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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: Abeer Samy Yousef Mohamed; Dina Ahmed Ahmed El-Meligy; Neveen Y. Azmy;

    Since ancient ages, humans sought to find a shelter. They started a long way of creating, modifying, and developing their shelters. During the continuous seek for luxury, he ignored architecture designs that respects the surrounding environment and climate. This type of architecture represented our historical roots that presents the local culture and environment of any country; and it is the result of creative interacts of humans and the nature. The aim of bioclimatic architecture is to create urban areas and buildings that are designed to fully cover their energy requirements without induce environmental damage. this study focuses the traditional /historical architecture in Arab countries, especially the natural conditions that influence the architectural decisions of sustainability, that could be used as a new vision for eco- adaptive architecture. In addition, it encourages analyzing and taking advantage of environmental conditions around buildings to maintain ideal living conditions through minimal consumption of energy, to achieve sustainability that recently has become a philosophy of architecture. EQA - International Journal of Environmental Quality, Vol 39 (2020)

    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/ EQAarrow_drop_down
    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/
    EQA
    Article . 2020
    Data sources: DOAJ
    AMS Acta
    Article . 2020
    License: CC BY NC
    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/ EQAarrow_drop_down
      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/
      EQA
      Article . 2020
      Data sources: DOAJ
      AMS Acta
      Article . 2020
      License: CC BY NC
      Data sources: Datacite
      addClaim

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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: Abeer Samy Yousef Mohamed; Dina Ahmed Ahmed El-Meligy; Neveen Y. Azmy;

    Since ancient ages, humans sought to find a shelter. They started a long way of creating, modifying, and developing their shelters. During the continuous seek for luxury, he ignored architecture designs that respects the surrounding environment and climate. This type of architecture represented our historical roots that presents the local culture and environment of any country; and it is the result of creative interacts of humans and the nature. The aim of bioclimatic architecture is to create urban areas and buildings that are designed to fully cover their energy requirements without induce environmental damage. this study focuses the traditional /historical architecture in Arab countries, especially the natural conditions that influence the architectural decisions of sustainability, that could be used as a new vision for eco- adaptive architecture. In addition, it encourages analyzing and taking advantage of environmental conditions around buildings to maintain ideal living conditions through minimal consumption of energy, to achieve sustainability that recently has become a philosophy of architecture. EQA - International Journal of Environmental Quality, Vol 39 (2020)

    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/ EQAarrow_drop_down
    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/
    EQA
    Article . 2020
    Data sources: DOAJ
    AMS Acta
    Article . 2020
    License: CC BY NC
    Data sources: Datacite
    addClaim

    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
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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/ EQAarrow_drop_down
      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/
      EQA
      Article . 2020
      Data sources: DOAJ
      AMS Acta
      Article . 2020
      License: CC BY NC
      Data sources: Datacite
      addClaim

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      You have already added works in your ORCID record related to the merged Research product.
  • 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: El Jery, Atef; Ramírez Coronel, Andrés Alexis; Orosco Gavilán, Juan Carlos; Al Ansari, Nadhir; +1 Authors

    Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model’s predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well. Sede virtual

    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/ Case Studies in Ther...arrow_drop_down
    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/
    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/
    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/
    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/
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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/ Case Studies in Ther...arrow_drop_down
      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/
      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/
      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/
      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/
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.
  • 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: El Jery, Atef; Ramírez Coronel, Andrés Alexis; Orosco Gavilán, Juan Carlos; Al Ansari, Nadhir; +1 Authors

    Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model’s predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well. Sede virtual

    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/ Case Studies in Ther...arrow_drop_down
    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/
    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/
    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/
    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/
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    You have already added works in your ORCID record related to the merged Research product.
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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/ Case Studies in Ther...arrow_drop_down
      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/
      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/
      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/
      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/
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.
  • 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: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; +5 Authors

    Future ocean acidification (OA) will affect physiological traits of marine species, with calcifying species being particularly vulnerable. As OA entails high energy demands, particularly during the rapid juvenile growth phase, food supply may play a key role in the response of marine organisms to OA. We experimentally evaluated the role of food supply in modulating physiological responses and biomineralization processes in juveniles of the Chilean scallop, Argopecten purpuratus, that were exposed to control (pH 8.0) and low pH (pH 7.6) conditions using three food supply treatments (high, intermediate, and low). We found that pH and food levels had additive effects on the physiological response of the juvenile scallops. Metabolic rates, shell growth, net calcification, and ingestion rates increased significantly at low pH conditions, independent of food. These physiological responses increased significantly in organisms exposed to intermediate and high levels of food supply. Hence, food supply seems to play a major role modulating organismal response by providing the energetic means to bolster the physiological response of OA stress. On the contrary, the relative expression of chitin synthase, a functional molecule for biomineralization, increased significantly in scallops exposed to low food supply and low pH, which resulted in a thicker periostracum enriched with chitin polysaccharides. Under reduced food and low pH conditions, the adaptive organismal response was to trade-off growth for the expression of biomineralization molecules and altering of the organic composition of shell periostracum, suggesting that the future performance of these calcifiers will depend on the trajectories of both OA and food supply. Thus, incorporating a suite of traits and multiple stressors in future studies of the adaptive organismal response may provide key insights on OA impacts on marine calcifiers. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2015) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation is 2016-05-16. Supplement to: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; Lardies, Marco A; Rodriguez-Navarro, Alejandro; Vargas, C A; Lagos, Nelson A; Duarte, Carlos Manuel (2016): Biomineralization changes with food supply confer juvenile scallops (Argopecten purpuratus) resistance to ocean acidification. Global Change Biology, 22(6), 2025-2037

    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/ PANGAEAarrow_drop_down
    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/
    PANGAEA
    Dataset . 2016
    Data sources: B2FIND
    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/
    B2FIND
    Other dataset type . 2016
    Data sources: B2FIND
    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/
    PANGAEA
    Dataset . 2016
    Data sources: PANGAEA
    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/
    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/
    PANGAEA
    Other dataset type . 2016
    License: CC BY
    Data sources: PANGAEA
    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/
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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/ PANGAEAarrow_drop_down
      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/
      PANGAEA
      Dataset . 2016
      Data sources: B2FIND
      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/
      B2FIND
      Other dataset type . 2016
      Data sources: B2FIND
      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/
      PANGAEA
      Dataset . 2016
      Data sources: PANGAEA
      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/
      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/
      PANGAEA
      Other dataset type . 2016
      License: CC BY
      Data sources: PANGAEA
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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: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; +5 Authors

    Future ocean acidification (OA) will affect physiological traits of marine species, with calcifying species being particularly vulnerable. As OA entails high energy demands, particularly during the rapid juvenile growth phase, food supply may play a key role in the response of marine organisms to OA. We experimentally evaluated the role of food supply in modulating physiological responses and biomineralization processes in juveniles of the Chilean scallop, Argopecten purpuratus, that were exposed to control (pH 8.0) and low pH (pH 7.6) conditions using three food supply treatments (high, intermediate, and low). We found that pH and food levels had additive effects on the physiological response of the juvenile scallops. Metabolic rates, shell growth, net calcification, and ingestion rates increased significantly at low pH conditions, independent of food. These physiological responses increased significantly in organisms exposed to intermediate and high levels of food supply. Hence, food supply seems to play a major role modulating organismal response by providing the energetic means to bolster the physiological response of OA stress. On the contrary, the relative expression of chitin synthase, a functional molecule for biomineralization, increased significantly in scallops exposed to low food supply and low pH, which resulted in a thicker periostracum enriched with chitin polysaccharides. Under reduced food and low pH conditions, the adaptive organismal response was to trade-off growth for the expression of biomineralization molecules and altering of the organic composition of shell periostracum, suggesting that the future performance of these calcifiers will depend on the trajectories of both OA and food supply. Thus, incorporating a suite of traits and multiple stressors in future studies of the adaptive organismal response may provide key insights on OA impacts on marine calcifiers. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2015) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation is 2016-05-16. Supplement to: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; Lardies, Marco A; Rodriguez-Navarro, Alejandro; Vargas, C A; Lagos, Nelson A; Duarte, Carlos Manuel (2016): Biomineralization changes with food supply confer juvenile scallops (Argopecten purpuratus) resistance to ocean acidification. Global Change Biology, 22(6), 2025-2037

    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/ PANGAEAarrow_drop_down
    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/
    PANGAEA
    Dataset . 2016
    Data sources: B2FIND
    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/
    B2FIND
    Other dataset type . 2016
    Data sources: B2FIND
    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/
    PANGAEA
    Dataset . 2016
    Data sources: PANGAEA
    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/
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    PANGAEA
    Other dataset type . 2016
    License: CC BY
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2016
      Data sources: B2FIND
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      B2FIND
      Other dataset type . 2016
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2016
      Data sources: PANGAEA
      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/
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      PANGAEA
      Other dataset type . 2016
      License: CC BY
      Data sources: PANGAEA
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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: Xiao, Xi; de Bettignies, Thibaut; Olsen, Ylva S.; Agusti, Susana; +2 Authors

    Canopy-forming seaweeds, as primary producers and foundation species, provide key ecological services. Their responses to multiple stressors associated with climate change could therefore have important knock-on effects on the functioning of coastal ecosystems. We examined interactive effects of UVB radiation and warming on juveniles of three habitat-forming subtidal seaweeds from Western Australia–Ecklonia radiata, Scytothalia dorycarpa and Sargassum sp. Fronds were incubated for 14 days at 16–30°C with or without UVB radiation and growth, health status, photosynthetic performance, and light absorbance measured. Furthermore, we used empirical models from the metabolic theory of ecology to evaluate the sensitivity of these important seaweeds to ocean warming. Results indicated that responses to UVB and warming were species specific, with Sargassum showing highest tolerance to a broad range of temperatures. Scytothalia was most sensitive to elevated temperature based on the reduced maximum quantum yields of PSII; however, Ecklonia was most sensitive, according to the comparison of activation energy calculated from Arrhenius’ model. UVB radiation caused reduction in the growth, physiological responses and thallus health in all three species. Our findings indicate that Scytothalia was capable of acclimating in response to UVB and increasing its light absorption efficiency in the UV bands, probably by up-regulating synthesis of photoprotective compounds. The other two species did not acclimate over the two weeks of exposure to UVB. Overall, UVB and warming would severely inhibit the growth and photosynthesis of these canopy-forming seaweeds and decrease their coverage. Differences in the sensitivity and acclimation of major seaweed species to temperature and UVB may alter the balance between species in future seaweed communities under climate change. XiaoWernberg_Temp_UV_PLoSone_raw_dataRaw data on growth, photosynthetic yield, Health status and absorption.XiaoWernberg_Temp_UV_PLoSone_raw data.xlsx

    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/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
    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/
    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 . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2016
    License: CC 0
    Data sources: Datacite
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    1
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      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/
      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 . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2016
      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: Xiao, Xi; de Bettignies, Thibaut; Olsen, Ylva S.; Agusti, Susana; +2 Authors

    Canopy-forming seaweeds, as primary producers and foundation species, provide key ecological services. Their responses to multiple stressors associated with climate change could therefore have important knock-on effects on the functioning of coastal ecosystems. We examined interactive effects of UVB radiation and warming on juveniles of three habitat-forming subtidal seaweeds from Western Australia–Ecklonia radiata, Scytothalia dorycarpa and Sargassum sp. Fronds were incubated for 14 days at 16–30°C with or without UVB radiation and growth, health status, photosynthetic performance, and light absorbance measured. Furthermore, we used empirical models from the metabolic theory of ecology to evaluate the sensitivity of these important seaweeds to ocean warming. Results indicated that responses to UVB and warming were species specific, with Sargassum showing highest tolerance to a broad range of temperatures. Scytothalia was most sensitive to elevated temperature based on the reduced maximum quantum yields of PSII; however, Ecklonia was most sensitive, according to the comparison of activation energy calculated from Arrhenius’ model. UVB radiation caused reduction in the growth, physiological responses and thallus health in all three species. Our findings indicate that Scytothalia was capable of acclimating in response to UVB and increasing its light absorption efficiency in the UV bands, probably by up-regulating synthesis of photoprotective compounds. The other two species did not acclimate over the two weeks of exposure to UVB. Overall, UVB and warming would severely inhibit the growth and photosynthesis of these canopy-forming seaweeds and decrease their coverage. Differences in the sensitivity and acclimation of major seaweed species to temperature and UVB may alter the balance between species in future seaweed communities under climate change. XiaoWernberg_Temp_UV_PLoSone_raw_dataRaw data on growth, photosynthetic yield, Health status and absorption.XiaoWernberg_Temp_UV_PLoSone_raw data.xlsx

    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/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
    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/
    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
    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/
    B2FIND
    Dataset . 2015
    Data sources: B2FIND
    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/
    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 . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2016
    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/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      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/
      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 . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2016
      License: CC 0
      Data sources: Datacite
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  • Authors: Klein, Shannon; Roch, Cassandra; Duarte, Carlos;

