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Yahaya I, Xu R, Zhou J, Jiang S, Su B, Huang J, Cheng J, Dong Z, Jiang T. Projected patterns of land uses in Africa under a warming climate. Sci Rep 2024; 14:12315. [PMID: 38811602 PMCID: PMC11136982 DOI: 10.1038/s41598-024-61035-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/30/2024] [Indexed: 05/31/2024] Open
Abstract
Land-use change is a direct driver of biodiversity loss, projection and future land use change often consider a topical issue in response to climate change. Yet few studies have projected land-use changes over Africa, owing to large uncertainties. We project changes in land-use and land-use transfer under future climate for three specified time periods: 2021-2040, 2041-2060, and 2081-2100, and compares the performance of various scenarios using observational land-use data for the year 2020 and projected land-use under seven Shared Socioeconomic Pathways Scenarios (SSP): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 from 2015 to 2100 in Africa. The observational land-use types for the year 2020 depict a change and show linear relationship between observational and simulated land-use with a strong correlation of 0.89 (P < 0.01) over Africa. Relative to the reference period (1995-2014), for (2021-2040), (2041-2060), (2081-2100), barren land and forest land are projected to decrease by an average of (6%, 11%, 16%), (9%, 19%, 38%) respectively, while, crop land, grassland and urban land area are projected to increase by (36%, 58%, and 105%), (4%, 7% and 11%), and (139%, 275% and 450%) respectively. Results show a substantial variations of land use transfer between scenarios with major from barren land to crop land, for the whole future period (2015-2100). Although SSP4-3.4 project the least transfer. Population and GDP show a relationship with cropland and barren land. The greatest conversion of barren land to crop land could endanger biodiversity and have negative effects on how well the African continent's ecosystem's function.
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Affiliation(s)
- Ibrahim Yahaya
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- Department of Geography, Gombe State University, P.M.B, 127, Gombe, Gombe State, Nigeria
| | - Runhong Xu
- School of Geographical Science, Qinghai Normal University, Xining, 810008, China
| | - Jian Zhou
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shan Jiang
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Buda Su
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
| | - Jinlong Huang
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Jing Cheng
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhibo Dong
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Tong Jiang
- Research Institute of Climatic and Environmental Governance, Institute for Disaster Risk Management/School of Geographical Science Nanjing, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
- School of Geographical Science, Jiangsu Second Normal University, Nanjing, 210013, China.
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Mignone BK, Clarke L, Edmonds JA, Gurgel A, Herzog HJ, Johnson JX, Mallapragada DS, McJeon H, Morris J, O'Rourke PR, Paltsev S, Rose SK, Steinberg DC, Venkatesh A. Drivers and implications of alternative routes to fuels decarbonization in net-zero energy systems. Nat Commun 2024; 15:3938. [PMID: 38729928 PMCID: PMC11087489 DOI: 10.1038/s41467-024-47059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/19/2024] [Indexed: 05/12/2024] Open
Abstract
Energy transition scenarios are characterized by increasing electrification and improving efficiency of energy end uses, rapid decarbonization of the electric power sector, and deployment of carbon dioxide removal (CDR) technologies to offset remaining emissions. Although hydrocarbon fuels typically decline in such scenarios, significant volumes remain in many scenarios even at the time of net-zero emissions. While scenarios rely on different approaches for decarbonizing remaining fuels, the underlying drivers for these differences are unclear. Here we develop several illustrative net-zero systems in a simple structural energy model and show that, for a given set of final energy demands, assumptions about the use of biomass and CO2 sequestration drive key differences in how emissions from remaining fuels are mitigated. Limiting one resource may increase reliance on another, implying that decisions about using or restricting resources in pursuit of net-zero objectives could have significant tradeoffs that will need to be evaluated and managed.
