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  • 15. Life on land
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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: Hermann Behling; John Carson; Bronwen S. Whitney; William D. Gosling; +12 Authors

    Abstract. An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to an absence of information on the spatial and temporal coverage of study sites. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last 2 millennia. We identify 60 vegetation (pollen) records from across South America which satisfy geochronological requirements set out for climate modelling, and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local-scale responses to climate modes; thus, it is necessary to understand how vegetation–climate interactions might diverge under variable settings. We provide a qualitative translation from pollen metrics to climate variables. Additionally, pollen is an excellent indicator of human impact through time. We discuss evidence for human land use in pollen records and provide an overview considered useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. This manuscript forms part of the wider LOng-Term multi-proxy climate REconstructions and Dynamics in South America – 2k initiative that provides the ideal framework for the integration of the various palaeoclimatic subdisciplines and palaeo-science, thereby jump-starting and fostering multidisciplinary research into environmental change on centennial and millennial timescales.

    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/ COREarrow_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/
    CORE
    Article . 2016
    License: CC BY
    Data sources: CORE
    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/
    CORE
    Article . 2016
    License: CC BY
    Data sources: CORE
    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/
    CORE
    Article . 2016
    License: CC BY
    Data sources: CORE
    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/
    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/
    Climate of the Past (CP)
    Article . 2016 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    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/
    Climate of the Past (CP)
    Article
    License: CC BY
    Data sources: UnpayWall
    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/
    Climate of the Past
    Other literature type . 2018
    Data sources: Copernicus
    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/
    Climate of the Past
    Article . 2016
    Data sources: DOAJ
    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/
    https://dx.doi.org/10.60692/ph...
    Other literature type . 2016
    Data sources: Datacite
    https://dx.doi.org/10.60692/0y...
    Other literature type . 2016
    Data sources: Datacite
    Digital.CSIC
    Article . 2016 . Peer-reviewed
    Data sources: Digital.CSIC
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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/ COREarrow_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/
      CORE
      Article . 2016
      License: CC BY
      Data sources: CORE
      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/
      CORE
      Article . 2016
      License: CC BY
      Data sources: CORE
      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/
      CORE
      Article . 2016
      License: CC BY
      Data sources: CORE
      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/
      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/
      Climate of the Past (CP)
      Article . 2016 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      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/
      Climate of the Past (CP)
      Article
      License: CC BY
      Data sources: UnpayWall
      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/
      Climate of the Past
      Other literature type . 2018
      Data sources: Copernicus
      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/
      Climate of the Past
      Article . 2016
      Data sources: DOAJ
      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/
      https://dx.doi.org/10.60692/ph...
      Other literature type . 2016
      Data sources: Datacite
      https://dx.doi.org/10.60692/0y...
      Other literature type . 2016
      Data sources: Datacite
      Digital.CSIC
      Article . 2016 . Peer-reviewed
      Data sources: Digital.CSIC
      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: Snyder, Katherine A.; Ludi, Eva; Cullen, Beth; Tucker, Josephine; +2 Authors

    SUMMARYThis article discusses how decentralisation policies are enacted in the planning and implementation of natural resource management interventions in rural Ethiopia. A key element of decentralisation policy is the emphasis on greater participation by local communities. Drawing on qualitative research conducted with government staff and farmers, this paper illustrates how different actors perceive and implement national policy and how these actions affect the longer‐term sustainability of land management interventions. Copyright © 2014 John Wiley & Sons, Ltd.

    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/ CGIAR CGSpace (Consu...arrow_drop_down
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    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
    Public Administration and Development
    Article . 2014 . Peer-reviewed
    License: Wiley Online Library User Agreement
    Data sources: Crossref
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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/ CGIAR CGSpace (Consu...arrow_drop_down
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      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
      Public Administration and Development
      Article . 2014 . Peer-reviewed
      License: Wiley Online Library User Agreement
      Data sources: Crossref
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    Authors: Felipe Jeferson de Medeiros; Cristiano Prestrelo de Oliveira; Alvaro Avila-Diaz;

    Extreme events usually cause numerous economic and social losses, especially in vulnerable countries, such as Brazil. Understanding whether the evolution of Earth System Models (ESMs) from Coupled Model Intercomparison Project (CMIP) improves the representation of extreme events and investigating their future change is fundamental because device policies of adaptation and mitigation to climate change generally consider the results of the most recent generation of ESMs. This study analyzes the performance of a subset of 40 ESMs from CMIP3, CMIP5, and CMIP6 in simulating eight extreme precipitation climate indices over Brazil during 1981–2000 and also estimates their projected changes for the middle (2046–2065) and far future (2081–2 100) under the worst-case scenario for each CMIP generation. Results reveal that CDD are the most challenging precipitation index to be simulated, while the best ones were PRCPTOT and R20mm. The model performance shows that CMIP3 has the best skill for Northeast Brazil, CMIP5 for Center-West, and CMIP6 for North, Southeast and South regions. Thus, at least for Brazil, the evolution of the ESMs from CMIP did not reflect a substantial improvement in the representation of precipitation climate extremes over all Brazilian regions. In addition, all the models across CMIP generations have difficulty in simulating the observed trends. This indicate that improvements are still needed in CMIP models. Despite the relative low performance in the historical climate, the climate projections indicate a consensus signal among most of precipitation climate extremes and CMIP generations, which increase its reliability. Overall, the extreme precipitation events are projected to be more severe, frequent, and long-lasting in all Brazilian regions, with the more pronounce changes expected in heavy rainfall and severe droughts in the central northern portion of Brazil and in the southern sector.

    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/ Weather and Climate ...arrow_drop_down
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    Weather and Climate Extremes
    Article . 2022 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    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/
    Weather and Climate Extremes
    Article . 2022
    Data sources: DOAJ
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      Weather and Climate Extremes
      Article . 2022 . Peer-reviewed
      License: CC BY
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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/
      Weather and Climate Extremes
      Article . 2022
      Data sources: DOAJ
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    Authors: Héctor Morales-Muñoz; Arwen Bailey; Katharina Löhr; Giulia Caroli; +5 Authors

    Climate disasters affect human security and development, moreso in fragile and conflict-affected contexts where population’ capacities to cope with climate change are compromised. Responses to such crises lie at the nexus of humanitarian assistance, development, and peacebuilding. Yet, there are still too few integrated programmatic responses coordinating peacebuilding and climate actions to ensure a progressive human development. This research develops a multi-scalar model to help actors identify thematic areas to inform synergistic efforts and programs at different scales to better coordinate their actions. Findings suggest that climate action and peacebuilding sectors can coordinate actions around climate and conflict risk assessments, the management of land and water resources, ecosystem restoration, nature-based climate adaptation, climate and conflict smart agriculture, natural resources governance, and sustainable market development. These collaborative efforts have the potential to generate co-benefits, such as increased social cohesion and livelihood creation.

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    Authors: Jacobo Arango; Alejandro Ruden; Deissy Martinez-Baron; Deissy Martinez-Baron; +13 Authors

