Thesis _ Luong Duc Anh

Faculty of Bioscience Engineering

Academic year 2011 2012

MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS:

A NETWORK ANALYSIS

Duc Anh Luong Promoter: Prof. Dr. Colin Janssen Co-promoter & Tutor: Dr. Frederik De Laender

Masters dissertation submitted in partial fulfillment of the requirements

for the degree of Master of Environmental Sanitation

i

COPYRIGHT

The author and promoters give permission to put this thesis to disposal for consultation and to copy

parts of it for personal use. Any other use falls under the limitations of copyright, in particular the

obligation to explicity mention the source when citing parts out of this thesis.

June 1st, 2012

Promoter

Prof. Dr. Colin Janssen

Co-promoter & tutor

Dr. Frederik De Laender

Author

Luong Duc Anh

ii

ACKNOWLEDGEMENTS

I am grateful to many people for help, both direct and indirect, in doing my thesis as well as my study

at Ghent University

First and foremost I would like to express my sincerest gratitude to my promoters: Prof. Dr Colin

Janssen, who has given me an opportunity to do my thesis in Laboratory of Environmental Toxicology,

and Dr. Frederik De Laender, who worked not only as my co-promoter but also as a tutor during my

thesis. This thesis cannot be finished without their encouragements and supports. I especially would

like to thank Dr Frederik De Laender because of his enthusiasm, patience, and sound advices. Under

the supervision of my promoters, I have gained not only much of knowledge in ecological modeling,

but also much of experiences in work organization for which I highly appreciate.

I would like to express my thankfulness to the colleagues in Norway charged by Prof. Olav Vadstein

and Prof. Yngvar Olsen for providing me with raw data from mesocosm experiment based on which I

have built the models.

Besides, I would like to thank VLIR who have provided me with financial supports, as well as all

teachers and staffs in Ghent University who made my learning desire become realistic. My sincere

thanks also go to CEC&T staffs, especially three wonderful coordinators: Veerle Lambert, Sylvie

Bauwens and Isabel Depotter, who have helped me a lot in organizing my life and my study in

Belgium. To all my colleagues at Environmental Sanitation Center, it is my honor to know you.

I owe my deeply gratitude to all my ex-teachers who have given me the knowledge and promotion to

pursue higher education level. I am grateful to Assoc.Prof.Dr. Luu Duc Hai, Assoc.Prof.Dr. Ho Thi Lam

Tra and Assoc Prof.Dr. Tran Duc Vien for their supports and encouragements.

Lastly, and most importantly, I wish to thank my family members, especially my parents. They raised

me, supported me, taught me, and loved me. To them I dedicate this thesis.

Luong Duc Anh

June 2012

iii

TABLE OF CONTENTS

LIST OF ABBREVIATION ...................................................................................................................... V

LIST OF FIGURES ................................................................................................................................ VI

LIST OF TABLES ................................................................................................................................. VII

ABSTRACT ......................................................................................................................................... VIII

INTRODUCTION AND GOALS .............................................................................................................. 1

1. LITERATURE REVIEW ...................................................................................................................... 2

1.1. FOOD WEBS ................................................................................................................................... 2

1.2. CLASSIFICATION AND CONTROL MECHANISMS OF PELAGIC MARINE FOOD WEBS ................................... 3

1.2.1. Herbivorous food webs versus microbial loops ..................................................................... 3

1.2.2. Bottom up versus top down control ....................................................................................... 5

1.3. CARBON FLOWS AND TRANSFER EFFICIENCY IN MARINE ECOSYSTEMS ................................................ 5

1.3.1. Carbon flows .......................................................................................................................... 5

1.3.2. Transfer efficiency ................................................................................................................. 6

1.4. ECOLOGICAL NETWORK THEORY ...................................................................................................... 8

1.4.1. Topological properties analysis ............................................................................................. 8

1.4.2. Estimation of network flows ................................................................................................. 10

1.4.3. Environmental extension of input-output analysis ............................................................... 11

1.4.4. Ecological network indices derived from information theory ................................................ 14

1.5. NUTRIENT ENRICHMENT OF MARINE ECOSYSTEMS ........................................................................... 15

1.5.1. Sources of nutrients for marine ecosystems ....................................................................... 15

1.5.2. Effects of nutrient enrichment on marine ecosystems ......................................................... 17

2. MATERIAL AND METHODOLOGY ................................................................................................. 20

2.1. THE MESOCOSM DATA ................................................................................................................... 20

2.2. ESTIMATION OF CARBON FLOWS IN THE MESOCOSMS BY LINEAR INVERSE MODELLING ...................... 21

2.2.1. Conceptual framework for applying Linear Inverse Models (LIM) ....................................... 21

2.2.2. Food web topology .............................................................................................................. 23

2.2.3. Data and constraints for set up of the linear inverse models ............................................... 24

2.2.4. Setup and solution of LIM .................................................................................................... 25

2.2.5. Analysis of the estimated carbon flows ............................................................................... 26

2.3. ECOLOGICAL NETWORK ANALYSIS .................................................................................................. 26

3. RESULTS ......................................................................................................................................... 27

3.1. CARBON FLOWS ........................................................................................................................... 27

3.1.1. Net primary production ........................................................................................................ 27

3.1.2. Response in net primary production of various phytoplankton groups ................................ 27

3.1.3. Total flows through phytoplankton (AUT), bacteria (BAC) and detritus (DET) .................... 28

iv

3.1.4. Carbon flows through phytoplankton, bacteria, and detritus to living compartments .......... 29

3.1.5. Carbon flows through the zooplankton compartments ........................................................ 31

3.2. TROPHIC STRUCTURE AND FOOD WEB EFFICIENCY .......................................................................... 32

3.2.1. Trophic levels of zooplankton .............................................................................................. 32

3.2.2. Dependency of zooplankton on DET ................................................................................... 33

3.2.3. Food web efficiency (FWE) calculated based on COP production ...................................... 35

3.3. CARBON CYCLING ......................................................................................................................... 35

3.3.1. Total system throughflow: cycled versus straight ................................................................ 35

3.3.2. Finns cycling index (FCI) and Average path length (APL) .................................................. 36

3.4. ECOSYSTEM STRUCTURE .............................................................................................................. 37

3.4.1. Total system flow throughput ............................................................................................... 37

3.4.2. Synergism ............................................................................................................................ 38

3.4.3. The dominance of indirect effect ......................................................................................... 38

3.4.4. The ratio of Ascendancy (A) to Development Capacity (C) ................................................. 39

3.4.5. Constraint efficiency ............................................................................................................ 40

4. DISCUSSION ................................................................................................................................... 42

4.1. CARBON FLOWS ........................................................................................................................... 42

4.1.1. Primary production .............................................................................................................. 42

4.1.2. Importance of bactivory, herbivory and detritivory in food webs .......................................... 42

4.2. TROPHIC STRUCTURE AND FOOD WEB EFFICIENCY (FWE) BASED ON COPEPODS PRODUCTION .......... 43

4.3. CARBON CYCLING ......................................................................................................................... 43

4.4. ECOSYSTEMS ACTIVITY AND ORGANIZATION .................................................................................... 44

5. CONCLUSION .................................................................................................................................. 45

