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Obesity continues to be a prevalent health concern affecting every race of the American population. According to data from the World Health Organization, 54% of U.S. adults are overweight (body mass index (BMI) >25 kg/m2 ) and 22% are obese (BMI >30 kg/m2) (1). In addition, 25% of U.S. children are overweight or obese (1). Studies show that obese children are likely to become obese adults (2-5). Also, recent studies report significant years of life lost due to the impact of being an obese adult (6, 7). Thus, insights into the pathogenesis of childhood obesity and preventative measures are needed to combat the inevitable increase in worldwide incidence of obesity and its associated co-morbidities. Recent studies have identified a new gastroenteric hormone, ghrelin, as a long-term regulator of energy balance in humans (12). Ghrelin is a 28 amino acid acylated peptide which is an endogenous ligand of the growth hormone secretagogue receptor (GHS-R), a hypothalamic G-protein-coupled receptor (13). Enteroendocrine cells (X/A-like cells) of the stomach are the major site of ghrelin synthesis, although a minor proportion of ghrelin synthesis occurs in other sites such as the hypothalamus, pituitary, duodenum, jejunum and lung (14) (15, 16). The hypothesis that hyperghrelinemia causes some of the features of PWS predicts that this disorder will be ameliorated (partially or completely) by lowering ghrelin levels. We have recently shown that the somatostatin agonist, octreotide, suppresses ghrelin levels in humans. If octreotide remains effective in longer term studies, the drug may become an adjuvant therapy, in addition to growth hormone, to control the insatiable appetite and morbid obesity seen in this condition. The purpose of this study is to investigate over a 6 month period the effect of octreotide therapy on food intake, sense of hunger, body weight, body composition, efficiency of burning calories, biomarkers of weight regulation and growth hormone markers in children and young Adults with Prader-Willi Syndrome(PWS).

A total of 256 surveys were analyzed for Caquetá and 227 surveys forYurimaguas. These surveys belong to the project "Sustainable Amazonian Landcapes" ( see below Related Datasets). All the households that participated in the survey did it freely and under prior informed consent. Households were asked about their farms’ agrobiodiversity (crops, cattle ranching, and pastoral diversity), farming activities, agroecological management practices and livelihood assets. These surveys provided the information required for defining the household typologies based on farming livelihood diversification and to obtain indicators of vulnerability to climate change. People surveyed usually were household heads or their spouses. The supervisors were the direct link with CIAT. The data was collected through Android Devices using the software CsPro 6.2 and 6.3. (Reporte Interno)

Leaching dataset of dissolved organic carbon (DOC) and nitrogen (DON), nitrate (NO3+) and ammonium (NH4+) were collected from 6 cropping treatments (corn, switchgrass, miscanthus, native grass mix, restored prairie and poplar) established in the Bioenergy Cropping System Experiment (BCSE) which is a part of Great Lakes Bioenergy Research Center (www.glbrc.org) and Long Termn Ecological Research (LTER) program (www.lter.kbs.msu.edu). The site is located at the W.K. Kellogg Biological Station (42.3956° N, 85.3749° W and 288 m above sea level), 25 km from Kalamazoo in southwestern Michigan, USA. Prenart soil water samplers made of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) were installed in blocks 1 and 2 of the BCSE (Fig. S1), and Eijkelkamp soil water samplers made of ceramic (http://www.eijkelkamp.com) were installed in blocks 3 and 4 (there were no soil water samplers in block 5). All samplers were installed at 1.2 m depth at a 45° angle from the soil surface, approximately 20 cm into the unconsolidated sand of the 2Bt2 and 2E/Bt horizons. Beginning in 2009, soil water was sampled at weekly to biweekly intervals during non-frozen periods (April to November) by applying 50 kPa of vacuum for 24 hours, during which water was collected in glass bottles. During the 2009 