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Fig. 1 in Fish assemblages along a morphodynamic continuum on three tropical beaches
Fig. 1. Map of Mamanguape River estuary with locations of the sampling sites: CPO, Curva do Pontal Beach; PON, Pontal Beach; and CAM, Campina Beach.
Fig. 2 in Diet and ecomorphological relationships of an endemic, species-poor fish assemblage in a stream in the Iguaçu National Park
Fig. 2. Ordenation of the fish fauna in Jumelo stream, Iguaçu National Park, Paraná, Brazil, produced by the first two axes of the principal components analysis (PCA 1 and PCA 2) applied to the correlation of 15 ecomorphological indices and the mouth orientations of the fish species.
Fig. 1 in Diet and ecomorphological relationships of an endemic, species-poor fish assemblage in a stream in the Iguaçu National Park
Fig. 1. Study area. Collection point in stream Jumelo, region of the Iguaçu National Park in the Brazilian state of Paraná and Gonçalves Dias River in the Iguaçu River Basin, Santa Tereza do Oeste, Paraná.
Fig. 1 in Functional differentiation between fish assemblages from forested and deforested streams
Fig. 1. Map of the study area, showing: the state of São Paulo (A); and the rio São José dos Dourados basin (B), where the forested streams (black circles), which were located in the largest forest fragments of the river basin (grey areas), and the deforested streams (grey circles) were located (C).
Fig. 3 in Functional differentiation between fish assemblages from forested and deforested streams
Fig. 3. Identification of the functional groups defined by partitions #30 and #33, with their respective trends of change in species abundance as a function of deforestation. The dominant traits of the functional groups that were important for stream differentiation according to the DPCoA (Fig. 4) are also shown. Species names are abbreviated according to Table 2.
Fig. 2 in Functional differentiation between fish assemblages from forested and deforested streams
Fig. 2. Dendrogram of the functional similarities of the 35 fish species included in the analyses. Mean species abundances within the forested (F1-F3) and deforested (D1-D3) streams are represented by the sizes of the black squares. Species names are abbreviated according to Table 2.
Fig. 4 in Functional differentiation between fish assemblages from forested and deforested streams
Fig. 4. Double Principal Coordinate Analysis (DPCoA) biplot ordination, describing the functional differences between forested (F1-F3) and deforested (D1-D3) streams. Black circles indicate each species, and their relative positions reflect their functional dissimilarities. Species are linked according to the functional groups originating from partitions #33 (a) and #30 (b). Species identities and their functional traits are shown in Fig. 3.
Figure 15 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 15. The variation of the Pianka mean niche overlap index deviation from random alternative within principal components 3 and 4. Spawning start: B_bjoerkna – Blicca bjoerkna, C_gibelio – Carassius gibelio, S_erythrophthalmus – Scardinius erythrophthalmus, A_brama – Abramis brama, P_fluviatilis – Perca fluviatilis, R_rutilus – Rutilus rutilus, E_lucius – Esox lucius; regression residuals of the spawning end dependence from the start: B_delta – Blicca bjoerkna, C_delta – Carassius gibelio, S_delta – Scardinius erythrophthalmus, A_delta – Abramis brama, P_delta – Perca fluviatilis, R_delta – Rutilus rutilus, E_delta – Esox lucius.
Figure 12 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 12. Spawning variance partitioning between broad-, medium-, and fine-scale temporal temperature patterns and biotope type explanatory variables. Notes: [a] – variation explained solely by broad temperature variables; [b] – variation explained solely by medium temperature variables; [c] – variation explained solely by fine temperature variables; [d] – variation explained solely by biotope type. The intersection of the ellipses corresponds to the variations explained by the respective sources together All the variance fractions shown are significant (p <0.001).
Figure 14 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 14. Relative variance of the principal components (in %). Method: ANOVA Method, Type I SS, columns denote cumulative sums of variance components.
Figure 13 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 13. The temporal variation of the principal components 1–5. the x-axis – years, the y-axis – the scores of the principal components 1–5.
Figure 10 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 10. Distribution histograms of the Pianka mean niche overlap indexes in different types of habitats: 1 – Nikolayev system of water bodies; 2 – river Protoch system and Obukhov floodplain; 3 – the channel of the river Dnipro; 4 – water bodies of the Taromske ledge.
