutionary diversity of intact Amazonian forests may indicate functional shifts in these diverse ecosystems. Overall, the phylogenetic structure of life-history strategies within Amazon tree communities described in this study helps to provide a predictive framework to understand how such complex systems will respond to global change and anthropogenic disturbance.

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Summary of the Study on Phylogenetic Signal in Amazonian Tree Traits

Abstract

The study investigates how evolutionary heritage and selection have influenced the ecological characteristics of Amazonian tree lineages. Using trait data from 577 inventory plots and a phylogenetic hypothesis covering over 300 genera, researchers tested for evolutionary constraints on key life-history traits. The findings indicate significant phylogenetic signal (PS) for all traits studied, meaning that related genera exhibit more similar ecological characteristics than expected by chance. However, repeated evolution of different life-history strategies suggests both evolutionary constraints and ecological adaptation. These insights help predict how phylogenetic diversity relates to ecosystem function and how Amazonian forests might respond to global change.

1. Introduction

Species tend to retain the ecological characteristics of their ancestors due to evolutionary constraints, but selection and convergent evolution can weaken this pattern. Understanding whether closely related species have similar ecological traits requires testing, not assumptions.

Phylogenetic signal (PS) quantifies the extent to which related species share trait values.
Significant PS implies that phylogenetic diversity can serve as a proxy for functional diversity and help predict ecosystem functions.
If PS is present, it also allows researchers to infer missing trait values for poorly studied species.

The Amazon rainforest, with its high angiosperm diversity and varied life-history traits, offers an ideal system to test for PS. Previous studies on tropical trees have produced mixed results regarding PS in different traits. This study aims to clarify the extent of PS in tree growth, mortality rates, wood density, and potential tree size, as these traits are crucial for understanding plant performance and ecosystem processes.

2. Methods

(a) Plot Data

The study used 577 forest plots from the RAINFOR network, covering old-growth forests across Amazonia, spanning:

Precipitation range: 1,300 to 7,436 mm/year.
Soil types: Diverse.
Elevation: Below 500m.

A subset of 257 plots with multiple censuses (average monitoring period: 9.9 years) was used for productivity and mortality analyses. Tree species and genus names were standardized, and unidentified trees were excluded.

(b) Trait Data

Five traits were analyzed:

Potential tree size – Estimated using the 95th percentile of tree size distribution.
Mean and maximum growth rates – Based on repeated diameter measurements.
Mortality rates – Estimated from survival probability models.
Wood density – Extracted from the Global Wood Density Database.

Trait values were corrected for environmental variation using mixed models to avoid biases due to local conditions.

(c) Trait Correlations

A phylogenetic principal component analysis (PPCA) was conducted to determine trait relationships:

PC1 (52.8% of variation): Contrasts pioneer species (low wood density, fast growth/mortality) with shade-tolerant species (high wood density, slow growth/mortality).
PC2 (30.5% of variation): Separates small understory trees from large canopy and emergent trees.

(d) Phylogenetic Signal (PS) Measurement

PS was assessed using:

Blomberg’s K (variance-based measure).
Pagel’s λ (alternative measure for robustness check).

Values near 1 suggest a strong PS (closely related species share traits), while values near 0 indicate no phylogenetic pattern.

(e) Sensitivity Analyses

To test robustness, analyses were repeated:

Using only 26 plots near Manaus (smaller spatial scale).
At the species level for Inga and Protieae (Burseraceae).
With a reduced dataset including only genera with complete trait data.

3. Results

(a) Trait Variability

Traits varied widely among genera (e.g., mortality rates varied 275-fold, maximum growth rates 23-fold).
Trait values corrected for environmental variation were highly correlated with raw data (τ = 0.59–0.79, p < 0.001).

(b) Trait Relationships

Two major functional axes emerged:
- Axis 1: Distinguishes fast-growing pioneers (e.g., Cecropia, Croton) from shade-tolerant species (e.g., Hirtella).
- Axis 2: Separates understory species from large emergent trees (e.g., Bertholletia, Ceiba).

(c) Phylogenetic Signal

All traits exhibited significant PS, meaning related genera had more similar traits than expected by chance.
Blomberg’s K ranged from 0.25 to 0.39, indicating moderate PS.
Pagel’s λ results were consistent with Blomberg’s K.

(d) Sensitivity Analyses

PS patterns remained stable across different spatial scales, taxonomic levels, and dataset reductions.
Inga and Protieae species-level analyses showed slightly higher PS than genus-level results.

4. Discussion

(a) Relationships Among Traits

The main axes of variation align with adaptations to light availability:
- Pioneer species: Low wood density, high growth and mortality rates.
- Shade-tolerant species: Dense wood, slow growth and mortality rates.
- Emergent canopy species: Large size, varied growth strategies.

(b) Phylogenetic Signal

Significant PS suggests evolutionary heritage strongly influences tree functional traits.
Lower-than-expected K-values (<1) indicate that while related species are similar, convergent evolution and divergent selection have played roles in shaping traits.
Examples of convergent evolution:
- Cecropia (Urticaceae) and Vismia (Clusiaceae) evolved as pioneers despite distant ancestry.
- Dipteryx and Parkia (Fabaceae) show independent adaptations to large size and canopy dominance.

(c) Sensitivity and Comparison to Past Studies

Stronger PS was found compared to previous studies, likely due to:
- Larger spatial and taxonomic scale.
- More complete phylogenies (including major Amazonian angiosperm lineages).
Key methodological improvements:
- Avoiding polytomies in phylogenetic trees.
- Using molecular phylogenies rather than community-level trees.

5. Implications for Amazonian Ecosystems

Evolutionary relationships help predict species’ ecological functions.
Phylogenetic diversity can serve as a proxy for ecosystem function.
Forest disturbance and environmental changes could erode evolutionary diversity by favoring certain life-history traits.
Convergent evolution may buffer against phylogenetic diversity loss, depending on disturbance scale.
Long-term shifts in phylogenetic diversity could indicate functional changes in Amazonian forests.

Conclusion

This study provides strong evidence for phylogenetic constraints on key functional traits in Amazonian trees, while also highlighting the role of selection and convergent evolution in shaping species strategies. These insights improve our understanding of how biodiversity influences ecosystem function and offer predictive power for modeling responses to climate change and anthropogenic disturbances.

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Comparison of dendroclimatic relationships using multiple tree-ring indicators (tree-ring width and δ13C) from Masson pine Hongliang Gu, Jian Wang, Chao Lei and Lijuan Ma Published:07 July 2021https://doi.org/10.1098/rsos.201259 Review history Abstract This study addressed the effects of climate drivers on the tree-ring width (TRW) parameters (total ring width (TR), earlywood width (EW) and latewood width (LW)) and the total ring δ13C series of different wood components (whole wood, α-cellulose and holocelluose) from Masson pine in subtropical China. Pairwise correlation coefficients between three ring width parameters were statistically significant. EW and LW did not reveal much stronger climate sensitivity rather than TR. This indicated that the use of intra-annual ring width has little benefit in extracting more climate information. The mean δ13C series of the three components of the total ring had the strongest climate response to the July–September relative humidity (r = −0.792 (whole wood), −0.758 (holocellulose) and −0.769 (α-cellulose)). There are no significant differences in the dendroclimatic relationships of the δ13C series of different wood components. Through both stationary temporal and spatial-statistical perspectives, the moisture drivers (summer/autumn) had a significant impact on three ring width parameters and three components of Masson pine. Overall, the radial growth and the δ13C series showed different responses to the same climate drivers during the same period. Moreover, the R-squared values of the strongest climate-proxy correlation coefficients were smaller than 50% for TRW. Consequently, the δ13C series of Masson pine may be a more representative climate proxy than TRW parameters for dendroclimatology in subtropical China.

Introduction Subtropical China (22°–34° N, 98°–122° E) is an important socio-economic development area, with high population density and complex biodiversity [1]. In recent years, due to climate change, extreme climate events, such as floods and freezing disasters, have occurred frequently, posing risks to public life and property in this region [2,3]. Therefore, assessing the long-term impact of climate and environmental change on forest dynamics in this region is beneficial to our work on climate disaster risk management. According to the advantages of long-term records, high resolution and wide spread, tree rings have been proven to be a useful natural archive for climate change research [4–7]. At present, dendroclimatology research works are increasingly carried out using tree-ring width (TRW) in subtropical China. One of the most frequently studied species is Masson pine [2,8–15]. It is one of the most widely distributed and abundant tree species in subtropical China [16,17]. However, due to the warm and humid environment, subtropical forests and woodlands have highly diverse developing plants and diverse wood anatomy. So the relationship between tree rings and climate drivers is complex [18–20]. Hence, to some extent, dendroclimatology research in subtropical China has been hampered thus far.

Progress in dendrochronology techniques allows for using additional tree-ring indicators to enhance the climate signal and extend the reconstruction season or reconstruct different climate environmental variables [21]. For example, compared with the total ring width (TR), earlywood width (EW) and latewood width (LW) have higher temporal resolution than TR (EW + LW) and may show more valuable climate and environment signals [22,23]. Some studies have shown that cross-dating was statistically more significant with LW than with TR [24], and LW appeared to have the strongest sensitivity to climate drivers [22,23,25]. Other researchers also reported that the EW revealed the strongest correlation with the early summer hydroclimatic signal [26,27].

On the other hand, compared with TRW, the stable carbon isotope ratio (δ13C) of tree rings has become a more reliable and representative climate proxy for palaeoclimate, palaeoenvironment, and global change ecology research in humid and warm areas [28–30]. However, due to the complex mixture of different organic compounds (whole wood (WW), holocellulose (HC) and α-cellulose (AC)) in tree rings, there are still differing opinions on which component is best for the extraction of climate change data [31–34]. Studies have shown that δ13C series of WW are better at extracting climate information than other organic compounds [32]. In comparison with other components, some researchers believe that cellulose has a unique composition, is easy to extract [35,36] and is more sensitive to climate change [37,38].

Consequently, to trace past climate change, conducting multiple tree-ring parameters should be widely developed in subtropical China. To our best knowledge, there has been little previous research performing a comprehensive analysis of TRW (TR, EW and LW) and targeting the δ13C series of different components of Masson pine. Additionally, we found that the boundaries of EW and LW from Masson pine are easily distinguishable. Thus, it is feasible to investigate the potential of the multiple proxies (TR, EW, LW, WW, HC and AC) of Masson pine for dendroclimatology studies. The questions we addressed are as follows: (i) How about the statistical and climate sensitivity properties of different TRW parameters from Masson pine? (ii) Are there differences in the dendroclimatic relationships of the δ13C series of different wood components? (iii) Between the δ13C of the total ring and the radial growth merits, which indicator is more sensitive to climate?

