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9,671 results for “Leaf”
Data from: Environmental conditions differently shape leaf, seed and seedling trait composition between and within elevations of tropical montane forests
<p>The composition of plant functional traits varies in response to environmental conditions due to processes of community assembly and species sorting. However, there is a lack of understanding of how plant trait composition responds to environmental conditions at different spatial scales and across the plant life cycle. We investigated the trait composition of leaves (specific leaf area), seeds (seed mass) and seedlings (initial seedling height) across elevations and within elevations in relation to soil and light conditions in a tropical montane forest in southern Ecuador. We surveyed traits and communities of adult trees, seeds and seedlings on nine plots at three elevations (1000-3000 m a.s.l.) and calculated community-weighted mean trait values to analyse trait variation across and within elevations. In addition, we measured two environmental factors (soil C/N ratio and canopy openness) to quantify local-scale variation in environmental conditions within elevations. We found that community-weighted means of specific leaf area, seed mass and initial seedling height decreased consistently with increasing elevation. Within elevations, mean trait values of trees, seeds and seedlings responded differently to local-scale environmental conditions. Specific leaf area decreased with increasing soil C/N ratio, and initial seedling height decreased with increasing canopy openness. Seed mass was associated neither with soil nor with light conditions. Our findings show that broad-scale and local-scale processes differently shape the composition of leaf, seed and seedling traits in tropical forests, indicating a scale-dependence in trait-environment associations. Furthermore, plant traits corresponding to different life stages were related differently to environmental conditions within elevations. Community assembly processes may therefore lead to differences in species sorting at early and late plant life stages.</p>
Figure 1 in Non-breeding season records of the Alpine Leaf Warbler Phylloscopus occisinensis
Figure 1. Sampling localities of Phylloscopus affinis (sensu lato) in Bangladesh and Thailand.
A novel ChlF-free leaf spectra dataset and the PROSPECT-DP model code
<p><span>A novel ChlF-free leaf spectra dataset (n = 849, species = 95) collected at three sites in Beijing, China during June and August 2023. Directional-directional reflectance and transmittance spectra with or without ChlF contributions were simultaneously measured using an ASD FieldSpec 3 spectrometer coupled with an FluoWat leaf clip.</span></p> <p><span><span>PROSPECT-DP, a coupled leaf optical properties model, in which a principal component analysis approach was applied on the training dataset to derive the spectral vectors for leaf spectra reconstruction, while the coefficients of spectral vectors were determined by leveraging the simulated leaf spectra derived from the PROSPECT-D model.</span></span></p>
ODP 1084 Hydrogen and Carbon Leaf Wax Isotopes
<p>This is the temporary home for ODP 1084 leaf wax isotope data. Upon publication, this data will be uploaded to the NOAA Paleo Database.</p>
FIGURE 3 in Leaf trait data of two Miocene floras from eastern China and its palaeoclimate implications
FIGURE 3. Distribution of the leaf size classes in the Toupi flora and Shengxian flora.