    # Systematic review of the uncertainty of coral reef futures under climate change, datasets Published paper resulting from this data can be found at: ## Summary This study conducted a systematic review of 79 published articles projecting coral reef responses to future climate change. This dataset contains qualitative and quantitative data extracted from the published studies, including model types, geographic focus, and projected impacts on coral reefs. ## Description of the data and file structure ### Supplementary Data File **Extracted Data**: **Source data for effect size calculations (n=8 published studies).** * Short.reference used to identify the published study from which the data were extracted. See Full Reference List within this Read.Me file * Scenario.ID identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * N.c is n/number of model runs for control scenario * N.e is n/number of model runs for future end-of-century (experimental) scenario * M.c is the Model estimate for baseline scenario * M.e is the model estimate of end of century projections * Sd.c is the standard deviation of end of century projection estimates * Sd.e is the standard deviation of the baseline scenario estimates ### Supplementary\_Data1 **Summary Database: Overview of the dataset including study details, geographic focus, spatial scale, modeling approach, and examined stressors.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref number identifies the full reference in the Full Reference List within this Read.Me file * Approach type classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) projective meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Focal projection(s) units is the unit in which the published studies delivered their projections * Spatial scale refers to the spatial scale of the projections published, classified as either regional or global * Geographic focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Major stressor(s) examined refer to the main drivers that were used to parameterize the models (e.g. warming, ocean acidification) ### Supplementary\_Data2 **Complete Database: Detailed information from all 79 reviewed studies (qualitative characteristics)** * Unique_ID is a random unique ID assigned to each of the published papers within the dataset * Author_list is a comprehensive list of all authors of the published studies within the dataset * Article_ttle is the title of the published article * Source_journal is the scientific journal in which the article was published * Publication_year refers to the year of publication of the published studies * Times_cited is the number of citations received by the published studies according to the Thomson Reuters Web of Science database on March 6, 2023. * Model_category classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Model_technique refers to the method used to model heat stress (thermal threshold technique versus continuous variable technique). For studies to be classified as threshold techniques, the use of these metrics had to form the primary framework of the models that delivered projections. The second technique represents approaches that abandon the central threshold concept to focus on empirical relationships between continuous variables. * if_TM_Threshold type records the type of thermal threshold used. N/a is used when the study did not use a thermal threshold, or it was not clearly reported. * Focal_projection_unit records the units in which the published studies delivered their projections. * Spatial_scale refers to the spatial scale of the projections published, classified as either regional or global. * Reported_geographic_focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Drivers_used_summary records a summary of drivers used to parameterise the models. * Underlying model structure/ description is a summary of the model structure and its purpose * Key_assumptions is a description of the main assumptions made by the model * Future_scenarios_examined refers to the exact future emissions pathways used * Model_geographic_resolution records the spatial resolution of the model output * Downscaled_yes_no records yes for when downscaling techniques were used to improve spatial resolution and no when downscaling techniques were not used * Downscaled_method records which type of downscaling technique was used (either statistical or dynamic). N/A is used when the study did not use a downscaling technique * Study_purpose is a summary of the published study's aims and its findings * Study advantages is a synthesis of the published study's key advantages * Study_gaps is a synthesis of the published study's key limitations ### Supplementary\_Data 3 **Exploratory Meta-analysis Database: Scenario descriptions for data included in the effect size analysis.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref is a number that identifies the full reference in the Full Reference List within this Read.Me file * Scenario identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * Scenario description is a summary of the future scneario modelled * Reported warming refers to the future emissions pathway used to model future warming * Classified warming categorizes these warming levels into different scenarios of 1.5 - 2ºC, 2 - 4ºC, and >4ºC represent projections at the end-of-century (years 2090-2100) * Reported projection unit is the unit in which the published studies delivered their projections * Classified projection unit represents the categories in which the projection units were analysed (e.g. % reef cells at risk) ### Klein\_et\_al.,\_2024 * **R script:** Script used for exploratory meta-analysis ## Reference List We use numbers that reference the sources we used to collect our data. Below is a list of the sources and their corresponding numbers. Supplementary References 1 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, doi:10.1136/bmj.n71 (2021). 2 Khalil, I., Muslim, A. M., Hossain, M. S. & Atkinson, P. M. Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region. International Journal of Remote Sensing 44, 194-216 (2023). 3 Abe, H., Kumagai, N. H. & Yamano, H. Priority coral conservation areas under global warming in the Amami Islands, Southern Japan. Coral Reefs 41, 1637-1650 (2022). 4 Sully, S., Hodgson, G. & van Woesik, R. Present and future bright and dark spots for coral reefs through climate change. Global Change Biology 28, 4509-4522, doi: (2022). 5 DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecological applications 32, e2650 (2022). 6 Holstein, D. M., Smith, T. B., van Hooidonk, R. & Paris, C. B. Predicting coral metapopulation decline in a changing thermal environment. Coral Reefs 41, 961-972, doi:10.1007/s00338-022-02252-9 (2022). 7 Raharinirina, N. A., Acevedo-Trejos, E. & Merico, A. Modelling the acclimation capacity of coral reefs to a warming ocean. PLOS Computational Biology 18, e1010099 (2022). 8 Chollett, I. et al. Planning for resilience: Incorporating scenario and model uncertainty and trade‐offs when prioritizing management of climate refugia. Global Change Biology 28, 4054-4068 (2022). 9 Setter, R. O., Franklin, E. C. & Mora, C. Co-occurring anthropogenic stressors reduce the timeframe of environmental viability for the world’s coral reefs. PLOS Biology 20, e3001821, doi:10.1371/journal.pbio.3001821 (2022). 10 McWhorter, J. K., Halloran, P. R., Roff, G., Skirving, W. J. & Mumby, P. J. Climate refugia on the Great Barrier Reef fail when global warming exceeds 3° C. Global Change Biology 28, 5768-5780 (2022). 11 Kalmus, P., Ekanayaka, A., Kang, E., Baird, M. & Gierach, M. Past the precipice? Projected coral habitability under global heating. Earth's Future 10, e2021EF002608 (2022). 12 McWhorter, J. K. et al. The importance of 1.5°C warming for the Great Barrier Reef. Global Change Biology 28, 1332-1341, doi: (2022). 13 Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Global Change Biology n/a, doi: (2021). 14 Adam, A. A. et al. Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions. Diversity and Distributions 27, 2245-2261 (2021). 15 Cant, J. et al. The projected degradation of subtropical coral assemblages by recurrent thermal stress. Journal of Animal Ecology 90, 233-247 (2021). 16 Principe, S. C., Acosta, A. L., Andrade, J. E. & Lotufo, T. M. Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Frontiers in Marine Science 8, 673086 (2021). 17 Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proceedings of the Royal Society B 288, 20210274 (2021). 18 Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Scientific Reports 11, 1-12 (2021). 19 Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences 118, e2015265118, doi:doi:10.1073/pnas.2015265118 (2021). 20 McManus, L. C. et al. Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle. Global change biology 27, 4307-4321 (2021). 21 McClanahan, T. R. & Azali, M. K. Environmental Variability and Threshold Model’s Predictions for Coral Reefs. Frontiers in Marine Science 8, doi:10.3389/fmars.2021.778121 (2021). 22 Zuo, X. et al. Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea. Remote Sensing 13, 2626 (2021). 23 McManus, L. C. et al. Extreme temperature events will drive coral decline in the Coral Triangle. Global Change Biology 26, 2120-2133 (2020). 24 Rodriguez, L., García, J. J., Tuya, F. & Martínez, B. Environmental factors driving the distribution of the tropical coral Pavona varians: predictions under a climate change scenario. Marine Ecology 41, 1-12 (2020). 25 Cacciapaglia, C. W. & van Woesik, R. Reduced carbon emissions and fishing pressure are both necessary for equatorial coral reefs to keep up with rising seas. Ecography 43, 789-800, doi: (2020). 26 Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Global Change Biology (2020). 27 Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Scientific Reports 9, 3721, doi:10.1038/s41598-019-38962-4 (2019). 28 Rodriguez, L., Martínez, B. & Tuya, F. Atlantic corals under climate change: modelling distribution shifts to predict richness, phylogenetic structure and trait-diversity changes. Biodiversity and Conservation 28, 3873-3890, doi:10.1007/s10531-019-01855-z (2019). 29 Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. Royal Society Open Science 6, 182111 (2019). 30 Yan, H. et al. Regional coral growth responses to seawater warming in the South China Sea. Science of the total environment 670, 595-605 (2019). 31 Woesik, R. v., Köksal, S., Ünal, A., Cacciapaglia, C. W. & Randall, C. J. Predicting coral dynamics through climate change. Scientific reports 8, 17997 (2018). 32 Wolff, N. H., Mumby, P. J., Devlin, M. & Anthony, K. R. N. Vulnerability of the Great Barrier Reef to climate change and local pressures. Global Change Biology 24, 1978-1991, doi:10.1111/gcb.14043 (2018). 33 Cacciapaglia, C. & van Woesik, R. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography 45, 154-163 (2018). 34 Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biology 24, 5084-5095, doi: (2018). 35 Langlais, C. et al. Coral bleaching pathways under the control of regional temperature variability. Nature Climate Change 7, 839-844 (2017). 36 Kendall, M. S., Poti, M. & Karnauskas, K. B. Climate change and larval transport in the ocean: fractional effects from physical and physiological factors. Global Change Biology 22, 1532-1547, doi: (2016). 37 Yara, Y. et al. Potential future coral habitats around Japan depend strongly on anthropogenic CO 2 emissions. Aquatic biodiversity conservation and ecosystem services, 41-56 (2016). 38 Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific reports 6, 39666 (2016). 39 Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 C and 2 C. Earth system dynamics 7, 327-351 (2016). 40 Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338-342, doi:doi:10.1126/science.aac7125 (2016). 41 Cooper, J. K., Spencer, M. & Bruno, J. F. Stochastic dynamics of a warmer Great Barrier Reef. Ecology 96, 1802-1811 (2015). 42 Bozec, Y.-M. & Mumby, P. J. Synergistic impacts of global warming on the resilience of coral reefs. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20130267 (2015). 43 Bozec, Y. M., Alvarez‐Filip, L. & Mumby, P. J. The dynamics of architectural complexity on coral reefs under climate change. Global change biology 21, 223-235 (2015). 44 van Hooidonk, R., Maynard, J. A., Liu, Y. & Lee, S. K. Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Global change biology 21, 3389-3401 (2015). 45 Kwiatkowski, L., Cox, P., Halloran, P. R., Mumby, P. J. & Wiltshire, A. J. Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nature Climate Change 5, 777-781 (2015). 46 Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change 5, 688-694 (2015). 47 Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Global Change Biology 21, 2479-2487 (2015). 48 Freeman, L. A. Robust performance of marginal Pacific coral reef habitats in future climate scenarios. PLoS One 10, e0128875 (2015). 49 Cacciapaglia, C. & van Woesik, R. Reef‐coral refugia in a rapidly changing ocean. Global Change Biology 21, 2272-2282 (2015). 50 Mumby, P. J., Wolff, N. H., Bozec, Y.-M., Chollett, I. & Halloran, P. Operationalizing the Resilience of Coral Reefs in an Era of Climate Change. Conservation Letters 7, 176-187, doi: (2014). 51 Yara, Y., Fujii, M., Yamano, H. & Yamanaka, Y. Projected coral bleaching in response to future sea surface temperature rises and the uncertainties among climate models. Hydrobiologia 733, 19-29 (2014). 52 Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Global Change Biology 20, 125-139 (2014). 53 van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology 20, 103-112, doi: (2014). 54 Ortiz, J. C., González-Rivero, M. & Mumby, P. J. An ecosystem-level perspective on the host and symbiont traits needed to mitigate climate change impacts on Caribbean coral reefs. Ecosystems 17, 1-13 (2014). 55 Lane, D. R. et al. Quantifying and valuing potential climate change impacts on coral reefs in the United States: Comparison of two scenarios. PloS one 8, e82579 (2013). 56 Kennedy, E. V. et al. Avoiding coral reef functional collapse requires local and global action. Current Biology 23, 912-918 (2013). 57 van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nature Climate Change 3, 508-511, doi:10.1038/nclimate1829 (2013). 58 Frieler, K. et al. Limiting global warming to 2 C is unlikely to save most coral reefs. Nature Climate Change 3, 165 (2013). 59 Ortiz, J. C., González‐Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs? Global change biology 19, 273-281 (2013). 60 Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral reef habitat response to climate change scenarios. PloS one 8, e82404 (2013). 61 Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Global Change Biology 19, 3592-3606, doi: (2013). 62 Couce, E., Irvine, P. J., Gregoire, L., Ridgwell, A. & Hendy, E. Tropical coral reef habitat in a geoengineered, high‐CO2 world. Geophysical Research Letters 40, 1799-1805 (2013). 63 Wooldridge, S. A. et al. Safeguarding coastal coral communities on the central Great Barrier Reef (Australia) against climate change: realizable local and global actions. Climatic Change 112, 945-961 (2012). 64 Meissner, K., Lippmann, T. & Sen Gupta, A. Large-scale stress factors affecting coral reefs: open ocean sea surface temperature and surface seawater aragonite saturation over the next 400 years. Coral Reefs 31, 309-319 (2012). 65 van Hooidonk, R. & Huber, M. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs 31, 121-131, doi:10.1007/s00338-011-0825-4 (2012). 66 Teneva, L. et al. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates. Coral Reefs 31, 1-12 (2012). 67 Yara, Y. et al. Ocean acidification limits temperature-induced poleward expansion of coral habitats around Japan. Biogeosciences 9, 4955-4968 (2012). 68 Edwards, H. J. et al. How much time can herbivore protection buy for coral reefs under realistic regimes of hurricanes and coral bleaching? Global Change Biology 17, 2033-2048 (2011). 69 Anthony, K. R. N. et al. Ocean acidification and warming will lower coral reef resilience. Global Change Biology 17, 1798-1808, doi: (2011). 70 Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227 (2011). 71 Hoeke, R. K., Jokiel, P. L., Buddemeier, R. W. & Brainard, R. E. Projected changes to growth and mortality of Hawaiian corals over the next 100 years. PloS one 6, e18038 (2011). 72 McLeod, E. et al. Warming seas in the Coral Triangle: coral reef vulnerability and management implications. Coastal Management 38, 518-539 (2010). 73 Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecological Applications 19, 3-17 (2009). 74 Vivekanandan, E., Ali, M. H., Jasper, B. & Rajagopalan, M. Vulnerability of corals to warming of the Indian seas: a projection for the 21st century. Current Science, 1654-1658 (2009). 75 Donner, S. D. Coping with commitment: projected thermal stress on coral reefs under different future scenarios. PLoS One 4, e5712 (2009). 76 Buddemeier, R. W. et al. A modeling tool to evaluate regional coral reef responses to changes in climate and ocean chemistry. Limnology and Oceanography: Methods 6, 395-411 (2008). 77 Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh‐Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology 11, 2251-2265 (2005). 78 McNeil, B. I., Matear, R. J. & Barnes, D. J. Coral reef calcification and climate change: The effect of ocean warming. Geophysical Research Letters 31 (2004). 79 Guinotte, J., Buddemeier, R. & Kleypas, J. Future coral reef habitat marginality: temporal and spatial effects of climate change in the Pacific basin. Coral reefs 22, 551-558 (2003). 80 Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world's coral reefs. Marine and freshwater research 50, 839-866 (1999). Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change (IPCC), consistently assert that limiting global warming to 1.5°C is unlikely to safeguard most of the world’s coral reefs. This prognosis primarily stems from 'excess heat’ threshold models, which assume that widespread coral bleaching predictably occurs when temperatures accumulate beyond a specific threshold. Our systematic review of research projecting coral reef futures to climate change (n=79) revealed that 'excess heat' models constituted only one third (32%) of all studies but attracted a high proportion (68%) of citations in the field. We observed that most methods employed deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainties of coral reef futures. In attempting to assess the consistency of projected impacts, we aimed to identify common coral reef metrics under the same emissions scenarios. However, disparate choices in metrics and emissions scenarios hindered a cohesive synthesis and limited the exploratory analysis to a small fraction of available studies. We found substantial discrepancies in expected impacts to coral reefs, suggesting that some 'excess heat' models may project more extreme impacts than other methods. Drawing on lessons from the field of climate change science, we propose that an IPCC ensemble-like approach to generating probabilistic projections for coral reef futures is feasible. Successful implementation will require improved coordination among modeling efforts to select common output metrics and emission scenarios, addressing existing geographical biases, among other gaps in current modeling efforts. We conducted a comprehensive literature search using the Thomson Reuters Web of Science database to identify studies that projecting the impacts of climate change on shallow tropical and sub-tropical coral reefs. This search, adhering to PRISMA guidelines, yielded 2705 peer-reviewed articles, which we refined to 79 relevant articles published between 1999 and 2023 based on a specific selection criteria (Dataset 1). These studies were categorized into five major methodology types and further classified based on their approaches to simulating heat stress. Key characteristics such as the model output variables, spatial scale, and geographic area of each study were extracted, along with their methodological approaches, assumptions, and the techniques used.Our study aimed to assess and compare the projected impacts and uncertainties of various model types using a meta-analysis approach. The database of 79 studies was considered for inclusion in the exploratory meta-analysis based on specific criteria (view published article and supplementary methods for detailed list and Supplementary Figure 1). Briefly, to enable a meaningful analysis, we identified the three most frequently used model outputs in our database. Among those, only studies that provided: 1) sufficient data for projection estimates and uncertainty measures to be reliably extracted or calculated, 2) reported end-of-century projections, and 3) used a baseline period between 2000 and 2015, were selected for the exploratory meta-analysis. In cases where projection and uncertainty estimates were presented in figures, values were extracted using PlotDigitizer, where possible.

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  • Authors: Klein, Shannon; Roch, Cassandra; Duarte, Carlos;