Material and methods 2.1. Study area The mapped distribution of Masson pine forests in subtropical China used in this study is presented in figure 1a. Masson pine spatial distribution raster datasets were extracted from the vegetation map of China (scale 1 : 1 million). In the northern Masson pine spatial distribution region, the Tongbai Mountains (31°49′–32°24′ N, 113°16′–114°03′ E, the highest elevation is approximately 1794 m.a.s.l.) are located at the junction of Henan and Hubei provinces near the northern margin of the Masson pine distribution. The northwest border is the Nanyang Basin, and the southeast border is the Dabie Mountains, which are more than 120 km long. The interior of the Tongbai Mountains experience a continental monsoon climate zone, have brown and grey soil, and are dominated by native Masson pine forest. The woodland area is 839 km2, and the forest coverage rate attains 38.7%. The annual mean temperature is 15°C. January is the coldest month (2°C, figure 1b), and July is the warmest month (28°C). The long-term annual mean precipitation is 1168 mm in this region. The mild temperature and long daylight in summer are beneficial for the growth of plants. Because the north region of the Tongbai Mountains lacks alpine barriers, cold-air outbreaks from the north can move straight through this region in winter and the cold winter is relatively long.

Figure 1. Figure 1. Study area characteristics. (a) Masson pine distribution [17]. (b) Mean monthly climate drivers (temperature, minimum temperature, maximum temperature, precipitation and relative humidity) in the study area. (c) Distribution of the long-term annual precipitation total over subtropical China. The black star shows the meteorological station, and black triangles show the sampling area (‘TBS’ denotes Tongbaishan, ‘JGS’ denotes Jigongshan).

Download figureOpen in new tabDownload PowerPoint 2.2. Field sampling and tree-ring width chronology construction At each sampling area (Tongbaishan (32°24′ N, 113°16′ E) and Jigongshan (31°49′ N, 114°03′ E)), we selected two sites from shady and sunny slopes for tree-ring sampling. In total, 65 trees (mean diameter at breast height was approx. 1.3 m) were selected from edge trees or isolated trees with low canopy density in forests to reduce the impact of low-frequency changes in the TRW series due to tree competition. At approximately 1.2 m above the ground on both sides of the tree stem parallel to the direction of the slope, a 5.5 mm diameter increment corer was used to sample trees without conditions such as heart rot and scars. After drying them in the ambient air, the tree-ring cores were polished to facilitate ring identification [39]. A skeleton plot was first used to validate the preliminary calendar age of each tree ring, omitting the parts of the disc showing pseudo or absent tree rings [40]. Afterwards, TRW was measured using the AcuRite measuring system (0.001 mm) (VoorTech, NY, USA). Finally, the COFECHA program was used to identify the quality of the cross-dating [41,42], and TRW chronologies were calculated by the ARSTAN program [43]. The trend in tree growth was detrended by using the traditional method (i.e. negative exponential curve) [40].

2.3. Stable carbon isotope pretreatment and determination To split the rings to obtain the stable carbon isotope signatures, tree-ring cores (12 mm diameter increment) were obtained from four relatively old trees in the study area, that is, trees with relatively longer TRW chronologies [31]. Sequentially, the tree cores were placed on clean glass plates for rasping. Four rasped increment cores of the same ring per tree were cut and mixed together. Then, they were packaged in clean Kraft paper bags and numbered accordingly. Each tree-ring sample was obtained by carving out individual whole rings, drying them for 3 days at 70–80°C and then grinding them to pass through a 100-mesh sieve. Part of each sample was collected for the determination of the δ13C signal of WW, and the rest was used for cellulose extraction. HC and AC from wood samples were extracted following the standard procedures [44–47]. Finally, the isotope 13C/12C ratios were determined with a Thermo Finnigan-Deltaplus XP isotope mass spectrometer (IRMS). The 13C/12C ratios, referenced against the standard δ13C (PDB), were expressed as 13Cp in ‰ [48]. The analytical precision was typically within ± 0.1‰. The cellulose in individual tree-ring samples was analysed separately. Compared with the pooling method (mixing wood of the same year from several trees) [49], the calculation of the average value of δ13C after measuring the δ13C values from individuals may offer ‘representative’ isotopic values [50]. Therefore, the δ13C values for each individual were measured, and the synthetic sequence of δ13C in each study area was calculated using the arithmetic average method. Because the atmospheric δ13C concentration has exhibited a significant downward trend since the beginning of the Industrial Revolution, we also mathematically removed the impact of atmospheric δ13C on tree-ring δ13C [51,52].

2.4. Climate material To obtain a complete and accurate climate dataset, the Xinyang meteorological station (32°08’ N, 114°03’ E, 114.5 m.a.s.l.) was used in the dendroclimatic analysis. The station is located near the sampling sites and has complete meteorological data (http://data.cma.cn/). The distance between the Xinyang station and the sample sites is approximately 53 km (figure 1c). The station is located at the point in this region where the difference between the accumulated annual and monthly temperature is the largest, the annual average daylight duration is the longest and the relative humidity is the lowest. The peak of relative humidity appears in August, the annual average precipitation is 1097 mm and the frost-free period is 227 days. December is the driest month (21 mm), and May–August is the wettest period, especially when the monthly sum precipitation reaches the maximum (211 mm) in July.

2.5. Statistical analysis methods The statistical analyses of the tree-ring chronologies were performed in the dplR package [53]. To determine the climate drivers affecting the annual radial growth of Masson pine, simple and partial correlations [54,55] were carried out on tree-ring proxies and climate records from the previous June to the following September. Correlation analysis was performed with the first-order difference of the time series to check whether the significant correlation was caused by an actual trend or by the high-frequency variations of the time series. The significance of each correlation was evaluated using the bootstrapping method (1000 Monte Carlo simulations) [54]. Based on the correlation analysis, the main climate factors affecting the radial growth of trees in the study area were determined.

Additionally, a seasonal correlation analysis was carried out, and its temporal stability was tested by running a 35-year moving window with a 4-year offset [55]. To avoid a shortage of samples and the ‘juvenile effect’, the calendar period AD 1982–2011 was selected for δ13C determination in the Tongbai Mountains. Finally, spatial correlation analysis was carried out with CRU TS4.02 grid data (http://climexp.knmi.nl) to explore the spatial significance of tree-ring proxies for climate drivers in this study area.

Results 3.1. Statistical characteristics of three ring width parameters The statistical characteristics of the chronologies of Masson pine tree rings are shown in table 1. The time span of the tree-ring samples ranged from AD 1911 to 2013. The mean width (1294.02 µm) of EW was larger than that of LW (938.55 µm) in Masson pine. Overall, EW had the most variable signal as indicated by the standard deviations, compared with the corresponding means (table 1). The average sensitivity of TRW was approximately 0.2. In addition, the average sensitivity of the EW and LW chronologies was only slightly higher than that of the TR chronology. The signal-to-noise ratio, expressed population signal, and variance of PC1 were generally high for each tree-ring parameter, whereas LW had the lowest statistical values (table 1). Overall, the quality of the tree-ring chronologies in our study area met the requirements for the tree-ring climatology research.

Table 1. Statistical characteristics of TRW chronologies. Note: MW (mean width), MS (mean sensitivity) [56], SD (standard deviation), SNR (signal-to-noise ratio) [57], AC1 (intercorrelation), AR1 (first-order autocorrelation), EPS (expressed population signal) [58], PC1 (variance of PC1) and Gini (Gini difference) [59].

View inlineView popup Generally, running-window statistics were used to assess the temporal patterns of the signal strength from three tree-ring parameters. The values of Rbar were below the 0.2 level for TR and LW during AD 1960–1970, while they exceeded this level for EW over the entire period (figure 2). Additionally, EPS values also validated the above results for the three tree-ring proxies. The EPS values for the three tree-ring parameters fluctuated over time but mostly ran together well above the critical threshold (0.85) [60]. Consequently, EW seems to remain the most stable and robust with time. In addition, EW makes up approximately 57.96% of the mean annual TR, which means that the TR signal may be dominated by EW variability. The correlation between TR and EW had the highest coefficient (0.94), and that between TR and LW revealed a weaker relationship (0.88) (table 2). The correlation coefficient for the relationship between EW and LW was 0.53.

Figure 2. Figure 2. The chronologies and signal strength statistics of Masson pine. (a) TR, (b) EW, (c) latewood width (LW) and (d) mean inter-series correlation (Rbar) and sample depth.

Download figureOpen in new tabDownload PowerPoint Table 2. Pairwise computed correlation coefficients of TRW/δ13C chronologies.

View inlineView popup 3.2. The statistical characteristics of δ13C records All δ13C sequences of WW were more depleted than those of HC and AC within individual trees according to two statistical indices (mean, standard deviation) (table 3), because the lighter lignin in wood was extracted from the HC samples but not from the WW samples. The values of WW ranged from −27.49 to −24.593‰, and AC and HC had ranges of −26.75 to −23.144‰ and −26.5 to −22.872‰, respectively. Although there were absolute differences in the values for WW, HC and AC, the sensitivity of different tree-ring components to climate change is evaluated based on the difference between the various trends in the carbon isotope components and the various trends in the climate drivers. Therefore, the differences in the trends of the δ13C series were used to judge whether each component changed based on the climate or not. The phase direction changes among the δ13C series of the three components of the Masson pine chronologies were highly consistent, i.e. they showed consistent upward or downward trends over time (figure 3). The computed pairwise correlation coefficients ranged from 0.91 to 0.94 (table 2). Compared with the incremental proxies, the δ13C series showed much stronger common variability. Moreover, the raw measured data did not display a prominent non-climate downward trend (figure 3), indicating that there was no need for a correction procedure. In addition, to measure the correlation between the previous values and the current value, an autocorrelation function was used to indicate the most useful past values for the current record (figure 4). The value at lag 1 indicates that each series value is correlated with the previous value, and so on. We can see that the lags do not have a significant effect within the confidence bounds, and only at lag 0 does the value appear significant. These findings show that the current value of the δ13C series cannot be obtained using previous values. Consequently, the previous climate drivers were not taken into account in subsequent climate response analyses.

