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Regime shifts limit the predictability of land-system change
Institution:1. Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany;2. Integrated Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;3. Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark;4. Faculty of Forestry, National University of Laos, P.O. Box 7322, Vientiane, Lao PDR;5. World Agroforestry Centre (ICRAF), c/o Kunming Institute of Botany, Chinese Academy of Sciences (CAS), 132# Lanhei Road, Heilongtan, Kumming 650201, PR China;1. University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands;2. Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, the Netherlands.;3. University of Groningen, Department of Psychometrics and Statistics, The Netherlands;1. International Institute of Tropical Agriculture-Tanzania, PO Box 34441, Dar es Salaam, Tanzania;2. IITA-Democratic Republic of Congo Avenue des Cliniques 13, Batiment INERA Commune de la GombeKinshasa, Democratic Republic of the Congo;3. IITA, Chitedze Research Station, PO Box 30258, Lilongwe 3, Malawi;4. Department of Crop Science, Faculty of Agricultural Sciences, University of Kinshasa, BP117 Kinshasa 11, People’s Republic of Congo;1. Department of Public Health and Clinical and Molecular Medicine, University of Cagliari, Italy;2. Consultation Liaison Psychiatric Unit at the University Hospital of Cagliari, University of Cagliari and AOU Cagliari, Italy;3. Centro Sclerosi Multipla, Cagliari, Italy;4. Mental Health Unit, Centre of Epidemiology, Surveillance and Health Promotion, Italian National Institute of Health, Rome, Italy;5. International Mood Center, University of California, San Diego, La Jolla, CA, United States;1. Southampton Solent University, East Park Terrace, Southampton, SO14 0YN, England, UK;2. Sabanc? University, Orta Mahalle, Üniversite Caddesi No: 27 Tuzla, 34956 ?stanbul, Turkey
Abstract:Payment schemes for ecosystem services such as Reducing Emissions from Deforestation and forest Degradation (REDD) rely on the prediction of ‘business-as-usual’ scenarios to ensure that emission reductions from carbon credits are additional. However, land systems often undergo periods of nonlinear and abrupt change that invalidate predictions calibrated on past trends. Rapid land-system change can occur when critical thresholds in broad-scale underlying drivers such as commodity prices and climate conditions are crossed or when sudden events such as political change or natural disasters punctuate long-term equilibria. As a result, land systems can shift to new regimes with markedly different economic and ecological characteristics. Anticipating the timing and nature of regime shifts of land systems is extremely challenging, as we demonstrate through empirical case studies in four countries in Southeast Asia (China, Laos, Vietnam and Indonesia). The results show how sudden events and gradual changes in underlying drivers caused rapid, surprising and widespread land-system changes, including shifts to different regimes in China, Vietnam and Indonesia, whereas land systems in Laos remained stable in the study period but show recent signs of rapid change. The observed regime shifts were difficult to anticipate, which compromises the validity of predictions of future land-system changes and the assessment of their impact on greenhouse gas emissions, hydrological processes, agriculture, biodiversity and livelihoods. This implies that long-term initiatives such as REDD must account for the substantial uncertainties inherent in future predictions of land-system change. Learning from past regime shifts and identifying early warning signs for future regime shifts are important challenges for land-system science.
Keywords:Land use  Deforestation  Prediction  REDD  Sustainability  Southeast Asia
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