    La production animale est une source essentielle de revenus et d'émissions de gaz à effet de serre (GES) agricoles en Colombie, au Brésil, en Argentine, au Costa Rica, en Uruguay, au Mexique et au Pérou. Plusieurs options de gestion et technologiques, avec un potentiel d'atténuation du méthane entérique, ont été évaluées et leur mise à l'échelle devrait contribuer à la réalisation des objectifs de réduction des émissions de GES. Pourtant, l'adoption généralisée d'options d'atténuation prometteuses reste limitée, ce qui soulève des questions quant à savoir si les objectifs de réduction des émissions envisagés sont réalisables. À l'aide de données générées localement, nous explorons les potentiels d'atténuation des technologies et des pratiques de gestion actuellement proposées pour atténuer les émissions de méthane entérique, pour les systèmes de production bovine dans les pays d'Amérique latine les plus émetteurs. Nous discutons ensuite des obstacles à l'adoption d'innovations qui réduisent considérablement les émissions de méthane entérique d'origine bovine et des changements majeurs dans les politiques et les pratiques qui sont nécessaires pour relever les ambitions nationales dans les pays à forte émission. En utilisant la science la plus récente et la pensée actuelle, nous fournissons notre point de vue sur une approche inclusive et ré-imaginons comment les secteurs universitaire, de la recherche, des affaires et des politiques publiques peuvent soutenir et encourager les changements nécessaires pour élever le niveau d'ambition et atteindre les objectifs de développement durable en envisageant des actions allant de la ferme à l'échelle nationale. La producción ganadera es una fuente fundamental de ingresos y emisiones de gases de efecto invernadero (GEI) agrícolas en Colombia, Brasil, Argentina, Costa Rica, Uruguay, México y Perú. Se han evaluado varias opciones de gestión y tecnológicas, con potencial de mitigación de metano entérico, y se prevé que su escalado contribuya al logro de los objetivos de reducción de emisiones de GEI. Sin embargo, la adopción generalizada de opciones de mitigación prometedoras sigue siendo limitada, lo que plantea dudas sobre si los objetivos de reducción de emisiones previstos son alcanzables. Utilizando datos generados localmente, exploramos los potenciales de mitigación de las tecnologías y prácticas de manejo actualmente propuestas para mitigar las emisiones de metano entérico, para los sistemas de producción ganadera en los países de mayor emisión de América Latina. Luego discutimos las barreras para adoptar innovaciones que reduzcan significativamente las emisiones de metano entérico en el ganado y los cambios importantes en las políticas y prácticas que se necesitan para aumentar las ambiciones nacionales en los países con altas emisiones. Utilizando la ciencia más reciente y el pensamiento actual, brindamos nuestra perspectiva sobre un enfoque inclusivo y reimaginamos cómo los sectores académico, de investigación, empresarial y de políticas públicas pueden apoyar e incentivar los cambios necesarios para elevar el nivel de ambición y alcanzar los objetivos de desarrollo sostenible considerando acciones desde la granja hasta la escala nacional. Livestock production is a pivotal source of income and agricultural greenhouse gas (GHG) emissions in Colombia, Brazil, Argentina, Costa Rica, Uruguay, Mexico and Peru. Several management and technological options, with enteric methane mitigation potential, have been evaluated and their scaling is anticipated to contribute towards achieving GHG emission reduction targets. Yet, widespread adoption of promising mitigation options remains limited, raising questions as to whether envisaged emission reduction targets are achievable. Using locally generated data, we explore the mitigation potentials of technologies and management practices currently proposed to mitigate enteric methane emissions, for cattle production systems in the higher emitting countries of Latin America. We then discuss barriers for adopting innovations that significantly reduce cattle-based enteric methane emissions and the major shifts in policy and practice that are needed to raise national ambitions in the high emitting countries. Using the latest science and current thinking, we provide our perspective on an inclusive approach and re-imagine how the academic, research, business and public policy sectors can support and incentivize the changes needed to raise the level of ambition and achieve sustainable development goals considering actions all the way from the farm to the national scale. الإنتاج الحيواني هو مصدر محوري للدخل وانبعاثات غازات الدفيئة الزراعية في كولومبيا والبرازيل والأرجنتين وكوستاريكا وأوروغواي والمكسيك وبيرو. تم تقييم العديد من خيارات الإدارة والخيارات التكنولوجية، مع إمكانية تخفيف الميثان المعوي، ومن المتوقع أن يساهم قياسها في تحقيق أهداف خفض انبعاثات غازات الدفيئة. ومع ذلك، لا يزال الاعتماد الواسع النطاق لخيارات التخفيف الواعدة محدودًا، مما يثير تساؤلات حول ما إذا كانت أهداف خفض الانبعاثات المتوخاة قابلة للتحقيق. باستخدام البيانات التي تم إنشاؤها محليًا، نستكشف إمكانات التخفيف من التقنيات وممارسات الإدارة المقترحة حاليًا للتخفيف من انبعاثات الميثان المعوية، لأنظمة إنتاج الماشية في البلدان ذات الانبعاثات الأعلى في أمريكا اللاتينية. ثم نناقش العوائق التي تحول دون اعتماد الابتكارات التي تقلل بشكل كبير من انبعاثات الميثان المعوي القائم على الماشية والتحولات الرئيسية في السياسات والممارسات اللازمة لرفع الطموحات الوطنية في البلدان ذات الانبعاثات العالية. باستخدام أحدث العلوم والتفكير الحالي، نقدم وجهة نظرنا حول نهج شامل ونعيد تصور كيف يمكن للقطاعات الأكاديمية والبحثية وقطاع الأعمال والسياسة العامة دعم وتحفيز التغييرات اللازمة لرفع مستوى الطموح وتحقيق أهداف التنمية المستدامة مع الأخذ في الاعتبار الإجراءات على طول الطريق من المزرعة إلى النطاق الوطني.

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    Frontiers in Sustainable Food Systems
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      Frontiers in Sustainable Food Systems
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    Authors: Jiahui Zhang; Jiahui Zhang; Tingting Ren; Junjie Yang; +7 Authors

    Elements are important functional traits reflecting plant response to climate change. Multiple elements work jointly in plant physiology. Although a large number of studies have focused on the variation and allocation of multiple elements in plants, it remains unclear how these elements co-vary to adapt to environmental change. We proposed a novel concept of the multi-element network including the mutual effects between element concentrations to more effectively explore the alterations in response to long-term nitrogen (N) deposition. Leaf multi-element networks were constructed with 18 elements (i.e., six macronutrients, six micronutrients, and six trace elements) in this study. Multi-element networks were species-specific, being effectively discriminated irrespective of N deposition level. Different sensitive elements and interactions to N addition were found in different species, mainly concentrating on N, Ca, Mg, Mn, Li, Sr, Ba, and their related stoichiometry. Interestingly, high plasticity of multi-element network increased or maintained relative aboveground biomass (species dominance) in community under simulated N deposition, which developed the multi-element network hypothesis. In summary, multi-element networks provide a novel approach for exploring the adaptation strategies of plants and to better predict the change of species dominance under altering nutrient availability or environmental stress associated with future global climate change.

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    Frontiers in Plant Science
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    Frontiers in Plant Science
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    Frontiers in Plant Science
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      Frontiers in Plant Science
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    Authors: Walter, Richard K.; Hamilton, Richard J.;

    International environmental organizations have an increasing commitment to the development of conservation programs in high-diversity regions where indigenous communities maintain customary rights to their lands and seas. A major challenge that these programs face is the alignment of international conservation values with those of the indigenous communities whose cooperation and support are vital. International environmental organizations are focused on biodiversity conservation, but local communities often have a different range of concerns and interests, only some of which relate to biodiversity. One solution to this problem involves adoption of a cultural landscape approach as the ethical and organizational foundation of the conservation program. In our conservation work in coastal Melanesia, we have developed a cultural landscape approach that involves the construction of a conceptual model of environment that reflects the indigenous perceptions of landscape. This model incorporates cultural, ideational, and spiritual values alongside other ecosystem services and underpins the conservation activities, priorities, and organizational structure of our programs. This cultural landscape model was a reaction to a survey of environmental values conducted by our team in which Solomon Islanders reported far greater interest in conserving cultural heritage sites than any other ecosystem resources. This caused a radical rethinking of community-based conservation programs. The methodologies we adopted are derived from the fields of archaeology and historical anthropology, in which there is an established practice of working through research problems within the framework of indigenous concepts of, and relationship to, landscape. In our work in Isabel Province, Solomon Islands, coastal communities have enthusiastically adopted conservation programs that are based on cultural landscape models that recognize indigenous values. A particularly useful tool is the Cultural Heritage Module, which identifies cultural heritage sites that become targets of conservation management and that are used as part of a holistic framework for thinking about broader conservation values.

