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14 results for “Plant hydraulics”
The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files
<p>This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository:</p> <p>model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows:</p> <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DC<em>k</em><sub>max</sub>)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (L<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (IL<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (H<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IH<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IIL<em>k</em><sub>max</sub>) for experiment 2</li> <li>ko_dc: model output with the results of the best <em>k</em><sub>max</sub> and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best <em>k</em><sub>max</sub> and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best <em>k</em><sub>max</sub> and the vulnerable configuration of the PVC used in experiment 3</li> </ul> <p>The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).</p>
Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species
<ol> <li><span>Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within six hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.</span></li> <li><span>Using pressure-volume curves, we measured these hydraulic traits distal shoots (<i>i.e.</i> twigs + leaves) in 62 plant species across four wooded communities in south-eastern Australia. For a subset of 30 of those species, we also measured hydraulic traits of twigs using moisture-release curves. Moisture content of fine fuels was then estimated for circumstances typical of fire weather. These projections were made assuming that under the hot, dry, windy conditions typical of large wildfires, leaves and fine twigs would function at internal water pressures close to wilting point (<i>i.e. </i>turgor loss point, TLP). The effect of different moisture contents at TLP on ignition time was then modelled using a fully mechanistic, finite element model of biomass ignition based on standard principles of physical chemistry.</span></li> <li><span>We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.</span></li> </ol>
Dataset on soil hydraulic properties under contrasted plant covers and agricultural practices
<p>Complete dataset on the temporal variation of soil infiltrability along a homogeneous fluvisol, on bare soil or soil planted with two plant species with contrasted root systems (a Malvaceae with a tap-root system and a Poaceae with a fibrous root system), and impacted by three different management practices (burning, mowing, and chemical weeding). An original protocol, based on specific ring infiltrometers, able to measure the temporal dynamics of soil infiltrability was used. This dispositive takes into account the variability of the measurement across space.</p> <p> </p>
Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species
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SurEau database : A database of hydraulic and stomatal traits for modelling drought resistance in plants
<p>This file contains a database of hydraulic and stomatal traits that accompanies the paper untitled "Pant resistance to drought relies on timely stomatal closure" publish in Ecology Letters. This database was used to built the Figure of this manuscript.</p> <p>> The first page ("Stem_VCurves") contains the parameter of vulnerability curve to embolism for 150 species. Family, genus and species names as well as original reference are provided.</p> <p>> The second page ("Pgs90") contains a first proxy for the water potential causing stomatal closure, it is the value of water potential causing 90% stomatal closure computed from gs versus water potential. Family, genus and species names as well as original reference are provided.</p> <p>> The third page (Ptlp) contains a second proxy for the water potential causing stomatal closure, it is the turgor loss point computed from pressure volume curves. Family, genus and species names as well as original reference are provided.</p> <p>> The fourth page (ALL) contains all the three previous pages together, allowing to reconstruct Figure 1.</p> <p>> The fifth page (PitlpAdultSeedlings) contains values of turgor loss point for adults and seedilngs for 15 species.</p> <p>> The sixth page (P50AdultSeedlings) contains values of embolism resistance for adults and seedilngs for 14 species.</p> <p>> The seventh page (Emin) contains values of minimum (i.e. cuticular) conductance and minimum transpiration for 33 species as well as embolism resistance values for theese species.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
<p>Hydraulic failure caused by severe drought contributes to aboveground dieback and whole-plant death. The extent to which dieback or whole-plant death can be predicted by plant hydraulic traits has rarely been tested among species with different leaf habits and/or growth forms. We investigated 19 hydraulic traits in 40 woody species in a tropical savanna and their potential correlations with drought response during an extreme drought event during the El Niño–Southern Oscillation in 2015. Plant hydraulic trait variation was partitioned substantially by leaf habit but not growth form along a trade-off axis between traits that support drought tolerance versus avoidance. Semi-deciduous species and shrubs had the highest branch dieback and top-kill (complete aboveground death) among the leaf habits or growth forms. Dieback and top-kill were well explained by combining hydraulic traits with leaf habit and growth form, suggesting integrating life history traits with hydraulic traits will yield better predictions.</p>
2015/16 El Niño increased water demand and pushed plants from a Mesic tropical montane grassland beyond their hydraulic safety limits
