Faculteit Bio-ingenieurswetenschappen
Academiejaar 2015 – 2016
The relative contribution of anthropogenic chemicals and
global change to phytoplankton growth: a modelling
exercise
Jolien De Decker
Promotor: prof. dr. Colin Janssen and prof dr. ir. Frederik De Laender
Tutor: dr. ir. Gert Everaert and ir. Karel Viaene
Masterproef voorgedragen tot het behalen van de graad van
Master in de bio-ingenieurswetenschappen: milieutechnologie
Faculteit Bio-ingenieurswetenschappen
Academiejaar 2015 – 2016
The relative contribution of anthropogenic chemicals and
global change to phytoplankton growth: a modelling
exercise
Jolien De Decker
Promotor: prof. dr. Colin Janssen and prof dr. ir. Frederik De Laender
Tutor: dr. ir. Gert Everaert and ir. Karel Viaene
Masterproef voorgedragen tot het behalen van de graad van
Master in de bio-ingenieurswetenschappen: milieutechnologie
II
Copyrights
De auteur en promotoren geven de toelating deze scriptie voor consultatie beschikbaar te stellen en
delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het
auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron te vermelden
bij aanhalen van resultaten uit deze scriptie.
The author and promotors give the permission to use this thesis for consultation and to copy parts of
it for personal use. Every other use is subject to copyright laws, more specifically the source must be
extensively specified when using results of this thesis.
Gent, juni 2016
De promotor,
De tutor,
Prof. dr. Colin Janssen
dr. ir. Gert Everaert
De auteur,
Jolien De Decker
III
IV
Acknowledgements
By writing this thesis I have learned a lot. Not only on scientific grounds but also on the personal level.
I wouldn’t have succeeded without the support and help of a few people.
First, I would like to thank my tutor dr. ir. Gert Everaert for the excellent support and fast feedback
over the entire year. I never had to hesitate to ask him for help or to discuss the encountered problems.
Of course I have to show my gratitude to prof. dr. Colin Janssen and prof. dr. ir. Karel De
Schamphelaere, who gave me the opportunity to conduct this research in their research department.
Prof. dr. ir. Frederik De Laender, ir. Karel Viaene and ir. Jan Baert should be thanked for all their
interesting and helpful remarks and insights.
A special thanks goes to my parents, who have offered me the opportunity to study and especially to
my mum, who has tolerated all my difficult moments over these 5 years. I would like to thank my
friends and housemates, with whom I studied and performed this thesis in all possible locations.
Finally, the person who stole my heart, Thomas, thank you for supporting me in everything I do and
thank you for always trying to understand.
V
VI
Table of contents
Copyrights............................................................................................................................................... III
Acknowledgements ................................................................................................................................. V
List of abbreviations ............................................................................................................................... IX
Abstract .................................................................................................................................................. XI
Samenvatting......................................................................................................................................... XII
Chapter 1: Literature Review ................................................................................................................. 1
1.
Phytoplankton ................................................................................................................................. 1
1.1
Definition ................................................................................................................................. 1
1.2
Driving Forces .......................................................................................................................... 2
1.3
Seasonal Pattern...................................................................................................................... 6
1.4
Anthropogenic influences ....................................................................................................... 7
2.
Climate Change................................................................................................................................ 8
3.
Chemical Pollution ......................................................................................................................... 10
4.
3.1
Introduction ........................................................................................................................... 10
3.2
Persistent Organic Pollutants ................................................................................................ 10
3.3
Emissions to the environment............................................................................................... 10
3.4
Physicochemical properties................................................................................................... 11
3.5
Temporal pattern .................................................................................................................. 13
3.6
Monitoring of chemical pollution .......................................................................................... 13
3.7
Ecotoxicological effects of POPs ............................................................................................ 14
3.8
Multistress quantification ..................................................................................................... 16
Modelling....................................................................................................................................... 17
Problem statement and goals ............................................................................................................... 19
Chapter 2: Materials and Methods ...................................................................................................... 21
1.
Theoretical limitations................................................................................................................... 22
1.1
Temperature limitation ......................................................................................................... 23
1.2
Nutrient limitation ................................................................................................................. 24
1.3
Limitation by chemicals ......................................................................................................... 25
1.4
Integration of limitation terms .............................................................................................. 26
2.
Verification theoretical model with experimental data ................................................................ 27
3.
Construction experimental limitations.......................................................................................... 28
4.
Validation experimental limitations .............................................................................................. 30
5.
Quantification of relative contributions ........................................................................................ 31
VII
5.1
BMDC Data ............................................................................................................................ 31
5.2
Relative Contributions ........................................................................................................... 31
5.3
Seasonal Pattern.................................................................................................................... 32
5.4
Variation of parameters ........................................................................................................ 32
5.5
Extreme situations................................................................................................................. 33
Chapter 3: Results ................................................................................................................................. 35
1.
2.
3.
Model development ...................................................................................................................... 35
1.1
Assessment theoretical model .............................................................................................. 35
1.2
Construction experimental limitations.................................................................................. 36
Model Validation ........................................................................................................................... 40
2.1
Validation experimental model ............................................................................................. 40
2.2
Validation experimental limitation functions........................................................................ 40
Relative Contributions ................................................................................................................... 41
3.1
Seasonal Pattern.................................................................................................................... 41
3.2
Variation of parameters ........................................................................................................ 43
3.3
Extreme situations................................................................................................................. 43
Chapter 4: Discussion ............................................................................................................................ 49
1.
2.
3.
Model development ...................................................................................................................... 49
1.1
Logistic model of Verhulst ..................................................................................................... 49
1.2
Theoretical equations for stress ............................................................................................ 49
1.3
Experimental limitations for stress ....................................................................................... 50
1.4
Validation of experimentally derived formulas ..................................................................... 52
Quantification of relative contributions ........................................................................................ 53
2.1
Seasonal pattern.................................................................................................................... 53
2.2
Variation of parameters ........................................................................................................ 54
2.3
Extreme situations................................................................................................................. 54
Recommendations and further research ...................................................................................... 55
Conclusion ............................................................................................................................................. 59
References ............................................................................................................................................. 61
Appendix................................................................................................................................................ 71
VIII
List of abbreviations
BCF
bio concentration factor
BCZ
Belgian coastal zone
BMDC
Belgian marine data center
Kow
octanol-water partition coefficient
PAH
polycyclic aromatic hydrocarbon
PCB
polychlorinated biphenyl
PDMS
polydimethylsiloxane
PEC
predicted environmental concentration
PNEC
predicted no effect concentration
POP
persistent organic pollutant
RMSE
root mean square error
TBT
tributyltin
ww
wet weight
IX
X
Abstract
Phytoplankton is the basis of the marine food chain and plays a fundamental role in the functioning of
marine ecosystems by providing half of the global primary production. Phytoplankton growth can thus
be a measure of marine ecosystem health and is defined by four key drivers: light, temperature,
nutrient availability and zooplankton grazing. These natural driving forces are altered by humaninduced impacts, of which climate change is the most important one. A major challenge for
oceanographers is to identify, quantify and understand how the changing climate will impact ocean
phytoplankton growth. Next to naturel drivers, also chemical pollution can define phytoplankton
growth. Indeed, since the years ’50 and ’60 the production of persistent organic pollutants (POPs) has
drastically increased in the northern hemisphere and the marine environment has been the main
receptor of these POPs. Despite restrictions in the use and production of primary compounds, a
complex chemical cocktail with unknown composition and concentrations is still present in marine
waters.
Hence, algae are continuously exposed to multistress conditions which change over the year. The
impact of these drivers has often been tested in single-stressor designs, but the combined impact of
these drivers on the growth of phytoplankton is poorly studied. In this study I aimed to quantify the
relative contribution of key drivers to the total limitation of phytoplankton growth in the Belgian part
of the North Sea. It is important to assess which stress factor determines phytoplankton growth and
in what matter this might change over the year.
For this purpose, I used an alternative approach as I integrated the theoretical assumptions made in
the past and the (limited) experimental data that are available. A logistic model of Verhulst was
combined with theoretical equations, accounting for stress limitations. This model was then optimized
by modifying and substituting the stress equations based on experimental observations. The model
was validated with an external dataset in order to be representable for marine phytoplankton in the
southern North Sea. Next, simulations were performed to quantify the relative contribution of the key
drivers of phytoplankton growth. Nutrient concentrations were clearly depicted as the main
determinants of algal growth, accounting for 70-80% of the total growth limitation. In winter
monitored nutrient concentrations were much higher than in summer and the relative contribution
consequently decreased. The median relative contribution of temperature was 25% of the total growth
limitation, with a bigger influence in winter than in summer. However, this is clearly dependent on the
optimal temperature of the phytoplankton organisms. Monitored POP concentrations were not
altering phytoplankton growth since relative contributions of the chemical exposure were less than
0.5%. However, when POP concentrations were multiplied with 10, 100 and 1000 the relative
contribution of the chemicals increased drastically. In the POP100 configuration chemical
concentrations accounted for approximately 10% of the total limitation and in the POP1000
configuration for approximately 40% of the total growth inhibition. In general it could be said that
chemical exposure is only drastically altering phytoplankton growth if environmental concentrations
exceeded monitored concentrations or when nutrients were not limiting the growth dynamics.
XI
Samenvatting
Phytoplankton ligt aan de basis van de mariene voedselketen en speelt een fundamentele rol in
mariene ecosystemen daar het verantwoordelijk is voor ongeveer de helft van de wereldwijde primaire
productie. Phytoplankton groei kan dus een maat zijn voor de gezondheid van het mariene ecosysteem
en hangt af van vier variabelen: licht, temperatuur, beschikbaarheid van nutriënten en het grazen door
zoöplankton. Deze natuurlijke drijvende krachten worden gewijzigd door menselijke invloeden, met
de klimaatsverandering als belangrijkste impact. Een grote uitdaging voor oceanografen bestaat uit
het identificeren, kwantificeren en begrijpen van hoe het veranderende klimaat mariene
phytoplankton groei zal beïnvloeden. Naast de natuurlijke invloeden, heeft ook chemische vervuiling
een impact op phytoplankton groei. Sinds de jaren ’50 en ’60 is de productie van persistente organische
polluenten (POPs) sterk toegenomen in de noordelijke hemisfeer en de meerderheid van deze stoffen
komt terecht in het mariene milieu. Ondanks restricties in het gebruik en de productie van primaire
polluenten, is er nog steeds een complexe chemische cocktail met ongekende compositie en
concentraties aanwezig in mariene waters.
Algen worden dus continu blootgesteld aan multistress omstandigheden, die variëren over het jaar.
De impact van deze drijvende krachten is vaak getest in single-stressor designs, maar weinig studies
bestuderen de gecombineerde invloed op phytoplankton groei. In deze studie tracht ik de relatieve
contributie van de drijvende krachten tot de totale phytoplankton groei inhibitie in het Belgische deel
van de Noordzee te kwantificeren. Het is belangrijk om in te schatten welke stress factor
phytoplankton groei bepaalt en hoe deze verhoudingen verschillen over het jaar.
Hiervoor gebruikte ik een alternatieve benadering door theoretische assumpties gemaakt in het
verleden te integreren met de (gelimiteerde) beschikbare data. Een logistisch Verhulst model werd
gecombineerd met theoretische stress vergelijkingen, die de stress limitaties uitdrukken. Dit model
werd geoptimaliseerd door de stress vergelijkingen aan te passen en te substitueren aan de hand van
experimentele observaties. Het model werd tenslotte gevalideerd met een externe dataset om te
verifiëren of het de groei van marien phytoplankton in de zuidelijke Noordzee correct kon voorspellen.
Daarna werden simulaties uitgevoerd om de relatieve contributie van de verschillende drijvende
krachten van phytoplankton groei te bepalen. Nutriënten concentraties waren duidelijk de
belangrijkste bepalende factor van de algen groei, met een relatieve contributie van 70-80% tot de
totale limitatie. In de winter waren de gemeten nutriëntenconcentraties veel hoger dan in de zomer
en werd er dus een lagere relatieve contributie berekend. De mediaan van de relatieve contributie van
de temperatuur tot de totale groei inhibitie was 25%, met meer invloed in de winter dan in de zomer.
Dit is echter afhankelijk van de gekozen optimum temperatuur van de phytoplankton organismen.
Gemeten POP concentraties wijzigden de phytoplankton groei niet, relatieve contributies waren
immers minder dan 0.5%. Nochtans, wanneer gemeten POP concentraties met 10, 100 en 1000
vermenigvuldigd werden, steeg de relatieve contributie grondig. In de POP100 configuratie was de
chemische vervuiling verantwoordelijk voor 10% van de totale limitatie en in de POP1000 configuratie
voor ongeveer 40%. Algemeen kan gesteld worden dat chemische polluenten de phytoplankton groei
enkel aanzienlijk wijzigen wanneer chemische milieuconcentraties sterk de gemeten concentraties
overschrijden en wanneer nutriënten niet limiterend zijn.
XII
Chapter 1: Literature Review
Chapter 1: Literature Review
1. Phytoplankton
1.1 Definition
The term ‘plankton’ was first used by the German biologist Victor Hensen in 1887 and included all
organic particles ‘which float freely and involuntarily in open water, independent on shores and
bottom’ (Reynolds, 1984). However, there are two big criticisms on the definition formulated by
Hensen (1887). First, not all plankton floats. Indeed, most of the organisms are more dense than water
and have developed mechanisms to live and move in the pelagic zone. Secondly, many planktonic
organisms are not exclusively confined to the pelagic zone but spend part of their life on sediments or
in other (littoral) habitats. Therefore, it is more correct to describe plankton as ‘the community of
plants and animals adapted to suspension in the sea or in fresh waters and which is liable to passive
movements by wind or current’ (Reynolds, 1984). This definition excludes non-living suspensoids or
fragments derived from biogenic sources (Reynolds, 2006).
Phytoplankton is the collective of photosynthetic microorganisms, adapted to live partly or
continuously in open water. It is the photoautotrophic part of the plankton (Reynolds, 2006). This
means they gain most of their energy from the sunlight. Some organisms are as well (partially)
dependent on organic substrates (Harris, 1986).
Within the phytoplankton group there are many different species. There is a large range of sizes, going
from unicellular prokaryotic or eukaryotic forms (nanometer scale) to multicellular species that are
visible for the human eye (millimetre scale). Some phytoplankton are prokaryotic and can be
characterized as bacteria while others have more animal-like characteristics. In general, phytoplankton
is a collection of small organisms which grow very rapidly (the fastest have a few doublings per day;
Harris, 1986). Within the phytoplankton, diatoms and dinoflagellates are the most abundant
taxonomic groups (Leterme et al., 2006). Temperate and polar oceans are dominated by diatoms,
which are unicellular, eukaryotic organisms (Stewart, 2005). Dinoflagellates tend to dominate in
regions of low turbulence and nutrients (Stewart, 2005).
Phytoplankton species, being at the bottom of the food web, are primary producers of organic matter
on which nearly all other forms of life in any large body of water depend (Fogg et al., 1987). Although
accounting for less than 1% of Earth’s photosynthetic biomass, these microscopic organisms are
responsible for more than 45% of the global annual net primary production (Falkowski, 2004) and 50
to 90% of all the oxygen in the air (Thangaradjou et al., 2012). Therefore, phytoplankton plays an
important role in climate regulation. By means of their photosynthetic carbon fixation, phytoplankton
extract carbon dioxide from the atmosphere, and they can export a considerable part of this carbon
by sinking downwards into the ocean interior (Ebert, 2001).
1
Chapter 1: Literature Review
1.2 Driving Forces
Four driving forces have been identified as the main factors influencing the community structure and
functioning of phytoplankton, i.e. light intensity, water temperature, nutrient availability and grazing
by zooplankton (Reynolds, 2008). Phytoplankton biomass increases when the production of organic
matter by photosynthesis exceeds the degradation through respiration. While respiration goes on
continuously, photosynthesis can only take place in the presence of light, carbon dioxide and nutrients
(Sverdrup, 1953). In the following paragraphs, the driving forces and their influence on phytoplankton
growth will be discussed.
1.2.1 Light
Light exerts a major control on phytoplankton growth (Thangaradjou et al., 2012). Phytoplankton cells
use energy from the sun to convert carbon dioxide and nutrients into complex organic compounds,
which form new plant material. This process, known as photosynthesis (Eq. 1), is how phytoplankton
organisms grow.
𝑃𝐴𝑅
6 𝐶𝑂2 + 12 𝐻2 𝑂 →
𝐶6 𝐻12 𝑂6 + 6𝑂2 + 6𝐻2 𝑂
(Eq. 1)
However, not all light can be used for photosynthesis (Wetzel, 2001). Only the visible light range (blue
to red) is considered photosynthetically active radiation (PAR). Ultraviolet light has too much energy
for photosynthesis, and infrared light does not have enough (Wetzel, 2001). The absorption of sun
energy is performed by chlorophyll, a colour pigment that is ubiquitous present in phytoplankton.
The light intensity entering the ocean decreases with depth due to absorption by water and
phytoplankton (Ebert, 2001). Turbidity, or the presence of suspended particles in the water, also
affects the amount of light that reaches into the water. The more sediment and other particles in the
water, the less light will be able to penetrate (Wetzel, 2001). The survival of the phytoplankton
organism depends on its ability to enter or remain in the upper, insolated part of the water, where
enough PAR is present to perform photosynthesis (Reynolds, 2006).
