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391 results for “morphometry”
North Temperate Lakes LTER Morphometry and Hypsometry data for core study lakes
Morphometric data for NTL study lakes. These are area and volume at one meter depth interval. hp-factor in percent volume for each depth layer NTL core study lakes
Seagrass morphometry and chlorophyll content at nine stations along a eutrophication gradient in West Falmouth Harbor, 2019
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, in 2019 we sampled seagrass (Zostera marina) to assess the relationship between sediment biogeochemistry and seagrass ecosystem parameters. This dataset contains information on aboveground and belowground biomass ratios, seagrass density, epiphyte biomass, and chlorophyll content from sites across the eelgrass bed in West Falmouth Harbor that receive varying inputs of nitrogen. The data supports findings reported in Haviland et al., 2022 (https://doi.org/10.1002/lno.12025).
Supplementary Data - "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio"
<p>Supplementary data for the manuscript entitled "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio".</p> <p>Includes data for topographic profiles of wrinkle ridges ("wrinkleridge_profiledata.xlsx"), COULOMB model inputs ("COULOMB_modelinputs.xlsx") and outputs ("COULOMB_modeloutputs.xlsx"), and GIS shapefile data for mapped wrinkle ridges (files labelled "allwrinkleridges" and "studiedwrinkleridges"), topographic profile lines (files labelled "topographicprofilelines"), and the regional profile (files labelled "regionalprofile"). </p>
Washover morphometry: lidar-derived and reported in literature
<p>This portfolio includes three sets of data, used and explained in Lazarus, Williams & Goldstein (2022, <a href="https://doi.org/10.1029/2022GL100098">https://doi.org/10.1029/2022GL100098</a>):</p> <ol> <li>washover morphometry measured from lidar-derived topographic change along the coastline of New Jersey, USA, following Hurricane Sandy (2012) ('NJ_Sandy_metrics.csv');</li> <li>the geospatial data layers used to generate those measurements ('WashoverGIS.zip');</li> <li>and a compilation of washover morphometry reported in the literature ('washover_LAV_literature_examples.csv').</li> </ol> <p> </p> <p><strong>Washover morphometry datasets</strong></p> <ul> <li><strong>NJ_Sandy_metrics.csv</strong> – The lidar-derived washover morphometry dataset includes: deposit width (m), intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), deposit perimeter (m), built fraction, the storm event (Sandy 2012), and a general location note.</li> <li><strong>washover_LAV_literature_examples.csv</strong> – Also included here are 35 measurements of washover morphometry reported in the literature by six different studies, sampling different storm events in different coastal barrier settings (Carruthers et al., 2013; Williams, 2015; Jamison-Todd et al., 2020; Rodriguez et al. 2020; Hansen et al., 2021; Williams & Rains, 2022). The literature-based dataset includes: intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), the reference (dataset) in which the measurements were reported, and additional notes.</li> </ul> <p> </p> <p><strong>Geospatial data layers ('WashoverGIS' [zipped])</strong></p> <p>The lidar data underpinning the geospatial data layers here are available from the NOAA Digital Coast Data Viewer (<a href="https://coast.noaa.gov/dataviewer/#/">https://coast.noaa.gov/dataviewer/#/</a>): "2012 USGS EAARL-B Lidar: Pre-Sandy" (pre-storm), and "2012 USGS EAARL-B Lidar: Post-Sandy" (post-storm).</p> <p>Geospatial analysis was done in QGIS version 3.22.5. We masked both the pre- and post-storm surfaces to isolate only positive elevations, and subtracted the pre-storm surface from the post-storm surface to calculated the difference between them; we then retained only the positive differences in the resulting surface to isolate sites of sediment deposition. We manually digitized the perimeters of depositional forms we interpreted as washover, corroborated by aerial imagery (<a href="https://storms.ngs.noaa.gov/">https://storms.ngs.noaa.gov/</a>).</p> <p>Basic geometric characteristics (perimeter, area) were taken directly from the washover polygons; washover length and width were taken from oriented minimum bounding boxes around each polygon. Volume for each washover polygon was measured using the Volume Calculation Tool (version 0.4) plugin for QGIS (<a href="https://github.com/REDcatch/Volume_calculation_for_QGIS3">https://github.com/REDcatch/Volume_calculation_for_QGIS3</a>). In built settings, each washover deposit was associated with a locally estimated built fraction (Lazarus et al., 2021). Elements of the built environment (i.e., buildings) were isolated by creating a binary mask of the pre-storm surface, such that all elevations ³5 m were set to a value = 1, and all elevations <5 m set to zero. Minimum enclosing circles were drawn around each washover polygon, and the total built area (masked value = 1) within each circle summed using the QGIS Zonal Statistics tool. Here, local built fraction is the total built area within a minimum enclosing circle divided by the area of that circle.