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TABLE 2 in Coexistence of two newt species in a transition zone of range overlap

TABLE 2 Numbers of adult newts observed in the focal pond near Ambleteuse, Pas de Calais, France<table><tbody><tr><th>Year</th><th>Method</th><th>Ichthyosaura alpestris</th><th>Lissotriton helveticus</th><th>Lissotriton vulgaris</th></tr></tbody><tbody><tr><th>1975</th><td>Dipnet</td><td>3</td><td>31</td><td>10</td></tr><tr><th>1979</th><td>Dipnet</td><td>5</td><td>47</td><td>18</td></tr><tr><th>1980</th><td>Dipnet</td><td>4</td><td>68</td><td>38</td></tr><tr><th>1981</th><td>Dipnet</td><td>7</td><td>118</td><td>40</td></tr><tr><th>1982</th><td>Dipnet</td><td>2</td><td>15</td><td>13</td></tr><tr><th>1983 $</th><td>Dipnet</td><td>28</td><td>363</td><td>208</td></tr><tr><th>1984</th><td>Dipnet</td><td>13</td><td>172</td><td>82</td></tr><tr><th>1986 $</th><td>Dipnet</td><td>22</td><td>226</td><td>184</td></tr><tr><th>1986 #</th><td>Dipnet</td><td>23</td><td>731</td><td>192</td></tr><tr><th>1988 $</th><td>Dipnet</td><td>29</td><td>413</td><td>132</td></tr><tr><th>1989 $</th><td>Dipnet</td><td>100</td><td>396</td><td>228</td></tr><tr><th>1990 $</th><td>Dipnet</td><td>24</td><td>297</td><td>71</td></tr><tr><th>1991 $</th><td>Dipnet</td><td>71</td><td>251</td><td>115</td></tr><tr><th>1992 $</th><td>Dipnet</td><td>19</td><td>388</td><td>162</td></tr><tr><th>1993 $</th><td>Dipnet</td><td>44</td><td>193</td><td>111</td></tr><tr><th>1994 $</th><td>Dipnet</td><td>26</td><td>132</td><td>9</td></tr><tr><th>1995 $</th><td>Dipnet</td><td>91</td><td>570</td><td>52</td></tr><tr><th>2003 $</th><td>Dipnet</td><td>98</td><td>156</td><td>4</td></tr><tr><th>2012 $</th><td>Funnel trap</td><td>213</td><td>426</td><td>81</td></tr><tr><th>2021</th><td>Funnel trap</td><td>30</td><td>71</td><td>12</td></tr></tbody></table>$ = for population size estimates see table 3; # = including animals found dead.

