Attached are CSV files for the moth occurence data used to create species distribution models for the baldcypress leafroller present in the Ecological Informatics puclication titled "Environmental niche model for the baldcypress leafroller, a significant defoliator of coastal forests, under current and projected climate scenarios" and authored by Kristy M. McAndrew, Joshua J. Granger, and Samuel F. Ward. Also included is an R script with data cleaning, processing, and model creation.Example code is included for projecting best fit models into future climate scenarios. Additionally, all projected future rasters are included, with code used to calculate area in each suitability category, and code used for statistical tests represented in the manuscript.Code to create each figure presented in the main text of the manuscript is present, including Climate data used is available either through R directly (see code) or from worldclim (https://www.worldclim.org/data/cmip6/cmip6climate.html). Host data are included both as shapefiles and rasters.

Attached are CSV files for the moth occurence data used to create species distribution models for the baldcypress leafroller present in the Ecological Informatics puclication titled "Environmental niche model for the baldcypress leafroller, a significant defoliator of coastal forests, under current and projected climate scenarios" and authored by Kristy M. McAndrew, Joshua J. Granger, and Samuel F. Ward. Also included is an R script with data cleaning, processing, and model creation.Example code is included for projecting best fit models into future climate scenarios. Additionally, all projected future rasters are included, with code used to calculate area in each suitability category, and code used for statistical tests represented in the manuscript.Code to create each figure presented in the main text of the manuscript is present, including Climate data used is available either through R directly (see code) or from worldclim (https://www.worldclim.org/data/cmip6/cmip6climate.html). Host data are included both as shapefiles and rasters.

A nation’s distribution of wealth is partially a political decision. However, research has shown that perceptions of inequality often diverge from reality. This divergence may in turn impact public support for policies. Norton and Ariely (2011) surveyed Americans in 2005 about views on wealth inequality and famously found that ‘Americans prefer Sweden.’ We reexamine this claim with a controlled laboratory experiment. We employ a more intuitive display of the distribution of wealth than the original study. Participants were shown three hypothetical distributions of wealth: an equal distribution, one highly unequal based on the United States, and a distribution between the two extremes. Cluster analysis reveals three distinct types of distributional preferences. Despite high inequality and political upheaval that have taken place since 2005, we find that our participants also prefer a wealth distribution that is more equal than that experienced in the United States. This result is consistent regardless of political affiliation. Additional research is needed to reconcile stated preferences for increased wealth equality with public policy enhancing inequality.

A nation’s distribution of wealth is partially a political decision. However, research has shown that perceptions of inequality often diverge from reality. This divergence may in turn impact public support for policies. Norton and Ariely (2011) surveyed Americans in 2005 about views on wealth inequality and famously found that ‘Americans prefer Sweden.’ We reexamine this claim with a controlled laboratory experiment. We employ a more intuitive display of the distribution of wealth than the original study. Participants were shown three hypothetical distributions of wealth: an equal distribution, one highly unequal based on the United States, and a distribution between the two extremes. Cluster analysis reveals three distinct types of distributional preferences. Despite high inequality and political upheaval that have taken place since 2005, we find that our participants also prefer a wealth distribution that is more equal than that experienced in the United States. This result is consistent regardless of political affiliation. Additional research is needed to reconcile stated preferences for increased wealth equality with public policy enhancing inequality.

The HWO Target Stars and Systems 2025 (TSS25) list is a community-developed catalog of potential stellar targets for the Habitable Worlds Observatory (HWO) in its survey to directly image Earth-sized planets in the habitable zone. The TSS25 list categorizes potential HWO targets into priority tiers based on their likelihood to be surveyed and the necessity of obtaining observations of their stellar properties prior to the launch of the mission. This target list builds upon previous efforts to identify direct imaging targets and incorporates the results of multiple yield calculations assessing the science return of current design concepts for HWO.  The TSS25 list identifies a sample of target stars that have a high probability to be observed by HWO (Tiers 1 and 2), independent of assumptions about the mission's final architecture. These stars should be the focus of community precursor science efforts in order to mitigate risks and maximize the science output of HWO. This target list is publicly available and is a living catalog that will be continually updated leading up to the mission.

