bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1

1 The mechanical stability of the world’s tallest broadleaf trees

2 T. Jackson1,2*, A. Shenkin1, N. Majalap3, J. bin Jami4, A. bin Sailim4, G. Reynolds4, D.A. Coomes2, C.J Chandler5, 3 D.S. Boyd5, A. Burt6, Phil Wilkes6,7, M. Disney6,7, Y. Malhi1

4 1 – Environmental Change Institute, School of Geography and the Environment, University of Oxford, OX1 3QY, UK 5 2 – Forest ecology and conservation group, Department of Plant Sciences, University of Cambridge, CB2 3EA, UK 6 3 – Phytochemistry Unit, Forest Research Centre, Jalan Sepilok, 90715 Sandakan, Sabah, Malaysia 7 4 – South East Asia Rainforest Research Partnership (SEARRP), Danum Valley Field Centre, 91112 Lahad Datu, Sabah, Malaysia 8 5 – School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK 9 6 – Department of Geography, University College London, WC1E 6BT, UK 10 7 – NERC National Centre for Earth Observation (NCEO), Leicester, UK 11 12 *Corresponding author – [email protected]

13 Abstract

14 The factors that limit the maximum height of trees, whether ecophysiological or mechanical, are the 15 subject of longstanding debate. Here we examine the role of mechanical stability in limiting tree 16 height and focus on trees from the tallest tropical forests on Earth, in Sabah, Malaysian Borneo, 17 including the recently discovered tallest tropical tree, a 100.8 m Shorea faguetiana. We use 18 terrestrial laser scans, in situ strain gauge data and finite-element simulations to map the 19 architecture of tall broadleaf trees and monitor their response to wind loading. We demonstrate 20 that a tree’s risk of breaking due to gravity or self-weight decreases with tree height and is much 21 more strongly affected by tree architecture than by material properties. In contrast, wind damage 22 risk increases with tree height despite the larger diameters of tall trees, resulting in a U-shaped 23 curve of mechanical risk with tree height. The relative rarity of extreme wind speeds in north Borneo

24 may be the reason it is home to the tallest trees in the tropics.

25 Introduction

26 Tall trees are an essential store of carbon and an inspiration to the public and to ecologists alike 27 (Bastin et al., 2018; Lindenmayer and Laurance, 2016; Lutz et al., 2018). Recently, the world’s tallest 28 tropical tree, a 100.8 m Shorea faguetiana (named “Menara”), has been discovered in Sabah, 29 Malaysian Borneo (Shenkin et al., 2019). Trees over 80 m tall have long been recognised in 30 temperate regions - notably the conifer Sequoia sempervirens in California and the broadleaf 31 Eucalyptus regnans in Tasmania, but the discovery of such tall trees in the tropics is recent. Record- 32 sized tropical trees have also recently been discovered Africa (Hemp et al., 2017) and South America 33 (Gorgens et al. in review). bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 2

1 2 Figure 1 – Tree height against trunk diameter measured at breast height (dbh), or above the buttress in 3 buttressed trees, based on the continental allometric equation for Asia (Feldpausch et al., 2011) with trees from 4 this study overlaid. Menara, the world’s tallest tropical tree is at the far right.

5 Tree height growth is driven by the intense competition for light in young forest stands (MacFarlane 6 and Kane, 2017) but why emergent trees continue to grow upwards once they have escaped 7 competition, and what determines the mean and maximum height of forest canopies, is less well 8 understood. Why do the world’s tallest broadleaf trees grow to about 100 m in height, and not 50 m 9 or 500 m?

10 Possible answers to this question are that tree height is limited by hydraulics, carbohydrate supply or 11 by mechanics. The hydraulic limitation theory posits that the negative water potentials in the 12 vascular systems at the tops of trees, driven by gravity and by demand outstripping supply around 13 midday, inhibits the transfer of water from the vascular system into the cytoplasm of plants (Ishii et 14 al., 2014; Koch et al., 2004). This effect, and the increased risk of embolism, is predicted to lead to 15 lower tree heights in drier regions and is supported by the correlation between canopy height and 16 water availability at the global scale (Klein et al., 2015; Moles et al., 2009a). The carbohydrate 17 transport theory posits that it is the ability of leaves to supply carbohydrates to distant tissues 18 against resistance that limits tree to around 100 m (Jensen and Zwieniecki, 2013), and explains the 19 relationship between water availability and tree height as being determined by climate constraints 20 on leaf size. The mechanical limitation theory suggests that tree height is limited by either gravity, 21 on which most of the literature has focussed due to its tractability, or wind damage risk, which is 22 addressed in the current study. Givnish et al., (2014) showed that the cost of producing and 23 sustaining support tissues increases disproportionately with tree height, meaning that the risk of 24 mechanical damage will also increase. The relative importance of mechanical, hydraulic and 25 carbohydrate constraints will vary according to local climate, and the tallest broadleaf trees are likely bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 3

1 to be found in regions with little seasonal water stress, such as aseasonal tropical (Klein et al., 2015; 2 Moles et al., 2009b).

3 In this study we focus on the mechanical limitation theory in the context of the tallest tropical 4 forests on earth, in northern Borneo. Our key questions are:

5 1. How does gravitational stability vary with tree height, architecture and material 6 properties? Terrestrial laser scanning (TLS) data allow us to estimate the weight of the 7 crowns of tall trees and so test the relative importance of variations in crown size and 8 material properties in terms of their effects on gravitational stability. 9 2. How does the risk of wind damage change with tree height? Taller trees are exposed to 10 higher wind speeds, but they also have better resistance to bending due to their higher trunk 11 diameter and modulus of elasticity. We use field measurements of strain under wind to 12 explicitly test whether these effects balance out. 13 3. Is Menara, the world’s tallest tropical tree, close to its mechanical limits? We focus 14 particular attention on Danum Valley in Sabah, home to Menara and many other tall trees, to 15 illustrate and explore the mechanical limits to tree height.

