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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.

We include one dataset with demographic data for birds, called RCW_demo_data. Each row in this csv file represents an individual x year combination, and columns include information about individual and territory characteristics in that year, as well as various vital rates. For reproductive vital rates, we include these rates only for female breeders. Thus, reproductive vital rates such as “successfirstnest” will be NA (indicating missing data) for all males and for female non-breeders. Each row includes a climate reference number (“clim.group”) that allows the demographic data to be matched with the climate data in the climate files (see below for more description about these climate data). Below we list each column individually. Year: year in which data were collected Surtonext: did this individual survive to the next breeding season (1) or not (0)? Nohelp: how many helpers were present in this territory? Firstnestattempt_bin: did this breeding female initiate a nest in that year’s breeding season? 1 indicates yes, 0 indicates no. Morenestattempt_bin: did this breeding female initiate more than one nest in that years breeding season? 1 indicates yes, 0 no. Fledgedfirstnest: how many fledged from the first nest. Fledgedlaternest: how many fledged from any later nests. Eggsfirstattempt: how many eggs in the first nest. Eggslaterattempt: how many eggs in the first nest. Clim.group: a grouping variable that matches the clim.group variable in the climate datasets. Note that the demographic data contains a space, the climate datasets a period, but SH 146 is the same climate grouping as SH.146. Site: one of SH, EG, or CL, representing Sandhills, Eglin, or Camp Lejeune Numericage: age of the bird Binned status: one of Breeder, Helper or Floater (B, H, or F). Sex: F or M Numericmalemateage: age of the male breeder which which a female bred. Only recorde for breeding females. Successfirstnest_bin: was the first nest successful? 1 indicates yes, 0 no. Frsurvivingfirst: what fraction of eggs survived to fledging from the first nest?Successmorenest_bin: were any later (i.e., 2nd or later) nests successful? 1 indicates yes, 0 no. Frsurvivinglater: what fraction of eggs survived to fledging from all later nests? We have included five datasets corresponding to the five climate variables. The name of the csv file indicates the climate variable that the dataset contains. Each dataset contains information on the date, the climate group (clim.grp, corresponds to the climate groups in the demographic dataset), and the value of the climate signal for that date. Units are indicated in the main text for this paper.

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.CAMS.CAMS-CSM1-0.ssp119' 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 CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

Seeds of 52 species of herbaceous plants typical from European grassland ecosystems were obtained from a commercial supplier (Planta naturalis). When species germinated in Petri dishes the seedlings were then transplanted to plastic pots (11 x 11 x 12 cm height, 1L volume). Pots were filled with a mixture of a potting substrate (Biolan Murumuld) and sand. Pots were randomly placed in the greenhouse of the University of Tartu, Estonia. Then, we established monocultures with seven individuals of a single species per pot which were grown under well-watered conditions. One month after transplanting the seedlings to the pots, a drought treatment was applied to half of the pots (five pots per species). The experiment was harvested in late July 2020, when the first individuals started flowering, after month-long drought treatment. Plant traits related to drought responses and resource use strategies were selected and measured for each species following established protocols. These included seven above- and belowground traits: Vegetative plant height (H, cm), Leaf Area (LA, mm2), Specific Leaf Area (SLA, mm2 mg-1), Leaf Dry Matter Content (LDMC, mg g-1), Specific Root Length (SRL, cm g-1), Average root Diameter (AvgD, mm), Root Dry Matter Content (RDMC, mg g-1). Before harvesting, we measured the plant height and collected one leaf per individual for three individuals per pot. Afterward, we collected the aboveground biomass and belowground biomass of all the individuals in each pot. Due to the difficulty in untangling the roots of the different individuals in a pot, root traits were estimated at the pot level. Roots were washed and a sample of finest roots (10-50mg) was collected. Leaves and fine roots were scanned at 300dpi and 600dpi, respectively, using an Epson perfection 3200 Photo scanner for leaves and Epson V700 Photo scanner for fine roots. After scanning, leaves and roots were oven-dried at 60°C for 72h. AvgD and root length were determined using WinRHIZO Pro 2015 (Regent Instruments Inc., Canada), and leaf area with ImageJ software. We averaged all traits values at the species level, attaining a single value for each trait in each treatment. The total aboveground biomass and total belowground biomass of each pot were oven-dried at 60°C for 72h and weighed. Drought is expected to increase in future climate scenarios. Although responses to drought of individual functional traits are relatively well-known, simultaneous changes across multiple traits in response to water scarcity remain poorly understood despite its importance to understand alternative strategies to resist drought. We grew 52 herbaceous species in monocultures under drought and control treatments and characterized the functional space using seven measured above- and belowground traits: plant height, leaf area, specific leaf area, leaf dry matter content, specific root length, average root diameter, and root dry matter content. Then, we estimated how each species occupied this space and the amount of functional space occupied in both treatments using trait probability density functions. We also estimated intraspecific trait variability (ITV) for each species as the dissimilarity in trait values between the individuals of each treatment. We then mapped drought resistance and ITV in the functional space using generalized additive models. The response of species to drought strongly depended on their traits, with species that invested more in root tissues and conserved small size being both more resistant to drought and having higher ITV. We also observed a significant trend of trait displacement towards less conservative strategies. However, these changes depended strongly on the trait values of species in the control treatment, with species with different traits having opposing responses to drought. These contrasting responses resulted in lower trait variability in the species pool in drought compared to control conditions. Our results suggest strong trait filtering acting on conservative species as well as the existence of an optimal part in the functional space to which species converge under drought. Our results show that changes in species trait-space occupancy are key to understand plant strategies to withstand drought, highlighting the importance of individual variation in response to environmental changes, and suggest that community-wide functional diversity and biomass productivity could decrease in a drier future. Knowing these shifts will help to anticipate changes in ecosystem functioning facing climate change. The complete dataset is in the file.