The zone of the water-column where light intensity is sufficient to support net photosynthesis is called
the euphotic zone (Letelier et al., 2004). Sverdrup (1953) recognized that there is a compensation
depth at which phytoplankton cell obtains just enough light for its own survival and daily production
thus equals daily respiration. Below the compensation depth the organisms are not able to gain weight
or reproduce. However, phytoplankton cells are able to survive below the compensation depth for
short periods if they have stored sufficient energy whilst they were residing above the compensation
depth (Janssen, 2010). The critical depth is defined as the depth below which vertical up and down
motion cannot make sure that the phytoplankton cells receive enough light (Sverdrup, 1953). If the
water motion takes them below this depth, the organisms spend so much time respiring in deep water
that they cannot make up for it by photosynthesis when they return to the surface (Janssen, 2010).
Both the compensation depth and the critical depth are dependent on the amount of incoming
radiation and the transparency of the water (Sverdrup, 1953) and together they define the limits of
phytoplankton survival (Janssen, 2010).
2
Chapter 1: Literature Review
1.2.2 Temperature
Temperature is one of the fundamental drivers of biological activity, influencing processes at multiple
levels of organization, from sub-cellular to ecosystem level (Mridul, 2013). The temperature in
phytoplankton cells is mainly determined by the surrounding water temperature (DeNicola, 1996).
Temperature has both direct and indirect effects on phytoplankton growth dynamics (Mridul et al.,
2012). Temperature directly affects algal specific growth rate, defined as the rate of increase of cell
substance per unit cell substance, through its control of enzyme reaction rates (Eppley, 1972). As
temperature increases, the increased kinetic energy of reaction molecules results in higher reaction
rates until the point where denaturation rate exceeds kinetic effects (DeNicola, 1996). Indeed, Eppley
(1972) found that the specific growth rate of phytoplankton increases exponentially when the
temperature increases up to an optimum temperature. When the ambient temperature exceeds the
optimum temperature, sharp declines in growth rate are observed (Mridul et al., 2012). This
relationship is illustrated in a thermal tolerance curve, in which the growth rate is plotted against the
ambient temperature (Figure 1). Close to the optimum temperature, the growth rate reaches its
maximum value. The temperature range in which the growth rate is positive is called the niche width.
Important to note is that the decline above the optimum temperature is much sharper than below the
optimum temperature, a property that is referred to as negative skewness (Kingsolver, 2009; Mridul
et al., 2012). This makes species living at their optimum temperature more sensitive to warming than
cooling, with important consequences for their performance in the environment (Mridul et al., 2012).
The optimum growth temperature of phytoplankton is typically between 20°C and 25°C (Fogg, 1987),
but is species-specific and even depends on the geographical region. Indeed, Mridul et al. (2012) found
a latitudinal trend in the optimum temperature for phytoplankton growth, i.e. close to the equator the
optimum temperature is much higher than the optimum temperature for species living at higher
latitudes.
Figure 1 Thermal tolerance curve of phytoplankton growth rate (Mridul., 2013). In this particular example, a water
temperature of 21°C corresponds to the optimum temperature and thus the maximum growth rate. At higher and lower
water temperature, the growth rate will be lower.
3
Chapter 1: Literature Review
An indirect effect of rising water temperature is an increase in ocean stratification, which leads to a
decrease of nutrients in the surface waters (Steinacher et al., 2010). As will be discussed later, the
deficient in nutrients has a big influence on phytoplankton growth. Also, increased sea surface
temperature may alter dynamics between phytoplankton and their zooplankton grazers (Eppley,
1972).
1.2.3 Nutrients
Since the earliest days of phytoplankton ecology, nutrients have been listed among the most important
variables controlling phytoplankton community structure and biomass (Tilman et al., 1982). Inorganic
nutrients such as nitrogen, phosphorus and silicon are part of the building blocks of phytoplankton
cellular structure and are at particular times limiting factors for the phytoplankton growth (Reid et al.,
1990). When nitrogen and phosphorus are deficient, algal growth rate will decrease (Kong et al., 2010).
Nitrogen exists in several dissolved forms in the ocean, but only reactive nitrogen: nitrate (NO3-), nitrite
(NO2-), and ammonium (NH4+), are easily taken up by phytoplankton (Tyrell, 1999). Marine
photosynthesizers, such as many cyanobacteria, can as well obtain their nitrogen from the
atmosphere-derived dinitrogen (N2). These organisms have an advantage when reactive nitrogen is
scarce since they do not reply on the dissolved supply. However, they are at a disadvantage compared
to other phytoplankton organisms when reactive nitrogen is more abundant, since N2-fixation requires
more energy (Tyrell, 1999). A significant loss of reactive nitrogen can be assigned to denitrification,
which transfers reactive nitrogen back to N2 (Jaffe, 1992). Phosphorus on the other hand gets in the
marine environment via river water (Jahnke, 1992), while the most significant output of phosphorus
from the oceans lays in organic debris sinking to the ocean floor and becoming incorporated into
sedimentary rocks (Tyrell, 1999).
A current dogma of aquatic science is that marine and estuarine phytoplankton tend to be nitrogen
limited, while freshwater phytoplankton tend to be phosphorus limited (Hecky & Kilham, 1988). This
can be related to different causes, for example that N-fixation is slow in the ocean and denitrification
is fast relative to their rates in freshwater environments (Hecky & Kilham, 1988). Although N-limitation
is more important in the marine environment, P-limitation should also be taken into account, since the
growth of marine phytoplankton gets inhibited by shortages of both nutrients. Indeed, both
phosphorus and nitrogen are essential to perform metabolic functions and for structural purposes
(Hecky & Kilham, 1988). Although there’s a large interspecies variation in nutrient requirements for
algal groups (Reid et al., 1990), Redfield (1934) suggested that the chemical composition of
phytoplankton tends to a constant N:P ratio of 16:1. When the nutrient concentration approaches this
ratio, growth rates are close to maximum growth rates (Redfield et al., 1986).
The uptake of nutrients and the kinetics of active transport through cell membranes resembles that of
enzyme reactions (Aksnes & Egge, 1991). Enzyme reactions usually follow a Michaelis-Menten rate and
therefore the rate of uptake of nutrients by phytoplankton mostly follows Michaelis-Menten uptake
kinetics (O’Brien, 1974). This rate of uptake governs the rate of population growth (O’Brien, 1974).
Consequently nutrient limitation of phytoplankton growth rate is commonly calculated using a
Michaelis-Menten equation, which relates directly the specific growth rate μ to the ambient nutrient
concentration (Schoemann et al., 2005). However, the relationships between the relative growth rate
and the nutrient concentrations are more complicated than this simple hyperbolic expression suggests.
4
Chapter 1: Literature Review
Relative growth rate is dependent more directly on intracellular concentration rather than the rate at
which the nutrient enters the cell (Fogg, 1987). Given a supply of phosphate for example, algae are
able to accumulate an excess which is stored within the cells in the form of polyphosphate granules.
The reserves resulting from this luxury consumption may then support growth in the absence of any
further external supply (Fogg, 1987). In this way phytoplankton can acclimate to changes in
environmental conditions by altering their chemical composition in response to environmental
variability (Bonachela et al., 2011).
1.2.4 Zooplankton
Next to light, temperature and nutrients also zooplankton grazing may control phytoplankton biomass
(Ruzicka et al., 2011). Oceanic zooplankton are the most widespread form of animal life on earth
(Verity & Smetacek, 1996) and get their energy from ingesting phytoplankton organisms. By grazing on
phytoplankton, zooplankton reduces the phytoplankton biomass in marine ecosystems. Calbet and
Landry (2004) state that microzooplankton grazing represents the major loss term for phytoplankton
cell growth and classically measured primary production across a broad range of ocean regions and
habitats.
Zooplankton organisms are the second trophic level of the marine food web (Figure 2). In every
transaction of energy from one trophic level to another, energy is lost (Janssen, 2010). This is lost to
use in two ways: by conversion to heat and by storage in the chemical bonds and other simple byproducts of the digestion. Given the energy losses, the result is that 2.5 tons of diatoms are required
to produce 250g of tuna (Janssen, 2010).
Figure 2 Marine food web (Maribus). In a marine food web several species of phytoplankton (primary producers) provide
food for many species of herbivorous zooplankton (primary consumers), which in turn are eaten by many species of
carnivores (secondary consumers).
5
Chapter 1: Literature Review
The most important group of marine zooplankton in the North Sea is copepods. Copepods are major
components of mesozooplankton (zooplankton with a body size of 0.2 ± 2.0 mm), and contribute
significantly to grazing pressure on phytoplankton (Sommer et al., 2001). In the southern North Sea,
herbivorous copepods constitute 70 to 80% of the total zooplankton biomass and consume 40% (on
an annual basis) of the net particulate production (Frangoulis et al., 2000).
1.3 Seasonal Pattern
In temperate regions phytoplankton densities fluctuate in one year, i.e. typically high densities of
marine diatoms can be found in spring and fall (Figure 3). During winter and summer phytoplankton
densities are low. The fluctuating densities are mainly related with the changes of the driving forces
throughout the year.
Winter is temperature regions in characterised by short days, low inclination of the sun and
phytoplankton growth is thus limited by low light levels. Phytoplankton are only in the sunlit surface
layers for a short period of time before being mixed down into deeper, darker water, far below the
critical depth (Henson, 2006). Phytoplankton cells are very scarce and inactive, and also zooplankton
concentrations are low (Janssen, 2010). High wind speeds and heat loss from the surface water drive
convective overturning. This deep turnover has an important consequence for the marine community:
plant nutrients that sank during the preceding summer are returned to the surface. Hence, nutrient
concentrations are high in winter but phytoplankton concentrations are low because photosynthesis
is limited by insufficient light (Henson, 2006).
As spring approaches, the days become longer, the insulation increases and light energy penetrates
deeper in the water. Additionally, wind speeds as well as water turnover reduce. This gives that
phytoplankton organisms cannot be carried below the critical depth anymore and they experience no
light limitation. Both phytoplankton and nutrients are strapped in the sunlit upper water column
(Henson, 2006). For a short period (i.e. few weeks), conditions for phytoplankton growth are optimal.
The surface water is rich in nutrients and is warming, stirring by the wind can no longer carry the plant
cells into dark water, and zooplankton populations are low (Janssen, 2010). Phytoplankton growth can
become explosive, which is then called a spring bloom. However, quite soon after the phytoplankton
bloom, densities of zooplankton herbivores start to increase as well (Janssen, 2010).
During spring bloom nutrients are transferred from the water through the phytoplankters into the
zooplankters and are no longer available for phytoplankton growth (Janssen, 2010). Indeed, the
phytoplankton community is rapidly using most of the available nutrients which gives that, as summer
approaches, nutrients get depleted and nutrient limitation gains importance (Henson, 2006). Next to
that, zooplankton communities impose a high grazing pressure on all phytoplankton species (Weithoff
et al., 2014). The combination of these factors bring the spring bloom to an end (Weithoff et al., 2014).
Phytoplankton concentrations start to decline while zooplankton concentrations are still increasing.
However, as summer approaches, zooplankton concentrations follow the phytoplankton pattern and
start to decrease as well. During the entire summer, nutrient concentrations are very low (Janssen,
2010).
During autumn, the daylight period shortens and the sun lowers in the sky, which moves the
compensation and critical depths to shallower levels. The sea surface cools, and this cooled water
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Chapter 1: Literature Review
sinks. Now the winds are able to stir the water at greater depths, returning nutrients to the surface. At
this point, a fall bloom of phytoplankton can occur, depending on the weather. If the weather is calm,
the nutrients brought to the surface stimulate a last seasonal surge of phytoplankton growth. If winds
prevail and stirring reaches the critical depth, however, there is no fall bloom. While fall advances, light
becomes more limited until phytoplankton growth stops. Animals enter their winter conditions, ready
to repeat the cycle with the advent of the following spring (Janssen, 2010).
Figure 3 Seasonal variability of nutrient concentrations (orange line), phytoplankton densities (green line) and zooplankton
densities (blue line) (modification of Janssen, 2010). In spring a big increase in phytoplankton densities can be observed,
known as the spring bloom.
1.4 Anthropogenic influences
The growth of phytoplankton is influenced by four main drivers (cfr. paragraph 1.3). However, in
addition to these drivers also anthropogenic influences may have an effect on the phytoplankton
growth. During past decades there is increasing awareness of the vulnerability of the marine
environment to human-induced impacts, as the anthropogenic pressure on the marine environment
has drastically increased (Zacharias and Gregr, 2005; Dachs and Mejanelle, 2010). This current epoch
in which humans and our societies have become a global geophysical force, is called the
“Anthropocene” (Steffen et al., 2007). In the next parts, the different ways in which humans can have
an impact on the marine environment and thus the stress experienced by phytoplankton organisms,
will be discussed. Focus will be on the potential effects of climate change and chemical pollution.
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Chapter 1: Literature Review
2. Climate Change
Since the pre-industrial area economic and population growth have influenced the anthropogenic
greenhouse gas (GHG) emissions. Concentrations of carbon dioxide (CO2), methane (CH4) and nitrous
oxide (N2O) have all shown large increases since 1850 (40%, 150% and 20%, respectively), with larger
absolute increases between 2000 and 2010 (Figure 4; IPCC, 2014). To date, the atmospheric
concentrations of CO2, CH4, N2O are the highest ever observed (IPCC, 2014).
Figure 4 Globally averaged greenhouse gas concentrations of carbon dioxide (CO2, green), methane (CH4, orange) and
nitrous oxide (N2O, red) determined from ice core data (dots) and from direct atmospheric measurements (lines) (IPCC
2014).
The rising atmospheric greenhouse gas concentrations have increased global average temperatures
with ca. 0.2°C per decade over the past 30 years. Most of this extra energy has been absorbed by the
world’s seas and oceans (Hoegh-Guldberg et al., 2010). Indeed, the marine environment stores more
than 90% of the accumulated energy, while the atmosphere stores only about 1% (IPCC, 2014). The
ocean warming is largest near the surface, i.e. the upper 75m warmed by approximately 0.11°C per
decade over the period 1971 to 2010 (IPCC, 2014). It is predicted that the global change in climate will
lead to a rise of the global mean surface temperature by 2.6°C-4.8°C (worst case scenario) or 0.3°C1.7°C (best case scenario) from 2081 to 2100, relative to 1986-2005 (Figure 5; IPCC, 2014).
Figure 5 The global average surface temperature change from 2006 to 2100, relative to 1986-2005, worst case (red) vs best
case (blue) scenarios (IPCC 2014). Projections are shown for a multi-model mean (solid lines) and the 5 to 95% range
(shading).
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Chapter 1: Literature Review
In addition to acting as a heat sink, the oceans have absorbed approximately one-third of the carbon
dioxide produced by human activities (Hoegh-Guldberg et al., 2010). However, this ocean CO2 uptake
is not harmless, as it induces ocean acidification, which is the combination of pH reductions and
alterations in fundamental chemical balances. The absorption of anthropogenic CO2 has acidified the
surface layers of the ocean, with a steady decrease of 0.02 pH units per decade over the past 30 years
(Doney et al., 2009).
The dissolution of CO2 gas from the atmosphere into seawater is a rapid chemical process, so that the
increase in CO2 concentration in the surface water of oceans is proportional to the increased CO2
concentration in the atmosphere (Feely et al., 2009). When CO2 gas is dissolved in sea water, it reacts
with water molecules to form carbonic acid (H2CO3). This acid can lose hydrogen atoms and dissociate
into bicarbonate (HCO3-) and carbonate (CO32-) ions (Eq. 2; Doney et al., 2009). All these reactions are
reversible and near equilibrium. For typical surface ocean conditions, about 90% of the total dissolved
inorganic carbon occurs as bicarbonate ions and 9% as carbonate ions, with only 1% remaining as
dissolved CO2(aq) and H2CO3 (Feely et al., 2009).
𝐶𝑂2(𝑎𝑞) + 𝐻2 𝑂 ⇄ 𝐻2 𝐶𝑂3 ⇄ 𝐻+ + 𝐻𝐶𝑂3− ⇄ 2𝐻+ + 𝐶𝑂32−
(Eq. 2)
Adding CO2 to oceans will increase carbonic acid concentrations, which will dissociate into bicarbonate
and hydrogen ions. The produced hydrogen ions will react with the carbonate ions to form extra
bicarbonate. A net increase in bicarbonate and hydrogen ion concentrations will occur (Feely et al.,
1009) and thus the pH of the oceans will decrease (Doney et al., 2009). Since the beginning of the
industrial area, the pH of the ocean surface water has decreased by 0.1 (IPCC, 2014). Furthermore, as
the CO32- concentrations decrease, solid calcium carbonate minerals dissolute (Eq. 3).
𝐶𝑎𝐶𝑂3 (𝑠) ⇄ 𝐶𝑎2+ + 𝐶𝑂32−
(Eq. 3)
This directly decreases the ability of some CaCO3 secreting organisms (e.g. coccolithophores,
foraminifera, crustaceans) to produce their shells or skeletons (Doney et al., 2009).