16 Results 17

Term Definition

Gravitational max height The tallest a tree can grow without collapsing under gravity

Modulus of elasticity A measure of a material’s resistance to bending

K The ratio of crown weight to trunk weight

Bending strain Extension per unit length – in this case along a tree trunk The slope of the best fit line relating bending strain to squared wind Wind-strain gradient speed The measured height of the tree divided by the maximum height Risk factor consistent with safety, either from wind or gravity 18

19 Table 1 – Definitions of key terms

20 Gravitational risk factors decrease with tree height within a forest canopy 21 We used two models to calculate the gravitational risk factors, the ratio of measured height to 22 theoretical maximum height each tree could reach before buckling under its own weight. The Euler 23 model (equation 1) represents a tree as a tapering cone with realistic trunk diameter and material 24 properties (Greenhill, 1881). The ‘top-weight’ model corresponds to a tapering cone with a top- 25 weight to represent the crown (King and Loucks, 1978). A novel aspect of this study is the inclusion bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 4

1 of detailed 3D tree architecture based on TLS data. This allowed us to estimate the woody volume in 2 different parts of the tree and so estimate the weight of the stem and the crown separately.

3 Both models predicted a decrease in gravitational risk factors with tree height (Figure 2a). This 4 means that, as trees get taller, their radial growth is more than sufficient to compensate for their 5 increased height. We also calculated gravitational risk factors directly from continental height- 6 diameter allometric equations (Feldpausch et al., 2011) and found the same trend (Figure 2b). The 7 intercept and exact shape of these risk factors depend on the relationship between tree height and 8 K, the ratio of crown weight to stem weight, but the negative trend and difference between 9 continents do not (S5). This result supports the hypothesis of King et al. (2009) who speculated that 10 understory trees have higher gravitational risk factors due to the prioritisation of vertical height 11 growth under intense competition for light, whereas overstory trees gain less advantage from 12 investing in height growth.

13 14 Figure 2 – a: Gravitational risk factors for trees in this study calculated using the classical model and top-weight 15 model. The hollow markers represent the same risk factor calculations using the mean material properties of all 16 trees, as opposed to species-specific material properties. Fit lines were calculated based on the latter. b: 17 Gravitational risk factors of trees based on the continental height-diameter allometries using the top-weight 18 model. Solid lines represent uniform material properties while dashed lines indicate material properties varying 19 with height as reported by (Jagels et al., 2018). The red dashed lines at risk factor = 1 shows the point at which 20 a tree would be expected to buckle under its own weight.

21 Crown size, not material properties, determine gravitational stability 22 The risk factor estimates based on the two models followed a similar pattern (adjusted R2 = 0.83) but 23 the magnitudes diverged substantially between models (Figure 2a). The ‘top-weight’ model 24 predicted that the trees are much closer to their gravitational stability limit than the ‘classical’ model 25 (increase in risk factor ranged from 0.062 to 0.581 with a mean of 0.214). This means that the 26 presence of a crown substantially decreases overall stability and demonstrates the importance of 27 crown dimensions in gravitational stability. bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 5

1 Comparing continents (Figure 2b) we find that trees in Africa and Asia (the latter data set being 2 dominated by sites in Borneo) have higher gravitational risk factors than those in South America. 3 These differences are driven by tree allometry, not by material properties. Clearly, trees experience 4 similar gravitational forces everywhere on Earth, and we suggest that these differences in 5 gravitational risk factors may be due to differences in wind regime.

6 Compared to changes in crown size, material properties (i.e. wood density and elasticity) had a 7 substantially lower effect on gravitational stability. Differences in risk factors using species-specific 8 or mean material properties ranged from -0.018 to 0.078 with a mean of 0.004. This small effect of 9 materials on gravitational stability is due to the fact that wood density and elasticity are strongly 10 correlated (Niklas and Spatz, 2010) and it is their ratio which affects gravitational stability.

11 Although its effect on gravitational stability is small, there is a consistent increase in the ratio of 12 wood elasticity to density with maximum tree height (Jagels et al., 2018). We found that including 13 species-specific material properties (Figure 2a) tended to amplify the difference between short and 14 tall trees, with gravitational risk factors increasing for shorter trees and decreasing for taller trees. 15 Similarly, Figure 2b used the systematic variation in material properties reported by Jagels et al. 16 (2018) and showed a comparable effect.

17 Wind risk increases with tree height

18 The second aspect of mechanical stability is resistance to wind-induced snapping or uprooting 19 (Niklas and Spatz, 2012). Mode-of-death surveys show a wide variation the relative likelihood of 20 snapping and uprooting (Everham and Brokaw, 1996), presumably driven by site conditions, and a 21 survey in Danum Valley found that slightly more trees snapped than uprooted in this site (Gale and 22 Hall, 2001). We do not know of any field technique that can measure the risk of uprooting for large 23 trees in a tropical forest environment. We argue that on average the risk of snapping and uprooting 24 should be similar, as trees are unlikely to have evolved strong mitigation of one risk at the expense 25 of the other (e.g. excessive protection against uprooting would make little sense if tree snapping is 26 the dominant mechanical risk, and vice versa). Therefore, we quantified the (more empirically and 27 analytically tractable) risk of snapping and assumed that uprooting is, on average, equally likely.

28 We measured the local wind speed using anemometers attached to tall trees and measured the 29 bending strains at the base of the trees using strain gauges. As expected, Danum Valley is not a 30 particularly windy site and the maximum recorded wind speed in almost 2000 hours of data 31 collected between August 2016 to March 2017 was a 10 s mean of 7.2 ms-1. We selected the 32 maximum wind speed and bending strain for each 10-minute window and calculated the wind-strain bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 6

1 gradient (Figures 3a, b). We then calculated each tree’s risk factor for a given maximum wind speed 2 by comparing strain produced at that wind speed with the species-specific breaking strain (Table S1). 3 The absolute value of the wind risk factor depends on the chosen wind measurement type and the 4 aggregation window length. We therefore focus on relative risk factors.

5 6 Figure 3 – 10-minute maximum bending strain against maximum squared wind speed measured with the sonic 7 anemometer for (a) 51 m tall Parashorea malaanonan logged with a CR1000 and (b) 17 m tall Orophea 8 myriantha logged with a CR23X. Note the difference in axes scales and data availability. c: Risk factor against 9 tree height assuming a 20 ms-1 wind speed at the position of our sonic anemometer. Individual wind-strain 10 gradients were calculated using a random effects model and a 10-minute aggregation period and risk factors 11 calculated as the ratio of the predicted strain at 20 ms-1 wind speed to the breaking strain for that species. For 12 details of the fitting process and sensitivity see S6 and S7. The red dashed line at risk factor = 1 shows the point 13 at which a tree would be expected to break.

