|Title||Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem|
|Author(s)||Sannigrahi, Srikanta; Zhang, Qi; Pilla, Francesco; Joshi, Pawan Kumar; Basu, Bidroha; Keesstra, Saskia; Roy, P.S.; Wang, Ying; Sutton, Paul C.; Chakraborti, Suman; Paul, Saikat Kumar; Sen, Somnath|
|Source||Science of the Total Environment 715 (2020). - ISSN 0048-9697|
Water and Food
Soil, Water and Land Use
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Biophysical and economic valuation - Climate change - Data dimensionality - Ecosystem services - Spatial regression - Sundarbans|
Most of the Earth's Ecosystem Services (ESs) have experienced a decreasing trend in the last few decades, primarily due to increasing human dominance in the natural environment. Identification and categorization of factors that affect the provision of ESs from global to local scales are challenging. This study makes an effort to identify the key driving factors and examine their effects on different ESs in the Sundarbans region, India. We carry out the analysis following five successive steps: (1) quantifying biophysical and economic values of ESs using three valuation approaches; (2) identifying six major driving forces on ESs; (3) categorizing principal data components with dimensionality reduction; (4) constructing multivariate regression models with variance partitioning; (5) implementing six spatial regression models to examine the causal effects of natural and anthropogenic forcings on ESs. Results show that climatic factors, biophysical factors, and environmental stressors significantly affect the ESs. Among the six driving factors, climate factors are highly associated with the ESs variation and explain the maximum model variances (R2 = 0.75–0.81). Socioeconomic (R2 = 0.44–0.66) and development (R2 = 27–0.44) factors have weak to moderate effects on the ESs. Furthermore, the joint effects of the driving factors are much higher than their individual effects. Among the six spatial regression models, Geographical Weighted Regression (GWR) performs the most accurately and explains the maximum model variances. The proposed hybrid valuation method aggregates biophysical and economic estimates of ESs and addresses methodological biases existing in the valuation process. The presented framework can be generalized and applied to other ecosystems at different scales. The outcome of this study could be a reference for decision-makers, planners, land administrators in formulating a suitable action plan and adopting relevant management practices to improve the overall socio-ecological status of the region.