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Figure 3 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 3. Number of four sea turtles nesting in each hypothetical station during survey period: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Fig. 1. a in Spatial and seasonal patterns in fish assemblage in Córrego Rico, upper Paraná River basin
Fig. 1. a) Map of SouthAmerica with Brazil location. b) Córrego Rico basin in São Paulo State. c) Córrego Rico basin (Modified from a figure by L. A. Amaral - unpublished) with the samples stretches.
Fig. 2 in Spatial and seasonal patterns in fish assemblage in Córrego Rico, upper Paraná River basin
Fig. 2. Non-metric multidimensional scaling (NMDS) ordination of environmental variables data from Córrego Rico stretches. a) All samples (stretches - S1 to S7; month and year of sample - mmyy): b) Samples divided in rainy (R) and dry (D) seasons (stretches - S1 to S7).
Data from: The spatial segregation patterns of sharks from Western Australia
The extent to which sharks segregate by size and sex determines the population structure and the scale at which populations should be managed. We summarized 20 years of fisheries-dependent and independent sampling to define the spatial patterns of size and sexual segregation for sharks in Western Australia. Carcharhinus obscurus and C. plumbeus showed a large-scale (more than 1000 km) latitudinal gradient in size. Large individuals occurred predominantly in the northwest and north whereas smaller individuals occurred predominantly in the southwest and south. Mustelus antarcticus and Furgaleus macki showed strong sexual segregation at very large scales. Females occurred predominantly in the west and southwest whereas the proportion of males in catches substantially increased in the southeast. The populations of other shark species did not show sex and size segregation patterns at very large scales; most species, however, showed varying degrees of segregation when data were analysed at a smaller scale. These findings highlight the importance of matching the scale of observation to the scale of the phenomenon observed. As many shark species are highly mobile, if sampling is opportunistic and constrained both temporally and spatially, the observed segregation patterns may not be representative of those at the population level, leading to inaccurate scientific advice.
Data from: Environmental conditions affect spatial genetic structures and dispersal patterns in a solitary rodent
The study of the spatial distribution of relatives in a population under contrasted environmental conditions provides critical insights into the flexibility of dispersal behaviour and the role of environmental conditions in shaping population relatedness and social structure. Yet few studies have evaluated the effects of fluctuating environmental conditions on relatedness structure of solitary species in the wild. The aim of this study was to determine the impact of interannual variations in environmental conditions on the spatial distribution of relatives [spatial genetic structure (SGS)] and dispersal patterns of a wild population of eastern chipmunks (Tamias striatus), a solitary rodent of North America. Eastern chipmunks depend on the seed of masting trees for reproduction and survival. Here, we combined the analysis of the SGS of adults with direct estimates of juvenile dispersal distance during six contrasted years with different dispersal seasons, population sizes and seed production. We found that environmental conditions influences the dispersal distances of juveniles and that male juveniles dispersed farther than females. The extent of the SGS of adult females varied between years and matched the variation in environmental conditions. In contrast, the SGS of males did not vary between years. We also found a difference in SGS between males and females that was consistent with male-biased dispersal. This study suggests that both the dispersal behaviour and the relatedness structure in a population of a solitary species can be relatively labile and change according to environmental conditions.
Data from: Spatial variation in Allee effects influences patterns of range expansion
Allee effects are thought to slow range expansion and contribute to stable range boundaries. Recent studies have shown Allee effects to vary spatiotemporally due to influences of environmental heterogeneity on population processes. Gradients in Allee effects might occur as a species' range approaches suboptimal conditions while expanding into new territory. Allee effects could exhibit patchiness if drivers of positive density dependence (e.g., mate finding rates) are influenced by habitat patchiness. However, theoretical studies have largely assumed Allee effects to be spatially constant. The goal of this study was to evaluate how spatiotemporal patterns of range expansion respond to spatial variations in Allee effects. We simulated spread in landscapes that differed in the spatial configuration and range of Allee thresholds. We compared spread with a constant Allee effect to spread in landscapes where the Allee threshold varied along a gradient or in a patchy fashion. Landscape configuration affected patterns of range expansion when Allee thresholds were near or exceeded the number of colonizing immigrants. In gradient landscapes, spread decelerated as the range edge approached higher Allee thresholds. In patchy landscapes, spread advanced quickly through areas with lower Allee thresholds and stalled in areas with higher Allee thresholds. Both focal and neighboring locations influencing spread. Spatial variation in Allee effects may be an underappreciated source of heterogeneity in patterns of range expansion. When Allee effects vary, spread estimates based on a spatially averaged Allee threshold may not accurately predict realized rates of spread. Our findings suggest that spread can occur despite generally high Allee thresholds if Allee thresholds are low in a subset of patches. This result has negative implications for controlling the spread of invasive species, but it also suggests range shifts by native species in response to climate change may be possible with even sparsely distributed refugia from Allee effects.
