Find research datasets worth reusing

opencc-zeroMay 2024View details →

Data from: Carbon isotope trends across a century of herbarium specimens suggest CO2 fertilization of C4 grasses

<p>Increasing atmospheric CO<sub>2</sub> is changing the dynamics of tropical savanna vegetation. C<sub>3</sub> trees and grasses are known to experience CO<sub>2</sub> fertilization, whereas responses to CO<sub>2</sub> by C<sub>4</sub> grasses are more ambiguous. Here, we sample stable carbon isotope trends in herbarium collections of South African C<sub>4</sub> and C<sub>3</sub> grasses to reconstruct <sup>13</sup>C discrimination. We found that C<sub>3</sub> grasses showed no trends in <sup>13</sup>C discrimination over the past century but that C<sub>4</sub> grasses increased their <sup>13</sup>C discrimination through time, especially since 1950. These changes were most strongly linked to changes in atmospheric CO<sub>2</sub> rather than to trends in rainfall climatology or temperature. Combined with previously published evidence that grass biomass has increased in C<sub>4</sub>-dominated savannas, these trends suggest that increasing water use efficiency due to CO<sub>2</sub> fertilization may be changing C<sub>4</sub> plant-water relations. CO<sub>2</sub> fertilization of C<sub>4</sub> grasses may thus be a neglected pathway for anthropogenic global change in tropical savanna ecosystems.</p>

opencc-by-4.0Apr 2024View details →

Data Accompanying "Leveraging successional facilitation to improve restoration of foundational dune grasses along a frequently disturbed coastline"

<div> <h1>Experimental Data accompanying "Leveraging successional facilitation to improve restoration of foundational dune grasses along a frequently disturbed coastline"</h1> Authors: Hallie Fischman, Copeland Cromwell, Joe Morton, Ralph Temmink, Tjisse van der Heide, Pete Adams, Christine Angelini <h1>Manuscript Abstract</h1> <a href="https://github.com/halliefischman1/Dune-Succession/blob/main/README.md#manuscript-abstract"></a></div> <p>Coastal ecosystems provide critical storm and flood protection services, but are rapidly degrading worldwide, making their restoration urgent. Here, we evaluated whether successional facilitation, where pioneers facilitate climax species, could be leveraged to accelerate coastal dune revegetation. A survey spanning 270km of Southeast US coastline revealed that Panicum amarum (bitter panicum) supported higher plant richness than Uniola paniculata (sea oats), and that sea oat cover was 230% greater on mature dunes than disturbed dunes, suggesting bitter panicum functions as a pioneer and sea oats is a climax species. A reciprocal transplant experiment confirmed this interpretation: bitter panicum stem production and height fell by 37% and >20 cm, respectively, when planted proximate to sea oats versus in isolation, whereas sea oats produced 38% more and >12 cm taller stems when planted proximate to bitter panicum versus in isolation. A second experiment evaluating the density-dependence of this facilitative interaction revealed that sea oats transplanted into low densities of bitter panicum grew >15% taller than isolated and high-density treatments. However, within seven months, wave inundation eliminated >60% propagules in both experiments. To explore foredune inundation frequency and its implications for dune revegetation, we applied empirical wave runup models at 101 locations throughout Volusia County, Florida. While disturbance frequency varied seasonally and annually, sites with low dune crests and steep beach slopes experienced frequent inundation (>50 events/year). Given the interactions between geomorphology and vegetation success, we present decision matrix to guide managers in determining optimal revegetation methods tailored to project goals and site conditions.</p> <div> <h1>Methods and Data Description</h1> <a href="https://github.com/halliefischman1/Dune-Succession/blob/main/README.md#methods-and-data-description"></a></div> <p>Experiment 1: Uniola paniculata (sea oats) and panicum amarum (bitter panicum) were planted in one of three treatments. Controls are in bare areas, Adjacent treatments (adj) are planted 50cm away from naturally occuring patches of the other species (ie, SO-adj-NPA is sea oats planted adjacent to natural bitter panicum), and Within treatments are planted in a naturally occuring patch of the other species (ie, SO-in-NPA is sea oats planted into a patch of natural bitter panicum). Each replicate consisted of 5 transplant propagules in an X configuration. At each monitoring, all stems were counted and the maximum height of each plot was measured. Plot treatments were reassigned during monitoring, if needed, such that any plot with the heterospecific growing within the 0.25 m2 area was considered a “within plot” and any plot with the heterospecific <50 cm away was considered an “adjacent plot”. See maunscript for additional details.</p> <p>Experiment 2: Sea oats were planted in the same configuration (5 propagules in an X) surrounded by varrying densities of natural bitter panicum. At each monitoring, we counted all bitter panicum stems within a 1m radius of the transplant, all propogule stems, and measured the height of the tallest transplant in each plot. Treatments were binned based on the number of surrounding bitter panicum stems at the final monitoring point: “Zero”, containing no stems within the 1 m radius (n=20 plots); “Low”, 1-9 stems in the surrounding 1 m radius (n=46 plots); “Mid” 10-34 stems in the 1 m radius (n=29 plots); and “High”, 35-70 stems in the 1m radius (n=25 plots). See manuscript for additional details.</p> <p>Datasets contains stem counts and heights of planted grasses over time. Stem counts are provided for each of the 5 propagules that comprise a single experimental unit, and a single maximum height is provided per plot. Plots were monitored approximately bimonthly until grasses were eroded by waves (see manuscript). Additional information on treatments are provided in the manuscript and data methods sections.</p>

