Digital Commons Network^™

Phenotypic And Biochemical Responses Of High-Yield Tomato Varieties To Salinity And Drought Stress, Shahida Ferdousee, Myung Hwangbo, Md Sakil Arman, Muhammad Abul Kalam Azad, Ajit Ghosh, Jongsun Kim

School of Earth, Environmental, & Marine Sciences Faculty Publications

Abiotic stresses can influence plant growth and productivity by causing physiological, biochemical, molecular, and morphological changes. Salinity and drought are increasing in frequency and intensity because of climate change. Therefore, this study assessed four high-yield tomato cultivars under 150 mM NaCl and 260 mM mannitol treatments to characterize physiological (i.e., chlorophyll level, root/shoot length, and relative water content) and biochemical responses (i.e., GLY I/II and DLDH enzyme activities, proline, and hydrogen peroxide). Results indicated that the four BARI tomato varieties showed genotype- and treatment-specific physiological and biochemical responses rather than a single uniform tolerance pattern. BARI tomato 2 maintained relatively …

A Granularity-Centered Taxonomy Of Personalized Federated Learning, Ei Ei Nyein Chan, Sergei Chuprov, Pretom Roy Ovi, Kamrul Hasan

Computer Science Faculty Publications

Personalized Federated Learning (PFL) has emerged as a key approach to address performance degradation in FL systems under heterogeneous client data. While existing surveys typically categorize PFL methods based on optimization strategies or system-level mechanisms, they often overlook a fundamental question: where is personalization embedded within the model architecture? In this survey, we bridge this knowledge gap and introduce a granularity-centered taxonomy that organizes PFL approaches according to the structural depth of personalization, ranging from head-layer and layer-wise adaptation to model-wise and parameter-wise customization. This novel perspective helps practitioners select appropriate personalization strategies based on model architecture, data heterogeneity, and …

The 2025 Measles Outbreak In Texas, Tamer Oraby, Martial L. Ndeffo-Mbah

School of Mathematical & Statistical Sciences Faculty Publications

Background: In 2025, Texas experienced its largest measles outbreak in decades, reporting 762 cases by mid-August. Measles is a highly contagious but vaccine-preventable infection transmitted mainly among unvaccinated individuals and capable of causing severe outcomes.

Methods: We investigate counterfactual measles control scenarios based on daycare and school closures and reactive vaccination of infants and children, including mixed interventions. We analyze the 2025 Texas outbreak using an age-structured multi-stage SEIR model formulated as a system of ordinary differential equations. The model is fit to case data using Bayesian inference to estimate the effective reproduction number and generate posterior predictive trajectories under …

Dry After-Ripening Increases Germination Of Critically Imperilled Physaria Thamnophila (Zapata Bladderpod), Christopher A. Gabler, Jerald T. Garrett

School of Earth, Environmental, & Marine Sciences Faculty Publications

Effective endangered plant conservation and recovery require empirical knowledge of seed dormancy release and responses to post-collection handling, yet such information is lacking for many rare taxa. Physaria thamnophila (Zapata bladderpod; Brassicaceae), a critically imperilled (G1) species endemic to southern Texas, USA, is poorly studied, and basic questions regarding seed storage and germination remain unresolved. We evaluated the effects of short-term cool-storage duration (14–180 d), storage temperature (refrigeration vs. freezing), dry after-ripening and maternal plant identity on germination probability and timing in P. thamnophila. Dry after-ripening and increased cool-storage duration consistently increased germination and shortened germination time, whereas storage temperature …

Influence Of Transboundary Wildfire Smoke On Fine Particulate Matter Concentrations At South Texas Border Schools, Sai Deepak Pinakana, Kabir Bahadur Shah, Owen Temby, Dawid K. Wladyka, Juan L. Gonzalez, Md Saydur Rahman, Katarzyna Sepielak, Amit U. Raysoni

