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FIGURE 8 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 8. Difference maps showing the percentage of events accurately detected by simulations without bioturbation subtracted from the percentage of events accurately detected by simulations with bioturbation. 100% sampling completeness and transition durations 0.001 times the event duration for A; 100% sampling completeness and transition durations five times the event duration for B; 25% sampling completeness and transition durations 0.001 times the event duration for C; 25% sampling completeness and transition durations five times the event duration for D. The solid black line marks the contour line for zero difference between the bioturbated and non-bioturbated simulations.
FIGURE 6 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 6. Effect of varying completeness with the duration of transition intervals. Simulation results with 25% completeness and transition lengths 0.001 times the event duration for A; and 25% completeness with transition lengths of five times the event duration for B. Background DCA-1 value is -0.5 and no bioturbation occurs. Excursion magnitude (y-axis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA- 1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval. Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.
FIGURE 12 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 12. Effect of sample thickness on accurate detection of events with excursion magnitudes of 2.8 DCA-1 units. Y-axis shows the percentage of accurately detected events at different sedimentation rates (x-axis) for three different event durations: 50 years, 100 years and 1000 years. Simulations are for the 2016 data set to approximate how a researcher might use a pilot data set to design a sampling procedure and are based on sampling with 25% completeness. A is simulations without the effect of bioturbation; B is simulations with the effect of bioturbation.
FIGURE 2 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 2. Simulation workflow in the paleontological assemblage mixer (paleoAM). A. Relative abundances of a given species (Epistominella pacifica) from the 355 samples of the 2021 data set, along the empirically derived DCA Axis 1 gradient. B. The per-bin mean of absolute abundance of E. pacifica across all samples within each bin. Absolute abundances are calculated from the relative abundances in A by rescaling the relative abundances to 10000 total specimens. C. Scaled kernel density estimates for E. pacifica, which depict the predicted abundance distribution of E. pacifica along DCA Axis 1 after fitting a kernel density estimate to the absolute abundances in B. D. Scaled kernel density estimates, like in C, for all taxa in the dataset showing their differing predicted abundance distributions along DCA Axis 1 with the kernel density of E. pacifica shown in C marked with an asterisk. E. Visual representation of parameters varied within the simulation along a vertical sediment core. From left to right: standard scenario, increased excursion magnitude, increased background value, increased resolution potential, sampling completeness, bioturbation and increased transition duration. Stacked rectangles represent potential sample intervals. In the first and last column, black dots indicate which intervals are sampled, gray dots indicate unsampled intervals. In the standard scenario, all potential sample intervals are sampled. Curved arrows denote sediment mixing among potential sample intervals.
FIGURE 1 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 1. Sample scores from detrended correspondence analysis (DCA) performed on benthic foraminiferal assemblages in the>63 µm size fraction from Integrated Ocean Drilling Program Expedition 341 Site U1419 in the Gulf of Alaska used in the simulation case study. A. DCA Axis 1 values for 355 assemblages from Sharon et al. (2021); B. DCA Axis 1 values for 47 assemblages representing a "pilot" data set of samples available and processed in 2016.
FIGURE 4 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 4. Percentage of events that are accurately detected and median excursion magnitudes for simulations performed with background DCA-1 values of -0.5, 0.5 and 1. For A and B, simulations used a background DCA-1 value of -0.5; for C and D, a background DCA-1 value of 0.5; for E and F, a background DCA-1 value of 1. Excursion magnitude (y-axis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA- 1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval. A, C and E. Color shading indicates the percentage of events that are accurately detected by at least one sample (i.e., the sample produces a DCA-1 value outside the 95% envelope of samples simulated at the background value, and within one DCA-1 unit of the simulated excursion magnitude). B, D and F. Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.
FIGURE 5 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 5. Effect of varying transition duration. Each panel represents a set of simulations generated at transition interval lengths of 0.001 times the event duration for A, 0.5 times the event duration for B, 1.0 times the event duration for C, and 5.0 times the event duration for D. Background DCA-1 value is -0.5 and completeness is 100%. Excursion magnitude (y-axis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA-1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval.Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.
FIGURE 9. DCA-1 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 9. DCA-1 values observed when assemblages simulated at different event values are mixed with different proportions of the background assemblage. For A, the maximum DCA-1 value observed is shown; for B, the median DCA-1 value observed; and for C, the minimum DCA-1 value observed. In all cases, background assemblages are simulated at a DCA-1 value of -0.5 and are mixed with an event assemblage with a DCA-1 value as given on the x-axis.
