Dry-Heat Tolerance of Common Tree Species in a Central Chinese City: Responses to Heterogeneous Habitat Characteristics

Dry-Heat Tolerance of Common Tree Species in a Central Chinese City: Responses to Heterogeneous Habitat Characteristics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dry-Heat Tolerance of Common Tree Species in a Central Chinese City: Responses to Heterogeneous Habitat Characteristics Li Zhang, Wenli Zhu, Xiaoyi Xing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4549982/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global warming has intensified the occurrence of dry heatwaves, and the heterogeneous urban habitats can influence trees' tolerance to dry-heat conditions, however the specific mechanisms remain unclear. This research investigated the dry-heat tolerance of nine common trees in Wuhan, a central Chinese city facing persistent heatwave threats, and explored the multifactorial influence of urban habitat variations on trees’ dry-heat tolerance. Diverse habitat sites with the sample plots in various spatial scales were established for each tree species. Dry-heat tolerance was evaluated through visual assessment, measuring the degree of leaf damage during a historic dry heatwave event. A significant interspecific difference was observed in trees’ dry-heat tolerance, with Pittosporum tobira exhibiting the highest dry-heat tolerance, while Prunus × yedoensis displayed the lowest, in relevance to the life forms and provenances of species. And the dry-heat tolerance of trees in a specific species varied across various habitats, influenced by surrounding building enclosure and land cover composition. The Degree of Building Enclosure was the most prominent habitat factor, followed by the Percentage of Canopy Coverage and the Percentage of Grove Area, all of which positively affected tree tolerance, in contrast with the Percentage of Impermeable Surface showing a negative effect. In addition, the scale effect was evident, that the 50m and 100m range were identified as the most effective spatial scale of campus habitats affecting trees’ dry-heat tolerance, especially for the species with weaker tolerance. The findings can support the selection of tree species and optimization of artificial habitats to enhance the heatwave resilience of urban vegetation in central Chinese cities. urban trees heatwave tolerance campus habitat land cover composition building enclosure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The introduction should not include subheadings. Urban trees have garnered considerable attention for their ecological benefits in carbon sequestration and microclimate moderation [ 1 – 3 ], which contribute to the migitation of climate risks in urban settlements[ 4 ]. However, the exacerbation of global warming has intensified the occurrence of extreme climate events, particularly heatwaves and droughts, posing unprecedented threats to the growth and adaptation of urban trees [ 5 , 6 ]. In recent years, heatwaves have often been accompanied by lower relative humidity, a phenomenon termed "dry heatwaves" [ 7 , 8 ]. Compared to the period before the 1970s, the frequency of simultaneous occurrence of summer heatwaves and droughts in China has increased tenfold in the 21st century [ 9 ]. And the historic dry heatwaves attacking the middle and lower reaches of the Yangtze River during the summer of 2022 has led to extensive forest mortality in Central China [ 10 ], which is anticipated to suffer even more from dry heatwaves in the future[ 11 ]. When trees experience heatwaves in conjection with drought conditions, additional challenges arise [ 12 ]. Dry heatwaves can exacerbate trees’ energy load and water deficienty, rendering them more susceptible to environmental stresses [ 13 , 14 ]. Urban trees exhibit substantial interspecific variability in their tolerance to dry heatwaves, referred to as “dry-heat tolerance” in abbreviation [ 15 ]. With the increasing prevalence of dry heatwave occurrences, the idenification of tree species possessing high dry-heat tolerance is crucial for enhancing the climate resilience of urban vegetation. Methods for assessing the responsive dry-heat tolerance of trees primarily involved physiological indicator measurement and visual tree assessment. Physiological measurement evaluates the dry-heat tolerance of tree individuals mainly based on parameters indicating photosynthetic and water use efficiency, conducted in controlled experiments or field investiations [ 16 – 19 ]. Visual tree assessment (VTA) evaluates tree vitality under climate stress by visually assessing the degree of morphological damage or abnormality for tree individuals, with higher vitality indicating greater tree tolerance [ 20 – 24 ]. Crown and leaf damage are considered important indicators for visual tree assessment, given that leaf damage is one of the most sensitive symptoms of plants in response to environmental hydrothermal stresses [ 25 , 26 ]. By scoring the percentage of leaf wiltering, scorch, discoloration, and defoliation, the morphorlogical tolerance of various urban tree species to heatwave or drought threats has been evaluated in Japan [ 27 ], China [ 28 ], Australia [ 19 , 29 ], and Central Europe [ 25 ]. Based on the long-term monitoring of crown dynamics in size, density, and epicormic growth, Callow evaluated the response characteristics of Holly Oak trees ( Quercus ilex ) to prolonged drought [ 26 ]. In addition to crown damage, damage symptoms on branches, stems, and other parts of the tree were also utilized in the tolerance assessment [ 30 – 32 ]. Visual tree assessment, as a straightforward and efficient method to evaluate trees’ health condition and morphological response to environmental stresses, can reflect the direct impacts of climate threats on the landscape of urban forests. Urban habitats act as predisposing factors influencing tree responses to climate change [ 23 ]. The adverse conditions prevalent in urban settings, such as shading, limited growing spaces, and conflicts with surrounding artificial infrastructure, can diminish tree vitality and further exacerbate the vulnerability of urban trees to climate change [ 33 – 36 ]. On the other hand, specific aspects of urban habitats may also provide refuge for certain tree species, potentially mitigating the adverse effects of climate change [ 37 ], and even offering essential resources for the conservation of threatened species [ 38 ]. Previous studies have examined the impacts of specific habitat charactersitics on the growth and adaptability of urban trees. Notable investigations have focused on variables such as the type of urban green spaces [ 39 – 41 ], as well as the impermeability of underlying surfaces Some scholars further explored how urban habitats influence trees’ responses to extreme climates [ 42 ]. For instance, Savi illustrated the correlation between the drought tolerance of Holm oak trees ( Quercus ilex ) and the proportion of impermeable surface at planting sites [ 43 ]. Rahman observed divergent transpiration water loss in small-leaved Lime trees ( Tilia cordata ) growing in different types of squares with varying degree of building enclosure under the summer heat stress in Munich, Germany [ 44 ]. The aforementioned studies mainly explored the impact of the variation in single habitat factor on the growth and acclimation of urban trees, however, the comprehensive impact mechanism of multiple habitat factors as a integrity remains unclear for typical urban settlements. And furthre investigation into the multifaceted comprehensive influence is imperative for illustrating how heterogeneous urban environment is shaping trees’ tolerance to climate threats. Among the diverse habitat types in urban built-up areas, campus habitats are distinguished by the integration of fragmented green-blue spaces, impervious surfaces and buildings, reflecting the typical characteristics of high-density habitat types such as residential and commercial areas [ 45 ]. Therefore, campus environments can offer ideal settings for exploring the influence of habiat features on tree heat tolerance in densely pupulated and constructed urban settlements. This study was conducted in a university campus green space in Wuhan, where heat risks are prevalent and escalating. Employing visual tree assessment method, we quantitatively measured the dry-heat tolerance of common urban tree species in response to a recent historic dry heatwave event, and explored the multifactorial impact mechanism of habitat characteristics on tree heat tolerance. Our objective is to elucidate the varying degrees of dry-heat tolerance among different tree species, reveal the key habitat factors influencing the dry-heat tolerance, and identify the spatial scales of habitat with the most prominent effects. The findings are expected to provide strategic insights for enhancing the climate resilience of urban vegetation in central Chinese cities. Material and Methods Research site The research site is located in Wuhan, China (29°58'N-31°22'N, 113°41'E-115°05'E), situated in the central region of the country on the plain of the middle reaches of the Yangtze River. The local forests are primarily constituted with evergreen and deciduous broad-leaved tree species, interspersed with some coniferous ones. Wuhan’s climate falls under the realm of the North Subtropical Monsoon Climate, characterized by chilly winters and scorching summers. According to the meteorology data from the Wuhan National Meteorological Observatory, the number of scorching days and the average temperature during the summer of 2022 in Wuhan were significantly higher than the long-term average, breaking the previous high-temperature records since 1961. A typical dry heatwave occurred from August 1st to August 23rd, with daily maximum temperatures consistently exceeding 35℃ and extreme peaks reaching 40℃, accompanied by no rainfall. The research was conducted in the educational and residential districts of Huazhong Agricultural University (HZAU), covering a flat terrain spanning 265 km 2 . As shown in Fig. 1 , the heterogeneous underlying surface composed of green-blue spaces, impervious surfaces and buildings, provides diverse habitats for tree growth. The campus hosts a diverse array of tree species, representative of common species in Wuhan. Many mature trees were planted 20–30 years ago, exhibiting consistent size and sourcing of seedling, as well as a robust state of health. These trees have endured harsh onslaught from the heatwave event in 2022 with less artificial intervention until responsive irrigation measures were implemented at the later phase of the heatwave stress. Research objects The research objects selected nine most common urban tree species in Wuhan, widely distributed across diverse habitats within the campus. These species include Pittosporum tobira (Japanese Cheesewood), Cercis chinensis (Chinese Redbud), Magnolia grandiflora (Southern Magnolia), Camphora officinarum (Camphor Tree), Prunus × yedoensis (Tokyo Cherry), Ginkgo biloba (Ginkgo), Platanus acerifolia (London Plane), Koelreuteria bipinnata (Chinese Flame Tree), and Metasequoia glyptostroboides (Dawn Redwood), as detailed in Table 1 . At least 5 sample trees were selected from each habitat site for every species, adhering to the following criteria: i) Mature individuals of similar ages, characterized with relatively consistent diameter at breast height and tree height. ii) Robust individuals without evident crown bias or damage, free from diseases and pests. Table 1 Information of the studied tree species Latin name abbreviation Life form Origin Areal-types of genera Pittosporum tobira Pto. Shrub Evergreen Native Old World Tropical Cercis chinensis Cch. Shrub Deciduous Exotic North Temperate Magnolia grandiflora Mgr. Arbor Evergreen Exotic East. Asia & North. American Disjuncted Camphora officinarum Cof. Arbor Evergreen Native Tropical Asia & Trop. Australasia Prunus × yedoensis Pve. Arbor Deciduous Exotic North Temperate Ginkgo biloba Gbi. Arbor Deciduous Native North Temperate Platanus acerifolia Pac. Arbor Deciduous Exotic North Temperate Koelreuteria bipinnata Kbi. Arbor Deciduous Native East. Asia Metasequoia glyptostroboides Mgl. Arbor Deciduous Native North Temperate “Origin” refers to the original natural distribution range of trees. Tree species are classified as indigenous species or exotic species based on whether they originate from China [ 46 ]. “Areal-type of genera” refers to the distinct geographical patterns of genera distribution, reflecting the ecological and evolutionary processes that influence where genera are found. The information of areal-types of genera for the studied species was derived from Wu [ 47 ]. Setting of Sample Plots For each tree species, eight habitat sites were selected, varying in the composition and structure of underlying surface. Sample plots with ranges of 50m, 100m, 150m, and 200m were established at each habitat site, as illustrated in Fig. 2 . The scale setting of the sample plots took reference from the revealed spatial scale of urban habitat in affecting microclimate [ 48 – 50 ], along with the characteristics of the sites' underlying surface. Evaluation of Tree Dry-Heat Tolerance The dry-heat tolerance of sample trees was evaluated using visual tree assessment, based on the degree of crown leaf damage or abnormality at the end of the 2022 heatwave event (before effective irrigation measures were implemented). This evaluation involved three symptom indicators: leaf discoloration, leaf wilting and leaf fall. Each indicator was scored from 0 to 10, with scoring criteria shown in Table 2 . Considering the varying severity of tree damage reflected by the three indicators — specifically, leaf fall > leaf discoloration > leaf wilting— they are weighted with 0.5, 0.3, and 0.2 respectively. And the weighted average score represents the dry-heat tolerance of each sample tree. Table 2 Indicator scoring criteria for evaluating the dry-heat tolerance of trees Indicators Score range Symptoms of tree leaf damage Leaf wilting 8 ~ 10 0 ~ 20% of the leaves are wilting, with slight curling at the edges. 6 ~ 8 20% ~ 40% of the leaves are wilting, with slight drooping at the edges, which can recover in the short term. 4 ~ 6 40% ~ 60% of the leaves are wilting and drooping, irreversibly. 2 ~ 4 60% ~ 80% of the leaves are wilting and drooping, irreversibly. 0 ~ 2 More than 80% of the leaves are wilting and drooping, irreversibly. Leaf discoloration 8 ~ 10 0 ~ 20% of the leaves turn yellowing, with overall normal appearance. 6 ~ 8 20% ~ 40% of the leaves turn yellow, with discoloration appearing along leaf tips and margins, accompanied by occasional scorched points. 4 ~ 6 40% ~ 60% of the leaves turn yellow, with intensified discoloration along leaf tips and margins, accompanied by marginal scorching. 2 ~ 4 60% ~ 80% of the leaves turn yellow, showing patchy scorching. 0 ~ 2 More than 80% of the leaves turn yellow, with extensive browning and desiccation. Leaf fall 8 ~ 10 0 ~ 20% of the leaves have fallen. 6 ~ 8 20% ~ 40% of the leaves have fallen. 4 ~ 6 40% ~ 60% of the leaves have fallen, exposing some small branches. 2 ~ 4 60% ~ 80% of the leaves have dropped, resulting in sparse canopy. 