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FIGURE 11 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 11 Muccanella cundalinensis gen. et sp. nov., male holotype. (A) Thoracopod I; (B) thoracopod II; (C) thoracopod III; (D) thoracopod IV; (E) thoracopod V; (F) Thoracopod VI; (G) thoracopod VII. Scale bar in mm.

FIGURE 7 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 7 Anguillanella callawaensis gen. et sp. nov., male holotype. (A) Thoracopod I; (B) thoracopod II; (C) thoracopod III; (D) thoracopod IV; (E) thoracopod V; (F) Thoracopod VI; (G) thoracopod VII. Scale bar in mm.

FIGURE 6 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 6 Anguillanella callawaensis gen. et sp. nov., male holotype (A–E, G); female allotype (F, H, K, L); male paratype (I, J). (A) Antennula (dorsal view); (B) antenna (dorsal view); (C) max Maxilla; (D) maxillula; (E) mandibular palp male holotype; (F) palp female allotype; (G) mandible male holotype; (H) mandi- ble female allotype; (I) paragnath male WAMC57657 (J) labrum male WAMC57423 (ventral view); (K) Paragnath female allotype; (L) labrum female allotype (dorsal view Downloaded). Scalefrom bar in Brill. mm com. 08/31/2023 03:13:03AM via free access

FIGURE 8 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 8 Anguillanella callawaensis gen. et sp. nov., (A–D, F, G, H) male holotype. (A, B) thoracopod VIII (posterior view); (C, D) thoracopod VIII (frontal view); (E) thoracopod VIII female allotype (frontal view); (F) first pleopod; (G) furcal rami and dorsal seta (dorsal view); (H) uropod (latero-internal view). Scale bar in mm. Abbreviations: O. lb, outer lobe; Bsp, basipod; Endp, endopod; Exp, exopod; P.pr, posterior projection; Fr.pr, frontal projection.

FIGURE 4 Maximum Clade Credibility Tree inferred using a concatenate COI, 16S, 28S and 18S alignment using BEAST. Node bars are 95 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 4 Maximum Clade Credibility Tree inferred using a concatenate COI, 16S, 28S and 18S alignment using BEAST. Node bars are 95% Higher Posterior Density, scale bar is in million years ago (Ma), starting from present 0. Numbers above bars = node age; numbers below bars (bold) = posterior probability of the node.

FIGURE 3 Bayesian consensus tree representing the known Bathynellidae taxa constructed using COI, 16S, 28S, ITS2 and 18S in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 3 Bayesian consensus tree representing the known Bathynellidae taxa constructed using COI, 16S, 28S, ITS2 and 18S alignments and model partitioning implemented in MrBayes. Numbers on branches represent Bayesian posterior probabilities followed by maximum likelihood bootstrap percentage. Bathynellinae and Gallobathynellinae clades are collapsed for easier interpretation.

FIGURE 5 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 5 Bathynellidae species distribution in the Goldsworthy area (Callawa, Cundaline, Yarrie ridges).

FIGURE 2 Bayesian consensus single gene trees for COI, 16S, 28S and ITS2 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 2 Bayesian consensus single gene trees for COI, 16S, 28S and ITS2. Numbers on branches represent Bayesian posterior probabilities followed by maximum likelihood bootstrap percentage. ABGD and PTP results are reported next to the trees. ABGD method: major partitions are showed; PTP: partitions with the highest support for each group are represented.

opencc-by-4.0Jun 2015View details →

Fig. 1 in Malacofauna of the catchment area of Rusenski Lom River (North Bulgaria)

Fig. 1. Approximate position of the study area: the catchment of Rusenski Lom River.

opencc-by-4.0Feb 2019View details →

Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge

1.Summary This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below.  The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes.  A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov). This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc. 2.Polyline geometry The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018).  Each line describes a meander and features the following attributes. 3.Attribute description <ul> <li>SegmentID: identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li>lakeFlag: 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li>Width: average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li>Elevation: mean elevation from SRTM (90m) per river meander in meters.  SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach.  The average elevation of all valid points per meander is reported here.  Locations where no elevation could be produced are marked as -9999.</li> <li>Slope: water surface slope in centimeter per kilometer.  Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate.  Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination.  Locations where no slope could be produced are marked as -9999.</li> <li>Meandwave: Meander wavelength in meters.  This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines.  Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li>Sinuosity: Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li>catch_area: Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner et al., 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location.  We translated flow accumulation given in number of pixels into catchment area (in m2) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li>QWBM: mean annual flow estimated with the water balance model WBMsed (Cohen et al., 2014).</li> <li>Strpwr_len: stream power normalized by width (W/m).</li> <li>Strpwr_are: stream power normalized by area (W/m2).</li> </ul> Acknowledgements Use of this database should be acknowledged appropriately. The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder. References Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, Geophysical Research Letters, 42(2), 395-402, doi: 10.1002/2014gl062764. Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, Science, doi: 10.1126/science.aat0636. Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, Glob. Planet. Change, 115, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011. LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, Geophysical Research Letters, 32(17), doi: 10.1029/2005gl023836. Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION, 89(10), 93-94, doi: doi:10.1029/2008EO100001. Leopold, L. B., and M. G. Wolman (1960), River meanders, Geological Society of America Bulletin, 71(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.