Understanding how climate change will affect the marine ecosystems is of global concern. Since
plankton are good indicators of climate change in the marine environment, studying plankton
dynamics can help to understand this worldwide problem (Hays et al., 2005). Unlike many other marine
organisms, few plankton species are commercially exploited, which means any long-term variations
can be attributed to climate change (Hays et al., 2005). Furthermore, plankton organisms can respond
easily to changes in ocean temperature by expanding and contracting their ranges (Hays et al., 2005).
Also, plankton population size is less limited by the persistence of individuals from previous years, since
most species are short lived. Due to these reasons environmental change and phytoplankton dynamics
are tightly coupled (Hays et al., 2005). If we want to measure and know the impacts of climate change,
plankton dynamics should be studied and monitored.
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Chapter 1: Literature Review
3. Chemical Pollution
3.1 Introduction
Over the past years, the industrial production of chemicals, plastics, and pharmaceuticals has increased
steadily (Burns, 2000). Thousands of industrial chemical substances are entering the marine
environment and often originate from anthropogenic activities, such as urbanization, industry and
agriculture (Laane et al., 2012). In the last four decades, there has been an increasing concern for the
potential harmful effects of organic pollutants to ecosystems and humans (Dachs & Méjanelle,
2010). The anthropogenic impact is constituted by the modification of the biosphere by thousands of
organic chemicals at ultra-trace levels and should be recognized as a potential driven force of marine
ecosystems (Dachs & Méjanelle, 2010).
3.2 Persistent Organic Pollutants
The chemical compounds of most concern in the marine environment are generally those that are
persistent, toxic and bioaccumulative (Law et al., 2010). These chemicals have a long half-life in soils,
sediments, air or biota and are called persistent organic pollutants (POPs) (Jones & De Voogt, 1999).
POPs are chemically stable, and therefore not easily degraded in the environment or in organisms (Rios
et al., 2007). They are lipophilic and accumulate in the food chain.
Important classes of POP chemicals are polychlorinated biphenyls (PCBs), organo-chlorine pesticides
and polycyclic aromatic hydrocarbons (PAHs). PCBs are stable chemicals, with low volatility at normal
temperature (Everaert, 2015). They are soluble in most organic solvents but almost insoluble in water.
They compromise a biphenyl ring with various chlorine substitutions and are environmentally
hazardous due to their extreme resistance against chemical and biological breakdown by natural
processes in the environment (Everaert, 2015). In the seventies PCBs were used globally as an additive
to lubricating oils and greases and in electrical installations (Pascall et al., 2005). Organo-chlorine
pesticides are synthetic compounds that are chemically stable and hydrophobic (Rios et al., 2007). A
well-known example of such as pesticide is DDT (dichloro-diphenyl-trichloroethane), used as an
insecticide in agriculture. Polycyclic aromatic hydrocarbons (PAHs) are a group of more than 100
different chemicals that are formed during the incomplete burning of coal, oil, garbage or other organic
substances (Rios et al., 2007). More than 50% of PAH inputs to the atmosphere are from transportation
and 28% from residential and industrial combustion (Webster et al., 2011). Examples of more polar
POPs are phenols and chlorinated phenols (Jones & De Voogt, 1999).
3.3 Emissions to the environment
Organic contaminants enter the environment through various routes: some are used intentionally in
industrial processes and agriculture applications, while others are released unintentionally from traffic
and waste (Everaert, 2015). Chemicals can be emitted to marine or fresh water, air, or soil
(Prevedouros et al., 2006). The behaviour and fate of chemicals between these environmental
compartments is dependent on both their physical and chemical properties (see further) and on the
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Chapter 1: Literature Review
nature of the environment. Indeed, ocean and atmosphere currents can distribute the pollutants
globally. For example, volatile contaminants can be atmospherically transported for long distances and
deposited in the aquatic environment, far away from the source area (Skei et al., 2000). Generally, the
marine environment acts as the main receptor of all organic contaminants (Law et al., 2010). The
pathways by which pollutants can get into the aquatic environment are shown in Figure 6.
Figure 6 Ways contaminants get into surface water (Van Leeuwen et al., 2007).
3.4 Physicochemical properties
The distribution, deposition and remobilization of organic chemicals in the marine environment does
not only depend on air and ocean currents, but also on the physical-chemical properties of the organic
chemicals. These properties include aqueous solubility, vapour pressure, partition coefficients
between the liquid, solid and air phase and half-lives in air, soil and water (Jones & De Voogt, 1999).
Once the chemicals are dissolved in water, they undergo many different processes and are distributed
amongst the different phases present (i.e. air, water, sediment and biota; Burns, 2000). Since POPs are
typically hydrophobic and lipophilic they partition strongly to solids (organic matter) and avoid the
aqueous phase (Jones & De Voogt, 1999). Both dissolved (DOM) and particulate organic matter (POM),
present in the water body, may act as ‘sponges’ to mop up the organic contaminants (Hylland &
Vethaak, 2011). In that way, they can be removed from the water column by sedimentation (Hylland
& Vethaak, 2011).
Apolar organic pollutants tend to accumulate as well in biological tissues in organisms (Jones & De
Voogt, 1999). Pelagic organisms can be exposed to contaminants in different ways: dissolved chemical
pollutants in water, through uptake of particles or organic material with associated contaminants or
through trophic transfer (Hylland & Vethaak, 2011). Once the contaminants get into an organism, their
lipophilic behaviour may ensure they accumulate in food chains (Jones & De Voogt, 1999). When the
concentration of the chemical in an organism exceeds the concentration in the consumed prey,
biomagnification occurs (Everaert, 2015). Due to biomagnification, toxic substances become
increasingly concentrated within living organisms as they move up each step of the food chain (Figure
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Chapter 1: Literature Review
7). Although chemical exposure in the water compartment may appear limited, POP concentrations in
phytoplankton can be 1,000 time the chemical concentration in water and concentrations in top
predators can even be 10,000,000 time the water concentration.
Figure 7 Biomagnification of POPs in a marine food chain (Goldberg).
Note that not all POPs will be mainly found in biological tissues, they can also volatilise from the water
body into the atmosphere under environmental temperatures (Jones & De Voogt, 1999). Organic
chemicals with high vapour pressure and low water solubility will mainly be found in the air at
equilibrium (Everaert, 2015). Lastly, the chemicals may degrade to other compounds (Burns, 2000),
but there half-life is often very long. Sinnkkonen and Paasivirta (2000) measured half-life times
between 60 days and 27 years for PCBs.
Two important concepts regarding the distribution and fate of POPs in the different compartments of
the marine environment should be mentioned: the biological pump mechanisms and the global
distillation effect. Both concepts are influenced by the partitioning behaviour, and thus the physicalchemical properties, of each individual organic compound (Everaert, 2015). The biological pump refers
to the process of accumulation of POPs in phytoplankton organic matter and the subsequently transfer
of these POPs to deeper water and sediment (Galban-Malagon et al., 2012). When phytoplankton
blooms, the dissolved concentrations of POPs in surface water are depleted (Everaert, 2015) and the
air-to-water flux of pollutants is promoted (Galban-Malagon et al., 2012). Galban-Malagon et al. (2012)
reported that seasonal changes in phytoplankton biomass result in seasonally variable POP
concentrations in the Arctic Ocean. The global distillation effect on the other hand is the cause of the
high concentrations of some pollutants found in Earth's arctic regions. The organic pollutants volatilize
in relatively warm source regions, move through the atmosphere and condense and accumulate at
colder, higher latitudes onto vegetation, soil, and bodies of water (Simonich & Hytes, 1995). The
pollutants get accumulated far away from their source area, due to this long-range transport. Global
distillation is driven by the change of a pollutant's subcooled liquid vapor pressure with temperature,
its environmental persistence, and its tendency to associate with lipids (Simonich & Hytes, 1995).
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Chapter 1: Literature Review
3.5 Temporal pattern
Because of the persistent, bio-accumulating and toxic character of the PCBs, production was stopped
in the late 1970s in Western Europe (Everaert, 2015). This gave rise to a general temporal pattern
found for many priority compounds, which are chemicals that have been prioritized in legislative
frameworks (e.g. PCBs and PAHs), in aqueous compartments (Figure 8). PCB concentrations peaked
between the 1970s and then declined in the 1990s (Laane et al., 2012). The decreasing concentrations
can be explained by restrictions in use, following the concerns over environmental persistence and
accumulation in the food web in the 1970s (Jones & De Voogt, 1999). Indeed, in 1992 the OSPAR
convention or Convention for the Protection of the Marine Environment of the North-East Atlantic,
which has been signed by all northwestern European countries, was initiated with the general aim of
the cessation of discharges, emissions and losses of hazardous substances by 2020 (Everaert, 2015).
The key objective was to achieve concentrations in the marine environment close to background values
for naturally occurring substances and close to zero for man-made synthetic substances (Everaert,
2016).
Figure 8 Relative emissions of PCBs to the environment between 1900 and 2000 (Jones & De Voogt, 1999).
Despite the decreasing emissions of organic chemicals and the decline in environmental
concentrations, the concentration of certain other chemical compounds in the coastal and marine
environment continue to increase (Laane et al., 2012). Moreover, after decades of primary emissions
reservoirs of PCBs have accumulated in soil, water and biota. These reservoirs can be remobilized due
to changes in temperature and soil organic matter (Cabrerizo et al., 2013) or physical disturbances due
to sand or gravel extractions and dredging activities (Martins et al., 2012) and thus keep providing an
ongoing supply of the chemicals to the water phase (Rios et al., 2007). So, as primary emissions cease,
secondary emissions from residues may significantly influence the fate of PCBs in the environment
(Nizzetto et al., 2012). The impact these secondary emissions might have is still poorly studied.
3.6 Monitoring of chemical pollution
In the scope of international agreements and conventions (e.g. Stockholm Convention (2001), Helsinki
Commission) organic chemicals have been monitored for several decades and from these monitoring
activities the ubiquitous presence of organic chemicals in the marine environment can be revealed
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Chapter 1: Literature Review
(Everaert, 2015). PCBs are widespread and detected in all environmental compartments (Everaert,
2015). Monitoring activities revealed that PCB concentrations in estuaries and harbours are up to three
orders of magnitude higher than in open sea (Schaanning et al., 2011). For example, Gioia et al. (2008)
reported that concentrations of the sum of seven PCBs in the open Atlantic Ocean are between 0.071
pg/L and 1.70 pg/L, while Emoluga et al. (2013) reported PCB concentrations in the order of ng/L in
Forth Estuary in Scotland. In addition, concentrations in the sediment compartment are even higher
than in the aqueous phase. Webster et al. (2011) reported concentrations of seven PCBs in sediment
in coastal waters around Scotland in the order µg/kg. Biomagnification is also visible in monitoring
activities, e.g. Ray et al. (1999) reported PCB concentrations in zooplankton in Newfoundland (Canada)
of 0.7 µg/kg ww, while Carballo et al. (2008) found PCB concentration in dolphins along the Canary
islands (Spain) between 300 and 33,000 μg/kg ww.
Important to note is that routine monitoring activities particularly focus on priority chemicals
(Everaert, 2015). As such, in comparison to the multitude of chemicals present in the marine
environment (Dachs and Mejanelle, 2010), only few chemicals are routinely monitored. By doing so,
several potentially harmful chemicals may be overlooked and the real chemical cocktail present in
marine waters is unknown.
3.7 Ecotoxicological effects of POPs
3.7.1 Field data
Since POPs bioaccumulate and magnify in the foodchain, effects of POPs often manifest themselves in
top predators such as marine mammals and predatory birds (Jones & De Voogt, 1999). There is a long
tradition of measuring the concentrations of contaminants in selected marine organisms and such data
have frequently formed the basis for environmental assessments (Law et al., 2010). Franke (1996)
stated that it is a reasonable assumption that bioaccumulation may be a prerequisite for long-term
effects in individuals, populations and ecosystems and that the bio concentration factor (BCF), i.e. the
ratio of the concentration in an organism and in the surrounding medium truly expresses the degree
of concern and the risk for the environment.
One of the most important effects of POPs is probably their disrupting effect on the endocrine system
of wildlife (Tanabe, 2004). Exposure to POPs during critical periods of life, may induce abnormal thyroid
function, decrease fertility rates and may also cause disruptions in the sex characteristics, hereby
altering the sex ratios of the population (Tanabe, 2004). Certain POPs are as well assumed to damage
the immune system of marine species, with an increase in disease outbreaks as result (Law et al., 2010).
In addition, a large variety of chemical contaminants may have a direct impact on plankton
communities by affecting photosynthesis and other aspects of energy utilization and incorporation
(Booij et al., 2013). The most important compounds causing toxic effects on marine phytoplankton are
biocides, especially those with a herbicidal mode of action (Booij et al., 2013). Effects can vary from
reductions in population development rate to shifts in species composition – i.e. towards species that
are more tolerant to a certain pollution (Hylland & Vethaak, 2011). Besides this, many POPs are known
or suspected carcinogens (Jones & De Voogt, 1999).
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Chapter 1: Literature Review
3.7.2 Laboratory toxicity tests
Next to collecting field data, a second way of gaining information about ecotoxicological effects of
POPs on marine species is by the use of laboratory toxicity tests (Walker et al., 2001). When performing
laboratory tests, organisms are exposed to a concentration range of a chemical to obtain a
concentration-response relationship, in which response refers to the effect caused by this chemical on
the organism. This allows the estimation of the ECx, the concentration at which x% effect is observed.
This ECx reflects the sensitivity of the tested species for the considered chemical: the lower an ECx of a
species is, the higher its sensitivity for the considered chemical (De Laender, 2007). Additionally, a
Predicted No Effect Concentration (PNEC) can be determined. In ecological risk assessment this PNEC
is than compared with the Predicted Environmental Concentration (PEC) of that chemical to indicate
the potential risk to the environment.
Ghekiere et al. (2013) assessed the ecological risk of different chemicals in the Belgian Coastal Zone
(BCZ) by using field data and comparing this with PNEC’s. Potential risks were reported for tributyltin
(TBT), polybrominated diphenylethers, PCBs and five PAHs in the water compartment of the BCZ
(Ghekiere et al., 2013). Despite the fact that concentrations of organic chemicals may exceed
environmental quality standards, not all their ecotoxicological effects are quantified yet (Rockström et
al., 2009). Moreover, usually singe-species tests are performed while the presence of other species
can affect the effect of chemicals (De Laender, 2007). To increase ecological realism, studies should be
performed with multiple species. Large scale experimental studies, i.e. micro-, mesocosm and field
enclosure studies, are capable of accounting for direct and indirect toxicant effects resulting from
ecological interactions between different species. Unfortunately, these types of studies are very
resource-demanding and can thus not be used for routine evaluation of chemical toxicity (De Laender,
2007).
3.7.3 Mixture toxicity
In most cases, aquatic organisms are not exposed to a single substance but to complex mixtures of
chemicals (Ghekiere et al., 2013). These mixtures are likely to contain organic chemicals that are not
monitored or assessed and thus with unknown ecotoxicological effects. In addition, there is increasing
concern about the potential adverse effect of mixtures since the effect of the mixture can be higher
than the effect of each individual component (Ghekiere et al., 2013).
A major limitation for the study of mixtures effects is that the knowledge about the chemical
composition of marine waters in terms of chemical pollution is poor (Everaert, 2015). Most studies
solve this problem by using artificial mixtures of organic chemicals. However, these mixtures are only
an approximation of the real mixtures of chemicals and may lack environmental realism (Backhaus et
al., 2003). Therefore, to increase environmental realism of laboratory-based ecotoxicological research,
passive samplers might be a useful alternative (Lohmann et al., 2012). In passive sampling, sheets are
deployed in an aquatic environment and here they accumulate hydrophobic chemicals. The sampling
procedure is based on free flow of chemical analyte molecules from the sampled medium (the marine
environment) to the passive sampler, as a result of a difference between the chemical potentials of
the chemical in the two media (Vrana et al., 2005). The net flow of analyte molecules from one medium
to the other continues until equilibrium is established in the system, or until the sampling period is
stopped (Vrana et al., 2005). After deployment, loaded sheets can be used as passive dosing devices
in ecotoxicological tests to establish constant realistic exposure concentrations (Claessens et al., 2015).
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Chapter 1: Literature Review
Despite the obvious advantages of using loaded sheets, few results of studies performed with passive
samplers, and thus environmental realistic mixtures, are reported.
3.8 Multistress quantification
Next to the inclusion of realistic chemical mixtures, the environmental realism of ecotoxicological
research would increase by using more environmentally realistic test conditions. Vieira and
Guilhermino (2012) concluded that temperature affects the toxicity of chemical contaminants and
suggest that in moderate or highly polluted ecosystems marine microalgae are particularly vulnerable
to temperature increase. Kong et al. (2010) on the other hand suggested that chemical toxicity to
phytoplankton under nutrient-starved conditions was lower than toxicity under nutrient-enriched
conditions. Also changes in light conditions (e.g. Wang et al., 2008) considerably alter the
ecotoxicological effects of organic chemicals in experimental assessments.
