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86 results for “Water Deficit”
Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset
<p><strong>Global CHIRPS MCWD Dataset</strong></p> <p>The MCWD (Maximum Cumulative Water Deficit) is a measure of drought severity, which corresponds to the maximum value of the monthly accumulated water deficit reached for each pixel within the year. The MCWD is a useful indicator of meteorologically induced water stress without taking into account local soil conditions and plant adaptations, which are poorly understood in Amazonia. The full method of MCWD is described in Aragão et al. (2007; <a href="https://doi.org/10.1029/2006GL028946">https://doi.org/10.1029/2006GL028946</a>). Detail about CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations) can be found in Funk et al. (2015; <a href="https://doi.org/10.1038/sdata.2015.66">https://doi.org/10.1038/sdata.2015.66</a>).</p> <p> </p> <p><strong>Coverage:</strong> Spanning 50°S-50°N (and all longitudes/land areas)</p> <p><strong>Period:</strong> 1981 to 2020</p> <p><strong>Spatial resolution:</strong> 0.05-degree</p> <p><strong>Temporal resolution:</strong> Annual</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System (Datum WGS84)</p> <p><strong>File format:</strong> The zip file containing 40 files (one per year) in compressed TIFF format.</p> <p><strong>Code:</strong> <a href="https://zenodo.org/record/5034650">https://zenodo.org/record/5034650</a></p> <p><strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration.</p> <p><strong>For the use of this dataset, please cite:</strong></p> <p>Silva Junior, C.H.L. et al. Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset. <em>Zenodo</em> (2021). DOI: 10.5281/zenodo.4903340. <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.5281/zenodo.4903340</a></p> <p>Silva Junior, C.H.L. et al. Fire Responses to the 2010 and 2015/2016 Amazonian Droughts. <em>Front. Earth Sci</em>. (2019). DOI: 10.3389/feart.2019.00097. <a href="https://doi.org/10.3389/feart.2019.00097">https://doi.org/10.3389/feart.2019.00097</a></p> <p>Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. <em>Scientific Data</em> (2015). DOI: 10.1038/sdata.2015.66. <a href="https://doi.org/10.1038/sdata.2015.66">https://doi.org/10.1038/sdata.2015.66</a></p>
Data for Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates
<p>This repository contains data and scripts to reproduce results that are presented in the manuscript: Van Laere, J., Merckx, R., Hood-Nowotny, R., Dercon, G. (2023) Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. <em>Front. Plant Sci</em>. 14:1222558 doi: 10.3389/fpls.2023.1222558</p>
Proteomic analysis reveals different molecular mechanisms to face water deficit in mycorrhizal and nonmycorrhizal sorghum plants
<p>Differential accumulated proteins in response to water deficit in mycorrhizal and nonmycorrhizal sorghum plants were recovered from 2D gels and identified by HPLC-MSMS. MS analysis was performed by a Nano acquity nanoflow LC system (Waters, Milford, MA, USA) coupled to a linear ion trap (LTQ) velos mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with a nanoelectrospray ion source.</p>
Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East
<p>This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS, FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI.<br> This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of the GPP-GLASS products, While, sGPPR dataset of GIMMS, FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS, FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit data see: <strong>Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. <em>Environmental Research Letters</em>. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a></strong></p>
Figure 6 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 6. Superoxide dismutase (SOD) activity in leaves (a) and o roots (b, c, d) ofCampomanesia xanthocarpa seedlings as a function of shading (0, 30 and 70%), continuous irrigation (CI) and intermittent (II) conditions, and experimental period (start - T0, 1st and 2nd photosynthesis zero – P0, 1st and 2nd Recovery - REC and END). Uppercase letters compare the same shading and irrigation conditions in different experimental periods.Lowercase letters compare the same irrigation condition and period in different shading.The asterisk compares irrigation conditions in the same shading and period (a). Uppercase letters compare the same condition shading in the different period (d). Lowercase letters compare different irrigation conditions in the different periods (b) and same shading (c) and same period in the different shading (d). The means of shading were compared by the Tukey test, the experimental periods by the Scott Knott test, and the irrigation conditions by the Bonferroni T test. In all cases, 5% probability was used.
Figure 5 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 5. Peroxidase activity (POD) in leaves (a) and roots (b, c, d) of Campomanesia xanthocarpa seedlings as a function of shading (0, 30 and 70%), continuous irrigation (CI) and intermittent (II) conditions, and experimental period (Start - T0, 1st and 2nd Photosynthesis Zero – P0, 1st and 2nd Recovery - REC and END). Uppercase letters compare the same shading and irrigation conditions in different experimental periods. Lowercase letters compare the same irrigation condition and period in different shading. The asterisk compares irrigation conditions in the same shading and period (a). Upper case letters compare the same irrigation condition in different shading and the same shading in the different period (d). Lowercase letters compare different irrigation conditions in the different periods (b) and same shading (c) and same period in the different shading (d). The means of shading were compared by the Tukey test, the experimental periods by the Scott Knott test, and the irrigation conditions by the Bonferroni T test. In all cases, 5% probability was used.
