Handbook of Ecological Indicators for Assessment of Ecosystem Health - Chapter 6 ppsx

Viaroli In order to study the management options in a coastal lagoon with intensiveshellfish Tapes philippinarum farming and macroalgal Ulva sp.. The model considers thenutrient cycles a

CHAPTER 6

Ecosystem Health Assessment and Bioeconomic Analysis in

Coastal Lagoons

J.M Zaldı´var, M Austoni, M Plus, G.A De Leo,

G Giordani, and P Viaroli

In order to study the management options in a coastal lagoon with intensiveshellfish (Tapes philippinarum) farming and macroalgal (Ulva sp.) blooms,

a biogeochemical model has been developed The model considers thenutrient cycles and oxygen in the water column as well as in the sediments,phytoplankton, zooplankton, and Ulva sp dynamics Furthermore, a discretestage-based model for the growth of Tapes philippinarum has been coupledwith this continuous biogeochemical model By studying the growth ofclams, it considers the nutrient contents in the water column as well as itstemperature, including the effects of harvesting and the mortality due toanoxic crisis The results from 1989 to 1999 show that the model is able tocapture the essential dynamics of the lagoon, with values in the same order ofmagnitude as the measurements from experimental campaigns and with data

on clam productivity The model has therefore been used to assess the effects

of Ulva’s mechanical removal on the lagoon’s eutrophication level using theexergy and specific exergy, as well as economic factors in terms of operatingvessel costs and averaged prices for clams as optimization parameters Theresults show that a combination of ecosystem models and health indicatorsconstitute a sound method for optimizing the management in such complexsystems.

6.1 INTRODUCTION

Coastal lagoons are subjected to strong anthropogenic pressures This ispartly due to freshwater inputs rich in organic and mineral nutrients derivedfrom urban, agricultural, or industrial effluents and domestic sewage, but alsodue to the intensive shellfish farming some of them support For example, theThau lagoon in southern France is an important site for the cultivation ofoysters (Crassostrea gigas) and mussels (Mytilus galloprovincialis) (Bacher etal., 1995) The Adriatic lagoons in northern Italy — the namely the Venice,Scardovari and Sacca di Goro lagoons — supported a production of around58,000 metric tonnes of clams (Tapes philippinarum) in 1995 (Solidoro et al.,2000), etc The combination of all these anthropic pressures call for anintegrated management that considers all the different aspects, from lagoonfluid dynamics, ecology, nutrient cycles, river runoff influence, shellfishfarming, macro-algal blooms, sediments, as well as the socio-economicalimplications of different possible management strategies However, histori-cally, coastal lagoons have been suffering from multiple and uncoordinatedmodifications undertaken with only limited sectorial objectives in mind — forexample, land-use modifications on the watershed affecting the nutrient loadsinto the lagoon; modifications in lagoon bathymetry by dredging or changingthe water circulation in the lagoon, and so on All these factors are responsiblefor important disruptions in ecosystem functioning characterized by eutrophicand dystrophic conditions in summer (Viaroli et al., 2001), algal blooms,oxygen depletion and sulfide production (Chapelle et al., 2001)

Obviously, to carry out such an integrated approach, biogeochemicalmodels that take into account the different mechanisms and importantvariables in the ecosystem are fundamental These models are able to handlethe complex link between human activities and the ecosystem functioning,something that is not possible to capture with more traditional statistical tools.However, in order to analyze the model results, it is necessary to use ecologicalindicators that will allow a comparison of the health of the ecosystem fromseveral scenario analyses Historically, the health of an ecosystem has beenmeasured using indices of particular species or components; for example,macrophytes and zooplankton Such indices are generally inadequate becausethey are not broad enough to reflect the complexity of ecosystems It istherefore necessary for the indicators to include structural, functional, andsystem-level aspects To cope with these aspects, new indices have been

developed (for a recent review see Rapport (1995)) Exergy and related values

— that is, structural exergy, specific exergy, etc — have recently been used toassess ecosystem health in freshwater ecosystems (Xu et al., 1999) as well asmarine ecosystems (Jørgensen, 2000)