The impact of natural drivers on chemical toxicity has often been tested in single-stressor designs, but
the combined impact of natural drivers of the growth of phytoplankton is poorly studied. It is necessary
to investigate phytoplankton growth under multistress conditions and to quantify the impact of all key
drivers. Two approaches have been recently used in this asset, i.e. an experimental approach (Everaert
et al., 2016) and a modelling approach based on field data (Everaert et al., 2015). In the experimental
approach the specific growth rate of a marine diatom, Phaeodactylum tricornutum, was tested in a
growth inhibition test with three nutrient regimes, two test temperatures, three light intensities and
three chemical exposures. Exposure to realistic mixtures of organic chemicals was achieved by the use
of passive samplers. Nutrient regime, temperature and time interval explained 85% of the observed
variability in the experimental data, while the variability explained by chemical exposure was only
about 1% (Everaert et al., 2016). The main conclusion made was that the mixture of hydrophobic
substances present in Belgian marine water did not affect the growth of P. tricornutum.
The second approach to quantify multistress is a modelling approach based on field data. Everaert et
al. (2015) modelled phytoplankton dynamics using four classical drivers (light and nutrient availability,
temperature and zooplankton grazing) and tested whether extending this model with a POP-induced
phytoplankton growth limitation term improved model fit to observed chlorophyll a concentrations.
Including monitored concentrations of seven PCBs and four pesticides did not lead to better
phytoplankton biomass simulations, suggesting that POP-induced growth limitation of marine
phytoplankton in the Belgium Part of the North Sea (ca. 1%) is relatively small compared to the
limitations caused by the classical drivers (Figure 9; Everaert et al., 2015).
Figure 9 Modelled contribution of photosynthetically active radiation (PAR), water temperature, nutrients, zooplankton
grazing and persistent organic pollutants (POPs) to the phytoplankton growth limitation in the Belgian part of the North Sea
(Everaert et al., 2015).
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Chapter 1: Literature Review
4. Modelling
Phytoplankton growth under non-stress conditions can be predicted by various mathematical models.
The simplest realistic model to address growth dynamics is the exponential growth model (Eq. 4;
Tsoularis & Wallace, 2002).
𝑑𝑁
𝑑𝑡
= 𝜇∗𝑁
(Eq. 4)
In Eq. 4, μ is called the intrinsic growth rate and represents the growth rate of a species per capita.
𝑁(𝑡) = 𝑁0 𝑒 𝜇𝑡
(Eq. 5)
The solution of Eq. 4 is written down as Eq. 5 and suggests that the size of a population is only
dependent on the initial size of the population (N0), the intrinsic growth rate (μ) and the time passed
(t). Note that intraspecific competition is not included here. Intraspecific competition refers to the
competition between members of the same species for a limited amount of resources. Therefore, the
population would increase unhindered or reduce to zero (if an initial growth reduction were present),
which is not a realistic situation. In 1920, Verhulst described that a population has a numerical upper
bound on the growth size. This upper bound is typically called the carrying capacity, K, and is defined
as the highest number or biomass of a certain species or functional group of organisms that can be
supported by an ecosystem (Mooij et al., 2005). Carrying capacity changes over time with the
abundance of resources. When population density approaches carrying capacity, population growth
rate approaches zero due to competition (Mooij et al., 2005).
Verhulst (1920) incorporated this limiting factor in the exponential growth model and introduced the
logistic growth equation.
𝑑𝑁
𝑑𝑡
𝑁
= 𝜇 ∗ 𝑁 ∗ (1 − )
𝐾
(Eq. 6)
N
In equation 6 the exponential model is multiplied with a factor 1 − K, which represents the fractional
deficiency of the current population size from the saturation level K. This logistic equation is often
referred to as the Verhulst logistic equation and is solved as:
𝑁 (𝑡) =
𝐾∗𝑁0
(𝐾−𝑁0 )∗𝑒 −𝜇𝑡 +𝑁0
(Eq. 7)
The Verhulst logistic growth equation (Eq. 7) is parametrized by an initial population size (N0), the initial
growth rate (µ) and the carrying capacity (K).
(1) Since lim 𝑁(𝑡) = K , each population will ultimately reach it’s saturation level.
𝑡→ ∞
1 dN
dt
(2) When the population size is increasing, the relative growth rate N
will decrease
𝐾
(3) At the inflection point the population size is exactly half of the carrying capacity: 2
In case that μ > 0, the Verhulst logistic equation has a sigmoid shape and is asymptotic to the saturation
level K. Note that under the same conditions, the exponential growth increases infinitely while the
logistic growth curve reaches its saturation level (Figure 10).
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Chapter 1: Literature Review
Figure 10 Variation in phytoplankton population size over time, modelled by an exponential growth curve (left) vs a logistic
growth curve (right).
Another way to empirically represent growth dynamics, is the Gompertz growth curve. The Gompertz
growth curve is similar to the logistic growth curve in the sense that both curves have an S-shape and
both curves go to an equilibrium value (Figure 11; Winsor, 1932). The main difference between both
curves is that the logistic function has its inflection point midway between its asymptotes, while the
Gompertz curve shows an inflection when about 37% of the total growth has been completed (Winsor,
1932).
Figure 11 Comparison of Gompertz and logistic function (after Winsor, 1932).
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Chapter 1: Literature Review
Problem statement and goals
Given the expected increase of the global human population, climate change and continued chemical
pollution, it is very likely that marine ecosystems will experience more stress in the future. Under these
multistress conditions the growth of marine species (including phytoplankton) might be affected. To
date however, little is known about the phytoplankton growth under multistress conditions. The main
reason is that often single stress experiments are performed and thus information about the
interaction between different stressors is limited. Next to that, few studies investigate the influence
of realistic environmental mixtures of hydrophobic compounds and the effect these mixtures might
have on phytoplankton growth is poorly understood. In order to partly address this knowledge gap, in
this master thesis, I aim to investigate the influence of key drivers on phytoplankton growth under
multistress conditions. To do so, I will investigate phytoplankton growth dynamics theoretically and
examine the influence the organisms experience by their key drivers. Experimental data will be used
to validate the theoretically observed relationships.
The research objective of the present work is to quantify the relative contribution of key drivers on
phytoplankton growth. I have divided this objective in four different research questions: (1) Are
theoretical descriptions of multistress growth conditions sufficient to characterize phytoplankton
growth under those conditions?; (2) Can experimental phytoplankton growth data improve the model
fit?; (3) What are the main contributing factors of marine phytoplankton growth in the Belgian Part of
the North Sea?; and finally (4) Given the natural key drivers, does chemical exposure have an influence
on phytoplankton growth dynamics?
Based on a literature review (cfr. Chapter 1) I hypothesize that it will be possible to predict
phytoplankton growth dynamics based on theoretical equations, and that experimental data can
definitely contribute to improve ecological correctness of this model. Literature review showed that
temperature and nutrient concentration are the most important contributors to marine
phytoplankton. Based on Echeveste et al. (2010) and Everaert (2015), it was hypothesized that the
influence of a realistic mixture of organic chemicals close to environmental concentrations, on marine
phytoplankton growth is limited.
19
Chapter 1: Literature Review
20
Chapter 2: Materials and Methods
Chapter 2: Materials and Methods
Phytoplankton is the basis of the pelagic food chain and can be used as a measure of the health of the
marine ecosystem (Siegel and Franz, 2010). Indeed, alterations in the phytoplankton community can
affect the entire ecosystem (Burkiewicz et al., 2005). Therefore, an improved understanding of the
ecotoxicological effects of both organic chemicals and variation in key drivers on phytoplankton
species is needed. One way to address this knowledge gap is by quantifying the relative contribution
of different stressors to the growth of marine phytoplankton. Recently, two approaches have been
used, i.e. an experimental approach (Everaert et al., 2016) and a modelling approach based on field
data (Everaert et al., 2015).
In this master thesis, I will use an alternative approach as I will integrate the theoretical assumptions
made in the past and the (limited) experimental data that are available. The aim was to quantify the
relative contribution of key drivers of phytoplankton growth. To do so, I applied a Verhulst model on
the data presented by Everaert et al. (2016). These data were generated by growing a marine diatom
under different multistress conditions. An overview of the steps followed is given in Figure 12. In a first
part the logistic model of Verhulst was combined with theoretical equations for stress limitations,
which were found in literature. The aim was to construct a theoretical model that could predict
phytoplankton growth dynamics under multistress conditions. This theoretical model was verified by
using an external dataset, provided by Everaert et al. (2016). It was tried to predict the observed algal
growth dynamics in the different tested situations.
Figure 12 Basic overview of the followed steps in this master thesis.
21
Chapter 2: Materials and Methods
In case that the theoretical model was not able to predict the observed growth dynamics, experimental
equations were constructed in the third step. Starting from the dataset provided by Everaert et al.
(2016) equations accounting for the stress were created in order to increase the fit to observed data.
In the fourth step, these experimental equations were combined with the theoretical model, which
was constructed in the first step. The aim was to modify or replace the theoretical equations to obtain
better predictions for the observed growth dynamics. The finalized model was then in step 5 validated
by an external dataset, created by Claeys M. (2016) at the Laboratory for Environmental Toxicology
and Aquatic Ecology at the University of Ghent. Once the model was finalized, it was used to perform
scenario-based simulations to quantify the relative contributions of the different stress limitation
functions.
The final aim of this thesis is to quantify the relative contribution of the most important drivers of
phytoplankton growth. Due to anthropogenic influences many drivers changed during the
“Anthropocene (Steffen et al., 2007)” and the combined effects of these drivers on the phytoplankton
dynamics are not quantified yet. Via this research, we aim to take the first step in quantifying the
effects of multistress conditions on the first trophic level of the marine food web and how these effects
might change in future, based on a scenario-based analysis. Scenarios are defined as combinations of
ecological realistic values of the key drivers.
1. Theoretical limitations
During this master thesis phytoplankton growth dynamics were modelled using a logistic model. The
formula of this logistic model is given by:
𝑑𝑁
𝑑𝑡
𝑁
= 𝜇 ∗ 𝑁 ∗ (1 − )
𝐾
(Eq. 6)
When no stress is experienced, the growth curve has a typical sigmoid shape (Figure 13). However,
phytoplankton organisms will always experience some kind of stress and these stress factors alter the
shape of the growth curve. To take these limitations into account, the different stress factors will be
integrated in the logistic model. The integration of the stress equations, will be explained in the
following paragraphs.
Figure 13 The logistic growth curve when no stress is experienced. A fictive example with a starting algal concentration of
100 cells/mL, an intrinsic growth rate μ of 0.4 d-1 and a carrying capacity K of 2000 cells/mL is given.
22
Chapter 2: Materials and Methods
1.1 Temperature limitation
As explained in literature review (cfr. Chapter 1), Eppley (1972) found that phytoplankton growth rates
increase when the ambient temperature increases. Mridul et al. (2012) stated that the growth rate is
at his maximum value at the optimum temperature and beyond that optimum, process rates decrease
until the lethal temperature is reached. The experienced stress by phytoplankton organisms at
temperatures different from this optimum temperature, was modelled by a nonlinear adaptive
response to temperature changes (Park, 1974).
𝑇𝑒𝑚𝑝𝑙𝑖𝑚𝑖𝑡𝑎𝑡𝑖𝑜𝑛 = 𝑉𝑇 𝑋𝑇 ∗ 𝑒𝑥𝑝(𝑋𝑇 ∗ (1 − 𝑉𝑇))
(Eq. 8)
The intermediate variable VT is defined as the ratio of (1) the difference between the maximum
temperature at which a process can occur and the water temperature; and (2) the difference between
the maximum temperature at which a process can occur and the optimal temperature (-)
𝑉𝑇 =
𝑇𝑚𝑎𝑥−𝑇
(Eq. 9)
𝑇𝑚𝑎𝑥−𝑇𝑜𝑝𝑡
with T = water temperature (°C), Tmax = maximum temperature for that population (°C) and Topt =
optimum temperature for that population (°C).
If VT < 0, the temperature limitation is set to zero.
The other variables of the temperature limitation function, are defined as follows:
𝑋𝑇 = 𝑊𝑇 2 ∗
(1+√1+
40
)
𝑌𝑇
2
400
(Eq. 10)
𝑊𝑇 = 𝑙𝑛(𝑄10) ∗ (𝑇𝑚𝑎𝑥 − 𝑇𝑜𝑝𝑡 )
(Eq. 11)
𝑌𝑇 = 𝑙𝑛(𝑄10) ∗ (𝑇𝑚𝑎𝑥 − 𝑇𝑜𝑝𝑡 + 2)
(Eq. 12)
with Q10 = rate of change per 10°C temperature change (-)
To calculate the Q10 value it is assumed that the ratio of rates over a given temperature interval is
constant (Cossins and Bowler, 1987). DeNicola (1996) reported a constant value of 2 for the Q10 for
most algal species, which was also stated by Cossins and Bowler (1987). Note that a Q10 of 2 leads to
a doubling of rate over a 10°C interval.
Collins & Wlosinski (1983) reported maximum and optimum temperatures that are rather constant
across phytoplankton species. These values were used by De Laender (2007) as well. Values for both
spring and summer situation are given and in order to consider the worst case conditions (most
limitation), it was decided to use the spring values. Following parameter values were used in the
temperature limitation function:
Tmax = 30°C, Topt = 8°C, Q10 = 2. (Collins & Wlosinski, 1983; DeNicola, 1996)
The limitation at the optimum temperature (8°C) equals 1, which means that the phytoplankton does
not experience growth limitation due to the ambient temperature (Figure 14). However, if the water
temperature deviates from the optimal temperature, the phytoplankton will experience stress and
thus the growth limitation due to temperature increases. The more deviation between the water
23
Chapter 2: Materials and Methods
temperature and the optimum temperature, the lower the value of the temperature limitation and
the more the growth of the phytoplankton organisms gets limited.
Figure 14 Theoretical temperature limitation experienced by phytoplankton organisms with an optimal temperature of 8°C.
1.2 Nutrient limitation
Field data and experimental data suggest that nutrients are amongst the most important drivers of
phytoplankton growth (cfr. literature review), so the influence of nutrient concentration on the growth
curve should definitely be quantified. The limitation phytoplankton organism experience due to
nutrient deficiency can be subdivided in nitrogen-limitation and phosphorus-limitation. Both were
modelled with a Michaelis-Menten function, as is often done in assessing influence of nitrogen and
phosphorus on primary production (e.g. Tyrell, 1999; Tilman et al., 1982):
𝑁𝑙𝑖𝑚𝑖𝑡 =
𝑃𝑙𝑖𝑚𝑖𝑡 =
𝑁
𝑁+ 𝐾𝑁
𝑃
𝑃+ 𝐾𝑃
(Eq. 13)
(Eq. 14)
N = nitrogen concentration in the water (mg N/L)
P = phosphorus concentration in the water (mg P/L)
KN = Michaelis-Menten constant for nitrogen limitation (mg N/L)
KP = Michaelis-Menten constant for phosphorus limitation (mg P/L)
De Laender (2007) reported a KN of 0.05 mg/L and a KP of 0.01 mg/L. The lower the nutrient
concentration, the lower the limitation values and the more inhibition of phytoplankton growth (Figure
15).
Figure 15 Theoretical nitrogen- and phosphorus-limitation modelled by Michaelis-Menten kinetics.
24
Chapter 2: Materials and Methods
1.3 Limitation by chemicals
Laboratory toxicity tests revealed that the presence of persistent organic pollutants has a big influence
on the growth of marine organisms. When the impact of hydrophobic chemicals on the phytoplankton
biomass dynamics has to be assessed, it is necessary to include the effect of mixtures of different POPs.
The toxicity of these complex mixtures of organic pollutants exceeds by 10³ times the toxicity expected
for a single pollutant (Echeveste et al., 2010).
In 1991, Warne observed that simple mixtures (containing less than five compounds) tended to have
highly variable toxicities: anything from highly antagonistic (combined effect is weaker than sum of
individual effects) to highly synergistic (stronger combined effect). However, Warne (1991) stated that
complex mixtures (greater than ten compounds) generally had additive toxicity. An explanation for the
observed trend was proposed by Warne and Hawker (1995) and was called the funnel hypothesis. This
hypothesis states that as the number of components in mixtures increases there is an increased
tendency for the toxicity to be additive. To state this hypothesis Warne and Hawker (1995) collected
toxicity data for 104 equitoxic mixtures composed of 182 chemicals and found that these data
conformed to the funnel hypothesis. These data included seven test species including bacteria,
crustacea, amphibia and fish, covered a variety of measures and endpoints of toxicity and included
both acute and chronic values. McCarty and Mackay (1993) also postulated that chemicals with
different mechanisms of action in complex mixtures are additive.
Based on the funnel hypothesis, the critical body burden (CBB) approach was formulated by McCarty
and Mackay (1993). This approach predicts that the potency measured at the site of toxic action should
be essentially constant for similar organisms. McCarty and Mackay (1993) found that the CBB is
relatively constant across species and toxic compounds. The CBB theory has been validated with a
variety of aquatic invertebrates, e.g. Landrum et al. (1994). The chemicals usually associated with the
hypothesis are nonpolar narcotics, which do not affect any specific organ, organ system or biochemical
pathway (Pawlisz & Peters, 1993). They cause a reversible disfunction called narcosis, in which cells
membranes are affected so that they become inoperative (Pawlisz & Peters, 1993).
