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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: García-Barros, Enrique; Álamo, Mario; Romo, Helena;

    # sRGB Reflectances from Iberian butterflies [https://doi.org/10.5061/dryad.1g1jwsv0q](https://doi.org/10.5061/dryad.1g1jwsv0q) Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (*C. webbianus*) from the Canaries. Average of male and female, sample size as indicated in column n. The data from *C. webbianus* and *C. marshalli* were not included in our analyses of reflectance. Text file, CSV format, columns delimited by periods, 225 rows (including headings) and 38 columns. Any means presented are weighted averages taking into account the areas of the parts involved. Wing reflectances refer to the parts of the wings exposed in a living butterfly (except FW\_AREA and HW\_AREA which are total wing surfaces). * **Ord**, row number (roughly a taxonomic arrangement) * **Species**, species name (abbreviated genus, contains a blank space, e.g., *Heteropterus morpheus*) * **N**, sample size * **FWL**, forewing length (mm) * **DFT**, reflectance, dorsal forewing * **DFp**, reflectance, dorsal forewing, proximal area * **DFd**, reflectance, dorsal forewing, distal area * **DHT**, reflectance, dorsal hindwing * **DHp**, reflectance, dorsal hindwing, proximal area * **DHd**, reflectance, dorsal hindwing, distal area * **DB**, reflectance, dorsal body area * **D(Tp+B)**, reflectance of the exposed dorsal body plus proximal wing surfaces * **DT**, reflectance of the dorsal areas (body plus whole wing) * **DTp**, reflectance of the dorsal, proximal wing areas * **DTd**, reflectance of the dorsal, distal wing areas * **VFT**, reflectance, ventral forewing * **VFp**, reflectance, ventral forewing, proximal area * **VFd**, reflectance, ventral forewing, distal area * **VHT**, reflectance, ventral hindwing * **VHp**, reflectance, ventral hindwing, proximal area * **VHd**, reflectance, ventral hindwing, distal area * **VB**, reflectance, ventral body area * **V(Tp+B)**, reflectance of the exposed ventral body plus proximal wing surfaces * **VT**, reflectance of the ventral areas (body plus whole wing) * **VTp**, reflectance of the ventral, proximal wing areas * **VTd**, reflectance of the ventral, distal wing areas * **Mean**, mean total reflectance (dorsal and ventral surfaces) * **p\_Mean**, mean reflectance of the proximal (dorsal and ventral) wing areas * **p\_Otimum**, mean reflectance of the proximal dorsal (for dorsal baskers) or ventral (for lateral basking species) wing areas. * **FW\_area**, total forewing area (mm2) * **HW\_area**, total hindwing area (mm2) * **T\_Mean\_Iberia\_10km**, Iberian mean species temperature, Centigrade degrees, 10 x 10 km resolution * **P\_Mean\_Iberia\_10km**, mean species annual precipitation, mm, Iberian Peninsula, 10 x 10 km resolution * **T\_Mean\_Ibera\_50km**, mean species temperature, Centigrade degrees, Iberian Peninsula, 50 x 50 km resolution * **P\_Mean\_Iberia\_50km**, mean species annual precipitation, mm, Iberian Peninsula, 50 x 50 km resolution Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (Cyclyrius webbianus) from the Canary Islands. Average of male and female, sample size as indicated in column n. The data from C. webbianus and Cacyreus marshalli are provided although these species were not included in our analyses of reflectance. The data were measured from digital images of set (collection) specimens taken in fixed conditions, with grey (average RGB) values standardized a posteriori to fit the scale white= 243.7= white, to black= 52. The data set includes the mean length of the forewing (mm) and the total areas (mm2) of the fore and hind wings.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    A total of 170 participants were initially enrolled in the comprehensive behavioral weight loss intervention.In this study, investigators will conduct a follow-up visit 3 years after the completion of the intervention. Only participants who completed the behavioral weight loss intervention will be enrolled in this study. Participants will undergo testing of body weight, body composition, physical activity patterns, energy intake patterns, sleep patterns, resting metabolic rate, and total daily energy expenditure. This study is designed as an observational trial. The objective of this study is to follow-up with participants 3 years after completion of an 18-month comprehensive behavioral weight loss intervention. Outcomes of interest include change in body weight, body composition, physical activity, energy intake, and sleep. In addition, investigators will explore the associations between current physical activity, sleep, and energy intake patterns and body weight regulation.

