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95 results for “climatic factors”
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Forest carbon removal factor variance by climate domain
<p>Uncertainty (variance) in removal factor (annual sequestration rate) for forest carbon in new and existing forests by climate domain (tropical, subtropical, temperate, boreal). Uncertainty analysis is from Harris et al. 2021 Nature Climate Change. Units are aboveground carbon Mg^2/ha^2/year^2. New and existing forest are distinguished by the presence or absence of Hansen et al. 2013 tree cover gain pixels. </p> <p>Note: Uncertainty for existing temperate forest removal factors is so high because the IPCC national greenhouse gas inventory guidelines have a very high uncertainty for these forests (2019 refinement of guidelines). </p> <p>Note: Uncertainty analysis is for published version of the model (v1.2.0).</p> <p>https://github.com/wri/carbon-budget</p>
Species richness: a pivotal factor mediating the effects of land use intensification and climate on grassland multifunctionality
<p>Temperate semi-natural grasslands harbour unique biodiversity, support livestock farming through forage production, and deliver many essential ecosystem services (ESs) to human society; they are highly multifunctional. However, temperate grassland ecosystems are also among the most threatened ecosystems on earth due to land use and climate change. Understanding how biodiversity, climate, and land use intensification impact grassland multifunctionality through complex direct and indirect pathways is critical to better anticipate the future of these fragile ecosystems. </p> <p>Here, we evaluate how local plant species richness (SR) modulates the effect of land use intensification and climate on grassland multifunctionality (using six key ESs: biomass productivity and stability, forage quality, carbon storage, pollination, and local plant rarity) in the French Massif Central, the largest grassland in Western-Europe. We sampled 100 grassland fields with contrasted fertilisation rates, and SR over large elevational and latitudinal gradients related to variation in mean annual temperature (MAT), and drought severity (DS), two key climate change drivers that are predicted to increase in the future.</p> <p>Using a confirmatory path analysis, we found that SR was the main driver of multifunctionality. We also found significant SR × MAT and SR × fertilization interactions suggesting that warm climate and high fertilization rates may alter the biodiversity-ecosystem multifunctionality relationships. Furthermore, increasing temperature and fertilization indirectly influenced multifunctionality by decreasing SR and consequent multifunctionality in warm low-land and highly fertilized grasslands compared to colder montane grasslands or less fertilised ones. DS only impacted some ES individually (e.g. forage quality).</p> <p>Synthesis and applications: we identified SR as a pivotal factor mediating the effects of land use intensification and climate on multifunctionality through both direct and indirect pathways. Failing to account for changes in SR could thus bias any prediction of – or aggravate – the effects of land use intensification and climate change on ESs delivery in temperate grassland ecosystems. Considering that SR, MAT, and fertilization are major proxies of three main global change drivers (biodiversity loss, climate change, and land use intensification) our study may help to better anticipate the effect of multiple interacting global change drivers on grassland ecosystems.</p>
Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China
<p>This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China.</p> <p><strong>Precipitation Reference Period (1979-2018)</strong></p> <p>The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> <p><strong>Ensemble Projections and Climate Change Factors</strong></p> <p>The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>
Fig. 5. The potential distribution map for F in Distribution Of The Freshwater Snail Species Fagotia (Gastropoda, Melanopsidae) In Ukraine According To Climatic Factors. I. Fagotia Esperi
Fig. 5. The potential distribution map for F. esperi in Ukraine under climatic conditions projected for 2050. Captions as in fig. 4, Bu — "Southern Buh".
Fig. 4. The potential distribution map for F in Distribution Of The Freshwater Snail Species Fagotia (Gastropoda, Melanopsidae) In Ukraine According To Climatic Factors. I. Fagotia Esperi
Fig. 4. The potential distribution map for F. esperi in Ukraine under contemporary climatic conditions (black squares represent pixels of 10-minute resolution, predicted to be suitable for the species). Convex polygons are drawn around assumed clusters: N — "northern", Ds — "Dnister", Du — "Danube", Dn — "Dnipro".
Climate is more influential to vegetation green-up than factors that contribute to erosion following high-severity wildfire
<p>Background</p> <p>In the southwestern United States, post-fire vegetation recovery is increasingly variable in forest burned at high-severity. Many factors, including temperature, drought, and erosion, can reduce post-fire vegetation recovery rates. Here, we examined how post-fire precipitation variability, topography, and soils influenced post-fire vegetation recovery in the southwestern United States as measured by greenness. We modeled relationships between post-fire vegetation and these predictors using Random Forest and examined changes in post-fire normalized burn ratio across fires in Arizona and New Mexico. We incorporated growing season climate to determine if year-of-fire effects were persistent during the subsequent five years or if temperature, water deficit, and precipitation in the years following fire were more influential for vegetation greenness.</p> <p>Results</p> <p>We found reductions in post-fire greenness in areas burned at high-severity when heavy and intense precipitation fell on more erodible soils immediately post-fire. In <a>highly erodible</a> scenarios, when accounting for growing season climate, coefficient of variation for year-of-fire precipitation, total precipitation, and soil erodibility decreased greenness in the fifth year. While the effects of year-of-fire factors related to erosion were significant, they were small, and the variability explained by growing season vapor pressure deficit and growing season precipitation were significantly greater.</p> <p>Conclusions</p> <p>Our results suggest that while the factors that contribute to post-fire erosion and its effects on vegetation recovery are important, at a regional scale, the majority of the variability in post-fire greenness in high-severity burned areas in southwestern forests is due to climatic drivers such as growing season precipitation and vapor pressure deficit. Given the scale of area burned at high-severity, the likelihood that high-severity burned area will continue to increase, and the potential for more post-fire erosion that can result in different vegetation trajectories, quantifying how these factors alter the trajectory of greenness and what that means in terms of ecosystem development is central to understanding how different ecosystem types will be distributed across these landscapes with additional climate change.</p>
Fig.11 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.11. Ranking of meteorological factors by the quantity of significant positive or negative correlations with the number of sun-basking Emys orbicularis in the interval 8d"Nsbd"21.
