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Expanding a Hurricane Wind Resistance Rating System for Tree Species

A peer-reviewed version of this preprint was published in:
Arboriculture & Urban Forestry 2025, jauf.2025.002. https://doi.org/10.48044/jauf.2025.002

Submitted:

22 April 2024

Posted:

23 April 2024

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Abstract
Background: Hurricanes and other wind events are significant disturbances that affect coastal urban forests around the world. Past research has led to the creation of wind resistance ratings for different tree species which can be used in urban forest management efforts to mitigate the effects of these storms. While useful, these ratings have been limited to species common to one global region (Florida, USA).Methods: Drawing on past ratings and data from a global literature review on tropical storm research, we created a machine learning model to broaden both the geographic coverage and the variety of species currently assessed for their resistance to wind.Results: We were able to assign wind resistance ratings to 281 new species based on the available data and our modelling efforts. The model accuracy and agreement with the original ratings when applied to the testing data set was high with 91% accuracy.Conclusions: The resulting list of wind resistance ratings has been adapted into a spreadsheet-based decision aid for managers, allowing them to assess the overall susceptibility of their urban forest to wind events like hurricanes.
Keywords: 
Cyclone
;  
Risk Management
;  
Species Selection
;  
Tree Failure
;  
Typhoon
Subject: 
Biology and Life Sciences  -   Forestry

Introduction

Hurricanes profoundly impact coastal communities and their urban forests. Extreme winds and flooding directly damage trees by breaking branches, knocking trees over, snapping trunks, or causing stress from prolonged inundation or salt exposure (e.g., Wang et al. 2000; Wiersma et al. 2012; Middleton 2016). In turn, broken trees can damage property and infrastructure, contributing to power outages and hindering emergency operations (Yum et al. 2020; Taylor et al. 2022). During cleanup after a hurricane, tree-generated debris removal can increase cleanup costs, significantly increasing the ecosystem disservices generated by the urban forest. For example, in Florida the 2004-2005 hurricane season produced an average of 233 m3 of debris per kilometer of street (Staudhammer et al. 2009). Furthermore, the cleanup process itself can lead to injuries among residents and professionals (Marshall et al. 2018). Ultimately, the loss of urban trees leads to a loss of ecosystem services (Olivero-Lora et al. 2022) and the harm damaged trees can cause to people, infrastructure, and property can all increase the public’s negative perception of trees (Wyman et al. 2012; Roman et al. 2020; Judice et al. 2021).
To mitigate some of the risks hurricanes pose to the urban forest, Duryea et al. (2007a, b) created a rating system that classified tree species based on their ability to resist hurricane wind damage. They based the rating system on observations of urban tree damage across Florida and Puerto Rico following the 2004-2005 hurricane season, encompassing 9 hurricanes. They also surveyed urban forest professionals in their region and asked them to estimate the wind resistance of common urban tree species (Cite). They combined the damage observations and expert opinions to rate 137 tree and palm species. This is the most comprehensive set of wind resistance ratings for urban trees, though the list focused on tree species common to Florida’s urban areas. Yet, hurricanes and typhoons pose risks to urban forests in many other regions of the world (e.g., Cole et al. 2021), highlighting the need for an expansion of the Duryea et al. (2007a, b) rating system.
Everham and Brokaw (1996) also compiled a list of tree species damaged by catastrophic winds (hurricanes, gales, severe windstorms) based on a literature review. They reported low, medium, and high damage ratings for 242 tree species. However, ratings were reported as originally published by the cited authors. No attempts were made to standardize these ratings to allow for formal comparisons of wind resistance across the entire collection of species. Their approach resulted in some species receiving multiple ratings; for example, Quercus virginiana Mill. was documented in four studies and given ratings of low (twice), medium, and high damage. While a substantial body of work, Everham and Brokaw’s collection only relied on damage observations from a wide range of forested environments and was not tailored to the conditions present in the urban forest. By contrast, Duryea et al. (2007a, b) designed their rating system explicitly for use by urban forestry professionals. As such, their rating system has been incorporated into planning documents such the City of Tampa, U.S.’s Tree Matrix (https://tampatreemap.org/tree-matrix).
Given both the utility and the limited geographic scope (e.g., Southeastern United States and the Carribean) of the work of Duryea et al. (2007a, b), our research aims to increase the number of tree species with wind resistance ratings beyond the original 137 species documented in their work. Since Duryea et al. (2007a, b) provided a limited description of the rating process and did not create clear definitions for each wind resistance category, we used a machine learning algorithm to mimic the original wind resistance rating system and create a model that could predict ratings for previously unrated species. We trained the model using a portion (70%) of the original Duryea et al. (2007a, b) tree species along with predictor variables such as intrinsic species traits, study site and hurricane characteristics, and observations of hurricane damage to a particular species. Once trained, we tested the model with other original species to validate its ability to produce similar ratings as the original system. Then we applied the model to previously unrated species with sufficient predictor data identified from a literature review. Finally, we combined the original and newly rated species into a spreadsheet tool for use by urban forestry professionals and community groups.