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    ClinicalTrials.gov
    Clinical Trial . 2019
    Data sources: ClinicalTrials.gov
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      ClinicalTrials.gov
      Clinical Trial . 2019
      Data sources: ClinicalTrials.gov
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    Authors: Pang, Rich; Van Breugel, Floris; Dickinson, Michael; Riffell, Jeffrey A.; +1 Authors

    Flight trajectories of fruit flies and mosquitoes in a wind tunnel.This data file is a MySQL database file which must be uploaded to a MySQL database management system (DBMS) (e.g., via the MAMP installation: http://localhost:8888/MAMP/?language=English, as was used in the associated manuscript). Once you have installed a MySQL DBMS on your machine, make a new database called “wind_tunnel_db”. To populate this database using the data file, first download all of the data files and join them together using: cat wind_tunnel_db_* > wind_tunnel_db.sql Then run the following command to populate the wind_tunnel_db MySQL database with the result. /path/to/mysql -uroot -proot wind_tunnel_db < /path/to/wind_tunnel_db.sql replacing the paths and username/passwords as appropriate. It will take several minutes since it is a large file. The database contains several tables, which are mostly self explanatory. The key tables of interest are the “experiment” table, which lists the 4 experiments contained in this data set, the “timepoint” table, which contains the position, velocity, etc., of every fly/mosquito at every measured time point, and the “trajectory” table, which indicates which set of time points correspond to which individual trajectories. Other useful tables that have been pre-populated are the “crossing” table, which specifies trajectory segments corresponding to each plume crossing, and the “crossing_group” table, which groups sets of crossings together according to experiment and crossing identification criteria. The code that interacts with this database and recreates the figures in the associated manuscript is contained at https://github.com/rkp8000/wind_tunnel.wind_tunnel_db_aaPart 2wind_tunnel_db_abPart 3wind_tunnel_db_acPart 4wind_tunnel_db_adPart 5wind_tunnel_db_aePart 6wind_tunnel_db_afPart 7wind_tunnel_db_agPart 8wind_tunnel_db_ahPart 9wind_tunnel_db_aiInfotaxis databaseBase database for running infotaxis simulations. To see how to prepare and populate this database with simulated trajectory data, see the file _paper_auxiliary_code in the GitHub repository http://github.com/rkp8000/wind_tunnel.infotaxis_db.sql Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, “infotaxis”, in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking.

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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2018
    Data sources: B2FIND
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    EASY
    Dataset . 2018
    Data sources: EASY
    DRYAD
    Dataset . 2019
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2018
      Data sources: B2FIND
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      EASY
      Dataset . 2018
      Data sources: EASY
      DRYAD
      Dataset . 2019
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Markus Stoffel; Daniel G. Trappmann; Mattias I. Coullie; Juan A. Ballesteros-Cánovas; +1 Authors

    This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Connie Weaver/Professor and Head of Department;

    Lifestyle choices,including diet,are conducive to healthy body weights in children. Dairy products and calcium supplementation have been associated with moderation of body weight and body fat. This study was designed to test the following hypotheses with overweight and obese adolescents consuming a controlled diet: - Dietary calcium supplementation as calcium carbonate or dairy calcium modulates energy balance in adolescents. - Increased calcium in the diet of adolescents will increase fecal fat excretion and thereby decrease fat absorption. - Calcium and dairy product supplementation will increase lipid oxidation resulting in an increase in energy expenditure. Subjects will consume a controlled diet containing 800 mg calcium for two three week periods. During one period they will also receive a frozen ice cream like product (smoothie) twice a day based on soy protein that contains no additional calcium. During the other period they will receive a similar product twice a day based on either dairy protein that contains 650 mg calcium or based on soy protein that contains 650 mg calcium as calcium carbonate

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    OpenTrials
    Clinical Trial . 2007
    Data sources: OpenTrials
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    ClinicalTrials.gov
    Clinical Trial . 2007
    Data sources: ClinicalTrials.gov
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      OpenTrials
      Clinical Trial . 2007
      Data sources: OpenTrials
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      ClinicalTrials.gov
      Clinical Trial . 2007
      Data sources: ClinicalTrials.gov
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    Authors: Opito, Emmanuel A.; Alanko, Timo; Kalbitzer, Urs; Nummelin, Matti; +3 Authors

    Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

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    https://dx.doi.org/10.17617/3....
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
    B2FIND
    Dataset . 2023
    Data sources: B2FIND
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      https://dx.doi.org/10.17617/3....
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
      B2FIND
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Reidy, Jennifer; Sinnott, Emily; Thompson, Frank; O'Donnell, Lisa;

