Faculty of Bioscience Engineering Academic year 2011 – 2012
Transcript of Faculty of Bioscience Engineering Academic year 2011 – 2012
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
Master’s dissertation submitted in partial fulfillment of the requirements
for the degree of Master of Environmental Sanitation
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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
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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
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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
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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. Finn’s 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
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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 Finn’s cycling index
FWE Food web efficiency
HNP Heterotrophic nanoplankton
ID Dominance of indirect effect index
JEL Jellyfish
LIM Linear Inverse Model
SED Sedimentation
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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
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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
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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 µg l–1 d–1 (Bag 1) to 30.2 µg l–1 d–1 (Bag 7). At low
nutrient addition rates (LN < 17.8 µg l–1 d–1), 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 µg l–1 d–1, 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 Finn’s
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.
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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
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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 Darwin’s 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).
LITERATURE REVIEW MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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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; Šolić 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) (Pavés and González, 2008). These food webs mainly occur in nutrient rich
environments and were previously thought to consist of large phytoplankton. However,
nanophytoplankton (2-20 μm) as well as picophytoplankton are now increasingly recognized as
important constituents in plankton communities (De Laender et al., 2010b; Fileman and Burkill, 2001;
Šolić 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) (Møller, 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).
LITERATURE REVIEW MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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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 (Pavés and González, 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)
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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. Šolić 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 bottom‒up 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
LITERATURE REVIEW MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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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 (<100
gC.m-2.year-1), mesotrophic (100-300 gC.m-2.year-1), eutrophic (300-500 gC.m-2.year-1), and
hypertrophic (>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 (Cadée 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.
LITERATURE REVIEW MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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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/Pn−1 × 100
The percentage of total productivity available at one trophic
level (Pn−1) 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/Pn−1 × 100
EE x AE x PE = consumer production/prey production.
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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 10.13±0.49% 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 diversity–stability relationships
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(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
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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).
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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-
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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*Δt/xj)nxn], where jij are the elements of the identity matrix and Δt 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
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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
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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.
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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 ..log2
j=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
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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
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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 N–NH4+, 4.95 kg N–NO2
−, 6.73 kg N–NO3− and 2.57 kg
P–PO43- 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
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(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):
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• 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
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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 µg/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 µgC/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
(<2 µm)
2 Autotrophic nanoplankton
(A2)
Diatoms (Rhizosolenia fragilissima, unidentified centric),
Rhodomonas sp. and unidentified pigmented flagellates,
small thecate dinoflagellates (traces) (width: 2-20 µm)
3 Autotrophic microplankton
(A3)
Diatom colonies (Skeletonema costatum), large dinoflagellates
(Ceratium spp., Dinophysis spp., Protoperidinium spp.), and
autotrophic ciliates (diameter>20 µm)
MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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4 Heterotrophic picoplankton
(BAC)
Heterotrophic bacteria, including Archaea (diameter: <1 µm)
5 Heterotrophic nanoplankton
(HNP)
Heterotrophic nanoflagellates (2 to 8 µm, 62%), Oikopleura
dioica (14%), Craspedophyceae (13%), bacterivore ciliates
(scuticociliates, small oligotrichs, 11%)
6 Heterotrophic microplankton
(CIL)
Herbivore ciliates (strombidids, strobilids, 20 to 50 µm),
Protoperidinium spp. (traces)
7 Heterotrophic mesoplankton
(COP)
Calanoid copepods (Acartia spp., Centrophages spp., Temora
longicornis, Pseudocalanus sp., Paracalanus parvus), cyclopoid
copepods (Oithona sp.)
8 Small medusa
(JEL)
Sarsi asp.
Some carbon flows (in µgC/l/d) were measured, including gross primary production (GPP) for each
group of phytoplankton (A1, A2, A3) and bacterial production. Also, the standing stock (in µgC/l) of
dead matter such as Dissolved Organic Carbon (DOC) and Detritus (DET) were determined.
2.2. Estimation of carbon flows in the mesocosms by Linear Inverse Modelling
2.2.1. Conceptual framework for constructing Linear Inverse Models (LIM)
In this study, carbon flows in the food webs were estimated by developing a Linear Inverse Model
(LIM) which was first applied 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). A linear
inverse model can be defined by three linear matrix expressions: approximate equalities that have to
be met as closely as possible, equalities that have to met exactly and inequalities.