Methods

Predictor Selection

Many biotic and abiotic factors influence a tree’s ability to resist damage from hurricane force winds (Salisbury et al. 2023). However, for the purpose of developing our predictive model, we limited our choice of predictors based on the availability of data within the studies identified in our literature review or within other databases. Some model variables represent generalizations of the species while others try to capture variation among study sites.
We used the proportion of a species’ population damaged or killed by a hurricane as the most direct measure of a species’ ability to resist hurricane damage. Duryea et al. (2007a, b) used the proportion of mortality as one factor when assigning wind resistance ratings to species.
Following observations from several hurricanes in Puerto Rico, Lugo (2008) hypothesized that tree growth rate could represent a hurricane response syndrome that includes architecture, elastic modulus (i.e. the ability to return back to its original shape when bent), successional status, and wood density. Of these traits, only wood density is widely and consistently documented. Species with denser wood, greater modulus of rupture (i.e., the ability to withstand bending) and modulus of elasticity can be more resistant to hurricane damage (Duryea et al. 2007a, b; Nakamura 2020, Francis 2000, Curran et al. 2008). Granted, other biotic and abiotic factors can moderate the effects of wood density (e.g. Paz et al. 2018; Uriarte et al. 2019). Wood density also strongly correlates with other wood properties and captures many aspects of wood functions (Chave et al. 2009).
Several researchers have observed greater rates of hurricane damage to early successional or pioneer species which tend to be fast growers (Zimmerman et al. 1994; Ostertag et al. 2005; Canham et al. 2010). Yet, without a consistent definition of early, mid, and late successional species across a range of biomes and continents, successional status did not lend itself to predictive modeling. Instead, we selected leaf mass per unit area as a proxy variable since it tends to correlate with shade tolerance or successional status (Wright et al. 2004; Reich et al. 2014; Lichstein et al. 2021). Generally, species with low leaf mass per unit area tend to be fast-growing and intolerant of shade, or early successional, while higher leaf mass per unit area species tend to be slow-growing and tolerant of shade – characteristics associated with late successional species.
We used maximum height potential (as reported in the literature) as a predictor since taller trees are often (Foster 1988; Johnsen et al. 2009; Xi et al. 2015), though not always prone to more damage (Gao and Yu 2021; Landry et al. 2021). Most of our data sources did not include height data, so we used maximum height to generalize results at the species level. Observations of multiple types of catastrophic windstorms suggest gymnosperms (conifers) tend to be less wind resistant compared to angiosperms (Everham and Brokaw 1996; Gardiner 2021). Similarly, deciduous or semi-deciduous trees may have an advantage in high winds compared to evergreen species though this effect has not been consistently observed (Everham and Brokaw 1996; Van Bloem et al. 2005).
Since some regions of the world and biomes are more prone to hurricanes than others, we included biome, latitude, and longitude in the model. Hurricane disturbance history may also influence a site’s susceptibility to future hurricane damage in diverging ways: previous storms could remove susceptible trees leaving the population more resistant to future damage or gaps created by previous storms could expose remaining trees to additional turbulence in future storms (Everham and Brokaw 1996; Ostertag et al. 2005). To the best of our knowledge, no study has compared hurricane damage to tree species between urban and rural settings (i.e., trees in highly built environments and trees growing in large forest stands). Nevertheless, considering these are two distinct settings, we included urban or rural setting as a model variable to account for these differences among studies.

Systematic Literature Search

We conducted a systematic literature search to identify peer-reviewed research and dissertations that contained hurricane damage data at the species level. We searched for papers and dissertations published between 1900 and 2022 in English, Chinese (Mandarin), French, Japanese, Portuguese, and Spanish. We searched in several search engines and databases in addition to forestry-related journals that may not have been indexed in a particular database (see Supplementary Table S3 in Salisbury et al. 2023). The last search was conducted on May 5, 2022. Our core search string in English was “forest AND (hurricane OR cyclone OR typhoon)”, its translation and synonyms in the five additional focal languages can be found in Supplementary Table S2 in Salisbury et al. 2023.
We screened the results of our search using the following criteria to include papers in the dataset: 1) data collection occurred within two years of a tropical cyclone; 2) the only disaster studied was a tropical cyclone or tropical storm; 3) researchers used ground-based methods of data collection, as opposed to techniques such as aerial surveys; and 4) the paper reported data at the species level as a proportion of a population or sample and provided the scientific binomial name of the species. We excluded mangrove ecosystems since these species are not typically planted in managed urban habitats.
We rated methodological completeness by answering the following questions for each study; did the study: 1) collect data using a randomized study design or by conducting a complete inventory? 2) report observations of damage based on the type of damage (e.g., broken branches, snapped trunk)? 3) conduct an assessment of the tree’s condition or potential risk of failure? and 4) measure tree size? We assigned one point for each question that received a “yes,” for a total potential score of 4.
After screening, we extracted damage data and other relevant information from each study. When possible, we used Tabula (https://tabula.technology) to extract damage data in table form, otherwise we manually copied the data into spreadsheet form. We extracted data from figures using WebPlotDigitizer (https://automeris.io/WebPlotDigitizer/). For papers written in Spanish, Japanese, or Chinese, a multi-lingual team member checked the translation to English made by Google Translate. We also recorded the location of the study site, the tropical cyclone name, and method details using a spreadsheet. We classified damage data into one of four categories (Table 1) and each study as urban or rural. An urban study collected data within a city or town, either in a highly managed environment (e.g., street trees) or in a natural area located within an urban matrix. A rural study collected data within a natural area or timber plantation that had little to no potential impact from urban development. We excluded observations that were only made to the genus or family level.

Tropical Cyclone and Study Site Characteristics

We used data from the International Best Track Archive for Climate Stewardship (IBTrACS) to determine the maximum sustained wind speed for each tropical cyclone in our dataset (Knapp et al. 2010, 2018). This provided a consistent metric to compare studies using one facet of storm intensity. We also used IBTrACS to determine the amount of time that had elapsed between a study’s tropical cyclone and the previous tropical cyclone that had passed within 50 km of the study site. We determined the biome of each study site using the typology developed by Olson et al. (2001). Note: although a territory of the United States, we counted Puerto Rico separately from other U.S. study sites because of its distinct tropical habitats not found in the continental U.S.

Tree Species Characteristics

Prior to extracting species’ traits from several datasets (Table 2), the names of species identified in our literature review and species in the trait datasets were harmonized to the Leipzig Catalog of Vascular Plants taxonomic backbone (Freiberg et al. 2020) using the lcvplants package v.2.1.0 (Freiberg et al. 2020) in R v.4.2.2 (R Core Team 2022). We first harmonized species to the LCVP backbone using exact matching, then we used fuzzy matching for species without an exact fit. All fuzzy matched species were manually checked to ensure a reasonable match. When we could not match a species the LCVP backbone with reasonable certainty (many of the species did not include botanical authorities), we excluded the species from further analysis.
Many of the traits (e.g., leaf type, leaf mass per unit area, maximum plant height, and wood density) came from the TRY PlantTr database’s publicly available data (Kattge et al. 2020, Table 2). Prior to the analysis, we removed TRY observations from experimental settings (e.g., growth chambers, glasshouses, etc.). We also removed TRY observations which had an error risk greater than 4, meaning that the trait value was more than four standard deviations away from the mean for other close relatives – as suggested by the database creators (https://www.try-db.org/TryWeb/TRY_Data_Release_Notes.pdf). Occasionally, a species had multiple trait values in a dataset. In those cases, we calculated the mean trait value for the species and used that value in our analysis. If a species had multiple leaf types, we either assigned the leaf type with the most observations or deciduous/semideciduous.

