Plastic responses of leaf functional traits in Rhizophora mucronata Lamk. to climate change-induced salinity in the Sundarbans

Forest trees, long-lived sessile organisms, survive to changing environmental conditions throughout their lifetime by expressing phenotypic plasticity. Particularly mangroves display functional trait plasticity in changing environmental conditions like increasing salinity. Salinity is the key regulating factor to determine growth and development of mangrove tree species. The Sundarbans is subjected to severe stresses including salinity. Sea-level rise and freshwater reduction from up-streams cause to increase salinity in the Sundarbans that alter leaf morphology and physiology of mangrove flora. Leaf is the most important organ of plant to sense environmental ques such as temperature and light to synchronize growth and development. Until recently, no such initiatives were taken to understand plasticity in leaf functional traits like leaf area, specific leaf area, leaf dry matter content, leaf thickness, leaf N, P, K and Na content to salinity variations. Understanding and predicting leaf plastic mechanism to salinity variations in the Sundarbans is crucial to understand the phenetic shift. In this study, Jahana Garjan (Rhizophora mucronata Lamk.) will be selected as candidate tree species to assess the leaf-level plasticity to salinity variations in the Sundarbans. From each saline zone, thirty trees will be selected to collect leaf sample. Collected fresh leaf will then be scanned with flatbed scanner. Leaf quantitative parameters will be measured using NIH ImageJ software and analyzed by univariate and multivariate statistical tools like PCA. A sub-sample of leaf (25 leaf from each saline zone) will be taken for measuring leaf thickness as well as taken green and oven-dried weight to obtain leaf dry matter content and specific leaf area (area-weight ratio). Leaf N, P, K and Na concentrations will also be measured using UV-VIS Spectrophotometer and Flame Photometer. Plasticity index (PI) for each trait will also be developed to understand the pattern of variations among the salinity zones. All collected data will be analyzed using the freely available (https://cran.r-project.org/) programming language R. The findings will contribute to mangrove science as well as to the effort of ecological monitoring of mangrove flora. 

• Research Project