# Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics

Thomas Ibanez, David Bauman, Shin-ichiro Aiba, Thomas Arsouze, Peter J

Bellingham, Chris Birkinshaw, Philippe Birnbaum, Timothy J Curran, Saara

J Dewalt, John Dwyer, et al.

► To cite this version:

Thomas Ibanez, David Bauman, Shin-ichiro Aiba, Thomas Arsouze, Peter J Bellingham, et al. | Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. Global Change Biology, 2024, 30, 10.1111/gcb.17317. [DOI: 10.1111/gcb.17317](https://doi.org/10.1111/gcb.17317)

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Submitted on 16 May 2024

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<figure><img src="image_1.png" /></figure>
RESEARCH ARTICLE

# Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics

Thomas Ibanez¹ ID

David Bauman¹,² ID

Shin-ichiro Aiba³ ID

Thomas Arsouze¹,⁴ ID

Peter J. Bellingham⁵ ID

Chris Birkinshaw⁶ ID

Philippe Birnbaum¹,⁴,⁷ ID

Timothy J. Curran⁸ ID

Saara J. DeWalt⁹ ID

John Dwyer¹⁰ ID

Thierry Fourcaud¹,⁴ ID

Janet Franklin¹¹,¹² ID

Takashi S. Kohyama³ ID

Christophe Menkes¹³ ID

Dan J. Metcalfe¹⁴ ID

Helen Murphy¹⁵ ID

Robert Muscarella¹⁶ ID

Gregory M. Plunkett¹⁷ ID

Chanel Sam¹⁸ ID

Edmund Tanner¹⁹ ID

Benton N. Taylor²⁰ ID

Jill Thompson²¹ ID

Tamara Ticktin²² ID

Marika V. Tuiwawa²³ ID

Maria Uriarte²⁴ ID

Edward L. Webb²⁵,²⁶ ID

Jess K. Zimmerman²⁷ ID

Gunnar Keppel¹,²⁸ ID

**Correspondence**

Thomas Ibanez, AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France.

Email: thomas.ibanez@ird.fr

**Funding information**

National Geographical Society; Marie Skłodowska-Curie, Grant/Award Number: 895799; Clemson Caribbean Initiative; Department of Marine and Wildlife Resources American Samoa; French Pacific Funds; San Diego State University Research Foundation; Hermon Slade Foundation, Grant/Award Number: HSF 19105; Swedish Research Council, Grant/Award Number: 2019-03758; Strategic Science Investment Fund of the New Zealand Ministry of Business; National Science Foundation, Grant/Award Number: 1555657 and 155793; Montpellier Advanced Knowledge Institute on Transitions; National Park of American Samoa

**Abstract**

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density ($ρ$) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher $ρ$ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of $ρ$ decreasing

<footnote>For affiliations refer to page 12.</footnote>

<footnote>This is an open access article under the terms of the [Creative Commons Attribution-NonCommercial License](https://creativecommons.org/licenses/by-nc), which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2024 The Authors. *Global Change Biology* published by John Wiley & Sons Ltd.</footnote>
RESEARCH ARTICLE

<figure><img src="image_2.png" /></figure>

WILEY

# Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics

Thomas Ibanez¹ ID | David Bauman¹,² ID | Shin-ichiro Aiba³ ID | Thomas Arsouze¹,⁴ ID |

Peter J. Bellingham⁵ ID | Chris Birkinshaw⁶ ID | Philippe Birnbaum¹,⁴,⁷ ID |

Timothy J. Curran⁸ ID | Saara J. DeWalt⁹ ID | John Dwyer¹⁰ ID | Thierry Fourcaud¹,⁴ ID |

Janet Franklin¹¹,¹² ID | Takashi S. Kohyama³ ID | Christophe Menkes¹³ ID |

Dan J. Metcalfe¹⁴ ID | Helen Murphy¹⁵ ID | Robert Muscarella¹⁶ ID |

Gregory M. Plunkett¹⁷ ID | Chanel Sam¹⁸ ID | Edmund Tanner¹⁹ ID | Benton N. Taylor²⁰ ID |

Jill Thompson²¹ ID | Tamara Ticktin²² ID | Marika V. Tuiwawa²³ ID | Maria Uriarte²⁴ ID |

Edward L. Webb²⁵,²⁶ ID | Jess K. Zimmerman²⁷ ID | Gunnar Keppel¹,²⁸ ID |

Correspondence

Thomas Ibanez, AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France.

Email: thomas.ibanez@ird.fr

Funding information

National Geographical Society; Marie Skłodowska-Curie, Grant/Award Number: 895799; Clemson Caribbean Initiative; Department of Marine and Wildlife Resources American Samoa; French Pacific Funds; San Diego State University Research Foundation; Hermon Slade Foundation, Grant/Award Number: HSF 19105; Swedish Research Council, Grant/Award Number: 2019-03758; Strategic Science Investment Fund of the New Zealand Ministry of Business; National Science Foundation, Grant/Award Number: 1555657 and 155793; Montpellier Advanced Knowledge Institute on Transitions; National Park of American Samoa

Abstract

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density ($ρ$) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher $ρ$ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of $ρ$ decreasing
<figure><img src="image_1.png" /></figure>

the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

KEYWORDS

hurricane, mechanical failure, snapping, storm, tree, tropical cyclones, tropics, typhoon, uprooting

# 1 | INTRODUCTION

Tropical cyclones (also known as hurricanes or typhoons) are large-scale disturbances that predominantly impact islands and coastal areas (Ibanez et al., 2022; Lin et al., 2020; Lugo, 2008). Globally, an average of almost 90 tropical cyclones per year occurred over the past four decades (Bourdin et al., 2022; Murakami et al., 2020), of which a half affected coastal areas (Wang & Toumi, 2021). The proportion of Categories 3–5 tropical cyclones (using the Saffir–Simpson hurricane wind scale; Simpson, 1974) has increased over the same period, likely because of global warming, and this trend has been predicted to continue (IPCC, 2021). Even if warming is limited to 1.5°C, which is now unlikely to occur (UNEP, 2023), a further increase of 10% is predicted in the proportion of the most intense tropical cyclones (Categories 4 and 5) (IPCC, 2021). In order to forecast the likely impacts of these changes, it is critical to understand how damage to forests caused by tropical cyclones varies as a function of wind intensity and the characteristics of trees.

The Saffir–Simpson hurricane wind scale (Simpson, 1974) is widely used to describe the strength of tropical cyclones and to anticipate potential damage. The scale is a function of the maximum sustained wind speed (MSW) generated by tropical cyclones and classifies them from Category 1 (MSW 33–42 ms⁻¹) to Category 5 (MSW ≥70 ms⁻¹). Given the increasing intensity of tropical cyclones, as exemplified by super-typhoon Haiyan (2013), an additional ‘Category 6’ has been proposed for tropical cyclones with MSW ≥80 ms⁻¹ (Lin et al., 2014) and more recently with MSW ≥86 ms⁻¹ (Wehner & Kossin, 2024). Higher MSW should increase mechanical wind loads on trees and result in higher proportions of trees that are either snapped (bole failure) or uprooted (root failure). As such, Category 1 cyclones are predicted to mostly damage foliage, while those in Category 5 are predicted to cause extensive tree snapping and/or uprooting.

Field data collected after tropical cyclones suggest that the proportion of damaged trees and the intensity of damage increase with wind speed. For example, in El Yunque National Forest (Puerto Rico), Category 4 tropical cyclone María (2017), tripled the proportion of snapped trees compared with Category 3 tropical cyclones Hugo (1989) and George (1998) (Uriarte et al., 2019). In Queensland (Australia), the level of damage was negatively correlated with the distance to trajectory of Category 4 tropical cyclone Larry (2006)

(Metcalfe et al., 2008). However, the relationship between MSW and the proportion of damaged trees or the probability of snapping and uprooting remains poorly explored, as most studies reported the damage caused by a single tropical cyclone on one or few plots (e.g., Asner & Goldstein, 1997; Basnet et al., 1992; Herbert et al., 1999; Webb et al., 2014; Zimmerman et al., 1994). For a given MSW, damage is also expected to be mediated by the topographical exposure to wind (EXP) with areas located on windward slopes and on ridges sustaining more damage than forests located on leeward slopes and valleys (e.g., Basnet et al., 1992; Bellingham, 1991; Franklin et al., 2004; Lugo et al., 1983; Reilly, 1991; Walker, 1991).

Tree dimensions (e.g., trunk diameter, height and crown size), together with wood strength, are believed to be the most important biotic characteristics affecting vulnerability to wind damage (Gardiner, 2021). Tree-winching experiments support that a larger diameter at breast height (DBH) should provide more mechanical stability (e.g., Cannon et al., 2015; Ribeiro et al., 2016), but DBH is usually positively related to tree height, crown size and tree weight (e.g., Blanchard et al., 2016; Chave et al., 2005), which should result in greater exposure to wind and mechanical loads (Gardiner, 2021). Some studies have indeed reported that, overall, larger trees experience more severe damage than smaller ones during tropical cyclones (Franklin et al., 2004; Ostertag et al., 2005; Reilly, 1991). However, the nature and the strength of the association between DBH and cyclone-induced uprooting or snapping remain unclear. Some studies found no significant association between tree DBH and snapping (Asner & Goldstein, 1997; Bellingham, 1991; Curran, Brown, et al., 2008; Zimmerman et al., 1994), while others found significant positive (Taylor et al., 2023; Uriarte et al., 2019) or negative associations (Webb et al., 2014). Likewise, most studies found no significant association between tree DBH and uprooting (Asner & Goldstein, 1997; Bellingham, 1991; Curran, Brown, et al., 2008; Elmqvist et al., 1994), while others found significant positive (Franklin et al., 2004; Taylor et al., 2023; Uriarte et al., 2019; Walker et al., 1992; Webb et al., 2014) or negative associations (Elmqvist et al., 1994). Furthermore, the direction and significance of these associations may vary among species (Zimmerman et al., 1994).

Wood density (ρ) is a relatively easy-to-measure trait that is positively associated with mechanical stability (Chave et al., 2009; Niklas & Spatz, 2010) as supported by tree-winching experiments (Ribeiro et al., 2016). Yet, the relationship between ρ and resistance
<figure><img src="image_3.png" /></figure>

the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

### KEYWORDS

hurricane, mechanical failure, snapping, storm, tree, tropical cyclones, tropics, typhoon, uprooting

# 1 | INTRODUCTION

Tropical cyclones (also known as hurricanes or typhoons) are large-scale disturbances that predominantly impact islands and coastal areas (Ibanez et al., 2022; Lin et al., 2020; Lugo, 2008). Globally, an average of almost 90 tropical cyclones per year occurred over the past four decades (Bourdin et al., 2022; Murakami et al., 2020), of which a half affected coastal areas (Wang & Toumi, 2021). The proportion of Categories 3-5 tropical cyclones (using the Saffir-Simpson hurricane wind scale; Simpson, 1974) has increased over the same period, likely because of global warming, and this trend has been predicted to continue (IPCC, 2021). Even if warming is limited to 1.5°C, which is now unlikely to occur (UNEP, 2023), a further increase of 10% is predicted in the proportion of the most intense tropical cyclones (Categories 4 and 5) (IPCC, 2021). In order to forecast the likely impacts of these changes, it is critical to understand how damage to forests caused by tropical cyclones varies as a function of wind intensity and the characteristics of trees.

The Saffir-Simpson hurricane wind scale (Simpson, 1974) is widely used to describe the strength of tropical cyclones and to anticipate potential damage. The scale is a function of the maximum sustained wind speed (MSW) generated by tropical cyclones and classifies them from Category 1 (MSW 33–42 ms⁻¹) to Category 5 (MSW ≥70 ms⁻¹). Given the increasing intensity of tropical cyclones, as exemplified by super-typhoon Haiyan (2013), an additional ‘Category 6’ has been proposed for tropical cyclones with MSW ≥80 ms⁻¹ (Lin et al., 2014) and more recently with MSW ≥86 ms⁻¹ (Wehner & Kossin, 2024). Higher MSW should increase mechanical wind loads on trees and result in higher proportions of trees that are either snapped (bole failure) or uprooted (root failure). As such, Category 1 cyclones are predicted to mostly damage foliage, while those in Category 5 are predicted to cause extensive tree snapping and/or uprooting.

Field data collected after tropical cyclones suggest that the proportion of damaged trees and the intensity of damage increase with wind speed. For example, in El Yunque National Forest (Puerto Rico), Category 4 tropical cyclone María (2017), tripled the proportion of snapped trees compared with Category 3 tropical cyclones Hugo (1989) and George (1998) (Uriarte et al., 2019). In Queensland (Australia), the level of damage was negatively correlated with the distance to trajectory of Category 4 tropical cyclone Larry (2006)

(Metcalfe et al., 2008). However, the relationship between MSW and the proportion of damaged trees or the probability of snapping and uprooting remains poorly explored, as most studies reported the damage caused by a single tropical cyclone on one or few plots (e.g., Asner & Goldstein, 1997; Basnet et al., 1992; Herbert et al., 1999; Webb et al., 2014; Zimmerman et al., 1994). For a given MSW, damage is also expected to be mediated by the topographical exposure to wind (EXP) with areas located on windward slopes and on ridges sustaining more damage than forests located on leeward slopes and valleys (e.g., Basnet et al., 1992; Bellingham, 1991; Franklin et al., 2004; Lugo et al., 1983; Reilly, 1991; Walker, 1991).

Tree dimensions (e.g., trunk diameter, height and crown size), together with wood strength, are believed to be the most important biotic characteristics affecting vulnerability to wind damage (Gardiner, 2021). Tree-winching experiments support that a larger diameter at breast height (DBH) should provide more mechanical stability (e.g., Cannon et al., 2015; Ribeiro et al., 2016), but DBH is usually positively related to tree height, crown size and tree weight (e.g., Blanchard et al., 2016; Chave et al., 2005), which should result in greater exposure to wind and mechanical loads (Gardiner, 2021). Some studies have indeed reported that, overall, larger trees experience more severe damage than smaller ones during tropical cyclones (Franklin et al., 2004; Ostertag et al., 2005; Reilly, 1991). However, the nature and the strength of the association between DBH and cyclone-induced uprooting or snapping remain unclear. Some studies found no significant association between tree DBH and snapping (Asner & Goldstein, 1997; Bellingham, 1991; Curran, Brown, et al., 2008; Zimmerman et al., 1994), while others found significant positive (Taylor et al., 2023; Uriarte et al., 2019) or negative associations (Webb et al., 2014). Likewise, most studies found no significant association between tree DBH and uprooting (Asner & Goldstein, 1997; Bellingham, 1991; Curran, Brown, et al., 2008; Elmqvist et al., 1994), while others found significant positive (Franklin et al., 2004; Taylor et al., 2023; Uriarte et al., 2019; Walker et al., 1992; Webb et al., 2014) or negative associations (Elmqvist et al., 1994). Furthermore, the direction and significance of these associations may vary among species (Zimmerman et al., 1994).

Wood density (ρ) is a relatively easy-to-measure trait that is positively associated with mechanical stability (Chave et al., 2009; Niklas & Spatz, 2010) as supported by tree-winching experiments (Ribeiro et al., 2016). Yet, the relationship between ρ and resistance
<figure><img src="image_2.png" /></figure>

to wind is not straightforward (Fournier et al., 2013). For instance, it has been argued that for a given DBH, higher $ρ$ provides higher wind resistance, but for the same construction cost, higher $ρ$ at the expense of smaller DBH decreases wind resistance (Larjavaara & Muller-Landau, 2010). Similar to DBH, no clear consensus on the nature and strength of the association between $ρ$ and vulnerability to tropical cyclones has emerged. Wood density has been found to be negatively associated with snapping (Curran, Gersbach, et al., 2008; Taylor et al., 2023; Walker et al., 1992; Webb et al., 2014; Zimmerman et al., 1994) but not always significantly so (Asner & Goldstein, 1997; Bellingham et al., 1995; Uriarte et al., 2019; Walker et al., 1992). Most studies found that $ρ$ was not significantly associated with uprooting (Asner & Goldstein, 1997; Bellingham et al., 1995; Curran, Gersbach, et al., 2008; Taylor et al., 2023; Zimmerman et al., 1994), while others found a significant negative association (Uriarte et al., 2019; Webb et al., 2014). Uprooting susceptibility should be primarily driven by soil and root properties, which determine tree anchorage, but for a given anchorage, heavier trees (larger DBH and/or $ρ$) should be more susceptible to uprooting (Gardiner, 2021).

Previous studies have been conducted mostly at single sites after one or two cyclones, and this approach has likely prevented the emergence of a consensus on the nature and strength of the relationships between tree characteristics and the damage trees experience. In this study, we analysed a consolidated dataset from 11 field-based studies conducted over a 40-year period to investigate the relationships among wind speed, tree size, wood density and cyclone-induced tree damage in forests. At the community level, we used reconstructed 2D surface wind speed and direction to test the relationships among MSW, topographical exposure to wind (EXP), and the proportion of snapped or uprooted trees. We then used the relationship between the proportion of damaged trees, MSW and EXP to forecast how tropical forests would be affected by higher intensity winds, including MSW $≥86 \text{ ms}^{-1}$. At the tree level, we explored whether tree size (DBH) and wood density ($ρ$) explained observed snapping and uprooting probabilities.

## 2 | MATERIALS AND METHODS

### 2.1 | Damage and tree characteristics

The categorisation of tree damage caused by tropical cyclones varies considerably across the literature. Tree snapping and uprooting are the most severe damage caused by tropical cyclones and are easy to identify in the field and are visible long after the passage of cyclones, and therefore are the most commonly quantified metrics of damage. Uprooting and, to a lesser extent, snapping increase tree mortality rates (Taylor et al., 2023; Webb et al., 2014) and result in structural changes to the forest by creating canopy gaps, which have important implications for regeneration and local diversity. Therefore, in this study we focus on tree snapping and uprooting damage.

We compiled published and unpublished data on tree snapping and uprooting resulting from tropical cyclones (Table 1) in the

Atlantic, Indian and Pacific Ocean basins. We avoided data where damage could not be clearly attributed to a specific cyclone. Data were available for damage caused by 11 cyclones occurring between 1988 and 2021, which affected plots surveyed in islands from nine archipelagos and in Australia (Figure 1). All plots were located in tropical rainforests except two, which were located in subtropical rainforests in Luquillo (Puerto Rico, 18.2°N) and Yakushima (Japan, 30.2°N). We prioritised data with pre- and post-cyclone forest data to prevent bias in plot location regarding the intensity of damage. For instance, we excluded the data set from Curran, Gersbach, et al. (2008), because they located their plots after the cyclone in the most severely damaged areas while in the same general area, plots before were set up before the cyclone by Metcalfe et al. (2008). However, we included data from Birkinshaw and Randrianjanahary (2007) and Zimmerman et al. (1994), which did not have pre-cyclone survey data. Damage was assessed 2–17 months after the cyclones.

