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Fig. 3 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 3. The ten-year mean values (1997–2006) of roe deer antler weights for each game management unit in Hungary. Source: National Game Management Database of Hungary
Fig. 2 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 2. The spatial distribution of main soil types, based on the soil types and subtypes from the Agrotopography Map of Hungary. (* For abbreviations see Table 1). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricultural Chemistry
Fig. 5 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 5. The means and standard deviations of soil-evaluation-numbers (SEN; soil fertility index representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) and percentages of land-use types (agriculture and forest) for each main soil types* in Hungary.
Fig. 1 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 1. The values of soil-evaluation-number (representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) from the Agrotopography Map of Hungary. (* 1, <10%; 2, 10–20%; 3, 20–30%; 4, 30–40%; 5, 40–50%; 6, 50–60%; 7, 60–70%; 8, 70–80%; 9, 80–90%; 10, 90–100%). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricul-
The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation
<p><span>Institute of Materia Medica, Chinese Academy of Medical Sciences purchased 10,000 ChemDiv databases.</span></p>
FIGURE 12 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 12. Comparison of slopes between the entire Cedar Mountain Formation and BYU fossil localities.
FIGURE 8 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 8. Number of cells assigned to each fossil potential value for the model. TABLE 6. Reclassified values for OLI/TIRS bands for revised model.
FIGURE 5 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 5. Differences of means between fossil localities and Cedar Mountain Formation (X1-X2). TABLE 3. Reclassified values for OLI/TIRS bands used in weighted suitability analysis.
FIGURE 1 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 1. Early Cretaceous Cedar Mountain Formation surface exposure, elevation, and fossil localities.
FIGURE 11 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 11. Comparison of aspects between the entire Cedar Mountain Formation and BYU fossil localities.
Swordtail fish hybrids reveal that genome evolution is surprisingly predictable after initial hybridization
<p>Over the past two decades, biologists have come to appreciate that hybridization, or genetic exchange between distinct lineages, is remarkably common – not just in particular lineages but in taxonomic groups across the tree of life. As a result, the genomes of many modern species harbor regions inherited from related species. This observation has raised fundamental questions about the degree to which the genomic outcomes of hybridization are repeatable and the degree to which natural selection drives such repeatability. However, a lack of appropriate systems to answer these questions has limited empirical progress in this area. Here, we leverage independently formed hybrid populations between the swordtail fish <em>Xiphophorus birchmanni </em>and <em>X. cortezi </em>to address this fundamental question. We find that local ancestry in one hybrid population is remarkably predictive of local ancestry in another, demographically independent hybrid population. Applying newly developed methods, we can attribute much of this repeatability to strong selection in the earliest generations after initial hybridization. We complement these analyses with time-series data that demonstrates that ancestry at regions under selection has remained stable over the past ~40 generations of evolution. Finally, we compare our results to the well-studied <em>X. birchmanni×X. malinche </em>hybrid populations and conclude that deeper evolutionary divergence has resulted in stronger selection and higher repeatability in patterns of local ancestry in hybrids between <em>X. birchmanni </em>and <em>X. cortezi</em>.</p>
Figure 1 in Testing predictions of length at first maturity of teleostean fishes, given their maximum length
Figure 1. – Schematic representation of how, in fish, a declining relative gill surface area (G-lines in A-D) and thus oxygen (O2) supply imposes a limit to body weight (Wmax) when the oxygen reaches a level just sufficient for maintenance (Qmaint in A). When a stress factor (e.g., a higher temperature) increases the O2 demand for maintenance, Wmax is reached at smaller sizes (B). Maturation and spawning must occur at a size Wm Qmaint (C). This contribution suggests that the ratio Qm/ Qmaint is similar and constant among fishes, thus enabling them to adjust their value of Wm under stress condition (D; see text).
FIGURE 6. Classification Tree results and predictions for the five fossil localities. A in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 6. Classification Tree results and predictions for the five fossil localities. A) Results and predictions for CT1, vegetative cover. B) Results and predictions for CT2, biogeographic realm. Abbreviations: LV, La Venta; QH, Quebrada Honda; RU, Rümikon; SC, Santa Cruz; TG, Tinguiririca.
Fig. 1 in Predictive factors of species composition of follower fishes in nuclear-follower feeding associations: a snapshot study
Fig. 1. Selected examples of nuclear-follower fish associations recorded in a stream in Southwestern Brazil: Leporellus vittatus begins to approach the bottom, while followed by a small group of Jupiaba acanthogaster (A); Leporinus macrocephalus disturbs the bottom, while Crenicichla vittata approaches to feed on fleeing or uncovered prey (B); L. macrocephalus stirs a little cloud, while a small group of Odontostilbe pequira and J. acanthogaster approaches (C); the same nuclear species stirs a large cloud, which congregates large numbers of O. pequira (D); Prochilodus lineatus begins to probe on the bottom, readily attracting a small group of O. pequira and J. acanthogaster (E); the same nuclear species stirs a large sediment cloud, which attracts large numbers of O. pequira (F). Photo credits: Sergio R. Floeter (A, E); José Sabino (B); Guilherme Ortigara Longo (C); Ivan Sazima (D, F).
Fig. 2 in Predictive factors of species composition of follower fishes in nuclear-follower feeding associations: a snapshot study
Fig. 2. Two-dimensional ordination of samples, considering the composition of follower fish species in the associations from Bray-Curtis similarity coefficient, and emphasizing the size of the sediment cloud (A), the duration of the sediment cloud (B), the identity of the nuclear species (C) and the abundance of the follower species Odontostilbe pequira, represented by the circumferences' sizes (D).
Figure 3 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 3. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2061-2080, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 2. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2041-2060, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Fig. 1 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 1. Methodology used to predict morning emergence time of Gambelia sila. Emergence was predicted as the time of day immediately preceding a distinct upward slope in the lizard's T b (triangles and dotted line) based on the assumption that it would take several minutes for the radio transmitter to heat in the sun. The rising Tb was also typically associated with the departure from the burrow physical model temperatures (diamonds and long-dashed line) and the approach of the open (sun) physical model temperatures (squares and short-dashed line). In each case, the predicted time was then compared to the observed emergence time when the lizard's head first appeared outside its burrow. The average difference between observed and predicted emergence times was 11 minutes and 37 seconds.
Figure 5 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 5. Illustration of the linear relationships between fetal age and growth measurements of biparietal diameter (BPD: top graph), thoracic diameter (TD: middle graph) and thoracic circumference (TC: bottom graph) in belugas.
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.