    # Systematic review of the uncertainty of coral reef futures under climate change, datasets Published paper resulting from this data can be found at: ## Summary This study conducted a systematic review of 79 published articles projecting coral reef responses to future climate change. This dataset contains qualitative and quantitative data extracted from the published studies, including model types, geographic focus, and projected impacts on coral reefs. ## Description of the data and file structure ### Supplementary Data File **Extracted Data**: **Source data for effect size calculations (n=8 published studies).** * Short.reference used to identify the published study from which the data were extracted. See Full Reference List within this Read.Me file * Scenario.ID identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * N.c is n/number of model runs for control scenario * N.e is n/number of model runs for future end-of-century (experimental) scenario * M.c is the Model estimate for baseline scenario * M.e is the model estimate of end of century projections * Sd.c is the standard deviation of end of century projection estimates * Sd.e is the standard deviation of the baseline scenario estimates ### Supplementary\_Data1 **Summary Database: Overview of the dataset including study details, geographic focus, spatial scale, modeling approach, and examined stressors.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref number identifies the full reference in the Full Reference List within this Read.Me file * Approach type classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) projective meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Focal projection(s) units is the unit in which the published studies delivered their projections * Spatial scale refers to the spatial scale of the projections published, classified as either regional or global * Geographic focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Major stressor(s) examined refer to the main drivers that were used to parameterize the models (e.g. warming, ocean acidification) ### Supplementary\_Data2 **Complete Database: Detailed information from all 79 reviewed studies (qualitative characteristics)** * Unique_ID is a random unique ID assigned to each of the published papers within the dataset * Author_list is a comprehensive list of all authors of the published studies within the dataset * Article_ttle is the title of the published article * Source_journal is the scientific journal in which the article was published * Publication_year refers to the year of publication of the published studies * Times_cited is the number of citations received by the published studies according to the Thomson Reuters Web of Science database on March 6, 2023. * Model_category classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Model_technique refers to the method used to model heat stress (thermal threshold technique versus continuous variable technique). For studies to be classified as threshold techniques, the use of these metrics had to form the primary framework of the models that delivered projections. The second technique represents approaches that abandon the central threshold concept to focus on empirical relationships between continuous variables. * if_TM_Threshold type records the type of thermal threshold used. N/a is used when the study did not use a thermal threshold, or it was not clearly reported. * Focal_projection_unit records the units in which the published studies delivered their projections. * Spatial_scale refers to the spatial scale of the projections published, classified as either regional or global. * Reported_geographic_focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Drivers_used_summary records a summary of drivers used to parameterise the models. * Underlying model structure/ description is a summary of the model structure and its purpose * Key_assumptions is a description of the main assumptions made by the model * Future_scenarios_examined refers to the exact future emissions pathways used * Model_geographic_resolution records the spatial resolution of the model output * Downscaled_yes_no records yes for when downscaling techniques were used to improve spatial resolution and no when downscaling techniques were not used * Downscaled_method records which type of downscaling technique was used (either statistical or dynamic). N/A is used when the study did not use a downscaling technique * Study_purpose is a summary of the published study's aims and its findings * Study advantages is a synthesis of the published study's key advantages * Study_gaps is a synthesis of the published study's key limitations ### Supplementary\_Data 3 **Exploratory Meta-analysis Database: Scenario descriptions for data included in the effect size analysis.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref is a number that identifies the full reference in the Full Reference List within this Read.Me file * Scenario identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * Scenario description is a summary of the future scneario modelled * Reported warming refers to the future emissions pathway used to model future warming * Classified warming categorizes these warming levels into different scenarios of 1.5 - 2ºC, 2 - 4ºC, and >4ºC represent projections at the end-of-century (years 2090-2100) * Reported projection unit is the unit in which the published studies delivered their projections * Classified projection unit represents the categories in which the projection units were analysed (e.g. % reef cells at risk) ### Klein\_et\_al.,\_2024 * **R script:** Script used for exploratory meta-analysis ## Reference List We use numbers that reference the sources we used to collect our data. Below is a list of the sources and their corresponding numbers. Supplementary References 1 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, doi:10.1136/bmj.n71 (2021). 2 Khalil, I., Muslim, A. M., Hossain, M. S. & Atkinson, P. M. Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region. International Journal of Remote Sensing 44, 194-216 (2023). 3 Abe, H., Kumagai, N. H. & Yamano, H. Priority coral conservation areas under global warming in the Amami Islands, Southern Japan. Coral Reefs 41, 1637-1650 (2022). 4 Sully, S., Hodgson, G. & van Woesik, R. Present and future bright and dark spots for coral reefs through climate change. Global Change Biology 28, 4509-4522, doi: (2022). 5 DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecological applications 32, e2650 (2022). 6 Holstein, D. M., Smith, T. B., van Hooidonk, R. & Paris, C. B. Predicting coral metapopulation decline in a changing thermal environment. Coral Reefs 41, 961-972, doi:10.1007/s00338-022-02252-9 (2022). 7 Raharinirina, N. A., Acevedo-Trejos, E. & Merico, A. Modelling the acclimation capacity of coral reefs to a warming ocean. PLOS Computational Biology 18, e1010099 (2022). 8 Chollett, I. et al. Planning for resilience: Incorporating scenario and model uncertainty and trade‐offs when prioritizing management of climate refugia. Global Change Biology 28, 4054-4068 (2022). 9 Setter, R. O., Franklin, E. C. & Mora, C. Co-occurring anthropogenic stressors reduce the timeframe of environmental viability for the world’s coral reefs. PLOS Biology 20, e3001821, doi:10.1371/journal.pbio.3001821 (2022). 10 McWhorter, J. K., Halloran, P. R., Roff, G., Skirving, W. J. & Mumby, P. J. Climate refugia on the Great Barrier Reef fail when global warming exceeds 3° C. Global Change Biology 28, 5768-5780 (2022). 11 Kalmus, P., Ekanayaka, A., Kang, E., Baird, M. & Gierach, M. Past the precipice? Projected coral habitability under global heating. Earth's Future 10, e2021EF002608 (2022). 12 McWhorter, J. K. et al. The importance of 1.5°C warming for the Great Barrier Reef. Global Change Biology 28, 1332-1341, doi: (2022). 13 Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Global Change Biology n/a, doi: (2021). 14 Adam, A. A. et al. Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions. Diversity and Distributions 27, 2245-2261 (2021). 15 Cant, J. et al. The projected degradation of subtropical coral assemblages by recurrent thermal stress. Journal of Animal Ecology 90, 233-247 (2021). 16 Principe, S. C., Acosta, A. L., Andrade, J. E. & Lotufo, T. M. Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Frontiers in Marine Science 8, 673086 (2021). 17 Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proceedings of the Royal Society B 288, 20210274 (2021). 18 Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Scientific Reports 11, 1-12 (2021). 19 Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences 118, e2015265118, doi:doi:10.1073/pnas.2015265118 (2021). 20 McManus, L. C. et al. Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle. Global change biology 27, 4307-4321 (2021). 21 McClanahan, T. R. & Azali, M. K. Environmental Variability and Threshold Model’s Predictions for Coral Reefs. Frontiers in Marine Science 8, doi:10.3389/fmars.2021.778121 (2021). 22 Zuo, X. et al. Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea. Remote Sensing 13, 2626 (2021). 23 McManus, L. C. et al. Extreme temperature events will drive coral decline in the Coral Triangle. Global Change Biology 26, 2120-2133 (2020). 24 Rodriguez, L., García, J. J., Tuya, F. & Martínez, B. Environmental factors driving the distribution of the tropical coral Pavona varians: predictions under a climate change scenario. Marine Ecology 41, 1-12 (2020). 25 Cacciapaglia, C. W. & van Woesik, R. Reduced carbon emissions and fishing pressure are both necessary for equatorial coral reefs to keep up with rising seas. Ecography 43, 789-800, doi: (2020). 26 Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Global Change Biology (2020). 27 Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Scientific Reports 9, 3721, doi:10.1038/s41598-019-38962-4 (2019). 28 Rodriguez, L., Martínez, B. & Tuya, F. Atlantic corals under climate change: modelling distribution shifts to predict richness, phylogenetic structure and trait-diversity changes. Biodiversity and Conservation 28, 3873-3890, doi:10.1007/s10531-019-01855-z (2019). 29 Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. Royal Society Open Science 6, 182111 (2019). 30 Yan, H. et al. Regional coral growth responses to seawater warming in the South China Sea. Science of the total environment 670, 595-605 (2019). 31 Woesik, R. v., Köksal, S., Ünal, A., Cacciapaglia, C. W. & Randall, C. J. Predicting coral dynamics through climate change. Scientific reports 8, 17997 (2018). 32 Wolff, N. H., Mumby, P. J., Devlin, M. & Anthony, K. R. N. Vulnerability of the Great Barrier Reef to climate change and local pressures. Global Change Biology 24, 1978-1991, doi:10.1111/gcb.14043 (2018). 33 Cacciapaglia, C. & van Woesik, R. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography 45, 154-163 (2018). 34 Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biology 24, 5084-5095, doi: (2018). 35 Langlais, C. et al. Coral bleaching pathways under the control of regional temperature variability. Nature Climate Change 7, 839-844 (2017). 36 Kendall, M. S., Poti, M. & Karnauskas, K. B. Climate change and larval transport in the ocean: fractional effects from physical and physiological factors. Global Change Biology 22, 1532-1547, doi: (2016). 37 Yara, Y. et al. Potential future coral habitats around Japan depend strongly on anthropogenic CO 2 emissions. Aquatic biodiversity conservation and ecosystem services, 41-56 (2016). 38 Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific reports 6, 39666 (2016). 39 Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 C and 2 C. Earth system dynamics 7, 327-351 (2016). 40 Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338-342, doi:doi:10.1126/science.aac7125 (2016). 41 Cooper, J. K., Spencer, M. & Bruno, J. F. Stochastic dynamics of a warmer Great Barrier Reef. Ecology 96, 1802-1811 (2015). 42 Bozec, Y.-M. & Mumby, P. J. Synergistic impacts of global warming on the resilience of coral reefs. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20130267 (2015). 43 Bozec, Y. M., Alvarez‐Filip, L. & Mumby, P. J. The dynamics of architectural complexity on coral reefs under climate change. Global change biology 21, 223-235 (2015). 44 van Hooidonk, R., Maynard, J. A., Liu, Y. & Lee, S. K. Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Global change biology 21, 3389-3401 (2015). 45 Kwiatkowski, L., Cox, P., Halloran, P. R., Mumby, P. J. & Wiltshire, A. J. Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nature Climate Change 5, 777-781 (2015). 46 Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change 5, 688-694 (2015). 47 Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Global Change Biology 21, 2479-2487 (2015). 48 Freeman, L. A. Robust performance of marginal Pacific coral reef habitats in future climate scenarios. PLoS One 10, e0128875 (2015). 49 Cacciapaglia, C. & van Woesik, R. Reef‐coral refugia in a rapidly changing ocean. Global Change Biology 21, 2272-2282 (2015). 50 Mumby, P. J., Wolff, N. H., Bozec, Y.-M., Chollett, I. & Halloran, P. Operationalizing the Resilience of Coral Reefs in an Era of Climate Change. Conservation Letters 7, 176-187, doi: (2014). 51 Yara, Y., Fujii, M., Yamano, H. & Yamanaka, Y. Projected coral bleaching in response to future sea surface temperature rises and the uncertainties among climate models. Hydrobiologia 733, 19-29 (2014). 52 Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Global Change Biology 20, 125-139 (2014). 53 van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology 20, 103-112, doi: (2014). 54 Ortiz, J. C., González-Rivero, M. & Mumby, P. J. An ecosystem-level perspective on the host and symbiont traits needed to mitigate climate change impacts on Caribbean coral reefs. Ecosystems 17, 1-13 (2014). 55 Lane, D. R. et al. Quantifying and valuing potential climate change impacts on coral reefs in the United States: Comparison of two scenarios. PloS one 8, e82579 (2013). 56 Kennedy, E. V. et al. Avoiding coral reef functional collapse requires local and global action. Current Biology 23, 912-918 (2013). 57 van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nature Climate Change 3, 508-511, doi:10.1038/nclimate1829 (2013). 58 Frieler, K. et al. Limiting global warming to 2 C is unlikely to save most coral reefs. Nature Climate Change 3, 165 (2013). 59 Ortiz, J. C., González‐Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs? Global change biology 19, 273-281 (2013). 60 Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral reef habitat response to climate change scenarios. PloS one 8, e82404 (2013). 61 Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Global Change Biology 19, 3592-3606, doi: (2013). 62 Couce, E., Irvine, P. J., Gregoire, L., Ridgwell, A. & Hendy, E. Tropical coral reef habitat in a geoengineered, high‐CO2 world. Geophysical Research Letters 40, 1799-1805 (2013). 63 Wooldridge, S. A. et al. Safeguarding coastal coral communities on the central Great Barrier Reef (Australia) against climate change: realizable local and global actions. Climatic Change 112, 945-961 (2012). 64 Meissner, K., Lippmann, T. & Sen Gupta, A. Large-scale stress factors affecting coral reefs: open ocean sea surface temperature and surface seawater aragonite saturation over the next 400 years. Coral Reefs 31, 309-319 (2012). 65 van Hooidonk, R. & Huber, M. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs 31, 121-131, doi:10.1007/s00338-011-0825-4 (2012). 66 Teneva, L. et al. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates. Coral Reefs 31, 1-12 (2012). 67 Yara, Y. et al. Ocean acidification limits temperature-induced poleward expansion of coral habitats around Japan. Biogeosciences 9, 4955-4968 (2012). 68 Edwards, H. J. et al. How much time can herbivore protection buy for coral reefs under realistic regimes of hurricanes and coral bleaching? Global Change Biology 17, 2033-2048 (2011). 69 Anthony, K. R. N. et al. Ocean acidification and warming will lower coral reef resilience. Global Change Biology 17, 1798-1808, doi: (2011). 70 Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227 (2011). 71 Hoeke, R. K., Jokiel, P. L., Buddemeier, R. W. & Brainard, R. E. Projected changes to growth and mortality of Hawaiian corals over the next 100 years. PloS one 6, e18038 (2011). 72 McLeod, E. et al. Warming seas in the Coral Triangle: coral reef vulnerability and management implications. Coastal Management 38, 518-539 (2010). 73 Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecological Applications 19, 3-17 (2009). 74 Vivekanandan, E., Ali, M. H., Jasper, B. & Rajagopalan, M. Vulnerability of corals to warming of the Indian seas: a projection for the 21st century. Current Science, 1654-1658 (2009). 75 Donner, S. D. Coping with commitment: projected thermal stress on coral reefs under different future scenarios. PLoS One 4, e5712 (2009). 76 Buddemeier, R. W. et al. A modeling tool to evaluate regional coral reef responses to changes in climate and ocean chemistry. Limnology and Oceanography: Methods 6, 395-411 (2008). 77 Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh‐Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology 11, 2251-2265 (2005). 78 McNeil, B. I., Matear, R. J. & Barnes, D. J. Coral reef calcification and climate change: The effect of ocean warming. Geophysical Research Letters 31 (2004). 79 Guinotte, J., Buddemeier, R. & Kleypas, J. Future coral reef habitat marginality: temporal and spatial effects of climate change in the Pacific basin. Coral reefs 22, 551-558 (2003). 80 Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world's coral reefs. Marine and freshwater research 50, 839-866 (1999). Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change (IPCC), consistently assert that limiting global warming to 1.5°C is unlikely to safeguard most of the world’s coral reefs. This prognosis primarily stems from 'excess heat’ threshold models, which assume that widespread coral bleaching predictably occurs when temperatures accumulate beyond a specific threshold. Our systematic review of research projecting coral reef futures to climate change (n=79) revealed that 'excess heat' models constituted only one third (32%) of all studies but attracted a high proportion (68%) of citations in the field. We observed that most methods employed deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainties of coral reef futures. In attempting to assess the consistency of projected impacts, we aimed to identify common coral reef metrics under the same emissions scenarios. However, disparate choices in metrics and emissions scenarios hindered a cohesive synthesis and limited the exploratory analysis to a small fraction of available studies. We found substantial discrepancies in expected impacts to coral reefs, suggesting that some 'excess heat' models may project more extreme impacts than other methods. Drawing on lessons from the field of climate change science, we propose that an IPCC ensemble-like approach to generating probabilistic projections for coral reef futures is feasible. Successful implementation will require improved coordination among modeling efforts to select common output metrics and emission scenarios, addressing existing geographical biases, among other gaps in current modeling efforts. We conducted a comprehensive literature search using the Thomson Reuters Web of Science database to identify studies that projecting the impacts of climate change on shallow tropical and sub-tropical coral reefs. This search, adhering to PRISMA guidelines, yielded 2705 peer-reviewed articles, which we refined to 79 relevant articles published between 1999 and 2023 based on a specific selection criteria (Dataset 1). These studies were categorized into five major methodology types and further classified based on their approaches to simulating heat stress. Key characteristics such as the model output variables, spatial scale, and geographic area of each study were extracted, along with their methodological approaches, assumptions, and the techniques used.Our study aimed to assess and compare the projected impacts and uncertainties of various model types using a meta-analysis approach. The database of 79 studies was considered for inclusion in the exploratory meta-analysis based on specific criteria (view published article and supplementary methods for detailed list and Supplementary Figure 1). Briefly, to enable a meaningful analysis, we identified the three most frequently used model outputs in our database. Among those, only studies that provided: 1) sufficient data for projection estimates and uncertainty measures to be reliably extracted or calculated, 2) reported end-of-century projections, and 3) used a baseline period between 2000 and 2015, were selected for the exploratory meta-analysis. In cases where projection and uncertainty estimates were presented in figures, values were extracted using PlotDigitizer, where possible.

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    Authors: Ager, Thomas Gjerluff; Sejr, Mikael K.; Duarte, Carlos M.; Mankoff, Kenneth D.; +5 Authors

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    Authors: K. Krishnaram; S. Sivamani; Zuhair Alaas; M.M.R. Ahmed; +2 Authors

    Les systèmes de production d'énergie photovoltaïque (PV) abstraits sont de plus en plus populaires en raison de leurs hautes performances et de leur propreté. Plusieurs facteurs influencent les performances d'un système PV, y compris les effets d'ombrage. Les systèmes photovoltaïques utilisent des méthodologies MPPT pour obtenir l'énergie du réseau photovoltaïque. Les MPPT conventionnels fonctionnent bien dans des conditions normales lorsqu'il n'y a pas d'effets d'ombre ou d'ombrage partiel. La présence d'un ombrage partiel affecte les performances du système et génère plusieurs pics de puissance. Cela complique le processus de découverte du pic global (GMPP) avec une efficacité de suivi améliorée et un temps de stabilisation réduit, y compris l'efficacité de conversion. Ce travail propose trois techniques MPPT hybrides : l'optimisation du cycle de l'eau - la perturbation et l'observation (WCO-PO), la perturbation et l'observation adaptables à échelle étagée prises en charge par le réseau de neurones artificiels (ANN-ASSPO), le contrôleur de mode de glissement rapide du terminal modifié par l'optimisation du loup gris (GWO-MFTSMC) et deux techniques MPPT conventionnelles WCO et P&O ont été mises en œuvre. Le système proposé utilise un convertisseur survolteur entrelacé avec trois phases. Les performances des stratégies MPPT hybrides proposées ont été comparées en termes de tension de sortie, de courant de sortie et de puissance extraite. La comparaison comprend également l'efficacité de conversion et le temps de stabilisation moyen. Pour analyser les performances, quatre cas différents ont été utilisés pour tester l'efficacité des MPPT hybrides dans des conditions climatiques changeantes. L'outil Matlab/Simulink a été utilisé pour analyser les performances du système PV. Dans les trois techniques MPPT hybrides, WCO-PO a obtenu de meilleurs résultats par rapport aux deux autres MPPT hybrides en termes d'efficacité de conversion (99,56 %) et de temps de sédimentation (1,4 m). Resumen Los sistemas de generación de energía basados en energía fotovoltaica (PV) son cada vez más populares debido a su alto rendimiento y limpieza. Varios factores influyen en el rendimiento de un sistema fotovoltaico, incluidos los efectos de sombreado. Los sistemas fotovoltaicos emplean metodologías MPPT para obtener la energía de la matriz fotovoltaica. Los MPPT convencionales funcionan bien en condiciones normales cuando no hay efectos de sombra o sombreado parcial. La presencia de sombreado parcial afecta al rendimiento del sistema y genera varios picos de potencia. Esto complica el proceso de determinar el pico global (GMPP) con una eficiencia de seguimiento mejorada y un tiempo de asentamiento reducido, incluida la eficiencia de conversión. En este trabajo se proponen tres técnicas híbridas de MPPT: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), y se han implementado dos técnicas convencionales de MPPT WCO y P&O. El sistema propuesto utiliza un convertidor elevador intercalado con tres fases. Los rendimientos de las estrategias de MPPT híbridas propuestas se compararon en términos de voltaje de salida, corriente de salida y potencia extraída. La comparación también incluye la eficiencia de conversión y el tiempo medio de asentamiento. Para analizar los rendimientos, se han utilizado cuatro casos diferentes para probar la eficacia de los MPPT híbridos en condiciones climáticas cambiantes. La herramienta MATLAB/Simulink se ha utilizado para analizar el rendimiento del sistema fotovoltaico. En las tres técnicas híbridas de MPPT, WCO-PO se ha desempeñado mejor en comparación con otros dos MPPT híbridos en términos de eficiencia de conversión (99.56%) y tiempo de asentamiento (1.4 m). Abstract Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). أصبحت أنظمة توليد الطاقة القائمة على الطاقة الكهروضوئية التجريدية شائعة بشكل متزايد نظرًا لأدائها العالي ونظافتها. تؤثر عدة عوامل على أداء النظام الكهروضوئي، بما في ذلك تأثيرات التظليل. تستخدم الأنظمة الكهروضوئية منهجيات MPPT للحصول على الطاقة من المصفوفة الكهروضوئية. تعمل MPPTs التقليدية بشكل جيد في الظروف العادية عندما لا يكون هناك تأثيرات ظل أو تظليل جزئي. يؤثر وجود التظليل الجزئي على أداء النظام ويولد العديد من قمم الطاقة. وهذا يعقد عملية اكتشاف الذروة العالمية (GMPP) مع تحسين كفاءة التتبع وتقليل وقت الاستقرار بما في ذلك كفاءة التحويل. يقترح هذا العمل ثلاث تقنيات MPPT هجينة: تحسين دورة المياه - الاضطراب والملاحظة (WCO - PO)، والاضطراب والملاحظة التدريجية المدعومة من الشبكة العصبية الاصطناعية (ANN - ASPO)، ووحدة التحكم في وضع انزلاق المحطة الطرفية السريعة المعدلة بالذئب الرمادي (GWO - MFTSMC)، وتم تنفيذ تقنيتين تقليديتين MPPT WCO و P&O. يستخدم النظام المقترح محول التعزيز المعشق مع ثلاث مراحل. تمت مقارنة أداء استراتيجيات MPPTs الهجينة المقترحة من حيث جهد الخرج وتيار الخرج والطاقة المستخرجة. تتضمن المقارنة أيضًا كفاءة التحويل ومتوسط وقت الاستقرار. لتحليل الأداء، تم استخدام أربع حالات مختلفة لاختبار فعالية MPPTs الهجينة في ظل الظروف المناخية المتغيرة. تم استخدام أداة MATLAB/Simulink لتحليل أداء النظام الكهروضوئي. في تقنيات MPPT الهجينة الثلاثة، كان أداء WCO - PO أفضل عند مقارنته بتقنيات MPPT الهجينة الأخرى من حيث كفاءة التحويل (99.56 ٪) ووقت الاستقرار (1.4 م).

