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Table 3 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia
<p><b>Table 3:</b> Comparision of operational cost, production and economic profit of IMC polyculture & GIFT mono-sex tilapia culture during 2018-19 at Jodamu village of district Ciuttack, Odisha</p><table><tbody><tr><th><b>Parameters</b></th><th><b>T1-IMC culture (area-.4 ha)</b></th><th><b>T2-GIFT Tilapia culture (area-.4 ha)</b></th></tr></tbody><tbody><tr><th><b>Operational Cost</b></th><td><b>Expenditure Expenditure (Rs/0.4ha/yr) (Rs/ha/yr)</b></td><td><b>Expenditure (Rs/0.4ha/yr)</b></td><td><b>Expenditure (Rs/ha/yr)</b></td></tr><tr><th><b>I. Expenditure</b></th><td></td><td></td><td></td><td></td></tr><tr><th>Watering/de-watering charges</th><td>3,000</td><td>7,500</td><td>3,000</td><td>7,500</td></tr><tr><th>Bleaching Powder 50kg@Rs30/kg</th><td>1,500</td><td>3,750</td><td>1,500</td><td>3,750</td></tr><tr><th>Organic Manure 1000kg@Rs 0.5/kg</th><td>500</td><td>1,250</td><td>500</td><td>1,250</td></tr><tr><th>DAP fertilizer 20kg@ 20/kg</th><td>400</td><td>1,000</td><td>400</td><td>1,000</td></tr><tr><th>Lime-800kg (IMC), 1000kg (GIFT tilapia) @ Rs 10/kg</th><td>8,000</td><td>20,000</td><td>10,000</td><td>25,000</td></tr><tr><th>GNOC-25kg (IMC), 42kg (GIFT tilapia) @ Rs 22/kg</th><td>550</td><td>1325</td><td>924</td><td>2310</td></tr><tr><th>Soyabin 21kg @Rs 30/kg</th><td>630</td><td>1,575</td><td>630</td><td>1,575</td></tr><tr><th>Curd 120 kg @ 40/kg</th><td>4800</td><td>12000</td><td>4800</td><td>12000</td></tr><tr><th>Yeast 5kg @ Rs 200/kg</th><td>1000</td><td>2500</td><td>1000</td><td>2500</td></tr><tr><th>Ricebran 80kg @ Rs 15.5/kg</th><td>1,240</td><td>3,100</td><td>1,240</td><td>3,100</td></tr><tr><th>Joggery-100kg (IMC), 208kg (GIFT tilapia) @ Rs 23/kg</th><td>2,300</td><td>5,750</td><td>4,784</td><td>11,960</td></tr><tr><th>IMC seed4000pc @ Rs 5/pc and GIFT seed cost 6400pc @ Rs 2/pc</th><td>20,000</td><td>50,000</td><td>12,800</td><td>32,000</td></tr><tr><th>IMC-F. Feed 5500kg @ Rs 40/kg and GIFT tilapia F. Feed 9800kg @ Rs 40/kg</th><td>2,20,000</td><td>5.50,000</td><td>3,92,000</td><td>9,80,000</td></tr><tr><th>Transport @ Rs10000/time</th><td>20,000</td><td>50,000</td><td>30,000</td><td>75,000</td></tr><tr><th>Man power for pond preparation, bio-security installation, Management, Feeding, 10,000 netting, watch and ward, marketing etc. @ 200/man day (IMC & GIFT)</th><td>25,000</td><td>30,000</td><td>75,000</td></tr><tr><th>Miscellaneous expenditure (medicine, aeration, transaction and coordination)</th><td>5,000</td><td>12,500</td><td>10,000</td><td>25,000</td></tr><tr><th>Total expenditure</th><td>2,98,920</td><td>7,47,300</td><td>5,03,578</td><td>15,22,070</td></tr><tr><th><b>IMC poly-culture & GIFT mono-sex tilapia Production and economic profit (2018-19)</b></th></tr><tr><th><b>II. Gross