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Fig. 11. Aquattuor denticulatus Frederiksen, 2013, paratype. A. Gonopods, posterior view. B in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 11. Aquattuor denticulatus Frederiksen, 2013, paratype. A. Gonopods, posterior view. B. Telomere, distal part, lateral view. C. Gonopods, anterior view. D. Telomere tip. li = lateral incision; bs = basomeral spine; pa = palette; mpl = mesal-posterior lamella of telomere. Scale bars: A, C = 0.1 mm; B = 0.02 mm; D = 0.01 mm.
Fig. 10 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 10. Aquattuor claudiahempae sp. nov., paratype. A. Gonopods, anterior view. B. Right palette, showing well developed meso-basal lobe. C. Telomere tip. D. Margin of telomere tip. E. Gonopods, poterior view. mbl = mesobasal lobe of palette; pa = palette; st9 = sternum of rudimentary 9th leg-pair. Scale bars: A, E = 0.1 mm; B = 0.05 mm; C = 0.02 mm; D = 0.01 mm.
Fig. 8 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 8. Aquattuor udzungwensis Enghoff sp. nov. A–B, D. Paratype. C. Specimen from West Kilombero Scarp FR. A. Gonopods in situ. B. Telopodite, ventral view. C. Right gonopod, anterior view. D. Telomere tip. li = lateral incision; pa = palette; mpl = mesal-posterior lamella of telomere. Scale bars: A–C = 0.1 mm; D = 0.05 mm.
Fig. 12 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 12. Map of part of Tanzania showing the mountains where Aquattuor species have been found. Based on Burgess et al. (2007: fig. 1).
Fig. 7 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 7. Aquattuor longipala Enghoff sp. nov., paratype. A. Gonopods, anterior view. B. Gonopods, posterior view. C. Gonopods, oblique apical (central) view. D. Detail of telomere tip. li = lateral incision; pa = palette; mpl = mesal-posterior lamella of telomere. Scale bars: A–C = 0.1 mm; D = 0.01 mm.
Fig. 4 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 4. Aquattuor submajor Enghoff sp. nov., paratype. A. Gonopods in situ, right gonopod coloured. B. Left gonopod coxa and basal part of telopodite. C. Left gonopod telopodite and part of coxa, dorsal (basal) view. Articifical colours: orange = coxa; blue = basomere; yellow = solenomere; pink = telomere; red = gonopod sternum; light green = ventral part of eighth pleurotergite (right side); dark green = sternum of reduced ninth leg-pair. mp = metaplica; pa = palette; pp = proplica. Scale bars = 0.1 mm.
Fig. 5 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 5. Aquattuor submajor Enghoff sp. nov., paratype, left gonopod. A. Anterior view. B. Close-up of mesal incision. C. Tip of telomere. D. Posterior view. btl = basal telomeral lamella; li = lateral incision; mi = mesal incision; mpl = mesal-posterior lamella of telomere; pa = palette; pn = posttorsal narrowing. Scale bars: A, D = 0.1 mm; B–C = 0.01 mm.
Fig. 3. Aquattuor spp. A. A in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 3. Aquattuor spp. A. A. stereosathe Enghoff sp. nov., paratype, front end. B–C. A. udzungwensis Enghoff sp. nov., paratype, telson, lateral and posterior view. D–E. A. udzungwensis Enghoff sp. nov., limbus. D. Specimen from West Kilombero Scarp FR. E. Paratype. Scale bars: A–D = 0.1 mm; E = 0.01 mm.
Fig. 2 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 2. Body size (numbers of podous rings and midbody vertical diameter) in ♂♂ of species of Aquattuor Frederiksen, 2013. In cases of (almost) coinciding values, symbols have been slightly displaced horizontally.
Fig. 1 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 1. Aquattuor claudiahempae sp. nov., two paratype ♂♂ after 18 months in alcohol. Scale bar = 1 mm (B). Photographs by S. Reboleira.
Fig. 9 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 9. Aquattuor stereosathe Enghoff sp. nov., paratype. A. Gonopods, anterior view. B. Gonopods, oblique lateral view. C. Tip of telomere. D. Gonopods, posterior view. li = lateral incision; pa = palette. Scale bars: A–C = 0.2 mm; D = 0.01 mm.
