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Table 4 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska

<p><i>Table 4.</i> Summary of harbor seal mother-pup pairs (MP) and seals without pups (nMP) counted during pupping, numbers of seals counted during the molt, and proportions of pups counted relative to total seals (including pups) observed during pupping and during the molt. Counts are presented as means, maximums, and sample sizes (in parentheses) for each region in each year and summarized across years by arithmetic mean, standard deviation and geometric mean.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>Geo</th></tr></tbody><tbody><tr><th>Region</th><td></td><td>2004</td><td>2005</td><td>2006</td><td>2007</td><td>2008</td><td>2009</td><td>2011</td><td>2012</td><td>2013</td><td>Mean</td><td>SD</td><td>Mean</td></tr><tr><th>Mother-pup pairs during pupping</th></tr><tr><th>AB</th><td>Mean</td><td>20.3</td><td>33.5</td><td>20.0</td><td>22.0</td><td>26.5</td><td>24.3</td><td>87.0</td><td>72.0</td><td>64.0</td><td>41.1</td><td>24.5</td><td>34.9</td></tr><tr><th></th><td>Max</td><td>28</td><td>39</td><td>20</td><td>23</td><td>38</td><td>31</td><td>87</td><td>77</td><td>64</td><td>45.2</td><td>23.2</td><td>39.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(1)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>38.3</td><td>57.5</td><td>39.0</td><td>52.0</td><td>41.5</td><td>38.7</td><td>116.0</td><td>83.0</td><td></td><td>58.2</td><td>26.0</td><td>53.7</td></tr><tr><th></th><td>Max</td><td>54</td><td>79</td><td>39</td><td>54</td><td>42</td><td>45</td><td>116</td><td>84</td><td></td><td>64.1</td><td>25.0</td><td>59.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>22.8</td><td>38.0</td><td>32.0</td><td>48.5</td><td>35.0</td><td>14.0</td><td></td><td></td><td></td><td>31.7</td><td>11.0</td><td>29.5</td></tr><tr><th></th><td>Max</td><td>26</td><td>38</td><td>32</td><td>58</td><td>35</td><td>14</td><td></td><td></td><td></td><td>33.8</td><td>13.3</td><td>31.1</td></tr><tr><th></th><td></td><td>(4)</td><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>RB-JB</th><td>Mean</td><td>19.5</td><td>11.0</td><td>33.5</td><td>40.0</td><td></td><td>36.3</td><td>8.0</td><td>29.0</td><td></td><td>25.3</td><td>11.7</td><td></td></tr><tr><th></th><td>Max</td><td>33</td><td>21</td><td>33.5</td><td>40</td><td></td><td>70</td><td>8</td><td>38</td><td></td><td>34.8</td><td>17.7</td><td></td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(3)</td><td>(1)</td><td>(3)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Seals without pups during pupping</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>124.8</td><td>103.0</td><td>86.0</td><td>178.0</td><td>179.0</td><td>206.7</td><td>178.0</td><td>406.5</td><td>140.0</td><td>178.0</td><td>89.1</td><td>161.3</td></tr><tr><th></th><td>Max</td><td>188</td><td>144</td><td>86</td><td>211</td><td>211</td><td>253</td><td>178</td><td>439</td><td>140</td><td>205.6</td><td>94.4</td><td>187.5</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(1)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>86.5</td><td>163.5</td><td>80.0</td><td>179.5</td><td>179.5</td><td>243.3</td><td>182.0</td><td>250.0</td><td></td><td>170.5</td><td>58.3</td><td>158.8</td></tr><tr><th></th><td>Max</td><td>96</td><td>179</td><td>80</td><td>205</td><td>202</td><td>342</td><td>182</td><td>257</td><td></td><td>192.9</td><td>78.3</td><td>175.8</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>50.5</td><td>145.0</td><td>106.0</td><td>119.5</td><td>173.0</td><td>89.0</td><td></td><td></td><td></td><td>113.8</td><td>39.1</td><td>106.1</td></tr><tr><th></th><td>Max</td><td>77</td><td>145</td><td>106</td><td>155</td><td>173</td><td>89</td><td></td><td></td><td></td><td>124.2</td><td>35.5</td><td>118.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Region</th><td></td><td>2004</td><td>2005</td><td>2006</td><td>2007</td><td>2008</td><td>2009</td><td>2011</td><td>2012</td><td>2013</td><td>Mean</td><td>SD</td><td>Mean</td></tr><tr><th>RB-JB</th><td>Mean</td><td>148.8</td><td>143.0</td><td>275.5</td><td>353.0</td><td></td><td>284.0</td><td>211.0</td><td>251.3</td><td></td><td>238.1</td><td>70.4</td><td>227.1</td></tr><tr><th></th><td>Max</td><td>174</td><td>233</td><td>275.5</td><td>353</td><td></td><td>448</td><td>211</td><td>329</td><td></td><td>289.1</td><td>87.5</td><td>276.2</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(3)</td><td>(1)</td><td>(3)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Total seals during molt</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>253.5</td><td>391.7</td><td>267.0</td><td>437.0</td><td>649.0</td><td></td><td></td><td>738.0</td><td></td><td>456.0</td><td>181.7</td><td>420.8</td></tr><tr><th></th><td>Max</td><td>303</td><td>503</td><td>267</td><td>437</td><td>649</td><td></td><td></td><td>738</td><td></td><td>482.8</td><td>170.4</td><td>451.