Figure 4 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 4. Scalogram illustrating the scaling of temporally structured variation in polynomial trend residuals data. The abscissa axis – dbMEMs ordered decreasingly according to the scale of temporal patterns they represent (dbMEM 1 represents the broadest scale, dbMEM 104 the finest scale). The ordinate axis – value of R2 is the variation explained adj by individual dbMEM variables.
Figure 5. Broad-scale components RDA 1-3 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 5. Broad-scale components RDA 1-3 of the annual temperature variation. Black line – the original data, colored lines – smoothed data. The abscissa axis – the number of days from 1 July of the previous year to June 31 of the next year
Figure 1 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 1. Map of the Rio de Janeiro state coast highlighting the 12 sampling stations in Araruama lagoon.
Figure 5 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 5. Cladocera Assemblage of Araruama Lagoon from January 2010 to December 2013. Stations 11 and 12 with different scales. E. spinifera (black and white lines arranged laterally); P. tergestina (black with small white spots);P. avitostris (vertical black and white lines); P. polyphemoides (chess pattern); P. sckmackeri (black and white lines waved horizontally).
Figure 3 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 3. BoxPolt of temperature presented from means and standard deviation, spatial variation (A) and temporal variation (B).
Data and Code from: Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden
<p>Data and Code supporting the analyses presented in: Fredriksson, Cromsigt & Hofmeester - Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden as published in Wildlife Biology</p> <p><strong>Abstract</strong></p> <p><span>Fires can strongly change the vegetation structure and the availability of resources for wildlife, but fire suppression has long affected the natural role of fire in shaping boreal ecosystems in northern Europe. Recently, wildfires have increased in frequency, possibly due to global warming. In contrast to the boreal systems in North America, there have been few studies on responses of wildlife to wildfires in northern Europe. Based on the findings from North America, we predict that responses of wildlife to wildfire vary among wildlife species: where mammalian herbivores, such as moose (<em>Alces alces</em>) and mountain hare (<em>Lepus timidus</em>), will be attracted to burnt areas following an increase in food availability, other species, such as reindeer (<em>Rangifer tarandus</em>), are negatively impacted due to fire reducing their preferred food. We then tested our predictions by contrasting wildlife utilization of sites that burnt by wildfire in 2006 with nearby unburnt control sites in three areas in northern Sweden. To measure wildlife utilization, we used 72 camera traps, equally divided between the burnt and control sites, with two placement strategies: random and on wildlife trails. The cameras recorded 27 mammal and bird species during summer 2018. Species assemblage differed between burnt and control sites. Fieldfare (<em>Turdus pilaris</em>) used burnt sites more than control sites, while pine marten (<em>Martes martes</em>) and western capercaillie (<em>Tetrao urogallus</em>) used control sites more than burnt sites. We however did not find support for a positive effect of past forest fires on any of the observed wild mammals. We discuss how, due to the impact of forestry, forage-rich habitat may not be as limiting in Scandinavia as in the North-American context, potentially leading to recently burnt sites being less attractive to herbivores such as moose.</span></p>
Figure 12 in Albian to Turonian agglutinated foraminiferal assemblages of the Lower Saxony Cretaceous sub-basins - implications for sequence stratigraphy and paleoenvironmental interpretation
Figure 12. Columnar section of the Cenomanian–Turonian boundary and the Lower and Middle Turonian part of the Wunstorf core Wu 2010/4 with agglutinated foraminiferal morphogroups, Fisher alpha index, species richness, and foraminiferal events (acmes) indicated by arrows. For log legend, see Fig. 4.
Figure 11 in Albian to Turonian agglutinated foraminiferal assemblages of the Lower Saxony Cretaceous sub-basins - implications for sequence stratigraphy and paleoenvironmental interpretation
Figure 11. Columnar section of the Cenomanian part of the Baddeckenstedt quarry with agglutinated foraminiferal morphogroups, Fisher alpha index, species richness, and foraminiferal events (acmes) indicated by arrows. For log legend, see Fig. 3; log redrawn after Wilmsen (2003: Fig. 8).
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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.