School of Earth, Environmental, & Marine Sciences Faculty Publications

Exposure to air pollutants in indoor and outdoor school environments poses significant health risks to children due to their heightened vulnerability. Increasing wildfire frequency has intensified transboundary smoke transport, with border communities among the first to experience associated air quality impacts. While the impacts of wildfires on air quality are increasingly recognized through satellite observations and modeling, ground-based observations remain scarce in under-monitored regions. Low-cost air sensors were deployed across schools in the Roma and Rio Grande City areas of Starr County, along the U.S.–Mexico border, for 146-days from November 2023 to April 2024. The raw sensor measurements were corrected …

Why Technical Readiness Is Not Enough: Institutional Barriers To Full Harvest Strategy Adoption In The International Pacific Halibut Commission, Evelyn Roozee, Owen Temby, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Harvest strategies are increasingly promoted as a mechanism to reduce political discretion and align fisheries management with pre-agreed scientific objectives. While substantial progress has been made in tuna regional fisheries management organizations (RFMOs), non-tuna RFMOs have lagged behind. Existing research has focused primarily on technical modeling challenges, with comparatively limited attention to the governance dynamics shaping adoption. Using the International Pacific Halibut Commission (IPHC) as an instrumental case study, we examine the progress and delays in the development of a harvest strategy policy (HSP) through semi-structured interviews and participant observation. We find that the IPHC successfully addressed commonly cited barriers, …

Remotely Assessing Foundational Skills Of 5–14-Year-Old Children: A Six-Country Psychometric Evaluation Of The Remote Assessment Of Learning (Real), Elizabeth Hentschel, Sascha Hein, Nan Li, Clay Westrope, Julia Taladay, Gillian Valentine, James Leckman, Amira Abdurahman, Fatime Bachir, Farah Darwazeh

Psychological Science Faculty Publications

Approximately 250 million children worldwide are out of school. There is growing consensus for investing in feasible, contextually appropriate, psychometrically tested, remote tools to support quality education in low-resource contexts, including low- and middle-income countries and crisis-affected contexts. Save the Children developed the Remote Assessment of Learning (ReAL) to assess 5–14-year-old children's foundational learning. Children (N = 4,840) were sampled from Cambodia, Mozambique, Niger, the occupied Palestinian territories, the Philippines, and Sudan, with an approximately equal proportion of male and female children within each country. The study assessed inter-rater reliability, factor structure, item slope and difficulty, criterion validity, …

Human-Centered Electric Vehicle Adoption Framework For Smart Mobility: Modeling Perceived Range And Charging Anxiety As A Psychological Barrier, Fatemeh Nazari, Abolfazl (Kouros) Mohammadian, Thomas Stephens

Civil Engineering Faculty Publications

Electric vehicles (EVs) offer a transformative pathway toward reducing the environmental, economic, and health-related externalities of internal combustion engine vehicles in urban settings. Despite substantial advances in battery technology, charging infrastructure expansion, and supportive policy incentives, EV penetration remains limited which poses challenges for smart and sustainable mobility planning. A critical yet insufficiently modeled barrier to adoption lies in the psychological perceptions surrounding electric driving range and charging reliability, which is commonly framed as “range anxiety,” but more broadly reflecting perceived range and charging anxiety. To address this gap, this study introduces a latent psychological construct capturing individuals’ perceived range …

Bayesian Variable Selection In High-Dimensional Ordinal Quantile Regression Models, Mai Dao, Md. Sakhawat Hossain, Zhuanzhuan Ma

School of Mathematical & Statistical Sciences Faculty Publications

Quantile regression (QR) provides a flexible statistical framework for modeling the entire conditional distribution of the response variable, making it useful for analysis in various fields. Despite its advantages, existing methods for QR often encounter numerical challenges in high-dimensional settings, especially for those with ordinal responses. In this paper, we use a latent-response framework to construct a Bayesian hierarchical model to conduct parameter estimation and variable selection for ordinal QR. Using the asymmetric Laplace working likelihood and the horseshoe prior for the regression coefficients, we obtain the posterior samples to be screened by the sequential two-means clustering process to identify …

A Finite Element Model To Analyze Crack-Tip Fields In A Transversely Isotropic Strain-Limiting Elastic Solid, Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah

School of Mathematical & Statistical Sciences Faculty Publications

This paper presents a finite element model for the analysis of crack-tip fields in a transversely isotropic strain-limiting elastic body. A nonlinear constitutive relationship between stress and linearized strain characterizes the material response. This algebraically nonlinear relationship is critical as it mitigates the physically inconsistent strain singularities that arise at crack tips. These strain-limiting relationships ensure that strains remain bounded near the crack tip, representing a significant advancement in the formulation of boundary value problems (BVPs) within the context of first-order approximate constitutive models. For a transversely isotropic elastic material containing a crack, the equilibrium equation, derived from the balance …

Greenhouse Gas Flux Response In Biochar- And Compost-Amended Lawn Soils Under Simulated Water Saturation Conditions, Angel Salinas, Chu-Lin Cheng, Engil Pereira, Rafael M. Almeida, James Jihoon Kang

School of Earth, Environmental, & Marine Sciences Faculty Publications

Understanding greenhouse gas emission dynamics in lawn soils is important for improving climate change mitigation strategies in urban and suburban landscapes. In this greenhouse mesocosm study, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes were measured from 12 turfgrass soil columns arranged in an unreplicated, completely randomized 4 × 3 factorial design. Four amendment treatments (biochar, compost, bio-com [1:1 volume ratio of biochar and compost], and unamended control) were combined with three water-table conditions (full saturation, half saturation, and unsaturated), with a single column representing each treatment combination. Columns were irrigated at 1 cm day−1 for 24 days, …

From Algorithm To Alarm: A Narrative Review On Insulin Delivery Innovation, Noor Ul Asaleem, Faraz Arshad, Maria Qadri, Muhammad Saqlain Mushtaq, Mishaim Khan, Pakeeza Shakoor, Tirath Patel, Anaya Noor, Nikhilesh Anand

School of Medicine Publications

Background and objective: The global rise in the incidence of diabetes mellitus necessitates innovative management strategies to mitigate the severity of the disease. The mainstay of diabetes treatment is still insulin therapy, although there are drawbacks, such as the need to maintain ideal glycemic control while lowering the risk of hypoglycemia and user fatigue. The development, clinical usefulness, and constraints of insulin delivery systems are examined in this narrative review, with particular attention to sensor-augmented and closed-loop technologies.

Methods: A thorough electronic database search was conducted to obtain pertinent data on major insulin delivery devices, including sensor-augmented and closed-loop systems. …

A Visitation Grid For Complete Coverage Foraging In Robot Swarms, Arturo Gonzalez, Yifeng Gao, Li Zhang, Qi Lu

Computer Science Faculty Publications

The complete collection of sparse resources in large, unknown environments remains a challenging problem for autonomous robot swarms. Previous studies have shown that a substantial portion of total mission time is consumed during the final stage of collection, where only a small fraction of randomly scattered resources remain. Consequently, many existing swarm foraging algorithms (search and collection) focus on collecting most resources within a limited time window, rather than improving end-stage efficiency for collecting all resources. We propose a grid-based stochastic foraging strategy that explicitly reduces redundant visits and accelerates late-stage collection. The unknown search area is partitioned into a …

Environmental Dna For Endangered Freshwater Mussel Monitoring: A Review On Global Synthesis Of Edna Methods, Challenges, And Innovative Strategies, Mizanur Rahman, Md. Saydur Rahman, Richard J. Kline

School of Earth, Environmental, & Marine Sciences Faculty Publications

Freshwater mussels (family Unionidae) are among the most endangered aquatic species worldwide, making effective monitoring vital for their conservation. Traditional surveys are laborious, time-consuming, and often miss rare or low-density populations. Environmental DNA (eDNA) is a highly sensitive, non-invasive approach for freshwater mussel detection and monitoring; however, uncertainties in ecological interpretation continue to limit its broader application in freshwater biodiversity monitoring. We conducted a systematic review of 49 peer-reviewed studies, identified through an extensive literature search, to assess temporal research trends, methodological advancements, ecological contexts, and regulatory readiness of freshwater mussel eDNA monitoring. Findings show that freshwater mussel eDNA research …

Deep Neural Networks: A Formulation Via Non-Archimedean Analysis, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

We introduce a new class of deep neural networks (DNNs) with multilayered tree-like architectures. The architectures are codified by using numbers in the ring of integers of non-Archimedean local fields. These rings have a natural hierarchical organization as infinite rooted trees. Natural morphisms on these rings allow us to construct finite multilayered architectures. The new DNNs are robust universal approximators of real-valued functions defined on the mentioned rings. We also show that the DNNs are robust universal approximators of real-valued square-integrable functions defined in the unit interval.