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>49.7</td><td>224.3</td><td>236.0</td><td>342.0</td><td>140.0</td><td></td><td></td><td>650.0</td><td></td><td>273.7</td><td>190.7</td><td>208.4</td></tr><tr><th></th><td>Max</td><td>85</td><td>306</td><td>236</td><td>342</td><td>140</td><td></td><td></td><td>650</td><td></td><td>293.2</td><td>182.5</td><td>240.0</td></tr><tr><th></th><td></td><td>(3)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>252.5</td><td>333.7</td><td></td><td>220.0</td><td>312.0</td><td></td><td></td><td></td><td></td><td>279.5</td><td>45.4</td><td>275.8</td></tr><tr><th></th><td>Max</td><td>552</td><td>380</td><td></td><td>220</td><td>312</td><td></td><td></td><td></td><td></td><td>366.0</td><td>121.5</td><td>346.4</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(0)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>RB-JB</th><td>Mean</td><td>146.8</td><td>308.7</td><td>165.5</td><td>513.5</td><td>564.0</td><td></td><td></td><td>477.0</td><td></td><td>362.6</td><td>165.7</td><td>318.1</td></tr><tr><th></th><td>Max</td><td>226</td><td>569</td><td>225</td><td>546</td><td>564</td><td></td><td></td><td>477</td><td></td><td>434.5</td><td>150.8</td><td>402.5</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(2)</td><td>(2)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Proportion pups relative to total seal including pups</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>11.8%</td><td>20.6%</td><td>15.9%</td><td>10.1%</td><td>10.9%</td><td>9.6%</td><td>24.7%</td><td>13.1%</td><td>23.9%</td><td>15.6%</td><td>6%</td><td>14.7%</td></tr><tr><th></th><td>Max</td><td>15.0%</td><td>23.7%</td><td>15.9%</td><td>11.2%</td><td>13.2%</td><td>13.3%</td><td>24.7%</td><td>14.6%</td><td>23.9%</td><td>17.3%</td><td>5%</td><td>16.6%</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(1)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>22.3%</td><td>20.1%</td><td>24.7%</td><td>18.5%</td><td>15.9%</td><td>12.7%</td><td>28.0%</td><td>20.0%</td><td></td><td>20.3%</td><td>5%</td><td>19.8%</td></tr><tr><th></th><td>Max</td><td>29.6%</td><td>25.8%</td><td>24.7%</td><td>19.7%</td><td>17.2%</td><td>15.7%</td><td>28.0%</td><td>20.4%</td><td></td><td>22.6%</td><td>5%</td><td>22.1%</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>26.3%</td><td>17.2%</td><td>18.8%</td><td>22.7%</td><td>14.4%</td><td>12.0%</td><td></td><td></td><td></td><td>18.6%</td><td>5%</td><td>17.9%</td></tr><tr><th></th><td>Max</td><td>42.0%</td><td>17.2%</td><td>18.8%</td><td>24.1%</td><td>14.4%</td><td>12.0%</td><td></td><td></td><td></td><td>21.4%10%</td><td>19.6%</td><td></td></tr><tr><th></th><td></td><td>(4)</td><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Region</th><td></td><td>2004</td><td>2005</td><td>2006</td><td>2007</td><td>2008</td><td>2009</td><td>2011</td><td>2012</td><td>2013</td><td>Mean</td><td>SD</td><td>Mean</td></tr><tr><th>RB-JB</th><td>Mean</td><td>10%</td><td>5%</td><td>10%</td><td>9%</td><td></td><td>11%</td><td>4%</td><td>10%</td><td></td><td>8.3%</td><td>3%</td><td>7.8%</td></tr><tr><th></th><td>Max</td><td>15%</td><td>8%</td><td>10%</td><td>9%</td><td></td><td>14%</td><td>4%</td><td>15%</td><td></td><td>10.6%</td><td>4%</td><td>9.6%</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(3)</td><td>(1)</td><td>(3)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Proportion pups relative to molting seals</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>8.0%</td><td>8.6%</td><td>7.5%</td><td>5.0%</td><td>4.1%</td><td></td><td></td><td>9.8%</td><td></td><td>7.2%</td><td>2%</td><td>6.8%</td></tr><tr><th></th><td>Max</td><td>9.2%</td><td>7.8%</td><td>7.5%</td><td>5.3%</td><td>5.9%</td><td></td><td></td><td>10.4%</td><td></td><td>7.7%</td><td>2%</td><td>7.5%</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>77.0%</td><td>25.6%</td><td>16.5%</td><td>15.2%</td><td>29.6%</td><td></td><td></td><td>12.8%</td><td></td><td>29.5%</td><td>22%</td><td>23.9%</td></tr><tr><th></th><td>Max</td><td>63.5%</td><td>25.8%</td><td>16.5%</td><td>15.8%</td><td>30.0%</td><td></td><td></td><td>12.9%</td><td></td><td>27.4%</td><td>17%</td><td>23.4%</td></tr><tr><th></th><td></td><td>(3)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>9.0%</td><td>11.4%</td><td></td><td>22.0%</td><td>11.2%</td><td></td><td></td><td></td><td></td><td>13.4%</td><td>5%</td><td>12.6%</td></tr><tr><th></th><td>Max</td><td>4.7%</td><td>10.0%</td><td></td><td>26.4%</td><td>11.2%</td><td></td><td></td><td></td><td></td><td>13.1%</td><td>8%</td><td>10.9%</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(0)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>RB-JB</th><td>Mean</td><td>13.3%</td><td>3.6%</td><td>20.2%</td><td>7.8%</td><td></td><td></td><td></td><td>6.1%</td><td></td><td>10.2%</td><td>6%</td><td>8.5%</td></tr><tr><th></th><td>Max</td><td>14.6%</td><td>3.7%</td><td>14.9%</td><td>7.3%</td><td></td><td></td><td></td><td>8.0%</td><td></td><td>9.7%</td><td>4%</td><td>8.6%</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(2)</td><td>(2)</td><td>(0)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr></tbody></table><p>(Continued)</p><p>(Continued)</p>

