Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
274
datasets available to search
ShareScore release 0.9.0
Dataset results
274 results for “climate change responses”
Differential response of Sichuan snub-nosed monkeys to climate change and human activities
<p><strong><span>Aim</span></strong><span>: Determining the mechanisms by which climate change and human activities affect patterns of ecological specialization in different genetic units of the same species is crucial for developing local or regionally-based conservation solutions. This study uses species distribution models and genetic analysis to 1) identify evidence of intraspecific differences in the population size and distribution of the three extant lineages (Sichuan/Gansu (SG), Qinling (QL), and Shennongjia (SNJ)) of Sichuan snub-nosed monkeys; and 2) determine why some lineages have lower population numbers, a smaller geographical distribution, and are more threatened with extinction.</span></p> <p><span><strong>Location</strong>:</span><span> China</span></p> <p><span><strong>Methods</strong>:</span><span> We used n-dimensional hypervolume modeling and Genotype–Environment Association (GEA) models to compare the climatic niches of three snub-nosed monkey lineages, SDMs to reconstruct the historical, current, and future distributions of each lineage, and SMC++ to calculate their effective population sizes. </span></p> <p><span><strong>Results</strong>:</span><span> We found evidence of: 1) climatic niche differentiation among the SG, QL, and SNJ lineages of Sichuan snub-nosed monkeys; 2) geographical isolation combined with a decrease in population size during the LGM resulted in ecological specialization among these three lineages; and 3) a decline in climatic suitability and anthropogenically-driven land conversion, combined with small population size and a narrow distributional range, indicate that the SNJ lineage is at a greater risk of extinction than the SG and QL lineages.</span></p> <p><span><strong>Main</strong> <strong>conclusions</strong>: </span><span>We demonstrate that during the LGM a reduction in habitat suitability</span> <span>driven by climate change, in concert with decreasing population size resulted in the geographical isolation of the three Sichuan snub-nosed monkey subpopulations, leading to lineage differences in ecological specialization</span><span>.</span> <span>GEA models and hypervolume models demonstrated that the three lineages occupy different ecological niches. Based on lineage-level models, the SNJ and QL lineages should be the immediate focus of conservation efforts due to their </span><span>small effective population size and expected future reductions in available suitable habitat</span><span>. The modeling approach used here</span> <span>is robust and can be applied effectively to examine the biogeography, recent evolutionary history, and effective population size of other endangered animal taxa.</span></p>
Forecasting climate change response in an alpine specialist songbird reveals the importance of considering novel climate
<p><span>Species persistence in the face of climate change depends on both ecological and evolutionary factors. Here, we integrate ecological and whole-genome sequencing data to describe how populations of an alpine specialist, the Brown-capped Rosy-Finch (<em>Leucosticte australis</em>) may be impacted by climate change.</span> <span>We sampled 116 Brown-capped Rosy-Finches from 11 sampling locations across the breeding range. Using 429,442 genetic markers from whole-genome sequencing, we described population genetic structure and identified a subset of 436 genomic variants associated with environmental data. We modelled future climate change impacts on habitat suitability using ecological niche models (ENMs) and impacts on putative local adaptation using gradient forest models (a genetic-environment association analysis; GEA). We used the metric of niche margin index (NMI) to determine regions of forecasting uncertainty due to climate shifts to novel conditions. Population genetic structure was characterized by weak genetic differentiation, indicating potential ongoing gene flow among populations. Precipitation as snow had high importance for both habitat suitability and changes in genetic variation across the landscape. Comparing ENM and gradient forest models with future climate predicted suitable habitat contracting at high elevations and population allele frequencies across the breeding range needing to shift to keep pace with climate change. NMI revealed large portions of the breeding range shifting to novel climate conditions. Our study demonstrates that forecasting climate vulnerability from ecological and evolutionary factors reveals insights into population-level vulnerability to climate change that are obfuscated when either approach is considered independently. For the Brown-capped Rosy-Finch, our results suggest that persistence may depend on rapid adaptation to novel climate conditions in a contracted breeding range. Importantly, we demonstrate the need to characterize novel climate conditions that influence uncertainty in forecasting methods.</span></p>
