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Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

# Data on: Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China [https://doi.org/10.5061/dryad.vdncjsz1z](https://doi.org/10.5061/dryad.vdncjsz1z) This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. ## Description of the data and file structure The predicted annual incidence of national SFTS cases with or without human population reduction under four RCPs under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The value represents the annual incidence, and the unit is 105/year. The Dataset-1 file includes the predicted annual incidence of national SFTS cases with a fixed future human population under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The Dataset-2 file includes the predicted annual incidence of national SFTS cases in the 2030s, 2050s, and 2080s with human population reduction (SSP2) under four RCPs. ## Sharing/Access information Data was derived from the following sources: * https://doi.org/10.1111/gcb.16969 This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. The SFTS incidence in three time periods (2030-2039, 2050-2059, 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. The projected spatiotemporal dynamics of SFTS will be heterogeneous across provinces. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.  See the Materials and methods section in the original paper. The code used in the statistical analyses are present in the paper and/or the Supplementary Materials.

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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

This README file was generated on [25/02/2024] by [Cooper Malanoski]. GENERAL INFORMATION 1. Title of Dataset: Climate change is an important predictor of extinction risk on macroevolutionary timescales 2. Author Information A. Principal Investigator Contact Information Name: [Cooper Malanoski] Institution: [Oxford University] Address: [Department of Earth Sciences, Oxford University, South Parks Road, Oxford, OX1 3AN, UK.] Email: [] B. Associate or Co-investigator Contact Information Name: [Dr. Erin Saupe] Institution: [Oxford University] Address: [1Department of Earth Sciences, Oxford University, South Parks Road, Oxford, OX1 3AN, UK.] Email: [] 3. Date of data collection: [NA] 4. Geographic location of data collection: [NA] 5. Information about funding sources: [National science research council (NERC), Award: NE/V011405/1 Leverhulme Prize Chinese Academy of Sciences Visiting Professorship for Senior International Scientists, Award: 2021FSE0001] SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: [Copyright © 2024 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. reuse] 2. Links to publications that cite or use the data: Malanoski et al. (2024). [Climate change is an important predictor of extinction risk on macroevolutionary timescales]. [Science]. 3. Links to other publicly accessible locations of the data: [NA] 4. Links/relationships to ancillary data sets: [Monarrez et al. (2021) was used to source the Generic_bodysize_data_monarrezetal2021.csv file] 5. Was data derived from another source? [Yes] A. If yes, list source(s): [Monarrez et al. (2021) was used to source the Generic_bodysize_data_monarrezetal2021.csv file] 6. Recommended citation for this dataset: Malanoski et al. (2024). Data from: Climate change is an important predictor of extinction risk on macroevolutionary timescales. Dryad Digital Repository. [doi:10.5061/dryad.1ns1rn91g] DATA & FILE OVERVIEW 1. File List: A) pbdbdata_code.Rmd B) Generic_bodysize_data_monarrezetal2021.csv C) raw_extracted_climatemodeldata.csv D) Geographic_range_code.Rmd E) Climate_based_variable_code.Rmd F) figures_code.Rmd G) intrinsic_and_extrinsic_variables.csv 2. Relationship between files: The utility of each dataset and code file is detailed below. 3. Additional related data collected that was not included in the current data package: [NA] 4. Are there multiple versions of the dataset? [No] A. If yes, name of file(s) that was updated: [NA] i. Why was the file updated? [NA] ii. When was the file updated? [NA] DATA-SPECIFIC INFORMATION \######################################################################### DATA-SPECIFIC INFORMATION FOR: Generic_bodysize_data_monarrezetal2021.csv includes the genera, log body volume and log body size estimates provided in Monarrez et al. (2021). For our analyses we use logvol, but logsize is retained for future studies. We removed bony fish from the original dataset, and the reasoning is provided in the Supplementary methods and materials. We join the log volume with our data based on the genus level. Higher taxonomic ranking revisions which Monarrez et al. (2021) revised were applied to our data using code found in Geographic_range_code.Rmd. 1. Number of variables: 7 2. Number of cases/rows: 9,461 3. Variable List: * genus: all invertebrate genera with body size information in Monarrez et al. (2021), except for Bony fish genera. * class: Linnean Class * logsize: log body size data from Monarrez et al. (2021) calculated from the treatise images in mm-squared. * logvol: log body volume data from Monarrez et al. (2021) calculated from the treatise images in mm-cubed. * phylum: Linnean Phylum * order: Linnean Order * family: Linnean Family 4. Missing data: Some Na's are present if a higher taxonomic ranking was not available for a genus. 5. Specialized formats or other abbreviations used: None \######################################################################### DATA-SPECIFIC INFORMATION FOR: raw_extracted_climatemodeldata.csv 1. Number of variables: 12 2. Number of cases/rows: 462,855 3. Variable List: * collection_no: Collection number of occurrence in the PBDB * stage: Geologic stage * Age: Age in millions of years before present * phylum: Linnean phylum * class: Linnean class * order: linnean order * family: linnean family * genus: linnean genus * paleolng: Paleolatitude coordinates * paleolat: Paleolongitude coordinates * Localized temperature: The temperature extracted for each occurrence in degrees Celsius * Localized change in temperature: Change in temperature between stages for each occurrence in degrees Celsius. 4. Missing data codes: Some Na's are present if a higher taxonomic ranking was not available for a genus. 