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    Ecology and Society
    Article . 2014 . Peer-reviewed
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    Ecology and Society
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    Authors: S. Bautista; M. Enjolras; P. Narvaez; M. Camargo; +1 Authors

    Sustainability assessment is a critical issue for the biodiesel production chain. The constant growth of the biodiesel industry has generated important sustainability concerns, such as competition with food production, indirect land use change, and impacts on water, biodiversity and social values. Government policies consider that quantitative, robust and independent sustainability assessment is vital to estimate the extent to which the biodiesel industry impacts sustainability issues. However, weaknesses in the definition of adequate indicators to measure the sustainability of biodiesel production have been identified. In this context, this work proposes a hierarchical assessment framework based on sustainability dimensions, principles, criteria and indicators. To do this, after a systematic literature review, a first version of the framework was proposed. Then, to define the final framework, a validation strategy based on expert survey consultations and a descriptive statistical analysis were developed. In order to define the principles and criteria importance for sustainability assessment of biodiesel, 62 experts answered an online survey assessing three attributes: relevance, ease of measurement and reliability. The first result of the validation analysis was the definition of a framework composed of five dimensions (social, economic, environmental, political and technological), 13 principles, and 30 criteria that would be part of a sustainability assessment of biodiesel production. The second result was the identification of potential groups and relationships between principles and criteria represented through data visualization techniques. The validated framework provides the basis for defining future studies about interdimensional principles and criteria. Also, the proposed sustainability assessment framework could be adapted and applied to biodiesel production in specific contexts.

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    Ecological Indicators
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    Authors: Mao, Zikun; Van Der Plas, Fons; Corrales, Adriana; Anderson-Teixeira, Kristina; +17 Authors

