Barba, J., Lloret, F., Poyatos, R., Molowny-Horas, R., Yuste, J.C. (2018) Multi-temporal influence of vegetation on soil respiration in a droughtaffected forest. IForest. 11: 189-198.EnlaceDoi: 10.3832/ifor2448-011
Cabon A., Martínez-Vilalta J., Martínez de Aragón J., Poyatos R., De Cáceres M. (2018) Applying the eco-hydrological equilibrium hypothesis to model root distribution in water-limited forests. Ecohydrology. : 0-0.EnlaceDoi: 10.1002/eco.2015
Drought is a key driver of vegetation dynamics, but plant water-uptake patterns and consequent plant responses to drought are poorly understood at large spatial scales. The capacity of vegetation to use soil water depends on its root distribution (RD). However, RD is extremely variable in space and difficult to measure in the field, which hinders accurate predictions of water fluxes and vegetation dynamics. We propose a new method to estimate RD within water balance models, assuming that vegetation is at eco-hydrological equilibrium (EHE). EHE conditions imply that vegetation optimizes RD such that transpiration is maximized within the limits of bearable drought stress, characterized here by species-specific hydraulic thresholds. Optimized RD estimates were validated against RD estimates obtained by model calibration from sap flow or soil moisture from 38 forest plots in Catalonia (NE Spain). In water-limited plots, optimized RD was similar to calibrated RD, but estimates diverged with higher water availability, suggesting that the EHE may not be assumed when water is not limiting. Thereafter, we applied the optimization procedure at the regional scale, to estimate RD for the water-limited forests of Catalonia. Regional variations of optimum RD reproduced many expected patterns in response to climate, soil physical properties, forest structure, and species hydraulic traits. We conclude that RD optimization, based on the EHE hypothesis and a simple description of plant hydraulics, produces realistic estimates of RD that can be used for model parameterization and shows promise to improve our ability to forecast vegetation dynamics under increased drought. © 2018 John Wiley & Sons, Ltd.
Peters R.L., Fonti P., Frank D.C., Poyatos R., Pappas C., Kahmen A., Carraro V., Prendin A.L., Schneider L., Baltzer J.L., Baron-Gafford G.A., Dietrich L., Heinrich I., Minor R.L., Sonnentag O., Matheny A.M., Wightman M.G., Steppe K. (2018) Quantification of uncertainties in conifer sap flow measured with the thermal dissipation method. New Phytologist. 219: 1283-1299.EnlaceDoi: 10.1111/nph.15241
Trees play a key role in the global hydrological cycle and measurements performed with the thermal dissipation method (TDM) have been crucial in providing whole-tree water-use estimates. Yet, different data processing to calculate whole-tree water use encapsulates uncertainties that have not been systematically assessed. We quantified uncertainties in conifer sap flux density (Fd) and stand water use caused by commonly applied methods for deriving zero-flow conditions, dampening and sensor calibration. Their contribution has been assessed using a stem segment calibration experiment and 4 yr of TDM measurements in Picea abies and Larix decidua growing in contrasting environments. Uncertainties were then projected on TDM data from different conifers across the northern hemisphere. Commonly applied methods mostly underestimated absolute Fd. Lacking a site- and species-specific calibrations reduced our stand water-use measurements by 37% and induced uncertainty in northern hemisphere Fd. Additionally, although the interdaily variability was maintained, disregarding dampening and/or applying zero-flow conditions that ignored night-time water use reduced the correlation between environment and Fd. The presented ensemble of calibration curves and proposed dampening correction, together with the systematic quantification of data-processing uncertainties, provide crucial steps in improving whole-tree water-use estimates across spatial and temporal scales. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust
Poyatos R., Aguadé D., Martínez-Vilalta J. (2018) Below-ground hydraulic constraints during drought-induced decline in Scots pine. Annals of Forest Science. 75: 0-0.EnlaceDoi: 10.1007/s13595-018-0778-7
Key message: Below-crown hydraulic resistance, a proxy for below-ground hydraulic resistance, increased during drought in Scots pine, but larger increases were not associated to drought-induced defoliation. Accounting for variable below-ground hydraulic conductance in response to drought may be needed for accurate predictions of forest water fluxes and drought responses in xeric forests. Context: Hydraulic deterioration is an important trigger of drought-induced tree mortality. However, the role of below-ground hydraulic constraints remains largely unknown. Aims: We investigated the association between drought-induced defoliation and seasonal dynamics of below-crown hydraulic resistance (a proxy for below-ground hydraulic resistance), associated to variations in water supply and demand in a field population of Scots pine (Pinus sylvestris L.) Methods: Below-crown hydraulic resistance (rbc) of defoliated and non-defoliated pines was obtained from the relationship between maximum leaf-specific sap flow rates and maximum stem pressure difference estimated from xylem radius variations. The percent contribution of rbc to whole-tree hydraulic resistance (%rbc) was calculated by comparing stem water potential variations with the water potential difference between the leaves and the soil. Results: rbc and %rbc increased with drought in both defoliated and non-defoliated pines. However, non-defoliated trees showed larger increases in rbc between spring and summer. The difference between defoliation classes is unexplained by differences in root embolism, and it is possibly related to seasonal changes in other properties of the roots and the soil-root interface. Conclusion: Our results highlight the importance of increasing below-ground hydraulic constraints during summer drought but do not clearly link drought-induced defoliation with severe below-ground hydraulic impairment in Scots pine. © 2018, INRA and Springer-Verlag France SAS, part of Springer Nature.
Poyatos R., Sus O., Badiella L., Mencuccini M., Martínez-Vilalta J. (2018) Gap-filling a spatially explicit plant trait database: Comparing imputation methods and different levels of environmental information. Biogeosciences. 15: 2601-2617.EnlaceDoi: 10.5194/bg-15-2601-2018
The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km2). We simulated gaps at different missingness levels (10-80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits. No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in the IEFC incomplete dataset (5495 plots) and quantify imputation uncertainty. Resulting spatial patterns of the studied traits in Catalan forests were broadly similar when using species means, regression kriging or the best-performing MICE application, but some important discrepancies were observed at the local level. Our results highlight the need to assess imputation quality beyond just imputation accuracy and show that including environmental information in statistical imputation approaches yields more plausible imputations in spatially explicit plant trait datasets. © 2018 Author(s).
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