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Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B in PhylOgeOgraphy and pOtential distributiOn OF Sturnira lilium and S. giannae (ChirOptera: PhyllOstOmidae) With range eXtensiOn FOr S. giannae in the CerradO and Pantanal biOmes
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B, and C were the ones that most affected the potential distribution of S. giannae, and D, E, and F most affected the potential distribution of S. lilium. The curves show the average response of the 10 replicate Maxent runs (red) and the standard deviation (blue).
Fig. 3 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 3. Cumulative distribution plots divided by year for (A) T. alpinus and (B) T. speciosus. For each species 2013 is shown in red, 2014 in teal, 2015 in pink. The x-axis represents each host individual, ordered from least to most flea infested, and the y-axis shows the cumulative proportion of total flea counts. The dotted line indicates individuals in the 90th percentile of flea abundances, illustrating that the top 10% most infected chipmunks usually account for close to 50% of all counted fleas. The proportion of individuals without fleas in each year is represented graphically as the proportion at which each colored line departs from the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 4. Relationships between fecal glucocorticoid metabolite levels, sex, and flea abundance for (A) T. alpinus and (B) T. speciosus. Points show the mean ± S.E. number of fleas counted for female (white) and male (black) individuals within FGM categories (FGM values were rounded to the nearest 10). Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each sex.
Fig. 2 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 2. Patterns of flea abundance across years, hosts, and flea species. Overall average flea abundances (A–B) and abundances of each flea species (C–D) in each year for T. alpinus (A, C) and T. speciosus (B, D). Abundances of each flea species on hosts of each sex (Males: closed circles, Females: open circles) on T. alpinus (E) and T. speciosus (F).
Fig. 5 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 5. Relationships between flea abundances and (A) the second principal component of temperature data; or (B) elevation for T. alpinus (white) and T. speciosus (black). Points show the mean ± S.E. number of fleas counted for a given study site in a given year. For each study site in each year, a mean ± S.E. temperature or elevation value is shown. Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each species.
Fig. 1 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 1. Map showing study sites. Sites (see Supplementary Data S1 for more information) located in and around Yosemite National Park (green) were visited either in all three years (2013, 2014, and 2015; black), in two of the years (yellow), or in only one year (red). Yellow and black lines show significant roadways in the area. Lakes are shown in blue, including Mono Lake at top right. Inset shows Yosemite National Park (green) on a map of California. Site codes: AL: Arrowhead Lake; CL: Cathedral Lake (upper); GA: Glen Aulin; GL: Gaylor Lakes; HC: Hoffmann Creek; MA: Mammoth Lakes; ML: May Lake; PC: Porcupine Creek; SL: Saddlebag Lake; SLN: Saddlebag Lake, north-side (Greenstone and Steelhead Lakes); TM: Tuolumne Meadows. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
Figure 6 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 6. Total cross-validation AUC (CV-AUC) and spatial congruence AUC (SC- AUC) for a range of grain sizes.
Figure 4 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 4. Divergence between the uncorrelated and pruned models estimated through Parolo divergence index at 250-m resolution. As is shown, there was little divergence (0– 0.2) between models in most of the study area.
Figure 1 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 1. The location of the study area on a map of western Asia (right). Inset shows DEM of study area with polygons indicating the protected areas where populations of goitered gazelle occur.
Figure 5 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 5. The change in performance index (AUC, left) and habitat suitability area (right) of the output model with increasing extent size (open circles) and grain size (filled circles) from 250 to 3000 m.
Figure 3 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 3. Goitered gazelle distribution maps based on the uncorrelated model (left) and the pruned model (right) for the 250-m grid size.
Figure 2. Predicted habitat suitability classification for N in Current and suitable habitat of the Critically endangered Northern white-cheeked gibbon (Nomascus leucogenys) in Lao PDR
Figure 2. Predicted habitat suitability classification for N. leucogenys (A, D) 2022, (B, E) 2050 and (C, F) 2070.
Fig. 2. Predicted probabilities and 95 in Is it best on the nest? Effects of avian life-history on haemosporidian parasitism
Fig. 2. Predicted probabilities and 95% confidence intervals of haemosporidian parasitism (Plasmodium, Haemoproteus, and Leucocytozoon). Expected prevalence illustrated according to haemosporidia genera; Plasmodium represented with "P" (a), Haemoproteus represented with "H" (b–c), Leucocytozoon represented with "L" (d–f). Note that in some instances symbol size exceeded the range of confidence intervals.
Fig. 2 in Predicting the risk of Alaria alata infestation in wild boar on the basis of environmental factors
Fig. 2. The prevalence of A. alata in wild boar in provinces in Poland calculated from literature values and data from the present study (A) and predicted by percentage of areas covered by WETLANDS (B) (for detailed information, see: Methods). The figure shows prevalence values for a given province and confidence intervals (lower; upper).
Fig. 4. Predicted lines from a in Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population
Fig. 4. Predicted lines from a generalized linear mixed model for significant effects of (a) the second principal component (PC2), (b) interaction between the first principal component (PC1) and MCP = area of 500-m buffered minimum convex polygon of detected nest sites per gorilla group, (see Minimum convex polygon calculation and Statistical analyses for details) and (c) interaction between monitoring (habituation) status and MCP on tapeworm infection (egg counts per gram in fecal sample). Principal components were computed from 10 correlated environmental variables (see Material and methods for details).
Fig. 3. Predicted lines from a in Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population
Fig. 3. Predicted lines from a generalized linear mixed model for significant effects of (a) the first (PC1) and (b) second principal component (PC2), (c) Density = mean relative density of gorillas per MCP and (d) MCP = area of 500-m buffered minimum convex polygon of detected nest sites per gorilla group, (see Minimum convex polygon calculation and Statistical analyses for details) on strongylid infection (egg counts per gram in fecal sample). Principal components were computed from 10 correlated environmental variables (see Material and methods for details).
Fig. 2 in Predicting size at first sexual maturity from length/weight relationship: a case study with an Amazonian catfish
Fig. 2. Length/weight relationship of male Auchenipterichthys longimanus. A and C represent Huxley regular length/weight relationship and its distribution of proportional residuals. B and D represent polyphasic length/weight relationship its and distribution of proportional residuals.
Fig. 1 in Predicting size at first sexual maturity from length/weight relationship: a case study with an Amazonian catfish
Fig. 1. Sampling area in the National Forest of Caxiuanã, Pará State, showing the rivers where the fish were collected; Curuá River - Ferreira Penna Research Station (ECFPn), Caxiuanã River; Puraquequara River and Caquajó River. A single point on the map may represent more than one colleting site.
Fig. 4 in Predicting size at first sexual maturity from length/weight relationship: a case study with an Amazonian catfish
Fig. 4. Frequency of mature individuals as a function of the total length. Female sexual maturity identified by visual inspection of ovarian (A) and G SI values (B). Male sexual maturity identified by visual inspection of testes (C) and G SI values (D).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.