Fig. 9. Aculodes capillarisi n in New Species Of Aculodes (Acari: Eriophyoidea) From Grasses In Poland

Fig. 9. Aculodes capillarisi n. sp., larva: D – dorsal aspect

Fig. 4. Aculodes calamaabditus n in New Species Of Aculodes (Acari: Eriophyoidea) From Grasses In Poland

Fig. 4. Aculodes calamaabditus n. sp., nymph: D – dorsal aspect, VO – ventral microtubercles

Fig. 6. Aculodes capillarisi n in New Species Of Aculodes (Acari: Eriophyoidea) From Grasses In Poland

Fig. 6. Aculodes capillarisi n. sp., female: CG – coxigenital region, PV – ventral telosoma

Fig. 2. Aculodes calamaabditus n in New Species Of Aculodes (Acari: Eriophyoidea) From Grasses In Poland

Fig. 2. Aculodes calamaabditus n. sp., female: CG – coxigenital region, PV – ventral telosoma

opencc-zeroJul 2024View details →

Foliar phosphorus concentration modulates the defensive mutualism of an endophytic fungus in a perennial host grass

<p>Grasses hosting <em>Epichloë</em> endophytes are protected against herbivores due to the production of various fungal alkaloids. Previous research has found that high foliar phosphorus concentrations reduce the level of the alkaloid ergovaline, thereby reducing the endophyte-mediated herbivore resistance. Yet, the impact of phosphorus on ergovaline biosynthesis versus its influence on endophyte growth and synthesis of other fungal alkaloids remains unresolved. Our objective was to elucidate these relationships. We grew endophyte-symbiotic and non-symbiotic <em>Festuca arundinacea</em> plants and fertilized them with different doses of phosphorus. Later, half of the plants from each treatment were challenged with larvae of the generalist chewing insect <em>Spodoptera frugiperda</em>. We assessed the relationships between foliar phosphorus levels, fungal mycelium, and alkaloid concentrations, as well as their impacts on larvae performance, herbivore-caused damage, and plant biomass. Endophyte mycelial biomass in plant tissue was found to be independent of foliar phosphorus concentration. The alkaloids lolines and peramine showed a linear relationship with mycelial biomass but no correlation with foliar phosphorus. Surprisingly, high ergovaline concentrations were positively associated with an interaction between endophyte mycelial biomass and foliar phosphorus concentration. Although herbivory increased loline concentration, only high concentrations of ergovaline and peramine were related to reduced <em>S. frugiperda</em> larvae weight gain. However, endophyte presence did not reduce herbivory damage to plants. Contrary to expectation, we did not find a negative but a positive association between concentrations of foliar phosphorus and ergovaline alkaloid, through its interaction with endophyte mycelial biomass. Alternatively, our findings suggest that phosphorus plays a crucial role in modulating the <em>Epichloë</em>-mediated defensive mutualism, primarily through its effects on ergovaline rather than on endophyte concentration or production of other alkaloids.</p>