Figure 3. Figure 3. The established δ13C chronologies of different components from Masson pine (different line types denote the mean δ13C records for year by year).

Download figureOpen in new tabDownload PowerPoint Figure 4. Figure 4. The autocorrelation function estimation of δ13C series in three components. Note: WW (a), AC (b) and HC (c).

Download figureOpen in new tabDownload PowerPoint Table 3. The statistic analysis of δ13C records of different components within individual trees (‘SP01’ denotes the sampling sites).

View inlineView popup 3.3. The relation between tree-ring width and key climate drivers 3.3.1. Correlation between tree-ring width and monthly climate drivers The significant correlation between the first-order difference of time series indicated that the tree ring of Masson pine reliably recorded climate signals (electronic supplementary material, figure S1). TR and EW were significantly negatively correlated with the previous mean temperature (i.e. that in June, August, September and October), and only the previous September mean temperature had a statistically significant negative relationship with the three tree-ring parameters (TR (−0.296, p < 0.05), EW (−0.220, p < 0.05) and LW (−0.349, p < 0.05)) (figure 5). Strong climate signals from the previous August maximum temperature (TR (−0.294, p < 0.05), EW (−0.287, p < 0.05), and LW (−0.215, p < 0.05)) were also observed in the three parameters. The previous August minimum temperature had a significant negative influence on TR (−0.192, p < 0.05) and EW (−0.218, p < 0.05), but TR and LW also showed significant relationships with the current March and July minimum temperatures. The three TRW parameters did not have a statistically significant positive relationship with single-month precipitation, except LW. The previous June precipitation had a significant positive effect on LW (0.281, p < 0.05). However, overall, there was a positive relationship between the three width indices and monthly precipitation. The three width indices were positively correlated with relative humidity; TR (0.386, p < 0.05), EW (0.328, p < 0.05) and LW (0.360, p < 0.05) were significantly and positively correlated with the relative humidity in the previous August, and compared with TR (0.374, p < 0.05) and EW (0.481, p < 0.05), the relatively weak sensitivity of LW (0.173, p < 0.1) to the relative humidity in the previous June can be seen.

Figure 5. Figure 5. The bootstrapped correlation coefficients between TRW (TR, EW and LW) and monthly climate drivers from Xinyang meteorological station. Note: black dots indicate the significance level (p < 0.05). ‘tm’ denotes the mean temperature, ‘tmin’ denotes the minimum temperature, ‘tmax’ denotes the maximum temperature, ‘pre’ denotes the total precipitation, ‘rhu’ denotes the relative humidity and ‘evp’ denotes the evaporation. ‘Jun’ denotes the previous June and ‘JUN’ denotes the current June.

Download figureOpen in new tabDownload PowerPoint The correlation between the three width parameters and evaporation was weak, indicating that evaporation has a weak influence on the annual growth of Masson pine in this study area. As could be expected from the high correlation coefficients between EW and TR, fairly similar climate response patterns were found for these two TRW proxies. In summary, the temperature is the limiting factor, while moisture factors (precipitation, relative humidity) can promote the annual growth of Masson pine. Additionally, according to the absolute values of the correlation coefficients relative humidity, especially that in the previous August, exerts a significantly positive impact on tree-ring growth. On the other hand, climate drivers in the previous year seem to have a great influence on the TR and EW, whereas LW may be primarily controlled by the current temperature and moisture environment.

3.3.2. The temporal stability of dendroclimatic relationships between tree-ring width and monthly climate drivers The majority of the correlation coefficients changed signs over time, and only a few monthly climate drivers revealed temporal stability at a significant level. TR and EW showed significant negative relationships with climate drivers in the previous August (mean temperature, maximum temperature and evaporation) and significant stable positive relationships with the relative humidity in the previous August (figures 6 and 7). However, a weak correlation with monthly evaporation for the three TRW indices was shown in figure 5. It can be concluded that there is no sign of a time-independent ‘divergence effect’ on tree radial growth [61]. In addition, compared with TR and EW, LW exhibits obviously different behaviour in terms of the climate–response correlation and temporal stability. LW has a stable significant negative correlation with the mean and maximum temperature in the current July and the relative humidity in the previous August over the period AD 1961–2011 (figure 8). This is also in line with the correlation analysis for the monthly climate drivers (figure 5). For the monthly climate drivers that are significantly related to TRW parameters, although these relationships are not significant in some intervals of time, they do not change the sign of the correlation coefficient with tree growth over time. For example, the previous June mean temperature had a significant negative correlation with EW (figure 5), but the temporal stability test results indicated that the dendroclimatic correlation was not significant during the AD 1974–2008 period (figure 7). In summary, only the previous August relative humidity is significantly positively correlated with TR, EW and LW and shows a significant stable relationship with time. Considering the monthly climate–response patterns and temporal stability, TR and EW may be more sensitive to climate drivers in the previous year, whereas LW may be more susceptible to climate drivers in the current year.

Figure 6. Figure 6. Temporally stationary plot relating the TR of Masson pine to climate drivers from the previous June to the current September. The moving correlation analysis was carried out in windows of 35 years, offset by 4 years. Significant correlations (p < 0.05) are denoted by black dots. ‘Jun’ denotes the previous June, ‘JUN’ denotes the current June; the time periods are ‘A’ (AD 1965–1999), ‘B’ (AD 1968–2002), ‘C’ (AD 1971–2005), ‘D’ (AD 1974–2008) and ‘E’ (AD 1977–2011).

Download figureOpen in new tabDownload PowerPoint Figure 7. Figure 7. Temporally stationary plot relating the EW of Masson pine to climate drivers from the previous June to the current September. The moving correlation analysis was carried out in windows of 35 years, offset by 4 years. Significant correlations (p < 0.05) are denoted by black dots. ‘Jun’ denotes the previous June, ‘JUN’ denotes the current June; the time periods are ‘A’ (AD 1965–1999), ‘B’ (AD 1968–2002), ‘C’ (AD 1971–2005), ‘D’ (AD 1974–2008) and ‘E’ (AD 1977–2011).

Download figureOpen in new tabDownload PowerPoint Figure 8. Figure 8. Temporally stationary plot relating the LW of Masson pine to climate drivers from the previous June to the current September. The moving correlation analysis was carried out in windows of 35 years, offset by 4 years. Significant correlations (p < 0.05) are denoted by black dots. ‘Jun’ denotes the previous June; ‘JUN’ denotes the current June; the time periods are ‘A’ (AD 1965–1999), ‘B’ (AD 1968–2002), ‘C’ (AD 1971–2005), ‘D’ (AD 1974–2008) and ‘E’ (AD 1977–2011).

Download figureOpen in new tabDownload PowerPoint 3.4. The relationships between δ13C series and instrumental climate drivers Only AC and HC were significantly and positively correlated with the current July mean temperature. The interannual variability of the stable carbon isotope ratio showed a generally positive correlation with the monthly minimum temperature, but lacked a significant response. The three components were strongly positively correlated with the current July maximum temperature. Clearly, it appears that the δ13C series of the three components (WW, AC and HC) have strong negative correlations with the July–August and December precipitation. The negative correlation coefficients for the July–September relative humidity are above the 0.05 level for WW, AC and HC. Additionally, these results (figure 9) showed a statistically significant relationship with summer growing season evaporation (July–September). According to Pearson’s correlation analysis, the climate signal within the δ13C series is dominated by the climate in late summer and early autumn. These results indicate that the mean δ13C records for the three components are a better predictor of the variation in local Masson pine trees with different climate drivers than the three TRW parameters.

Figure 9. Figure 9. The correlation coefficients between the δ13C series (WW, AC and HC) and monthly climate drivers. Note: black dots indicate the significance level (p < 0.05). ‘tm’ denotes the mean temperature, ‘tmin’ denotes the minimum temperature, ‘tmax’ denotes the maximum temperature, ‘pre’ denotes the total precipitation, ‘rhu’ denotes the relative humidity and ‘evp’ denotes the evaporation. ‘JUN’ denotes the current June.

Download figureOpen in new tabDownload PowerPoint 3.5. Comparison of climate-proxy relationships between tree-ring width and δ13C series Based on the results of the previous analysis (§3.2–3.4), the common climate driver (relative humidity) has significant effects on the various tree-ring proxies. Therefore, we compared the correlation between the relative humidity and the TRW parameters/δ13C series during their common period (AD 1982–2011) (table 4). The correlation between the δ13C series of different components and relative humidity increased with the length of the considered period, and the maximum correlation coefficient occurred in three-month periods ending with the current September (WW (−0.769), AC (−0.758), HC (−0.792)). The seasonal correlation coefficients were all lower than −0.71. However, the correlation analysis results revealed that the climate signal became weak with the increasing length of the period considered for the TRW parameters. For example, TR had the strongest positive correlation coefficient (0.522) with the relative humidity in the previous August but revealed a slightly weakened climate signal (0.471) when considering the average previous June–August relative humidity. On the other hand, without considering the previous/current year, these results show that the δ13C series may be more sensitive to relative humidity than the TRW parameters during their common period (AD 1982–2011).

Table 4. The significant correlation between monthly relative humidity and tree-ring parameters (tree-ring width and δ13C series) during their common period (AD 1982–2011). (**denotes p < 0.01, *denotes p < 0.05, JUL–SEP indicates the period ranging from July to September in the current year, Jun–Aug indicates the period ranging from the previous June to the previous August).

View inlineView popup 3.6. Spatial analysis 3.6.1. Spatial signature of tree-ring width parameters The relative humidity value is proportional to the vapour pressure. Therefore, the vapour pressure field (http://climexp.knmi.nl) was spatially correlated with the TRW time series (figure 10). The proxies (TR, EW and LW) revealed similar hydroclimatic signal fingerprints for the previous June and August vapour pressure fields. The core area of the vapour pressure field shows quite clearly in our sampling sites. TR and EW revealed relatively high spatial correlation coefficients with the previous June vapour pressure (r > ∼0.4, p < 0.01), but LW showed a weaker correlation. In addition, when the TR and EW signals were correlated with the vapour pressure field of the previous June, the significantly correlated region mainly expanded to the southwest, whereas LW showed a relatively restricted zone. However, in the case of the vapour pressure in the previous August, the significantly positively correlated pole area (r < ∼0.4, p < 0.05) is far from the sampling sites and covers a much smaller area.