Fig. 6 in A mountain of millipedes II: The genus Aquattuor Frederiksen, 2013 - five new species from the Udzungwa Mountains and one from Mt. Kilimanjaro, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 6. Aquattuor major Enghoff sp. nov., paratype. A. Left gonopod, anterior view. B. Left gonopod, posterior view. C–D. Telomere tip, two different views. li = lateral incision; pa = palette; mpl = mesal posterior lamella of telomere; pn = posttorsal narrowing; tt = triangular tooth of telomere. Scale bars: A–B = 0.1 mm; C–D = 0.01 mm.
Fig. 4 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 4. Phylogenetic analysis of the subgenus Sophophora and Lissocephala aff. diola Tsacas & Lachaise, 1979. Conventions as for Fig. 3.
Fig. 2 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 2. Percent divergence of the morphospecies DNA barcode from the closest neighbor found in the barcode database.
Fig. 3 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 3. Phylogenetic analysis of the genus Zaprionus and Microdrosophila aff. mamaru (Burla, 1954). This tree is the neighbor-joining tree. The maximum likelihood tree gives the same topology. Nodes with a bootstrap value lower than 50% were merged. Bootstrap values were calculated over 1000 repeats. Above nodes: bootstrap values for maximum likelihood using a GTR + G + I model. Below nodes: bootstrap values for neighbor-joining using the Kimura-2p distance.
Radon (222Rn) activity in air on the crater rim of Mt. Etna Central Crater (May-October 2018)
<p><em>The dataset in the file dataset_radon.xlsx compiles radon (<sup>222</sup>Rn) activity values measured in air on the rim of Mt. Etna Central Crater with passive dosimeters during summer 2018. Passive dosimeters were installed all around the crater and in four reference sites, at two different heights above the ground (5 cm and 1 m). Geographical coordinates of installation points are given in the file. Exposition periods given in the dataset started and ended as follows: May-Oct (24/05/18-11/10/18), May-Jul (24/05/18-06/07/18) and Jul-Oct (06/07/18-11/10/18). The uncertainty for each dosimeter is given with a confidence interval of 2-σ. Dosimeters are grouped according to the sector of the rim (Nort-West, North-East, South-East and South-West + reference sites). For group mean values, the uncertainty corresponds to the standard deviation of the mean (standard deviation of the population divided by the square root of the number of elements in the population). “lost” indicates a dosimeter that was lost during the exposition, “udl” refers to a dosimeter that was under detection limit, and “damaged” corresponds to a dosimeter that was corroded by acids and could not be analysed or that was clogged in soldered dust preventing radon from entering the capsule. Note that one station (namely, that closest to the Voragine vent) was excluded from the computation of the mean value of the NE sector. </em></p> <p><em>The dataset in the file SO2_flux.pdf contains the time series of the daily bulk SO<sub>2</sub> flux measured at Mount Etna during the period 01/04/18-30/10/18.</em></p>
Figure 2 in New Polypheretima and Pithemera (Oligochaeta: Megascolecidae) species from the Mt. Malindang Range, Mindanao Island, Philippines
Figure 2. Schematic drawings of the external anterio-ventral area of (A) Polypheretima mindanaoensis sp. nov., (B) Pithemera malindangensis sp. nov., (C) Pithemera duminagati sp. nov. and (D) Pithemera donvictorianoi sp. nov. Abbreviations: fp, female pore; cl, clitellum; mp, male pore; gm, genital markings. Scale bars: 5 mm.