0 ~ 2 More than 80% of the leaves have fallen, leaving the branches completely bare. Evaluation of Habitat Characteristics Two categories of structural indicators, including land cover composition and spatial enclosure, were selected to quantify the characteristics of campus habitats. To evaluate the land cover composition, we categorized the underlying surface of campus habitats into different land cover types, and calculated the proportional area of each type in the sample plots. This enables us to derive seven habitat factors: percentage of canopy coverage (PerCC), percentage of grove area (PerGA), percentage of canopy-covered impervious surface area (PerCS), percentage of lawn area (PerLA), percentage of building area (PerBA), percentage of impervious surface area (PerIS), and percentage of water area (PerWA). Each factor is detailed in Table 3 . Table 3 The habitat factors indicating land cover composition Habitat Factors Abbreviation Definition Percentage of Canopy Coverage PerCC The percentage of the total area covered by vertical projection of tree crowns. Percentage of Grove Area PerGA The percentage of the total area of groves or forestland. Percentage of Canopy-Covered Impervious Surface Area PerCS The percentage of the total area of impervious surface covered with tree canopy. Percentage of Lawn Area PerLA The percentage of the total area of open lawn without canopy cover. Percentage of Building Area PerBA The percentage of the building footprint area. Percentage of Impervious Surface Area PerIS The percentage of the total area of impervious artificial surfaces, including roads and paved squares, without canopy cover. Percentage of Water Area PerWA The percentage of the total area of water bodies, such as lakes. The spatial enclosure characteristics of campus habitats was quantified by the degree of building enclosure (DegBE). Serving as a morphological indicator of external spaces, DegBE refers to the extent of all-directions enclosure primarily created by buildings surrounding trees within a specific spatial range. It is derived from aspect ratio (H/W) and plan enclosure rate (L/C) that are commonly utilized to evaluate the spatial enclosure of typical urban open spaces like street canyons [ 51 ], and calculated using formula (1). $$DegBE=\sum _{i=1}^{n}\frac{{H}_{i}}{{D}_{i}}*\frac{{\alpha }_{i}}{360} \left(1\right)$$ where \({H}_{i}\) respectively refers to the height of Building \(i\) (m), \({D}_{i}\) refers to the minimum horizontal distance between the sample tree and the facade of Building \(i\) (m), \({\alpha }_{i}\) refers to the horizontal enclosed angle between the sample tree and the facade of Building \(i\) (°), \(i\) refers to the number of buildings surrounding sample trees, as illustrated as Fig. 3 . The data for these parameters were obtained through field measurement and visual interpretation of drone aerial images. Statistical analysis The interspecific difference in dry-heat tolerance among urban tree species was investigated through one-way analysis of variance (ANOVA) and multiple comparisons. Linear regression analysis was employed to explore the influence of habitat characteristics on the dry-heat tolerance of sample trees for specific tree species. Univariate correlation analysis was conducted to identify key habitat factors posing significant impacts on the dry-heat tolerance, and multiple regression analysis was performed to elucidate the multifactorial impact mechanism of campus habitat. The optimal regression equation between habitat factors and tree tolerance was selected for each tree species at every spatial scale, based on the significance ( p ) and goodness of fit ( R 2 ) of the equation, which indicates the overall sensitivity of tree dry-heat tolerance to habitat variation. Higher sensitivity was identified when the equation was significant ( p < 0.05 ) with a higher R 2 value. Results Interspecific difference in the dry-heat tolerance of urban tree species A significant interspecific difference in dry-heat tolerance was observed among various tree species. Based on their dry-heat tolerance, the nine tree species can be classified into three groups. Group 1 includes P. tobira and M. grandiflora , exhibiting the highest dry-heat tolerance with mean values above 7; Group 2 consists of K. bipinnata , P. acerifolia , and C. officinarum , showing moderate dry-heat tolerance with mean values of 5–6; Group 3 consists of C. chinensis , M. glyptostroboides , G. biloba , and P. yedoensis , displaying lower dry-heat tolerance with mean values of 3–5, with G. biloba and P. yedoensis exhibiting the lowest. The dry-heat tolerance of species in Group 1 is significantly higher than that of Group 3, as depicted in Fig. 4 . Detailed values of dry-heat tolerance for different tree species in every habitat site are presented in Table 4 . The dry-heat tolerance of tree species was found to be correlated with their life forms and geographical provenance. Evergreen species exhibited significantly higher dry-heat tolerance than deciduous species ( p < 0.05 ), indicating distinct adaptive strategies. While shrub species generally displayed higher dry-heat tolerance than arbor species, this difference did not reach statistical significance. Regarding geographical provenance, native tree species exhibited insignificantly higher dry-heat tolerance compared to exotic species. Further evidence of geographical influence was seen in the tolerance variations observed among species with different areal-types of genera. Specifically, the dry-heat tolerance of tree species with genera originating from North Temperate region was notably lower compared to those from Old World Tropical region, East. Asia & North. American Disjuncted and Tropical Asia & Trop. Australasia region ( p < 0.05 ). This suggests that species with genera from tropical origins tend to demonstrate superior resilience in coping with heatwave stress. Table 4 Dry-heat tolerance of various tree species. Species Habitat code Dry-heat tolerance Mean Value Species Habitat code Dry-heat tolerance Mean Value Species Habitat code Dry-heat tolerance Mean Value G. biloba 1 7.33 3.74 ± 3.80 P. tobira 1 9.67 7.23 ± 2.20 M. glyptostroboides 1 8.67 4.02 ± 3.10 2 10.00 2 9.33 2 4.18 3 3.00 3 9.20 3 3.00 4 0.00 4 9.00 4 3.67 5 0.19 5 6.33 5 2.00 6 0.00 6 5.33 6 3.33 7 4.67 7 4.67 7 0.00 8 4.76 8 4.33 8 7.33 K. bipinnata 1 9.67 5.90 ± 2.53 C. chinensis 1 6.33 4.57 ± 2.40 C. officinarum 1 7.73 5.47 ± 2.34 2 6.33 2 6.00 2 2.00 3 4.75 3 5.33 3 3.74 4 3.09 4 5.00 4 7.41 5 8.67 5 5.00 5 7.33 6 2.67 6 3.33 6 4.67 7 5.33 7 1.00 7 6.67 8 6.67 — — 8 4.20 P. yedoensis 1 7.33 3.28 ± 2.81 P. acerifolia 1 8.00 5.80 ± 2.64 M. Grandiflora 1 8.33 7.14 ± 1.80 2 2.60 2 6.67 2 8.00 3 3.67 3 2.67 3 6.33 4 2.86 4 7.83 4 9.67 5 4.67 5 7.33 5 7.67 6 2.78 6 7.84 6 6.00 7 2.33 7 4.72 7 4.00 8 0.00 8 1.33 — — "—" indicates data loss due to the absence of available habitat sites. Response of trees’ dry-heat tolerance to habitat variation The intra-specific variation in the dry-heat tolerance of sample trees was clearly evident across different habitats, with the variation being most pronounced for G. biloba and least apparent for M. grandiflora . To unveil the mechanism underlying the spatial variation, we explored the influence of habitat characteristics on the dry-heat tolerance of trees using linear regression analysis. (1) Key habitat factors and their combined effects on trees’ dry-heat tolerance The correlation between trees’ dry-heat tolerance and various habitat factors was analyzed for specific tree species. Among these factors, DegBE (Degree of Building Enclosure) showed a significant positive impact on the dry-heat tolerance of trees for most species, especially within the 50m and 100m scales. Other influential factors include PerGA (Percentage of Grove Area), PerCC (percentage of Canopy Coverage), and PerIS (Percentage of Impervious Surface Area), as illustrated in Fig. 5 . DegBE, PerGA and PerCC showed positive effects, indicating their roles in mitigating dry-heat stress and enhancing the climate adaptation of trees. Conversely, PerIS exhibited a negative correlation with trees’ dry-heat tolerance, suggesting that impervious surfaces are detrimental for trees in coping with dry-heat stress. "—" indicates no significant correlation, "●" indicates a significant positive correlation at the 0.05 level, "●●" indicates a significant positive correlation at the 0.01 level, "●" indicates a significant negative correlation at the 0.05 level, and "●●" indicates a significant negative correlation at the 0.01 level. Multiple regression analysis was conducted to explore the multifactorial impacts of habitat features on trees’ dry-heat tolerance. Optimal regression equations for each species at various spatial scales were identified and presented in Table 5 . The findings revealed that the variation in dry-heat tolerance among different habitats can be mainly explained by the combined effects of 24 habitat factors about building enclosure and surrounding green spaces, with varying magnitudes of effect for different habitat factors. At the 50m scale, PerCC emerged as the primary factor for P. tobira , C. chinensis , and M. glyptostroboides , with DegBE, PerIS, and PerGA playing a critical role for other species. At the 100m, 150m and 200m scales, however, the factor of DegBE was dominant for most species, exerting the highest explanatory power for trees’ dry-heat tolerance, and other habitat factors acted as supplementary ones. Table 5 Multivariate regression equations of trees’ dry-heat tolerance against habitat factors for various tree species Species Spatial scale of sample plot Optimal multiple regression equation R 2 p P. tobira 50m Y = 0.77 PerCC* + 0.26 PerLA 0.676 0.026* 100m Y = 0.59 DegBE* + 0.48 PerCC – 0.27 PerCS 0.855 0.013* 150m Y = 0.87 DegBE* − 0.40 PerTS 0.717 0.018* 200m Y = 0.83 DegBE* − 0.15 PerIS 0.733 0.016* C. chinensis 50m Y = 0.92 PerCC** + 0.13 DegBE 0.956 0.001** 100m — — > 0.050 150m Y = 0.70 PerCS* − 0.37 PerGA 0.648 0.050* 200m — — > 0.050 G. biloba 50m Y = 0.72 DegBE** + 0.56 PerCC* + 0.29 PerIS 0.832 0.017* 100m Y = 0.94 DegBE** + 0.24 PerGA 0.785 0.009** 150m Y = 0.71 DegBE* − 0.31PerGA 0.779 0.010** 200m Y = 0.79 DegBE** + 0.31 PerGA + 0.25 PerIS 0.872 0.010** K. bipinnata 50m Y = 0.85 PerGA* -0.36 PerCS 0.772 0.011* 100m Y = 0.73 DegBE** − 1.03 PerIS** − 0.63 PerLA* 0.925 0.003** 150m Y = 0.65 DegBE* − 0.37 PerIS 0.575 0.050* 200m — — > 0.050 M.Glyptostrob -oides 50m Y = 0.69 PerCC** + 0.85 DegBE* − 0.75 PerBA* 0.950 0.002** 100m Y = 3.20 PerCC* − 0.76 PerBA* − 1.42 PerCS- 2.17 PerGA 0.825 0.049* 150m Y = 5.19 PerCC* − 2.91 PerCS* − 3.41 PerGA*- 0.63 PerLA 0.794 0.050* 200m — — > 0.050 P. yedoensis 50m Y = − 0.76 PerIS* − 0.22 DegBE 0.803 0.007** 100m Y = 0.59 DegBE* + 0.48 PerCC − 0.27 PerCS 0.855 0.013* 150m Y = 0.69 DegBE* + 0.32 PerCC 0.796 0.008** 200m Y = 0.83 DegBE** − 0.15 PerIS 0.733 0.016* P. acerifolia 50m Y = − 0.90 PerIS** − 0.35 PerCS* 0.876 0.002** 100m Y = 0.72 DegBE* + 0.60 PerLA + 0.56 PerBA 0.801 0.023* 150m Y = 0.75 DegBE* − 0.57 PerCS 0.710 0.050* 200m Y = 0.67 PerCC* − 0.37 PerLA 0.712 0.019* C. officinarum 50m Y = 0.93 DegBE** + 0.48 PerGA 0.738 0.015* 100m Y = 0.88 DegBE* + 0.34 PerGA 0.743 0.034* 150m Y = − 0.99 PerIS* − 0.56 PerBA 0.609 0.041* 200m Y = 0.88 PerCS* − 0.49 PerLA − 0.46 PerGA 0.723 0.044* M. grandiflora 50m — — > 0.050 100m Y = 0.91 PerCS* + 0.54 PerIS 0.760 0.050* 150m — — > 0.050 200m — — > 0.050 "**" denotes a significant correlation at the 0.01 level, "*" denotes a significant correlation at 0.05 level "—" indicates that no significant regression equations were available at the spatial scale. (2) Scale effects and interspecific difference in the sensitivity of dry-heat tolerance to habitat variation The significance and goodness of fit ( R ²) of the optimal regression equations were used to evaluate the overall sensitivity of a specific species’ dry-heat tolerance to habitat variation at specific spatial scales. This sensitivity varied across different spatial scales. The R² values of multiple species at the 50m and 100m scales were observed to be higher compared to the 200m scale ( p > 0.05 ) and 150m scale ( p < 0.05 ), indicating that overall, the dry-heat tolerance of trees is most sensitive to the habitat variation at the 50m and 100m scales, a pivotal spatial scale for campus habitat in affecting trees’ climate adaptation. Significant interspecific differences existed in the sensitivity of trees’ dry-heat tolerance in response to habitat variations. G. biloba , P. yedoensis , P. acerifolia , C. officinarum , and P. tobira all showed sensitive responses to habitat characteristics across four spatial scales, with average R² values of 0.82 ± 0.04, 0.80 ± 0.04, 0.77 ± 0.07, 0.75 ± 0.07, and 0.70 ± 0.05, respectively. M. glyptostroboides exhibited sensitivity to habitat characteristics at three spatial scales, with average R² value of 0.86 ± 0.06. Additionally, C. chinensis and K. bipinnata exhibited sensitivity to habitat characteristics at two spatial scales, with average R ² values of 0.80 ± 0.15, and 0.76 ± 0.14, respectively. And M. grandiflora only showed sensitivity to habitat characteristics at the 150m spatial scale, with an R ² value of 0.76. This suggests that the sensitivity of G. biloba , P. yedoensis , and M. glyptostroboides , which exhibit weaker dry-heat tolerance, to habitat variation is more pronounced, while M. grandiflora with the prominently higher dry-heat tolerance displayed the lowest sensitivity. Furthermore, the aforementioned sensitivity of a certain specie was found to be linked with its life form. The dry-heat tolerance of deciduous tree species exhibited higher sensitivity to habitat variations compared to evergreen tree species, especially with a significant difference at the 50m scale ( p < 0.05 ). Discussion Interspecific difference in the dry-heat tolerance of urban tree species This study elucidated the significant interspecies variability in the dry-heat tolerance of common urban tree species in Wuhan. Notably, P. tobira showed the highest dry-heat tolerance, whereas G. biloba and P. yedoensis exhibited the lowest, and the observed low tolerance of G. biloba to dry heatwaves aligns with the findings of Okubo [ 27 ]. Moreover, our investigation revealed that the dry-heat tolerance of tree species correlates with their life forms and geographical distribution. The observed higher dry-heat tolerance of evergreen tree species compared to deciduous species concurs with the conclusion drawn by Okubo [ 27 ]. This variation can be ascribed to the inherent capacity of evergreen species to sustain photosynthetic stability under hydrothermal stresses, especially heat threat [ 52 ]. This research further revealed the superior dry-heat tolerance of species with tropical-origin genera like P. tobira , compared to those with temperate-origin genera such as G. biloba and P. yedoensis . Previous studies have also unveiled a correlation between the heat tolerance of tree species and the climate attributes of their natural distribution areas, suggesting that the species originating from warmer regions had stronger adaptation to heat stress [ 29 , 53 , 54 ]. In terms of the difference in tolerance between native and exotic species, we found that native tree species exhibited higher dry-heat tolerance compared to exotic counterparts, although the difference was not significant, consistent with the findings of Esperon-Rodriguez [ 19 ]. However, different viewpoints from other studies indicated that native species may possess markedly superior adaptability to extreme climates compared to exotic ones [ 55 – 57 ], or vice versa [ 58 , 59 ]. Such divergent outcomes highlight the need for further exploration of the differences between indigenous and alien species in their climate adaptation against the backdrop of climate change. Influence of habitat factors on trees’ dry-heat tolerance The dry-heat tolerance of trees in urban habitats is influenced not only by inherent species characteristics, but also by the enclosure structure of open spaces and land cover composition of underlying surface. This study found that the degree of building enclosure (DegBE) posed a significant positive impact on trees’ dry-heat tolerance for most investigated species, consistent with the findings of Rahman [ 44 ]. They observed that Tilia cordata growing on a plaza with a circular enclosure by buildings showed lower transpiration water loss in response to summer heatwaves compared to those growing on a counterpart plaza enclosed on two sides, indicating a stronger heat resilience. This correlation is closely associated with the impact of spatial enclosure characteristics on wind environments. Decreasing spatial enclosure, i.e., increasing openness, leads to higher local wind speeds and vapor pressure deficit (VPD) [ 60 , 61 ], resulting in an increased transpiration rates of trees during hot weather conditions [ 62 , 63 ], which is unfavorable for the physiological stability of plants under heat stress. Moreover, given that the influence of building enclosure on local climates depends not only on its degree but also on its orientation [ 64 ], investigating how the direction of building enclosure affects the climate adaptation of urban trees could be a promising avenue for future research. In addition to DegBE, indicators of land cover composition, including the percentage of green space area such as grove area (PerGA) and canopy coverage (PerCC), as well as the percentage of impervious surface area (PerIS), were also found to significantly influence the dry-heat tolerance of trees in this research. PerGA and PerCC were proven to be significantly positively correlated with the heat tolerance of plants, primarily because urban tree canopies can effectively reduce ambient temperatures through transpiration and shading effects, thereby alleviating extreme heat stress [ 16 , 65 ]. In contrast, the correlation between PerIS and dry-heat tolerance was revealed to be negative, aligning with the findings of Savi, Dale, McClung & Ibanez, and Zhao & Wang [ 43 , 66 – 68 ]. The extensive hardening of underlying surface can impede rainwater infiltration and soil moisture replenishment, exacerbating water stress and hindering tree growth under the threat of heatwaves [ 39 ]. Besides, the low albedo of impervious surfaces during hot periods enhances heat absorption, causing a rapid rise in surface and near-ground air temperature [ 69 , 70 ], increasing canopy transpiration and exacerbating the climatic vulnerability of trees [ 66 ]. Targeting the influence of heterogeneous and fragmented urban habitats, previous studies mainly focused on the impact of single habitat factor on the climate adaptation of urban trees [ 39 , 43 , 44 ], while the comprehensive multifactorial effects have been less explored. This research revealed that trees’ dry-heat tolerance in a specific habitat can be largely explained