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155 results for “Great Lakes”
Figure 4 in Phylogeny and biogeography of African Biomphalaria (Gastropoda: Planorbidae), with emphasis on endemic species of the great East African lakes
Figure 4. Biogeographical hypothesis of the evolutionary history of the African Biomphalaria species. A, Pliocene– Pleistocene dispersal of a Biomphalaria glabrata-like snail into western Africa where the ancestral African Biomphalaria evolved, probably similar to Biomphalaria camerunensis/Biomphalaria pfeifferi. B. pfeifferi colonized most of the sub- Saharan continent. The ancestral African Biomphalaria or a B. camerunensis/B. pfeifferi-like stock migrated to East Africa and evolved into a Biomphalaria angulosa-like species, which is ancestral to the choanomphala-group snails. Within this species group the Ugandan Biomphalaria sudanica and Biomphalaria alexandrina evolved with B. alexandrina subsequently migrating along the River Nile to eastern North Africa. The river Nile and the surrounding region is indicated in the small inset.
Figure 3 in Phylogeny and biogeography of African Biomphalaria (Gastropoda: Planorbidae), with emphasis on endemic species of the great East African lakes
Figure 3. Consensus cladogram resulting from the parsimony and maximum-likelihood analyses of the combined matrix (16S, cytochrome oxidase subunit I, COI, and internal transcribed spacer I, ITS1). maximum-parsimony (MP) bootstrap values greater than 50% are positioned below the nodes, and the posterior probabilities from the Bayesian inference analysis are placed above the nodes. Species icons represent the typical morphology of the species. Species without country nomination are from Uganda. The 'Nilotic species complex' first inferred by DeJong et al. (2001) is indicated by the vertical bar. Note the basal position of Biomphalaria angulosa to the Nilotic species complex. The weak bootstrap support of this complex in the parsimony analysis is a result of the close phylogenetic relationship with B. angulosa.
Figure 2 in Phylogeny and biogeography of African Biomphalaria (Gastropoda: Planorbidae), with emphasis on endemic species of the great East African lakes
Figure 2. Plots of transitions (TS) and transversions (TV) for Biomphalaria relative to the percentage sequence divergence (p-distance). The sliding window analysis for noncoding sequences and codon position for protein coding sequences are given in the insets. A, TS/TV plot of the partial sequence of the mitochondrial 16S gene. Regions with the most variation are identified in the sliding window analysis and correspond to loop regions in the secondary structure. B, TS/TV plot of the total internal transcribed spacer I (ITS1) gene that indicates a possible saturation. The sliding window analysis (inset) illustrates that the variation is not distributed uniformly across the sequence. C, TS/TV plot of the partial sequence of cytochrome oxidase subunit I (COI; primer pair LCO/HCO). The codon position plot is given in the inset. D, TS/TV plot of the partial sequence of COI (primer pair ASMIT 1/2). The codon position plot is given in the inset.
Figure A1 in Phylogeny and biogeography of African Biomphalaria (Gastropoda: Planorbidae), with emphasis on endemic species of the great East African lakes
Figure A1. Cladograms resulting from maximum-likelihood (ML) and maximum-parsimony (MP) analyses of the combined matrix and parsimony analyses of single-gene cladograms. Bootstrap support is indicated at the nodes. A, Consensus cladogram inferred from weighted and unweighted 16S by MP. B, Cladogram inferred from internal transcribed spacer I (ITS1) by MP. C, Cladogram inferred from cytochrome oxidase subunit I (COI) by MP (primer pair LCO/HCO). D, Cladogram inferred from COI (primer pair ASMIT 1/2) by MP. E, Cladogram inferred from the combined data matrix by MP. F, Cladogram inferred from the combined data matrix by ML.
Figure 1 in Phylogeny and biogeography of African Biomphalaria (Gastropoda: Planorbidae), with emphasis on endemic species of the great East African lakes
Figure 1. Summary cladogram from DeJong et al. (2001) showing the relationships of the African Biomphalaria. Note the position of Biomphalaria stanleyi within Biomphalaria pfeifferi.
