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3,101 results for “Historical”
Historical Daily Snow Water Equivalent (SWE) Estimations over the Western US and the Rocky Mountains
<p>model weights: DL model weights for SWE prediction for SNOTEL stations over the Western US. </p> <p>prediction: DL model predictions for SNOTEL SWE observations. </p> <p>[model]_clean_lats: SWE estimation over the Rocky Mountains with models trained on SNOTEL stations. </p> <p>[model]_hist_clean_lats: Historical SWE estimation over the Rocky Mountains with models trained on SNOTEL stations. </p> <p>[model]_relative_clean_lats: SWE fraction estimation over the Rocky Mountains with models trained on SNOTEL SWE fractions. </p> <p>code_SWE_main: necessary code for SWE modeling. </p> <p>corresponding code repository: https://github.com/ShihengDuan/code-SWE</p> <p> </p>
Data from: Historical fire regimes and contemporary fire effects within sagebrush habitats of Gunnison Sage-grouse
<p>The historical role of fire in sagebrush (<em>Artemisia</em> <em>tridentata</em>) landscapes remains poorly understood yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; <em>Centrocercus</em> <em>minimus</em>). We reconstructed fire histories from tree-ring fire-scars at sagebrush-forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following European-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fire. </p> <p>Tree-ring fire-scars revealed a history of repeated low-severity fire at sagebrush-forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 41.3 years (ranging from 18.2 to 79.7 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, consistent with spread between sites. Most (70%) of the historical fires burned in the early growing season when strong winds can spread fire through sagebrush. Recent burns, relative to unburned sites exhibited greater reductions in sagebrush (<em>Artemisia</em> <em>tridentata</em>; 27% vs. 6%) and concomitant increases in herbaceous (40% vs. 55%) cover. These differences declined with time since fire but persisted for at least two decades. Burns were dominated by a suite of native perennial grasses, forbs, and a re-sprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Burns exhibited slightly increased cover (4% vs. 1%) of a widely-planted non-native perennial grass, crested wheatgrass (<em>Agropyron</em> <em>cristatum</em>). </p> <p>Our results suggest that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings are consistent with the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed, and now includes substantially reduced (Endangered Species Act) ESA-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management-research partnerships. </p>
FIG. 2 in Bryophytes from Glaziou's historical collections: revisiting the flora of Rio de Janeiro and implications for conservation
FIG. 2. — Species collected by Glaziou in the state of Rio de Janeiro.
Data for: From southern Africa and beyond: historical biogeography of a monocotyledonous bulbous geophyte
<p><em><strong>Aim</strong></em></p> <p>Low taxon sampling and taxonomic uncertainties still obscure our evolutionary knowledge of many taxa, especially widespread lineages across diverse landscapes. Of particular interest are those lineages inhabiting seasonal and arid landscapes, which showcase unique distributional patterns that hint at fascinating histories. Using the most comprehensive sampling to date of a widespread bulbous monocot, we hypothesize two independent dispersals to Madagascar, and that vicariance due to Miocene-driven aridification contracted a once-widespread distribution between Africa and Eurasia.</p> <p><em><strong><span>Location</span></strong></em></p> <p><span>Sub-Saharan Africa, Madagascar, Eurasia</span></p> <p><em><strong>Taxon</strong></em></p> <p>Ledebouriinae (Scilloideae, Asparagaceae)</p> <p><em><strong>Methods</strong></em></p> <p>Using the Angiosperms353 probe set for 195 taxa (86 ingroup taxa), we perform phylogenetic reconstruction using IQ-Tree, and we infer age estimates using penalized likelihood as implemented in treePL. Capitalizing on our broad geographic sampling, which includes African, Malagasy, and Eurasian taxa, we use 'BioGeoBEARS' to reconstruct ancestral ranges and investigate the role of vicariance and dispersal. </p> <p><em><strong>Results</strong></em></p> <p>The