TABLE 1 in Coexistence of two newt species in a transition zone of range overlap

TABLE 1 Numbers of adult Lissotriton newts observed over 105 ponds in the western part of the department Pas de Calais in the 1974 to 2021 period. F v = Lv /(Lh + Lv)<table><tbody><tr><th>Pond</th><th>Coordinates</th><th>Sample size for species</th><th>F v</th></tr><tr><th>number</th><th>Northern latitude</th><th>Eastern longitude</th><th>Lissotriton helveticus (N)</th><th>Lissotriton vulgaris (N)</th></tr></tbody><tbody><tr><th>1</th><td>50.770</td><td>1.772</td><td>21</td><td>1</td><td>0.05</td></tr><tr><th>2</th><td>50.871</td><td>1.617</td><td>194</td><td>175</td><td>0.47</td></tr><tr><th>3</th><td>50.902</td><td>1.679</td><td>223</td><td>9</td><td>0.04</td></tr><tr><th>4</th><td>50.901</td><td>1.678</td><td>126</td><td>7</td><td>0.05</td></tr><tr><th>5</th><td>50.870</td><td>1.606</td><td>580</td><td>24</td><td>0.04</td></tr><tr><th>6</th><td>50.902</td><td>1.680</td><td>233</td><td>6</td><td>0.03</td></tr><tr><th>7</th><td>50.869</td><td>1.608</td><td>34</td><td>1</td><td>0.03</td></tr><tr><th>8</th><td>50.869</td><td>1.609</td><td>20</td><td>0</td><td>0.00</td></tr><tr><th>9</th><td>50.875</td><td>1.656</td><td>26</td><td>2</td><td>0.07</td></tr><tr><th>10</th><td>50.855</td><td>1.666</td><td>410</td><td>149</td><td>0.27</td></tr><tr><th>11</th><td>50.854</td><td>1.664</td><td>188</td><td>96</td><td>0.34</td></tr><tr><th>12</th><td>50.839</td><td>1.672</td><td>108</td><td>10</td><td>0.08</td></tr><tr><th>13</th><td>50.837</td><td>1.675</td><td>29</td><td>5</td><td>0.15</td></tr><tr><th>14</th><td>50.820</td><td>1.631</td><td>65</td><td>41</td><td>0.39</td></tr><tr><th>15</th><td>50.858</td><td>1.630</td><td>26</td><td>6</td><td>0.19</td></tr><tr><th>16</th><td>50.861</td><td>1.610</td><td>92</td><td>34</td><td>0.27</td></tr><tr><th>17</th><td>50.860</td><td>1.606</td><td>43</td><td>0</td><td>0.00</td></tr><tr><th>18</th><td>50.849</td><td>1.597</td><td>186</td><td>87</td><td>0.32</td></tr><tr><th>19</th><td>50.853</td><td>1.595</td><td>27</td><td>11</td><td>0.29</td></tr><tr><th>20</th><td>50.866</td><td>1.608</td><td>107</td><td>46</td><td>0.30</td></tr><tr><th>21</th><td>50.844</td><td>1.590</td><td>104</td><td>76</td><td>0.42</td></tr><tr><th>22</th><td>50.844</td><td>1.598</td><td>26</td><td>16</td><td>0.38</td></tr><tr><th>23</th><td>50.844</td><td>1.599</td><td>83</td><td>66</td><td>0.44</td></tr><tr><th>24</th><td>50.845</td><td>1.599</td><td>812</td><td>182</td><td>0.18</td></tr><tr><th>25</th><td>50.845</td><td>1.599</td><td>45</td><td>16</td><td>0.26</td></tr><tr><th>26</th><td>50.845</td><td>1.599</td><td>53</td><td>40</td><td>0.43</td></tr><tr><th>27</th><td>50.845</td><td>1.599</td><td>86</td><td>35</td><td>0.29</td></tr><tr><th>28</th><td>50.845</td><td>1.600</td><td>41</td><td>16</td><td>0.28</td></tr><tr><th>29</th><td>50.845</td><td>1.600</td><td>79</td><td>33</td><td>0.29</td></tr><tr><th>30</th><td>50.845</td><td>1.600</td><td>70</td><td>17</td><td>0.20</td></tr><tr><th>31</th><td>50.845</td><td>1.601</td><td>51</td><td>45</td><td>0.47</td></tr><tr><th>32</th><td>50.846</td><td>1.600</td><td>1712</td><td>450</td><td>0.21</td></tr><tr><th>33</th><td>50.845</td><td>1.600</td><td>576</td><td>152</td><td>0.21</td></tr><tr><th>34</th><td>50.846</td><td>1.603</td><td>10</td><td>10</td><td>0.50</td></tr><tr><th>35</th><td>50.849</td><td>1.598</td><td>4</td><td>6</td><td>0.60</td></tr><tr><th>36</th><td>50.861</td><td>1.591</td><td>31</td><td>61</td><td>0.66</td></tr><tr><th>37</th><td>50.860</td><td>1.586</td><td>113</td><td>244</td><td>0.68</td></tr><tr><th>38</th><td>50.860</td><td>1.583</td><td>72</td><td>347</td><td>0.83</td></tr><tr><th>39</th><td>50.857</td><td>1.664</td><td>36</td><td>2</td><td>0.05</td></tr><tr><th>40</th><td>50.864</td><td>1.616</td><td>18</td><td>4</td><td>0.18</td></tr><tr><th>41</th><td>50.865</td><td>1.602</td><td>82</td><td>24</td><td>0.23</td></tr><tr><th>42</th><td>50.855</td><td>1.664</td><td>112</td><td>35</td><td>0.24</td></tr><tr><th>43</th><td>50.832</td><td>1.626</td><td>19</td><td>12</td><td>0.39</td></tr><tr><th>44</th><td>50.844</td><td>1.593</td><td>19</td><td>12</td><td>0.39</td></tr><tr><th>45</th><td>50.850</td><td>1.582</td><td>1</td><td>10</td><td>0.91</td></tr><tr><th>46</th><td>50.819</td><td>1.611</td><td>17</td><td>4</td><td>0.19</td></tr><tr><th>47</th><td>50.818</td><td>1.608</td><td>35</td><td>35</td><td>0.50</td></tr><tr><th>48</th><td>50.822</td><td>1.600</td><td>11</td><td>3</td><td>0.21</td></tr><tr><th>49</th><td>50.823</td><td>1.602</td><td>618</td><td>159</td><td>0.20</td></tr><tr><th>50</th><td>50.821</td><td>1.619</