The modelling of POP limitations in this master thesis was based on the principles of the funnel
hypothesis and the CBB concept. This means that mixtures are assumed toxic if their summed
concentrations (additive toxicity according to the funnel hypothesis) exceed a certain level (the critical
body burden). De Laender et al. (2007) stated that it can be preferable to use simple external
concentration-effect functions, since these sub-models only require a limited set of single-species
toxicity test results. In contrast, toxicokinetic submodels are often characterized by a large number of
uncertain parameters (Sijm and Vanderlinde, 1995). Because of this, it was decided to work with a
logistic concentration-effect function (De Laender et al., 2008), based on the CBB theory, which can be
described as:
𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 =
1
𝑡𝑜𝑥 𝑠𝑙𝑜𝑝𝑒
1+(
)
𝐶𝐵𝐵50
(Eq. 15)
with tox = body burden of POPs, CBB50 = body residue concentration corresponding with a 50%
reduction of the maximum photosynthetic rate (mmol/kg wet weight) and slope = the slope of the
concentration-effect function.
25
Chapter 2: Materials and Methods
McCarty and Mackay (1993) found that the CBB varies between 2 and 8 mmol/kg wet weight. To take
maximal limitation into account, a worst case value of 2 mmol/kg ww was selected for the CBB. Despite
their importance, the slope of concentration-effect curves and the variations between these slopes
have seldom been investigated (Smit et al., 2001). Smit et al. (2001) collected concentration-effect
data for various chemicals and groups of aquatic species and concluded that 90% of all calculated slope
values were between 0.21 and 2.19. A median slope of 1.2 for algae was calculated (Smit et al., 2001).
This value of 1.2 was used as slope factor in the POP limitation function (Eq. 15).
The higher the chemical exposure, the higher the body burden in the phytoplankton organisms and
the more the growth of the organisms gets inhibited (Figure 16). When the body burden is 2 mmol/kg
ww and thus equals the critical body burden, a limitation of 50% is observed.
Figure 16 Theoretical POP-limitation based on the CBB theory.
1.4 Integration of limitation terms
In the previous paragraphs the influence of the most important driving forces on the growth curve
were discussed. The first step in the scheme in Figure 12 is to integrate the theoretical limitations
equations with the logistic model of Verhulst. To include maximal correction both the intrinsic growth
rate μ and the carrying capacity K were multiplied with the limitation equations, as in Eq. 17. It is thus
assumed that temperature, nutrient availability and chemical exposure are all affecting the growth
rate and the carrying capacity.
𝑑𝑁
𝑑𝑡
= 𝑇𝑙𝑖𝑚𝑖𝑡 ∗ 𝑁𝑙𝑖𝑚𝑖𝑡 ∗ 𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗ 𝜇 ∗ 𝑁 ∗ (1 − 𝑇
𝑁
𝑙𝑖𝑚𝑖𝑡 ∗𝑁𝑙𝑖𝑚𝑖𝑡 ∗𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗𝐾
)
(Eq. 16)
with Tlimit = limitation experienced because of temperature differences, Nlimit = limitation experienced
by nutrients deficiency and POPlimit = limitation experienced because of chemical exposure.
This is the final theoretical model, which in the scheme in Figure 12 is referred to as the ‘Theoretical
model’.
26
Chapter 2: Materials and Methods
2. Verification theoretical model with experimental data
The aim was to use the logistic growth model of Verhulst, combined with theoretical limitations
equations, (Eq. 17) to make future predictions of phytoplankton growth and to calculate relative
contributions of the different stressors. However first, it was necessary to verify whether this model
(including the different limitations) was able to predict experimental observations of marine
phytoplankton growth under multistress conditions (step 2 in Figure 12).
Everaert et al. (2016) generated a dataset where a marine diatom (Phaeodactylum tricornutum) was
grown under different circumstances: three nutrient regimes (14 μmol P/L & 588 μmol N/L; 2.8 μmol
P/L & 120 μmol N/L and 0.7 μmol P/L & 30 μmol N/L), two test temperatures (16 °C and 23 °C), three
light intensities and three chemical exposures were tested. This dataset was used to verify the
theoretical model built in the first step.
Since the highest nutrient regime is not ecologically relevant (highly saturated conditions), the data for
the algae grown under these high N- and P-concentrations was eliminated. The nutrient concentrations
were given in µmol P/L and µmol N/L. They were multiplied with the molar mass reported in EpiSuite
(2014) to obtain concentrations in mg P/L and mg N/L, which could then be integrated in the MichaelisMenten equation.
To expose the diatom to a realistic mixture of hydrophobic chemicals, polydimethylsiloxane (PDMS)
passive samplers were used in the experiment, as in Claessens et al. (2015). Following the algal growth
inhibition test, the freely dissolved concentrations of fifteen polycyclic aromatic hydrocarbons (PAHs)
and seven polychlorinated biphenyls (PCBs) were quantified. These water concentrations were
modified by using next formula:
𝑚𝑚𝑜𝑙
𝐵𝐵 (𝑘𝑔 𝑤.𝑤.) =
𝐶𝑜𝑛𝑐 (
𝑛𝑔
𝐿
) ∗ 𝐾𝑜𝑤 (
) ∗ 𝑙𝑖𝑝𝑖𝑑
𝐿𝑤𝑎𝑡𝑒𝑟
𝑘𝑔𝑙𝑖𝑝𝑖𝑑
𝑔
)
𝑚𝑜𝑙
𝑀𝑜𝑙𝑎𝑟 𝑀𝑎𝑠𝑠 (
𝑘𝑔 𝑙𝑖𝑝𝑖𝑑
)
𝑘𝑔 𝑤𝑒𝑡 𝑤𝑒𝑖𝑔ℎ𝑡
𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 (
∗ 10−6
(Eq. 17)
All molar masses (MM) and octanol-water coefficients (Kow) were reported in EpiSuite (2014). An
average lipid fraction of 24.5% for diatoms was used, as reported by Shifrin & Chisholm (2008). Since
the funnel hypothesis is followed, the final body burden was calculated as the sum of the different
body burdens for each organic chemical pollutant.
A logistic growth model was fit to the first experiment, a no-stress situation (optimal temperature,
optimal nutrient conditions and no presence of POPs). Next, I determined the nonlinear (weighted)
least squares estimate of the parameters μ (growth rate) and K (carrying capacity) of a nonlinear
model. In least squares estimation, the unknown values of the parameters, β0, β1, … (in this case μ and
K), in the regression function, 𝑓(𝑥⃗, 𝛽⃗ ) (in this case the logistic function in function of time), are
estimated by finding numerical values for the parameters that minimize the sum of squared deviations
between the observed responses and the functional portion of the model (NIST, 2012).
Mathematically, the least sum of squares criterion that is minimized to obtain the parameter estimates
is:
𝑄 = ∑𝑛𝑖=1[𝑦𝑖 − 𝑓(𝑥𝑖 , 𝛽̂ )]²
(Eq. 18)
with yi = the observed responses and 𝛽̂ = the estimates of the parameter values. For nonlinear models,
the minimization is done using iterative numerical algorithms (NIST, 2012).
27
Chapter 2: Materials and Methods
The obtained values of μ and K under non-stress conditions represent the maximum growth rate and
the maximum carrying capacity. De Hoop et al. (2012) calculated the intrinsic growth rate of the marine
diatom S. Robusta in an equal way. In this thesis the obtained values were used in the Verhulst model
combined with the theoretical limitation functions. Temperatures, nutrient and chemical
concentrations were inserted and it was tried to predict the growth dynamics of the marine diatom
under the different multistress conditions, as observed in the experiments of Everaert et al. (2016). In
order to increase the fit to the observational data, different combinations were made between the
limitation terms and the logistic model (besides the explained model in Eq.16)
𝑑𝑁
𝑑𝑡
𝑑𝑁
𝑑𝑡
𝑑𝑁
𝑑𝑡
𝑑𝑁
𝑑𝑡
= 𝑁𝑙𝑖𝑚𝑖𝑡 ∗ 𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗ 𝜇 ∗ 𝑁 ∗ (1 − 𝑇
𝑁
𝑙𝑖𝑚𝑖𝑡 ∗𝑁𝑙𝑖𝑚𝑖𝑡 ∗𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗𝐾
= 𝑇𝑙𝑖𝑚𝑖𝑡 ∗ 𝑁𝑙𝑖𝑚𝑖𝑡 ∗ 𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗ 𝜇 ∗ 𝑁 ∗ (1 − 𝑁
= 𝑁𝑙𝑖𝑚𝑖𝑡 ∗ 𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗ 𝜇 ∗ 𝑁 ∗ (1 − 𝑇
= 𝜇 ∗ 𝑁 ∗ (1 − 𝑇
𝑁
𝑙𝑖𝑚𝑖𝑡 ∗𝐾
𝑁
𝑙𝑖𝑚𝑖𝑡 ∗𝑁𝑙𝑖𝑚𝑖𝑡 ∗𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 ∗𝐾
𝑁
𝑙𝑖𝑚𝑖𝑡 ∗𝐾
)
(Eq. 19)
)
(Eq. 20)
)
(Eq. 21)
)
(Eq. 22)
All calculations were performed in R, a free environment for statistical computing.
3. Construction experimental limitations
In the previous step I tried to predict experimental phytoplankton growth dynamics by using
theoretical equations, found in literature. In case that the theoretical description of the limitations was
not in line with the observed limitation, I deducted limitation equations from the data themselves (step
3 in Figure 12). To do so, the dataset created by Everaert et al. (2016), in which a marine diatom was
grown under different multistress circumstances, was analysed. A logistic growth function was fitted
to each experiment and the nonlinear least square estimates of the growth rate and the carrying
capacity under all circumstances were determined. Experimental limitation functions were
constructed, expressing the influence of the different drivers on both the growth rate and the carrying
capacity under multistress conditions. The final aim of this step is to increase the fit to the observed
experimental growth dynamics of Everaert et al. (2016).
In each experiment a unique combination of nutrient regime, temperature and chemical exposure is
tested. By using the nls package in R, a logistic model is fitted to every experiment, as if no stress was
experienced. Recall that the logistic model is represented by next formula:
𝑑𝑁
𝑑𝑡
𝑁
= 𝜇 ∗ 𝑁 ∗ (1 − )
𝐾
(Eq. 6)
In this way, the values of the maximum growth rate μ and the carrying capacity K are determined for
all experiments. Each value of μ and K concurs with a certain temperature, nutrient concentration and
chemical exposure. The values are linked with the conditions they represent and listed in a table. This
table is explored by comparing the deducted growth rates and carrying capacities under different
28
Chapter 2: Materials and Methods
circumstances. In a first step the influence of one driver on respectively µ and K was investigated. It is
necessary to derive the kind of influence: linear/not linear, negative/positive relationship, etc. For this
purpose boxplots for the values of µ and K in function of one driver were constructed. All boxplots
cover the two other drivers, e.g. the boxplots in function of temperature cover the three chemical
exposures and the two nutrient regimes. Since different drivers can as well interact, the interactions
are investigated in the next part. It is determined whether limitation terms may be in function of two
or three driving forces. To do so, boxplots are again constructed for the values of µ and K in function
of one driver. In this case however the boxplots cover only one other driver, e.g. different boxplots in
function of temperature are created, covering or the three chemical exposures or the two nutrient
regimes.
After the boxplot exploration, I quantified the impact of each stress condition on the diatom growth
dynamics by calculating the ratios between deducted values of µ and K, as was done by Korsman et al.
(2014) in order to model the impact of multiple environmental stress factors on copepod populations.
µ(S)/µ(0) represents the ratio of the growth rate of the diatom as a function of one stress factor µ(Si)
or all stress factors combined µ(S) and the growth rate under optimal conditions µ(0), respectively.
The ratios of the carrying capacities were compared in an equal way. The influence of a particular stress
factor was quantified by calculating the ratios between values of µ and K for situations with two equal
drivers and one differing driver. For example, to evaluate the influence of the temperature on the
growth rate, the ratio of μ under one temperature to μ under a different temperature but under equal
nutrient regime and chemical exposure, is calculated.
The lm package in R was used to fit an ordinary linear model to the ratios of μ and K values with the
limitation term as response and the drivers as predictors. By doing so, a quantification of the effect of
each driver to the growth rate µ and carrying capacity K could be made. Different linear models were
built, in function of one, two or three drivers. In order to retain the best model, the coefficient of
determination R² was calculated for all different models. Its definition as the proportion of variance
'explained' by the regression model makes it useful as a measure of success of predicting the
dependent variable from the independent variables (Nagelkerke, 1991). If 𝑦𝑖 is denoted as the
observed value of the dependent variable, 𝑦
̅𝑖 as its mean, and 𝑦̂𝑖 as the fitted value, then the
coefficient of determination is:
𝑅2 =
∑(𝑦̂𝑖 −𝑦̅𝑖 )²
∑(𝑦𝑖 −𝑦̅𝑖 )²
(Eq. 23)
Since R² is clearly dependent on the sample size and the number of predictors (Zuur et al., 2007), the
adjusted R² value was used. This is a modified version of R² that only increases if a new term improves
the model more than would be expected by chance (Zuur et al., 2007). The model with the highest R²adj
is chosen as the best model and used in further calculations.
The final objective is to obtain experimental limitation equations which describe the influence of key
drivers on phytoplankton growth dynamics. For all drivers these experimental limitations were
compared with the theoretical limitations in model fitting to the observed data by Everaert et al.
(2016). Parameter values of the theoretical limitations were varied in order to better predict the
phytoplankton growth dynamics. The best representing functions were selected and combined to
develop the experimental model which is used in the next steps. Overall, this model should be able to
29
Chapter 2: Materials and Methods
predict the growth dynamics of the marine diatom under multistress conditions in the experiment that
Everaert et al. (2016) performed.
4. Validation experimental limitations
The aim of the previous step was to derive equations which express the limitation marine algae
experience under multistress conditions. These limitations are to be used to quantify the effect of
changing multistress conditions on phytoplankton growth. However, prior to using these limitations
for scenario-based quantification, they should be validated with external data (step 5 in the overview
in Figure 12). To do so, I will make use of data produced by Claeys, M. (2016). In her research Claeys et
al. (2016) tested the effect of different nutrient regimes and different temperatures on the growth
dynamics of three dinoflagellates: Alexandrium minutum, Protoceratium reticulatum and
Prorocentrum micans. As such, these data give the opportunity to validate the derived limitation
functions.
The nls package in R was used to fit a logistic model to the experimental observations of the
phytoplankton growth under non-stress conditions. The optimal growth rate μ and carrying capacity K
were determined. These values were inserted in the logistic growth model of Verhulst, combined with
the experimental derived limitations. This model was then used to predict the dinoflagellate growth
dynamics under multistress conditions. The model fit was investigated by calculating the Root Mean
Square Error (RMSE), which represents the sample standard deviation of the differences between
predicted values 𝑦̂𝑖 and observed values 𝑦𝑖 .
∑𝑛
̂𝑖 )²
𝑖=1(𝑦𝑖 −𝑦
𝑅𝑀𝑆𝐸 = √
𝑛
(Eq. 24)
In order to compare the RMSE of different experiments, the RMSE was divided by the observed
carrying capacity of each experiment. These ratios could then be compared and evaluated.
30
Chapter 2: Materials and Methods
5. Quantification of relative contributions
In the first part of this thesis a model is constructed by integrating theoretical assumptions made in
the past and (limited) experimental data that are available. After validating this model, it can be used
to quantify relative contribution of key drivers of phytoplankton growth, which forms the second part
of this thesis. In order to assess the relative contributions of the different stressfactors, scenario-based
simulations were performed.
5.1 BMDC Data
The first step in assessing relative contributions of different stressfactors consists out of obtaining
ecological relevant ranges for the temperature, nutrient concentration and POP concentration in the
Belgian part of the North Sea. The Belgium Marine Data Centre (BMDC) monitored these parameters
during the years 2009-2010 once a month and their data was used in this master thesis to determine
realistic ranges for all parameters.
The BMDC monitored the concentrations of 7 PCBs (PCB-28, PCB-52, PCB-101, PCB-118, PCB-138, PCB153 and PCB-180), 4 PAHs (benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(g,h,i)perylene and
indeno(1,2,3-cd)pyrene) and tributyltin (TBT) in the Belgian part of the North Sea. These
concentrations were summed to calculate the total POP concentration, following the funnel
hypothesis, and assumed to represent the overall trend of PCBs and pesticides in the marine
environment. However, the marine environment typically contains many more chemicals than those
available in monitoring data sets (Echeveste et al., 2010). Echeveste et al. (2010) stated that the effect
of complex mixtures of organic chemicals on marine phytoplankton can exceed the toxicity expected
for a single pollutant by a factor of 1000. To account for this, the model was run under different
conditions, analogous as in a study performed by Everaert et al. (2015). Configuration ‘1POP’ used the
total phytoplankton body burden of the 7 PCBs, 4 PAHs and TBT, i.e. the monitored POP concentration.
Configurations ‘10POP’, ‘100POP’ and ‘1000POP’ assumed the actual body burden was 10, 100 and
1000 times higher than the monitored concentration, respectively. As such, although being a rough
assumption, the multiplication of the monitored concentrations of PCBs and pesticides by these factors
serves as a surrogate for the unknown POPs present in the marine environment (Everaert et al., 2015).
Next, the BDMC also monitored the temperature, the nitrogen and the phosphorus concentrations in
the Belgian part of the North Sea during 2009-2010. These data were analysed in order to obtain
ecologic realistic ranges for the drivers of phytoplankton growth. The minimum and maximum values
reported for the three driving forces were selected and the stress factors were then varied between
these values. Multistress conditions were created by combining the values of the different driving
forces.