</p> <p>Geospatial files here include:</p> <ul> <li><strong>NJ_north_wash_metrics.shp</strong> // <strong>NJ_south_wash_metrics.shp </strong>– shapefiles of the digitized washover deposits, with morphometric characteristics compiled in their attribute tables</li> <li><strong>NJ_north_BBs.shp</strong> // <strong>NJ_south_BBs.shp</strong> – oriented bounding boxes to determine deposit intrusion length & width</li> <li><strong>NJ_north_MECs.shp</strong> // <strong>NJ_south_MECs.shp</strong> – minimum enclosing circles, used for calculating local built fraction</li> <li><strong>NJ_north_dSandy_POS.tif</strong> // <strong>NJ_south_dSandy_POS.tif </strong>– positive [post-storm - pre-storm] elevation differences</li> <li><strong>NJ_north_rooftops_th05.tif</strong> // <strong>NJ_south_rooftops_th05.tif </strong>– binary mask based on the pre-storm lidar layer ("2012 USGS EAARL-B Lidar: Pre-Sandy") used for calculating built fraction, in which all topographic elements >= 5 m are set = 1, and all < 5 m are set = 0</li> </ul> <p> </p>
Lake morphometry mediates the relationship between water color and fish biomass in small boreal lakes
<p>The data are for an analysis of the influence of water color and lake depth on fish biomass small (1-10 ha) lakes in boreal Sweden.</p> <p>AllBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.</p> <p> </p> <p>SmallBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area and less than or equal to 10 hectares in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.</p> <p> </p> <p>SNILLE_ms_data.csv contains data on fish biomass for 16 small boreal lakes. The geographic coordinates (Northing and Easting) are based on the Swedish Grid, see: http://www.lantmateriet.se. Lake surface areas based on the Swedish lake census (Nisell et al. 2007). Mean depth (meters) is based on echo sounding with an integrated GIS (Lowrance m52i). Volumes were calculated by calculating a triangulated irregular network and then mean depth subsequently calculated as volume divided by surface area. kd is the vertical light extinction coefficient (m^-1). We calculated <em>k</em><sub>d</sub> from the slope of the linear regression of the logarithm of photosynthetically active radiation (measured with LI-COR LI-193 spherical quantum sensor) versus measurement depth (measured in approximately 0.5 meter intervals over the deepest part of the lake). The shallowest measure was excluded from the calculation. The values in the table are the average of kd calculated from three visits to each lake (once each approximately in June, July, and August 2014). kd is an indicator of colored dissolved organic carbon and water color (brownness) in this region and there is relatively little contribution of phytoplankton or inorganic particulate. CPUE Catch-per-unit-effort (kg wet weight / net) is an indicator of fish biomass. For each lake, we set 8 multi mesh gill nets (Nordic 12 nets, 30 x 1.5 m; Mesh sizes: 5, 6.25, 8, 10, 12.5, 15.5, 19.5, 24, 29, 35, 43, 55 mm) over one night (approximately 12 hours) in August 2014. Four nets were deployed in the littoral zone perpendicular to the shoreline. These nets were approximately equally spaced. Two floating nets were deployed across the deepest point of the pelagic zone, and two benthic nets were set in the hypolimnion near the deepest point of the lake. Net-specific catches were averaged with weighting based on the relative extent of the different habitat types (see Karlsson et al. 2015). Specifically, the profundal nets were assumed to represent the total hypolimnetic volume and the pelagic nets were assumed to represent the volume above the hypolimnion. The volume of the littoral nets was calculated by subtracting the volume of the pelagic and profundal habitats from the total lake volume. These weighted CPUE values are given in the file. Species identified through gill netting are abbreviated as: P for European perch (<em>Perca fluviatilis</em>), R for common roach (<em>Rutilus rutilus</em>), N for northern pike (<em>Esox lucius</em>), B for burbot (<em>Lota lota</em>)</p> <p>Boreal_Area_kd_data.csv contains a list of estimated vertical light extinction coefficients (kd, m^-1) for lakes in boreal Sweden. Specifically, the