Approximate equalities: A.x ≈ b
Equalities: E.x = f
Inequalites: G.x ≥ h
In which x is the vector of unknown carbon flows that needs to be estimated; A, E, G are the matrices
containing coefficients of linear expression of the carbon flows and vectors b, f, and h hold numerical
data. Often, a linear inverse model only contains equations and inequalities, while approximate
equalities are added to single out one solution for x. Solving the three matrix expressions results in an
estimate for all carbon flows in the food web.
MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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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
MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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).
MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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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 20µm, 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.
MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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 0.01–3.02 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 0.03–0.11 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.
MATERIAL AND METHODOLOGY MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
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 µgC/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 µgC/l/d (day 1) to 13 µgC/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 µgC/l/d to just below 250 µgC/l/d). Microphytoplankton (A3)
5 10 15
1020
50100
200
500
1000
Days
NP
P (µ
gC/l/
d )
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
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 µgC/l/d in Bag 1 to 42 µgC/l/d in Bag 7. These increases were by far greater than that
of autotrophic picoplankton (marked as A1). At the low nutrient addition rate, A3 had the lowest NPP.
However, at higher nutrient addition rates it was the second most important contributor to NPP.
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).
3.1.3. Total flows through phytoplankton (AUT), bacteria (BAC) and detritus (DET)
In general, carbon flows through phytoplankton, bacteria and detritus increased with increasing
nutrient addition rates (Figure 11). The carbon flows through the BAC compartment were smaller than
those passing through the AUT and DET compartments. At no nutrient addition rate (Bag 1), the ratio
of mean gross bacterial production to mean gross primary production (GPP) for all phytoplankton
groups was nearly 1. However, this ratio decreased gradually with increasing nutrient addition rate
from Bag 2 to Bag 7 with the values of 0.61 and 0.48, respectively.
The role of DET was more pronounced at low nutrient addition rates, especially in Bag 1 (no nutrient
added) where the flow through the DET compartment was greater than the total flows through the
BAC and AUT compartments during the whole course of the experiment. At medium to high nutrient
addition rates (from Bag 4 to Bag 7), flows through the DET compartment dominated total GPP on at
1 2 3 4 5 6 7
050
100
150
200
250
300
(a)
Bags
NP
P (µ
gC/l/
d)
A1A2A3
1 2 3 4 5 6 7
(b)
Bags
Pro
porti
on (%
)
020
4060
80100
A1A2A3
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
29
least the first 7 days of the experiment. However, flows through the AUT compartment became more
important than flows through the DET at the end of the experiment (Figure 11a &11b). The higher the
nutrient addition rate was, the earlier AUT exceeded DET in terms of the total carbon flowing through.
For example, it took 9 days in Bag 6 and 7, whereas, these time in Bag 5 and Bag 4 were 11 and 13
days, respectively. Averaging over the whole course of the experiment for different nutrient addition
rates, DET had the highest total throughflow with only exceptions at very high nutrient addition rate in
Bag 6 and 7 (Figure 11d).
Figure 11. Changes in total flows through phytoplankton (AUT), detritus (DET) and bacteria (BAC) compartments with increasing nutrient addition rate from Bag 1 to Bag 7 (a,b,c: temporal changes during the experiment; d: average values for 7 nutrient addition rate).
3.1.4. Carbon flows through phytoplankton, bacteria, and detritus to living compartments
Only part of the GPP was transferred to higher trophic levels, as the rest was respired (flow to
dissolved inorganic carbon (DIC)), excreted to the dissolved organic carbon (DOC), or lost via
sedimentation (SED). Similarly, the carbon flows reaching BAC were partly lost as DIC or DOC. Also,
5 10 15
15
1050
100
500
1000
(a)
Days
Flow
thro
ugh
AU
T (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
15
1050
100
500
1000
(b)
Days
Flow
thro
ugh
DE
T (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
15
1050
100
500
1000
(c)
Days
Flow
thro
ugh
BA
C (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
1 2 3 4 5 6 7
BACAUTDET
(d)
Bags
Flow
(µgC
/L/d
)
0100
200
300
400
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
30
part of the flows through DET ended up at DOC and SED. As can be seen from Figure 12, it seems
that DET was a more important food source than primary production in these systems, which was
indicated by higher averaged flows from DET to higher trophic level in relation to that of phytoplankton
(AUT) and bacteria (BAC). However, the role of AUT as a food source relative to DET rose with
increasing nutrient addition rate, gaining more or less equal importance at Bag 7 (received highest
dosage of nutrients) (see in Figure 12d). Concerning temporal changes, AUT only outweighed DET to
become the most important food source at the end of the experiment in bags with high nutrient
addition rates (from day 13 in Bag 5, 6 and day 9 in Bag 7) (Figure 12a,b). The role of BAC as a food
source for zooplankton was limited, accounting for about 10% of total flows from these three food
sources to other living compartments.
