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FIG. 2 in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 2. Scheme of plots in a regular grid. A. Study site. B. Brephulopsis cylindrica and Xeropicta derbentina in the field. C. Scheme of plots in a regular grid. РИС. 2. Регулярная сетка плоЩадок. A. РасполоЖение исследуемого участка. B. Brephulopsis cylindrica и Xeropicta derbentina в месте обитания. C. Схема регулярной сетки плоЩадок.
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Data from: Disentangling the drivers of ground-dwelling macro-arthropod metacommunity structure at two different spatial scales
<p>The goal of this study was to explore the community assembly rules at local and regional scales.</p> <p> </p> <p><strong><em>Site description </em></strong></p> <p>All sampling locations were selected within the black soil region (Fig. 1), which is predominantly located in the temperate continental monsoon climatic zone in North China. It is characterized by a dry and cold winter and warm and humid summer. The soil was classified as black soil following the Chinese Soil Classification System, which is equivalent to a Typic Hapludoll in the USDA Soil Taxonomy. More specific details for this soil (such as black soil coverage area, geographical and ecological resources, etc.) can be obtained from Wen and Liang (2001). Samples were collected from three municipal districts: Bei'an, Hulan and Dehui.</p> <p> </p> <p><strong><em>Sampling design and setup</em></strong></p> <p>We conducted field sampling of ground-dwelling macro-arthropods and measured a set of environmental and spatial variables across all sampling locations three times: in May, July and September 2015. In total, 15 plots (five plots in each of the three municipal districts) were selected and sampled. At each plot, we further selected five sampling sites (approximately 10 m away from each other).</p> <p>We collected additional samples for estimating soil abiotic parameters at each site. Soil samples (5 × 5 cm and 10 cm depth) were collected near each pitfall trap site. The exact geographic coordinates of each sampling site were obtained by GPS.</p> <p>Ground-dwelling macro-arthropods were sampled by a pitfall trapping method. For pitfall traps, we used plastic cups (7 cm in diameter and 12 cm deep), which were partially filled with saturated salt water. The traps were exposed for one week in each sampling month. All collected ground-dwelling macro-arthropods were removed from the pitfall traps, sorted and preserved in a 95% alcohol solution. All adult macroarthropods from pitfalls were identified at the species or genus level using appropriate keys (e.g., Simon (1879), Martens (1978) and Barrientos (2004) for Opiliones; Roberts (1993, 1995) for Lycosidae; and Forel and Leplat (2001) and Ortuño and Marcos (2003) for Carabidae) and then were counted. Juvenile ground-dwelling arthropods were excluded from all analyses due to difficulties with their identification (Gao et al., 2016).</p> <p> </p> <p><strong><em>Environmental and spatial variables</em></strong></p> <p>Environmental variables used in our analysis included soil organic matter, soil total nitrogen, water content, pH, temperature. Soil water content (SWC%) was measured in the laboratory after the fresh soil was loaded into an aluminium box. Prior to estimating soil total nitrogen (TN) (Kjeldahl's method described by Duchaufour (1975)), soil organic matter (SOM) (Anne's method described by Duchaufour (1975)) and pH (Pansu and Gautheyrou, 2003), the collected soil samples were air-dried at 25℃ for one week and sieved (1 mm mesh size). Local temperature values were obtained from the publicly available datasets (The Local Chronicles of Bei’an, Hulan and Dehui). Geographic coordinates were recorded for further spatial modelling analysis.</p> <p> </p> <p>We have seven data files:</p> <p>env BAHLDH may.csv</p> <p>env BAHLDH july.csv</p> <p>env BAHLDH september.csv</p> <p>sp BAHLDH may.csv</p> <p>sp BAHLDH july.csv</p> <p>sp BAHLDH september.csv</p> <p>Geospatial coordinates.csv</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>Site: Bei’an, Hulan, Dehui represent sampling district; I-V represent sampling plot; 1-5 represent replicate</p> <p>SOM: soil organic matter</p> <p>pH: soil pH</p> <p>SWC: Soil water content</p> <p>TN: soil total nitrogen</p>
Health assessment of plantations based on LiDAR canopy spatial structure parameters
<p>The Yellow River Delta (YRD) has China's largest artificial <em>Robinia pseudoacacia</em> forest, which was planted in the late 1970s and suffered extensive dieback in the 1990s. The health grade of the <em>R.pseudoacacia</em> forest (named canopy vigor grade, CVG) could be achieved by using high-resolution images and canopy vigor indicators (CVIs). However, a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass (AGB) of <em>R.pseudoacacia</em> forest. Therefore, this study aims to construct forest health indicators (FHIs) based on canopy spatial structure parameters extracted from LiDAR. The FHIs included Weibull_α (the scale parameter of the Weibull density function that reflects the shape of the tree canopy), VCI (vertical complexity index), sdCC (the standard deviation of canopy cover), H<sub>99</sub> (the 99th percentile height) and cvLAD (the coefficient of variation of leaf area density), and could significantly distinguish three forest health grades (FHG) (<em>p</em> < 0.05). The FHG was positively correlated with forest AGB (<em>r<sub>s</sub></em> = 0.51, <em>p</em> = 0.004), and the similarity value with CVG was 63.33%. The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity, and distinguish forest health status without prior classification information.</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
<p class="RealLife">The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously, horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial dispersion of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that spatial structure moderated changes in AM fungal composition produced differential feedbacks on plant growth. Our results identify symbiont spatial structure within plant roots as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with cascading effects on plant communities.</p>