opencc-by-4.0Dec 2020View details →

Fig. 1 in New record of five ciliate species from temporary ponds on a grass lawn

Fig. 1. Sampling site. Temporary ponds on a lawn.

opencc-by-4.0Jul 2024View details →

Data for 'Nitrogen Availability and Summer Drought, but not N:P Imbalance, Drive Carbon Use Efficiency of a Mediterranean Tree-Grass Ecosystem

<p>These are flux, meteorology, phenological transition dates and a NDVI timeseries for the Majadas del Tietar 'MANIP' experimental site between 2014 and 2020. </p> <p>These data were used for the manuscript:</p> <p>Nair et al.  <span>Nitrogen Availability and Summer Drought, but not N:P Imbalance, Drive Carbon Use Efficiency of a Mediterranean Tree-Grass Ecosystem submitted to Global Change Biology. <br></span></p> <p><span>In this repository we provide partially processed data to reproduce the analysis in our manuscript.<br>Raw images and half-hourly flux data are available at the following locations:</span></p> <p><span>Phenocam Imagery: the Phenocam network (https://phenocam.nau.edu/webcam/, sites - CT: eslma, NT: eslma1, NPT: eslma2).<br></span><span>Flux and meteo:  the European Flux Database (https://www.europe-fluxdata.eu/, sites - CT: ES-LMa, NT: ES-LM1, NPT: ES-LM2). <br></span><span>Satellite NDVI: Sentinel-2A and 2B data available at https://dataspace.copernicus.eu/.</span></p>

opencc-by-4.0Sep 2018View details →

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL managed grass

<p>This is model output from LPJmL for managed grass as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>

opencc-by-4.0Sep 2024View details →

Updated dataset from "Exploring seed density and limiting similarity to reduce invasive grass performance for grassland restoration purposes"

<p>Here we aimed to compare the effect of two seed mixes and three density sowing treatments on the performance of the invasive grass Eragrostis plana, one of the major threats to the Campos Sulinos grasslands, at South Brazil. The experiment was carried out in a greenhouse experiment in Porto Alegre, Brazil. The seed mixes have the same species, but differ in terms of species abundance.</p> <p>Paper Thomas et al. (2024) "Exploring seed density and limiting similarity to reduce invasive grass performance for grassland restoration purposes", published at Applied Vegetation Science. <a href="https://doi.org/10.1111/avsc.12804">https://doi.org/10.1111/avsc.12804</a></p>

opencc-by-4.0Sep 2024View details →

Model outputs used in "Regional cooling potential from expansion of perennial grasses in Europe"

<p>This dataset accompanies a scientific publication: Xia Zhang, Bo Huang, Nariê Rinke Dias de Souza, Xiangping Hu, and Francesco Cherubini. 2024. Regional cooling potential from expansion of perennial grasses in Europe. Communications Earth & Environment <strong>5</strong>, 772 (2024). https://doi.org/10.1038/s43247-024-01923-5</p>

Table 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 1.</b> Number of field samples (including controls) for each qPCR assay at each site sampled for eDNA in 2018 and 2019 in western Lake Erie. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. Note that samples are site-specific.</p><table><tbody><tr><th>Site</th><th>Year</th></tr><tr><th>2018</th><th>2019</th></tr><tr><th>Assay</th><th>Samples</th><th>Assay</th><th>Samples</th></tr></tbody><tbody><tr><th>DR</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>81 81 81</td></tr><tr><th>HP</th><td>GCTM10 GCTM22 GCTM32</td><td>77 77 77</td><td>GCTM10 GCTM22 GCTM32</td><td>82 82 82</td></tr><tr><th>MB</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>80 80 80</td></tr></tbody></table>