Coordinated Great Lakes lake-wide average monthly mean water levels
<div> <pre> </pre> <p>This dataset consists of lake-wide average monthly mean water levels for Lakes Superior, Michigan-Huron, Saint Clair, Erie, and Ontario. The data are presented in meters with reference to the International Great Lakes Datum (IGLD) 1985. The dataset is computed and stewarded by Environment and Climate Change Canada (ECCC) and the United States Army Corps of Engineers (USACE), under the auspices of the Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data (Coordinating Committee)<strong>1</strong>. Each agency independently computes and manages their own version of the dataset. The data are binationally coordinated annually, where ECCC and USACE compare their versions to verify that they are consistent. </p> <p> </p> </div> <div> <p>The lake-wide average monthly mean water levels are computed from daily hydrometric observations collected from a network of gauging stations located around each lake and on both sides of the Canada-United States border. The gauges are owned and maintained by the Canadian Hydrographic Service (CHS)<strong>2</strong> and the National Oceanic and Atmospheric Administration (NOAA)<strong>3</strong>. </p> <p> </p> </div> <div> <p>Detailed metadata and data files for each lake are provided within this dataset. The data files are in comma separated value format (CSV). The CSV data files contain both the lake-wide average monthly mean water level and concise metadata described in accordance with the Climate and Forecasting (CF) conventions. The detailed metadata is in PDF format and provides extended descriptions of data sources, computation methods, dataset history, and related information for each great lake. </p> <p> </p> <p>This dataset is updated annually with the addition of the most recent complete calendar year of data. The version number reflects the year of the last available data. However, note that as well as adding the most recent data, data from previous years may be adjusted based on updated information from those years.</p> </div> <div> <pre> </pre> <p><strong>1</strong>Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data <a href="https://www.greatlakescc.org/en/home/" target="_blank" rel="noreferrer noopener">https://www.greatlakescc.org/en/home/</a> </p> <p> </p> </div> <div> <p><strong>2</strong>DFO 2024. Marine Environmental Data Section Archive, <a href="https://meds-sdmm.dfo-mpo.gc.ca/" target="_blank" rel="noreferrer noopener">https://meds-sdmm.dfo-mpo.gc.ca</a>, Ecosystem and Oceans Science, Department of Fisheries and Oceans Canada. </p> <p> </p> </div> <div> <p><strong>3</strong>NOAA 2024. Tides and Currents, <a href="https://tidesandcurrents.noaa.gov/" target="_blank" rel="noreferrer noopener">https://tidesandcurrents.noaa.gov/</a>, Center for Operational Oceanographic Products and Services, National Oceanic and Atmospheric Administration. </p> </div>
PAH concentrations in the Fram Strait, the Canadian Arctic Archipelago and the lower Great Lakes
<p>The PEs were deployed at different water depths of deep moorings deployed in the Fram Strait during 2014-2015 (9 samples) and 2018-2019 (22 samples), as well as in surface seawater of the Canadian Arctic Archipelago (6 samples), and in the air (6 samples) and surface water (3 samples) of the lower Great Lakes during 2018-2019. For atmospheric sampling, PEs have deployed at ~1-2 m height and fixed inside two inverted bowls to prevent rainfall and direct solar radiation. For water sampling, PEs were strung on stainless steel wires and attached to stainless steel cages. The surface-water cages were fixed to subsurface floats at ~4-5 m depth, while the deep-water cages were fastened to deep moorings at different depths for ~1 year. PAH concentration data were collected.</p> <p>Supporting information for "Zhang, L., Ma, Y., Vojta, S., Morales-McDevitt, M., Hoppmann, M., Soltwedel, T., et al. (2023). Presence, Sources and Transport of Polycyclic Aromatic Hydrocarbons in the Arctic Ocean. <em>Geophysical Research Letters</em>, 50, e2022GL101496". <a href="https://doi.org/10.1029/2022GL101496">https://doi.org/10.1029/2022GL101496</a></p>
Data from: Optimization of wetland environmental DNA metabarcoding protocols for Great Lakes region herpetofauna
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Newly identified nematodes from the Great Salt Lake are associated with microbialites and specially adapted to hypersaline conditions
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Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels
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Trophic structure and mercury transfer in the subarctic fish community of Great Slave Lake, Northwest Territories, Canada