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Affiliation(s)
- Bryan K Mignone
- ExxonMobil Technology and Engineering Company, Annandale, NJ, USA.
| | - Leon Clarke
- Bezos Earth Fund, Washington, DC, USA
- School of Public Policy, University of Maryland, College Park, MD, USA
| | - James A Edmonds
- Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD, USA
| | - Angelo Gurgel
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Jeremiah X Johnson
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Haewon McJeon
- Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD, USA
| | | | - Patrick R O'Rourke
- School of Public Policy, University of Maryland, College Park, MD, USA
- Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD, USA
| | - Sergey Paltsev
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven K Rose
- Energy Systems and Climate Analysis, EPRI, Washington, USA
| | | | - Aranya Venkatesh
- Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
- Energy Systems and Climate Analysis, EPRI, Washington, DC, USA
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Gao Y, Dong K, Yue Y. Projecting global fertilizer consumption under shared socioeconomic pathway (SSP) scenarios using an approach of ensemble machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169130. [PMID: 38070571 DOI: 10.1016/j.scitotenv.2023.169130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/02/2023] [Accepted: 12/03/2023] [Indexed: 01/01/2024]
Abstract
Comprehensively projecting global fertilizer consumption is essential for providing critical datasets in related fields such as earth system simulation, the fertilizer industry, and agricultural sciences. However, since previous studies have not fully considered the socioeconomic factors affecting fertilizer consumption, huge uncertainties may remain in fertilizer consumption projections. Here, an approach ensembled six machine learning algorithms was proposed in this study to predict global fertilizer consumption from 2020 to 2100 by considering the impact of socioeconomic factors under shared socioeconomic pathway (SSP) scenarios. It indicates that the proposed approach provides a rational and reliable framework for fertilizer consumption prediction that stably outperforms the single algorithms with relatively high accuracy (Nash-Sutcliffe efficiency of 0.93, Kling-Gupta efficiency of 0.89, and mean absolute percentage error of 10.97 %). We found that global N and P fertilizer consumption may decrease from 2020 to 2100, while K fertilizer may buck the trend. N fertilizer consumption showed a declining trend of -1 %, -17.13 %, and -3.43 % under the SSP1, SSP2, and SSP3 scenarios in 2100, respectively. For P fertilizer, those were -0.68 %, -9.68 %, and -2.03 %. In contrast, global K fertilizer consumption may increase by 18.03 %, 9.18 %, and 6.74 %, respectively. On average, N, P, and K fertilizer consumption is highest in China, and the lowest is in Kazakhstan. However, the hotspots of N fertilizer consumption may shift from China to Latin America and the Caribbean. This study highlighted the ensemble machine learning approach could potentially be a robust method for predicting future fertilizer consumption. Our prediction product will not only contribute to a better understanding of global fertilizer consumption trends and dynamics but also provide flexible and accurate key data/parameters for related research. The Projected Global Fertilizers Consumption Datasets are available at doi:https://doi.org/10.5281/zenodo.8195593 (Gao et al., 2023).
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Affiliation(s)
- Yulian Gao
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Kecui Dong
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Yaojie Yue
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
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Robertson RD, De Pinto A, Cenacchi N. Assessing the future global distribution of land ecosystems as determined by climate change and cropland incursion. CLIMATIC CHANGE 2023; 176:108. [PMID: 37520165 PMCID: PMC10382346 DOI: 10.1007/s10584-023-03584-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/10/2023] [Indexed: 08/01/2023]
Abstract
The geographic distribution of natural ecosystems is affected by both climate and cropland. Discussions of future land use/land cover usually focus on how cropland expands and displaces natural vegetation especially as climate change impacts become stronger. Less commonly considered is the direct influence of climate change on natural ecosystems simultaneously with cropland incursion. We combine a natural vegetation model responsive to climate with a cropland allocation algorithm to assess the relative importance of climate change compared to cropland incursion. Globally, the model indicates that climate change drives larger gains and losses than cropland incursion. For example, in the Amazonian rainforests, more than one sixth of the forest area could be lost due to climate change with cropland playing virtually no role. Our findings suggest that policies to protect specific ecosystems may be undercut by climate change and that localized analyses that fully account for the impacts of a changing climate on natural vegetation and agriculture are necessary to formulate policies that preserve natural ecosystems over the long term. Supplementary Information The online version contains supplementary material available at 10.1007/s10584-023-03584-3.