We focused on woody trees, that is, we excluded monocots (palms), tree ferns and cycads, and only considered individuals with a DBH $≥10 \text{ cm}$. Our final dataset consisted of 22,176 trees belonging to 815 species, 408 genera and 105 families. Data for each tree included its species name, DBH, and whether it had been uprooted (0/1) or snapped (0/1). The definitions of uprooting and snapping were fairly consistent across the different data sets. Uprooting is characterised by the leaning of the main bole an exposure of roots and snapping is characterised by the breakage of the main bole between the ground and the first branches of the crown. Species wood density ($ρ$) was taken directly from the primary studies when available, or from the getWoodDensity function of the BIOMASS R package (Réjou-Méchain et al., 2017). When species $ρ$ was not available from either of these two sources, we used the averaged $ρ$ from species of the genus or family, which generally explains ~75% and ~35%, respectively, of the species-level variation in $ρ$ (Chave et al., 2006). Wood density ($ρ$) ranged from 0.10 to $1.03 \text{ g cm}^{-3}$ (58%, 36% and 6% inferred at the species, genus and family level, respectively).

### 2.2 | Wind speed and exposure

For each plot, we reconstructed the 1-min MSW generated by tropical cyclones using the temporalBehaviour function of the R package StormR (Delaporte et al., 2023, 2024). This function allows reconstruction of wind speed and direction using a set of cyclone models and tropical cyclone characteristics from the International Best Track Archive for Climate Stewardship (IBTrACS) database (Knapp et al., 2010). We used the default setting, that is, the Willoughby et al. (2006) model with asymmetry following Chen (1994). These models reconstruct the 2D idealised surface wind speed structure generated by tropical cyclones based on the latitudes of their centres, the MSWs and the radius of MSW speed as provided by IBTrACS every 6 h for each tropical cyclone. We performed a linear interpolation of the original 6-h inputs from IBTrACS to reconstruct the 2D surface wind speed structure of each tropical cyclone every
<figure><img src="image_4.png" /></figure>

to wind is not straightforward (Fournier et al., 2013). For instance, it has been argued that for a given DBH, higher $ρ$ provides higher wind resistance, but for the same construction cost, higher $ρ$ at the expense of smaller DBH decreases wind resistance (Larjavaara & Muller-Landau, 2010). Similar to DBH, no clear consensus on the nature and strength of the association between $ρ$ and vulnerability to tropical cyclones has emerged. Wood density has been found to be negatively associated with snapping (Curran, Gersbach, et al., 2008; Taylor et al., 2023; Walker et al., 1992; Webb et al., 2014; Zimmerman et al., 1994) but not always significantly so (Asner & Goldstein, 1997; Bellingham et al., 1995; Uriarte et al., 2019; Walker et al., 1992). Most studies found that $ρ$ was not significantly associated with uprooting (Asner & Goldstein, 1997; Bellingham et al., 1995; Curran, Gersbach, et al., 2008; Taylor et al., 2023; Zimmerman et al., 1994), while others found a significant negative association (Uriarte et al., 2019; Webb et al., 2014). Uprooting susceptibility should be primarily driven by soil and root properties, which determine tree anchorage, but for a given anchorage, heavier trees (larger DBH and/or $ρ$) should be more susceptible to uprooting (Gardiner, 2021).

Previous studies have been conducted mostly at single sites after one or two cyclones, and this approach has likely prevented the emergence of a consensus on the nature and strength of the relationships between tree characteristics and the damage trees experience. In this study, we analysed a consolidated dataset from 11 field-based studies conducted over a 40-year period to investigate the relationships among wind speed, tree size, wood density and cyclone-induced tree damage in forests. At the community level, we used reconstructed 2D surface wind speed and direction to test the relationships among MSW, topographical exposure to wind (EXP), and the proportion of snapped or uprooted trees. We then used the relationship between the proportion of damaged trees, MSW and EXP to forecast how tropical forests would be affected by higher intensity winds, including MSW $≥86 \text{ ms}^{-1}$. At the tree level, we explored whether tree size (DBH) and wood density ($ρ$) explained observed snapping and uprooting probabilities.

## 2 | MATERIALS AND METHODS

### 2.1 | Damage and tree characteristics

The categorisation of tree damage caused by tropical cyclones varies considerably across the literature. Tree snapping and uprooting are the most severe damage caused by tropical cyclones and are easy to identify in the field and are visible long after the passage of cyclones, and therefore are the most commonly quantified metrics of damage. Uprooting and, to a lesser extent, snapping increase tree mortality rates (Taylor et al., 2023; Webb et al., 2014) and result in structural changes to the forest by creating canopy gaps, which have important implications for regeneration and local diversity. Therefore, in this study we focus on tree snapping and uprooting damage.

We compiled published and unpublished data on tree snapping and uprooting resulting from tropical cyclones (Table 1) in the

Atlantic, Indian and Pacific Ocean basins. We avoided data where damage could not be clearly attributed to a specific cyclone. Data were available for damage caused by 11 cyclones occurring between 1988 and 2021, which affected plots surveyed in islands from nine archipelagos and in Australia (Figure 1). All plots were located in tropical rainforests except two, which were located in subtropical rainforests in Luquillo (Puerto Rico, 18.2°N) and Yakushima (Japan, 30.2°N). We prioritised data with pre- and post-cyclone forest data to prevent bias in plot location regarding the intensity of damage. For instance, we excluded the data set from Curran, Gersbach, et al. (2008), because they located their plots after the cyclone in the most severely damaged areas while in the same general area, plots before were set up before the cyclone by Metcalfe et al. (2008). However, we included data from Birkinshaw and Randrianjanahary (2007) and Zimmerman et al. (1994), which did not have pre-cyclone survey data. Damage was assessed 2–17 months after the cyclones.

We focused on woody trees, that is, we excluded monocots (palms), tree ferns and cycads, and only considered individuals with a DBH $≥10 \text{ cm}$. Our final dataset consisted of 22,176 trees belonging to 815 species, 408 genera and 105 families. Data for each tree included its species name, DBH, and whether it had been uprooted (0/1) or snapped (0/1). The definitions of uprooting and snapping were fairly consistent across the different data sets. Uprooting is characterised by the leaning of the main bole an exposure of roots and snapping is characterised by the breakage of the main bole between the ground and the first branches of the crown. Species wood density ($ρ$) was taken directly from the primary studies when available, or from the getWoodDensity function of the BIOMASS R package (Réjou-Méchain et al., 2017). When species $ρ$ was not available from either of these two sources, we used the averaged $ρ$ from species of the genus or family, which generally explains ~75% and ~35%, respectively, of the species-level variation in $ρ$ (Chave et al., 2006). Wood density ($ρ$) ranged from 0.10 to $1.03 \text{ g cm}^{-3}$ (58%, 36% and 6% inferred at the species, genus and family level, respectively).

### 2.2 | Wind speed and exposure

For each plot, we reconstructed the 1-min MSW generated by tropical cyclones using the temporalBehaviour function of the R package StormR (Delaporte et al., 2023, 2024). This function allows reconstruction of wind speed and direction using a set of cyclone models and tropical cyclone characteristics from the International Best Track Archive for Climate Stewardship (IBTrACS) database (Knapp et al., 2010). We used the default setting, that is, the Willoughby et al. (2006) model with asymmetry following Chen (1994). These models reconstruct the 2D idealised surface wind speed structure generated by tropical cyclones based on the latitudes of their centres, the MSWs and the radius of MSW speed as provided by IBTrACS every 6 h for each tropical cyclone. We performed a linear interpolation of the original 6-h inputs from IBTrACS to reconstruct the 2D surface wind speed structure of each tropical cyclone every
<figure><img src="image_3.png" /></figure>

<table><caption>TABLE 1 Studies reporting damage by tropical cyclones in tropical and subtropical wet forests (Cat. is the maximum Saffir-Simpson hurricane wind scale category reached by the cyclone over its duration, species are the three most abundant species, maximum sustained wind speed (MSW) is the reconstructed maximum sustained wind speed at plot locations).</caption><thead><tr><th>Cyclone (year)</th><th>Cat.</th><th>Reference</th><th>Location (elevation)</th><th>Forest type (species)</th><th>No. of plots</th><th>MSW (m s<sup>-1</sup>)</th><th>Plot size (ha)</th><th>No. of trees</th></tr></thead><tbody><tr><td>Cook (2017)</td><td>2</td><td>This study</td><td>New Caledonia (80m)</td><td>Lowland tropical rainforest (Xylopia vieillardii, Annonaceae; Crossostylis grandiflora, Rhizophoraceae; Tabernaemontana cerifera, Apocynaceae)</td><td>1</td><td>38.2</td><td>1</td><td>896</td></tr><tr><td>Gilbert (1988)</td><td>5</td><td>Tanner and Bellingham (2006)</td><td>Jamaica (1560–1610m)</td><td>Montane tropical rainforest (Clethra occidentalis, Clethraceae; Podocarpus urbanii, Podocarpaceae; Cyrilla racemiflora, Cyrillaceae)</td><td>4</td><td>62.2–62.2</td><td>0.07–0.20</td><td>750</td></tr><tr><td>Hudah (2000)</td><td>4</td><td>Birkinshaw and Randrianjanahary (2007)</td><td>Madagascar (0–350m)</td><td>Lowland tropical rainforest (Brochoneura voru, Myristicaceae; Anthostema madagascariense, Euphorbiaceae; Uapaca louvelii, Phyllanthaceae)</td><td>15</td><td>65.9–71.0</td><td>0.1</td><td>1353</td></tr><tr><td>Hugo (1989)</td><td>5</td><td>Zimmerman et al. (1994)</td><td>Puerto Rico (300m)</td><td>Lowland subtropical rainforest (Casearia arborea, Salicaceae; Dacryodes excelsa, Burseraceae; Manilkara bidentata, Sapotaceae)</td><td>1</td><td>55.9</td><td>16</td><td>8848</td></tr><tr><td>Larry (2006)</td><td>4</td><td>Metcalfe et al. (2008)</td><td>Australia (50–1100m)</td><td>Lowland to montane tropical rainforest (Franciscodendron laurifolium, Malvaceae; Backhousia bancroftii, Myrtaceae; Myristica insipida, Myristicaceae)</td><td>9</td><td>29.9–45.9</td><td>0.5</td><td>1779</td></tr><tr><td>Maria (2017)</td><td>5</td><td>Taylor et al. (2023)</td><td>Dominica (250–800m)</td><td>Lower montane tropical rainforest (Amanoa caribaea, Phyllanthaceae; Sterculia caribaea, Malvaceae; Tapura latifolia, Dichapetalaceae)</td><td>9</td><td>37.1–42.5</td><td>0.25</td><td>1647</td></tr><tr><td>Niran (2021)</td><td>5</td><td>This study</td><td>New Caledonia (290m)</td><td>Lowland tropical rainforest (Sparattosyce dioica, Moraceae; Deplanchea speciosa, Bignoniaceae; Pleurocalyptus pancheri, Myrtaceae)</td><td>1</td><td>30.5</td><td>1</td><td>1225</td></tr><tr><td>Olaf (2005)</td><td>5</td><td>Webb et al. (2014)</td><td>American Samoa (20–260m)</td><td>Lowland tropical rainforest (Dysoxyulum samoense, Mellaceae; Hibiscus tiliaceus, Malvaceae; Artocarpus altilis, Moraceae)</td><td>4</td><td>72.4–72.8</td><td>1.00–2.00</td><td>3672</td></tr><tr><td>Pam (2015)</td><td>5</td><td>Ticktin et al. (2024)</td><td>Vanuatu (260–370m)</td><td>Lowland tropical rainforest (Myristica inutilis, Myristicaceae Syzygium myriadenum, Myrtaceae; Macaranga dioica, Euphorbiaceae)</td><td>8</td><td>56.5–67.7</td><td>0.08</td><td>509</td></tr></tbody></table>
<figure><img src="image_5.png" /></figure>

<table><caption>TABLE 1 Studies reporting damage by tropical cyclones in tropical and subtropical wet forests (Cat. is the maximum Saffir–Simpson hurricane wind scale category reached by the cyclone over its duration, species are the three most abundant species, maximum sustained wind speed (MSW) is the reconstructed maximum sustained wind speed at plot locations).</caption><thead><tr><th>Cyclone (year)</th><th>Cat.</th><th>Reference</th><th>Location (elevation)</th><th>Forest type (species)</th><th>No. of plots</th><th>MSW (m s<sup>-1</sup>)</th><th>Plot size (ha)</th><th>No. of trees</th></tr></thead><tbody><tr><td>Cook (2017)</td><td>2</td><td>This study</td><td>New Caledonia (80m)</td><td>Lowland tropical rainforest (Xylopia vieillardii, Annonaceae; Crossostylis grandiflora, Rhizophoraceae; Tabernaemontana cerifera, Apocynaceae)</td><td>1</td><td>38.2</td><td>1</td><td>896</td></tr><tr><td>Gilbert (1988)</td><td>5</td><td>Tanner and Bellingham (2006)</td><td>Jamaica (1560–1610m)</td><td>Montane tropical rainforest (Clethra occidentalis, Clethraceae; Podocarpus urbanii, Podocarpaceae; Cyrilla racemiflora, Cyrillaceae)</td><td>4</td><td>62.2–62.2</td><td>0.07–0.20</td><td>750</td></tr><tr><td>Hudah (2000)</td><td>4</td><td>Birkinshaw and Randrianjanahary (2007)</td><td>Madagascar (0–350m)</td><td>Lowland tropical rainforest (Brochoneura voru, Myristicaceae; Anthostema madagascariense, Euphorbiaceae; Uapaca louvelii, Phyllanthaceae)</td><td>15</td><td>65.9–71.0</td><td>0.1</td><td>1353</td></tr><tr><td>Hugo (1989)</td><td>5</td><td>Zimmerman et al. (1994)</td><td>Puerto Rico (300m)</td><td>Lowland subtropical rainforest (Casearia arborea, Salicaceae; Dacryodes excelsa, Burseraceae; Manilkara bidentata, Sapotaceae)</td><td>1</td><td>55.9</td><td>16</td><td>8848</td></tr><tr><td>Larry (2006)</td><td>4</td><td>Metcalfe et al. (2008)</td><td>Australia (50–1100m)</td><td>Lowland to montane tropical rainforest (Franciscodendron laurifolium, Malvaceae; Backhousia bancroftii, Myrtaceae; Myristica insipida, Myristicaceae)</td><td>9</td><td>29.9–45.9</td><td>0.5</td><td>1779</td></tr><tr><td>Maria (2017)</td><td>5</td><td>Taylor et al. (2023)</td><td>Dominica (250–800m)</td><td>Lower montane tropical rainforest (Amanoa caribaea, Phyllanthaceae; Sterculia caribaea, Malvaceae; Tapura latifolia, Dichapetalaceae)</td><td>9</td><td>37.1–42.5</td><td>0.25</td><td>1647</td></tr><tr><td>Niran (2021)</td><td>5</td><td>This study</td><td>New Caledonia (290m)</td><td>Lowland tropical rainforest (Sparattosyce dioica, Moraceae; Deplanchea speciosa, Bignoniaceae; Pleurocalyptus pancheri, Myrtaceae)</td><td>1</td><td>30.5</td><td>1</td><td>1225</td></tr><tr><td>Olaf (2005)</td><td>5</td><td>Webb et al. (2014)</td><td>American Samoa (20–260m)</td><td>Lowland tropical rainforest (Dysoxyulum samoense, Mellaceae; Hibiscus tiliaceus, Malvaceae; Artocarpus altilis, Moraceae)</td><td>4</td><td>72.4–72.8</td><td>1.00–2.00</td><td>3672</td></tr><tr><td>Pam (2015)</td><td>5</td><td>Ticktin et al. (2024)</td><td>Vanuatu (260–370m)</td><td>Lowland tropical rainforest (Myristica inutilis, Myristicaceae Syzygium myriadenum, Myrtaceae; Macaranga dioica, Euphorbiaceae)</td><td>8</td><td>56.5–67.7</td><td>0.08</td><td>509</td></tr></tbody></table>
<figure><img src="image_4.png" /></figure>

<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Cyclone (year)</th><th>Cat.</th><th>Reference</th><th>Location (elevation)</th><th>Forest type (species)</th><th>No. of plots</th><th>MSW (ms<sup>-1</sup>)</th><th>Plot size (ha)</th><th>No. of trees</th></tr></thead><tbody><tr><td>Waka (2001)</td><td>3</td><td>Franklin et al. (2004)</td><td>Tonga (10–180m)</td><td>Lowland tropical rainforest (Maniltoa grandiflora, Fabaceae; Zanthoxylum pinnatum, Rutaceae; Cryptocarya tumbinata; Lauraceae)</td><td>21</td><td>44.5–51.3</td><td>0.06</td><td>996</td></tr><tr><td>Yancy (1993)</td><td>4</td><td>Bellingham et al. (1996)</td><td>Japan (530m)</td><td>Lowland subtropical rainforest (Distylium racemosum, Hamamelidaceae; Actinodaphne acuminata, Lauraceae; Camellia oleifera, Theaceae)</td><td>1</td><td>60.1</td><td>0.62</td><td>501</td></tr></tbody></table>

1h. Then, for each plot and cyclone, we computed MSW as the largest value of the MSW observed along the passage of the cyclone at the plot location (see Figure 2a). MSW ranged from 29.9 to 72.8 ms⁻¹ (with mean of 52.8 ms⁻¹ and standard deviation of 12.7 ms⁻¹).

Topographical exposure to wind (EXP) was computed using the hillShade function of the raster R package (Hijmans, 2023) and the Copernicus 30-m spatial resolution global digital elevation model (GLO-30 Copernicus DEM, https://doi.org/10.5270/ESA-c5d3d65). We set the wind inflexion angle to 6° as suggested by Boose et al. (1994). Given that wind direction changes along the movement of the cyclone, we computed EXP every 1h. We then extracted the maximum EXP value (EXP) over the duration of the cyclone when the centre of the cyclone was located ≤300km away from the plot. Negative EXP values represent areas sheltered from the wind, while positive values represent areas exposed to the wind (e.g., see Figure 2b). For the site reporting impacts from Hurricane Hugo (1989) in Puerto Rico (Luquillo Experimental Forest), because the plot is large with a varying topography (e.g., elevation ranges from 330 to 430m), we used the average value within the extent of the 16-ha plot. The maximum topographical exposure to wind (EXP) across the full dataset ranged from 0.04 to 0.85 (with a mean of 0.29 and standard deviation of 0.17).

## 2.3 | Data analysis

We used a causal inference analytical framework (Structural Causal Modelling, Pearl, 2009) to understand the causal effects of MSW, wood density and tree size (further referred to as 'predictors of interest') on the outcomes of interest (see below) from these models. We first defined a causal diagram of the studied system—one at the community level and a second at the tree level—using directed acyclic graphs (DAGs; see Figure 3). For each predictor of interest (i.e., predictors whose total causal effect we aim to quantify), we then applied the 'backdoor criterion' to the DAG. This criterion allowed us to define the minimum set of covariates to condition on, to close non-causal paths and allow a causal interpretation of the slope of the predictor of interest (conditional on the DAG's assumptions being true, while avoiding common interpretational problems such as confounding, overcontrol or collider biases, Arif & MacNeil, 2023; McElreath, 2020).