by the combined effects of 2–4 key habitat factors, particularly DegBE and PerCC for most tree species, with PerCC having a more pronounced impact at the 50m scale, while DegBE is more prominent at the 100, 150m and 200m scales. The findings indicate that a habitat with building enclosure and extensive canopy coverage surrounding trees can potentially enhance their adaptation to dry heatwaves. Scale effects in the sensitivity of trees’ dry-heat tolerance to habitat variation Our research revealed that the sensitivity of trees’ dry-heat tolerance in response to habitat characteristics varied across different spatial scales, with most trees exhibiting the highest sensitivity to habitat variation at the spatial scale of 50 and 100 meters. Although some studies have illustrated the scale effects of urban habitat on influencing microclimates [ 50 , 71 ], very few have directly addressed the critical spatial scales at which habitat features can effectively impact tree climate adaptation. Given that habitat characteristics influence tree climate adaptation by affecting microclimates [ 72 , 73 ], the revealed scale effects of habitats in affecting microclimate can provide insights to their scale effects in shaping trees’ climatic adaptation. The optimal spatial range of habitat for effectively influencing microclimate varies across different habitat types, ranging from a radius of 20m in high-density urban residential areas [ 50 ] to a radius of approximately 150m for low-density suburban settlements [ 48 ]. This indicates potential habitat-type specificity in the critical spatial scale for affecting tree acclimation. This study specifically investigated the scale effects of campus habitats, and further exploration of other urban habitat types is warranted in future work. Limitations and Prospects Considering that the morphological characteristics of urban trees can intuitively reflect their response to extreme climates, this study utilized a visual tree assessment method to evaluate the dry-heat tolerance of urban trees based on the severity of leaf damage. While this approach facilitates efficient evaluation of trees’ adaptation to heatwave threats, it has certain limitations. During periods of extreme climate, trees initially undergo physiological changes, with morphological abnormality, such as leaf chlorosis and shedding, twig or branch die-back, and reduction in crown canopy, manifesting only after a certain tolerance threshold is exceeded [ 29 , 74 , 75 ]. Therefore, leaf damage symptoms may exhibit a delayed effect in detecting tree response to climate threats [ 76 ]. To decipher the timely responses of trees to hydrothermal threats throughout the entire stress period, it is essential to supplement visual assessment with physiological measurements, such as changes in photosynthesis and water potential, which presents the direction for future research. In addition, the structure and characteristics of urban habitats varied across different habitat types. Our investigation focused on the specific type of campus habitats, characterized by the integration of buildings, impervious surfaces, and green spaces, which are representative for the densely populated habitat types, such as residential and commercial areas. However, further exploration is needed to understand the influence of other urban habitat types, such as urban parks with their greenspace-dominated composition distinct from highly artificial environments, on the climate adaptation of urban trees. The frequency of heatwaves worldwide is projected to increase by 5–10 times over the next 40 years [ 77 ]. While artificial irrigation and shading can temporarily ease the heat and drought threat on urban trees, relying solely on high-density maintenance is insufficient and impractical for addressing long-term climate challenges, especially considering the sustainability of urban green spaces. Selecting climate-resilient tree species and optimizing tree habitats can offer sustainable approaches for improving the climate adaptation of urban vegetation. This study identified the varying dry-heat tolerance of common tree species in Wuhan, providing evidence for species selection to enhance the heatwave adaptation of urban species composition. We also elucidated the impact mechanism of typical artificial habitat factors, such as building enclosure and land cover composition, on the dry-heat tolerance of urban trees. This insight can inform the creation and optimization of climate-resilient habitats for local urban trees, especially the vulnerable ones, thereby enhancing their ability to withstand heatwaves and other climatic challenges. Conclusions This study investigated the dry-heat tolerance of common tree species and their responses to habitat variations in Wuhan. Significant interspecific differences were observed in the dry-heat tolerance, with P. yedoensis and G. biloba exhibiting the highest tolerance, while P. tobira and M. grandiflora displayed the lowest. The dry-heat tolerance of investigated species was associated with their life forms, with evergreen species showing significantly higher tolerance than deciduous species, and shrub species exhibiting higher tolerance than arbors, albeit with no significant difference. Additionally, the dry-heat tolerance of species was correlated with the climate attributes of their geographical provenance, with species originating from temperate regions exhibiting lower tolerance than those from tropical regions. While native tree species tended to show higher tolerance than exotic species, the difference was not statistically significant in our study. Besides species-specific traits, dry-heat tolerance was significantly influenced by building enclosure and land cover composition in urban habitats. Among habitat factors, the degree of buildings enclosure (DegBE) displayed the most pronounced positive impact, followed by percentage of grove area (PerGA) and canopy coverage (PerCC) with a positive effect, as well as impervious surface area (PerIS) with a negative effect. And the factors varied in their effect magnitudes when combinedly affecting trees’ dry-heat tolerance, with green space factors such as PerCC being the primary ones at the 50m scale and DegBE acting as the dominant factor at the 100m-200m scales for most species. The sensitivity of trees’ dry-heat tolerance to habitat variations varied across diverse spatial scales and different species, with the most prominent sensitivity observed at spatial scales of 100m and 50m, for species with weaker dry-heat tolerance such as P. yedoensis and G. biloba . These findings provide evidence supporting the selection of tree species and optimization of artificial habitats to enhance the heatwave resilience of urban vegetation in Wuhan and other cities in Central China. Declarations Acknowledgements I am deeply grateful to my supervisor, Xiaoyi Xing, for her invaluable guidance and unwavering support throughout this research. I also extend my thanks to my classmate, Wenli Zhu, for her assistance and insightful discussions. Funding: The research leading to these results received funding from The Fundamental Research Funds for the Central Universities, under Grant Agreement No 2662022YLQD002. Competing interests: The authors declare no competing interests related to this study. Data Availability: All data generated or analysed during this study are included in this published article. Code availability: No custom code was utilized in the analysis for this study. Author contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by WZ, XX and LZ. The first draft of the manuscript was written by LZ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Roy S, Byrne J, Pickering C. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban Forestry & Urban Greening . 2012; https://doi.org/10.1016/j.ufug.2012.06.006 Escobedo F, Palmas-Perez S, Dobbs C, et al. Spatio-temporal changes in structure for a Mediterranean urban forest: Santiago, Chile 2002 to 2014. Forests . 2016; https://doi.org/10.3390/f7060121 Greene CS, Robinson PJ, Millward AA. Canopy of advantage: Who benefits most from city trees? Journal of Environmental Management . 2017; https://doi.org/10.1016/j.jenvman.2017.12.015 Janowiak MK, Brandt LA, Wolf KL, et al. Climate adaptation actions for urban forests and human health. General Technical Report NRS-203 . 2021; https://doi.org/10.2737/NRS-GTR-203 Moore J, Pope J, Woods M, Ellis A. 2017 Aerial Survey Results: California. Forest Service . 2018; www.fs.usda.gov/detail/r5/forest-grasslandhealth. Accessed June 2018. Hartmann H, Bastos A, Das A J, et al. Climate change risks to global forest health: Emergence of unexpected events of elevated tree mortality worldwide. Annual Review of Plant Biology . 2022; https://doi.org/10.1146/annurev-arplant-102820-012804 Seo YW, Ha KJ, Park TW. Feedback attribution to dry heatwaves over East Asia. Environmental Research Letters . 2021; https://doi.org/10.1088/1748-9326/abf18f. Ha KJ, Seo YW, Yeo JH, et al. Dynamics and characteristics of dry and moist heatwaves over East Asia. Climate and Atmospheric Science . 2022; https://doi.org/10.1038/s41612-022-00272-4 Zscheischler J, Seneviratne SI. Dependence of drivers affects risks associated with compound events. Science Advances . 2017; https://doi.org/10.1126/sciadv.1700263 Zou X, Gao R, Chen X, et al. Monitoring and assessment of summer drought in the Yangtze River basin in 2022. China Flood & Drought Management . 2022; https://doi.org/10.16867/j.issn.1673-9264.2022417 Mei M, Gao G, Li Y, et al. Change characteristics in compound high temperature and drought extreme events over Yangtze River Basin from 1961 to 2022. Yangtze River . 2023; https://doi.org/10.16232/j.cnki.1001-4179.2023.02.003 Lalor AR, Law DJ, Breshears DD, Falk DA, Field JP, Loehman RA, Triepke FJ, Barron-Gafford GA. Mortality thresholds of juvenile trees to drought and heatwaves: implications for forest regeneration across a landscape gradient. Frontiers in Forests and Global Change . 2023; https://doi.org/10.3389/ffgc.2023.1198156 Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, et al. Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences . 2005; https://doi.org/10.1073/pnas.0505734102 Rouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warzée N. Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Annals of Forest Science .2006; https://doi.org/10.1051/forest:2006044 Zhang B, Brack CL. Urban forest responses to climate change: A case study in Canberra. Urban Forestry & Urban Greening . 2021; https://doi.org/10.1016/j.ufug.2020.126910 Gillner S, Vogt J, Tharang A, Dettmann S, Roloff A. Role of street trees in mitigating effects of heat and drought at highly sealed urban sites. Landscape and Urban Planning . 2015; https://doi.org/10.1016/j.landurbplan.2015.06.005 Gagne MA, Smith DD, McCulloh KA. Limited physiological acclimation to recurrent heatwaves in two boreal tree species. Tree Physiology . 2020; https://doi.org/10.1093/treephys/tpaa102 McClure AB, Coates TA, Hiers JK, Seiler JR, O'Brien JJ, Hoffman CM. Estimating heat tolerance of buds in southeastern US trees in fire-prone forests. Fire Ecology . 2022; https://doi.org/10.1186/s42408-022-00160-5 Esperon-Rodriguez M, Power SA, Tjoelker MG, Marchin RM, Rymer PD. Contrasting heat tolerance of urban trees to extreme temperatures during heatwaves. Urban Forestry & Urban Greening . 2021; https://doi.org/10.1016/j.ufug.2021.127387 Fite K. Impacts of root invigoration and its individual components on the performance of red maple ( Acer rubrum ). All Dissertations . 2008; https://tigerprints.clemson.edu/all_dissertations/260. Martinez-Trinidad T, Watson WT, Arnold MA, Lombardini L, Appel DN. Comparing various techniques to measure tree vitality of live oaks. Urban Forestry & Urban Greening . 2010; https://doi.org/10.1016/j.ufug.2010.02.003 Johnstone D, Moore G, Tausz M, Nicolas M. The measurement of plant vitality in landscape trees. Arboricultural Journal . 2013; https://doi.org/10.1080/03071375.2013.783746 Linhares CS, Gonçalves R, Martins LM, Knapic S. Structural stability of urban trees using visual and instrumental techniques: A review. Forests . 2021; https://doi.org/10.3390/f12121752 Takahashi K, Fukasawa Y. Association between corticolous myxomycetes and tree vitality in Cryptomeria japonica. Mycoscience . 2022; https://doi.org/10.47371/mycosci Schwenke MB, Schönlau V, Beierkuhnlein C. Tree sapling vitality and recovery following the unprecedented 2018 drought in central Europe. Forest Ecosystems . 2023; https://doi.org/10.1016/j.fecs.2023.100140 Callow D, May P, Johnstone DM. Tree vitality assessment in urban landscapes. Forests . 2018; https://doi.org/10.3390/f9050279 Okubo N, Inoue S, Ishii HR. Tolerance and acclimation of the leaves of nine urban tree species to high temperatures. Forests . 2023; https://doi.org/10.3390/f14081639 Li C, Su J, Liu X, et al. Comparison of ornamental and drought tolerance of different varieties of Rhododendron. Jiangsu Agricultural Sciences . 2019; https://doi.org/10.15889/j.issn.1002-1302.2019.22.031 Cunningham SC, Read J. Foliar temperature tolerance of temperate and tropical evergreen rainforest trees of Australia. Tree Physiology . 2006; https://doi.org/10.1093/treephys/26.11.1435 Fang J, Peng Z. Preliminary report on drought tolerance investigation of trees in Wuhan area. Journal of Huazhong Agricultural College . 1959; https://doi.org/10.13300/j.cnki.hnlkxb.1959.03.030 Zhang D, Li K, Liu M, Roloff A, Sun H. Approaches of drought-resistant woody species selection: A case study of Jinan, a semi-arid city in Shandong. Journal of Chinese Urban Forestry . 2019; https://doi.org/10.3969/j.issn.1672-4925.2018.00.002 Liu W. Evaluation and application of leaf scorch resistance of landscape tree species in Chongqing. (Master's thesis). Chongqing Jiaotong University. 2022; https://doi.org/10.27671/d.cnki.gcjtc.2022.001193 Nowak DJ, Stein SM, Randler PB, et al. Sustaining America's urban trees and forests: A Forests on the Edge report (General Technical Report NRS-62). Forest Service . 2010; https://doi.org/10.2737/NRS-GTR-62 Bussotti F, Pollastrini M, Killi DİLEK, Ferrini F, Fini A. Ecophysiology of urban trees in a perspective of climate change. Agrochimica . 2014; https://doi.org/10.12871/0021857201431 Locosselli GM, Miyahara AAL, Cerqueira P, Buckeridge MS. Climate drivers of tree fall on the streets of São Paulo, Brazil. Trees . 2021; https://doi.org/10.1007/s00468-021-02145-4 Manfra R, Massoca MS, Uras PMC, Cavalari AA, Locosselli GM. Average height of surrounding buildings and district age are the main predictors of tree failure on the streets of São Paulo/Brazil. Urban Forestry & Urban Greening . 2022; https://doi.org/10.1016/j.ufug.2022.127665 Soanes K, Lentini PE. When cities are the last chance for saving species. Frontiers in Ecology and the Environment . 2019; https://doi.org/10.1002/fee.2032 Ives CD, Lentini PE, Threlfall CG, et al. Cities are hotspots for threatened species. Global Ecology and Biogeography . 2015; https://doi.org/10.1111/geb.12404 Kjelgren RK, Clark JR. Microclimates and tree growth in three urban spaces. Journal of Environmental Horticulture . 1992; https://doi.org/10.24266/0738-2898-10.3.139 Zhao YY, Wang HY. Functional trait variation and its relationship with different habitats of woody plants in Chongqing. J ournal of Southwest University . 2019; https://doi.org/10.13718/j.cnki.xdzk.2019.11.002 Zhang Y, Tian Q, Huang R, Song L, Ma Y. Leaf functional traits of greening tree species and their tolerance to atmospheric pollution in heterogeneous urban habitats. Chinese Journal of Ecology . 2022; https://doi.org/10.13292/j.1000-4890.202210.010 Yu K, Van Geel M, Ceulemans T, et al. Foliar optical traits indicate that sealed planting conditions negatively affect urban tree health. Ecological Indicators . 2018; https://doi.org/10.1016/j.ecolind.2018.08.047 Savi T, Bertuzzi S, Branca S, et al. Drought-induced xylem cavitation and hydraulic deterioration: risk factors for urban trees under climate change? New Phytologist . 2015; https://doi.org/10.1111/nph.13112 Rahman MA, Moser A, Rötzer T, Pauleit S. Microclimatic differences and their influence on transpirational cooling of Tilia cordata in two contrasting street canyons in Munich, Germany. Agricultural and Forest Meteorology . 2017; https://doi.org/10.1016/j.agrformet.2016.10.006 Roman LA, Fristensky JP, Lundgren RE, Cerwinka CE, Lubar JE. Construction and Proactive Management Led to Tree Removals on an Urban College Campus. Forests . 2022;https://doi.org/10.3390/f13060871 Xu GY, Li HY, Mo XQ, et al. Composition and spatial-temporal distribution of Chinese naturalized plants. Chinese Journal of Plant Ecology . 2019; https://doi.org/10.17521/cjpe.2019.0053 Wu Z, Zhou Z, Sun H. The areal-types of seed plants and their origin and differentiation. Kunming: Yunnan Science and Technology Press . 2006. Yokohari M, Brown RD, Kato Y, Yamamoto S. The cooling effect of paddy fields on summertime air temperature in residential Tokyo, Japan. Landscape and Urban Planning . 