Table 4 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 4.</b> Percentage of positive eDNA replicate detections in each month and site in 2018 and 2019 in western Lake Erie (based on at least one positive detection on at least one marker and one replicate). All markers (GCTM10, GCTM 22, GCTM32) were used to calculate these proportions. Samples were collected monthly from June to November for each site. The number of telemetered grass carp detected within 7 days before sampling for eDNA is denoted in parentheses. Acoustic telemetry receivers in MB in 2018 were not available. DR = Detroit River, HP = Hot Ponds, and MB = Maumee Bay. NA denotes when acoustic telemetry receivers were not in operation.</p><table><tbody><tr><th>Year</th></tr><tr><th>Site</th><th>2018</th><th>2019</th></tr><tr><th></th><th>June</th><th>July</th><th>Aug</th><th>Sept</th><th>Oct</th><th>Nov</th><th>May</th><th>June</th><th>July</th><th>August</th><th>Oct</th><th>Nov</th></tr></tbody><tbody><tr><th>DR</th><td>0.0%</td><td>2.3%</td><td>2.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>4.5%</td><td>10.6</td><td>14.1</td><td>17.4%</td><td>14.1%</td><td>2.2%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(0)</td><td>(1)</td><td>% (1)</td><td>% (1)</td><td>(2)</td><td>(2)</td><td>(0)</td></tr><tr><th>HP</th><td>0.0%</td><td>0.1%</td><td>4.1%</td><td>2.2%</td><td>15.8%</td><td>0.0%</td><td>9.0%</td><td>1.5%</td><td>34.8</td><td>1.5%</td><td>15.8%</td><td>22.7%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(NA)</td><td>(2)</td><td>(2)</td><td>% (2)</td><td>(3)</td><td>(2)</td><td>(2)</td></tr><tr><th>MB</th><td>12.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.1%</td><td>37.1</td><td>8.3%</td><td>8.3%</td><td>0.0%</td></tr><tr><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(0)</td><td>(0)</td><td>% (0)</td><td>(0)</td><td>(0)</td><td>(0)</td></tr></tbody></table>