FIGURE 3 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 3. Multivariate comparison of empirical and simulated foraminiferal assemblages. A. Detrended correspondence analysis showing empirical (red filled) and simulated (black open) assemblages in the same ordination space. B. DCA1 scores for empirical samples paired with the DCA1 score of the corresponding simulated sample. Dotted line is the 1:1 line and is largely obscured by the points. C. All pairwise dissimilarities among empirical samples plotted against the average pairwise dissimilarity of 300 corresponding samples simulated at the same DCA1 values. Color scale depicts the density of dissimilarities with higher concentrations of dissimilarities in brighter colors. Black line is the 1:1 line.
FIGURE 7 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 7. The interaction of bioturbation, completeness and transition duration. All simulations figured include bioturbation and use a background DCA-1 value of -0.5. Simulation results for 100% sampling completeness and transition durations 0.001 times the event duration for A; 100% sampling completeness and transition durations five times the event duration for B; 25% sampling completeness and transition durations 0.001 times the event duration for C; 25% sampling completeness and transition durations five times the event duration for D. Excursion magnitude (yaxis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA-1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval. Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.
FIGURE 14 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 14. Estimated ability of the record to detect 100 year-long events with 3 cm samples, given a known sedimentation rate at three excursion magnitudes (0.4, 1.2 and 2.8), based on 'worst-case scenario' simulations with 25% sampling, bioturbation, rapid transitions between events and the background condition (-0.5 DCA-1 value). A and B are simulated with an excursion magnitude of 0.4; C and D are simulated with an excursion magnitude of 1.2; and E and F are simulated with an excursion magnitude of 2.8. A, C and E show the percentage of events that are detectable above a background value of -0.5. An event is detected if a sample has an observed DCA-1 value exceeding the 95% quantile of the DCA-1 value of assemblages simulated at the background value. B, D and F show the median DCA-1 values recovered from samples intersecting simulated events. Values that fall above the blue dashed line are detected; that is, they exceed the 95% quantile of the DCA-1 value of assemblages simulated at the background value. Values that fall above the red dotted line are accurately detected; that is, they exceed the 2.75% quantile on DCA-1 values recovered from simulations at the event value.
FIGURE 3 in Biostratigraphy and biochronology of late Cenozoic North American rodent assemblages
FIGURE 3. Examples of Microtus m1 morphology. A-C, Microtus pennsylvanicus (from Martin, 1990), D-F, Microtus paroperarius (from van der Meulen, 1978). ACC = anteroconid complex, BRA = buccal reentrant angle, LRA = lingual reentrant angle. Numbers in illustration D refer to triangle numbers.
FIGURE 1 in Biostratigraphy and biochronology of late Cenozoic North American rodent assemblages
FIGURE 1. Distribution of select pre-Rancholabrean Cenozoic rodent assemblages used to construct the database in Supplementary Material. 1 = Rancho el Ocote, MX; 2 = Concha, MX; 3 = Yepómera, MX; 4 = El Golfo, CA; 5 = Vallecito-Fish Creek sequence (e.g., Layer Cake, Arroyo Seco, Vallecito Creek), CA; 6 = San Pedro Valley sequence (e.g., Benson, Curtis Ranch, Duncan), AZ; 7 = Verde, AZ; 8 = McKay Reservoir, OR; 9 = Warren, CA; 10 = Panaca, NV; 11 = White Bluffs, WA; 12 = Kennewick, WA; 13 = Buckeye Creek, NV; 14 = Fish Springs Flat, NV; 15 = Grand View-Hagerman sequence (e.g., Grand View, Sand Point, Hagerman, Birch Creek, Froman Ferry), ID; 16 = Donnelly Ranch, CO; 17 = San Timoteo Badlands, CA; 18 = Wellington Hills, NV; 19 = Mesa del Sol, NM; 20 = Boyle Ditch, WY; 21 = Virden, NM; 22 = Ft. Selkirk, YT; 23 = El Casco, CA; 24 = SAM Cave, NM; 25 = Little Dell Dam, UT; 26 = Porcupine Cave, CO; 27 = Hansen Bluff, NM; 28 = Meade Basin reference section (see Figure 2 for all assemblages; includes Arlene's Ledge/Robin's Roost in OK), KS/NM; 29 = Little Sioux and Wright (new), IA; 30 = Cape Deceit, AL; 31 = Santee, NE; 32 = Mailbox, NE; 33 = Sand Draw area (Sand Draw, Zwiebel Channel), NE; 34 = Pipe Creek Sinkhole, IN; 35 = Hudspeth/Red Light, TX; 36 = Bull Draw/Deadman's Crk, TX; 37 = Red Corral, TX; 38 = Cita Canyon, TX; 39 = Blanco, TX; 40 = Vera, TX; 41 = Beck Ranch, TX; 42 = Fyllan Cave, TX; 43 = Conard Fissure, AK; 44 = Port Kennedy Cave, PA; 45 = Hanover Quarry, PA; 46 = Cumberland Cave, MD; 47 = Haile 15A, Haile 16A, FL; 48 = Inglis 1A/1C, FL; 49 = Hamilton Cave, WV; 50 = White Rock, KS; 51 = Dixon, KS, 52 = Leisey Shell Pit, FL, 53 = Hoye Canyon, NV.