Income from GIFT tilapia</b></th><td>0.4 ha/yr</td><td>ha/yr</td><td>0.4 ha/yr</td><td>ha/yr</td></tr><tr><th>Total production (kg/yr)</th><td>3383</td><td>8457.5</td><td>8000</td><td>20000</td></tr><tr><th>IMC & GIFT Cost of fish @ Rs 160 & 140/kg (Rs)</th><td>5,41,280</td><td>13,53,300</td><td>11,20,000</td><td>28,00,000</td></tr><tr><th>Net income from fish (Gross income-expenditure) (Rs)</th><td>2,42,360</td><td>6,05,900</td><td>6,31,172</td><td>15,77,930</td></tr><tr><th>Return on expenditure (%)</th><td>81.07</td><td>0.81</td><td>125.33</td><td>103.67</td></tr><tr><th>Cost benefit ratio (C:B)</th><td>0.810</td><td>0.81</td><td>1.25</td><td>1.25</td></tr></tbody></table>
Table 1 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia
<p><b>Table 1:</b> Ranges and mean values (± SD) of water parameters in T1 &T2</p><table><tbody><tr><th><b>Parameters</b></th><th><b>T1-IMC poly-culture</b></th><th><b>T2-GIFT tilapia mono-sex</b></th></tr></tbody><tbody><tr><th></th><td><b>Min</b></td><td><b>Max</b></td><td><b>Mean ± SD</b></td><td><b>Min</b></td><td><b>Max</b></td><td><b>Mean ± SD</b></td></tr><tr><th>Temp (°C)</th><td>21.1</td><td>33</td><td>26.3±4.56</td><td>21.2</td><td>34.1</td><td>27.7±6.5</td></tr><tr><th>Transparency (cm)</th><td>24.33</td><td>35.67</td><td>27.71± 0.86</td><td>25.00</td><td>36.00</td><td>29.29± 0.81</td></tr><tr><th>DO (ppm)</th><td>4.3</td><td>7.5</td><td>6.07±1.35</td><td>4.5-</td><td>6.0</td><td>5.3±0.51</td></tr><tr><th>pH</th><td>7.0</td><td>8.4</td><td>7.5±0.55</td><td>6.0</td><td>8.0</td><td>7.5±0.35</td></tr><tr><th>Alkalinity mg/l</th><td>86.7</td><td>114.7</td><td>100.4±12.8</td><td>80.1</td><td>114.0</td><td>98.3±14.2</td></tr><tr><th>Ammonia mg/l</th><td>0.51</td><td>0.61</td><td>0.55±0.05</td><td>0.55</td><td>0.64</td><td>0.58±0.04</td></tr></tbody></table>
Table 3 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 3:</b> T Tes</p><table><tbody><tr><th></th><th></th><th></th><th></th><th><b>Test Value = 0</b></th><th></th></tr></tbody><tbody><tr><th></th><td><b>t</b></td><td><b>95% Confidence interval of the difference df Sig.(2 tailed) Mean Difference Lower Upper</b></td></tr><tr><th>Catch</th><td>11.129</td><td>23</td><td>.000</td><td>144.275 117.46</td><td>171.09</td></tr><tr><th>Catching tool</th><td>14.387</td><td>23</td><td>.000</td><td>1.500 1.28</td><td>1.72</td></tr></tbody></table><p><b>Table 3:</b> T Tes</p>
Table 1 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 1:</b> Production of Red Snapper (<i>Lutjanus sp</i>) in Fishing Equipment Uni</p><table><tbody><tr><th><b>No</b></th><th><b>Months</b></th><th><b>Fishing Equipmen Hand Line Long Line</b></th><th><b>Production quantity (Kg)</b></th></tr></tbody><tbody><tr><th>1</th><td>Januari</td><td>96.4</td><td>78.3</td><td>174.7</td></tr><tr><th>2</th><td>Februari</td><td>98.2</td><td>57.4</td><td>155.6</td></tr><tr><th>3</th><td>Maret</td><td>112.1</td><td>102.4</td><td>214.5</td></tr><tr><th>4</th><td>April</td><td>195.2</td><td>144.5</td><td>339.7</td></tr><tr><th>5</th><td>Mei</td><td>135.2</td><td>118.7</td><td>253.9</td></tr><tr><th>6</th><td>Juni</td><td>174.2</td><td>154.2</td><td>328.4</td></tr><tr><th>7</th><td>Juli</td><td>213.3</td><td>215.4</td><td>428.7</td></tr><tr><th>8</th><td>Agustus</td><td>325.4</td><td>222.1</td><td>547.5</td></tr><tr><th>9</th><td>September</td><td>221.1</td><td>155.9</td><td>377</td></tr><tr><th>10</th><td>Oktober</td><td>154.2</td><td>124.2</td><td>278.4</td></tr><tr><th>11</th><td>November</td><td>89.3</td><td>130</td><td>219.3</td></tr><tr><th>12</th><td>Desember</td><td>89.5</td><td>55.4</td><td>144.9</td></tr><tr><th>TOTAL</th><td>1904.1</td><td>1558.5</td><td>3462.6</td></tr></tbody></table><p>t</p>
Table 2 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 2:</b> CPUE of Red Snapper</p><table><tbody><tr><th><b>No Month</b></th><th><b>Production (kg)</b></th><th><b>Standar Effort (Trip)</b></th><th><b>Standar CPUE (Kg/Trip)</b></th></tr></tbody><tbody><tr><th>1</th><td>Januari</td><td>174.7</td><td>24</td><td>7.279</td></tr><tr><th>2</th><td>Februari</td><td>155.6</td><td>24</td><td>6.483</td></tr><tr><th>3</th><td>Maret</td><td>214.5</td><td>27</td><td>7.944</td></tr><tr><th>4</th><td>April</td><td>339.7</td><td>30</td><td>11.323</td></tr><tr><th>5</th><td>Mei</td><td>253.9</td><td>27</td><td>9.404</td></tr><tr><th>6</th><td>Juni</td><td>328.4</td><td>27</td><td>12.163</td></tr><tr><th>7</th><td>Juli</td><td>428.7</td><td>27</td><td>15.878</td></tr><tr><th>8</th><td>Agustus</td><td>547.5</td><td>21</td><td>26.071</td></tr><tr><th>9</th><td>September</td><td>377</td><td>23</td><td>16.391</td></tr><tr><th>10</th><td>Oktober</td><td>278.4</td><td>26</td><td>10.708</td></tr><tr><th>11</th><td>November</td><td>219.3</td><td>25</td><td>8.772</td></tr><tr><th>12</th><td>Desember</td><td>144.9</td><td>26</td><td>5.573</td></tr><tr><th></th><td>Amount</td><td>3462.6</td><td>307</td><td>137.990</td></tr><tr><th></th><td>Average</td><td>288.550</td><td>26</td><td>11.499</td></tr></tbody></table>
Table II in AfriBasins: a new framework in FishBase for the analysis of African fresh and brackish water fish distributions, with a discussion on the Congo basin fauna
<p>Table II. – AfriBasin size, species and endemics per AfriBasin and different proxies of sampling effort based on 42022 georeferenced records from the RMCA fish collection and GBIF.