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Ensemble modeling approach to predict the past and future climate suitability for two mangrove species along the coastal wetlands of peninsular India
Institution:1. Department of Earth Sciences, Annamalai University, Tamil Nadu 608002, India;2. Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, India;3. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India;1. Jilin Provincial Laboratory of Grassland Farming/Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China;2. School of Information Science and Technology, Fudan University, Shanghai 200433, China;1. School of Resources, Environment and Materials, Guangxi University, 530004 Nanning, China;2. State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
Abstract:Mangroves support numerous ecosystem services and help in reducing coastal ecological risks, yet they are declining rapidly due to climate change, sea level fluctuations and human activities. It is important to understand their responses to climate and sea level changes and identify conservation target areas at spatio-temporal scales, specifically in regions of rich mangrove biodiversity. In this study, we predicted the potential impact of past (Middle Holocene, ~6000 years), current and future (2050s, 2070s; RCP 2.6 and RCP 8.5) climate change scenarios on the two dominant species in the coastal mangrove forest wetlands of India, i.e., Rhizophora mucronata and Avicennia officinalis through an ensemble species distribution modeling approach. The ensemble modeling has been carried out by integrating eight single algorithm methods. Based on the receiver operating characteristics of area under the curve (AUC) and true skill statistics (TSS) values the ensemble modeling has yielded the highest predictive performance for SVM for both the species and lowest by CART for R. mucronata and BIOCLIM for A. officinalis. The internal evaluation metrics of the resulting Species distribution models (SDMs) tested its robustness with AUC-0.97 and TSS-0.89 for A. officinalis and AUC-0.99 and TSS-0.90 for R. mucronata. Precipitation of Wettest Month (Bio 13) and Mean Temperature of Warmest Quarter (Bio 10) was the most important variable (54–67%) for the distribution of A. officinalis and Precipitation Seasonality (Bio 15) and Precipitation of Warmest Quarter (Bio 18) for R. mucronata. High precipitation and sea-level highstand during middle Holocene led to the maximum range expansion of suitable habitat for the mangrove species which is also validated in the present study by the fossil pollen datasets. Total mangrove habitat in current and future climatic scenarios decreased in 2.6 and 8.5 Representative Concentration Pathways (RCPs) for 2050 and 2070 which indicates the vulnerability of the species to climate change impacts. Mangrove species are projected to shift their ranges more towards land in future experiencing a decrease in the amount of suitable coastal area available to them throughout the Indian coastline. The plausible cause for this range shift may be due to higher precipitation that is usually associated with longer period of soil inundation and because of the rise in sea level. Our findings will assist in formulating species-specific restoration plans for these keystone species in context of climate change in the Indian Subcontinent.
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