Figure 12. Changes in flows from phytoplankton, detritus and bacteria to higher trophic levels with increasing nutrient addition rate from Bag 1 to Bag 7 (a,b,c: temporal changes during the experiment; d: average values for the 7 nutrient addition rates).
5 10 15
510
2050
100
200
500
1000
(a)
Days
Flow
from
AU
T (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
15
1050
100
500
1000
(b)
Days
Flow
from
DE
T (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
15
1050
100
500
1000
(c)
Days
Flow
from
BA
C (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
1 2 3 4 5 6 7
BACAUTDET
(d)
Bags
Flow
(µgC
/L/d
)
0100
200
300
400
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
31
3.1.5. Carbon flows through the zooplankton compartments
The total carbon flowing through all zooplankton compartments in general increased with the nutrient
addition rate, reaching just below 553 µgC/l/d in Bag 7 (the highest nutrient addition rate), which was
more than 4 times higher than that of Bag 1 (no nutrients added) (Figure 13b). Also, Bag 7 had the
highest peak of about 934 µgC/l/d, occurring on day 9, whereas the lowest flow through all
zooplankton compartments encountered on day 11 in Bag 1 (Figure 13a). The relative importance of
each zooplankton compartment is assessed by the carbon flowing through each of these
compartments, relative to the sum of all carbon flowing through all four compartments (FHNP, FCIL,
FCOP, FJEL for heterotrophic nanoplankton, ciliates, copepods, and jellyfish, respectively). Among all
groups of zooplankton, the CIL compartment had the largest total inflow of all consumer compartments
with FCIL ranging from 50.2 to 65.1 %. At medium nutrient addition rates (Bags 4, 5), the carbon flows
through the COP compartment was larger compared to the values of the HNP counterpart, making up
23.9 and 27.5% of total inflows of the zooplankton compartments. The value of FJEL was negligible
(<1%) in all cases.
Figure 13. Total carbon flows through zooplankton compartments: Jellyfishs (JEL), Copepods (COP), Ciliates (CIL), and Heterotrophic nanoplankton (HNP). (a) variation with time and nutrients addition rate in µgC/l/d; (b) average contribution of different zooplankton groups at 7 nutrient addition rates (Bag 1 to Bag 7). The proportion of carbon flows through JEL closed to zero (<1%). The number above each bar is the average carbon flows through all compartments in µgC/l/d).
5 10 15
0200
400
600
800
1000
(a) Total flows through zooplankton compartments
Days
Flow
s (µ
gC/l/
d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
1 2 3 4 5 6 7
(b)
Bags
Pro
port
ion
(%)
020
4060
80100
COPCILHNP
138 194 250 341 418 471 553
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
32
3.2. Trophic structure and food web efficiency
3.2.1. Trophic levels of zooplankton
The trophic levels of the different compartments are shown in Table 9. Only JEL, a strictly carnivorous
species, occupied a trophic level greater than 3 while zooplankton groups had trophic levels between
2.11 (lowest value for HNP) and 2.60 (highest value for COP). The trophic level of HNP was most
stable through time and between bags, as indicated by a fairly constant mean and standard deviation
compared with other zooplankton groups. This can be explained by the fact that HNP have the most
stringent dietary constraints, eating only small and medium-sized phytoplankton, DET and BAC. The
trophic level of COP showed a pronounced response to the nutrient addition rate, and varied from 2.44
in bag 7 to 2.60 in bag 1.
Table 9. Changes in trophic level of zooplankton (HNP: heterotrophic nanoplankton; CIL: ciliates; COP: copepods; JEL: jellyfishs) in the experiments with increasing nutrient addition rate (Bag 1 to Bag 7).