td><td>14</td><td>10</td><td>0.42</td></tr><tr><th>51</th><td>50.823</td><td>1.623</td><td>25</td><td>10</td><td>0.29</td></tr><tr><th>52</th><td>50.819</td><td>1.617</td><td>45</td><td>7</td><td>0.13</td></tr><tr><th>53</th><td>50.820</td><td>1.598</td><td>32</td><td>15</td><td>0.32</td></tr><tr><th>54</th><td>50.821</td><td>1.599</td><td>31</td><td>10</td><td>0.24</td></tr><tr><th>55</th><td>50.797</td><td>1.626</td><td>24</td><td>68</td><td>0.74</td></tr><tr><th>56</th><td>50.795</td><td>1.627</td><td>3</td><td>11</td><td>0.79</td></tr><tr><th>57</th><td>50.800</td><td>1.623</td><td>14</td><td>27</td><td>0.66</td></tr><tr><th>58</th><td>50.799</td><td>1.622</td><td>0</td><td>11</td><td>1.00</td></tr><tr><th>59</th><td>50.784</td><td>1.617</td><td>16</td><td>33</td><td>0.67</td></tr><tr><th>60</th><td>50.797</td><td>1.619</td><td>4</td><td>66</td><td>0.94</td></tr><tr><th>61</th><td>50.797</td><td>1.621</td><td>3</td><td>34</td><td>0.92</td></tr><tr><th>62</th><td>50.798</td><td>1.613</td><td>17</td><td>295</td><td>0.95</td></tr><tr><th>63</th><td>50.791</td><td>1.621</td><td>21</td><td>110</td><td>0.84</td></tr><tr><th>64</th><td>50.799</td><td>1.615</td><td>5</td><td>12</td><td>0.71</td></tr><tr><th>65</th><td>50.797</td><td>1.616</td><td>0</td><td>12</td><td>1.00</td></tr><tr><th>66</th><td>50.799</td><td>1.618</td><td>0</td><td>131</td><td>1.00</td></tr><tr><th>67</th><td>50.797</td><td>1.624</td><td>1</td><td>15</td><td>0.94</td></tr><tr><th>68</th><td>50.799</td><td>1.628</td><td>2</td><td>18</td><td>0.90</td></tr><tr><th>69</th><td>50.793</td><td>1.625</td><td>7</td><td>6</td><td>0.46</td></tr><tr><th>70</th><td>50.791</td><td>1.624</td><td>9</td><td>3</td><td>0.25</td></tr><tr><th>71</th><td>50.808</td><td>1.667</td><td>8</td><td>14</td><td>0.64</td></tr><tr><th>72</th><td>50.826</td><td>1.668</td><td>19</td><td>4</td><td>0.17</td></tr><tr><th>73</th><td>50.851</td><td>1.723</td><td>148</td><td>2</td><td>0.01</td></tr><tr><th>74</th><td>50.855</td><td>1.700</td><td>68</td><td>1</td><td>0.01</td></tr><tr><th>75</th><td>50.863</td><td>1.692</td><td>405</td><td>1</td><td>0.00</td></tr><tr><th>76</th><td>50.823</td><td>1.722</td><td>118</td><td>27</td><td>0.19</td></tr><tr><th>77</th><td>50.822</td><td>1.722</td><td>302</td><td>302</td><td>0.50</td></tr><tr><th>78</th><td>50.822</td><td>1.721</td><td>91</td><td>78</td><td>0.46</td></tr><tr><th>79</th><td>50.851</td><td>1.759</td><td>36</td><td>4</td><td>0.10</td></tr><tr><th>80</th><td>50.850</td><td>1.758</td><td>66</td><td>10</td><td>0.13</td></tr><tr><th>81</th><td>50.854</td><td>1.701</td><td>22</td><td>0</td><td>0.00</td></tr><tr><th>82</th><td>50.819</td><td>1.718</td><td>302</td><td>218</td><td>0.42</td></tr><tr><th>83</th><td>50.805</td><td>1.699</td><td>77</td><td>0</td><td>0.00</td></tr><tr><th>84</th><td>50.805</td><td>1.700</td><td>28</td><td>0</td><td>0.00</td></tr><tr><th>85</th><td>50.831</td><td>1.821</td><td>20</td><td>0</td><td>0.00</td></tr><tr><th>86</th><td>50.844</td><td>1.783</td><td>42</td><td>27</td><td>0.39</td></tr><tr><th>87</th><td>50.833</td><td>1.775</td><td>50</td><td>14</td><td>0.22</td></tr><tr><th>88</th><td>50.833</td><td>1.778</td><td>558</td><td>46</td><td>0.08</td></tr><tr><th>89</th><td>50.832</td><td>1.776</td><td>13</td><td>3</td><td>0.19</td></tr><tr><th>90</th><td>50.832</td><td>1.774</td><td>42</td><td>8</td><td>0.16</td></tr><tr><th>91</th><td>50.832</td><td>1.776</td><td>47</td><td>4</td><td>0.08</td></tr><tr><th>92</th><td>50.830</td><td>1.775</td><td>20</td><td>2</td><td>0.09</td></tr><tr><th>93</th><td>50.831</td><td>1.773</td><td>20</td><td>12</td><td>0.38</td></tr><tr><th>94</th><td>50.837</td><td>1.800</td><td>7</td><td>5</td><td>0.42</td></tr><tr><th>95</th><td>50.800</td><td>1.689</td><td>13</td><td>14</td><td>0.52</td></tr><tr><th>96</th><td>50.789</td><td>1.691</td><td>1883</td><td>6</td><td>0.00</td></tr><tr><th>97</th><td>50.778</td><td>1.679</td><td>66</td><td>1</td><td>0.01</td></tr><tr><th>98</th><td>50.779</td><td>1.659</td><td>13</td><td>0</td><td>0.00</td></tr><tr><th>99</th><td>50.771</td><td>1.666</td><td>327</td><td>3</td><td>0.01</td></tr><tr><th>100</th><td>50.774</td><td>1.673</td><td>20</td><td>0</td><td>0.00</td></tr><tr><th>101</th><td>50.779</td><td>1.606</td><td>87</td><td>50</td><td>0.36</td></tr><tr><th>102</th><td>50.807</td><td>1.690</td><td>32</td><td>0</td><td>0.00</td></tr><tr><th>103</th><td>50.797</td><td>1.688</td><td>27</td><td>1</td><td>0.04</td></tr><tr><th>104</th><td>50.801</td><td>1.690</td><td>17</td><td>1</td><td>0.06</td></tr><tr><th>105</th><td>50.789</td><td>1.690</td><td>13</td><td>0</td><td>0.00</td></tr></tbody></table>

