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1,936 results for “environmental data”
Code and Data for: "Signs of local adaptation and phenotypic plastic response to elevation shifted between environmental backgrounds in Snapdragon plants"
<p>Code and data for manuscript: "Signs of local adaptation and phenotypic plastic response to elevation shifted between environmental backgrounds in Snapdragon plants"</p>
data set regarding environmental impact of building facades
<p>The file includes a set of reference data and sources used for the determination of the environmental impact (LCA and EPD) for selected materials used for building facades, with a special emphasis on bio-based solutions.</p>
Data From: Powerful detection of polygenic selection and environmental adaptation in US beef cattle
<p>GEMMA output containing summary statistics for generation proxy selection mapping (GPSM) and environmental GWAS (envGWAS) selection analyses from <br> Rowan et al. "Powerful detection of polygenic selection and environmental adaptation in US beef cattle" 2021<br> https://doi.org/10.1101/2020.03.11.988121 </p> <p>File names identify the analysis run, for example<br> "Gelbvieh_envgwas_desert_summary_stats.txt.gz"<br> Is the Gelbvieh dataset analyzed using the Desert ecoregion as the dependent variable <br> in a univariate envGWAS model. </p> <p>Files are formated according to GEMMA output.</p>
Data from: The evolution of environmental tolerance and range size: A comparison of geographically restricted and widespread Mimulus
<p>The geographic ranges of closely related species can vary dramatically, yet we do not fully grasp the mechanisms underlying such variation. The niche breadth hypothesis posits that species that have evolved broad environmental tolerances can achieve larger geographic ranges than species with narrow environmental tolerances. In turn, plasticity and genetic variation in ecologically important traits and adaptation to environmentally variable areas can facilitate the evolution of broad environmental tolerance. We used five pairs of western North American monkeyflowers to experimentally test these ideas by quantifying performance across eight temperature regimes. In four species pairs, species with broader thermal tolerances had larger geographic ranges, supporting the niche breadth hypothesis. As predicted, species with broader thermal tolerances also had more within-population genetic variation in thermal reaction norms and experienced greater thermal variation across their geographic ranges than species with narrow thermal tolerances. Species with narrow thermal tolerance may be particularly vulnerable to changing climatic conditions due to a lack of plasticity and insufficient genetic variation to respond to novel selection pressures. Conversely, species experiencing high variation in temperature across their ranges may be buffered against extinction due to climatic changes because they have evolved tolerance to a broad range of temperatures.</p>
Data from: Local adaptation (mostly) remains local: reassessing environmental associations of climate-related candidate SNPs in Arabidopsis halleri
<p>Numerous landscape genomic studies have identified single-nucleotide polymorphisms (SNPs) and genes potentially involved in local adaptation. Rarely, it has been explicitly evaluated whether these environmental associations also hold true beyond the populations studied. We tested whether putatively adaptive SNPs in <em>Arabidopsis</em> <em>halleri</em> (Brassicaceae), characterized in a previous study investigating local adaptation to a highly heterogeneous environment, show the same environmental associations in an independent, geographically enlarged set of 18 populations. We analysed new SNP data of 444 plants with the same methodology (partial Mantel tests, PMTs) as in the original study and additionally with a latent factor mixed model (LFMM) approach. Of the 74 candidate SNPs, 41% (PMTs) and 51% (LFMM) were associated with environmental factors in the independent data set. However, only 5% (PMTs) and 15% (LFMM) of the associations showed the same environment–allele relationships as in the original study. In total, we found 11 genes (31%) containing the same association in the original and independent data set. These can be considered prime candidate genes for environmental adaptation at a broader geographical scale. Our results suggest that selection pressures in highly heterogeneous alpine environments vary locally and signatures of selection are likely to be population-specific. Thus, genotype-by-environment interactions underlying adaptation are more heterogeneous and complex than is often assumed, which might represent a problem when testing for adaptation at specific loci.</p>