    * File name: README.md * Authors: Zikun Mao, Xugao Wang * Other contributors: Fons van der Plas, Adriana Corrales, Kristina J. Anderson-Teixeira, Norman A. Bourg, Chengjin Chu, Zhanqing Hao, Guangze Jin, Juyu Lian, Fei Lin, Buhang Li, Wenqi Luo, William J. McShea, Jonathan A. Myers, Guochun Shen, Xihua Wang, En-Rong Yan, Ji Ye, Wanhui Ye, Zuoqiang Yuan * Date created: 2022-11-20 * Date modified: 2024-05-13 ## Dataset Attribution and Usage * Dataset Title: "Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility" * Persistent Identifier: [https://doi.org/10.5061/dryad.612jm646w](https://doi.org/10.5061/dryad.612jm646w) * Dataset Contributors: * Creators: Zikun Mao, Fons van der Plas, Adriana Corrales, Kristina J. Anderson-Teixeira, Norman A. Bourg, Chengjin Chu, Zhanqing Hao, Guangze Jin, Juyu Lian, Fei Lin, Buhang Li, Wenqi Luo, William J. McShea, Jonathan A. Myers, Guochun Shen, Xihua Wang, En-Rong Yan, Ji Ye, Wanhui Ye, Zuoqiang Yuan, Xugao Wang * License: Use of these data is covered by the following license: * Title: CC0 1.0 Universal (CC0 1.0) * Specification: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/); the authors respectfully request to be contacted by researchers interested in the re-use of these data so that the possibility of collaboration can be discussed. * Suggested Citations: * Dataset citation: > Mao, Z., F. van der Plas, A. Corrales, K. J. Anderson-Teixeira, N. A. Bourg, C. Chu, Z. Hao, G. Jin, J. Lian, F. Lin, et al. 2023. Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility. Dryad, Dataset, [https://doi.org/10.5061/dryad.612jm646w](https://doi.org/10.5061/dryad.612jm646w) * Corresponding publication: > Mao, Z., F. van der Plas, A. Corrales, K. J. Anderson-Teixeira, N. A. Bourg, C. Chu, Z. Hao, G. Jin, J. Lian, F. Lin, et al. 2023. Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility. Ecological Monographs, 93: e1568 ## Contact Information * Name: Zikun Mao * Affiliations: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China * ORCID ID: [https://orcid.org/0000-0002-7035-9129](https://orcid.org/0000-0002-7035-9129) * Email: [maozikun@iae.ac.cn](mailto:maozikun@iae.ac.cn) * Alternate Email: [maozikun15@mails.ucas.ac.cn](mailto:maozikun15@mails.ucas.ac.cn) * Alternate Email 2: [maozikun15@126.com](mailto:maozikun15@126.com) * Alternative Contact Name: Xugao Wang * Affiliations: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China * ORCID ID: [https://orcid.org/0000-0003-1207-8852](https://orcid.org/0000-0003-1207-8852) * Email: [wangxg@iae.ac.cn](mailto:wangxg@iae.ac.cn) --- # Additional Dataset Metadata ## Acknowledgements * Funding sources: This work was financially supported by the National Natural Science Foundation of China (Grant 31961133027), the National Key Research and Development Program of China (2022YFF1300501), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant ZDBS-LY-DQC019), the K. C. Wong Education Foundation, the General Program of China Postdoctoral Science Foundation (2021M703397), the Special Research Assistant Project of Chinese Academy of Sciences (2022000056), and the Major Program of Institute of Applied Ecology, Chinese Academy of Science (IAEMP202201). Chengjin Chu was funded by the National Natural Science Foundation of China (31925027). Funding for the data collections was provided by many organizations, including the Smithsonian Institution, the National Science Foundation (DEB 1557094), the National Zoological Park, the HSBC Climate Partnership, the International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis and the Tyson Research Center # Methodological Information * Methods of data collection/generation: see manuscript for details --- # Data and File Overview ## Summary Metrics * File count: 6 * Total file size: 42.4 MB * Range of individual file sizes: 12.3 KB - 41.5 MB * File formats: .RData, .R, .xlsx ## Table of Contents * 1\. Data source to run the R code.RData * 2\. Codispersion null model analysis.R * 3\. Generalized least squares model analysis.R * 4\. Structural equation modeling analysis.R * Observed data source.xlsx * Mycorrhizal types.xlsx Note: * These datasets contain the data for seven forest mega-plots, i.e., FL: Fenglin; TRC: Tyson Research Center; CBS: Changbaishan; SCBI: Smithsonian Conservation Biology Institute; TTS: Tiantongshan; DHS: Dinghushan; HSD: Heishiding * The authors respectfully request to be contacted by researchers interested in the datasets of other three scales (i.e., 10-m, 50-m, and 100-m) so that the possibility of collaboration can be discussed ## Setup * Recommended software/tools: R version 3.6.3 ([https://www.r-project.org/](https://www.r-project.org/)) for .RData and .R files; Microsoft Office EXCEL 2013 for .xlsx files --- * Relationship between data files * To run the R codes in the three .R files, you need to first open the R software and then load the R workspace "1. Data source to run the R code.RData" * The .xlsx file "Observed data source.xlsx" contains all the observed datasets in the .RData file "1. Data source to run the R code.RData" --- # File/Folder Details ## Details for: 1. Data source to run the R code.RData * General description: a .RData file containing the observed datasets and null model datasets at the 20-m scale to run the three analyses, i.e., codispersion null model analysis (codes in "2. Codispersion null model analysis.R"), generalized least squares model analysis ("3. Generalized least squares model analysis.R"), and structural equation modeling analysis ("4. Structural equation modeling analysis.R") * Format(s): .RData * Size(s): 41.5 MB * Contains: 14 datasets * Description for the 14 datasets: * Running "ls()" in the R software to see the names of these 14 datasets * The names of these 14 datasets are: "FL", "FL_Null_20", "TRC", "TRC_Null_20", "CBS", "CBS_Null_20", "SCBI", "SCBI_Null_20", "DHS", "DHS_Null_20", "TTS", "TTS_Null_20", "HSD", "HSD_Null_20" * FL: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for FL plot * FL_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for FL plot * TRC: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for TRC plot * TRC_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for TRC plot * CBS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for CBS plot * CBS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for CBS plot * SCBI: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for SCBI plot * SCBI_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for SCBI plot * DHS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for DHS plot * DHS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for DHS plot * TTS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for TTS plot * TTS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for TTS plot * HSD: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for HSD plot * HSD_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for HSD plot * Variables in these datasets: * Quad.num: The serial number of 20m * 20m quadrats * gx, gy: The coordinate of each 20m × 20m quadrat (m) * AGB.all: Aboveground biomass (AGB) of all trees in one quadrat (Mg/ha) * AGB.AM: AGB of AM (i.e., arbuscular mycorrhizal) trees in one quadrat (Mg/ha) * AGB.EM: AGB of EM (i.e., ectomycorrhizal) trees in one quadrat (Mg/ha) * SpNum.all: Tree species richness or number of tree species with > 1 individuals in one quadrat * SpNum.AM: AM tree species richness or number of AM tree species with > 1 individuals in one quadrat * SpNum.EM: EM tree species richness or number of EM tree species with > 1 individuals in one quadrat * Num.all: The number of tree individuals in one quadrat * Num.AM: The number of AM tree individuals in one quadrat * Num.EM: The number of EM tree individuals in one quadrat * AMdomi: AM tree dominance in one quadrat quantified using the proportion of AM tree individuals * EMdomi: EM tree dominance in one quadrat quantified using the proportion of EM tree AGB * Soil.PC1: Soil fertility index from the first principal component of the principal component analysis (only for observed datasets) * Soil.PC2: Soil fertility index from the second principal component of the principal component analysis (only for observed datasets) * Soil: Soil fertility index from the first principal component (for FL, TRC, CBS, SCBI, DHS plots) or the second principal component (for TTS and HSD plots) of the principal component analysis (only for null model datasets) ## Details for: 2. Codispersion null model analysis.R * Description: a .R file containing all codes to conduct our codispersion null model analyses (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 80 KB * Note: * Please open this file using R software * All necessary explanations for the "codispersion null model analysis" code can be found in the text after the "#" label in this .R file * Very important note: anyone who want to use this code to run the codispersion analysis, please cite the Buckley's paper in 2016 ([https://doi.org/10.1111/nph.13934](https://doi.org/10.1111/nph.13934)). ## Details for: 3. Generalized least squares model analysis.R * Description: a .R file containing all codes to conduct our generalized least squares model analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 12.3 KB * Note: * Please open this file using R software * All necessary explanations for the "generalized least squares model analysis" code can be found in the text after the "#" label in this .R file ## Details for: 4. Structural equation modeling analysis.R * Description: a .R file containing all codes to conduct our structural equation modeling analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 41.0 KB * Note: * Please open this file using R software * All necessary explanations for the "structural equation modeling analysis" code can be found in the text after the "#" label in this .R file ## Details for: Observed data source.xlsx * Description: a .xlsx file containing all the observed datasets of each 20m * 20m quadrats for the seven forests * Format(s): .xlsx * Size(s): 657 KB * Contents: 9 sheets * Description for each sheet: * Article information: listing the the article title, authors, and journal name * Column name: listing and explaining each column name in this dataset * Fenglin: the observed dataset containing 16 columns for FL plot * TRC: the observed dataset containing 16 columns for TRC plot * Changbaishan: the observed dataset containing 16 columns for CBS plot * SCBI: the observed dataset containing 16 columns for SCBI plot * Dinghushan: the observed dataset containing 16 columns for DHS plot * Tiantongshan: the observed dataset containing 16 columns for TTS plot * Heishiding: the observed dataset containing 16 columns for HSD plot * Note: please see the sheet "Column name" in this .xlsx file for the explanation of each column ## Details for: Mycorrhizal types.xlsx * Description: a .xlsx file showing the mycorrhizal type and the referred literature of each tree species * Format(s): .xlsx * Size(s): 70.9 KB * Contents: 10 sheets * Description for each sheet: * Article information: listing the the article title, authors, journal name, and abbreviation of mycorrhizal association * References: listing all the references (in total 49 items) used to classify the mycorrhizal type of studied species * Mycorrhizal associations: listing the basic information (including Family, Genera, and Species name), mycorrhizal classification, and the referred literatures for each tree species Column "Family": The Family name of each species Column "Genera": The Genera name of each species Column "Species": The Species name of each species Column "Mycorrhizal_type": Mycorrhizal types of each species to conduct our primary analyses, but for the species in red font, their mycorrhizal type was reassigned in the robustness test (see the note in the brackets for details) Column "Mycorrhizal_type_detailed": more detailed mycorrhizal types for each tree species Column "Reference and Note": referred literature and the detailed notes for each tree species * Fenglin: the mycorrhizal type and the referred literature of each tree species in FL plot * TRC: the mycorrhizal type and the referred literature of each tree species in TRC plot * Changbaishan: the mycorrhizal type and the referred literature of each tree species in CBS plot * SCBI: the mycorrhizal type and the referred literature of each tree species in SCBI plot * Dinghushan: the mycorrhizal type and the referred literature of each tree species in DHS plot * Tiantongshan: the mycorrhizal type and the referred literature of each tree species in TTS plot * Heishiding: the mycorrhizal type and the referred literature of each tree species in HSD plot * Access Information --- * To generate these datasets, we used the raw census and soil data of the ForestGEO network that can only be shared on request because most PIs have not made them publicly available. Forest census data from the ForestGEO data portal can be obtained by filling out the online Data RequestForm ([http://ctfs.si.edu/datarequest/index.php/main/plotdata](http://ctfs.si.edu/datarequest/index.php/main/plotdata)). Soil data are available to qualified researchers from ForestGEO network by contacting the mega-plot PIs ([https://forestgeo.si.edu/meet-team/principal-investigators](https://forestgeo.si.edu/meet-team/principal-investigators)). --- END OF README Diversity–biomass relationships (DBRs) often vary with spatial scale in terrestrial ecosystems, but the mechanisms driving these scale-dependent patterns remain unclear, especially for highly heterogeneous forest ecosystems. This study explores how mutualistic associations between trees and different mycorrhizal fungi (i.e., arbuscular mycorrhizal (AM) vs. ectomycorrhizal (EM) association) modulate scale-dependent DBRs. We hypothesized that in soil-heterogeneous forests with a mixture of AM and EM tree species, (i) AM and EM tree species respond in contrasting ways (i.e., positively vs. negatively respectively) to increasing soil fertility, (ii) AM tree dominance contributes to higher tree diversity and EM tree dominance contributes to greater standing biomass and that as a result, (iii) mycorrhizal associations exert an overall negative effect on DBRs across spatial scales. To empirically test these hypotheses, we collected detailed tree distribution and soil information (nitrogen, phosphorus, organic matter, pH, etc.) from seven temperate and subtropical AM-EM mixed forest mega-plots (16–50 ha). Using spatial codispersion null model and structural equation modeling, we identified the relationships among AM or EM tree dominance, soil fertility, tree species diversity and biomass, and thus DBRs across 0.01–1 ha scales. We found first evidence overall supporting the above three hypotheses in these AM-EM mixed forests: (i) In most forests, with increasing soil fertility tree communities changed from EM-dominated to AM-dominated. (ii) Increasing AM tree dominance had an overall positive effect on tree diversity and a negative effect on biomass, even after controlling for soil fertility and number of trees. Together, (iii) the changes in mycorrhizal dominance along soil fertility gradients weakened the positive DBR observed at 0.01–0.04 ha scales in nearly all forests and drove negative DBRs at 0.25–1 ha scales in four out of seven forests. Hence, this study highlights a soil-related mycorrhizal dominance mechanism that could partly explain why in many natural forests, biodiversity-ecosystem functioning (BEF) relationships shift from positive to negative with increasing spatial scale. See the "Materials and Methods" section in the manuscript for details.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    Research@WUR
    Dataset . 2022
    Data sources: Research@WUR
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    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      Research@WUR
      Dataset . 2022
      Data sources: Research@WUR
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      DRYAD
      Dataset . 2023
      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: Tatiana Agudelo Patiño; Jhonny Alejandro Poveda-Giraldo; Manuel Haminton Salas Moreno; Gysela Rengifo Mosquera; +1 Authors

    Tropical forests are a source of several high-value products that provide livelihood to small communities in different regions. Exotic fruits such as annatto are feedstock sources containing bioactive compounds with important applications in the food industry. Nevertheless, the integral use of annatto for community improvement and the crop’s contribution to carbon sequestration in tropical forests have not been analyzed. This paper aims to demonstrate the economic and environmental performance of small-scale alternatives to obtain natural colorants using annatto seed. The extraction of natural colorants (bixin and norbixin) was analyzed using ethanol and NaOH as solvents. The experimental results were used to simulate two scenarios. Scenario one involved bixin production, and scenario two comprised bixin and norbixin production. The economic and environmental assessments were performed considering the life cycle assessment (LCA) methodology based on a Colombian context. The best extraction yield was 72.65 mg g−1 for bixin and 193.82 mg g−1 for norbixin. From a simulation perspective, scenario two showed the best economic performance since a payback period of 3.1 years was obtained. The LCA showed a high CO2 sequestration potential (6.5 kg CO2 eq kg−1 seed) of the annatto crop. Moreover, the solvents used during the colorant extraction proved to be the most environmentally representative. Nevertheless, the CO2 sequestration of the crop continues to exceed the emissions generated by the process. This work demonstrates that the annatto is an alternative for small communities to reach equilibrium between the economic and environmental of the tropical forest.