TABLE 3 in Coexistence of two newt species in a transition zone of range overlap

TABLE 3 Estimates on the total adult newt population size (N̂) composed of Ichthyosaura alpestris, Lissotroton helveticus and L.vulgaris in the focal pond near Ambleteuse, Pas de Calais, France, by a weighted mean capture-recapture method and a removal method (details see text)<table><tbody><tr><th>Year</th><th>Number of visits</th><th>Capture event</th><th>Weighted mean</th><th>Removal</th><th>Different individuals</th><th>Capture efficiency</th></tr><tr><th>First</th><th>Last</th><th>N̂</th><th>SE</th><th>N̂</th><th>SE</th><th>95% Confidence interval</th></tr><tr><th>Lower</th><th>Upper</th></tr></tbody><tbody><tr><th>1983</th><td>3</td><td>06-May</td><td>19-May</td><td>2828</td><td>459.0</td><td>840</td><td>29.0</td><td>789</td><td>902</td><td>560</td><td>0.67</td></tr><tr><th>1986</th><td>5</td><td>28-Mar</td><td>13-May</td><td>806</td><td>93.0</td><td>508</td><td>27.6</td><td>463</td><td>572</td><td>356</td><td>0.70</td></tr><tr><th>1986a</th><td>6</td><td>13-Mar</td><td>13-May</td><td>1320</td><td>93.0</td><td>1022</td><td>27.6</td><td>977</td><td>1086</td><td>870</td><td>0.85</td></tr><tr><th>1988</th><td>6</td><td>18-Mar</td><td>22-May</td><td>1051</td><td>110.1</td><td>834</td><td>32.5</td><td>776</td><td>903</td><td>482</td><td>0.58</td></tr><tr><th>1989</th><td>2</td><td>01-Apr</td><td>02-Apr</td><td>707</td><td>53.7</td><td>722</td><td>19.6</td><td>688</td><td>764</td><td>530</td><td>0.73</td></tr><tr><th>1990</th><td>2</td><td>05-May</td><td>06-May</td><td>426</td><td>45.4</td><td>420</td><td>15.3</td><td>394</td><td>454</td><td>303</td><td>0.72</td></tr><tr><th>1991</th><td>3</td><td>10-May</td><td>19-May</td><td>493</td><td>46.1</td><td>404</td><td>22.7</td><td>455</td><td>544</td><td>322</td><td>0.80</td></tr><tr><th>1992</th><td>7</td><td>21-Mar</td><td>10-May</td><td>477</td><td>33.9</td><td>406</td><td>12,0</td><td>390</td><td>438</td><td>370</td><td>0.91</td></tr><tr><th>1993</th><td>4</td><td>28-Apr</td><td>05-May</td><td>464</td><td>38.9</td><td>436</td><td>21.6</td><td>401</td><td>486</td><td>319</td><td>0.73</td></tr><tr><th>1994</th><td>4</td><td>07-Apr</td><td>10-Apr</td><td>370</td><td>75.6</td><td>217</td><td>17.3</td><td>190</td><td>259</td><td>142</td><td>0.65</td></tr><tr><th>1995</th><td>4</td><td>27-Apr</td><td>06-May</td><td>950</td><td>72.7</td><td>667</td><td>22.4</td><td>631</td><td>719</td><td>541</td><td>0.81</td></tr><tr><th>2003</th><td>4</td><td>13-Apr</td><td>26-May</td><td>255</td><td>47.4</td><td>154</td><td>9.8</td><td>142</td><td>181</td><td>130</td><td>0.84</td></tr><tr><th>2012</th><td>4</td><td>09-Mar</td><td>13-Apr</td><td>990</td><td>75.9</td><td>909</td><td>37.9</td><td>842</td><td>991</td><td>549</td><td>0.60</td></tr></tbody></table>a Including animals found dead.