Linking individual and species-level leaf traits with ontogenetic development stage to explain tree performance under competition and environmental contexts
<p><span>To verify how species traits and individual traits link ontogenetic size, external biotic and abiotic factors to influence tree performance. In a temperate natural forest in northeastern China, we measured dynamic performance, size, as well as competition, topography, and soil variables as biotic and abiotic variables for all 1320 trees of 17 species in 62 monitoring plots from 2010-2020. For each individual tree, we also measured five typical functional traits representing leaf size and elemental content: leaf area, specific leaf area, leaf dry matter content, leaf nitrogen content, and leaf carbon : nitrogen ratios. These traits are not only strongly associated with performance, but trade-offs between traits have previously been shown to express plant acquisitive-conservative strategy characteristics. Reconceptualized based on previous understanding of trait-based approach (Fig. 1). We first tested the direct explanatory effects of species traits and individual traits on performance in multifactorial contexts.</span> <span>We first tested the direct explanatory effects of species traits and individual traits on performance in multifactorial contexts. Subsequently, we analyzed the moderating and mediating effects of these two levels of traits in explaining ontogenetic size, external competition, and environmental influences on performance. The following three questions were posed in response to the results:</span></p> <p><em><span>QI: Do species and individual traits differ in directly explaining performance in a multifactor context that includes ontogenetic size, external biotic and abiotic factors?</span></em></p> <p><em><span>QII: How species and individual traits explain the effect of ontogenetic size on performance</span> </em><em><span>by moderating and mediating effects.</span></em></p> <p><em><span>QIII: Can traits at the species and individual level influence competition- and environment-performance relationships</span> </em><em><span>through trait-based approaches? Does ontogenetic size work jointly with traits at different levels in this process?</span></em></p>
GLOBMAP global Leaf Area Index since 1981
<p>GLOBMAP LAI (Version 3) provides a consistent long-term global leaf area index (LAI) product (1981-2020, continuously updated) at 8km resolution on Geographic grid by quantitative fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and historical Advanced Very High Resolution Radiometer (AVHRR) data. The long-term LAI series was made up by combination of AVHRR LAI (1981–2000) and MODIS LAI (2001–). MODIS LAI series was generated from MODIS land surface reflectance data (MOD09A1 C6) based on the GLOBCARBON LAI algorithm (Deng et al., 2006). The relationships between AVHRR observations (GIMMS NDVI (Tucker et al., 2005)) and MODIS LAI were established pixel by pixel using two data series during overlapped period (2000–2006). Then the AVHRR LAI back to 1981 was estimated from historical AVHRR observations based on these pixel-level relationships. Detailed descriptions of algorithm and evaluation of the algorithm see Liu et al. (2012, JGR-B).</p> <p><strong>Several changes have been made compared with the JGR paper:</strong></p> <ol> <li>The MODIS C6 land surface reflectance products MOD09A1 was used to generate MODIS LAI in this GLOBMAP V3 products instead of C5 products.</li> <li>The clumping effects was considered at the pixel level by employing global clumping index map at 500m resolution (He et al., 2012) instead of land cover-specific clumping index in generation of MODIS LAI. And the new pixel-based AVHRR SR-MODIS LAI relationships were established based on these MODIS LAI series and used for AVHRR LAI retrieval.</li> <li>The cloud mask for MOD09A1 data were generated by a new cloud detection algorithm based on time series surface reflectance observations (Liu and Liu, 2013). And the contaminated pixels were filled by locally adjusted cubic spline capping approach (Chen et al., 2006).</li> </ol> <p><strong>Dataset Characteristics:</strong></p> <p><strong> </strong>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN;</p> <p> Temporal Coverage: July, 1981-Dec. 2020 (continuously updated);</p> <p> Spatial Resolution: 0.0727273º;</p> <p> Temporal Resolution: Half month (1981-2000), 8-day (2001-);</p> <p> Projection: Geographic;</p> <p> Data Format: HDF/Geotiff;</p> <p> Scale: 0.01;</p> <p> Valid Range: 0-1000.