and 2010 sampling periods we obtained fewer soil water samples from blocks 1 and 2 where Prenart lysimeters were installed. We observed no consistent differences between the two sampler types in concentrations of the analytes reported here. Depending on the volume of leachate collected, water samples were filtered using either 0.45 µm pore size, 33-mm-dia. cellulose acetate membrane filters when volumes were <50 ml, or 0.45 µm, 47-mm-dia. Supor 450 membrane filters for larger volumes. Samples were analyzed for NO3-, NH4+, total dissolved nitrogen (TDN), and DOC. The NO3- concentration was determined using a Dionex ICS1000 ion chromatograph system with membrane suppression and conductivity detection; the detection limit of the system was 0.006 mg NO3--N L-1. The NH4+ concentration in the samples was determined using a Thermo Scientific (formerly Dionex) ICS1100 ion chromatograph system with membrane suppression and conductivity detection; the detection limit of the system was similar. The DOC and TDN concentrations were determined using a Shimadzu TOC-Vcph carbon analyzer with a total nitrogen module (TNM-1); the detection limit of the system was ~0.08 mg C L-1 and ~0.04 mg N L-1. DON concentrations were estimated as the difference between TDN and dissolved inorganic N (NO3- + NH4+) concentrations. The NH4+ concentrations were only measured in the 2013-2015 crop-years, but they were always small relative to NO3- and thus their inclusion or lack of it was inconsequential to the DON estimation. Leaching rates were estimated on a crop-year basis, defined as the period from planting or emergence of the crop in the year indicated through the ensuing year until the next year’s planting or emergence. For each sampling point, the concentration was linearly interpolated between sampling dates during non-freezing periods (April through November). The concentrations in the unsampled winter period (December through March) were also linearly interpolated based on the preceding November and subsequent April samples. Solute leaching (kg ha-1) was calculated by multiplying the daily solute concentration in pore-water (mg L -1) by the modeled daily drainage rates (m3 ha-1) from the overlying soil. The drainage rates were obtained using the SALUS (Systems Approach for Land Use Sustainability) model (Basso and Ritchie, 2015). SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, nitrogen fertilizer application, tillage), and crop genetics. The SALUS water balance sub-model simulates surface run-off, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons (Basso and Ritchie, 2015). Drainage amounts and rates simulated by SALUS have been validated with measurements using large monolith lysimeters at a nearby site at KBS (Basso and Ritchie, 2005). On days when SALUS predicted no drainage, the leaching was assumed to be zero. The volume-weighted mean concentration for an entire crop-year was calculated as the sum of daily leaching (kg ha-1) divided by the sum of daily drainage rates (m3 ha-1). Weather data for the model were collected at the nearby KBS LTER meteorological station (lter.kbs.msu.edu). Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO3-, and NH4+ from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO3- comprised the majority of total N leaching in all systems (59-92%). Average NO3- leaching (kg N ha-1 yr-1) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH4+ concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L-1). Perennial crops leached more NO3- in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters. readme files are given that describe the data table

Tree cores were sampled using increment borers. At each site three trees were chosen for coring, with two or three cores taken per tree. Cores were sanded and ring widths measured based on high-resolution images of the sanded cores. Cores were cross-dated and summary statistics used to compare cross-dating accuracy. The dataset contains the resulting dated ring width series. This dataset includes tree ring width data, derived from tree cores, that were sampled from sites across the Rhön Biosphere Reserve (Germany). At each chosen site three trees were cored, with two or three cores taken per cored tree. Data was collected in August 2021.