opencc-by-4.0Oct 2021View details →

Data from: Extraction and identification of a wide range of microplastic polymers in soil and compost

Microplastic (MP) pollution is globally widespread, however their presence in soil systems is poorly understood due to complexity of soil and lack of standardised extraction methods. Datasets provided contain data from optimisation (recoveries) of MPs extraction protocol from soil and compost based on olive oil and density separation using zinc chloride, in case of low-density polyethylene and polyethylene terephthalate. Density separation was further used to extract five microplastic polymers (PET, PS, PE, PP and PVC), added to soil and compost at different concentrations, which is also included in the dataset. Additionally, identification data of extracted MPs from spiked soil and compost samples.

opencc-by-4.0Nov 2017View details →

Fig. 2 in Distributional Range Extension and Live Coloration of the Indo-Pacific Deepwater Cardinalfish Ostorhinchus cheni (Perciformes: Apogonidae)

Fig. 2. Preserved specimen of Ostorhinchus cheni. USNM 212791, 104.5 mm SL, Sri Lanka.

Immigrant males' memory acts to reduce ranging overlap and mating competition in wild baboons

Mechanistic models suggest that information acquired by animals ("knowledge") could shape home range patterns and dynamics, and how neighbours share space. In social species this would suggest that immigrants could bring new knowledge into social groups, potentially affecting the dynamics of home range overlap. We tested this "immigrant knowledge hypothesis" in a wild population of chacma baboons (Papio ursinus). We used data collected between 2005 and 2013 on two neighbouring troops in Namibia, comprising GPS records of daily ranges, male natal origins, daily females' reproductive status, and a satellite index of vegetation growth. We found that when the ratio of fertile females to adult males in the focal troop increased (i.e. increasing inter-troop mating competition costs for focal troop males), the focal troop tended to overlap less with a neighbouring troop's home range only when the alpha male had immigrated from that neighbouring troop and so was "knowledgeable" about its home range. When the alpha male was "not knowledgeable" the reverse was observed, with troops showing greater overlap, most likely reflecting an influence of fertile females on ranging patterns. Our findings support the immigrant knowledge hypothesis of group ranging behaviour, and suggest that dispersal and spatial memory may sometimes act to increase rather than decrease spatial segregation between groups. Very few previous studies have reported an effect of mating competition on ranging dynamics in social species, perhaps because previous studies did not consider divergent adaptive interest among group members and how they can be solved through collective-decision processes.

Both source and recipient range phylogenetic community structure can predict the outcome of avian introductions

Competing phylogenetic models have been proposed to explain the success of species introduced to other communities. Here, we present a study predicting the establishment success of birds introduced to Florida, Hawaii, and New Zealand using several alternative models, considering species' phylogenetic relatedness to source and recipient range taxa, propagule pressure, and traits. We find consistent support for the predictive ability of source region phylogenetic structure. However, we find that the effects of recipient region phylogenetic structure vary in sign and magnitude depending on inclusion of source region phylogenetic structure, delineation of the recipient species pool, and the use of phylogenetic correction in the models. We argue that tests of alternative phylogenetic hypotheses including the both source and recipient community phylogenetic structure, as well as important covariates such as propagule pressure, are likely to be critical for identifying general phylogenetic patterns in introduction success, predicting future invasions, and for stimulating further exploration of the underlying mechanisms of invasibility.