Model Development

We used a random forest classification procedure to create a model that could predict Wind Resistance Ratings for species based on observations of tropical cyclone damage and species characteristics. Random forests are a powerful and flexible nonparametric technique that does not make assumptions about data following a particular distribution (Breiman 2001, Cutler et al. 2007). Since random forests make predictions based on the consensus of hundreds of models, they generally produce models with low bias and variance which are considered to more accurate and consistent.
To create a random forest model that could predict species’ wind resistance rating, we first collected all observations for the original tree species in Duryea et al. (2007a, b) which had a complete set of wood density, leaf mass per unit area, and maximum height trait data. We then randomly selected 70% of those observations for use as training data while the remaining 30% were test data. During the random selection process to split the data, we stratified data by wind resistance rating to ensure even representation of each classification group.
Model predictors included urban or rural setting, angiosperm or gymnosperm, time since previous tropical cyclone within a 50 km radius of the study site, the latitude and longitude of the study site, biome, leaf type, wood density, maximum plant height, leaf mass per unit area, and damage (Table 2). Observations were sparsely distributed between the four types of damage identified in the literature review (mortality, multiple damage types, stem failure, and root failure). Consequently, we consolidated the four damage types into a single variable (damage). After testing different permutations of the damage data, when multiple types of damage were reported for a single observation (e.g., mortality and root failure), we would first assign “multiple damage types” to the final damage value. If the observation lacked multiple damage types data, then we would use mortality data, followed by root failure, then stem failure.
We fit a random forests model to the training data using the “rf” method of the caret package in R (Kuhn 2022). We set the model to contain 1,000 random forest trees and we used 10-fold cross validation with 5 repeats when fitting the model to reduce model variance. Model tuning indicated that the model should test 8 variables at each node in a tree.
We subsequently tested model performance using the test dataset to determine overall accuracy, adjusted Cohen’s Kappa with equal weights to each response category using the DescTools package (Signorell et al. 2022), sensitivity, and specificity. Sensitivity is the ratio of true positives to all positive predictions while specificity is the ratio of true negatives to all negative predictions. These values were calculated for each wind resistance category. For example, the specificity of the Highest category would be the number of correctly classified Highest observations to the total number of all observations predicted to have a Highest rating. We assessed the importance of each predictor using caret’s “varImp” function, which calculates the total decrease in node impurity, measured by the Gini index, that results from splitting data on a given variable and then averages that decrease across all trees.

Model Application

After training and testing the final model, we applied it to new tree species identified in our literature review (Salisbury et al. 2023) that were not part of the original wind resistance ratings list (observations = 440, species = 281). Since only 43% of these species had a complete set of wood density, mature height, and leaf mass per unit area data, we utilized imputation with bagged trees (Towards Data Science, 2020) to estimate missing data values. Earlier model testing revealed that observations with imputed wood density or imputed leaf mass per unit area and maximum height did not produce reliable predictions. Consequently, observations with missing wood density or missing leaf mass per unit area and maximum height were excluded from the prediction process.
We evaluated the confidence of each classification by examining the predicted probability that an observation was assigned to a given Wind Resistance Rating. The greater the predicted probability, the greater the confidence in the classification. For ease of interpretation by future users, we assigned each species a categorical confidence rating of Low Confidence (predicted probability ≤ 0.33), Moderate Confidence (0.33 < predicted probability ≤ 0.66), or High Confidence (predicted probability > 0.66).
Many species had multiple observations of damage data from different studies and consequently each observation received a unique predicted rating. Only 18 species with multiple observations received more than one rating. For these cases, the species was assigned the rating with the highest predicted probability and was marked as having Low Confidence. In other cases where multiple observations for a species were all assigned the same rating, we assigned the species the confidence rating from the highest predicted probability.
We then combined the original and new species into a single table that serves as the foundation for the Estimating Tree Community Hurricane Resistance Tool (ETCHR, v.01). We created ETCHR v.01 as an Excel Workbook which can use our database of wind resistance ratings and a community’s inventory data to estimate the proportion of wind resistant species in a tree population.

Results

Literature Search

The broad search terms produced 5,449 studies, of which 58 passed the screening process and had appropriate data for the study (Table A1 and Table A2). We attribute the low percentage of retained studies to the extremely general search terms we used and the apparent inability of some databases we searched to effectively utilize Boolean operators. The final studies in English, Chinese, Japanese, and Spanish produced 1,094 observations of species-level damage data. The studies took place 15 countries and examined 42 unique tropical cyclones (Figure 2; Appendix Table A1). Out of the original collection of observations, 285 observations representing 213 species lacked sufficient trait data to be used in the study (Figure 1).

Model Performance

We trained the random forest model using data from 73 species extracted from 39 studies and then tested the model using data from 52 species and 32 studies. Note that some species had multiple observations of damage. The model accuracy and agreement with the original ratings when applied to the testing data set was fairly high; accuracy was 0.91 while adjusted Cohen’s Kappa was 0.91 (Table 3). Within the four ratings, the model performed best for Medium High and Highest species and performed more poorly for Lowest and Medium Low.
Wood density, maximum height, and leaf mass per unit area were the most important predictors in the random forest model (Figure 3). When one of those variables were included at a node, they were better at splitting the data so that subgroups contained observations with the same classifications. Percent damaged, latitude, and longitude were also moderately important predictors.
Species in the training and testing data set with a high or medium high rating tend to have greater wood density compared to those with lower ratings (Figure 4). By contrast, high species tend to have shorter maximum heights. The rating groups had similar average leaf mass per unit area, though the maximum leaf mass per unit area in the low group was much greater than the other groups. Unsurprisingly, the average extent of damage decreased with increasing wind resistance rating, though within all groups damage varied substantially. The wide variability of predictor variables within the ratings and lack of linear relationships highlight the value of using a classification based approach and the difficulty of relying on a single characteristic to predict wind resistance.

