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 UniversitySantiniketan 731 235

IndiaEmail: sray@visva-

bharati.ac.in

IntroductionSundarban 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 infresh water runoff the basic characteristics of the eastern and western partsare 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 ofeastern part

• Due to salinity difference the mangrove plant species composition is markedlydifferent 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 forcomparative 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 shelland fin fishes. The productivity of the estuarine water is governed mainly bythe litterfall of adjacent mangrove forest. An island is selected in theeastern part of this ecosystem for study the contribution of Dissolvedinorganic 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

)(1

ixdrefT ii

n

jiji

)(1

ixdzfT i

n

jiji

Flow diversity (D) and flow specialization (S) are measured by using the following formulae:

sis

n

iis XPXPorXHD log

1

cisj

n

jcisc

n

ii XXPXXXPS /log/

11

Ascendency (A) and development capacity (C) are calculated with the help of following formulae:

)/log()/(1 1

jiij

n

i

n

jij TTTfTfTA

)/log()/(1

TTTTTC i

n

ii

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

Food web of virgin island

Information Indices (Kcal m-2 y-1)

Total system throughputDevelopment capacityRelative AscendencyimportsexportsRespirationRedundancy Finn cycling index

Virgin Reclaimed

539040 1365702571000 70030037% 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

LN

~ Tr

LDM

SON

Uptake

PON

WDIN

~

PUr

MDR

Con SON

~ Cr3

~

Cr1

~

SS

~

Cr2

SINLeach

LchR2

WDON

LchR1

Con DON

ConWPON

WTONDON deg

SON Leach

~

SpH

DIN loss

DIDAO

DON loss

STN

LD

PON set

T

PON Min

IDSO

HAFA

S IN

PIDAO

Con SIN

SON Min

~WpH

hr

Nremin

minitf T

f DO

kT

kDO

~

DO~

Temp

LrHAFAIrIDSO

ConSPONCrSPON

CrWPON

CrDON

LrD

IrPIDAO

IrDIDAO

LrDON

LrDIN

LrM

SrPON

~LB

~Nf

~

RP

Application of dynamic model

21

3

CrCrSTNConSON

CrSTNConSIN

NfLBLN

TrLNT

ConSONConSINTdt

dSTN

CrSPONSONConSPON

LrHAFASONHAFA

LchRSONSONLeach

MDRSONSONMin

IrIDSOIDSO

ConSPONHAFASONLeachSONMinIDSOConSONdt

dSON

1

2LchRSINSINLeach

LrMSINLDM

SINLeachLDMConSINSONMindt

dSIN

CrWPONWTONConWPON

CrDONWTONConDON

ConWPONConDONSONLeachdt

dWTON

hrWDONDON

LrDONWDONDONloss

IrDIDAODIDAO

DONDONlossDIDAOConDONdt

dWDON

deg

deg

PUrWDINUptake

LrDINWDINDINloss

UptakeDINlossDONPONMinSINLeachdt

dWDIN

deg

KDODO

DOfDO

efT

fDOfTitNre

NrePONPONMin

SrPONPONPONset

LrDPONLD

IrPIDAOPIDAO

PONMinPONsetLDConSPONPIDAOConWPONdt

dPON

TempKT

)20(

minmin

min

Sensitivity AnalysisParameter Description SSTN SSON SSIN STON SPON SDON SDIN System