</p><table><tbody><tr><th>Subbasin</th><th>Size (km 2)</th><th>Species</th><th>Species / 1000 km 2</th><th>Endemics</th><th>Endemics / 1000 km 2</th><th>Records</th><th>Records / 1000 km 2</th><th>Sampling localities</th><th>Sampling loc. / 1000 km 2</th><th>Sampling days</th><th>Sampling days / 1000 km 2</th><th>Expeditions</th><th>Expeditions / 1000 km 2</th></tr></tbody><tbody><tr><th>Middle Congo</th><td>75000</td><td>344</td><td>4.59</td><td>3</td><td>0.04</td><td>3622</td><td>48.29</td><td>172</td><td>2.29</td><td>495</td><td>6.60</td><td>154</td><td>2.05</td></tr><tr><th>Kasai</th><td>290000</td><td>259</td><td>0.89</td><td>23</td><td>0.08</td><td>1630</td><td>5.62</td><td>121</td><td>0.42</td><td>239</td><td>0.82</td><td>138</td><td>0.48</td></tr><tr><th>Pool Malebo</th><td>9500</td><td>254</td><td>26.74</td><td>10</td><td>1.05</td><td>4455</td><td>468.95</td><td>101</td><td>10.63</td><td>518</td><td>54.53</td><td>200</td><td>21.05</td></tr><tr><th>Ubangi</th><td>240000</td><td>248</td><td>1.03</td><td>19</td><td>0.08</td><td>1705</td><td>7.10</td><td>119</td><td>0.50</td><td>164</td><td>0.68</td><td>61</td><td>0.25</td></tr><tr><th>Lower Congo</th><td>50000</td><td>248</td><td>4.96</td><td>53</td><td>1.06</td><td>2233</td><td>44.66</td><td>264</td><td>5.28</td><td>366</td><td>7.32</td><td>131</td><td>2.62</td></tr><tr><th>Lualaba</th><td>323000</td><td>247</td><td>0.76</td><td>18</td><td>0.06</td><td>4354</td><td>13.48</td><td>388</td><td>1.20</td><td>645</td><td>2.00</td><td>226</td><td>0.70</td></tr><tr><th>Aruwimi</th><td>127500</td><td>233</td><td>1.83</td><td>12</td><td>0.09</td><td>1455</td><td>11.41</td><td>150</td><td>1.18</td><td>213</td><td>1.67</td><td>69</td><td>0.54</td></tr><tr><th>Ruki</th><td>177000</td><td>231</td><td>1.31</td><td>9</td><td>0.05</td><td>2211</td><td>12.49</td><td>81</td><td>0.46</td><td>328</td><td>1.85</td><td>103</td><td>0.58</td></tr><tr><th>Itimbiri</th><td>55000</td><td>228</td><td>4.15</td><td>4</td><td>0.07</td><td>1548</td><td>28.15</td><td>26</td><td>0.47</td><td>108</td><td>1.96</td><td>50</td><td>0.91</td></tr><tr><th>Upper Lualaba</th><td>144000</td><td>219</td><td>1.52</td><td>28</td><td>0.19</td><td>3449</td><td>23.95</td><td>371</td><td>2.58</td><td>644</td><td>4.47</td><td>160</td><td>1.11</td></tr><tr><th>Sangha</th><td>180000</td><td>215</td><td>1.19</td><td>8</td><td>0.04</td><td>2012</td><td>11.18</td><td>181</td><td>1.01</td><td>195</td><td>1.08</td><td>72</td><td>0.40</td></tr><tr><th>Uélé</th><td>122000</td><td>195</td><td>1.60</td><td>7</td><td>0.06</td><td>1104</td><td>9.05</td><td>85</td><td>0.70</td><td>184</td><td>1.51</td><td>83</td><td>0.68</td></tr><tr><th>Lindi-Tshopo</th><td>54000</td><td>182</td><td>3.37</td><td>1</td><td>0.02</td><td>931</td><td>17.24</td><td>86</td><td>1.59</td><td>128</td><td>2.37</td><td>47</td><td>0.87</td></tr><tr><th>Lomami</th><td>74000</td><td>181</td><td>2.45</td><td>1</td><td>0.01</td><td>738</td><td>9.97</td><td>151</td><td>2.04</td><td>161</td><td>2.18</td><td>44</td><td>0.59</td></tr><tr><th>Marine Lower