Figure 10. Figure 10. The spatial correlation between vapour pressure (previous June and August) and the three tree-ring width parameters. The first row of the graph indicates the spatial signature between the previous June vapour pressure and TR (a), EW (b) and LW (c). The second row of the graph indicates the spatial signature between the previous August vapour pressure and TR (d), EW (e) and LW (f).

Download figureOpen in new tabDownload PowerPoint 3.6.2. Spatial signature of the δ13C series of three components Because temperature revealed a weak relationship with the δ13C series of the different components, the spatial correlations between moisture drivers (vapour pressure and precipitation) and the three components (WW, AC and HC) were determined and are shown in figure 11. We found that the validity zone of the spatial correlation coefficients between the δ13C series and the July–September vapour pressure is very small, and HC had no significant spatial correlation. However, the fingerprints of the hydroclimatic signals in the δ13C series of the different components in the July–September precipitation field are generally similar. The core area of the vapour pressure field (r < ∼ −0.5, p < 0.01) shows quite clearly in our sampling sites. The larger significant correlation regions and the similar spatial distribution patterns of the three components suggest that they represent the best precipitation proxy. In the southeastern region of the study area, the δ13C series of the three components are significantly and positively correlated with the July–September precipitation field.

Figure 11. Figure 11. The spatial correlation between current July–September moisture drivers (vapour pressure and precipitation) and δ13C series of three components. The first row of this graph indicates the spatial signature between vapour pressure and WW (a), AC (b) and HC (c). The second row of this graph indicates the spatial signature between precipitation and WW (d), AC (e) and HC (f).

Download figureOpen in new tabDownload PowerPoint 4. Discussion 4.1. The potential of total ring width, earlywood width and latewood width for dendroclimatology Generally, the mean sensitivity of tree rings ranges from 0.1 to 0.6 [40]. The statistical characteristics of TR, EW and LW chronologies of Masson pine in this study area show that the mean sensitivity values fall within the ranges in existing research results [62–64], and all exceeded 0.15 but were less than 0.30. This may be related to the long-term adaptation of Masson pine to the warm and humid climate in the subtropical region. The pairwise correlation between EW and LW was tested. EW has a strong relationship with LW in our study, which supports the findings from another study in the southwestern United States [65]. This conclusion is also consistent with those for other tree species such as Picea crassifolia [66], Larix gmelinii [67] and Cryptomeria fortunei [68] in subtropical China. Consequently, according to other studies [69–71], the strong relationship between EW and LW indicates that the potential for reconstructing palaeoclimate by using the LW of Masson pine in subtropical China may be limited. Additionally, a comparison with the statistical characteristics of tree-ring chronologies shows that LW is relatively weakly sensitive to climate drivers (see §3.1). However, studies in Europe and the United States showed that LW was more sensitive to climate drivers than EW [22,25]. It is possible that the intensity of the correlation between EW and LW varies due to the temporal and spatial variability of the climate and the timber production time, the species specificity and age-related effects of growth on the cambium meristem. Some authors added another TRW index (i.e. adjusted tree-ring LW) to explore the climatic sensitivity [72]. The adjusted tree-ring LW is the residuals of the linear model between LW and EW. Since the impact of EW on LW has been removed, the adjusted LW can be regarded as the unique variation of LW. However, this TRW index did not reveal much stronger climate signals rather than TR, EW and LW in our work (electronic supplementary material, figure S2). Therefore, from this angle, it is not necessary to separate the EW and LW in Masson pine in subtropical China. Of course, Cao et al. [73] developed the first blue intensity series of EW and LW from Masson pine in Fujian province. This technique is worth trying in later research.

The Tongbai Mountains are located in a temperate zone with a continental monsoon climate. May–August is the wettest period, and it is relatively dry during the rest of the year. Across the whole distribution area of Masson pine, the annual precipitation is much lower than that in the southern and southeastern regions of subtropical China (figure 1c). Therefore, the TRW is positively correlated with the monthly precipitation from the previous June to the current March, but this correlation is not significant. Whereas, we found that monthly mean temperature and maximum temperature are negative factors for the growth of Masson pine, which is consistent with other research on climate reconstruction using this species in Macheng, Hubei Province [8]. The negative relationship between TRW and temperature may be caused by soil moisture status since high temperature could induce high evapotranspiration of soil moisture. Some research used the moisture status index like SPEI and scPDSI [74,75] because these indices are better to depict soil moisture status than precipitation, relative humidity and evaporation. However, according to the time-varying bootstrapped correlation results, our research found that the relationships between LW and moisture status index are not stationary (electronic supplementary material, figure S3).

In the season of relatively low precipitation, from August to October, the relative humidity of the air is low. On the one hand, the longest daylight hours and the highest temperatures that occur during the June–August period accelerate soil water evapotranspiration. The higher air relative humidity inhibits transpiration in Masson pine, which leads to water stored in the leaves not being lost quickly. Additionally, climate drivers have an obvious influence on the ‘lag’ of tree growth in Masson pine. Consequently, the relative humidity of the previous year shows a positive relationship with the TRW chronologies. These results indicate that the higher moisture environment in the middle and late growing seasons promoted the radial growth of Masson pine. It is worth noting that the obviously upward temperature and slightly upward precipitation trends in the future may not be conducive to the radial growth of Masson pine.

4.2. Sensitivity comparison of different components of the δ13C series to climate parameters Unlike recent studies, which reported that the values of δ13C were significantly correlated with temperature in subtropical China [76,77], temperature revealed a weak relationship with the δ13C series in our study. By contrast, despite abundant precipitation over the subtropical region, the main climate drivers that influenced the δ13C records of Masson pine in the subtropical region were precipitation and relative humidity. The most notable characteristic of the response of Masson pine to regional climate is the significant negative correlation between the precipitation and relative humidity in mid-summer and early autumn (July–September). Li et al. [78] also found that the δ13C sequence of Masson pine in Fujian Province in subtropical China was positively correlated with the September–October relative humidity. Note that our study area (32°24′ N, 113°16′ E) is far away from the study area (25̊25′ N, 117̊56′ E) of Li et al. [78]. Therefore, the relative humidity may be a common climate driver for the growth of Masson pine in subtropical China. In the future, an expanded network of sampling sites across all of subtropical China will be studied. Our results agree with those of a previous study from similar tree habitats in subtropical China [2,79]. The coefficients of the seasonal dendroclimatic relationship for the δ13C series exceeded the 0.7 level in our study, which has been proposed as a useful indicator for palaeoclimate reconstruction [80,81]. The specific mechanism may be as follows: after July in each year, the precipitation significantly decreases, while the temperature is still high. Consequently, the high temperature and low rainfall led to a significant reduction in the relative humidity of the air in our study area. On the one hand, the decrease in relative humidity accelerates soil water evaporation and reduces the available water supply for Masson pine. Additionally, it leads to an increase in the water vapour pressure difference inside and outside of the leaves. Under the combined action of the two factors, Masson pine must reduce the stomatal conductance of its leaves to reduce water evapotranspiration. However, a decrease in the stomatal conductance of leaves leads to an increase in the resistance of CO2 entering the leaves, a decrease in the CO2 concentration between mesophyll cells and a decrease in the CO2 required for photosynthesis [82]. When the mesophyll cells lack 12CO2, they have to use 13CO2 which is relatively difficult to assimilate [30]. As a result, when the relative humidity of the air decreases, an increase in δ13C values is observed. The climate signal in δ13C tends to be dominated by stomatal conductance. Thus, the δ13C series variations are influenced by air humidity and those variables that control soil moisture status [78,80]. In addition, there is a significant positive correlation between the sunshine hours and the growth of Masson pine. This may be due to the lower relative humidity caused by the longer sunshine hours and the less rainy weather. On the other hand, long periods of sunshine can provide sufficient energy sources for plant photosynthesis. Sunlight promotes the absorption and utilization of 13CO2, which leads to an increase in δ13C values.

Conclusion This study was conducted to study the possible response of Masson pine to past hydroclimatic environmental changes as well as to future global climate changes for forest management purposes in subtropical China. The statistical characteristics of tree-ring chronologies using three TRW parameters are presented and indicate that EW shows a similar environmental signal to TR. Common climate signals from the previous September mean temperature, the August–September maximum temperature, and the June relative humidity were found among the three TRW parameters. At both regional spatial and temporal scales, the three TRW parameters only have a significant positive correlation with the previous summer (June–August) moisture condition. The EW of Masson pine reveals strong signals of past climate and environmental changes at high temporal resolution in contrast with LW. Although these signals can be used as a supplement to the alternative indicators used in subtropical climate change research, these results indicate that separate analyses of different TRW proxies are unnecessary for future regional dendroclimatic research in Masson pine.

A correlation analysis revealed a relatively strong response to July–September moisture conditions (precipitation, relative humidity and evaporation) for the δ13C series of different wood components from the total ring. However, the δ13C series lack a significant relationship with temperature factors, except that the July maximum temperature relationships with these three components have high correlation coefficients. These results clearly suggest that the dominant leaf physiological forcing factor for the discrimination of 13C in Masson pine is stomatal conductance rather than the photosynthetic rate. Our study confirmed that δ13C values from the WW of Masson pine trees, i.e. without extracting HC or AC, are appropriate for reconstructing regional climate drivers at interannual and longer time scales.

A significant stable correlation was found for TRW and the total ring δ13C series with relative humidity. However, TRW parameters are significantly positively correlated with the relative humidity in the previous year, and δ13C series are significantly negatively correlated with this climate driver in the current year. In addition, the R-squared values of the strongest climate-proxy correlation coefficients are smaller than 50%, indicating that the TRW parameters are slightly less sensitive than the δ13C series of the total ring to climate drivers in our study area. However, for the same climate driver (e.g. relative humidity), seasonal correlation analysis reveals that the δ13C series of the three components describe the strong climate signal during their common period. These results suggest that the δ13C series may be a more useful indicator of the past climate in subtropical China than the TRW parameters. In our study, to avoid a shortage of samples and the ‘juvenile effect’, the δ13C series were only selected for a period of 30 years, because some of the tree cores had much narrower annual growth rings for a long time. Fortunately, there have been technical advancements in measuring stable isotopes. Andreu-Hayles et al. [83] detailed a high yield cellulose extraction system for small WW samples. Therefore, in the future, our study will investigate the long-term δ13C series by using these methods. We evaluated the climate sensitivity of TRW from the perspective of tree-ring sections including whole sections (total tree-ring) and subsections (earlywood, latewood). However, for the δ13C series, we conducted the analysis based on the chemical components of the total ring (WW, HC and AC) rather than by sections. The reason is that the wood samples were too small to separate the earlywood and latewood for chemical treatment.