14 The wind-strain gradient was steeper for taller trees (Figure 3c) meaning that taller trees have a 15 higher risk of wind damage. This seems intuitive, since taller trees are exposed to higher wind 16 speeds. However, trees are known to adapt to their local wind environment through increased radial 17 growth (Bonnesoeur et al., 2016; Telewski, 1995). Therefore, it seemed possible a priori that these 18 adaptations could counterbalance the increased wind loading. However, our field data demonstrate 19 that this is not the case - the larger diameters of tall trees are not sufficient to compensate for the 20 increased wind loading. This is likely because the support costs scale with trunk circumference and 21 so increase disproportionately with tree height, making it increasingly difficult for a tree to grow 22 radially sufficiently to support the increased wind loading (Givnish et al., 2014). In addition, these bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 7

1 results show that variations in material property, such as an increased stiffness for a given density, 2 do not compensate for the increased wind loading.

3 Post-damage surveys have shown that tall trees are more likely to be damaged by wind than shorter 4 trees (Rifai et al., 2016; Silvério et al., 2018). However, these data represent the first direct 5 measurements of bending strains and mechanistic study of wind damage risk in a tall tropical forest.

6 Mechanical risk factor follows a U-shaped curve 7 We found no significant correlation between the wind and gravitational risk factors for the 11 trees 8 where field and TLS data overlap (p=0.17, 0.08 for the ‘classical’ and ‘top-weight’ models 9 respectively). This demonstrates that overall mechanical stability cannot be approximated by 10 gravitational stability alone. It should be noted that the wind and gravitational risk factors are not 11 simply additive, since the gravitational risk factors are derived from a model whereas the wind risk 12 factors are extrapolated from field data which necessarily include gravitational effects. However, for 13 closed-canopy forests we show clearly that gravity risk factors play the dominant role in the 14 mechanical stability of small trees, while wind damage risk factors dominate for tall trees. The result 15 is a U-shaped mechanical risk profile (Figure 4) illustrating the shift from gravitational limitation to 16 wind limitation as the tree reaches the canopy. Figure 4 also demonstrates that the tree height at 17 which this shift takes place depends on the local wind conditions, with wind damage risk dominating 18 earlier in regions which experience more extreme wind storm events. One hypothesis generated 19 from this work is that the more continental climate system in South America develops larger-scale 20 and stronger storms and squall lines, resulting in shorter average canopy tree height and hence 21 lower biomass storage (Espírito-Santo et al., 2014). Note that it is rare extreme events (e.g. 22 convective downbursts) that would matter, and these rare maximum wind speeds are not 23 necessarily correlated to mean wind speeds. bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 8

1 2 Figure 4 – U-shaped variation in mechanical risk factor with tree height. This is derived from the combined 3 gravity and wind risk factors. The red dashed line at risk factor = 1 shows the point at which a tree would be 4 expected to break.

5 Is Menara, the world’s tallest tropical tree, close to its mechanical limits? 6 The world’s tallest tropical tree provides an important case study for questions about mechanical 7 stability. Our analysis shows that the greatest mechanical limitation on this tree’s height is likely due 8 to wind loading. Extrapolating from our field data to the height of this tree we estimate that it would 9 break at 24.1 ms-1 (measured at the location of the anemometer used in this study). However, this 10 tree, like the previous record holders from Malaysia (Jucker et al., 2017), is situated near the base of 11 a steep valley and will therefore be sheltered from the wind to a some degree.

12 In the above analysis we consider gravity and wind separately, but in nature they are combined. In 13 order to simulate the combined effects of wind and gravity we used finite element analysis (FEA), 14 based on a TLS-derived beam model of Menara (Figure 5). FEA is a well-established structural 15 engineering technique and has been successfully applied to conifers (Moore and Maguire, 2008) and 16 temperate broadleaf trees (Jackson et al., 2019; Sellier et al., 2006). Our results demonstrate that 17 the effect of wind dominates the bending response of the tree and that the effect of gravity is 18 secondary (Figure 5). The contribution of gravity increases with wind speed, since the tree deflection 19 is higher and so the overhanging weight of the crown causes a greater moment. Further FEA 20 simulations were carried out for the other TLS-scanned trees, but complications involving modelling 21 gravity for asymmetric trees without information on the asymmetries in material properties make 22 the results difficult to interpret (see S8). However, FEA is a promising technique in tree biomechanics bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 9

1 and could be used in future studies to estimate the strength of buttresses, which are characteristic 2 of these giant trees.

3 4 Figure 5 – a: Quantitative Structure Model (QSM) of Menara in its rest position. b: Finite element simulation of 5 the same tree under gravity and 12 ms-1 wind loading. c: Bending strain produced at the base of the stem of the 6 same tree under simulated wind loading with and without gravity.

7 Discussion

8 We studied the mechanical stability of tall trees in the lowland forests of Sabah, Malaysian Borneo, 9 in order to assess whether these tall trees are subject to mechanical constraints on maximum height. 10 We used TLS data to map the 3D architecture of tall trees and estimated the maximum possible 11 height consistent with gravitational stability. Gravitational risk factors decreased with tree height, 12 meaning that diameter growth more than compensated for increases in tree height, which is 13 consistent with previous work (King et al., 2009; Niklas, 1995). We also found that accounting for the 14 mass of the tree crown substantially reduced the maximum predicted height while material 15 properties had little effect. Our field measurements show that wind damage risk increases with tree 16 height, meaning that neither large diameters nor material properties counterbalanced this risk. 17 Therefore, overall mechanical risk factors follow a U-shaped curve with tree height (Figure 4). The 18 shape of this curve and the point at which mechanical risk shifts from gravity-dominated to wind- 19 dominated depends on the local wind regime.

