{"paper_id":"1fb447e2-0cdb-43c4-b107-6b3f4dec8d63","body_text":"Drought response of Siamese rosewood  Hung et al. 2025 \n 1 \n 2 \nEnvironmental and transcriptomic determinants of drought response 3 \nin critically endangered Siamese rosewood 4 \n 5 \nTin Hang Hung 1,2* , Kalyani Lenton 1, Phourin Chhang 3, Voradol Chamchumroon 4 ,  6 \nBansa Thammavong5, Riina Jalonen6 , Ida Theilade7 , John J. MacKay1,*  7 \n1. Department of Biology, University of Oxford, Oxford OX1 3EL, United Kingdom 8 \n2. Museum of Climate Change, The Chinese University of Hong Kong, Hong Kong 9 \n3. Institute of Forest and Wildlife Research and Development, Phnom Penh, Cambodia 10 \n4. The Forest Herbarium, Department of National Park, Wildlife and Plant 11 \nConservation, Ministry of Natural Resources and Environment, Bangkok 10900, 12 \nThailand 13 \n5. National Agriculture and Forestry Research Institute, Forestry Research Center, 14 \nVientiane, Laos 15 \n6. Bioversity International, 43400 UPM Serdang, Malaysia 16 \n7. Department of Geosciences and Natural Resource Management, University of 17 \nCopenhagen, Rolighedsvej 23, 1958 Frederiksberg C, Denmark 18 \n 19 \n* Corresponding authors:  20 \nTin Hang Hung (tin-hang.hung@biology.ox.ac.uk) 21 \nJohn J. MacKay (john.mackay@biology.ox.ac.uk)  22 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 2 of 33 \nCompeting interests statement 23 \nThe authors declare no competing interests. 24 \n 25 \nData availability statements 26 \nSequence and meta data of the transcriptomic analyses have been deposited under NCBI 27 \nBioProject PRJNA1378856, which contains all BioSample and Sequence Read Archive 28 \n(SRA) accessions. 29 \n  30 \nAuthor contributions 31 \nT.H.H.: designed the study, conducted the drought experiment, processed the RNA samples, 32 \nconducted the library preparation and sequencing, conducted the bioinformatic analyses, 33 \ndrafted the manuscript, and secured funding for the project; 34 \nK.L.: conducted the drought experiment and biochemical analyses and drafted the manuscript; 35 \nP.C.: collected the samples, revised the manuscript, and secured funding for the project; 36 \nV.C.: collected the samples, and revised the manuscript; 37 \nB.T.: collected the samples, revised the manuscript, and secured funding for the project; 38 \nR.J.: collected the samples, revised the manuscript, and secured funding for the project; 39 \nI.T.: collected the samples, revised the manuscript, and secured funding for the project; 40 \nJ.J.M.: supervised the study, revised the manuscript, and secured funding for the project. 41 \n 42 \nAcknowledgements 43 \nThis work is supported by funding to T.H.H., P.C., B.T., R.J., I.T., J.J.M. by the National 44 \nGeographic Society (EC-95234R-22). We would like to thank Kate A. Hardwick from the 45 \nRoyal Botanic Gardens, Kew, for assistance in supplying samples from Thailand by V.C. via 46 \nthe Millenium Seed Bank Partnership. T.H.H. is supported with a Croucher Fellowship. 47 \nT.H.H. wishes to personally thank the anonymous host of B612 for providing him a writing 48 \nretreat and food during the winter of 2023 that initiates this manuscript.  49 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 3 of 33 \nAbstract 50 \n 51 \nRosewoods account for up to 40% of the global illegal wildlife trade, with Dalbergia 52 \ncochinchinensis (Siamese rosewood) being the most heavily exploited species in Southeast 53 \nAsia. Its survival is further threatened by intensifying drought linked to climate change and 54 \nhydrological alteration.  Here we combine greenhouse drought experiments across six 55 \nprovenances with full-length cDNA-seq to uncover how water-relations and carbon-use 56 \nstrategies vary within this species. Multivariate trait analysis resolves a two-dimensional 57 \nisohydry space, in which a water-flux stringency axis ( gs–E) is largely orthogonal to a 58 \ncarbon-economics axis (A–WUEi). Provenances differed strikingly, where two (KKH and DN) 59 \nshowed a rare E↓ A↑  response, achieving high WUE i and maintaining growth under drought. 60 \nContrary to expectation, precipitation of the wettest month, not the driest, predicted isohydry, 61 \nindicating that wet-season conditions set a developmental and hydrological floor for later 62 \ndrought responses. We identified 76 drought-responsive genes and two genes associated with 63 \nisohydry axes, SEOR1 and a poorly characterised Notch-like protein AT4G14746. We also 64 \ndetected provenance-specific isoform switches, where drought favoured a loss-of-function 65 \nPRX52 isoform lacking its signal peptide in the anisohydric provenance THB, and gain-of-66 \nfunction isoforms of ANN3 and LTPG5 in NP. These results reveal previously hidden 67 \ndiversity in drought strategies, identify mechanism-related markers for screening, and provide 68 \na simple climatic lever for climate-adjusted provenancing. We reveal post-transcriptional 69 \nregulation as a novel candidate substrate for local adaptation in a threatened tropical tree, 70 \ndirectly linking ecophysiology, climate, and genomics for conservation and restoration. 71 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 4 of 33 \nIntroduction 72 \n Extreme droughts have emerged as a driver of accelerated forest mortality globally 1,2, 73 \nwhich threatens terrestrial biodiversity, climate forcing, and resource availability 3. 74 \nMechanistically, tree mortality reflects interacting processes such as xylem hydraulic failure, 75 \ncarbon depletion from prolonged stomatal closure, and heightened susceptibility to pests and 76 \npathogens, whose prevalences are expected to increase as warming intensifies atmospheric 77 \nwater demand 4. The IPCC’s Sixth Assessment Report concludes that many regions have 78 \nalready experienced anthropogenically influenced increases in agricultural and ecological 79 \ndrought, consistent with observed forest declines during recent heat-drought events 5. Many 80 \ntree species have evolved in response to spatial and temporal variability in water availability 81 \nthrough local adaptation and phenotypic plasticity6, but our understanding of these responses 82 \nmust be improved to predict the impacts of environmental change and to safeguard 83 \nbiodiversity. 84 \n Southeast Asia is a major biodiversity hotspot with disproportionally high levels of 85 \nendemism, which is experiencing unprecedented levels of drought threats due to climate and 86 \nland use changes. It also has the highest proportion of vascular plants, reptiles, birds, and 87 \nmammals classified as in the IUCN Red List7. Its main water body, the Mekong River Basin, 88 \nhas had record low flows and severe multi-year drought in the recent decade, with 89 \nwidespread soil-moisture deficits, delayed monsoons and saline intrusion in the delta 8. 90 \nDrought impacts are particularly amplified by strong climate variability, such as the 2015–91 \n2016 El Niño that brings exceptional heat and rainfall deficits, with recurrent moisture 92 \nshortfalls following the subsequent years 9. The extreme drought affected area has doubled 93 \nsince 1950s and both the intensity and frequency of drought risks will continue to increase in 94 \nall scenarios except for the lowest emission pathway 10. Simultaneously, these climatic 95 \nstresses now interact with one of the world’s fastest hydropower build-outs11, with more than 96 \n1,000 dams already in place with further expansion planned 12. The large storage of water 97 \nbehind dams during the wet season exacerbates the downstream impacts on water 98 \navailability8. Together these climate and infrastructure drivers are reshaping hydro-ecological 99 \nregimes across Southeast Asia and underlining the need to understand adaptive drought 100 \nresponses for the region’s forests. 