opencc-by-4.0May 2023View details →

River monitoring, particulate Cs-137 measurements and rainfall monintoring of the Mano Dam catchment (Japan) from 2014 to 2019

This dataset is composed of river monitoring, laboratory particulate Cs-137 measurements and rainfall monitoring used in the article: Vandromme, R., Hayashi, S.,Tsuji, H., Evrard, O., Grangeon, T., Landemaine, V., Laceby, J.P., Wakiyama, Y., Cerdan, O. The unprecedented soil decontamination program in Fukushima only reduced radioactive fluxes in rivers by 17% (PNAS, under review)  River measurements were conducted by Hayashi S. and Tsuji H.  A hydrological station was installed immediately upstream of Mano Dam Lake (Lake Hayama) in June 2014. The water height in the river was measured using an AquiStar PT12 pressure/temperature sensor (INW, Kirkland, WA, USA) at 5-min intervals and then converted into flow rate data using gage curves. Water and SS samples were also collected from the river section using an ISCO 6712 automatic water sampler (Teledyne Technologies, Lincoln, NE, USA) at 1-h intervals during flood events. The SS concentration was determined in the laboratory by filtration using a Whatman GF/F filter with 0.7-µm pores (Cytiva, Tokyo, Japan), and these concentrations were then used to convert the turbidity logs measured by a DTS-12 digital turbidity sensor (FTS Inc., Victoria, BC CANADA) at 5-min intervals into SS logs.  This station was damaged by Typhoon Etau in September 2015. Following this damage, the data analysis and discovery of anomalies that persisted until the end of 2016 prompted us to omit the data acquired during this period. The hydrological station was subsequently moved a few hundred meters upstream in 2017. Water flows and sediment concentrations were subsequently acquired using the same systems described above at 10-min intervals until the end of 2019.  River water samples were punctually collected to measure 137Cs concentration in Suspended Sediment (SS) (66 measurements during floods) from June 2014 to December 2019 (this chronicle ends in the middle of a flood). Station location before 2017 (coordinate system:WGS84) : lat 37.738391, long 140.803943 Station location after 2017 (coordinate system:WGS84) :  lat 37.733191, long 140.807709 Rainfall record was acquired by the Fukushima Prefecture (Maenori station, lat 37.73644, long 140.74233, coordinate system:WGS84) at 10-minute time steps.

FIGURE 9 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment

FIGURE 9 Anguillanella callawaensis furca variability (WAMC57370 photo).

publicFeb 2019View details →

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Data from: The influence of a semi-arid sub-catchment on suspended sediments in the Mara River, Kenya

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2020View details →

Nd isotopes reveal the human-induced change of sediment routing processes in the Huanghe (Yellow River) catchment

Table A1. Sampling locations, Sr-Nd isotopes, geochemical compositions and grain size parameters of Huanghe and loess sediments investigated. Table A2.  Annual sediment load  (Mt/yr) at major gauging stations along the Huanghe mainstream. Table A3. Nd isotopic compositions of major tectonic terranes and sources in the Huanghe basin (literature data). Table A4. Nd isotopes and geochemical compositions of rocks in the North China Craton. Table A5. Nd model ages of rocks from North China Craton (NCC), only rocks with Th/Sc, Th/Cr, Th/Co and Sm/Nd ratios within the range of references for UCC in the NCC have been selected. Table A6. Nd isotopes mixing of sediments from the lower Huanghe.

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Data from: Using a trait-based approach for assessing the vulnerability and resilience of hillslope seep wetland vegetation cover to disturbances in the Tsitsa River catchment, Eastern Cape, South Africa

Hill slope seep wetlands are ecologically and economically important ecosystems as they supply a variety of ecosystem services to society. In South Africa, livestock grazing is recognised as one of the most important disturbance factors changing the structure and function of hill slope seep wetlands. This study sought to investigate the potential effect of livestock grazing on the resilience and vulnerability of hillslope seep wetland vegetation cover using a trait based approach (TBA). Changes in vegetation cover were used as a surrogate for indicating grazing intensity. The degree of human disturbances was assessed using the Anthropogenic Activity Index (AAI). A TBA was developed using seven plant traits, resolved into 27 trait attributes. Based on the developed approach, plant species were grouped into vulnerable and resilient groups in relation to grazing pressure. It was then predicted that species belonging to the vulnerable group would be less dominant at the highly disturbed sites, as well as in the winter season when grazing pressure is at its peak. The approach developed enabled accurate predictions of the responses of hillslope plant species to grazing pressure seasonally, but spatially, only for the summer season. The predicted responses during the winter season across sites did not match the observed results, which could be attributed to the difficulty in species identification and accurate estimation of vegetation cover during winter. Overall, the approach developed here provides a general framework for applying the TBA and can thus be tested and applied elsewhere.