Figure 1 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 1. Water potential (Ψw) of Campomanesia xanthocarpa seedlings as a function of continuous irrigation (CI) and intermittent (II) conditions, shading (0, 30, and 70%) (a) and experimental period (Start: T0, 1st and 2nd Photosynthesis Zero: P0, 1st and 2nd Recovery: REC and END); (b). Uppercase letters compare the same irrigation condition in different shading. Lowercase letters compare the same shading in different irrigation conditions and shading (Figure 1a).
Figure 7 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 7. Schematic representation of the effects of shading (0, 30, and 70%) on the reduction (%) of water potential (Ψw) in the1st and 2nd photosynthesis zero (P0) in relation to initial fluorescence (F0), basal quantum production of the non-photochemical processes of photosystem II (F0/Fm), potential quantum efficiency of photosystem II (Fv/Fm), maximum efficiency of the photochemical process in photosystem II (Fv/F0), and peroxidase (POD) and superoxide dismutase (SOD) activities in the leaves of Campomanesia xanthocarpa.
Figure 3 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 3. Maximum efficiency of the photochemical process in photosystem II – Fv/F0 (a, b) and basal quantum production of the non-photochemical processes of photosystem – F0/Fm (c) of Campomanesia xanthocarpa seedlings as a function of continuous irrigation (CI) and intermittent (II) conditions, shading (0, 30, and 70%) and experimental period (Start - T0, 1st and 2nd Photosynthesis Zero – P0, 1st and 2nd Recovery - REC and END). Uppercase letters compare the same irrigation condition in different shading (a) and different experimental periods (b). Lowercase letters compare different irrigation conditions in the same shading (a) and different experimental periods (b). Uppercase letters compare the same shading and irrigation conditions in different experimental periods. Lowercase letters compare the same irrigation condition and period in different shading. The asterisk compares irrigation conditions in the same shading and period (c). The means of shading were compared by the Tukey test, the experimental periods by the Scott Knott test, and the irrigation conditions by the Bonferroni T test. In all cases, 5% probability was used.
Figure 4 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 4. Dickson quality index (DQI) (a, b) and chlorophyll index (c) of Campomanesia xanthocarpa seedlings as a function of continuous irrigation (CI) and intermittent (II) conditions, shading (0, 30, and 70%) and experimental period (Start: T0, 1st and 2nd Photosynthesis Zero: P0, 1st and 2nd Recovery: REC and END). Uppercase letters compare the same irrigation condition in different shading. Lowercase letters compare different irrigation conditions in the same shading (a). The means of shading were compared with the Tukey test; experimental periods, Scott Knott test; and irrigation conditions, Bonferroni t-test. In all cases, 5% probability was used.
TreeGOER Global Zones: Global atlas for the Climatic Moisture Index (CMI), Maximum Climatological Water Deficit (MCWD) and the number of months with average temperature > 10 degrees C (Tmo10)
<p>The <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database documents the environmental ranges for 48,129 tree species and is available from files archived at <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparation of this database from 30 arc-second global grid layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. The atlas from this archive was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p><strong>TreeGOER</strong> includes a file (<em>TreeGOER_Tmo10_classes.txt</em>) that documents the distribution of species in zones defined by the number of months with average temperature > 10 degrees C. <strong>TreeGOER</strong> also includes a file (<em>TreeGOER_CMI_classes.txt</em>) that documents the distribution of species in zones defined by the Climatic Moisture Index (CMI). The atlas provided here shows the global distribution of the Tmo10 zones and CMI zones at high resolution on six sheets each, including three sheets in the northern hemisphere and three sheets in the southern hemisphere.</p> <p>The atlas also includes six sheets that show the global distribution of the Maximum Climatological Water Deficit (MCWD), another environmental variable covered by the <strong>TreeGOER</strong> database.