Data from: Assessing the contributions of intraspecific and environmental sources of infection in urban wildlife: Salmonella enterica and white ibis as a case study
Conversion of natural habitats into urban landscapes can expose wildlife to novel pathogens and alter pathogen transmission pathways. Because transmission is difficult to quantify for many wildlife pathogens, mathematical models paired with field observations can help select among competing transmission pathways that might operate in urban landscapes. Here we develop a mathematical model for the enteric bacteria Salmonella enterica in urban-foraging white ibis (Eudocimus albus) in south Florida as a case study to determine (i) the relative importance of contact-based versus environmental transmission among ibis and (ii) whether transmission can be supported by ibis alone or requires external sources of infection. We use biannual field prevalence data to restrict model outputs generated from a Latin hypercube sample of parameter space and select among competing transmission scenarios. We find the most support for transmission from environmental uptake rather than between-host contact and that ibis–ibis transmission alone could maintain low infection prevalence. Our analysis provides the first parameter estimates for Salmonella shedding and uptake in a wild bird and provides a key starting point for predicting how ibis response to urbanization alters their exposure to a multi-host zoonotic enteric pathogen. More broadly, our study provides an analytical roadmap to assess transmission pathways of multi-host wildlife pathogens in the face of scarce infection data.
Data for: Environmental complexity mitigates the demographic impact of sexual selection
<p>Sexual selection and the evolution of costly mating strategies can negatively impact population demography and adaptive potential. While laboratory studies have documented outcomes stemming from these processes, theory suggests that the demographic impact of sexual selection is contingent on the environment and therefore may have been overestimated in simple laboratory settings. Here we find support for this claim. We exposed copies of beetle lines, previously evolved with or without sexual selection, to a 10-generation heatwave while maintaining half of them in a simple environment and the other half in a complex environment. Populations with an evolutionary history of sexual selection maintained larger sizes and more stable growth rates in complex (relative to simple) environments, an effect not seen in populations that evolved without sexual selection. These results have implications for evolutionary forecasting and suggest that the demographic impact of sexual selection in natural populations might be lower than predicted.</p>
Georeferenced data for the study Environmental suitability throughout the late Quaternary explains population genetic diversity
<p>Data filtered from GBIF (datasetKey: 50c9509d-22c7-4a22-a47d-8c48425ef4a7) Contains 150 records of the <i>Sciurus aberti </i>squirrel filtered in latitudinal windows of 5 degrees from 20 to 45 degrees N. </p>
Data from "Stability of genome-wide methylation patterns and parental environmental effects in the widespread, long-lived Lombardy poplar"
<p><strong>Data from : 'Stability of genome-wide methylation patterns and parental environmental effects in the widespread, long-lived Lombardy poplar'</strong></p> <p>An Vanden Broeck*, Tim Meese*, Pieter Verschelde, Karen Cox, Berthold Heinze, Dieter Deforce, Ellen De Meester and Filip Van Nieuwerburgh</p> <p> </p> <p>Related publication: Vanden Broeck, A.*, Meese, T.*, Verschelde, P. <em>et al.<strong> Genome-wide methylome stability and parental effects in the worldwide distributed Lombardy poplar</strong></em>. <em>BMC Biol</em> <strong>22</strong>, 30 (2024). https://doi.org/10.1186/s12915-024-01816-1</p> <ul> <li>* These authors contributed equally.</li> </ul> <p>--------------------------------------------------</p> <p><strong>Background: </strong>Despite the increasing number of epigenomic studies in plants, little is known about the forces that shape the methylome in long-lived woody perennials. The Lombardy poplar (<em>Populus nigra</em> cv. 'Italica' Duroi) offers an ideal opportunity to investigate the impact of the individual environmental history of trees on the methylome.