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    Authors: K. Krishnaram; S. Sivamani; Zuhair Alaas; M.M.R. Ahmed; +2 Authors

    Les systèmes de production d'énergie photovoltaïque (PV) abstraits sont de plus en plus populaires en raison de leurs hautes performances et de leur propreté. Plusieurs facteurs influencent les performances d'un système PV, y compris les effets d'ombrage. Les systèmes photovoltaïques utilisent des méthodologies MPPT pour obtenir l'énergie du réseau photovoltaïque. Les MPPT conventionnels fonctionnent bien dans des conditions normales lorsqu'il n'y a pas d'effets d'ombre ou d'ombrage partiel. La présence d'un ombrage partiel affecte les performances du système et génère plusieurs pics de puissance. Cela complique le processus de découverte du pic global (GMPP) avec une efficacité de suivi améliorée et un temps de stabilisation réduit, y compris l'efficacité de conversion. Ce travail propose trois techniques MPPT hybrides : l'optimisation du cycle de l'eau - la perturbation et l'observation (WCO-PO), la perturbation et l'observation adaptables à échelle étagée prises en charge par le réseau de neurones artificiels (ANN-ASSPO), le contrôleur de mode de glissement rapide du terminal modifié par l'optimisation du loup gris (GWO-MFTSMC) et deux techniques MPPT conventionnelles WCO et P&O ont été mises en œuvre. Le système proposé utilise un convertisseur survolteur entrelacé avec trois phases. Les performances des stratégies MPPT hybrides proposées ont été comparées en termes de tension de sortie, de courant de sortie et de puissance extraite. La comparaison comprend également l'efficacité de conversion et le temps de stabilisation moyen. Pour analyser les performances, quatre cas différents ont été utilisés pour tester l'efficacité des MPPT hybrides dans des conditions climatiques changeantes. L'outil Matlab/Simulink a été utilisé pour analyser les performances du système PV. Dans les trois techniques MPPT hybrides, WCO-PO a obtenu de meilleurs résultats par rapport aux deux autres MPPT hybrides en termes d'efficacité de conversion (99,56 %) et de temps de sédimentation (1,4 m). Resumen Los sistemas de generación de energía basados en energía fotovoltaica (PV) son cada vez más populares debido a su alto rendimiento y limpieza. Varios factores influyen en el rendimiento de un sistema fotovoltaico, incluidos los efectos de sombreado. Los sistemas fotovoltaicos emplean metodologías MPPT para obtener la energía de la matriz fotovoltaica. Los MPPT convencionales funcionan bien en condiciones normales cuando no hay efectos de sombra o sombreado parcial. La presencia de sombreado parcial afecta al rendimiento del sistema y genera varios picos de potencia. Esto complica el proceso de determinar el pico global (GMPP) con una eficiencia de seguimiento mejorada y un tiempo de asentamiento reducido, incluida la eficiencia de conversión. En este trabajo se proponen tres técnicas híbridas de MPPT: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), y se han implementado dos técnicas convencionales de MPPT WCO y P&O. El sistema propuesto utiliza un convertidor elevador intercalado con tres fases. Los rendimientos de las estrategias de MPPT híbridas propuestas se compararon en términos de voltaje de salida, corriente de salida y potencia extraída. La comparación también incluye la eficiencia de conversión y el tiempo medio de asentamiento. Para analizar los rendimientos, se han utilizado cuatro casos diferentes para probar la eficacia de los MPPT híbridos en condiciones climáticas cambiantes. La herramienta MATLAB/Simulink se ha utilizado para analizar el rendimiento del sistema fotovoltaico. En las tres técnicas híbridas de MPPT, WCO-PO se ha desempeñado mejor en comparación con otros dos MPPT híbridos en términos de eficiencia de conversión (99.56%) y tiempo de asentamiento (1.4 m). Abstract Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). أصبحت أنظمة توليد الطاقة القائمة على الطاقة الكهروضوئية التجريدية شائعة بشكل متزايد نظرًا لأدائها العالي ونظافتها. تؤثر عدة عوامل على أداء النظام الكهروضوئي، بما في ذلك تأثيرات التظليل. تستخدم الأنظمة الكهروضوئية منهجيات MPPT للحصول على الطاقة من المصفوفة الكهروضوئية. تعمل MPPTs التقليدية بشكل جيد في الظروف العادية عندما لا يكون هناك تأثيرات ظل أو تظليل جزئي. يؤثر وجود التظليل الجزئي على أداء النظام ويولد العديد من قمم الطاقة. وهذا يعقد عملية اكتشاف الذروة العالمية (GMPP) مع تحسين كفاءة التتبع وتقليل وقت الاستقرار بما في ذلك كفاءة التحويل. يقترح هذا العمل ثلاث تقنيات MPPT هجينة: تحسين دورة المياه - الاضطراب والملاحظة (WCO - PO)، والاضطراب والملاحظة التدريجية المدعومة من الشبكة العصبية الاصطناعية (ANN - ASPO)، ووحدة التحكم في وضع انزلاق المحطة الطرفية السريعة المعدلة بالذئب الرمادي (GWO - MFTSMC)، وتم تنفيذ تقنيتين تقليديتين MPPT WCO و P&O. يستخدم النظام المقترح محول التعزيز المعشق مع ثلاث مراحل. تمت مقارنة أداء استراتيجيات MPPTs الهجينة المقترحة من حيث جهد الخرج وتيار الخرج والطاقة المستخرجة. تتضمن المقارنة أيضًا كفاءة التحويل ومتوسط وقت الاستقرار. لتحليل الأداء، تم استخدام أربع حالات مختلفة لاختبار فعالية MPPTs الهجينة في ظل الظروف المناخية المتغيرة. تم استخدام أداة MATLAB/Simulink لتحليل أداء النظام الكهروضوئي. في تقنيات MPPT الهجينة الثلاثة، كان أداء WCO - PO أفضل عند مقارنته بتقنيات MPPT الهجينة الأخرى من حيث كفاءة التحويل (99.56 ٪) ووقت الاستقرار (1.4 م).

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    Authors: Mady Mohamed; Reem Okasha;

    With the rising environmental problems there are international movements towards sustainability and greening the built environments in order to mitigate the negative environmental impacts of buildings and human activities on environment and human health. This paper presents a range of K-12 Green Schools that were intentionally designed to utilize school building as a 3D-text book for Environmental Education (EE). The aim of this paper is to examine the methods and strategies of designing green school as a teaching tool through case study analysis of the selected schools. The cases provide a diversity of geographic locations, climates, green strategies and coasts. The research depends on the descriptive analytical approach for literature review; multiple-case study analysis to investigate the attributes of green schools that teach. The results revealed a set of approaches for utilizing green schools as a 3D-textbook for EE EQA - International Journal of Environmental Quality, Vol 39 (2020)

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      Article . 2020
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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: Mady Mohamed; Reem Okasha;

    With the rising environmental problems there are international movements towards sustainability and greening the built environments in order to mitigate the negative environmental impacts of buildings and human activities on environment and human health. This paper presents a range of K-12 Green Schools that were intentionally designed to utilize school building as a 3D-text book for Environmental Education (EE). The aim of this paper is to examine the methods and strategies of designing green school as a teaching tool through case study analysis of the selected schools. The cases provide a diversity of geographic locations, climates, green strategies and coasts. The research depends on the descriptive analytical approach for literature review; multiple-case study analysis to investigate the attributes of green schools that teach. The results revealed a set of approaches for utilizing green schools as a 3D-textbook for EE EQA - International Journal of Environmental Quality, Vol 39 (2020)

    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/ EQAarrow_drop_down
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  • Authors: Yasser B. Saddeek; V. Yu. Ivanov; Amr B. Saddek; Hesham M.H. Zakaly; +3 Authors

    La relaxation par ultrasons des verres de tellurite xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% a été étudiée en mesurant l'atténuation par ultrasons (αL) dans ces verres dans la gamme thermique 140–300 K (T) à diverses fréquences (f). Certaines caractéristiques peuvent être obtenues à partir de la relation de αL–T, telles que l'énergie d'activation moyenne (Ep) et la fréquence de tentative (f0). Les variables Ep et f0 sont fonction de la fréquence et de la teneur en B2O3. De plus, la relation αL–T rend bien compte des oscillations des atomes d'oxygène dans un processus de relaxation. Un tel processus physique provient de la transmission d'énergie ultrasonore aux atomes d'oxygène oscillants dans un potentiel à double puits. Le processus de relaxation est inspecté par un modèle appelé force centrale 1. Les différentes variables physiques soustraites d'un tel modèle sont fonction du contenu B2O3. La relajación ultrasónica de los vidrios de telurito xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% se investigó midiendo la atenuación ultrasónica (αL) en estos vidrios en el rango térmico 140–300 K (T) a varias frecuencias (f). Algunas características se pueden obtener de la relación de αL–T, como la energía de activación media (Ep) y la frecuencia de intento (f0). Las variables Ep y f0 son una función de la frecuencia y el contenido de B2O3. Además, la relación αL–T explica bien las oscilaciones de los átomos de oxígeno en un proceso de relajación. Dicho proceso físico se origina a partir de la transmisión de energía ultrasónica a los átomos de oxígeno oscilantes en un potencial de doble pozo. El proceso de relajación es inspeccionado por un modelo llamado fuerza central uno. Las diferentes variables físicas restadas de dicho modelo son una función del contenido de B2O3. The ultrasonic relaxation of tellurite glasses xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% was investigated by measuring the ultrasonic attenuation (αL) in these glasses in the thermal range 140–300 K (T) at various frequencies (f). Some characteristics can be obtained from the relation of αL–T, such as the average activation energy (Ep) and the attempt frequency (f0). The variables Ep and f0 are a function of frequency and B2O3 content. Moreover, the relation αL–T accounts well for the oscillations of the oxygen atoms in a relaxation process. Such a physical process is originated from transmitting ultrasonic energy to the oscillating oxygen atoms in a dual-well potential. The relaxation process is inspected by a model named central force one. The subtracted different physical variables from such a model are a function of B2O3 content. تم فحص الاسترخاء بالموجات فوق الصوتية لنظارات التيلوريت xB2O3 – 70 TeO2 – (30 – x) WO3، 0 ≤ x ≤ 30 mol% من خلال قياس التوهين بالموجات فوق الصوتية (αL) في هذه النظارات في النطاق الحراري 140–300 K (T) عند ترددات مختلفة (f). يمكن الحصول على بعض الخصائص من علاقة αL - T، مثل متوسط طاقة التنشيط (Ep) وتردد المحاولة (f0). المتغيران Ep و f0 هما دالة للتردد ومحتوى B2O3. علاوة على ذلك، فإن العلاقة αL - T تمثل بشكل جيد تذبذبات ذرات الأكسجين في عملية الاسترخاء. تنشأ هذه العملية الفيزيائية من نقل الطاقة فوق الصوتية إلى ذرات الأكسجين المتذبذبة في جهد بئر مزدوج. يتم فحص عملية الاسترخاء بواسطة نموذج يسمى القوة المركزية الأولى. المتغيرات الفيزيائية المختلفة المطروحة من هذا النموذج هي دالة لمحتوى B2O3.

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  • Authors: Yasser B. Saddeek; V. Yu. Ivanov; Amr B. Saddek; Hesham M.H. Zakaly; +3 Authors

    La relaxation par ultrasons des verres de tellurite xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% a été étudiée en mesurant l'atténuation par ultrasons (αL) dans ces verres dans la gamme thermique 140–300 K (T) à diverses fréquences (f). Certaines caractéristiques peuvent être obtenues à partir de la relation de αL–T, telles que l'énergie d'activation moyenne (Ep) et la fréquence de tentative (f0). Les variables Ep et f0 sont fonction de la fréquence et de la teneur en B2O3. De plus, la relation αL–T rend bien compte des oscillations des atomes d'oxygène dans un processus de relaxation. Un tel processus physique provient de la transmission d'énergie ultrasonore aux atomes d'oxygène oscillants dans un potentiel à double puits. Le processus de relaxation est inspecté par un modèle appelé force centrale 1. Les différentes variables physiques soustraites d'un tel modèle sont fonction du contenu B2O3. La relajación ultrasónica de los vidrios de telurito xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% se investigó midiendo la atenuación ultrasónica (αL) en estos vidrios en el rango térmico 140–300 K (T) a varias frecuencias (f). Algunas características se pueden obtener de la relación de αL–T, como la energía de activación media (Ep) y la frecuencia de intento (f0). Las variables Ep y f0 son una función de la frecuencia y el contenido de B2O3. Además, la relación αL–T explica bien las oscilaciones de los átomos de oxígeno en un proceso de relajación. Dicho proceso físico se origina a partir de la transmisión de energía ultrasónica a los átomos de oxígeno oscilantes en un potencial de doble pozo. El proceso de relajación es inspeccionado por un modelo llamado fuerza central uno. Las diferentes variables físicas restadas de dicho modelo son una función del contenido de B2O3. The ultrasonic relaxation of tellurite glasses xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% was investigated by measuring the ultrasonic attenuation (αL) in these glasses in the thermal range 140–300 K (T) at various frequencies (f). Some characteristics can be obtained from the relation of αL–T, such as the average activation energy (Ep) and the attempt frequency (f0). The variables Ep and f0 are a function of frequency and B2O3 content. Moreover, the relation αL–T accounts well for the oscillations of the oxygen atoms in a relaxation process. Such a physical process is originated from transmitting ultrasonic energy to the oscillating oxygen atoms in a dual-well potential. The relaxation process is inspected by a model named central force one. The subtracted different physical variables from such a model are a function of B2O3 content. تم فحص الاسترخاء بالموجات فوق الصوتية لنظارات التيلوريت xB2O3 – 70 TeO2 – (30 – x) WO3، 0 ≤ x ≤ 30 mol% من خلال قياس التوهين بالموجات فوق الصوتية (αL) في هذه النظارات في النطاق الحراري 140–300 K (T) عند ترددات مختلفة (f). يمكن الحصول على بعض الخصائص من علاقة αL - T، مثل متوسط طاقة التنشيط (Ep) وتردد المحاولة (f0). المتغيران Ep و f0 هما دالة للتردد ومحتوى B2O3. علاوة على ذلك، فإن العلاقة αL - T تمثل بشكل جيد تذبذبات ذرات الأكسجين في عملية الاسترخاء. تنشأ هذه العملية الفيزيائية من نقل الطاقة فوق الصوتية إلى ذرات الأكسجين المتذبذبة في جهد بئر مزدوج. يتم فحص عملية الاسترخاء بواسطة نموذج يسمى القوة المركزية الأولى. المتغيرات الفيزيائية المختلفة المطروحة من هذا النموذج هي دالة لمحتوى B2O3.

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    Authors: Mohamed Nejlaoui; Hussain Sadig; Abdullah Al-Ghafis;

    In Saudi Arabia, hot water for domestic uses consumes a great portion of home electricity. Thus, solar collectors can be considered as an important alternative to reduce the amount of consumed electricity. Therefore, in recent researches, a great attention was given to develop flat plate collector (FPC) with optimal performance. In this paper, a multi-objective modified imperialist competitive algorithm (MOMICA) was employed for optimizing the performance of a FPC. The optimization results showed a capability to reach higher FPC efficiency with a relatively small collector area and hence lower price. It has also been proved that the change of the insulator depth from 0.02 to 0.05 m has a strong influence on the system’s efficiency.