The HWO Target Stars and Systems 2025 (TSS25) list is a community-developed catalog of potential stellar targets for the Habitable Worlds Observatory (HWO) in its survey to directly image Earth-sized planets in the habitable zone. The TSS25 list categorizes potential HWO targets into priority tiers based on their likelihood to be surveyed and the necessity of obtaining observations of their stellar properties prior to the launch of the mission. This target list builds upon previous efforts to identify direct imaging targets and incorporates the results of multiple yield calculations assessing the science return of current design concepts for HWO.  The TSS25 list identifies a sample of target stars that have a high probability to be observed by HWO (Tiers 1 and 2), independent of assumptions about the mission's final architecture. These stars should be the focus of community precursor science efforts in order to mitigate risks and maximize the science output of HWO. This target list is publicly available and is a living catalog that will be continually updated leading up to the mission.

BioGeoBEARS folder: Contains the files, documents, and scripts used and generated during the Ancestral Range Reconstruction (ARR) analysis of the tribe Vernonieae (Asteraceae).Diversification Analysis folder: Contains the files, documents, and scripts used and generated during the Diversification Analysis of the tribe Vernonieae (Asteraceae).Trees folder: Contains the phylogenetic tree of the tribe Vernonieae (Asteraceae), which was dated using BEAST 2, treePL, and MEGA 11 and two calibration sets: M (Mandel et al. 2019) and P (Panero and Crozier 2016). Also includes scripts for building the violin plot and for the statistical analysis of divergence ages.Vernonieae.fas file: Contains the molecular sequences of the Vernonieae (Asteraceae) species.

BioGeoBEARS folder: Contains the files, documents, and scripts used and generated during the Ancestral Range Reconstruction (ARR) analysis of the tribe Vernonieae (Asteraceae).Diversification Analysis folder: Contains the files, documents, and scripts used and generated during the Diversification Analysis of the tribe Vernonieae (Asteraceae).Trees folder: Contains the phylogenetic tree of the tribe Vernonieae (Asteraceae), which was dated using BEAST 2, treePL, and MEGA 11 and two calibration sets: M (Mandel et al. 2019) and P (Panero and Crozier 2016). Also includes scripts for building the violin plot and for the statistical analysis of divergence ages.Vernonieae.fas file: Contains the molecular sequences of the Vernonieae (Asteraceae) species.

Lake Baikal ranks among the most species-rich freshwater environments on the planet; however, the evolutionary histories of endemic taxa remain poorly understood. The unique abiotic environments of Lake Baikal include the only bathybenthic, bathypelagic, and deep hydrothermal vent communities in freshwater, each of which supports species with derived morphological and physiological traits. As the only vertebrate radiation endemic to a non-tropical ancient lake, the Baikal sculpins represent an underappreciated resource for investigating evolutionary processes that underlie adaptive radiation. We examined morphological and ecological diversity among Baikal sculpins and present the first interspecific phylogeny inferred from unlinked nuclear genomic markers. The new phylogeny supports a holarctic lotic common ancestor to a limnetic radiation, and reveals convergent adaptations to bathyal and pelagic habitats. Ecomorphological shifts involved dramatic modification of skeletal elements, sensory systems, and reproductive mode. We apply the new phylogeny to revise a recent taxonomic update to Baikal sculpins, and we resurrect the monotypic genus Uranidea as the sister group to the Baikal sculpin radiation. According to previous research, our data support the independent evolution of pelagic and bathypelagic ecomorphs and multiple invasions of bathybenthic habitats. Analysis of morphological diversification and speciation rates in Lake Baikal sculpins supports this group as an understudied yet iconic adaptive radiation of freshwater fishes.

This dataset consists of shapefiles of training points names (SamplePoints.zip) which were collected on the Google Earth Engine platform for our land cover analysis. The code.zip contains the codes and the direct Google Earth Engine code showing how the land cover analysis with six (6) machine learning algorithms (Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Gradient Tree Boost (GTB), Classification and Regression Tree (CART), and Naive Bayes) was implemented. Each link to a code has been annotated with the correct season (either fall, spring, summer, and winter). Additionally, these links have been added as related links to this dataset strictly in the order Fall > Spring > Summer > Winter.