Table 3 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska

<p><i>Table 3.</i> Location-habitat categories, regions, and associated acronyms used in this study.</p><table><tbody><tr><th>Acronyms</th></tr><tr><th>Fjord-Habitat</th></tr></tbody><tbody><tr><th>Johnstone Bay Lake Ice</th><td>JBI</td></tr><tr><th>Johnstone Bay Land</th><td>JBL</td></tr><tr><th>Whidbey Bay Land</th><td>WBL</td></tr><tr><th>Day Harbor Land</th><td>DHL</td></tr><tr><th>Resurrection Bay Ice</th><td>RBI</td></tr><tr><th>Resurrection Bay Land</th><td>RBL</td></tr><tr><th>Aialik Bay Glacier Ice</th><td>AGI</td></tr><tr><th>Aialik Bay Land</th><td>AGL</td></tr><tr><th>Pedersen Glacier Ice</th><td>PGI</td></tr><tr><th>Pedersen Glacier Land</th><td>PGL</td></tr><tr><th>Northwestern Fjord Ice</th><td>NWFI</td></tr><tr><th>Northwestern Fjord Land</th><td>NWFL</td></tr><tr><th>McCarty Fjord Ice</th><td>MFI</td></tr><tr><th>McCarty Fjord Land</th><td>MFL</td></tr><tr><th>Regions</th><td></td></tr><tr><th>Resurrection Bay through Johnstone Bay</th><td>RB-JB</td></tr><tr><th>Aialik Bay</th><td>AB</td></tr><tr><th>Northwestern Fjord</th><td>NWF</td></tr><tr><th>McCarty Fjord</th><td>MCF</td></tr><tr><th>Southern Coast (McCarty Fjord-Harris Bay)</th><td>SC</td></tr></tbody></table>

Table 5 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska

<p><i>Table 5.</i> Summary of mean percent change, annually, in numbers of harbor seals counted from Johnstone Bay through Northwestern Fjord (JB-NWF) during the molt and of pups and nonpups counted during pupping, as determined from aerial surveys. For comparison, Aialik Bay trends, based on mean estimates adjusted for favorable standard conditions from 2004 to 2011, are included.</p><table><tbody><tr><th>Trend</th></tr></tbody><tbody><tr><th>Location/Time Period</th><td>Glacier</td><td>Terrestrial</td><td>Total</td></tr><tr><th>Molt</th><td></td><td></td><td></td></tr><tr><th>JB-NWF</th><td>5.40%</td><td>9.00%</td><td>6.10%</td></tr><tr><th>Aialik Bay</th><td>6.70%</td><td></td><td></td></tr><tr><th>Pupping</th><td></td><td></td><td></td></tr><tr><th>JB-NWF pups</th><td>5.00%</td><td>1.50%</td><td>6.90%</td></tr><tr><th>Aialik Bay pups</th><td>8.00%</td><td></td><td></td></tr><tr><th>JB-NWF seals without pups</th><td>7.96%</td><td>5.10%</td><td>8.15%</td></tr></tbody></table>

