An approach towards quantification of ecosystem

Ecological Indicators xxx (xxxx) xxx–xxx

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

An approach towards quantification of ecosystem trophic status and health through ecological network analysis applied in Hooghly-Matla estuarine system, India Joyita Mukherjeea,d,1, Samya Karanb, Moitreyee Chakrabartyc, Arnab Banerjeea, ⁎ Nabyendu Rakshita, Santanu Raya, a

Systems Ecology & Ecological Modelling Laboratory, Department of Zoology, Visva-Bharati University, Santiniketan 731235, India Department of Environmental Science, Vidyasagar University, West Bengal, India Department of Conservation Biology, Durgapur Govt. College, West Bengal, India d Department of Zoology, K.C. College, Hetampur, Birbhum, West Bengal, India b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Static model Ecopath Mangrove Food web Exergy

The structure and function of food web of Hooghly-Matla estuarine system (HMES) including Sundarban mangroves is studied to assess the health of the system. HMES, provides shelter and make a home to many economically important shell and fin fishes. This estuary is exposed to various threats such as increasing salinity, deterioration of soil fertility and productivity, pollution and loss of biodiversity. Ecological network analysis (ENA) is applied for the HMES to model the trophic flows in 22 ecological compartments using Ecopath (a software for network analysis), integrating ecological data for the 2013–2015. ENA is performed, including a set of indices, keystoneness and trophic spectrum analysis to describe the contribution of the 22 groups to the HMES functioning. Results show that 22 compartments of the HMES including primary producers (trophic level TL 1) to the top consumers (elasmobranch, TL 3.5), the ecotrophic efficiency ranges from 0.016 to 0.989. Small demersal fishes, prawns, shrimps and crabs are the most exploited groups of this ecosystem. Herbivory and detritivory ratio is 1:1 indicating relative absence of top predators and low maturation level. Maturity of the system, organization, relative order and disorder within the system, diversity of flow of material among compartments and overhead of the system has been assessed. Biomass over Total system throughput ratio (TB/TST), Total primary production over total respiration ratio (TPP/TR), Total primary production over biomass (TPP/TB) and system omnivory index (SOI) indicate the moderate maturity level of the system. The HMES trophic network has a moderate recycling level (FCI = 12.99%), a high total system throughput (TST = 22976.03 tonnes km−2 yr−1) and a low ascendency (A = 25799 tonnes km−2 yr−1), but a relatively low connectance (CI = 0.27), high internal relative ascendency (A = 29.6%) and a high omnivory index (OI = 0.203), indicating that this estuary is immature but relatively organized and complex, with strong production. HMES has some unique features in comparison to similar functioning geographically close estuaries or estuaries with similar environmental characteristics. System robustness and exergy are also estimated to assess ecosystem health and compared with other tropical systems. From a holistic point of view, present study conveys fundamental information and categorizes the status of the system.

1. Introduction Estuaries along with its vast biodiversity support extensive fisheries causing disproportionately high economic values (Blaber et al., 2000) and most extensively modified and threatened ecosystems on earth (Costanza et al., 1997; Blaber et al., 2000). Estuarine system endows

with abundant food supply for organisms of different trophic levels (Schelske and Odum, 1962; Teal, 1962; Odum, 1968; Nixon, 1980) and refuge from predation for juveniles of several fish and invertebrate species (Robertson and Blaber, 1993; Paterson and Whitfield, 2000). Estuaries serve as important sites for fish, both as nursery and reproduction grounds and also migration routes. Functioning of estuaries

⁎

Corresponding author. E-mail addresses: [email protected] (J. Mukherjee), [email protected] (S. Ray). 1 JM has done the work during her postdoctoral research at Systems Ecology & Ecological Modelling Laboratory. https://doi.org/10.1016/j.ecolind.2018.08.025 Received 12 March 2018; Received in revised form 4 August 2018; Accepted 13 August 2018 1470-160X/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Mukherjee, J., Ecological Indicators, https://doi.org/10.1016/j.ecolind.2018.08.025


J. Mukherjee et al.

nutrients and regulates the productivity and is the home for many economically important shell and fin fishes. Research on HMES have focused on the biogeochemical cycles (nitrogen and carbon), carbon flux, phytoplankton assemblage, water quality assessment and mangrove litterfall dynamics and fish faunal assemblage (Mukherjee et al., 2013; Roshith et al., 2013; Roy et al., 2012; Hossain et al., 2012; Dutta et al., 2012; Choudhury and Pal, 2011; Mukhopadhyay et al., 2006; Ray and Straskraba, 2001). Few works describing partly or whole estuarine area explaining the present condition of the system have been done (Ray et al., 2000; Ray, 2008; Ullah et al., 2012). Fishery in this area is at a stake due to over exploitation, the catch per unit effort (CPUE) and overall system health is declining as an invariable outcome (Dutta et al., 2016; Rakshit et al., 2017). Major ecological problems in this area are increasing soil and water salinity, subsequent deterioration of soil fertility and productivity, pollution of soil and water ecosystem and loss of biodiversity. Human society interaction with environment is intense and natural resource consumption to the extent of overexploitation is quite common here. Huge discharges of untreated domestic and industrial effluents, from nearby industries are the major cause of water pollution at this site. Alterations to and removal of mangrove vegetation over time has become another threat. This environmentally and economically important area can step towards an uncertain future if proper solutions are not taken into consideration. Therefore, the objective of this study are (1) construct a steady-state trophic model of HMES to reveal the trophic interactions among the compartments within this system through ENA, (2) analyze the ecosystem maturity and trophic functioning using an ecosystem characterization index resulting from network analysis, (3) to assess the health of this ecosystem.

engrosses complex interactions among the components of the ecosystem. The estimation of material or energy flow through diverse compartments of the system and efficiency of energy assimilation, transfer and dissipation provides an essential insight to reveal the structure and function of the system (Baird and Ulauowicz, 1993). Application of thermodynamic and network oriented indicators to coastal systems could have a powerful tool to assess ecosystem health and maturity (Vassallo et al., 2006). This study attempts to summarize and integrate existing trophic structure and to depict a larger picture of interactions among biological components and their functions in the ecosystem using a mass-balanced model of HMES. Stability of the estuarine system comes from the availability of resources for its components and trophic interactions among these components (Polis and Strong, 1996). Thus, information on material flow and trophic structure are fundamental for the understanding of the persistence of these communities through time (Polis, 1994; Polis and Strong, 1996). A good number of detailed food web studies have been done for lakes (Chea et al., 2016; Vander Zanden and Vadeboncoeur, 2002), rivers (Jepsen and Winemiller, 2002) and even large open ocean areas (Davenport and Bax, 2002; Sherwood and Rose, 2005) and also in estuaries (Lobry et al., 2008; Scharler and Baird, 2005; Baird et al., 2004a; Wolff et al., 2000). Generally, estuaries are in stressed condition due to anthropogenic activities (Diaz and Rosenberg, 2008; Blaber et al., 2000). There is still scope in detailing relationships between species and among the estuarine community specially, in tropics as a whole. Inspite of prevalent estuarine systems in tropics, there are only few of studies food web including network analysis (Patrıcio et al., 2004; Scharler and Baird, 2005; Mohamed et al., 2005). Under this circumstance, we study one important tropical estuarine system emphasizing on the system health. Health of an ecosystem is related to its fundamental attributes, functioning, maturity and stability. These factors are dependent on the complexity of the system which, in turn, dependent on the trophic interactions of the food web. ENA, a cluster of algorithms, with holistic approach towards ecosystem analysis to unravel several system properties is described in detail by Ulanowicz (1986, 2004), Wulff and Ulanowicz (1989) and Kay et al. (1989). Several studies have been carried out exploring various aspects of ecosystem functioning using ENA; such as, trophic models of the exploited benthic ecosystem (Ortiz and Wolff, 2002), evaluation of environmental and anthropogenic stress and use of these indices for ecosystem health management (Tecchio et al., 2015), trophic structure, function, efficiency and productivity among ecosystem types (Blomberg and Montagna, 2014), impact of the top predators of the system (Lercari et al., 2015), species relationships by energy transfers, trophic fluxes, and assimilation efficiency (de Mutsert et al., 2012), effect of stress on the structure and functioning of food web (Baeta et al., 2009), general status and development trends (Duan et al., 2009), assessment of nutrient supply and habitat structure (Scharler and Baird, 2005; Baird et al., 2004b) etc. Ecopath model using ENA was first applied by Christensen and Pauly (1992) to evaluate the maturity among 41 aquatic ecosystems through 31 attribute parameters provided by the statistics analysis. Ecopath is steady-state trophic model which evokes transfer of material or energy within the components of ecosystems (Brey, 2012). The tidal freshwater areas of estuaries have received little attention in ecological research although they are often heavily stressed by environmental impacts. Significant degradation and loss of coastal wetlands have been observed due to the establishment of increasing human populations near lagoons, gulfs and bays (Entsua-Mensah, 2002; Ibe and Sherman, 2002; Scheren et al., 2002; Wolanski et al., 2004). The Sundarbans, the lush mangrove vegetation between India and Bangladesh, is a unique ecosystem with high biodiversity including the HMES which is one of the most important estuarine systems in south-east Asia. HMES has been declared as World Heritage Site by UNESCO in 1985 and in 1989, a Biosphere Reserve in India which carry out a variety of ecosystem services. Litterfall of mangroves supplies the detritus,

2. Method 2.1. Study site The HMES, the first deltaic offshoot of the Ganges lies approximately between 21°31′–23°20′ N and 87°45′–88°45′ E. This tropical coastal estuary with its lush mangroves is shared between two countries – India and Bangladesh. The Indian part of HMES is shown in Fig. 1. The area considered for the study is from Diamond harbour to the coastal areas of Bay of Bengal along coastline of West Bengal state of India. Data on fish catch has been collected from fishing stations of Diamond harbor, Kakdwip, Namkhana, Bokkhali, Frasergunj.