20 Overall, this result suggests that wind plays a role in limiting tree height, which has wide-ranging 21 implications including for forest structure and carbon stocks. Two features of geographical variation 22 of tropical tree height support a role for wind constraints. One is the observation that tree heights in 23 Amazonia and Africa are generally lower than in Borneo (Feldpausch et al., 2011). Northwest 24 Amazonia, in particular, has a wet and aseasonal rainfall regime similar to north Borneo (and hence 25 little expected seasonal soil water stress (Malhi and Wright, 2004)), yet much shorter maximum tree 26 heights. We hypothesise that, compared to the insular climate of Borneo, the continental climates of 27 Amazonia or Central Africa are likely to generate more intense convective events and downbursts bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 10

1 that lead to a higher frequency of extreme winds. Secondly, at a local scale, we note that the many 2 of the tallest trees in the Danum Valley area, including Menara, appear to be found in somewhat 3 wind-sheltered regions on the lee side of ridges (Shenkin et al., 2019), again suggesting that 4 maximum winds speeds are a limiting factor. The general decline of tree height with increasing dry 5 season intensity does suggest that hydraulic or carbohydrate supply is a constraint on maximum 6 height in many tropical forests, but in forests with little seasonal drought our analysis suggest that 7 rare maximum wind speeds may provide the ultimate constraint.

8 Methods

9 Description of field sites 10 The island of Borneo is host to the world’s tallest tropical forests. Danum Valley is situated in the 11 state of Sabah, Malaysia (4°57'N 117°48'E) and is home to a permanent 50 ha forest monitoring plot. 12 Within this plot all the trees with dbh ≥ 1 cm have been tagged and measured and identified to 13 species level where possible. Our study site is located within a 1 ha intense monitoring plot 14 containing 450 trees ≥ 10 cm dbh with a basal area of 32 m2, 178 known species and 50 trees whose 15 species are not yet determined (Riutta et al., 2018). Tree height and crown dimensions have also 16 been estimated using a laser rangefinder (Sullivan et al., 2018). The Belian plot in the Maliau Basin 17 Conservation Area (4°44'N 116°58'E), also in Sabah, is a permanent sample plot with trees ≥ 10 cm 18 dbh monitored. It has a basal area of 41.5 m2, 135 known species, 4 species determined to genus, 19 and 5 individuals of undetermined taxonomy.

20 Terrestrial laser scanning and tree architecture 21 TLS data were collected in Danum Valley during April 2017 and in Maliau in July 2018 using standard 22 protocols (Wilkes et al., 2017). In this study we focussed on 16 trees from Danum Valley and 5 trees 23 from Maliau, in addition to Menara (Table S1). We manually extracted trees from the plot level point 24 cloud and trimmed the individual tree point clouds manually (Figure S3) using specifically designed 25 software (Vicari, 2017). We used a specifically designed cylinder fitting algorithm (TreeQSM; 26 Åkerblom, 2017) to fit 360 Quantitative Structure Models (QSMs) to each tree level point cloud, 27 varying the fitting parameters. The most plausible resulting QSM was selected manually by 28 comparing it to the point cloud (see S2). This process is subjective and all of the point clouds and 29 QSMs are available online. TLS scanning and data processing for Menara followed a similar protocol, 30 except that drone imagery of the crown was used to supplement the TLS data coverage using 31 structure-from-motion techniques (Shenkin et al., 2019). For this tree the QSM of the stem was 32 manually defined using a vertical profile of trunk diameter measurements taken directly from the 33 point cloud. Buttresses were removed from the tree-level point clouds prior to cylinder fitting to bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 11

1 generate the QSMs and a cylinder of the same height as the buttress attached to the bottom of the 2 QSM after fitting. The resolution of the point clouds decreased with tree height due to occlusion and 3 the divergence of the TLS laser beam. Therefore, the cylinder fitting process cannot detect the small 4 branches at the tops of the trees and the QSMs systematically underestimated tree height (Figure 5 S2). We therefore use height measurements taken from the point clouds for all analyses except for 6 Menara, which was directly measured by climbing.

7 As noted by Osunkoya et al., (2007) no generalizable definition of branching order is available in the 8 literature. We therefore manually defined the point at which the crown starts for each QSM (Figure 9 S3). We then calculated the ratio of crown mass to stem mass, K, and the positions of the centres of 10 mass for the crown and for the whole tree (Figure S3). Previous work in similar forests found that 11 variation in crown architecture was primarily intraspecific, with only a small proportion of the total 12 variance explained by species (Iida et al., 2011; Osunkoya et al., 2007). These studies also reported a 13 decrease in relative crown size with tree height, which was fit with a power law function (Osunkoya 14 et al., 2007). Therefore, in order to generalize the risk factor calculation, we assumed a power law 15 relationship 퐾 = 6.92퐻−0.69, where the parameters were derived by fitting to the data using the 16 Matlab curve fitting toolbox (Mathworks, 2017) . This same relationship was used for all trees in 17 figure 2b so that comparisons between continents or across material properties gradients are not 18 affected by it. The intercept and exact shape of the risk factors depend on the details of this 19 relationship, but the negative trend, difference between continents and effect of material properties 20 do not (S5). It would be interesting to test for systematic variability in this parameter in future work.

21 Gravitational stability

22 The most relevant material properties for mechanical stability are the green wood density, 휌푔푤,

23 modulus of elasticity, 퐸푔푤 and modulus of rupture, 푀푂푅푔푤. We collated material properties data 24 from the literature, see S1 for details.

25 We then calculated the theoretical maximum height each tree could reach before collapsing under

26 gravity, 퐻푚푎푥, using two different models. The ‘classical’ model (equation 1) of a tapering beam with 27 a circular cross-section (Greenhill, 1881) and the ‘top-weight’ model which includes a top-weight to 28 represent the crown centred at 0.9퐻 (King and Loucks, 1978). The difference between the two 29 models is the value of the constant, C. In the top-weight model the weight of the crown is included 30 as per equation 2 which depends on the parameter K, the ratio between stem and crown mass. In 31 the classical model we chose C=1.7464, so that the two models converge when the top weight is set 32 to 0. bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 12

1/3 2 1 퐻푚푎푥 = 퐶 (퐸푔푤푟0 ⁄휌푔푤) (1)

1/3 0.334퐾2+ 0.08655퐾+ 0.007601 2 퐶 = ( ) (2) 0.6125퐾3+0.1695퐾2+0.02907퐾+0.001427

3 Wind-strain data collection and processing 4 Since there is no tower near this plot, anemometers were attached to tall trees (trees 1 and 6) 5 boomed out from the stem. This means that our wind data are site-specific and cannot be translated 6 to the standard 10 m above canopy height point of measurement. However, we found that this 7 unusual method proved highly useful (see S6). In order to measure the tree’s bending in response to 8 wind we attached pairs of strain gauges to the stems of 19 trees at approximately 1.3 m. We used 9 three Campbell Scientific CR1000 data loggers and two CR23X data loggers to record the bending 10 strain data from October 2016 until April 2017. For the seven trees logged with CR1000 data loggers, 11 we collected hundreds of hours of data and found a clear bending strain signal (Figure 2a). We also 12 found clear signals for 9 of the 12 trees monitored with CR23X data loggers, although the data 13 availability was lower and the data were noisier. This complicated the uncertainty analysis and the 14 error propagation for the combined wind-strain gradient against tree height model.