We used Bayesian generalised linear mixed models to test the effects of the predictors of interest on the different response variables. In the first model, we aimed to quantify the causal effect of MSW on the total proportion of snapped or uprooted trees by a cyclone at the community (or plot) level. We also investigated how this effect may be mediated by the topographical exposure to wind (EXP)—that is, the interaction effect of MSW and EXP. Given that topography also affects forest structure and composition (e.g., Blanchard et al., 2019; Webb et al., 1999), which in turn can affect the proportion of damaged trees, we controlled for different forest structures by adding the plot's mean DBH and wood density (ρ) of the trees as predictors. We expect that forests with larger trees and
<figure><img src="image_6.png" /></figure>

<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Cyclone (year)</th><th>Cat.</th><th>Reference</th><th>Location (elevation)</th><th>Forest type (species)</th><th>No. of plots</th><th>MSW (ms<sup>-1</sup>)</th><th>Plot size (ha)</th><th>No. of trees</th></tr></thead><tbody><tr><td>Waka (2001)</td><td>3</td><td>Franklin et al. (2004)</td><td>Tonga (10–180m)</td><td>Lowland tropical rainforest (Maniltoa grandiflora, Fabaceae; Zanthoxylum pinnatum, Rutaceae; Cryptocarya turbinata; Lauraceae)</td><td>21</td><td>44.5–51.3</td><td>0.06</td><td>996</td></tr><tr><td>Yancy (1993)</td><td>4</td><td>Bellingham et al. (1996)</td><td>Japan (530m)</td><td>Lowland subtropical rainforest (Distylium racemosum, Hamamelidaceae; Actinodaphne acuminata, Lauraceae; Camellia oleifera, Theaceae)</td><td>1</td><td>60.1</td><td>0.62</td><td>501</td></tr></tbody></table>

1h. Then, for each plot and cyclone, we computed MSW as the largest value of the MSW observed along the passage of the cyclone at the plot location (see Figure 2a). MSW ranged from 29.9 to 72.8 ms⁻¹ (with mean of 52.8 ms⁻¹ and standard deviation of 12.7 ms⁻¹).

Topographical exposure to wind (EXP) was computed using the hillShade function of the raster R package (Hijmans, 2023) and the Copernicus 30-m spatial resolution global digital elevation model (GLO-30 Copernicus DEM, https://doi.org/10.5270/ESA-c5d3d65). We set the wind inflexion angle to 6° as suggested by Boose et al. (1994). Given that wind direction changes along the movement of the cyclone, we computed EXP every 1h. We then extracted the maximum EXP value (EXP) over the duration of the cyclone when the centre of the cyclone was located ≤300km away from the plot. Negative EXP values represent areas sheltered from the wind, while positive values represent areas exposed to the wind (e.g., see Figure 2b). For the site reporting impacts from Hurricane Hugo (1989) in Puerto Rico (Luquillo Experimental Forest), because the plot is large with a varying topography (e.g., elevation ranges from 330 to 430m), we used the average value within the extent of the 16-ha plot. The maximum topographical exposure to wind (EXP) across the full dataset ranged from 0.04 to 0.85 (with a mean of 0.29 and standard deviation of 0.17).

## 2.3 | Data analysis

We used a causal inference analytical framework (Structural Causal Modelling, Pearl, 2009) to understand the causal effects of MSW, wood density and tree size (further referred to as ‘predictors of interest’) on the outcomes of interest (see below) from these models. We first defined a causal diagram of the studied system—one at the community level and a second at the tree level—using directed acyclic graphs (DAGs; see Figure 3). For each predictor of interest (i.e., predictors whose total causal effect we aim to quantify), we then applied the ‘backdoor criterion’ to the DAG. This criterion allowed us to define the minimum set of covariates to condition on, to close non-causal paths and allow a causal interpretation of the slope of the predictor of interest (conditional on the DAG’s assumptions being true, while avoiding common interpretational problems such as confounding, overcontrol or collider biases, Arif & MacNeil, 2023; McElreath, 2020).

We used Bayesian generalised linear mixed models to test the effects of the predictors of interest on the different response variables. In the first model, we aimed to quantify the causal effect of MSW on the total proportion of snapped or uprooted trees by a cyclone at the community (or plot) level. We also investigated how this effect may be mediated by the topographical exposure to wind (EXP)—that is, the interaction effect of MSW and EXP. Given that topography also affects forest structure and composition (e.g., Blanchard et al., 2019; Webb et al., 1999), which in turn can affect the proportion of damaged trees, we controlled for different forest structures by adding the plot’s mean DBH and wood density (ρ) of the trees as predictors. We expect that forests with larger trees and
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>FIGURE 1 Location of the study sites (points) and trajectories of studied cyclones (lines). Map lines delineate study areas and do not necessarily depict accepted national boundaries.</figcaption></figure>

lower wood density would suffer more damage than forests with smaller trees and higher wood density. We used a binomial distribution with a logit link function and a varying intercept for the study identity, a variable that gathers plots from the same study (i.e., same region, cyclone and survey team; detailed model described in the **Supplementary Material S1**). We performed Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) with the *loo* function from the *loo* R package (Vehtari et al., 2017, 2023) to assess the predictive performance of our models. The *loo* function returns a summary table of Pareto *k* diagnostic; prediction are very well when *k* ≤ 0.5, well when 0.5 > *k* ≤ 0.7, poor when 0.7 > *k* ≤ 1, very poorly *k* > 1.

In the second and third models, we aimed to understand the causal effects of tree size (DBH) and the species' wood density (ρ) on the probability of individual tree snapping (Model 2) or uprooting (Model 3), and how these effects are mediated by MSW. We also accounted for the interaction between tree DBH and species ρ (the effect of ρ is expected to decrease with increasing DBH). We kept the effect of EXP (and its interaction with MSW) and added the study, plot and species identities as three varying intercepts to capture residual variation related to these grouping variables. Using the backdoor criterion, the defined set of necessary covariates was MSW, EXP, DBH and ρ. For Models 2 and 3, we used a Bernoulli family distribution with a logit link function (detailed model descriptions in the **Supplementary Material S1**). For all models, predictors were centred and scaled before fitting the model to facilitate prior assignment and exploration of the posterior distribution by the sampler.

We used the joint posterior distributions to generate posterior predictions of the response variables of Models 1, 2 and 3, to visualise the expected causal effects of interest and their uncertainty at different combinations of predictor values. For Model 1, we generated predictions of the proportion of damaged trees at 100 values of MSW equally spaced within the range of observed MSW and extrapolated wind speed values to 95 m.s⁻¹, the record MSW for tropical cyclones in the IBTrACS database (see Rogers et al., 2017). We

<figure><img src="image_7.png" /><figcaption>FIGURE 2 Example of (a) maximum sustained wind speed (MSW) generated by cyclone Niran (2021) over New Caledonia, including the location of the sample plot and surrounding area shown in (b), and (b) Maximum topographical exposure in the area around the example plot. Spatial resolutions are 1 km and 30 m, respectively (MSW = 25.0 m s⁻¹ and EXP = 0.26 at the plot location). In (b), white arrows represent wind direction blowing from North to South at the beginning of the cyclone and from South-West to North-East at the end of the cyclone.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>FIGURE 1 Location of the study sites (points) and trajectories of studied cyclones (lines). Map lines delineate study areas and do not necessarily depict accepted national boundaries.</figcaption></figure>

lower wood density would suffer more damage than forests with smaller trees and higher wood density. We used a binomial distribution with a logit link function and a varying intercept for the study identity, a variable that gathers plots from the same study (i.e., same region, cyclone and survey team; detailed model described in the **Supplementary Material S1**). We performed Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) with the *loo* function from the *loo* R package (Vehtari et al., 2017, 2023) to assess the predictive performance of our models. The *loo* function returns a summary table of Pareto *k* diagnostic; prediction are very well when *k* ≤ 0.5, well when 0.5 > *k* ≤ 0.7, poor when 0.7 > *k* ≤ 1, very poorly *k* > 1.

In the second and third models, we aimed to understand the causal effects of tree size (DBH) and the species' wood density (ρ) on the probability of individual tree snapping (Model 2) or uprooting (Model 3), and how these effects are mediated by MSW. We also accounted for the interaction between tree DBH and species ρ (the effect of ρ is expected to decrease with increasing DBH). We kept the effect of EXP (and its interaction with MSW) and added the study, plot and species identities as three varying intercepts to capture residual variation related to these grouping variables. Using the backdoor criterion, the defined set of necessary covariates was MSW, EXP, DBH and ρ. For Models 2 and 3, we used a Bernoulli family distribution with a logit link function (detailed model descriptions in the **Supplementary Material S1**). For all models, predictors were centred and scaled before fitting the model to facilitate prior assignment and exploration of the posterior distribution by the sampler.

We used the joint posterior distributions to generate posterior predictions of the response variables of Models 1, 2 and 3, to visualise the expected causal effects of interest and their uncertainty at different combinations of predictor values. For Model 1, we generated predictions of the proportion of damaged trees at 100 values of MSW equally spaced within the range of observed MSW and extrapolated wind speed values to 95 m.s⁻¹, the record MSW for tropical cyclones in the IBTrACS database (see Rogers et al., 2017). We

<figure><img src="image_8.png" /><figcaption>FIGURE 2 Example of (a) maximum sustained wind speed (MSW) generated by cyclone Niran (2021) over New Caledonia, including the location of the sample plot and surrounding area shown in (b), and (b) Maximum topographical exposure in the area around the example plot. Spatial resolutions are 1 km and 30 m, respectively (MSW = 25.0 m s⁻¹ and EXP = 0.26 at the plot location). In (b), white arrows represent wind direction blowing from North to South at the beginning of the cyclone and from South-West to North-East at the end of the cyclone.</figcaption></figure>
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 3 Directed acyclic graph (DAG) at the tree level. Outcomes are the probabilities of tree snapping or uprooting (DBH, diameter at breast height; EXP, topographical exposure to wind; H, tree height; MSW, maximum sustained wind speed; WD, wood density). At the community level, outcomes are the proportion of snapped or uprooted trees and averaged values were used as predictors.</figcaption></figure>

also compared the predicted posterior probability distributions of these proportions at the five threshold values of the Saffir-Simpson hurricane wind scale, namely 33, 43, 50, 58 and 70ms⁻¹, and at the suggested threshold for ‘Category 6’ cyclones (86ms⁻¹). These predictions were made at maximum EXP values of 0 (low), 0.3 (medium) and 0.6 (high) to visualise the implications of the interaction effect on the scale of the outcome variable. We used a mean DBH value of 20 cm (i.e., close to the median value, 21.3 cm) and a mean wood density of 0.60gcm⁻³ (i.e., close to the median value, 0.61gcm⁻³). For Models 2 and 3, we followed the same approach as for Model 1, but replaced EXP by ρ (low, ρ=0.35gcm⁻³, medium ρ=0.60gcm⁻³ and high-density ρ=0.85gcm⁻³), and used the three values of DBH (small, DBH=10cm, medium, DBH=45cm and large DBH=80cm), to visualise the predicted individual probabilities of being snapped or uprooted as MSW increases, depending on species ρ and at different DBHs, and at the same threshold wind values based on the Saffir-Simpson scale as for Model 1. Finally, we compared predicted individual probabilities of being snapped or uprooted for four different types of trees when exposed to high wind speed (70ms⁻¹): small trees with low wood density, small trees with high wood density, large trees with low wood density and large trees with high wood density.

Parameter or predicted outcome posterior distributions were shown in their entirety or were summarised using the median as the central point and a 90%-highest posterior density interval (90%-highest posterior density interval, HPDI), that is, the narrowest interval capturing 90% of the probability mass. All models were run in the R statistical environment (version 4.3.2, R Core Team, 2023) and were fitted using the No-U-Turn (NUTS) sampler of stan (Carpenter et al., 2017) through the brms R package (Bürkner, 2017).

Models were run on four chains, for 1500 iterations, with 500 warm-ups. Chain mixing was checked visually using trace plots and model convergence was verified using Rhats (all were <1.01, as required). Posterior draws were extracted using the *tidybayes* R package (Kay, 2023).

## 3 | RESULTS

At the plot scale, the proportion of uprooted or snapped trees increased with MSW and to a lesser extent with the maximum topographical exposure to wind (EXP) (Figure 4a; Figure S2). We also found a positive interaction between MSW and EXP (in 97.6% of iterations) showing that the proportion of damaged trees increased more quickly with increasing MSW on topographically exposed areas compared with topographically sheltered areas (Figure 4c). The proportion of damaged trees also increased with increasing mean DBH and to a lesser extent with decreasing mean wood density (in 83.2% of iterations). The uncertainty in the predicted proportion of damaged trees also increased with MSW. For an MSW of 33ms⁻¹ (Category 1 cyclones, Figure 4b), a mean DBH of 20 cm and a mean wood density of 0.60gcm⁻³, the predicted median proportion of damaged trees was low regardless of EXP (median=0.03%) with low uncertainty (90%-HPDI between 0.01% and 0.06%). For a MSW of 70ms⁻¹ (Category 5 cyclones, Figure 4f) the predicted proportion of damaged trees increased with increasing EXP but with large uncertainty around the median prediction, 0.36% (90%-HPDI between 0.22% and 0.51%), 0.41% (90%-HPDI between 0.27% and 0.57%), 0.45% (90%-HPDI between 0.31% and 0.63%), for EXP=0, 0.3 and 0.6, respectively. For extreme MSW of 86ms⁻¹ ('Category 6' cyclones, Figure 4g) the uncertainty in the predicted damage remained large, but the distribution of probabilities of predicted damage were skewed towards greater damage, especially in topographically exposed areas (median=0.78%, 90%-HPDI between 0.65% and 0.91% when EXP=0.6). Leave-one-out cross-validation indicated that our model was able to predict very well the observed proportion of damaged trees (Pareto's $k \le 0.5$) for 68.9% of the plots, well for 8.1% of the plots ($0.5 > Pareto's k \le 0.7$), poorly for 17.6% of the plots ($0.7 > Pareto's k \le 1$) and very poorly for only 5.4% of the plots (Pareto's $k > 1$).

At the tree level, the probabilities of snapping and uprooting decreased with increasing wood density ($\rho$), which had a negative slope estimate in 100% and 96.0% of the iterations in Models 2 and 3, respectively (Figure 5; Figure S3). The DBH of the trees had a lesser effect on the probability of snapping than $\rho$, with smaller trees tending to be more prone to snapping than large trees (DBH having a negative slope in 61.1% of the iterations). The negative effect of DBH on the probability of snapping increased with increasing MSW (starting to be noticeable for winds generated by Category 4 or higher tropical cyclones). The effect of DBH was more important for uprooting, with large trees being more likely to uproot than small trees (DBH having a positive slope in 100% of the iterations). This positive effect of DBH on the probability of
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**FIGURE 3** Directed acyclic graph (DAG) at the tree level. Outcomes are the probabilities of tree snapping or uprooting (DBH, diameter at breast height; EXP, topographical exposure to wind; H, tree height; MSW, maximum sustained wind speed; WD, wood density). At the community level, outcomes are the proportion of snapped or uprooted trees and averaged values were used as predictors.</figcaption></figure>

also compared the predicted posterior probability distributions of these proportions at the five threshold values of the Saffir-Simpson hurricane wind scale, namely 33, 43, 50, 58 and 70ms⁻¹, and at the suggested threshold for ‘Category 6’ cyclones (86ms⁻¹). These predictions were made at maximum EXP values of 0 (low), 0.3 (medium) and 0.6 (high) to visualise the implications of the interaction effect on the scale of the outcome variable. We used a mean DBH value of 20 cm (i.e., close to the median value, 21.3 cm) and a mean wood density of 0.60gcm⁻³ (i.e., close to the median value, 0.61gcm⁻³). For Models 2 and 3, we followed the same approach as for Model 1, but replaced EXP by ρ (low, ρ=0.35gcm⁻³, medium ρ=0.60gcm⁻³ and high-density ρ=0.85gcm⁻³), and used the three values of DBH (small, DBH=10cm, medium, DBH=45cm and large DBH=80cm), to visualise the predicted individual probabilities of being snapped or uprooted as MSW increases, depending on species ρ and at different DBHs, and at the same threshold wind values based on the Saffir-Simpson scale as for Model 1. Finally, we compared predicted individual probabilities of being snapped or uprooted for four different types of trees when exposed to high wind speed (70ms⁻¹): small trees with low wood density, small trees with high wood density, large trees with low wood density and large trees with high wood density.

Parameter or predicted outcome posterior distributions were shown in their entirety or were summarised using the median as the central point and a 90%-highest posterior density interval (90%-highest posterior density interval, HPDI), that is, the narrowest interval capturing 90% of the probability mass. All models were run in the R statistical environment (version 4.3.2, R Core Team, 2023) and were fitted using the No-U-Turn (NUTS) sampler of stan (Carpenter et al., 2017) through the brms R package (Bürkner, 2017).

Models were run on four chains, for 1500 iterations, with 500 warm-ups. Chain mixing was checked visually using trace plots and model convergence was verified using Rhats (all were <1.01, as required). Posterior draws were extracted using the *tidybayes* R package (Kay, 2023).

## 3 | RESULTS

At the plot scale, the proportion of uprooted or snapped trees increased with MSW and to a lesser extent with the maximum topographical exposure to wind (EXP) (Figure 4a; Figure S2). We also found a positive interaction between MSW and EXP (in 97.6% of iterations) showing that the proportion of damaged trees increased more quickly with increasing MSW on topographically exposed areas compared with topographically sheltered areas (Figure 4c). The proportion of damaged trees also increased with increasing mean DBH and to a lesser extent with decreasing mean wood density (in 83.2% of iterations). The uncertainty in the predicted proportion of damaged trees also increased with MSW. For an MSW of 33ms⁻¹ (Category 1 cyclones, Figure 4b), a mean DBH of 20 cm and a mean wood density of 0.60gcm⁻³, the predicted median proportion of damaged trees was low regardless of EXP (median=0.03%) with low uncertainty (90%-HPDI between 0.01% and 0.06%). For a MSW of 70ms⁻¹ (Category 5 cyclones, Figure 4f) the predicted proportion of damaged trees increased with increasing EXP but with large uncertainty around the median prediction, 0.36% (90%-HPDI between 0.22% and 0.51%), 0.41% (90%-HPDI between 0.27% and 0.57%), 0.45% (90%-HPDI between 0.31% and 0.63%), for EXP=0, 0.3 and 0.6, respectively. For extreme MSW of 86ms⁻¹ ('Category 6' cyclones, Figure 4g) the uncertainty in the predicted damage remained large, but the distribution of probabilities of predicted damage were skewed towards greater damage, especially in topographically exposed areas (median=0.78%, 90%-HPDI between 0.65% and 0.91% when EXP=0.6). Leave-one-out cross-validation indicated that our model was able to predict very well the observed proportion of damaged trees (Pareto's $k \le 0.5$) for 68.9% of the plots, well for 8.1% of the plots ($0.5 > Pareto's k \le 0.7$), poorly for 17.6% of the plots ($0.7 > Pareto's k \le 1$) and very poorly for only 5.4% of the plots (Pareto's $k > 1$).