2.2. Overview of ecopath model A potential key to understand ecological complexity is to apply ecosystem approach which considers all species in the system, the food web along with all flows and processes. The static modelling approach has been considered using Ecopath as a tool to understand the food web of the system, taking important functional groups from each of the trophic level. Static modelling approaches employ a series of linear equations to estimate values of all flows taking place in a system, which can then be analyzed with ENA indices. Ecopath is modelling software which captures a static mass balanced snapshot of the whole system (Christensen and Pauly, 1993; Christensen et al., 2005). The algorithm of Ecopath is based on parameterization of the two master equations known as: (1) Production = catch + predation + net migration + biomass accumulation + other mortality; (2) Consumption = production + respiration + unassimilated food, It can be simplified and expressed as follows (Christensen et al., 2005):

2


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Fig. 1. View of HMES (source: Google map). i

P Q Bi . ⎛ ⎞ . EEi− ∑ Bj . ⎛ ⎞ . DCji−EXi = 0 B ⎠j ⎝ B ⎠i ⎝ j=1 where Bi is the biomass of group i;

()

P the B i

ecological distribution and commercial importance. Preliminarily, the grouping was based on habitat -pelagic, benthopelagic and demersal habitats. They were divided again on the basis of asymptotic length, into (1) ‘small’ species with length equal or less than 30 cm, (2) ‘medium’ with length 30–89 cm and (3) ‘large’ with asymptotic length of 90 cm or more. Cluepids includes mainly the Hilsa fish which is economically highly important fish and are found approximately half the area of the study site. Elasmobranches are found mainly in the marine side of the study site. Most of the information regarding biomass value, P/B and Q/B ratios of different types of fish were taken from different available literatures and reports, single species stock assessments, fish survey data collected and published by Central Inland Fisheries Research Institute (Mitra et al., 2001, 1997; Nath et al., 2004; Sinha, 2004), Central Marine Fisheries Research Institute (Srinath et al., 2006) and Fishbase resources (Froese and Pauly, 2010). The biomass (B) and production/biomass ratio (P/B) of each fish functional group are calculated by the empirical relationship of Pauly (1980) and Beverton and Holt (1957) as follows (Christensen et al., 2005):

(1) production biomass ratio of

group i, which is equal to the coefficient of total mortality (Z) under the steady state condition; EEi is the ecotrophic efficiency of group i; Bj is the biomass of predator group j;

() Q B

j

is the consumption biomass ratio

of the predators j; EXi is the export of group i; i and j are respectively numbers of prey and predator groups. Biomass (B), production/biomass ratio (P/B), consumption/biomass ratio (Q/B), ecotrophic efficiency (EE) and diet composition (DC) are the main input parameters of the Ecopath model. To construct the model, the composition of the diet and at least three of the four parameters (B, P/B, EE, and Q/B) are needed as the basic input of the model for each functional group. Additionally, the one unknown parameter can be estimated by the model through the Ecopath parameterization algorithm. 2.3. Functional groups A quantitative representation of the trophic structure and energy transfers of the ecosystem under consideration is constructed using EwE network model. Ecologically similar species are grouped together to identify the components of the ecosystem (Coll et al., 2009). In HMES, 22 functional groups have been defined to construct the mass balance model in order to study the trophic relationship and they are described as follows (aggregation of functional groups of the ecosystem is given in Table 1; the basic estimates from EwE model run are listed in Table 2; diet matrix is shown in Table 6 (in supplementary) and Fig. 2 shows the flow diagram of the food web). Wet weight in tones per square kilometer is considered as the currency of the model. The aggregation of the fish functional groups is based mostly on the composition of diet, traits (size), ecological guilds and commercial landing statistics (Dutta et al., 2012; Manna and Goswami, 1985, Chakraborty et al., 2000).

B = Y / F , F = Z −M , Z =

P L −L¯ = ∞ B L¯ −L′

(2) −2

where B, Y, F, Z and M represent the biomass (t km ), the annual catch yield (t km−2 year−1), the fishing mortality (year−1), total mortality − (year−1), and natural mortality (year−1); K, L∞, L , L' represent the growth rate of VonBertalanffy Growth Function, asymptotic length of fish (cm), mean length of fish (cm), cut off length of fish (cm) − respectively. K, L∞, L , L' were derived from the FishBase website (www.fishbase.org) and previously published studies (Dutta et al., 2012; Manna and Goswami, 1985, Chakraborty et al., 2000). Annual fish yield of this estuarine region were derived from the Fishery Department, Government of West Bengal, India (www.wbfisheries.in). The consumption biomass ratio (Q/B) was estimated from FishBase website (www.fishbase.org). Absolute consumption computed by Ecopath is a flow expressed in, e.g., t/km2/year, while the corresponding Q/B would be per year. The Q/B has been calculated from log (Q/B) = 7.964–0.204logW∞ − 1.965 T′ + 0.083A + 0.532 h + 0.398d (Palomares and Pauly, 1998) where W∞ is the asymptotic weight (g), T ' is an expression for mean annual temperature of the water body, expressed using T ' = 1000/Kelvin (Kelvin = °C + 273.15), A is the aspect ratio, h is a dummy variable expressing food type (1 for herbivores, and 0 for detritivores and carnivores), and d is a dummy variable also expressing food type (1 for detritivores, and 0 for herbivores and carnivores).

2.3.1. Data collection and model construction Data used for this purpose has been collected over a period of two years (2014–2016). The parameter values which were not possible to collect directly from the field observations are obtained from literature survey. 2.3.1.1. Fish. Most common fish species with important economic value are selected for the study and are aggregated into 11 functional groups according to their ecological similarity. Grouping of fishes into compartments was based on similarities in feeding habits, body size, 3


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Table 1 Detailed composition of the functional groups at species level considered for the food web model of HMES. Sl. No.

Ecological Group

Family/species

Functionality

1.

Mangrove

2.

Phytoplankton and benthic algae

Avicennia marina, Avicenna alba, Porteresia coarctata, Excoecaria agallocha, Ceriops decandra, Acanthus ilicifolius and Derris trifoliate Coscinodiscus, Fragilaria, Diatoma, Amphipleura sp., Asterionellopsis sp., Bacillaria sp., Bellerochea sp., Ceratium sp., Chaetoceros spp., Coscinodiscus spp., Nitzschia spp., Pinnularia sp., Rhizosolenia spp., Skeletonema sp., Thalassionema sp. Thalassiosira sp., etc. Ulva sp., Enteromorpha sp., Vaucheria sp., Oscillatoria sp., Lyngbya sp., Catenella sp., Chaetomorpha sp., and Xenococcus sp.)

3.

Zooplankton

4.

Filter feeder

5. 6.

Prawn and Shrimp Small pelagic

few macrophytes and true mangroves, serve as primary producer and maintain ecological sustainability. Minute, photosynthetically active plants floating in the water columns, Various algal species found on the mud surface as a green mat, exposed during low tide but submerged during extreme high tide also breeding ground of many species. They are the active food source of herbivores zooplankton and filter feeder and continue the mineral and biochemical cycle. More than 18 species of small animal plankton are recorded. Primary food source of many juveniles and filter feeder. That feeds by filtering tiny organisms or fine particles of organic material from currents of water that pass through it. They are the absolute indicator of environmental changes and toxicity. high economic value, Food source for many carnivore groups. Mostly carnivorous, minutely omnivorous micro feeder.

7.

Cluepids

8.

Medium pelagic

9. 10.

Medium benthopelagic Large Benthopelagic

11. 12.

Medium mesopelagic Small demersal

13.

Medium Demersal

14.

Polynemid

15.

Large demersal

16. 17.

