Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Data from: Climatic conditions and landscape diversity predict plant-bee interactions and pollen deposition in bee-pollinated plants.

Climate change, landscape homogenization and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant-pollinator interactions and pollinator´s plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant-bee interactions in 68 Scandinavian plant communities in landscapes of varying land-cover heterogeneity along a latitudinal temperature gradient of 4–8 C°, and estimated pollen deposition as the number of pollen grains on flowers of the bee-pollinated plants Lotus corniculatus, and Vicia cracca. We show that plant-bee interactions, and the pollination potential for these bee-pollinated plants increase with landscape diversity, annual mean temperature, plant abundance, and decrease with distances to sand-dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant-bee interactions that incorporate pollinator-specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.

Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records, and reveals slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years — but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.

opencc-by-4.0Jun 2024View details →

TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats

TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.

opencc-by-4.0Jan 2024View details →

Data files for "Quantifying the global climate feedback from energy-based adaptation"

Data files for "Quantifying the global climate feedback from energy-based adaptation". Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.   Attribution: Some processed data contain excerpts of Non-Creative Commons Material as defined by the International Energy Agency (IEA -- see their terms of use at `https://www.iea.org/terms/terms-of-use-for-non-cc-material'). The emissions factors we use in our analysis are generated using IEA datasets. These data are aggregates of the underlying country-by-fuel level emissions factors and as presented contain only insubstantial amounts of the Non-CC Material. We attest they cannot be used to reconstruct individual data points in the original dataset. The factors we produce are attributable to the following two sources:  IEA. Emissions factors. Tech. Rep., International Energy Agency (IEA 2021). URL https://www.iea.org/data-and-statistics/data-product/910emissions-factors-2021. All Rights Reserved. IEA. World energy balances 2021. Tech. Rep., International Energy Agency (IEA) (2022). URL https://www.iea.org/data-and-statistics/data-product/world-energy-balances. All Rights Reserved.

Data for: Plasticity in mosquito size and thermal tolerance across a latitudinal climate gradient

Variations in heat tolerance among populations can determine whether a species can cope with ongoing climate change. Such variation may be especially important for ectotherms whose body temperatures, and consequently, physiological processes, are regulated by external conditions. Additionally, differences in body size are often associated with latitudinal clines, thought to be driven by climate gradients. While studies have begun to explore variation in body size and heat tolerance within species, our understanding of these patterns across large spatial scales, particularly regarding the roles of plasticity and genetic differences, remains incomplete. Here, we examine body size, as measured by wing length, and thermal tolerance, as measured by the time to immobilization at high temperatures ("thermal knockdown"), in populations of the mosquito Aedes sierrensis collected from across a large latitudinal climate gradient spanning 1300 km (34-44 °N). We find that mosquitoes collected from lower latitudes and warmer climates were more tolerant of high temperatures than those collected from higher latitudes and colder climates. Moreover, body size increased with latitude and decreased with temperature, a pattern consistent with James' rule, which appears to be a result of plasticity rather than genetic variation. Our results suggest that warmer environments produce smaller and more thermally tolerant populations.

opencc-by-4.0Jun 2024View details →

Mediterranean risk assessment data based on the concurrency between climate change, fisheries, stocks, and biodiversity

Data associated to the paper "Detecting Ecosystem Risk Hotspots: A Mediterranean Case Study" by G. Coro, L. Pavirani, A. Ellenbroek.

opencc-by-4.0Feb 2024View details →

Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"

Community Earth System Model 2 simulation data with marine cloud brightening perturbations used to compute climate impact metrics in "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region" by Romaric C. Odoulami, Haruki Hirasawa, Kouakou Kouadio, Trisha D. Patel, Kwesi A. Quagraine, Izidine Pinto, Temitope S. Egbebiyi, Babatunde J. Abiodun, Christopher Lennard, and Mark G. New. Simulation descriptions can be found in Hirasawa et al., 2023 Geophysical Research Letters doi.org/10.1029/2023GL104314.

opencc-by-4.0Dec 2009View details →

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.

opencc-by-4.0Dec 2009View details →

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.

opencc-by-4.0Jul 2024View details →

Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change

We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (Alle alle), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE.  In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via a model derived from a phylogenetically controlled meta-analysis, available within a web-based app (‘Seabird FMR Calculator’, <a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/">https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</a>) (Dunn et al. 2018).  These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.

opencc-zeroJul 2024View details →

Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe

This dataset contains primary, intermediate, and output data for "Climate change could fuel urinary schistosomiasis transmission in Africa and Europe". In this paper, we use mechanistic and correlative modelling to predict the distribution of schistosomiasis intermediate host snail Bulinus truncatus. Model projections suggest the suitable habitat for B. truncatus will increase by 17%, with new suitable habitat in Southern Europe and Central Africa, and a reduction in suitable habitat in the Sahel region.