101 \n Dalbergia cochinchinensis Pierre (Siamese rosewood) produces extremely valuable 102 \nrosewood timber in the Mekong Region, and is endemic to Cambodia, Laos, Thailand, and 103 \nVietnam. Rosewoods (Dalbergia spp.) amount to 30–40% of worldwide illegal wildlife trade, 104 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 5 of 33 \nwhich is valued in total at USD 7–23 billion annually, making it the target of the world’s 105 \nlargest wildlife crime 13. Siamese rosewood was assessed as ‘Critically Endangered’ in 2022 106 \ndue to an estimated ≥ 90% population decline and population fragmentation caused by illegal 107 \nlogging and habitat loss14. The species is on CITES Appendix II since 2013, but cross-border 108 \ntrafficking has continued to be a significant threat, especially in Cambodia, Laos and 109 \nVietnam14. In addition to overexploitation, habitat conversion, fire and overgrazing, 110 \nvulnerability analyses further indicate that a large share of the species range is exposed to 111 \nmedium to very high combined pressures, and a measurable fraction to climate-change risk 112 \nby mid-century15. Conservation actions initiated in the early 2000s included in situ and ex situ 113 \nconservation stands and seed production areas, but they were limited in scale, usually with < 114 \n50 seed-producing trees per country 16,17. Renewed efforts to conserve the remnant 115 \npopulations and their genetic diversity since the 2010s have involved the collection of genetic 116 \nmaterials, development of tree nurseries, and generation of value chains to incentivise local 117 \nlivelihoods18,19. 118 \nWith increasing drought risks in Mekong Region, our ability to safeguard the survival 119 \nand conservation tree species such as Siamese rosewood requires a better understanding of 120 \nthe variation in drought resistance and growth performance. 121 \nA useful lens to help fill this gap is the isohydry-anisohydry continuum. Isohydric 122 \nplants stabilise leaf water potential ( Ψ leaf) by closing stomata early as soils dry, while 123 \nanisohydric plants allow Ψ leaf to decline to maintain carbon assimilation. We previously 124 \nreported that Siamese rosewood had an anisohydric response to short-term drought, which 125 \nmaximised assimilation at the cost of water loss, whereas a isohydric response was found in 126 \nthe sympatric species D. oliveri20. While this anisohydric response can sustain photosynthesis 127 \nduring drought, it increases hydraulic risk and may lead to mortality under prolonged water 128 \ndeficits21. 129 \nIdentifying the molecular mechanisms and key genes in drought will provide 130 \nadditional insights for the physiological responses. Recent population genetic studies have 131 \nshown that Siamese rosewood is predominantly outcrossing and thus retains high levels of 132 \ngenetic diversity across much of its range despite severe demographic declines 22. Central 133 \npopulations in Cambodia and eastern Thailand harbour the highest allelic richness, whereas 134 \nperipheral and heavily exploited stands in northeastern Thailand, Laos, and Vietnam tend to 135 \nexhibit reduced diversity and higher relatedness, consistent with historical bottlenecks, 136 \nhabitat fragmentation, and local exploitation 23. In addition, our previous genomic scan has 137 \nidentified substantial genetic differentiation in D. cochinchinensis driven by temperature- and 138 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 6 of 33 \nprecipitation- related environmental factors 24. Local adaptation is also found to be the 139 \nstrongest in the periphery of the species range 24. Thus, it is very plausible that populations 140 \nwill display differentiated response to drought. However, it has also been shown that 141 \nenvironment has a stronger effect on gene expression than standing genetic variation 25. The 142 \ngene expression or transcriptomic response can be considered an intermediate phenotype that 143 \nis informative of high-level physiological traits 26. It is well-established that plants remodel 144 \ntheir transcriptome under drought stress, which regulates stress sensors, signalling pathways, 145 \nand synthesis of hormones and enzymes27,28. However, fundamental research in forest trees is 146 \nstill underrepresented compared to other plant crops 29, and systematic studies at population 147 \nlevel are scarce30. 148 \nThe endangered status of and the drought threats facing Siamese rosewood together 149 \nhighlight the need to improve our understanding of its drought tolerance variability and 150 \nadaptation across the remnant populations. The overarching aim of this study is to identify 151 \nthe environmental, physiological, and transcriptomic determinants of the species-wide 152 \nheterogeneity in drought response, by building on the recent capacity in genomic research in 153 \nSiamese rosewood, including its high-quality reference genome and range-wide genomic 154 \nscan24,31. Seedling recruitment is a critical bottleneck in Siamese rosewood 32 and early 155 \nphenotyping can still provide valuable insights into drought adaptation. First, we characterise 156 \nthe drought response in seedlings from six provenances by comparing various physiological 157 \ntraits in a greenhouse drought experiment. Second, we identify genes that are differentially 158 \nexpressed among different provenances under drought stress. Third, we analyse if different 159 \nprovenances have differential transcript usage in response to drought. This study will 160 \nultimately ensure drought resilience in germplasm of Siamese rosewood by integrating recent 161 \ndevelopments in conservation and cutting-edge genomic technologies.  162 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 7 of 33 \nMethods 163 \nPlant materials 164 \n Dried seeds of Dalbergia cochinchinensis were provided by the Institute of Forest and 165 \nWildlife Research and Development, Cambodia; the National Agriculture and Forestry 166 \nResearch Institute, Laos; and the Department of National Park, Wildlife and Plant 167 \nConservation, Thailand in 2020 from six local seed sources across the species range ( Figure 168 \n1a and Supplementary Table 1 ). The seeds were scarified by placing them in 70°C distilled 169 \nwater and left to cool to room temperature overnight. They were germinated on 1% agar in a 170 \ncontrolled greenhouse at 30°C and photoperiod 12L/12D. Germinants were first transferred 171 \nto 0.125L pots in a soil-perlite 3:1 (v:v) mixture and grown for three months. Healthy 172 \nseedlings were then transferred to 0.81 L pots with the same substrate and grown for further 173 \ntwo months. Throughout this period, plants were watered regularly to maintain at substrate 174 \ncapacity and fertilized once a week using N /i5 P/i5 K 20:20:20 fertilizer (Chempak, Suffolk, 175 \nUnited Kingdom). 