Ratings for New Species

We used the trained random forest model to assign wind resistance ratings to 281 new species we found in our literature search which had sufficient trait data (Appendix Table A2; data and original model also available at https://github.com/AllysonS/TreesForHurricanes). These species come from studies in the North Atlantic; Northwest and South Pacific; and North and South Indian tropical cyclone basins. They were studied in temperate conifer forests, tropical and subtropical moist broadleaf forests, tropical and subtropical dry broadleaf forests, and temperate broadleaf and mixed forests. Of these new species, 42% were assigned a low rating, 30% medium low, 14% medium high, and 14% high. both medium low and high wind resistance ratings had the greater proportion of species with high confidence in their predictions (23% and 21%, respectively, within each rating; Figure 5).
Twenty-two species were assigned more than one wind resistance rating since those species had data from multiple studies and we allowed the model to assign different ratings to different studies. We gave each of these species their final wind resistance rating based on the rating with the greatest predicted probability, then classified the species as having low confidence. These multi-rating species accounted for 24% of species with low confidence predictions. The majority of the other low confidence species received that classification because one or more of their traits was imputed prior to prediction. Examples of species with multiple ratings include Ficus religiosa L. from China, India, and Sri Lanka (Dittus 1985; Sundarapandian et al. 2014; Guo et al. 2020; Lin et al. 2017; Zhou and Dong 2018; Wang et al. 2000); Ginkgo biloba L. from Japan (Tabata et al 2020; Nakamura 2020); and Schefflera morototoni (Aubl.) Decne. & Planch. from Puerto Rico (Zimmerman et al. 1994; Francis 2000).

Tree Community Tropical Cyclone Resistance Calculator

We combined the newly rated species with the original ratings list into a spreadsheet tool (with associated guide and video tutorial) called the Estimating Tree Community Hurricane Resistance Tool (ETCHR, v.01, https://github.com/AllysonS/TreesForHurricanes). A community with a tree inventory can add their list of tree species and quantities into the "DataInput” tab of the spreadsheet. The Tool identifies the Wind Resistance Rating of the species from the inventory list and then calculates the proportion of the tree population that has Lowest, Medium Low, Medium High, and Highest ratings ("Summary” tab). Recognizing that tree resistance to tropical cyclone damage may vary by location and that new ratings may be inconsistent with practitioner experiences in different regions, the Tool gives users the option to adjust the rating based on local experience.
To provide an example application of the ETCHR Calculator, we evaluated three i-Tree inventories conducted in the City of Tampa, FL, USA in 2016 and 2021 (Table 4). The 2016 and 2021 inventories show consistent proportions of wind resistant species during this five-year period.

Discussion

Our analysis of the original rated species and new species demonstrated that our random forest model is a reasonable approach for predicting wind resistance ratings that align with original work by Duryea et al. (2007a, b). The random forest approach allowed us to accommodate many predictor variables which often had non-linear relationships with ratings groups (Figure 4). And importantly, the predictive model can be applied to other new species as trait and tropical cyclone damage data become available. It is possible that adding additional predictors could have further increased the performance of the model with the training data, however, trying to further improve model accuracy could have overfit the model and reduced its predictive capabilities (Kuhn and Johnson 2013).
The high importance value of wood density indicates that our model aligns well with the original Duryea et al.’s ratings. Wood density is a commonly reported trait and was one of the key tree characteristics that Duryea et al. (2007a, b) analyzed and considered in their determination of the ratings system. Other wood anatomy traits such as the modulus of rupture and wood fiber width can also predict tropical cyclone tree damage and other wind-based tree failures (Xu et al. 2014; Nakamura 2020; Gardiner 2021). However, for the purposes of prediction, wood density is a more widely reported trait and tends to be directly related to other wood characteristics (Chave et al. 2009). That noted, wood density and other mechanical properties and crown traits can vary within species that have broad ranges with varying exposure to windstorms (Cannon et al., 2023; Plourde et al. 2015).
The high importance values for leaf mass per unit area and max height emphasize that our predictive model is primarily driven by intrinsic characteristics and represents generalized predictions about species’ abilities to resist wind damage. The original rating system incorporated expert opinions, which provided substantial value to the rating system by capturing a broader range of experiences beyond the post-storm data collected by researchers (Duryea et al. 2007a, b). However, that approach was challenging to replicate for a large number of new species. Rather than directly incorporating expert opinions into the new predictive model, we incorporated a function in the ETCHR Tool to allow users to adjust the wind resistance rating based on expert knowledge of local conditions and species. In this way, our extension of the original rating system serves as a foundation for professionals to build on and tailor to their local environment.
There are several ways communities can utilize the wind resistance rating system to increase the resilience of their urban forests in the face of future tropical cyclones. Such activities can be considered mitigation, actions which preemptively eliminate or decrease the potential harm from a natural disaster (FEMA 2023). Many urban forestry and urban greening practices can facilitate recovery after natural disasters and with careful planning foster more resilient communities through recovery efforts (Campbell et al. 2019).
Many communities use urban forest management plans to set goals such as the extent of canopy cover or the diversity of tree species (Hauer and Peterson 2016). Output from the ETCHR Tool could be used to set and track goals related to the proportion of Medium High and High wind resistance rating species within a town or city. These goals could be achieved by incorporating Medium High and High species into new planting projects. Wind resistance ratings could be incorporated into forest climate change vulnerability assessments (e.g., Brandt et al. 2016). Many organizations use recommended species lists with details about site tolerances and species characteristics to encourage community members to plant the right tree in the right place (e.g., New York City Parks 2023; USF Water Institute 2023). Adding wind resistance ratings to such lists could help community members consider this characteristic when planting new trees.
Granted, the goal should not be achieving a tree community with 100% Medium High and Highest species as lower rated species are important in urban and rural forests. Indeed, maintaining functional diversity – a collection of species with a broad range of traits or characteristics – in urban forests minimizes vulnerability to changing climate and pest and disease outbreaks (Paquette et al, 2021). And in natural areas, fallen trees create gaps where younger trees establish, playing an important role in the life of the forest (Lugo 2008). Priority could be given to planting Medium High and High species in locations with high occupancy or high value targets, such as infrastructure or busy streets (Ellison 2005).
Conducting risk assessments and proactively pruning trees also contribute to mitigating hurricane damage to trees (Gilman et al. 2008; Koeser et al. 2020; Nelson et al. 2022). While resources for urban forestry programs can be limited compared to their needs (Hauer and Peterson 2016), wind resistance ratings could be used to complement other high volume risk assessment methods such as windshield surveys to identify trees with a high likelihood of failure (Rooney et al. 2005).
One drawback of the original rating system is the assignment of four wind resistance categories. Ideally, risk matrices clearly distinguish between very high and very low risk conditions but increasing the number of risk categories can muddy such distinctions (Cox 2008). And indeed, the properties of species in the Medium Low and Medium High categories tend to be similar (Figure 4). We maintained the original four categories to maintain consistency with the original research, though depending on local conditions, practitioners may find utility in combining the Medium Low and Medium High categories.
The application of the ETCHR Tool to the City of Tampa’s i-Tree inventories (Landry et al. 2023) demonstrates its potential in urban forest management as well as the need to continue expanding the wind resistance rating system to additional species since substantial portions of the inventories did not have wind resistance ratings. Notably, mangrove species, which lack wind resistance classifications, constituted a larger proportion of the 2011 inventory compared to 2016 and 2021 (50% versus 18% and 31%, respectively). This explains the larger percentage of trees with unknown wind resistance ratings in 2011. Because mangroves occupy coastal areas that bear the brunt of incoming storm surge (Sherman et al. 2001) and because they were not included in the original Duryea et al. (2007 a, b) rating system, we excluded them from our random forest model. Nevertheless, mangroves play an important role in subtropical and tropical coastal ecosystems (Barbier et al. 2011) and warrant further investigation to bring them into this wind resistance rating system.
To collect as many examples as possible to train a robust model, many of the species in this study come from rural settings and are not in nursery production. Nevertheless, the advantage of our modeling approach is that when data becomes available for unrated urban species, the model can use that new data to rate those species. Other research needs on this topic include examining the interaction between species’ wind resistance ratings and pruning techniques, and further evaluating the efficacy of practices intended to mitigate hurricane damage to urban trees.
While our work expands and helps synthesize past research on the wind resistance of trees, there are still gaps in our understanding. The literature referenced remains dominated by research published in English and focused on study sites in the Atlantic Ocean and Carribean Sea. The south Pacific is currently underrepresented, and we were unable to find research that met our criteria from Madagascar (Figure 2).
Finally, Duryea et al. (2007a, b) combined their post-hurricane field observations with a survey of the professional experiences of urban tree managers. It will be interesting so see if the predictions of our model reflect what our audience has witnessed in their post-storm cleanup efforts. We appreciate any feedback from readers who have worked in hurricane-prone areas.