<p>In recent decades, mercury concentrations have increased in fish of Great Slave Lake (GSL), a subarctic great lake in northern Canada with important recreational, subsistence, and commercial fisheries. This study characterized habitat use and trophic position of common fish species in GSL near the City of Yellowknife (Northwest Territories, Canada), measured mercury concentrations in water and in taxa from lower trophic levels of the food web, and examined trophic and biological influences on mercury concentrations within and among fish species. Northern pike (<i>Exos lucius</i>) and lake whitefish (<i>Coregonus clupeformis</i>) fed predominantly nearshore, cisco (<i>Coregonus artedi</i>) and longnose sucker (<i>Catostomus catostomus</i>) fed predominantly offshore, and burbot (<i>Lota lota</i>) fed roughly equally in both habitats. Habitat-specific feeding did not influence mercury bioaccumulation in fish, in contrast with published studies of smaller lakes. Water concentrations of total mercury and methylmercury were low and showed little spatial variation among sites or depths. Zooplankton (>200 µm) had similarly low methylmercury concentrations to littoral and profundal amphipods, suggesting little habitat-variation of mercury exposure near the base of the food web. Age, size, and trophic position were significant explanatory variables for muscle total mercury concentrations within populations of fish species. Among fish species, size and trophic position explained 80% of the variation in muscle total mercury concentrations. This study generated the most comprehensive dataset to date on mercury bioaccumulation in the food web of GSL, which will serve as a baseline for future studies of this great lake.</p>
Data from: Climate connectivity of the bobcat in the Great Lakes region
<p>The Great Lakes and the St. Lawrence River are imposing barriers for wildlife and the additive effect of urban and agricultural development that dominates the lower Great Lakes region likely further reduces functional connectivity for many terrestrial species. As the climate warms species will need to track climate across these barriers. It is important, therefore, to investigate land cover and bioclimatic hypotheses that may explain the northward expansion of species through the Great Lakes. We investigated the functional connectivity of a vagile generalist, the bobcat, as a representative generalist forest species common to the region. We genotyped tissue samples collected across the region at 14 microsatellite loci and compared different landscape hypotheses that might explain the observed gene flow or functional connectivity. We found that the Great Lakes and the additive influence of forest stands with either low or high canopy cover and deep lake-effect snow have disrupted gene flow, whereas intermediate forest cover has facilitated gene flow. Functional connectivity in southern Ontario is relatively low and was limited in part by the low amount of forest cover. Pathways across the Great Lakes were through the Niagara region and through the Lower Peninsula of Michigan over the Straits of Mackinac and the St. Mary's River. These pathways are important routes for bobcat range expansion north of the Great Lakes and are also likely pathways that many other mobile habitat generalists must navigate to track the changing climate. The extent to which species can navigate these routes will be important for determining the future biodiversity of areas north of the Great Lakes.</p>
Bioinformatic pipeline from: Increasing confidence for discerning species and population compositions from metabarcoding assays of environmental samples: case studies of fishes in the Laurentian Great Lakes and Wabash River
<p>Community composition data are essential for conservation management, facilitating identification of rare native and invasive species, along with abundant ones. However, traditional capture-based morphological surveys require considerable taxonomic expertise, are time consuming and expensive, can kill rare taxa and damage habitats, and often are prone to false negatives. Alternatively, metabarcode assays can be used to assess the genetic identity and compositions of entire communities from environmental samples, comprising a more sensitive, less damaging, and relatively time- and cost-efficient approach. However, there is a trade-off between the stringency of bioinformatic filtering needed to remove false positives and the potential for false negatives. The present investigation thus evaluated use of four mitochondrial (mt) DNA metabarcode assays and a customized