v

LIST OF ABBREVIATION

A1 Autotrophic picoplankton

A2 Autotrophic nanoplankton

A3 Autotrophic microplankton

APL Average path length

AUT Phytoplankton

BAC Bacteria

CIL Ciliates

COP Copepods

DET Detritus

DIC Dissolved Inorganic Carbon

DOC Dissolved Organic Carbon

ENA Ecological Network Analysis

FCI Finns cycling index

FWE Food web efficiency

HNP Heterotrophic nanoplankton

ID Dominance of indirect effect index

JEL Jellyfish

LIM Linear Inverse Model

SED Sedimentation

vi

LIST OF FIGURES

Figure 1. An example of a marine food web ........................................................................................... 2 Figure 2. Conceptual representation of the microbial food web. ............................................................ 4 Figure 3. Coupled herbivorous food web and microbial loop .................................................................. 4 Figure 4. The pattern of carbon flow through a trophic compartment. .................................................... 7 Figure 5. Frequency distribution of trophic-level transfer efficiencies. .................................................... 8 Figure 6. Diagram of systems ecology network analysis. ..................................................................... 12 Figure 7. Conceptual framework for constructing and solving a LIM. ................................................... 22 Figure 8. Food web topology of the constructed LIM. ........................................................................... 23 Figure 9. Changes in total net primary production with increasing nutrient addition rate ..................... 27 Figure 10. Response of NPP to increasing nutrient addition rates (Bag 1 to Bag 7) averaged over time of different phytoplankton groups (a) and the contribution of these groups to the total NPP (b). ......... 28 Figure 11. Changes in total flows through phytoplankton (AUT), detritus (DET) and bacteria (BAC) compartments with increasing nutrient addition rate ............................................................................ 29 Figure 12. Changes in flows from phytoplankton, detritus and bacteria to higher trophic levels with increasing nutrient addition rate. ........................................................................................................... 30 Figure 13. Total carbon flows through zooplankton compartments ...................................................... 31 Figure 14. Changes in diet of the zooplankton groups with increasing nutrient addition rate. .............. 33 Figure 15. Chaneges in dependency of Hetereotrophic nanoplankton (HNP), Ciliates (CIL), Copepods (COP) and Jelly fish (JEL) on detritus with increasing nutrient addition rate ........................................ 34 Figure 16. Food web efficiency calculated based on COP production. ................................................ 35 Figure 17. Changes in total system throughflow cycled (a) and total system throughflow straight (b) with increasing nutrient addition rates from Bag 1 to Bag 7 overtime. .................................................. 36 Figure 18. Changes in Finn Cycling Index (a) and Average Path Length (b) over time with increasing nutrient addition rates (Bag 1 to Bag 7). ............................................................................................... 36 Figure 19. Total system throughput vary over time with increasing nutrient addition rates .................. 37 Figure 20. Synergism index vary over time at different nutrient addition rates (Bag 1 to Bag 7) .......... 38 Figure 21. Changes in dominance of indirect effect overtime with increasing nutrient addition rates. . 39 Figure 22. Changes in relative ascendancy (A/C ratio) and relative internal ascendancy (Ai/Ci) over the experiment with increasing nutrient addition rates .......................................................................... 40 Figure 23. The variation of constraint efficiency overtime with increasing nutrient addition rates ........ 40

vii

LIST OF TABLES

Table 1. Gross primary production of various pelagic marine environments. ......................................... 6 Table 2. Definition of different types of transfer efficiency. ..................................................................... 7 Table 3. Definitions of food web concepts. ............................................................................................. 9 Table 4. Four emergent network properties and mathematical tests to determine their presence. ...... 13 Table 5. Some information measures of ecological networks. ............................................................. 15 Table 6. Daily nutrient addition rates applied in the 7 mesocosms. ...................................................... 20 Table 7. Classification of sampled species groups and the dominant organisms. ............................... 20 Table 8. The constraints on food web flows of carbon. ........................................................................ 25 Table 9. Changes in trophic level of zooplankton with increasing nutrient addition rate. ..................... 32

viii

ABSTRACT

This study constructed a Linear Inverse Model in combination with a Ecological Network Analysis to

quantify the response of marine ecosystems to nutrient stress. A data set from a single factor

mesocosm experiment (nutrient addition rate, balanced N:Si:P) that ran for 18 days was used to

construct this model. Specifically, nutrients were added with elemental ratio for N:Si:P of 16:16:1 and

daily nitrogen addition rate (LN) increased from 0 g l1 d1 (Bag 1) to 30.2 g l1 d1 (Bag 7). At low

nutrient addition rates (LN < 17.8 g l1 d1), carbon flows through the detritus compartment dominated

the carbon flows at the base of food webs (i.e. carbon flows through detritus, bacteria and

phytoplankton), and total gross primary production was only greater than detritus production at very

high nutrient addition rates (LN of 17.8 and 30.2 g l1 d1, respectively). However, regardless of the

nutrient treatment, detritus was more important as a food source for zooplankton than bacteria and

phytoplankton. Food web efficiency (FWE) - calculated by dividing copepod production by net primary

production - reduced with increasing nutrient addition rate. FWE ranged between 0.11% (Bag 7 with

highest nutrient addition rate) and 1.4% (Bag 1 with no nutrient added).

Based on the full estimation of carbon flows in the food webs by the Linear Inverse Model, ecological

network indices were calculated. Similar to the FWE, carbon cycling quantified using the Finns

cycling index (FCI) decreased with increasing nutrient addition rates. For example, the mean FCI in

Bag 1 was more than two times higher than the FCI in Bag 7 (73.2 vs. 32.1%, respectively). This

resulted in high values for the average path length and for the dominance of indirect effects, which

co-varied with FCI. System activity increased with increasing nutrient addition rate, and the system

was demonstrated to depend less on exogenous carbon sources (i.e. relative ascendancy and relative

internal ascendancy only differed marginally).

We conclude that detritus plays an important role in the carbon budget of the considered food web and

that nutrient stress changed ecosystem functioning. Cycling of carbon and the efficiency with which it

was transformed into zooplankton biomass decreased as nutrients were added. Lastly, the food web

under study was less dependence on exogenous carbon sources.

1

INTRODUCTION AND GOALS

Marine ecosystems provide a wide range of provisioning services, regulating services, cultural

services and supporting services to man (UNEP, 2006). However, they are under increasing

anthropogenic pressures. These anthropogenic activities affect marine ecosystems either directly (e.g.

overfishing, habitat modification) or indirectly (e.g. changes in equilibrium state between atmospheric-

ocean system).

A human impact on marine ecosystems that has been received much attention is nutrient enrichment.

It can result from various sources like the discharge of wastewater from industrial, agricultural and

municipal activities, seepage of groundwater contaminated with nutrients, marine aquaculture

activities (Arhonditsis et al., 2000; Caccia and Boyer, 2007; Tovar et al., 2000) and atmospheric

deposition induced by burning fossil fuels (Smith et al., 1999). Nutrient enrichment has been shown to

cause many changes in ecosystem structure and functioning (Raffaelli, 1999; Valiela et al., 1992).

Hence, studying and understanding these changes plays an important role in marine ecosystem

management. This requires techniques that allow for quantifying interactions between individual

species group as well as characterizing the whole ecosystem status.

Linear inverse modelling was first applied in ecology by Vezina and Platt (1988) and subsequently

used widely in ecological modeling (e.g. De Laender et al., 2010b; Kones et al., 2006; Van Oevelen et

al., 2010). This approach has been proved useful and relevant for quantifying energy and matter flows

transferred between different compartments in aquatic food webs from incompletely observed data

sets (Marquis et al., 2007; Niquil et al., 1999; Tortajada et al., 2012; Vezina and Pahlow, 2003). These

energy and matter flows can be then used as an input for Ecological Network Analysis (ENA), which

aims to characterize the structure and function of ecosystems through a set of indices (Niquil et al.,

1999; Tortajada et al., 2012; Ulanowicz, 1980, 1984; Ulanowicz and Abarca-Arenas, 1997). Based on

these indices, the status of an ecosystem can be evaluated as well as be compared with others (Baird

et al., 1991b; Baird and Ulanowicz, 1993; Heymans et al., 2007).

The goal of this thesis is to construct a Linear Inverse Model (LIM) and subsequently an Ecological

Network Analysis (ENA) to investigate changes in the structure and functions of an experimental

marine ecosystem exposed to nutrient stress. To do this, the following tasks were conducted:

1. Estimate all carbon flows in the exposed food webs using a LIM developed in this thesis and a

data set from a single factor mesocosm experiment (nutrient addition rate, balanced N:Si:P).

2. Examine the changes in key carbon flows (e.g. primary production, bacterial production) and

assess food web efficiency (FWE) at different nutrient addition rates.

3. Calculate ecological network indices that characterize food web structure and functioning and

investigate the effect of nutrient addition rates on them.

LITERATURE REVIEW MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS

2

1. LITERATURE REVIEW

1.1. Food webs

In ecosystems, organisms not only interact with their abiotic environment, but also exchange energy

and matter with other living organisms (Kumar, 1995). Food webs, which have become a central focus

of ecological studies at least since Darwins time, describe the trophic relationship between different

species in a community, in which all organisms consume and are consumed by other organisms

(Menge, 2008; Paine, 1988). Food webs can be visualized by means of simple descriptive diagrams,

which depict the general trophic structure of the community under study (Figure 1).

Figure 1. An example of a marine food web

(Source: http://oceanworld.tamu.edu/resources/oceanography-book/marinefoodwebs.htm)

Because of the complexity in species composition, even in a simple community, ecologists usually

resolve the food webs into different compartments with various degrees of trophic aggregation,

ranging from very general groups based on mode of feeding or size (e.g. heterotrophic

nanoplankton, microzooplankton, mesozooplankton) to highly specific groups (species) (Baird et

al., 1991a; Cohen and Briand, 1984; Menge, 2008; Olsen et al., 2007).