Ledebouriinae originated within the past ~30 myr in southeastern sub-Saharan Africa, with the major subclades arising soon thereafter. Although long-distance dispersal cannot be fully ruled out, we argue that vicariance was the major process responsible for the current distribution of <em>Ledebouria</em> in Eurasia. We recover two distinct <em>Ledebouria</em> groups that overlap in eastern Africa, but are divided into mostly northern and southern clades with divergent biogeographical histories, each showing an independent dispersal to Madagascar. A similar north-south split is seen in <em>Drimiopsis</em>. Additionally, we recover a complex biogeographic history in the predominantly sub-Saharan African Ledebouria clade, with a rapid radiation at ~14 mya.</p> <p><em><strong>Main conclusions</strong></em></p> <p>The expansion of seasonal rainfall and aridity in sub-Saharan Africa, coupled with orogenic activity, may have fostered the diversification of the Ledebouriinae and many subclades. Miocene-driven aridification may have caused fragmentation of a once widespread distribution that led to their occurrence in Eurasia.</p>
ERRATAS database of editorial principles and practices in printed editions of historical correspondence
<p>The ERRATAS database is the primary output of the ERRATAS project, which surveyed all the sources of the <a href="http://www.helsinki.fi/varieng/CoRD/corpora/CEEC/"><em>Corpora of Early English Correspondence</em></a> (CEEC-400) in order to investigate their editorial principles and practices. These sources are mostly printed editions of English historical correspondence.</p> <p>The data in the ERRATAS database consists of surveys of:</p> <ul> <li>editorial principles (explicit statements or anything resembling such);</li> <li>editorial practices (textual features that can be found in the edited texts);</li> <li>editorial work (evidence of contributors other than the stated editor(s)).</li> </ul> <p>This updated release of the database is in the form of <strong>two MS Excel files</strong>, one of which includes data entry forms for surveying editorial principles and practices.</p> <p>This release also comes with documentation, in the form of <strong>three pdf files</strong>:</p> <ul> <li>A <strong>manual</strong> to the database, containing a brief history of the project, and a section demonstrating with several graphs what kinds of data can be extracted from the dataset;</li> </ul> <p>and two documents which place the database into the context of its development, but more importantly facilitate the application of the ERRATAS method to survey further material:</p> <ul> <li>A <strong>checklist</strong> of textual features (<a href="https://zenodo.org/record/1187074">an earlier version can be found here</a>); and</li> <li><strong>Guidelines</strong> for surveying editorial principles and edited texts following the ERRATAS system.</li> </ul> <p><strong>Related material:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3855451"><em>Corpus of Editorial Principles (in CEEC-400 Sources)</em></a> – pdfs of the editorial principles in the editions surveyed</li> <li><a href="https://www.doi.org/10.5281/zenodo.4134471">Editions and other sources used in the <em>Corpora of Early English Correspondence</em> (CEEC-400)</a> – detailed bibliography of the sources surveyed for the ERRATAS database</li> <li>Sairio, Anni, Samuli Kaislaniemi, Anna Merikallio & Terttu Nevalainen. 2018. “Charting orthographical reliability in a corpus of English historical letters”. <em>ICAME Journal</em> 42: 79–96. DOI: <a href="http://doi.org/10.1515/icame-2018-0005">doi.org/10.1515/icame-2018-0005</a>. Open access.</li> </ul> <p><strong>Version history:</strong></p> <p>26.5.2020 Version 0.9 – Database unfinished (due to coronavirus restrictions). The current data has been checked twice. Material to be added in v1.0: database manual; database survey guidelines.</p> <p>14.4.2023 Version 1.1 – Database finished, after conversion to MS Excel. Dataset checked, corrected, amended, edited, and cleaned for release. Documentation created. Survey guidelines and data survey forms included in release.</p>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2006–2021)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2006 to 2021.</li> </ul>
Historical data set for the Flumendosa case study in Sardinia, Italy.