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Affiliation(s)
| | | | - Nicola Cenacchi
- International Food Policy Research Institute, Washington, DC USA
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Ren M, Huang C, Wu Y, Deppermann A, Frank S, Havlík P, Zhu Y, Fang C, Ma X, Liu Y, Zhao H, Chang J, Ma L, Bai Z, Xu S, Dai H. Enhanced food system efficiency is the key to China's 2060 carbon neutrality target. NATURE FOOD 2023:10.1038/s43016-023-00790-1. [PMID: 37400718 DOI: 10.1038/s43016-023-00790-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/09/2023] [Indexed: 07/05/2023]
Abstract
Bioenergy with carbon capture and storage, among other negative-emission technologies, is required for China to achieve carbon neutrality-yet it may hinder land-based Sustainable Development Goals. Using modelling and scenario analysis, we investigate how to mitigate the potential adverse impacts on the food system of ambitious bioenergy deployment in China and its trading partners. We find that producing bioenergy domestically while sticking to the food self-sufficiency ratio redlines would lower China's daily per capita calorie intake by 8% and increase domestic food prices by 23% by 2060. Removing China's food self-sufficiency ratio restrictions could halve the domestic food dilemma but risks transferring environmental burdens to other countries, whereas halving food loss and waste, shifting to healthier diets and narrowing crop yield gaps could effectively mitigate these external effects. Our results show that simultaneously achieving carbon neutrality, food security and global sustainability requires a careful combination of these measures.
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Affiliation(s)
- Ming Ren
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Chen Huang
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Yazhen Wu
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Andre Deppermann
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Stefan Frank
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Petr Havlík
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Yuyao Zhu
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Chen Fang
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Xiaotian Ma
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yong Liu
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Hao Zhao
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
- Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, China
| | - Jinfeng Chang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - Lin Ma
- Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, China
| | - Zhaohai Bai
- Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, China
| | - Shasha Xu
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, China.
- Institute of Carbon Neutrality, Peking University, Beijing, China.
- Institute for Global Health and Development, Peking University, Beijing, China.
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6
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Remote sensing-derived land surface temperature trends over South Asia. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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7
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Schneider JM, Zabel F, Mauser W. Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Sci Data 2022; 9:527. [PMID: 36030257 PMCID: PMC9420104 DOI: 10.1038/s41597-022-01632-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 08/12/2022] [Indexed: 11/19/2022] Open
Abstract
Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggregated at country level. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and bioenergy crops, and high resolution land cover data, our dataset indicates where cultivation is possible and how much land could potentially be used as cropland when biophysical constraints and different assumptions on land-use regulations are taken into account. By serving as an input for land-use models, the produced data could improve the comparability of the models and their output, and thereby contribute to a better understanding of potential land-use trade-offs.
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Affiliation(s)
- Julia M Schneider
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany.