2001; https://doi.org/10.1016/s0169-2046(00)00123-7 Sun CY. A street thermal environment study in summer by the mobile transect technique. Theoretical and Applied Climatology . 2011; https://doi.org/10.1007/s00704-011-0444-6 Yan H, Fan S, Guo C, Hu J, Dong L. Quantifying the Impact of Land Cover Composition on Intra-Urban Air Temperature Variations at a Mid-Latitude City. PLoS ONE . 2014; https://doi.org/10.1371/journal.pone.0102124 Zhang S, Chen Y. Degree of enclosure as outdoor space form parameter study based on the urban microclimate analysis: A case study on office building site design in Shanghai. Journal of East China Normal University . 2016; https://doi.org/10.3969/j.issn.1000-5641.2016.06.001 Colombo SJ, Timmer VR. Limits of tolerance to high temperatures causing direct and indirect damage to black spruce. Tree Physiology . 1992; https://doi.org/10.1093/treephys/11.1.95 Kumarathunge DP, Medlyn BE, Drake JE, Tjoelker MG. Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. New Phytol . 2019; https://doi.org/10.1111/nph.15668 Crous KY, Uddling J, De Kauwe MG. Temperature responses of photosynthesis and respiration in evergreen trees from boreal to tropical latitudes. New Phytologist . 2022; https://doi.org/10.1111/nph.17951 Zhou XH. Advantageous application analysis of native tree species in native landscaping. Contemporary Horticulture . 2019; https://doi.org/10.3969/j.issn.1673-9957.2013.08.214 Paudel I, Gerbi H, Wagner Y, Zisovich A, Sapir G, Brumfeld V, Klein T. Drought tolerance of wild versus cultivated tree species of almond and plum in the field. Tree Physiology . 2020; https://doi.org/10.1093/treephys/tpz134 Schmucker J, Uhl E, Schmied G, Pretzsch H. Growth and drought reaction of European hornbeam, European white elm, field maple and wild service tree. Trees . 2023; https://doi.org/10.1007/s00468-023-02441-1 Hoffmann N, Schall P, Ammer C, Leder B, Vor T. Drought sensitivity and stem growth variation of nine alien and native tree species on a productive forest site in Germany. Agricultural and Forest Meteorology . 2018; https://doi.org/10.1016/j.agrformet.2018.03.008 Vacek Z, Cukor J, Vacek S, et al. Production potential, biodiversity and soil properties of forest reclamations: Opportunities or risk of introduced coniferous tree species under climate change? European Journal of Forest Research . 2021; https://doi.org/10.1007/s10342-021-01392-x Tesfuhuney WA, Walker S, Van Rensburg LD, Steyn, AS. Micrometeorological measurements and vapour pressure deficit relations under in-field rainwater harvesting. Physics and Chemistry of the Earth . 2016; https://doi.org/10.1016/j.pce.2016.03.001 Montaldo N, Oren R. The way the wind blows matters to ecosystem water use efficiency. Agricultural and Forest Meteorology . 2016; https://doi.org/10.1016/j.agrformet.2015.11.002 Bush SE, Pataki DE, Hultine KR, West AG, Sperry JS, Ehleringer JR. Wood anatomy constrains stomatal responses to atmospheric vapor pressure deficit in irrigated urban trees. Oecologia . 2008; https://doi.org/10.1007/s00442-008-0966-5 Chen L, Zhang Z, Ewers BE. Urban tree species show the same hydraulic response to vapor pressure deficit across varying tree size and environmental conditions. PLOS ONE . 2012; https://doi.org/10.1371/journal.pone.0047882 Hebbert M. Urban microclimate designing the spaces between buildings. Urban design ; 2012. pp.49. Tracol Y, Gutierrez JR, Squeo FA. Plant area index and microclimate underneath shrub species from a Chilean semiarid community. Journal of Arid Environments . 2011; https://doi.org/10.1016/j.jaridenv.2010.08.002 Dale AG, Frank SD, Youngsteadt E, Fair B, Sherk J, Just M. Impervious surface thresholds and the pace to plant technique for planting urban red maple trees. EDIS . 2020; https://doi.org/10.32473/edis-in1185-2017 Mcclung T, Ibanez I. Quantifying the synergistic effects of impervious surface and drought on radial tree growth. Urban ecosystem . 2018; https://doi.org/10.1007/s11252-017-0699-5 Zhao YY, Wang HY. Functional trait variation and its relationship with different habitats of woody plants in Chongqing. Journal of Southwest University . 2019; https://doi.org/10.13718/j.cnki.xdzk.2019.11.002 Kim HH. Urban Heat Island. International Journal of Remote Sensing . 1992;13:319-336. Jiang YT, Fu P, Weng QH. Assessing the impacts of urbanization-associated land use/cover change on land surface temperature and surface moisture: a case study in the Midwestern United States. Remote Sens . 2015; https://doi.org/10.3390/rs70404880 Yan H, Yang S, Guo X, et al. Effects of Land Cover Composition and Structure on Air Temperature in Winter Based on the Local Climate Zone Scheme. Journal of Northwest Forestry University . 2023; https://doi.org/10.3969/j.issn.1001-7461.2023.03.30 Dobrowski SZ. A climatic basis for microrefugia: The influence of terrain on climate. Global Change Biology . 2011; https://doi.org/10.1111/j.1365-2486.2010.02263.x De Frenne P, Rodríguez-Sánchez F, Coomes, et al. Microclimate moderates plant responses to macroclimate warming. Proceedings of the National Academy of Sciences of the United States of America . 2013; https://doi.org/10.1073/pnas.1311190110 Vollenweider P, Günthardt-Goerg MS. Diagnosis of abiotic and biotic stress factors using the visible symptoms in foliage. Environmental Pollution . 2006; https://doi.org/10.1016/j.envpol.2006.01.002 Percival GC, Noviss K. Penconazole induced heat tolerance in scots pine ( pinus sylvestris ) and evergreen oak ( quercus ilex ). Arboriculture & Urban Forestry . 2010; https://doi.org/10.48044/jauf.2010.028 Percival GC. Heat tolerance of urban trees - A review. Urban Forestry & Urban Greening . 2023; https://doi.org/10.1016/j.ufug.2023.128021 Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, García-Herrera R. The hot summer of 2010: Redrawing the temperature record map of Europe. Science . 2011; https://doi.org/10.1126/science.1201224 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4549982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328331616,"identity":"864c9b06-f615-497b-bcc4-d0fb029916db","order_by":0,"name":"Li Zhang","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":328331617,"identity":"6337f8ac-c175-4b07-b861-d229999daebe","order_by":1,"name":"Wenli Zhu","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Zhu","suffix":""},{"id":328331618,"identity":"f9b624b3-fad5-4f77-80c5-3a0cdebdaead","order_by":2,"name":"Xiaoyi Xing","email":"data:image/png;base64,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","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2024-06-08 09:47:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4549982/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4549982/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60818346,"identity":"8d6e4ea6-c0b5-4b0c-83c8-af34f2138d39","added_by":"auto","created_at":"2024-07-22 12:27:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":387200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of research site (source: Google Map)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4549982/v1/cf87108a6ed009167734bf1b.png"},{"id":60818347,"identity":"8907484e-0c93-4dc6-b594-99376efb3f73","added_by":"auto","created_at":"2024-07-22 12:27:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":467943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe land cover composition in the sample plots of eight habitats for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCinnamomum officinarum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4549982/v1/3b400108419ea9ab3df02302.png"},{"id":60817539,"identity":"65179051-d4d8-43db-b40a-f0ae918e5922","added_by":"auto","created_at":"2024-07-22 12:19:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":220127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe illustration of parameters for the measurement of DegBE in a sample plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4549982/v1/f31eeaabca8420c44ddc1cef.png"},{"id":60817536,"identity":"7212e1e4-bedd-4c36-b331-b6e7920487de","added_by":"auto","created_at":"2024-07-22 12:19:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariations in dry-heat tolerance among different tree species\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePto.\u003c/em\u003e = \u003cem\u003ePittosporum tobira\u003c/em\u003e, \u003cem\u003eMgr. = Magnolia grandiflora\u003c/em\u003e, \u003cem\u003eKbi. = Koelreuteria bipinnata\u003c/em\u003e, \u003cem\u003ePac. = Platanus acerifolia\u003c/em\u003e, \u003cem\u003eCof. = Camphora officinarum\u003c/em\u003e, \u003cem\u003eCch. = Cercis chinensis\u003c/em\u003e, \u003cem\u003eMgl. = Magnolia grandiflora\u003c/em\u003e, \u003cem\u003eGbi. = Ginkgo biloba\u003c/em\u003e, \u003cem\u003ePve. = Prunus × yedoensis\u003c/em\u003e. The letters above the boxes (abc) indicate the significance of interspecific differences. The absence of shared letters between two species signifies a significant difference in their dry-heat tolerance.\u003cem\u003e \u003c/em\u003e(\u003cem\u003ep\u0026lt;0.05\u003c/em\u003e). The symbol (+) marks the mean value.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4549982/v1/558b365e95819c85c6846c08.png"},{"id":60817535,"identity":"5fe6ee6a-828a-4b3f-a83b-7fe54e020912","added_by":"auto","created_at":"2024-07-22 12:19:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":280558,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4549982/v1/de428639ef30c3cfb7af18fc.png"},{"id":68966469,"identity":"aa6f0973-301c-4330-bd19-15745fbb2e65","added_by":"auto","created_at":"2024-11-14 04:32:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2529372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4549982/v1/afecab76-5d57-4de8-ac65-181790f163dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dry-Heat Tolerance of Common Tree Species in a Central Chinese City: Responses to Heterogeneous Habitat Characteristics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe introduction should not include subheadings. Urban trees have garnered considerable attention for their ecological benefits in carbon sequestration and microclimate moderation [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which contribute to the migitation of climate risks in urban settlements[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the exacerbation of global warming has intensified the occurrence of extreme climate events, particularly heatwaves and droughts, posing unprecedented threats to the growth and adaptation of urban trees [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, heatwaves have often been accompanied by lower relative humidity, a phenomenon termed \"dry heatwaves\" [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Compared to the period before the 1970s, the frequency of simultaneous occurrence of summer heatwaves and droughts in China has increased tenfold in the 21st century [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. And the historic dry heatwaves attacking the middle and lower reaches of the Yangtze River during the summer of 2022 has led to extensive forest mortality in Central China [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which is anticipated to suffer even more from dry heatwaves in the future[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. When trees experience heatwaves in conjection with drought conditions, additional challenges arise [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Dry heatwaves can exacerbate trees\u0026rsquo; energy load and water deficienty, rendering them more susceptible to environmental stresses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUrban trees exhibit substantial interspecific variability in their tolerance to dry heatwaves, referred to as \u0026ldquo;dry-heat tolerance\u0026rdquo; in abbreviation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. With the increasing prevalence of dry heatwave occurrences, the idenification of tree species possessing high dry-heat tolerance is crucial for enhancing the climate resilience of urban vegetation. Methods for assessing the responsive dry-heat tolerance of trees primarily involved physiological indicator measurement and visual tree assessment. Physiological measurement evaluates the dry-heat tolerance of tree individuals mainly based on parameters indicating photosynthetic and water use efficiency, conducted in controlled experiments or field investiations [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Visual tree assessment (VTA) evaluates tree vitality under climate stress by visually assessing the degree of morphological damage or abnormality for tree individuals, with higher vitality indicating greater tree tolerance [\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Crown and leaf damage are considered important indicators for visual tree assessment, given that leaf damage is one of the most sensitive symptoms of plants in response to environmental hydrothermal stresses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. By scoring the percentage of leaf wiltering, scorch, discoloration, and defoliation, the morphorlogical tolerance of various urban tree species to heatwave or drought threats has been evaluated in Japan [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], China [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Australia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and Central Europe [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Based on the long-term monitoring of crown dynamics in size, density, and epicormic growth, Callow evaluated the response characteristics of Holly Oak trees (\u003cem\u003eQuercus ilex\u003c/em\u003e) to prolonged drought [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition to crown damage, damage symptoms on branches, stems, and other parts of the tree were also utilized in the tolerance assessment [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Visual tree assessment, as a straightforward and efficient method to evaluate trees\u0026rsquo; health condition and morphological response to environmental stresses, can reflect the direct impacts of climate threats on the landscape of urban forests.\u003c/p\u003e \u003cp\u003eUrban habitats act as predisposing factors influencing tree responses to climate change [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The adverse conditions prevalent in urban settings, such as shading, limited growing spaces, and conflicts with surrounding artificial infrastructure, can diminish tree vitality and further exacerbate the vulnerability of urban trees to climate change [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. On the other hand, specific aspects of urban habitats may also provide refuge for certain tree species, potentially mitigating the adverse effects of climate change [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and even offering essential resources for the conservation of threatened species [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have examined the impacts of specific habitat charactersitics on the growth and adaptability of urban trees. Notable investigations have focused on variables such as the type of urban green spaces [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], as well as the impermeability of underlying surfaces Some scholars further explored how urban habitats influence trees\u0026rsquo; responses to extreme climates [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For instance, Savi illustrated the correlation between the drought tolerance of Holm oak trees (\u003cem\u003eQuercus ilex\u003c/em\u003e) and the proportion of impermeable surface at planting sites [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Rahman observed divergent transpiration water loss in small-leaved Lime trees (\u003cem\u003eTilia cordata\u003c/em\u003e) growing in different types of squares with varying degree of building enclosure under the summer heat stress in Munich, Germany [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aforementioned studies mainly explored the impact of the variation in single habitat factor on the growth and acclimation of urban trees, however, the comprehensive impact mechanism of multiple habitat factors as a integrity remains unclear for typical urban settlements. And furthre investigation into the multifaceted comprehensive influence is imperative for illustrating how heterogeneous urban environment is shaping trees\u0026rsquo; tolerance to climate threats. Among the diverse habitat types in urban built-up areas, campus habitats are distinguished by the integration of fragmented green-blue spaces, impervious surfaces and buildings, reflecting the typical characteristics of high-density habitat types such as residential and commercial areas [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Therefore, campus environments can offer ideal settings for exploring the influence of habiat features on tree heat tolerance in densely pupulated and constructed urban settlements.