176 \n 177 \nExperimental design 178 \n We used a split-plot design with 10 plants per provenance randomly distributed into 179 \n10 trays, making up to 60 plants in total. We randomly assigned half of the trays to either 180 \nwell-watered control (C) or water-withholding treatment (D). The controls were watered 181 \nevery other day to maintain substrate capacity at 40–55%, whereas the droughted were not 182 \nwatered at all after the experiment started. We also randomly split the trays into two blocks 183 \nwith the start of experiment staggered by one day. 184 \n There were two types of data collected: (1) continuous data of water relation and 185 \nphotosynthetic measurements, and (2) end-point data of anatomical traits and biochemical 186 \nmeasurements. At the end of the experiment (day 14), three leaves were sampled from each 187 \nplant, snap-frozen in liquid nitrogen, and stored at –80°C. 188 \n 189 \nWater relation and photosynthetic measurements 190 \n Measurements were taken between 10 am and 2 pm (at least two hours after sunrise 191 \nand two hours before sunset), when photosynthetic activity reached its plateau (i.e. point of 192 \nsaturation). We measured soil water content ( SWC) by using a ML3 ThetaProbe Soil 193 \nMoisture Sensor (Delta-T Devices Ltd., Cambridge, England). We measured stomatal 194 \nconductance (g s), photosynthetic assimilation rate ( A), and transpiration rate ( E) using an 195 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 8 of 33 \ninfrared gas analyser LCpro T, Leaf Chamber & Soil Respiration System (SRS2000 T, ADC 196 \nBioScientific Limited, England) and its broad leaf chamber, with the light intensity set to 197 \nPAR 500 with equal parts of red, green, and blue lights. We calculated instantaneous water 198 \nuse efficiency (WUEi) using the following equation33: 199 \n/g1849/g1847/g1831 /g3036/g3404 /g1827\n/g1831  \n 200 \nAnatomical traits 201 \n We recorded changes in height ( Δ  Height), leaf number (Δ  Leaf), and branch number 202 \n(Δ  Branch) before and after the experiment. We measured leaf area using the equation for the 203 \narea of an ellipse ( S = ab π ). We measured fresh ( FW) and dry weights ( DW) of the leaves 204 \nbefore and after lyophilisation of two days in an Alpha 2-4LD-2 laboratory freeze-dryer 205 \n(Martin Christ GmbH, Germany). We calculated the leaf dry matter content ( LDMC) and 206 \nspecific leaf area (SLA) using the following equations34: 207 \n/g1838/g1830/g1839/g1829 /g3404 /g1830/g1849  /g4666mg/g4667\n/g1832/g1849  /g4666 mg/g4667  \n/g1845/g1838/g1827 /g3404 /g1845 /g4666c m /g2870/g4667\n/g1830/g1849  /g4666mg/g4667  \n 208 \nPigment extraction and quantification 209 \nWe ground ~25 mg of lyophilised leaves with a TissueLyzer (Retsch, Germany) at 210 \n25–1 s for 1 minute. We then extracted the pigments by adding an 80:20 mixture (v:v) of cold 211 \nacetone and 50 mM Tris buffer pH 8.0 and incubated for 72 hours, following Sims & 212 \nGamon’s protocols35. After centrifugation, we transferred the supernatant to a 15ml Falcon 213 \ntube and then topped the extracts up with 6–12 ml of the acetone-Tris buffer to ensure that 214 \nthe absorbances below were approximately in the range between 0 and 1. We used a 215 \nNanodrop One (Thermo Fisher Scientific, Waltham, Massachusetts, USA) to measure the 216 \nabsorbances of the extracts containing the leaves at 470, 537, 647, and 663 nm. We 217 \ndetermined the concentrations of anthocyanin (Ac ), chlorophyll a ( Chla) and b ( Chlb), and 218 \ncarotenoids (Carot) with the following equations: 219 \n/g1827/g1855 /g3404 0.08173A /g2873/g2871/g2875/g3398 0.00697A /g2874/g2872/g2875/g3398 0.002228A /g2874/g2874/g2871 \n/g1829/g1860/g1864 /g3028/g3404 0.01373A /g2874/g2874/g2871/g3398 0.000897A /g2873/g2871/g2875/g3398 0.003046A /g2874/g2872/g2875 \n/g1829/g1860/g1864 /g3029/g3404 0.02405A /g2874/g2872/g2875/g3398 0.004305A /g2873/g2871/g2875/g3398 0.005507A /g2874/g2874/g2871 \n/g1829/g1853/g1870/g1867/g1872 /g3404 /g4670/g1827 /g2872/g2875/g2868/g3398 /g466617.1 /g3400 /g1829/g1860/g1864 /g3028/g3397/g1829 /g1860 /g1864 /g3029/g4667 /g3398 9.479 /g3400 /g1827/g1855/g4667/g4671 /g3400 119.26  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 9 of 33 \n 220 \nSugar extraction and quantification 221 \n We heated ~25 mg of lyophilised leaves in 80% ethanol for one hour at 80°C. After 222 \ncentrifugation, we determined glucose concentration from the supernatant using Osaki’s 223 \nanthrone method36. We mixed the supernatant with anthrone and sulfuric acid and heated it at 224 \n100°C for 10 minutes sharply. We cooled the mixture on ice and measured their absorbances 225 \nat 625 nm using a Genesys 150 UV-Visible spectrophotometer (Thermo Fisher Scientific, 226 \nWaltham, Massachusetts, USA). We calculated the concentration of total soluble sugars (TSS) 227 \nin the samples according to a D-glucose standard curve. 228 \nWe washed the pellets in 80% ethanol to remove any glucose and then heated them at 229 \n100°C for 10 minutes with 30% perchloric acid to convert all starch to glucose. We again 230 \nmeasured absorbances of the extract at 625 nm again and used a D-glucose standard curve to 231 \ndetermine the concentration of starch (Starch). 232 \n 233 \nStatistical analyses on traits 234 \nAll statistical analyses were conducted in R 4.5.1. List of all traits included in this 235 \nanalysis is presented as Supplementary Table 2. 236 \nWe applied square-root transformation to g s, A, E, and WUE i and logarithmic 237 \ntransformation to Ac, Chla, Chlb, and Carot to correct for normality. We assessed normality 238 \nbased on the distribution of the residuals using a QQ plot. For continuous data, we conducted 239 \ntwo-way ANOVAs to examine the effects of SWC, provenance, and their interaction term on 240 \ngs, A, E, and WUE i. For end-point data, we conducted two-way ANOVAs to examine the 241 \neffects of treatment, provenance, and their interaction term anatomical and biomass traits ( Δ  242 \nHeight, Δ  Leaf, Δ  Branch, LDMA, SLA), pigments (Ac , Chla, Chlb, and Carot), and sugars 243 \n(TSS and Starch). We initially included the blocks as a random variable in the ANOVAs, but 244 \nno difference was found thus the random variable was excluded from the models. 245 \n 246 \nQuantification and climatic associations of isohydry 247 \nWe quantified the provenance-level isohydry based on the principal component 248 \nanalysis (PCA) of the coefficients of interaction effect of treatment × provenance on 249 \nphysiological traits that show significant interactions. We then quantified individual-level 250 \nisohydry also based on physiological traits that show significant interactions, with gs, A, E, 251 \nand WUEi divided by SWC to obtain a single-value slope for each individual. Visual 252 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 10 of 33 \ninspection confirmed that the loading of treatment aligned with the direction of second 253 \nprincipal component (PC2), thus the first principal component (PC1) was used as a composite 254 \nproxy for isohydry (denoted as Isohydry PC1 in this manuscript). 255 \nProvenance-level PCR is useful in quick visualisation and quantification of isohydry 256 \nat whole-provenance level, which is more spatially explicit and useful in conservation context. 257 \nHowever, individual-level PCR reveals a more substantial variation in isohydry among 258 \nindividual trees that are not attributable to provenances, and thus is more suitable for 259 \nsubsequent analyses on environmental and transcriptomic associations. 260 \nWe tested the effect of 19 climatic variables that were biologically meaningful, from 261 \nthe WorldClim 2 database ( bio_1–bio_19). To correct for the multicollinearity inherent in 262 \nclimatic data, we used a stepwise approach to remove climatic variables that have a variance 263 \ninflation factor (VIF) > 10. We then examined the effects of treatment, these climatic 264 \nvariables, and their interactions on both Isohydry PC1 and PC2, using multivariate analysis of 265 \nvariance (MANOVA). 