</p> <p><strong>Results: </strong>We present the results of three interconnected experiments on Lombardy poplar. In the first experiment, we investigated methylome variability during a growing season and across vegetatively reproduced generations. We found that ramets collected over Europe and raised in common conditions have stable methylomes in symmetrical CG-contexts. In contrast, seasonal dynamics occurred in methylation patterns in CHH-context. In the second experiment, we investigated whether methylome patterns of plants grown in a non-parental environment correlate with the parental climate. We did not observe any biological relevant pattern that significantly correlates with the parental climate. Finally, we investigated whether the parental environment has persistent carry-over effects on the vegetative offspring's' phenotype. We combined new bud set observations of three consecutive growing seasons with former published bud set data. Using a linear mixed effects analysis, we found a statistically significant but weak short-term, parental carry-over effect on the timing of bud set. However, this effect was negligible compared to the direct effects of the offspring environment.</p> <p><strong>Conclusions: </strong>Genome-wide cytosine methylation patterns in symmetrical GC-context are stable in Lombardy poplar and appear to be mainly the result of random processes. In this widespread poplar clone, methylation patterns in GC-context can be used as bio-markers to infer a common ancestor and thus to investigate the environmental history of a specific Lombardy poplar on short time-scales. The Lombardy poplar shows high phenotypic plasticity in a novel environment which enabled this clonal tree to adapt and survive all over the temperate regions of the world.</p> <p> </p> <p><strong>ADDITIONAL FILES</strong></p> <p><strong>Additional file 1.</strong> CSV-file with information on the Lombardy poplar trees samples used for whole genome bisulfite sequencing (WGBS) in the two methylome experiments (<em>metadata</em>). The raw fastq datafiles obtained by whole genome bisulfite sequencing (WGBS) are available at the <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO) database</a> (submission GSE225596).</p> <p><strong>Additional file 2.</strong> CSV-file with mapping statistics, bisulfite conversion rates and percentages of cytosine methylation for each DNA-sample analyzed by whole genome bisulfite sequencing (WGBS). (<em>processed data</em>).</p> <p><strong>Additional file 3</strong>. CSV-file with the total list of GO terms that were enriched in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (<em>processed data</em>).</p> <p><strong>Additional file 4. </strong>POWERPOINT-file. Heatmaps with GO terms over-represented in promoters containing DMRs in CpG-context per between-group pairwise comparison. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively). A. HUN4 versus ITS3; B. HUN4 versus UKD2, C; ITS3 versus SPC1; D. HUN4 versus SCP1, E. SPC1 versus UKD2</p> <p><strong>Additional file 5. </strong>CSV-file with the raw data of the bud set observations in the common garden experiment (<em>raw data</em>).</p> <p><strong>Additional file 6. </strong>HTML-file with the R source codes to reproduce the results of the bud set analysis (<em>code,</em> <em>R script</em>).</p> <p><strong>Additional file 7. </strong>A text-file representing the Snakefile (i.e. a readable Python-based workflow) including the different steps and rules of the bioinformatics of the WGBS data analyses (<em>code, Snakefile</em>).</p> <p><strong>Additional file 8. </strong>RMD-file<strong> </strong>with the code to reproduce the analyses to identify differential methylated predefined regions (<em>code,</em> <em>R script</em>).</p> <p><strong>Additional file 9. </strong>R-script with the code to reproduce the clustering and visualizing of the GO enrichment results (<em>code,</em> <em>R script</em>).</p> <p><strong>Supporting files 1</strong>. Zip-folder with: i) excel-files listing the genes in DMRs, and ii) PNG-files with the ‘Biological Coefficient of Variation (BCV)’-plots between any of the six pairwise comparisons of Lombardy poplars grouped per ortet and identified with Bioconductor package edgeR. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (<em>processed data</em>).</p> <p><strong>Supporting files 2</strong>. Zip-folder with PNG-files representing heatmaps and excel-files with clustered GO terms significant over-represented in promoters and gene regions located in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively. The files were obtained with the Bioconductor package simplifyEnrichment (<em>processed data</em>).</p> <p>---------------------------------------------------------</p> <p>Version 3:</p> <ul> <li>Renaming of file names according to the publisher's guidelines</li> <li>Additional file 2 includes also bisulfite conversion rates per sample</li> </ul>
Costa Rica mosquito community species occurrence and site environmental data, July - August 2017