15 The raw data consist of two mV readings per tree at 4 Hz and we calculated a single maximum strain 16 signal for each tree. This process involved (1) multiplying the raw strain signal by calibration 17 coefficients to transform from units of mV to strain; (2) bandpass filtering the data to smooth out 18 drift (3) re-projecting the signals onto into the North and East facing directions; and (4) combining 19 each pair of strain signals into a single maximum strain signal. We calculated the modal maximum 20 bending strain (Wellpott, 2008) and absolute maximum wind speed for each aggregation period (10 21 minutes or 1 minute). Using the maxima negates the need for a gust factor (Gardiner et al., 2000) 22 and focuses on the bending strains most relevant to wind damage. We then regressed maximum 23 wind against maximum strain and calculated the gradient. The wind speed measurement system 24 proved useful and wind-strain gradients did not vary much whichever anemometer they were 25 derived from (see S6 and S7).

26 Risk factor calculations 27 We calculated risk factors in order to compare the roles of wind and gravity in maximum height 28 limitation. The gravitational risk factors were defined as the ratio of the measured height (based on 29 the point clouds since the QSMs systematically under-estimate tree height) to the modelled

30 maximum height, 푅푔푟푎푣 = 퐻/퐻푚푎푥. We generalized gravitational risk factors using the continental 31 height-diameter allometries (Feldpausch et al., 2011) and, in the case of the top-weight model, the bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 13

1 power law relationship between K and H. We also used variation in material properties for tall and 2 short trees reported by Jagels et al., (2018) to estimate its effect on gravitational stability.

3 In order to calculate wind damage risk factors, we extrapolated from field data to estimate what the

-1 4 bending strain would be at a chosen threshold wind speed e.g. 20 ms , 휀20. The risk factor is then

5 푅푤푖푛푑 = 휀20/휀푏푟푒푎푘, where 휀푏푟푒푎푘 is the breaking strain (S1). These risk factors are necessarily 6 relative to the chosen wind speed as well as the point of measurement and aggregating window 7 length. We therefore focus on relative risk factors instead of absolute risk factors. In order to directly 8 compare these results with other sites we would need data on the return time of extreme wind 9 events and wind measurements from towers approximately 10 m above the canopy.

10 Finite element analysis of gravity and wind 11 Finite element analysis is a numerical method used to calculate the stresses and strains over a 12 complex geometry by splitting the problem up into a large number of finite elements. It has been 13 used to simulate the response of trees to wind loading in conifer forests (Moore and Maguire, 2008) 14 and temperate broadleaf forests (Jackson et al., 2019; Sellier et al., 2006). We used the QSM of 15 Menara as the input, and simulated the effect of increasing wind speed both with and without 16 gravity (for details see S8). Simulating the effect of gravity proved difficult, since the trees were 17 obviously scanned in the presence of gravity and are therefore pre-stressed structures. The best 18 approximation to a proper treatment of gravity was to apply a reversed gravity force, export the 19 deformed positions of all the branches into a new analysis, then apply a downwards gravity force 20 and maintain it throughout the simulation. This gave us the desired effect of increasing the moment 21 due to self-weight as the crown deflects under high wind speeds. However, in the cases of other, less 22 straight trees, they failed to stabilize under the downwards gravity load and collapsed. This is likely 23 due to the fact that, in nature, trees develop asymmetric material properties to compensate for their 24 asymmetric architecture, but this variation in material properties cannot yet be included in the 25 simulation.

26 Acknowledgments 27 We thank the Danum Valley Management Committee and Sabah Biodiversity Council for their assistance with 28 fieldwork. TJ was supported by NERC studentship NE/L0021612/1. TJ and DC are supported by NERC grant 29 NE/S010750/1. AS and YM are supported by NERC grant NE/P012337/1 (to YM) and YM is also supported by 30 the Frank Jackson Foundation. DB and CC are supported by NE/P004806/1 and NE/I528477/1. MD was 31 supported in part by NERC NCEO for travel and capital funding for lidar equipment, and NERC Standard Grants 32 NE/N00373X/1 and NE/ P011780/1. Some TLS data collection was funded through the Metrology for Earth 33 Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research 34 Program (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the 35 European Union. Some TLS data was contributed by the NASA Carbon Monitoring System, NASA Future 36 Mission Fusion for Improving Aboveground Biomass Estimation in High Biomass Forests bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 14

1 Data availability 2 The field data collected for this study are available at (https://doi.org/10.5285/657f420e-f956-4c33-b7d6- 3 98c7a18aa07a). The field data processing scripts, summary data, TLS point clouds and QSMs are all available 4 (https://github.com/TobyDJackson/WindAndTrees_Danum). Radius and mass taper exponents were calculated 5 in Matlab (https://github.com/TobyDJackson/TreeQSM_Architecture). An updated library for analysing tree 6 structural information is also available in R (https://github.com/ashenkin/treestruct).