At the tree level, the probabilities of snapping and uprooting decreased with increasing wood density (ρ), which had a negative slope estimate in 100% and 96.0% of the iterations in Models 2 and 3, respectively (Figure 5; Figure S3). The DBH of the trees had a lesser effect on the probability of snapping than ρ, with smaller trees tending to be more prone to snapping than large trees (DBH having a negative slope in 61.1% of the iterations). The negative effect of DBH on the probability of snapping increased with increasing MSW (starting to be noticeable for winds generated by Category 4 or higher tropical cyclones). The effect of DBH was more important for uprooting, with large trees being more likely to uproot than small trees (DBH having a positive slope in 100% of the iterations). This positive effect of DBH on the probability of
<figure><img src="image_10.png" /></figure>

FIGURE 4 Predicted proportion of damaged (snapped or uprooted) trees by Model 1 (N=74 plots), (a) median prediction (lines) and 90%-highest posterior density interval along the extended range of observed maximum sustained wind speed (MSW) for three different values of maximum topographical exposure to wind (EXP, 0.0, 0.3 and 0.6), (b-g) predicted posterior distribution of the proportion of damaged trees for different combinations of MSW (33, 43, 50, 58, 70 and 86 m s⁻¹) and EXP (0.0, 0.3 and 0.6). Predictions were made for an 'average Study Identity' (i.e., we ignored the varying Study Identity intercept to generate predictions), a mean DBH of 20 cm and a mean wood density of 0.60 g cm⁻³.

uprooting was modulated by species' $\rho$, such that large trees with low wood density were more likely to be uprooted than large trees with high wood density (Figure 5d).

The uncertainty in the predicted probability of snapping or uprooting increased with increasing MSW and with increasing DBH and also tended to be higher for uprooting than for snapping (Figure S4). Trees with light wood ($\rho$=0.35 g cm⁻³) showed very different responses when exposed to high wind speed (MSW=70 m s⁻¹) depending on their size (Figure 6). Small trees with light wood (DBH=10 cm) were 2.3 times as likely to be snapped than uprooted (median values, Figure 6a), while large trees (DBH=80 cm), were 1.7 times as likely to be uprooted than snapped (median values, Figure 6d). Differences were less pronounced for trees with dense wood ($\rho$=0.85 g cm⁻³). Small, dense-wooded trees were 1.9 times as likely to be snapped than to be uprooted (median values, Figure 6b) but large dense-wooded trees were 1.3 times as likely to be uprooted than to be snapped with probability distribution skewed towards small probabilities of damage (Figure 6d).

4 | DISCUSSION

We show that reconstructed MSW is a good predictor of observed cyclone-induced damage in the studied forests. We also demonstrate that modelled tree damage impacts broadly correspond with the predictions made based on the Saffir-Simpson hurricane wind scale (Simpson, 1974). Our community-level model predicts that for a forest with median average tree size (20 cm) and wood density (0.6 g cm⁻³), for Category 1 tropical cyclones (MSW=33-43 m s⁻¹), <15% of trees could be snapped or uprooted, while for MSW of 70 m s⁻¹ (Category 5 tropical cyclones threshold value) ~25%-65% of trees could be snapped or uprooted, depending on topographical exposure to wind. Based on our models, these proportions can reach ~45%-90% for MSW of 86 m s⁻¹ (the proposed 'Category 6' tropical cyclone threshold value suggested by Wehner & Kossin, 2024). Our model indicates a sharp increase in damage when wind speeds reach Category 3 intensity or higher (≥50 m s⁻¹); similar patterns were found using a remote sensing vegetation index in the southwest Pacific
<figure><img src="image_11.png" /></figure>

FIGURE 4 Predicted proportion of damaged (snapped or uprooted) trees by Model 1 (N=74 plots), (a) median prediction (lines) and 90%-highest posterior density interval along the extended range of observed maximum sustained wind speed (MSW) for three different values of maximum topographical exposure to wind (EXP, 0.0, 0.3 and 0.6), (b–g) predicted posterior distribution of the proportion of damaged trees for different combinations of MSW (33, 43, 50, 58, 70 and 86 m s⁻¹) and EXP (0.0, 0.3 and 0.6). Predictions were made for an ‘average Study Identity’ (i.e., we ignored the varying Study Identity intercept to generate predictions), a mean DBH of 20 cm and a mean wood density of 0.60 g cm⁻³.

uprooting was modulated by species' *ρ*, such that large trees with low wood density were more likely to be uprooted than large trees with high wood density (Figure 5d).

The uncertainty in the predicted probability of snapping or uprooting increased with increasing MSW and with increasing DBH and also tended to be higher for uprooting than for snapping (Figure S4). Trees with light wood ($\rho$=0.35 g cm⁻³) showed very different responses when exposed to high wind speed (MSW = 70 m s⁻¹) depending on their size (Figure 6). Small trees with light wood (DBH=10 cm) were 2.3 times as likely to be snapped than uprooted (median values, Figure 6a), while large trees (DBH=80 cm), were 1.7 times as likely to be uprooted than snapped (median values, Figure 6d). Differences were less pronounced for trees with dense wood ($\rho$=0.85 g cm⁻³). Small, dense-wooded trees were 1.9 times as likely to be snapped than to be uprooted (median values, Figure 6b) but large dense-wooded trees were 1.3 times as likely to be uprooted than to be snapped with probability distribution skewed towards small probabilities of damage (Figure 6d).

4 | DISCUSSION

We show that reconstructed MSW is a good predictor of observed cyclone-induced damage in the studied forests. We also demonstrate that modelled tree damage impacts broadly correspond with the predictions made based on the Saffir-Simpson hurricane wind scale (Simpson, 1974). Our community-level model predicts that for a forest with median average tree size (20 cm) and wood density (0.6 g cm⁻³), for Category 1 tropical cyclones (MSW = 33–43 m s⁻¹), <15% of trees could be snapped or uprooted, while for MSW of 70 m s⁻¹ (Category 5 tropical cyclones threshold value) ~25%–65% of trees could be snapped or uprooted, depending on topographical exposure to wind. Based on our models, these proportions can reach ~45%–90% for MSW of 86 m s⁻¹ (the proposed ‘Category 6’ tropical cyclone threshold value suggested by Wehner & Kossin, 2024). Our model indicates a sharp increase in damage when wind speeds reach Category 3 intensity or higher (≥50 m s⁻¹); similar patterns were found using a remote sensing vegetation index in the southwest Pacific
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>FIGURE 5 Predicted probabilities of snapping by Model 2 (a-c) and uprooting by Model 3 (d-f). Median prediction (lines) and 90%-highest posterior density interval along the extended range of observed maximum sustained wind speed (MSW) for three different values of diameter at breast height (DBH, 10, 45 and 80 cm) and three different values of wood density (ρ, 0.35, 0.60 and 0.85 g cm⁻³). Predictions were made for a medium topographical exposure to wind (EXP=0.3) and ignore the varying intercept between the identities of studies, plots and species.</figcaption></figure>

(Delaporte et al., 2022). This nonlinear increase in tree damage with greater wind speed is due to the geometric scaling of wind effects on trees, which is proportional to the square of the horizontal wind speed (Ancelin et al., 2004; Gardiner et al., 2000; Mayhead, 1973). Such non-linearity has critical implications for forests in the context of climate change, because the proportion of the most intense tropical cyclones (Categories 3-5) has increased over the past four decades and is predicted to further increase in the future (IPCC, 2021), which will lead to more snapped and uprooted trees.

A large amount of uncertainty remained in the predictions of the damaged tree proportion for a given MSW, which increased with greater MSW. As suggested by previous observations (e.g., Basnet et al., 1992; Bellingham, 1991; Franklin et al., 2004; Lugo et al., 1983; Reilly, 1991; Walker, 1991), we found that the effect of MSW was mediated by the topographical exposure to wind

(EXP) and that the mediating effect of topography increased with increasing wind speed. Forests located on windward slopes and on ridges sustained more damage than forests located on leeward slopes and valleys. However, our results support the conclusion of an analysis of remotely sensed damage caused by the major tropical cyclone María (2017) in Puerto Rico suggesting that the effects of EXP are negligible compared with that of MSW (Hall et al., 2020). The relatively small effect of topography in our model and in the earlier study by Hall et al. (2020) could be partially explained by how the effect of topography on winds was integrated, where wind is treated analogously to light (e.g., Boose et al., 1994). Indeed, unlike light, topography does not only act as a barrier to wind, sheltering some parts of the landscape, but also changes its direction and speed (e.g., Ruel et al., 1998). Rainfall, before and during tropical cyclones, has also been suggested to be a better predictor
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 5 Predicted probabilities of snapping by Model 2 (a-c) and uprooting by Model 3 (d-f). Median prediction (lines) and 90%-highest posterior density interval along the extended range of observed maximum sustained wind speed (MSW) for three different values of diameter at breast height (DBH, 10, 45 and 80 cm) and three different values of wood density (ρ, 0.35, 0.60 and 0.85 g cm⁻³). Predictions were made for a medium topographical exposure to wind (EXP=0.3) and ignore the varying intercept between the identities of studies, plots and species.</figcaption></figure>

(Delaporte et al., 2022). This nonlinear increase in tree damage with greater wind speed is due to the geometric scaling of wind effects on trees, which is proportional to the square of the horizontal wind speed (Ancelin et al., 2004; Gardiner et al., 2000; Mayhead, 1973). Such non-linearity has critical implications for forests in the context of climate change, because the proportion of the most intense tropical cyclones (Categories 3-5) has increased over the past four decades and is predicted to further increase in the future (IPCC, 2021), which will lead to more snapped and uprooted trees.

A large amount of uncertainty remained in the predictions of the damaged tree proportion for a given MSW, which increased with greater MSW. As suggested by previous observations (e.g., Basnet et al., 1992; Bellingham, 1991; Franklin et al., 2004; Lugo et al., 1983; Reilly, 1991; Walker, 1991), we found that the effect of MSW was mediated by the topographical exposure to wind

(EXP) and that the mediating effect of topography increased with increasing wind speed. Forests located on windward slopes and on ridges sustained more damage than forests located on leeward slopes and valleys. However, our results support the conclusion of an analysis of remotely sensed damage caused by the major tropical cyclone María (2017) in Puerto Rico suggesting that the effects of EXP are negligible compared with that of MSW (Hall et al., 2020). The relatively small effect of topography in our model and in the earlier study by Hall et al. (2020) could be partially explained by how the effect of topography on winds was integrated, where wind is treated analogously to light (e.g., Boose et al., 1994). Indeed, unlike light, topography does not only act as a barrier to wind, sheltering some parts of the landscape, but also changes its direction and speed (e.g., Ruel et al., 1998). Rainfall, before and during tropical cyclones, has also been suggested to be a better predictor
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>FIGURE 6 Predicted posterior distribution of the probability of snapping by Model 2 and uprooting by Model 3 for different combinations of tree size (diameter at breast height, DBH = 10 cm in (a) and (b), and DBH = 80 cm in (c) and (d)) and wood density ($ρ$ = 0.35 g cm$^{-3}$ in (a) and (c), and $ρ$ = 0.85 g cm$^{-3}$ in (b) and (d)), as illustrated by the grey circles. Predictions were made for a maximum sustained wind speed (MSW) of 70 m s$^{-1}$, a medium topographical exposure to wind (EXP = 0.3) and generalised across the identities of studies, plots and species (i.e., used the grand effects, ignoring the varying intercept parameters).</figcaption></figure>

of cyclone-induced damages than MSW (Hall et al., 2020). Heavy rainfall can decrease the resistance of trees to uprooting by saturating soils with water, reducing soil strength (Gardiner, 2021) and also by increasing the weight of trees, and thus mechanical stress, because of large amount of water intercepted and stored by the canopy (e.g., in the bark, leaves or epiphytes) (e.g., Herwitz, 1985). However, we suggest that wind is the main factor causing the mechanical stress while rainfall mediates its effect.

We also found that tree communities with larger DBH experience more damage than communities with smaller DBH. This is likely due to the positive relationship between DBH and tree height with taller trees being more exposed to wind and experiencing greater mechanical stress due to longer lever arm (Gardiner, 2021). We lack reliable tree height data from our sites to directly test the relationship between tree height and the damage experienced by forests during cyclones. However, forests exposed to frequent cyclones have lower canopy heights than other areas at equivalent latitudes where cyclones are absent or infrequent (Ankori-Karlinsky et al., 2024; Ibanez et al., 2019; Quigley & Platt, 2003) and trees in forests exposed to frequent cyclones invest more in secondary (diameter) than primary (height) growth which increases their mechanical stability (Blanchard et al., 2016; Thomas et al., 2015). Canopy height is also driven by environmental factors such that, in drier areas or at higher elevations, canopy heights are lower and forests may be less damaged by cyclones (Boose et al., 1994).

Our results support studies concluding that species with higher wood density ($ρ$) have a lower snapping probability than species with lower $ρ$ (Curran, Gersbach, et al., 2008; Taylor et al., 2023; Walker et al., 1992; Webb et al., 2014; Zimmerman et al., 1994). We also found that species with higher wood density ($ρ$) tend to be less likely to uproot than species with lower $ρ$, but this effect was

smaller than for snapping, as had been suggested in previous studies (Asner & Goldstein, 1997; Bellingham et al., 1995; Curran, Gersbach, et al., 2008; Taylor et al., 2023; Zimmerman et al., 1994). This is not surprising because uprooting should involve complex interactions between roots and soils (Gardiner, 2021). To our knowledge, few studies have explored the effect of soils (e.g., Franklin et al., 2004) or root characteristics (e.g., Basnet et al., 1993) on cyclone-induced damage in tropical wet forests. For instance, uprooting is likely to be more frequent on poorly drained soils than on well-drained soils because trees have shallower rooting depth the poorer the drainage (Rutledge et al., 2021; Wang & Xu, 2009). Root dimensions (e.g., the root branching organisation, the diameter of lateral roots or the rooting depth) were identified as key parameters to better explain uprooting (Freschet et al., 2021; Gardiner, 2021; Stokes, 2002) and need to be validated in a tropical context.

We also showed that the effect of $ρ$ on snapping or uprooting probabilities was mediated by both the size of the trees and the strength of the wind they had been exposed to. This could explain why other studies did not find that increasing $ρ$ noticeably decreases the probability of snapping (Asner & Goldstein, 1997; Bellingham et al., 1995; Uriarte et al., 2019; Walker et al., 1992). This is also supported by a study in the dry forests of the Yucatán Peninsula, where $ρ$ only had a significant effect in areas affected by winds $≥58$ m s$^{-1}$ (Vandecar et al., 2011). The differences between studies in the effects of $ρ$ on wind-induced damage might also reflect differences in the ranges of $ρ$ studied. For instance, $ρ$ tends to be higher in dry forests (e.g., 0.30 to 1.10 g cm$^{-3}$ in Vandecar et al., 2011) than in wet forests (e.g., 0.10 to 1.03 g cm$^{-3}$ in our dataset), potentially due to adaptations to other factors, such as drought (O'Brien et al., 2017).

The negative effect of $ρ$ on the probability of snapping accords with $ρ$ being well correlated with many other physical properties of
<figure><img src="image_14.png" /></figure>

FIGURE 6 Predicted posterior distribution of the probability of snapping by Model 2 and uprooting by Model 3 for different combinations of tree size (diameter at breast height, DBH = 10 cm in (a) and (b), and DBH = 80 cm in (c) and (d)) and wood density ($ρ = 0.35 g cm^{-3}$ in (a) and (c), and $ρ = 0.85 g cm^{-3}$ in (b) and (d)), as illustrated by the grey circles. Predictions were made for a maximum sustained wind speed (MSW) of $70 m s^{-1}$, a medium topographical exposure to wind (EXP = 0.3) and generalised across the identities of studies, plots and species (i.e., used the grand effects, ignoring the varying intercept parameters).

of cyclone-induced damages than MSW (Hall et al., 2020). Heavy rainfall can decrease the resistance of trees to uprooting by saturating soils with water, reducing soil strength (Gardiner, 2021) and also by increasing the weight of trees, and thus mechanical stress, because of large amount of water intercepted and stored by the canopy (e.g., in the bark, leaves or epiphytes) (e.g., Herwitz, 1985). However, we suggest that wind is the main factor causing the mechanical stress while rainfall mediates its effect.

We also found that tree communities with larger DBH experience more damage than communities with smaller DBH. This is likely due to the positive relationship between DBH and tree height with taller trees being more exposed to wind and experiencing greater mechanical stress due to longer lever arm (Gardiner, 2021). We lack reliable tree height data from our sites to directly test the relationship between tree height and the damage experienced by forests during cyclones. However, forests exposed to frequent cyclones have lower canopy heights than other areas at equivalent latitudes where cyclones are absent or infrequent (Ankori-Karlinsky et al., 2024; Ibanez et al., 2019; Quigley & Platt, 2003) and trees in forests exposed to frequent cyclones invest more in secondary (diameter) than primary (height) growth which increases their mechanical stability (Blanchard et al., 2016; Thomas et al., 2015). Canopy height is also driven by environmental factors such that, in drier areas or at higher elevations, canopy heights are lower and forests may be less damaged by cyclones (Boose et al., 1994).

Our results support studies concluding that species with higher wood density ($ρ$) have a lower snapping probability than species with lower $ρ$ (Curran, Gersbach, et al., 2008; Taylor et al., 2023; Walker et al., 1992; Webb et al., 2014; Zimmerman et al., 1994). We also found that species with higher wood density ($ρ$) tend to be less likely to uproot than species with lower $ρ$, but this effect was

smaller than for snapping, as had been suggested in previous studies (Asner & Goldstein, 1997; Bellingham et al., 1995; Curran, Gersbach, et al., 2008; Taylor et al., 2023; Zimmerman et al., 1994). This is not surprising because uprooting should involve complex interactions between roots and soils (Gardiner, 2021). To our knowledge, few studies have explored the effect of soils (e.g., Franklin et al., 2004) or root characteristics (e.g., Basnet et al., 1993) on cyclone-induced damage in tropical wet forests. For instance, uprooting is likely to be more frequent on poorly drained soils than on well-drained soils because trees have shallower rooting depth the poorer the drainage (Rutledge et al., 2021; Wang & Xu, 2009). Root dimensions (e.g., the root branching organisation, the diameter of lateral roots or the rooting depth) were identified as key parameters to better explain uprooting (Freschet et al., 2021; Gardiner, 2021; Stokes, 2002) and need to be validated in a tropical context.