5.2 Relative Contributions
In order to assess the relative contributions of the different drivers, the logistic model of Verhulst was
first plotted under nonstress conditions, i.e. optimal temperature, optimal nutrient concentration and
no chemical exposure. This model is in the following paragraphs referred to as the ‘Basic model’. Next,
simulations were performed with the logistic model of Verhulst and one influencing stressfactor
(suboptimal temperature, nutrient deficiency or chemical exposure). Finally simulations were
31
Chapter 2: Materials and Methods
performed with the logistic model of Verhulst and the three influencing stressfactors combined. All
simulations were performed for a time of 28 days, since this is a typical length of an algae growth test.
The algal growth dynamics under these three conditions (no stress, one stressfactor, three
stressfactors) were compared in order to quantify the relative contribution of each driver.
Contributions were calculated with algal biomass as response variable since Nyholm (1985) stated that
this is the most common way to express inhibitory effects in algal growth tests.
The contribution of stressfactor j on day i is calculated as:
𝐶𝑜𝑛𝑡𝑟𝑖,𝑗 =
([ ]𝑖,𝐵𝑎𝑠𝑖𝑐 −[ ]𝑖,𝐵𝑎𝑠𝑖𝑐+𝑠𝑡𝑟𝑒𝑠𝑠𝑜𝑟 𝑗 )
[ ]𝑖,𝐵𝑎𝑠𝑖𝑐
(Eq. 25)
with [ ]i,Basic = the algal concentration in cells/ml on day i obtained with the basic model and
[ ]i,Basic+stressor j = the algal concentration in cells/ml on day i obtained with the basic model combined
with stressfactor j. j is varied between the temperature, the nutrient concentration and the chemical
exposure.
Analogously the total contribution of the three stressfactors combined on day i is calculated as:
𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑛𝑡𝑟𝑖 =
([ ]𝑖,𝐵𝑎𝑠𝑖𝑐 −[ ]𝑖,𝐵𝑎𝑠𝑖𝑐+𝑡ℎ𝑟𝑒𝑒 𝑠𝑡𝑟𝑒𝑠𝑠𝑜𝑟𝑠 )
[ ]𝑖,𝐵𝑎𝑠𝑖𝑐
(Eq. 26)
For each simulation the day i where the total contribution was maximal, and thus the influence of the
stress factors on the growth dynamics the highest, was calculated. The relative contribution (in%) of
each driver to the total limitation of the phytoplankton growth (the sum of all absolute limitations
terms) was calculated as in Eq. 27.
𝐶𝑜𝑛𝑡𝑟
𝑖,𝑗
𝑅𝑒𝑙 𝐶𝑜𝑛𝑡𝑟𝑖,𝑗 = 𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑛𝑡𝑟
𝑖
(Eq. 27)
5.3 Seasonal Pattern
The BMDC monitored the temperature, nutrient concentration and POP concentration during the year
2009 once a month. For every month the relative contributions of each driver were calculated as
explained in the previous paragraphs. The importance of each driving force during each month is
analysed. The 12 situations were then compared and the change in relative contributions over the year
was assessed.
5.4 Variation of parameters
The BMDC monitored the values of the parameters only once a month which gives very particular
situations. During the year much more possible situations and combinations exist. To account for this
variation, the minimum and maximum value of each parameter over the years 2009-2010 were
calculated. Each parameter was varied between its minimum and maximum and thus a range of
ecological realistic values was obtained. The ranges of the different stress factors were combined to
obtain all possible multistress conditions. Simulations were performed for all these scenarios and for
every scenario the relative contributions were calculated in the way explained in previous chapters.
32
Chapter 2: Materials and Methods
The median and mean are calculated for the relative contributions in order to assess which parameter
has more influence on phytoplankton growth dynamics.
5.5 Extreme situations
Since it is important to know how phytoplankton organisms are affected by extreme conditions, the
ranges of the parameters reported by the BMDC were investigated. The maximum and minimum
values for temperature, nutrient concentration and chemical exposure were determined in summer
months (June, July, August, September) and winter months (December, January, February and March).
Extreme scenarios were created by combining these extreme conditions.
It is useful to assess how the growth curves are altered by the extreme situations. First of all a
simulation was performed with the logistic model of Verhulst without any stress limitation (‘Basic
Model’), this means under optimal temperature, optimal nutrient concentration and no chemical
exposure. Secondly simulations were performed with the logistic model and a temperature limitation,
then with the logistic model and both temperature and nutrient limitation and finally simulations with
limitation of temperature, nutrient concentrations and chemical exposure were performed. All of them
were modelled by the derived stress limitation equations during the model development. The growth
curves for the models with stress factors included and not included were compared and visualised.
33
Chapter 2: Materials and Methods
34
Chapter 3: Results
Chapter 3: Results
1. Model development
1.1 Assessment theoretical model
The aim of the present research was to quantify the relative contributions of key drivers on
phytoplankton growth dynamics. This was done by integrating theoretical assumptions made in the
past and the (limited) experimental date that are available. To do so, six different steps described in
the previous chapter were done. The result of each of these steps is presented in the following chapter.
Hence, I selected the most important figures. Other figures are available via supportive information.
In the first step a theoretical model was built by combining the logistic growth model of Verhulst with
theoretical equations. This theoretical model was used to predict the growth dynamics of a marine
diatom, as observed in an experiment performed by Everaert et al. (2016). Under optimal conditions
(no stress) an optimal value of 1.41 d-1 was obtained for the intrinsic growth rate μ and an optimal
value of 1.22 * 106 cells/mL for the carrying capacity K for the marine diatom Phaeodactylum
tricornutum. The 95% confidence intervals are given in Table 1.
Table 1 Intrinsic growth rate µ and carrying capacity K under non-stress conditions.
µ
a
Kb
a
Mean
1.41
2.5%
1.35
97.5%
1.46
1.22 * 106
1.18 * 106
1.26 * 106
: expressed in d-1, b: expressed in cells/mL.
The estimated µ and K (Table 1) were integrated in the theoretical model and it was tried to predict
the growth dynamics of the marine diatom under multistress conditions, as observed by Everaert et
al. (2016). The theoretical model consisted out of a combination of the logistic model of Verhulst and
theoretical limitation functions for the drivers. Each experiment tested different conditions of the
drivers and the goodness of fit was evaluated for all experiments. The RMSE were calculated (Table S1
in appendix) and suggested that the outcome of the theoretical model was not close to the observed
growth dynamics. Indeed, high RMSE values were obtained and predictions were thus not approaching
observations (Figure 17).
Figure 17 Model fit of the theoretical model (line) to the experimental observations (black points). The model predictions
are not approaching the observed values.
35
Chapter 3: Results
In order to increase the fit of the Verhulst model to the experiment observations, the limitation terms
for temperature, nutrients and POP were integrated in the logistic growth model in different ways (cfr.
Chapter 2).
None of the combinations succeeded in giving a good prediction of the marine diatom growth
dynamics (Figure 18). The RMSE was calculated for all experiments and had an average value of 61%
of the observed carrying capacity.
Figure 18 Different predictions of the theoretical model compared with experimental observations (black points). The
predictions of Eq. 17 (red line), Eq. 20 (green line), Eq. 22 (orange line) and Eq. 24 (blue line) are given.
The observed limitations on the growth rate µ and carrying capacities K were compared with the
predicted limitations. It was clear that the influence of nutrient deficiencies could not be predicted
with the theoretical Michaelis-Menten equation (Table 2). The predicted limitations deviate highly
from the observed limitations. A mean deviation of 57% was calculated between the observed and the
predicted limitations for nutrient deficiencies.
Table 2 Comparison of observed and predicted limitations (Michaelis-Menten kinetics) for nutrient deficiency.
T (°C)
N-conca
P-concb
BBc
Observed limit µ
Observed limit K
Predicted limit
16
16
16
23
23
23
30
30
30
30
30
30
0.7
0.7
0.7
0.7
0.7
0.7
0
0
0.00231
0
0
0.00231
0.2646491
0.3054598
0.3222201
0.2128708
0.2593001
0.2391421
1.0491639
0.9668341
0.9508605
0.953623
0.963306
0.8570797
0.6143617
0.6143617
0.6143617
0.6143617
0.6143617
0.6143617
a:
expressed in µmol N/L, b: expressed in µmol P/L, c: expressed in mmol/kg ww.
1.2 Construction experimental limitations
1.2.1 Nutrient limitation
Since the logistic model of Verhulst combined with theoretical limitation terms was not able to give
good fits to the experimental data, experimental limitation terms were constructed. In a first step, the
intrinsic growth rate μ and carrying capacity K were determined for every experiment with the nls
package (Table 3).
36
Chapter 3: Results
Table 3 Optimal μ and K for every experiment performed by Everaert et al. (2016).
T (°C)
N (µmol N/L)
16
16
16
16
16
16
23
23
23
23
23
23
120
30
120
30
120
30
120
30
120
30
120
30
P (µmol P/L) BB (mmol/kg)
2.8
0.7
2.8
0.7
2.8
0.7
2.8
0.7
2.8
0.7
2.8
0.7
0
0
0
0
0.233
0.233
0
0
0
0
0.233
0.233
µ (d-1)
K (cells/mL)
1.404737
1.473799
1.367585
1.322228
1.410423
1.341115
1.484919
1.416053
1.598244
1.539598
1.517652
1.300749
1219174
322653.2
1037195
316821.5
1151382
370998.5
1131412
240844.5
872745.9
226303.1
1039203
248517.1
Since the outcome of the Michaelis-Menten kinetics was not close to the observed limitation due to
nutrients deficiency, an experimental equation was determined for the nutrient limitation. Based on
the results presented in Table 2, it is clear that nutrients deficiency has an influence on both the growth
rate μ and the carrying capacity K of the marine diatom. Therefore two different limitation functions
were derived, one for the limitation on the growth rate and one for the limitation on the carrying
capacity.
From the boxplots it can be concluded that the limitation on µ and the on K are in function of both the
nutrient concentration and the temperature (Figure 19).
Figure 19 Boxplots of the derived growth rates µ (A and B) and carrying capacities K (C and D) for different nutrient regimes
at a temperature of 16°C and 23°C. Nutrient regime 1 represents the highest nutrient concentration (2.8 µmol P/L & 120 µmol
N/L) and nutrient regime 2 represents the lowest nutrient concentration (0.7 µmol P/L and 30 µmol N/L).
37
Chapter 3: Results
In order to determine the influence of nutrient deficiency, the ratios of carrying capacities and growth
rates of experiments with the same temperature and chemical exposure, but different nutrient
concentrations, were calculated (e.g. Exp3 vs Exp2, Exp6 vs Exp5). A linear model was fit to the ratios
of K and µ, with the limitation term as response factor and the temperature, nutrient concentration
and POP concentration as predictors. The models were built in function of one, two or three drivers
and the coefficients of determination of all models are given in Table 4. The limitation on the growth
rate was best represented by a linear model in function of the three drivers (T, nutr and POP) since this
gave the highest R²adj. For the limitation on the carrying capacity however, the addition of the chemical
exposure as a predictor didn’t give an increase in the R²adj. The best model for impact on the carrying
capacity was thus in function of only two drivers (T and nutr) and could be visualised as a plane in
function of these two drivers (Figure S1 in appendix).The finalized limitation functions are given in Eq.
28 and Eq. 29.
Table 4 Coefficients of determination for different models for the limitation on the growth rate and the carrying capacity.
Predictors
R²adj µ limit
R²adj K limit
T
0.2307
0.9918
T + nutr
0.3584
0.9958
T + nutr + POP
0.5311
0.9914
𝑁𝑢𝑡𝑟𝑙𝑖𝑚𝑖𝑡,𝜇 = 0.0576 ∗ 𝑛𝑢𝑡𝑟 − 0.0046 ∗ 𝑇 − 17.156 ∗ 𝑃𝑂𝑃 + 1.045
(Eq. 28)
𝑁𝑢𝑡𝑟𝑙𝑖𝑚𝑖𝑡,𝐾 = 0.977 ∗ 𝑛𝑢𝑡𝑟 − 0.0043 ∗ 𝑇 + 0.1071
(Eq. 29)
with T = temperature (°C), nutr = the ratio of the nutrient concentration to the optimal concentration
of 2.8 µmol P/L and 120 µmol N/L and POP = body burden (mmol/kg).
1.2.2 Temperature limitation
First boxplots were constructed in order to determine which parameter was affected by the
temperature. No significant differences between the growth rates derived at the two temperatures
could be observed and thus no limitation on the growth rate was assumed (Figure 20; Table S2). On
the carrying capacity however, a significant effect was observed (Figure 20; Table S2). This limitation
was best approximated by the theoretical limitation equation, but with an optimal temperature of 18
°C. This temperature gave the lowest RMSE (0.098), compared to a RMSE of 0.64 when the optimum
temperature was 8°C. All the other parameter values are kept equal to the values explained in Chapter
2. The limitation is represented by Eq. 9, for which the explanation of the intermediate variables XT
and VT is given in Materials & Methods (Eq. 10-13).
𝑇𝑒𝑚𝑝𝑙𝑖𝑚𝑖𝑡𝑎𝑡𝑖𝑜𝑛 = 𝑉𝑇 𝑋𝑇 ∗ 𝑒𝑥𝑝(𝑋𝑇 ∗ (1 − 𝑉𝑇))
(Eq. 9)
38
Chapter 3: Results
Figure 20 Boxplots illustrating the effect of two temperatures (16°C and 23°C) on the growth rate µ (d-1) and the carrying
capacity K (cells/mL).
1.2.3 Limitation by chemicals
In the experiment performed by Everaert et al. (2016) no effect of the chemical exposure was
encountered. The same results were obtained in this thesis when the effect of a blanco (BI), unloaded
PDMS sheets (POP-) and loaded PDMS sheets (POP+) on the growth dynamics were visualised. Indeed,
no significant differences were encountered between the growth rates and carrying capacities derived
at the three chemical exposures (Figure 21; Table S3). Since the research question is to determine the
influence of chemical exposure on phytoplankton growth dynamics, a limitation term should be
included. The theoretical logistic concentration-effect function (De Laender et al., 2008), based on the
CBB theory, was therefore used (Eq.12), as in Everaert et al. (2015). De Hoop et al. (2013) found that
the influence of atrazine on the growth rate and the carrying capacity of the marine diatom S. robusta
was similar. An equal decrease in both parameters was observed and one concentration-response
function could thus be used to model the effect of atrazine on both µ and K. In this thesis the same
pattern was assumed so that the limitation term (Eq. 12) was altering both the growth rate and the
carrying capacity.
𝑃𝑂𝑃𝑙𝑖𝑚𝑖𝑡 =
1
𝑡𝑜𝑥 𝑠𝑙𝑜𝑝𝑒
1+(
)
𝐶𝐵𝐵50
(Eq. 12)
Figure 21 Boxplots representing the influence of different chemical exposures (blanco (BI), presence of unloaded PDMS sheets
(POP-) and presence of loaded PDMS sheets (POP+)) on the growth rate (µ) and the carrying capacity (K). No significant
differences between the obtained growth rates and carrying capacities can be observed, suggesting no alteration of the
growth dynamics of P. tricornutum because of chemical exposure.
39
Chapter 3: Results
2. Model Validation
2.1 Validation experimental model
In the previous part a model was made by combining the logistic growth model of Verhulst with
theoretical equations found in literature and experimental observations. These steps are represented
as step 1-4 in the overview in Chapter 2 (Figure 12). This model was first validated by checking whether
it was able to predict the growth dynamics of the experiment it was based on. As an example some
figures with the model fit to the experimental observations are shown (Figure 22), other figures are
available in appendix (Figure S2). The calculated RMSE values varied between 5 and 14 % of the
carrying capacity (Table S4), which gives a remarkable improvement compared with the theoretical
model. Indeed, when the theoretical model was used the average RMSE was 61% of the observed
carrying capacity (Table S1).
Figure 22 Model fit of experimental modified model (line) to data (points) observed by Everaert et al. (2016). A clear
improvement of the model fit compared with Figure 17 is made.
2.2 Validation experimental limitation functions
Since the equations for nutrient limitation were made based on the growth dynamics of just one
marine diatom, they should be verified with an external dataset. For this purpose the growth dynamics
of three dinoflagellates observed by Claeys et al. (2016) was used. The optimal μ and K were
determined with the nls package and by using these values, the growth dynamics under stress
conditions were predicted. The model approximates the observed growth dynamics (Figure 23; Figure
S4). For the Protoceratium reticulatum and Prorocentrum micans organisms the RMSE varied between
11 and 18% of the observed carrying capacity (Table S5). For the Alexandrium minutum organism was
in one case a higher RMSE of 55% of the observed carrying capacity calculated (Table S5), which means
that the model was not able to predict the observed growth dynamics.
40
Chapter 3: Results
Figure 23 Validation of experimental nutrient limitation function by external dataset (Claeys et al., 2016). The effect of
multistress conditions was tested on the growth dynamics of three dinoflagellates: A. minutum (AM), P. micans (PM) and P.
reticulatum (PR).
3. Relative Contributions
Since the experimental model was verified and validated, I used this model to quantify the relative
contribution of the driving forces. For this purpose the monitored data of the BMDC was used. In this
part the results of the scenario-based quantification of the relative contributions are shown.
3.1 Seasonal Pattern
First the relative contribution of each stress factor was assessed for each month in the year 2009. The
changes in relative contributions can be seen in Figure 24.
