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85 results for “lake assessment”
Fig. 2 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 2. Percent contribution of T. stagnicolae, T. szidati, T. physellae and A. brantae species to each lake. Water samples from different locations and dates were tested using the species-specific qPCR assay and results were pooled by lake to understand the relative contribution overall of each species to each lake. The percent contribution (based on gene copy number) of each species was calculated.
Fig. 3 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 3. Lifecycles of T. stagnicolae, A. brantae, T. szidati, and T. physellae. life cycle summary of the avian schistosome species targeted for species-specific qPCR tests designed in this study.
Cyanobacterial blooms in a warming climate: Paleolimnological assessments of three Boreal Shield lakes in central Ontario, Canada
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Paleolimnological assessment of a hyper-eutrophic lake (Nowlans Lake, N.S., Canada) Cladoceran communities
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Figure 2 in Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey)
Figure 2. Age and length (FL) relationship due to age groups in A. mossulensis females.
Figure 3 in Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey)
Figure 3. Age and length (FL) relationship due to age groups in A. mossulensis males.
Figure 1 in Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey)
Figure 1. Map of the study area (modified from Gokce and Ozhan, 2011).
Assessing the conservation priority of freshwater lake sites based on taxonomic, functional and environmental uniqueness
<p><strong>Aim: </strong>We propose a novel approach that considers taxonomic uniqueness, functional uniqueness and environmental uniqueness, and show how it can be used in guiding conservation planning. We illustrate the approach using data for lake biota and environment.</p> <p><strong>Location: </strong>Lake Puruvesi, Finland.</p> <p><strong>Methods: </strong>We sampled macrophytes and macroinvertebrates from the same 18 littoral sites. By adapting the original 'ecological uniqueness' approach, we used distance‒based methods to calculate measures of taxonomic (LCBD‒t), functional (LCBD‒f) and environmental (LCEH) uniqueness for each site. We also considered the numbers and locations of the sites needed to protect up to 70% of total variation in taxonomic, functional or environmental features in the studied part of the lake.</p> <p><strong>Results: </strong>Relationships between taxonomic (LCBD‒t), functional (LCBD‒f) and environmental (LCEH) uniqueness were generally weak, and only the relationship between macrophyte LCBD‒t and LCBD‒f was statistically significant. Overall, however, if the whole biotic dataset was considered, macroinvertebrate LCBD‒f values showed a consistent positive relationship with macrophyte LCBD‒f. Depending on the measure of site uniqueness, between one third to one half of the sites could help protect up to 70% of the ecological uniqueness of the studied part of Lake Puruvesi.</p> <p><strong>Main conclusions: </strong>Although the dataset examined originated from a large lake system, the approach we proposed here can be applied in different ecosystems and at various spatial scales. An important consideration is that a set of sites has been sampled using the same methods, resulting in species and environmental matrices that can be analysed using the methodological approach proposed here. This framework can be easily applied to grid‒based data, sets of islands, or sets of forest fragments. We suggest that the approach based on taxonomic, functional and environmental uniqueness will be a useful tool in guiding nature conservation and ecosystem management, especially if associated with meta‒system ideas or network thinking.</p>
Figure 5 in Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey)
Figure 5. Age and length (FL) relationship due to age groups in A. marmid females.
Figure 6 in Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey)
Figure 6. Age and length (FL) relationship due to age groups in A. marmid males.
Figure 2 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 2. Results of Cluster Analysis.
Figure 1 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 1. Rusałka Lake in Szczecin City, own elaboration, after Poleszczuk et al. (2012).
Figure 3 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 3. Results of discriminant analysis.
Do we similarly assess diversity with microscopy and High Throughput Sequencing? Case of microalgae in lakes.
<p>These are the repository files of the paper "Do we similarly assess diversity with microscopy and High Throughput Sequencing? Case of microalgae in lakes" published in Organisms Diversity and Evolution</p> <p>In these files are given:</p> <p>- the sampling sites descrptions (coordinates, lake names)</p> <p>- species relative abundances obtained with light microscopy for each sampling site</p> <p>- OTUs amounts and relative abundances obtained High-Throughput Sequencing for each sampling site</p> <p>- code correspondence between lake codes and FastQ files codes</p> <p>- FastQ files of the sampling sites</p>
Figure. Location of the Beyşehir and Eğirdir lakes in Türkiye. in Health risk assessments of heavy metal concentrations via consumption of an invasive species, Carassius gibelio, from two large freshwater lakes of Türkiye
Figure. Location of the Beyşehir and Eğirdir lakes in Türkiye.
