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511 results for “climate effects”
Fig. 6 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 6. Podolica cattle in the Gallipoli Cognato Regional Park, Basilicata, southern Italy. These cattle move freely within the park's territory, helping in disseminating Ixodes ricinus to different altitudes (from 200 m to over 1000 m).
Fig. 5. A in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 5. A male of the winter tick Haemaphysalis inermis collected in a cold winter day in January 2010 in Basilicata, southern Italy.
Fig. 4 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 4. Shanghai, China: the largest city proper by population in the world. China is the world's largest carbon emitter; it accounted for 29% of global total emissions in 2012 (Olivier et al., 2013).
Fig. 3 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 3. Deforestation of Atlantic rainforest for the establishment of banana tree plantations in Amaraji, north-eastern Brazil.
Fig. 1 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 1. Climate change is contributing to sea level rise. The Boa Viagem beach is a tourist destination in Recife, north-eastern Brazil. If current trends in sea level rise persist, cities like Recife may be literally swallowed the sea in the coming decades.
Fig. 2 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 2. Sloth found on a road that crosses a region of Atlantic rainforest in Aldeia, north-eastern Brazil. Crab-eating foxes (Cerdocyon thous) and other wild animals are commonly seen crossing this road and are frequently victims of car crashes.
Figure 3. The effective population size through recent time for 3 in Comparative analyses of past population dynamics between two subterranean zokor species and the response to climate changes
Figure 3. The effective population size through recent time for 3 clades of Gansu zokor (Eospalax cansus).
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.
Fig. 3 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 3. GI parasite genera types identified from fecal samples of toque macaques. I. Protozoan types: (A) Balantidium cyst, (B) Balantidium trophozoite, (C) Endolimax cyst, (D) Entamoeba cyst, (E) Isospora cyst. (F) Unidentified protozoan cyst; II. Cestode types: (G) Bertiella ova, (H) Diphyllobothrium ova, (I) Hymenolepis ova; III. Trematode types: (J–K) Unidentified trematode ova; IV. Acanthocephalan type: (L) Moniliformis ova; V. Nematode types: (M) Oesophagostomum ova, (N) Strongyloides ova, (O) Ascaris ova, (P) Trichuris ova, (Q) Strongyle/ Hookworm ova, (R) Enterobius ova, (S)Trichostrongylus ova, (T) Unidentified nematode ova.
Fig. 2 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 2. Map of Sri Lanka with sampling localities in the dry and the wet zones and the montane region.
Fig. 4 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 4. Number of parasite genera types (species richness) infecting M. s. aurifrons, M. s. sinica and M. s. opisthomelas in urban, suburban, and wild habitats.
Fig. 1 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 1. The three subspecies of macaque's endemic to Sri Lanka. (A) Common macaque (Macaca sinica sinica), (B) dusky or pale-fronted macaque (M. s. aurifrons), and (C) hill-zone macaque (M. s. opisthomelas) (image courtesy: Madura De Silva).
Length of day residuals after the removal of tidal friction, glacial isostatic adjustment, and climatic effects: 720 BC to 2020
<p>LOD residuals after the removal of tidal friction, glacial isostatic adjustment, and climatic effects.<br>Time range 720 BC to 2020 AD.<br>Data are with respect to 2020.<br>First column: time in year (negative years mean BC)<br>Second column: LOD residuals in milliseconds<br>Third column: uncertainty of the LOD residuals in milliseconds</p> <p>If you use the data, please cite the following references:<br>1. The increasingly dominant role of climate change on length of day variations: Kiani Shahvandi et al. 2024 published in PNAS, https://doi.org/10.1073/pnas.2406930121<br>2. Length of day variations explained in a Bayesian framework: Kiani Shahvandi et al. 2024 published in GRL<br>3. Addendum 2020 to ‘Measurement of the Earth’s rotation: 720 BC to AD 2015’: Morrison et al. 2021 published in Proceedings of the Royal Society A, https://doi.org/10.1098/rspa.2020.0776</p>
Figure 2 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 2 Abundances (number of individuals per beating sample) of phytoseiid mite species on seedlings in August. A – mean Amblyseius swirskii abundance with and without pollen provisioning. B – The relationship betweenTyphlodromus athiasae andA. swirskii abundances on different cultivars. The order of cultivars appearing in the legend corresponds to the magnitudes of their fitted intercepts (Pomello> Volka> …> Shamouti). Error bars are ± 1 SE
Figure 1 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 1 Phytoseiid species abundances (number of individuals per beating sample) on different cul- tivars in April, 5 weeks post release, on seedlings where Euseius stipulatus was released, with pollen provisioning (white bars), on seedlings where Euseius scutalis was released, with pollen provision- ing (gray bars), and on seedlings where no predator was released, without pollen provisioning (black bars). A – Euseius stipulatus abundances. B –Iphiseius degeneransabundances. C –Amblyseius swirskii abundances. Error bars are ± 1 SE.
Figure 3 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 3 Mean daily reproductive output per female (panels A and B) and survival rate (of both sexes, panels C and D), ofA. swirskii and E. stipulatus on Pomelo and Shamouti leaf discs in climate-controlled chambers. Panels A and C – Temperature regime 1 (simulating spring temperatures). Panels B and D – Temperature regime 2 (simulating summer temperatures). See Table 2 for the daily temperature schedule of each regime. Note the different scales of reproductive output between the two temperature regimes. Error bars are ± 1 SE.
Fig. 2 in Projected Climate Change Effects On Nuthatch Distribution And Diversity Across Asia
Fig. 2. Model predictions of species distribution area retained (gray) and lost (black) due to climate change for two example species, Sitta tephronota (white triangles, western area) and S. frontalis (dotted squares, eastern area).
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.