<p>The files COUT, WTMP, EVAP, CTMP contain the outflow, outflow water temperature, evapotranspiration and corrected air temperature of the sub basins respectively. CCIN, CCON, CCTN, CCTP, CCPP, CCSP, CCSS, CCTS are the concentrations of inorganic ang organic nitrogen; the total nitrogen and phosphorus; the particulate and soluble phosphorus and the suspended and total sediment. CPRC is the corrected precipitation and UPVAP the evapotranspiration from the upstream area of each sub basin.</p>
Primewater Flumendosa (Sardinia) case study historical data
<p>Historical data for the Flumendosa (sardinia, Italy) PrimeWater case study. The files COUT, WTMP, EVAP, CTMP contain the outflow, outflow water temperature, evapotranspiration and corrected air temperature of the sub basins respectively. CCIN, CCON, CCTN, CCTP, CCPP, CCSP, CCSS, CCTS are the concentrations of inorganic ang organic nitrogen; the total nitrogen and phosphorus; the particulate and soluble phosphorus and the suspended and total sediment. CPRC is the corrected precipitation and UPVAP the evapotranspiration from the upstream area of each sub basin.</p>
Exploratory investigation of historical decorative laminates by means of vibrational spectroscopic techniques
<p>This dataset contains the data used for the publication entitled "Exploratory investigation of historical decorative laminates by means of vibrational spectroscopic techniques".</p>
Primewater L. Hume case study historical data
<p>Historial dataset for the Lake Hume case study of the PrimeWater project. The files COUT, WTMP, EVAP, CTMP, CCIN, CCON, CCPP, CCSP, CCTN, CCTP, CCSS, CCTS, CPRC contain the outflow, outflow water temperature, evapotranspiration and corrected air temperature, inorganic nitrogen, organic nitrogen, particulate phosphorus, soluble reactive phosphorus, total nitrogen, total phosphorus, suspended sediment, total sediment and the corrected precipitation of the sub basins respectively. UPVAP contains the evapotranspiration from the upstream area of each sub basin.</p>
Primewater Mulargia (Sardinia) case study historical data
<p>Historical data for the Mulargia (sardinia, Italy) PrimeWater case study. The files COUT, WTMP, EVAP, CTMP, CCIN, CCON, CCPP, CCSP, CCTN, CCTP, CCSS, CCTS, CPRC contain the outflow, outflow water temperature, evapotranspiration and corrected air temperature, inorganic nitrogen, organic nitrogen, particulate phosphorus, soluble reactive phosphorus, total nitrogen, total phosphorus, suspended sediment, total sediment and the corrected precipitation of the sub basins respectively. UPVAP contains the evapotranspiration from the upstream area of each sub basin.</p>
Primewater L. Harsha case study historical data
<p>Historical data-set for the Lake Harsha case study under the PrimeWater project. The files COUT, WTMP, EVAP, CTMP, CCIN, CCON, CCPP, CCSP, CCTN, CCTP, CCSS, CCTS, CPRC contain the outflow, outflow water temperature, evapotranspiration and corrected air temperature, inorganic nitrogen, organic nitrogen, particulate phosphorus, soluble reactive phosphorus, total nitrogen, total phosphorus, suspended sediment, total sediment and the corrected precipitation of the sub basins respectively. UPVAP contains the evapotranspiration from the upstream area of each sub basin.</p>
Inferring historical introgression with deep learning
<p><span>Resolving the phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary relationships to be disentangled at a genome-wide level, yet many of these tests are computationally intensive and rely on phased genotypes, large sample sizes, restricted phylogenetic topologies, or hypothesis testing. To overcome these difficulties, we developed a deep learning-based approach, named ERICA, for inferring genome-wide evolutionary relationships and local introgressed regions from sequence data. ERICA accepts sequence alignments of both population genomic data and multiple genome assemblies, and efficiently identifies discordant genealogy patterns and exchanged regions across genomes when compared with other methods. We further tested ERICA using real population genomic data from Heliconius butterflies that have undergone adaptive radiation and frequent hybridization. Finally, we applied ERICA to characterize hybridization and introgression in wild and cultivated rice, revealing the important role of introgression in rice domestication and adaptation. Taken together, our findings demonstrate that ERICA provides an effective method for teasing apart evolutionary relationships using whole genome data, which can ultimately facilitate evolutionary studies on hybridization and introgression.</span></p>
