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593 results for “fish distribution”
Figure 3 in CHIPS: a database of historic fish distribution in the Seine River basin (France)
Figure 3. - Waterbodies with historical fish data.
Figure 1 in CHIPS: a database of historic fish distribution in the Seine River basin (France)
Figure 1. - Historical data: origin and percentages for observations from all sources.
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>
Fig. 2 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 2. Schematic view of the Baguari Dam with the positioning of the fish ladder entrance.
Fig. 1 in Fish passage post-construction issues: analysis of distribution, attraction and passage efficiency metrics at the Baguari Dam fish ladder to approach the problem
Fig. 1. Location of Baguari Dam in the middle Doce River Basin, Minas Gerais State, Brazil.
Figure 1 in Occurrence and distribution of tetraodontiform fishes of the Andaman and Nicobar Islands, India
Figure 1. Map of the Andaman and Nicobar archipelago, showing the location of sampling sites.
Fig. 3 in Spatial and temporal distribution of fish eggs and larvae in a subtropical coastal lagoon, Santa Catarina State, Brazil
Fig. 3. Mean abundances · 100 m-3 (± sd) of fish eggs and larvae in Ibiraquera Lagoon, by month.
Data from: Current distributions and future climate‐driven changes in diatoms, insects and fish in U.S. streams
<span>Aim</span> <p class="abstract_para">Biodiversity on Earth is threatened by climate change. Despite the vulnerability of freshwater habitats to human impacts, most climate change projections have focused on terrestrial systems. Here, we examined how the current distributions and biodiversity of stream taxa might change under mitigated, stabilizing and increasing greenhouse gas emissions.</p> <span>Location</span> <p class="abstract_para">Conterminous USA.</p> <span>Time period</span> <p class="abstract_para">Present day to 2070.</p> <span>Major taxa studied</span> <p class="abstract_para">Stream diatoms, insects and fish.</p> <span>Methods</span> <p class="abstract_para">We developed species distribution models for 336 freshwater taxa from 1,227 distinct stream localities using water chemistry, watershed and climatic variables. Models based only on climate were used to project changes in the distributions and biodiversity of cold‐ versus warm‐water taxa under representative concentration pathways (RCPs) ranging from 2.6 to 8.5 W/m<sup>2</sup>.</p> <span>Results</span> <p class="abstract_para">In all three organismal groups, climate emerged as the strongest predictor of species distributions, providing comparable explanatory power to water chemistry and watershed variables combined. The RCP‐based projections suggested a widespread expansion of warm‐water taxa, outpacing the decline of cold‐water taxa. Consequently, overall species richness would increase, but beta diversity would decrease drastically with the severity of climate change. A closer look at individual taxa and functional guilds revealed that vulnerable cold‐water taxa included: (a) diatom guilds forming the base and bulk of the biofilm; (b) environmentally sensitive insects, characteristic of unimpacted streams; and (c) ecologically and recreationally important salmonids, which were forecast to diminish dramatically in source habitats. Warm‐water fish projected to increase their distributions include bait bucket release minnows and dominant predators.</p> <span>Main conclusions</span> <p class="abstract_para">Our results suggest potentially devastating impacts of climate change on stream ecosystems, with the restructuring of diatom, insect and fish communities, diminished distributions of functionally important taxa and widespread expansion of warm‐water taxa, giving rise to biotic homogenization. Given that the magnitude of these biotic shifts depends on the severity of climate change, appropriate current policy decisions are necessary to preserve freshwater ecosystems.</p>
Data from: Two for the price of one: eDNA metabarcoding reveals temporal and spatial variability of mussel and fish co-distributions in Michigan riverine systems
<p>Freshwater mussels (family Unionidae) are among the world's most endangered taxa, with almost 75% of North American taxa classified as a species of concern, threatened, or endangered. Despite the critical importance of comprehensive distributional data for the conservation of unionids and fishes, these data are often lacking because of the labor and resources associated with traditional survey methods. During their larval stage, unionid mussels use various fish species as obligate hosts, making native fish species vital to unionid persistence and an understanding of host distribution similarly important. Here, we utilized an eDNA metabarcoding approach to evaluate patterns of co-distribution of unionid mussels and fishes along ~362 km of the densely sampled Grand River network as well as the outlets of 19 tributaries along the eastern shore of Lake Michigan, USA. We detected a total of 21 mussel and 40 fish taxa, with distinctive composition of both mussel and fish assemblages across tributaries and differences in fish taxa between sampling periods. Notably, we detected more mussel taxa within the Grand River watershed than at the outlets of all 20 rivers combined. Within the Grand River network, two fish taxa (<em>Pylodictus</em> <em>olivaris</em> and <em>Cyprinella</em>) were found more frequently in areas of high mussel diversity, and three fish taxa more frequently in areas of low mussel diversity (<em>Umbra</em>, Leuciscidae, and <em>Etheostoma</em>). There was little difference between eDNA detections of mussels from samples collected in June versus August, but we detected significantly more fish taxa in August compared to June. Taken together, our findings demonstrate the value of eDNA metabarcoding for evaluating co-distribution of ecologically connected taxa. The use of eDNA as a tool for determining distributions of mussels and their obligate hosts may facilitate conservation efforts for these imperiled taxa.</p>
Data set to: Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
<p>Data for the manuscript "Mapping a brain parasite: occurrence and spatial distribution in fish encephalon", doi: 10.1016/j.ijppaw.2023.03.004. Description of the distribution of metacercariae from the trematode species <em>Cardiocephaloides longicollis</em> in the brain of fish. </p>
Fig. 50 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 50. Micropterus salmoides, Lam Tei Irrigation Reservoir.
Fig. 60 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 60. Mugilogobius myxodermus, Plover Cove Reservoir.
Fig. 58. Glossogobius olivaceus, 65 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 58. Glossogobius olivaceus, 65 mm SL, Plover Cove Reservoir.
Fig. 56. Hemiculter leucisculus, 120.2 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 56. Hemiculter leucisculus, 120.2 mm SL, Tai Lam Chung Reservoir.
Fig. 42. Aplocheilus lineatus, 54.5 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 42. Aplocheilus lineatus, 54.5 mm SL, Deep Water Bay Stream.
Fig. 40 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 40. Vieja melanurus, Lai King area.
Fig. 41 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 41. Melanotaenia trifasciata, Black Hill.
Fig. 54 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 54. Distribution of the six most widespread non-native freshwater fish species in Hong Kong.
Fig. 57. Tanichthys albonubes, 29.5 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 57. Tanichthys albonubes, 29.5 mm SL, trade material.
Fig. 55. Osteochilus salsburyi, 109 in The non-native freshwater fishes of Hong Kong: diversity, distributions, and origins
Fig. 55. Osteochilus salsburyi, 109 mm SL, Lower Aberdeen Reservoir.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.