Table 2 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska

<p><i>Table 2.</i> Habitat haul-out categories.</p><table><tbody><tr><th>Tidewater</th><th>Tidewater glacier ice habitats include ice calved from Aialik Glacier in</th></tr></tbody><tbody><tr><th>glacier ice</th><td>Aialik Bay; Northwestern, Ogive, and Anchor Glaciers in Northwestern</td></tr><tr><th></th><td>Fjord; and McCarty Glacier in McCarty Fjord and comprise primary</td></tr><tr><th></th><td>habitats for seals on the Kenai Peninsula. Harbor seals rarely hauled out on</td></tr><tr><th></th><td>ice calved from Holgate Glacier in Aialik Bay.</td></tr><tr><th>Glacial</th><td>Glacial Lake ice habitats, including Johnstone Lake (terminus of Excelsior</td></tr><tr><th>lake ice</th><td>Glacier in Johnstone Bay), Bear Glacier Lake (terminus of Bear Glacier in</td></tr><tr><th></th><td>Resurrection Bay), and Pedersen Glacier Lake (terminus of Pedersen Glacier</td></tr><tr><th></th><td>in Aialik Bay), are highly estuarine and have restricted access <i>via</i> tidally</td></tr><tr><th>influenced streams. During the winter, surface waters freeze. In this paper,</th></tr><tr><th></th><td>seals using lake-ice habitats were associated with adjoining habitats.</td></tr><tr><th></th><td>Pedersen Lake was associated with tidewater glacial habitats while lakes at</td></tr><tr><th></th><td>the terminus of Excelsior and Bear glaciers were associated with terrestrial</td></tr><tr><th></th><td>habitats.</td></tr><tr><th>Traditional</th><td>Traditional terrestrial haul-outs along the southeastern Kenai Peninsula are</td></tr><tr><th>terrestrial</th><td>primarily rocky shorelines and include all terrestrial haul-outs from</td></tr><tr><th></th><td>Johnstone Bay through Resurrection Bay.</td></tr><tr><th>Incidental</th><td>Incidental terrestrial haul-outs are those associated with tidewater glacier</td></tr><tr><th>terrestrial</th><td>systems in Aialik Bay, Northwestern Fjord and McCarty Fjord. Primary</td></tr><tr><th></th><td>haul-out activity occurs on the glacial ice and occupancy at the terrestrial</td></tr><tr><th></th><td>haul-outs is irregular and involved small numbers of seals (<20 seals).</td></tr><tr><th></th><td>In this study, survey times in the tidewater glacier fjords often occurred</td></tr><tr><th></th><td>late in the low tide cycle which, in conjunction with the irregularity in</td></tr><tr><th></th><td>the seal’s use of these terrestrial haul-outs, resulted in suboptimal</td></tr><tr><th></th><td>assessments of habitat use.</td></tr></tbody></table>

Table 1 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska

<p><i>Table 1.</i> Summary of the location, terminus habitat and status of glaciers that calve ice used by harbor seals in this study.</p><table><tbody><tr><th>Glacier</th><th>Status</th><th>Habitat</th><th>Location</th></tr></tbody><tbody><tr><th>Excelsior Glacier</th><td>Retreating</td><td>Tidally influenced Lake</td><td>Johnstone Bay</td></tr><tr><th>Bear Glacier</th><td>Retreating</td><td>Tidally influenced Lake</td><td>Resurrection Bay</td></tr><tr><th>Holgate Glacier Pedersen Glacier</th><td>Stable Retracted Retreating a</td><td>Tidewater Tidally Influenced Lake</td><td>Aialik Bay Aialik Bay</td></tr><tr><th>Aialik Glacier</th><td>Stable Retracted/</td><td>Tidewater</td><td>Aialik Bay</td></tr><tr><th>Anchor Glacier Ogive Glacier Northwestern Glacier</th><td>Thinning Retracted a Retracted a Retracted a,b</td><td>Tidewater Tidewater Tidewater</td><td>Northwestern Fjord Northwestern Fjord Northwestern Fjord</td></tr><tr><th>McCarty Glacier</th><td>Retreating</td><td>Tidewater</td><td>McCarty Fjord</td></tr></tbody></table><p><sup>a</sup> Detaching from icefield. <sup>b</sup> Partially grounded.</p>

opencc-by-4.0Sep 2024View details →

Novel Endpoint Characterization Factors for Life Cycle Impact Assessment of Terrestrial Acidification