7 References 8

9 Åkerblom, M., 2017. Inversetampere/Treeqsm: Initial Release. https://doi.org/10.5281/zenodo.844626 10 Bastin, J.F., Rutishauser, E., Kellner, J.R., Saatchi, S., Pélissier, R., Hérault, B., Slik, F., Bogaert, J., De Cannière, C., 11 Marshall, A.R., Poulsen, J., Alvarez-Loyayza, P., Andrade, A., Angbonga-Basia, A., Araujo-Murakami, A., 12 Arroyo, L., Ayyappan, N., de Azevedo, C.P., Banki, O., Barbier, N., Barroso, J.G., Beeckman, H., Bitariho, 13 R., Boeckx, P., Boehning-Gaese, K., Brandão, H., Brearley, F.Q., Breuer Ndoundou Hockemba, M., 14 Brienen, R., Camargo, J.L.C., Campos-Arceiz, A., Cassart, B., Chave, J., Chazdon, R., Chuyong, G., Clark, 15 D.B., Clark, C.J., Condit, R., Honorio Coronado, E.N., Davidar, P., de Haulleville, T., Descroix, L., Doucet, 16 J.L., Dourdain, A., Droissart, V., Duncan, T., Silva Espejo, J., Espinosa, S., Farwig, N., Fayolle, A., 17 Feldpausch, T.R., Ferraz, A., Fletcher, C., Gajapersad, K., Gillet, J.F., do Amaral, I.L., Gonmadje, C., Grogan, 18 J., Harris, D., Herzog, S.K., Homeier, J., Hubau, W., Hubbell, S.P., Hufkens, K., Hurtado, J., Kamdem, N.G., 19 Kearsley, E., Kenfack, D., Kessler, M., Labrière, N., Laumonier, Y., Laurance, S., Laurance, W.F., Lewis, S.L., 20 Libalah, M.B., Ligot, G., Lloyd, J., Lovejoy, T.E., Malhi, Y., Marimon, B.S., Marimon Junior, B.H., Martin, 21 E.H., Matius, P., Meyer, V., Mendoza Bautista, C., Monteagudo-Mendoza, A., Mtui, A., Neill, D., Parada 22 Gutierrez, G.A., Pardo, G., Parren, M., Parthasarathy, N., Phillips, O.L., Pitman, N.C.A., Ploton, P., Ponette, 23 Q., Ramesh, B.R., Razafimahaimodison, J.C., Réjou-Méchain, M., Rolim, S.G., Saltos, H.R., Rossi, L.M.B., 24 Spironello, W.R., Rovero, F., Saner, P., Sasaki, D., Schulze, M., Silveira, M., Singh, J., Sist, P., Sonke, B., 25 Soto, J.D., de Souza, C.R., Stropp, J., Sullivan, M.J.P., Swanepoel, B., ter Steege, H., Terborgh, J., Texier, 26 N., Toma, T., Valencia, R., Valenzuela, L., Ferreira, L.V., Valverde, F.C., Van Andel, T.R., Vasque, R., 27 Verbeeck, H., Vivek, P., Vleminckx, J., Vos, V.A., Wagner, F.H., Warsudi, P.P., Wortel, V., Zagt, R.J., 28 Zebaze, D., 2018. Pan-tropical prediction of forest structure from the largest trees. Glob. Ecol. Biogeogr. 29 27. https://doi.org/10.1111/geb.12803 30 Bonnesoeur, V., Constant, T., Moulia, B., Fournier, M., 2016. Forest trees filter chronic wind-signals to 31 acclimate to high winds. New Phytol. 210, 850–860. https://doi.org/10.1111/nph.13836 32 Espírito-Santo, F.D.B., Gloor, M., Keller, M., Malhi, Y., Saatchi, S., Nelson, B., Junior, R.C.O., Pereira, C., Lloyd, J., 33 Frolking, S., Palace, M., Shimabukuro, Y.E., Duarte, V., Mendoza, A.M., López-González, G., Baker, T.R., 34 Feldpausch, T.R., Brienen, R.J.W., Asner, G.P., Boyd, D.S., Phillips, O.L., 2014. Size and frequency of 35 natural forest disturbances and the Amazon forest carbon balance. Nat. Commun. 5. 36 https://doi.org/10.1038/ncomms4434 37 Everham, E.M., Brokaw, N.V.L., 1996. Forest damage and recovery from catastrophic wind. Bot. Rev. 62, 113– 38 185. https://doi.org/10.1007/BF02857920 39 Feldpausch, T.R., Banin, L., Phillips, O.L., Baker, T.R., Lewis, S.L., Quesada, C.A., Affum-Baffoe, K., Arets, 40 E.J.M.M., Berry, N.J., Bird, M., Brondizio, E.S., De Camargo, P., Chave, J., Djagbletey, G., Domingues, T.F., 41 Drescher, M., Fearnside, P.M., França, M.B., Fyllas, N.M., Lopez-Gonzalez, G., Hladik, A., Higuchi, N., 42 Hunter, M.O., Iida, Y., Salim, K.A., Kassim, A.R., Keller, M., Kemp, J., King, D.A., Lovett, J.C., Marimon, B.S., 43 Marimon-Junior, B.H., Lenza, E., Marshall, A.R., Metcalfe, D.J., Mitchard, E.T.A., Moran, E.F., Nelson, 44 B.W., Nilus, R., Nogueira, E.M., Palace, M., Patiño, S., Peh, K.S.H., Raventos, M.T., Reitsma, J.M., Saiz, G., 45 Schrodt, F., Sonké, B., Taedoumg, H.E., Tan, S., White, L., Wöll, H., Lloyd, J., 2011. Height-diameter 46 allometry of tropical forest trees. Biogeosciences 8, 1081–1106. https://doi.org/10.5194/bg-8-1081-2011 47 Gale, N., Hall, P., 2001. Factors determining the modes of tree death in three Bornean rain forests. J. Veg. Sci. 48 12, 337–346. https://doi.org/10.2307/3236847 49 Gardiner, B., Peltola, H., Kellomäki, S., 2000. Comparison of two models for predicting the critical wind speeds bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 15