Bag HNP CIL COP JEL
1 2.13±0.11 2.38±0.14 2.60±0.23 3.50±0.17
2 2.13±0.12 2.49±0.16 2.57±0.22 3.53±0.17
3 2.14±0.12 2.37±0.19 2.54±0.20 3.46±0.17
4 2.11±0.10 2.35±0.17 2.51±0.20 3.43±0.16
5 2.16±0.19 2.33±0.17 2.45±0.19 3.39±0.15
6 2.14±1.16 2.32±0.16 2.46±0.20 3.39±0.15
7 2.15±0.15 2.29±0.17 2.44±0.18 3.36±0.16
The trophic relationship among different zooplankton species was partly reflected by their trophic level
values. Except for JEL, all zooplankton species are omnivores, feeding both on phytoplankton, DET
and lower trophic levels, i.e. COP can feed on CIL which, in turn, can feed on HNP. If carbon is
transferred in straight food chains like AUT/DET->HNP->CIL->COP or BAC->HNP->CIL->COP, one
expects that CIL and COP will occupy a trophic level higher than 3. Thus, the actual variation of
trophic levels between different compartments indicated the important role of phytoplankton as well as
of DET in the diet of zooplankton (i.e. CIL and COP), limiting the trophic level of CIL and COP to 3.
The sequence of trophic level increased from HNP to JEL. The diets of the zooplankton were
demonstrated in Figure 14.
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
33
Figure 14. Changes in diet of the zooplankton groups with increasing nutrient addition rate (Bag 1 to Bag 7). (a) Heterotrophic nanoplankton (HNP), (b) Ciliates (CIL); (c) Copepods (COP); (d) Jellyfishs (JEL).
3.2.2. Dependency of zooplankton on detritus
The dependency of different zooplankton compartments on DET were assessed relative to the
dependency on phytoplankton, which was set at 1 as a benchmark. Not only protozoa (HNP) but also
all other groups of zooplankton relied heavily on the DET in their extended diet. CIL was the group that
depended most on DET, followed by HNP, whereas COP depended less on DET, preceded by JEL.
Interestingly, there was no direct flow from DET to JEL. Still, JEL relied more on DET than
phytoplankton, illustrating the importance of indirect pathways of DET, e.g. DET->CIL->JEL or DET-
>COP->JEL. One could expect that the dependency of JEL on DET being in between that of COP and
CIL. Furthermore, Figure 15 reveals that the dependency of protozoa and zooplankton on DET
1 2 3 4 5 6 7
(a)
Bags
Pro
porti
on (%
)
020
4060
80100
A1A2DETBAC
1 2 3 4 5 6 7
(b)
Bags
Pro
porti
on (%
)
020
4060
80100
A1A2DETBACHNP
1 2 3 4 5 6 7
(c)
Bags
Pro
porti
on (%
)
020
4060
80100
A1A2A3DETBACHNPCIL
1 2 3 4 5 6 7
(d)
Bags
Pro
porti
on (%
)
020
4060
80100
CILCOP
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
34
declined with the increasing nutrient addition rate. However, the dependency on autotrophic
phytoplankton merely outstripped that on DET at the end of experience in bag with high nutrient addition
rate (bag 5, 6, and 7).
Figure 15. Chaneges in dependency of Hetereotrophic nanoplankton (HNP), Ciliates (CIL), Copepods (COP) and Jelly fish (JEL) on detritus in their extended diet with increasing nutrient addition rate from Bag 1 to Bag 7 over the experiment.
5 10 15
05
1015
2025
Dependency of HNP on DET
Days
Dependency
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 150
510
1520
25
Dependency of CIL on DET
Days
Dependency
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
05
1015
2025
Dependency of COP on DET
Days
Dependency
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
05
1015
2025
Dependency of JEL on DET
Days
Dependency
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
35
3.2.3. Food web efficiency (FWE) calculated based on COP production
The FWE calculated as the ratio of COP production and NPP varied over time. On average, the FWE
decreased with increasing nutrient addition rate. Bag 1 had the highest efficiency (about 1.4%),
followed by bag 2 (just above 0.6%), whereas FWE in bag 7 was only 0.11%. Over the period of the
experiment, the FWE in bag 1 reached a peak of 2.6% on day 11, and once again on day 17 with a
FWE of 2.7%. Bag 2 had the second highest FWE with two peaks on day 3 and day 18 (about 1%).
Figure 16. Food web efficiency calculated based on COP production. (a) the variation of FWE at different nutrient addition rate over time (Bag 1 to Bag 7); (b) the changes in time-averaged FWE with increasing nutrient addition rate (Bag 1 to Bag 7).
3.3. Carbon cycling
3.3.1. Total system throughflow: cycled versus straight
The cycled (TSTC) and straight (TSTS) total system throughflows increased with the nutrient addition
rate (Figure 17), but the difference between the low and high nutrient addition rates was more
pronounced for TSTS than for TSTC. The trends found for TSTC are similar to those found for the
carbon flowing through DET (Figure 11b and 17a), and likewise TSTS and GPP show similar patterns
over the employed nutrient gradient, but with higher magnitude (Figure 11a and 17b).