Detritivorous Fish Meiobenthic carnivore

18.

Other benthic harbivore

19.

Elasmobranch

20.

Dolphin

21. 22.

Bird Detritus

Copepoda, Rotifera, cladocera, ostracoda, crustacean larva, Protozoan Clams, mollusks, siphunculid and echurids, sponges

Panaeus spp., Macrobrachium rosenbergii, Sergestidae (Acetesindicus) Setipinna spp, Coilia spp., Raconda russeliana Gray, Rhinomugil corsula, Leionilver Tenulosa sp., Ilisha elongata, Ilisha megaloptera, Nematalosa nasus, Hilsa sp. Rastrelliger kanagurta, Herpodon nehereus, Caranx spp, Thunnus albacares Stolephorus spp., Thryssa spp., Trachynocephalus mylops Trichiurus spp., Lepidopus spp. (Ribbon fish), Upneus spp. (goatfish) Chirocentrus spp., Scatophagus sp., (butter fish), Pampus argenteus, Boleophthalmus dussmieri, Cynoglossus spp., Pseudapocryptes lanceolatus, Scatophagus argus, Sillaginopus panijus Eleutheronema tetradactylum, Otolithoides biauritus, Terapon jarbua, Sciaena biauritus Johnius spp., Croackers, Muraenesox spp., Polynemidae (Indian salmon), Congresox sp. Lates calcarifer, Arius spp., Lutjanus sp.,Polynemus spp., Scomberoides spp., Anguilla spp., Liza parsia, Liza tade, Mystus gulio, etc Nematodes, some insect larvae (Culicoides spp.), sea anemone, holothuroids Gastropod (Neritasp, Littorinaseabra, Lambislambis) & bivalve mollusk (Glaucomya sp., Razor calm, Teredo sp., Batissa inflate), crustecean, Annelids (polychaetes; Dioptera sp., Lepidonotus sp.; Nemartines), insect larvae (Chironomid spp.), Isopod, zoobenthos Skates or Guitarfish (Rhinobatos sp.), Sawfish (Pristis microdon), rays (Himantura sp. Aetomylaeus sp., Narcine sp., Rhinoptera sp.), Shark (Scoliodon laticaudus, Carcharhinus spp.). Ganges River dolphins (Platanista gangetica) and Irrawaddy dolphins (Orcaella brevirostris) Fish eating birds dead and decaying parts of the organisms present in the system

Omnivorous micro feeder, contains maximum biomass of fish group, sensitive to seasonal changes. Mainly carnivore, abundant all the seasons and showing less pronounced seasonal changes in abundance pattern. Carnivore, habitat shifting behaviour is not much observed. Carnivore, slight habitat shifting behaviour is shown and maintain territory. Omnivore, seasonal variation in abundance found. Omnivore, seasonal variation in abundance found but less. Changing niche status observed in this group. Omnivore, minutely changing niche status. Top carnivores Mainly the mullet group and cat fish group and high vulnerability. Carnivore micro consumer and food source of other juvenile. Micro consumer, high seasonal variation in habitat.

apex predator, found in part of the study area

mammal, top predator top predators, eat on fish mainly The nonliving compartment is formed mostly within the food web but also with some input from upper zone of Phoenix sp. patches

four major groups viz. Copepoda, Cladocera, Rotifera and Ostracoda. The biomass of zooplankton and zoobenthos are estimated from our field survey and literature (Vivekanandan et al., 2003). Here, the zooplankton communities are considered as single functional group. Zoobenthos communities (except fish, as fish were considered in separate groups) were categorized into 2 different functional groups: 1) Meiobenthic Carnivore (Nematodes, some insect larvae, sea anemone, holothuroids) and 2) other Benthic Herbivore (i.e., mollusk, oligochaeta, crustecean larva). The P/B and Q/B of zooplankton is taken from Vivekanandan et al. (2003). The P/B values for herbivores were estimated by the EwE models with the provided inputs of Q/B and P/Q values adapted from literature (Mohamed and Zacharia, 2009). For the meiobenthic carnivore, the biomass was calculated by EwE with input of P/B, Q/B and EE values which was kept constant at 0.95. P/B and Q/B for this group is modified from literature (Mohamed and Zacharia, 2009).

2.3.1.2. Dolphin. Each input parameter values for this compartment have been taken from Mohamed and Zacharia (2009). 2.3.1.3. Birds. All the inputs for this compartment have been taken from a tropical reservoir system (Moreau et al., 2000). 2.3.1.4. Elasmobranch. Biomass is calculated from catch data of West Bengal Marine Fisheries Unit and Q/B is estimated from Fishbase.org. P/B is calculated by Ecopath as P/Q was set to 0.1. 2.3.1.5. Prawn and shrimp and filter feeders. The P/B and Q/B of prawn and shrimp is estimated from previous literature and similar system models (Mohamed and Zacharia, 2009; Khan and Latif, 1997; Khan et al., 1992). Biomass is estimated by EwE. Biomass of filter feeder was calculated from secondary data source (Chakraborthy and Choudhury, 1992; Nirmale et al., 2012) and The P/B and Q/B values of this group are modified from the literature (Nirmale et al., 2012; Chakraborthy and Choudhury, 1992; Mohamed and Zacharia, 2009).

2.3.1.7. Primary producer. The primary producers are characterized into two groups- 1) phytoplankton and benthic algae and 2) Mangroves. In the HMES, the phytoplankton was constituted mainly

2.3.1.6. Zooplankton and zoobenthos. Zooplankton in HMES include 4


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Table 2 All the basic parameter values of mass balanced model of HMES using Ecopath. Group name

Trophic level

Habitat area (fraction)

Biomass (t/km2)

P/B

Q/B

EE

Mangrove Phytoplankton and benthic algae Zooplankton Filter feeder Prawn and shrimp Small pelagic Cluepids Medium pelagic Medium benthopelagic Large benthopelagic Medium mesopelagic Small demersal Medium demersal Polynemid Large demersal fish Detritivorous fish Meiobenthic carnivore Other benthic herbivore Elasmobranch Dolphin Birds Detritus

1 1 2.109 2.249 2 2.946 2.106 3.004 3.288 3.151 2.186 2.870 2.811 2.991 3.023 2.094 2.184 2 3.538 3.496 3.360 1

1 1 1 1 1 1 0.5 1 1 1 1 1 1 1 1 1 1 1 0.75 1 1 1

124 60 24 4.876 33.14119 9.188301 7.539299 12.37987 5.4 3.4 2.19 2.219939 0.752311 1.184 0.18 6.551532 17.10641 8.122 1.30725 0.019 0.25 24

40 70 44 4.564 5.97 5.9 1.68 3.13 4.7 2.34 1.3 4.97 3.42 3.38 3.69 3.5 1.5 6.855 1.356 0.2 0.35

0 0 300 8.14 25 29.5 12.9 12.8 26 13.3 4.5 24.85 24.7 11.84 14 22.5 13.4 22.85 6.78 12.75 65

0.016 0.754 0.951 0.838 0.95 0.83 0.988 0.860 0.921 0.944 0.633 0.569 0.425 0.700 0.951 0.855 0.95 0.814 0.657 0 0 0.786

0.146 0.560 0.238 0.2 0.130 0.244 0.180 0.175 0.288 0.2 0.138 0.285 0.263 0.155 0.111 0.3 0.2 0.015 0.005

where D is the detrital biomass in (g C m−2), PP is the primary production in (g C m−2); E is the euphotic depth in meters. The depth of the euphotic zone was calculated as follows: E = 2.5 × SD (Secchi depth in meters).

by algae (i.e., diatoms, chlorophyta, cyanobacteria, bacillariophyta, euglenophyta). Biomass of phytoplankton is estimated from the field survey. The P/B ratios of phytoplankton are taken from Vivekanandan et al. (2003), similar to many tropical systems. Due to lack of data, biomass and P/B ratio of mangroves was calculated from Wolff et al. (2000).