We have studied, based on previously developed models (Zaldı´var et al.,2003a, 2003b) for Sacca di Goro, the effects of Ulva’s mechanical removal onthe lagoon’s eutrophication level using specific exergy (Jørgensen, 1997), andcosts and benefits (De Leo et al., 2002; Cellina et al., 2003) The costs areassociated with the normal operation of the vessels and with the disposal of thecollected Ulva biomass whereas the benefits consider the increased productivity

of shellfish as well as the decrease in mortality due to anoxic crises Foranalyzing the ecosystem health we used specific exergy calculated in terms ofbiomass of the different model’s variables and its information content(Jørgensen, 1997) The comparison between both approaches has allowed us

to develop a management strategy that improves the ecosystem health in Sacca

di Goro and at the same time reduces the economic losses associated with clammortality during anoxic crises

The Sacca di Goro (see Figure 6.1) is a shallow-water embayment of the PoDelta (44
470 to 44
500N and 12
150 to 12
200E) The surface area is

26 km2and the total water volume is approximately 40  106m3 Numericalmodels (O’Kane et al., 1992) have demonstrated a clear zonation of the lagoonwith the low-energy eastern area separated from two higher-energy zones,including both the western area influenced by freshwater inflow from the Po diVolano and the central area influenced by the Adriatic Sea The eastern zone(called Valle di Gorino) is very shallow (with a maximum depth of 1 m) and

Figure 6.1 General layout of Sacca di Goro with the main farming areas indicated in gray and

freshwater inflows by arrows.

accounts for one-half of the total surface area and for one fourth of the watervolume of the lagoon.

The bottom is flat and the sediment is composed of typical alluvial mudwith a high clay and silt content in the northern and central zones, while sand

is more abundant near the southern shoreline, and sandy mud is predominates

in the eastern area

The watershed, Burana-Volano, is a lowland, flat basin located in the PoDelta and covering an area of about 3000 km2 On the northern and easternside it is bordered by a branch of the Po River entering the Adriatic Sea Alarge part of the catchment area is below sea level with an average elevation of

0 m, a maximum elevation of 24 m and a minimum of 4 m About 80%

of the watershed is dedicated to agriculture All the land is drained (irrigated)through an integrated channel network and various pumping stations Pointand nonpoint pollution sources discharge a considerable amount of nutrients

in the lagoon from small tributaries and drainage channels (Po di Volano andCanal Bianco)

The catchment is heavily exploited for agriculture, while the lagoon is one

of the most important aquacultural systems in Italy About 10 km2 of theaquatic surface are exploited for Manila clam (Tapes philippinarum) farming,with an annual production of about 8000 metric tons (Figure 6.2) Fish and

Figure 6.2 Averaged prices for Tapes philippinarum in the northern Adriatic (Bencivelli, private

communication and Solidoro et al., 2000) and time evolution of estimated clams annual production in Sacca di Goro (Bencivelli, private communication).

shellfish production provides work, directly or indirectly, for 5000 people Theeconomical annual revenue has been varying during the last few years aroundE100 million.

Water quality is a major problem due to: (1) the large supply of nutrients,organic matter, and sediments that arrive from the freshwater inflows; (2) thelimited water circulation due to little water exchange with the sea (total waterexchange time is between 2 to 4 days); and (3) the intensive shellfishproduction In fact from 1987 to 1992 the Sacca di Goro experienced anabnormal proliferation of macroalgae (Ulva sp.), which gradually replacedphytoplankton populations (Viaroli et al., 1992) (see Figure 6.3) This was aclear symptom of the rapid degradation of environmental conditions and of

an increase in the eutrophication of this ecosystem

The decomposition of Ulva in summer (at temperatures of 25 to 30
C)produces the depletion of oxygen (Figure 6.4) that can lead to anoxia in thewater column In the beginning of August 1992, after a particularly severeanoxic event that resulted in a high mortality of farmed populations of musselsand clams, a 300- to 400-m-wide, 2-m-deep channel was cut through the sandbank to allow an increase in the sea water inflow and the water renewal in theValle di Gorino This measure temporarily solved the situation — during thefollowing years a reduction of the Ulva cover (Viaroli et al., 1995) and a clear

Figure 6.3 Measured annual trends of Ulva biomasses in the water column in the sheltered

zone of Sacca di Goro (Viaroli et al., 2001).

increase in phytoplankton biomass values were observed (Sei et al., 1996).However, in 1997 another anoxic event took place when an estimated Ulvabiomass of 100,000 to 150,000 metric tons (enough to cover half of the lagoon)started to decompose The economic losses due to mortality of the farmed clampopulations were estimated at aroundE7.5–10 million (Bencivelli, 1998).