Table 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 1.</b> Number of field samples (including controls) for each qPCR assay at each site sampled for eDNA in 2018 and 2019 in western Lake Erie. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. Note that samples are site-specific.</p><table><tbody><tr><th>Site</th><th>Year</th></tr><tr><th>2018</th><th>2019</th></tr><tr><th>Assay</th><th>Samples</th><th>Assay</th><th>Samples</th></tr></tbody><tbody><tr><th>DR</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>81 81 81</td></tr><tr><th>HP</th><td>GCTM10 GCTM22 GCTM32</td><td>77 77 77</td><td>GCTM10 GCTM22 GCTM32</td><td>82 82 82</td></tr><tr><th>MB</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>80 80 80</td></tr></tbody></table>
Table 4 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 4.</b> Percentage of positive eDNA replicate detections in each month and site in 2018 and 2019 in western Lake Erie (based on at least one positive detection on at least one marker and one replicate). All markers (GCTM10, GCTM 22, GCTM32) were used to calculate these proportions. Samples were collected monthly from June to November for each site. The number of telemetered grass carp detected within 7 days before sampling for eDNA is denoted in parentheses. Acoustic telemetry receivers in MB in 2018 were not available. DR = Detroit River, HP = Hot Ponds, and MB = Maumee Bay. NA denotes when acoustic telemetry receivers were not in operation.</p><table><tbody><tr><th>Year</th></tr><tr><th>Site</th><th>2018</th><th>2019</th></tr><tr><th></th><th>June</th><th>July</th><th>Aug</th><th>Sept</th><th>Oct</th><th>Nov</th><th>May</th><th>June</th><th>July</th><th>August</th><th>Oct</th><th>Nov</th></tr></tbody><tbody><tr><th>DR</th><td>0.0%</td><td>2.3%</td><td>2.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>4.5%</td><td>10.6</td><td>14.1</td><td>17.4%</td><td>14.1%</td><td>2.2%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(0)</td><td>(1)</td><td>% (1)</td><td>% (1)</td><td>(2)</td><td>(2)</td><td>(0)</td></tr><tr><th>HP</th><td>0.0%</td><td>0.1%</td><td>4.1%</td><td>2.2%</td><td>15.8%</td><td>0.0%</td><td>9.0%</td><td>1.5%</td><td>34.8</td><td>1.5%</td><td>15.8%</td><td>22.7%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(NA)</td><td>(2)</td><td>(2)</td><td>% (2)</td><td>(3)</td><td>(2)</td><td>(2)</td></tr><tr><th>MB</th><td>12.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.1%</td><td>37.1</td><td>8.3%</td><td>8.3%</td><td>0.0%</td></tr><tr><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(0)</td><td>(0)</td><td>% (0)</td><td>(0)</td><td>(0)</td><td>(0)</td></tr></tbody></table>
Table 3 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 3.</b> Candidate set of hierarchical occupancy models used to estimate probability of grass carp eDNA occurrence among sites (ψ), the conditional probability of grass carp eDNA occurrence at a sampling locality within a site given that grass carp were present at the site (Θ), and the conditional probability of eDNA detection on replicate filters collected at a sampling locality given that the species is present at the sampling locality <i>(p</i>) from three sites in western Lake Erie sampled in 2018 and 2019. Covariates included location (site), time (Month) and probe type (GCTM10, GCTM22, GCTM32). Model comparison was evaluated with the Widely Applicable Information Criterion (WAIC).</p><table><tbody><tr><th>Model</th><th>WAIC</th><th>Δ WAIC</th><th>Lack of fit</th><th>Predicted Variance</th></tr></tbody><tbody><tr><th>ψ(Site)Θ(Site)p(.)</th><td>309.44</td><td>-</td><td>298.70</td><td>18.63</td></tr><tr><th>ψ(.)Θ(Site)p(.)</th><td>309.50</td><td>0.06</td><td>298.99</td><td>10.73</td></tr><tr><th>ψ(.)Θ(Month)p(.)</th><td>317.61</td><td>8.18</td><td>299.05</td><td>18.56</td></tr><tr><th>ψ(Season)Θ(.)p(.)</th><td>317.67</td><td>8.24</td><td>299.01</td><td>18.65</td></tr><tr><th>ψ(Month)Θ(.)p(.)</th><td>317.72</td><td>8.28</td><td>299.04</td><td>18.67</td></tr><tr><th>ψ(Season)Θ(Season)p(.)