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Conservation of thermally sensitive species depends on monitoring organismal and population-level responses to environmental change in real time. Epigenetic processes are increasingly recognized as key integrators of environmental conditions into developmentally plastic responses, and attendant epigenomic datasets hold potential for revealing cryptic phenotypes relevant to conservation efforts. Here, we demonstrate the utility of genome-wide DNA methylation (DNAm) patterns in the face of climate change for a group of especially vulnerable species, those with temperature-dependent sex determination (TSD). Due to their reliance on thermal cues during development to determine sexual fate, contemporary shifts in temperature are predicted to skew offspring sex ratios and ultimately destabilize sensitive populations. Using reduced-representation bisulfite sequencing, we profiled the DNA methylome in blood cells of hatchling American alligator (Alligator mississippiensis), a TSD species lacking reliable markers of sexual dimorphism in early life-stages. We identified 120 sex-associated differentially methylated cytosines (DMCs; FDR < 0.1) in hatchlings incubated under a range of temperatures, as well as 707 unique temperature-associated DMCs. We further developed DNAm-based models capable of predicting hatchling sex with 100% accuracy (in 20 training samples and 4 test samples) and past incubation temperature with a mean absolute error of 1.2˚C (in 4 test samples) based on the methylation status of 20 and 24 loci, respectively. Though largely independent of epigenomic patterning occurring in the embryonic gonad during TSD, DNAm patterns in blood cells may serve as non-lethal markers of hatchling sex and past incubation conditions in conservation applications. These findings also raise intriguing questions regarding tissue-specific epigenomic patterning in the context of developmental plasticity. 

The ecological impacts of multiple stressors are hard to predict but important to understand. When multiple stressors influence foundation species, the effects can cascade throughout the ecosystem. Gulf of Mexico seagrass ecosystems are currently experiencing a suite of novel stressors, including warmer water temperatures and increased herbivory due to tropicalization and conservation efforts. We investigated the impact of warming temperatures and grazing history on plant performance, morphology, and palatability by integrating a mesocosm study using the seagrass Thalassia testudinum with feeding trials using the sea urchin Lytechinus variegatus. Warming temperatures negatively impacted T. testudinum tolerance traits, reducing belowground biomass by 34%, productivity by 74%, shoot density by 10%, and the number of leaves per plant by 24%, and negatively impacted resistance traits through 13% lower toughness of young leaves and a trend for reduced leaf carbon:nitrogen. Lytechinus variegatus individuals preferred to consume plants grown under heated conditions, which supports findings of enhanced palatability. Simulated turtle grazing impacted more plant traits than grazing by other herbivores, potentially diminishing plant resilience to future disturbances through reduced rhizome non-structural carbohydrate concentrations and increasing palatability through reduced fiber content and 23% lower leaf carbon:phosphorus. Simulated turtle, simulated parrotfish, and urchin grazing reduced leaf carbon:nitrogen by 11%, also potentially increasing nutritive value. Interactions between warming temperatures and grazers on plant traits were additive for 16 out of 19 response variables. However, the stressors non-additively impacted the number of leaves per plant, fiber content, and epiphyte load. We suggest that the impacts of grazers on leaf turnover rate and leaf age may vary based on water temperature, potentially driving these interactions. Overall, increased temperatures and grazing pressure will likely reduce seagrass resilience, structure, and biomass, potentially impacting feedback systems and producing negative consequences for seagrass cover, associated species, and ecosystem services.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

An EPA list of greenhouse gas reports.

