Assessment of different aspects of Sundarban mangrove ecosystem through static and dynamic modelling

Assessment of different aspects of Sundarban mangrove ecosystem through static and dynamic modelling Santanu Ray Ecological Modeling Laboratory Department of Zoology (Centre for Advance Studies) Visva-Bharati University Santiniketan 731 235 India Email: [email_address]

Introduction Sundarban Mangrove Ecosystem Situated in the Gangetic delta of the Hooghly-Brahamputra estuarine complex Extend over two countries, India and Bangladesh Seven major rivers in this zone Approximately 170 km in length and 60 km width, greatest halophytic formation (4200 sq km) of the world Many mangrove plant species are found in this ecosystem About 450 deltaic islands, out of which 40% are reclaimed and rests virgin

Due to less snow cover in Himalaya fresh water runoff have been decreasing gradually in the Hooghly-Brahamputra estuarine complex particularly in the western part of Sundarban mangrove system Fresh water is added in this complex through Padma river in the eastern part whereas the western part is supplied fresh water by river Hooghly. Padma carries much more water than Hooghly, due to the difference in fresh water runoff the basic characteristics of the eastern and western parts are different The major difference is the salinity of the estuarine water of eastern and western part, average salinity of the western part is much more than that of eastern part Due to salinity difference the mangrove plant species composition is markedly different in these two regions The impact of climate change particularly in the difference of fresh water runoff two sites are selected in this mangrove ecosystem, one is in eastern part and another in western part and studied through system ecology perspective

First network modelling (popularly known as static model) are performed for comparative study of benthic ecosystem of mudflat of eastern and western parts of Sundarban Mangrove Ecosystem The estuarine water is highly productive and nursery ground of many shell and fin fishes. The productivity of the estuarine water is governed mainly by the litterfall of adjacent mangrove forest. An island is selected in the eastern part of this ecosystem for study the contribution of Dissolved inorganic nitrogen from mangrove litter fall to the adjacent estuary. For this purpose a dynamic model has been constructed

Elements of Ecological Network Node – Collection of elements, each node represents a compartment (biotic or abiotic) Edges – Line connects the nodes are called edges, directed edges are called arcs. Arcs are named using the numerical identifiers of the nodes they connect. Each arc in an ecological flow network can have an associated value. This value represents the magnitude of flow that occurs from the initial to the terminal node of the arc in a given unit of time.

F = Flow Matrix, Z = Input Vector, E = Export Vector and R = Respiration Vector

Four Major Tasks Performed by Network Analysis The evaluation of all direct and indirect bilateral relationships in a network of trophic exchanges The elucidation of the trophic structure immanent in the network The identification and quantification of all pathways for recycling medium extant in the network The quantification of the overall status of the network’s structure

The equations for throughflow become either for outflow and for inflow respectively

Flow diversity (D) and flow specialization (S) are measured by using the following formulae: Ascendency (A) and development capacity (C) are calculated with the help of following formulae:

Dynamic Model Model elements: State variables, forcing variables or control variables, rate parameters, constants Modelling procedure: Conceptualization of the system and construction of conceputal model Transformation of conceptual model into mathematical model Run model with realistic data of the system Sensitivity analysis and calibration of the unknown rate parameters Validation of the model with real data base Verification of the model

Application of static model in Hooghly-Matla estuarine ecosystem Food web of reclaimed island

Information Indices (Kcal m-2 y-1) Total system throughput Development capacity Relative Ascendency imports exports Respiration Redundancy Finn cycling index Virgin Reclaimed 539040 136570 2571000 700300 37% 29% 13.8% 12.75% 12.2% 5.37% 17.3% 19.2% 19.6% 33.5% 21.3% 8.3%

Conclusion: Magnitude of inputs and outputs (export) is much higher in virgin than reclaimed forests Primary productivity of virgin system is almost threefold greater than that of the reclaimed Detritus production is about eight times greater in virgin system than Reclaimed counterpart The phytoplankton community makes a significant contribution to the community production of mudflat in reclaimed system but in virgin it is dominated by benthic community Virgin system is more efficient in producing commercially valuable resources Detritivory (from D to II strongly predominates over herbivory (from I to II) in virgin system and in reclaimed system herbivory is greater than detritivory The ratio of herbivory : detritivory is almost 1:1(13400 Kcal m^2 y-1 : 15700 Kcal m^2 y-1 ) in reclaimed Island and in virgin counterpart it is about 1:3 (31700 Kcal m^2 y-1 : 83604 Kcal m^2 y-1 )

Relative Ascendency (37%) is higher than Redundancy (19.6%) in virgin forest whereas the reclaimed system shows high redundancy (33.5%) than ascendency (29%) of trophic pathways and therefore reclaimed system is probably highly resilient to subsequent perturbations About 21.3% of the total energy flow travels over cyclical pathways in virgin forest and only 8.4% in reclaimed forest. Only (31) cycles existing the in reclaimed system and and (38) cycles in virgin forest Contribution of litterfall into detritus is almost 16 times higher in virgin than reclaimed counterpart.