Fig. 1 in Spatial and temporal distribution patterns of ichthyoplankton in a region affected by water regulation by dams
Fig. 1. Location of the sampling sites in the Ilha Grande National Park (1: Bandeirantes right channel; 2: Amambaí; 3: Triângulo; 4: Porto Santo Antônio; 5: Peruzzi; 6: Paraná/ Iguatemi; 7: Iguatemi; 8: Paraná/Saraiva; 9: Saraiva middle; 10: Saraiva Channel; 11: Ilha Grande right channel; 12: Bandeirantes left channel; 13: Ilha Grande Pontal; 14: Alvarenga; 15: Esmeralda; 16: Três Coqueiros; 17: São João; 18: Porto Luiz; 19 Porto Cerâmica; 20: Piquiri; 21: Porto Terra Roxa; 22 Ilha Grande left channel; 23: Xambrê River; 24: Xambrê middle).
Fig. 2 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 2. Distribution of the relative abundance of the 49 species of fish captured in the 22 plots in Site of Long-Term Sampling (SLTS), related to the depth at each of the collection plots.
Figure 4 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 4 - Female genitalia, Megalocraerus rubricatus. A 8th tergite, dorsal view B 8th sternite, ventral view C Bursa copulatrix (bc), common oviduct (co), spermatheca (st) and attached spermathecal gland (stg) D Ovipositor.
Figure 3 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 3 - Male genitalia, Megalocraerus rubricatus. A 8th tergite, dorsal view B 8th sternite, dorsal view C 8th tergite and sternite, lateral view, in situ D 9th and 10th tergites, dorsal view E 9th sternite, dorsal view F Aedeagus, dorsal view G Tegmen, lateral view.
Figure 6 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 6 - Aedeagi of Megalocraerus spp. A Megalocraerus mandibularis, dorsal view B Megalocraerus mandibularis, lateral view C Megalocraerus chico, dorsal view D Megalocraerus chico, lateral view E Megalocraerus madrededios, dorsal view F Megalocraerus madrededios, lateral view G unnamed Megalocraerus sp. from Rio de Janeiro, dorsal view H unnamed Megalocraerus sp. from Rio de Janeiro, lateral view.
Figure 1 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 1 - Generic characters of Megalocraerus. A Frons B Antenna C Mouthparts, ventral view (one maxilla and labial palpus omitted for clarity).
Figure 5 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 5 - A Dorsal habitus Megalocraerus mandibularis B Mandibles male Megalocraerus mandibularis C Dorsal habitus Megalocraerus chico.
Figure 2 from: Freijeiro A, Baselga A (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. In: Jolivet P, Santiago-Blay J, Schmitt M (Eds) Research on Chrysomelidae 6. ZooKeys 597: 81–99. https://doi.org/10.3897/zookeys.597.6792
Figure 2 - Partitioning of the variation (%) in species richness (a, b) and species composition (c, d) among groups of explanatory sets (A=area, E=environment and S=spatial variables) for European Cryptocephalinae (left column: a, c) and Chrysomelinae (right column: b, d).
Figure 1 from: Freijeiro A, Baselga A (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. In: Jolivet P, Santiago-Blay J, Schmitt M (Eds) Research on Chrysomelidae 6. ZooKeys 597: 81–99. https://doi.org/10.3897/zookeys.597.6792
Figure 1 - Patterns of variation in species richness (a, b), hierarchical clustering based in βsim (c, d) and mapping of 4 major clusters (e, f) for cryptocephalines (left column: a, c, e) and chrysomelines (right column: b, d, f). Colours correspond to the 4 major clusters. Countries' abbreviations follow those of Löbl and Smetana (2010).
Figure 3 from: Freijeiro A, Baselga A (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. In: Jolivet P, Santiago-Blay J, Schmitt M (Eds) Research on Chrysomelidae 6. ZooKeys 597: 81–99. https://doi.org/10.3897/zookeys.597.6792
Figure 3 - Distance decay of similarity with spatial distance in Northern Europe (solid dots, solid line) and southern Europe (hollow dots, dashed line) for cryptocephalines (a, blue), chrysomelines (b, red) and longhorn beetles (c, green). The density plots in (d) show the distribution of 1000 bootstrap replicates of the distance decay slopes (solid lines: northern Europe, dashed lines: southern Europe, colours corresponding to a, b, and c).
Data and code for "High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra"
<p><strong>Repository structure</strong></p> <p>The zipped folder includes the following subfolders:</p> <p><em>data</em></p> <p>In-situ measurement data from the plots. Remotely-sensed data could not be included in the repository due to their large size. </p> <p><em>src</em></p> <p>R codes to reproduce the data cleaning, prosessing, and statistical analysis steps.</p> <p><em>results</em></p> <p>Model parameters, performance statistics, model files, figures, edited tables together with some summary tables produces from upscaling results.</p> <p><em>raster data and upscaled results</em></p> <p>Averaged flux, soil moisture and temperature maps for the growing season (July 1-August 2nd, 8 am - 8 pm) as well as static maps produced in this study. All the upscaled results could not be included in the repository due to their large size. </p> <p>Note that the analysis to produce the vegetation classification map are described here: https://github.com/poniitty/kilpisjarvi_vegclass</p>
Data from: Life history determines biogeographical patterns of soil bacterial communities over multiple spatial scales
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Data from: Spatial variation in Allee effects influences patterns of range expansion
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Data from: Spatial heterogeneity of a parasitic plant drives the seed-dispersal pattern of a zoochorous plant community in a generalist dispersal system
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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.