1 required to damage coniferous trees. Ecol. Modell. 129, 1–23. https://doi.org/10.1016/S0304- 2 3800(00)00220-9 3 Givnish, T.J., Wong, S.C., Stuart-Williams, H., Holloway-Phillips, M., Farquhar, G.D., 2014. Determinants of 4 maximum tree height in Eucalyptusspecies along a rainfall gradient in Victoria, Australia. Ecology 95, 5 2991–3007. https://doi.org/10.1890/14-0240.1 6 Greenhill, 1881. Determination of the greatest height consistent with stability that a vertical pole or mast can 7 be made, and of the greatest height to which a tree of given proportions can grow, in: Proceedings of the 8 Cambridge Philosophical Society. pp. 65–73. 9 Hemp, A., Zimmermann, R., Remmele, S., Pommer, U., Berauer, B., Hemp, C., Fischer, M., 2017. Africa’s highest 10 mountain harbours Africa’s tallest trees. Biodivers. Conserv. 26, 103–113. 11 https://doi.org/10.1007/s10531-016-1226-3 12 Iida, Y., Kohyama, T.S., Kubo, T., Kassim, A.R., Poorter, L., Sterck, F., Potts, M.D., 2011. Tree architecture and 13 life-history strategies across 200 co-occurring tropical tree species. Funct. Ecol. 25, 1260–1268. 14 https://doi.org/10.1111/j.1365-2435.2011.01884.x 15 Ishii, H.R., Azuma, W., Kuroda, K., Sillett, S.C., 2014. Pushing the limits to tree height: could foliar water storage 16 compensate for hydraulic constraints in Sequoia sempervirens? https://doi.org/10.1111/1365- 17 2435.12284 18 Jackson, T, Shenkin, A., Kalyan, B., Zionts, J., Calders, K., Origo, N., Disney, M., Burt, A., Raumonen, P., Malhi, Y., 19 2019. A New Architectural Perspective on Wind Damage in a Natural Forest. Front. For. Glob. Chang. 1, 20 13. https://doi.org/10.3389/ffgc.2018.00013 21 Jackson, T., Shenkin, A., Wellpott, A., Calders, K., Origo, N., Disney, M., Burt, A., Raumonen, P., Gardiner, B., 22 Herold, M., Fourcaud, T., Malhi, Y., 2019. Finite element analysis of trees in the wind based on terrestrial 23 laser scanning data. Agric. For. Meteorol. 265. https://doi.org/10.1016/j.agrformet.2018.11.014 24 Jagels, R., Equiza, M.A., Maguire, D.A., Cirelli, D., 2018. Do tall tree species have higher relative stiffness than 25 shorter species? Am. J. Bot. 105, 1617–1630. https://doi.org/10.1002/ajb2.1171 26 Jensen, K.H., Zwieniecki, M.A., 2013. Physical Limits to Leaf Size in Tall Trees. Phys. Rev. Lett. 110, 018104. 27 https://doi.org/10.1103/PhysRevLett.110.018104 28 Jucker, T., Asner, G.P., Dalponte, M., Brodrick, P., Philipson, C.D., Vaughn, N., Brelsford, C., Burslem, D.F.R.P., 29 Deere, N.J., Ewers, R.M., Kvasnica, J., Lewis, S.L., Malhi, Y., Milne, S., Nilus, R., Pfeifer, M., Phillips, O., Qie, 30 L., Renneboog, N., Reynolds, G., Riutta, T., Struebig, M.J., Svátek, M., Teh, Y.A., Turner, E.C., Coomes, 31 D.A., 2017. A regional model for estimating the aboveground carbon density of Borneo’s tropical forests 32 from airborne laser scanning. arxiv.org. https://doi.org/10.5194/bg-2018-74 33 King, D., Loucks, O.L., 1978. The theory of tree bole and branch form. Radiat. Environ. Biophys. 15, 141–165. 34 https://doi.org/10.1007/BF01323263 35 King, D.A., Davies, S.J., Tan, S., Nur Supardi, M.N., 2009. Trees approach gravitational limits to height in tall 36 lowland forests of Malaysia. Funct. Ecol. 23, 284–291. https://doi.org/10.1111/j.1365-2435.2008.01514.x 37 Klein, T., Randin, C., Körner, C., 2015. Water availability predicts forest canopy height at the global scale. Ecol. 38 Lett. 18, 1311–1320. https://doi.org/10.1111/ele.12525 39 Koch, G.W., Stillet, S.C., Jennings, G.M., Davis, S.D., 2004. The limits to tree height. Nature 428, 851–854. 40 https://doi.org/10.1038/nature02417 41 Lindenmayer, D.B., Laurance, W.F., 2016. The Unique Challenges of Conserving Large Old Trees. Trends Ecol. 42 Evol. https://doi.org/10.1016/j.tree.2016.03.003 43 Lutz, J.A., Furniss, T.J., Johnson, D.J., Davies, S.J., Allen, D., Alonso, A., Anderson-Teixeira, K.J., Andrade, A., 44 Baltzer, J., Becker, K.M.L., Blomdahl, E.M., Bourg, N.A., Bunyavejchewin, S., Burslem, D.F.R.P., Cansler, 45 C.A., Cao, K., Cao, M., Cárdenas, D., Chang, L.W., Chao, K.J., Chao, W.C., Chiang, J.M., Chu, C., Chuyong, 46 G.B., Clay, K., Condit, R., Cordell, S., Dattaraja, H.S., Duque, A., Ewango, C.E.N., Fischer, G.A., Fletcher, C., bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 16