opencc-zeroJul 2024View details →

Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations

The amount of water vapor injected into the stratosphere after the eruption of Hunga Tonga-Hunga Ha'apai (HTHH) was unprecedented, and it is therefore unclear what it might mean for surface climate. We use chemistry climate model simulations to assess the long-term surface impacts of stratospheric water vapor (SWV) anomalies similar to those caused by HTHH, but neglect the relatively minor aerosol loading from the eruption. The simulations show that the SWV anomalies lead to strong and persistent warming of Northern Hemisphere landmasses in boreal winter, and austral winter cooling over Australia, years after eruption, demonstrating that large SWV forcing can have surface impacts on a decadal timescale. We also emphasize that the surface response to SWV anomalies is more complex than simple warming due to greenhouse forcing and is influenced by factors such as regional circulation patterns and cloud feedbacks. Further research is needed to fully understand the multi-year effects of SWV anomalies and their relationship with climate phenomena like El Nino Southern Oscillation.

opencc-by-4.0Jun 2019View details →

CESM1.2 simulation data for "Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks"

CESM1.2 simulation data for Early Eocene Citations: Zhu, J., Poulsen, C. J., & Tierney, J. E. (2019). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Science Advances, 5(9), eaax1874. <a href="https://doi.org/10.1126/sciadv.aax1874">https://doi.org/10.1126/sciadv.aax1874</a> Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164" rel="nofollow">https://doi.org/10.1016/j.epsl.2020.116164</a>   <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP), surface temperature (TS) and surface temperature at reference height (TREFHT) from four Eocene simulations with 1×, 3×, 6× and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>TS and TREFHT are on the atmosphere grid of 1.9 × 2.5° (latitude × longitude).</li> <li>TEMP is on the POP ocean grid (~1°; see here: http://www.cesm.ucar.edu/models/cesm1.2/pop2/).</li> <li>NEW on July 09, 2024: restart files for the Eocene simulations.</li> </ul> A case folder is available on GitHub: <a href="https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne">https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne</a>

opencc-zeroJul 2024View details →

Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic

The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. We used macroecological modeling to estimate the community-level species richness across the Western Siberian tundra, with climate variables and anthropogenic influence identified as main explanatory factors. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary. Here we present a supplementing dataset to the analysis of our paper "Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic" (<a href="https://doi.org/10.1002/ece3.11140">https://doi.org/10.1002/ece3.11140</a>). Our research is based on the Western Siberian part of the Russian Arctic Vegetation Archive (AVA-RUS, <a href="http://avarus.space">http://avarus.space</a>), with 1483 Braun-Blanquet plots observed from 2005-2018. The dataset contains geolocated species richness data along with sampled raster data on environmental and anthropogenic predictors used for modeling. The scripts are used for paleoclimatic data sampling; testing univariate predictive performance and limited collinearity for all predictors; fitting four different modes: random forest, gradient boosting machine, generalized linear model, and generalized additive model; their validation and projection. Detailed information regarding the data structure and the applied methods could be found in the paper.

opencc-by-4.0Oct 2017View details →

Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia

Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.

opencc-by-4.0Feb 2024View details →

Climate model and proxy input data for PaleoDA South America reconstruction

This repository contains  input data needed to run the paleoclimate reconstruction code for  "A continental reconstruction of hydroclimatic variability in South America during the past 2000 years", submitted to Climate of the Past in February 2024 [https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/].  The Github repository is located here: https://github.com/mchoblet/paleoda_sa/tree/main Structure: model_data: One File for each Model (GISS, CCSM (isoGSM), CESM, ECHAM5, iHADCM3) and variable (prec,tsurf,d18O, SPEI). Monthly resolution. proxy_data: One File for each proxy record type (Trees and corals contain a separate file for annual and djf linear regression parameters, the proxy data as such is the same). The data has yearly resolution, and thus also contains NaNs for when a year is not covered by a proxy. Note, that these time series are resampled to a regular resolution in the multi-time scale PaleoDA code. Climate Model Data: The original data can be found in https://zenodo.org/records/6610684. The data in this repository here has been slightly modified and regridded for easier processing by the reconstruction algorithm.  When using the data here, please also cite https://zenodo.org/records/6610684 and the publication  "Investigating stable oxygen and carbon isotopic variability in speleothem records over the last millennium using multiple isotope-enabled climate models", by  <div>Janica C. Bühler, Josefine Axelsson, Franziska A. Lechleitner, Jens Fohlmeister, Allegra N. LeGrande, Madhavan Midhun, Jesper Sjolte, Martin Werner, Kei Yoshimura, and Kira Rehfeld (https://cp.copernicus.org/articles/18/1625/2022/cp-18-1625-2022.html)</div> Climate Proxy Data: A regional proxy record subselection for South America. See References in Appendix A Choblet et al. (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/). The DOI of each record is stored as Metadata. How were these files created? The steps are documented in the the Github repository https://github.com/mchoblet/paleoda_sa/tree/main (data_preprocessing). The SPEI drought index has ben computed from modeled precipitation and temperature using Thornthwaite's method (using the Climate Indices package, https://github.com/monocongo/climate_indices). Manuscript revision in July 2024: <ul> <li>Added historical documentary indices time series and the Puyehue lake record. For technical reasons in the PaleoDA algorithm, it is kept apart from the other lake records. The reconstruction code on Github has been updated for including these datasets.</li> </ul>         <div> </div>

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

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 (Fraxinus pennsylvanica) 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.

opencc-by-4.0Jul 2024View details →

Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"

Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in Earth's Future Dear reader, reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in Earth's Future. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below. The research data covers the following files: <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> Meta-data files are provided with data files in the different subfolders for clarification. iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State. Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/ The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep). Literature: Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092. Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/ Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737 Contact one of the authors if you have further questions: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)