Global insect herbivory and its response to climate change
<p>Data and code for paper '<em>Global insect herbivory and its response to climate change</em>'. To obtain more comprehensive data, please download the <a href="../records/11047796"><strong>new version</strong></a> of the file from Zenodo.</p> <p>Mu Liu, Peixi Jiang, Jonathan M. Chase, Xiang Liu,<br>Global insect herbivory and its response to climate change,<br>Current Biology,<br>2024,<br><a href="https://doi.org/10.1016/j.cub.2024.04.062">https://doi.org/10.1016/j.cub.2024.04.062</a>.</p>
Goldenberg, J., Bisschop, K., Bruni, G., Di Nicola, M. R., Banfi, F., Faraone, F. P. "Replication Data for: Melanin-based color variation in response to changing climates in snakes"
<p>This repository contains the data used to produce the manuscrpit "Melanin-based color variation in response to changing climates in snakes" by Goldenberg, J., Bisschop, K., Bruni, G., Di Nicola, M. R., Banfi, F., Faraone, F. P.</p> <p>Article DOI: 10.1002/ece3.11627</p> <p>Journal: Ecology and Evolution</p>
Data and figure production code for 'Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry'
<p>Time mean data, and python code, used to create figures in 'Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry', Biogeosciences, Hogikyan and Resplandy 2024</p>
SDM results for 10,590 tree species from "Regional uniqueness of tree species composition and response to forest loss and climate change"
<p>Output from species distribution models (SDMs) with geographic constraints to estimate the spatial distribution of tree species at the global level at a 30-arc second resolution, presented in the publication "Regional uniqueness of tree species composition and response to forest loss and climate change". </p> <h2>Data</h2> <p>This file contains the results for 10,590 tree species. The results for each species are contained in a directory with the species name connected by an underscore. For most species, the directory contains several .tif files that make up the tiles of the distribution maps for that species and a metadata file. The .tif files can be merged with the gdal_merge.py function to obtain a single .tif file per species (see example below). For some species, the directory contains a single .tif file which does not require merging. In all cases, the .tif files contain 9 bands that correspond to the predicted species distribution using climatic variables corresponding to various climate projections from Chelsa 2.1.</p> <h3>Band order</h3> <ol> <li>covariates_1981_2010: average of historical climate measurements from 1981 to 2010</li> <li>covariates_2011_2040_ssp126: average future climate projection for 2011-2040 under shared socioeconomic pathway (SSP) 1.26</li> <li>covariates_2011_2040_ssp370: average future climate projection for 2011-2040 under SSP 3.70</li> <li>covariates_2011_2040_ssp585: average future climate projection for 2011-2040 under SSP 5.85</li> <li>covariates_2041_2070_ssp126: average future climate projection for 2041-2070 under SSP 1.26</li> <li>covariates_2041_2070_ssp370: average future climate projection for 2041-2070 under SSP 3.70</li> <li>covariates_2041_2070_ssp585: average future climate projection for 2041-2070 under SSP 5.85</li> <li>covariates_2071_2100_ssp126: average future climate projection for 2071-2100 under SSP 1.26</li> <li>covariates_2071_2100_ssp370: average future climate projection for 2071-2100 under SSP 3.70</li> <li>covariates_2071_2100_ssp585: average future climate projection for 2071-2100 under SSP 5.85</li> </ol> <h3>Metadata</h3> <p>The metadata contains more information about the bands, as well as the following species-level properties:</p> <ul> <li>nobs: number of spatially distinct occurrence records used in model training</li> <li>precision: precision of binarised model output computed through 3-fold cross-validation</li> <li>threshold: threshold used to binarise probabilistic model output, determined as the threshold maximizing the true skill statistic (TSS) during 3-fold cross-validation</li> <li>f1: F1 score of binarised model output computed through 3-fold cross-validation</li> <li>auc: area under the ROC curve (AUC) of model output computed through 3-fold cross-validation</li> <li>prevalence: prevalence of presences (ie. occurrences records) throughout the training data which consisted of occurrence records and pseudo-absences</li> <li>tss: TSS of binarised model output computed through 3-fold cross-validation</li> <li>recall: recall of binarised model output computed through 3-fold cross-validation</li> <li>nativeness_info: indicates whether reported native countries were available for this species (possible values: "yes" or "no", should be "yes" for all species included)</li> <li>npa: number of pseudo-absences used in model training</li> <li>system:index: species name </li> </ul> <h3>Merging example</h3> <p>For example, the directory Abarema_barbouriana contains files Abarema_barbouriana_0.tif, Abarema_barbouriana_2.tif, ..., Abarema_barbouriana_9.tif and metadata.json. The tiles can be merged with the command "gdal_merge.py -o Abarema_barbouriana_merged.tif Abarema_barbouriana/Abarema_barbouriana_*.tif".</p>