We also showed that the effect of $ρ$ on snapping or uprooting probabilities was mediated by both the size of the trees and the strength of the wind they had been exposed to. This could explain why other studies did not find that increasing $ρ$ noticeably decreases the probability of snapping (Asner & Goldstein, 1997; Bellingham et al., 1995; Uriarte et al., 2019; Walker et al., 1992). This is also supported by a study in the dry forests of the Yucatán Peninsula, where $ρ$ only had a significant effect in areas affected by winds $≥58 m s^{-1}$ (Vandecar et al., 2011). The differences between studies in the effects of $ρ$ on wind-induced damage might also reflect differences in the ranges of $ρ$ studied. For instance, $ρ$ tends to be higher in dry forests (e.g., 0.30 to $1.10 g cm^{-3}$ in Vandecar et al., 2011) than in wet forests (e.g., 0.10 to $1.03 g cm^{-3}$ in our dataset), potentially due to adaptations to other factors, such as drought (O'Brien et al., 2017).

The negative effect of $ρ$ on the probability of snapping accords with $ρ$ being well correlated with many other physical properties of
wood (Niklas & Spatz, 2010). However, the relatively large effect of $\rho$ compared with DBH in determining snapping probabilities contradicts physics-based modelling of tree resistance to wind. In these models, tree DBH (together with tree height) is the most important factor determining the resistance of trees, with the probability of snapping and uprooting being proportional to $DBH^3$ and $DBH^2$, respectively (Gardiner, 2021). The discrepancy between the physics and our findings might stem from the fact that those models have been developed using properties of wooden beams and have been mostly applied in forestry to monospecific stands (often conifers) (e.g., Ancelin et al., 2004; Virot et al., 2016). Indeed, in addition to its effect on the mechanical properties of wood (e.g., Young's modulus, modulus of rupture and modulus of elasticity), $\rho$ is very likely to capture other architectural features in species-rich tropical forests (e.g., Poorter et al., 2006) that can also affect tree resistance to wind. Higher $\rho$ is also associated with better resistance to pathogens and wood decay, which allows trees to better maintain wood mechanical properties (Chave et al., 2009; Larjavaara & Muller-Landau, 2010).

Most studies used in our analysis covered the damage caused by major tropical cyclones that reached Category 4 or 5 at their maximum intensity, but only half reported damage in areas affected by winds $\ge 58 \text{ms}^{-1}$ (Bellingham et al., 1996; Birkinshaw & Randrianjanahary, 2007; Tanner & Bellingham, 2006; Ticktin et al., 2024; Webb et al., 2014) and only two in areas affected by winds $\ge 70 \text{ms}^{-1}$ (Birkinshaw & Randrianjanahary, 2007; Webb et al., 2014). This likely contributed to uncertainty in our model predictions for high wind speeds. This is important because the most severe damage occurs at these extreme speeds and because of the predicted increasing frequency of the most extreme tropical cyclones. Finally, as noted in recent reviews (e.g., Heartsill-Scalley & López-Marrero, 2021; Lin et al., 2020), available data is highly biased towards the North Atlantic Basin or individual cyclone events. Notable in our dataset, we only have one site in each of the Indian Ocean and the North Pacific.

A network of permanent plots with standardised post-cyclone damage assessment survey protocols across the main tropical cyclone basins that had a greater focus on tree biomechanical traits and on measuring actual wind speeds (and rainfalls) would help to further understand how tropical cyclones are shaping forest ecosystems. Integrating a combination of traits that are more directly linked to the biomechanical properties of trees than $\rho$ (e.g., modulus of rupture and elasticity) and additional dimensional (e.g., tree height and crown size) or architectural (e.g., growth and branching patterns, root architecture and rooting depth) tree characteristics, may provide a more nuanced understanding of the responses of trees to tropical cyclones (Asner & Goldstein, 1997; Fournier et al., 2013; Laurans et al., 2024; Read et al., 2011). Conducting tree-winching experiments would also help in assessing the mechanical stability of tropical trees and their potential resistance to wind (e.g., Ribeiro et al., 2016); so far, tree-winching experiments have mostly been conducted in temperate forests (e.g., Peltola, 2006). This knowledge will be critical for understanding tropical forest resistance and

resilience to increasing frequency of intense cyclones in the coming decades resulting from global warming.

Our finding that greater wood density reduces the likelihood of cyclone damage has implications for large-scale extrapolation of the future effects of more intense tropical cyclones and as their tracks move more poleward (Studholme et al., 2022). Since community-level variation in wood density declines as latitude increases (Swenson & Enquist, 2007), forests at higher latitudes may have less resistance to cyclones, especially when community-level mean wood density that is lower than that required to withstand high wind speeds. This is manifest when tropical cyclones affect cool temperate and boreal forests dominated by conifers of low wood density, especially Pinaceae, causing very high levels of damage (e.g., Foster, 1988; Korznikov et al., 2022), and a lack of resilience in forests such as these to the effect of high wind speeds could turn them from being carbon sinks to carbon sources (e.g., Zeng et al., 2009). Moreover, a changing cyclone disturbance regime as high-intensity cyclones become more prevalent and move to higher latitudes could alter selection pressures (e.g. Cannon et al., 2023). If cyclones of proposed Category-6 intensity became recurrent in a region, it may become impossible to maintain populations of tree species of low wood density if they cannot grow to maturity fast enough to set seed between cyclones or cannot be maintained by resprouting (Batista & Platt, 2003; Bellingham et al., 1995). This would result in chronic loss of forest diversity and, potentially, reduced resilience to other disturbances that interact with cyclones, such as pathogens, drought or fire (Ibanez et al., 2022; Seidl et al., 2017).

## AUTHOR CONTRIBUTIONS

**Thomas Ibanez:** Conceptualization; formal analysis; investigation; writing – original draft; writing – review and editing. **David Bauman:** Formal analysis; writing – review and editing. **Shin-ichiro Aiba:** Investigation; writing – review and editing. **Thomas Arsouze:** Methodology; writing – review and editing. **Peter J. Bellingham:** Investigation; writing – review and editing. **Chris Birkinshaw:** Investigation; writing – review and editing. **Philippe Birnbaum:** Investigation; writing – review and editing. **Timothy J. Curran:** Writing – review and editing. **Saara J. DeWalt:** Investigation; writing – review and editing. **John Dwyer:** Writing – review and editing. **Thierry Fourcaud:** Writing – review and editing. **Janet Franklin:** Investigation; writing – review and editing. **Takashi S. Kohyama:** Investigation; writing – review and editing. **Christophe Menkes:** Methodology; writing – review and editing. **Dan J. Metcalfe:** Investigation; writing – review and editing. **Helen Murphy:** Investigation; writing – review and editing. **Robert Muscarella:** Investigation; writing – review and editing. **Gregory M. Plunkett:** Investigation; writing – review and editing. **Chanel Sam:** Investigation; writing – review and editing. **Edmund Tanner:** Investigation; writing – review and editing. **Benton N. Taylor:** Investigation; writing – review and editing. **Jill Thompson:** Investigation; writing – review and editing. **Tamara Ticktin:** Investigation; writing – review and editing. **Marika V. Tuiwawa:** Investigation; writing – review and editing. **Maria Uriarte:**
wood (Niklas & Spatz, 2010). However, the relatively large effect of $\rho$ compared with DBH in determining snapping probabilities contradicts physics-based modelling of tree resistance to wind. In these models, tree DBH (together with tree height) is the most important factor determining the resistance of trees, with the probability of snapping and uprooting being proportional to $DBH^3$ and $DBH^2$, respectively (Gardiner, 2021). The discrepancy between the physics and our findings might stem from the fact that those models have been developed using properties of wooden beams and have been mostly applied in forestry to monospecific stands (often conifers) (e.g., Ancelin et al., 2004; Virot et al., 2016). Indeed, in addition to its effect on the mechanical properties of wood (e.g., Young's modulus, modulus of rupture and modulus of elasticity), $\rho$ is very likely to capture other architectural features in species-rich tropical forests (e.g., Poorter et al., 2006) that can also affect tree resistance to wind. Higher $\rho$ is also associated with better resistance to pathogens and wood decay, which allows trees to better maintain wood mechanical properties (Chave et al., 2009; Larjavaara & Muller-Landau, 2010).

Most studies used in our analysis covered the damage caused by major tropical cyclones that reached Category 4 or 5 at their maximum intensity, but only half reported damage in areas affected by winds $\ge 58 \text{ms}^{-1}$ (Bellingham et al., 1996; Birkinshaw & Randrianjanahary, 2007; Tanner & Bellingham, 2006; Ticktin et al., 2024; Webb et al., 2014) and only two in areas affected by winds $\ge 70 \text{ms}^{-1}$ (Birkinshaw & Randrianjanahary, 2007; Webb et al., 2014). This likely contributed to uncertainty in our model predictions for high wind speeds. This is important because the most severe damage occurs at these extreme speeds and because of the predicted increasing frequency of the most extreme tropical cyclones. Finally, as noted in recent reviews (e.g., Heartsill-Scalley & López-Marrero, 2021; Lin et al., 2020), available data is highly biased towards the North Atlantic Basin or individual cyclone events. Notable in our dataset, we only have one site in each of the Indian Ocean and the North Pacific.

A network of permanent plots with standardised post-cyclone damage assessment survey protocols across the main tropical cyclone basins that had a greater focus on tree biomechanical traits and on measuring actual wind speeds (and rainfalls) would help to further understand how tropical cyclones are shaping forest ecosystems. Integrating a combination of traits that are more directly linked to the biomechanical properties of trees than $\rho$ (e.g., modulus of rupture and elasticity) and additional dimensional (e.g., tree height and crown size) or architectural (e.g., growth and branching patterns, root architecture and rooting depth) tree characteristics, may provide a more nuanced understanding of the responses of trees to tropical cyclones (Asner & Goldstein, 1997; Fournier et al., 2013; Laurans et al., 2024; Read et al., 2011). Conducting tree-winching experiments would also help in assessing the mechanical stability of tropical trees and their potential resistance to wind (e.g., Ribeiro et al., 2016); so far, tree-winching experiments have mostly been conducted in temperate forests (e.g., Peltola, 2006). This knowledge will be critical for understanding tropical forest resistance and

resilience to increasing frequency of intense cyclones in the coming decades resulting from global warming.

Our finding that greater wood density reduces the likelihood of cyclone damage has implications for large-scale extrapolation of the future effects of more intense tropical cyclones and as their tracks move more poleward (Studholme et al., 2022). Since community-level variation in wood density declines as latitude increases (Swenson & Enquist, 2007), forests at higher latitudes may have less resistance to cyclones, especially when community-level mean wood density that is lower than that required to withstand high wind speeds. This is manifest when tropical cyclones affect cool temperate and boreal forests dominated by conifers of low wood density, especially Pinaceae, causing very high levels of damage (e.g., Foster, 1988; Korznikov et al., 2022), and a lack of resilience in forests such as these to the effect of high wind speeds could turn them from being carbon sinks to carbon sources (e.g., Zeng et al., 2009). Moreover, a changing cyclone disturbance regime as high-intensity cyclones become more prevalent and move to higher latitudes could alter selection pressures (e.g. Cannon et al., 2023). If cyclones of proposed Category-6 intensity became recurrent in a region, it may become impossible to maintain populations of tree species of low wood density if they cannot grow to maturity fast enough to set seed between cyclones or cannot be maintained by resprouting (Batista & Platt, 2003; Bellingham et al., 1995). This would result in chronic loss of forest diversity and, potentially, reduced resilience to other disturbances that interact with cyclones, such as pathogens, drought or fire (Ibanez et al., 2022; Seidl et al., 2017).

## AUTHOR CONTRIBUTIONS

**Thomas Ibanez:** Conceptualization; formal analysis; investigation; writing – original draft; writing – review and editing. **David Bauman:** Formal analysis; writing – review and editing. **Shin-ichiro Aiba:** Investigation; writing – review and editing. **Thomas Arsouze:** Methodology; writing – review and editing. **Peter J. Bellingham:** Investigation; writing – review and editing. **Chris Birkinshaw:** Investigation; writing – review and editing. **Philippe Birnbaum:** Investigation; writing – review and editing. **Timothy J. Curran:** Writing – review and editing. **Saara J. DeWalt:** Investigation; writing – review and editing. **John Dwyer:** Writing – review and editing. **Thierry Fourcaud:** Writing – review and editing. **Janet Franklin:** Investigation; writing – review and editing. **Takashi S. Kohyama:** Investigation; writing – review and editing. **Christophe Menkes:** Methodology; writing – review and editing. **Dan J. Metcalfe:** Investigation; writing – review and editing. **Helen Murphy:** Investigation; writing – review and editing. **Robert Muscarella:** Investigation; writing – review and editing. **Gregory M. Plunkett:** Investigation; writing – review and editing. **Chanel Sam:** Investigation; writing – review and editing. **Edmund Tanner:** Investigation; writing – review and editing. **Benton N. Taylor:** Investigation; writing – review and editing. **Jill Thompson:** Investigation; writing – review and editing. **Tamara Ticktin:** Investigation; writing – review and editing. **Marika V. Tuiwawa:** Investigation; writing – review and editing. **Maria Uriarte:**
Investigation; writing – review and editing. **Edward L. Webb:**
Investigation; writing – review and editing. **Jess K. Zimmerman:**
Investigation; writing – review and editing. **Gunnar Keppel:**
Conceptualization; writing – review and editing.

### AFFILIATIONS

¹AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France

²Plant Ecology and Biogeochemistry Lab, Faculty of Sciences, Université Libre de Bruxelles, Brussels, Belgium

³Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan

⁴CIRAD, UMR AMAP, Montpellier, France

⁵Manaaki Whenua–Landcare Research, Lincoln, New Zealand

⁶Missouri Botanical Garden, Madagascar Research and Conservation Program, Antananarivo, Madagascar

⁷Institut Agronomique néo-Calédonien (IAC), Nouméa, New Caledonia

⁸Department of Pest-Management and Conservation, Lincoln University, Lincoln, New Zealand

⁹Department of Plant and Microbial Biology, College of Biological Sciences, University of Minnesota-Twin Cities, St. Paul, Minnesota, USA

¹⁰School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia

¹¹Department of Geography, San Diego State University, San Diego, California, USA

¹²Botany and Plant Sciences Department, University of California, Riverside, California, USA

¹³ENTROPIE, UMR 9220, IRD, Univ. de la Réunion, CNRS, Nouméa, New Caledonia

¹⁴Ecosciences Precinct, CSIRO, Dutton Park, Queensland, Australia

¹⁵CSIRO, Australian Tropical Sciences and Innovation Precinct, James Cook University, Townsville, Queensland, Australia

¹⁶Plant Ecology and Evolution, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden

¹⁷Cullman Program for Molecular Systematics, New York Botanical Garden, Bronx, New York, USA

¹⁸Vanuatu National Herbarium, Vanuatu Department of Forestry, Port Vila, Vanuatu

¹⁹Department of Plant Sciences, University of Cambridge, Cambridge, UK

²⁰Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA

²¹UK Centre for Ecology & Hydrology, Penicuik, Midlothian, UK

²²School of Life Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA

²³South Pacific Regional Herbarium and Biodiversity Center, Institute of Applied Sciences, University of the South Pacific, Suva, Fiji

²⁴Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, New York, USA

²⁵Department of Forest Sciences, Viikki Tropical Resources Institute, University of Helsinki, Helsinki, Finland

²⁶Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, Finland

²⁷Environmental Science, University of Puerto Rico, San Juan, Puerto Rico, USA

²⁸UniSA STEM and Future Industries Institute, University of South Australia, Adelaide, South Australia, Australia

### ACKNOWLEDGMENTS

TI, JD, CM and GK were supported by a Hermon Slade Foundation grant (HSF 19105). TI was also supported by the French Pacific Funds and GK was partially supported by the Montpellier Advanced Knowledge Institute on Transitions (MAK'IT) as part of a Visiting Scientist Fellowship while working on parts of the manuscript. DB was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie

grant agreement no. 895799. PJB was supported by the Strategic Science Investment Fund of the New Zealand Ministry of Business, Innovation and Employment. RM was supported by grant 2019-03758 from the Swedish Research Council and grant 2020-00921 from Formas. Field data collection by ELW was supported by the National Park of American Samoa and the Wildlife Restoration Grant to the Department of Marine and Wildlife Resources, Government of American Samoa. Field data collection by JF was supported by the National Geographical Society (pre-cyclone) and San Diego State University Research Foundation (post-cyclone), and conducted with permission from the Prime Minister's Office, Kingdom of Tonga. Field data collection by BNT and SJD was funded by the Clemson Caribbean Initiative and aided by Kalan Ickes and Elvis Stedman. Field data collection by TT, GMP, MT and SC was supported by National Science Foundation grants DEB 1555657 and 155793. The Luquillo forest dynamics plot was supported by grants BSR-8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447 and DEB-129764 from the NSF to the Department of Environmental Science, University of Puerto Rico, and to the International Institute of Tropical Forestry, USDA Forest Service, as part of the Luquillo Long-Term Ecological Research Program. The US Forest Service (Dept. of Agriculture) and the University of Puerto Rico, Mellon Foundation and Smithsonian Tropical Research Institute gave additional support. We are grateful to the many volunteers who helped to censused the plots. We thank Aaron Hogan, Jeffery Cannon and an anonymous reviewer for helpful comments.

### CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

### DATA AVAILABILITY STATEMENT

The data that support the findings of this study are archived and publicly available on Zenodo (https://doi.org/10.5281/zenodo.11062523). Tree-level data for forests of Dominica after the hurricane Maria (2017) are only available on request.

### ORCID

Thomas Ibanez https://orcid.org/0000-0002-3192-1721

David Bauman https://orcid.org/0000-0001-9115-6518

Shin-ichiro Aiba https://orcid.org/0000-0002-3844-0138

Thomas Arsouze https://orcid.org/0000-0002-8871-6120

Peter J. Bellingham https://orcid.org/0000-0001-9646-4819

Philippe Birnbaum https://orcid.org/0000-0003-4830-5500

Timothy J. Curran https://orcid.org/0000-0001-8817-4360

Saara J. DeWalt https://orcid.org/0000-0002-7541-3008

John Dwyer https://orcid.org/0000-0001-7389-5528

Janet Franklin https://orcid.org/0000-0003-0314-4598

Takashi S. Kohyama https://orcid.org/0000-0001-7186-8585

Christophe Menkes https://orcid.org/0000-0002-1457-9696

Dan J. Metcalfe https://orcid.org/0000-0001-6853-7072

Robert Muscarella https://orcid.org/0000-0003-3039-1076

Gregory M. Plunkett https://orcid.org/0000-0002-0751-4309
<figure><img src="image_15.png" /></figure>

Investigation; writing – review and editing. **Edward L. Webb:**
Investigation; writing – review and editing. **Jess K. Zimmerman:**
Investigation; writing – review and editing. **Gunnar Keppel:**
Conceptualization; writing – review and editing.