    We monitored golden-cheeked warbler territories in 10 plots within an urban preserve to determine abundance, delineate territories, and document breeding success. We determined environmental conditions across the study period to examine temporal and landscape effects. We then used these data to estimate adult survival and productivity and relate these vital rates to environmental conditions experienced during our study period. We used supported covariates to predict potential effects on this population 25 years into the future. These data and code are associated with the publication in Ecosphere entitled "Urban land cover and El Nino events negatively impact population viability of an endangered North American songbird." We performed an integrated population model to evaluate the effect of climate patterns and urban land cover on the viability of an endangered wood-warbler breeding in central Texas. We used territory monitroing data from 2011–2019 to predict viability of the population 25 years into the future. We assembled and conducted the analysis in R.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; +47 Authors

    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.DKRZ.MPI-ESM1-2-HR.ssp126' 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-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 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 Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Luisa Barrera; Bradley W Layne; Zejie Chen; Kenta Wantanabe; +4 Authors

    Raw datasets (.mat and .fig files) and codes (.mlx and .m files) used in our manuscript of the same title. Figure numbers correspond with the figure numbers in the corresponding manuscript. Figure 4: Effects of kinetic parameters on Solar-to-chemical (STC) efficiencies and reaction selectivity Figure 5: Solar-to-chemical (STC) efficiencies for a model incorporating competing undesired redox reactions implemented for different redox shuttle pairs Figure 7: Solar-to-chemical efficiencies for an ensemble of light absorbers Figure 8: Maximum solar-to-chemical (STC) efficiencies and corresponding number of light absorbers as a function of asymmetry factors in limiting current density for redox shuttle reduction Figure 9: Solar-to-chemical efficiencies for an increasing number of light absorbers for different total absorptance values (99%, 75%, 50%). Figure 10: Qualitative comparisons between experimental measurements and model predictions for a photocatalytic suspension reactor The main piece of the code developed is provided as an interactive .mlx file; not all subfunction calls within the main code is included, and can be shared upon reasonable request via email from the lead (luisab@umich.edu) and the corresponding authors (rbchan@umich.edu) of this paper.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
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    Authors: Marbà, Núria; Jordá, Gabriel; Bennett, Scott; Duarte, Carlos M.;

    Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity. 6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. Peer reviewed

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    Digital.CSIC
    Dataset . 2022 . Peer-reviewed
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2022 . Peer-reviewed
      Data sources: Digital.CSIC
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    Authors: García-Barros, Enrique; Álamo, Mario; Romo, Helena;

    # sRGB Reflectances from Iberian butterflies [https://doi.org/10.5061/dryad.1g1jwsv0q](https://doi.org/10.5061/dryad.1g1jwsv0q) Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (*C. webbianus*) from the Canaries. Average of male and female, sample size as indicated in column n. The data from *C. webbianus* and *C. marshalli* were not included in our analyses of reflectance. Text file, CSV format, columns delimited by periods, 225 rows (including headings) and 38 columns. Any means presented are weighted averages taking into account the areas of the parts involved. Wing reflectances refer to the parts of the wings exposed in a living butterfly (except FW\_AREA and HW\_AREA which are total wing surfaces). * **Ord**, row number (roughly a taxonomic arrangement) * **Species**, species name (abbreviated genus, contains a blank space, e.g., *Heteropterus morpheus*) * **N**, sample size * **FWL**, forewing length (mm) * **DFT**, reflectance, dorsal forewing * **DFp**, reflectance, dorsal forewing, proximal area * **DFd**, reflectance, dorsal forewing, distal area * **DHT**, reflectance, dorsal hindwing * **DHp**, reflectance, dorsal hindwing, proximal area * **DHd**, reflectance, dorsal hindwing, distal area * **DB**, reflectance, dorsal body area * **D(Tp+B)**, reflectance of the exposed dorsal body plus proximal wing surfaces * **DT**, reflectance of the dorsal areas (body plus whole wing) * **DTp**, reflectance of the dorsal, proximal wing areas * **DTd**, reflectance of the dorsal, distal wing areas * **VFT**, reflectance, ventral forewing * **VFp**, reflectance, ventral forewing, proximal area * **VFd**, reflectance, ventral forewing, distal area * **VHT**, reflectance, ventral hindwing * **VHp**, reflectance, ventral hindwing, proximal area * **VHd**, reflectance, ventral hindwing, distal area * **VB**, reflectance, ventral body area * **V(Tp+B)**, reflectance of the exposed ventral body plus proximal wing surfaces * **VT**, reflectance of the ventral areas (body plus whole wing) * **VTp**, reflectance of the ventral, proximal wing areas * **VTd**, reflectance of the ventral, distal wing areas * **Mean**, mean total reflectance (dorsal and ventral surfaces) * **p\_Mean**, mean reflectance of the proximal (dorsal and ventral) wing areas * **p\_Otimum**, mean reflectance of the proximal dorsal (for dorsal baskers) or ventral (for lateral basking species) wing areas. * **FW\_area**, total forewing area (mm2) * **HW\_area**, total hindwing area (mm2) * **T\_Mean\_Iberia\_10km**, Iberian mean species temperature, Centigrade degrees, 10 x 10 km resolution * **P\_Mean\_Iberia\_10km**, mean species annual precipitation, mm, Iberian Peninsula, 10 x 10 km resolution * **T\_Mean\_Ibera\_50km**, mean species temperature, Centigrade degrees, Iberian Peninsula, 50 x 50 km resolution * **P\_Mean\_Iberia\_50km**, mean species annual precipitation, mm, Iberian Peninsula, 50 x 50 km resolution Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (Cyclyrius webbianus) from the Canary Islands. Average of male and female, sample size as indicated in column n. The data from C. webbianus and Cacyreus marshalli are provided although these species were not included in our analyses of reflectance. The data were measured from digital images of set (collection) specimens taken in fixed conditions, with grey (average RGB) values standardized a posteriori to fit the scale white= 243.7= white, to black= 52. The data set includes the mean length of the forewing (mm) and the total areas (mm2) of the fore and hind wings.

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    DRYAD
    Dataset . 2023
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    A total of 170 participants were initially enrolled in the comprehensive behavioral weight loss intervention.In this study, investigators will conduct a follow-up visit 3 years after the completion of the intervention. Only participants who completed the behavioral weight loss intervention will be enrolled in this study. Participants will undergo testing of body weight, body composition, physical activity patterns, energy intake patterns, sleep patterns, resting metabolic rate, and total daily energy expenditure. This study is designed as an observational trial. The objective of this study is to follow-up with participants 3 years after completion of an 18-month comprehensive behavioral weight loss intervention. Outcomes of interest include change in body weight, body composition, physical activity, energy intake, and sleep. In addition, investigators will explore the associations between current physical activity, sleep, and energy intake patterns and body weight regulation.

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    ClinicalTrials.gov
    Clinical Trial . 2019
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      Clinical Trial . 2019
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    Authors: Pang, Rich; Van Breugel, Floris; Dickinson, Michael; Riffell, Jeffrey A.; +1 Authors

    Flight trajectories of fruit flies and mosquitoes in a wind tunnel.This data file is a MySQL database file which must be uploaded to a MySQL database management system (DBMS) (e.g., via the MAMP installation: http://localhost:8888/MAMP/?language=English, as was used in the associated manuscript). Once you have installed a MySQL DBMS on your machine, make a new database called “wind_tunnel_db”. To populate this database using the data file, first download all of the data files and join them together using: cat wind_tunnel_db_* > wind_tunnel_db.sql Then run the following command to populate the wind_tunnel_db MySQL database with the result. /path/to/mysql -uroot -proot wind_tunnel_db < /path/to/wind_tunnel_db.sql replacing the paths and username/passwords as appropriate. It will take several minutes since it is a large file. The database contains several tables, which are mostly self explanatory. The key tables of interest are the “experiment” table, which lists the 4 experiments contained in this data set, the “timepoint” table, which contains the position, velocity, etc., of every fly/mosquito at every measured time point, and the “trajectory” table, which indicates which set of time points correspond to which individual trajectories. Other useful tables that have been pre-populated are the “crossing” table, which specifies trajectory segments corresponding to each plume crossing, and the “crossing_group” table, which groups sets of crossings together according to experiment and crossing identification criteria. The code that interacts with this database and recreates the figures in the associated manuscript is contained at https://github.com/rkp8000/wind_tunnel.wind_tunnel_db_aaPart 2wind_tunnel_db_abPart 3wind_tunnel_db_acPart 4wind_tunnel_db_adPart 5wind_tunnel_db_aePart 6wind_tunnel_db_afPart 7wind_tunnel_db_agPart 8wind_tunnel_db_ahPart 9wind_tunnel_db_aiInfotaxis databaseBase database for running infotaxis simulations. To see how to prepare and populate this database with simulated trajectory data, see the file _paper_auxiliary_code in the GitHub repository http://github.com/rkp8000/wind_tunnel.infotaxis_db.sql Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, “infotaxis”, in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking.