3

1.2. Classification and control mechanisms of pelagic marine food webs

1.2.1. Herbivorous food webs versus microbial loops

By the middle of 20th century, some ecologists conducted pioneering experimental studies about

species interactions in rocky shore intertidal habitats, demonstrating the specific advantages of marine

systems as model systems for community analysis (Menge, 2008). Food web structure in different

regions of the world have adapted to the regional circulation and climate conditions. Most marine food

webs can be classified into two groups, namely: herbivorous food webs and microbial loops (De

Laender et al., 2010b; Legendre and Rassoulzadegan, 1995). These types of food webs differ in terms

of the energy sources they rely on (De Laender et al., 2010b).

The marine herbivorous or classical marine food webs consist of the producers belonging to

phytoplankton groups (e.g. large diatom). Phytoplankton utilizes solar radiation as the primary source

of energy via photosynthetic process. Energy and matter are transferred in the food web by grazing of

herbivores and subsequently by carnivores. These food webs are characterized by short and simple

energy and material pathways with a high potential for carbon export (e.g. via sedimentation of algal

aggregates) (De Laender et al., 2010b; oli et al., 2010). On the other hand, these classical food

webs have been considered as being efficient in transferring energy and matter from phytoplankton to

fishes or higher trophic levels (e.g. marine birds, mammals) in productive zones (e.g. upwelling

ecosystems) (Pavs and Gonzlez, 2008). These food webs mainly occur in nutrient rich

environments and were previously thought to consist of large phytoplankton. However,

nanophytoplankton (2-20 m) as well as picophytoplankton are now increasingly recognized as

important constituents in plankton communities (De Laender et al., 2010b; Fileman and Burkill, 2001;

oli et al., 2010).

Azam et al. (1983) proposed the hypothesis of the microbial loop in which bacteria play a role as

producers, processing significant quantities of organic matter, which can be fed on by larger

zooplankton (Figure 2). As opposed to phytoplankton in herbivorous food webs, bacteria in the water

column utilize dissolved organic matter (DOM) as an energy source (Azam et al., 1983). This energy

source for bacteria is generated through exudation of phytoplankton (Sharp, 1977), sloppy feeding

from zooplankton (e.g. Copepods) (Mller, 2005), viral lysis of phytoplankton and bacterial cells

(Fuhrman, 1992), or excretion by zooplankton (Saba et al., 2011). Among these autochthonous

sources, Fuhrman (1992) regarded the first two as the main food sources for bacteria, next to

allochthonous sources in estuaries and coastal zones (Mantoura and Woodward, 1983). Bacteria are

grazed by heterotrophic nanoflagellates (HNF), which in turn are preyed upon by heterotrophic protists

(e.g. ciliates) and larger zooplankton (e.g. copepods).


4

Figure 2. Conceptual representation of the microbial food web. (Source: Landry (2009))

The classification of pelagic marine food webs into the two aforementioned groups is merely a

theoretical exercise. Actually, both of them are simultaneously present in most ecosystems and are

well-linked (Pavs and Gonzlez, 2008). For example, both heterotrophic nanoflagellates and

microzooplankton can be preyed upon by mesozooplankton (e.g. copepods), thus playing a role as a

linkage between the microbial and herbivorous food webs (De Laender et al., 2010b; Sherr and Sherr,

1998). However, the relative importance of the two food web types varies with the environmental

conditions. Microbial food webs may be more dominant in oligotrophic environments where most of

the necessary nutrients are recycled through the grazing of protozoa on picoplankton (Goldman et al.,

1985). The relative importance of the microbial loop decreases in productive conditions (Cotner and

Biddanda, 2002)

Figure 3. Coupled herbivorous food web and microbial loop (Sherr and Sherr, 1998)


5

1.2.2. Bottom up versus top down control

Food web structure is regulated by interactions between a set of biotic and abiotic factors in the

ecosystem. The question which mechanisms control the biomass of a population, or more broadly the

food web structure, has been of concern among ecologists from the 1960s on (Hairston et al., 1960;

McQueen et al., 1989; Menge and Sutherland, 1976). Although ecologists have agreed on the

importance of trophic interaction in determining distributions and abundance of organisms, they still

debate on the relative strength of bottom-up and top-down control (Power, 1992).

The effects of nutrient enrichment on food web structure depends on the type of control that governs

the abundance of the various trophic levels, i.e. bottom-up or top-down (Loeuille and Loreau, 2004). If

bottom-up control is dominant, i.e. the biomass of each trophic level is controlled by the amount of its

resources, the biomass will increase at all trophic levels. For example, increases in nutrient supply

from bird guano modified community structure via enhancement of algal production, resulting in the

increased growth of limpets and greater abundance of algal-dwelling invertebrates (Bosman et al.,

1986; Bosman and Hockey, 1986). On the contrary, if top-down control is dominant, i.e. the biomass

at each trophic level is controlled by the level above it, nutrient enrichment will increase the biomass of

top predators and all odd-numbered lower trophic levels, but it will leave even-numbered

compartments of the food chain unaffected (Smith, 1969). The removal of a predator is expected to

yield an effect on the biomass of other trophic levels. This effect depends on the type of control that

drives the food web (small influences of predator removal if bottom-up control prevails, major effects if

top-down control dominates).

Determining the relative importance of and linkage between top-down and bottom-up controls is

crucial to understanding variation in community structure. However, this relationship changes over

time depending on the environmental conditions. oli et al. (2010) indicated the changes in the

control mechanism toward microbial food web structure in Vranjic basin with changing environmental

trophic status. Structural changes in the pelagic food web resulted in a shift from bottomup and top

down control of some groups of microorganisms, including bacteria. In eutrophic condition, bacteria

were controlled by bottom-up mechanism, whereas, heterotrophic flagellates became the controlling

factor (top-down control) in oligotrophic conditions. The results of this were consistent with some other

studies (Billen et al., 1990; Gasol et al., 2002).

1.3. Carbon flows and transfer efficiency in marine ecosystems

1.3.1. Carbon flows

Ecosystems normally include primary producers, decomposers and detritivores, a pool of dead

organic matter, herbivores, carnivores and parasites plus the physicochemical environment that

provides the living conditions and acts both as a source and a sink for energy and matter. In the

marine pelagic environment, phytoplankton and cyanobacteria are the main producers that are


6

responsible for generating primary production. These organisms have the ability to absorb solar

radiation to utilize CO2 as a carbon source for synthesizing organic matters via photosynthesis, the

starting point for carbon transfer in ecosystems. The total amount of carbon fixed by photosynthesis is

referred to as gross primary production (GPP; e.g. in gC.m-2.year-1) (Begon et al., 2006). Castro and

Huber (2003) summarized the typical values of GPP for various pelagic marine environments (see

Table 1). GPP can used as basis for the classification of marine ecosystems into oligotrophic (500 gC.m-2.year-1) ecosystems (Kaiser et al., 2005). However, it should be noted that

the GPP of a given ecosystem can vary considerably with time, both seasonally and inter-annually.

For example, GPP in the Dutch Wadden Sea increased up to more than 400 gC.m-2.year-1 until the

1990s, followed by a decline to 200-250 gC.m-2.year-1 in 2000 (Cade and Hegeman, 2002).

Table 1. Gross primary production of various pelagic marine environments.

Pelagic environment GPP (gC.m-2.year-1)

Artic Ocean 1-100

Southern Ocean (Antarctica) 40-260

Subpolar areas 50-110

Temperate areas (Oceanic) 70-180

Temperate areas (Coastal) 110-220

Central ocean gyres 4-40

Coastal upwelling areas 110-370

Not all the organic matter synthesized by primary producers is available for consumers. Phytoplankton

use part of the carbon fixed through photosynthesis for their maintenance, accounting for 5 to 30% of

GPP (Vezina and Platt, 1988). On the other hand, some carbon is released to the environment via

exudation of phytoplankton in form of DOC, which varies from less than 1% up to 40% of total carbon

fixed (Fogg, 1983; Lignell, 1990; Smith and Wiebe, 1976). This is an important source of DOC for

heterotrophic bacteria in the water column (Azam et al., 1983). In addition, part of primary production

will be lost via sedimentation.