\u003c/p\u003e \u003cp\u003eThis study was conducted in a university campus green space in Wuhan, where heat risks are prevalent and escalating. Employing visual tree assessment method, we quantitatively measured the dry-heat tolerance of common urban tree species in response to a recent historic dry heatwave event, and explored the multifactorial impact mechanism of habitat characteristics on tree heat tolerance. Our objective is to elucidate the varying degrees of dry-heat tolerance among different tree species, reveal the key habitat factors influencing the dry-heat tolerance, and identify the spatial scales of habitat with the most prominent effects. The findings are expected to provide strategic insights for enhancing the climate resilience of urban vegetation in central Chinese cities.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch site\u003c/h2\u003e \u003cp\u003eThe research site is located in Wuhan, China (29\u0026deg;58'N-31\u0026deg;22'N, 113\u0026deg;41'E-115\u0026deg;05'E), situated in the central region of the country on the plain of the middle reaches of the Yangtze River. The local forests are primarily constituted with evergreen and deciduous broad-leaved tree species, interspersed with some coniferous ones. Wuhan\u0026rsquo;s climate falls under the realm of the North Subtropical Monsoon Climate, characterized by chilly winters and scorching summers. According to the meteorology data from the Wuhan National Meteorological Observatory, the number of scorching days and the average temperature during the summer of 2022 in Wuhan were significantly higher than the long-term average, breaking the previous high-temperature records since 1961. A typical dry heatwave occurred from August 1st to August 23rd, with daily maximum temperatures consistently exceeding 35℃ and extreme peaks reaching 40℃, accompanied by no rainfall.\u003c/p\u003e \u003cp\u003eThe research was conducted in the educational and residential districts of Huazhong Agricultural University (HZAU), covering a flat terrain spanning 265 km\u003csup\u003e2\u003c/sup\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the heterogeneous underlying surface composed of green-blue spaces, impervious surfaces and buildings, provides diverse habitats for tree growth. The campus hosts a diverse array of tree species, representative of common species in Wuhan. Many mature trees were planted 20\u0026ndash;30 years ago, exhibiting consistent size and sourcing of seedling, as well as a robust state of health. These trees have endured harsh onslaught from the heatwave event in 2022 with less artificial intervention until responsive irrigation measures were implemented at the later phase of the heatwave stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch objects\u003c/h2\u003e \u003cp\u003eThe research objects selected nine most common urban tree species in Wuhan, widely distributed across diverse habitats within the campus. These species include \u003cem\u003ePittosporum tobira\u003c/em\u003e (Japanese Cheesewood), \u003cem\u003eCercis chinensis\u003c/em\u003e (Chinese Redbud), \u003cem\u003eMagnolia grandiflora\u003c/em\u003e (Southern Magnolia), \u003cem\u003eCamphora officinarum\u003c/em\u003e (Camphor Tree), \u003cem\u003ePrunus \u0026times; yedoensis\u003c/em\u003e (Tokyo Cherry), \u003cem\u003eGinkgo biloba\u003c/em\u003e (Ginkgo), \u003cem\u003ePlatanus acerifolia\u003c/em\u003e (London Plane), \u003cem\u003eKoelreuteria bipinnata\u003c/em\u003e (Chinese Flame Tree), and \u003cem\u003eMetasequoia glyptostroboides\u003c/em\u003e (Dawn Redwood), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. At least 5 sample trees were selected from each habitat site for every species, adhering to the following criteria: i) Mature individuals of similar ages, characterized with relatively consistent diameter at breast height and tree height. ii) Robust individuals without evident crown bias or damage, free from diseases and pests.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of the studied tree species\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eabbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLife form\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAreal-types of genera\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePittosporum tobira\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePto.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShrub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvergreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld World Tropical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCercis\u0026nbsp;chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCch.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShrub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeciduous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNorth Temperate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMagnolia grandiflora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMgr.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvergreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast. Asia \u0026amp; North. American Disjuncted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCamphora\u0026nbsp;officinarum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCof.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvergreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTropical Asia \u0026amp; Trop. Australasia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus\u0026nbsp;\u0026times; yedoensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePve.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeciduous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNorth Temperate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGinkgo\u0026nbsp;biloba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGbi.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeciduous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNorth Temperate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlatanus acerifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePac.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeciduous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNorth Temperate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKoelreuteria\u0026nbsp;bipinnata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKbi.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeciduous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast. Asia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMetasequoia\u0026nbsp;glyptostroboides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMgl.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeciduous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNorth Temperate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026ldquo;Origin\u0026rdquo; refers to the original natural distribution range of trees. Tree species are classified as indigenous species or exotic species based on whether they originate from China [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u0026ldquo;Areal-type of genera\u0026rdquo; refers to the distinct geographical patterns of genera distribution, reflecting the ecological and evolutionary processes that influence where genera are found. The information of areal-types of genera for the studied species was derived from Wu [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSetting of Sample Plots\u003c/h2\u003e \u003cp\u003eFor each tree species, eight habitat sites were selected, varying in the composition and structure of underlying surface. Sample plots with ranges of 50m, 100m, 150m, and 200m were established at each habitat site, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The scale setting of the sample plots took reference from the revealed spatial scale of urban habitat in affecting microclimate [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], along with the characteristics of the sites' underlying surface.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Tree Dry-Heat Tolerance\u003c/h2\u003e \u003cp\u003eThe dry-heat tolerance of sample trees was evaluated using visual tree assessment, based on the degree of crown leaf damage or abnormality at the end of the 2022 heatwave event (before effective irrigation measures were implemented). This evaluation involved three symptom indicators: leaf discoloration, leaf wilting and leaf fall. Each indicator was scored from 0 to 10, with scoring criteria shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Considering the varying severity of tree damage reflected by the three indicators \u0026mdash; specifically, leaf fall\u0026thinsp;\u0026gt;\u0026thinsp;leaf discoloration\u0026thinsp;\u0026gt;\u0026thinsp;leaf wilting\u0026mdash; they are weighted with 0.5, 0.3, and 0.2 respectively. And the weighted average score represents the dry-heat tolerance of each sample tree.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicator scoring criteria for evaluating the dry-heat tolerance of trees\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms of tree leaf damage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeaf wilting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20% of the leaves are wilting, with slight curling at the edges.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026thinsp;~\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20% ~ 40% of the leaves are wilting, with slight drooping at the edges, which can recover in the short term.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40% ~ 60% of the leaves are wilting and drooping, irreversibly.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;~\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60% ~ 80% of the leaves are wilting and drooping, irreversibly.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore than 80% of the leaves are wilting and drooping, irreversibly.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeaf discoloration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20% of the leaves turn yellowing, with overall normal appearance.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026thinsp;~\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20% ~ 40% of the leaves turn yellow, with discoloration appearing along leaf tips and margins, accompanied by occasional scorched points.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40% ~ 60% of the leaves turn yellow, with intensified discoloration along leaf tips and margins, accompanied by marginal scorching.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;~\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60% ~ 80% of the leaves turn yellow, showing patchy scorching.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore than 80% of the leaves turn yellow, with extensive browning and desiccation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeaf fall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20% of the leaves have fallen.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026thinsp;~\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20% ~ 40% of the leaves have fallen.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40% ~ 60% of the leaves have fallen, exposing some small branches.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;~\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60% ~ 80% of the leaves have dropped, resulting in sparse canopy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore than 80% of the leaves have fallen, leaving the branches completely bare.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Habitat Characteristics\u003c/h2\u003e \u003cp\u003eTwo categories of structural indicators, including land cover composition and spatial enclosure, were selected to quantify the characteristics of campus habitats. To evaluate the land cover composition, we categorized the underlying surface of campus habitats into different land cover types, and calculated the proportional area of each type in the sample plots. This enables us to derive seven habitat factors: percentage of canopy coverage (PerCC), percentage of grove area (PerGA), percentage of canopy-covered impervious surface area (PerCS), percentage of lawn area (PerLA), percentage of building area (PerBA), percentage of impervious surface area (PerIS), and percentage of water area (PerWA). Each factor is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe habitat factors indicating land cover composition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Canopy Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the total area covered by vertical projection of tree crowns.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Grove Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the total area of groves or forestland.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Canopy-Covered Impervious Surface Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the total area of impervious surface covered with tree canopy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Lawn Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the total area of open lawn without canopy cover.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Building Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the building footprint area.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Impervious Surface Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the total area of impervious artificial surfaces, including roads and paved squares, without canopy cover.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of Water Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe percentage of the total area of water bodies, such as lakes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe spatial enclosure characteristics of campus habitats was quantified by the degree of building enclosure (DegBE). Serving as a morphological indicator of external spaces, DegBE refers to the extent of all-directions enclosure primarily created by buildings surrounding trees within a specific spatial range. It is derived from aspect ratio (H/W) and plan enclosure rate (L/C) that are commonly utilized to evaluate the spatial enclosure of typical urban open spaces like street canyons [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and calculated using formula (1).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$DegBE=\\sum _{i=1}^{n}\\frac{{H}_{i}}{{D}_{i}}*\\frac{{\\alpha }_{i}}{360} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{i}\\)\u003c/span\u003e\u003c/span\u003e respectively refers to the height of Building \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e (m), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}\\)\u003c/span\u003e\u003c/span\u003e refers to the minimum horizontal distance between the sample tree and the facade of Building \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e (m), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{i}\\)\u003c/span\u003e\u003c/span\u003e refers to the horizontal enclosed angle between the sample tree and the facade of Building \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e (\u0026deg;), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e refers to the number of buildings surrounding sample trees, as illustrated as Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The data for these parameters were obtained through field measurement and visual interpretation of drone aerial images.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe interspecific difference in dry-heat tolerance among urban tree species was investigated through one-way analysis of variance (ANOVA) and multiple comparisons. Linear regression analysis was employed to explore the influence of habitat characteristics on the dry-heat tolerance of sample trees for specific tree species. Univariate correlation analysis was conducted to identify key habitat factors posing significant impacts on the dry-heat tolerance, and multiple regression analysis was performed to elucidate the multifactorial impact mechanism of campus habitat. The optimal regression equation between habitat factors and tree tolerance was selected for each tree species at every spatial scale, based on the significance (\u003cem\u003ep\u003c/em\u003e) and goodness of fit (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) of the equation, which indicates the overall sensitivity of tree dry-heat tolerance to habitat variation. Higher sensitivity was identified when the equation was significant (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) with a higher \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e value.