<p>Land use change is an important driver of both biodiversity loss and zoonotic disease transmission in tropical countryside landscapes. Developing solutions for protecting biodiversity, public health, and livelihoods in working landscapes requires understanding the spatial scales at which habitat characteristics such as land cover shape biodiversity, especially for arthropods that transmit pathogens. A growing body of evidence shows that species richness for many taxa correlates with tree cover at small spatial scales of <100 m, indicating that local tree cover management is a promising conservation tool. To investigate whether mosquito species richness, community composition, and presence of specific disease vector species respond to tree cover—and if so, whether at spatial scales similar to other taxa—we surveyed mosquito communities along a tree cover gradient and across agricultural, residential, and forested land uses in rural southern Costa Rica. We found that tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector <em>Aedes albopictus</em>, particularly at small spatial scales of 80 – 200m<em>. </em>Beyond tree cover, land use type predicted community composition and <em>Ae. albopictus </em>presence, but not species richness. The results suggest that preservation and expansion of tree cover at local scales can protect biodiversity for a wide range of taxa and also confer protection against disease vector occurrence.</p>
R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
<p>An important part of infectious disease management is predicting factors that influence disease outbreaks, such as <em>R</em>, the number of secondary infections arising from an infected individual. Estimating <em>R</em> is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated <em>R</em> for <em>Bacillus anthracis</em> infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that <em>R</em> is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (<em>R</em><1) to explosive outbreaks (<em>R</em>>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.</p>
Amazona obscured occurrences, background points, and environmental data
<p><strong>Aim:</strong> Introduced species offer insight on whether and how organisms can shift their ecological niches during translocation. The genus <em>Amazona</em> offers a clear test case, where sister species Red-crowned (<em>A. viridigenalis</em>) and Lilac-crowned Parrots (<em>A. finschi</em>) have established breeding populations in southern California following introduction via the pet trade from Mexico where they do not coexist. After establishment in the 1980s, introduced population sizes have increased, with mixed species flocks found throughout urban Los Angeles. Here, we investigate the differences between the environmental conditions of the native and introduced ranges of these now co-occurring species.</p> <p><strong>Location:</strong> Southern California and Mexico.</p> <p><strong>Method</strong>s: Using environmental data on climate and habitat from their native and introduced ranges, we tested whether Red-crowned and Lilac-crowned Parrots have divergent realized niches between their native ranges, and whether each species has significantly shifted its realized niche to inhabit urban southern California. We also analyzed data from Texas and Florida introductions of Red-crowned Parrots for comparative analysis.</p> <p><strong>Results: </strong>There are significant differences in the native-range niches of both parrot species, but a convergence into a novel, shared environmental niche into urban southern California, characterized by colder temperatures, less tree cover, and lower rainfall. Texas and Florida Red-crowned Parrots also show evidence for niche shifts with varying levels of niche conservatism through the establishment of somewhat different realized niches.</p> <p><strong>Main Conclusions: </strong>Despite significant niche shifts, introduced parrots are thriving, suggesting a broad fundamental niche and an ability to exploit urban resources. Unique niche shifts in different U.S. introductions indicate that <em>Amazona</em> parrots can adapt to diverse environmental conditions, with cities offering a resource niche and the timing of introduction playing a crucial role. Cities can potentially serve as refugia for threatened parrot species, but the risk of hybridization between species emphasizes the need for ongoing monitoring and genetic investigations.</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