Data for the manuscript: Demographic basis of spatially structured fluctuations in a threespine stickleback metapopulation
<p>Uncovering the demographic basis of population fluctuations is a central goal of population biology. This is particularly challenging for spatially structured populations, which require disentangling synchrony in demographic rates from coupling via immigration. In this study, we fit a stage-structured metapopulation model to a 29-year times series of threespine stickleback abundance in the heterogeneous and productive Lake Myvatn, Iceland. The lake comprises two basins (North and South) connected by a channel through which the stickleback disperse. The model includes time-varying demographic rates, allowing us to assess the potential contributions of recruitment and survival, spatial coupling via immigration, and demographic transience to the population's large fluctuations in abundance. Our analyses indicate that recruitment was only modestly synchronized between the two basins, whereas survival probabilities of adults were more strongly synchronized, contributing to cyclic fluctuations in the lake-wide population size with a period of approximately six years. The analyses further show that the two basins are coupled through immigration, with the North Basin subsidizing the South Basin and playing a dominant role in driving the lake-wide dynamics. Our results show that cyclic fluctuations of a metapopulation can be explained in terms of the combined effects of synchronized demographic rates and spatial coupling.</p>
Fig. 2 in Spatial variation of dung beetle assemblages associated with forest structure in remnants of southern Brazilian Atlantic Forest
Fig. 2. Principal coordinates analysis (PCoA) of dung beetle species based on Bray–Curtis similarity and environmental variables based on Euclidean distance. The analysis was performed using presence–absence (a), abundance (b) and biomass (c) data of dung beetles, and 15 environmental variables (d). ANH: Anhatomirim Environmental Protection Area; ITA: Permanent Protection Areas of Itapema; PER: Peri Lagoon Municipal Park; RAT: Permanent Protection Areas of Ratones.
Figure 3 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 3. Abundance, number of species and Berger-Parker index in samples in different management types and fields. Colors show fields. Boxplots show data distribution (n = 81 per field), horizontal lines represent the medians.
Fig. 2 in Planktonic Ciliates of the Neva Estuary (Baltic Sea): Community Structure and Spatial Distribution
Fig. 2. Two groups of samples, distinguished by ordination (MDS) on the basis of similarity of the ciliate community structure (p <0.05). Upper and lower parts of the inner Neva Estuary (white and grey symbols) slightly differed by community structure (Global R = 0.163).
Fig. 1 in Planktonic Ciliates of the Neva Estuary (Baltic Sea): Community Structure and Spatial Distribution
Fig. 1. Scheme of the inner Neva Estuary and location of sampling stations; modified from Telesh et al. (2008). Broken line indicates the storm-surge barrier.
Figure 5 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 5. Station-wise variation in the 0–500 m column integrated mesozooplankton abundance/density and biomass in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 6 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 6. Depth-wise variation in the number of zooplankton groups at each station in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 1 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 1. Map of the sampling site in the Bay of Bengal. Stations CB1 to CB5 are located along the central (88°E) and WB1 to WB4 along the western margin of the bay.
Figure 13 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 13. Multivariate cluster analysis of the data of all 129 copepod species combined from all the stations and depths in the central and western bay using the 30% cut-off level of Bray–Curtis similarity. Cluster/Group I are assemblages mostly from the mixed layer (M) and thermocline (T) from central and western transects. Group II comprises assemblages found between the thermocline and 500 m and Group III includes only a few species found exclusively from 200–300 m depth at stations CB3–CB5.
Figure 4 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 4. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the western Bay of Bengal during spring intermonsoon. ng: negligible biovolume; NO DATA is where the net failed to open/close. *At WB3, medusae (100 mL 100 m–3) and at WB4 salps (200 mL 100 m–3) were observed at the surface during the day.
Figure 9 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 9. Vertical distribution of abundance (log number 100 m–3) of the major copepod species in the central Bay of Bengal during spring intermonsoon.
Figure 3 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 3. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the central Bay of Bengal during spring intermonsoon. ng: Negligible biovolume; NO DATA is where the net failed to open/close. *Swarms of medusae were observed at CB3 (their biovolume 90 mL 100 m–3) and CB4 (200 mL 100 m–3) at the surface at night.
Figure 8 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 8. Vertical distribution of the various types (orders) of copepods in the central (a) and western (b) Bay of Bengal during the spring intermonsoon. The percentages at every depth are averages from 5 stations in the central and 4 stations in the western bay. Data are unavailable at 300–500 m in the central bay due to negligible abundance.
Figure 12 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 12. Variation in multivariate dispersion (MVDISP) indices between different depth strata (9 stations data combined) and between the central and western transects in the Bay of Bengal.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.