A Machine Learning Approach For Water Quality Assessment In The Lower Rio Grande Valley Watershed, Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An, Fatemeh Nazari

Civil Engineering Faculty Publications

Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. …

Using A Cell Flux Model To Investigate Phytoplankton Growth And Macromolecular Allocation Under Iron Limitation, Margaret Bernish, Meng Gao, Jongsun Kim, Benoit Pasquier

School of Earth, Environmental, & Marine Sciences Faculty Publications

Global biogeochemical models are powerful tools used to interpret observations and empirical data, create hypotheses, and make predictions. Many of these models, however, represent marine microbes as static stoichiometric reactions that convert nutrients into biomass. To better constrain the biological processes that control nutrient cycling in the global ocean, we extend the Cell Flux Model of Phytoplankton to explore the impact of dissolved iron concentration on the allocation of macromolecules and elemental ratios within phytoplankton cells. This model is supported by data obtained from a range of incubation experiments conducted with phytoplankton of varying species and size categories. Model output …

Tile-Based Knot Assembly With Celtic!, Divya Bajaj, Ryan Knobel, Juan Manuel Perez, Rene Reyes, Ramiro Santos, Tim Wylie

Computer Science Faculty Publications

In this paper we focus on the intersection of tile assembling systems, edge-matching puzzles, combinatorial games, and knot construction and identity. As a basis, we utilize the game Celtic!, which is a 2-player board game where the goal of the game is to construct knots where one knot uses more of a player’s pieces than the other player over all knots. All pieces must build off an existing knot and a valid knot must be closed. We consider three variations: a 0-player self-assembly variation that deterministically places pieces to form a closed knot of some length, a 1-player puzzle variation …

Cultural Traits May Replace Human Mobility Data In Forecasting Covid-19 Mortality: A Deep Learning Approach, Saif Abbas, Tamer Oraby, Michael G. Tyshenko, Samit Bhattacharyya

School of Mathematical & Statistical Sciences Faculty Publications

The COVID-19 pandemic highlighted the need for accurate epidemic forecasting to support public health decision-making. Most existing approaches depend heavily on human mobility data, while largely neglecting population behavior shaped by socio-cultural norms. In this study, we analyze daily COVID-19 mortality and Google mobility data from 72 countries during the first 130 d of the pandemic, a period characterized by high uncertainty and behavioral heterogeneity. In particular, we examine whether Hofstede’s country-level cultural dimensions can serve as latent behavioral forecasters of mortality in lieu of dynamic mobility indicators. Using 100 d for training and 30 d for forecasting, we employ …

A Bayesian-Optimized Ensemble Deep Learning Framework For Automated Detection And Classification Of Retinal Diseases In Ghana Using Oct Images, Gifty Duah, Eric Nyarko, Gideon Nana Amo, Theophilus Dwamena Frimpong, Anani Lotsi

School of Mathematical & Statistical Sciences Faculty Publications

Retinal diseases pose a significant global health challenge due to their potential to cause severe visual impairment and blindness. This study aimed to develop a robust deep learning ensemble framework for the automated detection and classification of retinal diseases from optical coherence tomography (OCT) images. This study used OCT images from WATBORG Eye Services in Ghana, including glaucoma, macular edema, posterior vitreous detachment (PVD), and healthy eyes. The data preprocessing steps included augmentation, resizing, and one-hot encoding. The dataset was divided into training (56%), validation (14%), and testing (30%) sets using stratified sampling. Six convolutional neural network (CNN) architectures, Visual …

Ni2-Imido Bispincer Platforms: Cooperative Reactivity Enabled Through Substrate Addition Across A Ni2n Unit, David De Los Santos, Kritika Gour, Manar M. Shoshani