## AFFILIATIONS

¹AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France

²Plant Ecology and Biogeochemistry Lab, Faculty of Sciences, Université Libre de Bruxelles, Brussels, Belgium

³Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan

⁴CIRAD, UMR AMAP, Montpellier, France

⁵Manaaki Whenua–Landcare Research, Lincoln, New Zealand

⁶Missouri Botanical Garden, Madagascar Research and Conservation Program, Antananarivo, Madagascar

⁷Institut Agronomique néo-Calédonien (IAC), Nouméa, New Caledonia

⁸Department of Pest-Management and Conservation, Lincoln University, Lincoln, New Zealand

⁹Department of Plant and Microbial Biology, College of Biological Sciences, University of Minnesota-Twin Cities, St. Paul, Minnesota, USA

¹⁰School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia

¹¹Department of Geography, San Diego State University, San Diego, California, USA

¹²Botany and Plant Sciences Department, University of California, Riverside, California, USA

¹³ENTROPIE, UMR 9220, IRD, Univ. de la Réunion, CNRS, Nouméa, New Caledonia

¹⁴Ecosciences Precinct, CSIRO, Dutton Park, Queensland, Australia

¹⁵CSIRO, Australian Tropical Sciences and Innovation Precinct, James Cook University, Townsville, Queensland, Australia

¹⁶Plant Ecology and Evolution, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden

¹⁷Cullman Program for Molecular Systematics, New York Botanical Garden, Bronx, New York, USA

¹⁸Vanuatu National Herbarium, Vanuatu Department of Forestry, Port Vila, Vanuatu

¹⁹Department of Plant Sciences, University of Cambridge, Cambridge, UK

²⁰Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA

²¹UK Centre for Ecology & Hydrology, Penicuik, Midlothian, UK

²²School of Life Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA

²³South Pacific Regional Herbarium and Biodiversity Center, Institute of Applied Sciences, University of the South Pacific, Suva, Fiji

²⁴Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, New York, USA

²⁵Department of Forest Sciences, Viikki Tropical Resources Institute, University of Helsinki, Helsinki, Finland

²⁶Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, Finland

²⁷Environmental Science, University of Puerto Rico, San Juan, Puerto Rico, USA

²⁸UniSA STEM and Future Industries Institute, University of South Australia, Adelaide, South Australia, Australia

## ACKNOWLEDGMENTS

TI, JD, CM and GK were supported by a Hermon Slade Foundation grant (HSF 19105). TI was also supported by the French Pacific Funds and GK was partially supported by the Montpellier Advanced Knowledge Institute on Transitions (MAK'IT) as part of a Visiting Scientist Fellowship while working on parts of the manuscript. DB was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie

grant agreement no. 895799. PJB was supported by the Strategic Science Investment Fund of the New Zealand Ministry of Business, Innovation and Employment. RM was supported by grant 2019-03758 from the Swedish Research Council and grant 2020-00921 from Formas. Field data collection by ELW was supported by the National Park of American Samoa and the Wildlife Restoration Grant to the Department of Marine and Wildlife Resources, Government of American Samoa. Field data collection by JF was supported by the National Geographical Society (pre-cyclone) and San Diego State University Research Foundation (post-cyclone), and conducted with permission from the Prime Minister's Office, Kingdom of Tonga. Field data collection by BNT and SJD was funded by the Clemson Caribbean Initiative and aided by Kalan Ickes and Elvis Stedman. Field data collection by TT, GMP, MT and SC was supported by National Science Foundation grants DEB 1555657 and 155793. The Luquillo forest dynamics plot was supported by grants BSR-8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447 and DEB-129764 from the NSF to the Department of Environmental Science, University of Puerto Rico, and to the International Institute of Tropical Forestry, USDA Forest Service, as part of the Luquillo Long-Term Ecological Research Program. The US Forest Service (Dept. of Agriculture) and the University of Puerto Rico, Mellon Foundation and Smithsonian Tropical Research Institute gave additional support. We are grateful to the many volunteers who helped to censused the plots. We thank Aaron Hogan, Jeffery Cannon and an anonymous reviewer for helpful comments.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are archived and publicly available on Zenodo (https://doi.org/10.5281/zenodo.11062523). Tree-level data for forests of Dominica after the hurricane Maria (2017) are only available on request.

## ORCID

Thomas Ibanez https://orcid.org/0000-0002-3192-1721

David Bauman https://orcid.org/0000-0001-9115-6518

Shin-ichiro Aiba https://orcid.org/0000-0002-3844-0138

Thomas Arsouze https://orcid.org/0000-0002-8871-6120

Peter J. Bellingham https://orcid.org/0000-0001-9646-4819

Philippe Birnbaum https://orcid.org/0000-0003-4830-5500

Timothy J. Curran https://orcid.org/0000-0001-8817-4360

Saara J. DeWalt https://orcid.org/0000-0002-7541-3008

John Dwyer https://orcid.org/0000-0001-7389-5528

Janet Franklin https://orcid.org/0000-0003-0314-4598

Takashi S. Kohyama https://orcid.org/0000-0001-7186-8585

Christophe Menkes https://orcid.org/0000-0002-1457-9696

Dan J. Metcalfe https://orcid.org/0000-0001-6853-7072

Robert Muscarella https://orcid.org/0000-0003-3039-1076

Gregory M. Plunkett https://orcid.org/0000-0002-0751-4309
<figure><img src="image_15.png" /></figure>

Edmund Tanner ID https://orcid.org/0000-0002-4961-9993

Benton N. Taylor ID https://orcid.org/0000-0002-9834-9192

Jill Thompson ID https://orcid.org/0000-0002-4370-2593

Tamara Ticktin ID https://orcid.org/0000-0003-4227-2584

Maria Uriarte ID https://orcid.org/0000-0002-0484-0758

Edward L. Webb ID https://orcid.org/0000-0001-5554-9955

Gunnar Keppel ID https://orcid.org/0000-0001-7092-6149

REFERENCES

Ancelin, P., Courbaud, B., & Fourcaud, T. (2004). Developing an individual tree based mechanical model to predict wind damage within forest stands. *Forest Ecology and Management*, 203(1-3), 101-121. https://doi.org/10.1016/j.foreco.2004.07.067

Ankori-Karlinsky, R., Hall, J., Murphy, L., Muscarella, R., Martinuzzi, S., Fahey, R., Zimmerman, J. K., & Uriarte, M. (2024). Chronic winds reduce tropical forest structural complexity regardless of climate, topography, or forest age. *Ecosystems*, 27, 479-491. https://doi.org/10.1007/s10021-024-00900-5

Arif, S., & MacNeil, M. A. (2023). Applying the structural causal model framework for observational causal inference in ecology. *Ecological Monographs*, 93(1), e1554. https://doi.org/10.1002/ecm.1554

Asner, G. P., & Goldstein, G. (1997). Correlating stem biomechanical properties of Hawaiian canopy trees with hurricane wind damage. *Biotropica*, 29(2), 145-150.

Basnet, K., Likens, G. E., Scatena, F. N., & Lugo, A. E. (1992). Hurricane Hugo: Damage to a tropical rainforest in Puerto Rico. *Journal of Tropical Ecology*, 8(1), 47-55.

Basnet, K., Scatena, F. N., Likens, G. E., & Lugo, A. E. (1993). Ecological consequences of root grafting in tabonuco (*Dacryodes excelsa*) trees in the Luquillo experimental Forest, Puerto Rico. *Biotropica*, 25(1), 28-35. https://doi.org/10.2307/2388976

Batista, W. B., & Platt, W. J. (2003). Tree population responses to hurricane disturbance: Syndromes in a south-eastern USA old-growth forest. *Journal of Ecology*, 91(2), 197-212. https://www.jstor.org/stable/3599755

Bellingham, P. (1991). Landforms influence patterns of hurricane damage—Evidence from Jamaican montane forests. *Biotropica*, 23(4), 427-433. https://doi.org/10.2307/2388262

Bellingham, P. J., Kohyama, T., & Aiba, S. (1996). The effects of a typhoon on Japanese warm temperate rainforests. *Ecological Research*, 11(3), 229-247. https://doi.org/10.1007/BF02347781

Bellingham, P. J., Tanner, E. V. J., & Healey, J. R. (1995). Damage and responsiveness of Jamaican montane tree species after disturbance by a hurricane. *Ecology*, 76(8), 2562-2580. https://doi.org/10.2307/2265828

Birkinshaw, C., & Randrianjanahary, M. (2007). The effects of cyclone Hudah on the forest of Masoala Peninsula, Madagascar. *Madagascar Conservation & Development*, 2(1), Article 1. https://doi.org/10.4314/mcd.v2i1.44125

Blanchard, E., Birnbaum, P., Ibanez, T., Boutreux, T., Antin, C., Ploton, P., Vincent, G., Pouteau, R., Vandrot, H., Hequet, V., Barbier, N., Droissart, V., Sonké, B., Texier, N., Kamdem, N. G., Zebaze, D., Libalah, M., & Couteron, P. (2016). Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. *Trees*, 30, 1953-1968. https://doi.org/10.1007/s00468-016-1424-3

Blanchard, G., Munoz, F., Ibanez, T., Hequet, V., Vandrot, H., Girardi, J., & Birnbaum, P. (2019). Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia. *Journal of Vegetation Science*, 30(5), Article 5. https://doi.org/10.1111/jvs.12781

Boose, E. R., Foster, D. R., & Fluet, M. (1994). Hurricane impacts to tropical and temperate forest landscapes. *Ecological Monographs*, 64(4), 369-400. https://doi.org/10.2307/2937142

Bourdin, S., Fromang, S., Dulac, W., Cattiaux, J., & Chauvin, F. (2022). Intercomparison of four algorithms for detecting tropical cyclones using ERA5. *Geoscientific Model Development*, 15(17), 6759-6786. https://doi.org/10.5194/gmd-15-6759-2022

Bürkner, P.-C. (2017). brms: An R package for bayesian multilevel models using Stan. *Journal of Statistical Software*, 80, 1-28. https://doi.org/10.18637/jss.v080.i01

Cannon, J. B., Barrett, M. E., & Peterson, C. J. (2015). The effect of species, size, failure mode, and fire-scarring on tree stability. *Forest Ecology and Management*, 356, 196-203. https://doi.org/10.1016/j.foreco.2015.07.014

Cannon, J. B., Peterson, C. J., Godfrey, C. M., & Whelan, A. W. (2023). Hurricane wind regimes for forests of North America. *Proceedings of the National Academy of Sciences of the United States of America*, 120(42), e2309076120. https://doi.org/10.1073/pnas.2309076120

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. *Journal of Statistical Software*, 76, 1. https://doi.org/10.18637/jss.v076.i01

Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. *Oecologia*, 145(1), 87-99. https://doi.org/10.1007/s00442-005-0100-x

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12(4), 351-366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

Chave, J., Muller-Landau, H. C., Baker, T. R., Easdale, T. A., Ter Steege, H., & Webb, C. O. (2006). Regional and phylogenetic variation of wood density across 2456 neotropical tree species. *Ecological Applications*, 16(6), 2356-2367. https://doi.org/10.1890/1051-0761(2006)016[2356:rapvow]2.0.co;2

Chen, K.-M. (1994). A computation method for typhoon wind field. *Tropic Oceanology*, 13(2), 41-48.

Curran, T. J., Brown, R. L., Edwards, E., Hopkins, K., Kelley, C., McCarthy, E., Pounds, E., Solan, R., & Wolf, J. (2008). Plant functional traits explain interspecific differences in immediate cyclone damage to trees of an endangered rainforest community in north Queensland. *Austral Ecology*, 33(4), 451-461. https://doi.org/10.1111/j.1442-9993.2008.01900.x

Curran, T. J., Gersbach, L. N., Edwards, W., & Krockenberger, A. K. (2008). Wood density predicts plant damage and vegetative recovery rates caused by cyclone disturbance in tropical rainforest tree species of North Queensland, Australia. *Austral Ecology*, 33(4), 442-450. https://doi.org/10.1111/j.1442-9993.2008.01899.x

Delaporte, B., Arsouze, T., Keppel, G., Jullien, S., Menkes, C., & Ibanez, T. (2024). StormR: An R package to quantify and map the tropical storms and cyclones' winds characteristics. *Journal of Open Source Software*, 9(93), 5766. https://doi.org/10.21105/joss.05766

Delaporte, B., Ibanez, T., Despinoy, M., Mangeas, M., & Menkes, C. (2022). Tropical cyclone impact and forest resilience in the Southwestern Pacific. *Remote Sensing*, 14(5), 1245. https://doi.org/10.3390/rs14051245

Delaporte, B., Ibanez, T., Keppel, G., Jullien, S., Menkes, C., & Arsouze, T. (2023). *StormR: Analyzing the behaviour of wind generated by tropical storms and cyclones*. R package version 0.1.1. https://CRAN.R-proje ct.org/package=StormR

Elmqvist, T., Rainey, W. E., Pierson, E. D., & Cox, P. A. (1994). Effects of tropical cyclones Ofa and Val on the structure of a Samoan lowland rain-forest. *Biotropica*, 26(4), 384-391. https://doi.org/10.2307/2389232

Foster, D. R. (1988). Species and stand response to catastrophic wind in central New England, USA. *Journal of Ecology*, 76(1), 135-151. https://doi.org/10.2307/2260458
<figure><img src="image_16.png" /></figure>

Edmund Tanner ID https://orcid.org/0000-0002-4961-9993

Benton N. Taylor ID https://orcid.org/0000-0002-9834-9192

Jill Thompson ID https://orcid.org/0000-0002-4370-2593

Tamara Ticktin ID https://orcid.org/0000-0003-4227-2584

Maria Uriarte ID https://orcid.org/0000-0002-0484-0758

Edward L. Webb ID https://orcid.org/0000-0001-5554-9955

Gunnar Keppel ID https://orcid.org/0000-0001-7092-6149

REFERENCES

Ancelin, P., Courbaud, B., & Fourcaud, T. (2004). Developing an individual tree based mechanical model to predict wind damage within forest stands. *Forest Ecology and Management*, 203(1-3), 101-121. https://doi.org/10.1016/j.foreco.2004.07.067

Ankori-Karlinsky, R., Hall, J., Murphy, L., Muscarella, R., Martinuzzi, S., Fahey, R., Zimmerman, J. K., & Uriarte, M. (2024). Chronic winds reduce tropical forest structural complexity regardless of climate, topography, or forest age. *Ecosystems*, 27, 479-491. https://doi.org/10.1007/s10021-024-00900-5

Arif, S., & MacNeil, M. A. (2023). Applying the structural causal model framework for observational causal inference in ecology. *Ecological Monographs*, 93(1), e1554. https://doi.org/10.1002/ecm.1554

Asner, G. P., & Goldstein, G. (1997). Correlating stem biomechanical properties of Hawaiian canopy trees with hurricane wind damage. *Biotropica*, 29(2), 145-150.

Basnet, K., Likens, G. E., Scatena, F. N., & Lugo, A. E. (1992). Hurricane Hugo: Damage to a tropical rainforest in Puerto Rico. *Journal of Tropical Ecology*, 8(1), 47-55.

Basnet, K., Scatena, F. N., Likens, G. E., & Lugo, A. E. (1993). Ecological consequences of root grafting in tabonuco (*Dacryodes excelsa*) trees in the Luquillo experimental Forest, Puerto Rico. *Biotropica*, 25(1), 28-35. https://doi.org/10.2307/2388976

Batista, W. B., & Platt, W. J. (2003). Tree population responses to hurricane disturbance: Syndromes in a south-eastern USA old-growth forest. *Journal of Ecology*, 91(2), 197-212. https://www.jstor.org/stable/3599755

Bellingham, P. (1991). Landforms influence patterns of hurricane damage—Evidence from Jamaican montane forests. *Biotropica*, 23(4), 427-433. https://doi.org/10.2307/2388262

Bellingham, P. J., Kohyama, T., & Aiba, S. (1996). The effects of a typhoon on Japanese warm temperate rainforests. *Ecological Research*, 11(3), 229-247. https://doi.org/10.1007/BF02347781

Bellingham, P. J., Tanner, E. V. J., & Healey, J. R. (1995). Damage and responsiveness of Jamaican montane tree species after disturbance by a hurricane. *Ecology*, 76(8), 2562-2580. https://doi.org/10.2307/2265828

Birkinshaw, C., & Randrianjanahary, M. (2007). The effects of cyclone Hudah on the forest of Masoala Peninsula, Madagascar. *Madagascar Conservation & Development*, 2(1), Article 1. https://doi.org/10.4314/mcd.v2i1.44125

Blanchard, E., Birnbaum, P., Ibanez, T., Boutreux, T., Antin, C., Ploton, P., Vincent, G., Pouteau, R., Vandrot, H., Hequet, V., Barbier, N., Droissart, V., Sonké, B., Texier, N., Kamdem, N. G., Zebaze, D., Libalah, M., & Couteron, P. (2016). Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. *Trees*, 30, 1953-1968. https://doi.org/10.1007/s00468-016-1424-3

Blanchard, G., Munoz, F., Ibanez, T., Hequet, V., Vandrot, H., Girardi, J., & Birnbaum, P. (2019). Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia. *Journal of Vegetation Science*, 30(5), Article 5. https://doi.org/10.1111/jvs.12781

Boose, E. R., Foster, D. R., & Fluet, M. (1994). Hurricane impacts to tropical and temperate forest landscapes. *Ecological Monographs*, 64(4), 369-400. https://doi.org/10.2307/2937142

Bourdin, S., Fromang, S., Dulac, W., Cattiaux, J., & Chauvin, F. (2022). Intercomparison of four algorithms for detecting tropical cyclones using ERA5. *Geoscientific Model Development*, 15(17), 6759-6786. https://doi.org/10.5194/gmd-15-6759-2022

Bürkner, P.-C. (2017). brms: An R package for bayesian multilevel models using Stan. *Journal of Statistical Software*, 80, 1-28. https://doi.org/10.18637/jss.v080.i01

Cannon, J. B., Barrett, M. E., & Peterson, C. J. (2015). The effect of species, size, failure mode, and fire-scarring on tree stability. *Forest Ecology and Management*, 356, 196-203. https://doi.org/10.1016/j.foreco.2015.07.014

Cannon, J. B., Peterson, C. J., Godfrey, C. M., & Whelan, A. W. (2023). Hurricane wind regimes for forests of North America. *Proceedings of the National Academy of Sciences of the United States of America*, 120(42), e2309076120. https://doi.org/10.1073/pnas.2309076120

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. *Journal of Statistical Software*, 76, 1. https://doi.org/10.18637/jss.v076.i01

Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. *Oecologia*, 145(1), 87-99. https://doi.org/10.1007/s00442-005-0100-x

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12(4), 351-366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

Chave, J., Muller-Landau, H. C., Baker, T. R., Easdale, T. A., Ter Steege, H., & Webb, C. O. (2006). Regional and phylogenetic variation of wood density across 2456 neotropical tree species. *Ecological Applications*, 16(6), 2356-2367. https://doi.org/10.1890/1051-0761(2006)016[2356:rapvow]2.0.co;2

Chen, K.-M. (1994). A computation method for typhoon wind field. *Tropic Oceanology*, 13(2), 41-48.