Table 3 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 3.</b> Candidate set of hierarchical occupancy models used to estimate probability of grass carp eDNA occurrence among sites (ψ), the conditional probability of grass carp eDNA occurrence at a sampling locality within a site given that grass carp were present at the site (Θ), and the conditional probability of eDNA detection on replicate filters collected at a sampling locality given that the species is present at the sampling locality <i>(p</i>) from three sites in western Lake Erie sampled in 2018 and 2019. Covariates included location (site), time (Month) and probe type (GCTM10, GCTM22, GCTM32). Model comparison was evaluated with the Widely Applicable Information Criterion (WAIC).</p><table><tbody><tr><th>Model</th><th>WAIC</th><th>Δ WAIC</th><th>Lack of fit</th><th>Predicted Variance</th></tr></tbody><tbody><tr><th>ψ(Site)Θ(Site)p(.)</th><td>309.44</td><td>-</td><td>298.70</td><td>18.63</td></tr><tr><th>ψ(.)Θ(Site)p(.)</th><td>309.50</td><td>0.06</td><td>298.99</td><td>10.73</td></tr><tr><th>ψ(.)Θ(Month)p(.)</th><td>317.61</td><td>8.18</td><td>299.05</td><td>18.56</td></tr><tr><th>ψ(Season)Θ(.)p(.)</th><td>317.67</td><td>8.24</td><td>299.01</td><td>18.65</td></tr><tr><th>ψ(Month)Θ(.)p(.)</th><td>317.72</td><td>8.28</td><td>299.04</td><td>18.67</td></tr><tr><th>ψ(Season)Θ(Season)p(.)</th><td>317.84</td><td>8.40</td><td>299.04</td><td>18.79</td></tr><tr><th>ψ(.)Θ(Season)p(.)</th><td>317.74</td><td>8.31</td><td>299.07</td><td>18.66</td></tr><tr><th>ψ(Site)Θ(.)p(.)</th><td>317.94</td><td>8.51</td><td>299.04</td><td>18.90</td></tr><tr><th>ψ(.)Θ(.)p(.)</th><td>325.00</td><td>15.57</td><td>305.50</td><td>20.21</td></tr><tr><th>ψ(Season)Θ(Site)p(.)</th><td>325.41</td><td>15.98</td><td>305.49</td><td>19.91</td></tr><tr><th>ψ(Site + Season)Θ(.)p(.)</th><td>325.59</td><td>16.16</td><td>305.44</td><td>20.15</td></tr><tr><th>ψ(Site)Θ(Season)p(.)</th><td>325.72</td><td>16.29</td><td>305.48</td><td>20.23</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(.)</th><td>325.92</td><td>16.49</td><td>305.49</td><td>20.43</td></tr><tr><th>ψ(.)Θ(Site + Season)p(.)</th><td>326.37</td><td>16.94</td><td>305.52</td><td>20.84</td></tr><tr><th>ψ(Site)Θ(Month)p(.)</th><td>329.57</td><td>20.14</td><td>308.65</td><td>20.92</td></tr><tr><th>ψ(Month)Θ(Month)p(.)</th><td>330.14</td><td>20.71</td><td>308.66</td><td>21.84</td></tr><tr><th>ψ(Site + Season)Θ(Site)p(Probe)</th><td>377.63</td><td>68.20</td><td>276.90</td><td>100.72</td></tr><tr><th>ψ(Site + Season)Θ(.)p(Probe)</th><td>378.35</td><td>68.92</td><td>277.00</td><td>101.34</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(Probe)</th><td>378.42</td><td>68.99</td><td>277.00</td><td>101.41</td></tr><tr><th>ψ(Site + Season)Θ(Season)p(Probe)</th><td>378.55</td><td>69.12</td><td>277.09</td><td>101.46</td></tr><tr><th>ψ(Season)Θ(Site + Season)p(Probe)</th><td>379.41</td><td>69.98</td><td>277.28</td><td>102.10</td></tr><tr><th>ψ(Site)Θ(Site + Season)p(Probe)</th><td>379.62</td><td>70.19</td><td>277.40</td><td>102.21</td></tr><tr><th>ψ(.)Θ(Site + Season)p(Probe)</th><td>379.88</td><td>70.45</td><td>277.31</td><td>102.57</td></tr><tr><th>ψ(Site + Month)Θ(.)p(.)</th><td>383.56</td><td>74.13</td><td>282.86</td><td>100.69</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(.)</th><td>385.47</td><td>76.04</td><td>283.38</td><td>102.09</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(Probe)</th><td>386.95</td><td>77.52</td><td>282.90</td><td>104.05</td></tr><tr><th>ψ(.)Θ(Site + Month)p(.)</th><td>396.44</td><td>87.01</td><td>292.14</td><td>104.29</td></tr><tr><th>ψ(.)Θ(.)p(Probe)</th><td>396.45</td><td>87.01</td><td>292.14</td><td>104.29</td></tr></tbody></table>

Table 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 2.</b> Gene region, primer, and probe sequences used to amplify GCTM10,GCTM22, and GCTM32 for grass carp.</p><table><tbody><tr><th>Gene</th><th>Primers and Probes</th><th>Sequence</th></tr></tbody><tbody><tr><th>ND2</th><td>Forward</td><td>5′- CCYTACGTACTCGCAATTCTAC -3′</td></tr><tr><th>ND2</th><td>Reverse</td><td>5′- GTGGTGGTGTTGGGCTATTA -3′</td></tr><tr><th>ND2</th><td>Probe</td><td>5′- VIC- ACCCTAACCTTTGCTAGCTCCCAC -MGBNFQ-3′</td></tr><tr><th>COII</th><td>Forward</td><td>5′- CCGACTCCTAGAAACAGATCAC -3′</td></tr><tr><th>COII</th><td>Reverse</td><td>5′- GGGACAGCTCAGGAATGTAATA -3′</td></tr><tr><th>COII</th><td>Probe</td><td>5′- 56-FAM- CCAGTTCGT/ZEN/GTCCTAGTATCTGCCGA -3IABkFQ -3′</td></tr><tr><th>COIII</th><td>Forward</td><td>5′- CCACGGACTACACGTCATTATT -3′</td></tr><tr><th>COIII</th><td>Reverse</td><td>5′-GATGTTCGGATGTAAAGTGGTATTG -3′</td></tr><tr><th>COIII</th><td>Probe</td><td>5′-NED- TTCCTAGCTGTTTGCCTTCTCCGT -MGBNFQ-3′</td></tr></tbody></table>