School of Integrative Biological & Chemical Sciences Faculty Publications

Transition metal imido complexes are renowned for their capacity to undertake challenging 1,2 additions owing to the high basicity of the imido moiety. Access to transition metal imidos is often limited to the addition of organic azides to metal precursors, hindering the design of molecular constructs incorporating imido units. Bimetallic bispincer complexes, 1 and 2, are presented, which are anchored by a central imido moiety supported by pyridyl imine donors, forming highly planarized Ni2N-aryl cores. Complex 2, which also features a Ni─Ni σ-bond, is able to engage in cooperative substrate cleavage of main group hydrides via a formal 1,2 addition …

Propagation Of Dirac Spherical Waves In The Expanding Universe, Karen Yagdjian

School of Mathematical & Statistical Sciences Faculty Publications

The explicit formulas for the spherical solutions of the Dirac equation in the expanding universe are given. The initial value of the solution can be, in particular, a wave function of the hydrogen-like atom or a spherical wave in the Minkowski space, that then propagates in the Friedmann-Lemaître-Robertson-Walker space-time, which is expanding with the de Sitter scale factor.

Comparative Analysis Of Traditional And Deep Learning Time Series Architectures For Influenza A Infectious Disease Forecasting, Edmund Fosu Agyemang, Hansapani Rodrigo, Vincent Agbenyeavu

School of Mathematical & Statistical Sciences Faculty Publications

Influenza A remains a major cause of respiratory mortality worldwide, motivating accurate forecasting to support timely preparedness and resource allocation. This study presents a comparative evaluation of two traditional seasonal time series baselines, ARIMA and Holt–Winters exponential smoothing (ETS), and six deep learning (DL) architectures (Simple RNN, LSTM, GRU, BiLSTM, BiGRU, and a Transformer) for forecasting monthly Influenza A case counts in the United States. Data from January 2009 to December 2023 were analyzed, using January 2009 to December 2022 for training and January 2023 to December 2023 for out-of-sample testing. Models were tuned using a validation split and assessed …

Human-Driven, Autonomous, Or Hybrid? The Optimal Fleet Configurations For Ride-Hailing Platforms, Wenjing Li, Yali Zhang, Jun Sun, Zhaojun Yang

Information Systems Faculty Publications

The growing commercialization of autonomous vehicles (AVs) is reshaping consumer service preferences and prompting ride-hailing platforms to redesign fleet structures that accommodate the coexistence of human-driven vehicles (HVs) and AVs. This article develops a queueing game framework that incorporates vehicle heterogeneity and consumer preference differences to systematically compare three fleet configuration strategies: the pure HV (PHV) strategy (HVs only), the pure AV (PAV) strategy (AVs only), and the hybrid strategy (both HVs and AVs). The analysis highlights how consumer mismatch losses, AV operating costs, and service rates jointly shape equilibrium outcomes. Results show that when consumer mismatch losses are moderate, …

Gwtc-4.0: Population Properties Of Merging Compact Binaries, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

We detail the population properties of binary neutron star, neutron star–black hole binary, and binary black hole mergers using 158 events from the cumulative Gravitational-Wave Transient Catalog 4.0. The black hole primary mass distribution consists of a power-law-like continuum that steepens above 35 M⊙ with overdensities at 10 M⊙ and 35 M⊙. Binary black holes with primary masses near 10 M⊙ are more likely to have less massive secondaries, with a mass ratio distribution peaking at  q=0.74+0.13−0.13, potentially a signature of stable mass transfer during binary evolution. Black hole spins are inferred to be nonextremal, with …

Narrowband Searches For Continuous Gravitational Waves From Known Pulsars In The First Two Parts Of The Fourth Ligo–Virgo–Kagra Observing Run, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J. Lawrence, Francisco Llamas Villarreal, Miriam Ramos Arevalo, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

Rotating nonaxisymmetric neutron stars (NSs) are promising sources for continuous gravitational waves (CWs). CWs may, if detected, inform us about the internal structure and equation of state of NSs. Here, we present a narrowband search for CWs from known pulsars, for which a matched-filter search can be applied. Narrowband searches are robust to mismatches between electromagnetic (EM) and gravitational emissions, in contrast to fully targeted searches where they are assumed to be phase-locked. In this work, we search for the CW counterparts emitted by 34 pulsars using data from the first and second parts of the fourth LIGO–Virgo–KAGRA observing run. …