Curran, T. J., Brown, R. L., Edwards, E., Hopkins, K., Kelley, C., McCarthy, E., Pounds, E., Solan, R., & Wolf, J. (2008). Plant functional traits explain interspecific differences in immediate cyclone damage to trees of an endangered rainforest community in north Queensland. *Austral Ecology*, 33(4), 451-461. https://doi.org/10.1111/j.1442-9993.2008.01900.x

Curran, T. J., Gersbach, L. N., Edwards, W., & Krockenberger, A. K. (2008). Wood density predicts plant damage and vegetative recovery rates caused by cyclone disturbance in tropical rainforest tree species of North Queensland, Australia. *Austral Ecology*, 33(4), 442-450. https://doi.org/10.1111/j.1442-9993.2008.01899.x

Delaporte, B., Arsouze, T., Keppel, G., Jullien, S., Menkes, C., & Ibanez, T. (2024). StormR: An R package to quantify and map the tropical storms and cyclones' winds characteristics. *Journal of Open Source Software*, 9(93), 5766. https://doi.org/10.21105/joss.05766

Delaporte, B., Ibanez, T., Despinoy, M., Mangeas, M., & Menkes, C. (2022). Tropical cyclone impact and forest resilience in the Southwestern Pacific. *Remote Sensing*, 14(5), 1245. https://doi.org/10.3390/rs14051245

Delaporte, B., Ibanez, T., Keppel, G., Jullien, S., Menkes, C., & Arsouze, T. (2023). *StormR: Analyzing the behaviour of wind generated by tropical storms and cyclones*. R package version 0.1.1. https://CRAN.R-proje ct.org/package=StormR

Elmqvist, T., Rainey, W. E., Pierson, E. D., & Cox, P. A. (1994). Effects of tropical cyclones ofa and Val on the structure of a Samoan lowland rain- forest. *Biotropica*, 26(4), 384-391. https://doi.org/10.2307/2389232

Foster, D. R. (1988). Species and stand response to catastrophic wind in central New England, USA. *Journal of Ecology*, 76(1), 135-151. https://doi.org/10.2307/2260458
Fournier, M., Dlouhá, J., Jaouen, G., & Almeras, T. (2013). Integrative bio-
mechanics for tree ecology: Beyond wood density and strength.
*Journal of Experimental Botany*, **64**(15), 4793-4815. https://doi.org/
10.1093/jxb/ert279

Franklin, J., Drake, D. R., McConkey, K. R., Tonga, F., & Smith, L. B. (2004).
The effects of Cyclone Waka on the structure of lowland tropical
rain forest in Vava'u, Tonga. *Journal of Tropical Ecology*, **20**, 409–
420. https://doi.org/10.1017/s0266467404001543

Freschet, G. T., Roumet, C., Comas, L. H., Weemstra, M., Bengough, A. G., Rewald, B., Bardgett, R. D., De Deyn, G. B., Johnson, D., Klimešová, J., Lukac, M., McCormack, M. L., Meier, I. C., Pagès, L., Poorter, H., Prieto, I., Wurzburger, N., Zadworny, M., Bagniewskazadworna, A., ... Stokes, A. (2021). Root traits as drivers of plant and ecosystem functioning: Current understanding, pitfalls and future research needs. *New Phytologist*, **232**(3), 1123–1158. https://doi.org/10.1111/nph.17072

Gardiner, B. (2021). Wind damage to forests and trees: A review with an emphasis on planted and managed forests. *Journal of Forest Research*, 26(4), 248–266. https://doi.org/10.1080/13416979.2021.1940665

Gardiner, B., Peltola, H., & Kellomäki, S. (2000). Comparison of two models for predicting the critical wind speeds required to damage coniferous trees. *Ecological Modelling*, **129**(1), 1–23. https://doi.org/10.1016/S0304-3800(00)00220-9

Hall, J., Muscarella, R., Quebbeman, A., Arellano, G., Thompson, J., Zimmerman, J. K., & Uriarte, M. (2020). Hurricane-induced rainfall is a stronger predictor of tropical forest damage in Puerto Rico than maximum wind speeds. *Scientific Reports*, **10**(1), 4318. https://doi.org/10.1038/s41598-020-61164-2

Heartsill-Scalley, T., & López-Marrero, T. (2021). Beyond tropical storms: Understanding disturbance and forest dynamics. *Frontiers in Forests and Global Change*, **4**, 698733. https://doi.org/10.3389/ffgc.2021.698733

Herbert, D. A., Fownes, J. H., & Vitousek, P. M. (1999). Hurricane damage to a Hawaiian forest: Nutrient supply rate affects resistance and resilience. *Ecology*, **80**(3), 908–920. https://doi.org/10.1890/0012-9658(1999)080[0908:HTDAHF]2.0.CO;2

Herwitz, S. R. (1985). Interception storage capacities of tropical rainfor-
est canopy trees. *Journal of Hydrology*, **77**(1), 237–252. https://doi.
org/10.1016/0022-1694(85)90209-4

Hijmans, R. (2023). *Raster: Geographic data analysis and modelling*. R pack-
age version 3.6-20. https://cran.r-project.org/web/packages/ras-
ter/index.html

Ibanez, T., Keppel, G., Menkes, C., Gillespie, T. W., Lengaigne, M., Mangeas, M., Rivas-Torres, G., & Birnbaum, P. (2019). Globally consistent impact of tropical cyclones on the structure of tropical and subtropical forests. *Journal of Ecology*, **107**(1), 279–292. https://doi.org/10.1111/1365-2745.13039

Ibanez, T., Platt, W. J., Bellingham, P. J., Vieilledent, G., Franklin, J., Martin, P. H., Menkes, C., Pérez-Salicrup, D. R., Russell-Smith, J., & Keppel, G. (2022). Altered cyclone-fire interactions are changing ecosystems. *Trends in Plant Science*, **27**(12), 1218–1230. https://doi.org/10.1016/j.tplants.2022.08.005

IPCC. (2021). *Climate change 2021: The physical science basis*. Contribution of Working Group I to the sixth assessment report of the Intergovernmental Panel on Climate Change [V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.)]. Cambridge University Press. https://doi.org/10.1017/9781009157896

Kay, M. (2023). *tidybayes: Tidy Data and Geoms for bayesian models*. https://doi.org/10.5281/zenodo.1308151

Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J. (2010). The international best track archive for climate stewardship (IBTrACS). *Bulletin of the American Meteorological Society*, **91**(3), 363–376. https://doi.org/10.1175/2009bams2755.1

Korznikov, K., Kislov, D., Doležal, J., Petrenko, T., & Altman, J. (2022). Tropical cyclones moving into boreal forests: Relationships between disturbance areas and environmental drivers. *Science of the Total Environment*, **844**, 156931. https://doi.org/10.1016/j.scitenv.2022.156931

Larjavaara, M., & Muller-Landau, H. C. (2010). Rethinking the value of high wood density. *Functional Ecology*, **24**(4), 701–705. https://doi.org/10.1111/j.1365-2435.2010.01698.x

Laurans, M., Munoz, F., Charles-Dominique, T., Heuret, P., Fortunel, C., Isnard, S., Sabatier, S.-A., Caraglio, Y., & Violle, C. (2024). Why incorporate plant architecture into trait-based ecology? *Trends in Ecology & Evolution*, **1-13**. https://doi.org/10.1016/j.tree.2023.11.011

Lin, I.-I., Pun, I.-F., & Lien, C.-C. (2014). “Category-6” supertyphoon Haiyan in global warming hiatus: Contribution from subsurface ocean warming. *Geophysical Research Letters*, **41**(23), 8547–8553. https://doi.org/10.1002/2014GL061281

Lin, T.-C., Hogan, J. A., & Chang, C.-T. (2020). Tropical cyclone ecology: A scale-link perspective. *Trends in Ecology & Evolution*, **35**(7), 594–604. https://doi.org/10.1016/j.tree.2020.02.012

Lugo, A. E. (2008). Visible and invisible effects of hurricanes on forest ecosystems: An international review. *Austral Ecology*, **33**(4), 368–398. https://doi.org/10.1111/j.1442-9993.2008.01894.x

Lugo, A. E., Applefield, M., Douglas, J. P., & Macdonald, R. B. (1983). The impact of hurricane David on the forests of Dominica. *Canadian Journal of Forest Research*, **13**, 201–211.

Mayhead, G. J. (1973). Some drag coefficients for British forest trees derived from wind tunnel studies. *Agricultural Meteorology*, **12**, 123–130. https://doi.org/10.1016/0002-1571(73)90013-7

McElreath, R. (2020). Statistical rethinking: A bayesian course with examples in *R* and *STAN* (2nd ed.). Chapman and Hall/CRC Press. https://doi.org/10.1201/9780429029608

Metcalfe, D. J., Bradford, M. G., & Ford, A. J. (2008). Cyclone damage to tropical rain forests: Species- and community-level impacts. *Austral Ecology*, **33**(4), 432–441. https://doi.org/10.1111/j.1442-9993.2008.01898.x

Murakami, H., Delworth, T. L., Cooke, W. F., Zhao, M., Xiang, B., & Hsu, P.-C. (2020). Detected climatic change in global distribution of tropical cyclones. *Proceedings of the National Academy of Sciences of the United States of America*, **117**(20), 10706–10714. https://doi.org/10.1073/pnas.1922500117

Niklas, K. J., & Spatz, H.-C. (2010). Worldwide correlations of mechanical properties and green wood density. *American Journal of Botany*, **97**(10), 1587–1594. https://doi.org/10.3732/ajb.1000150

O'Brien, M. J., Engelbrecht, B. M. J., Joswig, J., Pereyra, G., Schuldt, B., Jansen, S., Kattge, J., Landhäusser, S. M., Levick, S. R., Preisler, Y., Väänänen, P., & Macinnis-Ng, C. (2017). A synthesis of tree functional traits related to drought-induced mortality in forests across climatic zones. *Journal of Applied Ecology*, **54**(6), 1669–1686. https://doi.org/10.1111/1365-2664.12874

Ostertag, R., Silver, W. L., & Lugo, A. E. (2005). Factors affecting mortality and resistance to damage following hurricanes in a rehabilitated subtropical moist forest. *Biotropica*, **37**(1), 16–24.

Pearl, J. (2009). *Causality: Models, reasoning and inference* (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161

Peltola, H. M. (2006). Mechanical stability of trees under static loads. *American Journal of Botany*, **93**(10), 1501–1511. https://doi.org/10.3732/ajb.93.10.1501

Poorter, L., Bongers, L., & Bongers, F. (2006). Architecture of 54 moist-forest tree species: Traits, trade-offs, and functional groups. *Ecology*, **87**(5), 1289–1301. https://doi.org/10.1890/0012-9658(2006)87[1289:AOMTST]2.0.CO;2

Quigley, M. F., & Platt, W. J. (2003). Composition and structure of seasonally deciduous forests in the Americas. *Ecological Monographs*, **73**(1), 87–106. https://doi.org/10.1890/0012-9615(2003)073[0087:CASOSD]2.0.CO;2
Fournier, M., Dlouhá, J., Jaouen, G., & Almeras, T. (2013). Integrative bio-
mechanics for tree ecology: Beyond wood density and strength.
*Journal of Experimental Botany*, 64(15), 4793-4815. https://doi.org/
10.1093/jxb/ert279

Franklin, J., Drake, D. R., McConkey, K. R., Tonga, F., & Smith, L. B. (2004).
The effects of Cyclone Waka on the structure of lowland tropical
rain forest in Vava'u, Tonga. *Journal of Tropical Ecology*, 20, 409-
420. https://doi.org/10.1017/s0266467404001543

Freschet, G. T., Roumet, C., Comas, L. H., Weemstra, M., Bengough, A. G., Rewald, B., Bardgett, R. D., De Deyn, G. B., Johnson, D., Klimešová, J., Lukac, M., McCormack, M. L., Meier, I. C., Pagès, L., Poorter, H., Prieto, I., Wurzburger, N., Zadworny, M., Bagniewskazadworna, A., Stokes, A. (2021). Root traits as drivers of plant and ecosystem functioning: Current understanding, pitfalls and future research needs. *New Phytologist*, 232(3), 1123-1158. https://doi.org/10.1111/nph.17072

Gardiner, B. (2021). Wind damage to forests and trees: A review with an emphasis on planted and managed forests. *Journal of Forest Research*, 26(4), 248–266. https://doi.org/10.1080/13416979.2021.1940665

Gardiner, B., Peltola, H., & Kellomäki, S. (2000). Comparison of two models for predicting the critical wind speeds required to damage coniferous trees. *Ecological Modelling*, 129(1), 1–23. https://doi.org/10.1016/S0304-3800(00)00220-9

Hall, J., Muscarella, R., Quebbeman, A., Arellano, G., Thompson, J., Zimmerman, J. K., & Uriarte, M. (2020). Hurricane-induced rainfall is a stronger predictor of tropical forest damage in Puerto Rico than maximum wind speeds. *Scientific Reports*, 10(1), 4318. https://doi.org/10.1038/s41598-020-61164-2

Heartsill-Scalley, T., & López-Marrero, T. (2021). Beyond tropical storms: Understanding disturbance and forest dynamics. *Frontiers in Forests and Global Change*, 4, 698733. https://doi.org/10.3389/ffgc.2021.698733

Herbert, D. A., Fownes, J. H., & Vitousek, P. M. (1999). Hurricane damage to a Hawaiian forest: Nutrient supply rate affects resistance and resilience. *Ecology*, 80(3), 908–920. https://doi.org/10.1890/0012-9658(1999)080[0908:HTDAHF]2.0.CO;2

Herwitz, S. R. (1985). Interception storage capacities of tropical rainfor-
est canopy trees. *Journal of Hydrology*, 77(1), 237-252. https://doi.
org/10.1016/0022-1694(85)90209-4

Hijmans, R. (2023). *Raster: Geographic data analysis and modelling*. R pack-
age version 3.6-20. https://cran.r-project.org/web/packages/ras-
ter/index.html

Ibanez, T., Keppel, G., Menkes, C., Gillespie, T. W., Lengaigne, M., Mangeas, M., Rivas-Torres, G., & Birnbaum, P. (2019). Globally consistent impact of tropical cyclones on the structure of tropical and subtropical forests. *Journal of Ecology*, 107(1), 279–292. https://doi.org/10.1111/1365-2745.13039

Ibanez, T., Platt, W. J., Bellingham, P. J., Vieilledent, G., Franklin, J., Martin, P. H., Menkes, C., Pérez-Salicrup, D. R., Russell-Smith, J., & Keppel, G. (2022). Altered cyclone-fire interactions are changing ecosystems. *Trends in Plant Science*, 27(12), 1218–1230. https://doi.org/10.1016/j.tplants.2022.08.005

IPCC. (2021). *Climate change 2021: The physical science basis*. Contribution of Working Group I to the sixth assessment report of the Intergovernmental Panel on Climate Change [V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.)]. Cambridge University Press. https://doi.org/10.1017/9781009157896

Kay, M. (2023). *tidybayes: Tidy Data and Geoms for bayesian models*. https://doi.org/10.5281/zenodo.1308151

Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J. (2010). The international best track archive for climate stewardship (IBTrACS). *Bulletin of the American Meteorological Society*, 91(3), 363–376. https://doi.org/10.1175/2009bams2755.1

Korznikov, K., Kislov, D., Doležal, J., Petrenko, T., & Altman, J. (2022). Tropical cyclones moving into boreal forests: Relationships between disturbance areas and environmental drivers. *Science of the Total Environment*, 844, 156931. https://doi.org/10.1016/j.scitenv.2022.156931

Larjavaara, M., & Muller-Landau, H. C. (2010). Rethinking the value of high wood density. *Functional Ecology*, 24(4), 701–705. https://doi.org/10.1111/j.1365-2435.2010.01698.x

Laurans, M., Munoz, F., Charles-Dominique, T., Heuret, P., Fortunel, C., Isnard, S., Sabatier, S.-A., Caraglio, Y., & Violle, C. (2024). Why incorporate plant architecture into trait-based ecology? *Trends in Ecology & Evolution*, 1–13. https://doi.org/10.1016/j.tree.2023.11.011

Lin, I.-I., Pun, I.-F., & Lien, C.-C. (2014). "Category-6" supertyphoon Haiyan in global warming hiatus: Contribution from subsurface ocean warming. *Geophysical Research Letters*, 41(23), 8547–8553. https://doi.org/10.1002/2014GL061281

Lin, T.-C., Hogan, J. A., & Chang, C.-T. (2020). Tropical cyclone ecology: A scale-link perspective. *Trends in Ecology & Evolution*, 35(7), 594–604. https://doi.org/10.1016/j.tree.2020.02.012

Lugo, A. E. (2008). Visible and invisible effects of hurricanes on forest ecosystems: An international review. *Austral Ecology*, 33(4), 368–398. https://doi.org/10.1111/j.1442-9993.2008.01894.x

Lugo, A. E., Applefield, M., Douglas, J. P., & Macdonald, R. B. (1983). The impact of hurricane David on the forests of Dominica. *Canadian Journal of Forest Research*, 13, 201–211.

Mayhead, G. J. (1973). Some drag coefficients for British forest trees derived from wind tunnel studies. *Agricultural Meteorology*, 12, 123–130. https://doi.org/10.1016/0002-1571(73)90013-7

McElreath, R. (2020). Statistical rethinking: A bayesian course with examples in *R* and *STAN* (2nd ed.). Chapman and Hall/CRC Press. https://doi.org/10.1201/9780429029608

Metcalfe, D. J., Bradford, M. G., & Ford, A. J. (2008). Cyclone damage to tropical rain forests: Species- and community-level impacts. *Austral Ecology*, 33(4), 432–441. https://doi.org/10.1111/j.1442-9993.2008.01898.x

Murakami, H., Delworth, T. L., Cooke, W. F., Zhao, M., Xiang, B., & Hsu, P.-C. (2020). Detected climatic change in global distribution of tropical cyclones. *Proceedings of the National Academy of Sciences of the United States of America*, 117(20), 10706–10714. https://doi.org/10.1073/pnas.1922500117

Niklas, K. J., & Spatz, H.-C. (2010). Worldwide correlations of mechanical properties and green wood density. *American Journal of Botany*, 97(10), 1587–1594. https://doi.org/10.3732/ajb.1000150

O'Brien, M. J., Engelbrecht, B. M. J., Joswig, J., Pereyra, G., Schuldt, B., Jansen, S., Kattge, J., Landhäusser, S. M., Levick, S. R., Preisler, Y., Väänänen, P., & Macinnis-Ng, C. (2017). A synthesis of tree functional traits related to drought-induced mortality in forests across climatic zones. *Journal of Applied Ecology*, 54(6), 1669–1686. https://doi.org/10.1111/1365-2664.12874

Ostertag, R., Silver, W. L., & Lugo, A. E. (2005). Factors affecting mortality and resistance to damage following hurricanes in a rehabilitated subtropical moist forest. *Biotropica*, 37(1), 16–24.

