The PSInet Plant Water Potential Database: advancing new perspectives on plant water status, traits, and hydraulic processes
Guo, J.; Restrepo Acevedo, A. M.; Browne, M.; Johnson, D. M.; McCulloh, K. A.; Nippert, J. B.; Poyatos, R.; Kannenberg, S. A.; Beverly, D. P.; Endsley, A.; Feldman, A. F.; Konings, A. G.; Liu, Y.; Martinez-Vilalta, J.; Hammond, W. M.; Hultine, K. R.; Lowman, L. E. L.; Dukes, J. S.; Green, J. K.; Sack, L.; Vinod, N.; Hu, J.; Allen, J.; Adams, C. E.; Adams, H. D.; Adet, L. L. A.; Ambrose, A.; Anderegg, L. D. L.; Anderegg, W. R. L.; Aparecido, L. M. T.; Aranda, I.; Arcoverde de Mattos, E.; Avila-Lovera, E.; Bailey, K. C.; Baldocchi, D. D.; Bassiouni, M.; Batllori, E.; Batterton, B. E.; Baugh, T.;
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CactEcoDB: Trait, spatial, environmental, phylogenetic and diversification data for the cactus family 94%
- VegVault dataset: linking global paleo-, and neo-vegetation data with functional traits and abiotic drivers 92%
- A 0.6-meter resolution canopy height and structure model for the contiguous United States 92%
Similar papers in this journal
Similar papers in this journal
- Root hydraulic properties: an exploration of their variability across scales 91%
- Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis efficiency, and rhizosphere acidification of sorghum accessions under osmotic stress 90%
- Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.