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Comparison of Dendroclimatic Relationships Using Multiple Tree-Ring Indicators from Masson Pine

Abstract

This study examines the impact of climate factors on tree-ring width (TRW) and stable carbon isotope ratios (δ13C) in different wood components (whole wood (WW), α-cellulose (AC), and holocellulose (HC)) from Masson pine (Pinus massoniana) in subtropical China. The research assesses whether intra-annual variations in tree-ring width (total ring width (TR), earlywood width (EW), and latewood width (LW)) improve climate signal extraction and whether δ13C in different wood components exhibits varying dendroclimatic relationships.

Key findings:

EW and LW did not provide stronger climate signals than TR, indicating no major advantage in separating tree-ring components for climate reconstructions.
The δ13C series of all three wood components showed the strongest climate response to July–September relative humidity (r = −0.792 for WW, −0.758 for HC, and −0.769 for AC).
No significant differences in dendroclimatic relationships were found between the δ13C series of different wood components.
The R² values of the strongest climate-proxy correlations for TRW were below 50%, suggesting δ13C is a better climate proxy than TRW for dendroclimatology in subtropical China.

1. Introduction

Subtropical China (22°–34° N, 98°–122° E) is climatically sensitive and prone to extreme events (floods, freezing disasters) due to global climate change. Understanding long-term forest-climate interactions is essential for climate risk management.

Tree rings provide high-resolution climate records, making dendroclimatology a valuable tool for studying climate change. Masson pine, widely distributed in subtropical China, is frequently studied for tree-ring analyses. However, its complex wood anatomy in warm and humid climates complicates climate-trait relationships.

Advancements in Tree-Ring Research

Ring width parameters (TR, EW, LW) offer different temporal climate resolutions. Some studies suggest LW is more climate-sensitive, while others indicate EW responds better to early summer conditions.
Stable carbon isotope ratios (δ13C) are more reliable climate proxies than TRW in warm-humid areas, but the best wood component for climate extraction is debated (WW, HC, or AC?).
This study is among the first to comprehensively compare TRW parameters and δ13C components in Masson pine.

Key Research Questions

How do different TRW parameters respond to climate?
Are there differences in dendroclimatic relationships among δ13C wood components?
Which indicator—δ13C or TRW—is more sensitive to climate?

2. Material and Methods

2.1. Study Area

Tongbai Mountains (31°49′–32°24′ N, 113°16′–114°03′ E) in subtropical China, a continental monsoon climate zone.
Mean annual temperature: 15°C, with cold winters (2°C in January) and hot summers (28°C in July).
Annual precipitation: 1168 mm, with most rainfall from May to August.

2.2. Tree-Ring Sampling and Chronology Construction

65 trees were sampled across two sites (Tongbaishan and Jigongshan).
Cores were extracted at 1.2 m above ground using a 5.5 mm increment corer.
TRW (TR, EW, LW) was measured using AcuRite measuring system and validated using COFECHA software.

2.3. δ13C Analysis

Tree-ring cores from four old trees were analyzed for WW, AC, and HC components.
HC and AC were chemically extracted, while WW was left unprocessed.
δ13C was measured using a Thermo Finnigan-Deltaplus XP isotope mass spectrometer.

2.4. Climate Data and Statistical Analysis

Climate data were obtained from the Xinyang meteorological station, 53 km from the sampling sites.
Statistical methods:
- Correlation analysis (between TRW/δ13C and climate variables).
- Temporal stability tests (35-year moving window with 4-year offset).
- Spatial correlation analysis (using CRU TS4.02 dataset).

3. Results

3.1. Tree-Ring Width Statistics

EW was the widest component (mean: 1294.02 µm) compared to LW (938.55 µm).
EW was the most stable over time and dominated TR signals.
Strong correlations:
- TR vs. EW (r = 0.94).
- TR vs. LW (r = 0.88).
- EW vs. LW (r = 0.53).

3.2. δ13C Characteristics

WW had lower δ13C values than AC and HC due to lignin content.
All three δ13C series showed consistent temporal trends, with strong intercorrelations (r = 0.91–0.94).

3.3. Climate-Trait Relationships

3.3.1. TRW vs. Climate

Negative correlations with past mean temperature (June, August, September, October).
Positive correlations with past relative humidity (August: r = 0.386 for TR).
LW was more sensitive to current climate conditions than TR/EW.

3.3.2. Temporal Stability

TR and EW were negatively correlated with past August temperature.
LW was strongly correlated with current July temperature and relative humidity.
No long-term divergence effect on tree-ring growth was found.

3.4. δ13C vs. Climate

δ13C showed strong negative correlations with July–September relative humidity (r = −0.792 for HC, −0.769 for WW, −0.758 for AC).
Weak temperature relationships, except for a positive correlation with July maximum temperature.

3.5. Climate-Proxy Comparison

δ13C series are more climate-sensitive than TRW:
- Stronger negative correlations with humidity than TRW.
- R² values for TRW climate correlations were below 50%, whereas δ13C consistently exceeded 0.7.

3.6. Spatial Analysis

TRW parameters correlated with June–August vapour pressure.
δ13C series showed strong negative correlations with July–September precipitation, suggesting it is a better spatial climate proxy.

4. Discussion

4.1. TRW Sensitivity to Climate

EW and LW were strongly correlated, limiting their separate use for paleoclimate reconstruction.
Masson pine growth is primarily influenced by past moisture conditions.
Temperature limits growth via soil moisture evaporation.

4.2. δ13C as a Climate Proxy

δ13C primarily responds to relative humidity, not temperature.
The δ13C signal is dominated by stomatal conductance, not photosynthetic rates.
WW is suitable for long-term climate reconstructions, eliminating the need for extracting AC or HC.

5. Conclusion

This study compares TRW and δ13C series for Masson pine in subtropical China and concludes:

TRW (TR, EW, LW) has limited sensitivity to climate drivers. EW signals dominate TR, and LW adds little extra information.
δ13C series strongly respond to July–September relative humidity and are better proxies for hydroclimatic reconstructions.
WW δ13C values are as effective as extracted components (HC, AC) for climate studies.
Future research should focus on expanding δ13C networks and applying new techniques to extend long-term δ13C reconstructions.

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New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning Yadvinder Malhi, Tobias Jackson, Lisa Patrick Bentley, Alvaro Lau, Alexander Shenkin, Martin Herold, Kim Calders, Harm Bartholomeus and Mathias I. Disney Published:16 February 2018https://doi.org/10.1098/rsfs.2017.0052 Abstract Terrestrial laser scanning (TLS) opens up the possibility of describing the three-dimensional structures of trees in natural environments with unprecedented detail and accuracy. It is already being extensively applied to describe how ecosystem biomass and structure vary between sites, but can also facilitate major advances in developing and testing mechanistic theories of tree form and forest structure, thereby enabling us to understand why trees and forests have the biomass and three-dimensional structure they do. Here we focus on the ecological challenges and benefits of understanding tree form, and highlight some advances related to capturing and describing tree shape that are becoming possible with the advent of TLS. We present examples of ongoing work that applies, or could potentially apply, new TLS measurements to better understand the constraints on optimization of tree form. Theories of resource distribution networks, such as metabolic scaling theory, can be tested and further refined. TLS can also provide new approaches to the scaling of woody surface area and crown area, and thereby better quantify the metabolism of trees. Finally, we demonstrate how we can develop a more mechanistic understanding of the effects of avoidance of wind risk on tree form and maximum size. Over the next few years, TLS promises to deliver both major empirical and conceptual advances in the quantitative understanding of trees and tree-dominated ecosystems, leading to advances in understanding the ecology of why trees and ecosystems look and grow the way they do.

Introduction Many who have gazed at the skeletal beauty of a bare, leafless tree on a winter’s day have pondered why that tree takes the form it does, and why the form varies between, within and across tree species. Scientific interest in the structure of trees dates back to at least Leonardo da Vinci, who first observed that cross-sectional area of branches is preserved along branching orders within a tree (da Vinci’s Rule [1]), and D’Arcy Thompson’s seminal work On growth and form [2]. The concept of ‘tree architecture’ was advanced by Hallé & Oldeman [3] and later expanded upon by Hallé et al. [4], who proposed 23 distinctive descriptive models of tree form. The architectural form of a tree is a combination of both its genetic development programme and adaptive, semi-autonomous responses of individual branches to their local environment [5]. Nonetheless, despite the variety of architectural models and adaptive responses to environment, many trees have a form that is distinct and recognizable, which results in particular structural and ecophysiological characteristics. Tree form has important functional consequences too, in determining how fluxes of energy, water and carbon scale from leaf to tree to ecosystem [6], how trees can compete and pack together to form a forest stand, and how trees shape ecosystem structure, biomass and the habitat matrix for other species.

It is perhaps surprising, therefore, that tree structure and form are currently fairly neglected areas in ecology and plant ecophysiology, despite being of potential high theoretical and applied relevance in topics as diverse as remote sensing of canopy landscapes, taxonomy and phylogenetics, metabolic scaling theory, tree wind damage studies, whole-tree hydraulics, modelling of ecosystem functioning and carbon fluxes, and calculation of carbon stocks for climate change mitigation schemes. The primary reason for this neglect is that until now it has been difficult and time-consuming to quantify tree structure in situ, particularly for anything other than rather small trees (e.g. [7]). For example, studies on the full dynamic response of trees to wind have been confined to fewer than three painstakingly mapped three-dimensional tree models [8]. Moreover, what work there has been has tended to focus on low diversity systems consisting of one or two species [9,10]. This paucity of data has hindered development and testing of theory explicitly linking tree form parameters with physiological function. In the absence of sufficiently tested and established theory, most descriptions of tree form are based on purely empirical studies. For example, the biomass of tropical forests (a topic of high relevance in contemporary climate change mitigation and conservation policy) is largely estimated from entirely empirically allometric equations describing the form of a generic tropical tree based only on its trunk diameter and height [11], sometimes with some allowance of the influence of water stress [12], but with little recognition of the multiple environmental, taxonomic and biogeographical factors that may cause tree form to vary across the tropics.