</p> <p><strong>Citation (</strong>Please cite this paper whenever these data are used)<strong>:</strong></p> <p>Liu, Y., R. Liu, and J. M. Chen (2012), Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data, J. Geophys. Res., 117, G04003, doi:10.1029/2012JG002084.</p> <p>If you have any questions, please contact <strong>Prof. Ronggao Liu (liurg@igsnrr.ac.cn)</strong> or <strong>Dr. Yang Liu (liuyang@igsnrr.ac.cn)</strong>.</p> <p><strong>Related publications with this dataset:</strong><strong> </strong></p> <ol> <li>Chen, J. M., F. Deng, and M. Chen (2006), Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter, <em>IEEE Trans. Geosci. Remote Sens.</em>, 44, 2230-2238</li> <li>Deng, F., J. M. Chen, S. Plummer, M. Z. Chen, and J. Pisek (2006), Algorithm for global leaf area index retrieval using satellite imagery, <em>IEEE Trans. Geosci. Remote Sens.</em>, 44(8), 2219–2229.</li> <li>He, L. M., J. M. Chen, J. Pisek, C. B. Schaaf, and A. H. Strahler (2012), Global clumping index map derived from the MODIS BRDF product, <em>Remote Sens. Environ.</em>, 119, 118-130.</li> <li>Liu, R. G., and Y. Liu (2013), Generation of new cloud masks from MODIS land surface reflectance products, <em>Remote Sens. Environ.</em>, 133, 21-37.</li> <li>Tucker, C. J., J. E. Pinzon, M. E. Brown, D. A. Slayback, E. W. Pak,R. Mahoney, E. F. Vermote, and N. El Saleous (2005), An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, <em>Int. J. Remote Sens.</em>, 26(20), 4485–4498.</li> </ol>
Regional differences in leaf evolution facilitate photosynthesis following severe drought
<p>Characterizing physiological and anatomical changes that underlie rapid evolution following climatic perturbation can broaden our understanding of how climate change is affecting biodiversity. It can also provide evidence of cryptic adaptation despite stasis at higher levels of biological organization.</p> <p>Here we compared evolutionary changes in populations of <em>Mimulus cardinalis </em>from historically different climates in the north and south of the species' range following an exceptional drought. We grew seeds produced from pre-drought ancestral plants alongside peak-drought descendants in a common greenhouse and exposed them to wet and dry conditions.</p> <p>Prior to the drought, northern ancestral populations expressed traits contributing to drought escape, while southern ancestral populations expressed drought avoidance. Following the drought, both regions evolved to reduce water loss and maintain photosynthesis in dry treatments (drought avoidance), but via different anatomical alterations in stomata, trichomes, and palisade mesophyll. Additionally, southern populations lost the ability to take advantage of wet conditions.</p> <p>These results reveal rapid evolution towards drought avoidance at an anatomical level following an exceptional drought, but suggest that differences in the mechanisms between regions incur different trade-offs. This sheds light on the importance of characterizing underlying mechanisms for downstream life-history and macromorphological traits.</p>
Leaf area predicts conspecific spatial aggregation of woody species
<p><strong>Aim:</strong> Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co-existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention.</p> <p><strong>Location:</strong> Global synthesis.</p> <p><strong>Time period:</strong> 1997 - 2022.</p> <p><strong>Major taxa studied:</strong> Woody angiosperms and conifers.</p> <p><strong>Methods:</strong> We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat.</p> <p><strong>Results:</strong> We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observe aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza.</p> <p><strong>Main Conclusions:</strong> Compared to climatic variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area.</p>
Fig. 1 in Host specificity studies on Gynaikothrips (Thysanoptera: Phlaeothripidae) associated with leaf galls of cultivated Ficus (Rosales: Moraceae) trees
Fig. 1. Pronotal posteroangular setae showing variation in length.
Fig. 2. Four Ficus benjamina plants inside a in Host specificity studies on Gynaikothrips (Thysanoptera: Phlaeothripidae) associated with leaf galls of cultivated Ficus (Rosales: Moraceae) trees
Fig. 2. Four Ficus benjamina plants inside a collapsible cage.
Fig. 1 in Identity of the citrus leaf mining flea beetle in northeast India and nomenclatural changes in Amphimela (Coleoptera: Chrysomelidae: Galerucinae: Alticini)
Fig. 1. Podagricomela nigripes, dorsal habitus; scale bar = 0.5 mm.