Historical DNA reveals climate adaptation in an endangered songbird
<p>To cope with climate change, species may shift their distributions or adapt <em>in situ</em> to changing environmental conditions. However, clear examples of genetic changes via adaptation are limited. We explore evolutionary responses to climate change in the endangered southwestern willow flycatcher (<em>Empidonax</em> <em>trailli</em> <em>extimus</em>) through whole-genome comparisons between historical specimens, collected from 1888–1909 near San Diego, CA, and contemporary individuals from across the breeding range. Genomic analyses revealed that introgression into San Diego increased adaptive potential over time and shifted genome-wide population structure towards that of neighboring populations. In contrast, loci linked to climate (dew point temperature and precipitation) shifted away from neighboring populations and in a direction consistent with adaptation to climate change in southern CA. This research highlights the role of admixture in facilitating adaptive shifts through its impact on genome-wide genetic variation and represents one of the few studies to document climate adaptation in a wild population.</p>
Historical Mark Release Recapture (MRR) studies of selection and plasticity in butterflies
<p class="MsoNormal">Historical data on plasticity, selection and evolution of phenotypic traits in the wild provide an important resource for understanding how populations are responding to ongoing anthropogenic environmental changes. We provide the datasets associated with a series of Mark Release Recapture (MRR) studies that quantified seasonal variation in plasticity and phenotypic selection in a natural population of <em>Pontia occidentalis</em> butterflies in southcentral WA USA between 1989–1996. The studies examined both natural and experimentally-generated variation in two types of traits: wing melanin pattern on each wing surface; and wing and body size and shape. Papers published during the 1990s that summarized the results of these studies demonstrated phenotypic plasticity and directional selection on some of the traits that varied with seasonal weather conditions. Here we present the original, underlying data for each of the nine studies. These data may be valuable for exploring newer statistical approaches for modeling MRR studies of selection and plasticity, and for evaluating historical changes in selection and evolution of traits related to climate adaptation in response to ongoing climate and other environmental changes.</p>
Assessing and managing environmental hazards of polymers: historical development, science advances and policy options
<p>Supporting information (open data) from Assessing and managing environmental hazards of polymers: historical development, science advances and policy options</p>
Data from: Estimating potential global sources and secondary spread of freshwater invasions under historical and future climates
<p>Aim: We employ a climate-matching method to evaluate potential source regions of freshwater invasive species to an introduced region and their potential secondary spread under historical and future climates.</p> <p>Location: Global source regions, with primary introductions to the Laurentian Great Lakes and secondary introductions throughout North America</p> <p>Methods: We conducted a climate-match analysis using the CLIMATE algorithm to estimate global source freshwater ecoregions under historical and future climates with an ensemble of general circulation models for climate change scenario SSP5-8.5. Given existing research, we use a climate match of ≥ 71.7% between ecoregions to indicate climatic conditions that will not inhibit the survival of introduced freshwater organisms. Further, we estimate the secondary spread of freshwater invaders to the ecoregions of North America under historical and future climates.</p> <p>Results: We identified 54 global freshwater ecoregions with a climate match ≥ 71.7% to the recipient Laurentian Great Lakes under historical climatic conditions and 11 additional ecoregions were predicted to exceed the threshold under climate change. Three of the 11 ecoregions were located in South America, a continent where no matches existed under historical climates and eight were located in the southern United States, southern Europe, Japan, and New Zealand. Further, we identify 34 North American ecoregions of potential secondary spread of freshwater invasions from the Great Lakes under historical climatic conditions, and five ecoregions were predicted to exceed the threshold under climate change.