opencc-by-4.0Oct 2024View details →

Assemblies and annotations from the paper "Interspecies hybridization as a route of accessory chromosome origin in fungal species infecting wild grasses"

<div> <p>This repository contains the whole genome assemblies, gene and TE annotations generated and analyzed in the manuscript entitled "Interspecies hybridization as a route of accessory chromosome origin in fungal species infecting wild grasses". </p> <p>Preprint available on BioRxiv:</p> <p>https://www.biorxiv.org/content/10.1101/2024.10.03.616481v1</p> <p> </p> </div>

opencc-zeroOct 2019View details →

Data from: Diversity, dynamics and effects of long terminal repeat retrotransposons in the model grass Brachypodium distachyon

<ul> <li><span>Transposable elements (TEs) are the main reason for the high plasticity of plant genomes, where they occur as communities of diverse evolutionary lineages. Because research has typically focused on single abundant families or summarized TEs at a coarse taxonomic level, our knowledge about how these lineages differ in their effects on genome evolution is still rudimentary. </span></li> <li><span>Here we investigate the community composition and dynamics of 32 long terminal repeat retrotransposon (LTR-RT) families in the 272 Mb genome of the Mediterranean grass <i>Brachypodium distachyon. </i></span></li> <li><span>We find that much of the recent transpositional activity in the <i>B. distachyon </i>genome is due to centromeric <i>Gypsy </i>families and <i>Copia </i>elements belonging to the Angela lineage. With a half-life as low as 66 ky, the latter are the most dynamic part of the genome and an important source of within-species polymorphisms. Second, GC-rich <i>Gypsy </i>elements of the Retand lineage are the most abundant TEs in the genome. Their presence explains more than 20 percent of the genome-wide variation in GC content and is associated with higher methylation levels. </span></li> <li><span>Our study shows how individual TE lineages change the genetic and epigenetic constitution of the host beyond simple changes in genome size. </span></li> </ul>

opencc-zeroDec 2020View details →

Plant traits of grass and legume species for flood resilience and N2O mitigation

<p>Flooding threatens the functioning of managed grasslands by decreasing primary productivity and increasing nitrogen losses, notably as the potent greenhouse gas nitrous oxide (N2O). Sowing species with traits that promote flood resilience and mitigate flood-induced N2O emissions within these grasslands could safeguard their productivity while mitigating nitrogen losses.</p> <p>We tested how plant traits and resource acquisition strategies could predict flood resilience and N2O emissions of 12 common grassland species (eight grasses and four legumes) grown in field soil in monocultures in a 14-week greenhouse experiment.</p> <p>We found that grasses were more resistant to flooding, while legumes recovered better. Resource-conservative grass species had higher resistance, while resource-acquisitive grasses species recovered better. Resilient grass and legume species lowered cumulative N2O emissions. Grasses with lower inherent leaf and root δ13C (and legumes with lower root δ13C) lowered cumulative N2O emissions during and after the flood.</p> <p>Our results highlight the differing responses of grasses with contrasting resource acquisition strategies, and of legumes to flooding. Combining grasses and legumes based on their traits and resource acquisition strategies could increase the flood-resilience of managed grasslands, and their capability to mitigate flood-induced N2O emissions.</p>