2.3.2. Feeding relationship in the food web of HMES The diet matrix is an important input for the mass balanced network model. It corresponds to all the flows connecting each compartment shaping the prey predator groups in the system. The outflows from each compartment are dispersed amongst the predators of that particular group as inputs to the latter. This matrix is based upon the principle of

2.3.1.8. Detritus. The biomass of detritus was calculated using the empirical equation from the primary production and euphotic depth suggested by Christensen and Pauly (1993):

log D= 0.954 log PP + 0.863 logE−2.41

P/Q

(3)

Fig. 2. Food web structure of HMES depicting the trophic structure of the system, size of the nodes and the thickness of the arches are according to their biomass and quantity of material flow respectively. Trophic levels are denoted in the left side. 5


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“who eats whom and by how much”. This diet matrix illustrates the material flows from compartment i (prey) to compartment j (predator) (Fath et al., 2007). Data on average diet composition from feeding ecology (Mustafa, 2003; Mohamed et al., 2005; Ullah et al., 2012; Vivekanandan et al., 2003; Khan and Latif, 1997) or stomach analyses of each functional group of this ecosystem or related species of other similar ecosystems are used for development of this matrix (Table 6; see appendix). Food composition of the predators (DCji) is the fraction (in weight) of every prey i in the stomach content of predator j. In cases for which published information was not available, the diet of the same species in a similar ecosystem or of another similar species was considered, assuming that there are no significant differences (Christensen et al., 2005).

t ∗ = P.

where N is the number of living groups in the given model (Christensen et al., 2005). 2.5. Ecosystem properties, ENA indices, and network analysis Every system is related to a steady state condition which can be represented through mass balanced model based on some assumptions that all the inputs and outputs of every compartment are balanced. An approach of discrete trophic level to each functional group is incorporated in Ecopath as suggested by Ulanowicz (1995) following Lindeman (1942). MTI is employed in Ecopath following Ulanowicz and Puccia (1990) to describe how one group impacts directly and indirectly i.e., both predatory and competitive interactions (Christensen et al., 2005) on all the other groups in an ecosystem. Maturity and stability are the factors to determine the system functioning (Odum, 1969). A panel of indicators are exercised to illustrate and evaluate these two properties (Christensen et al., 2005). Food chain structure converges from linear to web like following the maturity of the system. The connectance index (CI) and system omnivory index (SOI) are correlated (Odum, 1971) with maturity of the system. The CI is a measure of the observed number of food links in a system relative to the number of possible links (Gardner and Ashby, 1970). The value of CI depends on both the size of a system and on diet matrices, while the SOI expresses how the feeding interactions are distributed between trophic levels. Besides, a routine based on the approach suggested by Ulanowicz (1986) is implemented to describe the numerous cycles and pathways implied by the food web representing an ecosystem. The FCI describes the amount of material or energy which is cycled in the ecosystem, as opposed to straight-through flows (Kay et al., 1989). In addition, the average path length (APL) can be calculated, which describes how many times a unit of energy will be transferred (between compartments) between entering and leaving the ecosystem. Each species play different role towards the functioning of the system; some species are more important than the rest of the species in upholding the integrity of the system. Even they (having low biomass) donate disproportionately in relation to their biomass to the functioning of the system. These are called keystone species (Paine, 1969; Power et al., 1996). The Keystoneness routine in Ecopath software allocates definite Keystoneness index (KSi) values to every compartment of the network. The KSi is calculated following the proposal of (Power et al., 1996) and later modified by Libralato et al. (2006) as where pi is the biomass component represented by the biomass of the functional group that it contributes to the total food web biomass (Power et al., 1996) and εi is the overall effect of each group in the system. The average mutual information (AMI) is an index quantifying the flow specialization. It is the difference between the probability (probability of occurrence of known inflow into a compartment) and the conditional probability of a flow event occurring (probability of occurrence of known inflow and known outflow from source compartment a time step earlier) (Ulanowicz, 1986). Unspecialized flows are the complement part of the specialized flows, i.e., AMI. Parallel pathways in the network such as, flow redundancy, respiration (energy which is of no further use to the ecosystem) and imports and exports across the ecosystem boundary (Ulanowicz, 1986, 2004), contribute to the unspecialized flows. Theoretically, AMI has its upper limit called Shannon’s diversity index, represented by H′. TST is the sum of all flows through all compartments of the network in an ecosystem. It is the measure of the size and activity of the system itself (Ulanowicz and Kay, 1991). Calculation of TST is as follows,

2.4. Model balancing and uncertainty The ENA model for the HMES is comprehended using the Ecopath software. Generally, a manual alteration of the input data is exercised to balance the model (Christensen et al., 2005), following ecological knowledge and reasoning, rather than sole computer algorithm. The diet compositions of some functional groups are slightly modified in order to get the mass-balance model. Available literature is used to bridge the gap where the information on the specific system is unavailable (Ullah et al., 2012; Froese and Pauly, 2010; Mustafa, 2003; Vivekanandan et al., 2003; Mohamed and Zacharia, 2009). Few recent papers using ENA has emphasized on the uncertainty analysis (Hines et al., 2018). There has been a growing trend of trying to incorporate more global uncertainty analyses and sensitivity analyses in the ENA literature, but that we are still in the infancy of figuring how to do this appropriately (Borrett et al., 2018). Validity of the input values are confirmed by running the pedigree algorithm of Ecopath for this model (Funtowicz and Ravetz, 1990) which satisfies two fold purposes of describing the data origin and assigning confidence interval to the data based on origin (Pauly et al., 2000). The pedigree routine of an Ecopath input is realized as a coded statement categorizing the origin of a given input (i.e., the type of data on which it is based), and by the same token, specifying the likely uncertainty associated with the input. The pedigree routine allows you to mark the data origin using a pre-defined table for each type of input parameters. Ecoranger module can subsequently pick up the confidence intervals from the pedigree tables and use these as prior probability distributions for all input data. The key criterion used here is that input estimated from local data (i.e., from the area covered by the model is question) as a rule is better than date from elsewhere, be it a guesstimate, derived from empirical relationships or derived from other Ecopath models (Christensen et al., 2005). Based on the options selected for each parameter for each group a pedigree index, P, can be calculated as the product of all the pedigree parameter specific indices. The P scales between 0 and 1 (inclusive). The pedigree index values are also used to calculate an overall pedigree index for a given model. The index values for input data scale from 0 for data that is not rooted in local data up to a value of 1 for data that are fully rooted in local data. Based on the individual index value an overall ‘pedigree index’ P is calculated based on: n

P=

∑∑ i=1 j=1

(N −2) / 1−P 2

lij n

where lij is the pedigree index for model group i and parameter j, n is the total number of functional groups. 2.4.1. Measure of fit The pedigree index, P, will be, among other things, a function of the number of groups in the system. We are therefore also using a measure of fit, t*:

TST =

∑ Tij ij

6


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where Tij is the flow from compartment i to compartment j. Size of the system is associated to growth which in turn, is determined by magnitude of flows and number of compartments (Bodini, 2012; Ulanowicz, 1986). The fraction of material or energy flow of a system which determines the organization level of the system is denoted as Ascendency (A). It is a single measure of ecosystem growth and development (Kay et al., 1989). Measure of ascendency is done using the formula given below,

A=

cannot be calculated in exact as all the possible contributions to exergy of a system cannot be determined (Jorgensen and Nielsen, 2007). Exergy can be considered as one of the major goal functions of ecosystem which reflects the energy of the components of the system to keep the system functioning (Zhang et al., 2003) and thus can serve as a good indicator of the system’s status. Different organisms have different exergy values depending on their genetic complexity and more an organism is genetically complex more it has exergy value in comparison to simpler organisms. Jørgensen (1997, 2007) proposed a relative exergy index (eco-Exergy), Ex, can be approximately calculated as below:

Tij T ⎞ ⎟ ⎝ i Tj ⎠

∑ Tij log ⎛⎜ T i, j

n

Ex =

where βi is the weighting factor of the ith component in the system and Ci is the corresponding concentration of this component. Given i = 1 represents detritus. Exergy can be expressed as a detritus exergy equivalent (mg l−1, β1 = 1 for detritus), in which conversion factors for different trophic levels are taken from different literature (Jørgensen, 1997).

Tij ⎞ C = − ∑ Tij log ⎛ ⎝ TST ⎠ i, j ⎟

Ascendency (A) is the product of AMI and TST, and development capacity (C) that of H′ and TST. A/C ratio shows the degree of organization of the network (Ulanowicz et al., 2009). Redundancy quantifies the functional group of parallel pathways along which biomass may exchange between the compartments (Ulanowicz, 1986). Due to the larger number of parallel pathways, greater redundancy corresponds to a system that should be able to maintain stability when it is perturbed (Mukherjee et al., 2015). It is calculated using the following formula n

R=

n

⎛

∑ ∑ (Tij). log ⎜ i=1 j=1

Tij2

n ∑ Tij ⎝ j=1

n ∑i = 1 Tij

3. Results 3.1. Basic input and output variables A set of basic input variables and the estimated parameters from the model are listed in Table 2. Generally, the EE values of all functional groups are less than 1, and most of the P/Q values are in between 0.05 and 0.3, meeting the requirements of a balanced model. Here, The EE values of all the commercial fishery objectives (e.g., fishes, shrimps and crabs) were much higher than 0.5. The EE values of all the functional groups and their respective continuous trophic levels are shown in Table 2. P/Q values for each group are also within the permissible range.

⎞ ⎟ ⎠

Finn’s Cycling Index (1976) measures the fraction of total system throughflow which is cycled, i.e., flow that returns to any specific compartment after having exited from that same compartment.