Response of phosphorus burial and post-depositional diagenesis to postglacial climate change in the coastal system
<p><strong>The dataset includes geochemical data comprising major elements, TOC and TN, phosphorus and iron speciation, and EDS analysis results, as used in the manuscript titled "Response of phosphorus burial and post-depositional diagenesis to postglacial climate change in the coastal system".</strong></p>
Data from: Response of net primary production to land use and climate changes in the middle-reaches of the Heihe River basin
Net primary production (NPP) supplies matter, energy, and services to facilitate the sustainable development of human society and ecosystem. The response mechanism of NPP to land use and climate changes is essential for food security and biodiversity conservation but lacks a comprehensive understanding, especially in arid and semi-arid regions. To this end, taking the middle-reaches of the Heihe River basin (MHRB) as an example, we uncovered the NPP responses to land use and climate changes by integrating multi-source data (e.g., MOD17A3 NPP, land use, temperature, and precipitation) and multiple methods. The results showed that: (1) land use intensity (LUI) increasing, and climate warming and wetting promoted NPP. From 2000 to 2014, the LUI, temperature and precipitation of MHRB increased by 1.46, 0.58 °C and 15.76 mm, respectively, resulting in an increase of 14.62 gC/m2 in annual average NPP. (2) The conversion of low-yield cropland to forest and grassland increased NPP. Although the widespread conversion of unused land and grassland to cropland boosted both LUI and NPP, it was not conducive to ecosystem sustainability and stability due to huge water consumption and human-appropriated NPP. Urban sprawl occupied cropland, forest and grassland, and reduced NPP. (3) Increase in temperature and precipitation generally improved NPP. The temperature decreasing less than 1.2 °C also promoted the NPP of hardy vegetation due to the simultaneous precipitation increasing. However, warming-induced water stress compromised the NPP in arid sparse grassland and deserts. Cropland had greater NPP and NPP increase than natural vegetation due to the irrigation, fertilizers and other artificial inputs it received. Decrease in both temperature and precipitation generally reduced NPP, but the NPP in the well-protection or less-disturbance areas still increased slightly.
R-scripts for the calculation of HW and Bioclimatic models in: "Small vertebrate and mollusc community response to the Holocene environment and climate changes in the Kraków-Częstochowa Upland (Poland)"
<p>HW_Holocene: Tables and R script used for calculation of HW percentage values.</p> <p>PalBER_Bioclimaticmodel_modified: Tables and R script used for the calculation of the climate values through Bioclimatic model. Modified after Royer et al., 2020.</p>
Data from: Blue mussel (Genus Mytilus) transcriptome response to simulated climate change in the Gulf of Maine
<p>The biogeochemistry of the Gulf of Maine is rapidly changing in response to a changing climate, including rising temperatures, acidification, and declining primary productivity. These impacts are projected to worsen over the next hundred years and will apply selective pressure on populations of marine calcifiers. This study investigates the transcriptome expression response to these changes in ecologically and economically important marine calcifiers, blue mussels. Wild mussels (<i>Mytilus edulis</i> and <i>M. trossulus</i>) were sampled from sites spanning the Gulf of Maine and exposed to two different biogeochemical water conditions: i. present-day conditions in the Gulf of Maine and ii. simulated future conditions that included elevated temperature, increased acidity, and decreased food supply. Patterns of gene expression were measured using RNA-seq from 24 mussel samples and contrasted between ambient and future conditions. The net calcification rate, a trait predicted to be under climate-induced stress, was measured for each individual over a 2-week exposure period and used as a covariate along with gene expression patterns. Generalized linear models, with and without the calcification rate, were used to identify differentially expressed transcripts between ambient and future conditions. The comparison revealed transcripts that likely comprise a core stress response characterized by the induction of molecular chaperones, genes involved in aerobic metabolism, and indicators of cellular stress. Furthermore, the model contrasts revealed transcripts that may be associated with individual variation in calcification rate and suggest possible biological processes that may have downstream effects on calcification phenotypes, such as zinc-ion binding and protein degradation. Overall, these findings contribute to the understanding of blue mussel adaptive responses to imminent climate change and suggest metabolic pathways are resilient in variable environments.</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Adaptation of sea turtles to climate warming: will phenological responses be sufficient to counteract changes in reproductive output?