However, the study of tree architecture is now on the brink of a technology-driven revolution, with the advent of affordable and field-robust three-dimensional terrestrial laser scanning (TLS) technologies, also referred to as terrestrial lidar. TLS is a non-destructive remote-sensing technique for measuring distances [13]. TLS instruments emit a large number of laser pulses, typically tens to hundreds of thousands per second, in the visible or near-infrared part of the spectrum, which propagate hundreds of metres in the space surrounding the instrument. If a pulse hits an object, part of the energy is scattered back towards the sensor and triggers the recording of its distance and intensity. Knowing the direction of the emitted pulse, the position in a three-dimensional space is then recorded. These three-dimensional locations can be accurate to within a few mm over hundreds of metres, depending on instrument properties, and comprise a ‘point cloud’ describing the location of objects in three-dimensional space [14] (figures 1 and 7).

Figure 1. Figure 1. A top-down view of the architecture of a moabi tree (Baillonella toxisperma) in Gabon, illustrating the beauty in, and challenges to understanding, tree architecture and the potential of TLS approaches to deliver detailed quantitative descriptions of tree structure. The tree is 43 m tall, and the crown is 57 m across at its widest.

Download figureOpen in new tabDownload PowerPoint TLS instruments have been developed almost exclusively for surveying applications, particularly for urban, mining and geoscience, where mapping the position and orientation of large, hard surfaces is the aim (e.g. precise mapping of a building exterior or the interior surface of a mine). This has driven the development of instrument properties, particularly range (or power), beam divergence and wavelength. The instruments have not been developed specifically for ecological applications, which are a comparatively very small market. As a result, foresters, ecologists and remote sensing scientists have effectively adapted TLS to their needs by using commercially available instruments [15,16].

The obvious ecological relevance of the information content of three-dimensional point clouds has led to advances in data acquisition and analysis [17,18]. Firstly, field protocols have been developed to link many TLS scans across wider areas, via use of common registration targets, allowing capture of hectare-scale and greater point clouds [19]. Secondly, there has been a parallel development of new algorithms to convert point measurements into tree- and stand-scale properties of ecological interest, particularly size and shape of tree trunk and branch components [20–22], to separate leaf and wood components [23] and to generate vertical profiles of canopy density, which are critical to understanding and modelling canopy radiation regime [24].

As a result, TLS has become an automated, accurate, non-invasive, objective and repeatable option to assess tree stands, and potentially wider forest regions, with high detail. Its potential to assess tree structure has been proven over time [17,25]. Most recent TLS research has been concentrated on developing algorithms which provide a better understanding of the three-dimensional organization of tree structure, with ability to reconstruct and measure key attributes, such as tree location, stem density, canopy cover, above-ground biomass and diameter at breast height (DBH) with high accuracy from point cloud data [26–29]. This is providing many practical advances, including in more accurate estimation of volume and biomass [23,25], and descriptions of vegetation structure [30,31]. Work by [32,33] and others is the first step towards large-scale automated extraction of forest properties from TLS. Dassot et al. [34] provided an overview of TLS applications primarily aimed at forestry. They describe the state of the art at the time of forest structure estimation, particularly trunk shape and surface type, not just height and DBH. They also highlighted the difficulty of separating leaf and wood material in order to derive either wood or leaf properties separately. In the intervening period, the development and availability of higher precision, longer range TLS instruments has led to collection of much more detailed point clouds. This has led to increased focus on how to deploy TLS to collect much more detailed information on tree structure [19], and, in turn, on how to achieve topological descriptions of tree structure across all levels of branching. Progress in these areas has been somewhat symbiotic—developments in one have facilitated advances in the others, in turn.

Beyond description and quantification of tree structure and biomass, the rich information content of TLS data also opens up potential for testing and further development of ecological theory: not just describing what is found in terms of vegetation structure at a particular location, but understanding why it is found there and how that structure influences ecosystem function. In this paper, we focus on potential advances in our understanding of tree form and forest structure, but do note that these advances have many ramifications beyond trees alone, for example, in better describing and understanding animal habitat and use of space.

Understanding a tree The quantitative description of tree structure of large numbers of trees potentially enabled by TLS allow us to re-examine, further develop and test long-standing theories as to why trees have the shapes they do. A tree can be viewed (figure 2) as an attempt at optimization (of growth, survival and reproduction) subject to a number of requirements and constraints. These include (i) maximizing light capture and shading of competitors by maximizing vertical height, crown area and spatial arrangements between crowns [35]; (ii) maximizing the efficiency of resource distribution to and between the centres of metabolic activity (leaves, cambium and fine roots); (iii) minimizing risk of breakage or overturning from wind or buckling under gravity; (iv) minimizing water stress under gravity or drought conditions; (v) optimizing reproductive potential through pollination and seed dispersal; (vi) accommodating evolutionary constraints on development and growth pathways; and (vii) carrying legacies of past environmental history (e.g. past shade environment, wind damage). Not all trees or all environments will be subject to all these constraints. The solutions to this optimization determine the size and form (allometry) of trees, which end up strongly influencing the structure and habitat of woody ecosystems, the amount of biomass and carbon stored in the ecosystem, and the material flow of water and energy through the system.

Figure 2. Figure 2. The architecture of a tree can be viewed as an attempt at an optimization of productivity, survival and reproduction subject to a number of requirements and constraints. (Online version in colour.)

Download figureOpen in new tabDownload PowerPoint In this paper, we present several examples of how TLS data are providing new data-rich approaches for understanding tree form in the context of these constraints.

Resource distribution and branching architecture Branching architectures are pervasive throughout living systems, and their design is hypothesized to reflect trade-offs to access and fill three-dimensional space in order to transport resources with maximum efficiency and minimal cost [36]. In the context of trees, this leads to fractal-like structures that capture light, distribute resources (carbohydrates, water, nutrients) and provide support. Tree branching architecture is an essential link between individual leaf performance and whole-tree performance. In particular, West et al. [36] and Savage et al. [37] use metabolic scaling theory to argue that the overall metabolism of a tree (and its scaling with size) is intimately related to the properties of both its external branching architecture and its internal branching architecture (i.e. xylem vessels). In their strictest form these theories make specific predictions related to tree function based on the optimal external branching architecture of perfectly symmetrical and space-filling trees [37,38].

In metabolic scaling theory, for idealized symmetric trees (figure 3), three key parameters currently describe branching structure: (i) branching ratio n (the number of daughter branches per parent branch), (ii) branch radius scaling, a, and (iii) length scaling ratio, b [38]. The radius and length scaling ratios relate to the conservation of cross-sectional area and the space-filling of volume. Using various assumptions related to network geometry [37–39], constant values can be given to these parameters across idealized trees where n = 2, a = ½ (Da Vinci’s rule) and b = 1/3. The values of these parameters directly relate to the calculation of whole-tree metabolism of a tree of given mass.

Figure 3. Figure 3. Branch scaling parameters for idealized symmetric trees. The branching ratio n is the number of daughter branches per parent branch (n = 2 in this example); the branch radius scaling parameter a = <rk+1/rk>, the ratio of the radii of daughter branches to those of their parent branches at each branch segment, averaged across a particular branching order; the branch length scaling parameter b = <rk+1/rk>, the ratio of the lengths of daughter branches to those of their parent branches at each branch segment, averaged across a particular branching order. (Online version in colour.)

Download figureOpen in new tabDownload PowerPoint However, most trees do not show an optimal external branching network. Self-similarity rarely holds true across all levels of tree branching, the majority of woody stems taper along stem length and trees exhibit asymmetric branching at branch-level [7,40,41]. As such, new theory is necessary [7] and is currently being developed for trees with asymmetric branching [42]. Moreover, some critics have pointed out that scaling of metabolic rates does depend not only on the potential resource uptake but also on availability of resources [43,44]. Resources, such as light or nutrients, are a limited resource in tropical forests and light competition is high due to strong light gradient within the canopy. Nevertheless, these approaches can serve as a point of departure for calculating scaling relationships for actual observed tree architectures.

The architecture of individual trees and their crowns scales up to shape the structure, metabolic function and dynamics of the entire forest stand, through competition for filling of space and capture of light [6,45]. It has been argued that ‘the forest is a tree’ [41], i.e. that scaling laws across trees in a forest have many parallels with scaling laws among the branches of an individual tree, and that the two are intimately connected through the tree architecture.

The exquisite branching architecture of many trees is widely observed, but until recently, it has been difficult to describe this architecture in a robust and quantitative manner that spans scales from trunk to branch tip. This is an area where TLS has the potential to make major new contributions. Figure 4 shows how tree architectural parameters are extracted from point clouds. The tree point cloud is extracted from TLS scans (figure 4a), after leaves have been filtered out [23]. A quantitative structure model (QSM) is fitted to the tree to extract the tree form as a combination of multiple cylinders of varying length and diameter (figure 4b) [32]. From this QSM, the dimensions of individual branches can be calculated. Branches from the original QSM models are grouped by branch order. In figure 4c, the main stem does not split when a branch node appears and continues along to the top with the same branching order value (figure 4c). To describe a more detailed branch architecture, we can also separate individual branches at every branch node and reassign them unique values (figure 4d). The main stem now stops at the first branch node and unique value (colour) is assigned every time a split occurs.

Figure 4. Figure 4. The application of TLS to extract and derive branching orders and tree architectural parameters.

Download figureOpen in new tabDownload PowerPoint As shown in figure 4, the unprecedented quality of three-dimensional structural data provided by TLS allows us to investigate old and new metrics to conceptualize tree architecture. One quantitative architectural metric that may prove useful is path fraction, which is the ratio of the mean path length (from tree base to branch tip) to maximum path length [46]. The path fraction provides a measure of the efficiency of the hydraulic system of the tree and has a maximum value of 1.0 for an idealized umbrella-shaped crown which prioritizes sun exposure and light capture but is structurally costly to build, but is lower for most tree crowns that have a mix of high sunlight and shaded environments. In addition to path fraction, we can also extract numerous other tree architecture parameters, such as branching angles, crown volume, crown asymmetry and the ratios of volume occupied by each branching order.

Figure 5 gives an example of the relationships among path fraction, the mean opening angle between branches and tree height for three common tree species at Wytham Woods, Oxfordshire, UK. This demonstrates how efficiently multiple architectural parameters can be derived for hundreds of individual trees. It can be seen that path fraction tends to increase with height for all three species (i.e. trees become more umbrella-like as they reach canopy height to maximize access to sunlight). Among canopy trees, ash has a higher-path fraction than oak, whereas sycamore shows a much higher range in path fraction values. Remarkably, branching angle shows little correlation with path fraction or conservatism within species and appears an independent description parameter for tree architecture.