# Nitrogen-fixing plants increase soil nitrogen and neighboring plant biomass, but decrease community diversity: A meta-analysis reveals the mediating role of soil texture [https://doi.org/10.5061/dryad.4qrfj6qk1](https://doi.org/10.5061/dryad.4qrfj6qk1) ## Description of the data and file structure This data file was constructed by gathering and extracting data from published scientific papers identified using a rigorous selection process (see manuscript for details on the selection process). The papers included in this data are identified within the primary dataset here, but also in the supplementary file of the manuscript. This dataset includes original manuscript information, data extractor, geographical and ecological data, and notation of any treatments or differences in groups, along with the means and error terms for each data point extracted. This is the raw data used for this paper. The 'yi' and 'vi' terms in the dataset are the individual log response ratio (lnRR) and variation, respectively. These are the terms used in all analyses presented in the final manuscript. There are moderators included in this dataset to account for and test for heterogeneity within the response of interest. However, given the nature of this type of analysis, there are quite a few missing data points from the various moderators; these are noted with an "N/A" in the dataset. The data file is titled "Ortiz-Wolf-2024-JoE.xslx" - this data file contains two spreadsheets: 'metadata' and 'dataset'. The 'metadata' spreadsheet describes each attribute (including abbreviations and units) in 'dataset'. The 'dataset' spreadsheet contains the independent effect sizes (Log Response Ratio) for each data point and the available moderator data there were used in our meta-analyses and used to generate figures presented in our manuscript and supplemental file. ## Sharing/Access Information NA Several recent regional studies have cast doubt on the widespread assumption that nitrogen-fixing plants (N-fixers) act as facilitators of neighboring plant communities. We conducted a meta-analysis to synthesize the effects of N-fixers on plant communities and to understand how ecological context moderates these effects. We analyzed studies that assessed paired effects of N-fixers and non-fixers on soil N, neighboring-plant (non-fixer) biomass, and plant community diversity; ecological moderators included climate, soil texture, and N-fixer growth form and invasive status. N-fixers led to higher soil N and neighboring plant biomass, but lower community diversity compared to non-fixers. The effect of N-fixers on neighboring plant biomass was strongly mediated by soil texture; N-fixer invasive status and growth form were also significant mediators of the facilitative effects of N-fixers. N-fixer effects lie on a continuum between facilitation and suppression that is moderated by intrinsic and extrinsic processes, and this analysis provides insight into how these factors moderate the effects of N-fixers. Overall, N-fixers facilitate neighbor biomass but suppress diversity, though high variation in these effects can be explained in part by ecological context.

Replication code and data for the paper: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity." The following files are included in this repository: 1) R scripts (numbered 0 through 9) include replication code for data analysis 2) Datasets (6 zip folders) contain the data analyzed in the R scripts

This paper evidenced sensitivity of US immigration judge decisions to temperature in the city of arbitration on date of adjudication. This note serves to correct errors noted since publication. Main results from both the main linear specifications are qualitatively unchanged, with estimated treatment effects similar in size to the original and retaining statistical significance at conventional levels. Some secondary results lose significance with erosion of sample size. We also acknowledge the additional finding by Spamann (2020) with respect to external validity. Immigration judges in the United States.

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.C4MIP.E3SM-Project.E3SM-1-1' 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 E3SM 1.1 (Energy Exascale Earth System Model) climate model, released in 2019, includes the following components: aerosol: MAM4 with resuspension, marine organics, and secondary organics (same grid as atmos), atmos: EAM (v1.1, cubed sphere spectral-element grid; 5400 elements with p=3; 1 deg average grid spacing; 90 x 90 x 6 longitude/latitude/cubeface; 72 levels; top level 0.1 hPa), atmosChem: Troposphere specified oxidants for aerosols. Stratosphere linearized interactive ozone (LINOZ v2) (same grid as atmos), land: ELM (v1.1, same grid as atmos; active biogeochemistry using the Converging Trophic Cascade plant and soil carbon and nutrient mechanisms to represent carbon, nitrogen and phosphorus cycles), MOSART (v1.1, 0.5 degree latitude/longitude grid), ocean: MPAS-Ocean (v6.0, oEC60to30 unstructured SVTs mesh with 235160 cells and 714274 edges, variable resolution 60 km to 30 km; 60 levels; top grid cell 0-10 m), ocnBgchem: BEC (Biogeochemical Elemental Cycling model, NPZD-type with C/N/P/Fe/Si/O; same grid as ocean), seaIce: MPAS-Seaice (v6.0; same grid as ocean). The model was run by the LLNL (Lawrence Livermore National Laboratory, Livermore, CA 94550, USA); ANL (Argonne National Laboratory, Argonne, IL 60439, USA); BNL (Brookhaven National Laboratory, Upton, NY 11973, USA); LANL (Los Alamos National Laboratory, Los Alamos, NM 87545, USA); LBNL (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA); ORNL (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA); PNNL (Pacific Northwest National Laboratory, Richland, WA 99352, USA); SNL (Sandia National Laboratories, Albuquerque, NM 87185, USA). Mailing address: LLNL Climate Program, c/o David C. Bader, Principal Investigator, L-103, 7000 East Avenue, Livermore, CA 94550, USA (E3SM-Project) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.