opencc-zeroDec 2020View details →

opencc-by-4.0Jun 2021View details →

Sediment routing and anthropogenic impact in the Huanghe River catchment, China: an investigation using Nd isotopes of river sediments

Table A1. Sampling locations, Sr-Nd isotopes, geochemical compositions and grain size parameters of Huanghe and loess sediments investigated. Table A2.  Annual sediment load  (Mt/yr) at major gauging stations along the Huanghe mainstream. Table A3. Nd isotopic compositions of major tectonic terranes and sources in the Huanghe basin (literature data). Table A4. Nd isotopes and geochemical compositions of rocks in the North China Craton. Table A5. Nd model ages of rocks from North China Craton (NCC), only rocks with Th/Sc, Th/Cr, Th/Co and Sm/Nd ratios within the range of references for  NCC-UC have been selected. Table A6. Nd isotopes mixing of sediments from the lower Huanghe.

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Data from: Habitat usage of Daubenton's bat (Myotis daubentonii), common pipistrelle (Pipistrellus pipistrellus), and soprano pipistrelle (Pipistrellus pygmaeus) in a North Wales upland river catchment

Distributions of Daubenton's bat (Myotis daubentonii), common pipistrelle, (Pipistrellus pipistrellus), and soprano pipistrelle (Pipistrellus pygmaeus) were investigated along and altitudinal gradient of the Lledr River, Conwy, North Wales, and presence assessed in relation to the water surface condition, presence/absence of bank‐side trees, and elevation. Ultrasound recordings of bats made on timed transects in summer 1999 were used to quantify habitat usage. All species significantly preferred smooth water sections of the river with trees on either one or both banks; P. pygmaeus also preferred smooth water with no trees. Bats avoided rough and cluttered water areas, as rapids may generate high‐frequency echolocation‐interfering noise and cluttered areas present obstacles to flight. In lower river regions, detections of bats reflected the proportion of suitable habitat available. At higher elevations, sufficient habitat was available; however, bats were likely restricted due to other factors such as a less predictable food source. This study emphasizes the importance of riparian habitat, bank‐side trees, and smooth water as foraging habitat for bats in marginal upland areas until a certain elevation, beyond which bats in these areas likely cease to forage. These small‐scale altitudinal differences in habitat selection should be factored in when designing future bat distribution studies and taken into consideration by conservation planners when reviewing habitat requirements of these species in Welsh river valleys, and elsewhere within the United Kingdom.

opencc-zeroDec 2018View details →

opencc-by-4.0Aug 2024View details →

Dataset: Geochemical - mineralogical constraints on the provenance of sediment supplied from South African river catchments draining into the southwestern Indian Ocean

Supplementary Information Table 1 from manuscript in AGU Geochemistry, Geophysics, Geosystems, titled: Geochemical - mineralogical constraints on the provenance of sediment supplied from South African river catchments draining into the southwestern Indian Ocean. Pryor, E.J1,&dagger;*; Hall, I.R1; Simon, M.H2,3; Andersen, M1; Babin, D4; Starr, A5; Lipp, A6; van der Lubbe, H.J.L7 1Cardiff University, School of Earth and Environmental Sciences, Main Building, United Kingdom 2NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway 3 SFF Centre for Early Sapiens Behaviour (SapienCE), Bergen, Norway 4Lamont-Doherty Earth Observatory of Columbia University, 61 Rt 9W, Palisades, New York 10964-8000, USA 5Department of Geography, University of Cambridge, United Kingdom 6Department of Earth Sciences, University College London, United Kingdom 7Department of Earth Sciences, Cluster Geochemistry & Geology, Vrije Universiteit Amsterdam (VU). &dagger;Now at Department of Earth Sciences, University of Bergen, Norway; SFF Centre for Early Sapiens Behaviour (SapienCE), Bergen, Norway *Corresponding author: Ellie Pryor (ellie.pryor@uib.no) This table provides the bedrock geology data for each river catchment between Durban and Cape Town, South Africa which was required for the endmember mixing model discussed in the submitted manuscript. This data can be used for endmember mixing calculations or used for GIS mapping.  We also provide the grain size data measured on a Sympatec HELOS KR laser diffraction particle sizer. This grain size was inputted into the grain size endmember mixing model Analysize package within MATLAB 2022b (from Paterson and Heslop, 2015).

opennotspecifiedJun 2015View details →

Distribution. SE Australia, from the McPherson and Border ranges in SE Queensland, S to Victoria and SE South Australia; it is absent from the coastal drainages of the Great Dividing Range, S at least to the Wallamba River, and W of the Great Dividing Range in New South Wales (but it probably occurs to the limit of tree growth on the Southern Tableland), and from the inland draining catchments of the Murray Basin in Victoria. in Acrobatidae

Distribution. SE Australia, from the McPherson and Border ranges in SE Queensland, S to Victoria and SE South Australia; it is absent from the coastal drainages of the Great Dividing Range, S at least to the Wallamba River, and W of the Great Dividing Range in New South Wales (but it probably occurs to the limit of tree growth on the Southern Tableland), and from the inland draining catchments of the Murray Basin in Victoria.