</p> <p> </p> <table> <tbody> <tr> <td><strong>Zone</strong></td> <td><strong>Classes</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>Tmo10</td> <td> Tmo10 = 12 + Bio06 >= 18</td> <td>tropical (minimum temperature of coldest month 18 degrees C or higher)</td> </tr> <tr> <td> </td> <td>Tmo10 = 12 + Bio06< 18</td> <td>tropical (minimum temperature of coldest month less than 18 degrees C)</td> </tr> <tr> <td> </td> <td>8 ≤ Tmo10 < 12</td> <td>subtropical</td> </tr> <tr> <td> </td> <td>4 ≤ Tmo10 < 8</td> <td>temperate</td> </tr> <tr> <td> </td> <td>1 ≤ Tmo10 < 4</td> <td>boreal</td> </tr> <tr> <td> </td> <td>Tmo10 < 1</td> <td>polar</td> </tr> <tr> <td>CMI</td> <td>CMI ≥ 0.5</td> <td>P >= 2 * PET</td> </tr> <tr> <td> </td> <td>0 ≤ CMI < 0.5</td> <td>PET <= P < 2 * PET</td> </tr> <tr> <td> </td> <td>−0.35 ≤ CMI < 0</td> <td>0.65 <= P/PET < 1</td> </tr> <tr> <td> </td> <td>−0.5 ≤ CMI < −0.35</td> <td>dry sub-humid</td> </tr> <tr> <td> </td> <td>−0.8 ≤ CMI < −0.5</td> <td>semi-arid</td> </tr> <tr> <td> </td> <td>−0.95 ≤ CMI < −0.8</td> <td>arid</td> </tr> <tr> <td> </td> <td>CMI < −0.95</td> <td> hyper-arid</td> </tr> <tr> <td>MCWD</td> <td>MCWD ≤ -100</td> <td> </td> </tr> <tr> <td> </td> <td>−200 ≤ MCWD < −100</td> <td> </td> </tr> <tr> <td> </td> <td>−400 ≤ MCWD < −200</td> <td> </td> </tr> <tr> <td> </td> <td>−600 ≤ MCWD < −400 </td> <td> </td> </tr> <tr> <td> </td> <td>−800 ≤ MCWD < −600</td> <td> </td> </tr> <tr> <td> </td> <td>−1000 ≤ MCWD < −800</td> <td> </td> </tr> <tr> <td> </td> <td>−1250 ≤ MCWD < −1000</td> <td> </td> </tr> <tr> <td> </td> <td>−1500 ≤ MCWD < −1250</td> <td> </td> </tr> <tr> <td> </td> <td>−1750 ≤ MCWD < −1500 </td> <td> </td> </tr> <tr> <td> </td> <td>−2000 ≤ MCWD < −1750 </td> <td> </td> </tr> <tr> <td> </td> <td>−2500 ≤ MCWD < −2000 </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>A fourth map series in the atlas combines information from the Climatic Moisture Index with the distribution of 52,602 cities that were included in the CitiesGOER database, available from <a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a><a name="_Hlk141002106"></a><br></p> <p>Maps were created from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas), <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</p> <p>For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>When using the TreeGOER Global Zones atlas in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> </ul> <p> </p> <p>The development of the TreeGOER Global Zones atlas (including development of version 2024.06) was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
Figure 3 in Does silicon help to alleviate water deficit stress and in the recovery of Dipteryx alata seedlings?
Figure 3. Potential quantum efficiency of photosystem II - F V/FM (a and d), absorbed energy conversion efficiency - F V/F (b and e), basal 0 quantum production of non-photochemical processes - F 0 /FM (c), maximum chlorophyll-a fluorescence - FM (f) and initial fluorescence - F 0 (g) in D. alata seedlings produced under different water regimes (I: Irrigated; II: combined intermittent irrigation without and with 0.75 and 1.50 Si) in different evaluation periods (T0: time zero; P0: photosynthesis close to zero; REC: recovery: END: end of evaluations). Capital letters compare water regimes within each assessment period (Tukey; p <0.05); Lowercase letters compare the evaluation periods within each water regime. (Tukey; p <0.05).
Figure 2. Photosynthetic rate – A in Does silicon help to alleviate water deficit stress and in the recovery of Dipteryx alata seedlings?
Figure 2. Photosynthetic rate – A (a), intracellular CO concentration – C (b), transpiration – E (c), stomatal conductance – gs (d), intrinsic 2 i Rubisco A/C i carboxylation efficiency (e) and efficiency of water use – WUE (f) in D. alata seedlings produced under different water regimes (I: Irrigated; II: combined intermittent irrigation without and with 0.75 and 1. Si) in different evaluation periods (T0: zero time; P0: photosynthesis close to zero; REC: recovery: END: end of evaluations). Capital letters compare water regimes within each assessment period (Tukey; p <0.05); Lowercase letters compare the evaluation periods within each water regime. (Tukey; p <0.05).
Figure 4 in Photosynthetic metabolism and antioxidant in Ormosia arborea are modulated by abscisic acid under water deficit?
Figure 4. Activity of the catalase enzyme in leaf (A) and root (B) of Ormosia arborea seedlings irrigated (I and I 10 µM ABA) and submitted to water deficit conditions (SI and SI 10 µM ABA) in the different evaluation periods: zero time (T0), first null photosynthesis (1 st P0), second null photosynthesis (2nd P0), recovery (REC) and final evaluation (END). Upper case letters differ between trial times and lowercase letters between treatments.