Conclusion

Duryea et al. (2007a, b) developed a wind resistance rating system that arborists and urban foresters have used as a planning tool to improve species selection and identify species at greater risk of failure during hurricanes. In this paper, we demonstrated how a random forests predictive model can extend the original Duryea et al. rating system to include new tree species not observed in their original study. Our model assigned many new species a rating with moderate to high confidence, though ultimately future observations of hurricane damage to these species will support or refute these ratings. By sharing the model code, it can be adjusted to incorporate new data and further improve the rating system. We intend for our model and its interactive spreadsheet, ETCHR, to be an additional tool in the toolbox of urban forest hurricane mitigation strategies. As more storms occur in regions previously unstudied, our methods can be replicated to continue to expand our understanding or relative wind resistance ratings.

Acknowledgements

Funding for this research was provided by the Florida Forest Service.

Appendix A

Table A1. The quantities of studies found by the literature search and passed screening criteria, in addition to the number of unique tropical cyclones observed in the studies and the countries or territories where the study took place.
Table A1. The quantities of studies found by the literature search and passed screening criteria, in addition to the number of unique tropical cyclones observed in the studies and the countries or territories where the study took place.
LANGUAGE SEARCH MET CRITERIA OBSERVATIONS TROPICAL CYCLONES COUNTRIES/TERRITORIES
English 483 43 728 28 American Samoa, Australia, Hawaii, India, Japan, Mexico, Puerto Rico, Samoa, Solomon Islands, Sri Lanka, Tonga, conterminous U.S.
Chinese 97 9 199 8 China
French 140
Japanese 3709 3 60 3 Japan
Portuguese 72
Spanish 948 3* 107 3 Honduras, Mexico, Nicaragua
Total 5449 58 1094 42 15
* Note: One study was published in both Spanish and English.
Table A2. Research studies that documented hurricane damage to tree populations grouped by tropical cyclone basin. We used these studies as data sources for the predictive model.
Table A2. Research studies that documented hurricane damage to tree populations grouped by tropical cyclone basin. We used these studies as data sources for the predictive model.
North Atlantic North Pacific South Pacific
Basnet et al. 1992 Harrington et al. 1997 Burslem et al. 2000
Batista and Platt 2003 Bellingham et al. 1996 Elmqvist et al. 1994
Chapman et al. 2008 Guo et al. 2020 Franklin et al. 2004
Doyle et al. 1995 Huanglong 2002 Webb et al. 2014
Duryea et al. 2007a Ida and Nakagoshi 1997
Duryea et al. 2007b Lin et al. 2017
Francis 2000 Nakamura 2021
Gao and Yu 2021 Saito 2002 North Indian
Gresham et al. 1991 Sato et al. 2009 Dittus 1985
Harcombe et al. 2009 Tabata et al. 2020 Sundarapandian et al. 2014
Henkel et al. 2016 Tian et al. 2020
Howard 2012 Wang et al. 2000
Johnsen et al. 2009 Xu et al. 2008
Klein et al. 2020 Xu et al. 2014 South Indian
Koeser et al. 2020 Zhang et al. 2009 Curran et al. 2008
Kribel and Ware 2014 Zhou et al. 2018 Metcalfe et al. 2008
Middleton 2009
Negrón-Juárez et al. 2010
Ogle et al. 2006
Ostertag et al. 2005
Oswalt and Oswalt 2008
Pascarella 1997
Prengaman et al. 2008
Putz and Sharitz 1991
Rivas-Cooper 1999
Rodriguez-Robles et al. 1990
Rutledge et al. 2021
Sánchez Sánchez and Islebe 1999
Van Bloem et al. 2005
Vandecar et al. 2011
Vandermeer et al. 1990
Williams Linera et al. 2021
Xi 2005
Zimmerman et al. 1994
Table A3. Tree species with High wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. Asterisks (*) denote palm species from Duryea et al. (2007a, b). More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Table A3. Tree species with High wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. Asterisks (*) denote palm species from Duryea et al. (2007a, b). More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Scientific Name Confidence Scientific Name Confidence
Adonidia merrillii* na Kruglodendron ferreum na
Alstonia rostrata High Jupunba macradenia Moderate
Alstonia scholaris Low Lagerstroemia indica na
Amyris elemifera High Larix kaempferi Moderate
Astronium graveolens Moderate Latania toddigesii* na
Bombax ceiba High Livistona chinensis* na
Bursera simaruba na Luehea candida Low
Butia capitata* na Maclura tinctoria Moderate
Carya floridana na Magnolia grandiflora na
Ceiba pentandra High Manilkara hexandra High
Cenostigma gaumeri Low Melicoccus bijugatus Moderate
Coccothrinax argentata* na Metasequoia glyptostroboides Moderate
Conocarpus erectus na Myrsine seguinii Moderate
Chrysobalanus icaco na Osmanthus fragrans High
Cordia sebestena na Paraserianthes falcataria Moderate
Cornus florida na Phoenix canariensis* na
Dendropanax arboreus Low Phoenix dactylifera* na
Diospyros ferrea Low Phoenix reclinata* na
Dodonaea viscosa Moderate Phoenix roebelenii* na
Dypsis lutescens* na Photinia glabra High
Elaeocarpus angustifolius High Podocarpus spp. na
Eugenia axillaris na Ptychosperma elegans* na
Eugenia confuse na Quercus geminata na
Eugenia foetida na Quercus incana Moderate
Eugenia reinwardtiana Low Quercus laevis na