When expansion stalls: an extension to the concept of range pinning in ecology

Identifying the factors modulating range expansion is essential to accurately predict changes in the spatial distribution of populations. By preventing population growth after dispersal, Allee effects can lead to front stops in discrete space, called 'pinning' if permanent. However, other mechanisms, such as positive density-dependent dispersal, have also been shown to affect the rate of range expansion and generate discrete-space front stops, albeit temporarily. In this study, we investigated the stability of the front stops generated by such mechanisms in relation to the carrying capacity of the environment. To this end, we performed artificial range expansions in discrete space using stochastic simulations and microcosm experiments. Simulation results confirmed that density-dependent dispersal alone can generate sustained front stops, albeit for a limited range of carrying capacities. We also highlighted the synergy between Allee effects and density-dependent dispersal on pinning emergence. Experimental results, obtained using a model species known to exhibit density-dependent dispersal, but without Allee effects, confirmed the model results. Furthermore, our study raises the issue of carefully considering the conditions for pinning stability, in a stochastic context and depending on the time-scale considered.

Maternal effects of climate warming and nitrogen deposition vary with home and introduced ranges

Maternal effects allow offspring to cope with changing environments. While the immediate effects of climate warming and nitrogen (N) deposition are well documented, their maternal effects have been little studied. We conducted a 6-year maternal experiment with Solidago canadensis, native to North America and invasive in China, and two offspring experiments to address how maternal warming, maternal N-addition and population source interacted to influence offspring performance. Maternal effects of warming and N-addition on seed traits, leaf dry matter content, and whole-plant biomass were stronger in S. canadensis offspring from China than in offspring from North America. Matched maternal-offspring environments allowed offspring to perform better compared to mismatched environments; offspring grown under warming flowered and produced seeds within a growing season only when their maternal plants were previously exposed to warming. Offspring environments influenced its performance and also modulated maternal effects. We suggest that the maternal effects of simulated climate warming and N deposition could vary ranges, and our findings imply that maternal warming could advance the reproductive phenology of offspring.

opencc-by-4.0Jan 2022View details →

Comparison of stopping powers and CSDA range values between libdEdx and dedx web

This data is a collection of energy and corresponding stopping power and CSDA range values. It was collected from the<a href="http://github.com/APTG/libdedx/"> libdEdx library</a> and <a href="http://github.com/APTG/dedx_web">dedx website</a> Data is a part of the thesis titled:  “Webassembly technology used in web interfaces presenting scientific calculations results”  AGH UST Poland Data Programs: ASTAR, PSTAR, MSTAR, BETHE_EXT, ICRU Ions: Hydrogen, Helium, Carbon, Oxygen, Iron, Uranium Materials: Water, Silicon, Copper, Gold, Lithiumfluoride, PMMA (polymethyl methacrylate)

opencc-zeroJan 2022View details →

Data from: Wildfire catalyzes upward range expansion of trembling aspen in southern Rocky Mountain beetle-killed forests

In this study, we assessed whether recent wildfires and spruce beetle outbreaks promoted upward range expansion of trembling aspen (Populus tremuloides) in the San Juan Mountains of southern Colorado. We assessed aerial imagery for presence/absence of aspen in the study area and ground truthed a subset of these points to compare the aerial imagery analysis with field records of aspen. From these data we determined the upper elevational limit of aspen in the study area before fire. We then conducted field surveys at and above the upper elevational limit of aspen in burned and unburned, but beetle killed areas and collected data on stand structure, surface cover, seedling density and microsite conditions associated with seedlings.

opencc-by-4.0Jul 2021View details →

Datasets of Indoor UWB Measurements for Ranging and Positioning in Good and Challenging Scenarios

This is a dataset of ranging and positioning measurements collected from an UWB development board. The Real Time Location System based on UWB is set up in a laboratory. Data were captured in the static laboratory environment with different conditions that affects to the positioning performance. In the lab, scenarios with different propagation conditions between the nodes and different geometries were set up. We consider good, challenging, and intermediate scenarios with: Line of Sight (LOS) and Non-LOS propagation conditions as well as easy and challenging geometries. These datasets may be used, for example, for investing and validating ranging and positioning algorithms in different scenarios. A detailed description is provided in the file README.pdf

opencc-by-4.0Feb 2022View details →

Supplemental Data for "Plus and minus ends of microtubule respond asymmetrically to kinesin binding by a long range directionally driven allosteric mechanism"

This is raw data set associated with all the figures in the article "Plus and minus ends of microtubule respond asymmetrically to kinesin binding by a long range directionally driven allosteric mechanism" published in Science Advances by Huong T Vu, Zhechun Zhang, Riina Tehver and D. Thirumalai.