<p>In 2015/16, a strong El Niño event caused anomalously high temperatures and reduced precipitation resulting in Pantropical drought‐induced diebacks and wildfires. Although many studies have documented the El Niño impacts on tropical forests, little we know about its effects on tropical grasslands. Here, we investigated plant drought responses during and after the 2015/16 El Niño event (Jun 2016 to Aug 2017) in 12 species with contrasting drought strategies (tolerance, avoidance and escape) in a Brazilian tropical montane grassland. We tested if (1) the El Niño event induced meteorological drought anomalies, (2) the atmospheric and/or soil drought led to plant water stress and (3) plants showed signs of drought recovery. In contrast to other tropical areas, we found that the 2015/16 El Niño event did not strongly affect precipitation in our study site. However, it increased air temperature and vapour pressure deficit, thus pushing all grassland species, even the most drought‐tolerant ones, beyond their hydraulic safety margins during the dry season. Most species showed signs of drought recovery, returning to positive hydraulic margins in the wet season after the El Niño. However, the finding that all evaluated species, regardless of their drought‐response strategy, are already operating close to their hydraulic safe thresholds for stomatal closure and turgor loss suggests that this cool–humid tropical montane grassland is especially vulnerable to meteorological extremes exacerbated by the additive effects of El Niño and climate change.</p>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
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2015/16 El Niño increased water demand and pushed plants from a Mesic tropical montane grassland beyond their hydraulic safety limits
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Vulnerability segmentation is vital to hydraulic strategy of tropical‒subtropical woody plants
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Data from: Plasticity in plant hydraulic traits: An evaluation of a common-taxa experiment across a climatic gradient in the Western U.S.
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Dataset and R code for "Relationship between wind speed and plant hydraulics at the global scale"
<p><strong><span>Data collection</span></strong><strong><span> </span></strong></p> <p><span> Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; </span><span><a href="https://xylemfunctionaltraits.org/"><span>https://xylemfunctionaltraits.org</span></a></span><span>).</span></p> <p><span>To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) <span>height data were reported at the same site where plant hydraulic traits were measured.</span> </span></p> <p><span>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km<sup>2</sup> scale were extracted from WorldClim: mean annual wind speed (<span>μ</span>), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (<span>μS; </span>coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET. </span></p> <p><strong><span>Data analysis</span></strong></p> <p><span>Trait and environment data were log<sub>10</sub>-transformed to achieve approximate normality, except for <em>P</em><sub>50</sub> and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index.</span></p> <p><span>To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters. </span></p> <p><span>To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry <em>vs</em>. wet sites, and tropical <em>vs</em>. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012).</span><span> </span><span>A grouping factor was added in each SMA to test whether species groups share a common slope, with <em>p</em> > 0.05 indicating species groups share a common slope. </span></p> <p><span>Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics. </span></p> <p><span>A Random Forest </span><span>machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest. </span></p> <p> </p>
Data and R code for "Negative effects of wind on plant hydraulics at the global scale"
<p>To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources.</p> <p>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET.</p> <p>Simple linear regression was used to examine the relationships between two variables, utilizing the 'lm' function in R software. Partial regression analysis was conducted using the R package VISREG to investigate the relationships between wind speed and plant hydraulics while controlling for other variables. This analysis helped to illustrate the independent effect of wind on plant hydraulics. The Random Forest machine-learning algorithm (implemented using the R package randomForest) was utilized to assess the relative importance of environmental variables for each plant hydraulic trait. The Mean Decrease in Gini was calculated as the average of a variable's total decrease in node impurity, taking into account the proportion of samples that reach that node in each individual decision tree in the random forest. This provides a measure of a variable's importance in estimating the value of the target variable across all of the trees in the forest. A higher Mean Decrease in Gini value indicates greater importance of the variable. Multiple regression analyses were performed to develop predictive equations for plant hydraulic traits using environmental variables. To test for hydraulic traits-wind speed slope directions and differences among species groups in different climatic regions, we used standardized major axis (SMA) analyses. The R package SMATR was employed for these analyses. We considered <em>p </em>< 0.05 as the threshold for statistical significance in all models.</p>
Data for "Negative effects of wind on plant hydraulics at the global scale"
<p>To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources.</p> <p>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET.</p>
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