1.3.2. Transfer efficiency

As can be seen from Figure 4, a proportion of the carbon is lost when transferring from one trophic

level to the next. The determination of transfer efficiencies in planktonic food webs is of great value in

understanding the dynamics and energetics of aquatic ecosystems (Kumar, 1995). The utilization of

primary production in the pelagic zone very often depends on the nature of the dominant species of

producers and consumers. For example, in a system of nano-planktonic algae macroconsumer (e.g.


7

calanoid, cladocerans) effective utilization occurs mostly via grazing due to suitable size of preys in

relation to comsumers. On the other hand, in the case of large algae (e.g. colonial forms,

dinoflagellates, cyanophytes) and smaller consumers, primary production is mainly utilized via

bacterial detritus medium.

Figure 4. The pattern of carbon flow through a trophic compartment (modified after Begon et al. (2006)).

Begon et al. (2006) indicated three major categories of efficiency in carbon flow transfer: (a)

consumption efficiency; (b) assimilation efficiency; and (c) net production efficiency (see in Table 2).

Table 2. Definition of different types of transfer efficiency.

Type Definition

Consumption efficiency (CE)

CE = In/Pn1 100

The percentage of total productivity available at one trophic

level (Pn1) that is actually consumed (ingested) by a trophic

compartment one level up (In).

Assimilation efficiency (AE)

AE = An/In 100

The percentage of carbon taken up by consumers in a trophic

compartment (In) that is assimilated across the gut wall (An)

and becomes available for growth or maintenance.

Production efficiency (PE)

PE = Pn/An 100.

The percentage of assimilated carbon (An) that is

incorporated into new biomass (Pn).

Trophic level transfer efficiency

TLTE = Pn/Pn1 100

EE x AE x PE = consumer production/prey production.


8

Consumption efficiency is highest in phytoplankton-dominated communities (about 50%) because the

consumers can obtain greater density and the proportion of structural tissue in producers is lower in

these systems than in terrestrial communities (Begon et al., 2006). However, the consumption

efficiency of carnivores feeding on their prey is less well known. Typical values of assimilation

efficiency for herbivores, detritivores, and microbivores are quite low (20-50%), whereas assimilation

efficiency can be up to 80% for carnivores. The concept of assimilation efficiency is not applicable for

bacteria because they digest food externally. As far as production efficiency is concerned, it depends

much more on the taxonomic class of organisms. While invertebrates have production efficiencies of

30-40%, their vertebrate counterparts exhibit much lower efficiencies with about 10% for ectotherms

and only 1-2% for endotherms (Begon et al., 2006). Pauly and Christensen (1995) re-estimated the

trophic level transfer efficiency based on 48 empirical trophic models of aquatic ecosystems and found

the mean of 10.130.49% which is close to assumed value of 10% from Linderman (1942).

Figure 5. Frequency distribution of trophic-level transfer efficiencies in 48 trophic studies of aquatic communities (source: Begon et al. (2006) after Pauly and Christensen (1995)).

1.4. Ecological network theory

Network theory has been applied in various fields of research, including food web ecology. Network

theory is employed by food web ecologists in many ways, e.g. to represent trophic relations in food

webs and more generally flows of energy and matter in ecosystems. In these trophic networks,

species are usually classified into different functional groups which are expressed as nodes while the

presence of energy and matter transfers and transformations are represented by links (Borrett et al.,

2007).

1.4.1. Topological properties analysis

Description of feeding relationships among species has been under study at least from the 1800s;

however, quantitative, comparative studies on potential generalities in the network structure of food

webs did not arise until the late of 1970s (Dunne, 2006). The topological properties of empirical food

webs that were first analyzed emerged from research on ecological diversitystability relationships


9

(e.g. MacArthur (1955)). MacArthur (1955) concluded that the stability increases as the number of

links increases and that stability can be achieved either by large numbers of species with a fairly

restricted diet, or by a smaller number of species eating a wide variety of other species. On the other

hand, May (1972, 1973) held the opposite view in which simple, abstract communities of interacting

species will tend to change sharply from stable to unstable behavior as the complexity of the

ecosystem increases. During last decades of the 20th century, there was a transformation in ecology

from questions about stability to questions about ecosystem responses to perturbations and the

relationship between ecosystem complexity and stability (McCann, 2000).

There is a rigorous set of definitions of food web concepts which have been developed to examine the

structure of food webs (Cohen, 1978; Cohen and Briand, 1984) (see in Table 3).

Table 3. Definitions of food web concepts.

Concept Definition

Trophic species Set of species with the same diets and same predators.

Links (trophic links,

edge, direct effects)

The connection between consumer and prey.

Basal species Species at the base or bottom of the food web feeding on no other species

but being fed on by others.

Intermediate species Species that are both prey and predator.

Top predator Species feeding on basal or intermediate species with no predator of their own.

Trophic level Number of links +1 between a basal species and the species of interest.

Food chain Path of links from a basal to top species.

Cycle (feeding loop) Directed sequence of links starting and ending at the same species.

Community webs Entire set of feeding relationships.

Omnivory Predation on prey occurring on more than one trophic level.

Ecologists have attempted to make generalizations about the structure of natural food webs by

formulating the relationship between some parameters derived from food web topology, such as:

number of species (S), number of links (L), connectance (C). Connectance refers to the probability

that any two species will interact with each other. It can be expressed either as C=L/S2 in which all

potential directed trophic links among S species are taken into account or C=L/[S(S-1)/2] when loops

are excluded. There has been a great deal of contributions studying the relationships between linkage

density (L/S), the scale or size (S) of the community and ecosystem stability and diversity (Dunne,

2006). Based on the trends in published webs, three scaling laws have been proposed (Briand and


10

Cohen, 1984; Cohen and Newman, 1985). The first, the species scaling law, proposes that the

proportions of basal, intermediate, and top species do not vary with the total number of species (S) in

the web, and are approximately 0.19, 0.52 and 0.29, respectively. The second, the link scaling law,

postulates that the mean fraction of links between top and basal, top and intermediate, intermediate

and intermediate, and intermediate and basal links remain invariant with S at respective values of

about 0.08, 0.35, 0.3, and 0.27. The third, the link species scaling law states that the total number of

links (L) is proportional to S and that mean linkage density (L/S) does not vary with S at about 1.86.

Most ecologists readily acknowledged problems with resolving taxa within food webs in gross and

uneven ways, potential impacting the scaling laws (Martinez, 1993; Pimm et al., 1991). For example,

some food web studies include various whale species as distinct compartments, whereas other make

whales as a single group that feeds on plankton, macroinvertebrates, and seals. Martinez (1993)

analyzed 11 large food webs and found significant effects of taxonomic resolution on food web

structure. Particularly, mean chain length, linkage density, and the fraction of intermediate species as

well as links between them decreased as the number of trophic species decreased because of trophic

aggregation. However, proportions of top species, basal species and links between them increased.

These findings contrasted with the scaling laws, which stated that most topological properties are

robust to the number of trophic species, which in turn depends on the degree of species aggregation.

These results also supported the hypothesis of scale-dependence, which was first tested statistically

by Schoener (1989). Thus, early patterns of scale invariance are due to artifacts of poorly resolved

data, whereas scale dependence of most topological properties is likely to be observed across higher

quality datasets (Dunne, 2006).

1.4.2. Estimation of network flows

Along with the topological analyses mentioned above, there are other types of ecological network

analysis which focus on quantifying energy and matter transfer and cycling. Many ecological studies in

the past concentrated on the qualitative description of feeding relationship, and to a lesser extent on

quantifying the main material and energy flows. This results from the fact that not all material and

energy flows in food web can be readily measured (Van Oevelen et al., 2010). Therefore, a lot of effort

has been devoted to finding a framework for incorporating observational data and empirical data in

food web reconstruction. Vezina and Platt (1988) are pioneers in the use of Linear Inverse Models

(LIMs) to estimate unobserved flows in food webs. These estimations are based on incomplete

observed datasets, physiological constraints from the literature, food web topology and the mass

balance principle. LIMs have been applied in marine ecosystems for a wide variety of purposes, e.g.

characterization of planktonic food webs (Niquil et al., 1999), analysis of planktonic food web

dynamics (Marquis et al., 2007), comparative studies about the response of different coastal system to

nutrient enrichment (Olsen et al., 2006), or in ecological risk assessment (De Laender et al., 2011).