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    Sustainability
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Sustainability
    Article . 2023
    Data sources: DOAJ
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      Sustainability
      Article . 2023 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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      Article . 2023
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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: Hermann Behling; John Carson; Bronwen S. Whitney; William D. Gosling; +12 Authors

    Abstract. An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to an absence of information on the spatial and temporal coverage of study sites. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last 2 millennia. We identify 60 vegetation (pollen) records from across South America which satisfy geochronological requirements set out for climate modelling, and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local-scale responses to climate modes; thus, it is necessary to understand how vegetation–climate interactions might diverge under variable settings. We provide a qualitative translation from pollen metrics to climate variables. Additionally, pollen is an excellent indicator of human impact through time. We discuss evidence for human land use in pollen records and provide an overview considered useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. This manuscript forms part of the wider LOng-Term multi-proxy climate REconstructions and Dynamics in South America – 2k initiative that provides the ideal framework for the integration of the various palaeoclimatic subdisciplines and palaeo-science, thereby jump-starting and fostering multidisciplinary research into environmental change on centennial and millennial timescales.

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    CORE
    Article . 2016
    License: CC BY
    Data sources: CORE
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    CORE
    Article . 2016
    License: CC BY
    Data sources: CORE
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    CORE
    Article . 2016
    License: CC BY
    Data sources: CORE
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    Climate of the Past (CP)
    Article . 2016 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Climate of the Past (CP)
    Article
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    Data sources: UnpayWall
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    Climate of the Past
    Other literature type . 2018
    Data sources: Copernicus
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    Climate of the Past
    Article . 2016
    Data sources: DOAJ
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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/
    https://dx.doi.org/10.60692/ph...
    Other literature type . 2016
    Data sources: Datacite
    https://dx.doi.org/10.60692/0y...
    Other literature type . 2016
    Data sources: Datacite
    Digital.CSIC
    Article . 2016 . Peer-reviewed
    Data sources: Digital.CSIC
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    Access Routes
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      CORE
      Article . 2016
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      CORE
      Article . 2016
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      Article . 2016
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      Climate of the Past (CP)
      Article . 2016 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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      Climate of the Past (CP)
      Article
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      Data sources: UnpayWall
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      Climate of the Past
      Other literature type . 2018
      Data sources: Copernicus
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      Climate of the Past
      Article . 2016
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      https://dx.doi.org/10.60692/ph...
      Other literature type . 2016
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      https://dx.doi.org/10.60692/0y...
      Other literature type . 2016
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      Digital.CSIC
      Article . 2016 . Peer-reviewed
      Data sources: Digital.CSIC
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    Authors: Snyder, Katherine A.; Ludi, Eva; Cullen, Beth; Tucker, Josephine; +2 Authors

    SUMMARYThis article discusses how decentralisation policies are enacted in the planning and implementation of natural resource management interventions in rural Ethiopia. A key element of decentralisation policy is the emphasis on greater participation by local communities. Drawing on qualitative research conducted with government staff and farmers, this paper illustrates how different actors perceive and implement national policy and how these actions affect the longer‐term sustainability of land management interventions. Copyright © 2014 John Wiley & Sons, Ltd.

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    Public Administration and Development
    Article . 2014 . Peer-reviewed
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      Public Administration and Development
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    Authors: Felipe Jeferson de Medeiros; Cristiano Prestrelo de Oliveira; Alvaro Avila-Diaz;

    Extreme events usually cause numerous economic and social losses, especially in vulnerable countries, such as Brazil. Understanding whether the evolution of Earth System Models (ESMs) from Coupled Model Intercomparison Project (CMIP) improves the representation of extreme events and investigating their future change is fundamental because device policies of adaptation and mitigation to climate change generally consider the results of the most recent generation of ESMs. This study analyzes the performance of a subset of 40 ESMs from CMIP3, CMIP5, and CMIP6 in simulating eight extreme precipitation climate indices over Brazil during 1981–2000 and also estimates their projected changes for the middle (2046–2065) and far future (2081–2 100) under the worst-case scenario for each CMIP generation. Results reveal that CDD are the most challenging precipitation index to be simulated, while the best ones were PRCPTOT and R20mm. The model performance shows that CMIP3 has the best skill for Northeast Brazil, CMIP5 for Center-West, and CMIP6 for North, Southeast and South regions. Thus, at least for Brazil, the evolution of the ESMs from CMIP did not reflect a substantial improvement in the representation of precipitation climate extremes over all Brazilian regions. In addition, all the models across CMIP generations have difficulty in simulating the observed trends. This indicate that improvements are still needed in CMIP models. Despite the relative low performance in the historical climate, the climate projections indicate a consensus signal among most of precipitation climate extremes and CMIP generations, which increase its reliability. Overall, the extreme precipitation events are projected to be more severe, frequent, and long-lasting in all Brazilian regions, with the more pronounce changes expected in heavy rainfall and severe droughts in the central northern portion of Brazil and in the southern sector.

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    Weather and Climate Extremes
    Article . 2022 . Peer-reviewed
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    Weather and Climate Extremes
    Article . 2022
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    Authors: Héctor Morales-Muñoz; Arwen Bailey; Katharina Löhr; Giulia Caroli; +5 Authors

    Climate disasters affect human security and development, moreso in fragile and conflict-affected contexts where population’ capacities to cope with climate change are compromised. Responses to such crises lie at the nexus of humanitarian assistance, development, and peacebuilding. Yet, there are still too few integrated programmatic responses coordinating peacebuilding and climate actions to ensure a progressive human development. This research develops a multi-scalar model to help actors identify thematic areas to inform synergistic efforts and programs at different scales to better coordinate their actions. Findings suggest that climate action and peacebuilding sectors can coordinate actions around climate and conflict risk assessments, the management of land and water resources, ecosystem restoration, nature-based climate adaptation, climate and conflict smart agriculture, natural resources governance, and sustainable market development. These collaborative efforts have the potential to generate co-benefits, such as increased social cohesion and livelihood creation.

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    Authors: Jacobo Arango; Alejandro Ruden; Deissy Martinez-Baron; Deissy Martinez-Baron; +13 Authors