</th><td>317.84</td><td>8.40</td><td>299.04</td><td>18.79</td></tr><tr><th>ψ(.)Θ(Season)p(.)</th><td>317.74</td><td>8.31</td><td>299.07</td><td>18.66</td></tr><tr><th>ψ(Site)Θ(.)p(.)</th><td>317.94</td><td>8.51</td><td>299.04</td><td>18.90</td></tr><tr><th>ψ(.)Θ(.)p(.)</th><td>325.00</td><td>15.57</td><td>305.50</td><td>20.21</td></tr><tr><th>ψ(Season)Θ(Site)p(.)</th><td>325.41</td><td>15.98</td><td>305.49</td><td>19.91</td></tr><tr><th>ψ(Site + Season)Θ(.)p(.)</th><td>325.59</td><td>16.16</td><td>305.44</td><td>20.15</td></tr><tr><th>ψ(Site)Θ(Season)p(.)</th><td>325.72</td><td>16.29</td><td>305.48</td><td>20.23</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(.)</th><td>325.92</td><td>16.49</td><td>305.49</td><td>20.43</td></tr><tr><th>ψ(.)Θ(Site + Season)p(.)</th><td>326.37</td><td>16.94</td><td>305.52</td><td>20.84</td></tr><tr><th>ψ(Site)Θ(Month)p(.)</th><td>329.57</td><td>20.14</td><td>308.65</td><td>20.92</td></tr><tr><th>ψ(Month)Θ(Month)p(.)</th><td>330.14</td><td>20.71</td><td>308.66</td><td>21.84</td></tr><tr><th>ψ(Site + Season)Θ(Site)p(Probe)</th><td>377.63</td><td>68.20</td><td>276.90</td><td>100.72</td></tr><tr><th>ψ(Site + Season)Θ(.)p(Probe)</th><td>378.35</td><td>68.92</td><td>277.00</td><td>101.34</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(Probe)</th><td>378.42</td><td>68.99</td><td>277.00</td><td>101.41</td></tr><tr><th>ψ(Site + Season)Θ(Season)p(Probe)</th><td>378.55</td><td>69.12</td><td>277.09</td><td>101.46</td></tr><tr><th>ψ(Season)Θ(Site + Season)p(Probe)</th><td>379.41</td><td>69.98</td><td>277.28</td><td>102.10</td></tr><tr><th>ψ(Site)Θ(Site + Season)p(Probe)</th><td>379.62</td><td>70.19</td><td>277.40</td><td>102.21</td></tr><tr><th>ψ(.)Θ(Site + Season)p(Probe)</th><td>379.88</td><td>70.45</td><td>277.31</td><td>102.57</td></tr><tr><th>ψ(Site + Month)Θ(.)p(.)</th><td>383.56</td><td>74.13</td><td>282.86</td><td>100.69</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(.)</th><td>385.47</td><td>76.04</td><td>283.38</td><td>102.09</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(Probe)</th><td>386.95</td><td>77.52</td><td>282.90</td><td>104.05</td></tr><tr><th>ψ(.)Θ(Site + Month)p(.)</th><td>396.44</td><td>87.01</td><td>292.14</td><td>104.29</td></tr><tr><th>ψ(.)Θ(.)p(Probe)</th><td>396.45</td><td>87.01</td><td>292.14</td><td>104.29</td></tr></tbody></table>
Table 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 2.</b> Gene region, primer, and probe sequences used to amplify GCTM10,GCTM22, and GCTM32 for grass carp.</p><table><tbody><tr><th>Gene</th><th>Primers and Probes</th><th>Sequence</th></tr></tbody><tbody><tr><th>ND2</th><td>Forward</td><td>5′- CCYTACGTACTCGCAATTCTAC -3′</td></tr><tr><th>ND2</th><td>Reverse</td><td>5′- GTGGTGGTGTTGGGCTATTA -3′</td></tr><tr><th>ND2</th><td>Probe</td><td>5′- VIC- ACCCTAACCTTTGCTAGCTCCCAC -MGBNFQ-3′</td></tr><tr><th>COII</th><td>Forward</td><td>5′- CCGACTCCTAGAAACAGATCAC -3′</td></tr><tr><th>COII</th><td>Reverse</td><td>5′- GGGACAGCTCAGGAATGTAATA -3′</td></tr><tr><th>COII</th><td>Probe</td><td>5′- 56-FAM- CCAGTTCGT/ZEN/GTCCTAGTATCTGCCGA -3IABkFQ -3′</td></tr><tr><th>COIII</th><td>Forward</td><td>5′- CCACGGACTACACGTCATTATT -3′</td></tr><tr><th>COIII</th><td>Reverse</td><td>5′-GATGTTCGGATGTAAAGTGGTATTG -3′</td></tr><tr><th>COIII</th><td>Probe</td><td>5′-NED- TTCCTAGCTGTTTGCCTTCTCCGT -MGBNFQ-3′</td></tr></tbody></table>
Greenland-wide assessment of supraglacial lake area fluctuations between 2017 and 2022
<p>The area of supraglacial lake (SGL) on Greenland ice sheet over 2017-2022 melting season has been extracted from approximately 300,000 passive optical satellite images. Each time series consists of four columns of decimal time, standard date, SGL area, and the corresponding uncertainty, respectively. The SGL area was calculated based on the SGL occurrence grids, and a total of 3,006 grids with valid observations were obtained.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
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.