    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/ Journal of Applied a...arrow_drop_down
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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: Mohamed Nejlaoui; Hussain Sadig; Abdullah Al-Ghafis;

    In Saudi Arabia, hot water for domestic uses consumes a great portion of home electricity. Thus, solar collectors can be considered as an important alternative to reduce the amount of consumed electricity. Therefore, in recent researches, a great attention was given to develop flat plate collector (FPC) with optimal performance. In this paper, a multi-objective modified imperialist competitive algorithm (MOMICA) was employed for optimizing the performance of a FPC. The optimization results showed a capability to reach higher FPC efficiency with a relatively small collector area and hence lower price. It has also been proved that the change of the insulator depth from 0.02 to 0.05 m has a strong influence on the system’s efficiency.

    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/ Journal of Applied a...arrow_drop_down
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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: Abeer Samy Yousef Mohamed; Dina Ahmed Ahmed El-Meligy; Neveen Y. Azmy;

    Since ancient ages, humans sought to find a shelter. They started a long way of creating, modifying, and developing their shelters. During the continuous seek for luxury, he ignored architecture designs that respects the surrounding environment and climate. This type of architecture represented our historical roots that presents the local culture and environment of any country; and it is the result of creative interacts of humans and the nature. The aim of bioclimatic architecture is to create urban areas and buildings that are designed to fully cover their energy requirements without induce environmental damage. this study focuses the traditional /historical architecture in Arab countries, especially the natural conditions that influence the architectural decisions of sustainability, that could be used as a new vision for eco- adaptive architecture. In addition, it encourages analyzing and taking advantage of environmental conditions around buildings to maintain ideal living conditions through minimal consumption of energy, to achieve sustainability that recently has become a philosophy of architecture. EQA - International Journal of Environmental Quality, Vol 39 (2020)

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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: Abeer Samy Yousef Mohamed; Dina Ahmed Ahmed El-Meligy; Neveen Y. Azmy;

    Since ancient ages, humans sought to find a shelter. They started a long way of creating, modifying, and developing their shelters. During the continuous seek for luxury, he ignored architecture designs that respects the surrounding environment and climate. This type of architecture represented our historical roots that presents the local culture and environment of any country; and it is the result of creative interacts of humans and the nature. The aim of bioclimatic architecture is to create urban areas and buildings that are designed to fully cover their energy requirements without induce environmental damage. this study focuses the traditional /historical architecture in Arab countries, especially the natural conditions that influence the architectural decisions of sustainability, that could be used as a new vision for eco- adaptive architecture. In addition, it encourages analyzing and taking advantage of environmental conditions around buildings to maintain ideal living conditions through minimal consumption of energy, to achieve sustainability that recently has become a philosophy of architecture. EQA - International Journal of Environmental Quality, Vol 39 (2020)

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      EQA
      Article . 2020
      Data sources: DOAJ
      AMS Acta
      Article . 2020
      License: CC BY NC
      Data sources: Datacite
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    Authors: El Jery, Atef; Ramírez Coronel, Andrés Alexis; Orosco Gavilán, Juan Carlos; Al Ansari, Nadhir; +1 Authors

    Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model’s predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well. Sede virtual

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    Authors: El Jery, Atef; Ramírez Coronel, Andrés Alexis; Orosco Gavilán, Juan Carlos; Al Ansari, Nadhir; +1 Authors

    Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model’s predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well. Sede virtual

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    Authors: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; +5 Authors

    Future ocean acidification (OA) will affect physiological traits of marine species, with calcifying species being particularly vulnerable. As OA entails high energy demands, particularly during the rapid juvenile growth phase, food supply may play a key role in the response of marine organisms to OA. We experimentally evaluated the role of food supply in modulating physiological responses and biomineralization processes in juveniles of the Chilean scallop, Argopecten purpuratus, that were exposed to control (pH 8.0) and low pH (pH 7.6) conditions using three food supply treatments (high, intermediate, and low). We found that pH and food levels had additive effects on the physiological response of the juvenile scallops. Metabolic rates, shell growth, net calcification, and ingestion rates increased significantly at low pH conditions, independent of food. These physiological responses increased significantly in organisms exposed to intermediate and high levels of food supply. Hence, food supply seems to play a major role modulating organismal response by providing the energetic means to bolster the physiological response of OA stress. On the contrary, the relative expression of chitin synthase, a functional molecule for biomineralization, increased significantly in scallops exposed to low food supply and low pH, which resulted in a thicker periostracum enriched with chitin polysaccharides. Under reduced food and low pH conditions, the adaptive organismal response was to trade-off growth for the expression of biomineralization molecules and altering of the organic composition of shell periostracum, suggesting that the future performance of these calcifiers will depend on the trajectories of both OA and food supply. Thus, incorporating a suite of traits and multiple stressors in future studies of the adaptive organismal response may provide key insights on OA impacts on marine calcifiers. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2015) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation is 2016-05-16. Supplement to: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; Lardies, Marco A; Rodriguez-Navarro, Alejandro; Vargas, C A; Lagos, Nelson A; Duarte, Carlos Manuel (2016): Biomineralization changes with food supply confer juvenile scallops (Argopecten purpuratus) resistance to ocean acidification. Global Change Biology, 22(6), 2025-2037

    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/ PANGAEAarrow_drop_down
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    PANGAEA
    Dataset . 2016
    Data sources: B2FIND
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    B2FIND
    Other dataset type . 2016
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2016
    Data sources: PANGAEA
    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/
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    PANGAEA
    Other dataset type . 2016
    License: CC BY
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2016
      Data sources: B2FIND
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      B2FIND
      Other dataset type . 2016
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2016
      Data sources: PANGAEA
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      PANGAEA
      Other dataset type . 2016
      License: CC BY
      Data sources: PANGAEA
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    Authors: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; +5 Authors

    Future ocean acidification (OA) will affect physiological traits of marine species, with calcifying species being particularly vulnerable. As OA entails high energy demands, particularly during the rapid juvenile growth phase, food supply may play a key role in the response of marine organisms to OA. We experimentally evaluated the role of food supply in modulating physiological responses and biomineralization processes in juveniles of the Chilean scallop, Argopecten purpuratus, that were exposed to control (pH 8.0) and low pH (pH 7.6) conditions using three food supply treatments (high, intermediate, and low). We found that pH and food levels had additive effects on the physiological response of the juvenile scallops. Metabolic rates, shell growth, net calcification, and ingestion rates increased significantly at low pH conditions, independent of food. These physiological responses increased significantly in organisms exposed to intermediate and high levels of food supply. Hence, food supply seems to play a major role modulating organismal response by providing the energetic means to bolster the physiological response of OA stress. On the contrary, the relative expression of chitin synthase, a functional molecule for biomineralization, increased significantly in scallops exposed to low food supply and low pH, which resulted in a thicker periostracum enriched with chitin polysaccharides. Under reduced food and low pH conditions, the adaptive organismal response was to trade-off growth for the expression of biomineralization molecules and altering of the organic composition of shell periostracum, suggesting that the future performance of these calcifiers will depend on the trajectories of both OA and food supply. Thus, incorporating a suite of traits and multiple stressors in future studies of the adaptive organismal response may provide key insights on OA impacts on marine calcifiers. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2015) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation is 2016-05-16. Supplement to: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; Lardies, Marco A; Rodriguez-Navarro, Alejandro; Vargas, C A; Lagos, Nelson A; Duarte, Carlos Manuel (2016): Biomineralization changes with food supply confer juvenile scallops (Argopecten purpuratus) resistance to ocean acidification. Global Change Biology, 22(6), 2025-2037

    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/ PANGAEAarrow_drop_down
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    PANGAEA
    Dataset . 2016
    Data sources: B2FIND
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    B2FIND
    Other dataset type . 2016
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2016
    Data sources: PANGAEA
    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/
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    PANGAEA
    Other dataset type . 2016
    License: CC BY
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2016
      Data sources: B2FIND
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      B2FIND
      Other dataset type . 2016
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2016
      Data sources: PANGAEA
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      PANGAEA
      Other dataset type . 2016
      License: CC BY
      Data sources: PANGAEA
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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: Xiao, Xi; de Bettignies, Thibaut; Olsen, Ylva S.; Agusti, Susana; +2 Authors

    Canopy-forming seaweeds, as primary producers and foundation species, provide key ecological services. Their responses to multiple stressors associated with climate change could therefore have important knock-on effects on the functioning of coastal ecosystems. We examined interactive effects of UVB radiation and warming on juveniles of three habitat-forming subtidal seaweeds from Western Australia–Ecklonia radiata, Scytothalia dorycarpa and Sargassum sp. Fronds were incubated for 14 days at 16–30°C with or without UVB radiation and growth, health status, photosynthetic performance, and light absorbance measured. Furthermore, we used empirical models from the metabolic theory of ecology to evaluate the sensitivity of these important seaweeds to ocean warming. Results indicated that responses to UVB and warming were species specific, with Sargassum showing highest tolerance to a broad range of temperatures. Scytothalia was most sensitive to elevated temperature based on the reduced maximum quantum yields of PSII; however, Ecklonia was most sensitive, according to the comparison of activation energy calculated from Arrhenius’ model. UVB radiation caused reduction in the growth, physiological responses and thallus health in all three species. Our findings indicate that Scytothalia was capable of acclimating in response to UVB and increasing its light absorption efficiency in the UV bands, probably by up-regulating synthesis of photoprotective compounds. The other two species did not acclimate over the two weeks of exposure to UVB. Overall, UVB and warming would severely inhibit the growth and photosynthesis of these canopy-forming seaweeds and decrease their coverage. Differences in the sensitivity and acclimation of major seaweed species to temperature and UVB may alter the balance between species in future seaweed communities under climate change. XiaoWernberg_Temp_UV_PLoSone_raw_dataRaw data on growth, photosynthetic yield, Health status and absorption.XiaoWernberg_Temp_UV_PLoSone_raw data.xlsx

    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/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
    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/
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    EASY
    Dataset . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2016
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      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/
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      EASY
      Dataset . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2016
      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: Xiao, Xi; de Bettignies, Thibaut; Olsen, Ylva S.; Agusti, Susana; +2 Authors

    Canopy-forming seaweeds, as primary producers and foundation species, provide key ecological services. Their responses to multiple stressors associated with climate change could therefore have important knock-on effects on the functioning of coastal ecosystems. We examined interactive effects of UVB radiation and warming on juveniles of three habitat-forming subtidal seaweeds from Western Australia–Ecklonia radiata, Scytothalia dorycarpa and Sargassum sp. Fronds were incubated for 14 days at 16–30°C with or without UVB radiation and growth, health status, photosynthetic performance, and light absorbance measured. Furthermore, we used empirical models from the metabolic theory of ecology to evaluate the sensitivity of these important seaweeds to ocean warming. Results indicated that responses to UVB and warming were species specific, with Sargassum showing highest tolerance to a broad range of temperatures. Scytothalia was most sensitive to elevated temperature based on the reduced maximum quantum yields of PSII; however, Ecklonia was most sensitive, according to the comparison of activation energy calculated from Arrhenius’ model. UVB radiation caused reduction in the growth, physiological responses and thallus health in all three species. Our findings indicate that Scytothalia was capable of acclimating in response to UVB and increasing its light absorption efficiency in the UV bands, probably by up-regulating synthesis of photoprotective compounds. The other two species did not acclimate over the two weeks of exposure to UVB. Overall, UVB and warming would severely inhibit the growth and photosynthesis of these canopy-forming seaweeds and decrease their coverage. Differences in the sensitivity and acclimation of major seaweed species to temperature and UVB may alter the balance between species in future seaweed communities under climate change. XiaoWernberg_Temp_UV_PLoSone_raw_dataRaw data on growth, photosynthetic yield, Health status and absorption.XiaoWernberg_Temp_UV_PLoSone_raw data.xlsx

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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2016
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
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    EASY
    Dataset . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2016
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
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      EASY
      Dataset . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2016
      License: CC 0
      Data sources: Datacite
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  • Authors: Klein, Shannon; Roch, Cassandra; Duarte, Carlos;