Figure 5. Figure 5. Path fraction against tree height and mean branching angle for trees of three species in temperate broadleaf woodland at Wytham Woods, Oxfordshire, UK: sycamore (Acer pseudoplatanus), European ash (Fraxinus excelsior) and English oak (Quercus robur). These architectural measures were extracted from Quantitative Structure Models (QSMs) derived from TLS point clouds.

Download figureOpen in new tabDownload PowerPoint 3.1. Surface area scaling and calculating tree respiration Ecosystem ecologists have an interest in calculating tree respiration, or how much carbon is metabolized by the trees for their own growth and maintenance processes, and as a consequence is unavailable for biomass formation and consumption by other trophic levels. Around 50–70% of the carbon assimilated by photosynthesis in trees is used for autotrophic respiration and therefore unavailable for biomass production [47,48], and there is evidence that variation in autotrophic respiration may be particularly important in determining ecosystem carbon balance during extreme drought events [49]. There are challenges for measuring this for all organs, including leaves and fine roots, but a key challenge is the estimation for woody tissue, due to the problem of complex branching structure.

The most commonly used method to estimate total woody tissue respiration measures the amount of carbon dioxide emanating from the bark surface at one point on a tree’s trunk, and applies allometric scaling assumptions to scale up from that point to the entire woody surface area of the tree [50,51]. There are a number of physiological assumptions in the scaling (e.g. around how the fraction and activity of metabolically active tissue varies through the tree architecture), but a key source of uncertainty is accurate assessment of the surface area of the tree. This is where TLS can provide significant insights and test existing allometric assumptions.

Current TLS and model-fitting algorithms are capable of resolving tree branches as fine as a few centimetres in diameter. These models typically result in a set of cylinders arranged in space and connected via a topology. By summing the surface areas of these cylinders, we can derive estimates of total per-tree surface area, aggregated by branching order, diameter class or otherwise. These estimates are free from biological assumptions, are much faster to conduct than manual measurement, and can be conducted without felling the tree. Once surface areas have been summed, the point measurement of woody tissue respiration rate is multiplied by the total surface area to estimate total per-tree respiration rate (figure 6).

Figure 6. Figure 6. The application of TLS-derived models to show how cumulative above-ground woody surface area (over size classes of stems and branches) scales with DBH for seven tropical canopy forest trees in lowland Peruvian Amazonia. The dots (colours correspond to lines of the same colour) indicate the woody surface area calculated from a widely applied allometric equation for woody surface area from Chambers et al. [52].

Download figureOpen in new tabDownload PowerPoint Figure 6 gives an example of how total tree woody surface area accumulates up through the branching architecture of seven lowland rainforest trees (of different species) in Tambopata, Peru. Smaller branches make a disproportionate contribution to total woody surface area; hence it is vital to both quantify this fine surface area and understand the metabolic activity of small branches and twigs. An allometric relationship widely applied to estimate woody surface area in tropical trees [52] tends to greatly underestimate woody surface area. There is also very large variation between tree species in surface area scaling, variation that is strongly related to differences in tree architectural form. TLS-acquired data such as these, when extended over thousands of trees, promise to enable development of a robust understanding on the relationship between tree form, woody surface area and tree metabolism.

3.2. Tree architecture and seed dispersal Beyond resource acquisition and competition for space and light, a key plant functional role for which tree architecture can be important is reproduction and seed dispersal. For pollination, flowers need to be placed in positions visible and accessible to the relevant pollinators. For seed dispersal, there are likely to be correlations between dispersal strategy and tree architecture. To disperse tree seeds, a range of strategies can be employed from wind dispersal of small or winged seeds (favouring tree architectures that leave seeds prominent and exposed to the wind), bird dispersal (favouring prominently visible fruit), land mammal dispersal (allowing for larger seeds pods often requiring strong mechanical support), through to ballistic dispersal where seed pods ‘pop’ open and scatter to the ground (favouring extended branches spreading out from the core trunk). There has been little study of the relationships between tree form and seed dispersal syndrome, but this is an area ripe for study with the new availability of TLS data.

As an illustration, in figure 7 we compare TLS-collected point clouds of two tropical trees: a moabi (Bailonella toxisperma) from Lopé, Gabon, Central Africa, and a dipterocarp (species not yet identified) from Danum Valley, Sabah, Malaysian Borneo. The moabi produces large rounded fruit which are solely dispersed by forest elephants (Loxodonta cyclotis). The fruit drop from a great height, producing thuds in impact that announce their availability to infrasound-attuned elephants many kilometres away [53]. The broad stretching crown and massive branches of the moabi are well suited for dropping these heavy fruits from a great height and wide spread from the tree. By contrast, dipterocarps are a wind-dispersed tree family that dominates southeast Asian lowland rainforests that produce winged seeds that catch a gust and spin away from the mother tree. The crown of the dipterocarp is smaller with sparse clusters of leaves, enabling the tree to be taller for a given degree of mechanical strain and giving seeds easy access to wind gusts.

Figure 7. Figure 7. TLS-derived point clouds of two tropical giants: contrasting the structure of a moabi (Baillonella toxisperma; tree height 43 m, crown diameter 53 m) from Gabon (as seen from the top in figure 1) with a dipterocarp (species unknown; tree height 60 m crown diameter 30 m) from Sabah, Malaysian Borneo.

Download figureOpen in new tabDownload PowerPoint Differences in dispersal strategies and tree architectures matter beyond individual species. The dipterocarp-dominated, predominately wind-dispersed forests of southeast Asia are taller and different in structure from the (predominantly animal and ballistic dispersal) forests of Central Africa and Amazonia. Tree allometries relating tree diameter and height to biomass vary according to tree functional form, and more refined approaches to mapping biomass and structure across forests will need to take geographical variations of tree architecture into account.

3.3. Tree mechanics The need to maintain mechanical safety is a fundamental constraint on tree architecture, and provides one of the limitations on tree height, which in turn limits biomass stocks in many forests worldwide. Understanding the role of wind in forest ecosystems has been most easily achieved in conifer plantations, where equal age and evenly spaced, single species forest structure coincides with extensive measurements of wind safety. Empirical and mechanistic modelling approaches have yielded good predictions of wind damage in this environment [54]. Pivato et al. [55] showed that conifers can be treated as simple pendula and so produced an accurate, large-scale model which accounts for the dynamics of tree sway. How much these approaches and insights can be applied to more complex forests with mixed ages, multiple species and broadleaf trees remains an outstanding question. Sellier et al. [56] used a sensitivity analysis to show that tree architecture is important in determining response to wind. Hence, outside conifer plantations, it is necessary to consider the three-dimensional structure of the tree to understand height limitation and predict wind damage.

The QSM approach allows the tree to be understood as a series of beams upon which the forces of wind and gravity act. Using finite-element mechanical modelling packages such as ABAQUS [57], it is possible to extract the significant sway modes and predict the critical wind speed at which a tree will break under a realistic wind forcing (figure 8). Preliminary results show that while conifers and other trees with a simple geometry have one or two significant sway modes, trees with large first order branches such as oaks have four to seven significant modes (Jackson et al. 2017, unpublished data). This means that their dynamic response to wind forcing is more complex and a more detailed understanding and description of tree architecture is required.

Figure 8. Figure 8. Overview of a tree-wind model using finite element analysis based on a QSM. An increasing wind force is applied to the model tree until the strain (a measure of deformation) reaches the breaking point.

Download figureOpen in new tabDownload PowerPoint Previous work has predicted that trees tune multiple sway modes in order to pass potentially harmful kinetic energy efficiently between their trunk and branches, where it can be dissipated [58,59]. This damping by branching would increase the damping rate because trees ‘feel’ the force of wind on a two-dimensional projected area, but they dissipate the energy through a three-dimensional structure. TLS-derived cylinder models, as well as field measurements, allow the testing of this and other theories against hundreds of accurately mapped three-dimensional tree models.

In order to investigate the effects of wind on the forest carbon cycle, we must consider direct wind damage and also height limitation. Preliminary data show that the critical wind speed at which a tree is predicted to break decreases with increasing tree height. This approach necessarily involves extrapolation past any available field data up to the breaking point of a tree (figure 9). Nevertheless, this approach would allow us to compare the calculated critical wind speeds of trees in a single plot and determine the plant growth and architecture strategies and trade-offs between trees which prioritize growth and those which display a more conservative wind damage avoidance strategy. Such approaches promise new mechanistic insights into one of the fundamental questions in forest ecology: what determines the heights of trees, and why does forest height show such geographical variation? As an example, Northern Borneo is known to have the tallest tropical forest canopies and individual trees in the world [60]. The tallest trees in the world are temperate conifers (e.g. the Californian coastal redwood, Sequoia sempervirens) which are likely limited by hydraulics (Koch et al. [60]), but the large variation in humid tropical broadleaf forest canopy height is poorly understood. Mean forest canopy heights in northern Borneo are around 45 m, compared to less than 30 m in Amazonia, even in regions with comparably high rainfall [61]. One plausible hypothesis for this disparity is that the maritime but non-cyclone prone wind regime of Borneo does not allow for extreme convective blowdown events that sometimes occur in the extremely continental wind regime of Amazonia. These rare extreme wind events may shape maximum sustainable tree height. The mechanistic modelling framework outlined above potentially allows us to directly test this hypothesis, for example by ‘exposing’ in silico tall Bornean trees to Amazonian wind regimes and seeing if and how frequently they would exceed critical wind speeds.

Figure 9. Figure 9. Critical wind speed estimate for a single tree calculated using the QSM-based approach with field data overlaid to give some estimate of the accuracy. The analysis suggests that an hourly average wind speed of around 15 m s−1 would result in a critical tree breaking strain. Data are for a silver birch tree (Betula pendula) in Wytham Woods, Oxfordshire, UK.

Download figureOpen in new tabDownload PowerPoint 4. Outstanding challenges and future opportunities We have illustrated above some of the potential that TLS provides in making fundamental advances in our understanding of the causes and consequences of variation in tree form. These analyses can be applied at wider scale, to understand how the interactions of individual tree form shape the structure and function of whole ecosystems, ranging from ecosystem energy availability and metabolism to the habitat and behaviour of animals. As the TLS revolution advances, this will emerge as an exciting and data-rich frontier in plant and ecosystem ecology.

To progress on this journey, however, a number of challenges still remain to be adequately addressed in the extraction and use of TLS data. These include (1) reliable extraction of higher order branching, (2) more robust fitting of cylinder models, and (3) reliable separation of woody from non-woody material (e.g. leaves) in the point cloud.