<p>README: Supplementary Information</p> <p> </p> <p>In this document, we list the supplementary material that supports our results and conclusions and we provide a description of what each file contains.</p> <p> </p> <p>Supplementary Tables</p> <p> </p> <p><strong>Supp. Info. Table S1: metadata</strong></p> <p>This file contains various information about the nesting sites studied, including their location and respective Regional Management Unit (RMU, as per Wallace, B. P., et al. 2010. "Regional management units for marine turtles: a novel framework for prioritizing conservation and research across multiple scales." PLoS One 5(12): e15465), the number of nesting seasons with nest count data and how monitoring is conducted, the number of temperature loggers deployed, their type and where they were placed in the clutch, hatchling measurements in terms of straight carapace length (SCL in mm), and whether data from the literature (incubation experiments at constant temperature) was available at the RMU level to estimate thermal tolerance curves (see the column labeled ‘hatching success lab. data for this RMU (literature)’) and sex ratio thermal reaction norms (see the column labeled ‘sex ratio lab. data for this RMU (literature)’).</p> <p>The file also summarizes the parameters used to reconstruct nest temperature following the method in Monsinjon, J. R., et al. (2019) "The climatic debt of loggerhead sea turtle populations in a warming world." Ecological Indicators 107: 105657: mean diel thermal amplitude in °C (daily maxima minus daily minima), average time of daily min. temperatures in decimal hours, average time of daily max. temperatures in decimal hours, number of days lagged with sea surface temperature (SST), number of days lagged with two-meter air temperature (T2M), GLM coefficient of the Intercept, GLM coefficient of the relationship with SST, GLM coefficient of the relationship with T2M, GLM coefficient of the relationship with the proportion of incubation time used to infer metabolic heating (MH), and standard deviation of the coefficients of the nests from the GLMM random effect used to estimate the thermal heterogeneity.</p> <p>IPCC regions from which predicted increases in air and sea temperatures were extracted are indicated along with the values extracted for the future changes in temperature (median). The settings selected to extract the warming scenarios from the online interface are given below:</p> <ul> <li>IPCC's atlas: https://interactive-atlas.ipcc.ch/regional-information</li> <li>Dataset = CMIP6 (Model projections)</li> <li>Variable = Mean temperature (T) and Sea Surface Temperature (SST) anomalies (change in deg C)</li> <li>Region set = WGI reference-regions (or Small islands for Tetiaroa, French Polynesia)</li> <li>Uncertainty = Advanced</li> <li>Baseline period = 1981-2010</li> <li>Future period = 2081-2100</li> <li>Season = Annual</li> <li>Scenario “Middle of the road” (SSP2-4.5): Approximately in line with the upper end of combined pledges under the Paris Agreement. The scenario “deviates mildly from a ‘no-additional climate-policy’ reference scenario, resulting in a best-estimate warming around 2.7°C by the end of the 21st century”.</li> </ul> <p>The remaining columns show the shift (number of days) in nesting phenology estimated for the IPCC regions according to seawater warming scenarios, and using either the mean or the extreme (max.) coefficient of the negative linear relationship between nesting dates and sea water temperature (using the literature data presented in <strong>Supp. Info. Table S6</strong>).</p> <p> </p> <p><strong>Supp. Info. Table S2: hatching success literature data</strong></p> <p>This file contains the literature data on hatching success from incubation experiments conducted at various constant temperatures. For each species, the Regional Management Unit (RMU) is specified.</p> <p> </p> <p><strong>Supp. Info. Table S3: mean temperature and hatching success</strong></p> <p>This file contains in-situ hatching success data and associated mean temperatures during the whole incubation period. Data are from the literature and the present study (refer to <strong>Supp. Info. Table S1</strong> for the 3-letter beach codes). Note that the nesting season is specified only for the present study.</p> <p> </p> <p><strong>Supp. Info. Table S4:</strong> <strong>sex ratio literature data</strong></p> <p>This file contains the literature data on sex ratio from incubation experiments conducted at various constant temperatures. For each species, the Regional Management Unit (RMU) is specified.</p> <p> </p> <p><strong>Supp. Info. Table S5: in situ hatching success</strong></p> <p>This file contains in-situ hatching success data measured at 19 of our 24 study sites encompassing the four species considered for this study: <em>Caretta caretta</em>, <em>Chelonia mydas</em>, <em>Eretmochelys imbricata</em>, and <em>Lepidochelys olivacea</em> (refer to <strong>Supp. Info Table S1</strong> for the 3-letter beach codes). When the number of eggs was not available, we calculated the number that hatched by multiplying the survival proportion by 100 and rounding the value. And we calculated the number of eggs that did not hatch by subtracting the number that hatched from 100.