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Fig. 5 in Distribution patterns and trophic characteristics of salmonids and native species inhabiting high altitude rivers of Pampa de Achala region, Argentina

Fig. 5. Dendrogram of diet similitude of Trichomycterus corduvensis (TC), Salvelinus fontinalis (SF) and Oncorhynchus mykiss (OM) in high altitude streams in Pampa de Achala area, Córdoba.

Fig. 6 in Distribution patterns and trophic characteristics of salmonids and native species inhabiting high altitude rivers of Pampa de Achala region, Argentina

Fig. 6. Graphic representation of Tokeshi analysis. ID = mean individual feeding diversity; PD = population feeding diversity. a) Trichomycterus corduvensis, b) Salvelinus fontinalis, and c) Oncorhynchus mykiss. Numbers correspond to streams from Table 1.

Fig. 1 in Distribution patterns and trophic characteristics of salmonids and native species inhabiting high altitude rivers of Pampa de Achala region, Argentina

Fig. 1. Sampling sites and surveyed rivers in the Reserva Provincial Pampa de Achala and Quebrada del Condorito National Park.

Fig. 4 in Spatial and temporal distribution patterns of ichthyoplankton in a region affected by water regulation by dams

Fig. 4. Average abundance of fish eggs (a) and larvae (b) in the Ilha Grande National Park, from October 2001 to March 2005.

Fig. 2 in Spatial and temporal distribution patterns of ichthyoplankton in a region affected by water regulation by dams

Fig. 2. Average egg abundances (rectangles) and standard errors (bars) by period (a), month (b) and sampling area (c) in the Ilha Grande National Park, from October 2001 to March 2005.

opencc-by-4.0Apr 2023View details →

Regional distribution of annual sea-to-air OCS fluxes for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb.

This dataset was prepared for a publication by von Hobe et al. (2023): Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) In that publication, the data are displayed in Figure 3. Average annual sea-to-air OCS fluxes on a 2.8 ° latitude x 2.8 ° longitude grid for the present day atmosphere and for the two OCS emission scenarios considered by Quaglia et al. (2022) were obtained from a 2003 - 2019 simulation, using a model described in Lennartz et al. (2021). - - - - - - - - - - - File format:      netCDF Index Variables:      latitude      longitude Parameters:      ocsem500:    mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 500 ppt      ocsem4800:  mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 4.8 ppb      ocsem35500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 35.5 ppb - - - - - - - - - - - References: Lennartz, S. T., Gauss, M., von Hobe, M., and Marandino, C. A.: Monthly resolved modelled oceanic emissions of carbonyl sulphide and carbon disulphide for the period 2000–2019, Earth Syst. Sci. Data, 13, 2095-2110, 10.5194/essd-13-2095-2021, 2021. Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022. von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.

Fig. 6 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 6. PCA distribution of the sampling sites and the subclasses of life forms (according to Sharova 1981): Z_Phytob – Zoophagous phytobionts; Z_Strat – Zoophagous stratobionts; M_Strat – Mixophytophagous stratobionts; M_Short – Mixophytophagous stratohortobionts; M_Geoh – Mixophytophagous geobionts.

Fig. 7 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 7. PCA distribution of the sampling sites and categories of life forms (according to SHAROVA 1981): Life form class 1. Zoophagous. Life form subclass: 1.1 – Phytobios; 1.2 – Epigeobios; 1.3 – Stratobios; 1.4 – Geobios. Life form groups: 1.1.2 – stemdwelling hortobionts; 1.1.3 – leaf-dwelling dendrohortobionts; 1.2.2 – large walking epigeobionts; 1.2.2(1) – large walking dendroepigeobionts; 1.2.3 – running epigeobionts; 1.2.4 – flying epigeobionts; 1.3(1) – series crevice-dwelling stratobionts; 1.3(1).1 – surface & litter-dwelling; 1.3(1).2 – litter-dwelling; 1.3(1).3 – litter & crevice-dwelling; 1.3(1).4 – endogeobionts; 1.3(1).5 – litter & bark-dwelling; 1.3(1).6 – bothrobionts; 1.3(2).1 – litter & soil-dwelling; 1.4.2(1) – large digging geobionts. Life form class 2. Mixophytophagous. Life form subclass: 2.1 – Stratobios; 2.2 – Stratohortobios; 2.3 – Geohortobios. Life form groups: 2.1.1 – crevice-dwelling stratobionts; 2.2.1 – stratohortobionts; 2.3.1 – harpaloid geohortobionts; 2.3.1(1) – crevice-dwelling harpaloid geohortobionts; 2.3.2 – zabroid geohortobionts; 2.3.3 – dytomeoid geohortobionts.

Fig. 4 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 4. Ordination of the sampling sites in relation to the humidity and vegetation. The calculations were performed by the use of the results from all of the sampling sites and all of the catches, standardized through the recalculation of the data as number of specimens per 100 trapdays.