Exostema caribaeum Low Quercus margarettae High
Ficus macrophylla Moderate Quercus myrtifolia na
Ficus racemose High Quercus virginiana na
Fraxinus griffithii High Sabal palmetto* na
Geniostoma rupestre Low Senna atomaria Low
Guaiacum officinale Low Simarouba amara Moderate
Guaiacum sanctum na Taxodium distichum na
Guazuma ulmifolia Moderate Taxodium ascendens na
Gymnanthes lucida High Thouinia paucidentata Low
Heliocarpus donnellsmithii Moderate Thouinia striata Low
Hyophorbe lagenicaulis* na Thrinax morrissii* na
Hyophorbe verschaffeltii* na Thrinax radiata* na
Ilex cassine na Toxicodendron succedaneum Moderate
Ilex glabra na Vochysia ferruginea Moderate
Ilex opaca na Vochysia guatemalensis Moderate
Ilex vomitoria na
Table A4. Tree species with Medium High wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. Asterisks (*) denote palm species from Duryea et al. (2007a, b). More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Table A4. Tree species with Medium High wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. Asterisks (*) denote palm species from Duryea et al. (2007a, b). More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Scientific Name Confidence Scientific Name Confidence
Acacia crassicarpa Moderate Liquidambar styraciflua na
Acer floridanum na Litchi chinensis na
Acer palmatum na Lithocarpus longipedicellatus High
Acer pictum Moderate Lysiloma latisiliquum na
Albizia odoratissima Moderate Magnolia x soulangiana na
Annona glabra na Magnolia virginiana na
Aphananthe aspera Low Myrcia schiedeana Low
Artocarpus altilis Moderate Nageia nagi Moderate
Betula nigra na Nyssa aquatica na
Bischofia javanica Low Nyssa sylvatica na
Blastus cochinchinensis Moderate Ostrya virginiana na
Calophyllum antillanum na Pictetia aculeata Low
Calophyllum calaba Low Pistacia chinensis Moderate
Camellia oleifera Moderate Plectrocarpa arborea Low
Carpinus caroliniana na Pleiogynium timoriense Low
Carya aquatica Low Pometia pinnata Low
Carya glabra na Pouteria reticulata Moderate
Carya tomentosa na Prunus angustifolia na
Caryota mitis* na Quercus hemisphaerica Low
Casearia nitida Low Quercus michauxii na
Casearia thamnia Low Quercus myrsinifolia Moderate
Castanopsis fissa Moderate Quercus shumardii na
Castanospermum australe Moderate Quercus stellata na
Ceiba aesculifolia Low Roystonea elata* na
Cercis canadensis na Sassafras albidum High
Celtis sinensis Low Senna siamea Moderate
Chionanthus virginicus na Sideroxylon foetidissimum na
Chrysophyllum oliviforme na Simarouba glauca na
Coccoloba diversifolia na Swietenia mahagoni na
Coccoloba uvifera na Symplocos lancifolia Low
Cocos nucifera* na Syzygium buxifolium Low
Diospyros virginiana na Syzygium cumini Moderate
Dypsis decaryl* na Tabernaemontana arborea Moderate
Fraxinus americana na Terminalia tetraphylla Low
Hirtella triandra Moderate Trichilia trifolia Low
Inga coruscans Moderate Ulmus alata na
Lannea coromandelica Moderate
Table A5. Tree species with Medium Low wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Table A5. Tree species with Medium Low wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Scientific Name Confidence Scientific Name Confidence
Acacia mangium Moderate Guarea guidonia Moderate
Acer negundo na Juniperus chinensis Moderate
Acer rubrum na Kigelia pinnata na
Acer saccharinum na Lagerstroemia speciosa High
Acronychia acidula Low Leucaena leucocephala Moderate
Aegle marmelos High Ligustrum lucidum High
Anacardium occidentale Low Lindera kwangtungensis Moderate
Averrhoa carambola na Lithocarpus glaber Moderate
Azadirachta indica Moderate Machilus thunbergii Moderate
Bauhinia blakeana na Magnolia champaca Moderate
Betula platyphylla Moderate Mangifera indica na
Brosimum alicastrum Low Matayba domingensis Low
Brosimum utile Moderate Melia azedarach Low
Bucidas buceras na Meliosma angustifolia High
Callistemon spp. na Miconia elata Moderate
Calophyllum inophyllum High Morisonia flexuosa Moderate
Calophyllum neoebudicum Low Morus rubra na
Carapa guianensis Moderate Myrcia deflexa Low
Carya texana High Myrica cerifera na
Casearia sylvestris Moderate Myristica globosa Low
Cecropia peltata Moderate Olea europaea Moderate
Cedrus deodara Moderate Ormosia krugii Low
Celtis laevigata na Persea borbonia na
Celtis occidentalis na Pinus caribaea High
Cinnamomum camphora na Pinus echinata High
Chimarrhis parviflora Moderate Pinus elliottii na
Chukrasia tabularis High Pinus palustris na
Cinnamomum bejolghota Moderate Pinus serotina Moderate
Cinnamomum burmanni High Pinus taeda na
Citrus japonica Moderate Pinus thunbergii High
Citrus spp. na Piscidia piscipula Moderate
Cleyera japonica High Platanus x hispanica High
Coccoloba tuerckheimii Low Platanus occidentalis na
Coccoloba uvifera Moderate Platycladus orientalis High
Cochlospermum vitifolium Low Plumeria rubra Moderate
Colubrina arborescens High Populus heterophylla Moderate
Cryptocarya chinensis Moderate Pouteria campechiana Moderate
Dacryodes excelsa High Prunus serotina na
Damburneya coriacea High Psidium guajava Moderate
Delonix regia na Quecus alba na
Distylium racemosum Moderate Quercus laurifolia na
Drypetes lateriflora Moderate Quercus lyrata High
Enterolobium cyclocarpum na Quercus phellos na