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eInterspecific difference in the dry-heat tolerance of urban tree species\u003c/h2\u003e\n \u003cp\u003eA significant interspecific difference in dry-heat tolerance was observed among various tree species. Based on their dry-heat tolerance, the nine tree species can be classified into three groups. Group 1 includes \u003cem\u003eP. tobira\u003c/em\u003e and \u003cem\u003eM. grandiflora\u003c/em\u003e, exhibiting the highest dry-heat tolerance with mean values above 7; Group 2 consists of \u003cem\u003eK. bipinnata\u003c/em\u003e, \u003cem\u003eP. acerifolia\u003c/em\u003e, and \u003cem\u003eC. officinarum\u003c/em\u003e, showing moderate dry-heat tolerance with mean values of 5\u0026ndash;6; Group 3 consists of \u003cem\u003eC. chinensis\u003c/em\u003e, \u003cem\u003eM. glyptostroboides\u003c/em\u003e, \u003cem\u003eG. biloba\u003c/em\u003e, and \u003cem\u003eP. yedoensis\u003c/em\u003e, displaying lower dry-heat tolerance with mean values of 3\u0026ndash;5, with \u003cem\u003eG. biloba\u003c/em\u003e and \u003cem\u003eP. yedoensis\u003c/em\u003e exhibiting the lowest. The dry-heat tolerance of species in Group 1 is significantly higher than that of Group 3, as depicted in Fig. \u003cspan\u003e4\u003c/span\u003e. Detailed values of dry-heat tolerance for different tree species in every habitat site are presented in Table \u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe dry-heat tolerance of tree species was found to be correlated with their life forms and geographical provenance. Evergreen species exhibited significantly higher dry-heat tolerance than deciduous species (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), indicating distinct adaptive strategies. While shrub species generally displayed higher dry-heat tolerance than arbor species, this difference did not reach statistical significance. Regarding geographical provenance, native tree species exhibited insignificantly higher dry-heat tolerance compared to exotic species. Further evidence of geographical influence was seen in the tolerance variations observed among species with different areal-types of genera. Specifically, the dry-heat tolerance of tree species with genera originating from North Temperate region was notably lower compared to those from Old World Tropical region, East. Asia \u0026amp; North. American Disjuncted and Tropical Asia \u0026amp; Trop. Australasia region (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). This suggests that species with genera from tropical origins tend to demonstrate superior resilience in coping with heatwave stress.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDry-heat tolerance of various tree species.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHabitat code\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDry-heat tolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHabitat code\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDry-heat tolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHabitat code\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDry-heat tolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eG. biloba\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eP. tobira\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eM. glyptostroboides\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eK.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ebipinnata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eC.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003echinensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eC. officinarum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eP.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eyedoensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eP. acerifolia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eM.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eGrandiflora\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026quot;\u0026mdash;\u0026quot; indicates data loss due to the absence of available habitat sites.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eResponse of trees\u0026rsquo; dry-heat tolerance to habitat variation\u003c/h2\u003e\n \u003cp\u003eThe intra-specific variation in the dry-heat tolerance of sample trees was clearly evident across different habitats, with the variation being most pronounced for \u003cem\u003eG. biloba\u003c/em\u003e and least apparent for \u003cem\u003eM. grandiflora\u003c/em\u003e. To unveil the mechanism underlying the spatial variation, we explored the influence of habitat characteristics on the dry-heat tolerance of trees using linear regression analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e(1) Key habitat factors and their combined effects on trees\u0026rsquo; dry-heat tolerance\u003c/h2\u003e\n \u003cp\u003eThe correlation between trees\u0026rsquo; dry-heat tolerance and various habitat factors was analyzed for specific tree species. Among these factors, DegBE (Degree of Building Enclosure) showed a significant positive impact on the dry-heat tolerance of trees for most species, especially within the 50m and 100m scales. Other influential factors include PerGA (Percentage of Grove Area), PerCC (percentage of Canopy Coverage), and PerIS (Percentage of Impervious Surface Area), as illustrated in Fig. \u003cspan\u003e5\u003c/span\u003e. DegBE, PerGA and PerCC showed positive effects, indicating their roles in mitigating dry-heat stress and enhancing the climate adaptation of trees. Conversely, PerIS exhibited a negative correlation with trees\u0026rsquo; dry-heat tolerance, suggesting that impervious surfaces are detrimental for trees in coping with dry-heat stress.\u003c/p\u003e\n \u003cp\u003e\u0026quot;\u0026mdash;\u0026quot; indicates no significant correlation, \u0026quot;●\u0026quot; indicates a significant positive correlation at the 0.05 level, \u0026quot;●●\u0026quot; indicates a significant positive correlation at the 0.01 level, \u0026quot;●\u0026quot; indicates a significant negative correlation at the 0.05 level, and \u0026quot;●●\u0026quot; indicates a significant negative correlation at the 0.01 level.\u003c/p\u003e\n \u003cp\u003eMultiple regression analysis was conducted to explore the multifactorial impacts of habitat features on trees\u0026rsquo; dry-heat tolerance. Optimal regression equations for each species at various spatial scales were identified and presented in Table \u003cspan\u003e5\u003c/span\u003e. The findings revealed that the variation in dry-heat tolerance among different habitats can be mainly explained by the combined effects of 24 habitat factors about building enclosure and surrounding green spaces, with varying magnitudes of effect for different habitat factors. At the 50m scale, PerCC emerged as the primary factor for \u003cem\u003eP. tobira\u003c/em\u003e, \u003cem\u003eC. chinensis\u003c/em\u003e, and \u003cem\u003eM. glyptostroboides\u003c/em\u003e, with DegBE, PerIS, and PerGA playing a critical role for other species. At the 100m, 150m and 200m scales, however, the factor of DegBE was dominant for most species, exerting the highest explanatory power for trees\u0026rsquo; dry-heat tolerance, and other habitat factors acted as supplementary ones.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariate regression equations of trees\u0026rsquo; dry-heat tolerance against habitat factors for various tree species\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpatial scale of sample plot\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOptimal multiple regression equation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eP. tobira\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.77 PerCC* + 0.26 PerLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.59 DegBE* + 0.48 PerCC \u0026ndash; 0.27 PerCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.87 DegBE* \u0026minus;\u0026thinsp;0.40 PerTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.83 DegBE* \u0026minus;\u0026thinsp;0.15 PerIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eC. chinensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.92 PerCC** + 0.13 DegBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.70 PerCS* \u0026minus;\u0026thinsp;0.37 PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eG. biloba\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.72 DegBE** + 0.56 PerCC* + 0.29 PerIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.94 DegBE** + 0.24 PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.71 DegBE* \u0026minus;\u0026thinsp;0.31PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.79 DegBE** + 0.31 PerGA\u0026thinsp;+\u0026thinsp;0.25 PerIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eK. bipinnata\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.85 PerGA* -0.36 PerCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.73 DegBE** \u0026minus;\u0026thinsp;1.03 PerIS** \u0026minus;\u0026thinsp;0.63 PerLA*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.65 DegBE* \u0026minus;\u0026thinsp;0.37 PerIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eM.Glyptostrob\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e-oides\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.69 PerCC** + 0.85 DegBE* \u0026minus;\u0026thinsp;0.75 PerBA*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;3.20 PerCC* \u0026minus;\u0026thinsp;0.76 PerBA* \u0026minus;\u0026thinsp;1.42 PerCS- 2.17 PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;5.19 PerCC* \u0026minus;\u0026thinsp;2.91 PerCS* \u0026minus;\u0026thinsp;3.41 PerGA*- 0.63 PerLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eP. yedoensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.76 PerIS* \u0026minus;\u0026thinsp;0.22 DegBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.59 DegBE* + 0.48 PerCC \u0026minus;\u0026thinsp;0.27 PerCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.69 DegBE* + 0.32 PerCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.83 DegBE** \u0026minus;\u0026thinsp;0.15 PerIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eP. acerifolia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.90 PerIS** \u0026minus;\u0026thinsp;0.35 PerCS*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.72 DegBE* + 0.60 PerLA\u0026thinsp;+\u0026thinsp;0.56 PerBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.75 DegBE* \u0026minus;\u0026thinsp;0.57 PerCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.67 PerCC* \u0026minus;\u0026thinsp;0.37 PerLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eC. officinarum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.93 DegBE** + 0.48 PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.88 DegBE* + 0.34 PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.99 PerIS* \u0026minus;\u0026thinsp;0.56 PerBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.88 PerCS* \u0026minus;\u0026thinsp;0.49 PerLA \u0026minus;\u0026thinsp;0.46 PerGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eM. grandiflora\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.91 PerCS* + 0.54 PerIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026quot;**\u0026quot; denotes a significant correlation at the 0.01 level, \u0026quot;*\u0026quot; denotes a significant correlation at 0.05 level \u0026quot;\u0026mdash;\u0026quot; indicates that no significant regression equations were available at the spatial scale.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e(2) Scale effects and interspecific difference in the sensitivity of dry-heat tolerance to habitat variation\u003c/h2\u003e\n \u003cp\u003eThe significance and goodness of fit (\u003cem\u003eR\u003c/em\u003e\u0026sup2;) of the optimal regression equations were used to evaluate the overall sensitivity of a specific species\u0026rsquo; dry-heat tolerance to habitat variation at specific spatial scales. This sensitivity varied across different spatial scales. The R\u0026sup2; values of multiple species at the 50m and 100m scales were observed to be higher compared to the 200m scale (\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e) and 150m scale (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), indicating that overall, the dry-heat tolerance of trees is most sensitive to the habitat variation at the 50m and 100m scales, a pivotal spatial scale for campus habitat in affecting trees\u0026rsquo; climate adaptation.\u003c/p\u003e\n \u003cp\u003eSignificant interspecific differences existed in the sensitivity of trees\u0026rsquo; dry-heat tolerance in response to habitat variations. \u003cem\u003eG. biloba\u003c/em\u003e, \u003cem\u003eP. yedoensis\u003c/em\u003e, \u003cem\u003eP. acerifolia\u003c/em\u003e, \u003cem\u003eC. officinarum\u003c/em\u003e, and \u003cem\u003eP. tobira\u003c/em\u003e all showed sensitive responses to habitat characteristics across four spatial scales, with average R\u0026sup2; values of 0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, 0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, and 0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, respectively. \u003cem\u003eM. glyptostroboides\u003c/em\u003e exhibited sensitivity to habitat characteristics at three spatial scales, with average R\u0026sup2; value of 0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06. Additionally, \u003cem\u003eC. chinensis\u003c/em\u003e and \u003cem\u003eK. bipinnata\u003c/em\u003e exhibited sensitivity to habitat characteristics at two spatial scales, with average \u003cem\u003eR\u003c/em\u003e\u0026sup2; values of 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15, and 0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, respectively. And \u003cem\u003eM. grandiflora\u003c/em\u003e only showed sensitivity to habitat characteristics at the 150m spatial scale, with an \u003cem\u003eR\u003c/em\u003e\u0026sup2; value of 0.76. This suggests that the sensitivity of \u003cem\u003eG. biloba\u003c/em\u003e, \u003cem\u003eP. yedoensis\u003c/em\u003e, and \u003cem\u003eM. glyptostroboides\u003c/em\u003e, which exhibit weaker dry-heat tolerance, to habitat variation is more pronounced, while \u003cem\u003eM. grandiflora\u003c/em\u003e with the prominently higher dry-heat tolerance displayed the lowest sensitivity. Furthermore, the aforementioned sensitivity of a certain specie was found to be linked with its life form. The dry-heat tolerance of deciduous tree species exhibited higher sensitivity to habitat variations compared to evergreen tree species, especially with a significant difference at the 50m scale (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInterspecific difference in the dry-heat tolerance of urban tree species\u003c/h2\u003e \u003cp\u003eThis study elucidated the significant interspecies variability in the dry-heat tolerance of common urban tree species in Wuhan. Notably, \u003cem\u003eP. tobira\u003c/em\u003e showed the highest dry-heat tolerance, whereas \u003cem\u003eG. biloba\u003c/em\u003e and \u003cem\u003eP. yedoensis\u003c/em\u003e exhibited the lowest, and the observed low tolerance of \u003cem\u003eG. biloba\u003c/em\u003e to dry heatwaves aligns with the findings of Okubo [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, our investigation revealed that the dry-heat tolerance of tree species correlates with their life forms and geographical distribution.\u003c/p\u003e \u003cp\u003eThe observed higher dry-heat tolerance of evergreen tree species compared to deciduous species concurs with the conclusion drawn by Okubo [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This variation can be ascribed to the inherent capacity of evergreen species to sustain photosynthetic stability under hydrothermal stresses, especially heat threat [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This research further revealed the superior dry-heat tolerance of species with tropical-origin genera like \u003cem\u003eP. tobira\u003c/em\u003e, compared to those with temperate-origin genera such as \u003cem\u003eG. biloba\u003c/em\u003e and \u003cem\u003eP. yedoensis\u003c/em\u003e. Previous studies have also unveiled a correlation between the heat tolerance of tree species and the climate attributes of their natural distribution areas, suggesting that the species originating from warmer regions had stronger adaptation to heat stress [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In terms of the difference in tolerance between native and exotic species, we found that native tree species exhibited higher dry-heat tolerance compared to exotic counterparts, although the difference was not significant, consistent with the findings of Esperon-Rodriguez [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, different viewpoints from other studies indicated that native species may possess markedly superior adaptability to extreme climates compared to exotic ones [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], or vice versa [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Such divergent outcomes highlight the need for further exploration of the differences between indigenous and alien species in their climate adaptation against the backdrop of climate change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of habitat factors on trees\u0026rsquo; dry-heat tolerance\u003c/h2\u003e \u003cp\u003eThe dry-heat tolerance of trees in urban habitats is influenced not only by inherent species characteristics, but also by the enclosure structure of open spaces and land cover composition of underlying surface. This study found that the degree of building enclosure (DegBE) posed a significant positive impact on trees\u0026rsquo; dry-heat tolerance for most investigated species, consistent with the findings of Rahman [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. They observed that \u003cem\u003eTilia cordata\u003c/em\u003e growing on a plaza with a circular enclosure by buildings showed lower transpiration water loss in response to summer heatwaves compared to those growing on a counterpart plaza enclosed on two sides, indicating a stronger heat resilience. This correlation is closely associated with the impact of spatial enclosure characteristics on wind environments. Decreasing spatial enclosure, i.e., increasing openness, leads to higher local wind speeds and vapor pressure deficit (VPD) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], resulting in an increased transpiration rates of trees during hot weather conditions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], which is unfavorable for the physiological stability of plants under heat stress. Moreover, given that the influence of building enclosure on local climates depends not only on its degree but also on its orientation [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], investigating how the direction of building enclosure affects the climate adaptation of urban trees could be a promising avenue for future research.