</p> <p> </p> <p><strong>Supp. Info. Table S6: phenological shifts</strong></p> <p>This file contains the literature data on the relationship between nesting dates and thermal environmental cues. The grey rows (*) indicate cases that were not considered because the study reported either non-significant relationships or positive relationships between the proxy for nesting phenology and the environmental cue (i.e., a delay of nesting dates with increasing temperatures instead of a shift earlier as assumed in the present study).</p> <p> </p> <p><strong>Supp. Info. Table S7: required phenological shifts earlier and later in the nesting season</strong></p> <p>This file contains the estimated required phenological shifts (number of days earlier or later in the season to stay within present-day conditions) and associated rates (number of days earlier or later per 1°C increase in sea surface temperature) that would be necessary to achieve required shifts. Rates were calculated by dividing the required shifts by projected increases in sea surface temperature at our sites (see <strong>Supp. Info. Table S1</strong>). Required shifts and rates were calculated for our indicators of incubation temperature (IT in column labels; phenological shifts required for the future median incubation temperature index to remain below the 75<sup>th</sup> percentile of current conditions), hatching success (HS in column labels; phenological shifts required for the future median hatching success index to remain above the 25<sup>th</sup> percentile of current conditions), and sex ratio (SR in column labels; phenological shifts required for the future median sex ratio index, in proportion of males, to remain above the 25<sup>th</sup> percentile of current conditions). NAs mean that no shift was found to remain within present-day conditions. Refer to <strong>Supp. Info Table S1</strong> for the 3-letter beach codes.</p> <p> </p> <p><strong>Supp. Info. Table S8: hatching success and sex ratio ranges per site</strong></p> <p>This file contains statistics (minimum, median, and maximum) on hatching success (HS in survival proportion) and sex ratio (SR in male proportion) for each study site, and the climate (SSP2-4.5) and phenology (no shift, mean shift, and max. shift) scenarios presented in the core manuscript. For each study site (see <strong>Supp. Info. Table S1</strong> for the 3-letter beach codes in the first column), ranges are calculated between 2007-2020 for the present conditions and between 2059-2100 for the future conditions.</p> <p> </p> <p>Supplementary Figures</p> <p> </p> <p><strong>Supp. Info. Figure S1: hatching success and sex ratio reaction norms</strong></p> <p>This figure shows the hatching success and sex ratio thermal reaction norms estimated using literature data at controlled incubation temperatures (see data in <strong>Supp. Info. Table S2</strong> and <strong>Supp. Info. Table S4</strong>) for the four species considered here: <em>Caretta caretta</em>, <em>Chelonia mydas</em>, <em>Eretmochelys imbricata</em>, and <em>Lepidochelys olivacea</em>. Points are observations and error bars are their confidence intervals. Continuous lines are estimated curves with shades of grey being the confidence intervals. Reaction norms were fitted using data at the species-level (black) and the RMU-level (red) when available.</p> <p> </p> <p><strong>Supp. Info. Figure S2: output examples in 2018-2020</strong></p> <p>This figure shows five successive panels for each study site. In the first panel, we show reconstructed nest temperature without metabolic heating under present (black) and future (SSP2-4.5 in red) scenarios. The blue line shows the estimated pattern of nesting activity assuming no change in phenology and the purple and pink ones corresponds to those assuming a maximum shift (i.e., -18.85 d.°C<sup>-1</sup>) and a mean shift (i.e., -6.86 d.°C<sup>-1</sup>), respectively, in nesting dates under the SSP2-4.5 warming scenario (see median values in <strong>Supp. Info. Table S1</strong>). The following panels show the outputs for hatching success and sex ratio under the present and SSP2-4.5 climate scenarios. Information on the study sites is shown in the title (species, location, 3-letter beach codes).</p> <p> </p> <p><strong>Supp. Info. Figure S3: required shift for incubation temperature</strong></p> <p>This figure shows the backward (earlier nesting) and forward (later nesting) phenological shifts required (vertical arrows) for the future median incubation temperature index (the black continuous line, with grey shaded areas representing the 25<sup>th</sup> and 75<sup>th</sup> percentiles) to remain below the 75<sup>th</sup> percentile of current conditions. The black rectangle represents the 25<sup>th</sup> and 75<sup>th</sup> percentiles of current conditions and the black point is the median. The red point shows the median of future conditions under the SSP2-4.5 warming scenario assuming no phenological shift, the blue point according to the mean phenological shift, and the green point according to the maximum phenological shift. Refer to <strong>Supp. Info. Table S1</strong> for the 3-letter beach codes.