<p>Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. In this study, we compare the relative efficacy of linear and second-order LTRE analyses in capturing changes in population growth rate caused by environmental driver changes. To explore this question, we analyze demographic data collected for three long-lived plant species: <em>Ardisia escallonioides</em> (Pascarella & Horvitz, 1998), <em>Silene acaulis</em>, and <em>Bistorta vivipara</em> (Doak & Morris, 2010). This repository includes data files containing vital rate (survival, growth, reproduction) observations or models for our three case studies, as well as an R script in which we use these demographic data to calculate linear and second-order LTRE approximations of changes in population growth rate for each system and generate the figures we present in our paper.</p>
CABO forest inventory survey data: Canopy-level spectra, species abundances, and environmental conditions
<p>This dataset contains the forest inventory survey data as aqcuired by airborne imaging spectroscopy (i.e., continuum removed spectral reflectance) and field inventory surveys (i.e., relative species abundance) for 65 field plots along a temperate-to-boreal forest gradient in southern Québec, Canada. Additionally, it contains plot-level average environmental variables (e.g., elevation, slope, etc.) and species-mean foliar traits. This data supports the manuscript titled "Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis" <em>in press </em>at <em>Ecological Monographs </em>(ID: ECM23-0177) and accompanying code (Crofts 2024) is archieved on Zenodo (<a href="https://doi.org/10.5281/zenodo.10735410)">doi: 10.5281/zenodo.10735410)</a>. Please refer to the methods section of the associated paper for methodological details, with additional details available at protocols.io (<a href="https://doi.org/10.17504/protocols.io.q26g7rn23vwz/v2">doi: 10.17504/protocols.io.q26g7rn23vwz/v2).</a> This data was aqcuired as a part of the Canadian Airborne Biodiversity Observatory (CABO; caboscience.org) and has the licence Creative Commons by Attribution (cc-by). Note: this dataset is also published of EcoSiS (<a href="https://ecosis.org/package/cabo-canopy-level-spectra-from-forest-sites)">https://ecosis.org/package/cabo-canopy-level-spectra-from-forest-sites)</a></p>
Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
<p>Environmental DNA (eDNA) once shed can exist in numerous states with varying behaviors including degradation rates and transport potential. In this study we consider three states of eDNA: 1) a membrane-bound state referring to DNA enveloped in a cellular or organellar membrane, 2) a dissolved state defined as the extracellular DNA molecule in the environment without any interaction with other particles, and 3) an adsorbed state defined as extracellular DNA adsorbed to a particle surface in the environment. Capturing, isolating, and analyzing a target state of eDNA provides utility for better interpretation of eDNA degradation rates and transport potential. While methods for separating different states of DNA have been developed, they remain poorly evaluated due to the lack of state-controlled experimentation. We evaluated the methods for separating states of eDNA from a single sample by spiking DNA from three different species to represent the three states of eDNA as state-specific controls. We used chicken DNA to represent the dissolved state, cultured mouse cells for the membrane-bound state, and salmon DNA adsorbed to clay particles as the adsorbed state. We performed the separation in three water matrices, two environmental and one synthetic, spiked with the three eDNA states. The membrane-bound state was the only state that was isolated with minimal contamination from non-target states. The membrane-bound state also had the highest recovery (54.11 ± 19.24 %), followed by the adsorbed state (5.08 ± 2.28 %), and the dissolved state had the lowest total recovery (2.21 ± 2.36 %). This study highlights the potential to sort the states of eDNA from a single sample and independently analyze them for more informed biodiversity assessments. However, further method development is needed to improve recovery and reduce cross-contamination.</p>
Data from: Phenology of penaeid shrimp nursery habitat use: trends and environmental drivers over four decades
<p>These datasets are those used in analysis published in Batchelder et al. 2024 (for full abstract see: https://doi.org/10.3354/meps14741). </p>
ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations
