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2,260 results for “climate change”
Figure 4 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 4. Overlay of Iranian Conservation Network with the habitat suitability map of the Mesopotamian spiny-tailed lizard.
Figure 3 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 3. Model of habitat suitability for the species based on the present climatic data (A) and 2.6 (B) and 8.5 (C) scenarios of the CCSM for the future.
Figure 1 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 1. The presence records (black dots) used for the development of a maximum entropy model for predicting the habitat suitability of the Mesopotamian spiny-tailed lizard.
Figure 4 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 4. Mean suitability for Arborophila crudigularis under baseline climate conditions and future climate scenarios. cccma and csiro represent two general circulation models; RCP2.6, and RCP8.5 represent two greenhouse gas emission scenarios; EN is entire suitable habitat; PR is presence records.
Figure 2 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 2. Performance of each model for predicting the suitable habitat for Arborophila crudigularis. GLM: Generalized linear model; GBM: generalized boosting model; GAM: generalized additive model; CTA: classification tree analysis; ANN: artificial neural network; FDA: flexible discriminant analysis; MARS: multiple adaptive regression splines; RF: random forest; MAXENT: maximum entropy model.
Figure 6 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 6. Changes in suitable habitat for Arborophila crudigularis under the RCP8.5 emission scenario. cccma and csiro represent two general circulation models.
Figure 5 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 5. Changes in suitable habitat for Arborophila crudigularis under the RCP2.6 emission scenario. cccma and csiro represent two general circulation models.
Figure 2 in Range dynamics of some nemoral species of Lepidoptera in the Russian Far East due to climate change
Figure 2. Some species of Lepidoptera from Amur region (Russia): A – Lobocla bifasciata, 1.07.2021; B, C – Chrysozephyrus brillantinus, 16.07.2021; D – Clanis undulosa, 30.06.2021; E – Acosmeryx naga, 3.07.2021; F – Ambulyx tobii, 1.07.2021; G – Rhagastis mongoliana, 3.07.2021; H, I, J – Siglophora sanguinolenta (H, I –25– 27.07.2021, J – live specimen, 9.09.2021). A, B, D–J – upperside, C – underside. A–H, J – males, I – female. Localities: A – 7 km N Tarmanchukan; B, C – 2 km S Voronezhskoe–1; D, F, H–J – 8 km SE Boitsovo; E – Mokhovaya Pad'; G – 4.5 km NW Rachi.
Figure 1 in Range dynamics of some nemoral species of Lepidoptera in the Russian Far East due to climate change
Figure 1. Distribution records of the some nemoral species of Lepidoptera in the southern part of the Amur region (Russia).
Figure 3 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay
Figure 3. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in summer and autumn.
Figure 2 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay
Figure 2. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in winter and spring
Figure 4 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 4. The relative size distribution of females (1) and males (2) in the spawning of 2016 -2019 (gonads on V maturity stage).
Figure 6 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 6. The average weight – length relationship for the red mullet that lived in the coastal waters of Crimea in the spring and summer of 2016–2019.
Figure 2 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 2. The average annual GSI values (1) of females and males of red mullet, the average monthly temperature of water (2) during the spawning period, and their standard deviations.
Figure 7 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 7. Map of potential invasion range of S. woodiana in Europe under the RCP 8.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 6 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 6. Map of potential invasion range of S. woodiana in Europe under the RCP 4.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 4 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 4. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 8.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 5 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 5. Map of potential invasion range of S. woodiana in Europe under the recent climate conditions: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 3 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 3. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 4.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 1 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 1. Map of records of S. woodiana in Europe obtained from GBIF database and published sources (Vikhrev et al., 2024).
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.