Leaf litter fall
<b>Description: </b><p>Wet and dry weight of leaf litter</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/110"><b>Above ground net primary productivity</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=23">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Leaf litter</b> (Worksheet LitterWeights)</p><p>Dimensions: 396 rows by 16 columns</p><p>Description: Wet and dry weight of leaf litterfall at SAFE vegetation plots</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE Project 2nd order sample point (Field type: Location)</li><li><b>SurveyNum</b>: Collection period (Field type: Numeric)</li><li><b>DateSet</b>: Date litter trap was set (Field type: Date)</li><li><b>TimeSet</b>: Time of data litter trap was set (Field type: Time)</li><li><b>DateCollected</b>: Date litter trap was collected (Field type: Date)</li><li><b>TimeCollected</b>: Time of dat litter trap was collected (Field type: Time)</li><li><b>WW.litter</b>: Wet weight of leaf litter (Field type: Numeric)</li><li><b>DW.litter</b>: Dry weight of litter (Field type: Numeric)</li><li><b>DW.leaves.photo</b>: Dry weight of leaves separated out for herbivory photographs (Field type: Numeric)</li><li><b>DW.leaves.other</b>: Dry weight of leaves not used for herbivory photographs (Field type: Numeric)</li><li><b>DW.wood</b>: Dry weight of wood and twigs (Field type: Numeric)</li><li><b>DW.flower</b>: Dry weight of flowers (Field type: Numeric)</li><li><b>DW.fruit</b>: Dry weight of fruits (Field type: Numeric)</li><li><b>DW.seed</b>: Dry weight of seeds (Field type: Numeric)</li><li><b>DW.other</b>: Dry weight of remaining debris (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2011-03-24 to 2011-10-05</p><p><b>Latitudinal extent: </b>4.6353 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
Active thermography to estimate leaf heat transfer - leaf level data to manuscript
<p>Active thermography is an informative methodology to measure that we adopted to plant sciences to measure leaf heat transfer and derive spatial maps of thermal responsiveness of leaves. This method and its usability is described in a publication in 'Frontiers of Plant Sciences'.</p> <p>Here we publish the data-set of active thermography measurements that were used in this publication. The Mat-lab code that was developed with this publication is also published at Zenodo under the doi 10.5281/zenodo.1195869</p>
Leaf litter amphibian communities
<b>Description: </b><p>Leaf litter amphibian communities</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/84"><b>Composition of amphibian communities</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=96">here</a></p><p><b>Files: </b>This consists of 1 file: template_Faruk.xlsx</p><p><b>template_Faruk.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Plot environmental data</b> (described in worksheet EnvironData)</p><p>Description: Descriptions of vegetation and plot biophysical characteristics</p><p>Number of fields: 15</p><p>Number of data rows: 141</p><p>Fields: </p><ul><li><b>Locality</b>: SAFE Project sample site (Field type: Location)</li><li><b>Date</b>: Date of observations (Field type: Date)</li><li><b>TreeBigger50m</b>: Number of large trees (Field type: Numeric)</li><li><b>TreeSmaller50m</b>: Number of small trees (Field type: Numeric)</li><li><b>Deadwood</b>: Number of deadwood items (Field type: Numeric)</li><li><b>Litter1</b>: Leaf litter weight (one of four measurements) (Field type: Numeric)</li><li><b>Litter2</b>: Leaf litter weight (one of four measurements) (Field type: Numeric)</li><li><b>Litter3</b>: Leaf litter weight (one of four measurements) (Field type: Numeric)</li><li><b>Litter4</b>: Leaf litter weight (one of four measurements) (Field type: Numeric)</li><li><b>Veg1</b>: Unknown (Field type: Numeric)</li><li><b>Veg2</b>: Unknown (Field type: Numeric)</li><li><b>Veg3</b>: Unknown (Field type: Numeric)</li><li><b>TADPOLE</b>: Number of frog tadpoles observed (Field type: Numeric)</li><li><b>ADULT</b>: Number of frog adults observed (Field type: Numeric)</li><li><b>SPAWN</b>: Number of frog spawn observed (Field type: Numeric)</li></ul></li><li><p><b>Species records</b> (described in worksheet SpeciesData)</p><p>Description: Identity and abundance of frog species</p><p>Number of fields: 5</p><p>Number of data rows: 10</p><p>Fields: </p><ul><li><b>Locality</b>: SAFE Project sample site (Field type: Location)</li><li><b>Date</b>: Date of observation (Field type: Date)</li><li><b>TaxonomicName</b>: Species identity (Field type: Taxa)</li><li><b>Quantity</b>: Number of individuals observed (Field type: Numeric Trait)</li><li><b>Stage</b>: Life history stage (Field type: Categorical)</li></ul></li></ol><p><b>Date range: </b>2011-06-19 to 2011-06-29</p><p><b>Latitudinal extent: </b>4.6882 to 4.7369</p><p><b>Longitudinal extent: </b>117.5746 to 117.6549</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Amphibia<br> -  -  - Anura<br> -  -  -  - Dicroglossidae<br> -  -  -  -  - <i>Fejervarya</i><br> -  -  -  -  -  - <i>Fejervarya limnocharis</i><br> -  -  -  - Microhylidae<br> -  -  -  -  - <i>Chaperina</i><br> -  -  -  -  -  - <i>Chaperina fusca</i><br></div><p></p>
Neotropical leaf-litter beetle genus Motonerus (Coleoptera: Hydrophilidae): new species, distribution data and description of third instar larva
<p>Numerous new material of the genus <em>Motonerus </em>Hansen, 1989 (Coleoptera: Hydrophilidae: Sphaeridiinae) was examined over the past decade, resulting in the discovery of several undescribed species as well as new distributional records for most of the previously described species. Three species are here described as new: <em>Motonerus explanatus </em>sp. nov. (Panama), <em>M. inca </em>sp. nov. (Venezuela, Ecuador, Peru, Bolivia) and <em>M. sofiae </em>sp. nov. (Peru). New country records are provided for <em>M. depressus </em>Fikáček & Short (new for Venezuela, Ecuador, Peru and Bolivia), <em>M. hanseni </em>Fikáček & Short (new for Panama) and <em>M. problematicus </em>Fikáček & Short (new for Panama). An updated identification key and distributional maps are provided for all species, along with complete occurrence data in a DarwinCore formatted file. Potential distribution of the genus is modelled using the maximum entropy approach. Wing morphology is examined in detail, which revealed most species are macropterous, with <em>M. andersoni </em>Fikáček & Short being brachypterous, and <em>M. apterus </em>Fikáček & Short, <em>M. oosternoides </em>Fikáček & Short and <em>M. explanatus </em>sp. nov. micropterous. The third instar of the putative larva of <em>M. obscurus </em>Hansen is briefly described based on larvae collected in association with adults.</p>
Data from: Heritability of growth and leaf loss compensation in a long-lived tropical understorey palm
<p>Data from: Heritability of growth and leaf loss compensation in a long-lived tropical understorey palm, PLOS ONE.</p> <p>Quantitative genetic data related to growth parameters and leaf loss compensation of 1402 <em>Chamaedorea elegans</em> seedlings. Data was collected in a greenhouse experiment performed at the Unifarm experimental facilities of Wageningen University between January 2013 and July 2014, in which a defoliation treatment was applied. Seeds were collected in the Montes Azules Biosphere Reserve in Chiapas, Mexico. See PLOS ONE publication for more details.</p> <p>The file contains a separate "read me" tab in which the meaning of the variable codes is explained. </p> <p> </p>
Leaf litter decomposition in old-growth and selectively logged forest
<p><strong>Description: </strong></p> <p>In a multifactorial experiment we investigated the consequences of selective logging for decomposition and nutrient cycling in Southeast Asia by testing the effects of chemical composition of leaf litter and site factors on leaf litter mass loss. Litterbags were used to estimate decomposition over a period of 24 weeks, litterbags were collected 2, 4, 6, 8, 13, 24 weeks after the start of the experiment.