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Myroslava"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"International Institute for Applied Systems Analysis (IIASA) Laxenburg, Austria"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Fritz, Steffen"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"International Institute for Applied Systems Analysis (IIASA) Laxenburg, Austria"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0000-0003-0420-8549"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Verburg, Peter"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Vrije Universiteit 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However, their benefits, trade-offs and growing-success are heavily location-dependent. Therefore, spatial data on the distribution of SRWPs are indispensable for assessing current distribution, trade-offs with other uses and potential contributions to climate mitigation. As current global datasets lack reliable information on SRWPs and full global mapping is difficult, we provide a consistent and systematic approach to estimate the spatial distribution of SRWPs in (sub-)tropical biomes under current and future climate. We combined three advanced methods (maximum entropy, random forest and multinomial regression) to evaluate spatially explicit probabilities of SRWPs. As inputs served a large empirical dataset on SRWP observations and 17 predictor variables, covering biophysical and socio-economic conditions. This dataset can help adding a more nuanced treatment of mitigation options and forest management in research on biodiversity and land use change."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2023"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Earth and Environmental Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Tree plantations"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"forest management"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Land-based climate change mitigation"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Natural climate solutions"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Spatial probability mapping"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Land use modelling"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Schulze K, Malek Ž, Schepaschenko D, Lesiv M, Fritz S, Verburg PH. 2023.  \nPantropical distribution of short-rotation woody plantations: spatial probabilities under current and future climate. 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