FCI =

Ci

i=1

Developmental capacity (C) is the maximum potential a system has to achieve further development. It is the theoretical upper limit of ascendency (Ulanowicz et al., 2009). It is calculated in the following way, ⎜

∑ βi

3.2. Food web structure and trophic analysis

TST TSTC

A more concisely web-like figure is also shown in Fig. 2 to indicate the whole interactions and energy flows in the estuarine food web.

where TSTC represents the amount of TST that is recycled within the system. FCI decreases following stressed condition of a system (Odum, 1985). The network properties ascendency and redundancy capture opposing tendencies in system organization, which can be combined in a single measure called system robustness The ratio a = A/C is an appropriate and standardized measure of degree of order and disorder in the system (Ulanowicz, 2009b). Value of robustness of a system ranges between 0 and 1, according to this idea. For a particular system, when the ascendency value approaches to developmental capacity, then the A/C value approaches 1. Contrary, as the ascendency value is very low, the A/C ratio reaches near 0. Empirical evidence from 17 ecosystems, mostly from estuarine and coastal marine habitats, illustrates that ecosystems find a balance between these properties (Ulanowicz, 2009a). This phenomenon specifies that natural systems maintain a balance between the organization (Ascendency) and overhead (redundancy) to achieve maximum robustness (Ulanowicz et al., 2009). Exergy (also called eco-Exergy, Jørgensen, 2007) is the amount of stored workable energy of a system when it is fetched into the thermodynamic equilibrium with its environment (Jørgensen, 1982, 1992a,b, 1997, 1999a). An ecosystem is inclined to move away from its thermodynamic equilibrium. The maximum possible value of exergy is possible when the system shows maximum distance from thermodynamic equilibrium (Jørgensen, 2007, 1999a, 1992a,b, 1982; Jørgensen et al., 2005). With the increasing complexity of organism exergy is going high; for example, phytoplankton has higher exergy than detritus due to higher information content. Exergy of a system

3.2.1. Transfer efficiencies (TEs) From the Lindeman spine of the system, two main food chains, a detritus-based food chain and a grazing food chain can be identified (Fig. 3) for the system under study. However, material flow along the grazing chain is 3249 t km−2 y−1, whereas 4822 t km−2 y−1 flows through the detritus food chain, despite the high biomass of the primary producers (184 t km−2 y−1) (Fig. 3). Seven discrete trophic levels (TL from I to VII) including all the functional groups of the HMES are also portrayed according to Lindeman (1942) (Fig. 3). The transfer efficiencies (TEs) are the ratios between sum of exports and flows predated by the next level and the throughput on the trophic level. TE from primary producers is 8.8% and from the detritus is 8.6%. Proportion of total flow originating from detritus is 0.51. Trophic level I is composed of primary producers and detritus which are further considered for analyses of the flows related to each group. Flows are mostly linked to TL I-II and fast declined for higher levels. The %TST is very high in TL-I. This emphasizes the impact of the primary producers and detritus compartments in the HMES. 3.2.2. Mixed trophic impact (MTI) The result of MTI shows both positive and negative impacts among every compartment of the system (Fig. 4). MTI routine is a tool for indicating the possible impact of direct and indirect interactions (including competition) in a steady-state system (Ulanowicz and Puccia, 1990). The groups with the most positive impact on most of the other groups are detritus and the producer viz. phytoplankton and benthic 7


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Fig. 3. Transfer efficiency of material from lower to higher trophic levels in the HMES. Amount of material flows along the grazing and detritus food chain are also represented.

Fig. 4. Mixed trophic impact plot of HMES indicating the positive and negative interactions among the functional groups.

3.2.3. Keystoneness From this routine, elasmobranch is seen to have the most relative total impact (RTI) in the system (1.0) followed by the medium benthopelagic (0.972), zooplankton (0. 853), birds (0.828) and polenemids (0.767) with all the other species having lower RTI values (Table 4). Taking into consideration the MTI and RTI values, the medium benthopelagic, elasmobranch, birds and zooplankton can be regarded as the most important compartments in terms of relative total impact in the HMES (Fig. 4, Table 4, see appendix). Combined with their position in the food web (Fig. 2), results indicate that zooplankton, medium benthopelagic fish and polynemids are efficient carriers for material transfer from lower TL to top predators. The major difficulty in constructing a food web model (or rather an ecological model) is the scarcity in data availability. In the current study, this inconvenience is overcome by using data obtained from published literature with similar consumer organisms (Piet, 1998) and also from direct field observations. The validity of the data used and the reliability and dependability of the model has been tested through the

algae and detritus. Interestingly, prawns and shrimps also show similar trends of positive impact on four other groups. In contrast, medium benthopelagic shows the most negative effects on other groups in comparison to others. Polynemids and elasmobranch impart high negative impact on medium demersal and large demersals respectively. Prey predator interactions, competition and cascading effects are observed among the compartments. It is also seen that most of the groups exerted a negative impact on itself probably due to competition for resources (Christensen et al., 2005). As zooplankton shows cannibalism, it has high negative impact on itself. A dominant trophic interaction between detritus and medium mesopelagic fish is also underlined by the highest positive impact value of the whole matrix (MTI 0.507, Fig. 4). On the contrary, negative impacts specify predation and/or competition. The highest negative impact is seen between mangrove and benthic herbivore (MTI −0.649); followed by polynemids on medium demersal (MTI −0.637), zooplankton on itself and birds on cluepids (MTI −0.570 and −0.502 respectively), indicating intraspecies competition and/or cannibalism among zooplankton.

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Vivekanandan et al., 2003; Mohamed and Zacharia, 2009) and also from direct field observations. Pedigree index and the associated ‘t’ measurement reveals the uncertainty of the data used for the model and the results are quite acceptable when compared to the pedigree value of other Ecopath models. Additionally, the model also makes out the importance to bridge in data gaps and highlights the requirement to progress knowledge about specific parts of the system. For instance, the microbial and microphytobenthos compartments, which could be imperative for the trophic structure and symbolize high biomass (AbarcaArenas and Ulanowicz, 2002; Rybarczyk et al., 2003), are not measured in the model. In spite of the problem of merging bacteria with detritus, bacteria is allocated to the detritus compartment here, as suggested by Christensen and Pauly (1992) and practiced by numerous authors. All the eleven fish groups in HMES having the total biomass 50.94 tonnes km−2 y−1 and shows EE values towards the higher range (> 0.5). Five fish groups have trophic level higher than 3. High predation pressure of the top predators poses a high exploitation rate to the middle trophic layer, such as zooplankton, prawns, benthic herbivore having EE values 0.951, 0.95, 0.814 respectively. This highly exploited middle layer gives less grazing pressure to the primary producers level as evident from low EE values of this level. This exerts top down effect in the system. The high exploitation of fish groups due to fishing pressure in this region because of high market value of this resource and this region serves as one of the most economically important export stations in south-east Asia. Though mangroves have the largest individual biomass among all the other components of the estuarine trophic structure, is not utilized in efficient manner by the consumers as evident from EE values. It may be due to the difficulty of successful utilization of live individuals (Christensen et al., 2005). It might also be an indication of non-availability of their predators in the ecosystem or in the model. Though crabs feed on the fallen leaves of the mangroves and the dead and decayoing parts mainly contribute to the detritus pool of the system (Mukherjee et al., 2013). EE value implies low exploitation of some fish groups (small demersal, medium demersal) which belong to the higher trophic levels. This may be due to the fact that export of these groups (via fishing) is not beneficial to the fishermen and is thus unusual. High predation pressure of top predators (mostly by many fish groups) exerts on middle trophic groups. Due to top-down effects the biomass of these middle groups decrease and in turn the biomass of primary groups (lower trophic levels) increase. However, external pressure such as overfishing on top predators balances this condition and maintains the normal functioning of this system. In present study, it is specified that both gazing pathway as well as detrital pathway are almost equally important in this estuary (consider the similar EE values for primary producers (except mangroves) and the detritus group). This fact is to some extent contradictory to most available literature where the detrital pathway is of higher importance (Fig. 3). But the transfer efficiencies (TE) varied greatly between the successive trophic levels. The initial values of TEs increase from TL-II to TL-III, towards the higher trophic levels. The TE between trophic level 1 (TL-I) and trophic level 2 (TL-II) of ecosystems is higher whereas, much food is available for TL-III. TE from primary producers is 8.8% whereas from detritus is 8.6%. TE put forward an example of low herbivore transfer efficiencies, higher efficiencies on trophic level 3 and lower efficiencies at the higher levels. Most of the production may not be originated from phytoplankton but also from the mangroves whose energy is only available to its consumer after decaying into detritus (Patrício and Marques, 2006). This kind of TE has already been seen in different systems (Christensen and Pauly, 1992; Baird and Ulanowicz, 1989). The mean transfer efficiency among different trophic levels of the ecosystem is 8.6% in our study, slightly lower than the 10% proposed by Lindeman (1942), but lying within the acceptable range of EE values reported in the published literature (Libralato et al., 2008; Pauly and Christensen, 1995). Among all groups of HMES, the maximum primary production is required by medium benthopelagic fish, followed