5 10 15
0.00
0.01
0.02
0.03
0.04
0.05
0.06
(a)
Days
FWE
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
1 2 3 4 5 6 7
0.000
0.005
0.010
0.015
(b)
Bags
FWE
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
36
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.
3.3.2. Finn’s cycling index (FCI) and Average path length (APL)
FCI is the ratio of TSTC over the total system throughflow (TST). TST is the sum of TSTC and TSTS,
both discussed in section 3.3.1. Although TSTC increased with increasing nutrient addition rate, FCI
was inversely proportional to the rate of nutrient addition because TSTS increased more with nutrient
additions than TSTC. Bag 1 had the greatest FCI with an average of about 73.2% during the course of
the experiment, which is 2 times more than that of bag 7 which showed the lowest FCI (34.1% on
average). Also, the FCI index during the first 5 days increased gradually in all bags, with the exception
of Bag 2 that had a slight reduction between Day 3 and Day 5. In general, FCI index decreased
through the experiment; except for bags with no nutrient added (Bag 1) and lowest nutrient addition
rate (Bag 2).
Figure 18. Changes in Finn’s Cycling Index (a) and Average Path Length (b) over time with increasing nutrient addition rates (Bag 1 to Bag 7).
5 10 15
2050
100
200
500
1000
2000
(a) Total system throughflow cycled
Days
TSTC
(µgC
/l/d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
2050
100
200
500
1000
2000
(b) Total system throughflow straight
Days
TSTS
(µgC
/l/d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
0.0
0.2
0.4
0.6
0.8
1.0
(a) Cycling index
Days
FCI
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
010
2030
4050
(b) Average Path Length
Days
APL
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
37
APL co-varied with FCI, and decreased with increasing nutrient addition rates. Because of the high
FCI of bag 1, the APL of carbon in this bag was highest with a maximum of 34.4 on day 7 while for
bag 7, APL ranged from about 3.4 to just above 10 and showed a clear gradually decreasing trend
during the period of experiment.
3.4. Ecosystem structure and activity
3.4.1. Total system flow throughput
System activity, or system size, can be characterized by means of the Total System Throughput index
(T..), which is the sum of all flows in the network. Food webs transporting higher amounts of material
have higher T.. values (Baird et al., 1991a). Generally, system activity increased with nutrient addition
rate during the experiment. On day 9, the total system throughput in Bag 7, 6, 3 and 2 reached the
maximum values, while the peaks occurred 2 days earlier in Bag 4 and 5. The variation in system
activity is more pronounced in bags with very high amount of nutrients added (Bag 6 and 7). After
reaching peaks of 2521 and 3747 µgC/l/d, the T.. dropped dramatically by about 40% in Bag 6 and
50% in Bag 7 to 1551 and 1895 µgC/l/d, respectively. From day 15, the total system throughput
increased, and again declined on day 18, except for Bag 7 which still increased on day 18.
Figure 19. Total system throughput vary over time with increasing nutrient addition rates (Bag 1 to Bag 7).
5 10 15
100
200
500
1000
2000
5000
Days
T.. (
µgC
/l/d)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
38
3.4.2. Synergism
Synergistic relations in ecological systems have been demonstrated frequently at many levels of
biological organization, regardless of system size and complexity (Fath, 2004; Fath and Patten,
1998). In the used R package ‘NetIndices’ (Soetaert and Kones, 2011), this index is calculated based
on the ratio of the sum of all positive elements in the integral utility matrix to the absolute value of all
negative elements. Values of the synergism index (B/C) > 1 indicate that synergism occurs in the food
webs when indirect flows are taken into account, whereas values = 1 indicate there is no synergism in
the food web. Synergism was apparent in the inspected food webs, with synergism indices ranging
from 3.20±0.15 (Bag 6, day 17) to 7.62±0.88 (Bag 4, day 5). The synergism index increased during
the first five days of experiment, gradually decreased and rose again at the end of experiment, except
for Bag 1. Also, only in Bag 1 did the synergism index increase, whereas other bags showed a
decrease in this index during the experiment.