Conceptual Model of the Nitrogen Dynamics of Mangrove Litterfall Application of dynamic model

Sensitivity Analysis +0.69 +1.11 +0.42 +0.16 +0.82 +0.09 -0.21 +0.21 +0.21 -0.12 +0.12 -0.12 0.00 0.00 -0.11 0.00 0.00 0.00 +0.90 +0.90 -0.14 0.00 +0.21 +0.21 0.00 0.00 -0.1 0.00 +0.21 0.00 0.00 0.00 +0.26 +0.28 +0.07 0.00 0.00 0.00 +0.30 0.00 +0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Temperature coefficient Half saturation constant for oxygen Leaching rate for SON to TON in water Leaching rate for SIN to DIN in water Microbial degradation rate for SON Nitrogen mineralization rate KT KDO LchR1 LchR2 MDR minit System Sensitivity S DIN S DON S PON S TON S SIN S SON S STN Description Parameter

+0.41 +0.72 +0.61 0.00 +0.90 +0.90 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 +0.23 0.41 0.00 0.00 0.00 +0.21 -0.19 -0.21 0.00 +0.90 +0.90 +0.21 +0.23 +0.41 0.00 0.00 0.00 0.00 +0.22 0.00 0.00 0.00 0.00 0.00 +0.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Conversion rate for WTON to DON Conversion rate for SON to PON Conversion rate for WTON to PON Input rate for DIDAO Input rate for IDSO Input rate for PIDAO KCrDON CrSPON CrWPON IrDIDAO IrIDSO IrPIDAO System Sensitivity S DIN S DON S PON S TON S SIN S SON S STN Description Parameter

Sensitivity analysis has been carried out using the formula S= [  x /x]/ [  p /p] (Jorgensen, 1994) +0.61 +0.29 +0.41 +1.64 +0.60 +0.47 +0.26 +0.29 0.00 +0.26 +0.27 +0.21 0.00 0.00 +0.41 +0.28 0.00 0.00 +0.35 0.00 0.00 +0.28 0.00 +0.26 0.00 0.00 0.00 +0.28 0.00 0.00 0.00 0.00 0.00 +0.26 +0.33 0.00 0.00 0.00 0.00 +0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Loss rate due to detritivores Loss rate of DIN from system Loss rate of DON from the system Loss rate of HAFA from SON of system Loss rate due to Mangroves Settling rate for PON LrD LrDIN LrDON LrHAFA LrM SrPON System Sensitivity S DIN S DON S PON S TON S SIN S SON S STN Description Parameter

Simulated & Observed results of Soil Total Nitrogen (STN) and Soil Organic Nitrogen (SON) during Calibration of parameters p < 0.05, Chi-square=312.42 (STN) and 294.11 (SON)

Simulated & Observed results of Soil Inorganic Nitrogen (SIN) during Calibration of parameters P < 0.05, Chi-square= 68.67

Simulated & Observed results of Dissolved Organic Nitrogen (DON) and Dissolved Inorganic Nitrogen (DIN) during Calibration of parameters p < 0.05, Chi-square = 4.94 (DON) and 38.21 (DIN)

Simulated & Observed results of Soil Total Nitrogen (STN) and Soil Organic Nitrogen (SON) during Validation p <0.05, Chi-square= 261.16 (STN) and 199.46 (SON)

Simulated & Observed results of Soil Inorganic Nitrogen (SIN) during Validation p < 0.05, Chi-square= 72.06

Simulated & Observed results of Dissolved Organic Nitrogen (DON) and Dissolved Inorganic Nitrogen (DIN) during Validation p <0.05, Chi-square= 8.54 (DON) and 161.47 (DIN)

Summary Contribution of DIN to the Hooghly-Matla estuary is dependent on high litter production. Soil pH and soil salinity are considered to be key factors for the conversion of STN to SON whereas redox potential plays an important role in the conversion of STN to SIN. Redox potential and conditions of soil are important factors determining the saturation of oxygen in soil. Mineralization of SON to SIN is governed by microbial activity that depends on soil temperature; while the mineralization of PON to DIN is controlled by water temperature and dissolved oxygen. DON degradation is governed by hydrolysis which is dependent on water pH. DIN dynamics of the estuary depends on the mineralization of the PON (fed by detritivores), DON degradation and leaching of SIN. Rainfall plays a major role in the accumulation of DON and DIN in the estuary that’s why maximum nutrient load in estuary occurs in monsoon (July to October). Loss rate of humic acid and fulvic acid from SON is most system sensitive parameter. Leaching rate of SON to WTON, microbial degradation rate of SON to SIN, conversion rate of SON to PON are the sensitive parameters in this system

Assessment of different aspects of Sundarban mangrove ecosystem through static and dynamic modelling

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Assessment of different aspects of Sundarban mangrove ecosystem through static and dynamic modelling