Sensitivity

KT

KDO

LchR1

LchR2

MDR

minit

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 mineralizatio

n rate

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

+0.30

0.00

+0.21

0.00

0.00

0.00

+0.26

+0.28

+0.07

0.00

0.00

0.00

-0.1

0.00

+0.21

0.00

+0.90

+0.90

-0.14

0.00

+0.21

+0.21

0.00

0.00

-0.11

0.00

0.00

0.00

-0.21

+0.21

+0.21

-0.12

+0.12

-0.12

+0.69

+1.11

+0.42

+0.16

+0.82

+0.09

Parameter Description SSTN SSON SSIN STON SPON SDON SDIN SystemSensitivity

KCrDON

CrSPON

CrWPON

IrDIDAO

IrIDSO

IrPIDAO

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

0.00

0.00

0.00

0.00

0.00

0.00

0.00

+0.23

0.00

0.00

0.00

0.00

0.00

+0.22

0.00

0.00

0.00

0.00

+0.21

+0.23

+0.41

0.00

0.00

0.00

+0.21

-0.19

-0.21

0.00

+0.90

+0.90

-0.01

+0.23

0.41

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

+0.41

+0.72

+0.61

0.00

+0.90

+0.90

Parameter Description SSTN SSON SSIN STON SPON SDON SDIN SystemSensitivit

y

LrD

LrDIN

LrDON

LrHAFA

LrM

SrPON

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

0.00

0.00

0.00

0.00

0.00

0.00

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

+0.35

0.00

0.00

+0.28

0.00

+0.26

0.00

0.00

+0.41

+0.28

0.00

0.00

+0.26

+0.29

0.00

+0.26

+0.27

+0.21

+0.61

+0.29

+0.41

+1.64

+0.60

+0.47

Sensitivity analysis has been carried out using the formula S= [x /x]/ [p /p] (Jorgensen, 1994)

1:00 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

150.00

300.00

1: STN 2: Observ ed STN

1

1

1

1

2

2

22

Graph 1 (STN (mg/kg))

Simulated & Observed results of Soil Total Nitrogen (STN) and Soil Organic Nitrogen (SON) during Calibration of parameters

1:00 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

150.00

300.00

1: SON 2: Observ eed SON

1

1

1

1

2

2

22

Graph 4 (SON(mg/kg))

p < 0.05, Chi-square=312.42 (STN) and 294.11 (SON)

1:00 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

5.00

10.00

1: S IN 2: Observ ed SIN

11

1 1

22

22

Graph 2 (SIN (mg/kg))

Simulated & Observed results of Soil Inorganic Nitrogen (SIN) during Calibration of parameters

P < 0.05, Chi-square= 68.67

1:00 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

5.00

10.00

1: DON 2: Observ ed DON

1 11 12

22 2

Graph 5 (DIN (mg/l))

Simulated & Observed results of Dissolved Organic Nitrogen (DON) and Dissolved Inorganic Nitrogen (DIN) during Calibration of parameters

1:00 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

5.00

10.00

1: DIN 2: Observ edDIN

1

11

12

2

2 2

Graph 3 (DIN (mg/l))

p < 0.05, Chi-square = 4.94 (DON) and 38.21 (DIN)

1:07 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

150.00

300.00

1: STN 2: Observ ed STN

11

1

1

2

2

22

Graph 1 (STN (mg/kg))

Simulated & Observed results of Soil Total Nitrogen (STN) and Soil Organic Nitrogen (SON) during Validation

1:07 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

150.00

300.00

1: SON 2: Observ eed SON

11

1

1

2

2

22

Graph 2 (SON (mg/kg))

p <0.05, Chi-square= 261.16 (STN) and 199.46 (SON)

1:07 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

5.00

10.00

1: S IN 2: Observ ed SIN

1 1

11

22

22

Graph 3 (SIN (mg/kg))

Simulated & Observed results of Soil Inorganic Nitrogen (SIN) during Validation

p < 0.05, Chi-square= 72.06

1:07 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

5.00

10.00

1: DON 2: Observ ed DON

1 1 1 12 2

2 2

Graph 4 (DON (mg/l))

Simulated & Observed results of Dissolved Organic Nitrogen (DON) and Dissolved Inorganic Nitrogen (DIN) during Validation

1:07 PM Fri, Sep 14, 2007

0.00 91.25 182.50 273.75 365.00

Day s

1:

1:

1:

2:

2:

2:

0.00

5.00

10.00

1: DIN 2: Observ edDIN

1

11

12

2

2 2

Graph 5 (DIN (mg/l))

p <0.05, Chi-square= 8.54 (DON) and 161.47 (DIN)

SummaryContribution 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

Thank You