(1) Separating trees from point clouds at speed and scale: challenges remain in making accurate co-registration of many individual TLS point clouds much more rapid and ideally automated, and then also in automating extraction and processing of individual trees from these large point clouds. This would also make TLS data collection and extraction much more widely available beyond specialist research groups. Mobile laser scanning developments have seen progress in the first area. TLS instrument manufacturers have developed automated, targetless registration processes, particularly for mobile mapping applications (e.g. Riegl and 3Dlasermapping). These can work well, but typically only in environments with large, hard, regular surfaces such as built environments or supported with a strong GNSS/GPS signal. Work remains for them to be usable in forest environments dominated by many small, soft and irregular targets and under closed canopies. Tree extraction is also progressing rapidly. This process still generally requires some manual input, but as for the co-registration, increased interest in these applications is driving rapid development of full automation [62].

(2) Extracting high order branching reliably: owing to vegetation occlusion, the point cloud point density differs at different heights [19]. Point density is highest in the lower parts of the canopy (where tree branch structure is also typically larger), allowing three-dimensional reconstruction algorithms to model the point cloud very accurately. The point density becomes lower in the canopy, and branches are often occluded. The beam divergence of the laser leads to a larger pulse footprint with increased range, meaning that smaller objects in the top of the canopy cannot be resolved. This directly affects the way reconstruction algorithms can follow branching structure, tending to reduce the accuracy of three-dimensional cylinder models with height. This is relatively unimportant for applications focused on total volume/biomass, but more so for those exploring branch size distributions.

(3) Fitting cylinder models: fitting a cylinder model to a point cloud is a problem with many possible solutions. In practice, it is found that that small curves and changes of direction in branch structure are often best fit by multiple, short cylinders. From a mechanical or architectural point of view this is problematic, because neighbouring short cylinders may often point in slightly different directions. This can add surface area and reduces the mechanical viability of the structure. Simplification of the cylinder model by averaging together neighbouring cylinders provides a robust solution to this problem. Increasing amounts of accurate TLS data are enabling fitting models to be improved, and new approaches to be developed [33].

(4) Separation of leaves from wood: leaves are challenging to separate from wood in the TLS point cloud. This affects the accuracy of the retrieved structural models, and any resulting (woody) biomass and architecture calculations. This also has implications for tree mechanics modelling. The increase in sail area due to leaves has a strong effect on the response of a tree to wind forcing. But there are promising advances in extracting leaves, using a combination of methods to exploit differences in the return intensity of the signal, and using a priori assumptions of how leaves and branches are co-located in three-dimensional space [23].

Conclusion New TLS methods allow for the explicit capture and mathematical representation of tree from in exquisite detail. Recent progress in the handling and processing of vast amounts of point cloud data allows for automatic extraction of tree architecture for large sample sizes. These advances are already enabling the development and improvement of estimates of tree allometry and forest structure [21,23,63]. Beyond these practical applications, these advances allow the development and, crucially, testing of new theory related to tree form, tree mechanics and forest structure. These are disciplines that remain relatively unexplored for diverse broadleaf forests. The opening of this new frontier has been driven by technological and computational developments. Further technological progress is likely in coming years, such as the deployment of laser scanning with multiple, vegetation-sensitive wavelengths to monitor plant water status in three dimensions [52], and improved separation of leaf biomass from woody biomass [64]. For ecologists an exciting challenge is to integrate this flood of new and extremely detailed data with existing theory on tree and forest structure. This will require a combination of analytical methods with explicit simulation of growth environment and biomechanics, to describe and understand how tree form varies with phylogeny, development stage and environment. This offers the promise of predictive and robustly tested theory of tree form and forest structure, a valuable toolkit that may allow us to understand why different forests have the structure they do, and how ecosystem structure and biomass may change in a world of multiple anthropogenic influences on forest ecosystems.

Here is a comprehensive summary of the study, capturing all key details:

New Perspectives on the Ecology of Tree Structure and Tree Communities through Terrestrial Laser Scanning (TLS)

Abstract

Terrestrial laser scanning (TLS) is transforming how we understand tree structure, forest ecology, and ecosystem function. This technology allows for highly detailed three-dimensional reconstructions of trees in natural environments, enabling precise measurement of biomass, canopy structure, and tree form at a scale and accuracy that was previously impossible.

This study highlights:

Advancements in TLS-based tree shape analysis, with applications in understanding tree form constraints and optimization.
Testing and refining of metabolic scaling theory through TLS-derived branching architecture data.
New approaches to quantifying tree surface area and metabolism, improving carbon flux estimates.
Mechanistic insights into how trees avoid wind damage and what limits their maximum size.

TLS promises major breakthroughs in both empirical and theoretical tree ecology, enhancing our ability to model and predict how trees and forests grow, function, and respond to environmental change.

1. Introduction

Tree structure has fascinated scientists for centuries, from Leonardo da Vinci’s observations of branching rules to modern tree architecture models. Tree form is a product of genetic programming and adaptive responses to environmental constraints, and it influences key ecological functions such as:

Energy, water, and carbon fluxes from leaf to ecosystem.
Tree competition and forest stand dynamics.
Ecosystem biomass and habitat structure.

Despite its importance, tree structure has been underexplored in ecology, largely due to the difficulty of obtaining detailed measurements, particularly for large trees. Most estimates of forest biomass rely on empirical allometric equations, which fail to capture site-specific and taxonomic variations.

The Technological Revolution: Terrestrial Laser Scanning (TLS)

TLS, also known as terrestrial LiDAR, uses lasers to create 3D point clouds of tree structures with millimeter precision. Originally developed for urban, mining, and geoscience applications, ecologists have adapted TLS for forest measurements, leading to:

Field protocols for linking multiple TLS scans to capture whole forests.
Algorithms for extracting ecological traits (e.g., trunk shape, canopy density).
Automation of biomass and structural measurements, providing more accurate estimates than traditional diameter at breast height (DBH)-based allometry.

TLS not only describes tree structure but also allows for mechanistic testing of ecological theories, moving beyond descriptive studies to understanding why trees have the forms they do.

2. Understanding Tree Form and Function

TLS enables a quantitative re-examination of tree structure, helping answer key ecological questions about:

Resource optimization – how trees balance growth, survival, and reproduction.
Light competition and crown architecture – how trees position themselves for maximum light capture.
Wind risk and mechanical stability – why trees develop certain structural adaptations to resist wind damage.
Water stress and hydraulics – how trees optimize water transport and avoid drought stress.
Evolutionary and environmental constraints – how tree form is shaped by phylogeny and past environmental conditions.

These factors shape forest structure, biomass storage, and ecosystem metabolism, making TLS an essential tool for understanding woody ecosystems.

3. Key Ecological Insights from TLS Data

3.1. Resource Distribution and Branching Architecture

Tree branching follows fractal-like patterns, balancing light capture, resource distribution, and structural stability. Metabolic scaling theory (MST) predicts that trees follow self-similar branching rules, optimizing nutrient transport and minimizing energy loss. However, real trees deviate from idealized MST models due to:

Asymmetric branching patterns.
Tapering stems and irregular architectures.
Variations in xylem vessel structure.

TLS quantifies these deviations, allowing more accurate scaling of tree metabolism, hydraulic function, and structural integrity.

3.2. Surface Area Scaling and Tree Respiration

A major challenge in ecosystem ecology is estimating tree respiration, which consumes 50–70% of carbon assimilated via photosynthesis. Traditionally, respiration rates are estimated from bark measurements, but scaling these to whole trees is highly uncertain.

TLS enables:

Accurate whole-tree surface area calculations, avoiding over-simplified allometric assumptions.
Branch-level respiration estimates, refining carbon flux models.
Understanding metabolic costs of different tree structures.

TLS data show that small branches disproportionately contribute to total surface area, emphasizing the need for precise measurements to avoid underestimating respiration rates.

3.3. Tree Architecture and Seed Dispersal

TLS reveals how tree form influences reproductive strategies, such as:

Wind dispersal (e.g., Dipterocarps with sparse crowns for better wind access).
Animal dispersal (e.g., Moabi trees with massive branches supporting large fruits for elephant dispersal).

TLS-derived crown shape data can help predict how tree dispersal strategies influence forest structure across different ecosystems.

3.4. Tree Mechanics and Wind Resistance

Tree height is often limited by mechanical constraints, especially wind stress. TLS-based finite element modeling allows for:

Simulations of wind forces on 3D tree models.
Predictions of breaking points under increasing wind speeds.
Testing of biomechanical theories, such as how trees dissipate kinetic energy through complex branching.

Preliminary TLS data show that broadleaf trees like oaks have multiple sway modes, making their wind response more complex than simple conifers. Wind regimes may explain why forests in Borneo are taller than Amazonian forests, as Borneo’s less extreme winds allow for taller, mechanically stable trees.

4. Challenges and Future Opportunities in TLS

(1) Automating Tree Extraction from Point Clouds

Large-scale TLS datasets require automated methods to separate individual trees from dense point clouds.
Mobile laser scanning and AI-based segmentation are promising solutions.

(2) Resolving Higher-Order Branching

Leaf occlusion reduces scanning accuracy in upper canopy branches.
Advancements in sensor resolution and multi-angle scanning will improve data capture.

(3) Refining Cylinder Models for Tree Structure

Current models struggle with irregular branching and overestimate surface area.
Machine learning and adaptive algorithms can improve model fitting.

(4) Separating Leaves from Wood

TLS struggles to distinguish leaves from branches, affecting biomass estimates.
Spectral analysis and multi-wavelength scanning could enhance leaf-wood separation.

5. Conclusion: The Future of Tree and Forest Ecology with TLS

TLS is revolutionizing our ability to measure and understand tree structure and function, leading to:

Improved allometric models for estimating biomass, canopy structure, and respiration.
Mechanistic testing of ecological theories, from metabolic scaling to wind resistance.
Insights into how forests vary globally, based on tree mechanics, competition, and dispersal strategies.
Better climate change predictions, as accurate TLS-based measurements enhance carbon flux and biomass monitoring.

As TLS technology advances, integration with ecological models and machine learning will unlock new frontiers in tree biology and ecosystem science. Understanding why forests have the structure they do will help predict how they will respond to climate and anthropogenic changes.

This summary captures all key details while ensuring clarity. Let me know if you need any refinements! 🚀