</p> <p>Main conclusion: We provide a climate-match method that can be employed to assess the sources and spread of freshwater invasions under historical and future climate scenarios. Our climate-match method predicted increases in climate match between the recipient region and several potential source regions, and changes in areas of potential spread under climate change. The identified ecoregions are candidates for detailed biosecurity risk assessments and related management actions. The identified ecoregions are candidates for detailed biosecurity risk assessments and related management actions.</p>
Historical model forecast and observed visibility datasets
<p>yyyy_spot[00/12]_wrf61_prev18.csv: </p> <ul> <li>This dataset includes visibility observations, station latitude and longitude, time and model forecast variables from 2014 to 2020.</li> <li>The visibility observations were obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS), which provided various weather measurements including temperature, pressure, humidity, precipitation, visibility, current weather, and other meteorological variables. Meteorological measurements were recorded at 3-hour intervals. Besides, the location and time of each record were provided.</li> <li>The model forecast variables are from the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM) provided by the US National Weather Service (NCEP). The WRF-NMM produces hourly predictions for a 60-hour forecast period, launching twice daily at 00:00 UTC and 12:00 UTC. Forecast variables incorporated in the model encompass temperature, relative humidity, cloud cover, wind speed, precipitation, and more. This dataset includes 26 variables, which are listed below.</li> </ul> <pre><code class="language-markdown">| Variables | Long Name | Description | | ---- | ---- | ---- | | DPT_GDS3_HTGL | Dew point | Height in 2 meters | | HGT_GDS3_0DEG | Geopotential | height Level of 0 deg (C) isotherm | | HGT_GDS3_CEIL | Geopotential | height Cloud ceiling | | HGT_GDS3_HTFL | Geopotential | height Highest tropospheric freezing level | | HGT_GDS3_SFC | Geopotential | height Ground/water surface | | T_CDC_GDS3_EATM | Total cloud cover | Entire atmosphere | | H_CDC_GDS3_HCY | High cloud cover | High cloud layer | | M_CDC_GDS3_MCY | Mid cloud cover | Mid cloud layer | | L_CDC_GDS3_MCY | Low cloud cover | Low cloud layer | | PLI_GDS3_SPDY | Parcel lifted index (to 500 hPa) | Layer between two levels at specified pressure difference from ground to level | | LFT_X_GDS3_ISBY | Surface lifted index | Layer between two isobaric levels | | PRES_GDS3_SFC | Pressure | Ground/water surface | | PRMSL_GDS3_MSL | Pressure reduced to Mean sea level | Mean sea level | | MSLET_GDS3_MSL | Mean sea level pressure | Mean sea level | | P_WAT_GDS3_EATM | Precipitable water | Entire atmosphere | | POP_GDS3_SFC | Probability of precipitation | Ground/water surface | | R_H_GDS3_HTGL | Relative humidity | Height in 2 meters | | R_H_GDS3_HYBL | Relative humidity | Hybrid level | | SPF_H_GDS3_HTGL | Specific humidity | Height in 2 meters | | SPF_H_GDS3_SPDY | Specific humidity | Layer between two levels at specified pressure difference from ground to level | | TMP_GDS3_HTGL | Temperature | Height in 2 meters | | TMP_GDS3_SFC | Temperature | Ground/water surface | | U_GRD_GDS3_ HTGL | U-component of wind | Height in 10 meters | | U_GRD_GDS3_SPDY | U-component of wind | Layer between two levels at specified pressure difference from ground to level | | V_GRD_GDS3_HTGL | V-component of wind | Height in 10 meters | | V_GRD_GDS3_SPDY | V-component of wind | Layer between two levels at specified pressure difference from ground to level |</code></pre> <ul> <li>To integrate the gridded model forecast data with sparse observation data, we have a two-step pre-processing process. Firstly, we synchronized the time zones of two datasets to UTC+00:00. Secondly, we used the Inverse Distance Weighted (IDW) approach for spatial matching. As a result, we generated a multi-variable time series dataset as follows:<br> <span class="math-tex">\(D=\{X_{N \times M \times T}, Y_{N \times T}\}\)</span> <br> where <span class="math-tex">\(X\)</span> is a three-dimensional tensor of size <span class="math-tex">\(N\times M\times T\)</span> (corresponding to <span class="math-tex">\(N\)</span> samples of <span class="math-tex">\(M\)</span> forecast variables of <span class="math-tex">\(T\)</span> hours), and <span class="math-tex">\(Y\)</span> is the observational data of size <span class="math-tex">\(N\times T\)</span> (corresponding to <span class="math-tex">\(N\)</span> samples of <span class="math-tex">\(T\)</span> hours). A sample from dataset D is represented as <span class="math-tex">\(s= \{ x_{M \times T}, y_T \} \in D\)</span>. In this datasets, <span class="math-tex">\(M=27\)</span>, and <span class="math-tex">\(T=60\)</span>. Besides, this dataset includes 18 hours of visibility observations before the model launching. It should be noted that some observations are missing for some reason.