41
Chapter 3: Results
Figure 24 Relative contributions of temperature, nutrients and POP concentrations during the year 2009. Configurations POP1 (A), POP10 (B) , POP100 (C) and POP1000 (D) are visualised.
42
Chapter 3: Results
3.2 Variation of parameters
The driving forces were varied between their minimum and maximum value, measured by the BMDC
during the years 2009-2010, and combined in all possible ways. For all possible scenarios the relative
contributions of the three driving forces to the total growth limitation were calculated. The mean and
median values are shown in Table 5 for the POP1, POP10, POP100 and POP1000 simulations.
Table 5 Mean and median values for relative contributions of temperature, POP and nutrient concentrations.
POP1
Relative Contr. T
Relative Contr. POP
Relative Contr. Nutr
Mean
30.71%
0.25%
69.04%
Median
24.51%
0.12%
75.38%
POP10
Relative Contr. T
Relative Contr. POP
Relative Contr. Nutr
Mean
20.36%
1.34%
78.30%
Median
15.97%
1.30%
82.19%
POP100
Relative Contr. T
Relative Contr. POP
Relative Contr. Nutr
Mean
15.23%
12.60%
72.17%
Median
11.51%
11.50%
72.83%
POP1000
Relative Contr. T
Relative Contr. POP
Relative Contr. Nutr
Mean
11.91%
36.46%
51.64%
Median
8.37%
41.42%
49.93%
3.3 Extreme situations
The BMDC parameter values measured in the Belgian part of the North Sea during the years 2009 and
2010 were analysed and the extreme values in summer (June, July, August, September) and winter
months (December, January, February, March) were identified (Table 6). Note that nutrient
concentrations are given as the ratio to the optimal concentration of 2.8 µmol P/L and 120 µmol N/L.
Table 6 Minimum and maximum values for temperature, nutrient and POP concentration in summer and winter months
(BMDC data, 2012).
Summer
Winter
Tmin
12.236
°C
3.175
°C
Tmax
21.11
°C
11.168
°C
Nutrmin
3.39
%
10.65
%
Nutrmax
58.12
%
96.54
%
POPmin
0.000663 mmol/kg
0.000747 mmol/kg
POPmax
0.005038 mmol/kg
0.003596 mmol/kg
These extreme values were combined in order to create extreme situations. The relative contributions
in an extreme summer situation (highest temperature, lowest nutrient concentration and highest
chemical exposure monitored) are visualised in Figure 25, while Figure 26 represents another summer
situation, where other parameters are less limiting: lowest temperature monitored, highest nutrient
concentration and highest chemical exposure. The relative contributions in an extreme winter
situation (lowest temperature, lowest nutrient concentration and highest chemical exposure) are
visualised in Figure 27. And Figure 28 shows a winter situation with the highest temperature, highest
nutrient concentration and lowest chemical exposure observed in winter months in 2009-2010.
43
Chapter 3: Results
Figure 25 Relative contributions of temperature, POP and nutrient concentrations on phytoplankton concentration in an extreme summer situation. Temperature is the highest observed
(21.11°C), nutrient concentration is the lowest observed (3.39% of optimal concentrations) and chemical concentrations is the highest observed (0.005038 mmol/kg in situation POP1). Note
that the POP100 and POP1000 configurations are given in a logarithmic scale.
44
Chapter 3: Results
Figure 26 Relative contributions of temperature, POP and nutrient concentrations on phytoplankton concentration in a summer situation. Temperature is the lowest observed (12.24°C), nutrient
concentration is the highest observed (58.12% of optimal concentrations) and chemical concentrations is the highest observed (0.005038 mmol/kg in situation POP1). Note that the POP1000
configuration is given in a logarithmic scale.
45
Chapter 3: Results
Figure 27 Relative contributions of temperature, POP and nutrient concentrations on phytoplankton concentration in an extreme winter situation. Temperature is the lowest observed (3.18°C),
nutrient concentration is the lowest observed (10.65% of optimal concentrations) and chemical concentrations is the highest observed (0.003596 mmol/kg in situation POP1). POP1000
configuration is visualised in a logarithmic scale.
46
Chapter 3: Results
Figure 28 Relative contributions of temperature, POP and nutrient concentrations on phytoplankton concentration in an extreme winter situation. Temperature is the highest observed (11.17°C),
nutrient concentration is the highest observed (96.53% of optimal concentrations) and chemical concentrations is the highest observed (0.003596 mmol/kg in situation POP1).The POP1000
configuration is visualised in a logarithmic scale.
47
Chapter 3: Results
48
Chapter 4: Discussion
Chapter 4: Discussion
The aim of the present research is to investigate the growth dynamics of phytoplankton organisms and
to assess the relative contribution of key drivers on the growth limitation under multistress conditions.
To partly address this research question we made use of an alternative approach, integrating the
theoretical assumptions made in the past and the (limited) experimental data that are available.
We found that phytoplankton growth dynamics under multistress conditions could not be predicted
with a logistic model of Verhulst, combined with theoretical assumptions. However, when this model
was combined with experimental derived stress equations, a significant improvement in model fit was
observed. Relative contributions of each driver to the total growth limitation of marine algae were
calculated. It was clear that nutrients were the determining factor but also temperature had a
significant influence on phytoplankton growth. The relative contribution of chemicals on the total
growth limitation was minimal. All results presented in Chapter 3 will be discussed and compared with
literature findings. An answer will be formulated on the previous formulated research questions.
Finally, some remarks and recommendations for future research will be made.
1. Model development
1.1 Logistic model of Verhulst
The growth of the marine diatom Phaeodactylum tricornutum followed a logistic Verhulst growth
function. Under optimal conditions (no stress) an optimal value of 1.40 d-1 was obtained for the
intrinsic growth rate μ (Table 1). This approaches the maximum growth rate of 1.47 d-1 for P.
tricornutum, measured by Geider et al (1985). However, note that the Verhulst logistic function does
not account for suboptimal growth under multistress conditions. To model suboptimal growth
limitations functions were integrated with the logistic function. The performance and development of
those limitations functions will be discussed next.
1.2 Theoretical equations for stress
The theoretical model, created by combining theoretical equations from literature with the logistic
function of Verhulst, was not able to predict the algal growth dynamics of the marine diatom (Figure
17 and 18). None of the different combinations of the stress equations had a good fit to the observed
growth dynamics (Figure 18). The most important deviance was encountered for the nutrient
limitation (average of 57% deviance between predicted and observed limitation; Table 2). The
Michaelis-Menten equations predicted higher algal concentrations and thus less growth inhibition of
the nutrients deficiency. Indeed, in the past, multiple experiments have demonstrated shortcomings
of using Michaelis-Menten kinetics on phytoplankton nutrient uptake. For example, Rhee (1974) found
that Michaelis-Menten kinetics overestimated the uptake rates of nutrients compared with data from
experiments. Therefore Michaelis-Menten kinetics may give an overestimation of the algal growth
under nutrient depleted conditions (as in Figure 17). In 2009, Franks addressed two issues regarding
the use of Michaelis-Menten kinetics when modelling the uptake of nutrients by phytoplankton. First,
49
Chapter 4: Discussion
the substrate concentration decreases markedly during experiments, while the Michaelis-Menten
equation assumes a constant concentration (Franks, 2009). While substrate concentrations decrease,
phytoplankton growth gets more limited and for this extra limitation the Michaelis-Menten equation
is not accounting. Second, there is no particular reason an individual phytoplankton cell should behave
(kinetically) like an enzyme (recall that Michaelis and Menten (1913) published their equation as the
reaction rate of an enzyme). Indeed, phytoplankton actively respond to their environment, and can
acclimate metabolically to changing conditions (Franks, 2009). One important example of
phytoplankton acclimation is their ability to regulate the number of uptake sites (proteins that
incorporate the nutrient from the cell membrane into the cytoplasm; Bonachela et al., 2011). Aksnes
& Egge (1991) showed that the uptake rate increases linearly with the number of transporters on the
cell’s surface. This gives that the affinity of phytoplankton cells for nutrients is not constant, while in
the Michaelis-Menten equation a constant half-saturation parameter is assumed. Aksnes & Egge
(1991) already addressed difficulties with the interpretation of the kinetic parameters, and in particular
the half-saturation constant, derived from experimental measurements. In order to search an
alternative for the Michaelis-Menten equation, the experimental data from Everaert et al. (2016) was
analysed and the derivation of experimental stress limitations will be discussed in the following part.
1.3 Experimental limitations for stress
1.3.1 Nutrient limitation
The supply of macronutrients (that contain bioavailable N, P) largely determines phytoplankton
production in the ocean (Li et al., 2015). Although Michaelis-Menten kinetics was not able to predict
the observed nutrient limitation (Table 2), nutrient limitation should definitely be included in the
model. Therefore, an experimental limitation equation was constructed, accounting for the altered
growth dynamics under nutrient deficit conditions. As only a limited amount of experimental data was
available (Table 3), a linear relationship between the nutrient concentration and the growth dynamics
was assumed in line with findings of Dugdale et al. (2006). Dugdale et al. (2006) found that nitrate
uptake in a coastal upwelling plume in California by phytoplankton as a function of nitrate
concentration was linear rather than Michaelis-Menten-like.
The highest R²adj was calculated when the experimental equation for nutrient limitation was in
function of both the nutrient concentration and the water temperature (Table 4). Indeed, at higher
temperatures, phytoplankton organisms were less inhibited by the shortage of nutrients than at lower
temperatures (Eq. 28-29). Rhee & Gotham (2003) stated that temperature stress is one of the most
important factors which may interact with nutrient limitation in nature. Several studies (e.g. Nyholm,
1978) described the simultaneous effect of temperature and nutrient concentration by multiplying an
equation for temperature limitation and Michaelis-Menten growth function of nutrient-limited
growth. Multiplicative models assume essentially that temperature affects only the maximum growth
rate. There is evidence, however, that the half-saturation constant of the Michaelis-Menten equation
also changes with temperature (e.g. Ahlgren, 1978). Moreover, Rhee & Gotham (2003) argued that the
combined effect of nutrient limitation and suboptimal temperature is larger than the sum of the
individual effects (i.e. synergism). The experimentally derived equations for nutrient limitation (Eq. 2829) account for the influence of both temperature and nutrient concentration and gave thus a much
better fit to the experimental observations (Figure 22). The calculated RMSE improved from 61% of
50
Chapter 4: Discussion
the carrying capacity when the theoretical functions were used, to only 9% of the carrying capacity
with the modified model (Table S1 and S4). For the experimental equation affecting the growth rate µ,
the highest R²adj was calculated when the limitation function was affected by the chemical exposure as
well (Table 4). The negative relation (Eq.28) suggests that at higher chemical exposure, phytoplankton
organisms are more affected by N,P-starved conditions. This results corroborate with Kong et al. (2010)
who found that a single-cell green alga Chlorella vulgaris was more sensitive to organic chemicals
under nutrient-enriched growth conditions than under nutrient-starved growth conditions. Other
studies about the influence of organic pollutants on the effect of nutrient limitation to phytoplankton
growth are limited. In future research additional research about the combined effect of chemical
pollution and nutrient limitation is needed.
The impact of nutrient deficiency on the carrying capacity K was much higher than on the growth rate
µ (Table 2). The ratio of the growth rates determined at equal temperatures and chemical exposures
but varying nutrient concentrations, had an average value 0.27 while the average ratio of the carrying
capacities equalled 0.96 (Table 2). The higher this ratio, the more difference between the determined
values and the more limitation observed. According to Li et al. (2015) the nutritional status of marine
water is mainly affecting the final biomass, i.e. the carrying capacity. Mooij et al. (2005) also noted that
the carrying capacity of phytoplankton increases with nutrient enrichment. Li et al. (2015) stated as
well that N-nutrients have been more deficient than P-nutrients in a wide range of oceanic surface
waters and that N is thus mainly controlling marine plankton production.
1.3.2 Temperature limitation
Higher temperatures will affect physiological processes such as photosynthesis and respiration, and
thus stimulate primary production (Kilham et al. 1996; Hughes 2000). The effect of temperature is
often integrated in ecological model by using simple laws, like the exponential Arrhenius law (1889).
However, the Arrhenius law only describes the increasing phase of the temperature response curve
and does not predict the growth drop beyond the optimal temperature (Ras et al., 2013). The
decreasing part of the growth response curve for temperatures higher than Topt has received little
attention, but is very likely to have a profound impact on the phytoplankton productivity. Indeed, due
to its high negative slope, a small temperature variation (e.g. + 2°C) induces a significant decrease of
the growth rate (e.g. -30%; Ras et al., 2013). To quantify the impact of high temperatures, a model
developed by Park (1974) is used that accounts over a range of temperatures, including those higher
than the optimum temperature. However, this model includes two cardinal temperatures (Tmax and
Topt) that should be estimated. The estimation of these parameters is not always straightforward and
the underlying uncertainty can be very large (Ras et al., 2013). The optimum temperature proposed by
Collins & Wlosinski (1983) was not acceptable for the P. tricornutum organism, since no good fit could
be given to the observed growth dynamics (Figure 17). An optimal temperature of 18°C was found
instead, which approximates the optimal temperature of 20°C for the P. tricornutum organism,
reported by Fawley (1984). The maximum temperature of 30°C reported by Collins & Wlosinski (1983)
was maintained. The third parameter, the Q10 value, was assumed a value of 2, as reported by
DeNicola (1996).
The experimental data observed by Everaert et al. (2016) showed that temperature had a big influence
on the carrying capacity K (Figure 20; Table S2). Indeed, Mooij et al. (2005) argued that increased
51
Chapter 4: Discussion
primary production leads to an increase in carrying capacity with increasing temperatures (Mooij et
al., 2005). Many other authors (e.g. Bernard and Rémond, 2012) observe an influence of temperature
on the growth rate. In this experiment however, no significant differences between the growth rates
at different temperatures were observed (Figure 20; Table S2). Although this may seem contraintuitive no temperature limitation on the growth rate was accounted for.
1.3.3 Limitation by chemicals
No significant differences were observed between the derived growth rates and carrying capacities of
the P. tricornutum grown under different chemical exposures (Figure 21; Table S3), hence suggesting
that the marine diatom growth was not altered by the presence of a realistic environmental mixture
of hydrophobic compounds. The same conclusion was made by Everaert et al. (2016), they stated that
the mixture of hydrophobic compounds accumulated using passive samplers in Belgian coastal waters
did not exert a direct effect on the specific growth rate of the marine diatom P. tricornutum in a growth
inhibition test. Also Echeveste et al. (2010) made similar observations for marine phytoplankton
species.
1.4 Validation of experimentally derived formulas
A new model was created by integrating the experimental derived limitation functions with the logistic
growth function of Verhulst. This model gave an optimal fit to the observed growth dynamics of the P.
tricornutum diatom (Figure 22). RMSE values were calculated for all experiments and the RMSE was
between 5 and 14% of the observed carrying capacity (Table S4). This is a significant improvement of
the theoretical model, where the RMSE varied between 23% and 98% of the carrying capacity, with a
mean value of 61% (Table S1). The experimentally derived equations gave thus a better fit to the
observed growth dynamics.
The empirical description of the growth limiting terms (i.e. Eq. 28-29) was validated using an external
dataset of Claeys et al. (2016). We found that the empirical equations of the limitations gave good fit
to the observed growth dynamics of three dinoflagellates (Figure 23). The RMSE values were calculated
for the three organisms (Table S5) and it was notably that RMSE for the Alexandrium minutum
organism was much higher (towards 55% of the experimentally observed carrying capacity) than for
the Protoceratium reticulatum and Prorocentrum micans organisms (between 11 and 18% of the
carrying capacity). The reason for the higher RMSE for A. minutum is that one experiment could not be
predicted with the derived model. This could be caused by a coincidence or because the derived
equations do not apply for this organism. The other experiments performed with A. minutum though
could be predicted by the derived model. Overall, the experimentally derived equations were able to
predict phytoplankton growth under nutrient-starved conditions and were further used in the suite of
the research.
52
Chapter 4: Discussion
2. Quantification of relative contributions
Based on the combination of the logistic model of Verhulst, the theoretical limitation functions and
the experimental equations the relative contributions of the different key drivers of algal growth were
quantified. The contribution of each driver was calculated by comparing the algal densities under stress
and under non-stress circumstances. Next, the relative contribution of every stress factor to the total
limitation was assessed.
2.1 Seasonal pattern
The relative contribution of each driver (temperature, nutrient concentration and chemical exposure)
to the total monthly phytoplankton growth limitation was quantified and visualised in Figure 24. A
typical seasonal pattern for temperate regions can be observed: in winter nutrient concentrations are
high and thus not limiting (cfr. Chapter 1), hence the relative contribution of nutrients is low (ca. 30%,
Figure 24a). In addition, in the first months of the year the temperature is the lowest observed (ca.