<p>This repository contains Excel files with the characterization factorsand the soil response factors for the publication entitled "Novel Endpoint Characterization Factors for Life Cycle Impact Assessment of Terrestrial Acidification", published in the "Journal of Ecological Indicators" .<br><br></p> <p>Content:</p> <p><strong>Datasets.zip</strong> is an folder containing the following Excel files: </p> <ul> <li><strong>CF_Terrestrial_Acidification_2024-08-01.xlsx</strong> with the following sheets <ul> <li><em>Dataframe</em> gathering terrestrial acidification marginal endpoint CF [PDF.yr/kg_emitted] values at country level (with the world average value), for 3 acidifying substances (NOx, NHx, SOx) at global and regional impact scales (with and without the inclusion of the Global Extinction Probability - GEP - respectively)</li> <li><em>Calc Info</em> summing up calculation informations</li> <li><em>Calc Table</em> registering input parameters used to run the calculations leading to the <em>Dataframe</em> sheet (substance used, original and adapted resolutions for emission and deposition compartments, resolutions for each CF components  and spatial transformations, GEP normalization method)</li> </ul> </li> <li><strong>RF_NO3_2024-03-27.xlsx</strong> with the following sheet:<br> <ul> <li><em>RFs</em> containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in NO3 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs) </li> </ul> </li> <li><strong>RF_NH4_2024-03-25.xlsx</strong> with the following sheet:<br> <ul> <li><em>RFs</em> containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in NH4 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs)</li> </ul> </li> <li><strong>RF_SO4_2024-03-25.xlsx</strong> with the following sheet:<br> <ul> <li><em>RFs</em> containing the soil response values [(molH+ / L).(yr / kg_dep)] at ecoregion level (referred to as "idTarget") for a marginal increase of 10% in SO4 deposition rate. It also gathers the intermediate results leading the final RFs (sustances' deposition rates, reference and post-deposition increase pHs)</li> </ul> </li> </ul>

Table 10 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 10. Number of forest communities, and percent of total forest area by forest community in all measured plots across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands. Agroforest was not sampled in Palau, mangrove was not sampled in Guam or CNMI, and Montane rainforest only occurs in FSM.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Communities</b></th><th><b>Lowland [80%CI]</b></th><th><b>Strand [80%CI]</b></th><th><b>Agroforest [80%CI]</b></th><th><b>Mangrove [80%CI]</b></th><th><b>Montane [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>4</td><td>39.3% [27.2-51.4%]</td><td>27.5% [17.6-37.4%]</td><td>31.7% [21.4-42%]</td><td>1.5% [0-3.4%]</td><td>-</td></tr><tr><th>FSM</th><td>5</td><td>57.5% [51.2-63.8%]</td><td>2.8% [1-4.6%]</td><td>17.9% [13.1-22.7%]</td><td>17.1% [12.2-22%]</td><td>4.6% [1.8-7.4%]</td></tr><tr><th>Palau</th><td>3</td><td>84.1% [78.1-90.1%]</td><td>4.2% [1-7.4%]</td><td>-</td><td>11.7% [6.6-16.8%]</td><td>-</td></tr><tr><th>Guam</th><td>3</td><td>94.3% [90.3-98.3%]</td><td>2.6% [0-5.2%]</td><td>3.1% [0-7.1%]</td><td>-</td><td>-</td></tr><tr><th>CNMI</th><td>3</td><td>93% [87.8-98.2%]</td><td>6.3% [1.5-10.8%]</td><td>0.7% [0-1.6%]</td><td>-</td><td>-</td></tr></tbody></table>

Table 7 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 7. Estimated percent of forest area with live canopy cover percent of greater than or equal to 50%, 80% and 90% across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>% Forest area with canopy cover>=50% [80%CI]</b></th><th><b>Canopy cover>=80% [80%CI]</b></th><th><b>Canopy cover>=90% [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>92.3% [87.2-97.4%]</td><td>77.4% [68.3-86.5%]</td><td>50.1% [38-62.2%]</td></tr><tr><th>FSM</th><td>90.4% [86.4-90.8%]</td><td>52.5% [46-59%]</td><td>6.4% [3.2-9.6%]</td></tr><tr><th>Palau</th><td>92.7% [88.8-96.7%]</td><td>82.3% [76.6-88%]</td><td>61.6% [53.8-69.4%]</td></tr><tr><th>Guam</th><td>90.5% [85.3-95.7%]</td><td>62.9% [54.6-71.2%]</td><td>31.4% [23.9-38.9%]</td></tr><tr><th>CNMI</th><td>81.6% [72.8-90.4%]</td><td>58.9% [48.2-69.6%]</td><td>30.8% [20-41.6%]</td></tr></tbody></table>

Table 9 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 9. Number of live tree species per plot and total number of forest plots sampled for each forest community across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Forest community</b></th><th><b># of Tree species per plot [80%CI]</b></th><th><b># of Plots</b></th></tr></tbody><tbody><tr><th>Lowland Rainforest</th><td>7.5 [7.1-7.9]</td><td>253</td></tr><tr><th>Montane Rainforest</th><td>7.5 [6-9]</td><td>4</td></tr><tr><th>Strand Forest</th><td>3.8 [3.3-4.3]</td><td>32</td></tr><tr><th>Agroforest</th><td>3.4 [3-3.8]</td><td>44</td></tr><tr><th>Mangrove</th><td>3.4 [2.7-4.1]</td><td>24</td></tr></tbody></table>