opencc-zeroFeb 2022View details →

Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

Species distribution models (SDMs) are a popular tool in theoretical and quantitative ecology, and constitute the most widely used modelling framework in global change science and biodiversity conservation. As main data sources, SDMs require georeferenced biodiversity observations as a response or dependent variable (e.g. species occurrence, species richness, etc) and geographic layers of environmental information as predictors or independent variables (e.g. climate, land cover, vegetation indices derived from remote sensing, etc). However, although SDMs have become one of the most important quantitative tools for addressing regular and timely biodiversity assessments worldwide, these techniques are still subject to different sources of uncertainty that have been unequally assessed. Thus, despite uncertainty related to niche-based or distribution-based models has been addressed at different stages in the modelling process, an analysis of the effect of uncertainty coming from alternative data sources on the predictive ability of SDMs is still limited. Citizen-collected species occurrence data (e.g. eBird) are often used for fitting SDMs when data from standardized and expert-supported surveys (e.g. Atlases) are unavailable. On the other hand, macroclimate variables are much more commonly used as predictors in SDMs than other sources of information coming from remote sensing data. We assessed the effects of using different data sources (in both response and predictor variables) on SDM performance across a wide range of bird species with contrasting distributional ranges in the Iberian Peninsula (Portugal and Spain). To do that, a SDM ensemble-forecasting approach was implemented by using bird data from two different data sources: the semi-structured eBird project and standardized Atlases. We fitted SDMs with three predictor types: macroclimate, remotely sensed ecosystem functional attributes (EFAs) from vegetation indices, and their combination. Species were grouped in four range size classes. We also used different evaluation metrics to better assess the uncertainty of model predictions. We then applied generalized linear mixed-effects models to test the effect on model performance of input data source across distributional range sizes while accounting for different accuracy metrics. Pairwise comparisons between range projections were used to assess their spatial similarity. Our models demonstrated the usefulness and complementarity of different input data sources when modelling species distribution across different distributional ranges. Citizen science and remote sensing data contribute to update the knowledge of the distribution of the most threatened bird species by increasing the model accuracy. These findings highlight the need to integrate different data sources to improve the model predictions at regional scale. Our framework also underlines that model uncertainty should be examined more exhaustively at early stages of the modelling process. To perfom and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable; ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four *.csv files named as: 1) The_Iberian_bird_species_occurrences_dataset_10km.csv 2) CHELSA_bioclimate_variables_IP10km.csv 3) MODIS_EVI-based_EFAs_IP10km.csv 4) Combined_bioclimate_EFA_dataset_IP10km.csv For a more detailed description of the main dataset and each of these subdatasets, please refer to the attached README file. Keywords: bird atlas, eBird data, ecosystem functional attributes (EFAs), Iberian Peninsula, IUCN categories, Model accuracy, MODIS EVI, narrow-ranged species, remote sensing, species distribution models (SDMs), widespread species

opencc-by-4.0Feb 2022View details →

Physiological host range of three Trissolcus parasitoids in New Zealand

This archive contains a 'data' folder holding an Excel workbook with the results of no-choice oviposition experiments conducted with Trissolcus basalis (Wollaston) & Trissolcus oenone (Dodd) (Scelionidae: Hymenoptera) and New Zealand species of Pentatomidae. Trissolcus basalis is a biological control agent released in New Zealand in 1949 against Nezara viridula L., and Trissolcus oenone is a native parasitoid also present in Australia. This is the first time the physiological host ranges of each parasitoid have been established quantitatively in New Zealand, and for Trissolcus basalis, this study provides a retrospective view of its physiological host range after 80 years in the country. This workbook also contains a second sheet holding a combined dataset - a combination of results from the previously mentioned study and those from: "Experimental assessment of the biosafety of Trissolcus japonicus in New Zealand, prior to the anticipated arrival of the invasive pest Halyomorpha halys" (DOI: 10.1007/s10526-019-09949-x). The archive also contains a 'src' folder containing an R markdown file with the R code used to perform analyses.

opencc-zeroFeb 2022View details →

Genetic structure in the nonbreeding range of rufa Red Knots suggests distinct Arctic breeding populations

An understanding of the migratory connectivity between breeding and nonbreeding areas is fundamental to the management of long-distance migrants under pressure from habitat change along their flyways. Here we describe evidence for genetic structure within the nonbreeding range of the endangered Arctic-Canadian rufa subspecies of Red Knots (Calidris canutus). Using blood and tissue samples from the major nonbreeding regions in Argentina (Tierra del Fuego and Río Negro), northern Brazil (Maranhão), and southeastern USA (Florida), we estimated genetic structure in 514 amplified fragment length polymorphism (AFLP) loci, applying cluster assignment analyses in DAPC, assignPOP, and STRUCTURE. Using a priori location information, individuals could be correctly re-assigned to their nonbreeding regions, which validated that the assignment accuracy of the data was sufficient. Without using a priori location information, we detected 3–5 genotype clusters, and posterior assignment probabilities of samples to these genotype clusters varied among the three regions. Lastly a chi-square test confirmed that allele frequencies varied significantly among nonbreeding regions, rejecting the hypothesis that samples were drawn from a single gene pool. Our findings hint at undescribed structure within the Red Knot rufa breeding range in the Canadian Arctic and indicate that each rufa nonbreeding area in this study hosts a different subsample of these breeding populations. The observation that nonbreeding sites of rufa Red Knots contain different genetic pools argues for separate conservation management of these sites.