3°C) and consequently the relative contribution of temperature limitation is the highest (ca. 70%,
Figure 24a). As spring approaches, water temperatures increase and start to approximate the optimum
temperature. Indeed, the relative contribution of temperature decreases drastically in Spring and
Summer months to 2-3% in June and July (Figure 24a). At the same time nutrients get more depleted
and algal growth starts to get limited by nutrient deficiency. The relative contribution of nutrient
concentration on phytoplankton growth dynamics is maximal (98%) in June and July until it becomes
the only driver influencing algal growth (Figure 24a). In August, the relative contribution of nutrients
decreases again, due to turnover which brings nutrients from great depths back to the surface
(Janssen, 2010). In this period a fall bloom can occur. For the POP1 configuration, i.e. the monitored
POP concentration of 7 PCBs, 4 PAHs and TBT, no relative contribution of the chemical exposure is
visible over the year 2009 (Figure 24a). The average relative contribution of the POP concentration is
0.14% (with a maximum of 0.24%) and chemical exposure is thus not altering phytoplankton growth.
However, the marine environment typically contains many more chemicals than those available in
monitoring data sets (Echeveste et al., 2010). Therefore, the model was run under different conditions:
POP10, representing a chemical exposure 10 times higher than the monitored concentration, POP100
and POP1000, representing respectively a multiplication of 100 and 1000 of the monitored
concentrations. When doing so, different patterns can be observed. In the POP10 configuration (Figure
24b) the relative contribution of the chemical exposure varies between 1.13% and 1.97% and is thus,
although a noticeable increase, still minimal. However, an important difference with Figure 24a (POP1
configuration) can be detected. The relative contribution of nutrient concentrations (Figure 24b) has
increased with approximately 20% in respect to Figure 24a (POP1) in winter months. This can be
attributed to the fact that the experimental derived equation for the influence of nutrient limitation
on the growth rate is as well determined by the chemical concentration. As POP concentrations were
multiplied by 10, the body burden has increased with a factor 10 as well and phytoplankton growth
was thus more inhibited by nutrient deficiency in winter months.
In the POP100 configuration though the relative contribution of POP exposure is clearly increased and
varies between 14% and 20% (Figure 24c). Monitored chemical concentrations varied seasonally: in
spring (when phytoplankton concentrations are high) low dissolved aqueous POP concentrations were
observed by the BMDC. This can be attributed to the fact that phytoplankton is known to accumulate
53
Chapter 4: Discussion
POPs and in this way deplete dissolved concentrations of POPs (Dachs, 1999). Despite this seasonal
variation in POP concentration the relative contribution of the hydrophobic chemicals is approximately
constant over the year, with an average of 16% (Figure 24c). The lowest contribution of the chemicals
is observed in January. When monitored POP concentrations are multiplied with 1000, the relative
contribution of the chemicals is drastically increased and the pattern of relative contributions is altered
(Figure 24d). The POP concentration becomes, together with the nutrient concentration, the biggest
contributor of phytoplankton growth. The relative contribution of chemicals is approximately constant
over the year and has an average value of 44% (Figure 24d). Nutrient concentration stays an important
contributor and accounts to minimal 41% and maximal 52% of the growth inhibition. Influence of
temperature on the other hand becomes minimal, only in winter a relative contribution of
approximately 20% can be observed.
2.2 Variation of parameters
Temperature, nutrient concentrations and POP concentrations were varied between ecologic realistic
ranges and for every combination the relative contribution of each driving force was determined. In
order to assess these relative contributions the mean and median were calculated for each stress
factor. It is clear that nutrient concentration is the main determinant of phytoplankton growth, that
temperature follows and that contribution of chemical exposure is limited (Table 5). The relative
contribution of the POPs is not reaching 1%, while nutrients account for approximately 70% of the total
limitation. When the monitored chemical concentrations are multiplied with 10, a small increase in
relative contribution is observed, but it is only when a multiplication with 100 is performed that their
mean relative contributions exceed 10%. Still, at the POP100 configuration nutrient concentrations
have the most influence on growth dynamics (more than 70%), and POP concentration still the lowest
relative contribution (Table 5). When monitored POP concentrations are multiplied with 1000,
chemical exposure become a big contributor, as its average relative contribution is more than 35%
(Table 5). Nutrient availability stays the main factor determining phytoplankton growth with a mean
relative contribution of more than 50% (Table 5).
2.3 Extreme situations
Since it is important to investigate how phytoplankton growth is altered by extreme situations, this is
discussed next. Extreme situations are defined as combinations of minimum and maximum values of
the drivers of algal growth during summer or winter months. It is clear that in both extreme summer
situations discussed (respectively a combination of highest temperature, lowest nutrient
concentration and highest chemical concentration monitored, Figure 25; and lowest temperature,
highest nutrient concentration and highest chemical concentration monitored, Figure 26) the nutrient
concentration has the biggest influence on phytoplankton growth dynamics, accounting for
respectively 90% and 70% of the total limitation. When nutrient concentration is the lowest monitored
in summer, the growth of the algae organisms is drastically altered. Carrying capacities lower to less
than 5% of the originally found carrying capacity (in non-stress conditions) and phytoplankton
organisms are thus not able to continue their growth (Figure 25). When the lowest temperature is
combined with the highest nutrient concentration monitored in summer months, a 50% reduction of
the carrying capacity is still observed (Figure 26). In both summer situations, chemical exposure is not
54
Chapter 4: Discussion
altering phytoplankton growth in the POP1 and POP10 configuration. When the monitored POP
concentrations are multiplied with 100 however, some contribution can be seen to the algal growth
dynamics (Figure 25c-26c). If nutrient availability is low, the relative contribution of the chemicals is
still low (approximately 1%; Figure 25c), while if nutrients are available in higher concentrations and
thus less limiting, a relative contribution of 17% of the chemical exposure is calculated (Figure 26c). It
is only when the POP concentrations are multiplied with 1000 that the phytoplankton growth dynamics
are drastically altered by the chemical exposure. In this two particular extreme situations the chemical
exposure accounts for respectively 10% and 60% of the growth inhibition (Figure 25-26).
Next two extreme winter situations are investigated. Clearly the relative contribution of temperature
has gained importance (Figure 27-28) compared with the extreme summer situations. When
temperature equals the lowest monitored value in winter months, algal growth is altered and carrying
capacities decrease to 50% of the value obtained under optimal conditions. This may be dedicated to
the fact that an optimal temperature of 18°C is selected. In winter temperatures are much lower and
the deviation between the actual water temperature and the optimum temperature is consequently
high. It should be noted that some phytoplankton organisms have a much lower optimum temperature
and for these organisms the growth inhibition of low temperatures in winter situations will be much
lower. Nutrient contribution on the other hand has drastically decreased, compared with summer
situations. Indeed, nutrient concentrations are much higher in winter and thus less limiting. When
nutrient concentrations are the highest observed, these concentrations almost equal the optimal
values and the limitation is thus minimal (Figure 28). When nutrient concentrations are the lowest
observed on the other hand, the contribution of nutrients accounts to approximately 50% of the total
limitation (Figure 27). For the relative contribution of chemical exposures, an equal pattern as in
extreme situations in summer can be observed: no influence in the POP1 and POP10 configurations, a
small influence in the POP100 configuration (1% when nutrient concentrations are low and 35% when
nutrient concentrations are high) and a big contribution in the POP1000 configuration. In the POP1000
configurations the chemical exposure accounts to respectively 7% and 80% of the total growth
limitation (Figure 27-28). It is thus only when nutrient concentrations are not limiting, that chemical
exposure becomes the main contributor.
3. Recommendations and further research
Some remarks can be made to the followed procedure and the derived conclusions. First, it should be
noted that each algal species is characterized by a different optimal growth temperature (Ras et al.,
2013). The derived optimum temperature of 18°C for the P. tricornutum organism is not valid for all
phytoplankton species since the optimum temperature of different species is dependent on the area
where they live. Indeed, phytoplankton cells living in polar regions tend to have a lower optimal
temperature than phytoplankton cells dominating in temperate regions (Tang et al., 1997). As the
optimum temperature changes, the relative contribution of deviating temperatures could change.
Secondly, the way of deriving the experimental equations should be questioned. The equation for
nutrient limitation was constructed by using the experimental data produced by Everaert et al. (2016).
This dataset was small and the different nutrient conditions measured were limited. Moreover the
equation for POP limitation could never be verified with experimental data. Therefore, it is
recommended to further investigate whether this theoretical logistic concentration-effect function
55
Chapter 4: Discussion
predicts the growth inhibition by the presence of hydrophobic compounds in a good way. For this
purpose experiments should be realised where phytoplankton organisms are exposed to realistic
environmental mixtures, which can be achieved by the use of passive samplers.
Third, interactions between chemical toxicities and environmental drivers have been reported in
various papers. For example, Kong et al. (2010) reported increased toxicity under elevated nutrient
concentrations and Vieiria and Guilhermino (2012) declared that also test temperature may alter the
toxicity of organic chemicals. These interactions are not included in the equation accounting for POP
limitation and thus not integrated in the model. It is suggested to perform more research in the effect
of natural drivers on the toxicity of hydrophobic compounds. Environmental realistic laboratory
experiments should be performed in order to determine the degree of interaction between the
different key drivers. Since field observations have historically yielded significant insight into the
environmental and biotic controls on phytoplankton standing stock and rates of primary production
(Irwin & Finkel, 2008), it is recommended to increase the monitoring of environmental variables and
dissolved POP concentrations in the North Sea.
In this study the main focus was on the first trophic level of the marine food web, i.e. phytoplankton.
A very important remark that can be made is that also other organisms present in the marine
environment have an influence on the phytoplankton growth dynamics. Everaert et al. (2015)
concluded that the main inhibitor of phytoplankton growth is zooplankton grazing. Besides
zooplankton grazing, phytoplankton growth is as well altered by interspecies competition, i.e.
competition in which individuals of different species compete for the same resource. These drivers
were not included in the model, although they may have a remarkable impact on growth dynamics.
The reason that they were not used in the derived model is that effects on phytoplankton biomass due
to zooplankton grazing and competitive interactions are poorly constrained because of limitations in
both data and mechanistic understanding (Irwin & Finkel, 2008). The addition of these drivers to the
model may change the relative contributions drastically and it is thus recommended to perform more
analysis and laboratory experiments on the impact of zooplankton grazing and interspecies interaction
on phytoplankton growth under multistress conditions. Even if the relative contribution of these
drivers is minimal, they might alter the balances between populations and communities. In this way
even effects in higher trophic levels could be induced.
Moreover higher trophic levels are more vulnerable to chemical pollution since POPs accumulate in
the food chain (cfr. Chapter 1) and highest chemical concentrations are thus found in top predators.
Although the relative contribution of chemical exposure to the total limitation of phytoplankton
growth is limited, important ecotoxicological effects could be expressed at higher trophic levels, e.g.
Letcher et al. (2010). As such, the question is from which point in the trophic food web the exposure
to POPs and the accumulated body burden turns out to be problematic. To do so, bioaccumulation
models such as the one proposed by Hendriks et al. (2001) can be used.
Lastly, the chemicals that were used for the quantifications should be questioned. Monitored
concentrations of 7 PCBs, 4 PAHs and TBT were integrated to represent the chemical pollution in the
North Sea. However, the marine environment contains much more chemicals, of which emerging
chemicals form an important group (Hutchinson et al., 2013). Emerging contaminants are often
defined as chemicals that have been detected in the environment, but which are currently not included
in routine monitoring programmes in the EU (la Farré et al., 2008). Indeed, monitoring mainly focuses
on the detection and measurement of a few priority substances. The fate, behaviour and toxicological
56
Chapter 4: Discussion
impacts of emerging contaminants are thus poorly understood (la Farré et al., 2008). Examples of
emerging contaminants are pharmaceuticals, antibiotics, personal care products etc. The main reason
why these contaminants were not used in the model, is the limited amount of long-term monitoring
data in the marine environment (Law et al., 2010). However, toxic effects of emerging pollutants on
phytoplankton growth have been reported, e.g. Richardson & Ternes (2011). Therefore, the
concentrations of emerging contaminants in the North Sea should be monitored and also the effect of
these emerging organic chemicals on phytoplankton growth requires further investigation.
57
Chapter 4: Discussion
58
Conclusion
In the present master thesis I tried to model phytoplankton growth dynamics with the logistic model
of Verhulst, combined with theoretical stress limitation functions. When predictions were compared
with observed growth dynamics, problems were encountered with the Michaelis-Menten function for
nutrient limitation. Theoretical descriptions of multistress growth conditions were thus not sufficient
to characterize phytoplankton growth under those conditions. A new experimentally derived equation
for the influence of nutrient availability was constructed. Integrating this function to the logistic model
of Verhulst gave an important increase in model fit. The constructed model was used to quantify the
relative contribution of temperature, nutrient availability and chemical exposure on marine
phytoplankton growth.
The monitored concentrations of 7 PCBs, 5 PAHs and TBT in the Belgian part of the North Sea gave no
alteration of phytoplankton growth. The relative contribution of the chemical exposure to the total
limitation was never exceeding 1%. Nutrient availability on the other hand was the main determinant
of phytoplankton growth with the highest limitation in summer months. In winter, temperature gained
importance as contributor. This can mainly be attributed to the fact that a high optimal temperature
of 18°C was selected and low winter temperatures were consequently limiting the phytoplankton
growth. To account for unmonitored chemicals, POP concentrations were multiplied with 10, 100 and
1000. In the POP100 configuration a significant increase in the relative contribution was observed, i.e.
chemical exposure accounted for approximately 12% of the total growth limitation. In the POP1000
configuration a relative contribution of approximately 40% was calculated for the chemical pollution.
In general it could be concluded that monitored POP concentrations are not significantly affecting the
phytoplankton growth dynamics in the Belgian part of the North Sea. Important to note is that the
North Sea contains as well emerging contaminants, which are not monitored and of which the effects
are not known. These emerging chemicals should be incorporated in existing monitoring and
assessment programmes to improve environmental realism.
59
60
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70
Appendix
Table S1: RMSE between growth dynamics predicted with the theoretical model and observed growth dynamics. RMSE was
divided by the observed carrying capacity and thus expressed as a percentage.
Exp2
Exp3
Exp5
Exp6
Exp8
Exp9
Exp11
Exp12
Exp14
Exp15
Exp17
Exp18
RMSE
803301
339304
919474
348093
827810
310196
478597
60575
322582
67413
427533
75987
%
60.87%
91.87%
84.07%
98.44%
67.95%
74.69%
41.43%
23.42%
36.43%
27.90%
40.38%
28.56%
Table S2 Observed limitation of temperature on growth rate and carrying capacity of P. tricornutum.
Exp2
Exp3
Exp5
Exp6
Exp8
Exp9
Exp11
Exp12
Exp14
Exp15
Exp17
Exp18
T (°C)
16
16
16
16
16
16
23
23
23
23
23
23
N (µmol N/L)
120
30
120
30
120
30
120
30
120
30
120
30
P (µmol P/L)
2.8
0.7
2.8
0.7
2.8
0.7
2.8
0.7
2.8
0.7
2.8
0.7
BB (mmol/kg)
0
0
0
0
0.00231
0.00231
0
0
0
0
0.00231
0.00231
K limit
1
1
1
1
1
1
0.928015
0.74645
0.841448
0.714292
0.90257
0.66986
µ limit
1
1
1
1
1
1
1.05708
0.960818
1.168662
1.164397
1.076026
0.969901
71
Table S3 Observed limitation of chemical exposures on growth rate and carrying capacity of P. tricornutum.
Exp2
Exp3
Exp5
Exp6
Exp8
Exp9
Exp11
Exp12
Exp14
Exp15
Exp17
Exp18
T (°C)
16
16
16
16
16
16
23
23
23
23
23
23
N (µmol N/L) P (µmol P/L)
120
2.8
30
0.7
120
2.8
30
0.7
120
2.8
30
0.7
120
2.8
30
0.7
120
2.8
30
0.7
120
2.8
30
0.7
POP
BI
BI
POPPOPPOP+
POP+
BI
BI
POPPOPPOP+
POP+
K limit
1
1
0.850737
0.981926
0.944396
1.149837
1
1
0.771378
0.939623
0.918501
1.031857
µ limit
1
1
0.973553
0.897156
1.004048
0.909972
1
1
1.076317
1.087246
1.022043
0.918573
Table S4 RMSE between the observed growth dynamics of P. tricornutum and the growth dynamics predicted by the
experimental model.
Exp2
Exp3
Exp5
Exp6
Exp8
Exp9
Exp11
Exp12
Exp14
Exp15
Exp17
Exp18
RMSE
64973
31136
152439
35859.9
68706.28
36633.99
164591.4
15824.41
56695.89
16049.35
127239.6
19084.32
K
1319667
369346.7
1093667
353606.7
1218333
415320
1155333
258640
885500
241626.7
1058667
266020
%
4.92%
8.43%
13.94%
10.14%
5.64%
8.82%
14.25%
6.12%
6.40%
6.64%
12.02%
7.17%
Table S5 RMSE between the predicted and observed growth dynamics for the experiments performed by Claeys et al. (2016).
PR
PR
PM
PM
AM
AM
RMSE
422
650
454
644
2555
6009
%
11.41%
16.57%
14.10%
18.41%
13.30%
55.19%
72
Figure S1 Nutrient limitation function affecting the carrying capacity in function of the water temperature and the nutrient
concentration.
73
Figure S2 Validation of the experimental model with the observed data (Everaert et al., 2016).
74
Figure S3 Comparison of the observed growth dynamics of the P. Reticulatum (PR), P. Micans (PM) and A. minutum (AM)
organisms, observed by Claeys et al. (2016), and the growth dynamics predicted with the experimental model.
75