1 Freund, J.A., Giardina, C., Germain, S.J., Gilbert, G.S., Hao, Z., Hart, T., Hau, B.C.H., He, F., Hector, A., 2 Howe, R.W., Hsieh, C.F., Hu, Y.H., Hubbell, S.P., Inman-Narahari, F.M., Itoh, A., Janík, D., Kassim, A.R., 3 Kenfack, D., Korte, L., Král, K., Larson, A.J., Li, Y. De, Lin, Y., Liu, S., Lum, S., Ma, K., Makana, J.R., Malhi, Y., 4 McMahon, S.M., McShea, W.J., Memiaghe, H.R., Mi, X., Morecroft, M., Musili, P.M., Myers, J.A., 5 Novotny, V., de Oliveira, A., Ong, P., Orwig, D.A., Ostertag, R., Parker, G.G., Patankar, R., Phillips, R.P., 6 Reynolds, G., Sack, L., Song, G.Z.M., Su, S.H., Sukumar, R., Sun, I.F., Suresh, H.S., Swanson, M.E., Tan, S., 7 Thomas, D.W., Thompson, J., Uriarte, M., Valencia, R., Vicentini, A., Vrška, T., Wang, X., Weiblen, G.D., 8 Wolf, A., Wu, S.H., Xu, H., Yamakura, T., Yap, S., Zimmerman, J.K., 2018. Global importance of large- 9 diameter trees. Glob. Ecol. Biogeogr. 27. https://doi.org/10.1111/geb.12747 10 MacFarlane, D.W., Kane, B., 2017. Neighbour effects on tree architecture: functional trade-offs balancing 11 crown competitiveness with wind resistance. Funct. Ecol. 31, 1624–1636. https://doi.org/10.1111/1365- 12 2435.12865 13 Malhi, Y., Wright, J., 2004. Spatial patterns and recent trends in the climate of tropical rainforest regions. 14 Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 359, 311–329. https://doi.org/10.1098/rstb.2003.1433 15 Mathworks, M., 2017. Matlab. 16 Moles, A.T., Warton, D.I., Warman, L., Swenson, N.G., Laffan, S.W., Zanne, A.E., Pitman, A., Hemmings, F.A., 17 Leishman, M.R., 2009a. Global patterns in plant height. J. Ecol. 97, 923–932. 18 https://doi.org/10.1111/j.1365-2745.2009.01526.x 19 Moles, A.T., Warton, D.I., Warman, L., Swenson, N.G., Laffan, S.W., Zanne, A.E., Pitman, A., Hemmings, F.A., 20 Leishman, M.R., 2009b. Global patterns in plant height. J Ecol 97, 923–932. 21 Moore, J.R., Maguire, D.A., 2008. Simulating the dynamic behavior of Douglas-fir trees under applied loads by 22 the finite element method. Tree Physiol. 28, 75–83. https://doi.org/10.1093/treephys/28.1.75 23 Niklas, K.J., 1995. Size-dependent allometry of tree height, diameter and trunk-taper. Ann. Bot. 75, 217–227. 24 https://doi.org/10.1006/anbo.1995.1015 25 Niklas, K.J., Spatz, H.C., 2010. Worldwide correlations of mechanical properties and green wood density. Am. J. 26 Bot. 97, 1587–1594. https://doi.org/10.3732/ajb.1000150 27 Niklas, K.K.J., Spatz, H.H.-C., 2012. Plant physics. University of Chicago Press. 28 Osunkoya, O.O., Omar-Ali, K., Amit, N., Dayan, J., Daud, D.S., Sheng, T.K., 2007. Comparative height-crown 29 allometry and mechanical design in 22 tree species of Kuala Belalong rainforest, Brunei, Borneo. Am. J. 30 Bot. 94, 1951–1962. https://doi.org/10.3732/ajb.94.12.1951 31 Rifai, S.W., Urquiza Muñoz, J.D., Negrón-Juárez, R.I., Ramírez Arévalo, F.R., Tello-Espinoza, R., Vanderwel, M.C., 32 Lichstein, J.W., Chambers, J.Q., Bohlman, S.A., 2016. Landscape-scale consequences of differential tree 33 mortality from catastrophic wind disturbance in the Amazon. Ecol. Appl. 26, 2225–2237. 34 https://doi.org/10.1002/eap.1368 35 Riutta, T., Malhi, Y., Kho, L.K., Marthews, T.R., Huaraca Huasco, W., Khoo, M., Tan, S., Turner, E., Reynolds, G., 36 Both, S., Burslem, D.F.R.P., Teh, Y.A., Vairappan, C.S., Majalap, N., Ewers, R.M., 2018. Logging 37 disturbance shifts net primary productivity and its allocation in Bornean tropical forests. Glob. Chang. 38 Biol. 24, 2913–2928. https://doi.org/10.1111/gcb.14068 39 Sellier, D., Fourcaud, T., Lac, P., 2006. A finite element model for investigating effects of aerial architecture on 40 tree oscillations. Tree Physiol. 26, 799–806. https://doi.org/10.1093/treephys/26.6.799 41 Shenkin, A., Chandler, C., Boyd, D., Jackson, T., bin Jami, J., Disney, M., Majalap, N., Nilus, R., Foody, G., 42 Reynolds, G., Wilkes, P., Cutler, M., Van Der Heijden, G.M.F., Burslem, D., Coomes, D., Patrick Bentley, L., 43 Malhi, Y., 2019. The world’s tallest tropical tree in three dimensions. Front. For. Glob. Chang. 2, 32. 44 https://doi.org/10.3389/FFGC.2019.00032 45 Silvério, D. V, Brando, P.M., Bustamante, M.M.C., Putz, F.E., Marra, D.M., Levick, S.R., Trumbore, S.E., 2018. 46 Fire, fragmentation, and windstorms: A recipe for tropical forest degradation. J. Ecol. 47 https://doi.org/10.1111/1365-2745.13076 bioRxiv preprint doi: https://doi.org/10.1101/664292; this version posted June 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 17

1 Sullivan, M.J.P., Lewis, S.L., Hubau, W., Qie, L., Baker, T.R., Banin, L.F., Chave, J., Cuni-Sanchez, A., Feldpausch, 2 T.R., Lopez-Gonzalez, G., Arets, E., Ashton, P., Bastin, J.-F., Berry, N.J., Bogaert, J., Boot, R., Brearley, F.Q., 3 Brienen, R., Burslem, D.F.R.P., de Canniere, C., Chudomelová, M., Dančák, M., Ewango, C., Hédl, R., Lloyd, 4 J., Makana, J.-R., Malhi, Y., Marimon, B.S., Junior, B.H.M., Metali, F., Moore, S., Nagy, L., Vargas, P.N., 5 Pendry, C.A., Ramírez-Angulo, H., Reitsma, J., Rutishauser, E., Salim, K.A., Sonké, B., Sukri, R.S., 6 Sunderland, T., Svátek, M., Umunay, P.M., Martinez, R.V., Vernimmen, R.R.E., Torre, E.V., Vleminckx, J., 7 Vos, V., Phillips, O.L., 2018. Field methods for sampling tree height for tropical forest biomass estimation. 8 Methods Ecol. Evol. 9, 1179–1189. https://doi.org/10.1111/2041-210X.12962 9 Telewski, F.W., 1995. Wind-induced physiological and developmental responses in trees. Wind Trees 237, 237– 10 263. https://doi.org/10.1017/CBO9780511600425 11 Vicari, M.B., 2017. Leaf separation in TLS data. https://doi.org/10.5281/ZENODO.802233 12 Wellpott, a, 2008. The stability of continuous cover forests. Sch. Geosci. PhD, 160. 13 Wilkes, P., Lau, A., Disney, M., Calders, K., Burt, A., Gonzalez de Tanago, J., Bartholomeus, H., Brede, B., Herold, 14 M., 2017. Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sens. 15 Environ. 196, 140–153. https://doi.org/10.1016/j.rse.2017.04.030 16