\u003c/p\u003e \u003cp\u003eIn addition to DegBE, indicators of land cover composition, including the percentage of green space area such as grove area (PerGA) and canopy coverage (PerCC), as well as the percentage of impervious surface area (PerIS), were also found to significantly influence the dry-heat tolerance of trees in this research. PerGA and PerCC were proven to be significantly positively correlated with the heat tolerance of plants, primarily because urban tree canopies can effectively reduce ambient temperatures through transpiration and shading effects, thereby alleviating extreme heat stress [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In contrast, the correlation between PerIS and dry-heat tolerance was revealed to be negative, aligning with the findings of Savi, Dale, McClung \u0026amp; Ibanez, and Zhao \u0026amp; Wang [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The extensive hardening of underlying surface can impede rainwater infiltration and soil moisture replenishment, exacerbating water stress and hindering tree growth under the threat of heatwaves [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Besides, the low albedo of impervious surfaces during hot periods enhances heat absorption, causing a rapid rise in surface and near-ground air temperature [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], increasing canopy transpiration and exacerbating the climatic vulnerability of trees [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTargeting the influence of heterogeneous and fragmented urban habitats, previous studies mainly focused on the impact of single habitat factor on the climate adaptation of urban trees [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], while the comprehensive multifactorial effects have been less explored. This research revealed that trees\u0026rsquo; dry-heat tolerance in a specific habitat can be largely explained by the combined effects of 2\u0026ndash;4 key habitat factors, particularly DegBE and PerCC for most tree species, with PerCC having a more pronounced impact at the 50m scale, while DegBE is more prominent at the 100, 150m and 200m scales. The findings indicate that a habitat with building enclosure and extensive canopy coverage surrounding trees can potentially enhance their adaptation to dry heatwaves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eScale effects in the sensitivity of trees\u0026rsquo; dry-heat tolerance to habitat variation\u003c/h2\u003e \u003cp\u003eOur research revealed that the sensitivity of trees\u0026rsquo; dry-heat tolerance in response to habitat characteristics varied across different spatial scales, with most trees exhibiting the highest sensitivity to habitat variation at the spatial scale of 50 and 100 meters. Although some studies have illustrated the scale effects of urban habitat on influencing microclimates [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], very few have directly addressed the critical spatial scales at which habitat features can effectively impact tree climate adaptation. Given that habitat characteristics influence tree climate adaptation by affecting microclimates [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], the revealed scale effects of habitats in affecting microclimate can provide insights to their scale effects in shaping trees\u0026rsquo; climatic adaptation. The optimal spatial range of habitat for effectively influencing microclimate varies across different habitat types, ranging from a radius of 20m in high-density urban residential areas [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] to a radius of approximately 150m for low-density suburban settlements [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This indicates potential habitat-type specificity in the critical spatial scale for affecting tree acclimation. This study specifically investigated the scale effects of campus habitats, and further exploration of other urban habitat types is warranted in future work.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Prospects\u003c/h2\u003e \u003cp\u003eConsidering that the morphological characteristics of urban trees can intuitively reflect their response to extreme climates, this study utilized a visual tree assessment method to evaluate the dry-heat tolerance of urban trees based on the severity of leaf damage. While this approach facilitates efficient evaluation of trees\u0026rsquo; adaptation to heatwave threats, it has certain limitations. During periods of extreme climate, trees initially undergo physiological changes, with morphological abnormality, such as leaf chlorosis and shedding, twig or branch die-back, and reduction in crown canopy, manifesting only after a certain tolerance threshold is exceeded [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Therefore, leaf damage symptoms may exhibit a delayed effect in detecting tree response to climate threats [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. To decipher the timely responses of trees to hydrothermal threats throughout the entire stress period, it is essential to supplement visual assessment with physiological measurements, such as changes in photosynthesis and water potential, which presents the direction for future research.\u003c/p\u003e \u003cp\u003eIn addition, the structure and characteristics of urban habitats varied across different habitat types. Our investigation focused on the specific type of campus habitats, characterized by the integration of buildings, impervious surfaces, and green spaces, which are representative for the densely populated habitat types, such as residential and commercial areas. However, further exploration is needed to understand the influence of other urban habitat types, such as urban parks with their greenspace-dominated composition distinct from highly artificial environments, on the climate adaptation of urban trees.\u003c/p\u003e \u003cp\u003eThe frequency of heatwaves worldwide is projected to increase by 5\u0026ndash;10 times over the next 40 years [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. While artificial irrigation and shading can temporarily ease the heat and drought threat on urban trees, relying solely on high-density maintenance is insufficient and impractical for addressing long-term climate challenges, especially considering the sustainability of urban green spaces. Selecting climate-resilient tree species and optimizing tree habitats can offer sustainable approaches for improving the climate adaptation of urban vegetation. This study identified the varying dry-heat tolerance of common tree species in Wuhan, providing evidence for species selection to enhance the heatwave adaptation of urban species composition. We also elucidated the impact mechanism of typical artificial habitat factors, such as building enclosure and land cover composition, on the dry-heat tolerance of urban trees. This insight can inform the creation and optimization of climate-resilient habitats for local urban trees, especially the vulnerable ones, thereby enhancing their ability to withstand heatwaves and other climatic challenges.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated the dry-heat tolerance of common tree species and their responses to habitat variations in Wuhan. Significant interspecific differences were observed in the dry-heat tolerance, with \u003cem\u003eP. yedoensis\u003c/em\u003e and \u003cem\u003eG. biloba\u003c/em\u003e exhibiting the highest tolerance, while \u003cem\u003eP. tobira and M. grandiflora\u003c/em\u003e displayed the lowest. The dry-heat tolerance of investigated species was associated with their life forms, with evergreen species showing significantly higher tolerance than deciduous species, and shrub species exhibiting higher tolerance than arbors, albeit with no significant difference. Additionally, the dry-heat tolerance of species was correlated with the climate attributes of their geographical provenance, with species originating from temperate regions exhibiting lower tolerance than those from tropical regions. While native tree species tended to show higher tolerance than exotic species, the difference was not statistically significant in our study. Besides species-specific traits, dry-heat tolerance was significantly influenced by building enclosure and land cover composition in urban habitats. Among habitat factors, the degree of buildings enclosure (DegBE) displayed the most pronounced positive impact, followed by percentage of grove area (PerGA) and canopy coverage (PerCC) with a positive effect, as well as impervious surface area (PerIS) with a negative effect. And the factors varied in their effect magnitudes when combinedly affecting trees\u0026rsquo; dry-heat tolerance, with green space factors such as PerCC being the primary ones at the 50m scale and DegBE acting as the dominant factor at the 100m-200m scales for most species. The sensitivity of trees\u0026rsquo; dry-heat tolerance to habitat variations varied across diverse spatial scales and different species, with the most prominent sensitivity observed at spatial scales of 100m and 50m, for species with weaker dry-heat tolerance such as \u003cem\u003eP. yedoensis\u003c/em\u003e and \u003cem\u003eG. biloba\u003c/em\u003e. These findings provide evidence supporting the selection of tree species and optimization of artificial habitats to enhance the heatwave resilience of urban vegetation in Wuhan and other cities in Central China.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI am deeply grateful to my supervisor, Xiaoyi Xing, for her invaluable guidance and unwavering support throughout this research. I also extend my thanks to my classmate, Wenli Zhu, for her assistance and insightful discussions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe research leading to these results received funding from The Fundamental Research Funds for the Central Universities, under Grant Agreement No 2662022YLQD002.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e No custom code was utilized in the analysis for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by WZ, XX and LZ. The first draft of the manuscript was written by LZ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoy S, Byrne J, Pickering C. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e. 2012; https://doi.org/10.1016/j.ufug.2012.06.006\u003c/li\u003e\n\u003cli\u003eEscobedo F, Palmas-Perez S, Dobbs C, et al. Spatio-temporal changes in structure for a Mediterranean urban forest: Santiago, Chile 2002 to 2014. \u003cem\u003eForests\u003c/em\u003e. 2016; https://doi.org/10.3390/f7060121\u003c/li\u003e\n\u003cli\u003eGreene CS, Robinson PJ, Millward AA. Canopy of advantage: Who benefits most from city trees? \u003cem\u003eJournal of Environmental Management\u003c/em\u003e. 2017; https://doi.org/10.1016/j.jenvman.2017.12.015\u003c/li\u003e\n\u003cli\u003eJanowiak MK, Brandt LA, Wolf KL, et al. Climate adaptation actions for urban forests and human health.\u003cem\u003e General Technical Report NRS-203\u003c/em\u003e. 2021; https://doi.org/10.2737/NRS-GTR-203\u003c/li\u003e\n\u003cli\u003eMoore J, Pope J, Woods M, Ellis A. 2017 Aerial Survey Results: California.\u003cem\u003e Forest Service\u003c/em\u003e. 2018; www.fs.usda.gov/detail/r5/forest-grasslandhealth. Accessed June 2018.\u003c/li\u003e\n\u003cli\u003eHartmann H, Bastos A, Das A J, et al. Climate change risks to global forest health: Emergence of unexpected events of elevated tree mortality worldwide. \u003cem\u003eAnnual Review of Plant Biology\u003c/em\u003e. 2022; https://doi.org/10.1146/annurev-arplant-102820-012804\u003c/li\u003e\n\u003cli\u003eSeo YW, Ha KJ, Park TW. Feedback attribution to dry heatwaves over East Asia. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e. 2021; https://doi.org/10.1088/1748-9326/abf18f.\u003c/li\u003e\n\u003cli\u003eHa KJ, Seo YW, Yeo JH, et al. Dynamics and characteristics of dry and moist heatwaves over East Asia. \u003cem\u003eClimate and Atmospheric Science\u003c/em\u003e. 2022; https://doi.org/10.1038/s41612-022-00272-4\u003c/li\u003e\n\u003cli\u003eZscheischler J, Seneviratne SI. Dependence of drivers affects risks associated with compound events. \u003cem\u003eScience Advances\u003c/em\u003e. 2017; https://doi.org/10.1126/sciadv.1700263\u003c/li\u003e\n\u003cli\u003eZou X, Gao R, Chen X, et al. Monitoring and assessment of summer drought in the Yangtze River basin in 2022. \u003cem\u003eChina Flood \u0026amp; Drought Management\u003c/em\u003e. 2022; https://doi.org/10.16867/j.issn.1673-9264.2022417\u003c/li\u003e\n\u003cli\u003eMei M, Gao G, Li Y, et al. Change characteristics in compound high temperature and drought extreme events over Yangtze River Basin from 1961 to 2022. \u003cem\u003eYangtze River\u003c/em\u003e. 2023; https://doi.org/10.16232/j.cnki.1001-4179.2023.02.003\u003c/li\u003e\n\u003cli\u003eLalor AR, Law DJ, Breshears DD, Falk DA, Field JP, Loehman RA, Triepke FJ, Barron-Gafford GA. Mortality thresholds of juvenile trees to drought and heatwaves: implications for forest regeneration across a landscape gradient.\u003cem\u003e Frontiers in Forests and Global Change\u003c/em\u003e. 2023; https://doi.org/10.3389/ffgc.2023.1198156\u003c/li\u003e\n\u003cli\u003eBreshears DD, Cobb NS, Rich PM, Price KP, Allen CD, et al. Regional vegetation die-off in response to global-change-type drought.\u003cem\u003e Proceedings of the National Academy of Sciences\u003c/em\u003e. 2005; https://doi.org/10.1073/pnas.0505734102\u003c/li\u003e\n\u003cli\u003eRouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warz\u0026eacute;e N. Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. \u003cem\u003eAnnals of Forest Science\u003c/em\u003e.2006; https://doi.org/10.1051/forest:2006044\u003c/li\u003e\n\u003cli\u003eZhang B, Brack CL. Urban forest responses to climate change: A case study in Canberra. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e. 2021; https://doi.org/10.1016/j.ufug.2020.126910\u003c/li\u003e\n\u003cli\u003eGillner S, Vogt J, Tharang A, Dettmann S, Roloff A. Role of street trees in mitigating effects of heat and drought at highly sealed urban sites. \u003cem\u003eLandscape and Urban Planning\u003c/em\u003e. 2015; https://doi.org/10.1016/j.landurbplan.2015.06.005\u003c/li\u003e\n\u003cli\u003eGagne MA, Smith DD, McCulloh KA. Limited physiological acclimation to recurrent heatwaves in two boreal tree species. \u003cem\u003eTree Physiology\u003c/em\u003e. 2020; https://doi.org/10.1093/treephys/tpaa102\u003c/li\u003e\n\u003cli\u003eMcClure AB, Coates TA, Hiers JK, Seiler JR, O\u0026apos;Brien JJ, Hoffman CM. Estimating heat tolerance of buds in southeastern US trees in fire-prone forests. \u003cem\u003eFire Ecology\u003c/em\u003e. 2022; https://doi.org/10.1186/s42408-022-00160-5\u003c/li\u003e\n\u003cli\u003eEsperon-Rodriguez M, Power SA, Tjoelker MG, Marchin RM, Rymer PD. Contrasting heat tolerance of urban trees to extreme temperatures during heatwaves. Urban \u003cem\u003eForestry \u0026amp; Urban Greening\u003c/em\u003e. 2021; https://doi.org/10.1016/j.ufug.2021.127387\u003c/li\u003e\n\u003cli\u003eFite K. Impacts of root invigoration and its individual components on the performance of red maple (\u003cem\u003eAcer rubrum\u003c/em\u003e). \u003cem\u003eAll Dissertations\u003c/em\u003e. 2008; https://tigerprints.clemson.edu/all_dissertations/260. \u003c/li\u003e\n\u003cli\u003eMartinez-Trinidad T, Watson WT, Arnold MA, Lombardini L, Appel DN. Comparing various techniques to measure tree vitality of live oaks. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e. 2010; https://doi.org/10.1016/j.ufug.2010.02.003\u003c/li\u003e\n\u003cli\u003eJohnstone D, Moore G, Tausz M, Nicolas M. The measurement of plant vitality in landscape trees.