The Multi-Temporal Dual Channel Algorithm (MT-DCA)
<p>I) SUMMARY</p> <p>This soil moisture and vegetation optical depth product is called the Multi-Temporal Dual Channel Algorithm (MT-DCA). It retrieves surface soil moisture and vegetation optical depth (directly related to total water volume in the vegetation canopy) from <a href="https://nsidc.org/data/SPL1CTB_E">SMAP level 1C brightness temperature</a> observations using a robust estimation technique. It is an in-house MIT algorithm and is not an official SMAP product. The data are freely available on 9km and 36km grids from April 2015 to July 2021 in daily time steps.</p> <p>No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using the MT-DCA, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference <a href="https://www.sciencedirect.com/science/article/pii/S0034425717302961">Konings et al. (2017</a>) when referring to the MT-DCA in the text. Feel free to send us an email at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a> to let us know how you are using the data. </p> <p>The version 5 update is a re-implementation of the MT-DCA using the updated SMAP L1C brightness temperatures. It extends the data through July 2021.</p> <p>II) CONTACT</p> <p>For questions, please email Andrew Feldman at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a>.</p> <p>III) ALGORITHM DESCRIPTION</p> <p>The algorithmic approach uses both horizontally and vertically polarized brightness temperatures to retrieve soil moisture and VOD simultaneously. The key innovation of the MT-DCA is that it recognizes that classical dual-channel algorithms are under-determined: brightness temperature observations are correlated and cannot retrieve two unknowns (soil moisture and VOD) (as illustrated in Konings et al, RSE 2016). This creates amplifying errors in retrievals from snapshot dual-channel algorithms. The MT-DCA uses a viable assumption that VOD changes more slowly than soil moisture between overpasses, and uses information from multiple SMAP overpasses to stabilize the retrieval. It is considered a regularization approach similar to the Sobolev Norm regularization. Specifically, this approach is applied to each temporally adjacent pair of overpasses (for SMAP, two overpasses approximately 2-3-days apart), which includes four brightness temperature measurements. For each overpass pair, the soil moisture at both overpasses is retrieved, along with a constant VOD for both overpasses. This leads to two retrievals of each of soil moisture and VOD at any given overpass time: one where the parameters are retrieved using additional TB information from the overpass before and one from the overpass after. Both retrievals of VOD and soil moisture values at each overpass are averaged. Ultimately, VOD is not held constant, but rather is slowed in time between overpasses. A second key innovation of the MT-DCA is that, because the retrievals are no longer under determined, it is also possible to retrieve a constant single scattering albedo for each pixel. The single scattering albedo is estimated through model selection of the value of the parameter that minimizes the sum of all overpass cost functions. The retrieved albedo is also included in the files here. VOD is reported at nadir.</p> <p>The single scattering albedo is assumed constant over the full record of SMAP data, as is currently accepted practice across approaches with SMAP, SMOS, and AMSR. There is a high amount of computational power required to retrieve an albedo over more than three years of SMAP data. Therefore, an adjustment was made: the single scattering albedo was retrieved over the third year of SMAP data (April 1st, 2017 to March 31st, 2018). This constant value was then applied to the other years without requiring the albedo optimization loop. Tests across many individual pixels revealed that albedo in the third year does not differ greatly from albedo over all years and the other individual years. </p> <p>The algorithm is described in more detail in Konings et al. (2017). The algorithm is based on principles explained in more detail in Konings et al. (2016), which describes the original algorithm development using Aquarius observations. See also the related Konings et al. (2015) publication for quantitative justification for the approach. While the dataset has not been officially validated, the MT-DCA soil moisture retrievals show in-situ comparison statistics similarly to the official baseline SMAP soil moisture product (SMAP soil moisture retrieval in-situ assessment can be found in Chan et al. (2016)). Finally, the MT-DCA vegetation optical depth retrievals are not validated due to only sparsely available ground information related to vegetation water content. Nevertheless, information about error propagation into the MT-DCA soil moisture and VOD retrievals as well as VOD error reductions using the MT-DCA regularization technique can be found in Feldman et al. (2021).</p> <p>Chan, S.K., Bindlish, R., O’Neill, P.E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.C., Crow, W.T., Kerr, Y., 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54, 4994–5007. <a href="https://doi.org/10.1109/TGRS.2016.2561938">https://doi.org/10.1109/TGRS.2016.2561938</a></p> <p>Feldman, A.F., D. Chaparro, and D. Entekhabi (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. In Press.