6.3 SIMULATION MODELS

6.3.1 Biogeochemical Model

A model of the Sacca di Goro ecosystem has been developed and partiallyvalidated with field data from 1989 to 1998 (Zaldı´var et al., 2003a) The modelconsiders the nutrient cycles in the water column and in the sediments asschematically shown in Figure 6.5 Nitrogen (nitrates plus nitrites andammonium) and phosphorous have been included into the model, since thesetwo nutrients are involved in phytoplankton growth in coastal areas Silicatehas been introduced to distinguish between diatoms and flagellates, whereasconsideration of the dissolved oxygen was necessary in order to study theevolution of hypoxia and the anoxic events that have occurred in the Sacca diGoro during the past few years

Figure 6.4 Experimental annual trends of dissolved oxygen saturation concentrations in the

water column in the sheltered zone of Sacca di Goro (Viaroli et al., 2001).

With regard to the biology, the model considers two types of ton and zooplankton communities The phytoplankton model, based on theAquaphy model (Lancelot et al., 2002), explicitly distinguishes betweenphotosynthesis (directly dependent on irradiance and temperature) andphytoplankton growth (dependent on both nutrients and energy availability).The microbial loop includes the release of dissolved and particulate organicmatter with two different classes of biodegradability into the water (Lancelot

phytoplank-et al., 2002) Dphytoplank-etrital particulate organic matter undergoes sedimentation.Furthermore, the evolution of bacteria biomass is explicitly taken into account

In shallow lagoons, sediments play an important role in biogeochemicalcycles (Chapelle et al., 2000) The sediments have several roles: they act as sinks

of organic detritus material through sedimentation and they consume oxygenand supply nutrients through bacterial mineralization, nitrification and benthicfauna respiration Indeed, depending on the dissolved oxygen concentration,nitrification or denitrification takes place in sediments, and for thephosphorous the sediments usually act as a buffer through adsorption anddesorption processes For all these reasons, the model considers the sediments

to be dynamic

Ulvasp has become an important component of the ecosystem in Sacca diGoro The massive presence of this macroalgae has heavily affected the lagoonecosystem and has prompted several interventions aimed at removing itsbiomass in order to avoid anoxic crises, especially during the summer In thiscase, Ulva biomass as well as the nitrogen concentration in macroalgae tissuesare considered as other state variables (Solidoro et al., 1997)

Figure 6.5 General schema of the biogeochemical model for Sacca di Goro.

The state space of dynamical variables considered is summarized inTable 6.1 We consider 38 state variables: there are 5 for nutrients in the watercolumn and 5 in the sediments; organic matter is represented by 15 statevariables in the water column and 2 in the sediments; 11 state variablesrepresent the biological variables: 6 for phytoplankton, 2 for zooplankton,

1 for bacteria and 2 for Ulva

6.3.2 Discrete Stage-Based Model of Tapes Philippinarum

Knowing the importance of Tapes philippinarum in the Sacca di Goroecosystem, it is clear that a trophic model that takes into account the effect ofshellfish farming activities in the lagoon is necessary For this reason a discretestage-based model has been developed (Zaldı´var et al., 2003b) The modelconsiders six stage-based classes (seeFigure 6.5) The first one corresponds totypical seeding sizes whereas the last two correspond to the marketable sizes

‘‘medium’’ (37 mm) and ‘‘large’’ (40 mm) according to Solidoro et al (2000).The growth of Tapes philippinarum is based on the continuous growth modelfrom Solidoro et al (2000) that depends on the temperature and phytoplank-ton in the water column This model has been transformed into a variable stageduration for each class in the discrete stage-based model Furthermore, the