Figure 20. Synergism index vary over time at different nutrient addition rates (Bag 1 to Bag 7)
3.4.3. The dominance of indirect effect
Carbon in the food webs does not only flow between two adjacent compartments, but also non-
adjacent nodes are capable of exchanging carbon, albeit in an indirect way. The latter is defined as an
indirect effect. Indirect flows are those in which a species receives energy indirectly from another
species, such as when a polar bear receives energy from krill by consuming penguins that directly ate
the krill. As can be seen from Figure 21, indirect effects dominated direct effects at lower addition
rates of nutrient addition, and the dominance of indirect effects was universal over all combinations of
5 10 15
34
56
78
910
Days
Syn
ergi
sm in
dex
(B/C
)
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
39
day and nutrient addition rate (ID>1). Bag 1 showed a clear trend among all bags. This index in Bag 1
reached a peak of 37.5±1.8 on day 7, after which it decreased steadily during the next 6 days before
reaching a peak again on day 17. There were no consistent effects of different nutrient addition rates
during the first 11 days on this index. However, the nutrient addition rate had a negative effect on the
dominance of indirect effects from day 11 onward. Particularly, the more nutrients were added, the
lesser dominance of indirect effect occurred.
Figure 21. Changes in dominance of indirect effect over the experiment with increasing nutrient addition rates from Bag 1 to Bag 7.
3.4.4. The ratio of Ascendancy (A) to Development Capacity (C)
Figure 22 depicts the relative ascendancy (A/C ratio) and relative internal ascendency (Ai/Ci ratio) for
different bags and through time. Ulanowicz and Norden (1990) stated that highly organized
ecosystems have a tendency of internalizing most of their activities, i.e. the difference between A/C
and Ai/Ci is low. As can be seen from Figure 22, A/C and Ai/Ci were almost the same, varying
between 0.38 and 0.53 for the former and 0.37 to 0.53 for the latter. This indicates that the systems
were less dependent on exogenous connections to adjacent ecological and physical systems (Baird et
al., 1991a). During the experiment course, no general effect of nutrient addition rate on relative
ascendancy was observed. Still, relative ascendancy (A/C ratio) decreased with the rate of nutrient
addition on day 18.
5 10 15
010
2030
4050
Dominance of indirect index
Days
ID
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
40
Figure 22. Changes in relative ascendancy (A/C ratio) and relative internal ascendancy (Ai/Ci) over the experiment with increasing nutrient addition rates from Bag 1 to Bag 7.
3.4.5. Constraint efficiency
Constraint efficiency (CE ratio) is the uncertainty reduction caused by food web topology divided by
the maximum network uncertainty. It provides a simple measure of constraint across the flow network.
For example, if each compartment in the network connects with only one another compartment, then
we are 100% confident about the destination of any outflow from any compartment. Hence, the
constraint efficiency will be 100%, meaning that 100% of uncertainty about the flows is captured in the
food web topology. Figure 23 showed the changes in CE ratio with increasing nutrient addition rate
during the experiment.
Figure 23. The variation of constraint efficiency over experiment with increasing nutrient addition rates from Bag 1 to Bag 7.
5 10 15
0.30
0.35
0.40
0.45
0.50
0.55
(a)
Days
ACratio.total
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
0.30
0.35
0.40
0.45
0.50
0.55
(b)
Days
ACratio.Internal
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
5 10 15
0.55
0.60
0.65
0.70
Constraint efficiency
Days
CE
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
Bag 1Bag 2Bag 3Bag 4Bag 5Bag 6Bag 7
RESULTS MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
41
The values for CE ranged from 55.7% to 65.4% for all bags. In general, CE decreased over the
experiment in all bags except for Bag 1, which had a CE value of 63.3% on day 18 in relation to 60.6%
at the beginning of experiment. The decrease in CE ratio indicated that the outflows of the
compartments became more uncertain or less constrained.
DISCUSSION MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
42
4. DISCUSSION
4.1. Carbon flows
4.1.1. Primary production
The gross primary production (sum for all phytoplankton groups, GPP) represented a positive
relationship with increasing nutrient addition rate in this experiment (Figure 11a). The decrease in total
gross primary production (GPP), occurring after GPP reaching maximum values around day 9 and day
11, can be explained by nutrient depletion, a self-shading effect or a combination of both. Regarding
the self-shading effect, algal blooms can cause the turbidity of water to increase which hampers the
penetration of sunlight into the water column (Shigesada and Okubo, 1981). Thus, this can cause
lowered phytoplankton productivity (Drake et al., 2010). This is also true for net primary production
(NPP).