266 \n 267 \nRNA extraction, full-length cDNA library construction, and sequencing 268 \n We isolated total RNA from leaves using the Monarch Total RNA Miniprep Kit (New 269 \nEngland Biolabs, United Kingdom). We determined their quantity on a  Qubit 4 Fluorometer 270 \n(Thermo Fisher Scientific, United Kingdom), assessed their purity using a NanoDrop One 271 \nSpectrophotometer (Thermo Fisher Scientific), with A260/280 and A260/230 above 1.80, and 272 \nverified their integrity on a 1% bleach-agarose gel. 273 \n The 6-µl reverse transcription reaction contained 3 µl of ~200 ng total RNA, 2 µl of 274 \n10 µM Reverse Transcription Primer (5’–AGCAGTGGTATCAACGCAGAGTAC(T) 30V–3’) 275 \nand 1 µl of 10 mM dNTP. The reaction was incubated at 70°C for 5 min and held on ice to 276 \nallow the primer to anneal to mRNA with poly(A) tail. cDNA synthesis was performed by 277 \nadding 2.5 µl of Template Switching RT Buffer, 1 µl of Template Switching RT Enzyme 278 \nMix (#M0466, New England Biolabs, United Kingdom), and 0.5 µl of 75 μ M Template 279 \nSwitching Oligo (TSO) (5’–280 \nGCTAATCATTGCAAGCAGTGGTATCAACGCAGAGTACATrGrGrG–3’) to the 6-µl 281 \nprimer-annealed reaction. The reaction was incubated at 42°C for 90 min, 85°C for 5 min, 282 \nand held at 4°C. Full-length cDNA (fl-cDNA) was amplified by adding 12.5 µl of Q5 Hot-283 \nStart High-Fidelity 2X Master Mix (New England Biolabs, United Kingdom), 10 µl of 284 \nnuclease-free water, and 1 µl of 10 µM cDNA PCR Primer (5’–285 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 11 of 33 \nAAGCAGTGGTATCAACGCAGAGT–3’) to the 10 µl cDNA product. The thermal cycling 286 \nprofile was 98°C 45 s, 20× [98°C 10 s, 62°C 15 s, 72°C 3 min], 72°C 5 min, for yielding ~1 287 \nµg fl-cDNA product. We cleaned up the fl-cDNA using 1.2× AMPure XP (Beckman Coulter, 288 \nUnited States). 289 \n Nanopore libraries were constructed with the ligation sequencing chemistry using 290 \n~200 fmol pooled library (~250 ng for 2,000 bp c DNA). Nanopore libraries were then 291 \nsequenced and basecalled using the super-accuracy model on a GridION system (Oxford 292 \nNanopore Technologies, United Kingdom) at the Department of Biology, University of 293 \nOxford. 294 \n Basecalled reads were trimmed for Nanopore adaptors, the primer sequences, and 295 \nsplit for chimeras using dorado 0.6.2+14a7067. We then identified and quantified known and 296 \nnovel transcripts using IsoQuant 3.4.137 and minimap 2.28-r1209, supplied with the reference 297 \ngenome and gene annotation of D. cochinchinensis (Dacoc 1.2). We also extracted the 298 \ntranscript sequences from the transcript model generated by IsoQuant with gffread 0.12.7. 299 \n 300 \nDifferential gene expression analysis 301 \n We imported the gene-level count data from IsoQuant into R 4.4.1 and performed the 302 \nanalysis with DESeq2 1.44.0 38. We removed low-expression genes where there were less 303 \nthan 6 samples (the size of the smallest experimental unit) with normalised counts greater 304 \nthan or equal to 10. We conducted likelihood ratio tests to compare a full model which 305 \naccounts for provenance, ~ Treatment + Provenance + PC1 + PC2 + Treatment:PC1 + 306 \nTreatment:PC2, with a reduced model in which the effect of interest is removed. First, we 307 \ntested the main effect of drought treatment on gene expression ( the drought effect ) with a 308 \nreduced model of ~ Provenance + PC1 + PC2 + Treatment:PC1 + Treatment:PC2. Second, we 309 \ntested the differential effect of treatment on gene expression in isohydry (the isohydry effect), 310 \nas represented by the two principal axes (PC1 and PC2) using two LRTs with reduced models 311 \nof ~ Treatment + Provenance + PC1 + PC2 + Treatment:PC2 and ~ Treatment + Provenance 312 \n+ PC1 + PC2 + Treatment:PC1  for PC1 and PC2 respectively. For both tests, we applied 313 \nindependent hypothesis weighting, which could increase detection power in genome-scale 314 \nmultiple testing 39, with an FDR threshold of 0.05 to discover significantly differentially 315 \nexpressed genes (DEG). 316 \n We conducted gene set enrichment analyses (GSEA) on the effect size, which is the 317 \nχ 2-statistics in LRT, to search for Gene Ontology (GO) terms and Kyoto Encyclopedia of 318 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 12 of 33 \nGenes and Genomes (KEGG) pathways that are significantly enriched using clusterProfiler 319 \n4.12.040 and fgsea 1.30.041. 320 \n 321 \nDifferential transcript usage, annotations, and functional consequences 322 \n We analysed differential transcript usage with the pipeline IsoformSwitchAnalyzeR 323 \n2.8.042, which incorporated DEXSeq 1.55.1 43. First, we assessed isoform switching between 324 \ncontrol and drought-treated individuals, and set provenances as the batch effect ( the overall 325 \ndrought effect). Second, we assessed isoform switching between control and drought-treated 326 \nindividuals for each provenance ( the provenance effect ). We determined significant 327 \ndifferential transcript usage as those with a difference in isoform usage (dIF) > 0.01 and an 328 \nisoform switch Q-value < 0.05. Individual-level isohydry could be not used as a factor, like in 329 \nthe case of differential gene expression analysis, because continuous variables are not 330 \ncompatible with existing differential transcript usage analysis pipelines. 331 \n We annotated isoforms exhibiting significant differential transcript usage using a suite 332 \nof complementary tools. Coding potential was assessed with CPC2 1.0.144. Conserved protein 333 \ndomains were identified using pfam_scan.pl on Pfam database version 38.045. Signal peptides 334 \nwere predicted with SignalP 6.0 46. Subcellular localisation was inferred using DeepLoc 2.0 47 335 \nin Accurate mode. Transmembrane helices were predicted with DeepTMHMM 1.0.44 48. 336 \nIntrinsically disordered protein regions were identified using AIUPred v2.1.2 49 (aka IUPred 337 \n3). These annotations were integrated within the IsoformSwitchAnalyzeR framework to 338 \nevaluate the functional consequences of isoform switching events, including potential 339 \nchanges in coding potential, protein domain architecture, secretion signals, membrane 340 \nlocalisation, and structural disorder.  341 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 13 of 33 \nResults 342 \nVariable responses in water relation and photosynthetic traits  343 \nAll four water relation and photosynthetic traits varied significantly in response to the 344 \nchanges in soil water content (SWC) caused by the drought treatment. We also observed  345 \nprovenances effects and provenance x SWC interactions for several of the same traits. 346 \nStomatal conductance (g s) decreased with declining SWC by a coefficient of –8.063e-05 347 \n(F1,408 = 4.11, p  = 0.043), with significant variation among provenances (F 5,408 = 2.93, p = 348 \n0.013) and a significant SWC × provenance interaction (F 5,408 = 2.74, p = 0.019). Positive 349 \nslopes (higher gs with higher SWC) were observed in THA (1.20e-03), NP (1.03e-03), and PT 350 \n(1.01e-03) , while negative slopes (lower gs with higher SWC) were found in KKH (–5.52e-351 \n05), DN (–8.06e-05), and THB (–7.73e-04) (Figure 3a–b). Photosynthetic assimilation rate (A) 352 \nalso showed significant effects of SWC (F 1,408 = 5.39, p = 0.021) and provenance (F 5,408 = 353 \n4.47, p < 0.001), as well as a strong SWC × provenance interaction (F 5,408 = 4.97, p < 0.001). 