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/124"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>NERC (Standard grant, NE/K016253/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2(383))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247639">here</a></p> <p><strong>Files: </strong>This consists of 1 file: Both_litter_decomposition_experiment.xlsx</p> <p><strong>Both_litter_decomposition_experiment.xlsx</strong></p> <p>This file contains dataset metadata and 3 data tables:</p> <ol> <li> <p><strong>chemical_composition_start</strong> (described in worksheet chemical_composition_start)</p> <p>Description: Chemical properties from the two leaf litter types before the experiment</p> <p>Number of fields: 19</p> <p>Number of data rows: 10</p> <p>Fields:</p> <ul> <li><strong>replicate</strong>: Replicate number (Field type: Replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: ID)</li> <li><strong>P_mg.g</strong>: Phosporus concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>N_perc</strong>: Nitrogen concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C_perc</strong>: Carbon concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C.N</strong>: Carbon nitrogen ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>P_perc</strong>: Phosporus concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C.P</strong>: Carbon phosporus ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>N.P</strong>: Nitrogen phosporus ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>Ca_mg.g</strong>: Calcium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>Mg_mg.g</strong>: Magnesium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>Al_mg.g</strong>: Aluminium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>K_mg.g</strong>: Potassium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>soluble_cell_content</strong>: Soluble cell content in percent (Field type: Numeric)</li> <li><strong>nonsoluble_cell_content</strong>: Non-soluble cell content in percent (Field type: Numeric)</li> <li><strong>hemicellulose_bound_proteins</strong>: Hemicellulose and bound proteins content in percent (Field type: Numeric)</li> <li><strong>cellulose_lignin_recalcitrants</strong>: Soluble cell content in percent (Field type: Numeric)</li> <li><strong>cellulose</strong>: Cellulose content in percent (Field type: Numeric)</li> <li><strong>lignin_recalcitrants</strong>: Lignin and recalcitrants content in percent (Field type: Numeric)</li> </ul> </li> <li> <p><strong>chemical_composition_end</strong> (described in worksheet chemical_composition_end)</p> <p>Description: Chemical properties from individual leaf litter bags at the end of the experiment</p> <p>Number of fields: 18</p> <p>Number of data rows: 64</p> <p>Fields:</p> <ul> <li><strong>code</strong>: Identifyer for each leaf litter bag, coding for location-plotname-subplot pair-leaf litter type-mesh size-replicate (Field type: ID)</li> <li><strong>location</strong>: Location of experimental plots (M: Maliau; S: SAFE) (Field type: ID)</li> <li><strong>location_name</strong>: Plot name (Field type: Location)</li> <li><strong>plot</strong>: Running number of experimental plots (Field type: ID)</li> <li><strong>pair</strong>: Each plot contains two experimental units (making up a pair) (Field type: ID)</li> <li><strong>replicate</strong>: Each pair contained two replicates of the same treatment (litter type x mesh size) (Field type: replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: Categorical)</li> <li><strong>mesh</strong>: Mesh size of the litter bags (Field type: Categorical)</li> <li><strong>P_mg.g</strong>: Phosporus concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>K_mg.g</strong>: Potassium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Ca_mg.g</strong>: Calcium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Mg_mg.g</strong>: Magnesium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Al_mg.g</strong>: Aluminium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>N_perc</strong>: Nitrogen concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>C_perc</strong>: Carbon concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>C.N</strong>: Carbon nitrogen ratio of dry leaf litter (Field type: Numeric)</li> <li><strong>cellulose</strong>: Cellulose concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>lignin_recalcitrants</strong>: Lignin and recalcitrants concentration in % of dry leaf litter (Field type: Numeric)</li> </ul> </li> <li> <p><strong>litterbags_massloss</strong> (described in worksheet litterbags_massloss)</p> <p>Description: Mass loss of litter in litterbags over the experimental period of 24 weeks</p> <p>Number of fields: 17</p> <p>Number of data rows: 128</p> <p>Fields:</p> <ul> <li><strong>code</strong>: Identifyer for each leaf litter bag, coding for location-plotname-subplot pair-leaf litter type-mesh size-replicate (Field type: ID)</li> <li><strong>location</strong>: Location of experimental plots (Field type: ID)</li> <li><strong>plotname</strong>: Plot name (Field type: Location)</li> <li><strong>plot</strong>: Running number of experimental plots (Field type: ID)</li> <li><strong>pair</strong>: Each plot contains two experimental units (making up a pair) (Field type: ID)</li> <li><strong>replicate</strong>: Each pair contained two replicates of the same treatment (litter type x mesh size) (Field type: Replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: Categorical)</li> <li><strong>mesh</strong>: Mesh size of the litter bags (Field type: Categorical)</li> <li><strong>weight_t0</strong>: Initial weight at the beginning of the experiment, around 10 g per litter bag (Field type: Numeric)</li> <li><strong>weight_t1</strong>: Weight at time step 1 after 2 weeks (Field type: Numeric)</li> <li><strong>weight_t2</strong>: Weight at time step 1 after 4 weeks (Field type: Numeric)</li> <li><strong>weight_t3</strong>: Weight at time step 1 after 6 weeks (Field type: Numeric)</li> <li><strong>weight_t4</strong>: Weight at time step 1 after 8 weeks (Field type: Numeric)</li> <li><strong>weight_t5</strong>: Weight at time step 1 after 13 weeks (Field type: Numeric)</li> <li><strong>weight_t6</strong>: Weight at time step 1 after 24 weeks (Field type: Numeric)</li> <li><strong>t6_corrected</strong>: Weight at time step 1 after 24 weeks, corrected for contaminating material, mostly ingrown plant roots and fungal hyphae. This is the data to use. (Field type: Numeric)</li> <li><strong>mass_loss_%</strong>: Mass loss at the end of the experiment compared to the start of the experiment, in percent (Field type: Numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2014-05-01 to 2018-09-01</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p>
Leaf responses to mild drought stress in natural variants of Arabidopsis.
<p>Plant growth and crop yield are negatively affected by a reduction in water availability. However, a clear understanding of how growth is regulated under non-lethal drought conditions is lacking. Recent advances in genomics, phenomics and transcriptomics allow in-depth analysis of natural variation. In this study, we conducted a detailed screening of leaf growth responses to mild drought in six <em>Arabidopsis thaliana</em> accessions. </p> <p>The raw phenotyping can be found in:<br> - cellularData.txt -> mature (22 days after stratification; DAS) leaf epidermis (third leaf) analysed for cell area, cell number, pavement cell area, pavement cell number, stomatal index and leaf area of the analysed leaf.</p> <p>- leaf3_maturity.txt -> area of the third leaf at maturity (22DAs) in mm<sup>2.</sup></p> <p>- leaf3_transition.txt -> area of the third leaf at transition (last day of cell proliferation; 11 DAS) in mm<sup>2</sup>.</p> <p>- rosetteArea.txt -> projected rosette area (mm<sup>2</sup>) over time (from 9-21DAS).</p> <p>The phenotyping results have been normalised for batch effects ('experiment' in raw data) and are found in cellularData_normalised.txt, leaf3_normalised.txt and rosetteArea_normalised.txt.</p> <p>Conversion from accession names (indiated as genotype in the data files) to the ID number used in the 1001genomes project (www.1001genomes.org) can be found in accessionIDs.txt.</p> <p>In each file 'C' indicates well-watered plants, while 'S' stand for mild-drought treatment.</p> <p>These results and methodological results are described in Clauw et al. (2015, Plant Physiology).</p> <p> </p> <p> </p>
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