5. Specialized formats or other abbreviations used: None \######################################################################### DATA-SPECIFIC INFORMATION FOR: intrinsic_and_extrinsic_variables.csv includes the climate model data for each occurrence in the PBDB data that can be sourced from pbdbdata_code.Rmd. This includes the localized temperatures and climate change estimates necessary to carry out future studies, and all analyses in Malanoski et al. (2024) 1. Number of variables: 17 2. Number of cases/rows: 22,222 3. Variable List: * ext: Binary extinction variable based on range through methods. A value of 0 indicates that the genus survived into subsequent stages and 1 indicates that the genus went extinct and is absent from subsequent stages. * Genus: Linnean genus * Stage: Geologic stage * Phylum: Linnean phylum * Class: Linnean class * Order: Linnean order * Family: Linnean family * Realized_thermal_niche_breadth: Realized thermal niche breadth calculated as the difference between the maximum and minimum occuppied temperatures for each genus. This variable is based on the median of all subsampled ranges for a genus. * Absolute_realized_thermal_preference: Realized thermal preference is calculated as the absolute value of the deviation in median occuppied temperature for a genus from the median for all occurrences within a stage. This variable is based on the median of all subsampled preferences for a genus. * Geographic_range_size: Geographic range size is calculated using the log convex hull area (km-squared). This variable is based on the median of all subsampled areas for a genus. * Body_size: Body size is calculated as the log body volume (mm-cubed) for each genus, derived from Monarrez et al. (2021). * Absolute_temperature_change: Change in temperature or climate change is calculated as the absolute change in temperature from stage n to n+1. This variable is based on the median of all subsampled ranges for a genus. * Realized_thermal_niche_breadth_std: Standardized realized thermal niche breadth * Realized_thermal_preference_std: Standardized realized thermal preference * Geographic_range_size_std: Standardized geographic range size * Body_size_std: Standardized body size * Absolute_temperature_change_std: Standardized absolute temperature change 4. Missing data codes: NA (data not applicable). Higher taxonomic levels may contain NA values if there are none applicable for a genus. 5. Specialized formats or other abbreviations used: \######################################################################### DATA-SPECIFIC INFORMATION FOR: pbdbdata_code.Rmd pbdbdata_code.Rmd is based on Kocsis et al. (2019) it can be used to download a dataset from the Paleobiology database (PBDB), and process and clean the data using the methods used in this manuscript. The code filters out occurrences which cannot be assigned to a stage and assigns up to date stages, filters out taxa which are not included in Generic_bodysize_data_monarrezetal2021.csv, and vets the occurrences for spatial duplicates and data without coordinates. \######################################################################### DATA-SPECIFIC INFORMATION FOR: Geographic_range_code.Rmd and Climate_based_variable_code.Rmd Geographic_range_code.Rmd and Climate_based_variable_code.Rmd are used to calculate geographic range size, absolute realized thermal preference, realized thermal niche breadth, and absolute change in occupied temperature. These R-markdown files rely on the raw_extracted_climatemodeldata.csv file and We provide code to calculate these variables for both jackknife and bootstrap subsampling methods. The geographic range code is adapted from Casey et al. (2021). The output from these files is provided as intrinsic_and_extrinsic_variables.csv and is used as the input for our statistical models, which can be made using figures_code.Rmd. \######################################################################### DATA-SPECIFIC INFORMATION FOR: figures_code.Rmd figures_code.Rmd can be used and modified to reproduce the main text and supplementary tables and figures. The code initially runs all model combinations for our 5 predictors using a generalized linear mixed effect model. Then we use the output from the best model total.glmer2 to make the marginal effects plots seen in figure 2, the supplementary conditional mode plots, and the AIC tables seen in the supplemental materials and methods. Lastly, we provide the code to produce figure 1. \######################################################################### Anthropogenic climate change is increasing rapidly and already impacting biodiversity. Despite the importance for future projections, understanding of the underlying mechanisms by which climate mediates extinction remains limited. We present an integrated approach examining the role of intrinsic traits vs. extrinsic climate change in mediating extinction risk for marine invertebrates over the past 485 million years. We found that a combination of physiological traits and the magnitude of climate change are necessary to explain marine invertebrate extinction patterns. Our results suggest that taxa previously identified as extinction resistant may still succumb to extinction if the magnitude of climate change is great enough.

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.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' 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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

This package generates all of the model runs and plotting code for "Current infrastructure does not yet commit us to 1.5°C warming". See enclosed README file for dependencies and how to run.

Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s “Stated Policies” Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution. Primary input data was collected from the following sources: Baseline household fuel choices - WHO household energy database (https://www.nature.com/articles/s41467-021-26036-x) End-use emissions - US EPA lifecycle assessment of household fuels (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339679&Lab=NRMRL&simplesearch=0&showcriteria=2&sortby=pubDate&timstype=Published+Report&datebeginpublishedpresented) Upstream emissions - Argonne National Labs GREET Model (https://greet.es.anl.gov/index.php) Current and future population estimates - UNECA (http://data.un.org/Explorer.aspx?d=EDATA) Input data was processed by defining household fuel choice scenarios, estimating national household fuel consumption based on these scenarios, and applying fuel-specific emission factors to create country-specific emission pathways. These emission pathways were input into the FaIR model (https://zenodo.org/record/5513022#.Yt_jfHbMLb0) which generated additional data for each scenario including time series of pollution concentrations, radiative forcing, and temperature changes. All data is provided in CSV format. Nothing proprietary is required. 

The underlying dataset for the Net Zero Tracker's Net Zero Stocktake Report 2024.