Congo</th><td>10000</td><td>164</td><td>16.40</td><td>8</td><td>0.80</td><td>1421</td><td>142.10</td><td>104</td><td>10.40</td><td>192</td><td>19.20</td><td>123</td><td>12.30</td></tr><tr><th>Mweru</th><td>94000</td><td>163</td><td>1.73</td><td>29</td><td>0.31</td><td>3376</td><td>35.91</td><td>327</td><td>3.48</td><td>569</td><td>6.05</td><td>155</td><td>1.65</td></tr><tr><th>Léfini-Likouala</th><td>110000</td><td>140</td><td>1.27</td><td>10</td><td>0.09</td><td>2278</td><td>20.71</td><td>128</td><td>1.16</td><td>195</td><td>1.77</td><td>50</td><td>0.45</td></tr><tr><th>Sankuru</th><td>130000</td><td>119</td><td>0.92</td><td>10</td><td>0.08</td><td>659</td><td>5.07</td><td>33</td><td>0.25</td><td>80</td><td>0.62</td><td>34</td><td>0.26</td></tr><tr><th>Tumba</th><td>6500</td><td>110</td><td>16.92</td><td>3</td><td>0.46</td><td>357</td><td>54.92</td><td>12</td><td>1.85</td><td>67</td><td>10.31</td><td>23</td><td>3.54</td></tr><tr><th>Bangweulu</th><td>110000</td><td>103</td><td>0.94</td><td>1</td><td>0.01</td><td>1325</td><td>12.05</td><td>172</td><td>1.56</td><td>190</td><td>1.73</td><td>70</td><td>0.64</td></tr><tr><th>Kwilu</th><td>85000</td><td>86</td><td>1.01</td><td>5</td><td>0.06</td><td>130</td><td>1.53</td><td>19</td><td>0.22</td><td>34</td><td>0.40</td><td>26</td><td>0.31</td></tr><tr><th>Kwango</th><td>195000</td><td>84</td><td>0.43</td><td>10</td><td>0.05</td><td>299</td><td>1.53</td><td>24</td><td>0.12</td><td>33</td><td>0.17</td><td>18</td><td>0.09</td></tr><tr><th>Mai-Ndombe</th><td>40000</td><td>72</td><td>1.80</td><td>8</td><td>0.20</td><td>89</td><td>2.23</td><td>27</td><td>0.68</td><td>16</td><td>0.40</td><td>25</td><td>0.63</td></tr><tr><th>Lukenie</th><td>76500</td><td>51</td><td>0.67</td><td>2</td><td>0.03</td><td>275</td><td>3.59</td><td>18</td><td>0.24</td><td>32</td><td>0.42</td><td>21</td><td>0.27</td></tr><tr><th>Mongala</th><td>45000</td><td>41</td><td>0.91</td><td>0</td><td>0</td><td>93</td><td>2.07</td><td>9</td><td>0.20</td><td>17</td><td>0.38</td><td>14</td><td>0.31</td></tr><tr><th>Lulonga</th><td>67000</td><td>20</td><td>0.30</td><td>0</td><td>0</td><td>80</td><td>1.19</td><td>13</td><td>0.19</td><td>17</td><td>0.25</td><td>12</td><td>0.18</td></tr><tr><th>Kotto</th><td>71000</td><td>19</td><td>0.27</td><td>1</td><td>0.01</td><td>126</td><td>1.77</td><td>15</td><td>0.21</td><td>12</td><td>0.17</td><td>16</td><td>0.23</td></tr><tr><th>Bomu</th><td>130000</td><td>14</td><td>0.11</td><td>1</td><td>0.01</td><td>67</td><td>0.52</td><td>11</td><td>0.08</td><td>14</td><td>0.11</td><td>9</td><td>0.07</td></tr></tbody></table>
Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
Table 2 Unidentified species with a in Checklist of Water mites in Mexico. Historical background and DNA barcoding perspectives