This archive includes a minimal dataset needed to reproduce the analysis as well as a table (CSV) and spatial polygons (ESRI shapefile) of the resulting output from the publication: Hoecker, T.J., S. A. Parks, M. Krosby & S. Z. Dobrowski. 2023. Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States. Communications Earth and Environment. Publication abstract: Changes in wildfire frequency and severity are altering conifer forests and pose threats to biodiversity and natural climate solutions. Where and when feedbacks between vegetation and fire could mediate forest transformation are unresolved. Here, for the western U.S., we used climate analogs to measure exposure to fire-regime change; quantified the direction and spatial distribution of changes in burn severity; and intersected exposure with fire-resistance trait data. We measured exposure as multivariate dissimilarities between contemporary distributions of fire frequency, burn severity, and vegetation productivity and distributions supported by a 2 °C-warmer climate. We project exposure to fire-regime change across 65% of western US conifer forests and mean burn severity to ultimately decline across 63% because of feedbacks with forest productivity and fire frequency. We find that forests occupying disparate portions of climate space are vulnerable to projected fire-regime changes. Forests may adapt to future disturbance regimes, but trajectories remain uncertain.

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.MIROC.MIROC6.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 MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

The dataset contains artificial phenological observations of 17 species from 2009 to 2018 and consists of a woody plant subset and an herbaceous plant subset. A total of 97 pieces of woody plant subset data records phenological information such as leaf bud breaking phase, leaf unfolding phase, first bloom phase, full flowering phase, fruit or seed ripening phase, leaf turning to autumn color phase and leaf falling phase. A total of 66 pieces of herbaceous plant subset data records phenological information such as germination or turning green phase, flowering phase, fruit or seed ripening phase, seed dispersal phase and autumn wilting phase. The dataset contains artificial phenological observations of 17 species from 2009 to 2018 and consists of a woody plant subset and an herbaceous plant subset. A total of 97 pieces of woody plant subset data records phenological information such as leaf bud breaking phase, leaf unfolding phase, first bloom phase, full flowering phase, fruit or seed ripening phase, leaf turning to autumn color phase and leaf falling phase. A total of 66 pieces of herbaceous plant subset data records phenological information such as germination or turning green phase, flowering phase, fruit or seed ripening phase, seed dispersal phase and autumn wilting phase.

Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per section PMedian;PIQR,PSkewness,PCV: Median, interquartile range,skewness and coefficient of variation of preessure per section TKEMedian;TKEIQR,TKESkewness,TKECV: Median, interquartile range,skewness and coefficient of variation of turbulent kinetic energy per section TIMedian;TIIQR,TISkewness,TICV: Median, interquartile range,skewness and coefficient of variation of turbulence intensity per section U_2Median;lU_2IQR;U_2Skewness;U_2CV: Median, interquartile range,skewness and coefficient of variation of vertical wind speed per section EpsilonMedian;EpsilonIQR,EpsilonSkewness,EpsilonCV: Median, interquartile range,skewness and coefficient of variation of turbulent dissipation rate per section NutMedian;NutIQR,NutSkewness,NutCV: Median, interquartile range,skewness and coefficient of variation of kinematic viscosity per section GustsMedian;GustsIQR,GustsSkewness,GustsCV: Median, interquartile range,skewness and coefficient of variation of instataneous gusts per section MeanSectorSlope: Mean slope per section ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: Section table on Skomer - with Mean cliff orientation and Slope (NOT-Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section ApsectClass: Factor indicating whether the mean cliff orientation is lee- or windward to the SW wind ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: SW wind - Sections on Skokholm to predict colonies using cliff orientation and slope model from Skomer (NON - Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. Methods detailed in manuscript: https://doi.org/10.1111/ecog.05733.

# Coral reef state influences resilience to acute climate-mediated disturbances\_Table S1 [https://doi.org/10.5061/dryad.rfj6q57gz](https://doi.org/10.5061/dryad.rfj6q57gz) The dataset provides a summary of all publications included in the analysis for this study and the key statistics obtained from the studies and used in the analyses. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. ## Description of the data and file structure Each column provides the following information: | Column | Detail | | ------ | ------ | | Realm | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Province | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Ecoregion | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Unique study identifier | Unique identifiers for the lowest sampling unit in the dataset. In cases where there were data for different regions, reefs, islands/atolls, sites, reef zones, depths, and/or multiple disturbances within a publication or time-series, data from these publications were divided into separate ‘studies’. | | Publication/Dataset | Unique identifiers for the publication or dataset (generally the surname of the first author followed by the year of publication). | | Publication title | Title of the publication or dataset from which the data were sourced. | | Publication year | Year the publication from the which the data were sourced was published. | | Country/Territory | Name of the country or location from which the data came. | | Site latitude | Latitude of the study site from where the data came. | | Site longitude | Longitude of the study site from where the data came. | | Disturbance type | Classification of disturbance: Temperature stress, Cyclone/ severe storm, Runoff or Multiple. | | Disturbance.year | Year of the disturbance. | | Mean coral cover pre-disturbance | Pre-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Mean coral cover post-disturbance | Post-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Impact (lnRR) | Impact measure: the log response ratio of pre- to post-disturbance percentage coral cover. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Time-averaged recovery rate | Recovery rate as percentage coral cover per year in the approximate 5-year time window following disturbance. See main Methods text in manuscript for more detail. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in the calculation of recovery rate. | | Recovery shape | Recovery shape category: linear, accelerating, decelerating, logistic, flatline or null. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Recovery completeness | Recovery completeness category: complete recovery – coral is observed to reach its pre-disturbance coral cover, signs of recovery – a positive trajectory but not reaching pre-disturbance cover in the time period examined, undetermined – no clear pattern in recovery, the null model was the top model, no recovery – the null model was the top model but the linear model had slope and standard error in slope near zero and further decline – the top model had a negative trend. 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Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Wismer, S., Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2019). Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Science of the Total Environment, 650, 1487-1498. Woolsey, E., Bainbridge, S. J., Kingsford, M. J., & Byrne, M. (2012). Impacts of cyclone Hamish at One Tree Reef: Integrating environmental and benthic habitat data. Marine Biology, 159(4), 793-803. Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales. Location: Tropical coral reefs globally. Time period: 1966 to 2017. Major taxa studied: Scleratinian hard corals. Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating). Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period. Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience. The dataset provides a summary of all studies included in the analysis and the key statistics obtained from the studies and used in the analyses for the manuscript entitled "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography.