bioRxivPlant BiologyPreprint
The PSInet Plant Water Potential Database: advancing new perspectives on plant water status, traits, and hydraulic processes
Jessica GuoiD, Ana Maria Restrepo AcevedoiD, Marvin G. BrowneiD, Daniel M. JohnsoniD, Katherine A. McCullohiD, Jesse B. NippertiD, Rafael PoyatosiD, Steven A. KannenbergiD, Daniel P. BeverlyiD, K. Arthur EndsleyiD, Andrew F. FeldmaniD, Alexandra G. KoningsiD, Yanlan LiuiD, Jordi Martínez‐VilaltaiD, William M. HammondiD, Kevin R. HultineiD, Lauren E. L. LowmaniD, Jeffrey S. DukesiD, Julia K. GreeniD, Lawren SackiD, Nidhi VinodiD, Jie HuiD, Jean AlleniD, Caleb E. Adams, Henry D. AdamsiD, , Ignacio Gónzalez-FernándeziD
Abstract
Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.
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