| | - Florian Zabel
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Wolfram Mauser
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
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8
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Mashilingi SK, Zhang H, Chen W, Vaissière BE, Garibaldi LA, An J. Temporal Trends in Pollination Deficits and Its Potential Impacts on Chinese Agriculture. JOURNAL OF ECONOMIC ENTOMOLOGY 2021; 114:1431-1440. [PMID: 34050664 DOI: 10.1093/jee/toab100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Indexed: 06/12/2023]
Abstract
Worldwide, there is increasing evidence that shows a decline in pollinators, limiting crop pollination and production. However, it is unclear to what extent Chinese agriculture could be impacted by pollinator deficits. Data for 84 major crops in China between 1961 and 2018 were analyzed for the temporal trends in crop area and production, agricultural economic contribution of pollination, crop yield deficits, and honey bee pollination demand. We found a rapid increase in agricultural dependence on insect pollinators: both the cultivated area and total production of pollinator-dependent crops increased faster than those of pollinator-independent crops during 1961-2018. The total economic value of pollination amounted to US$ 106.08 billion in 2010, representing 19.12% of the total production value of Chinese agriculture, approximately twice the 9.5% value estimated for global agriculture. Crops with higher pollinator dependence showed greater mean growth in cultivated area than those with lower dependence, but lower mean growth of crop production and yield. Crop yield growth was also more unstable with increasing pollinator dependence. The minimum pollination demand for honey bee colonies was about three times the stock of honey bee colonies available in 2018. Furthermore, we found a decline in crop yield deficit with the increase in honey bee colony pollination service capacity. We considered that the shortage of pollinators resulted in the yield deficits for pollinator-dependent crops. Future increase in the area of pollinator-dependent crops will increase the need for more pollinators, suggesting the importance of implementing measures to protect pollinators to ensure a better-secured future for agricultural production in China.
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Affiliation(s)
- Shibonage K Mashilingi
- Key Laboratory for Insect-Pollinator Biology of the Ministry of Agriculture and Rural Affairs, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Hong Zhang
- Key Laboratory for Insect-Pollinator Biology of the Ministry of Agriculture and Rural Affairs, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Wenfeng Chen
- Institute of Life Sciences, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Bernard E Vaissière
- INRAE, Laboratoire Pollinisation & Ecologie des Abeilles, UR406 Abeilles & Environnement, Avignon, France
| | - Lucas A Garibaldi
- Instituto de Investigaciones en Recursos Naturales, Universidad Nacional de Río Negro, Agroecología y Desarrollo Rural. Río Negro, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural, Río Negro, Argentina
| | - Jiandong An
- Key Laboratory for Insect-Pollinator Biology of the Ministry of Agriculture and Rural Affairs, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
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9
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Malhi Y, Franklin J, Seddon N, Solan M, Turner MG, Field CB, Knowlton N. Climate change and ecosystems: threats, opportunities and solutions. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190104. [PMID: 31983329 DOI: 10.1098/rstb.2019.0104] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The rapid anthropogenic climate change that is being experienced in the early twenty-first century is intimately entwined with the health and functioning of the biosphere. Climate change is impacting ecosystems through changes in mean conditions and in climate variability, coupled with other associated changes such as increased ocean acidification and atmospheric carbon dioxide concentrations. It also interacts with other pressures on ecosystems, including degradation, defaunation and fragmentation. There is a need to understand the ecological dynamics of these climate impacts, to identify hotspots of vulnerability and resilience and to identify management interventions that may assist biosphere resilience to climate change. At the same time, ecosystems can also assist in the mitigation of, and adaptation to, climate change. The mechanisms, potential and limits of such nature-based solutions to climate change need to be explored and quantified. This paper introduces a thematic issue dedicated to the interaction between climate change and the biosphere. It explores novel perspectives on how ecosystems respond to climate change, how ecosystem resilience can be enhanced and how ecosystems can assist in addressing the challenge of a changing climate. It draws on a Royal Society-National Academy of Sciences Forum held in Washington DC in November 2018, where these themes and issues were discussed. We conclude by identifying some priorities for academic research and practical implementation, in order to maximize the potential for maintaining a diverse, resilient and well-functioning biosphere under the challenging conditions of the twenty-first century. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.
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Affiliation(s)
- Yadvinder Malhi
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
| | - Janet Franklin
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Nathalie Seddon
- Nature-based Solutions Initiative, Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK
| | - Martin Solan
- School of Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Waterfront Campus, European Way, Southampton SO14 3ZH, UK
| | - Monica G Turner
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Christopher B Field
- Stanford Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA
| | - Nancy Knowlton
- National Museum of Natural History, Smithsonian, MRC 163, PO Box 37012, Washington, DC 20013-7012, USA
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