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    Dataset . 2018
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    Dataset . 2018
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      Dataset . 2018
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      EASY
      Dataset . 2018
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      Dataset . 2019
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    Authors: Markus Stoffel; Daniel G. Trappmann; Mattias I. Coullie; Juan A. Ballesteros-Cánovas; +1 Authors

    This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.

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    ZENODO
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    Dataset . 2023
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      ZENODO
      Dataset . 2023
      License: CC BY
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Connie Weaver/Professor and Head of Department;

    Lifestyle choices,including diet,are conducive to healthy body weights in children. Dairy products and calcium supplementation have been associated with moderation of body weight and body fat. This study was designed to test the following hypotheses with overweight and obese adolescents consuming a controlled diet: - Dietary calcium supplementation as calcium carbonate or dairy calcium modulates energy balance in adolescents. - Increased calcium in the diet of adolescents will increase fecal fat excretion and thereby decrease fat absorption. - Calcium and dairy product supplementation will increase lipid oxidation resulting in an increase in energy expenditure. Subjects will consume a controlled diet containing 800 mg calcium for two three week periods. During one period they will also receive a frozen ice cream like product (smoothie) twice a day based on soy protein that contains no additional calcium. During the other period they will receive a similar product twice a day based on either dairy protein that contains 650 mg calcium or based on soy protein that contains 650 mg calcium as calcium carbonate

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    OpenTrials
    Clinical Trial . 2007
    Data sources: OpenTrials
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    ClinicalTrials.gov
    Clinical Trial . 2007
    Data sources: ClinicalTrials.gov
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      OpenTrials
      Clinical Trial . 2007
      Data sources: OpenTrials
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      ClinicalTrials.gov
      Clinical Trial . 2007
      Data sources: ClinicalTrials.gov
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    Authors: Opito, Emmanuel A.; Alanko, Timo; Kalbitzer, Urs; Nummelin, Matti; +3 Authors

    Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

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    https://dx.doi.org/10.17617/3....
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
    B2FIND
    Dataset . 2023
    Data sources: B2FIND
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      https://dx.doi.org/10.17617/3....
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
      B2FIND
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Reidy, Jennifer; Sinnott, Emily; Thompson, Frank; O'Donnell, Lisa;

    We monitored golden-cheeked warbler territories in 10 plots within an urban preserve to determine abundance, delineate territories, and document breeding success. We determined environmental conditions across the study period to examine temporal and landscape effects. We then used these data to estimate adult survival and productivity and relate these vital rates to environmental conditions experienced during our study period. We used supported covariates to predict potential effects on this population 25 years into the future. These data and code are associated with the publication in Ecosphere entitled "Urban land cover and El Nino events negatively impact population viability of an endangered North American songbird." We performed an integrated population model to evaluate the effect of climate patterns and urban land cover on the viability of an endangered wood-warbler breeding in central Texas. We used territory monitroing data from 2011–2019 to predict viability of the population 25 years into the future. We assembled and conducted the analysis in R.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; +47 Authors

    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.DKRZ.MPI-ESM1-2-HR.ssp126' 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-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 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 Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Luisa Barrera; Bradley W Layne; Zejie Chen; Kenta Wantanabe; +4 Authors

    Raw datasets (.mat and .fig files) and codes (.mlx and .m files) used in our manuscript of the same title. Figure numbers correspond with the figure numbers in the corresponding manuscript. Figure 4: Effects of kinetic parameters on Solar-to-chemical (STC) efficiencies and reaction selectivity Figure 5: Solar-to-chemical (STC) efficiencies for a model incorporating competing undesired redox reactions implemented for different redox shuttle pairs Figure 7: Solar-to-chemical efficiencies for an ensemble of light absorbers Figure 8: Maximum solar-to-chemical (STC) efficiencies and corresponding number of light absorbers as a function of asymmetry factors in limiting current density for redox shuttle reduction Figure 9: Solar-to-chemical efficiencies for an increasing number of light absorbers for different total absorptance values (99%, 75%, 50%). Figure 10: Qualitative comparisons between experimental measurements and model predictions for a photocatalytic suspension reactor The main piece of the code developed is provided as an interactive .mlx file; not all subfunction calls within the main code is included, and can be shared upon reasonable request via email from the lead (luisab@umich.edu) and the corresponding authors (rbchan@umich.edu) of this paper.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
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      ZENODO
      Dataset . 2024
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      ZENODO
      Dataset . 2024
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    Authors: Marbà, Núria; Jordá, Gabriel; Bennett, Scott; Duarte, Carlos M.;

    Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity. 6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. Peer reviewed

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    Digital.CSIC
    Dataset . 2022 . Peer-reviewed
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2022 . Peer-reviewed
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