    # Systematic review of the uncertainty of coral reef futures under climate change, datasets Published paper resulting from this data can be found at: ## Summary This study conducted a systematic review of 79 published articles projecting coral reef responses to future climate change. This dataset contains qualitative and quantitative data extracted from the published studies, including model types, geographic focus, and projected impacts on coral reefs. ## Description of the data and file structure ### Supplementary Data File **Extracted Data**: **Source data for effect size calculations (n=8 published studies).** * Short.reference used to identify the published study from which the data were extracted. See Full Reference List within this Read.Me file * Scenario.ID identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * N.c is n/number of model runs for control scenario * N.e is n/number of model runs for future end-of-century (experimental) scenario * M.c is the Model estimate for baseline scenario * M.e is the model estimate of end of century projections * Sd.c is the standard deviation of end of century projection estimates * Sd.e is the standard deviation of the baseline scenario estimates ### Supplementary\_Data1 **Summary Database: Overview of the dataset including study details, geographic focus, spatial scale, modeling approach, and examined stressors.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref number identifies the full reference in the Full Reference List within this Read.Me file * Approach type classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) projective meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Focal projection(s) units is the unit in which the published studies delivered their projections * Spatial scale refers to the spatial scale of the projections published, classified as either regional or global * Geographic focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Major stressor(s) examined refer to the main drivers that were used to parameterize the models (e.g. warming, ocean acidification) ### Supplementary\_Data2 **Complete Database: Detailed information from all 79 reviewed studies (qualitative characteristics)** * Unique_ID is a random unique ID assigned to each of the published papers within the dataset * Author_list is a comprehensive list of all authors of the published studies within the dataset * Article_ttle is the title of the published article * Source_journal is the scientific journal in which the article was published * Publication_year refers to the year of publication of the published studies * Times_cited is the number of citations received by the published studies according to the Thomson Reuters Web of Science database on March 6, 2023. * Model_category classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Model_technique refers to the method used to model heat stress (thermal threshold technique versus continuous variable technique). For studies to be classified as threshold techniques, the use of these metrics had to form the primary framework of the models that delivered projections. The second technique represents approaches that abandon the central threshold concept to focus on empirical relationships between continuous variables. * if_TM_Threshold type records the type of thermal threshold used. N/a is used when the study did not use a thermal threshold, or it was not clearly reported. * Focal_projection_unit records the units in which the published studies delivered their projections. * Spatial_scale refers to the spatial scale of the projections published, classified as either regional or global. * Reported_geographic_focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Drivers_used_summary records a summary of drivers used to parameterise the models. * Underlying model structure/ description is a summary of the model structure and its purpose * Key_assumptions is a description of the main assumptions made by the model * Future_scenarios_examined refers to the exact future emissions pathways used * Model_geographic_resolution records the spatial resolution of the model output * Downscaled_yes_no records yes for when downscaling techniques were used to improve spatial resolution and no when downscaling techniques were not used * Downscaled_method records which type of downscaling technique was used (either statistical or dynamic). N/A is used when the study did not use a downscaling technique * Study_purpose is a summary of the published study's aims and its findings * Study advantages is a synthesis of the published study's key advantages * Study_gaps is a synthesis of the published study's key limitations ### Supplementary\_Data 3 **Exploratory Meta-analysis Database: Scenario descriptions for data included in the effect size analysis.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref is a number that identifies the full reference in the Full Reference List within this Read.Me file * Scenario identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * Scenario description is a summary of the future scneario modelled * Reported warming refers to the future emissions pathway used to model future warming * Classified warming categorizes these warming levels into different scenarios of 1.5 - 2ºC, 2 - 4ºC, and >4ºC represent projections at the end-of-century (years 2090-2100) * Reported projection unit is the unit in which the published studies delivered their projections * Classified projection unit represents the categories in which the projection units were analysed (e.g. % reef cells at risk) ### Klein\_et\_al.,\_2024 * **R script:** Script used for exploratory meta-analysis ## Reference List We use numbers that reference the sources we used to collect our data. Below is a list of the sources and their corresponding numbers. Supplementary References 1 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, doi:10.1136/bmj.n71 (2021). 2 Khalil, I., Muslim, A. M., Hossain, M. S. & Atkinson, P. M. Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region. International Journal of Remote Sensing 44, 194-216 (2023). 3 Abe, H., Kumagai, N. H. & Yamano, H. Priority coral conservation areas under global warming in the Amami Islands, Southern Japan. Coral Reefs 41, 1637-1650 (2022). 4 Sully, S., Hodgson, G. & van Woesik, R. Present and future bright and dark spots for coral reefs through climate change. Global Change Biology 28, 4509-4522, doi: (2022). 5 DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecological applications 32, e2650 (2022). 6 Holstein, D. M., Smith, T. B., van Hooidonk, R. & Paris, C. B. Predicting coral metapopulation decline in a changing thermal environment. Coral Reefs 41, 961-972, doi:10.1007/s00338-022-02252-9 (2022). 7 Raharinirina, N. A., Acevedo-Trejos, E. & Merico, A. Modelling the acclimation capacity of coral reefs to a warming ocean. PLOS Computational Biology 18, e1010099 (2022). 8 Chollett, I. et al. Planning for resilience: Incorporating scenario and model uncertainty and trade‐offs when prioritizing management of climate refugia. Global Change Biology 28, 4054-4068 (2022). 9 Setter, R. O., Franklin, E. C. & Mora, C. Co-occurring anthropogenic stressors reduce the timeframe of environmental viability for the world’s coral reefs. PLOS Biology 20, e3001821, doi:10.1371/journal.pbio.3001821 (2022). 10 McWhorter, J. K., Halloran, P. R., Roff, G., Skirving, W. J. & Mumby, P. J. Climate refugia on the Great Barrier Reef fail when global warming exceeds 3° C. Global Change Biology 28, 5768-5780 (2022). 11 Kalmus, P., Ekanayaka, A., Kang, E., Baird, M. & Gierach, M. Past the precipice? Projected coral habitability under global heating. Earth's Future 10, e2021EF002608 (2022). 12 McWhorter, J. K. et al. The importance of 1.5°C warming for the Great Barrier Reef. Global Change Biology 28, 1332-1341, doi: (2022). 13 Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Global Change Biology n/a, doi: (2021). 14 Adam, A. A. et al. Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions. Diversity and Distributions 27, 2245-2261 (2021). 15 Cant, J. et al. The projected degradation of subtropical coral assemblages by recurrent thermal stress. Journal of Animal Ecology 90, 233-247 (2021). 16 Principe, S. C., Acosta, A. L., Andrade, J. E. & Lotufo, T. M. Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Frontiers in Marine Science 8, 673086 (2021). 17 Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proceedings of the Royal Society B 288, 20210274 (2021). 18 Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Scientific Reports 11, 1-12 (2021). 19 Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences 118, e2015265118, doi:doi:10.1073/pnas.2015265118 (2021). 20 McManus, L. C. et al. Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle. Global change biology 27, 4307-4321 (2021). 21 McClanahan, T. R. & Azali, M. K. Environmental Variability and Threshold Model’s Predictions for Coral Reefs. Frontiers in Marine Science 8, doi:10.3389/fmars.2021.778121 (2021). 22 Zuo, X. et al. Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea. Remote Sensing 13, 2626 (2021). 23 McManus, L. C. et al. Extreme temperature events will drive coral decline in the Coral Triangle. Global Change Biology 26, 2120-2133 (2020). 24 Rodriguez, L., García, J. J., Tuya, F. & Martínez, B. Environmental factors driving the distribution of the tropical coral Pavona varians: predictions under a climate change scenario. Marine Ecology 41, 1-12 (2020). 25 Cacciapaglia, C. W. & van Woesik, R. Reduced carbon emissions and fishing pressure are both necessary for equatorial coral reefs to keep up with rising seas. Ecography 43, 789-800, doi: (2020). 26 Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Global Change Biology (2020). 27 Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Scientific Reports 9, 3721, doi:10.1038/s41598-019-38962-4 (2019). 28 Rodriguez, L., Martínez, B. & Tuya, F. Atlantic corals under climate change: modelling distribution shifts to predict richness, phylogenetic structure and trait-diversity changes. Biodiversity and Conservation 28, 3873-3890, doi:10.1007/s10531-019-01855-z (2019). 29 Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. Royal Society Open Science 6, 182111 (2019). 30 Yan, H. et al. Regional coral growth responses to seawater warming in the South China Sea. Science of the total environment 670, 595-605 (2019). 31 Woesik, R. v., Köksal, S., Ünal, A., Cacciapaglia, C. W. & Randall, C. J. Predicting coral dynamics through climate change. Scientific reports 8, 17997 (2018). 32 Wolff, N. H., Mumby, P. J., Devlin, M. & Anthony, K. R. N. Vulnerability of the Great Barrier Reef to climate change and local pressures. Global Change Biology 24, 1978-1991, doi:10.1111/gcb.14043 (2018). 33 Cacciapaglia, C. & van Woesik, R. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography 45, 154-163 (2018). 34 Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biology 24, 5084-5095, doi: (2018). 35 Langlais, C. et al. Coral bleaching pathways under the control of regional temperature variability. Nature Climate Change 7, 839-844 (2017). 36 Kendall, M. S., Poti, M. & Karnauskas, K. B. Climate change and larval transport in the ocean: fractional effects from physical and physiological factors. Global Change Biology 22, 1532-1547, doi: (2016). 37 Yara, Y. et al. Potential future coral habitats around Japan depend strongly on anthropogenic CO 2 emissions. Aquatic biodiversity conservation and ecosystem services, 41-56 (2016). 38 Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific reports 6, 39666 (2016). 39 Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 C and 2 C. Earth system dynamics 7, 327-351 (2016). 40 Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338-342, doi:doi:10.1126/science.aac7125 (2016). 41 Cooper, J. K., Spencer, M. & Bruno, J. F. Stochastic dynamics of a warmer Great Barrier Reef. Ecology 96, 1802-1811 (2015). 42 Bozec, Y.-M. & Mumby, P. J. Synergistic impacts of global warming on the resilience of coral reefs. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20130267 (2015). 43 Bozec, Y. M., Alvarez‐Filip, L. & Mumby, P. J. The dynamics of architectural complexity on coral reefs under climate change. Global change biology 21, 223-235 (2015). 44 van Hooidonk, R., Maynard, J. A., Liu, Y. & Lee, S. K. Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Global change biology 21, 3389-3401 (2015). 45 Kwiatkowski, L., Cox, P., Halloran, P. R., Mumby, P. J. & Wiltshire, A. J. Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nature Climate Change 5, 777-781 (2015). 46 Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change 5, 688-694 (2015). 47 Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Global Change Biology 21, 2479-2487 (2015). 48 Freeman, L. A. Robust performance of marginal Pacific coral reef habitats in future climate scenarios. PLoS One 10, e0128875 (2015). 49 Cacciapaglia, C. & van Woesik, R. Reef‐coral refugia in a rapidly changing ocean. Global Change Biology 21, 2272-2282 (2015). 50 Mumby, P. J., Wolff, N. H., Bozec, Y.-M., Chollett, I. & Halloran, P. Operationalizing the Resilience of Coral Reefs in an Era of Climate Change. Conservation Letters 7, 176-187, doi: (2014). 51 Yara, Y., Fujii, M., Yamano, H. & Yamanaka, Y. Projected coral bleaching in response to future sea surface temperature rises and the uncertainties among climate models. Hydrobiologia 733, 19-29 (2014). 52 Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Global Change Biology 20, 125-139 (2014). 53 van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology 20, 103-112, doi: (2014). 54 Ortiz, J. C., González-Rivero, M. & Mumby, P. J. An ecosystem-level perspective on the host and symbiont traits needed to mitigate climate change impacts on Caribbean coral reefs. Ecosystems 17, 1-13 (2014). 55 Lane, D. R. et al. Quantifying and valuing potential climate change impacts on coral reefs in the United States: Comparison of two scenarios. PloS one 8, e82579 (2013). 56 Kennedy, E. V. et al. Avoiding coral reef functional collapse requires local and global action. Current Biology 23, 912-918 (2013). 57 van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nature Climate Change 3, 508-511, doi:10.1038/nclimate1829 (2013). 58 Frieler, K. et al. Limiting global warming to 2 C is unlikely to save most coral reefs. Nature Climate Change 3, 165 (2013). 59 Ortiz, J. C., González‐Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs? Global change biology 19, 273-281 (2013). 60 Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral reef habitat response to climate change scenarios. PloS one 8, e82404 (2013). 61 Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Global Change Biology 19, 3592-3606, doi: (2013). 62 Couce, E., Irvine, P. J., Gregoire, L., Ridgwell, A. & Hendy, E. Tropical coral reef habitat in a geoengineered, high‐CO2 world. Geophysical Research Letters 40, 1799-1805 (2013). 63 Wooldridge, S. A. et al. Safeguarding coastal coral communities on the central Great Barrier Reef (Australia) against climate change: realizable local and global actions. Climatic Change 112, 945-961 (2012). 64 Meissner, K., Lippmann, T. & Sen Gupta, A. Large-scale stress factors affecting coral reefs: open ocean sea surface temperature and surface seawater aragonite saturation over the next 400 years. Coral Reefs 31, 309-319 (2012). 65 van Hooidonk, R. & Huber, M. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs 31, 121-131, doi:10.1007/s00338-011-0825-4 (2012). 66 Teneva, L. et al. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates. Coral Reefs 31, 1-12 (2012). 67 Yara, Y. et al. Ocean acidification limits temperature-induced poleward expansion of coral habitats around Japan. Biogeosciences 9, 4955-4968 (2012). 68 Edwards, H. J. et al. How much time can herbivore protection buy for coral reefs under realistic regimes of hurricanes and coral bleaching? Global Change Biology 17, 2033-2048 (2011). 69 Anthony, K. R. N. et al. Ocean acidification and warming will lower coral reef resilience. Global Change Biology 17, 1798-1808, doi: (2011). 70 Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227 (2011). 71 Hoeke, R. K., Jokiel, P. L., Buddemeier, R. W. & Brainard, R. E. Projected changes to growth and mortality of Hawaiian corals over the next 100 years. PloS one 6, e18038 (2011). 72 McLeod, E. et al. Warming seas in the Coral Triangle: coral reef vulnerability and management implications. Coastal Management 38, 518-539 (2010). 73 Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecological Applications 19, 3-17 (2009). 74 Vivekanandan, E., Ali, M. H., Jasper, B. & Rajagopalan, M. Vulnerability of corals to warming of the Indian seas: a projection for the 21st century. Current Science, 1654-1658 (2009). 75 Donner, S. D. Coping with commitment: projected thermal stress on coral reefs under different future scenarios. PLoS One 4, e5712 (2009). 76 Buddemeier, R. W. et al. A modeling tool to evaluate regional coral reef responses to changes in climate and ocean chemistry. Limnology and Oceanography: Methods 6, 395-411 (2008). 77 Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh‐Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology 11, 2251-2265 (2005). 78 McNeil, B. I., Matear, R. J. & Barnes, D. J. Coral reef calcification and climate change: The effect of ocean warming. Geophysical Research Letters 31 (2004). 79 Guinotte, J., Buddemeier, R. & Kleypas, J. Future coral reef habitat marginality: temporal and spatial effects of climate change in the Pacific basin. Coral reefs 22, 551-558 (2003). 80 Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world's coral reefs. Marine and freshwater research 50, 839-866 (1999). Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change (IPCC), consistently assert that limiting global warming to 1.5°C is unlikely to safeguard most of the world’s coral reefs. This prognosis primarily stems from 'excess heat’ threshold models, which assume that widespread coral bleaching predictably occurs when temperatures accumulate beyond a specific threshold. Our systematic review of research projecting coral reef futures to climate change (n=79) revealed that 'excess heat' models constituted only one third (32%) of all studies but attracted a high proportion (68%) of citations in the field. We observed that most methods employed deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainties of coral reef futures. In attempting to assess the consistency of projected impacts, we aimed to identify common coral reef metrics under the same emissions scenarios. However, disparate choices in metrics and emissions scenarios hindered a cohesive synthesis and limited the exploratory analysis to a small fraction of available studies. We found substantial discrepancies in expected impacts to coral reefs, suggesting that some 'excess heat' models may project more extreme impacts than other methods. Drawing on lessons from the field of climate change science, we propose that an IPCC ensemble-like approach to generating probabilistic projections for coral reef futures is feasible. Successful implementation will require improved coordination among modeling efforts to select common output metrics and emission scenarios, addressing existing geographical biases, among other gaps in current modeling efforts. We conducted a comprehensive literature search using the Thomson Reuters Web of Science database to identify studies that projecting the impacts of climate change on shallow tropical and sub-tropical coral reefs. This search, adhering to PRISMA guidelines, yielded 2705 peer-reviewed articles, which we refined to 79 relevant articles published between 1999 and 2023 based on a specific selection criteria (Dataset 1). These studies were categorized into five major methodology types and further classified based on their approaches to simulating heat stress. Key characteristics such as the model output variables, spatial scale, and geographic area of each study were extracted, along with their methodological approaches, assumptions, and the techniques used.Our study aimed to assess and compare the projected impacts and uncertainties of various model types using a meta-analysis approach. The database of 79 studies was considered for inclusion in the exploratory meta-analysis based on specific criteria (view published article and supplementary methods for detailed list and Supplementary Figure 1). Briefly, to enable a meaningful analysis, we identified the three most frequently used model outputs in our database. Among those, only studies that provided: 1) sufficient data for projection estimates and uncertainty measures to be reliably extracted or calculated, 2) reported end-of-century projections, and 3) used a baseline period between 2000 and 2015, were selected for the exploratory meta-analysis. In cases where projection and uncertainty estimates were presented in figures, values were extracted using PlotDigitizer, where possible.

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  • Authors: Klein, Shannon; Roch, Cassandra; Duarte, Carlos;