11

The problem with application of LIMs in ecology is that there are usually more unknown food web

flows than formulated equations, with an average ratio of 4 to 1 (Vezina and Pahlow, 2003). Therefore,

solving a LIM generates an infinite amount of possible solutions of food web flows (Van Oevelen et al.,

2010; Vezina and Platt, 1988). One of the approaches to determine the best solution was proposed by

Vezina and Platt (1988), later followed by many other scientists (e.g. Marquis et al., 2007; Niquil et

al., 1999): parsimony or minimum-norm strategy (LIM-MN). This approach finds the food web

configuration that agrees with quantitative data and is minimal in the sum of squared flow values.

However, there is no ecological basis for the parsimony principle and the solution is typically an

extreme rather than the most likely one. Also, some flows may be set to zero and many flows may be

close to the bounds of their ranges (Kones et al., 2006). Kones et al. (2006) used a Monte Carlo

approach (LIM-MCA) as an alternative for the parsimony approach. They argued that the averaged

flows obtained from randomly generated plausible food webs are more likely flow values than those

derived using the parsimony method. The two approaches (LIM-MN and LIM-MCA) were also

compared in the study of Stukel et al. (2012). These authors revealed that LIM-MCA gives a robust

depiction of ecosystem processes when primary production is an input of model.

1.4.3. Environmental extension of input-output analysis

Input-output analysis was developed by (Leontief, 1936) to analyze the interdependence of industrial

sectors in economy in which the relationships between different industries are summarized in a matrix,

with the direct transactions. Likewise, ecologists have used matrices to describe the trophic

relationships between trophic functional groups in the food webs (Dunne, 2006; Fath and Patten,

1999b; Finn, 1976; Latham II, 2006). The simplest way to construct such a matrix is arranging all

trophic groups in rows and columns and using the binary digits 0 and 1 to indicate whether or not a

species in row i feeds on the species in column j. This is called a non-dimensional direct flow matrix.

Often, 1 is replaced by the absolute value of the flow from species j to species i (fij), making it a

dimensional direct flow matrix (Fath and Patten, 1998). All inputs of n internal compartments of the

food web are represented by n x 1 column vector (z) and a 1 x n row vector is used to represent the

outflow from each compartment to the environment. By using matrix notation and manipulations, one

can investigate both direct and indirect trophic interactions between functional groups. Fath and

Patten (1998) defined transactions and relation. A transaction is a directly observable transfer of

conservative resources between two organisms or functional groups, whereas a relation is the direct

or indirect consequence of these transfers. For example, in a food chain consisting of 3 species with

the matter transfer: k -> j ->i, there are two direct transactions from k to j and from j to i, which leads to

the presence of 1 type of relation, namely prey predator. Although there is no direct transaction

between k and i, there is still an indirect relationship between them. Specifically, species k can benefit

from species i because species j can be suppressed by i while j is predator of k.

In the excellent review about the foundations of network environ analysis, Fath and Patten (1999b)

summarized four main domains of ecological network analysis which borrowed the principle of input-


12

output analysis founded by Leontief (Figure 6). Structural path analysis is considered as the basis for

functional analysis (i.e. flow analysis, storage analysis, utilities analysis). Trophic structural analysis

has been used extensively in characterizing and comparing food webs (Baird et al., 2011; Baird et al.,

1991b; Baird and Ulanowicz, 1993; Monaco and Ulanowicz, 1997; Niquil et al., 1999). In these

studies, the number and distribution of cycles and average path length as well as trophic position of

species were elaborated. In a comparative study with six marine ecosystems, Baird et al. (1991b)

found that the average path lengths of two upwelling systems (i.e. Peruvian and Benguela upwelling

systems) were much shorter than that of other systems. Besides, the trophic structure can be used as

a surrogate for assessing the degree of stress that ecosystems experience (Baird et al., 1991b; Baird

and Ulanowicz, 1993).

Figure 6. Diagram of systems ecology network analysis (adopted from Fath and Patten (1999b)).

Each functional analysis is based on a different nondimensional normalization of dimensional direct

flow matrix (F). In flow analysis, each element (fij) in the direct flow matrix is normalized by the total

flow through donor compartment j (Tj), [G=(gij)nxn = (fij/Tj)nxn] with n is the number of internal

compartments. Similarly, the flows are normalized by the steady-state storage at the donor

compartment j (xj), [P=(pij)nxn = (iij + fij*t/xj)nxn], where jij are the elements of the identity matrix and t is

small enough time step. Therefore, all elements in the non-dimensional flow intensity matrix are bound

between 0 and 1. On the contrary, the elements in the direct utility matrix is bound between -1 and 1

because these elements are derived from net flow between two compartment i and j normalized by the

through flow over receiving compartment i, [D=(dij)nxn = ((fij fji)/Ti)nxn]. Based on these matrices, one

can quantify direct, indirect and integral relations within a system via mathematical algorithms (Fath

and Patten, 1998; Fath and Patten, 1999a; Finn, 1976). In functional analysis, the indirect effects

associated with a path of sequences of length k are identified by computing the kth power of the non-

dimensional quantity matrix of interest (flow, storage, and utility). Thus, the integral interaction

NETWORK ENVIRON ANALYSIS Structural alalysis Pathway analysis

Functional analysis Flow analysis Storage analysis Utility analysis


13

matrices are found by summing all infinite power series of the direct interaction matrices (Fath and

Patten, 1999b):

The integral interaction matrices account for the contribution of all direct and indirect interactions. For

example, a simple test shows that the product of integral flow matrix (N) by input vector returns the

throughflow vector, T=N.z (z is the column vector of all inputs of n internal compartments), confirming

each elements in integral flow matrix either directly or indirectly contribute to the overall throughflow in

the network. This can be also applied to non-steady state cases (Fath and Patten, 1999b). Through

the flow and utility analysis, four network properties have been identified (Table 4) which have been

already subsequently tested by large-scale computer models of ecosystems (Fath, 2004). The

hypothesis about the existence of these four properties is also supported by several empirical food

web analyses. For example, Salas and Borrett (2011) investigated 50 empirical food webs and found

that indirect flows dominate direct flows in 74% of the cases and increased to 88.5% if only models

with cycling structure were taken into account.

Table 4. Four emergent network properties and mathematical tests to determine their presence.

Property Definition Test

Dominance of

indirect effects

A system receives more influence from

indirect process than from direct

process.

Amplification Components in a network get back more

than they put in.

Homogenization Action of the network makes the flow

distribution more uniform. >1

Synergism Systemwide relation in the network are

inherently positive. (nij, iij, gij: elements in integral, initial and direct normalized non-dimensional flow matrices; , : mean of

elements in integral and direct normalized non-dimensional flow matrices; CV(N), CV(G): coefficient of variation of

elements in integral and direct normalized non-dimensional flow matrices; : dimensional integral utility matrix)

id =

(nij ! iij ! gij )i, j=1

n

"

giji, j=1

n

">1

nij >1 for i ! j

Hp =CV (N )CV (G)

bc =

positive elements in !!negative elements in ! !

>1

n g

!

Flow: N = I + G + G2 + G3 + G4 + . = (I-G)-1

Storage: Q = I + P + P2 + P3 + P4 + . = (I-P)-1

Utility: U = I + D + D2 + D3 + D4 + .= (I-P)-1

Integral = Initial input Direct Indirect


14

1.4.4. Ecological network indices derived from information theory

There is an alternative approach exists in ecological network analysis where ecologists try to

characterize the structure and function of ecosystems by means of information theory (e.g. Rutledge

et al., 1976; Ulanowicz, 1980; Ulanowicz and Abarca-Arenas, 1997). Rutledge et al. (1976) were the

first to apply the average mutual information index (AMI) as an indicator of maturity in ecological

networks. They suggested that AMI should decrease as ecosystems become mature. However,

Ulanowicz (1980) suggested that the AMI should increase with ecosystem development as the flow

patterns become more constrained, indicating the elimination of inefficient flows.

Ulanowicz (1980) developed a new index, namely Ascendancy (A), that quantifies both the level of

system activity and the degree of the organization, two important factors in the development of

ecosystems. He hypothesized that ascendancy should increase during maturation of the ecosystem.

The system activity component of ascendancy is measured by total system throughput (T..),

calculated as the sum of all the trophic exchanges occurring in the system. Also, AMI, as introduced

by Rutledge et al. (1976), measures system organization. The natural upper bound of ascendancy is

defined as the development capacity of an ecosystem (C) (Ulanowicz, 1980). The ascendancy index

has shown its usefulness, both in ecosystem characterization and in comparative studies of

various ecosystems (Baird et al., 1991b; Baird and Ulanowicz, 1993; Heymans et al., 2007;

Patricio et al., 2006).