</p> <p> </p> <p><strong>Supp. Info. Figure S4: required shift for hatching success</strong></p> <p>This figure shows the backward (earlier nesting) and forward (later nesting) phenological shifts required (vertical arrows) for the future median hatching success index (the black continuous line, with grey shaded areas representing the 25<sup>th</sup> and 75<sup>th</sup> percentiles) to remain above the 25<sup>th</sup> percentile of current conditions. The black rectangle represents the 25<sup>th</sup> and 75<sup>th</sup> percentiles of current conditions and the black point is the median. The red point shows the median of future conditions under the SSP2-4.5 warming scenario assuming no phenological shift, the blue point according to the mean phenological shift, and the green point according to the maximum phenological shift. Refer to <strong>Supp. Info. Table S1</strong> for the 3-letter beach codes.</p> <p> </p> <p><strong>Supp. Info. Figure S5: required shift for sex ratio</strong></p> <p>This figure shows the backward (earlier nesting) and forward (later nesting) phenological shifts required (vertical arrows) for the future median sex ratio index, in proportion of males (the black continuous line, with grey shaded areas representing the 25<sup>th</sup> and 75<sup>th</sup> percentiles), to remain above the 25<sup>th</sup> percentile of current conditions. The black rectangle represents the 25<sup>th</sup> and 75<sup>th</sup> percentiles of current conditions and the black point is the median. The red point shows the median of future conditions under the SSP2-4.5 warming scenario assuming no phenological shift, the blue point according to the mean phenological shift, and the green point according to the maximum phenological shift. Refer to <strong>Supp. Info. Table S1</strong> for the 3-letter beach codes.</p> <p> </p> <p><strong>Supp. Info. Figure S6: incubation temperature fit quality for each site</strong></p> <p>This figure shows the predicted vs observed daily mean incubation temperatures (individually for each site). The grey dashed line is the line of equality, and the red line shows the orthogonal regression. Refer to <strong>Supp. Info. Table S1</strong> for the 3-letter beach codes.</p> <p> </p> <p><strong>Supp. Info. Figure S7: sensitivity analysis</strong></p> <p>This figure shows the differences in hatching success (survival proportion) and sex ratio (male proportion) when predicted using laboratory data (from constant temperature experiments found in the literature: see <strong>Supp. Info. Table S2 </strong>and <strong>Supp. Info. Table S4</strong>) either at the species level or at the Regional Management Unit (RMU) level. Differences are plotted for two climate scenarios (present and SSP2-4.5) and three phenology scenarios (no shift, mean shift, max. shift: see values in number of days shifted earlier in <strong>Supp. Info. Table S1</strong>). We considered only predictions for <em>Caretta caretta</em> (Cc in orange), <em>Eretmochelys imbricata</em> (Ei in yellow) and <em>Lepidochelys olivacea</em> (Lo in green). Data at the RMU level were not available at our study sites for <em>Chelonia mydas</em>. Black dashed lines represent the line of equality.</p> <p> </p>
Data from: Plant genetics and interspecific competitive interactions determine ectomycorrhizal fungal community responses to climate change
Open the record for dataset details and reuse information.
Data from: The evolution of mammal body sizes: responses to Cenozoic climate change in North American mammals
Open the record for dataset details and reuse information.
Data from: What is a mild winter? Regional differences in within-species responses to climate change
Open the record for dataset details and reuse information.
Data from: Sex-specific responses to climate change in plants alter population sex ratio and performance
Open the record for dataset details and reuse information.
Data from: Growth-competition-herbivore resistance trade-offs and the responses of alpine plant communities to climate change
Open the record for dataset details and reuse information.
Data from: Quantifying how short-term environmental variation leads to long-term demographic responses to climate change
Open the record for dataset details and reuse information.
Data from: Investigating yellow dung fly body size evolution in the field: response to climate change?
Open the record for dataset details and reuse information.
Data from: Altitudinal migration and the future of an iconic Hawaiian honeycreeper in response to climate change and management
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.