bioinformatic pipeline to increase confidence in species identifications by removing false positives, while achieving high detection probability. Positive controls were used to calculate sequencing error, and results that fell below those cutoff values were removed, unless found with multiple assays. The performance of this approach was tested to discern and identify North American freshwater fishes using lab experiments (mock communities and aquarium experiments) and processing of a bulk ichthyoplankton sample. The method then was applied to field environmental (e)DNA water samples taken concomitant with electrofishing surveys and morphological identifications. This protocol detected 100% of species present in concomitant electrofishing surveys in the Wabash River and an additional 21 that were absent from traditional sampling. Using single 1 L water samples collected from just four locations, the metabarcoding assays discerned 73% of the total fish species that were discerned in comparison to four months of an extensive electrofishing river survey in the Maumee River, along with an additional nine species. In both rivers, total fish species diversity was best resolved when all four metabarcode assays were used together, which identified 35 additional species missed by electrofishing. Ecological distinction and diversity levels among the fish communities also were better resolved with the metabarcode assays than with morphological sampling and identifications, especially with the combined assays. At the population-level, metabarcode analyses targeting the invasive round goby <i>Neogobius melanostomus</i> and the silver carp <i>Hypophthalmichthys molitrix</i> identified all population haplotype variants found using Sanger sequencing of morphologically sampled fish, along with additional intra-specific diversity, meriting further investigation. Overall findings demonstrated that the use of multiple metabarcode assays and custom bioinformatics that filter potential error from true positive detections improves confidence in evaluating biodiversity.</p>
FishPass Sortable Attribute Database: Phenological, morphological, physiological, and behavioural characteristics related to passage and movement of Great Lakes fishes
<p>In-stream barriers pose threats to fishes, including habitat loss, constraints on migration, and reduced connectivity between populations. Despite many negative consequences, barriers can serve to protect native species by limiting the spread of invasive species. For example, in the Laurentian Great Lakes, physical barriers have long been used to control invasive Sea Lamprey (<em>Petromyzon marinus</em>) populations by limiting access to potential upstream spawning and rearing habitat. Selective fish passage systems could solve this connectivity conundrum but must efficiently pass multiple native or desirable species while blocking invasive species. Designing such fish passage systems requires an understanding of the phenology, morphology, physiology, and behaviour (attribute dimensions) of fishes in the community. Here, we describe the first comprehensive collection of sortable attributes associated with fish passage. The integrated database consists of 21 biological attributes that influence the movement and passage of 220 species in the Great Lakes. Data coverage varies with species, taxonomic orders, and attribute dimensions. Behavioural attributes were typically underrepresented in the literature and the ecology of potential invaders was not well understood. The synthesis described herein is a critical step towards a holistic approach to fish passage design and may help to inform management actions related to population connectivity.</p>
Data for: Aquatic connectivity treatments increase fish and macroinvertebrate use of Typha invaded Great Lakes coastal wetlands
<p>Coastal wetlands provide critical habitat for aquatic organisms and important ecosystem services for the terrestrial and aquatic landscapes they bridge, but increasingly common invasive macrophytes disrupt plant communities, food webs, habitat structure and littoral-pelagic linkages. In Laurentian Great Lakes coastal wetlands, invasive cattails (<em>Typha</em> ×<em>glauca</em> and<em> T. angustifolia</em>, hereafter <em>Typha</em>) homogenize ecosystem structure and reduce nearshore dissolved oxygen, and plant, fish, and macroinvertebrate diversity. We hypothesize that management treatments that reduce<em> Typha </em>and its abundant litter promote structural heterogeneity and mitigate physiochemical and biodiversity impacts.</p> <div>To test this hypothesis, we implemented a large-scale (2048 m<sup>2</sup> treatment units), multi-site (four coastal wetlands) experiment in northern Michigan (USA) to examine how invasive Typha mechanical harvesting treatments (biomass harvest, aquatic connectivity channels, Typha-dominated control) altered fish, macroinvertebrate, plant, larval amphibian abundance and diversity, and water quality for two-years post-treatment. We collected fish, macroinvertebrates, plant, larval amphibian, and water quality data from for two years following the implementation of management treatments. These data are presented in this archived dataset. </div>