    La production animale est une source essentielle de revenus et d'émissions de gaz à effet de serre (GES) agricoles en Colombie, au Brésil, en Argentine, au Costa Rica, en Uruguay, au Mexique et au Pérou. Plusieurs options de gestion et technologiques, avec un potentiel d'atténuation du méthane entérique, ont été évaluées et leur mise à l'échelle devrait contribuer à la réalisation des objectifs de réduction des émissions de GES. Pourtant, l'adoption généralisée d'options d'atténuation prometteuses reste limitée, ce qui soulève des questions quant à savoir si les objectifs de réduction des émissions envisagés sont réalisables. À l'aide de données générées localement, nous explorons les potentiels d'atténuation des technologies et des pratiques de gestion actuellement proposées pour atténuer les émissions de méthane entérique, pour les systèmes de production bovine dans les pays d'Amérique latine les plus émetteurs. Nous discutons ensuite des obstacles à l'adoption d'innovations qui réduisent considérablement les émissions de méthane entérique d'origine bovine et des changements majeurs dans les politiques et les pratiques qui sont nécessaires pour relever les ambitions nationales dans les pays à forte émission. En utilisant la science la plus récente et la pensée actuelle, nous fournissons notre point de vue sur une approche inclusive et ré-imaginons comment les secteurs universitaire, de la recherche, des affaires et des politiques publiques peuvent soutenir et encourager les changements nécessaires pour élever le niveau d'ambition et atteindre les objectifs de développement durable en envisageant des actions allant de la ferme à l'échelle nationale. La producción ganadera es una fuente fundamental de ingresos y emisiones de gases de efecto invernadero (GEI) agrícolas en Colombia, Brasil, Argentina, Costa Rica, Uruguay, México y Perú. Se han evaluado varias opciones de gestión y tecnológicas, con potencial de mitigación de metano entérico, y se prevé que su escalado contribuya al logro de los objetivos de reducción de emisiones de GEI. Sin embargo, la adopción generalizada de opciones de mitigación prometedoras sigue siendo limitada, lo que plantea dudas sobre si los objetivos de reducción de emisiones previstos son alcanzables. Utilizando datos generados localmente, exploramos los potenciales de mitigación de las tecnologías y prácticas de manejo actualmente propuestas para mitigar las emisiones de metano entérico, para los sistemas de producción ganadera en los países de mayor emisión de América Latina. Luego discutimos las barreras para adoptar innovaciones que reduzcan significativamente las emisiones de metano entérico en el ganado y los cambios importantes en las políticas y prácticas que se necesitan para aumentar las ambiciones nacionales en los países con altas emisiones. Utilizando la ciencia más reciente y el pensamiento actual, brindamos nuestra perspectiva sobre un enfoque inclusivo y reimaginamos cómo los sectores académico, de investigación, empresarial y de políticas públicas pueden apoyar e incentivar los cambios necesarios para elevar el nivel de ambición y alcanzar los objetivos de desarrollo sostenible considerando acciones desde la granja hasta la escala nacional. Livestock production is a pivotal source of income and agricultural greenhouse gas (GHG) emissions in Colombia, Brazil, Argentina, Costa Rica, Uruguay, Mexico and Peru. Several management and technological options, with enteric methane mitigation potential, have been evaluated and their scaling is anticipated to contribute towards achieving GHG emission reduction targets. Yet, widespread adoption of promising mitigation options remains limited, raising questions as to whether envisaged emission reduction targets are achievable. Using locally generated data, we explore the mitigation potentials of technologies and management practices currently proposed to mitigate enteric methane emissions, for cattle production systems in the higher emitting countries of Latin America. We then discuss barriers for adopting innovations that significantly reduce cattle-based enteric methane emissions and the major shifts in policy and practice that are needed to raise national ambitions in the high emitting countries. Using the latest science and current thinking, we provide our perspective on an inclusive approach and re-imagine how the academic, research, business and public policy sectors can support and incentivize the changes needed to raise the level of ambition and achieve sustainable development goals considering actions all the way from the farm to the national scale. الإنتاج الحيواني هو مصدر محوري للدخل وانبعاثات غازات الدفيئة الزراعية في كولومبيا والبرازيل والأرجنتين وكوستاريكا وأوروغواي والمكسيك وبيرو. تم تقييم العديد من خيارات الإدارة والخيارات التكنولوجية، مع إمكانية تخفيف الميثان المعوي، ومن المتوقع أن يساهم قياسها في تحقيق أهداف خفض انبعاثات غازات الدفيئة. ومع ذلك، لا يزال الاعتماد الواسع النطاق لخيارات التخفيف الواعدة محدودًا، مما يثير تساؤلات حول ما إذا كانت أهداف خفض الانبعاثات المتوخاة قابلة للتحقيق. باستخدام البيانات التي تم إنشاؤها محليًا، نستكشف إمكانات التخفيف من التقنيات وممارسات الإدارة المقترحة حاليًا للتخفيف من انبعاثات الميثان المعوية، لأنظمة إنتاج الماشية في البلدان ذات الانبعاثات الأعلى في أمريكا اللاتينية. ثم نناقش العوائق التي تحول دون اعتماد الابتكارات التي تقلل بشكل كبير من انبعاثات الميثان المعوي القائم على الماشية والتحولات الرئيسية في السياسات والممارسات اللازمة لرفع الطموحات الوطنية في البلدان ذات الانبعاثات العالية. باستخدام أحدث العلوم والتفكير الحالي، نقدم وجهة نظرنا حول نهج شامل ونعيد تصور كيف يمكن للقطاعات الأكاديمية والبحثية وقطاع الأعمال والسياسة العامة دعم وتحفيز التغييرات اللازمة لرفع مستوى الطموح وتحقيق أهداف التنمية المستدامة مع الأخذ في الاعتبار الإجراءات على طول الطريق من المزرعة إلى النطاق الوطني.

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    Frontiers in Sustainable Food Systems
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    Authors: Jiahui Zhang; Jiahui Zhang; Tingting Ren; Junjie Yang; +7 Authors

    Elements are important functional traits reflecting plant response to climate change. Multiple elements work jointly in plant physiology. Although a large number of studies have focused on the variation and allocation of multiple elements in plants, it remains unclear how these elements co-vary to adapt to environmental change. We proposed a novel concept of the multi-element network including the mutual effects between element concentrations to more effectively explore the alterations in response to long-term nitrogen (N) deposition. Leaf multi-element networks were constructed with 18 elements (i.e., six macronutrients, six micronutrients, and six trace elements) in this study. Multi-element networks were species-specific, being effectively discriminated irrespective of N deposition level. Different sensitive elements and interactions to N addition were found in different species, mainly concentrating on N, Ca, Mg, Mn, Li, Sr, Ba, and their related stoichiometry. Interestingly, high plasticity of multi-element network increased or maintained relative aboveground biomass (species dominance) in community under simulated N deposition, which developed the multi-element network hypothesis. In summary, multi-element networks provide a novel approach for exploring the adaptation strategies of plants and to better predict the change of species dominance under altering nutrient availability or environmental stress associated with future global climate change.

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    Frontiers in Plant Science
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    Authors: Walter, Richard K.; Hamilton, Richard J.;

    International environmental organizations have an increasing commitment to the development of conservation programs in high-diversity regions where indigenous communities maintain customary rights to their lands and seas. A major challenge that these programs face is the alignment of international conservation values with those of the indigenous communities whose cooperation and support are vital. International environmental organizations are focused on biodiversity conservation, but local communities often have a different range of concerns and interests, only some of which relate to biodiversity. One solution to this problem involves adoption of a cultural landscape approach as the ethical and organizational foundation of the conservation program. In our conservation work in coastal Melanesia, we have developed a cultural landscape approach that involves the construction of a conceptual model of environment that reflects the indigenous perceptions of landscape. This model incorporates cultural, ideational, and spiritual values alongside other ecosystem services and underpins the conservation activities, priorities, and organizational structure of our programs. This cultural landscape model was a reaction to a survey of environmental values conducted by our team in which Solomon Islanders reported far greater interest in conserving cultural heritage sites than any other ecosystem resources. This caused a radical rethinking of community-based conservation programs. The methodologies we adopted are derived from the fields of archaeology and historical anthropology, in which there is an established practice of working through research problems within the framework of indigenous concepts of, and relationship to, landscape. In our work in Isabel Province, Solomon Islands, coastal communities have enthusiastically adopted conservation programs that are based on cultural landscape models that recognize indigenous values. A particularly useful tool is the Cultural Heritage Module, which identifies cultural heritage sites that become targets of conservation management and that are used as part of a holistic framework for thinking about broader conservation values.

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    Ecology and Society
    Article . 2014 . Peer-reviewed
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    Authors: S. Bautista; M. Enjolras; P. Narvaez; M. Camargo; +1 Authors

    Sustainability assessment is a critical issue for the biodiesel production chain. The constant growth of the biodiesel industry has generated important sustainability concerns, such as competition with food production, indirect land use change, and impacts on water, biodiversity and social values. Government policies consider that quantitative, robust and independent sustainability assessment is vital to estimate the extent to which the biodiesel industry impacts sustainability issues. However, weaknesses in the definition of adequate indicators to measure the sustainability of biodiesel production have been identified. In this context, this work proposes a hierarchical assessment framework based on sustainability dimensions, principles, criteria and indicators. To do this, after a systematic literature review, a first version of the framework was proposed. Then, to define the final framework, a validation strategy based on expert survey consultations and a descriptive statistical analysis were developed. In order to define the principles and criteria importance for sustainability assessment of biodiesel, 62 experts answered an online survey assessing three attributes: relevance, ease of measurement and reliability. The first result of the validation analysis was the definition of a framework composed of five dimensions (social, economic, environmental, political and technological), 13 principles, and 30 criteria that would be part of a sustainability assessment of biodiesel production. The second result was the identification of potential groups and relationships between principles and criteria represented through data visualization techniques. The validated framework provides the basis for defining future studies about interdimensional principles and criteria. Also, the proposed sustainability assessment framework could be adapted and applied to biodiesel production in specific contexts.