<p><b>Table 2</b> Unidentified species with a DNA barcode in BOLD database. BINs indicate putative species. * Unique BINs this database.</p><table><tbody><tr><th><i>Taxa</i></th><th>BIN</th><th><i>Taxa</i></th><th>BIN</th><th><i>Taxa</i></th><th>BIN</th></tr></tbody><tbody><tr><th>Arrenuridae</th><td></td><td>Limnesiidae</td><td></td><td><b>Pionidae</b></td><td></td></tr><tr><th>Arrenuridae</th><td>AEA 4828*</td><td><i>Limnesiidae</i></td><td>AEA 4382*</td><td><i>Pionidae</i></td><td>AEA 4809*</td></tr><tr><th><i>Arrenurus cristinae</i></th><td>AEA 7842*</td><td><i>Centrolimnesia</i></td><td>AEA 3914*</td><td><i>Piona</i></td><td>AEA 5358*</td></tr><tr><th><i>Arrenurus eduardoi</i></th><td>AEA 7844*</td><td><i>Limnesia</i></td><td>AEA 6471*</td><td><i>Piona</i></td><td>AEE 5501*</td></tr><tr><th><i>Arrenurus federicoi</i></th><td>AEB 7095*</td><td><i>Limnesia</i></td><td>ACX 7759</td><td><i>Piona</i></td><td>AEN 6046*</td></tr><tr><th><i>Arrenurus ecosur</i></th><td>ACX 8463</td><td><i>Limnesia</i></td><td>ACY 7380</td><td><i>Piona</i></td><td>AEO 7290*</td></tr><tr><th><i>Arrenurus marshallae</i></th><td>ACL2521</td><td><i>Limnesia</i></td><td>AEA 5595</td><td><i>Piona</i></td><td>AER1599 *</td></tr><tr><th><i>Arrenurus</i></th><td>ACX 8462*</td><td><b>Hygrobatidae</b></td><td></td><td><i>Piona</i></td><td>AER1600 *</td></tr><tr><th><i>Arrenurus</i></th><td>ACX 8788*</td><td>Hygrobatidae</td><td>AEA 5236*</td><td><i>Piona</i></td><td>AER1601 *</td></tr><tr><th><i>Arrenurus</i></th><td>ACX 8789*</td><td>Hygrobatidae</td><td>AEA 4089*</td><td><b>Mideopsidae</b></td><td></td></tr><tr><th><i>Arrenurus</i></th><td>ACY 6809*</td><td><i>Atractides</i></td><td>ACX 7786*</td><td>Mideopsidae</td><td>AEB 4633*</td></tr><tr><th><i>Arrenurus</i></th><td>ADI 3752*</td><td><i>Hygrobates</i></td><td>AEA 3689*</td><td><i>Mideopsis</i></td><td>ACY 7169*</td></tr><tr><th><i>Arrenurus</i></th><td>ADI 4458*</td><td><i>Hygrobates</i></td><td>AEA 3690*</td><td><i>Mideopsis</i></td><td>AEA 6512*</td></tr><tr><th><i>Arrenurus</i></th><td>AEA 3972*</td><td><i>Hygrobates</i></td><td>AEA 3924*</td><td><i>Mideopsis</i></td><td>ACX 8679</td></tr><tr><th><i>Arrenurus</i></th><td>AEA 7182*</td><td><i>Hygrobates</i></td><td>ACX 7887</td><td><b>Krendowskiidae</b></td><td></td></tr><tr><th><i>Arrenurus</i></th><td>AEA 7843*</td><td><i>Hygrobates</i></td><td>ADO 7098</td><td><i>Krendowskia</i></td><td>ACX 8435*</td></tr><tr><th><i>Arrenurus</i></th><td>AEA 8234*</td><td><b>Unionicolidae</b></td><td></td><td><i>Geayia</i></td><td>ACT 6195</td></tr><tr><th><i>Arrenurus</i></th><td>AEF1989 *</td><td>Unionicolidae</td><td>AEA 6658*</td><td><b>Hydrachnidia</b></td><td>AEA3823*</td></tr><tr><th><i>Arrenurus</i></th><td>AEF 8444*</td><td>Unionicolidae</td><td>ACY 7381</td><td></td><td>AEA4343*</td></tr><tr><th><i>Arrenurus</i></th><td>ACL2418</td><td>Unionicolidae</td><td>AEB1594 *</td><td></td><td>AEF3494*</td></tr><tr><th><i>Arrenurus</i></th><td>ACX 8464</td><td>Unionicolidae</td><td>AEA 