<p><strong>ClimateForecasts</strong> is a database that provides environmental data for 15,504 weather station locations and 49 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database that is available from <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a><a name="_Hlk141002106"></a>. Details on the preparations of these layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. A similar extraction process was used for the <strong>CitiesGOER</strong> database that is also available from Zenodo via <a href="../doi/10.5281/zenodo.8175429">https://zenodo.org/doi/10.5281/zenodo.8175429</a>.</p> <p><strong>ClimateForecasts</strong> (as the CitiesGOER) was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site. One example of combining data from these different sets in the R statistical environment is available from this Rpub: <a href="https://rpubs.com/Roeland-KINDT/1114902">https://rpubs.com/Roeland-KINDT/1114902</a>.</p> <p>The identities including the geographical coordinates of weather stations were sourced from <a href="https://meteostat.net/en/">Meteostat</a>, specifically by downloading (17-FEB-2024) the <a href="https://dev.meteostat.net/bulk/stations.html">‘lite dump’ data set</a> with information for active weather stations only. Two weather stations where the country could not be determined from the ISO 3166-1 code of ‘XA’ were removed. If weather stations had the same name, but occurred in different ISO 3166-2 regions, this region code was added to the name of the weather station between square brackets. Afterwards duplicates (weather stations of the same name and region) were manually removed.</p> <p>Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Similar methods were used to calculate these median values as in the case studies for the <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">TreeGOER manuscript</a> (calculations were partially done via the <a href="https://rdrr.io/cran/BiodiversityR/man/ensemble.envirem.html">BiodiversityR::ensemble.envirem.run</a> function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes <a href="https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html">future grid layers available from WorldClim 2.1</a>).</p> <p>Maps were added in version 2024.03 where locations of weather stations were shown on a map of the Climatic Moisture Index (CMI). These maps were created by a similar process as in the <a href="../doi/10.5281/zenodo.8252756">TreeGOER Global Zones Atlas</a> from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>. Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas). For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>Maps for version 2024.07 modified the dimensions of the sheets to those used in version 2024.06 of the <a href="../doi/10.5281/zenodo.8252756">TreeGOER Global Zones Atlas</a>. Another modification was the inclusion of Natural Earth boundaries for <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</p> <p>Version 2024.10 includes a new data set that documents the location of the city locations in <strong>Holdridge Life Zones</strong>. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) that are <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnff">available for download from DRYAD</a> and were created for the following article: Elsen et al. 2022. Accelerated shifts in terrestrial life zones under rapid climate change. <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a>. Version 2024.10 further includes Holdridge Life Zones for the climates available from the previously included climates, calculating biotemperatures and life zones with similar methods as used by Holdridge (<a href="https://www.jstor.org/stable/1675393?seq=1">1947</a>; <a href="https://app.ingemmet.gob.pe/biblioteca/pdf/Amb-56.pdf">1967</a>) and Elsen et al. (<a href="https://doi.org/10.1111/gcb.15962">2022</a>) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>.</p> <p>Version 2024.11 includes a new data set that documents the location of the weather stations in <strong>Köppen-Geiger climate zones</strong>. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a>.</p> <p>Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database. One of these zones are the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">Whittaker biome types</a>, available as a polygon from the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">plotbiomes</a> package (see also <a href="https://www.davidzeleny.net/wiki/lib/exe/fetch.php/vegecol:materials:ricklefs_bioms_chapter_5.pdf">here</a>). Whittaker biome types were extracted with similar R scripts as described by <a href="https://rpubs.com/Roeland-KINDT/1275232">Kindt 2025</a> (these were also used to calculate environmental ranges of TreeGOER species, as archived <a href="https://zenodo.org/records/14908944">here</a>).</p> <p>Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. <a href="https://gmd.copernicus.org/articles/17/275/2024/">2024</a>).