Table 6.1 State variables used and units in the biogeochemical model

Inorganic nutrients,

water column

Biological variables, Water column

Silicate mmol Si(OH) 4 /m 3 Micro-zoopk (40–200 mm) mg C/m 3

Dissolved oxygen g O 2 /m3 Meso-zoopk (>200 mm) mg C/m3Organic matter (OM),

water column

Bacteria Ulva Nitrogen in Ulva tissue

mg C/m 3

g dw**/l

mg N/g dw Monomeric dissolved

OM (C)

mg C/m3

Sediments Monomeric dissolved

OM (N)

mmol N/m 3 (i w ¼ interstitial waters)

Ammonium (i w.) mmol/m3Detrital biogenic silica mmol Si/m 3 Nitrate (i w.) mmol/m 3

High biodegradability:

Phosphorous (i w.) Inorganic adsorbed phosphor.

mmol/m3

mg P/g PS*** Dissolved polymers (C) mg C/m3 Dissolved oxygen (i w.) g O 2 /m3Dissolved polymers (N, P) mmol N, P/m 3 Organic particulate phosphor mg P/g PS Particulate OM (C) mg C/m 3 Organic particulate nitrogen mg N/g PS Particulate OM (N, P) mmol N, P/m3

effects of harvesting as well as the mortality due to anoxic crisis are taken intoaccount by appropriate functions, as well as the evolution of cultivable areaand the seeding and harvesting strategies in use in Sacca di Goro.

6.3.3 Ulva’s Harvesting Model

In order to model the Ulva biomass harvested by one vessel per unit of time,

we followed the model developed by De Leo et al (2002) assuming that thevessel harvesting capacity, q, is 1.3  05g dry weight per l (gdw/l) per hour,which corresponds approximately to 100 metric tons of wet weight of Ulva perday Therefore, we have incorporated into the Ulva’s model a term that takesthis into account:

HðU, EÞ ¼ q  E  RðUÞ if UðtÞ  Uth

result in an increase in benefits The total value obtained (CB ^ Costs 2

Benefits) is the difference between the costs associated with the operation of

the vessels as well as the disposal of the harvested Ulva biomass minus theprofits obtained by selling the shellfish biomass harvested in Sacca di Goro.

6.4 RESULTS AND DISCUSSION

6.4.1 The Existing Situation

Sacca di Goro has been suffering from anoxic crises during the warmseason Such crises are responsible for considerable damage to the aquaculture

Table 6.2 Parameters used to evaluate the genetic information content,

from Jørgensen (2000)

Ecosystem component

Number of information genes

Conversion factor (W i )

*Coffaro et al (1997), y Marques et al (1997), Fonseca et al (2000).

Table 6.3 Parameters used for the calculation of the exergy

for the Sacca di Goro lagoon model

C:dw (gC/gdw) –ln P i

industry and to the ecosystem functioning In order to individuate the mosteffective way to avoid such crises, it is important to understand the processesleading to anoxia in the lagoon Figure 6.6a shows the experimental andsimulated Ulva biomasses The model is able to predict the Ulva peaks and forsome years their magnitude For comparing experimental and simulatedresults we have assumed a constant area in the lagoon of 16.5 km2 As hasbeen observed in Viaroli et al (2001), the rapid growth of Ulva sp in spring

is followed by a decomposition process, usually starting from mid-June Thisdecomposition stimulates microbial growth The combination of organicmatter decomposition and microbial respiration produces anoxia in the watercolumn, mostly in the bottom water This is followed by a peak of solublereactive phosphorous that is liberated from the sediments

Oxygen evolution in the water column is highly influenced by the Ulvadynamics In fact, high concentrations are simulated in corresponding highalgal biomass growth rates Furthermore, when the Ulva biomass starts todecompose the oxygen starts to deplete Experimental and simulated data areshown in Figure 6.6c As can be seen, anoxic crises have occurred practicallyevery year in the lagoon

Figure 6.7shows the comparison between the estimated and simulated totalclam biomass in Sacca di Goro It can be seen that there is a general agreementbetween experimental and estimated values Oxygen also has a strong influence

Figure 6.6 Experimental and simulated Ulva biomasses; Chlorophyl-a and oxygen

concentra-tion in Sacca di Goro.