Table 3 System attributes and important ratios of HMES. Parameters

Value

Unit

Sum of all consumption Sum of all exports Sum of all respiratory flows Sum of all flows into detritus Total system throughput Sum of all production Mean trophic level of the catch Calculated total net primary production Total primary production/total respiration Net system production Total primary production/total biomass Total biomass/total throughput Total biomass (excluding detritus) Throughput cycled (excluding detritus) Predatory cycling index

9468.333 1346.267 6032.349 6129.079 22976.03 10702.32 2.347 9160 1.51848 3127.651 28.28844 0.01409326 323.8072 759.41 7.09

t/km2/yr t/km2/yr t/km2/yr t/km2/yr t/km2/yr t/km2/yr

Throughput cycled (including detritus) Finn's cycling index Finn's mean path length Ascendency Overhead Developmental capacity Average mutual information Shanon’s diversity index

2984.02 12.99 3.114 25799.0 61478.3 87277.2 1.123 3.799

t/km2/yr t/km2/yr /year t/km2 t/km2/yr % of throughput without detritus t/km2/year % of total throughput flowbits flowbits flowbits

pedigree routine for the EwE model. The current model has a pedigree index of 0.567 with a measure of fit 3.0 which shows that the model is reliable with a high level of confidence. 3.3. Ecosystem properties and ENA indices The statistics routine of Ecopath and flow indices list all the ecosystem attributes (Christensen et al., 2005; Lindeman, 1942; Ulanowicz, 1986) for the HMES in Table 3. The total system throughput of the estuarine ecosystem has reached 22,976.03 t km−2 y−1, of which 41.20% derived from consumption, 5.85% from exports and 26.25% from respiration with 26.67% eventually flowing into detritus. The values of ascendancy and overhead are 29.6% and 70.4% respectively for HMES (Table 3). 3.4. Ecosystem health assessment 3.4.1. Robustness For HMES, the robustness curve demonstrates that the robustness value for the system is near the apex (“window of vitality”, Ulanowicz et al., 2009) if we compare that with the hypothetical curve. 3.4.2. Exergy Table 5 (see appendix) lists the relative β values for different organisms proposed by Jørgensen (1997). It is applied to calculate the exergy of the system. Using the β values listed in Table 5 and the tonnes km−2 biomass in Table 2 following exergy of the each compartment of the system is obtained expressed as g detritus equivalents m-2 (Appendix 1, Table 5). Total exergy of the system is 20192.16315 gm detritus equivalents m−2 and specific exergy is 58.05563783. 4. Discussion 4.1. HMES trophic structure Salient features of ecosystem such as maturity, organization, fundamental processes involved with material and energy transfer can be determined by static modelling of a system using Ecopath as a tool. In the current study, data obtained from published literature with models having similar consumer organisms (Mustafa, 2003; Ullah et al., 2012; 9


J. Mukherjee et al.

by medium pelagic, small pelagic fish and meiobenthic carnivores. As revealed in the model, the trophic efficiency (TE) decreased as the trophic level increased. It emphasizes the significance of the primary production and detritus in the upholding of ecosystem health. Decreasing trend of TE values from lower to higher trophic level is in accordance with theoretical ecology (Abdul and Adekoya, 2016). Abundance of prey and predator of a particular organism specifies the position of that organism in a food chain. This means that, rise in the abundance of one species may be as a result of the decline of another species. High biodiversity strengthens the reliability of an ecosystem through increasing number of redundant species per functional group, whereas some groups with a particular TL are consistent in maintaining ecosystem functioning by compensating for temporary loss of other groups in the same or lower TL (Villanueva et al., 2006). Present study also showed that the biomass, production and consumption of TL II indicated the importance of zooplankton in the food web. Zooplankton serve not only the direct consumer of primary production and detritus, but also thrive as the main food for higher TL dominated by ominivorous fish species. The transfer efficiencies declined gradually from trophic levels III (11.3%), IV (5.3%) to V (5.1). This decline might also be endorsed to the alteration in energy used, as it is converted from one form to another. Similar observation of this is reported by Angelini and Agostinho (2005); Abdul and Adekoya (2016), where, top predators having high species richness induces increased redundancy and mediated decline in transfer efficiencies from one discrete TL to another. In present study elasmobranches has a high keystoneness index similar with some other studies such as, Bay of Biscaya (Ainsworth et al., 2001), Eastern tropical Pacific (Watters et al., 2003), Floreana of Galapagos (Okey et al., 2004) and Hong Kong (Buchary et al., 2002). This result emphasizes on the importance of mid trophic level organisms such as zooplankton, prawn, small pelagic fish in transferring energy from primary producer level to the top predators for the functioning of the system and thus highlighting bottom up effect. These results are in accordance with the MTI values. These results are comparable with other studies in coastal and semi-enclosed marine environments such as Northern and Central Adriatic Sea (Coll et al., 2007), Georgia Strait (Jarre-Teichmann and Christensen, 1998; Pauly et al., 1998; Walterset al., 2005), Chesapeake Bay (Baird and Ulanowicz, 1989), Bolinao reef (Alĩno et al., 1993) and Gulf of Thailand (Christensen, 1998; Walters et al., 2005). Elasmobranch and medium benthopelagic fish have the most negative impacts on other groups in the system as is evident from analysis of the MTI values. This is due to their overlapping feeding habit. MTI indicated the importance of detritus based food chain in HMES as detritus has been utilized as food by most of the groups in the system, where almost all the biomass is concentrated in the first level. Detritus and the producers show the maximum positive effects on other components of the system which is ecologically quite reasonable and can be explained following bottom-up effect in the system. Zooplankton and prawns and shrimps also show negative impact on most of the groups. In present study, Zooplankton serve as the prey for most of the fish groups, so it poses indirect effect on other groups. Similar outcome is also found in some other studies (Bondavalli and Ulanowicz, 1999; Scharler and Fath, 2009). According to Patten (1995) for ecosystem functioning the indirect effect is more important than direct effect. In present study trophic cascade effect is evident, i.e. predators have negative impact on their prey but has an indirect positive impact on prey of their prey (top down control). For example, medium benthopelagic fish has high negative impact on itself, small demersal fish, detritivorous fish, elasmobranches and filter feeder but indirect positive impact on large demersal fish that is consumed by elasmobranches. The trophic level of consumer groups ranged from 2 to 3.5 in HMES. Primary producers and detritus are the direct or indirect food sources for most of the groups indicating that these groups, mainly fish, are composed of juveniles which utilize this estuarine ecosystem as a