Table 8 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 8. Estimated percent of endemic tree species, and number of endemic tree species inventoried across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>% Endemic trees [80%CI]</b></th><th><b># of Endemic species</b></th></tr></tbody><tbody><tr><th>RMI</th><td>0%</td><td>1</td></tr><tr><th>FSM</th><td>23.6% [19.3-27.9%]</td><td>26</td></tr><tr><th>Palau</th><td>36.7% [32.4-41%]</td><td>38</td></tr><tr><th>Guam</th><td>18.4% [11.9-22.3%]</td><td>13</td></tr><tr><th>CNMI</th><td>17.1% [9.3-27.5%]</td><td>9</td></tr></tbody></table>

Table 1 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 1. List of Micronesia Challenge terrestrial measures, FIA data used, and analyses performed to describe each measure. BA is basal area. N/A = indicator uses different units.</p><table><tbody><tr><th><b>MC Terrestrial Measure</b></th><th><b>FIA Data Used</b></th><th><b>Area or # of Trees</b></th><th><b>% of Forest or % of Trees</b></th></tr></tbody><tbody><tr><th><i>Human disturbance</i></th><td>Disturbance (human and fire)</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Species diversity</i></th><td>Tree species per plot</td><td>N/A</td><td>N/A</td></tr><tr><th></th><td>Dominant vascular plant species per plot</td><td>N/A</td><td>N/A</td></tr><tr><th></th><td>Tree species: relative dominance</td><td>Square feet per acre</td><td>BA/BA of All Trees</td></tr><tr><th></th><td>% Cover of understory species</td><td>Acreage</td><td>% of Forest Area</td></tr><tr><th><i>Forest structure</i></th><td>Tree DBH (diameter at breast height)</td><td># of Trees</td><td>% by DBH class</td></tr><tr><th></th><td>Tree height</td><td># of Trees</td><td>% by Height class</td></tr><tr><th></th><td>Basal area</td><td>Square feet per acre</td><td>N/A</td></tr><tr><th></th><td>Stem density (per plot and per acre)</td><td># of Trees per plot/acre</td><td>N/A</td></tr><tr><th><i>Invasive species</i></th><td>Tree species</td><td># of Trees</td><td>% of All trees</td></tr><tr><th></th><td>Invasive vegetation subplot cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Forest cover</i></th><td>% Live canopy cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Tree abundance</i></th><td>Tree species</td><td># of Trees</td><td>% of All trees</td></tr><tr><th></th><td>Tree rank order: endemics and invasives</td><td># of Trees</td><td>% of All trees</td></tr><tr><th><i>Mangrove stem density</i></th><td>Stem density (per acre)</td><td># of Trees per acre</td><td>N/A</td></tr><tr><th><i>Mangrove basal area</i></th><td>Basal area</td><td>Square feet per acre</td><td>Relative dominance</td></tr><tr><th><i>Forest community</i></th><td>Forest community</td><td>Acreage</td><td>% of Forest area</td></tr></tbody></table>

Table 6 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 6. Percent of forest area with invasive plant species present or covered with invasive plant species, and percent of all trees that are invasive across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Invasives present [80%CI]</b></th><th><b>Covered in invasives [80%CI]</b></th><th><b>Invasive trees [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>11.4% [4.9-17.9%]</td><td>1% [0-2%]</td><td>0</td></tr><tr><th>FSM</th><td>49.2% [43.9-54.5%]</td><td>11.3% [9-13.6%]</td><td>5.4% [2.1-8.7%]</td></tr><tr><th>Palau</th><td>13.3% [9.1-17.5%]</td><td>0.9% [0.2-1.6%]</td><td>1.2% [0-2.4%]</td></tr><tr><th>Guam</th><td>85.7% [81.8-89.6%]</td><td>39.9% [34.5-45.3%]</td><td>30.1% [23.9-36.3%]</td></tr><tr><th>CNMI</th><td>84.9% [80.4-89.4%]</td><td>54.6% [44.2-65%]</td><td>43.6% [31.8-55.4%]</td></tr></tbody></table>

