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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.

Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

Raw data and script relevant to the article: ‘The public perception of the role, importance and vulnerability of seagrass. A case study from the Great Barrier Reef’ Software/equipment used to create/collect the data: Survey Monkey for data collection Software/equipment used to manipulate/analyse the data: R for statistical analysis Leximancer for content analysis