Latham II and Scully (2002) used uncertainty from network flows (Hsys) as a descriptive tool to assess

levels of topological constraints and defined Hc as the uncertainty reduced by the structure of network,

or the constraint information inherent in the network. Hc can be normalized by its upper bound

(maximum uncertainty of flows in the network, Hmax), after which it is termed constraint efficiency (CE).

Baird et al. (1991b) proposed three important criteria that need to be satisfied for using information

theory to compare different ecosystems: ecosystems should have more or less the same food web

topology (e.g. same number of compartments), their flows of matter or energy should be expressed by

the same currency (e.g. carbon flows) and appropriate dimensionless indices. As for the last criterion,

the relative ascendancy (A/C ratio) is a good parameter to compare two or more different ecosystems.

Another aspect to note is that highly organized systems have a tendency of internalizing most of their

activity, thus the internal relative ascendancy (Ai/Ci ratio) is regarded as the most suitable index for

the status of system development (Ai and Ci are the internal ascendancy and development capacity,

respectively) (Baird et al., 1991b). Also, the constraint efficiency index - that is scale-independent -

can be appropriate to compare various ecosystems.


15

Table 5. Some information measures of ecological networks.

Index Definition Formula

Average mutual

information

(AMI)

Measure the average amount of constraint

exerted upon an arbitrary quantum of

currency passing from any one compartment

to the next

Statistical

uncertainty (HR)

Upper bound of AMI

Ascendancy (A) Quantify system activity/size and organization

in system

Development

Capacity (C)

Natural upper bound of Ascendancy

Constraint

efficiency (CE)

The fraction of total uncertainty reduced by

network topology

(Tij: flow from compartment j to i; Ti.: Total inflows to compartment i; Tj. : total outflows from

compartment j; T.. : Total system throughput; Hc: constraint information; Hsys: network efficiency; Hmax:

maximum uncertainty; n: the number of internal compartments which does not include compartment 0,

n+1 and n+2. Compartment 0 is the source of exogenous import to the system; compartment n+1 and

n+2 are the destination of usable export and unusable export (dissipation/respiration), respectively)

1.5. Nutrient enrichment of marine ecosystems

1.5.1. Sources of nutrients for marine ecosystems

Many studies have indicated that human activities on land, especially in coastal regions, can be

considered as the main sources of nutrients entering shallow coastal ecosystems (UNEP, 1994;

Valiela et al., 1992). These sources include agricultural activities, sewage outfalls, septic tanks, runoff,

deforestation, fossil fuel combustion and atmospheric deposition. The pollution sources can be

classified into two categories, including nonpoint source and point sources (Arhonditsis et al., 2000).

Nonpoint agricultural and rural runoffs are primary contributors of nutrients (Total Nitrogen - TN and

Total Phosphate - TP) to the coastal areas of the Wide Caribbean Region while domestic and

industrial point sources are less important contributors (UNEP, 1994). Note that the nutrient pollution

TijT .. log2j=0

n

!i=1

n+2

! TijT..Ti.T. j

!T. jT..j=0

n

" log2T. jT..

Tij *log2TijT..Ti.T. jj=0

n

!i=1

n+2

!

! Tij log2TijT..j=0

n

"i=1

n+2

"

Hmax = log2(n + 2)i=1

n

!

Hsys = !TijT..log2

TijT. jj=1

n

"i=1

n+2

"

Hc = Hmax ! Hsys and CE=Hc /Hmax


16

sources can be also divided into groups based on their origin (e.g., land-based sources, atmospheric

deposition and sea-based sources).

1.5.1.1. Land-based sources and atmospheric deposition

Land-based nutrient sources are considered one of the most important threats to the marine

environment (UNEP, 1994). The nitrogen (N) and Phosphorous (P) cycle have been changed

significantly at all scales as a result of population growth and natural resources consumption

pressures (Shadiul Islam and Tanaka, 2004). The annual input of nutrients from the catchment area to

the Baltic Sea was estimated to be around 1000 kt N and 46 kt P (Nausch et al., 1999). Coastal zones

can act as a filter between land and the open sea retaining suspended solids and nutrients (Nixon and

Pilson, 1983; Sharp et al., 1984). Therefore, the terrestrial input and fate of nutrients is essential for

the evaluation and prediction of coastal marine eutrophication (Borum, 1996). Several human

activities, such as overharvesting of land, deforestation, river fish farming, domestic and industrial

sewage discharge may directly or indirectly affect the nutrient inflow into the sea (Carpenter et al.,

1998; Mc Clelland and Valiela, 1998; and Pergent-Martine et al., 2006). Globally, coastal watersheds

receive 103 Tg.yr-1 of N from the combination of synthetic fertilizer (73.6 Tg yr-1), atmospheric

deposition (22.5 Tg yr-1), and human sewage (9.1 Tg yr-1) (Caccia and Boyer, 2007).

Agricultural activities are reported to contribute about 50% of the total pollution source of surface water

by means of the higher nutrient enrichment, mainly as NH4+ and NO3- derived from agricultural inputs

(Shadiul Islam and Tanaka, 2004). Fertilizer production has increased dramatically from 3 TgN yr-1 to

80 TgN yr-1 between 1950 and 2000 (Galloway, 1998). A significant fraction of the total agricultural N

applied to soil exceeds the requirements for plant growth and this surplus N may move into surface

waters or migrate to ground water which in turn enters the sea, usually as dissolved inorganic nitrogen

(NO3-, NO2-, NH4+), contributing to nutrient enrichment in these regions (Smith et al., 1999). In their

study at the Biscayne Bay, Caccia and Boyer (2007) found that the NOx- (NO3- and NO2-) loading

made up a much greater proportion than that of ammonium to the total amount of N loading (NOx- was

1294 ton N.yr-1; NH4+ was 392.6 ton N.yr-1). The relative proportion of these N forms in the total N

loading may indicate the primary activities that contribute to N emission into surface waters. In the

industrialized north of Biscayne Bay, the dissolved inorganic nitrogen load into the canals was evenly

split between NO3- and NH4+, whereas 95% of the dissolved inorganic nitrogen load in the south was

in the form of NO3- reflecting more agricultural land use (Caccia and Boyer, 2007). Also in the Greek

Gulf, surrounded by an intensively cultivated watershed, the agriculture runoff was regarded as the

primary contributor to nutrient loading during winter, accounting for 40-60% of the total nitrogen stock

(Arhonditsis et al., 2000). In the Mar Meno coastal lagoon in Spain, 50% of dissolved inorganic

nitrogen was from agricultural sources, while these sources contribute for up to more than 80% of the

nitrogen load in Danish waters (Garcia-Pintado et al., 2006; Nausch et al., 1999).

According to the data obtained from the study in the Mediterranean Sea of Arhonditsis et al. (2000),

estimated combined fluxes of nitrogen and organic carbon from sewage and industrial activity are up


17

to 10% of the total stock. It is known that inadequately treated sewage effluent leads to increasing

nutrient loads discharged into rivers or wet land, which eventually flows into coastal waters (UNEP,

1994) and many industries located on the coastal region, including food processing, chemical

industries and soap contribute to increasing nutrient loads to coastal waters (Kucuksezgin et al.,

2006). Wastewater and increased inputs of P eroded from the landscape into rivers have caused a

three-fold increase of global fluxes of P to oceans from ca. 8 million metric tones per year to ca. 22

million metric tones per year (Howarth et al., 1995).

The role of atmospheric deposition as a source of nutrients depends on the locations and type of

nutrients. Arhonditsis et al. (2000) showed that the contribution of wet and dry atmospheric deposition

to the total nitrogen and organic carbon in the Mediterranean Sea is insignificant. This is also true for

the nitrogen budget in Biscayne Bay with only 231.7 ton atmospheric N.yr-1 compared to

approximately 1300 ton N.yr-1 arriving via canals. However, atmospheric deposition is the main source

of Phosphorous in the south of this Bay (Caccia and Boyer, 2007). Anthropogenic activities can cause

an increase in atmospheric deposition of nutrients on water systems. The combustion of fossil fuels

causes an additional emission of N into the atmosphere and a significant fraction of this emission

subsequently returns to the land and ocean surface via wet and dry deposition (Smith et al., 1999).

Atmospheric deposition is regarded as the most rapidly growing source of N loading (Caccia and

Boyer, 2007).

1.5.1.2. Sea-based sources

Marine aquaculture is one of the most important activities in many areas (Shadiul Islam, 2005; Tovar

et al., 2000) and is considered an alternative to land-based aquaculture (Sara et al., 2011). It is an

important industry that continues to grow rapidly with an average global annual growth rate of 8.8%

per year since 1970, compared with only 1.2% for capture fisheries and 2.8% for terrestrial farmed

meat production systems (FAO, 2007). However, the development of marine aquaculture has caused

some notable environmental effects, particularly the increase of dissolved nutrient loads, suspended

solids and organic matter. Tovar et al. (2000) estimated that culturing one ton of fishes (gilthead

seabream Sparus aurata) discharges 36.41 kg NNH4+, 4.95 kg NNO2, 6.73 kg NNO3 and 2.57 kg

PPO43- into the seawater.

1.5.2. Effects of nutrient enrichment on marine ecosystems

Nutrient loadings from watersheds and other land-based sources alter the structure and function of

receiving aquatic ecosystems (Valiela et al., 1992). This is because the growth of algae and vascular

plants in freshwater and marine ecosystems are strongly influenced by the supply rate of N and P.

Responses of coastal marine waters to nutrient addition largely depend on whether they are mixed or

stratified (Kennish, 1992) and also on the specific environmental conditions (e.g. N limitation or P

limitation). For example, Phaeocystis becomes dominant under N-limitations which is coincided with

stronger P-loadings relative to the increase in N-discharge in the Dutch coastal zone of the North Sea


18

(Riegman, 1995). Generally, the addition of nutrients often promotes an increase in biomass and

productivity (Riegman, 1995) and leads to eutrophication, a process often only observable towards its

end-point, when ecological effects become obvious and dramatic (Raffaelli, 1999). These events

create hypoxia or anoxia in susceptible water bodies and eventually lead to the death of aquatic life

(Paez-Osuna et al., 1998; Valiela et al., 1992). Anderson et al. (2002) have indicated that moderate

eutrophication may enhance the ecological and commercial value of an estuary; however, excessive

nutrient loadings can lead to a rapid deterioration of the shallow water environment when dissolved

oxygen is depleted as a result of too much organic matter as well as the occurrence of toxic

phytoplankton blooms.

Eutrophication has been observed to result in great changes in species composition, and cause

alterations of the structure and function of marine communities over large areas (Shadiul Islam and

Tanaka, 2004). However, these changes are not always similar across ecosystems. Kimor (1992) has

found a shift from diatoms to dinoflagellates, and a decrease of phytoplankton size towards a

dominance of small size nanoplankton (e.g. microflagellates and coccoids). A similar response was

observed in zooplankton communities, with herbivorous copepods being replaced by small-size and

gelatinous zooplankton (Zaitsev, 1992). Also, eutrophication stimulates proliferation of macroalgae

and filamentous algae (Shadiul Islam and Tanaka, 2004; Valiela et al., 1992). Eutrophication favors

the downward transport of carbon and nutrients towards the sediments, not only due to higher algal

biomasses but also as a consequence of a shift towards larger algal species with higher sedimentation

rates (Riegman, 1995). In addition to the increasing biomass, there is also a remarkable change in

species composition of the macrophyte canopy. Nutrient loading in some places in Waquoit Bay

(country) eliminated eelgrass and enhanced the growth of a green (Cladophora vagabunda) and a red

(Gracilaria tikvahiae) algal species (Valiela et al., 1992). Teichberg et al. (2008) has indicated nutrient

availability as an important factor governing composition of seaweed assemblages due to the fact that

nutrient enrichment may promote the spread of annual fast growing algae while inhibiting the growth of

perennial species (Worm and Lotze, 2006).

The effects of eutrophication on pelagic food webs are also presented in a shift from bottom-up to top-

down control. Implementation of this concept generates the prediction that algal blooms in the marine

environment are dominated by species that escape from grazing by microzooplankton species. This,

in turn, leads to the dominance of poorly edible algal species (Riegman, 1995). Nutrient enrichment

not only reduces biodiversity and changes the identity of the dominant species but also causes

harmful algal blooms (Anderson et al., 2002). In estuaries with a relatively long retention time, blooms

of phytoplankton utilize the excess nutrient, lowering dissolved oxygen in the water column, shading

sea-grasses, increasing inputs of organic material into the sediment and often enhancing the growth of

opportunistic macro-algae. By contrast, an increase in opportunistic macro-algae is the most obvious

biological response of estuaries with short flushing time. Some main effects of eutrophication on

estuarine and coastal marine ecosystems can be summary as below (Smith et al., 1999):


19

Increased biomass of marine phytoplankton and epiphytic algae

Shifts in phytoplankton species composition to taxa that may be toxic or inedible (e.g., bloom-

forming dinoflagellates)

Increases in nuisance blooms of gelatinous zooplankton

Changes in macroalgal production, biomass, and species composition

Changes in vascular plant production, biomass, and species composition

Reduced water clarity

Death and losses of coral reef communities

Decreases in the perceived aesthetic value of the water body

Shifts in composition towards less desirable animal species Increased probability of kills of

recreationally and commercially important animal species

MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS

20

2. MATERIAL AND METHODOLOGY

2.1. The mesocosm data

The used data come from a mesocosm experiment conducted in a tidally driven lagoon system on the

west coast of central Norway. The experiment consisted of 7 mesocosms (denoted Bag 1 to Bag 7)

made from transparent polyethylene with a volume of about 38 m2 each and moored on floating

stands (Olsen et al., 2007). This was a single factor experiment (variable nutrient addition rate with a

element ratio of 16:16:1 for Si:N:P) lasting 18 days (from 19 August to 5 September 1997). Nutrients

were added on a daily basis with the rate indicated in Table 6.

Table 6. Daily nutrient addition rates applied in the 7 mesocosms (LN, LP and LS for Nitrogen, Phosphorous and Silicon, respectively, in g/l/d). N was added as NH4NO3, P as Na2HPO4, Si as SiO2.

Nutrient Addition Bag 1 Bag 2 Bag 3 Bag 4 Bag 5 Bag 6 Bag 7

LN 0.00 2.13 3.61 6.14 10.40 17.80 30.20

LP 0.00 0.29 0.50 0.85 1.45 2.46 4.18

LS 0.00 4.27 7.25 12.30 21.00 35.60 60.60

During the experiment, integrated samples over the whole water column (0-10m) were collected every

2 days. The planktonic organisms in the samples were classified based on their size and carbon

source (i.e. autotrophic and heterotrophic organism). The standing stocks (in gC/l) of the different

phytoplankton and small zooplankton groups were determined either by conversion factors or bio-

volumes and group-specific regressions between carbon content and cell volume. The biomass of

copepods was based on length-carbon biomass relations estimated during the experiment, whereas

length-weight relationships of other mesozooplankton were taken from the literature (for more details

see in Olsen et al. (2007))

Table 7. Classification of sampled species groups and the dominant organisms.

No Group Dominant taxonomic groups/species

1 Autotrphic picoplankton

(A1)

Prokatyotic picocyanobacteria, traces of picoeukaryotes

(20 m)


21

4 Heterotrophic picoplankton

(BAC)

Heterotrophic bacteria, including Archaea (diameter:


22

Two above set of equalities and inequalities were constructed based on: (1) food web topology, (2) the

site-specific data (measured stocks and flows), and (3) physiological constraints. This conceptual

framework is presented in Figure 7. Often, the food web topology and physiological constraints are

adjusted in case of incompatible matrix expressions and thus no solution for x can be found (refine

arrow in Figure 7).

Figure 7. Conceptual framework for constructing and solving a LIM.

INPUT FILE

Site-specific data Food web topology Constraints

LIM

SOLUTION

ANALYSIS OF

SOLUTION

Ref

ine


23

2.2.2. Food web topology

The considered food webs in the 7 bags contain 10 internal compartments (i.e. A1, A2, A3, BAC,

HNP, CIL, COP, JEL, DOC and DET; see Table 7) and 2 external compartments (i.e. Dissolved

inorganic carbon (DIC) and sedimentation carbon (SED)). All the carbon flows between food web

compartments represent the metabolism of and the feeding relationships between living compartments

as found in the literature (Figure 8). Autotrophic phytoplankton (A1, A2, A3) and bacteria (BAC) play a

role as basal trophic levels, which are the starting points of the herbivorous food chain and the

microbial loop, respectively. The former can utilize solar radiation to convert inorganic carbon to

biomass via photosynthesis, whereas BAC can use dissolved organic carbon (DOC) as a food

source. All zooplankton groups are able to feed on DET and egest DET, except for HNP, which do

not egest DET.

Figure 8. Food web topology of the constructed LIM. Abbreviations are A1: autotrophic picoplankton; A2: autotrophic nanoplankton; A3: autotrophic microplankton; BAC: bacteria; HNP: heterotrophic nanoplankton; CIL: ciliates; COP: copepods; JEL: jellyfishs; DET: detritus; DIC: dissolved inorganic carbon; DOC: dissolved organic carbon).


24

HNP are commonly considered the major consumers of autotrophic picoplankton (A1) (Weisse, 1993),

and studies have demonstrated that A1, in addition to BAC, constitute the majority of the diet of HNP

(Dolan and Simek, 1999). Also, HNP can graze on A2. HNP is a constituent in the diet of larger

zooplankton (i.e. CIL and COP).

CIL can feed not only on HNP but also directly on BAC, which is a prey of HNP. CIL also preys upon

small phytoplankton, including A1 and A2, which have a smaller size than 20m, and on DET as

mentioned above. COP represents one of the most well-known and important mesozooplankton

groups in marine food webs (Drilleta et al., 2011) and have a broad diet. They can feed on smaller

zooplankton (i.e. HNP, CIL), BAC and on phytoplankton of various sizes. Only A1 are too small to be

grazed by COP unless they aggregate (Richardson and Jackson, 2007; Stukel and Landry, 2010),

thus COP can graze upon them. Adults copepods have been found to be inefficient in consuming

BAC, but their nauplii can consume large amounts of BAC (Roff et al., 1995). JEL, which occupy the

highest trophic level in the food webs, are compulsory carnivores. They only feed on CIL, COP and

also egest DET.

In each living compartment, part of the ingested carbon will be respired or excreted, forming carbon

flows from all living compartments to the DOC and DIC pools. The sources of sedimentary carbon are

the sinking of DET and phytoplankton.

2.2.3. Data and constraints for set up of the linear inverse models

LIM makes a distinction between internal and external compartments. There are no mass balance

equations for the external compartments (e.g. DIC). The dynamics of the internal compartments are

fully described in the model and the LIM will create mass balance equations for them. The sets of

equalities (E.x = f in section 2.2.1) are constructed based on mass balance equations for each model

compartment and site-specific data, which are primary production and bacterial production in this

study. The systems was assumed to be in steady state, hence all growth rates of standing stock of each

internal compartment were set equal to zero. The assumption of steady state has been shown to only

marginally influence the derived carbon flows (Vezina and Pahlow, 2003)

A large number of constraints on the food web flows were included in the inverse model (Table 8).

These constraints reflect limits on the physiology and biological functioning of marine organisms (e.g.

respiration and excretion account for only part of ingestion, thus each of these flows never exceeds

total ingestion) and were taken from the literature. In this study, constraints on the following quantities

were taken into account: respiration, excretion, production, assimilation efficiency, viral lysis and DET

dissolution to DOC. Based on these constraints and on the standing stock of the different

compartments which were measured during the experiment, the set of inequalities (G.x h in section

2.2.1) is created.


25

Table 8. The constraints on food web flows of carbon.

Compartment Characteristic Unit Ranges Source

All phytoplankton

Respiration rate Fraction of GPP 0.05 0.3 Vezina and Platt (1988)

Excretion rate Fraction of NPP 0.05 0.5 Vezina and Platt (1988)

Sedimentation rate

Fraction of SS < 0.07 Tamelander and Heiskanen (2004)

Bacteria Viral mortality of

bacteria

Fraction of

production rate

10 40% Fuhrman (2000)

Heterotrophic nanoplankton and Ciliates

Respiration rate d-1 < 0.18 Vezina and Platt (1988)

Ingestion rate d-1 < 15.44 Vezina and Platt (1988)

Excretion Fraction of respiration

0.33 1 Vezina and Platt (1988)

Copepods Assimilation efficiency

Unitless 0.5 0.9 Besiktepe and Dam (2002)

Respiration d-1 > 0.065 Vezina and Platt (1988)

Ingestion d-1 0.013.02 Mauchline (1998)

Excretion Fraction of respiration

0.3 1 Vezina and Platt (1988)

Jellyfish Respiration d-1 0.005 1.15 Schneider (1992)

Ingestion d-1 0.030.11 Gibson and Spitz (2011)

Detritus Dissolution Fraction of SS < 0.02 Bever et al. (2010)

(GPP: gross primary production; NPP: net primary production; SS: standing stock; d: day)

2.2.4. Setup and solution of LIM

Inverse food web models are typically under-determined (i.e. the number of equalities is smaller than

the number of unknown flows), with an average ratio of unknown flows to formulated equalities of 4:1

(Vezina and Pahlow, 2003). Thus, there is an infinite number of solutions and each unknown flow can

only be quantified within a certain range. The inverse models constructed here were solved in the R

environment for statistical computation version 2.12.2 for Macintosh (R Development Core Team,

2009) using the package LIM (Van Oevelen et al., 2009). The function Xranges was used to obtain

the ranges (min-max) of all carbon flows in food webs. The function Lsei, which minimizes some set

of linear functions (A.x b) in least square sense, gives the most parsimonious solution. The solutions

obtained using Xranges and Lsei were subsequently used as an initial condition for a Markov Chain

Monte Carlo technique (MCMC) - using the function Xsample - with a step size of (max(Ranges)-

min(Ranges))/4. The number of MCMC iterations was set at 5000, thus realizing 5000 possible

solutions for each of the carbon flows. This approach allowed quantifying the uncertainty associated

with each flow.


26

2.2.5. Analysis of the estimated carbon flows

From the solution of LIMs, the main carbon flows were analysed, including gross primary production,

carbon flows through DET, BAC, phytoplankton, and zooplankton groups. The food web efficiency

(FWE) was calculated based on copepod production with the following formula:

FWE = COP productionNPP

In which COP production was calculated by taking all the flows to COP subtracting the flows

representing COP respiration, excretion and egestion; NPP is the sum of net phytoplankton primary

production.

2.3. Ecological network analysis

From solutions obtained in section 1.2.3, network indices which can be used to quantify the function

and structure of food webs, were calculated by using package NetIndices version 1.4 (Soetaert and

Kones, 2011). These indices are discussed in detail in the literature review (section 1.4.3 and 1.4.4).

RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS

27

3. RESULTS

3.1. Carbon flows

3.1.1. Net primary production

Net primary production (NPP; sum across all phytoplankton groups) increased with increasing nutrient

addition rate (Figure 9). This response of NPP was quite fast and reached a peak on day 9 (bag 4, 6

and 7) or day 11 (bag 2,3 and 5) and again on day 17 (with exception of bag 1 and 7 whose NPP

continued increasing). The temporal changes in NPP differed among treatments and were more

pronounced in Bag 6 and 7 where a reduction of 50% was observed after reaching a peaking of 414

and 683 gC/l/d on day 9, respectively. In general, NPP of all bags increased during the experiment

with the exception of the bag receiving no additional nutrients (Bag 1) in which NPP decreased from

37 gC/l/d (day 1) to 13 gC/l/d (day 18).

Figure 9. Changes in total net primary production with increasing nutrient addition rate (Bag 1 to Bag 7) during the experiment.

3.1.2. Response in net primary production of various phytoplankton groups

NPP of autotrophic nanoplankton (A2) responded strongest to the nutrient input and its contribution to

total NPP increased dramatically with increasing nutrient addition rates. It made up 30% of the total

NPP in Bag 1 and nearly 80% in Bag 7 (Figure 10). The corresponding absolute value of NPP for this

group increased almost 60 times (from 4 gC/l/d to just below 250 gC/l/d). Microphytoplankton (A3)

5 10 15

1020

50100

200

500

1000

Days

NP

P (

gC/l/

d )

Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7







RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS

28

had similar changes in NPP, but at lower magnitudes relative to A2. NPP of A3 increased about 64

times from 0.7 gC/l/d in Bag 1 to 42 gC/l/d in Bag 7. These increases were by far greater than that

of autotrophic picoplankton (marked as A1). At the low nutr

Date post:	05-Nov-2015
Category:	Documents
Upload:	thuy-anh
View:	289 times
Download:	6 times

Thesis _ Luong Duc Anh

Documents