    # Systematic review of the uncertainty of coral reef futures under climate change, datasets Published paper resulting from this data can be found at: ## Summary This study conducted a systematic review of 79 published articles projecting coral reef responses to future climate change. This dataset contains qualitative and quantitative data extracted from the published studies, including model types, geographic focus, and projected impacts on coral reefs. ## Description of the data and file structure ### Supplementary Data File **Extracted Data**: **Source data for effect size calculations (n=8 published studies).** * Short.reference used to identify the published study from which the data were extracted. See Full Reference List within this Read.Me file * Scenario.ID identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * N.c is n/number of model runs for control scenario * N.e is n/number of model runs for future end-of-century (experimental) scenario * M.c is the Model estimate for baseline scenario * M.e is the model estimate of end of century projections * Sd.c is the standard deviation of end of century projection estimates * Sd.e is the standard deviation of the baseline scenario estimates ### Supplementary\_Data1 **Summary Database: Overview of the dataset including study details, geographic focus, spatial scale, modeling approach, and examined stressors.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref number identifies the full reference in the Full Reference List within this Read.Me file * Approach type classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) projective meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Focal projection(s) units is the unit in which the published studies delivered their projections * Spatial scale refers to the spatial scale of the projections published, classified as either regional or global * Geographic focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Major stressor(s) examined refer to the main drivers that were used to parameterize the models (e.g. warming, ocean acidification) ### Supplementary\_Data2 **Complete Database: Detailed information from all 79 reviewed studies (qualitative characteristics)** * Unique_ID is a random unique ID assigned to each of the published papers within the dataset * Author_list is a comprehensive list of all authors of the published studies within the dataset * Article_ttle is the title of the published article * Source_journal is the scientific journal in which the article was published * Publication_year refers to the year of publication of the published studies * Times_cited is the number of citations received by the published studies according to the Thomson Reuters Web of Science database on March 6, 2023. * Model_category classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Model_technique refers to the method used to model heat stress (thermal threshold technique versus continuous variable technique). For studies to be classified as threshold techniques, the use of these metrics had to form the primary framework of the models that delivered projections. The second technique represents approaches that abandon the central threshold concept to focus on empirical relationships between continuous variables. * if_TM_Threshold type records the type of thermal threshold used. N/a is used when the study did not use a thermal threshold, or it was not clearly reported. * Focal_projection_unit records the units in which the published studies delivered their projections. * Spatial_scale refers to the spatial scale of the projections published, classified as either regional or global. * Reported_geographic_focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Drivers_used_summary records a summary of drivers used to parameterise the models. * Underlying model structure/ description is a summary of the model structure and its purpose * Key_assumptions is a description of the main assumptions made by the model * Future_scenarios_examined refers to the exact future emissions pathways used * Model_geographic_resolution records the spatial resolution of the model output * Downscaled_yes_no records yes for when downscaling techniques were used to improve spatial resolution and no when downscaling techniques were not used * Downscaled_method records which type of downscaling technique was used (either statistical or dynamic). N/A is used when the study did not use a downscaling technique * Study_purpose is a summary of the published study's aims and its findings * Study advantages is a synthesis of the published study's key advantages * Study_gaps is a synthesis of the published study's key limitations ### Supplementary\_Data 3 **Exploratory Meta-analysis Database: Scenario descriptions for data included in the effect size analysis.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref is a number that identifies the full reference in the Full Reference List within this Read.Me file * Scenario identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * Scenario description is a summary of the future scneario modelled * Reported warming refers to the future emissions pathway used to model future warming * Classified warming categorizes these warming levels into different scenarios of 1.5 - 2ºC, 2 - 4ºC, and >4ºC represent projections at the end-of-century (years 2090-2100) * Reported projection unit is the unit in which the published studies delivered their projections * Classified projection unit represents the categories in which the projection units were analysed (e.g. % reef cells at risk) ### Klein\_et\_al.,\_2024 * **R script:** Script used for exploratory meta-analysis ## Reference List We use numbers that reference the sources we used to collect our data. Below is a list of the sources and their corresponding numbers. Supplementary References 1 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, doi:10.1136/bmj.n71 (2021). 2 Khalil, I., Muslim, A. M., Hossain, M. S. & Atkinson, P. M. Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region. International Journal of Remote Sensing 44, 194-216 (2023). 3 Abe, H., Kumagai, N. H. & Yamano, H. Priority coral conservation areas under global warming in the Amami Islands, Southern Japan. Coral Reefs 41, 1637-1650 (2022). 4 Sully, S., Hodgson, G. & van Woesik, R. Present and future bright and dark spots for coral reefs through climate change. Global Change Biology 28, 4509-4522, doi: (2022). 5 DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecological applications 32, e2650 (2022). 6 Holstein, D. M., Smith, T. B., van Hooidonk, R. & Paris, C. B. Predicting coral metapopulation decline in a changing thermal environment. Coral Reefs 41, 961-972, doi:10.1007/s00338-022-02252-9 (2022). 7 Raharinirina, N. A., Acevedo-Trejos, E. & Merico, A. Modelling the acclimation capacity of coral reefs to a warming ocean. PLOS Computational Biology 18, e1010099 (2022). 8 Chollett, I. et al. Planning for resilience: Incorporating scenario and model uncertainty and trade‐offs when prioritizing management of climate refugia. Global Change Biology 28, 4054-4068 (2022). 9 Setter, R. O., Franklin, E. C. & Mora, C. Co-occurring anthropogenic stressors reduce the timeframe of environmental viability for the world’s coral reefs. PLOS Biology 20, e3001821, doi:10.1371/journal.pbio.3001821 (2022). 10 McWhorter, J. K., Halloran, P. R., Roff, G., Skirving, W. J. & Mumby, P. J. Climate refugia on the Great Barrier Reef fail when global warming exceeds 3° C. Global Change Biology 28, 5768-5780 (2022). 11 Kalmus, P., Ekanayaka, A., Kang, E., Baird, M. & Gierach, M. Past the precipice? Projected coral habitability under global heating. Earth's Future 10, e2021EF002608 (2022). 12 McWhorter, J. K. et al. The importance of 1.5°C warming for the Great Barrier Reef. Global Change Biology 28, 1332-1341, doi: (2022). 13 Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Global Change Biology n/a, doi: (2021). 14 Adam, A. A. et al. Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions. Diversity and Distributions 27, 2245-2261 (2021). 15 Cant, J. et al. The projected degradation of subtropical coral assemblages by recurrent thermal stress. Journal of Animal Ecology 90, 233-247 (2021). 16 Principe, S. C., Acosta, A. L., Andrade, J. E. & Lotufo, T. M. Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Frontiers in Marine Science 8, 673086 (2021). 17 Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proceedings of the Royal Society B 288, 20210274 (2021). 18 Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Scientific Reports 11, 1-12 (2021). 19 Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences 118, e2015265118, doi:doi:10.1073/pnas.2015265118 (2021). 20 McManus, L. C. et al. Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle. Global change biology 27, 4307-4321 (2021). 21 McClanahan, T. R. & Azali, M. K. Environmental Variability and Threshold Model’s Predictions for Coral Reefs. Frontiers in Marine Science 8, doi:10.3389/fmars.2021.778121 (2021). 22 Zuo, X. et al. Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea. Remote Sensing 13, 2626 (2021). 23 McManus, L. C. et al. Extreme temperature events will drive coral decline in the Coral Triangle. Global Change Biology 26, 2120-2133 (2020). 24 Rodriguez, L., García, J. J., Tuya, F. & Martínez, B. Environmental factors driving the distribution of the tropical coral Pavona varians: predictions under a climate change scenario. Marine Ecology 41, 1-12 (2020). 25 Cacciapaglia, C. W. & van Woesik, R. Reduced carbon emissions and fishing pressure are both necessary for equatorial coral reefs to keep up with rising seas. Ecography 43, 789-800, doi: (2020). 26 Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Global Change Biology (2020). 27 Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Scientific Reports 9, 3721, doi:10.1038/s41598-019-38962-4 (2019). 28 Rodriguez, L., Martínez, B. & Tuya, F. Atlantic corals under climate change: modelling distribution shifts to predict richness, phylogenetic structure and trait-diversity changes. Biodiversity and Conservation 28, 3873-3890, doi:10.1007/s10531-019-01855-z (2019). 29 Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. Royal Society Open Science 6, 182111 (2019). 30 Yan, H. et al. Regional coral growth responses to seawater warming in the South China Sea. Science of the total environment 670, 595-605 (2019). 31 Woesik, R. v., Köksal, S., Ünal, A., Cacciapaglia, C. W. & Randall, C. J. Predicting coral dynamics through climate change. Scientific reports 8, 17997 (2018). 32 Wolff, N. H., Mumby, P. J., Devlin, M. & Anthony, K. R. N. Vulnerability of the Great Barrier Reef to climate change and local pressures. Global Change Biology 24, 1978-1991, doi:10.1111/gcb.14043 (2018). 33 Cacciapaglia, C. & van Woesik, R. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography 45, 154-163 (2018). 34 Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biology 24, 5084-5095, doi: (2018). 35 Langlais, C. et al. Coral bleaching pathways under the control of regional temperature variability. Nature Climate Change 7, 839-844 (2017). 36 Kendall, M. S., Poti, M. & Karnauskas, K. B. Climate change and larval transport in the ocean: fractional effects from physical and physiological factors. Global Change Biology 22, 1532-1547, doi: (2016). 37 Yara, Y. et al. Potential future coral habitats around Japan depend strongly on anthropogenic CO 2 emissions. Aquatic biodiversity conservation and ecosystem services, 41-56 (2016). 38 Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific reports 6, 39666 (2016). 39 Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 C and 2 C. Earth system dynamics 7, 327-351 (2016). 40 Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338-342, doi:doi:10.1126/science.aac7125 (2016). 41 Cooper, J. K., Spencer, M. & Bruno, J. F. Stochastic dynamics of a warmer Great Barrier Reef. Ecology 96, 1802-1811 (2015). 42 Bozec, Y.-M. & Mumby, P. J. Synergistic impacts of global warming on the resilience of coral reefs. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20130267 (2015). 43 Bozec, Y. M., Alvarez‐Filip, L. & Mumby, P. J. The dynamics of architectural complexity on coral reefs under climate change. Global change biology 21, 223-235 (2015). 44 van Hooidonk, R., Maynard, J. A., Liu, Y. & Lee, S. K. Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Global change biology 21, 3389-3401 (2015). 45 Kwiatkowski, L., Cox, P., Halloran, P. R., Mumby, P. J. & Wiltshire, A. J. Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nature Climate Change 5, 777-781 (2015). 46 Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change 5, 688-694 (2015). 47 Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Global Change Biology 21, 2479-2487 (2015). 48 Freeman, L. A. Robust performance of marginal Pacific coral reef habitats in future climate scenarios. PLoS One 10, e0128875 (2015). 49 Cacciapaglia, C. & van Woesik, R. Reef‐coral refugia in a rapidly changing ocean. Global Change Biology 21, 2272-2282 (2015). 50 Mumby, P. J., Wolff, N. H., Bozec, Y.-M., Chollett, I. & Halloran, P. Operationalizing the Resilience of Coral Reefs in an Era of Climate Change. Conservation Letters 7, 176-187, doi: (2014). 51 Yara, Y., Fujii, M., Yamano, H. & Yamanaka, Y. Projected coral bleaching in response to future sea surface temperature rises and the uncertainties among climate models. Hydrobiologia 733, 19-29 (2014). 52 Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Global Change Biology 20, 125-139 (2014). 53 van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology 20, 103-112, doi: (2014). 54 Ortiz, J. C., González-Rivero, M. & Mumby, P. J. An ecosystem-level perspective on the host and symbiont traits needed to mitigate climate change impacts on Caribbean coral reefs. Ecosystems 17, 1-13 (2014). 55 Lane, D. R. et al. Quantifying and valuing potential climate change impacts on coral reefs in the United States: Comparison of two scenarios. PloS one 8, e82579 (2013). 56 Kennedy, E. V. et al. Avoiding coral reef functional collapse requires local and global action. Current Biology 23, 912-918 (2013). 57 van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nature Climate Change 3, 508-511, doi:10.1038/nclimate1829 (2013). 58 Frieler, K. et al. Limiting global warming to 2 C is unlikely to save most coral reefs. Nature Climate Change 3, 165 (2013). 59 Ortiz, J. C., González‐Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs? Global change biology 19, 273-281 (2013). 60 Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral reef habitat response to climate change scenarios. PloS one 8, e82404 (2013). 61 Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Global Change Biology 19, 3592-3606, doi: (2013). 62 Couce, E., Irvine, P. J., Gregoire, L., Ridgwell, A. & Hendy, E. Tropical coral reef habitat in a geoengineered, high‐CO2 world. Geophysical Research Letters 40, 1799-1805 (2013). 63 Wooldridge, S. A. et al. Safeguarding coastal coral communities on the central Great Barrier Reef (Australia) against climate change: realizable local and global actions. Climatic Change 112, 945-961 (2012). 64 Meissner, K., Lippmann, T. & Sen Gupta, A. Large-scale stress factors affecting coral reefs: open ocean sea surface temperature and surface seawater aragonite saturation over the next 400 years. Coral Reefs 31, 309-319 (2012). 65 van Hooidonk, R. & Huber, M. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs 31, 121-131, doi:10.1007/s00338-011-0825-4 (2012). 66 Teneva, L. et al. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates. Coral Reefs 31, 1-12 (2012). 67 Yara, Y. et al. Ocean acidification limits temperature-induced poleward expansion of coral habitats around Japan. Biogeosciences 9, 4955-4968 (2012). 68 Edwards, H. J. et al. How much time can herbivore protection buy for coral reefs under realistic regimes of hurricanes and coral bleaching? Global Change Biology 17, 2033-2048 (2011). 69 Anthony, K. R. N. et al. Ocean acidification and warming will lower coral reef resilience. Global Change Biology 17, 1798-1808, doi: (2011). 70 Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227 (2011). 71 Hoeke, R. K., Jokiel, P. L., Buddemeier, R. W. & Brainard, R. E. Projected changes to growth and mortality of Hawaiian corals over the next 100 years. PloS one 6, e18038 (2011). 72 McLeod, E. et al. Warming seas in the Coral Triangle: coral reef vulnerability and management implications. Coastal Management 38, 518-539 (2010). 73 Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecological Applications 19, 3-17 (2009). 74 Vivekanandan, E., Ali, M. H., Jasper, B. & Rajagopalan, M. Vulnerability of corals to warming of the Indian seas: a projection for the 21st century. Current Science, 1654-1658 (2009). 75 Donner, S. D. Coping with commitment: projected thermal stress on coral reefs under different future scenarios. PLoS One 4, e5712 (2009). 76 Buddemeier, R. W. et al. A modeling tool to evaluate regional coral reef responses to changes in climate and ocean chemistry. Limnology and Oceanography: Methods 6, 395-411 (2008). 77 Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh‐Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology 11, 2251-2265 (2005). 78 McNeil, B. I., Matear, R. J. & Barnes, D. J. Coral reef calcification and climate change: The effect of ocean warming. Geophysical Research Letters 31 (2004). 79 Guinotte, J., Buddemeier, R. & Kleypas, J. Future coral reef habitat marginality: temporal and spatial effects of climate change in the Pacific basin. Coral reefs 22, 551-558 (2003). 80 Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world's coral reefs. Marine and freshwater research 50, 839-866 (1999). Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change (IPCC), consistently assert that limiting global warming to 1.5°C is unlikely to safeguard most of the world’s coral reefs. This prognosis primarily stems from 'excess heat’ threshold models, which assume that widespread coral bleaching predictably occurs when temperatures accumulate beyond a specific threshold. Our systematic review of research projecting coral reef futures to climate change (n=79) revealed that 'excess heat' models constituted only one third (32%) of all studies but attracted a high proportion (68%) of citations in the field. We observed that most methods employed deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainties of coral reef futures. In attempting to assess the consistency of projected impacts, we aimed to identify common coral reef metrics under the same emissions scenarios. However, disparate choices in metrics and emissions scenarios hindered a cohesive synthesis and limited the exploratory analysis to a small fraction of available studies. We found substantial discrepancies in expected impacts to coral reefs, suggesting that some 'excess heat' models may project more extreme impacts than other methods. Drawing on lessons from the field of climate change science, we propose that an IPCC ensemble-like approach to generating probabilistic projections for coral reef futures is feasible. Successful implementation will require improved coordination among modeling efforts to select common output metrics and emission scenarios, addressing existing geographical biases, among other gaps in current modeling efforts. We conducted a comprehensive literature search using the Thomson Reuters Web of Science database to identify studies that projecting the impacts of climate change on shallow tropical and sub-tropical coral reefs. This search, adhering to PRISMA guidelines, yielded 2705 peer-reviewed articles, which we refined to 79 relevant articles published between 1999 and 2023 based on a specific selection criteria (Dataset 1). These studies were categorized into five major methodology types and further classified based on their approaches to simulating heat stress. Key characteristics such as the model output variables, spatial scale, and geographic area of each study were extracted, along with their methodological approaches, assumptions, and the techniques used.Our study aimed to assess and compare the projected impacts and uncertainties of various model types using a meta-analysis approach. The database of 79 studies was considered for inclusion in the exploratory meta-analysis based on specific criteria (view published article and supplementary methods for detailed list and Supplementary Figure 1). Briefly, to enable a meaningful analysis, we identified the three most frequently used model outputs in our database. Among those, only studies that provided: 1) sufficient data for projection estimates and uncertainty measures to be reliably extracted or calculated, 2) reported end-of-century projections, and 3) used a baseline period between 2000 and 2015, were selected for the exploratory meta-analysis. In cases where projection and uncertainty estimates were presented in figures, values were extracted using PlotDigitizer, where possible.

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    Authors: Ager, Thomas Gjerluff; Sejr, Mikael K.; Duarte, Carlos M.; Mankoff, Kenneth D.; +5 Authors

    This dataset includes data on sea surface temperatures, sea ice concentration, sea ice seasonality, salinity, runoff form the Greenland ice sheet, cholorophyll a, and a litterature review. The data is divided into six regions around Greenland stretching 200 km of the coastline. Each region spans 9 degrees latitude.

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    Authors: Ager, Thomas Gjerluff; Sejr, Mikael K.; Duarte, Carlos M.; Mankoff, Kenneth D.; +5 Authors

    This dataset includes data on sea surface temperatures, sea ice concentration, sea ice seasonality, salinity, runoff form the Greenland ice sheet, cholorophyll a, and a litterature review. The data is divided into six regions around Greenland stretching 200 km of the coastline. Each region spans 9 degrees latitude.

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    Authors: K. Krishnaram; S. Sivamani; Zuhair Alaas; M.M.R. Ahmed; +2 Authors

    Les systèmes de production d'énergie photovoltaïque (PV) abstraits sont de plus en plus populaires en raison de leurs hautes performances et de leur propreté. Plusieurs facteurs influencent les performances d'un système PV, y compris les effets d'ombrage. Les systèmes photovoltaïques utilisent des méthodologies MPPT pour obtenir l'énergie du réseau photovoltaïque. Les MPPT conventionnels fonctionnent bien dans des conditions normales lorsqu'il n'y a pas d'effets d'ombre ou d'ombrage partiel. La présence d'un ombrage partiel affecte les performances du système et génère plusieurs pics de puissance. Cela complique le processus de découverte du pic global (GMPP) avec une efficacité de suivi améliorée et un temps de stabilisation réduit, y compris l'efficacité de conversion. Ce travail propose trois techniques MPPT hybrides : l'optimisation du cycle de l'eau - la perturbation et l'observation (WCO-PO), la perturbation et l'observation adaptables à échelle étagée prises en charge par le réseau de neurones artificiels (ANN-ASSPO), le contrôleur de mode de glissement rapide du terminal modifié par l'optimisation du loup gris (GWO-MFTSMC) et deux techniques MPPT conventionnelles WCO et P&O ont été mises en œuvre. Le système proposé utilise un convertisseur survolteur entrelacé avec trois phases. Les performances des stratégies MPPT hybrides proposées ont été comparées en termes de tension de sortie, de courant de sortie et de puissance extraite. La comparaison comprend également l'efficacité de conversion et le temps de stabilisation moyen. Pour analyser les performances, quatre cas différents ont été utilisés pour tester l'efficacité des MPPT hybrides dans des conditions climatiques changeantes. L'outil Matlab/Simulink a été utilisé pour analyser les performances du système PV. Dans les trois techniques MPPT hybrides, WCO-PO a obtenu de meilleurs résultats par rapport aux deux autres MPPT hybrides en termes d'efficacité de conversion (99,56 %) et de temps de sédimentation (1,4 m). Resumen Los sistemas de generación de energía basados en energía fotovoltaica (PV) son cada vez más populares debido a su alto rendimiento y limpieza. Varios factores influyen en el rendimiento de un sistema fotovoltaico, incluidos los efectos de sombreado. Los sistemas fotovoltaicos emplean metodologías MPPT para obtener la energía de la matriz fotovoltaica. Los MPPT convencionales funcionan bien en condiciones normales cuando no hay efectos de sombra o sombreado parcial. La presencia de sombreado parcial afecta al rendimiento del sistema y genera varios picos de potencia. Esto complica el proceso de determinar el pico global (GMPP) con una eficiencia de seguimiento mejorada y un tiempo de asentamiento reducido, incluida la eficiencia de conversión. En este trabajo se proponen tres técnicas híbridas de MPPT: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), y se han implementado dos técnicas convencionales de MPPT WCO y P&O. El sistema propuesto utiliza un convertidor elevador intercalado con tres fases. Los rendimientos de las estrategias de MPPT híbridas propuestas se compararon en términos de voltaje de salida, corriente de salida y potencia extraída. La comparación también incluye la eficiencia de conversión y el tiempo medio de asentamiento. Para analizar los rendimientos, se han utilizado cuatro casos diferentes para probar la eficacia de los MPPT híbridos en condiciones climáticas cambiantes. La herramienta MATLAB/Simulink se ha utilizado para analizar el rendimiento del sistema fotovoltaico. En las tres técnicas híbridas de MPPT, WCO-PO se ha desempeñado mejor en comparación con otros dos MPPT híbridos en términos de eficiencia de conversión (99.56%) y tiempo de asentamiento (1.4 m). Abstract Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). أصبحت أنظمة توليد الطاقة القائمة على الطاقة الكهروضوئية التجريدية شائعة بشكل متزايد نظرًا لأدائها العالي ونظافتها. تؤثر عدة عوامل على أداء النظام الكهروضوئي، بما في ذلك تأثيرات التظليل. تستخدم الأنظمة الكهروضوئية منهجيات MPPT للحصول على الطاقة من المصفوفة الكهروضوئية. تعمل MPPTs التقليدية بشكل جيد في الظروف العادية عندما لا يكون هناك تأثيرات ظل أو تظليل جزئي. يؤثر وجود التظليل الجزئي على أداء النظام ويولد العديد من قمم الطاقة. وهذا يعقد عملية اكتشاف الذروة العالمية (GMPP) مع تحسين كفاءة التتبع وتقليل وقت الاستقرار بما في ذلك كفاءة التحويل. يقترح هذا العمل ثلاث تقنيات MPPT هجينة: تحسين دورة المياه - الاضطراب والملاحظة (WCO - PO)، والاضطراب والملاحظة التدريجية المدعومة من الشبكة العصبية الاصطناعية (ANN - ASPO)، ووحدة التحكم في وضع انزلاق المحطة الطرفية السريعة المعدلة بالذئب الرمادي (GWO - MFTSMC)، وتم تنفيذ تقنيتين تقليديتين MPPT WCO و P&O. يستخدم النظام المقترح محول التعزيز المعشق مع ثلاث مراحل. تمت مقارنة أداء استراتيجيات MPPTs الهجينة المقترحة من حيث جهد الخرج وتيار الخرج والطاقة المستخرجة. تتضمن المقارنة أيضًا كفاءة التحويل ومتوسط وقت الاستقرار. لتحليل الأداء، تم استخدام أربع حالات مختلفة لاختبار فعالية MPPTs الهجينة في ظل الظروف المناخية المتغيرة. تم استخدام أداة MATLAB/Simulink لتحليل أداء النظام الكهروضوئي. في تقنيات MPPT الهجينة الثلاثة، كان أداء WCO - PO أفضل عند مقارنته بتقنيات MPPT الهجينة الأخرى من حيث كفاءة التحويل (99.56 ٪) ووقت الاستقرار (1.4 م).