This work models a last-mile network design problem for an e-retailer with a capacitated two-echelon distribution structure - typical in e-retail last-mile distribution, catering to a market with a stochastic and dynamic daily customer demand requesting delivery within time-windows. Considering the distribution evnironment, this work formulates last-mile network design problem for this e-retailer as a dynamic-stochastic two capacitated location routing problem with time-windows. In doing so, this work splits the last-mile network design problem into its constituent strategic, tactical, and operational decisions. Here, the strategic decisions undertake long-term planning to develop a distribution structure with appropriate distribution facilities and a suitable delivery fleet to service the expected customer demand in the planning horizon. The tactical decisions pertain to medium-term day-to-day planning of last-mile delivery operations to establish efficient goods flow in this distribution structure to service the daily stochastic customer demand. And finally, operational decisions involve immediate short-term planning to fine-tune this last-mile delivery to service the requests arriving dynamically through the day. Note, the last-mile network design problem formulated as a location routing problem constitutes three subproblems encompassing facility location problem, customer allocation problem, and vehicle routing problem, each of which are NP-hard combinatorial optimization problems. To this end, this work develops an adaptive large neighborhood search meta-heuristic algorithm that searches through the neighborhood by destroying and consequently repairing the solution thereby reconfiguring large portions of the solution with specific operators that are chosen adaptively in each iteration of the algorithm, hence the name adaptive large neighborhood search. Further, considering the stochastic and dynamic nature of the delivery environment, this work develops a Monte-Carlo framework simulating each day in the planning horizon, with each day divided into 1-hr timeslots, and with each time-slot accepting customer requests for service by the end of the day. In particular, the framework assumes the e-retailer will delay route commitments until the last-feasible time-slot to accumulate customer requests and consequently assign them to an uncommitted delivery route. Note, a delivery route is committed once the e-retailer starts loading packages assigned to this delivery route onto the delivery vehicle assigned for this delivery route. At the end of every time-slot then, this framework assumes the e-retailer integrates the new customer requests by inserting these customer nodes into such uncommitted delivery routes in a manner that results in the least increase in distribution cost keeping the customer-distribution facility allocation fixed. Thus, the framework iterates through the time-slots with the e-retailer processing route commitments, accumulating customer requests, and subsequently integrating them into the delivery operations for the day. E-commerce has the potential to make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with such alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NOAA-GFDL.GFDL-ESM4.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The GFDL-ESM4 climate model, released in 2018, includes the following components: aerosol: interactive, atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa), atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry), land: GFDL-LM4.1, landIce: GFDL-LM4.1, ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-COBALTv2, seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories). The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

The purpose of this study is to evaluate the safety and tolerability of the JAK2 inhibitor XL019 administered orally in adults with Polycythemia Vera.

This digital GIS dataset and accompanying nonspatial files synthesize model outputs from a regional-scale volumetric 3-D geologic model that portrays the generalized subsurface geology of the Powder River Basin and Williston Basin regions from a wide variety of input data sources. The study area includes the Hartville Uplift, Laramie Range, Bighorn Mountains, Powder River Basin, and Williston Basin. The model data released here consist of the stratigraphic contact elevation of major Phanerozoic sedimentary units that broadly define the geometry of the subsurface, the elevation of Tertiary intrusive and Precambrian basement rocks, and point data that illustrate an estimation of the three-dimensional geometry of fault surfaces. The presence of folds and unconformities are implied by the 3D geometry of the stratigraphic units, but these are not included as discrete features in this data release. The 3D geologic model was constructed from a wide variety of publicly available surface and subsurface geologic data; none of these input data are part of this Data Release, but data sources are thoroughly documented such that a user could obtain these data from other sources if desired. The PowderRiverWilliston3D geodatabase contains 40 subsurface horizons in raster format that represent the tops of modeled subsurface units, and a feature dataset “GeologicModel”. The GeologicModel feature dataset contains a feature class of 30 estimated faults served in elevation grid format (FaultPoints), a feature class illustrating the spatial extent of 22 fault blocks (FaultBlockFootprints), and a feature class containing a polygon delineating the study areas (ModelBoundary). Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfModelUnits). Separate file folders contain the vector data in shapefile format, the raster data in ASCII format, and the tables as comma-separated values. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). An included READ_ME file documents the process of manipulating and interpreting publicly available surface and subsurface geologic data to create the model. It additionally contains critical information about model units, and uncertainty regarding their ability to predict true ground conditions. Accompanying this data release is the “PowderRiverWillistonInputSummaryTable.csv”, which tabulates the global settings for each fault block, the stratigraphic horizons modeled in each fault block, the types and quantity of data inputs for each stratigraphic horizon, and then the settings associated with each data input.