Eriobotrya japonica na Quercus rubra High
Erythroxylum rotundifolium Low Quercus velutina High
Eucalyptus cinerea na Rockinghamia angustifolia Low
Eucalyptus tereticornis Moderate Salix nigra Moderate
Eucalyptus urophylla Low Salix x sepulcralis na
Fagus grandifolia High Sarcosperma laurinum Moderate
Ficus aurea na Symplocos sumuntia High
Ficus benghalensis High Syzygium jambos High
Flacourtia rukam Moderate Tabebuia heterophylla na
Fraxinus mandshurica Moderate Talipariti tiliaceum Low
Fraxinus pennsylvanica na Terminalia catappa na
Fraxinus profunda High Toona ciliata Moderate
Garcinia madruno Moderate Ulmus americana na
Ginkgo biloba Low Ulmus rubra Moderate
Gironniera subaequalis Moderate Vachellia farnesiana Moderate
Guarea glabra Moderate
Table A6. Tree species with Low wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. Asterisks (*) denote palm species from Duryea et al. (2007a, b). More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Table A6. Tree species with Low wind resistance ratings. Bolded species are our additions to the original Duryea et al. (2007a, b) rating lists. These new species come from hurricane studies that met our inclusion criteria (Appendix Table A1) and the ratings were assigned using our random forests model. Confidence indicates the probability a rating was correctly assigned according to the model. Asterisks (*) denote palm species from Duryea et al. (2007a, b). More detailed information for the species modeled in this study can be found at https://github.com/AllysonS/TreesForHurricanes.
Scientific Name Confidence Scientific Name Confidence
Acacia auriculiformis Moderate Laetia procera Moderate
Adina cordifolia Low Lepisanthes tetraphylla Low
Aglaia pinnata High Licania hypoleuca Moderate
Albizia julibrissin High Lindackeria laurina Moderate
Albizia procera High Liquidambar formosana Moderate
Alchornea latifolia Low Liriodendron tulipifera na
Aleurites moluccanus High Luehea alternifolia Low
Andira inermis Moderate Magnolia obovata Moderate
Apeiba membranacea Low Manilkara bidentata Low
Araucaria cunninghamii High Manilkara zapota Low
Araucaria heterophylla na Maranthes panamensis Low
Barringtonia asiatica Moderate Melaleuca quinquenervia na
Bridelia retusa Low Miconia tetrandra Low
Brosimum guianense Moderate Micromelum minutum Low
Brosimum lactescens Low Mitragyna parvifolia Low
Byrsonima crispa Low Morinda citrifolia Low
Byrsonima spicata Low Neea psychotrioides Moderate
Calophyllum brasiliense Moderate Ocotea leucoxylon Low
Cananga odorata Moderate Otoba novogranatensis Low
Carya illinoensis na Oxydendrum arboreum Moderate
Casearia arborea Low Pachira aquatica Moderate
Casearia commersoniana Moderate Persea americana na
Cassia fistula na Peltophorum pterocarpa na
Casuarina equisetifolia na Picea abies High
Catalpa bignonioides Moderate Pinus clausa na
Cespedesia spathulata Moderate Pinus glabra na
Chorisia speciosa na Pipturus argenteus Low
Cordia bicolor Low Planera aquatica Moderate
Cordia gerascanthus Low Populus x canadensis Moderate
Cordia sulcata Low Populus deltoides Moderate
Crescentia cujete Moderate Pourouma bicolor Moderate
Croton poecilanthus Low Protium pittieri Low
x Cupressocyparis leylandii na Protium stevensonii Moderate
Cupressus sempervirens High Prunus caroliniana na
Dimocarpus longan High Prunus jamasakura Low
Dipteryx oleifera Moderate Pseudolmedia spuria Low
Dussia macroprophyllata Low Psychotria asiatica Moderate
Erythrina variegata High Pterocarpus indicus Moderate
Eucalyptus robusta Moderate Pterocarpus officinalis Moderate
Eurya japonica Moderate Pyrus calleryana na
Fagus crenata Moderate Quassia amara Moderate
Ficus benjamina na Quercus acutissima Moderate
Ficus concinna High Quercus aliena Moderate
Ficus elastica Moderate Quercus falcata na
Ficus microcarpa Low Quercus laurifolia na
Ficus religiosa Low Quercus gilva Moderate
Ficus virens High Quercus glauca Low
Firmiana simplex Moderate Quercus nigra na
Fraxinus caroliniana Moderate Quercus serrata Moderate
Gliricidia sepium Moderate Robinia pseudoacacia Moderate
Grevillea robusta na Salix babylonica Moderate
Guarea bullata Low Sapindus mukorossi Moderate
Guarea grandifolia Moderate Sapium laurocerasus Low
Guarea kunthiana Moderate Sapium sebiferum na
Guarea pterorhachis High Schefflera morototoni Low
Gyrocarpus jatrophifolius Low Schleichera oleosa Low
Handroanthus chrysanthus Moderate Sloanea berteroana Low
Handroanthus impetiginosus Moderate Spathodea campanulata na
Heptapleurum actinophyllum Moderate Stereospermum colais Moderate
Heptapleurum heptaphyllum Moderate Styphnolobium japonicum High
Hernandia didymantha Low Swietenia macrophylla Moderate
Holoptelea integrifolia Low Syagrus romanzoffiana* na
Homalium racemosum Low Symphonia globulifera Moderate
Hymenaea courbaril High Tabebuia caraiba na
Ilex verticillata Moderate Tamarindus indicus Moderate
Inga laurina Moderate Tapirira guianensis Moderate
Inga pezizifera Moderate Tectona grandis Low
Inga thibaudiana Moderate Terminalia amazonia Low
Ipomoea wolcottiana Low Ulmus parvifolia na
Jacaranda mimosifolia na Washingtonia robusta na
Juniperus silicicola na Xylopia sericophylla Low
Khaya senegalensis Moderate Xylosma intermedia Moderate
Lacistema aggregatum Moderate