opencc-zeroMar 2022View details →

Extreme home range sizes among Eurasian lynx at the northern edge of their biogeographic range

Eurasian lynx (Lynx lynx) have a wide distribution across Eurasia. The northern edge of this distribution is in Norway, where they reach up to 72 degrees north. We conducted a study of lynx space use in this region from 2007 to 2013 using GPS telemetry. The home range sizes averaged 2,606 (± 438 SE) km2 for males (n=9 ranges) and 1,456 (± 179 SE) km2 for females (n=24 ranges). These are the largest home ranges reported for any large felid, and indeed are only matched by polar bears, arctic living wolves, and grizzly bears among all the Carnivora. The habitat occupied was almost entirely treeless alpine tundra, with home ranges only containing from 20 to 25% of forest. These data have clear implications for the spatial planning of lynx management in the far north as the current management zones are located in unsuitable habitats and are not large enough to encompass individual lynx movements.

Evolution of selfing syndrome and its influence on genetic diversity and inbreeding: A range-wide study in Oenothera primiveris.

Premise of the study: To avoid inbreeding depression plants have evolved diverse breeding systems to favor outcrossing such as self-incompatibility. However, changes in biotic and abiotic conditions can result in selective pressures that lead to a breakdown in self-incompatibility. The shift to increased selfing is commonly associated with reduced floral features, lower attractiveness to pollinators, and increased inbreeding. In this study, we tested the hypothesis that the loss of self-incompatibility, a shift to self-fertilization (autogamy), and concomitant evolution of the selfing syndrome (reduction in floral traits associated with cross-fertilization) will lead to increased inbreeding and population differentiation in Oenothera primiveris. Across its range, this species exhibits a shift in its breeding system and floral traits from a self-incompatible population with large flowers to self-compatible populations with smaller flowers. Methods: We conducted a breeding system assessment, evaluated floral traits in the field and under controlled conditions, and measured population genetic parameters using RADseq data.  Key results: Our results reveal a bimodal transition to the selfing syndrome from the west to the east of the range of O. primiveris. This shift includes variation in the breeding system, mating system, a reduction in floral traits (flower diameter, herkogamy, and scent production), a shift to greater autogamy, reduced genetic diversity, and increased inbreeding. Conclusions: The observed variation highlights the importance of range-wide studies to understand breeding system variation and the evolution of the selfing syndrome within populations and species.

opencc-zeroMar 2022View details →

Functional trait variation of Anemone nemorosa along macro‐ and microclimatic gradients close to the northern range edge

Climate warming is affecting ecosystems worldwide, and slow-colonizing forest under- storey species are particularly vulnerable if they are unable to track climate change. However, species' responses to climatic conditions in terms of growth, reproduction and colonization capacity may vary with the distance to their distribution range edge. Anemone nemorosa is known to be a slow colonizing forest herb dependent on forest cover in the southern and central part of its distribution range, whereas at its northern distribution range (and at higher elevations) it also occurs in open habitats. Here, we investigated the response of plant functional traits of Anemone nemorosa in cen- tral Norway (close to its northern distribution range edge) to a macroclimatic gra- dient (elevation), two microclimatic gradients (forest density and distance to forest edge) and a competition treatment (removal of neighbouring vegetation). We aimed to identify which environmental conditions (light, temperature, soil pH and/or soil organic matter) drive A. nemorosa's responses. In a total of 90 plots, we measured six functional traits of A. nemorosa (plant height, biomass, specific leaf area, seed number, seed mass,and germination percentage). We found stronger variation in environmental conditions along the macroclimatic elevational gradient, than along the microclimatic gradients of forest density and distance to forest edge, and this was also reflected in A. nemorosa's responses. Light availability, in interaction with temperature, was the key environmental variable driving plant performance, while soil conditions were less important. The competition release treatment had a negative effect on A. nemorosa in our study sites, indicating that the facilitative effect of the neighbouring vegetation may be stronger than the competitive effect. Our study suggests that A. nemorosa, close to its northern distribution range edge, has the capacity to cope with climate change through phenotypic responses, and that light and temperature are the key drivers of these responses.