Table 2 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 2. Source of largest forest disturbance by percent of forest area disturbed, and total percent of forest area affected by all disturbance types across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Largest disturbance type</b></th><th><b>% of Forest area [80% CI]</b></th><th><b>All disturbances [80% CI]</b></th><th><b>Endemics [80%CI]</b></th><th><b>Most common understory species</b></th><th><b>% Forest area [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>Weather</td><td>4.3% [0-8.6%]</td><td>8.1% [2.3-13.9%]</td><td>0%</td><td><i>Cocos nucifera</i></td><td>11.1% [7.7-14.5%]</td></tr><tr><th>FSM</th><td>Human</td><td>21.2% [16.2-26.2%]</td><td>39.2% [33.5-45.7%]</td><td>8.3% [6.9-9.7%]</td><td><i>Hibiscus tiliaceus</i></td><td>8.1% [6.3-9.9%]</td></tr><tr><th>Palau</th><td>Weather</td><td>10.9% [5.6-16.2%]</td><td>25.1% [18.2-32%]</td><td>20.6% [17.9-23.3%]</td><td><i>Pinanga insignis</i></td><td>16.5% [13-20%]</td></tr><tr><th>Guam</th><td>Animals</td><td>29.8% [22.2-37.4%]</td><td>49.1% [40.8-57.4%]</td><td>6.9% [5-8.8%]</td><td><i>Hibiscus tiliaceus</i></td><td>9.9% [7.8-12%]</td></tr><tr><th>CNMI</th><td>Tree Disease</td><td>40.1% [33.1-47.1%]</td><td>67.7% [58.7-76.7%]</td><td>9.3% [5.8-12.8%]</td><td><i>Leucaena leucocephala</i></td><td>23.5% [17.9-29.1%]</td></tr></tbody></table>

Table 5 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

<p>Table 5. Mean tree DBH, height, stem density, and basal area for forest across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>DBH in inches [80%CI]</b></th><th><b>Height in feet [80%CI]</b></th><th><b>Stems/acre [80%CI]</b></th><th><b>Square Feet/acre [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>4 [3.3-4.7]</td><td>23.1 [20.5-25.7]</td><td>726 [556-896]</td><td>124 [112-136]</td></tr><tr><th>FSM</th><td>4.4 [4.1-4.7]</td><td>27.3 [26.3-28.3]</td><td>611 [558-664]</td><td>145 [132-158]</td></tr><tr><th>Palau</th><td>3.9 [3.8-4]</td><td>26.9 [25.7-28.1]</td><td>937 [866-1008]</td><td>143 [131-155]</td></tr><tr><th>Guam</th><td>3.1 [3-3.2]</td><td>22.7 [22.2-23.6]</td><td>1014 [885-1143]</td><td>84 [78-90]</td></tr><tr><th>CNMI</th><td>2.8 [2.5-3.1]</td><td>20.8 [19.9-21.7]</td><td>1392 [1148-1636]</td><td>84 [72-96]</td></tr></tbody></table>

opencc-by-4.0Sep 2024View details →

Codes for identification of the earliest terrestrial trace fossils

<p>This repository contains the CFDEM (<a href="http://www.cfdem.com/" target="_blank" rel="noopener">www.cfdem.com<span>(opens in new window)</span></a>) and Matlab codes with our own developments to reproduce trace fossils in different environments (three zip files) and deduce putative trace-makers (CurveGithub.m). You can also get access to a free software for trace-maker identification through <a href="https://zenodo.org/records/10259553" target="_blank" rel="noopener">https://zenodo.org/records/10259553<span>(opens in new window)</span></a>.</p> <p>Codes.zip contains the CFDEM solvers to reproduce translational and rotational motion of the trace-makers/appendages (cfdemSolverPisoSTL0, cfdemSolverPisoSTL), along with the necessary semi-resolved force models (gradPForce, viscForce, GidaspowDragsemi). Steps to compile these codes are written in Readme within the compressed file. If you want to use relatively coarse mesh (mesh size at least three times larger than sediment diameter) to accelerate simulations, please use the default unresolved forces models. In that case, only the provided solvers needs to be compiled. But the force model lists in case/CFD/constant/couplingProperies needs to be adjusted. Please refer to the benchmark cases in <a href="http://www.cfdem.com/" target="_blank" rel="noopener">www.cfdem.com<span>(opens in new window)</span></a> to learn to use CFDEM.</p> <p>Benchmark_case.zip contains a benchmark validation of the moving cube on submerged sands. Appendage.zip contains a moving appendage on subaerial wet sands. The details about how to run the cases are written in the Readme file in each compressed file.</p>

opencc-by-4.0Oct 2024View details →

Weakening of the terrestrial carbon sink through enhanced fluvial carbon export

<p>This dataset provides the original data simulated by ORCHIDEE-Clateral model (Zhang et al., 2022, Earth System Dynamics) and an evaluation of the simulation results.</p> <p>Global-hist-yearly-COMPARISON_STAT_TimeSeries_GloRegion.xlsx provides the time series of lateral carbon transfers, as well as the terrestrial carbon fluxes simulated by the ORCHIDEE-Clateral model.</p> <p>Evaluation_SiteLevel_runoff_sediment_TOC_POC_DOC.xlsx provides the observed and simulated riverine water, sediment and carbon fluxes at hydrological gauging sites across the world.</p> <p>Evaluation_LargeScale_NPPSOC_Sediment_POCDOCCO2.xlsx provides the estimates of global and continental lateral carbon fluxes from this study and previous studies.</p> <p>ClateralCbalance.mat provides the raster maps of  simulated lateral carbon transfers, as well as the terrestrial carbon fluxes by the ORCHIDEE-Clateral model. For each variable, the first dimension is longitude (-180 W - 180E), the second dimension is latitude (90N - -90S), and the thrid dimension is the year (1898-2020).</p>

opencc-by-4.0Oct 2024View details →

Data for "A neural correlate of learning fails to predict foraging efficiency in the bumble bee Bombus terrestris"