values are based on water chemistry data from a national water quality survey conducted in Sweden every five years. Lake surface water (0.5 m) was sampled from above the deepest part of the lake during early autumn when the water column is mixed. Water quality analyses were performed using standard limnological techniques (detailed methods available on the internet at: http://www.slu.se/en/departments/aquatic-sciences-assessment/laboratories/geochemicallaboratory/water-chemical-analyses/) by a certified water analysis laboratory at the Swedish University of Agricultural Sciences. The data are freely available on the Internet at http://www.slu.se/vatten-miljo. Absorbance at 420 nm (D) which is a metric of water color (brownness) was used to calculate absorption coefficients per meter (a, m-1) from the initial measurement: a = (D * 2.303) / L. where L is the optical path length in meters, 0.05 in the case of the monitoring data. We then estimated kd (m^-1) based on the calibration curve reported by Seekell et al. (2015): = kd = 0.3121 + 0.1327a. These values were associated with surface areas from the Swedish lake census (Nisell et al. 2007) using a identification number common to both the Swedish water chemistry and lake census datasets. Finally, the file was trimmed to only include lakes with surface areas greater or equal to 1 hectare and less than or equal to 10 hectares.</p> <p>References:</p> <ul> <li>Nisell, J., A. Lindsjö, and J. Temnerud (2007), Rikstäckande virtuellt vattendrags nätverk för flödesbaserad modellering VIVAN, [In Swedish], Rapport 2007:17, Institutionen för miljöanalys, SLU.</li> <li>Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions o the world: A new map of life on Earth. <em>BioScience</em> 51:933-938.</li> <li> <p>Karlsson J, Bergström AK, Byström P, Gudasz C, Rodriguez P, Hein C (2015) Terrestrial organic matter input suppresses biomass production in lake ecosystems. <em>Ecology</em> 96:2870-2876. doi: 10.1890/15-0515.1</p> </li> <li> <p>Seekell DA, Lapierre JF, Karlsson J (2015) Trade-offs between light and nutrient availability across gradients of dissolved organic carbon concentration in Swedish lakes: Implications for patterns in primary production. <em>Canadian Journal of Fisheries and Aquatic Sciences</em> 72:1663-1671. doi: 10.1139/cjfas-2015-0187</p> </li> </ul>
MCR LTER: Reference: Fish Taxonomy, Trophic Groups and Morphometry
These reference data document the maximum length, length to weight conversion parameters, trophic status, and items consumed for fish species observed in the Annual Fish Survey Time-series core data set (knb-lter-mcr.6). With knb-lter-mcr.6, these data enable the investigation of temporal trends in the biomass of taxa as well as the abundance and biomass of different trophic groups. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Fig. 3 in Sexual dimorphism in the catfish Genidens genidens (Siluriformes: Ariidae) based on otolith morphometry and relative growth
Fig. 3. Lapilli otoliths from Genidens genidens females and males from Guanabara Bay, Rio de Janeiro, southeastern Brazil.
Fig. 6 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)
Fig. 6. Kruskall-Wallis results of the lapillus length and weight in each Prochilodus lineatus age; ages 2 and 7 were not included in analysis because they only have one sample.
Fig. 1 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)
Fig. 1. Study area and location of sampling sites in the floodplain of the Upper Paraná River (Baía River – 1; Ivinhema Ri- ver – 2; Paraná River – 3; lagoa guaraná – 4; lagoa dos patos – 5; lagoa das garças – 6; lagoa do Osmar – 7; ressaco do paú véio – 8; lagoa fechada – 9; lagoa ventura – 10).
Fig. 3 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)
Fig. 3. Fits of the linear regressions between lapillus otolith and standard length of Prochilodus lineatus: a. Otolith Length (r2 = 0.80) and b. Otolith Weight (r2 = 0.82); the shaded area represents the confidence interval of 95% of the estimate; loess fit of raw residuals are in right panels.
Fig. 9 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa - Corrigendum
Fig. 9. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 36) of E. cf. atromaculatus (Nichols & Griscom, 1917): Epulu 2 (▲), and Ituri 8 (). Also shown are the type specimens of E. atromaculatus (Nichols & Griscom, 1917) (○).
Fig. 8 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa - Corrigendum
Fig. 8. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 42) of E. cf. atromaculatus (Nichols & Griscom, 1917): Ituri 5 (◊), Ituri 6 (♦), Ituri/'Kisangani region' (∆), Epulu 2 (▲), and Ituri 8 (). Also shown are the type specimens of E. atromaculatus (Nichols & Griscom, 1917) (○).
