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Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach

Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).

Рис. 2. Диаграммы распределениЯ обилиЯ наЗемного моллюска M. cartusiana: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). Fig. 2. Diagram of the abundance distribution of the land snail M. cartusiana: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach

Рис. 2. Диаграммы распределениЯ обилиЯ наЗемного моллюска M. cartusiana: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). Fig. 2. Diagram of the abundance distribution of the land snail M. cartusiana: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes).

Fig. 1 in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach

Fig. 1. Diagram of the abundance distribution of the land snail B. cylindrica: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes).

Рис. 1. Диаграммы распределениЯ обилиЯ наЗемного моллюска B. cylindrica: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach

Рис. 1. Диаграммы распределениЯ обилиЯ наЗемного моллюска B. cylindrica: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков).

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Fig. 3 in Spatial distribution of radar bright band intensity

Fig. 3 ― Variation of longitudinally averaged monthly BBI with latitude in (a) February 1999, (b) July 2007, (c) September 2001, and (d) October 2002

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Fig. 2 in Spatial distribution of tuna larvae in the Gulf of Gabes (Eastern Mediterranean) in relation with environmental parameters

Fig. 2: Spatial distribution of environmental parameters: Temperature (a), Salinity (b), Oxygen content (c), Zooplankton biomass (d) and Chlorophyll a (e).

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Fig. 5 in Spatial distribution of tuna larvae in the Gulf of Gabes (Eastern Mediterranean) in relation with environmental parameters

Fig. 5: Vertical profiles of different environmental parameters [Temperature (a), Salinity (b), Oxygen content (c) and Chlorophyll a (d)] for coastal () and oceanic () stations.

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Fig. 3 in Spatial distribution of tuna larvae in the Gulf of Gabes (Eastern Mediterranean) in relation with environmental parameters

Fig. 3: Dendrogram of the Euclidean distance between the sampling stations based on the environmental factor (temperature, salinity, oxygen content, chlorophyll a and depth).

Fig. 2 in Spatial Distribution Of Nematodes In The Forest Ecosystem Of The Mezin National Nature Park, Ukraine

Fig. 2. Abundance of nematodes belonging to different orders in the studied microhabitats of forest ecosystem.

Fig. 1 in Spatial Distribution Of Nematodes In The Forest Ecosystem Of The Mezin National Nature Park, Ukraine

Fig. 1. Species diversity of nematodes belonging to different orders in the studied microhabitats of forest ecosystem.

Fig. 3 in Spatial Distribution Of Nematodes In The Forest Ecosystem Of The Mezin National Nature Park, Ukraine

Fig. 3. Structure of nematode fauna of the studied microhabitats of forest ecosystem according to the dominance criterion: A — species diversity, %; B — abundance, %.

Fig. 1 in Spatial distribution of Hyalella patagonica Cunningham, 1871 (Amphipoda) on Andean Patagonian river (Truful-Truful river, 38°S, Araucania region, Chile)

Fig. 1. Map of studied site, Truful-Truful, Conguillío National Park, Chile. Fig. 1. Mapa del sitio en estudio, Truful-Truful, Parque Nacional Conguillío, Chile.

Fig. 2 in Spatial distribution of Hyalella patagonica Cunningham, 1871 (Amphipoda) on Andean Patagonian river (Truful-Truful river, 38°S, Araucania region, Chile)

Fig. 2. Graph of estimation of negative binomial distribution for H. patagonica population of Truful-Truful river.

Spatially Coherent 3D Distributions of HI and CO in the Milky Way - Data Products

Data products from the joint reconstruction of Galactic HI and H2 (via CO). <h3>Primary data products:</h3> These are the posterior samples of the "densities" (HI and H2) in cm^-3 and "auxiliary" fields (i.e. the three components of the Galactic velocity field and the two spatially resolved line-widths) in km/s on our Sun-centered HEALPix-times-radius grid. These files also contain two tables with the centres and edges of the pixelisation in radial direction. The nearest (farthest) bin is at approximately 50 pc (28 kpc). The HEALPix dimension is ordered using the "nested" scheme. <ul> <li>samples_densities_hpixr.fits </li> <li>samples_auxiliary_hpixr.fits</li> </ul> <h3>Interpolated to a regular grid:</h3> For convenience, we also provide versions linearly interpolated to regular, Cartesian grids. Due to the strongly inhomogeneous original numerical grid, these interpolated versions contain regions of significant over/undersampling. To mitigate this a little, we provide a "local" (800 x 800 x 320 grid points with -1.25 kpc < x < 1.25 kpc, -1.25 kpc < y < 1.25 kpc and -0.5 kpc < z < 0.5 kpc) and a "global" (1250 x 1250 x 125 grid points with -12 kpc < x < 28 kpc, -20 kpc < y < 20 kpc, -2 kpc < z < 2 kpc) version. The origin (0,0,0) is defined by the position of the Sun and positive x points towards the Galactic centre. In an attempt to keep the file sizes reasonable, we provide the mean and standard deviation of each field instead of all eight individual samples. <ul> <li>mean_std_densities_xyz_global.fits</li> <li>mean_std_densities_xyz_local.fits</li> <li>mean_std_auxiliary_xyz_global.fits</li> <li>mean_std_auxiliary_xyz_local.fits</li> </ul>

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Fig. 2 in Relationships between morphology, diet and spatial distribution: testing the effects of intra and interspecific morphological variations on the patterns of resource use in two Neotropical Cichlids

Fig. 2. Head of Satanoperca pappaterra (a) and Crenicichla britskii (b), showing differences in the mouth protrusion.

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Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range

Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.

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Figure 2 in Spatial Distribution of the Content of Heavy Metals in the Belaya River Ecosystem

Figure 2. Normalized values of Pb (a) and Mn (b) by Fe in gauge stations I-III (gauges I–II-aerobic conditions; gauge III – anaerobic conditions).

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Figure 3 in Spatial distribution of Ceratitis capitata in guava orchards and influences from orchard management

Figure 3. Estimates of the parameters of the regression analysis by the Taylor power model, adjusted by the F test to evaluate the spatial distribution and the t-test to compare the hypotheses of the aggregation index of Ceratitis capitata in guava orchards, Ivinhema-MS. Tests: ANOVA (F = 304.05; p <0.001; g.l = 20); t (5.17; p <0.001) for the Alpha hypothesis (h0: a = 1 vs h1 a ≠ 1, where: Alpha (a <0, a = 0 and a>0) and; t (17.44; p <0.001) for the Beta hypothesis (h0: b = 1 vs h1: b ≠ 1), where: Beta (b <1, b = 1 and b> 1).

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Figure 2 in Spatial distribution of Ceratitis capitata in guava orchards and influences from orchard management

Figure 2. Number of fruit fly/trap/day (FTD) of Ceratitis capitata and range of negative binomial thresholds (Bn) with other distributions, establishing the levels of safety and control activities for the Mediterranean fly in three guava orchards, Ivinhema, MS, Brazil.