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Figure 1. Wind Resistance Rating model development process.
Figure 1. Wind Resistance Rating model development process.
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Figure 2. The location of rural and urban study sites for papers with species-damage data identified in the literature search. Light blue lines indicate the paths of tropical cyclones that have made landfall since 1970.
Figure 2. The location of rural and urban study sites for papers with species-damage data identified in the literature search. Light blue lines indicate the paths of tropical cyclones that have made landfall since 1970.
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Figure 3. Variable importance scores for the model predictors. A greater Mean Gini Decrease indicates a greater importance in the model.
Figure 3. Variable importance scores for the model predictors. A greater Mean Gini Decrease indicates a greater importance in the model.
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Figure 4. The distribution of percent damaged, leaf mass per unit area (LMA), maximum species height, and wood density among wind resistance ratings. Data from training and testing sets. L = low, ML = medium low, MH = medium high, H = high.
Figure 4. The distribution of percent damaged, leaf mass per unit area (LMA), maximum species height, and wood density among wind resistance ratings. Data from training and testing sets. L = low, ML = medium low, MH = medium high, H = high.
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Figure 5. The proportion of Confidence Levels within each Wind Resistance Rating category.
Figure 5. The proportion of Confidence Levels within each Wind Resistance Rating category.
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Table 1. Damage categories and definitions used to classify data extract from the literature review.
Table 1. Damage categories and definitions used to classify data extract from the literature review.
DAMAGE TYPE DEFINITION
MORTALITY (%) Trees considered dead.
ALL DAMAGE TYPES (%) Multiple types of damage that were combined (e.g., snapped trunks and uprooted).
ROOT FAILURE (%) Trees that fell over because root or root plate anchorage failed. Also described as tipped up, uprooted, or windthrow.
STEM FAILURE (%) Trees with broken or snapped main stem/trunk/bole.
Table 2. Predictors used in the random forests model.
Table 2. Predictors used in the random forests model.
PREDICTOR DEFINITION SOURCES
ANGIOSPERM OR GYMNOSPERM The tree type Multiple
BIOME General habitat type at study location Olson et al., 2001
DAMAGE Proportion of species that died or were damaged during a tropical cyclone Original source of data
LATITUDE Latitude of study site Original source of data
LEAF TYPE Leaf phenological type: evergreen or deciduous/semi-deciduous Kattge et al. 2020
LEAF MASS PER UNIT AREA Leaf mass per unit area in g/m2 Kattge et al. 2020
LONGITUDE Longitude of study site Original source of data
MAXIMUM PLANT HEIGHT Mean height at maturity (m) Moles et al. 2004; Kattge et al. 2020
PREVIOUS TROPICAL CYCLONE Time elapsed between the study’s focal storm and the previous tropical cyclone occurring within 50 km of the study site IBTrACs
URBAN OR RURAL General landscape setting of study Original source of data
WOOD DENSITY Mean wood density (ratio of dry wood weight to fresh volume; g/cm3) Zanne et al. 2009; Kattge et al. 2020
Table 3. Performance metrics for the testing dataset. Wind resistance rating accuracy across all data was calculated at 0.91 (0.84-0.96) with an adjusted Kappa of 0.91 (0.9-0.91).
Table 3. Performance metrics for the testing dataset. Wind resistance rating accuracy across all data was calculated at 0.91 (0.84-0.96) with an adjusted Kappa of 0.91 (0.9-0.91).
Sensitivity Specificity Positive Predictive Value Negative Predictive Value Balanced Accuracy Obs. Quantity
Lowest 0.83 0.98 0.92 0.94 0.9 29
Medium Low 0.9 0.93 0.88 0.94 0.92 42
Medium High 0.95 0.98 0.9 0.99 0.96 20
Highest 1 0.99 0.95 1 0.99 20
Table 4. The percentage of trees in each wind resistance rating category from the City of Tampa’s 2011, 2016, and 2021 i-Tree inventories determined using the ETCHR Tool.
Table 4. The percentage of trees in each wind resistance rating category from the City of Tampa’s 2011, 2016, and 2021 i-Tree inventories determined using the ETCHR Tool.
WIND RESISTANCE RATING 2016Y 2021Z
LOWEST 11% 6%
MEDIUM LOW 17% 17%
MEDIUM HIGH 9% 6%
HIGHEST 23% 20%
UNKNOWN 40% 51%
TOTAL ESTIMATED TREES 9,234,900 10,401,415
zLandry et al. 2023. yLandry et al. 2018.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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