7951*</td><td></td><td>AEF8255*</td></tr><tr><th><b>Limnocharidae</b></th><td></td><td>Unionicolidae</td><td>AEA 6062*</td><td></td><td>AEF0324*</td></tr><tr><th>Limnocharidae</th><td>AEA 4515*</td><td>Unionicolidae</td><td>AEA 4829*</td><td></td><td>AEI2293*</td></tr><tr><th><i>Limnochares</i></th><td>ACY 6840*</td><td>Unionicolidae</td><td>AEA 3726*</td><td></td><td>AEI2954*</td></tr><tr><th><i>Limnochares</i></th><td>ADI 4862*</td><td>Unionicolidae</td><td>AEA 4514*</td><td></td><td>AEI4296*</td></tr><tr><th><i>Eylaidae</i></th><td></td><td><i>Unionicola</i></td><td>ACX 8035*</td><td></td><td>AEI4327*</td></tr><tr><th><i>Eylaidae</i></th><td>AEA 4696*</td><td><i>Unionicola</i></td><td>ADM 7936*</td><td></td><td>AEI4328*</td></tr><tr><th><i>Eylaidae</i></th><td>AEA 5669*</td><td><i>Unionicola</i></td><td>AEB 4634*</td><td></td><td>AEI4329*</td></tr><tr><th><i>Eylais</i></th><td>ADD 9174*</td><td><i>Unionicola</i></td><td>AEE 0841*</td><td></td><td>AEI8266*</td></tr><tr><th><b>Hydryphantidae</b></th><td></td><td><i>Unionicola</i></td><td>ACX 9008</td><td></td><td>AEI8707*</td></tr><tr><th><i>Hydryphantes</i></th><td>AEA 5005*</td><td><i>Unionicola</i></td><td>AEF2345</td><td></td><td>AEI 9124*</td></tr><tr><th><b>Hydrodromidae</b></th><td></td><td><i>Unionicola</i></td><td>ACX 8034</td><td></td><td>AEJ2119*</td></tr><tr><th><i>Hydrodroma</i></th><td>ADF 3732</td><td><i>Neumania</i></td><td>AEA 8101*</td><td></td><td>AEN6047*</td></tr><tr><th><b>Anisitsiellidae</b></th><td></td><td><i>Neumania</i></td><td>ACY 6829</td><td></td><td>AEN8226*</td></tr><tr><th><i>Mamersellides</i></th><td>AEA 6955*</td><td><i>Koenikea</i></td><td>ACY 7384*</td><td></td><td>AEN8227*</td></tr><tr><th><i>Mamersellides</i></th><td>AEA 6956*</td><td><i>Koenikea</i></td><td>ACB 9299</td><td></td><td>AEO5260*</td></tr><tr><th><i>Torrenticolidae</i></th><td></td><td><i>Koenikea</i></td><td>ADI2928</td><td></td><td>AER2566 *</td></tr><tr><th><i>Torrenticolidae</i></th><td>AEA 4395*</td><td><i>Koenikea</i></td><td>ADI 3114</td><td></td><td>AEB1898</td></tr><tr><th><i>Torrenticola</i></th><td>AEA 7372*</td><td></td><td></td><td></td><td></td></tr></tbody></table>
A deepened water table increases the vulnerability of peat mosses to periodic drought
Open the record for dataset details and reuse information.
No evidence for trade-offs between bird diversity, yield and water table depth on oil palm smallholdings: implications for tropical peatland landscape restoration
Open the record for dataset details and reuse information.
Diatom cell-size composition as a novel tool for quantitative estimates of water table in peatlands
Open the record for dataset details and reuse information.
Marcell Experimental Forest site, station Watershed S2, peatland water table bogwell site, study of water table elevation above mean sea level in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a monthly timescale.