nursery area (Ray, 2008). This is consistent with other studies (Ray et al., 2000; Ray, 2008), which showed that HMES is the nursery bed for many species of fish and macrocrustaceans. These results suggest that some of the fish after juvenile maturity can take part in exporting energy out of the system because of natural emigration. 4.2. Ecosystem maturity and complexity linked with system health Based on ecological theories, the complexity and stability of the system could be indicated by the system omnivory index (SOI) and connectance index (CI). Furthermore, some system attributes – total system throughput (TST), ascendency (A), relative ascendency (A/C) and Finn’s cycling index (FCI) – can be related to maturity (Odum, 1969) and ecosystem stress (e.g., Patrıcio et al., 2004). Present study shows the detritus based organization and low transfer efficiency, which in turn directed towards a moderate FCI value (12.99%) and a high net system production (3127.651 tonne/km2/year), suggesting that HMES is not a highly mature ecosystem. CI and SOI could describe the system maturity since the food chain is expected to change from linear to web-like as the ecosystem matures. In HMES, the value of CI and SOI are 0.270 and 0.203. The values of HMES when compared with other systems give a mixed impression. CI and SOI values of different systems are as follows- Gulf of Maxico (CI and SOI 0.3) (Cruz-Escalona et al., 2007), Seine estuary (CI 0.24, SOI 0.11) (Rybarczyk and Elkaım, 2003), Bay of Somme (CI 0.25, SOI 0.01) (Rybarczyk et al., 2003), Narragansett Bay (CI 2.6, SOI 0.3) (Monaco and Ulanowicz, 1997), Delaware Bay (CI 2.6, SOI 0.3) (Monaco and Ulanowicz, 1997), Chesapeake Bay (CI 2.5, SOI 0.2) (Monaco and Ulanowicz, 1997), Gulf of Paria (CI 0.31). Tropical coastal estuarine ecosystem model shows less web-like features than the Gulf of Paria ecosystem. The low CI and SOI indicate that the system lacks complexity in food web structure and also in the diversity of diet composition. This corresponds to the fact as observed from other indices that the system is not a highly mature one, but moderately mature. Diversity of functional groups along with their feeding links affects the CI and SOI values of the system strongly (Abdul and Adekoya, 2016). With ecosystem development, as stated by Odum (1971), the ratio of total primary production to respiration (TPP/TR) and total primary production to biomass (TPP/B) are two important indicators to measure the maturity of an ecosystem. Ecosystems having TPP/TR values much higher or lower than 1 are thought to be immature, while only those with TPP/TR ratios approaching 1 are considered to be mature (Odum, 1969; PérezEspaña and Arreguin-Sánchez, 1999). In HMES, the TPP/TR ratio is 1.518, higher than most of the coastal areas, as southwest coast of India (1.34), Seine (1.37), Gironde (1.05), Kuosheng Bay (1.06), but much lower than Yucatan Peninsula, Mexico (15.9), Canche (22.6), Somme (15.5), St. Michel (6.1), Loire (139.59), Caete’ Estuary (3.3) (Vivekanandan et al., 2003; Vega-Cendejas and Arreguın-Sánchez, 2001; Selleslagh et al., 2012; Wolff et al., 2000). The ratio of total system biomass to the total system throughput (TB/TST) is directly proportional to system maturity. Comparing HMES with other systems, the presence of high zooplankton biomass and the low value (0.014) of TB/TST indicate that HMES ecosystem is moderately mature one (Christensen, 1995; Xu et al., 2001). This TB/TST is similar with some estuarine and lagoon system e.g., Seine estuary (0.01) (Rybarczyk and Elkaım, 2003), Bay of Somme (0.01) (Rybarczyk et al., 2003), Narragansett Bay (0.04) (Monaco and Ulanowicz, 1997), Delaware Bay (0.04) (Monaco and Ulanowicz, 1997), Chesapeake Bay (0.03) (Monaco and Ulanowicz, 1997), but lower than Gulf of Maxico (0.03) (CruzEscalona et al., 2007). According to Ulanowicz and Norden (1990) and Ulanowicz (1986), the ratio of Ascendency (A) to TST and overhead could also be the measurement of ecosystem growth and development. Ecosystems with high values of A (%) indeed reflect high levels of efficiency of the system. Ascendency in HMES (25799.0 flowbits) is higher than Canche (1439.5 flowbits), Somme (2401.6 flowbits), Seine (3944.3 flowbits), 10


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St. Michel (451.6 flowbits), Loire (647.0 flowbits), Gironde (939.5 flowbits), which suggested that the estuarine system has achieved efficiency to some extent towards its own functioning. The relative ascendancy (A/C) is the fraction of possible organization which is actually realized (Ulanowicz, 1986) in the system and it shows negative correlation with maturity (Christensen, 1995). Relative ascendency of different estuaries when compared with HMES is similar. Relative ascendency of Seine estuary (34.8%) (Rybarczyk and Elkaım, 2003), Bay of Somme (35%) (Rybarczyk et al., 2003), Swartkops estuary (28%) (Baird and Ulauowicz, 1993), Narragansett Bay (33.5%) (Monaco and Ulanowicz, 1997), Ems Estuary (38.2%) (Baird and Ulauowicz, 1993), Delaware Bay (33.4%) (Monaco and Ulanowicz, 1997), Chesapeake Bay (31.3%) (Monaco and Ulanowicz, 1997). Overhead or redundancy corresponds to the number of parallel pathways among the compartments and reveals the complexity of the system. This value illustrates a system with abundant connections that can withstand some disturbances. Therefore, the energy transfer can prolong through the trophic network by supplementary ways after these perturbations. The high overhead value indicates the stability of the ecosystem. Although it is immature, the high relative ascendency A/C (29.5%) and high overhead indicate that it is quite stable. A similar context is found in a study on Canche estuary (Sellesbagh et al., 2012). This suggests that HMES has significant ‘strength in reserve’. However, it is indistinct whether the ‘strength in reserve’ indicates resilience or resistance of the system under study. That is, the system may either be opposed to perturbations, or it may be flexible and ‘bounce back’ quickly (Cruz-Escalona et al., 2007). The system may have a blend of both the qualities. H′ typifies the flow distribution in accordance with the number of links in an ecosystem. The more specialization occurs in an ecosystem, the higher the AMI value relative to H′. AMI value is expected to be lower at early successional stage, i.e., immature stage, of the estuary and value would be higher at low disturbance condition. Here, AMI value is low (1.12) which is little lower from the Kromme, Swartkope and Sundays estuary (1.5, 1.6 and 1.9 respectively) (Scharler and Baird, 2005) which are also tropical estuaries with anthropogenic influence. The value of H′ which represents the diversity of flow within the system is also not very high (3.798) for HMES. It put forward the thought that HMES not very complex system and still have possibility to grow till maturity.

Fig. 5. Depiction of Robustness of HMES which specifies the relative order of the system.

(Mukherjee et al, 2015; Myers and Knoll, 2001; Chapin et al., 2000). Sustainability of the system can be achieved through the ability to maintain its metabolic activity and internal structure or organization. In general, more robust food webs exhibit smaller deviations from the original state of the system in its activity level when facing stress; this means sustainability is related to robustness of the system (Kharrazi et al., 2013; Fath, 2015). Robustness of HMES during the present study shows that the system is very near to the optimal robustness condition which may also be compared with the “window of vitality” by Ulanowicz et al. (2009). The trade-off between system’s organization and redundancy determines the robustness of the system. The robustness value of HMES shows that (Fig. 5) optimal functioning of the system, as can be found within the‘window of vitality’. Anthropogenic factors such as overexploitation of natural resources, pollution-industrial and domestic, mangrove deforestation and dam construction have played crucial role in degrading the quality and ecological value of estuaries (Whitfield and Elliott, 2002). These factors also seem to affect HMES where a significant stress has been forced during the last few decades (Hazra et al., 2010; Nath et al., 2004; Sinha, 2004). Exergy is a system performance indicator (Mandal et al., 2009; Ray, 2006; Ray et al., 2001). It should be expected that the ecosystems with relatively high eco-exergy are well-developed (mature) ecosystems, which entails that the specific eco-exergy is high due to a relative high concentration of developed organisms (with relatively high-values). If the eco-exergy and specific eco-exergy (exergy divided by biomass) is compared for the 26 ecosystems (Table 3; Jørgensen, 2007) HMES shows intermediate values, which again indicates that the system is moderately mature. It has similar status of eco-exergy storage with few tropical systems such as, Lake Tanganyika, Lake George, Campede Bank, Mexico, French Frig. Shoals and Venezuela. The specific exergy, is however not very high (58.05). It means that the average β-value is 58.05, which is only slightly more than filter feeders, prawn and shrimp. The ecosystem therefore cannot be considered very developed or highly mature. HMES has similar specific eco-exergy value with Laguna de Bay, Phillippines, Etang de Thau, France, Brunei and S. China Sea (Table 3; Jørgensen, 2007). All the ecosystem attributes discussed above have revealed the structure of HMES food web. All the ENA indices indicated the functional level and activities of this system through estimation of material flow pattern within the system. Analyses of these properties actually revealed the maturity and health of the system. Exergy calculation and comparison of this system indicates that the system is not at high developmental stage. The attributes of this estuary has been compared with other estuaries to compare its activity level and health with other similar systems. As this type of studies in tropical estuaries is scarce, so,

4.3. Cycling within the system Finn (1976) stated that FCI strongly correlates with ecosystem maturity, resilience and stability and recovery time (Vasconcellos et al., 1997). Indeed, cycling index is assumed to increase as systems mature and become more stable (Odum, 1969). This is because low cycling is highly dependent on energy passing rapidly through and is rather unstable and vulnerable to the changes in nutrient input (Christensen and Pauly, 1992). The value of FCI in HMES is 12.99%, lower than many systems; like in Caete’ Estuary (17.9%), Seine (16.10%) but higher than other estuaries such as, Canche (0.8%), St. Michel (0.64%), Loire (0.19%) and Gironde (3.99%) and similar like Somme (12.20%). Consequently, the higher the cycling index the more the ecosystem is released from stress (Guo et al., 2013; Jia et al., 2012; Liu et al., 2007). Most of the systems are found to vary FCI value ranging from 5 to 20% (Kones et al., 2009; Anh et al., 2015). For the current observation, it can be concluded that the HMES ecosystem is a not vulnerable to disruptions as it lies within the global range. 4.4. Relationship with ecosystem health Ecosystem health is a concept which is been made measurable by applying some indices and indicators. Anthropogenic activities even including expansion of agriculture, urban development and deforestation, affect the ecosystem health in a direct or indirect manner 11


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Declaration of interest

the comparison sometimes has been done with estuarine system beyond tropics also. Our overall study regarding the status of this ecosystem, it shows that the system is moderately mature. Generally, all mature estuarine ecosystem the detritivory is dominant over herbivory, but virtually this ecosystem is controlled equally by detritivory and herbivory (almost ratio is 1:1). Adjacent mangrove forest is the major source of detritus into this estuary. Increasing deforestation of the mangrove forest causes less production of detritus and hence the system does not show its maturity. Both the ecological indicators i.e. robustness and eco-exergy values for the HMES are been established as competent tools for assessing the ecosystem health. These properties along with the other ecosystem indices (ENA indices), have employed in agreement to evoke an effective representation of the system’s health. The indices establish the fact that this estuarine system is able to cope up with stress to a fair extent and is well organized in terms of ecological components.