At no nutrient addition (Bag 1), picophytoplankton (A1) had the highest average NPP followed by
nanophytoplankton (A2) and microphytoplankton (A3), successively. The fact that large algae are
outcompeted by small algae at low nutrient level has been reported in the literature (e.g. Stibor et al.
(2004)). However, at higher nutrient addition rates, the response in NPP of large algae (i.e. A2 and
A3) was stronger than that of picophytoplankton (A1). This was consistent with the fact that nutrient
enrichment is known to cause increases in the productivity of large algae (Bell and Kalff, 2001). Olsen
et al. (2001) also indicated that high nutrient additions could not be utilized efficiently by A1 but
supported blooms of diatoms which were dominant groups in A2 and A3. It has been assumed that
eutrophication in the form of increases in nitrogen and phosphorus - rather than silicon - may favor
non-silicon dependent algae over diatoms (Officer and Ryther, 1980). However, diatom groups
dominated the phytoplankton community in this experiment even at the highest nutrient addition rate
because nutrients (N, P, Si) were added at the Redfield ratio (elemental ratio of N:Si:P was 16:16:1).
This prevented silicon limitation and may have allowed diatoms to grow faster than other taxa (Banse,
1982).
4.1.2. Importance of bactivory, herbivory and detritivory in food webs
The ratio of detritivory to bacterivory (D:B) and detritivory to herbivory (D:H) declined dramatically with
increasing nutrient addition rate from 10.4 and 9.2 (Bag 1) to 4.2 and 1 (Bag 7), respectively
(detritivory, baterivory and herbivory are measured by carbon consumption of zooplanktons on DET,
BAC and AUT, respectively). When consumption of DOC by bacteria is included in the detritivory, the
D:H ratio will be even greater. The lower D:H ratio at higher nutrient addition rate resulted in the
decrease in the Finn’s cycling index, which will be discussed latter. Herbivory dominated bacterivory
and overall, the herbivory to bacterivory ratio was positively related to the nutrient addition rate.
Rybarczyk et al. (2003) found that lower values of D:H indicate the more efficient use of primary
production, whereas Odum (1969) used this ratio as an indicator of surplus production. Hence, under
DISCUSSION MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
43
high nutrient addition rates, food webs showed a higher degree of utilization of primary production.
This was also confirmed by Baird et al. (1991b) when calculating D:H ratio for the Benguela and
Peruvian upwelling ecosystems with ratios of 1:100 and 3:10, respectively. They suggested that this
ratio can be used to differentiate upwelling systems from other ecosystems such as estuarine
ecosystems.
4.2. Trophic structure and food web efficiency (FWE) based on copepods production
Some studies have showed that copepods dominate the herbivore community in the pelagic marine
environment. However, it should be noted that copepods are not strictly herbivorous. They can feed on
other small zooplankton (e.g. ciliates) when larger phytoplankton such as diatoms and dinoflagellates
are rare (Gismervik and Andersen, 1997; Stibor et al., 2004; Stoecker and Capuzzo, 1990). Thus,
COP may occupy a variable trophic level, depending on the nutrient supply. At low nutrient levels,
small phytoplankton cells dominate larger cells and COP may feed more on nano- and
microzooplankton (i.e. heterotrophic nanoplankton, HNP, and ciliates, CIL) than on phytoplankton
(Caron et al., 1999; Sherr and Sherr, 2002). As a result of this, they occupy a higher trophic level at
lower nutrient levels. In this thesis, it was found that the proportion of CIL and HNP in the diet of COP
decreased gradually with increasing nutrient addition rates: from 36% (Bag 1) to 28.5% (Bag 7). Also
the total contribution of phytoplankton and detritus increased from 49.2% to 61.9% with increasing
nutrient addition rates. This resulted in a decline of the trophic level of copepods from 2.6 (Bag 1) to
2.4 (Bag 7)(Table 9), i.e. well in line with what has been reported in the literature. In addition, the
trophic levels of COP that were estimated in this thesis were comparable to those found by Sommer et
al. (2005), which ranged from 2.4 to 2.6.
Berglund et al. (2007) reported a FWE of 22%, calculated based on mesozooplankton production in a
phytoplankton based food web (i.e herbivorous food web). This value is much higher than what was
found here (FWE around 0.11-1.4%). The FWE in this study was comparable to the results of some
earlier studies in both marine and freshwater systems with FWE (also based on mesozooplankton
production) between 0.1 and 1% (Havens et al., 2000; Koshikawa et al., 1996).