</p> <p>Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. <a href="https://doi.org/10.1016/j.rse.2017.06.037">https://doi.org/10.1016/j.rse.2017.06.037</a></p> <p>Konings, A.G., M. Piles, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. https://doi.org/10.1016/j.rse.2015.11.009</p> <p>Konings, A.G., K.A. McColl, M. Piles and D. Entekhabi (2015): How many parameters can be maximally estimated from a set of measurements? IEEE Geoscience and Remote Sensing Letters, 12(5), 1081-1085. https://doi.org/10.1109/LGRS.2014.2381641</p> <p>IV) QUALITY CONTROL</p> <p>Several conditions can create uncertainty in the MT-DCA retrievals including surface water bodies (lakes, rivers, coastal areas, etc.), radio frequency interference (RFI), highly sloped surfaces (mountainous regions), dense vegetation, frozen ground, and others. The MT-DCA removes time periods of frozen ground and removes pixels with water body fractions of greater than 0.5. SMAP L1C brightness temperatures are adjusted considering RFI and surface water body information. Nevertheless, the MT-DCA retrievals are purposefully not substantially quality controlled to increase the range of science applications of the data. Therefore, the retrievals are subject to uncertainty in regions where and times when these aforementioned issues occur. We suggest the data user familiarize themselves with quality flags in the SMAP algorithm theoretical basis document in <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>. Conservative quality control can be applied using SMAP quality flag information directly applicable to the dataset here. These quality flags can be downloaded from the SMAP official product files at <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>.</p> <p>V) DATA FORMATTING AND FILE NAMES </p> <p>Data are provided in zipped folders in both netcdf4 (.nc) and matfile (.mat) formats. Each zipped folder contains soil moisture, vegetation optical depth, single scattering albedo, latitude, longitude, and time vector information. Note that as an update in Version 5, the zipped folders for 9km .mat files are separated into soil moisture and vegetation optical depth to reduce zip folder size. The other zipped folders still have all variables within them. These variables are provided at a 9km resolution as well as upscaled to 36km. For both .nc and .mat files, the 9km data are provided in 3-month periods with a naming convention of ‘YYYYMM_YYYYMM’ where YYYY is the 4-digit year, and MM is the 2-digit month. The first YYYYMM string represents the first month and the second YYYYMM string is the final month of the period. The 36km data are provided in 12-month periods with the same naming conventions in the file names.</p> <p>Retrievals are obtained from enhanced-resolution brightness temperatures from SMAP that are gridded at 9km. As such, they are on a 9km EASE2-grid. These retrievals are upscaled to 36km and gridded on a 36km EASE2 grid. Additional information and geolocation tools are available at <a href="https://nsidc.org/data/ease/ease_grid2.html">https://nsidc.org/data/ease/ease_grid2.html</a>. </p> <p>Information specific to folders with .nc and .mat formats is given below:</p> <p>a) NETCDF Files (.nc): The folders with netcdf files contain files with the convention MTDCA_YYYYMM_YYYYMM_Xkm_VX.nc where VX is the version number, Xkm is the grid scale, and YYYYMM strings are the first and last months of the range of data saved in the file. Soil moisture, vegetation optical depth, latitude, longitude, and time index information are provided in these files. A map of single scattering albedo for the full time series is saved in a separate file as MTDCA_OMEGA_Xkm_VX.nc along with latitude and longitude information.</p> <p>b) MATFILES (.mat): The folders with matfiles contain individual files for:</p> <ol> <li>Soil moisture: MTDCA_VX_SM_YYYYXX_YYYYXX_Xkm.mat</li> <li>Vegetation Optical Depth: MTDCA_VX_TAU_YYYYXX_YYYYXX_Xkm.mat</li> <li>Single Scattering Albedo: MTDCA_VX_OMEGA_Xkm.mat</li> <li>Latitude/Longitude: SMAPCenterCoordinatesXKM.mat</li> </ol> <p>A datevector variable in each soil moisture and vegetation optical depth file contains information on the year, month, and day corresponding to the timestep of each variable.</p>
Alpine and subalpine vegetation of Mt. Midzhur, Stara Planina, Bulgaria
<p>This dataset contains data used in my diploma thesis. The fieldwork was carried out in the Western Stara Planina Mountains, mainly in the area of Mt. Midzhur. I recorded 78 vegetation plots using the Braun-Blanquet approach. The dataset was classified using modified Twinspan algorithm. A DCA ordination graph was created to present the dissimilarity of the vegetation types. The vegetation was divided into 12 classes. Seven alliances not previously known from Bulgaria and one newly described alliance of subalpine tall-herb vegetation on screes were reported. Numerous associations were newly reported for Bulgaria or were newly described. The role of altitude and soil pH on the vegetation was analyzed using the general linear model; the floristic composition was analyzed on the level of floristic elements.</p>
Dataset of blow fly (Diptera: Calliphoridae) species observed along an elevational gradient on Mt. Etna, Sicily.
<p>This dataset contains count data for blow flies (Diptera: Calliphoridae) collected at four different elevations along an altitudinal gradient around Mt. Etna, in Sicily (Italy). Samples were collected to determine changes in blow fly community assembly as elevation changes.</p> <p>BlowflyAltitudeSicily_Data.csv is a file that contains the raw count data for species separated by both elevation and sex of the identified specimens.</p> <p>BlowflyAltitudeSicily_Methods.docx is a summarized version of the sampling method relevant to interpreting the data.</p> <p>BlowflyAltitudeSicily_Descriptive.txt is a file describing the column headers in "BlowflyAltitudeSicily_Data.csv".</p>
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