16-Dec-2016 Swapan Sarker “Integrating multiple species and functional traits to anticipate whole-ecosystem productivity in a complex and changing environment"

Published: 11 February 2016

Please join us for a special research talk titled “Integrating multiple species and functional traits to anticipate whole-ecosystem productivity in a complex and changing environment" by PhD student, Swapan Sarker

Friday 16th December 12-1 pm
Graham Kerr Building, library

Swapan Sarker (Institute of Biodiversity, Animal Health, and Comparative Medicine) will be giving a talk, entitled “Integrating multiple species and functional traits to anticipate whole-ecosystem productivity in a complex and changing environment" on the Friday the 16th of December at 12 pm in the GK library

Predicting productivity and stress levels in primary producers is crucial given the impacts of climate change on Earth’s forest systems. However, such predictions are challenging because of the complex interactions between competing species and levels of trait plasticity within species and the complex dependencies between the traits. Simultaneous changes in more than one environmental drivers could influence such interconnected system in counter-intuitive ways. Therefore, we need a conceptual and quantitative synthesis of how several species and functional traits interact with each other and with multiple changing attributes of the environment. We propose such a synthesis and apply it to world’s largest mangrove forest - Sundarbans, a sentinel ecosystem that is being impacted simultaneously by climate change, human exploitation, and forest management. We find strong intraspecific trade-offs among the plant traits and serious detrimental effects of several environmental drivers on the growth traits with subsequent decline in ecosystem productivity. Our integrated approach can identify and resolve trade-offs at sub-organismal, community and ecosystem levels. Further, it can do this in an explicitly spatial and dynamic way with computational efficiency to arrive at forecasts that would have been impossible from a single trait – single species perspective.


First published: 11 February 2016