4.3. Carbon cycling
Decrease in the Finn’s cycling index (FCI) with increasing nutrient addition rate in this experiment was
consistent with what has been reported in the literature (e.g. Baird et al., 1991b). Baird et al. (1991b)
examined 6 marine ecosystems and found that the FCI varied greatly with more than 3 orders of
magnitude. Nutrient enriched ecosystems (i.e. upwelling regions) had lowest FCIs, ranging between
0.01 and 3.2% for Benguela and Peruvian ecosystems, respectively.
The very high FCI in Bag 1 (73.2%) reflected the higher importance of detritivory than that of
herbivory, as was also indicated by a D:H ratio of 10.4. In this bag, carbon was recycled through the
DET and DOC compartments with the dominance of DET. The high FCI made carbon cycling many
DISCUSSION MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
44
times in the ecosystem before being dissipated as dissolved inorganic carbon (DIC), resulting in very
high values for the average path length index (APL) with a maximum of 34.4 (Bag 1 on day 7). The
APL in this study was extremely high compared to values found in the literature for carbon and were
comparable to APL values found for mineral elements (e.g. K and Ca). In the models of the Hubbard
Brook ecosystem, Finn (1980) found APL values for K and Ca of 24.3 and 16.7, respectively, and
corresponding FCI values of 82.6 and 79.9%. It is noted that mineral elements like K and Ca tend to
be recycled more in ecosystems than carbon. The high value for APL as well as FCI in our study can
be explained by the importance of DET in the food webs.
4.4. Ecosystems activity and organization
Ecosystem activity, measured by total system throughput (T..), increased with increasing nutrient
addition rates (Figure 19). However, the rate of T.. increase was slightly different from that of GPP.
From Figures 11 and 17, the total system through straight (TSTS) seemed to co-vary with GPP. Also,
the total system through cycled (TSTC) seemed to be correlated with the flows through DET.
The changes in the dominance of indirect effect showed the same pattern as FCI and APL. This can
be explained by the fact that greater cycling has increased the importance of indirect effects. The
positive correlation between the dominance of indirect effect and FCI has been reported in the
literature (e.g. Fath, 2004). In his models consisting of 60 compartments, the index varied from about
7 to 12 and FCI varied between 0.14 and 0.25. However, comparing different ecosystems needs to be
done with caution because the relationship between these indices is not the same over different
ecosystems. For example, Kones et al. (2009) calculated the APL for Takapoto and the Gulf of Riga
was about 4 while FCI smaller than 25%.
The relative ascendancy (A/C) is an indication of organization of the system and its efficiency. It can
be interpreted as the level of development reached by an ecosystem compared with its theoretical
maturity (development capacity, C). Rybarczyk et al. (2003) concluded that large difference between
A/C and Ai/Ci ratio reflected the heavy dependence of the food web in the Bay of Somme on external
carbon resources such as detritus. Their conclusions were based on high D/H and low FCI they found
for the Bay of Somme. In this study the difference between A/C and Ai/Ci ratio is very small, reflecting
that these systems were less dependent on external carbon sources. However, the D/H ratio and FCI
were high, which is in contrast with the argumentation of Rybarczyk et al. (2003). This disagreement
can be explained by the fact that DET is an internal compartment in our study.
CONCLUSION MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
45
5. CONCLUSION
Some main conclusions can be drawn from the results of this study:
1. Primary production of phytoplankton responded positively with nutrient addition rate; however,
there was no linear relationship found between both, potentially due to nutrient exhaustion or
self-shading effects.
2. The carbon flows through detritus compartment as well as from this compartment to
zooplankton compartments dominated that of bacteria and phytoplankton. The relative
importance of these flows decreased with increasing nutrient addition rate.
3. Food web efficiency ranged between 0.11 to 1.4% and decreased with increasing nutrient
addition rate.
4. Finn’s cycling index (FCI) decreased with increasing nutrient addition rate from 73.2% (Bag 1
with no nutrient added) to 32.1% (Bag 7 received the highest nutrient addition rate). The
average path length (APL) and the dominance of indirect effects (ID index) co-varied with FCI.
5. The food webs in this study are not heavily dependent on the exogenous input, proved by the
little differences between relative ascendancy and relative internal ascendancy
6. The carbon flows become more uncertain when the nutrient addition rate increased at the end
of the experiment and synergism was demonstrated to occur in these food webs.
REFERENCES MARINE ECOSYSTEMS EXPOSED TO NUTRIENT STRESS: A NETWORK ANALYSIS
46
REFERENCES
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