<p>This file contain the foraging and synaptic density data collected in the summer of 2019 by Pasquier, Pull, Ott and Leadbeater. Used in the paper titled "A neural correlate of learning fails to predict foraging efficiency in the bumble bee <em>Bombus terrestris</em>".</p>

dryad36/100

Data from: Aquatic and terrestrial resources are not nutritionally reciprocal for consumers

1.Aquatic and terrestrial ecosystems are connected through reciprocal fluxes of energy and nutrients that can subsidize consumers. Past research on reciprocal aquatic‐terrestrial subsidies to consumers has generally focused on subsidy quantity while ignoring major differences in the nutritional composition of aquatic and terrestrial resources. Because aquatic resources contain substantially more highly unsaturated omega‐3 fatty acids (HUFAs) than terrestrial resources, aquatic subsidies may play a unique role by supplying these critical compounds to both aquatic and terrestrial consumers. 2. Here, we first characterized nutritional quality in terms of HUFA content in aquatic and terrestrial insect prey. We then used bulk stable isotope analyses to estimate subsidy use by stream and riparian consumers coupled with compound‐specific stable isotope analyses, which allowed us to document consumer HUFA sources. Finally, in order to understand the nutritional importance of aquatic‐derived HUFAs for riparian consumers, we conducted manipulative diet experiments on Eastern Phoebe (Sayornis phoebe) chicks in the laboratory. 3. Aquatic insects were significantly enriched in HUFAs, mainly in terms of eicosapentaenoic acid (EPA), compared with terrestrial insects. Stream fishes relied mainly upon aquatic resources, while insectivorous birds varied in their use of aquatic subsidies across sites. However, like stream fishes, Eastern Phoebe chicks received HUFAs from aquatic insects, even when they were heavily reliant upon terrestrial insects for their overall diet. In the laboratory, dietary HUFAs, such as those supplied by aquatic insects, increased the growth rate and condition of Eastern Phoebe chicks. 4. This study demonstrates that aquatic and terrestrial subsidies are not nutritionally reciprocal from the perspective of consumers because aquatic resources are the main source of critical fatty acids for both stream and riparian consumers. It also confirms previous findings on the nutritional importance of HUFAs for riparian birds, demonstrating that an insectivorous riparian lifestyle influences avian nutritional needs. Finally, our findings raise the possibility that birds and other riparian insectivores may experience nutritional mismatches with terrestrial prey if they do not have access to high quality aquatic subsidies as a consequence of aquatic habitat degradation or shifts in consumer and resource phenology.

opencc-zeroJul 2019View details →

dryad36/100

Data from: An inexpensive and open-source method to study large terrestrial animal diet and behavior using time-lapse video and GPS

1. The behavior of free-ranging animals is difficult to study, especially on the large spatial and temporal scales relevant to long-lived large species. Animal-borne video and environmental data collection systems (AVEDs) record behavior and other data in real time as animals conduct daily activities. However, few studies have combined systematically collected, long term AVED foraging data with environmental and movement data to test hypotheses on animal foraging. Additionally, AVEDs are often either prohibitively expensive, or require extensive fabrication and programming knowledge. 2. The video and coordinate animal-mounted system (VACAMS) is an animal-mounted data collection system based on a modified GoPro® action camera platform that records short, first "person" perspective videos of animal behavior on an automated time-lapse schedule. As most videos are georeferenced, researchers can return to the locations of specific behaviors and collect accurate, fine-grained data on non-woody vegetation and other habitat characteristics that may influence animal behavior. Moreover, VACAMS are inexpensive and easy to use. 3. This study describes VACAMS preliminary data on cattle foraging and a hypothesis exploring free-ranging cattle browsing habits throughout the rainy season in the tropical dry forest of Sonora, Mexico. I generated a database of vegetation types consumed by cows each month (Annual, Woody, and Leaf litter) and compared actual vegetation type frequencies to a priori assumptions based on seasonal patterns of forage availability. During the monsoons, when palatable vegetation was abundant, frequencies of annual and woody perennial vegetation in cattle diets did not differ from month to month. When the rains ceased and palatable vegetation became scarce, cows switched to leaf litter, dead annual vegetation, twigs, and dried leguminous fruits. 4. Open source software and commercially available hardware make VACAMS financially attainable for many researchers, land managers, students, and other user groups. VACAMS could be used on a range of domestic and semi-domestic free-ranging animals, particularly in dense forests where conventional observations are impossible. With improvements to GPS battery life and durability, the weakest points of the system, VACAMS could also potentially apply to studies of other large terrestrial animals.

opencc-zeroDec 2018View details →