Fig.3 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.3. Basic forms of sun-basking activity of Emys Fig.4. Basic forms of sun-basking activity of Emys orbicularis registered in the study: lying in the orbicularis registered in the study: heating under shadow. the sun in the shoal.
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 1 in Impact of climatic factors on sexual size dimorphism in ground beetle Pterostichus melanarius (Illiger, 1798) (Coleoptera, Carabidae)
Figure 1. Elytra length variation in P. melanarius from different habitats (a – females, b – males). Habitats are designated as follows: 1 – meadow, 2 – birch-forest, 3 – elm, 4 – oak-wood, 6 – pine forest, 7 – willow, 8 – shrubs, 9 – lawn, 10 – fir-forest, 11 – garden, 12 – rape field.
Beyond the usual climate? Factors determining flowering and fruiting phenology across a genus over 117 years
<p><span>Premise</span>: Although changes in plant phenology are largely attributed to changes in climate, the roles of other factors, such as genetic constraints, competition, and self-compatibility, are underexplored. </p> <p><span>Methods</span>: We compiled >900 herbarium records spanning 117 years for all 8 nominal species of the winter-annual genus <em>Leavenworthia</em> (Brassicaceae). We used linear regression to determine the rate of phenological change across years and phenological sensitivity to climate. Using a variance partitioning analysis, we assessed the relative influence of climatic and non-climatic factors (self-compatibility, range overlap, latitude, and year) on <em>Leavenworthia</em> reproductive phenology. </p> <p><span>Key Results</span>: Flowering advanced by ~2.0 days and fruiting ~1.3 days per decade. For every 1°C increase in spring temperature, flowering advanced ~2.3 days and fruiting ~3.3 days. For every 100 mm decrease in spring precipitation, each advanced ~6-7 days. The best models explained 35.4% of flowering variance and 33.9% of fruiting. Spring precipitation accounted for 51.3% of explained variance in flowering date and 44.6% in fruiting. Mean spring temperature accounted for 10.6% and 19.3%, respectively. Year accounted for 16.6% of flowering variance and 5.4% of fruiting, and latitude 2.3% and 15.1%, respectively. Non-climatic variables combined accounted for <11% of the variance across phenophases.</p> <p><span>Conclusions</span>: Spring precipitation, alongside other climate and climatically-related factors, were dominant predictors of phenological variance. Our results emphasize the strong effect of precipitation on phenology, especially in moisture-limited habitats preferred by <em>Leavenworthia</em>. Amongst the many factors that determine phenology, climate is the dominant influence, indicating the effects of climate change on phenology are expected to increase.</p>
Data and code from: Western larch regeneration more sensitive to wildfire-related factors than seasonal climate variability
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Species richness: a pivotal factor mediating the effects of land use intensification and climate on grassland multifunctionality
Open the record for dataset details and reuse information.
Beyond the usual climate? Factors determining flowering and fruiting phenology across a genus over 117 years
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Climate is more influential to vegetation green-up than factors that contribute to erosion following high-severity wildfire
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Data from: Steep and deep: Terrain and climate factors explain brown bear (Ursus arctos) alpine den site selection to guide heli-skiing management
<p>Winter recreation and tourism continue to expand worldwide, and where these activities overlap with valuable wildlife habitat, there is greater potential for conservation concerns. Wildlife populations can be particularly vulnerable to disturbance in alpine habitats as helicopters and snowmachines are increasingly used to access remote backcountry terrain. Brown bears (<i>Ursus arctos</i>) have adapted hibernation strategies to survive this period when resources and energy reserves are limited, and disturbance could negatively impact fitness and survival. To help identify areas of potential conflict between helicopter skiing and denning brown bears in Alaska, we developed a model to predict alpine denning habitat and an associated data-based framework for mitigating disturbance activities. Following den emergence in spring, we conducted three annual aerial surveys (2015–2017) and used locations from three GPS-collared bears (2008–2014) to identify 89 brown bear dens above the forest line. We evaluated brown bear den site selection of land cover, terrain, and climate factors using resource selection function (RSF) models. Our top model supported the hypothesis that bears selected dens based on terrain and climate factors that maximized thermal efficiency. Brown bears selected den sites characterized by steep slopes at moderate elevations in smooth, well-drained topographies that promoted vegetation and deep snow. We used the RSF model to map relative probability of den selection and found 85% of dens occurred within terrain predicted as prime denning habitat. Brown bear exposure to helicopter disturbance was evident as moderate to high intensities of helicopter flight tracking data overlapped prime denning habitat, and we quantified where the risk of these impact was greatest. We also documented evidence of late season den abandonment due to disturbance from helicopter skiing. The results from this study provide valuable insights into bear denning habitat requirements in subalpine and alpine landscapes. Our quantitative framework can be used to support conservation planning for winter recreation industries operating in habitats occupied by denning brown bears.</p>
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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.