\u003cem\u003e Arboricultural Journal\u003c/em\u003e. 2013; https://doi.org/10.1080/03071375.2013.783746 \u003c/li\u003e\n\u003cli\u003eLinhares CS, Gon\u0026ccedil;alves R, Martins LM, Knapic S. Structural stability of urban trees using visual and instrumental techniques: A review. \u003cem\u003eForests\u003c/em\u003e. 2021; https://doi.org/10.3390/f12121752\u003c/li\u003e\n\u003cli\u003eTakahashi K, Fukasawa Y. Association between corticolous myxomycetes and tree vitality in Cryptomeria japonica. \u003cem\u003eMycoscience\u003c/em\u003e. 2022; https://doi.org/10.47371/mycosci\u003c/li\u003e\n\u003cli\u003eSchwenke MB, Sch\u0026ouml;nlau V, Beierkuhnlein C. Tree sapling vitality and recovery following the unprecedented 2018 drought in central Europe. \u003cem\u003eForest Ecosystems\u003c/em\u003e. 2023; https://doi.org/10.1016/j.fecs.2023.100140\u003c/li\u003e\n\u003cli\u003eCallow D, May P, Johnstone DM. Tree vitality assessment in urban landscapes. \u003cem\u003eForests\u003c/em\u003e. 2018; https://doi.org/10.3390/f9050279\u003c/li\u003e\n\u003cli\u003eOkubo N, Inoue S, Ishii HR. Tolerance and acclimation of the leaves of nine urban tree species to high temperatures. \u003cem\u003eForests\u003c/em\u003e. 2023; https://doi.org/10.3390/f14081639\u003c/li\u003e\n\u003cli\u003eLi C, Su J, Liu X, et al. Comparison of ornamental and drought tolerance of different varieties of Rhododendron. \u003cem\u003eJiangsu Agricultural Sciences\u003c/em\u003e. 2019; https://doi.org/10.15889/j.issn.1002-1302.2019.22.031\u003c/li\u003e\n\u003cli\u003eCunningham SC, Read J. Foliar temperature tolerance of temperate and tropical evergreen rainforest trees of Australia. \u003cem\u003eTree Physiology\u003c/em\u003e. 2006; https://doi.org/10.1093/treephys/26.11.1435\u003c/li\u003e\n\u003cli\u003eFang J, Peng Z. Preliminary report on drought tolerance investigation of trees in Wuhan area. \u003cem\u003eJournal of Huazhong Agricultural College\u003c/em\u003e. 1959; https://doi.org/10.13300/j.cnki.hnlkxb.1959.03.030\u003c/li\u003e\n\u003cli\u003eZhang D, Li K, Liu M, Roloff A, Sun H. Approaches of drought-resistant woody species selection: A case study of Jinan, a semi-arid city in Shandong. \u003cem\u003eJournal of Chinese Urban Forestry\u003c/em\u003e. 2019; https://doi.org/10.3969/j.issn.1672-4925.2018.00.002\u003c/li\u003e\n\u003cli\u003eLiu W. Evaluation and application of leaf scorch resistance of landscape tree species in Chongqing. (Master\u0026apos;s thesis). Chongqing Jiaotong University. 2022; https://doi.org/10.27671/d.cnki.gcjtc.2022.001193 \u003c/li\u003e\n\u003cli\u003eNowak DJ, Stein SM, Randler PB, et al. Sustaining America\u0026apos;s urban trees and forests: A Forests on the Edge report (General Technical Report NRS-62). \u003cem\u003eForest Service\u003c/em\u003e. 2010; https://doi.org/10.2737/NRS-GTR-62\u003c/li\u003e\n\u003cli\u003eBussotti F, Pollastrini M, Killi DİLEK, Ferrini F, Fini A. Ecophysiology of urban trees in a perspective of climate change. \u003cem\u003eAgrochimica\u003c/em\u003e. 2014; https://doi.org/10.12871/0021857201431\u003c/li\u003e\n\u003cli\u003eLocosselli GM, Miyahara AAL, Cerqueira P, Buckeridge MS. Climate drivers of tree fall on the streets of S\u0026atilde;o Paulo, Brazil. \u003cem\u003eTrees\u003c/em\u003e. 2021; https://doi.org/10.1007/s00468-021-02145-4\u003c/li\u003e\n\u003cli\u003eManfra R, Massoca MS, Uras PMC, Cavalari AA, Locosselli GM. Average height of surrounding buildings and district age are the main predictors of tree failure on the streets of S\u0026atilde;o Paulo/Brazil. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e. 2022; https://doi.org/10.1016/j.ufug.2022.127665\u003c/li\u003e\n\u003cli\u003eSoanes K, Lentini PE. When cities are the last chance for saving species. \u003cem\u003eFrontiers in Ecology and the Environment\u003c/em\u003e. 2019; https://doi.org/10.1002/fee.2032\u003c/li\u003e\n\u003cli\u003eIves CD, Lentini PE, Threlfall CG, et al. Cities are hotspots for threatened species. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e. 2015; https://doi.org/10.1111/geb.12404\u003c/li\u003e\n\u003cli\u003eKjelgren RK, Clark JR. Microclimates and tree growth in three urban spaces. \u003cem\u003eJournal of Environmental Horticulture\u003c/em\u003e. 1992; https://doi.org/10.24266/0738-2898-10.3.139\u003c/li\u003e\n\u003cli\u003eZhao YY, Wang HY. Functional trait variation and its relationship with different habitats of woody plants in Chongqing. J\u003cem\u003eournal of Southwest University\u003c/em\u003e. 2019; https://doi.org/10.13718/j.cnki.xdzk.2019.11.002 \u003c/li\u003e\n\u003cli\u003eZhang Y, Tian Q, Huang R, Song L, Ma Y. Leaf functional traits of greening tree species and their tolerance to atmospheric pollution in heterogeneous urban habitats.\u003cem\u003e Chinese Journal of Ecology\u003c/em\u003e. 2022; https://doi.org/10.13292/j.1000-4890.202210.010\u003c/li\u003e\n\u003cli\u003eYu K, Van Geel M, Ceulemans T, et al. Foliar optical traits indicate that sealed planting conditions negatively affect urban tree health. \u003cem\u003eEcological Indicators\u003c/em\u003e. 2018; https://doi.org/10.1016/j.ecolind.2018.08.047\u003c/li\u003e\n\u003cli\u003eSavi T, Bertuzzi S, Branca S, et al. Drought-induced xylem cavitation and hydraulic deterioration: risk factors for urban trees under climate change? \u003cem\u003eNew Phytologist\u003c/em\u003e. 2015; https://doi.org/10.1111/nph.13112\u003c/li\u003e\n\u003cli\u003eRahman MA, Moser A, R\u0026ouml;tzer T, Pauleit S. Microclimatic differences and their influence on transpirational cooling of \u003cem\u003eTilia cordata\u003c/em\u003e in two contrasting street canyons in Munich, Germany. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e. 2017; https://doi.org/10.1016/j.agrformet.2016.10.006\u003c/li\u003e\n\u003cli\u003eRoman LA, Fristensky JP, Lundgren RE, Cerwinka CE, Lubar JE. Construction and Proactive Management Led to Tree Removals on an Urban College Campus. \u003cem\u003eForests\u003c/em\u003e. 2022;https://doi.org/10.3390/f13060871\u003c/li\u003e\n\u003cli\u003eXu GY, Li HY, Mo XQ, et al. Composition and spatial-temporal distribution of Chinese naturalized plants. \u003cem\u003eChinese Journal of Plant Ecology\u003c/em\u003e. 2019; https://doi.org/10.17521/cjpe.2019.0053\u003c/li\u003e\n\u003cli\u003eWu Z, Zhou Z, Sun H. The areal-types of seed plants and their origin and differentiation. \u003cem\u003eKunming: Yunnan Science and Technology Press\u003c/em\u003e. 2006.\u003c/li\u003e\n\u003cli\u003eYokohari M, Brown RD, Kato Y, Yamamoto S. The cooling effect of paddy fields on summertime air temperature in residential Tokyo, Japan. \u003cem\u003eLandscape and Urban Planning\u003c/em\u003e. 2001; https://doi.org/10.1016/s0169-2046(00)00123-7\u003c/li\u003e\n\u003cli\u003eSun CY. A street thermal environment study in summer by the mobile transect technique. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e. 2011; https://doi.org/10.1007/s00704-011-0444-6\u003c/li\u003e\n\u003cli\u003eYan H, Fan S, Guo C, Hu J, Dong L. Quantifying the Impact of Land Cover Composition on Intra-Urban Air Temperature Variations at a Mid-Latitude City. \u003cem\u003ePLoS\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eONE\u003c/em\u003e. 2014; https://doi.org/10.1371/journal.pone.0102124\u003c/li\u003e\n\u003cli\u003eZhang S, Chen Y. Degree of enclosure as outdoor space form parameter study based on the urban microclimate analysis: A case study on office building site design in Shanghai. \u003cem\u003eJournal of East China Normal University\u003c/em\u003e. 2016; https://doi.org/10.3969/j.issn.1000-5641.2016.06.001\u003c/li\u003e\n\u003cli\u003eColombo SJ, Timmer VR. Limits of tolerance to high temperatures causing direct and indirect damage to black spruce. \u003cem\u003eTree Physiology\u003c/em\u003e. 1992; https://doi.org/10.1093/treephys/11.1.95\u003c/li\u003e\n\u003cli\u003eKumarathunge DP, Medlyn BE, Drake JE, Tjoelker MG. Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. \u003cem\u003eNew Phytol\u003c/em\u003e. 2019; https://doi.org/10.1111/nph.15668\u003c/li\u003e\n\u003cli\u003eCrous KY, Uddling J, De Kauwe MG. Temperature responses of photosynthesis and respiration in evergreen trees from boreal to tropical latitudes. \u003cem\u003eNew Phytologist\u003c/em\u003e. 2022; https://doi.org/10.1111/nph.17951 \u003c/li\u003e\n\u003cli\u003eZhou XH. Advantageous application analysis of native tree species in native landscaping. \u003cem\u003eContemporary Horticulture\u003c/em\u003e. 2019; https://doi.org/10.3969/j.issn.1673-9957.2013.08.214\u003c/li\u003e\n\u003cli\u003ePaudel I, Gerbi H, Wagner Y, Zisovich A, Sapir G, Brumfeld V, Klein T. Drought tolerance of wild versus cultivated tree species of almond and plum in the field.\u003cem\u003e Tree Physiology\u003c/em\u003e. 2020; https://doi.org/10.1093/treephys/tpz134\u003c/li\u003e\n\u003cli\u003eSchmucker J, Uhl E, Schmied G, Pretzsch H. Growth and drought reaction of European hornbeam, European white elm, field maple and wild service tree. \u003cem\u003eTrees\u003c/em\u003e. 2023; https://doi.org/10.1007/s00468-023-02441-1\u003c/li\u003e\n\u003cli\u003eHoffmann N, Schall P, Ammer C, Leder B, Vor T. Drought sensitivity and stem growth variation of nine alien and native tree species on a productive forest site in Germany. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e. 2018; https://doi.org/10.1016/j.agrformet.2018.03.008\u003c/li\u003e\n\u003cli\u003eVacek Z, Cukor J, Vacek S, et al. Production potential, biodiversity and soil properties of forest reclamations: Opportunities or risk of introduced coniferous tree species under climate change? \u003cem\u003eEuropean Journal of Forest Research\u003c/em\u003e. 2021; https://doi.org/10.1007/s10342-021-01392-x\u003c/li\u003e\n\u003cli\u003eTesfuhuney WA, Walker S, Van Rensburg LD, Steyn, AS. Micrometeorological measurements and vapour pressure deficit relations under in-field rainwater harvesting. \u003cem\u003ePhysics and Chemistry of the Earth\u003c/em\u003e. 2016; https://doi.org/10.1016/j.pce.2016.03.001\u003c/li\u003e\n\u003cli\u003eMontaldo N, Oren R. The way the wind blows matters to ecosystem water use efficiency. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e. 2016; https://doi.org/10.1016/j.agrformet.2015.11.002\u003c/li\u003e\n\u003cli\u003eBush SE, Pataki DE, Hultine KR, West AG, Sperry JS, Ehleringer JR. Wood anatomy constrains stomatal responses to atmospheric vapor pressure deficit in irrigated urban trees. \u003cem\u003eOecologia\u003c/em\u003e. 2008; https://doi.org/10.1007/s00442-008-0966-5\u003c/li\u003e\n\u003cli\u003eChen L, Zhang Z, Ewers BE. Urban tree species show the same hydraulic response to vapor pressure deficit across varying tree size and environmental conditions. \u003cem\u003ePLOS ONE\u003c/em\u003e. 2012; https://doi.org/10.1371/journal.pone.0047882\u003c/li\u003e\n\u003cli\u003eHebbert M. Urban microclimate designing the spaces between buildings. \u003cem\u003eUrban design\u003c/em\u003e; 2012. pp.49.\u003c/li\u003e\n\u003cli\u003eTracol Y, Gutierrez JR, Squeo FA. Plant area index and microclimate underneath shrub species from a Chilean semiarid community. \u003cem\u003eJournal of Arid Environments\u003c/em\u003e. 2011; https://doi.org/10.1016/j.jaridenv.2010.08.002\u003c/li\u003e\n\u003cli\u003eDale AG, Frank SD, Youngsteadt E, Fair B, Sherk J, Just M. Impervious surface thresholds and the pace to plant technique for planting urban red maple trees. \u003cem\u003eEDIS\u003c/em\u003e. 2020; https://doi.org/10.32473/edis-in1185-2017\u003c/li\u003e\n\u003cli\u003eMcclung T, Ibanez I. Quantifying the synergistic effects of impervious surface and drought on radial tree growth. \u003cem\u003eUrban ecosystem\u003c/em\u003e.\u003cem\u003e \u003c/em\u003e2018; https://doi.org/10.1007/s11252-017-0699-5\u003c/li\u003e\n\u003cli\u003eZhao YY, Wang HY. Functional trait variation and its relationship with different habitats of woody plants in Chongqing. \u003cem\u003eJournal of Southwest University\u003c/em\u003e. 2019; https://doi.org/10.13718/j.cnki.xdzk.2019.11.002\u003c/li\u003e\n\u003cli\u003eKim HH. Urban Heat Island. \u003cem\u003eInternational Journal of Remote Sensing\u003c/em\u003e. 1992;13:319-336. \u003c/li\u003e\n\u003cli\u003eJiang YT, Fu P, Weng QH. Assessing the impacts of urbanization-associated land use/cover change on land surface temperature and surface moisture: a case study in the Midwestern United States. \u003cem\u003eRemote Sens\u003c/em\u003e. 2015; https://doi.org/10.3390/rs70404880\u003c/li\u003e\n\u003cli\u003eYan H, Yang S, Guo X, et al. Effects of Land Cover Composition and Structure on Air Temperature in Winter Based on the Local Climate Zone Scheme. \u003cem\u003eJournal of Northwest Forestry University\u003c/em\u003e. 2023; https://doi.org/10.3969/j.issn.1001-7461.2023.03.30\u003c/li\u003e\n\u003cli\u003eDobrowski SZ. A climatic basis for microrefugia: The influence of terrain on climate. \u003cem\u003eGlobal Change Biology\u003c/em\u003e. 2011; https://doi.org/10.1111/j.1365-2486.2010.02263.x\u003c/li\u003e\n\u003cli\u003eDe Frenne P, Rodr\u0026iacute;guez-S\u0026aacute;nchez F, Coomes, et al. Microclimate moderates plant responses to macroclimate warming. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e. 2013; https://doi.org/10.1073/pnas.1311190110\u003c/li\u003e\n\u003cli\u003eVollenweider P, G\u0026uuml;nthardt-Goerg MS. Diagnosis of abiotic and biotic stress factors using the visible symptoms in foliage.\u003cem\u003e Environmental Pollution\u003c/em\u003e. 2006; https://doi.org/10.1016/j.envpol.2006.01.002\u003c/li\u003e\n\u003cli\u003ePercival GC, Noviss K. Penconazole induced heat tolerance in scots pine (\u003cem\u003epinus sylvestris\u003c/em\u003e) and evergreen oak (\u003cem\u003equercus\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eilex\u003c/em\u003e). \u003cem\u003eArboriculture \u0026amp; Urban Forestry\u003c/em\u003e. 2010; https://doi.org/10.48044/jauf.2010.028\u003c/li\u003e\n\u003cli\u003ePercival GC. Heat tolerance of urban trees - A review. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e. 2023; https://doi.org/10.1016/j.ufug.2023.128021\u003c/li\u003e\n\u003cli\u003eBarriopedro D, Fischer EM, Luterbacher J, Trigo RM, Garc\u0026iacute;a-Herrera R. The hot summer of 2010: Redrawing the temperature record map of Europe. \u003cem\u003eScience\u003c/em\u003e. 2011; https://doi.org/10.1126/science.1201224\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"urban trees, heatwave, tolerance, campus habitat, land cover composition, building enclosure","lastPublishedDoi":"10.21203/rs.3.rs-4549982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4549982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal warming has intensified the occurrence of dry heatwaves, and the heterogeneous urban habitats can influence trees' tolerance to dry-heat conditions, however the specific mechanisms remain unclear. This research investigated the dry-heat tolerance of nine common trees in Wuhan, a central Chinese city facing persistent heatwave threats, and explored the multifactorial influence of urban habitat variations on trees\u0026rsquo; dry-heat tolerance. Diverse habitat sites with the sample plots in various spatial scales were established for each tree species. Dry-heat tolerance was evaluated through visual assessment, measuring the degree of leaf damage during a historic dry heatwave event. A significant interspecific difference was observed in trees\u0026rsquo; dry-heat tolerance, with \u003cem\u003ePittosporum tobira\u003c/em\u003e exhibiting the highest dry-heat tolerance, while \u003cem\u003ePrunus \u0026times; yedoensis\u003c/em\u003e displayed the lowest, in relevance to the life forms and provenances of species. And the dry-heat tolerance of trees in a specific species varied across various habitats, influenced by surrounding building enclosure and land cover composition. The Degree of Building Enclosure was the most prominent habitat factor, followed by the Percentage of Canopy Coverage and the Percentage of Grove Area, all of which positively affected tree tolerance, in contrast with the Percentage of Impermeable Surface showing a negative effect. In addition, the scale effect was evident, that the 50m and 100m range were identified as the most effective spatial scale of campus habitats affecting trees\u0026rsquo; dry-heat tolerance, especially for the species with weaker tolerance. The findings can support the selection of tree species and optimization of artificial habitats to enhance the heatwave resilience of urban vegetation in central Chinese cities.\u003c/p\u003e","manuscriptTitle":"Dry-Heat Tolerance of Common Tree Species in a Central Chinese City: Responses to Heterogeneous Habitat Characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 12:19:51","doi":"10.21203/rs.3.rs-4549982/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a3f85fa-6623-429f-bdff-921f4deb9444","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-14T04:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 12:19:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4549982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4549982","identity":"rs-4549982","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}