Fig. 7 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa - Corrigendum
Fig. 7. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 22) of E. cf. brazzai (Pellegrin, 1901): 'Kisangani region' 2 (◊), Ituri 3 (♦) and 'Kisangani region' 3 (∆). Also shown are the type specimens examined of E. brazzai (Pellegrin, 1901) (○) and E. tshopoensis (De Vos, 1991) (●).
Fig. 5 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa - Corrigendum
Fig. 5. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 36) of E. cf. miolepis specimens from the Lower Congo: Inkisi (◊), Luki 1 (♦) and Luki 2 (∆). Also shown are the type specimens examined of: E. miolepis (Boulenger, 1902) (○), E. holotaenia (Boulenger, 1904) (●), E. eutaenia (Boulenger, 1904) (□) and E. kerstenii (Peters, 1868) (■).
Fig. 3 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa - Corrigendum
Fig. 3. Scatterplot of PC2 against PC1 for a PCA on 17 log-transformed measurements (n = 177) of Enteromius Cope, 1867: E. cf. miolepis (Boulenger, 1902) (◊), E. cf. brazzai (Pellegrin, 1901) (♦), E. cf. pellegrini (Poll, 1939) (∆), and E. cf. atromaculatus (Nichols & Griscom, 1917) (▲). Also shown are the type specimens examined of: E. miolepis (Boulenger, 1902) (○), E. holotaenia (Boulenger, 1904) (●), E. eutaenia (Boulenger, 1904) (□), E. kerstenii (Peters, 1868) (■), E. brazzai (Pellegrin, 1901) (), E. tshopoensis (De Vos, 1991) (▼), E. pellegrini (Poll, 1939) (+), and E. atromaculatus (Nichols & Griscom, 1917) ().
Fig. 4 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa - Corrigendum
Fig. 4. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 177) of Enteromius: E. cf. miolepis (Boulenger, 1902) (◊), E. cf. brazzai (Pellegrin, 1901) (♦), E. cf. pellegrini (Poll, 1939) (∆), and E. cf. atromaculatus (Nichols & Griscom, 1917) (▲). Also shown are the type specimens examined of: E. miolepis (Boulenger, 1902) (○), E. holotaenia (Boulenger, 1904) (●), E. eutaenia (Boulenger, 1904) (□), E. kerstenii (Peters, 1868) (■), E. brazzai (Pellegrin, 1901) (), E. tshopoensis (De Vos, 1991) (▼), E. pellegrini (Poll, 1939) (+), and E. atromaculatus (Nichols & Griscom, 1917) ().
Fig. 2. A in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa
Fig. 2. A. ML tree based on 558-bp-long Enteromius COI sequences with 1000 bootstrap replications, with node support shown as NJ/ML bootstrap (bootstrap values> 95% are shown; lineages <2% sequence divergence were collapsed), the label 'Kisangani region' contains samples from the Lomami/ Lobaye system and the Lobilo. B. Map of the Congo basin with the sampled river stretches indicated according to the phylogenetic lineages.
Fig. 9 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa
Fig. 9. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 36) of E. cf. atromaculatus (Nichols & Griscom, 1917): Epulu 2 (▲), and Ituri 8 (). Also shown are the type specimens of E. atromaculatus (Nichols & Griscom, 1917) (○).
Fig. 8 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa
Fig. 8. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 42) of E. cf. atromaculatus (Nichols & Griscom, 1917): Ituri 5 (◊), Ituri 6 (♦), Ituri/'Kisangani region' (∆), Epulu 2 (▲), and Ituri 8 (). Also shown are the type specimens of E. atromaculatus (Nichols & Griscom, 1917) (○).
Fig. 4 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa
Fig. 4. Scatterplot of PC2 against PC1 for a PCA on 10 meristics (n = 177) of Enteromius: E. cf. miolepis (Boulenger, 1902) (◊), E. cf. brazzai (Pellegrin, 1901) (♦), E. cf. pellegrini (Poll, 1939) (∆), and E. cf. atromaculatus (Nichols & Griscom, 1917) (▲). Also shown are the type specimens examined of: E. miolepis (Boulenger, 1902) (○), E. holotaenia (Boulenger, 1904) (●), E. eutaenia (Boulenger, 1904) (□), E. kerstenii (Peters, 1868) (■), E. brazzai (Pellegrin, 1901) (), E. tshopoensis (De Vos, 1991) (▼), E. pellegrini (Poll, 1939) (+), and E. atromaculatus (Nichols & Griscom, 1917) ().
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