Authors have no conflict of interest. Contributors JM carried out field sampling at the study site with the help of Samya Karan. JM executed the modelling procedure and data analysis and the research question design for this work. MB, NR and AB helped in manuscript preparation. Prof. Santanu Ray conceived the conceptual model design, manuscript preparation and correction. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.ecolind.2018.08.025. References

5. Conclusion Abarca-Arenas, L.G., Ulanowicz, R.E., 2002. The effects of taxonomic aggregation on network analysis. Ecol. Model. 149, 285–296. Abdul, W.O., Adekoya, E.O., 2016. Preliminary Ecopath model of a tropical coastal estuarine ecosystem around bight of Benin, Nigeria. Environ. Biol. Fish. 99 (12), 909–923. Ainsworth, C., Feriss, B., Leblond, E., Guénette, S., 2001. The bay of biscay, France: 1998 and 1970 models. Fish. Cent. Res. Rep. 9, 271–313. Alĩno, P.M., McManus, L.T., McManus, J.W., Nãnola Jr., C.L., Fortes, M.D., Trono Jr.,G.C., Jacinto, G.S., 1993. Initial parameter estimations of a coral reef flatecosystem in Bolinao, Pangasinan, northwestern Philippines. Trophic Modelsof Aquatic Ecosystems ICLARM Conf. Proc., 252–258. Angelini, R., Agostinho, A.A., 2005. Food web model of the upper Parana River foodplain: description and aggregation effects. Ecol. Model. 181, 109–121. Anh, P.V., Everaert, G., Goethals, P., Vinh, C.T., De Laender, F., 2015. Production and food web efficiency decrease as fishing activity increases in a coastal ecosystem. Estuar. Coast. Shelf Sci. 165, 226–236. Baeta, A., Pinto, R., Valiela, I., Richard, P., Niquil, N., Marques, J.C., 2009. δ 15 N and δ 13 C in the Mondego estuary food web: seasonal variation in producers and consumers. Mar. Environ. Res. 67, 109–116. Baird, D., Ulanowicz, R.E., 1989. The seasonal dynamics of the Chesapeake Bay ecosystem. Ecol. Monogr. 59, 329–364. Baird, D., Ulauowicz, R.E., 1993. Comparative study on the trophic structure, cycling and ecosystem properties of four tidal. Mar. Ecol. Prog. Ser. 99, 221–237. Baird, D., Christian, R.R., Peterson, C.H., Johnson, G.A., 2004b. Consequences of hypoxia on estuarine food webs and ecosystem function: energy diversion from consumer to microbes. Ecol. Appl. 14, 805–822. Baird, D., Asmus, H., Asmus, R., 2004a. Energy flow of a boreal intertidal ecosystem, the Sylt-Romo Bight. Mar. Ecol. Prog. Ser. 279, 45–61. Beverton, R.J.H., Holt, S.J. 1957. On the dynamics of exploited fish populations. Fisheries Investigation Series 2, volume 19, UK Ministry of Agriculture. Fisheries and Food, London, UK. Blaber, S.J.M., Cyrus, D.P., Albaret, J.J., Ching, C.V., Day, J.W., Elliott, M., Fonseca, M.S., Hoss, D.E., Orensanz, J., Potter, I.C., Silvert, W., 2000. Effects of fishing on the structure and functioning of estuarine and nearshore ecosystems. ICES J. Mar. Sci.: J. Conseil 57 (3), 590–602. Blomberg, B.N., Montagna, P.A., 2014. Meta-analysis of Ecopath models reveals secondary productivity patterns across the Gulf of Mexico. Ocean Coastal Manage. 100, 32–40. Bodini, A., 2012. Building a systemic environmental monitoring and indicators for sustainability: what has the ecological network approach to offer? Ecol. Indic. 15, 140–148. Bondavalli, C., Ulanowicz, R.E., 1999. Unexpected effects of predators upon their prey: the case of the American alligator. Ecosystems 2, 49–63. Borrett, S.R., Sheble, L., Moody, J., Anway, E.C., 2018. Bibliometric review of ecological network analysis: 2010–2016. Ecol. Modell. 382, 63–82. Brey, M.K., 2012. Quantifying the trophic interactions of introduced fishes on a reservoir food web. [dissertation thesis]. North Carolina State University, Raleigh (NC). Buchary, E.A., Alder, J., Nurhakim, S., Wagey, T., 2002. The use of ecosystem-basedmodelling to investigate multi-species management strategies for capturefisheries in the Bali Strait, Indonesia. Fish. Cent. Res. Rep. 10, 24. Chakraborthy, S.K., Choudhury, A., 1992. Ecological studies on the zonation of Brachyuran crabs in a virgin mangrove island of Sunderbans, Indian. J. Mar. Biol. Ass. India 34 (1–2), 189–194. Chakraborty, S.K., Devadoss, P., Manojkumar, P.P., Feroz Khan, M., Jayasankar, P., Sivakami, S., Gandhi, V., Appanasastry, Y., Raju, A., Livingston, P., Amcer Hamsa, K.M.S., Badruddin, M., Ramalingam, P., Dhareswar, V.M., Seshagirl Rao, C.V., Nandakumaran, K., Chavan, B.B., Seetha, P.K., 2000. The fishery, biology andstock assessment of jew fish resources of India. Mar. Fish. Res. Manage. 604–616. Chapin III, F.S., Zavaleta, E.S., Eviner, V.T., Naylor, R.L., Vitousek, P.M., Reynolds, H.L., Hooper, D.U., Lavorel, S., Sala, O.E., Hobbie, S.E., Mack, M.C., Díaz, S., 2000. Consequences of changing biodiversity. Nature 405, 234–242. Chea, R., Guo, C., Grenouillet, G., Lek, S., 2016. Toward an ecological understanding of a flood-pulse system lake in a tropical ecosystem: food web structure and ecosystem

This study was a preliminary attempt to establish an Ecopath model to estimate trophic interactions, trophic transfer and energy flows among 22 defined aquatic functional groups representing the species structure and composition in HMES. Present model indicated successful utilization of Ecopath to enlighten the trophic relationships, health and maturity of estuarine ecosystems. The simulated results obtained from the mass-balance model can provide some information for fishes and other aquatic groups. Ecosystem analyses must include a number of disciplines in a harmonized approach to respond specific questions regarding how big, multidimensional systems work (Jørgensen and Marques, 2001). Considering global parameters such as, TST, C, A, A/C, R, OI, it has been found that in the HMES values are very close to those estimated for some American, European and African estuaries but for some indices the values are quite unique. ENA offers a systematic approach to realize what is occurring at the whole-system level. The current study on this estuarine ecosystem seems to provide how the estimates of ENA channelize an improved understanding of ecosystem health. The trophic network of HMES is complex and heterogeneous in its area, depicting a clear benthic pelagic coupling of the food web. A good representation of the benthic compartments (zooplankton and fishes) is observed and the primary production (mangrove, phytoplankton, benthic algae) especially planktonic, is high in HMES. HMES can develop the ecological network further and increase the information embodied in the system. Almost 1:1 herbivory and detritivory ratio and low FCI indicate the disturbance in the system. Therefore, management of the HMES is needed for sustainable use of resources and maintaining biodiversity. Snapshots of the HMES structure and health interpreted using Ecopath software show that this moderately mature system has good robust nature and high ‘reserve’ (high redundancy) to balance any stress situation and maintain its normal functioning. Shallow systems, estuaries in particular, are dynamic rather than static systems, which seems inconsistent when they are described as stable systems (Selleslagh and Amara, 2008). In this respect, it would be fascinating in future to investigate the seasonal aspects in this model. Despite this, the present model seems realistic and could be a reference status for the trophic functioning of the HMES.

Acknowledgement Joyita Mukherjee would like to acknowledge Central Inland Fisheries Research Institute (CIFRI), Kolkata for providing necessary literature from the institute library. JM is grateful to the UGC Postdoctoral fellowship for Women (Ref. No. F.151/201415/ PDFWM201415 GEWES25053 (SAII)) for providing necessary funds.

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