354 \nTHA (0.0037) and NP (0.00051) had positive slopes, while PT (–0.0014), THB (–0.0018), 355 \nDN (–0.0038), and KKH (–0.010) had negative slopes ( Figure 3c–d). Transpiration rate ( E) 356 \nwas strongly influenced by SWC (F 1,408 = 14.52, p  < 0.001) and provenance (F 5,408 = 4.14, p  357 \n= 0.0011), while the SWC × provenance interaction was nearly significant (F 5,408 = 2.06, p = 358 \n0.069). Five of the provenances produced positive slopes PT (0.0060), THA (0.0059), NP 359 \n(0.0051), KKH (0.0020), and DN (0.0014) had and a negative slope was only observed in 360 \nTHB (–0.0014) ( Figure 3 e–f). Instantaneous water-use efficiency (WUE i) increased 361 \nsignificantly under lower SWC (F 1,408 = 19.82, p < 0.001), with a significant SWC × 362 \nprovenance interaction (F 5,408 = 4.23, p < 0.001) but no main effect of provenance. 363 \nAccordingly, most of the provenances had negative slopes, while THA (–0.0012), NP (–364 \n0.0029), PT (–0.0057), DN (–0.0065), and KKH (–0.015), while a positive slope was found 365 \nfor THB (0.00057) (Figure 3g–h). 366 \n 367 \nAnatomical and biochemical traits 368 \n Height change ( Δ  Height) showed a significant treatment × provenance interaction 369 \n(F5,48 = 3.06, p = 0.0177), although treatment (F1,48 = 1.47, p = 0.23) and provenance (F5,48 = 370 \n1.34, p = 0.26) alone were not significant. Final height was higher in drought-treated 371 \nindividuals in KKH (+1.2 cm), DN (+0.8), and NP (+0.2), and lower in drought-treated 372 \nindividuals in THB (–0.8), PT (–1.2), and THA (–2.6) ( Figure 4 a–b). Leaf ( Δ  Leaf) and 373 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 14 of 33 \nbranch ( Δ  Branch) number changes were unaffected by treatment, provenance, or their 374 \ninteraction (p > 0.05) (Supplementary Figure 1a–b). 375 \nChlorophyll a content (Chla) showed significant effects of provenance (F5,48 = 2.65, p 376 \n= 0.0342) and a significant treatment × provenance interaction (F5,48 = 3.14, p = 0.0156). Chla 377 \nwas higher in drought-treated individuals in KKH (+1.94), DN (+1.50), THB (+1.48), and 378 \nlower in THA (–0.20), NP (–0.73), and PT (–0.91) ( Figure 4c–d). No significant effects were 379 \ndetected for anthocyanin (Ac ), chlorophyll b ( Chlb), or carotenoids (Carot ), although the 380 \nprovenance effect on carotenoids was marginal (F 5,48 = 1.99, p = 0.0966) ( Supplementary 381 \nFigure 1c–e). 382 \nNeither leaf dry matter content (LDMC) nor specific leaf area (SLA) was significantly 383 \naffected by treatment, provenance, or their interaction, although SLA showed a marginal 384 \ntreatment effect (F1,48 = 2.99, p = 0.0904) (Supplementary Figure 1f–g). 385 \nNeither total soluble sugars ( TSS) nor starch ( Starch) concentrations differed 386 \nsignificantly among treatments, provenances, or their interactions ( p > 0.05) (Supplementary 387 \nFigure 1h–i). 388 \nAll ANOVA tables were summarised in Supplementary Table 3. 389 \n 390 \nCharacterisation and environmental association of isohydry 391 \n We characterised provenance- and individual-level isohydry using the first two 392 \nprincipal components (PC1 and PC2) that summarise the water relations and photosynthetic 393 \ntraits. The provenance-level two-dimensional isohydry space captured 67.58 + 32.05 = 394 \n99.63% of the variation in the coefficients of effect of SWC × Provenance on the water 395 \nrelations and photosynthetic traits (Figure 1b). PC1 co-varied largely with A and WUEi, while 396 \nPC2 co-varied largely with gs and E. The co-direction of loadings suggested that gs and E 397 \nwere positively correlated, and the same between A and WUEi. However, the orthogonality 398 \nsuggested that gs–E and A–WUEi were largely independent from each other. 399 \n The individual-level two-dimensional isohydry space captured 55.18 + 38.94 = 400 \n94.12% of the variation in water relations and photosynthetic traits (Figure 1c). PC1 co-varied 401 \nlargely with gs and E, while PC2 co-varied largely with A and WUEi. Similarly, gs–E and A–402 \nWUEi were largely independent from each other. 403 \n Bioclimatic variables were largely inter-correlated and after filtering, only four 404 \nvariables were retained in the model (VIF < 10), namely precipitation of wettest month 405 \n(bio_13), precipitation of driest month ( bio_14), and precipitation seasonality (bio_15). Only 406 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 15 of 33 \nprecipitation of wettest month (bio_13) showed a significant treatment × isohydry interaction 407 \n(F2,51 = 3.20, p  = 0.049) ( Figure 1d–e ). Higher precipitation of the wettest month led to a 408 \nhigher isohydry score. 409 \n 410 \nDifferentially expressed genes for drought response and isohydry 411 \n Full-length transcriptome sequencing yielded an average of 2.40 Gb (SD ± 0.85) for 412 \n36 individuals. The mean read length was 729.20 bp and N50 was 952.33 bp. 413 \n For the drought effect , we detected 76 genes that were significantly differentially 414 \nexpressed ( Figure 5a and b and Supplementary Table 4 ). The most statistically significant, 415 \nannotated genes were GASA14 (Dacoc22547), CYP77A4 (Dacoc12206), PME1 416 \n(Dacoc00037), DEG11 (Dacoc04747), CXE20 (Dacoc12653), RDUF2 (Dacoc26635), XTH9 417 \n(Dacoc27022), and THE1 (Dacoc27311). There were 21 drought-response genes that were 418 \nunannotated. Five gene ontology terms were enriched, including Golgi cis cisterna 419 \n(GO:0000137) (q = 0.0017), cell wall organization or biogenesis (GO:0071554) ( q = 0.016), 420 \nsmall molecule biosynthetic process (GO:0044283) ( q = 0.016), external encapsulating 421 \nstructure organization (GO:0045229) ( q = 0.019), and cell wall organization (GO:0071555) 422 \n(q = 0.025) (Supplementary Table 5). 423 \n For the  isohydry effect , we only detected two genes that were significantly 424 \ndifferentially expressed. One strongly associated with PC1 ( gs–E) was Dacoc11700 425 \n(AT4G14746) ( q = 0.025, Figure 5c ), that encodes a neurogenic locus notch-like protein. 426 \nHigher expression of Dacoc11700 led to a higher PC1 score, which implies steeper gs–E 427 \nslopes and thus early stomatal closure, a traditional isohydric response ( Figure 5d ). 428 \nDacoc11700 was predicted to contain a signal peptide and locate on the outside of cell 429 \nmembrane. The gene strongly associated with PC2 (A–WUE i) was Dacoc11936 (SEOR1, 430 \nSieve Element Occlusion Related 1) (q  = 0.00092, Figure 5 e), that encodes a protein 431 \nprimarily located within the sieve elements in the phloem. Similarly, higher expression of 432 \nSEOR1 led to a higher PC2 score (Figure 5f), which implies steeper A–WUEi slopes and thus 433 \ndecreased photosynthetic efficiency, also a traditional isohydric response. 434 \n 435 \nDifferential transcript usage 436 \n We detected significant provenance-specific isoform switches between well-watered 437 \ncontrols and drought-treated individuals for two provenances NP and THB, but not any 438 \noverall effect of drought treatment or provenance alone. 