The conservation genetics of Iris lacustris (Dwarf Lake Iris), a Great Lakes endemic
<p><em>Iris lacustris</em>, a northern Great Lakes endemic, is a rare species known from 165 occurrences across Lake Michigan and Huron in the United States and Canada. Due to multiple factors, including habitat loss, lack of seed dispersal, patterns of reproduction, and forest succession, the species is threatened. Early population genetic studies using isozymes and allozymes recovered no to limited genetic variation within the species. To better explore genetic variation across the geographic range of <em>I. lacustris </em>and to identify units for conservation, we used tunable Genotyping-by-Sequencing (tGBS) with 171 individuals across 24 populations from Michigan and Wisconsin, and because the species is polyploid, we filtered the single nucleotide polymorphism (SNP) matrices using polyRAD to recognize diploid and tetraploid loci. Based on multiple population genetic approaches, we resolved three to four population clusters that are geographically structured across the two ranges of the species. The species migrated from west to east across its geographic range, and minimal genetic exchange has occurred among populations. Four units for conservation are recognized, but nine adaptive units were identified, providing evidence for local adaptation across the geographic range of the species. Population genetic analyses with all, diploid, and tetraploid loci recovered similar results, which suggests that methods may be robust to variation in ploidy level. </p>
Data from: Great Lakes Restoration Initiative human wellbeing survey
<p>This dataset represents a comprehensive exploration of ecosystem restoration practices and their impacts on both ecological and human wellbeing indicators. Traditionally, ecosystem restoration efforts have focused on ecological benchmarks such as water and habitat quality, species abundance, and vegetation cover. However, there is an increasing recognition of the interplay between restoration and human communities, evidenced by positive socio-ecological connections like property value, natural hazard mitigation, recreation opportunities, and overall happiness. With the United Nations declaring 2021-2030 as the "Decade of Ecosystem Restoration" and a push for more socio-ecological goals in restoration, this dataset delves into the degree to which restoration practitioners consider human wellbeing. It is based on a case study of the Great Lakes Restoration Initiative (GLRI), a federally funded program that has awarded over $3.5 billion to 5,300 projects. A total of 1,574 GLRI projects were surveyed, with 437 responses received, revealing that almost half of these projects set human wellbeing goals, and more than 70% believed they achieved them. In comparison, 90% of project managers believed they met their ecological goals. This dataset highlights the documented perceptions of positive impacts on both people and nature, suggesting that restoration efforts often go beyond traditional indicators. As such, it advocates for the adoption of a socio-ecological perspective in ecosystem restoration programs to comprehensively document the full extent of restoration outcomes. The data collection process included a survey methodology, and the dataset provides insights into project design, implementation, and success measurements. The data was collected between November 2020 and March 2021, with a maximum of three contact attempts for each project. It offers a unique perspective on the relationship between ecosystem restoration and human wellbeing, emphasizing the importance of capturing the often "unseen" benefits of these projects.</p>
A Stochastically Generated meteorological forcings for the Laurentian Great Lakes to support the Great Lakes Restoration Initiative