Pearl, J. (2009). *Causality: Models, reasoning and inference* (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161

Peltola, H. M. (2006). Mechanical stability of trees under static loads. *American Journal of Botany*, 93(10), 1501–1511. https://doi.org/10.3732/ajb.93.10.1501

Poorter, L., Bongers, L., & Bongers, F. (2006). Architecture of 54 moist-forest tree species: Traits, trade-offs, and functional groups. *Ecology*, 87(5), 1289–1301. https://doi.org/10.1890/0012-9658(2006)87[1289:AOMTST]2.0.CO;2

Quigley, M. F., & Platt, W. J. (2003). Composition and structure of seasonally deciduous forests in the Americas. *Ecological Monographs*, 73(1), 87–106. https://doi.org/10.1890/0012-9615(2003)073[0087:CASOSD]2.0.CO;2
R Core Team. (2023). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. https://www.R-proje ct.org/

Read, J., Evans, R., Sanson, G. D., Kerr, S., & Jaffré, T. (2011). Wood properties and trunk allometry of co-occurring rainforest canopy trees in a cyclone-prone environment. *American Journal of Botany*, 98(11), 1762–1772. https://doi.org/10.3732/ajb.1100080

Reilly, A. E. (1991). The effects of hurricane Hugo in three tropical forests in the U.S. Virgin Islands. *Biotropica*, 23(4), 414–419. https:// doi.org/10.2307/2388260

Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J., & Hérault, B. (2017). biomass: An r package for estimating above-ground biomass and its uncertainty in tropical forests. *Methods in Ecology and Evolution*, 8(9), 1163–1167. https://doi.org/10.1111/2041-210X. 12753

Ribeiro, G. H. P. M., Chambers, J. Q., Peterson, C. J., Trumbore, S. E., Magnabosco Marra, D., Wirth, C., Cannon, J. B., Négron-Juárez, R. I., Lima, A. J. N., de Paula, E. V. C. M., Santos, J., & Higuchi, N. (2016). Mechanical vulnerability and resistance to snapping and uprooting for Central Amazon tree species. *Forest Ecology and Management*, 380, 1–10. https://doi.org/10.1016/j.foreco.2016.08.039

Rogers, R. F., Aberson, S., Bell, M. M., Cecil, D. J., Doyle, J. D., Kimberlain, T. B., Morgerman, J., Shay, L. K., & Velden, C. (2017). Rewriting the tropical record books: The extraordinary intensification of Hurricane Patricia (2015). *Bulletin of the American Meteorological Society*, 98(10), 2091–2112. https://doi.org/10.1175/BAMS-D-16- 0039.1

Ruel, J.-C., Pin, D., & Cooper, K. (1998). Effect of topography on wind behaviour in a complex terrain. *Forestry*, 71(3), 261–265. https://doi. org/10.1093/forestry/71.3.261

Rutledge, B. T., Cannon, J. B., McIntyre, R. K., Holland, A. M., & Jack, S. B. (2021). Tree, stand, and landscape factors contributing to hurricane damage in a coastal plain forest: Post-hurricane assessment in a longleaf pine landscape. *Forest Ecology and Management*, 481, 118724. https://doi.org/10.1016/j.foreco.2020.118724

Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J., Leker, M. J., Trotsiuk, V., Mairota, P., Svoboda, M., Fabrika, M., Nagel, T. A., & Reyer, C. P. O. (2017). Forest disturbances under climate change. *Nature Climate Change*, 7(6), 395–402. https://doi.org/10.1038/ nclimate3303

Simpson, R. H. (1974). The hurricane disaster potential scale. *Weatherwise*, 27, 169–186.

Stokes, A. (2002). Biomechanics of tree root anchorage. In Y. Waisel, A. Eshel, & U. Kafkafi (Eds.), *Plant roots* (pp. 297–314). CRC Press.

Studholme, J., Fedorov, A. V., Gulev, S. K., Emanuel, K., & Hodges, K. (2022). Poleward expansion of tropical cyclone latitudes in warming climates. *Nature Geoscience*, 15(1), 14–28. https://doi.org/10. 1038/s41561-021-00859-1

Swenson, N. G., & Enquist, B. J. (2007). Ecological and evolutionary determinants of a key plant functional trait: Wood density and its community-wide variation across latitude and elevation. *American Journal of Botany*, 94(3), 451–459. https://doi.org/10. 3732/ajb.94.3.451

Tanner, E. V. J., & Bellingham, P. J. (2006). Less diverse forest is more resistant to hurricane disturbance: Evidence from montane rain forests in Jamaica. *Journal of Ecology*, 94(5), 1003–1010. https://doi. org/10.1111/j.1365-2745.2006.01149.x

Taylor, B. N., Stedman, E., Van Bloem, S. J., Whitmire, S. L., & DeWalt, S. J. (2023). Widespread stem snapping but limited mortality caused by a category 5 hurricane on the Caribbean Island of Dominica. *Forest Ecology and Management*, 532, 120833. https://doi.org/10.1016/j. foreco.2023.120833

Thomas, S. C., Martin, A. R., & Mycroft, E. E. (2015). Tropical trees in a wind-exposed Island ecosystem: Height-diameter allometry

and size at onset of maturity. *Journal of Ecology*, 103(3), 594–605. https://doi.org/10.1111/1365-2745.12378

Ticktin, T., McGuigan, A., Alo, F., Balick, M., Boraks, A., Sam, C., Doro, T., Dovo, P., Ibanez, T., Naikitini, A., Ranker, T. A., Tuiwawa, M. V., Wahe, J.-P., & Plunkett, G. M. (2024). High resilience of Pacific Island forests to a category-5 cyclone. *Science of the Total Environment*, 922, 170973. https://doi.org/10.1016/j.scitotenv. 2024.170973

United Nations Environment Programme. (2023). *Adaptation gap report 2023: Underfinanced. Underprepared. Inadequate investment and planning on climate adaptation leaves world exposed*. https://wedocs. unep.org/20.500.11822/43796

Uriarte, M., Thompson, J., & Zimmerman, J. K. (2019). Hurricane María tripled stem breaks and doubled tree mortality relative to other major storms. *Nature Communications*, 10(1), 1362. https://doi.org/ 10.1038/s41467-019-09319-2

Vandecar, K. L., Lawrence, D., Richards, D., Schneider, L., Rogan, J., Schmook, B., & Wilbur, H. (2011). High mortality for rare species following hurricane disturbance in the southern Yucatán. *Biotropica*, 43(6), 676–684. https://doi.org/10.1111/j.1744-7429. 2011.00756.x

Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P., Paananen, T., & Gelman, A. (2023). *loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models*. R package version 2.6.0. https://mc-stan. org/loo/

Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Statistics and Computing*, 27(5), 1413–1432. https://doi.org/10.1007/s1122 2-016-9696-4

Virot, E., Ponomarenko, A., Dehandschoewercker, É., Quéré, D., & Clanet, C. (2016). Critical wind speed at which trees break. *Physical Review E*, 93(2), 023001. https://doi.org/10.1103/PhysRevE.93. 023001

Walker, L. (1991). Tree damage and recovery from hurricane Hugo in Luquillo Experimental Forest, Puerto-Rico. *Biotropica*, 23(4), 375– 385. https://doi.org/10.2307/2388255

Walker, L., Voltzow, J., Ackerman, J., Fernandez, D., & Fetcher, N. (1992). Immediate impact of hurricane Hugo on a Puerto-Rican rain-forest. *Ecology*, 73(2), 691–694. https://doi.org/10.2307/ 1940775

Wang, F., & Xu, Y. J. (2009). Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale. *Environmental Monitoring and Assessment*, 156(1), 491–507. https://doi.org/10. 1007/s10661-008-0500-6

Wang, S., & Toumi, R. (2021). Recent migration of tropical cyclones toward coasts. *Science*, 371(6528), 514–517. https://doi.org/10.1126/ science.abb9038

Webb, E. L., Stanfield, B. J., & Jensen, M. L. (1999). Effects of topography on rainforest tree community structure and diversity in American Samoa, and implications for frugivore and nectarivore populations. *Journal of Biogeography*, 26(4), 887–897. https://doi.org/10.1046/j. 1365-2699.1999.00326.x

Webb, E. L., van de Bult, M., Fa'aumu, S., Webb, R. C., Tualaulelei, A., & Carrasco, L. R. (2014). Factors affecting tropical tree damage and survival after catastrophic wind disturbance. *Biotropica*, 46(1), 32– 41. https://doi.org/10.1111/btp.12067

Wehner, M. F., & Kossin, J. P. (2024). The growing inadequacy of an open-ended Saffir-Simpson hurricane wind scale in a warming world. *Proceedings of the National Academy of Sciences of the United States of America*, 121(7), e2308901121. https://doi.org/10.1073/pnas. 2308901121

Willoughby, H. E., Darling, R. W. R., & Rahn, M. E. (2006). Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles. *Monthly Weather Review*, 134(4), 1102–1120. https://doi.org/10.1175/mwr3106.1
R Core Team. (2023). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. https://www.R-proje ct.org/

Read, J., Evans, R., Sanson, G. D., Kerr, S., & Jaffré, T. (2011). Wood properties and trunk allometry of co-occurring rainforest canopy trees in a cyclone-prone environment. *American Journal of Botany*, 98(11), 1762-1772. https://doi.org/10.3732/ajb.1100080

Reilly, A. E. (1991). The effects of hurricane Hugo in three tropical forests in the U.S. Virgin Islands. *Biotropica*, 23(4), 414-419. https://doi.org/10.2307/2388260

Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J., & Hérault, B. (2017). biomass: An r package for estimating above-ground biomass and its uncertainty in tropical forests. *Methods in Ecology and Evolution*, 8(9), 1163-1167. https://doi.org/10.1111/2041-210X.12753

Ribeiro, G. H. P. M., Chambers, J. Q., Peterson, C. J., Trumbore, S. E., Magnabosco Marra, D., Wirth, C., Cannon, J. B., Négron-Juárez, R. I., Lima, A. J. N., de Paula, E. V. C. M., Santos, J., & Higuchi, N. (2016). Mechanical vulnerability and resistance to snapping and uprooting for Central Amazon tree species. *Forest Ecology and Management*, 380, 1-10. https://doi.org/10.1016/j.foreco.2016.08.039

Rogers, R. F., Aberson, S., Bell, M. M., Cecil, D. J., Doyle, J. D., Kimberlain, T. B., Morgerman, J., Shay, L. K., & Velden, C. (2017). Rewriting the tropical record books: The extraordinary intensification of Hurricane Patricia (2015). *Bulletin of the American Meteorological Society*, 98(10), 2091-2112. https://doi.org/10.1175/BAMS-D-16-0039.1

Ruel, J.-C., Pin, D., & Cooper, K. (1998). Effect of topography on wind behaviour in a complex terrain. *Forestry*, 71(3), 261-265. https://doi.org/10.1093/forestry/71.3.261

Rutledge, B. T., Cannon, J. B., McIntyre, R. K., Holland, A. M., & Jack, S. B. (2021). Tree, stand, and landscape factors contributing to hurricane damage in a coastal plain forest: Post-hurricane assessment in a longleaf pine landscape. *Forest Ecology and Management*, 481, 118724. https://doi.org/10.1016/j.foreco.2020.118724

Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J., Leker, M. J., Trotsiuk, V., Mairota, P., Svoboda, M., Fabrika, M., Nagel, T. A., & Reyer, C. P. O. (2017). Forest disturbances under climate change. *Nature Climate Change*, 7(6), 395-402. https://doi.org/10.1038/nclimate3303

Simpson, R. H. (1974). The hurricane disaster potential scale. *Weatherwise*, 27, 169-186.

Stokes, A. (2002). Biomechanics of tree root anchorage. In Y. Waisel, A. Eshel, & U. Kafkafi (Eds.), *Plant roots* (pp. 297-314). CRC Press.

Studholme, J., Fedorov, A. V., Gulev, S. K., Emanuel, K., & Hodges, K. (2022). Poleward expansion of tropical cyclone latitudes in warming climates. *Nature Geoscience*, 15(1), 14-28. https://doi.org/10.1038/s41561-021-00859-1

Swenson, N. G., & Enquist, B. J. (2007). Ecological and evolutionary determinants of a key plant functional trait: Wood density and its community-wide variation across latitude and elevation. *American Journal of Botany*, 94(3), 451-459. https://doi.org/10.3732/ajb.94.3.451

Tanner, E. V. J., & Bellingham, P. J. (2006). Less diverse forest is more resistant to hurricane disturbance: Evidence from montane rain forests in Jamaica. *Journal of Ecology*, 94(5), 1003-1010. https://doi.org/10.1111/j.1365-2745.2006.01149.x

Taylor, B. N., Stedman, E., Van Bloem, S. J., Whitmire, S. L., & DeWalt, S. J. (2023). Widespread stem snapping but limited mortality caused by a category 5 hurricane on the Caribbean Island of Dominica. *Forest Ecology and Management*, 532, 120833. https://doi.org/10.1016/j.foreco.2023.120833

Thomas, S. C., Martin, A. R., & Mycroft, E. E. (2015). Tropical trees in a wind-exposed Island ecosystem: Height-diameter allometry

and size at onset of maturity. *Journal of Ecology*, 103(3), 594-605. https://doi.org/10.1111/1365-2745.12378

Ticktin, T., McGuigan, A., Alo, F., Balick, M., Boraks, A., Sam, C., Doro, T., Dovo, P., Ibanez, T., Naikitini, A., Ranker, T. A., Tuiwawa, M. V., Wahe, J.-P., & Plunkett, G. M. (2024). High resilience of Pacific Island forests to a category-5 cyclone. *Science of the Total Environment*, 922, 170973. https://doi.org/10.1016/j.scitotenv.2024.170973

United Nations Environment Programme. (2023). *Adaptation gap report 2023: Underfinanced. Underprepared. Inadequate investment and planning on climate adaptation leaves world exposed*. https://wedocs.unep.org/20.500.11822/43796

Uriarte, M., Thompson, J., & Zimmerman, J. K. (2019). Hurricane María tripled stem breaks and doubled tree mortality relative to other major storms. *Nature Communications*, 10(1), 1362. https://doi.org/10.1038/s41467-019-09319-2

Vandecar, K. L., Lawrence, D., Richards, D., Schneider, L., Rogan, J., Schmook, B., & Wilbur, H. (2011). High mortality for rare species following hurricane disturbance in the southern Yucatán. *Biotropica*, 43(6), 676-684. https://doi.org/10.1111/j.1744-7429.2011.00756.x

Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P., Paananen, T., & Gelman, A. (2023). *loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models*. R package version 2.6.0. https://mc-stan.org/loo/

Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Statistics and Computing*, 27(5), 1413-1432. https://doi.org/10.1007/s11222-016-9696-4

Virot, E., Ponomarenko, A., Dehandschoewercker, É., Quéré, D., & Clanet, C. (2016). Critical wind speed at which trees break. *Physical Review E*, 93(2), 023001. https://doi.org/10.1103/PhysRevE.93.023001

Walker, L. (1991). Tree damage and recovery from hurricane Hugo in Luquillo Experimental Forest, Puerto-Rico. *Biotropica*, 23(4), 375-385. https://doi.org/10.2307/2388255

Walker, L., Voltzow, J., Ackerman, J., Fernandez, D., & Fetcher, N. (1992). Immediate impact of hurricane Hugo on a Puerto-Rican rain-forest. *Ecology*, 73(2), 691-694. https://doi.org/10.2307/1940775

Wang, F., & Xu, Y. J. (2009). Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale. *Environmental Monitoring and Assessment*, 156(1), 491-507. https://doi.org/10.1007/s10661-008-0500-6

Wang, S., & Toumi, R. (2021). Recent migration of tropical cyclones toward coasts. *Science*, 371(6528), 514-517. https://doi.org/10.1126/science.abb9038

Webb, E. L., Stanfield, B. J., & Jensen, M. L. (1999). Effects of topography on rainforest tree community structure and diversity in American Samoa, and implications for frugivore and nectarivore populations. *Journal of Biogeography*, 26(4), 887-897. https://doi.org/10.1046/j.1365-2699.1999.00326.x

Webb, E. L., van de Bult, M., Fa'aumu, S., Webb, R. C., Tualaulelei, A., & Carrasco, L. R. (2014). Factors affecting tropical tree damage and survival after catastrophic wind disturbance. *Biotropica*, 46(1), 32-41. https://doi.org/10.1111/btp.12067

Wehner, M. F., & Kossin, J. P. (2024). The growing inadequacy of an open-ended Saffir-Simpson hurricane wind scale in a warming world. *Proceedings of the National Academy of Sciences of the United States of America*, 121(7), e2308901121. https://doi.org/10.1073/pnas.2308901121

Willoughby, H. E., Darling, R. W. R., & Rahn, M. E. (2006). Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles. *Monthly Weather Review*, 134(4), 1102-1120. https://doi.org/10.1175/mwr3106.1
<figure><img src="image_16.png" /></figure>

Zeng, H., Chambers, J. Q., Negrón-Juárez, R. I., Hurtt, G. C., Baker, D. B., & Powell, M. D. (2009). Impacts of tropical cyclones on US forest tree mortality and carbon flux from 1851 to 2000. *Proceedings of the National Academy of Sciences of the United States of America, 106*(19), 7888-7892. https://doi.org/10.1073/pnas.0808914106

Zimmerman, J. K., Everham, E. M., III, Waide, R. B., Lodge, D. J., Taylor, C. M., & Brokaw, N. V. L. (1994). Responses of tree species to hurricane winds in subtropical wet forest in Puerto Rico: Implications for tropical tree life histories. *Journal of Ecology, 82*(4), 911-922.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Ibanez, T., Bauman, D., Aiba, S.-i., Arsouze, T., Bellingham, P. J., Birkinshaw, C., Birnbaum, P., Curran, T. J., DeWalt, S. J., Dwyer, J., Fourcaud, T., Franklin, J., Kohyama, T. S., Menkes, C., Metcalfe, D. J., Murphy, H., Muscarella, R., Plunkett, G. M., Sam, C., ... Keppel, G. (2024). Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. *Global Change Biology*, 30, e17317. https://doi.org/10.1111/gcb.17317
<figure><img src="image_17.png" /></figure>

Zeng, H., Chambers, J. Q., Negrón-Juárez, R. I., Hurtt, G. C., Baker, D. B., & Powell, M. D. (2009). Impacts of tropical cyclones on US forest tree mortality and carbon flux from 1851 to 2000. *Proceedings of the National Academy of Sciences of the United States of America, 106*(19), 7888-7892. https://doi.org/10.1073/pnas.0808914106

Zimmerman, J. K., Everham, E. M., III, Waide, R. B., Lodge, D. J., Taylor, C. M., & Brokaw, N. V. L. (1994). Responses of tree species to hurricane winds in subtropical wet forest in Puerto Rico: Implications for tropical tree life histories. *Journal of Ecology, 82*(4), 911-922.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Ibanez, T., Bauman, D., Aiba, S.-i., Arsouze, T., Bellingham, P. J., Birkinshaw, C., Birnbaum, P., Curran, T. J., DeWalt, S. J., Dwyer, J., Fourcaud, T., Franklin, J., Kohyama, T. S., Menkes, C., Metcalfe, D. J., Murphy, H., Muscarella, R., Plunkett, G. M., Sam, C., ... Keppel, G. (2024). Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. *Global Change Biology*, 30, e17317. https://doi.org/10.1111/gcb.17317