439 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 16 of 33 \n For THB, we detected 2 isoform switches. Dacoc21458 (PRX52, Peroxidase 52) 440 \nshowed a significant isoform switch (q = 0.020) between a full functional isoform RB and an 441 \ntruncated isoform transcript7028 which had an alternative transcription start site with a 442 \nmissing signal peptide ( Figure 6a ). The isoform fraction of RB decreased from 98.89% to 443 \n41.60% between control and drought, whereas that of transcript7028 increased from 1.11% to 444 \n58.40%, constituting a change of 57.29% ( Figure 6b ). We also discovered a novel and 445 \npreviously unannotated gene 16703 on chromosome 7 that showed a significant isoform 446 \nswitch (q = 4.72e-15) (Supplementary Figure 3a), which had 4 non-coding isoforms of varied 447 \nlengths and no domain or topology could be predicted. All 4 isoforms had relatively similar 448 \nabundance in control, but the longest isoform transcript16686 dominated 94.30% of the 449 \nabundance in drought, constituting a change in isoform fraction of 80.06% ( Supplementary 450 \nFigure 3b). 451 \nFor NP, we detected 2 further isoform switches. Dacoc26112 (ANN3, Annexin 3) 452 \nshowed a significant switch (q  = 0.0056) between a full functional isoform RA and three 453 \nother alternative isoforms ( Supplementary Figure 4a ). The isoform fraction of RA increased 454 \nfrom 10.22% to 45.23% between control and drought, whereas the non-coding isoform 455 \ntranscript41999 decreased from 67.18% to 8.03% ( Supplementary Figure 4b ). Another gene 456 \nDacoc06197 (LTPG5, Non-specific lipid transfer protein GPI-anchored 5) switched between 457 \na full functional isoform RA and an alternative isoform transcript43524 with an earlier start 458 \nsite ( q = 0.034) ( Supplementary Figure 5a ). The isoform fraction of RA increased from 459 \n55.62% to 94.59% between control and drought, whereas that of transcript43524 decreased 460 \nfrom 44.38% to 5.41%, constituting a change of 38.98% (Supplementary Figure 5b). 461 \nHowever most importantly, these genes have no significant difference in gene-level 462 \nexpression between control and treatment, and would not have been detected in the 463 \ndifferential gene expression analysis ( Figure 6c , Supplementary Figure 3c , Supplementary 464 \nFigure 4c, and Supplementary Figure 5c ). Therefore, isoform switches are the sole mechanism 465 \nthat regulates the response to drought in these genes.  466 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 17 of 33 \nDiscussion 467 \nDrought response and isohydry in Siamese rosewood 468 \n Decreased soil water content (SWC) imposes significant constraints on gs in Siamese 469 \nrosewood, but the magnitude and direction of responses are provenance-dependent, as shown 470 \nby the SWC × provenance interaction, which was significant for gs and A, and marginal for E. 471 \nThe near-orthogonality of gs–E and A–WUEi implies an independent carbon-economics 472 \ndimension, in which adjustments in stomatal water-flux is decoupled from photosynthetic 473 \nperformance. This observation means that different provenances adopt distinct combinations 474 \nof flux control and carbon maintenance in response to drought stress. Some provenances, 475 \nsuch as THB exhibited sharp reductions in E with relatively modest declines in A. In contrast, 476 \nindividuals from KKH and DN decreased E in response to declining water availability but 477 \nincreased A, thus increasing WUEi even when the level of drought increases. 478 \nThe E–A decoupling has been observed when a change in stomatal flux does not 479 \nalways correspond to a change in photosynthetic capacity. In some cases of extreme heat, 480 \ntranspiration continues even as photosynthesis declines to near zero ( E↑ A↓ )50, which may 481 \nstem from a passive physical effect of high temperatures that increases the fluidity of water 482 \nand the permeability of guard cells 51. This response also has an adaptive advantage where 483 \nsacrificing water is necessary for cooling by transpiration to enhance the survival of leaves 484 \nbut ultimately will deplete water stores 52. However, Siamese rosewood provenances KKH 485 \nand DN display a rare, opposite relationship, where transpiration decreases and 486 \nphotosynthesis continues to increase (E↓ A↑ ). There may be a completely different mechanism 487 \nvia mesophyll CO 2 conductance, thus maintaining comparatively high CO 2 supply to 488 \nchloroplasts even as stomata are tightly closed53. Aquaporin-mediated CO2 diffusion has been 489 \nfound to modulate mesophyll conductance and to respond to stress and hormones, such as 490 \ndrought and ABA priming 54–56. Mesophyll conductance is not measured in our study and its 491 \noccurrence in Siamese rosewoods is speculative, however, the observation of elevated WUE i 492 \nduring drought for KKH and DN may be linked to improved sustainability and growth 493 \noutcomes of trees57, especially for a species such as Siamese rosewood, which is fast growing, 494 \necologically pioneering species with high water consumption20. 495 \n Pigment and growth responses indicate functional acclimation without large structural 496 \nshifts over our experimental timeframe. We observed that Δ  Height and Chl a exhibit 497 \ntreatment × provenance interactions, whereas SLA, LDMC, TSS, and Starch  show no 498 \ndetectable treatment or interaction effects. Such patterns are broadly consistent with the 499 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 18 of 33 \nnotion that short-term drought primarily elicits adjustments in gas exchange, pigments, and 500 \nhormonal signalling, while structural reconfiguration often requires longer or more severe 501 \nstress and can vary with species and context 58. The high-WUEi provenances, KKH and DN, 502 \ncontinue to reflect the adaptive benefits of E–A decoupling, where in both provenances Chla 503 \nand Δ  Height has increased in drought-treated samples. 504 \n Characterising isohydry in a gs–E/A–WUEi framework as developed in the present 505 \nstudy depends on its definition. If isohydry is defined solely by the stomatal closure in 506 \ndeclining water availability, then judging by the direction of gs–E in the provenance-PCA, PT 507 \nand THA would be the most isohydric, and THB would be the most anisohydric at whole-508 \nprovenance levels. If the definition of isohydry also considers the assimilation and water use 509 \nefficiency, then judging by the direction of PC2 in the provenance-PCA, THA would be the 510 \nmost isohydric and KKH would be the most anisohydric at whole-provenance levels. Our 511 \nstudy highlights the heterogeneity in drought response in line with contemporary views of the 512 \nisohydry-anisohydry continuum, which emphasises that water-potential regulation emerges 513 \nfrom multiple, only partly covarying stomatal, hydraulic, osmotic, and metabolic processes, 514 \nrather than a single trait or score59,60. 515 \n 516 \nWettest months, not dry, determine the isohydry-anisohydry continuum 517 \n Precipitation in wettest months is found to be the single determinant factor for the 518 \nisohydry-anisohydry continuum in Siamese rosewood, with more isohydric the provenance 519 \nassociated with higher the precipitation. This may be explained by the high wet-season 520 \nprecipitation acting as a trait construction window which promotes anatomical features, such 521 \nas larger, more conductive earlywood vessels 61. However, larger vessels often track greater 522 \nvulnerability to cavitation, that is, higher P5062, and thus requires stronger stomatal control to 523 \nprotect the vulnerable xylem63. Moreover, stomatal density and size and cuticle properties are 524 \ngradually set towards leaf maturation 64, which may peak after the wettest month. Higher 525 \nprecipitation is reported to increase stomatal density across plant communities 65, and may 526 \nalso reduce cuticle thickness 66. Thus, the development and growth in wettest months are 527 \nlikely to determine anatomical adaptation that predicts drought response. 