<p>The dataset consists of three folders, each with time series for the Laurentian Great Lakes and their contributing watersheds: </p> <ol> <li>"observed_historical.zip" : containing daily forcings for Large Lake Thermodynamics Model and Large Basin Runoff Model and Overlake Precipitation time series from GLERL Hydrometeorological Database and consistent with the inputs of GLSGyFS software.</li> <li>"baseline.zip" : 1000-yr long simulated daily meteorological time series using the Stochastic Weather Generator, without any thermodynamic climate perturbations. </li> <li>"scenarios.zip" : 30 scenarios of each 1000-yr long simulated daily meteorological time series using the Stochastic Weather Generator with different thermodynamic perturbations, consistent with Coupled Model Intercomparison Project 6 (CMIP6) projections for the Great Lakes region. </li> </ol> <p> </p> <p><strong>Acknowledgements: </strong></p> <p>This work was funded by the Great Lakes Restoration Initiative (GLRI) Action Plan 3, Focus Area 5.2 Conduct Comprehensive Science Programs and Projects. Funding for this work was also provided through the National Science Foundation Grant No. CBET-2144332, and the U.S. Geological Survey Northeast Climate Adaptation Science Center, which is managed by the USGS National Climate Adaptation Science Center, under Grant/Cooperative Agreement No. G21AC10601-00. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the Northeast Climate Adaptation Science Center or the U.S. Geological Survey.</p>
Data from: Comparative productivity of six bioenergy cropping systems on marginal lands in the Great Lakes Region, United States
<p>Growing lignocellulosic crops on marginal lands is a promising solution for sustainable biofuel production. We evaluated the productivity of bioenergy cropping systems (switchgrass [<em>Panicum</em> <em>virgatum</em> L., var. Cave‐In‐Rock], miscanthus [<em>Miscanthus</em> × <em>giganteus</em>, 'Illinois clone'], hybrid poplar [<em>Populus</em> <em>nigra</em> × <em>P. maximowiczii</em> A. Henry 'NM6'], native grasses [five species], early successional vegetation, and restored prairie vs. historical vegetation [as reference control]) with and without nitrogen fertilization on low‐fertility former cropland at five sites in the Great Lakes Region, United States. We reported biomass yields for the first 7 years after establishment. Switchgrass was most consistently productive across all sites, but miscanthus was more productive at three of the five sites. When averaged across sites, years, and nitrogen (N) treatments, biomass yields followed the order miscanthus > switchgrass > hybrid poplar ≈ native grasses > restored prairie > early successional vegetation ≈ historical vegetation, but varied substantially by crop and site, with a significant crop by site interaction. Yields of miscanthus and switchgrass peaked after four to five growing seasons and declined thereafter, while yields of both native grasses and restored prairie increased throughout 6 years with no sign of follow‐on decline, suggesting that polycultures may outperform monocultures over the long term. Yields of early successional vegetation—similar in composition to historical vegetation at each site—did not improve with time. Nitrogen fertilization increased the yields of all cropping systems at all sites. Our results demonstrate the viability of low‐productivity former cropland for long‐term bioenergy production and suggest there is no single crop best suited for all low-fertility soils.</p>
Functional traits reveal the dominant drivers of long‐term community change across a North American Great Lake
<p>The datasets here were used to determine annual sentinel fish species and trait composition in Lake Erie's western and central basins during 1969–2018 in relation to multiple anthropogenic stressors. Here, we provide three datasets, which are used in the paper by Sinclair et al. titled: "Functional traits reveal the dominant drivers of long-term community change across a North American Great Lake". Each dataset is provided as a separate tab in a single Excel worksheet. The first dataset ("Western basin") provides the annual catch-per-unit-effort (CPUE; individuals per trawl minute) for all sentinel fish species caught in Lake Erie's western basin during 1969–2018 in fall (September-October) trawl surveys conducted by the Ohio Department of Natural Resources - Division of Wildlife. The second dataset ("Central basin") provides the CPUE values for Lake Erie's central basin. The third dataset ("Environmental stressors") provides the annual values for nutrient inputs, water transparency, temperature, white perch abundances, and goby abundances for the western and central basins. A summary and explanation of each variable are also provided in the "Info" tab. Further information on how fish CPUE and stressor values were calculated is provided in the methods and supporting information of the associated article.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.