Fig. 3 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 3. Distribution of the permanent species in relation to the humidity and vegetation. The analysis included only the permanent species – those with a frequency above 50% (see TEOFILOVA 2015): A aenea – Amara aenea; A anth – Amara anthobia; A famil – Amara familiaris; Ac megac – Acinopus megacephalus; Agon sp – Agonum (Europhilus) sp.; Br crep – Brachinus crepitans; Br expl – Brachinus explodens; C ambig – Calathus ambiguus; C cinct – Calathus cinctus; C fuscip – Calathus fuscipes; Car conv – Carabus convexus; Car cor – Carabus coriaceus; Car ullr – Carabus ullrichi; Chl nit – Chlaenius nitidulus; H dimid – Harpalus dimidiatus; H dist – Harpalus distinguendus; H flavic – Harpalus flavicornis; H rubrip – Harpalus rubripes; H tardus – Harpalus tardus; Laem ter – Laemostenus terricola; Lei ruf – Leistus rufomarginatus; M maurus – Microlestes maurus; M minut – Microlestes minutulus; Myas ch – Myas chalybaeus; N brevic – Nebria brevicollis; O azur – Ophonus azureus; Par mend – Parophonus mendax; Ps rufip – Pseudoophonus rufipes; Pt melas – Pterostichus melas; Tr q – Trechus quadristriatus.

openCC (other)Mar 2022View details →

edi40/100

Spatial soil properties distribution in “Hoya del río Suárez” region in Colombia.

There is a spatial soil properties distribution surface in raster format. This is the result of a study about digital soil mapping. Implementing geoestatistics (regression kriging RK) and machine learning algorithms (random forest RF, support vector machines SVM, and ensemble models), the study found the best performance for 5 soil properties (clay fraction, bulk density, total porosity, pH, and cation exchange capacity). The study was located in a region named “Hoya del río Suárez”, which is the main sugarcane-producing region in Colombia, and its land area is around 47000 hectares. Those raster surfaces were carried out in 2021, and the database used for doing this study was compiled between 2015 and 2016.

opencc-by-4.0May 2020View details →

Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"

This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225

dryad36/100

Species Abundance Distributions (SADs) for local tree communities in 1-ha forest plots on 20 tropical islands in the Indo-Pacific region

Species abundance distributions (SADs) characterise the distribution of individuals among species. This dataset was used to investigate the relative importance of disturbance regime (tropical cyclone regime) and island geography (the area and isolation of islands) on the shape of SADs.

opencc-zeroAug 2020View details →

dryad36/100

Data from: Effects of roads and land use on frog distributions across spatial scales and regions in the eastern and central United States

Aim: Understanding the scales over which land use affects animal populations is critical for conservation planning, and it can provide information about the mechanisms that underlie correlations between species distributions and land use. We used a citizen-science database of anuran surveys to examine the relationship between road density, land use, and the distribution of frogs and toads across spatial scales and regions of the United States. Location: Eastern and Central United States Methods: We compiled data on anuran occupancy collected from 1999-2013 across 13 states in the North American Amphibian Monitoring Program, a citizen science survey of calling frogs. These data were indexed to measures of land use within buffers ranging from 300 m to 10 km. Results: The negative effects of road density and development on anuran richness were strongest at the smallest scales (300 – 1000 m), and this pattern was consistent across regions. In contrast, the relationships of anuran richness to agriculture and forest cover were similar across local scales but varied among regions. Richness had a negative relationship with agriculture/ forest loss in the Midwest but a positive relationship with agriculture in the Northeast. Anuran richness was more closely related to primary/secondary road density than to rural road density, and the negative effects of larger roads increased at smaller scales. Individual species differed in the scales over which roads and development affected their distributions, but these differences were not closely related to either body size or movement ability. Main conclusions: This study further refines our understanding of the relationship between roads and amphibian populations and highlights the need for research into the specific mechanisms by which roads affect amphibians. Additionally, we find that relationships between land use and species richness can differ substantially across regions, demonstrating that one should use caution in generalizing from one region to another, even when species composition is similar.

opencc-zeroDec 2015View details →

dryad36/100

Simulated distribution of the fluid salinity, Cu and temperature in a sub-volcanic region

The files brinelens.00.vtu, brinelens.01.vtu,... contain the simulated distributions of the fluid bulk salinity (a), the Cu concentration in fluid for the 'no sulfur' case (b), the Cu concenration in fluid for the 'sulfur unlimited' case (c), the Cu deposition for the 'sulfur unlimited' case (d), and the temperature (e) at t=0 yr, 10000 yr, 20000 yr,..., respectively. The distributions shown in Fig. 6a-e are in brinelens.10.vtu. The file brinelens.csv contains the simulated time evolution of the total mass of dissolved and precipitated Cu. These data are plotted in Fig. 7.

opencc-zeroJun 2021View details →

opencc-by-4.0Apr 2020View details →

Fig. 10 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 10. Drawing of Parus variegatus Gmelin, 1774, after Ivan Borisov's watercolour (Taf. 20, 3 R).

Figure 3 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 3. Points of the studied samples for Ace-2 marker.

Figure 2 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 2. Distribution of Culex species in sample collection areas.

Figure 1 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 1. The study area (study areas are red-lined areas).

Figure 6 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 6. Points of the studied samples for CQ11 marker.