528 \nAn additional effect of higher precipitation in wettest months may be to reduce the 529 \ncarbon penalty commonly associated with isohydry during drought. Wet-season carbon gain 530 \ncan build up larger non-structural carbohydrate (NSC) pools, which are used for maintenance 531 \nrespiration, osmotic adjustment, and defence 67. NSC might persist into the dry season such 532 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 19 of 33 \nthat carry-over carbon can buffer the risk of carbon starvation under stomatal closure 68. A 533 \nwetter peak month also boosts soil and plant water stores, and thus higher capacitance 69. 534 \nStomata spend less total time closed to maintain the same hydraulic safety, thus the area 535 \nunder the photosynthetic curve can be larger even under isohydry 70,71. In contrast, drought 536 \nstresses in drier months only act on the anatomical and hydraulic settings after they are built 537 \nin wetter months, and thus stomatal and hydraulic limits are unlikely to be constrained by the 538 \ndry season.  539 \n 540 \nDifferential gene expression reveals different pathways 541 \n Most of our understanding of water status regulation along an isohydry-anisohydry 542 \ncontinuum in plants is at the level of hydraulics and stomatal physiology while very little is 543 \nknown of its genetic control. Comparative omics has begun to reveal drought-response 544 \nmodules in model and crop plants, and a few tree systems, but explicit searches for putative 545 \nregulators of isohydry, beyond generic drought or ABA pathways, remain scarce and largely 546 \nindirect72,73. Here we take a different tack by mapping transcript abundance onto composite 547 \naxes of water-use and carbon strategies, thereby nominating candidate regulators of these key 548 \ntraits. 549 \n The Dacoc11936 gene, which is homologous to SEOR1, is the only gene found to be 550 \nassociated with the phenotypic variation along the A–WUEi axis. It encodes a structural 551 \nphloem P-protein that polymerises and occludes sieve elements upon wounding 74. Although 552 \nlive-imaging studies show that SEOR1 agglomerations do not significantly alter phloem flow 553 \nunder non-wounding conditions in Arabidopsis thaliana75, phloem protection could represent 554 \nan adaptive response to recurrent mechanical stress in Siamese rosewood due to seasonally 555 \ndry conditions of southeast Asian forests. This prediction is plausible given that assimilation 556 \ncan be feedback-limited by sink unloading and transport capacity as phloem unloading is 557 \nlargely convective with a diffusion component 76. Therefore, it is plausible that SEOR1-558 \nmedicated occlusion stabilises phloem transport, buffering carbon allocation and thus A–559 \nWUEi. 560 \nDacoc11700 (AT4G14746) is the only gene found to be associated with the 561 \nphenotypic variation along the gs–E axis. It is annotated as a “neurogenic locus notch-like 562 \nprotein” and remains poorly experimentally characterised in Arabidopsis, but it is predicted 563 \nto be extracellular 77. It contains a signal peptide and thus could hypothetically contribute to 564 \ncell-cell or apoplastic signal perception of cues such as ABA. It represents a candidate 565 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 20 of 33 \nregulator for isohydry that requires functional validation, such as a CRISPR/Cas9 knock-out 566 \nor overexpression complemented by sub-cellular localisation analyses to verify its apoplastic 567 \ntargeting. 568 \nWe have identified many drought-responsive genes that have been validated in 569 \nprevious research. For example, GASA14 is a small, cysteine-rich, secreted apoplastic 570 \npeptide that integrates GA and ABA responses and modulates reactive oxygen species (ROS) 571 \naccumulation78. DEG11 is a chloroplastic protease that helps degrade photodamaged 572 \nphotosystem during photoinhibition, which is important for limiting photooxidative damage 573 \nwith drought-induced stomatal closure and excess excitation 79. CXE20 binds strigolactones 574 \n(SLs) which impact root system architecture and mycorrhizal signalling, such that altered SL 575 \navailability can shift water foraging and carbon allocation under drought stress 80. PME1, 576 \nXTH9, and THE1 all contribute to the modulation of cell wall organisation: PME1 is 577 \nresponsible for pectin de-esterification of homogalacturonan in the cell wall that changes 578 \npectin stiffness and porosity 81; XTH9 remodels hemicellulose and drives xylem cell 579 \nexpansion82; THE1 responses to cell wall damage and transduces into hormonal response 580 \nsuch as abscisic acid (ABA) production and wall remodelling 83. The associated matrix 581 \npolysaccharides are synthesised or methyl-esterified in the Golgi cis-cisterna before secretion 582 \nand thus can adjust wall composition during drought84. 583 \n 584 \nDifferential transcript usage is a potential mechanism of local adaptation 585 \n Alternative transcript usage can enable rapid loss- or gain-of-function under drought, 586 \nby toggling coding potential, reshaping domains, exposing transcripts to nonsense-mediated 587 \ndecay, or adding or removing signal peptides that redirect secretion and subcellular 588 \nlocalization, thereby tuning processes from guard-cell signalling to cell-wall and cuticle 589 \nbiogenesis85–87. With most plant drought transcriptomics aggregating data to the gene level, 590 \nisoform diversity and its functional consequences are obscured 88, and therefore population-591 \nspecific or locally adapted mechanisms to distinct hydroclimates are missed. 592 \n We detected two isoform switches between control and drought treatment specific to 593 \nthe provenance THB, which was identified as the most anisohydric considering gs–E axis. 594 \nWe observed that PRX52 switched to a drought-dominant, loss-of-function isoform with a 595 \nmissing signal peptide. PRX52 is a class III apoplastic peroxidase involved in the synthesis of 596 \nS units in interfascicular fibres during lignification 89. Class III peroxidases characteristically 597 \npossess an N-terminal signal peptide for entry into the secretory pathway 90, and co-localise 598 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 21 of 33 \nwith laccases to lignifying wall domains during secondary cell-wall deposition 91. Therefore, 599 \nthe drought-dominant isoform of PRX52 is likely to result in reduced lignin synthesis and 600 \nhigher cell extensibility, which might facilitate stomatal opening and mesophyll expansion at 601 \nlower turgour92. Class III peroxidases also broadly modulate reactive oxygen species (ROS) 602 \nhomeostasis during stress responses 93. Reduced secretion of PRX52 may dampen the 603 \napoplastic hydrogen peroxide (H2O2) burst, which is a proximate trigger of stomatal closure, 604 \nand thus sustain a higher gs. Other class III peroxidases PRX4, PRX33, PRX34, and PRX71 605 \nhave been shown in guard cells 94. This discovery of reduced secretion in PRX52 regulating 606 \nanisohydry opens novel avenues for drought research, which may be validated in future 607 \nstudies with ROS staining and imaging in guard cells. 