FIGURE 2 in Biostratigraphy and biochronology of late Cenozoic North American rodent assemblages
FIGURE 2. Depositional basin framework on which chronological ordering of assemblages (= localities) in Supplementary Material is mostly based. See Supplementary Material and text for full rodent communities, locality data, and information sources. LSD = lowest stratigraphic datum, HSD = highest stratigraphic datum. LSDs and HSDs are regional basin limits, but may also represent global limits (highest or lowest records anywhere in North America). Esr = estimated age based on sedimentation rate from Hart and Brueseke, 1999), ft = fission track date from Walkup et al., 2016), CMZ = Cenozoic Mammal Zone, xxx... = dated volcanic ash beds. Continued on next page.
FIGURE 4 in Biostratigraphy and biochronology of late Cenozoic North American rodent assemblages
FIGURE 4. Plot of arvicoline species richness on ordinate against CMZ midpoints on abscissa. Numbers on graph are CMZs. CMZ 1 omitted.
Fig. 2 in Patterns in fish species composition and assemblage structure in the upper Salado River lakes, Pampa Plain, Argentina
Fig. 2. Relationship between diversity and species richness in fish assemblages and the NO3:NH 4 ratio.
Fig. 3 in Patterns in fish species composition and assemblage structure in the upper Salado River lakes, Pampa Plain, Argentina
Fig. 3. Bar chart showing the distribution of total fish collected of each species within the upper Salado River lakes. Species codes as listed in Table 2. Species are intentionally sorted by means of their spatial distribution to ease the interpretation. From left to right, from clear to dark filled bars: Mch = Mar Chiquita, Go = Gómez, Crp = Carpincho, and Rch = Rocha.
Fig. 4 in The role of vegetated areas on fish assemblage of the Paraná River floodplain: effects of different hydrological conditions
Fig. 4. NMDS ordination of dominant fish for different hydrologic condition (HW = high water, FW receding water, IS isolation) and sites (S). Ope = Odontostilbe pequira, Cvo = Cyphocharax voga, Spi = Serrapinnus calliurus, Abi = Astyanax bimaculatus, Pli = Prochilodus lineatus, Mdi = Moenkhausia dichroura, Gba = Gymnogeophagus balzanii, Rbo = Roeboides microlepis, Opa = Odontostilbe paraguayensis, Dte = Diapoma terofali.
Fig. 2 in The role of vegetated areas on fish assemblage of the Paraná River floodplain: effects of different hydrological conditions
Fig. 2. Water level fluctuations of the Paraná River at Corrientes between 1997 and 2001. The Sites were connected with the Paraná River above the hydrological level indicated by the horizontal lines. The number of flooding days (in parentheses) indicates the connectivity between the floodplain and the river channel.
Fig. 3 in The role of vegetated areas on fish assemblage of the Paraná River floodplain: effects of different hydrological conditions
Fig. 3. Cluster analysis based on Jaccard distance (UPGMA method) of fish assemblages in the seven floodplain lakes. A. March 1999 (after a long lasting inundation phase of the Paraná River), B-September 1999 (at receding water) and C- February 2000 (during isolation).
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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)
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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.