Marcell Experimental Forest site, station Watershed S2, peatland water table bogwell site, study of water table elevation above mean sea level in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a yearly timescale.
Marcell Experimental Forest site, station Watershed S2, groundwater deepwell #202, study of water table elevation above mean sea level in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a monthly timescale.
Marcell Experimental Forest site, station Watershed S2, groundwater deepwell #202, study of water table elevation above mean sea level in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a yearly timescale.
Plant species percent cover data: Distribution of Wetland Plant Species in Relation to the Level of the Water Table
This study established permanent plots along transects in fifteen wetlands covering a range of wetland types. The plot are used to: 1) characterize the non-wooded wetlands at CCESR; 2) determine correlations of plant community type with environmental factors; 3) examine changes over a five year period.
Depth to water table
Eighty-seven piezometers (cased bore holes) were installed along topographic transects within the Cedar Creek Natural History Area. Depth to water table is measured at each well at a monthly to quarterly interval. Water samples are collected periodically and analyzed for pH and major cations and anions.
Water table depth dataset collected at Frasne peatland (192ha, Jura Mountains, France)
<p>Data of water table depth (m) measured on Frasne peatland in two piezometers (frn/pz_plot4 and frn/pz_plot9). Measurements start on 06-11-2008 and are regularly updated with new data.</p> <p>Zip file contain :</p> <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> <p>Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a></p> <p>We also recommend to contact sno-tourbieres for additional information about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a></p>
Water table depth dataset collected at La Guette peatland (23 ha, Loiret, France)
<p>Data of water table depth (m) and water temperature (°C) measured on La Guette peatland in 6 piezometers (lgt/pz_dc, lgt/pz_wc, lgt/pz_cbdv_amont, lgt/pz_cbdv_aval, lgt/pz_do, lgt/pz_wo). Measurements start on 15-06-2008 and are regularly updated with new data.<br> <br> Zip file contain :</p> <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> <p>Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a><br> We also recommend to contact sno-tourbieres to talk about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a></p>
Palms and trees resist extreme drought in Amazon forests with shallow water tables
1. The intensity and frequency of severe droughts in the Amazon region has increase in recent decades. These extreme events are associated with changes in forest dynamics, biomass and floristic composition. However, most studies of drought response have focused on upland forests with deep water tables, which may be especially sensitive to drought. Palms, which tend to dominate the less well-drained soils, have also been neglected. The relative neglect of shallow water tables and palms is a significant concern for our understanding of tropical drought impacts, especially as one third of Amazon forests grow on shallow water tables (<5m deep). 2. We evaluated the drought response of palms and trees in forests distributed over a 600 km transect in central-southern Amazonia, where the landscape is dominated by shallow water table forests. We compared vegetation dynamics before and following the 2015-16 El Nino drought, the hottest and driest on record for the region (-214 mm of cumulative water deficit). 3. We observed no change in stand mortality rates and no biomass loss in response to drought in these forests. Instead, we observed an increase in recruitment rates, which doubled to 6.78% y-1 ± 4.40 (mean ± SD) during 2015-16 for palms and increased by half for trees (to 2.92% y-1 ± 1.21), compared to rates in the pre-El-Nino interval. Within these shallow water table forests, mortality and recruitment rates varied as a function of climatic drought intensity and water table depth for both palms and trees, with mortality being greatest in climatically and hydrologically wetter environments and recruitment greatest in drier environments. Across our transect there was a significant increase over time in tree biomass. 4. Synthesis: Our results indicate that forests growing over shallow water tables – relatively under-studied vegetation that nonetheless occupies one-third of Amazon forests - are remarkably resistant to drought. These findings are consistent with the hypothesis that local hydrology and its interactions with climate strongly constrain forest drought effects, and has implications for climate change feedbacks. This work enhances our understanding of integrated drought effects on tropical forest dynamics and highlights the importance of incorporating neglected forest types into both the modeling of forest climate responses and into public decisions about priorities for conservation.
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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
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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
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