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    Ecological Indicators
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    Authors: Mao, Zikun; Van Der Plas, Fons; Corrales, Adriana; Anderson-Teixeira, Kristina; +17 Authors

    * File name: README.md * Authors: Zikun Mao, Xugao Wang * Other contributors: Fons van der Plas, Adriana Corrales, Kristina J. Anderson-Teixeira, Norman A. Bourg, Chengjin Chu, Zhanqing Hao, Guangze Jin, Juyu Lian, Fei Lin, Buhang Li, Wenqi Luo, William J. McShea, Jonathan A. Myers, Guochun Shen, Xihua Wang, En-Rong Yan, Ji Ye, Wanhui Ye, Zuoqiang Yuan * Date created: 2022-11-20 * Date modified: 2024-05-13 ## Dataset Attribution and Usage * Dataset Title: "Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility" * Persistent Identifier: [https://doi.org/10.5061/dryad.612jm646w](https://doi.org/10.5061/dryad.612jm646w) * Dataset Contributors: * Creators: Zikun Mao, Fons van der Plas, Adriana Corrales, Kristina J. Anderson-Teixeira, Norman A. Bourg, Chengjin Chu, Zhanqing Hao, Guangze Jin, Juyu Lian, Fei Lin, Buhang Li, Wenqi Luo, William J. McShea, Jonathan A. Myers, Guochun Shen, Xihua Wang, En-Rong Yan, Ji Ye, Wanhui Ye, Zuoqiang Yuan, Xugao Wang * License: Use of these data is covered by the following license: * Title: CC0 1.0 Universal (CC0 1.0) * Specification: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/); the authors respectfully request to be contacted by researchers interested in the re-use of these data so that the possibility of collaboration can be discussed. * Suggested Citations: * Dataset citation: > Mao, Z., F. van der Plas, A. Corrales, K. J. Anderson-Teixeira, N. A. Bourg, C. Chu, Z. Hao, G. Jin, J. Lian, F. Lin, et al. 2023. Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility. Dryad, Dataset, [https://doi.org/10.5061/dryad.612jm646w](https://doi.org/10.5061/dryad.612jm646w) * Corresponding publication: > Mao, Z., F. van der Plas, A. Corrales, K. J. Anderson-Teixeira, N. A. Bourg, C. Chu, Z. Hao, G. Jin, J. Lian, F. Lin, et al. 2023. Scale-dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility. Ecological Monographs, 93: e1568 ## Contact Information * Name: Zikun Mao * Affiliations: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China * ORCID ID: [https://orcid.org/0000-0002-7035-9129](https://orcid.org/0000-0002-7035-9129) * Email: [maozikun@iae.ac.cn](mailto:maozikun@iae.ac.cn) * Alternate Email: [maozikun15@mails.ucas.ac.cn](mailto:maozikun15@mails.ucas.ac.cn) * Alternate Email 2: [maozikun15@126.com](mailto:maozikun15@126.com) * Alternative Contact Name: Xugao Wang * Affiliations: CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China * ORCID ID: [https://orcid.org/0000-0003-1207-8852](https://orcid.org/0000-0003-1207-8852) * Email: [wangxg@iae.ac.cn](mailto:wangxg@iae.ac.cn) --- # Additional Dataset Metadata ## Acknowledgements * Funding sources: This work was financially supported by the National Natural Science Foundation of China (Grant 31961133027), the National Key Research and Development Program of China (2022YFF1300501), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant ZDBS-LY-DQC019), the K. C. Wong Education Foundation, the General Program of China Postdoctoral Science Foundation (2021M703397), the Special Research Assistant Project of Chinese Academy of Sciences (2022000056), and the Major Program of Institute of Applied Ecology, Chinese Academy of Science (IAEMP202201). Chengjin Chu was funded by the National Natural Science Foundation of China (31925027). Funding for the data collections was provided by many organizations, including the Smithsonian Institution, the National Science Foundation (DEB 1557094), the National Zoological Park, the HSBC Climate Partnership, the International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis and the Tyson Research Center # Methodological Information * Methods of data collection/generation: see manuscript for details --- # Data and File Overview ## Summary Metrics * File count: 6 * Total file size: 42.4 MB * Range of individual file sizes: 12.3 KB - 41.5 MB * File formats: .RData, .R, .xlsx ## Table of Contents * 1\. Data source to run the R code.RData * 2\. Codispersion null model analysis.R * 3\. Generalized least squares model analysis.R * 4\. Structural equation modeling analysis.R * Observed data source.xlsx * Mycorrhizal types.xlsx Note: * These datasets contain the data for seven forest mega-plots, i.e., FL: Fenglin; TRC: Tyson Research Center; CBS: Changbaishan; SCBI: Smithsonian Conservation Biology Institute; TTS: Tiantongshan; DHS: Dinghushan; HSD: Heishiding * The authors respectfully request to be contacted by researchers interested in the datasets of other three scales (i.e., 10-m, 50-m, and 100-m) so that the possibility of collaboration can be discussed ## Setup * Recommended software/tools: R version 3.6.3 ([https://www.r-project.org/](https://www.r-project.org/)) for .RData and .R files; Microsoft Office EXCEL 2013 for .xlsx files --- * Relationship between data files * To run the R codes in the three .R files, you need to first open the R software and then load the R workspace "1. Data source to run the R code.RData" * The .xlsx file "Observed data source.xlsx" contains all the observed datasets in the .RData file "1. Data source to run the R code.RData" --- # File/Folder Details ## Details for: 1. Data source to run the R code.RData * General description: a .RData file containing the observed datasets and null model datasets at the 20-m scale to run the three analyses, i.e., codispersion null model analysis (codes in "2. Codispersion null model analysis.R"), generalized least squares model analysis ("3. Generalized least squares model analysis.R"), and structural equation modeling analysis ("4. Structural equation modeling analysis.R") * Format(s): .RData * Size(s): 41.5 MB * Contains: 14 datasets * Description for the 14 datasets: * Running "ls()" in the R software to see the names of these 14 datasets * The names of these 14 datasets are: "FL", "FL_Null_20", "TRC", "TRC_Null_20", "CBS", "CBS_Null_20", "SCBI", "SCBI_Null_20", "DHS", "DHS_Null_20", "TTS", "TTS_Null_20", "HSD", "HSD_Null_20" * FL: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for FL plot * FL_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for FL plot * TRC: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for TRC plot * TRC_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for TRC plot * CBS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for CBS plot * CBS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for CBS plot * SCBI: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for SCBI plot * SCBI_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model data to conduct the codispersion null model analysis for SCBI plot * DHS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for DHS plot * DHS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for DHS plot * TTS: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for TTS plot * TTS_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for TTS plot * HSD: R data with "data.frame" format; the observed data of each 20m * 20m quadrat for HSD plot * HSD_Null_20: R data with "list" format containing 199 "data.frame" subdata; the null model to conduct the codispersion null model analysis for HSD plot * Variables in these datasets: * Quad.num: The serial number of 20m * 20m quadrats * gx, gy: The coordinate of each 20m × 20m quadrat (m) * AGB.all: Aboveground biomass (AGB) of all trees in one quadrat (Mg/ha) * AGB.AM: AGB of AM (i.e., arbuscular mycorrhizal) trees in one quadrat (Mg/ha) * AGB.EM: AGB of EM (i.e., ectomycorrhizal) trees in one quadrat (Mg/ha) * SpNum.all: Tree species richness or number of tree species with > 1 individuals in one quadrat * SpNum.AM: AM tree species richness or number of AM tree species with > 1 individuals in one quadrat * SpNum.EM: EM tree species richness or number of EM tree species with > 1 individuals in one quadrat * Num.all: The number of tree individuals in one quadrat * Num.AM: The number of AM tree individuals in one quadrat * Num.EM: The number of EM tree individuals in one quadrat * AMdomi: AM tree dominance in one quadrat quantified using the proportion of AM tree individuals * EMdomi: EM tree dominance in one quadrat quantified using the proportion of EM tree AGB * Soil.PC1: Soil fertility index from the first principal component of the principal component analysis (only for observed datasets) * Soil.PC2: Soil fertility index from the second principal component of the principal component analysis (only for observed datasets) * Soil: Soil fertility index from the first principal component (for FL, TRC, CBS, SCBI, DHS plots) or the second principal component (for TTS and HSD plots) of the principal component analysis (only for null model datasets) ## Details for: 2. Codispersion null model analysis.R * Description: a .R file containing all codes to conduct our codispersion null model analyses (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 80 KB * Note: * Please open this file using R software * All necessary explanations for the "codispersion null model analysis" code can be found in the text after the "#" label in this .R file * Very important note: anyone who want to use this code to run the codispersion analysis, please cite the Buckley's paper in 2016 ([https://doi.org/10.1111/nph.13934](https://doi.org/10.1111/nph.13934)). ## Details for: 3. Generalized least squares model analysis.R * Description: a .R file containing all codes to conduct our generalized least squares model analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 12.3 KB * Note: * Please open this file using R software * All necessary explanations for the "generalized least squares model analysis" code can be found in the text after the "#" label in this .R file ## Details for: 4. Structural equation modeling analysis.R * Description: a .R file containing all codes to conduct our structural equation modeling analysis (see the Method section in the manuscript for details) * Format(s): .R * Size(s): 41.0 KB * Note: * Please open this file using R software * All necessary explanations for the "structural equation modeling analysis" code can be found in the text after the "#" label in this .R file ## Details for: Observed data source.xlsx * Description: a .xlsx file containing all the observed datasets of each 20m * 20m quadrats for the seven forests * Format(s): .xlsx * Size(s): 657 KB * Contents: 9 sheets * Description for each sheet: * Article information: listing the the article title, authors, and journal name * Column name: listing and explaining each column name in this dataset * Fenglin: the observed dataset containing 16 columns for FL plot * TRC: the observed dataset containing 16 columns for TRC plot * Changbaishan: the observed dataset containing 16 columns for CBS plot * SCBI: the observed dataset containing 16 columns for SCBI plot * Dinghushan: the observed dataset containing 16 columns for DHS plot * Tiantongshan: the observed dataset containing 16 columns for TTS plot * Heishiding: the observed dataset containing 16 columns for HSD plot * Note: please see the sheet "Column name" in this .xlsx file for the explanation of each column ## Details for: Mycorrhizal types.xlsx * Description: a .xlsx file showing the mycorrhizal type and the referred literature of each tree species * Format(s): .xlsx * Size(s): 70.9 KB * Contents: 10 sheets * Description for each sheet: * Article information: listing the the article title, authors, journal name, and abbreviation of mycorrhizal association * References: listing all the references (in total 49 items) used to classify the mycorrhizal type of studied species * Mycorrhizal associations: listing the basic information (including Family, Genera, and Species name), mycorrhizal classification, and the referred literatures for each tree species Column "Family": The Family name of each species Column "Genera": The Genera name of each species Column "Species": The Species name of each species Column "Mycorrhizal_type": Mycorrhizal types of each species to conduct our primary analyses, but for the species in red font, their mycorrhizal type was reassigned in the robustness test (see the note in the brackets for details) Column "Mycorrhizal_type_detailed": more detailed mycorrhizal types for each tree species Column "Reference and Note": referred literature and the detailed notes for each tree species * Fenglin: the mycorrhizal type and the referred literature of each tree species in FL plot * TRC: the mycorrhizal type and the referred literature of each tree species in TRC plot * Changbaishan: the mycorrhizal type and the referred literature of each tree species in CBS plot * SCBI: the mycorrhizal type and the referred literature of each tree species in SCBI plot * Dinghushan: the mycorrhizal type and the referred literature of each tree species in DHS plot * Tiantongshan: the mycorrhizal type and the referred literature of each tree species in TTS plot * Heishiding: the mycorrhizal type and the referred literature of each tree species in HSD plot * Access Information --- * To generate these datasets, we used the raw census and soil data of the ForestGEO network that can only be shared on request because most PIs have not made them publicly available. Forest census data from the ForestGEO data portal can be obtained by filling out the online Data RequestForm ([http://ctfs.si.edu/datarequest/index.php/main/plotdata](http://ctfs.si.edu/datarequest/index.php/main/plotdata)). Soil data are available to qualified researchers from ForestGEO network by contacting the mega-plot PIs ([https://forestgeo.si.edu/meet-team/principal-investigators](https://forestgeo.si.edu/meet-team/principal-investigators)). --- END OF README Diversity–biomass relationships (DBRs) often vary with spatial scale in terrestrial ecosystems, but the mechanisms driving these scale-dependent patterns remain unclear, especially for highly heterogeneous forest ecosystems. This study explores how mutualistic associations between trees and different mycorrhizal fungi (i.e., arbuscular mycorrhizal (AM) vs. ectomycorrhizal (EM) association) modulate scale-dependent DBRs. We hypothesized that in soil-heterogeneous forests with a mixture of AM and EM tree species, (i) AM and EM tree species respond in contrasting ways (i.e., positively vs. negatively respectively) to increasing soil fertility, (ii) AM tree dominance contributes to higher tree diversity and EM tree dominance contributes to greater standing biomass and that as a result, (iii) mycorrhizal associations exert an overall negative effect on DBRs across spatial scales. To empirically test these hypotheses, we collected detailed tree distribution and soil information (nitrogen, phosphorus, organic matter, pH, etc.) from seven temperate and subtropical AM-EM mixed forest mega-plots (16–50 ha). Using spatial codispersion null model and structural equation modeling, we identified the relationships among AM or EM tree dominance, soil fertility, tree species diversity and biomass, and thus DBRs across 0.01–1 ha scales. We found first evidence overall supporting the above three hypotheses in these AM-EM mixed forests: (i) In most forests, with increasing soil fertility tree communities changed from EM-dominated to AM-dominated. (ii) Increasing AM tree dominance had an overall positive effect on tree diversity and a negative effect on biomass, even after controlling for soil fertility and number of trees. Together, (iii) the changes in mycorrhizal dominance along soil fertility gradients weakened the positive DBR observed at 0.01–0.04 ha scales in nearly all forests and drove negative DBRs at 0.25–1 ha scales in four out of seven forests. Hence, this study highlights a soil-related mycorrhizal dominance mechanism that could partly explain why in many natural forests, biodiversity-ecosystem functioning (BEF) relationships shift from positive to negative with increasing spatial scale. See the "Materials and Methods" section in the manuscript for details.