For this manuscript, there were three types of methods performed to make our main conclusions: genetic diversity and structure analyses, downloading and mapping butternut fossil pollen during the last 20,000 years, and modeling and hindcasting butternut's (Juglans cinerea) distribution 20,000 years ago to present. Genetic analyses and species distribution modeling were performed in Emily Schumacher’s Github repository (https://github.com/ekschumacher/butternut) and pollen analyses and mapping were performed in Alissa Brown’s repository (https://github.com/alissab/juglans). Here is information detailing the Genetic data Data collection description: To perform genetic diversity and structure analyses on butternut, we used genetic data from the publication Hoban et al. (2010) and genetic data from newer sampling efforts on butternut from 2011 - 2015. Individuals were collected by Jeanne Romero-Severson, Sean Hoban, and Martin Williams over the course of ~ten years with a major sampling effort closer to 2009 followed up by another round of sampling 2012 - 2015. The initial 1,004 butternut individuals that were collected were genotyped by Sean Hoban and then the subsequent 757 individuals were genotyped in the Romero-Severson lab at Notre Dame non-consecutively. Genotyping was performed according to Hoban et al. (2008); DNA was extracted from fresh cut twigs using DNeasy Plant Mini kits (QIAGEN). PCR was performed by using 1.5 mM MgCl2, 1x PCR buffer [50 mm KCl, 10 mm Tris-HCl (pH 9.0), 0.1% Triton-X-100 (Fisher BioTech)], 0.2 mm dNTPs, 4 pm each forward and reverse primer, 4% Bovine Serum Albumin, 0.25 U TaKaRa Ex Taq Polymerase (Panvera), and 20 ng DNA template (10 μL total volume). The PCR temperature profile was as follows: 2 min at 94 °C; 30 cycles of 94 °C for 30 s, Ta for 30 s, and 72 °C for 30 s; 45 min at 60 °C; and 10 min at 72 °C on a PTC-225 Peltier Thermal Cycler (MJ Research). The process of assessing loci and rebinning for differences in years is detailed in the Schumacher et al. (2022) manuscript. Data files butternut_44pop.gen: Genepop file of original 1,761 butternut individuals, sampling described above, separated into original 44 sampling populations. butternut_24pop_nomd.gen: Genepop file of 1,635 butternut individuals, following rebinning based on researcher binning, reduced based on geographic isolation and missing data, organized into 24 populations. Used to generate all genetic diversity results. butternut_24pop_relate_red.gen: Genepop file of 993 butternut individuals, reduced for 25% relatedness, used to generate all clustering analyses. butternut_26pop_nomd.gen: Genepop file of 1,662 butternut individuals, reduced based on geographic isolation and missing data, including Quebec individuals, organized into 26 populations. Used to generate genetic diversity results with Quebec individuals. butternut_26pop_relate_red.gen: Genepop file of 1,015 butternut individuals, including Quebec individuals, reduced for 25% relatedness, used to generate clustering analyses with Quebec individuals. Fossil Pollen Data collection description: Pollen records for butternut were downloaded from Neotoma Paleoecology Database in 500-year time increments and visualized in 1,000 year-time increments 20,000 years ago to present. Data files butternut_pollen_data.csv: CSV of pollen records used for analyses and mapping. Includes original coordinates for each record (“og_long”, “og_lat”), the count of Juglans cinerea pollen at each site (“Juglans_cinerea_count”), and the age of the record (“Age”). To create the final maps, the coordinates were projected into Albers for each record (“Proj_Long,” “Proj_Lat”). Species Distribution Modeling and Hindcast Modeling Data collection description: We wanted to identify butternut's ecological preferences using boosted regression trees (BRT) and then hindcast distribution models into the past to identify migration pathways and locations of glacial refugia. Species distribution modeling was performed using boosted regression trees according to Elith et al. (2008). To run BRT, we needed to: 1. Reduce occurrence records to account for spatial autocorrelation, 2. Generate pseudo-absence points to identify the habitat where butternut is not found, 3. Obtain and extract the 19 bioclimatic variables at all points, 4. Select ecological variables least correlated with each other and most correlated with butternut presence. The BRT model that