608 \n We detected a gain-of-function phenomenon in the provenance NP, where both 609 \nANN3 and LTPG5 switched from non-coding or non-native isoforms, respectively, to the 610 \nfunctional coding isoform during drought. Annexins are Ca² /i5 -dependent phospholipid-611 \nbinding proteins that relocalise to the plasma membrane when cytosolic Ca² /i5  rises, 612 \nparticipate in vesicle trafficking and exocytosis, and modulate ROS–Ca² /i5  signalling in 613 \nstress95. ANN3 specifically has been linked to eATP-regulated growth and vesicle polarity 96. 614 \nThe drought-biased ATTS switch may thus upgreulate ANN3 protein to stabilise both the 615 \nplasma membrane and exocytosis that maintains cell wall supply and repair 97. On the other 616 \nhand, GPI-anchored LTPGs  localise to the outer leaflet of the plasma membrane and are 617 \nrequired for exporting cuticular lipids and depositing suberin to the plant surface 98. LTPG5 618 \nhas confirmed roles in defence signalling, typical of many GPI-APs that interface with RLKs 619 \nat the cell surface 99. The drought-responsive LTPG5 full-length isoform may act by 620 \nreinforcing the cuticle and reducing non-stomatal water loss. 621 \n 622 \nImplications for conservation and drought research 623 \n This study provides spatially explicit evidence for drought-responsive conservation of 624 \nSiamese rosewood, which is crucial in times of intensifying drought and extreme weather 625 \nconditions in their habitat. It enables climate-adjusted provenancing and assisted gene flow, 626 \nsuch as matching seed sources to planting sites using a dual filter that combines each 627 \nprovenance’s position in the isohydry space with wettest-month precipitation of both source 628 \nand target sites. High- WUE/i1, growth-maintaining provenances, such as KKH and DN, are 629 \nprime candidates for dry, variable environments, but should be deployed in diverse, mosaic 630 \nmixes to hedge risk and preserve genetic diversity. We present marker panels supported by 631 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 22 of 33 \nunderstanding of underlying mechanism useful for drought tolerance screening and breeding, 632 \nsuch as common variants responsible for cell wall, but other key drought-responsive genes 633 \nand isoform switches also offer novel avenues in engineering drought-tolerant plants. Future 634 \ndrought research in Siamese rosewood and at large may include common garden validation of 635 \nwettest-month precipitation in shaping isohydry behaviour. Our work could support several 636 \nobjectives: (1) extend measurements into mesophyll conductance and test aquaporin 637 \ninvolvement in improving water-use efficiency; (2) integrate our genomic markers into 638 \ngenomic selection for water-efficient growth; and (3) run operational pilots that compare 639 \nsurvival, growth, and hydrological impacts of provenance mixes under real reforestation 640 \nsettings. Siamese rosewood is a promising species that has pioneering ability suitable for 641 \nforest landscape restoration. Conserving this critically endangered species will continue to 642 \nrealise its ecological and socioeconomic value and may set a valuable model for other 643 \nthreatened tropical tree species in Southeast Asia.  644 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nFigures  645 \n 646 \n 647 \nFigure 1. (a) Six local seed provenances of Dalbergia cochinchinensis in this study. The map is colour coded with the 648 \nprecipitation of wettest month (mm). The colour scheme of the provenances is consistent in all figures throughout this 649 \nmanuscript. (b) Provenance-level PCA on the coefficients of effect of Treatment × Provenance on physiological traits that 650 \nshow significant interactions. (c) Individual-level PCA on physiological traits that show significant interactions. (d) The 651 \ninteraction effect of precipitation of wettest month and treatment on the isohydry score (which is a composite score derived 652 \nfrom the first principal component (PC1) of the individual-level PCA in Figure 1c, see Methods for details).  (e)  653 \nCorresponding interaction plot, which shows model-predicted relationships and the fitted values between precipitation of 654 \nwettest month and isohydry score. 655 \n  656 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \n 657 \n 658 \nFigure 2. Experimental design of the greenhouse experiment. We studied 10 plants per provenance and randomly assigned 659 \nhalf of the plants to either well-watered control (C) or water-withholding treatment (D). 660 \n 661 \n  662 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \n 663 \nFigure 3. (a)  Stomatal conductance ( gs), (c) assimilation rate ( A), (e) transpiration rate ( E), and (g)  water use efficiency 664 \n(WUEi) along the gradient of soil water content (SWC). All traits were square-root transformed to correct for normality. (b), 665 \n(d), (f), and (h)  Corresponding interaction plots of g s, A, E, and WUE i, which show model-predicted relationships and the 666 \nfitted values between SWC and the corresponding trait. 667 \n  668 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \n 669 \n 670 \n 671 \nFigure 4. (a)  Change in height ( Δ  Height) and (c)  chlorophyll a content ( Chla) between control (C) and drought (D) among 672 \nsix provenances. (b) and (d) are the corresponding interaction plots, which show model-predicted relationships and the fitted 673 \nvalues between treatment and the corresponding traits. Chla was log-transformed to correct for normality in the model in 674 \nFigure 3d, but raw values were visualised in Figure 3c. 675 \n  676 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \n 677 \n 678 \nFigure 5. Volcano plots of –log 10 q-values against log2 fold change for (a) the drought effect, (c) the isohydry effect (using 679 \nPC1), and (e) the isohydry effect (using PC2)  using likelihood ratio test (LRT) between a full model and a reduced model 680 \nwithout the effect of concern. (b) Manhattan plot of –log 10 q-values against chromosome position for the drought effect. (d)  681 \nVST-transformed gene expression of Isohydry PC1-associated gene Dacoc11700 (AT4G14746). (f) VST-transformed gene 682 \nexpression of Isohydry PC2-associated gene Dacoc11936 ( AtSEOR1) C and D denote well-watered control and drought 683 \ntreatment respectively. 684 \n  685 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \n686 \nFigure 6. Isoform switch of Dacoc21458 (PRX52) between control and drought treatment in provenance THB. (a) 687 \nStructures and annotations of the two isoforms. (b)  Gene expression between control (C) and drought (D) conditions. (c) 688 \nIsoform fraction of the two isoforms between control and drought conditions. (c) Gene expression between control (C) and 689 \ndrought (D) conditions. 690 \n 691 \n692 \n \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint \n\nDrought response of Siamese rosewood  Hung et al. 2025 \nPage 29 of 33 \nReferences 693 \n 694 \n1. Martínez-Vilalta, J., Lloret, F. & Breshears, D. D. Drought-induced forest decline: causes, 695 \nscope and implications. Biol Lett 8, 689–691 (2012). 696 \n2. Hammond, W. M. et al. Global field observations of tree die-off reveal hotter-drought 697 \nfingerprint for Earth’s forests. Nat Commun 13, 1–11 (2022). 698 \n3. Gazol, A., Pizarro, M., Hammond, W. 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Int J Mol Sci 21, 1774 (2020). 936 \n  937 \n 938 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 16, 2025. ; https://doi.org/10.64898/2025.12.14.692107doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}