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    Authors: Tatiana Agudelo Patiño; Jhonny Alejandro Poveda-Giraldo; Manuel Haminton Salas Moreno; Gysela Rengifo Mosquera; +1 Authors

    Tropical forests are a source of several high-value products that provide livelihood to small communities in different regions. Exotic fruits such as annatto are feedstock sources containing bioactive compounds with important applications in the food industry. Nevertheless, the integral use of annatto for community improvement and the crop’s contribution to carbon sequestration in tropical forests have not been analyzed. This paper aims to demonstrate the economic and environmental performance of small-scale alternatives to obtain natural colorants using annatto seed. The extraction of natural colorants (bixin and norbixin) was analyzed using ethanol and NaOH as solvents. The experimental results were used to simulate two scenarios. Scenario one involved bixin production, and scenario two comprised bixin and norbixin production. The economic and environmental assessments were performed considering the life cycle assessment (LCA) methodology based on a Colombian context. The best extraction yield was 72.65 mg g−1 for bixin and 193.82 mg g−1 for norbixin. From a simulation perspective, scenario two showed the best economic performance since a payback period of 3.1 years was obtained. The LCA showed a high CO2 sequestration potential (6.5 kg CO2 eq kg−1 seed) of the annatto crop. Moreover, the solvents used during the colorant extraction proved to be the most environmentally representative. Nevertheless, the CO2 sequestration of the crop continues to exceed the emissions generated by the process. This work demonstrates that the annatto is an alternative for small communities to reach equilibrium between the economic and environmental of the tropical forest.

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    Sustainability
    Article . 2023 . Peer-reviewed
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    Sustainability
    Article . 2023
    Data sources: DOAJ
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