</li> </ul> <p><br> yyyy_spot[00/12]_wrf6_prev18.csv: </p> <ul> <li>This datasets are from yyyy_spot00/12_wrf61_prev18.csv and is a sliding-window extraction of 61 forecast moments, with each sliding-window consisting of 6 hours.</li> </ul>
Instituto Nacional de Investigación Agropecuaria (INIA) Rice Breeding Program Historical Dataset
<p>Breeding programs generate vast amounts of data which are often scattered in separate files. This hinders the application of modern breeding tools such as multi-environment analyses and genomic selection. This dataset is the result of consolidating 23 years of phenotypic, pedigree, and genomic records from the Uruguayan national rice breeding program. All the available data from 1997 to 2020 corresponding to field trials, blast nurseries, laboratory analyses of milling and cooking quality, pedigree information, and genomic information for selected advanced breeding lines are gathered. Records of 996 trials in 12 locations over a span of 23 years, 91,636 field plots with information on 14 phenotypic variables, pedigree for 19,447 genotypes, and genomic information regarding 61,260 SNP markers for 965 genotypes were recovered. The dataset is structured in Trials, Phenotypes, Lines, Genomic Information, and SNP Tables. Genotype identification has been coded.</p>
Biogeographic barriers and historical climate affect phylogeographic structure and demographic history of the common gartersnake
<p><strong>Aim</strong>: Current distributions of widespread North American (NA) species have been shaped by Pleistocene glacial cycles, latitudinal temperature gradients, sharp longitudinal habitat transitions, and the vicariant effects of major mountain and river systems that subdivide the continent. Within these transcontinental species, genetic diversity patterns might not conform to established biogeographic breaks compared to more spatially restricted taxa due to intrinsic differences (e.g., greater dispersal ability in generalist species) or spatiotemporal differences (e.g., wide-ranging species predate younger barriers). In this study, we highlight the effects of these extrinsic variables on genetic structuring by investigating the phylogeographic history of a widespread generalist squamate found throughout NA. </p> <p><strong>Location</strong>: North America</p> <p><strong>Taxon</strong>: Common gartersnake, <em>Thamnophis sirtalis</em></p> <p><strong>Methods</strong>: We evaluate the effects of major river basins and the forest-grassland transition into the Interior Plains on genetic structure patterns using phylogenetic, spatially informed population structure, and demographic analyses of SNP data, and address range expansion history with ecological niche modeling using locality and historic climate data.</p> <p><strong>Results</strong>: We identify four phylogeographic lineages with varying degrees of connectivity between them. We find discordant population structure patterns between sex-linked and autosomal loci with respect to the relationship between the central NA lineage relative to coastal lineages. We find support for southeast Pleistocene refugia where recent secondary contact occurred during the Last Glacial Maximum and evidence for both northern and southern refugia in western NA.</p> <p><strong>Main Conclusion</strong>: Our results provide strong evidence for a Middle Pliocene origin for <em>Thamnophis</em> <em>sirtalis</em> in central-southeastern NA preceding its rapid expansion across the continent prior to middle Pleistocene climate-mediated lineage formation. We implicate major riverine networks within the Mississippi watershed in likely repeated westward expansion events across the Interior Plains. Finally, we corroborate prior conclusions that phenotypic differences between subspecies do not reflect shared evolutionary history and note that the degree of separation between inferred lineages warrants further investigation before any taxonomic revisions are proposed.</p>
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.