</p> <p> </p> <p>When using <strong>ClimateForecasts</strong> in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. <a href="https://doi.org/10.5194/soil-7-217-2021">https://doi.org/10.5194/soil-7-217-2021</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> </ul> <p>When using information from the Holdridge Life Zones, also cite:</p> <ul> <li>Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a></li> </ul> <p>When using information from Köppen-Geiger climate zones, also cite:</p> <ul> <li>Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a></li> </ul> <p>When using information on the Whittaker biome types, also cite:</p> <ul> <li>Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.</li> <li>Whittaker, R. H. (1970). Communities and ecosystems.</li> <li>Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7145245">https://doi.org/10.5281/zenodo.7145245</a></li> </ul> <p>When using information on the steady state water table depth, also cite:</p> <ul> <li>Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. <a href="https://gmd.copernicus.org/articles/17/275/2024/">https://gmd.copernicus.org/articles/17/275/2024/</a></li> </ul> <p> </p> <p>The development of <strong>ClimateForecasts</strong> and its partial integration in version 2024.03 of the GlobalUsefulNativeTrees database was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p>
Data from: Environmental variation associated with topography explains butterfly diversity along a tropical elevation gradient
<p>Few studies have evaluated the role of topography on the diversity patterns of biological communities along elevation gradients. We evaluated the influence of microclimate and vegetation structure associated with topographic variation on the richness and composition of species of different families of butterflies on a mountain located in a dry enclave (Chicamocha River Canyon) in the northern Andes, Colombia. We captured butterflies over four months at 18 elevations (300 to 1500 m a.s.l.) in two topographic positions (riverbed and hillslope) using an entomological net and traps baited with fermented fruit. In general, butterfly richness increased with elevation in both topographic positions. However, the richness-elevation relationship changed with butterfly family. The riverbed and hillslope sites host different assemblages of butterflies, and this pattern that was consistent for most families. In the riverbed, two sets of species are recognized along the elevation gradient (one below 700 m a.s.l. and the other above 1000 m a.s.l.), mainly owing to species replacement. On the hillslopes there was no clear pattern of grouping associated with elevation. Microclimate differences between the riverbed and hillslope sites along the elevation gradient were related to the vegetation structure and explained the variation in butterfly species composition. Our results highlight the role of topography not only by explaining the response of species richness and composition to environmental variation determined by elevation, but also as a factor that must be considered in the planning and management of biodiversity conservation in the mountains.</p>
Data for - EU's bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol
<p>To reduce greenhouse gas (GHG) emissions, the European Union (EU) has targets for utilizing energy from renewable sources. By 2030, a minimum of 3.5% of energy in the EU’s transport sector should come from renewable biological sources, such as crop residues. This paper analyzed EU’s “advanced bioethanol” potential from wheat straw and maize stover and evaluated its environmental (land, water, and carbon) footprint. We differentiated between gross and net bioethanol output, the latter by subtracting the energy inputs in production. Results suggest that the annual amount of the sustainably harvestable wheat straw and maize stover is 81.9 Megatonnes (Mt) at field moisture weight (65.3 Mt as dry weight), yielding 470 PJ as gross (404 PJ as net) advanced bioethanol output. Calculated net advanced bioethanol can replace 2.95% of EU transport sector’s energy consumption. EU’s advanced bioethanol has a land footprint of 0.28 m<sup>2</sup> MJ<sup>−1</sup> for wheat straw and 0.18 m<sup>2</sup> MJ<sup>−1</sup> for maize stover. The average water footprint of advanced bioethanol is 173 L MJ<sup>−1</sup> for wheat straw and 113 L MJ<sup>−1</sup> for maize stover. The average carbon footprint per unit of advanced bioethanol is 19.4 and 19.6 g CO<sub>2</sub>eq MJ<sup>−1</sup> for wheat straw and maize stover, respectively. Using advanced bioethanol can lead to emission savings, but EU’s advanced bioethanol production potential is insufficient to achieve EU’s target of a minimum share of 3.5% of advanced biofuels in the transport sector by 2030, and the associated water and land footprints are not smaller than footprints of conventional bioethanol.</p>
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.