predicted butternut's ecological niche was then used to hypothesize butternut's suitable habitat and range shifts in the past. We downloaded occurrence records according to Beckman et al. (2019) as described here: https://github.com/MortonArb-ForestEcology/IMLS_CollectionsValue. The habitat suitability map generated from the BRT were projected into the past 20,000 years using Paleoclim variables (Brown et al., 2018). Data files butternut_BRT_var.csv: A CSV of the butternut presence and pseudoabsence points and extracted Bioclim variables (Fick & Hijman, 2017) used to run BRT in the final manuscript. Longitude and latitude coordinates are projected into Albers Equal Area Conic project, same with all of the ecological variables. Presence points are indicated with a 1 in the “PA” column and pseudo-absence points are indicated with a “0.” The variables most correlated with presence and least correlated with each other in this analysis were precipitation of the wettest month (“PwetM”), mean diurnal range (“MDR”), mean temperature of the driest quarter (“MTDQ”), mean temperature of the wettest quarter (“MTwetQ”), and seasonal precipitation (“precip_season”). References Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C., & Haywood, A. M. (2018). PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Scientific Data, 5, 1-9 Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802-813. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302-4315. Hoban, S., Anderson, R., McCleary, T., Schlarbaum, S., and Romero-Severson, J. (2008). Thirteen nuclear microsatellite loci for butternut (Juglans cinerea L.). Molecular Ecology Resources, 8, 643-646. Hoban, S. M., Borkowski, D. S., Brosi, S. L., McCleary, T. S., Thompson, L. M., McLachlan, J. S., ... Romero-Severson, J. (2010). Range‐wide distribution of genetic diversity in the North American tree Juglans cinerea: A product of range shifts, not ecological marginality or recent population decline. Molecular Ecology, 19, 4876-4891. Aim: Range shifts are a key process that determine species distributions and genetic patterns. A previous investigation reported that Juglans cinerea (butternut) has lower genetic diversity at higher latitudes, hypothesized to be the result of range shifts following the last glacial period. However, genetic patterns can also be impacted by modern ecogeographic conditions. Therefore, we re-investigate genetic patterns of butternut with additional northern population sampling, hindcasted species distribution models, and fossil pollen records to clarify the impact of glaciation on butternut. Location: Eastern North America Taxon: Juglans cinerea (L., Juglandaceae) (butternut) Methods: Using 11 microsatellites, we examined range-wide spatial patterns of genetic diversity metrics (allelic richness, heterozygosity, FST) for previously studied butternut individuals and an additional 757 samples. We constructed hindcast species distribution models and mapped fossil pollen records to evaluate habitat suitability and evidence of species’ presence throughout space and time. Results: Contrary to previous work on butternut, we found that genetic diversity increased with distance to range edge, and previous latitudinal clines in diversity were likely due to a few outlier populations. Populations in New Brunswick, Canada were genetically distinct from other populations. At the Last Glacial Maximum, pollen records demonstrate butternut likely persisted near the glacial margin, and hindcast species distribution models identified suitable habitat in the southern United States and near Nova Scotia. Main conclusions: Genetic patterns in butternut may be shaped by both glaciation and modern environmental conditions. Pollen records and hindcast species distribution models combined with genetic distinctiveness in New Brunswick suggest that butternut may have persisted in cryptic northern refugia. We suggest that thorough sampling across a species range and evaluating multiple lines of evidence are essential to understanding past species movements. Data was cleaned and processed in R - genetic data cleaning and analyses and species distribution modeling methods were performed in Emily Schumacher's butternut repository and fossil pollen data cleaning and modeling was performed in Alissa Brown's juglans repository. Steps for performing data cleanining, analyses, and generating figures for the manuscript are described within each repo.