Figure 1 in Photosynthetic metabolism and antioxidant in Ormosia arborea are modulated by abscisic acid under water deficit?
Figure 1. Photosynthetic rate (A) – (A), transpiration rate (E); (B) and water use efficiency (A/E); (C) as a function of the evaluation days of Ormosia arborea irrigated seedlings (I and I 10 µM ABA) and submitted to water deficit conditions (SI and SI 10 µM ABA). Continuous vertical line indicates the periods of evaluation: time zero (T0), first null photosynthesis (1st P0), second null photosynthesis (2nd P0), recovery (REC) and final evaluation (END).
Figure 2 in Photosynthetic metabolism and antioxidant in Ormosia arborea are modulated by abscisic acid under water deficit?
Figure 2. Stomatal conductance (gs) – (A) internal CO 2 concentration (Ci); (B) and instantaneous carboxylation efficiency CO 2 (A/Ci); (C) of Ormosia arborea irrigated seedlings (I and I 10 µM ABA) and submitted to water deficit conditions (SI and SI 10 µM ABA). Continuous vertical line indicates the periods of evaluation: time zero (T0), first null photosynthesis (1st P0), second null photosynthesis (2nd P0), recovery (REC) and final evaluation (END).
Figure 3 in Photosynthetic metabolism and antioxidant in Ormosia arborea are modulated by abscisic acid under water deficit?
Figure 3. Water potential (Ψw) (A) and Potential efficiency quantum of photosystem II (Fv/Fm) (B) as a function of the evaluation periods between irrigated seedlings (I) of Ormosia arborea (I and I 10 µM ABA) and submitted to the water deficit condition (SI and SI 10 µM ABA). Lowercase letters compare the different treatments in the same evaluation period and uppercase letters compare the same treatment in the different evaluation periods.
Figure 6 in Photosynthetic metabolism and antioxidant in Ormosia arborea are modulated by abscisic acid under water deficit?
Figure 6. Enzymatic activity of superoxide dismutase in leaves (SOD Leaves) (A) and roots (SOD roots) (B) of Ormosia arborea seedlings irrigated (I and I 10 µM ABA) and submitted to water deficit conditions (SI and SI 10 µM ABA) in the different evaluation periods: zero time (T0), first null photosynthesis (1st P0), second null photosynthesis (2nd P0), recovery (REC) and final evaluation (END). Upper case letters differ between trial times and lowercase letters between treatments.
Reduced seed set under water deficit is driven mainly by reduced flower numbers and not by changes in flower visitations and pollination
<p><span>Water deficit can alter floral traits with cascading effects on flower-visitor interactions and plant fitness. </span><span>Water stress induction can </span><span>diminish </span><span>productivity, directly resulting in lower flower production and consequently seed set. Changes in floral traits, such as floral scent or reward amount, may in turn alter pollinator visitations and behavior and consequently can reduce pollination services resulting in lower reproduction output. </span><span>However, </span><span>the relative contribution of this indirect in comparison to the direct effects of changes in seed set are not fully understood.</span></p> <p><span>We manipulated water availability using rain-out shelters in a field experiment and measured effects on floral scent bouquet, morphology, phenology, flower-visitor interactions, pollination, and seed set</span><span>.</span><span> Plant individuals of </span><em><span>Sinapis</span> <span>arvensis</span></em><span> (</span><span>Brassicaceae)</span><span> were randomly assigned to one of three treatments: mean precipitation (= control), reduced mean precipitation, or drought period treatment.</span></p> <p><span>Our results show that decreasing water availability lowers the number of flowers and seed set. This indicates a direct link between water stress and seed set, as seed mass increases with increasing flower number. </span><span>The indirect link of water stress <em>via</em> floral traits, pollinator visits, and pollination has weaker effects on seed set. However, floral traits remain relatively stable under decreased water availability, whereas plant growth and flower abundance decrease, potentially in order to allow investment in more resources in fewer flowers to maintain pollination success. Thus, plants are able to compensate for water stress and can maintain floral trait expression, such as a stable scent emission and bouquet, to retain pollinator attraction.</span></p> <p><span>These findings indicate that the direct link from water stress to seed set has a stronger impact on plants' reproductive success than the indirect link through altered floral trait expression and pollinator visits in a generalist plant species.</span></p>
Cotton root cross-sections under water deficit
<p>This experiment is describing the analysis of the root cross-sections. The roots were collected in the lab of Prof. Avat Shekoofa (Uni. of Tennessee), and the root samples were sent to Julkowska lab (BTI). Magda did hand cross-sections and stained them with toluidine blue to visualize the xylem vessels. The individual images were stitched into an ortho-mosaic using Photoshop.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.