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    Authors: K. Krishnaram; S. Sivamani; Zuhair Alaas; M.M.R. Ahmed; +2 Authors

    Les systèmes de production d'énergie photovoltaïque (PV) abstraits sont de plus en plus populaires en raison de leurs hautes performances et de leur propreté. Plusieurs facteurs influencent les performances d'un système PV, y compris les effets d'ombrage. Les systèmes photovoltaïques utilisent des méthodologies MPPT pour obtenir l'énergie du réseau photovoltaïque. Les MPPT conventionnels fonctionnent bien dans des conditions normales lorsqu'il n'y a pas d'effets d'ombre ou d'ombrage partiel. La présence d'un ombrage partiel affecte les performances du système et génère plusieurs pics de puissance. Cela complique le processus de découverte du pic global (GMPP) avec une efficacité de suivi améliorée et un temps de stabilisation réduit, y compris l'efficacité de conversion. Ce travail propose trois techniques MPPT hybrides : l'optimisation du cycle de l'eau - la perturbation et l'observation (WCO-PO), la perturbation et l'observation adaptables à échelle étagée prises en charge par le réseau de neurones artificiels (ANN-ASSPO), le contrôleur de mode de glissement rapide du terminal modifié par l'optimisation du loup gris (GWO-MFTSMC) et deux techniques MPPT conventionnelles WCO et P&O ont été mises en œuvre. Le système proposé utilise un convertisseur survolteur entrelacé avec trois phases. Les performances des stratégies MPPT hybrides proposées ont été comparées en termes de tension de sortie, de courant de sortie et de puissance extraite. La comparaison comprend également l'efficacité de conversion et le temps de stabilisation moyen. Pour analyser les performances, quatre cas différents ont été utilisés pour tester l'efficacité des MPPT hybrides dans des conditions climatiques changeantes. L'outil Matlab/Simulink a été utilisé pour analyser les performances du système PV. Dans les trois techniques MPPT hybrides, WCO-PO a obtenu de meilleurs résultats par rapport aux deux autres MPPT hybrides en termes d'efficacité de conversion (99,56 %) et de temps de sédimentation (1,4 m). Resumen Los sistemas de generación de energía basados en energía fotovoltaica (PV) son cada vez más populares debido a su alto rendimiento y limpieza. Varios factores influyen en el rendimiento de un sistema fotovoltaico, incluidos los efectos de sombreado. Los sistemas fotovoltaicos emplean metodologías MPPT para obtener la energía de la matriz fotovoltaica. Los MPPT convencionales funcionan bien en condiciones normales cuando no hay efectos de sombra o sombreado parcial. La presencia de sombreado parcial afecta al rendimiento del sistema y genera varios picos de potencia. Esto complica el proceso de determinar el pico global (GMPP) con una eficiencia de seguimiento mejorada y un tiempo de asentamiento reducido, incluida la eficiencia de conversión. En este trabajo se proponen tres técnicas híbridas de MPPT: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), y se han implementado dos técnicas convencionales de MPPT WCO y P&O. El sistema propuesto utiliza un convertidor elevador intercalado con tres fases. Los rendimientos de las estrategias de MPPT híbridas propuestas se compararon en términos de voltaje de salida, corriente de salida y potencia extraída. La comparación también incluye la eficiencia de conversión y el tiempo medio de asentamiento. Para analizar los rendimientos, se han utilizado cuatro casos diferentes para probar la eficacia de los MPPT híbridos en condiciones climáticas cambiantes. La herramienta MATLAB/Simulink se ha utilizado para analizar el rendimiento del sistema fotovoltaico. En las tres técnicas híbridas de MPPT, WCO-PO se ha desempeñado mejor en comparación con otros dos MPPT híbridos en términos de eficiencia de conversión (99.56%) y tiempo de asentamiento (1.4 m). Abstract Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). أصبحت أنظمة توليد الطاقة القائمة على الطاقة الكهروضوئية التجريدية شائعة بشكل متزايد نظرًا لأدائها العالي ونظافتها. تؤثر عدة عوامل على أداء النظام الكهروضوئي، بما في ذلك تأثيرات التظليل. تستخدم الأنظمة الكهروضوئية منهجيات MPPT للحصول على الطاقة من المصفوفة الكهروضوئية. تعمل MPPTs التقليدية بشكل جيد في الظروف العادية عندما لا يكون هناك تأثيرات ظل أو تظليل جزئي. يؤثر وجود التظليل الجزئي على أداء النظام ويولد العديد من قمم الطاقة. وهذا يعقد عملية اكتشاف الذروة العالمية (GMPP) مع تحسين كفاءة التتبع وتقليل وقت الاستقرار بما في ذلك كفاءة التحويل. يقترح هذا العمل ثلاث تقنيات MPPT هجينة: تحسين دورة المياه - الاضطراب والملاحظة (WCO - PO)، والاضطراب والملاحظة التدريجية المدعومة من الشبكة العصبية الاصطناعية (ANN - ASPO)، ووحدة التحكم في وضع انزلاق المحطة الطرفية السريعة المعدلة بالذئب الرمادي (GWO - MFTSMC)، وتم تنفيذ تقنيتين تقليديتين MPPT WCO و P&O. يستخدم النظام المقترح محول التعزيز المعشق مع ثلاث مراحل. تمت مقارنة أداء استراتيجيات MPPTs الهجينة المقترحة من حيث جهد الخرج وتيار الخرج والطاقة المستخرجة. تتضمن المقارنة أيضًا كفاءة التحويل ومتوسط وقت الاستقرار. لتحليل الأداء، تم استخدام أربع حالات مختلفة لاختبار فعالية MPPTs الهجينة في ظل الظروف المناخية المتغيرة. تم استخدام أداة MATLAB/Simulink لتحليل أداء النظام الكهروضوئي. في تقنيات MPPT الهجينة الثلاثة، كان أداء WCO - PO أفضل عند مقارنته بتقنيات MPPT الهجينة الأخرى من حيث كفاءة التحويل (99.56 ٪) ووقت الاستقرار (1.4 م).

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    Authors: Mady Mohamed; Reem Okasha;

    With the rising environmental problems there are international movements towards sustainability and greening the built environments in order to mitigate the negative environmental impacts of buildings and human activities on environment and human health. This paper presents a range of K-12 Green Schools that were intentionally designed to utilize school building as a 3D-text book for Environmental Education (EE). The aim of this paper is to examine the methods and strategies of designing green school as a teaching tool through case study analysis of the selected schools. The cases provide a diversity of geographic locations, climates, green strategies and coasts. The research depends on the descriptive analytical approach for literature review; multiple-case study analysis to investigate the attributes of green schools that teach. The results revealed a set of approaches for utilizing green schools as a 3D-textbook for EE EQA - International Journal of Environmental Quality, Vol 39 (2020)

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    Authors: Mady Mohamed; Reem Okasha;

    With the rising environmental problems there are international movements towards sustainability and greening the built environments in order to mitigate the negative environmental impacts of buildings and human activities on environment and human health. This paper presents a range of K-12 Green Schools that were intentionally designed to utilize school building as a 3D-text book for Environmental Education (EE). The aim of this paper is to examine the methods and strategies of designing green school as a teaching tool through case study analysis of the selected schools. The cases provide a diversity of geographic locations, climates, green strategies and coasts. The research depends on the descriptive analytical approach for literature review; multiple-case study analysis to investigate the attributes of green schools that teach. The results revealed a set of approaches for utilizing green schools as a 3D-textbook for EE EQA - International Journal of Environmental Quality, Vol 39 (2020)

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  • Authors: Yasser B. Saddeek; V. Yu. Ivanov; Amr B. Saddek; Hesham M.H. Zakaly; +3 Authors

    La relaxation par ultrasons des verres de tellurite xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% a été étudiée en mesurant l'atténuation par ultrasons (αL) dans ces verres dans la gamme thermique 140–300 K (T) à diverses fréquences (f). Certaines caractéristiques peuvent être obtenues à partir de la relation de αL–T, telles que l'énergie d'activation moyenne (Ep) et la fréquence de tentative (f0). Les variables Ep et f0 sont fonction de la fréquence et de la teneur en B2O3. De plus, la relation αL–T rend bien compte des oscillations des atomes d'oxygène dans un processus de relaxation. Un tel processus physique provient de la transmission d'énergie ultrasonore aux atomes d'oxygène oscillants dans un potentiel à double puits. Le processus de relaxation est inspecté par un modèle appelé force centrale 1. Les différentes variables physiques soustraites d'un tel modèle sont fonction du contenu B2O3. La relajación ultrasónica de los vidrios de telurito xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% se investigó midiendo la atenuación ultrasónica (αL) en estos vidrios en el rango térmico 140–300 K (T) a varias frecuencias (f). Algunas características se pueden obtener de la relación de αL–T, como la energía de activación media (Ep) y la frecuencia de intento (f0). Las variables Ep y f0 son una función de la frecuencia y el contenido de B2O3. Además, la relación αL–T explica bien las oscilaciones de los átomos de oxígeno en un proceso de relajación. Dicho proceso físico se origina a partir de la transmisión de energía ultrasónica a los átomos de oxígeno oscilantes en un potencial de doble pozo. El proceso de relajación es inspeccionado por un modelo llamado fuerza central uno. Las diferentes variables físicas restadas de dicho modelo son una función del contenido de B2O3. The ultrasonic relaxation of tellurite glasses xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% was investigated by measuring the ultrasonic attenuation (αL) in these glasses in the thermal range 140–300 K (T) at various frequencies (f). Some characteristics can be obtained from the relation of αL–T, such as the average activation energy (Ep) and the attempt frequency (f0). The variables Ep and f0 are a function of frequency and B2O3 content. Moreover, the relation αL–T accounts well for the oscillations of the oxygen atoms in a relaxation process. Such a physical process is originated from transmitting ultrasonic energy to the oscillating oxygen atoms in a dual-well potential. The relaxation process is inspected by a model named central force one. The subtracted different physical variables from such a model are a function of B2O3 content. تم فحص الاسترخاء بالموجات فوق الصوتية لنظارات التيلوريت xB2O3 – 70 TeO2 – (30 – x) WO3، 0 ≤ x ≤ 30 mol% من خلال قياس التوهين بالموجات فوق الصوتية (αL) في هذه النظارات في النطاق الحراري 140–300 K (T) عند ترددات مختلفة (f). يمكن الحصول على بعض الخصائص من علاقة αL - T، مثل متوسط طاقة التنشيط (Ep) وتردد المحاولة (f0). المتغيران Ep و f0 هما دالة للتردد ومحتوى B2O3. علاوة على ذلك، فإن العلاقة αL - T تمثل بشكل جيد تذبذبات ذرات الأكسجين في عملية الاسترخاء. تنشأ هذه العملية الفيزيائية من نقل الطاقة فوق الصوتية إلى ذرات الأكسجين المتذبذبة في جهد بئر مزدوج. يتم فحص عملية الاسترخاء بواسطة نموذج يسمى القوة المركزية الأولى. المتغيرات الفيزيائية المختلفة المطروحة من هذا النموذج هي دالة لمحتوى B2O3.

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  • Authors: Yasser B. Saddeek; V. Yu. Ivanov; Amr B. Saddek; Hesham M.H. Zakaly; +3 Authors

    La relaxation par ultrasons des verres de tellurite xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% a été étudiée en mesurant l'atténuation par ultrasons (αL) dans ces verres dans la gamme thermique 140–300 K (T) à diverses fréquences (f). Certaines caractéristiques peuvent être obtenues à partir de la relation de αL–T, telles que l'énergie d'activation moyenne (Ep) et la fréquence de tentative (f0). Les variables Ep et f0 sont fonction de la fréquence et de la teneur en B2O3. De plus, la relation αL–T rend bien compte des oscillations des atomes d'oxygène dans un processus de relaxation. Un tel processus physique provient de la transmission d'énergie ultrasonore aux atomes d'oxygène oscillants dans un potentiel à double puits. Le processus de relaxation est inspecté par un modèle appelé force centrale 1. Les différentes variables physiques soustraites d'un tel modèle sont fonction du contenu B2O3. La relajación ultrasónica de los vidrios de telurito xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% se investigó midiendo la atenuación ultrasónica (αL) en estos vidrios en el rango térmico 140–300 K (T) a varias frecuencias (f). Algunas características se pueden obtener de la relación de αL–T, como la energía de activación media (Ep) y la frecuencia de intento (f0). Las variables Ep y f0 son una función de la frecuencia y el contenido de B2O3. Además, la relación αL–T explica bien las oscilaciones de los átomos de oxígeno en un proceso de relajación. Dicho proceso físico se origina a partir de la transmisión de energía ultrasónica a los átomos de oxígeno oscilantes en un potencial de doble pozo. El proceso de relajación es inspeccionado por un modelo llamado fuerza central uno. Las diferentes variables físicas restadas de dicho modelo son una función del contenido de B2O3. The ultrasonic relaxation of tellurite glasses xB2O3 – 70 TeO2 – (30 – x) WO3, 0 ≤ x ≤ 30 mol% was investigated by measuring the ultrasonic attenuation (αL) in these glasses in the thermal range 140–300 K (T) at various frequencies (f). Some characteristics can be obtained from the relation of αL–T, such as the average activation energy (Ep) and the attempt frequency (f0). The variables Ep and f0 are a function of frequency and B2O3 content. Moreover, the relation αL–T accounts well for the oscillations of the oxygen atoms in a relaxation process. Such a physical process is originated from transmitting ultrasonic energy to the oscillating oxygen atoms in a dual-well potential. The relaxation process is inspected by a model named central force one. The subtracted different physical variables from such a model are a function of B2O3 content. تم فحص الاسترخاء بالموجات فوق الصوتية لنظارات التيلوريت xB2O3 – 70 TeO2 – (30 – x) WO3، 0 ≤ x ≤ 30 mol% من خلال قياس التوهين بالموجات فوق الصوتية (αL) في هذه النظارات في النطاق الحراري 140–300 K (T) عند ترددات مختلفة (f). يمكن الحصول على بعض الخصائص من علاقة αL - T، مثل متوسط طاقة التنشيط (Ep) وتردد المحاولة (f0). المتغيران Ep و f0 هما دالة للتردد ومحتوى B2O3. علاوة على ذلك، فإن العلاقة αL - T تمثل بشكل جيد تذبذبات ذرات الأكسجين في عملية الاسترخاء. تنشأ هذه العملية الفيزيائية من نقل الطاقة فوق الصوتية إلى ذرات الأكسجين المتذبذبة في جهد بئر مزدوج. يتم فحص عملية الاسترخاء بواسطة نموذج يسمى القوة المركزية الأولى. المتغيرات الفيزيائية المختلفة المطروحة من هذا النموذج هي دالة لمحتوى B2O3.

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    Authors: Mohamed Nejlaoui; Hussain Sadig; Abdullah Al-Ghafis;

    In Saudi Arabia, hot water for domestic uses consumes a great portion of home electricity. Thus, solar collectors can be considered as an important alternative to reduce the amount of consumed electricity. Therefore, in recent researches, a great attention was given to develop flat plate collector (FPC) with optimal performance. In this paper, a multi-objective modified imperialist competitive algorithm (MOMICA) was employed for optimizing the performance of a FPC. The optimization results showed a capability to reach higher FPC efficiency with a relatively small collector area and hence lower price. It has also been proved that the change of the insulator depth from 0.02 to 0.05 m has a strong influence on the system’s efficiency.

    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/ Journal of Applied a...arrow_drop_down
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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/ Journal of Applied a...arrow_drop_down
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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: Mohamed Nejlaoui; Hussain Sadig; Abdullah Al-Ghafis;

    In Saudi Arabia, hot water for domestic uses consumes a great portion of home electricity. Thus, solar collectors can be considered as an important alternative to reduce the amount of consumed electricity. Therefore, in recent researches, a great attention was given to develop flat plate collector (FPC) with optimal performance. In this paper, a multi-objective modified imperialist competitive algorithm (MOMICA) was employed for optimizing the performance of a FPC. The optimization results showed a capability to reach higher FPC efficiency with a relatively small collector area and hence lower price. It has also been proved that the change of the insulator depth from 0.02 to 0.05 m has a strong influence on the system’s efficiency.

    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/ Journal of Applied a...arrow_drop_down
    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/
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