Flo V., Martinez-Vilalta J., Steppe K., Schuldt B., Poyatos R. (2019) A synthesis of bias and uncertainty in sap flow methods. Agricultural and Forest Meteorology. 271: 362-374.LinkDoi: 10.1016/j.agrformet.2019.03.012
Sap flow measurements with thermometric methods are widely used to measure transpiration in plants. Different method families exist depending on how they apply heat and track sapwood temperature (heat pulse, heat dissipation, heat field deformation or heat balance). These methods have been calibrated for many species, but a global assessment of their uncertainty and reliability has not yet been conducted. Here we perform a meta-analysis of 290 individual calibration experiments assembled from the literature to assess calibration performance and how this varies across methods, experimental conditions and wood properties (density and porosity types). We used different metrics to characterize mean accuracy (closeness of the measurements to the true, reference value), proportional bias (resulting from an effect of measured flow on the magnitude of the error), linearity in the relationship between measurements and reference values, and precision (reproducibility and repeatability). We found a large intra- and inter-method variability in calibration performance, with a low proportion of this variability explained by species. Calibration performance was best when using stem segments. We did not find evidence of strong effects of wood density or porosity type in calibration performance. Dissipation methods showed lower accuracy and higher proportional bias than the other methods but they showed relatively high linearity and precision. Pulse methods also showed significant proportional bias, driven by their overestimation of low flows. These results suggest that Dissipation methods may be more appropriate to assess relative sap flow (e.g., treatment effects within a study) and Pulse methods may be more suitable to quantify absolute flows. Nevertheless, all sap flow methods showed high precision, allowing potential correction of the measurements when a study-specific calibration is performed. Our understanding of how sap flow methods perform across species would be greatly improved if experimental conditions and wood properties, including changes in wood moisture, were better reported. © 2019 Elsevier B.V.
Martinez-Vilalta J., Anderegg W.R.L., Sapes G., Sala A. (2019) Greater focus on water pools may improve our ability to understand and anticipate drought-induced mortality in plants. New Phytologist. : 0-0.LinkDoi: 10.1111/nph.15644
Drought-induced tree mortality has major impacts on ecosystem carbon and water cycles, and is expected to increase in forests across the globe with climate change. A large body of research in the past decade has advanced our understanding of plant water and carbon relations under drought. However, despite intense research, we still lack generalizable, cross-scale indicators of mortality risk. In this Viewpoint, we propose that a more explicit consideration of water pools could improve our ability to monitor and anticipate mortality risk. Specifically, we focus on the relative water content (RWC), a classic metric in plant water relations, as a potential indicator of mortality risk that is physiologically relevant and integrates different aspects related to hydraulics, stomatal responses and carbon economy under drought. Measures of plant water content are likely to have a strong mechanistic link with mortality and to be integrative, threshold-prone and relatively easy to measure and monitor at large spatial scales, and may complement current mortality metrics based on water potential, loss of hydraulic conductivity and nonstructural carbohydrates. We discuss some of the potential advantages and limitations of these metrics to improve our capacity to monitor and predict drought-induced tree mortality. © 2018 The Authors New Phytologist © 2018 New Phytologist Trust
Poyatos R., Aguadé D., Martínez-Vilalta J. (2019) Correction to: Below-ground hydraulic constraints during drought-induced decline in Scots pine (Annals of Forest Science, (2018), 75, 4, (100), 10.1007/s13595-018-0778-7). Annals of Forest Science. 76: 0-0.LinkDoi: 10.1007/s13595-019-0825-z
The article was published without the submitted data availability statement linking readers to a public repository. Due to publication modifications, the information appears missing in the original article. The following corrects previous version of the statement: Data availability The datasets generated and/or analysed during the current study are available in Zenodo Repository (Poyatos et al. 2018). The datasets were not peer reviewed. The original article has been corrected. © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature.
Rao K., Anderegg W.R.L., Sala A., Martínez-Vilalta J., Konings A.G. (2019) Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality. Remote Sensing of Environment. 227: 125-136.LinkDoi: 10.1016/j.rse.2019.03.026
Drought-induced tree mortality events are expected to increase in frequency under climate change. However, monitoring and modeling of tree mortality is limited by the high spatial variability in vegetation response to climatic drought stress and lack of physiologically meaningful stress variables that can be monitored at large scales. In this study, we test the hypothesis that relative water content (RWC) estimated by passive microwave remote sensing through vegetation optical depth can be used as an empirical indicator of tree mortality that both integrates variations in plant drought stress and is accessible across large areas. The hypothesis was tested in a recent severe drought in California, USA. The RWC showed a stronger threshold relationship with mortality than climatic water deficit (CWD) – a commonly used mortality indicator – although both relationships were noisy due to the coarse spatial resolution of the data (0.25° or approximately 25 km). In addition, the threshold for RWC was more uniform than that for CWD when compared between Northern and Southern regions of California. A random forests regression (machine learning) with 32 variables describing topography, climate, and vegetation characteristics predicted forest mortality extent i.e. fractional area of mortality (FAM) with satisfactory accuracy-coefficient of determination R test 2 = 0.66, root mean square error = 0.023. Importantly, RWC was more than twice as important as any other variable in the model in estimating mortality, confirming its strong link to mortality rates. Moreover, RWC showed a moderate ability to aid in forecasting mortality, with a relative importance of RWC measured one year in advance of mortality similar to that of other relevant explanatory variables measured in the mortality year. The results of this study present a promising new approach to estimate drought stress of forests linked to mortality risk. © 2019 Elsevier Inc.
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