Synergistic Effects Of Arbuscular Mycorrhizal Fungi And Foliar Nitrogen–Phosphorus Application On Maize Productivity Under Irrigated And Rainfed Conditions, Mst. Lailatul Ferdows, Saima Biswas, Mizanur Rahman, F. M.Jamil Uddin, Md. Nayan, Aporna Tabassum Api, Mohaiminul Islam, Nadia Islam, Swapan Kumar Paul, Md. Harun Rashid

School of Earth, Environmental, & Marine Sciences Faculty Publications

A field experiment was conducted at the Agronomy Field Laboratory of Bangladesh Agricultural University to evaluate the effects of arbuscular mycorrhizal fungi (AMF) inoculation and foliar supplementation of nitrogen (N) and phosphorus (P) on the performance of maize (Zea mays L.) under irrigated and rainfed conditions. The experiment followed a randomised complete block design with two levels of AMF (inoculated and non-inoculated) and four foliar treatments (no N and P, N only, P only, and combined N + P). The recommended dose of fertiliser (RDF) was applied as a soil application to all treatments. AMF inoculation significantly increased grain yield …

Rapid Recovery Of Peripheral Oxygen Saturation In Hypoxic Covid-19 Patients With Ivermectin/Doxycycline/Zinc Multidrug Therapy, Sabine Hazan, Adriana C. Vidal, Eleftherios Gkioulekas, Anoja W. Gunaratne, Sibaish Dolai, Robert L. Clancy, Peter A. Mccullough, Thomas J. Borody

School of Mathematical & Statistical Sciences Faculty Publications

Several combination therapies for the early outpatient treatment of COVID-19 were proposed by independent research groups at the onset of the pandemic during 2020 and 2021. In this observational study, we report on the outcomes of an off-label triple combination therapy, consisting of ivermectin, doxycycline, and zinc, with adjunct vitamin C and D3 supplementation, which was used on high-risk COVID-19 patients. These patients refused an initial recommendation to seek inpatient care, despite a high-risk presentation compounded with one or more comorbidities and/or severe hypoxia. Telemedicine was used to administer personalized treatment to patients at home, who did not have access …

Ai-Driven Detection Of Neurodevelopmental Disorder From Emotional Speech Using A Hybrid Cnn–Bilstm–Attention Framework, Nayarah Shabir, Parveen Lehana, Sheema Khan

School of Medicine Publications

Neurodevelopmental disorders (NDDs) are associated with impairments in communication, behavior, and social interaction, making accurate diagnosis clinically challenging. Autism Spectrum Disorder (ASD), a major NDD, often exhibits atypical speech patterns characterized by altered prosody and reduced emotional expressiveness. The study proposes a hybrid dual-path framework for ASD detection from emotional speech using two strategies: PCA–GMM-based acoustic modeling and a CNN–BiLSTM–Attention architecture for spectral–temporal feature learning. The proposed framework captures probabilistic, spectral, and temporal speech characteristics for robust ASD classification. Acoustic analysis demonstrated clear separability between ASD and non-ASD speech, while the deep learning framework achieved stable and reliable performance across …

Height Does Not Impair The Hydraulic System Of The Tallest Tropical Dipterocarp Trees, Paulo Bittencourt, Arne Scheire, Palasiah Jotan, Jehova Lourenço-Junior, Lindsay F. Banin, Mohd. Aminur Faiz Bin Suis, David Frp Burslem, Bradley O. Christoffersen, David Coomes, Peter Groenendijk

School of Integrative Biological & Chemical Sciences Faculty Publications

Half of the aboveground biomass in forests is stored in a disproportionately small number of very tall trees. These giants are predicted to be more vulnerable to drought-induced damage because height impairs their hydraulic system. We evaluated whether the hydraulic system of world’s tallest tropical tree species—Southeast Asian dipterocarps—are negatively affected by their height. The more negative xylem pressures caused by tree height were fully compensated for through adjustment of vessel anatomy and leaf hydraulic traits, and the trees suffered no height-related loss in growth during a severe drought. Therefore, height does not make the hydraulic systems of the world’s …