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Home , Regime shift

Multiple forces drive the Baltic Sea dynamics and its response to environmental change

Susa Niiranen

Doctoral thesis in Marine Susa Niiranen, [email protected]

Stockholm Resilience Centre Stockholm University SE-106 91 Stockholm, Sweden

©Susa Niiranen, Stockholm 2013

ISBN 978-91-7447-744-3

Printed in Sweden by US-AB, Stockholm 2013 Distributor: Department of Ecology, Environment and Plant Sciences, Stockholm University Cover by Jens Öhlund

Abstract

Understanding the interaction of multiple drivers and their compounded effects on dynamics is a key challenge for marine resource management. The Baltic Sea is one of the world’s seas most strongly impacted by effects from both human activities and . In the late 1980’s changes in climate in combination with intensive fishing initiated a reorganization of the Central Baltic Sea (CBS) food web resulting in the current sprat-dominated state. In the future, climate change is projected to cause drastic changes in hydrodynamic conditions of the world oceans in general, and the Baltic Sea in particular. In this thesis, CBS food web responses to the combined effects of fishing, nutrient loads and climate were tested for the past (1974-2006) and projected into the future (2010-2098). A new food web model for the CBS (BaltProWeb) was developed using extensive monitoring data across trophic levels. This model described the past food web dynamics well, and was hence also used for future (2010-2098) projections. Different ensemble modeling approaches were employed when testing the food web response to future scenarios. The results show that regardless the climate change, the management of nutrient loads and cod fishing are likely to determine the food web dynamics and trophic control mechanisms in the future Baltic Sea. Consequently, the variation in the food web projections was large, ranging from a strongly eutrophied and sprat- dominated to a cod-dominated CBS with levels close to today’s values. The results also suggest a potential risk of abrupt ecosystem changes in the future CBS, particularly if the nutrient loads are not reduced. Finally, the studies illustrate the usefulness of the ensemble modeling approach, both from the perspective of ecosystem-based management as well as for studying the importance of different mechanisms in the ecosystem response.

Keywords: Baltic Sea, climate change, with Ecosim, food web interactions, multiple drivers, regime shift, trophic control

Sammanfattning

Förståelse av hur drivkrafter interagerar och tillsammans ändrar ett ekosystems dynamik är en fundamental del av marin naturresursförvaltning. Östersjön är ett av världens mest påverkade hav, där både mänskliga aktiviteter och klimat har haft ett stort inflytande. Under 1980-talet startade en omorganisering av födoväven i egentliga Östersjön på grund av klimatförändringar och intensivt fiske, vilket resulterade i det nuvarande tillståndet där födoväven domineras av skarpsill. Prognoser för framtidens klimat visar att dramatiska förändringar av havens hydrodynamik är troliga, vilket särskilt kommer påverka Östersjön. Avhandlingen visar hur kombinationer av effekter från fiske, tillförsel av näringsämnen och klimatpåverkan kan ha skapat förändringar i egentliga Östersjöns födoväv under 1974-2006 och hur dessa kan samverka i framtiden (2010-2098). Omfattande miljöövervakningsdata från olika trofinivåer användes för att utveckla en ny födovävsmodell för egentliga Östersjön, BaltProWeb. Modellen beskrev den tidigare dynamiken väl och användes därför vid projektionerna för framtiden. Flera ensembler av modeller användes för att testa hur födoväven förändras under olika framtida scenarier. Resultaten visar att förvaltning av näringstillförsel och torskfiske med stor sannolikhet kommer styra hur födovävsdynamik och trofisk kontroll förändras i framtida Östersjön, oavsett hur klimatet ändras. Variationen mellan projektionerna var stor, där vissa scenarier gav ett kraftigt övergött och skarpsillsdominerat hav, medan vi under andra förutsättningar kan få torskdominans och effekter av övergödningen på samma nivå som idag. Resultaten visar också att det finns risk för abrupta ekosystemförändringar i framtiden i egentliga Östersjön, särskilt om näringstillförseln inte minskar. Slutligen visar studierna att ensembler av modeller är en användbar metod, både för att inkorporera ekosystembaserad förvaltning och för att studera olika mekanismers betydelse för ekosystemförändringar.

Nyckelord: Östersjön, klimatförändringar, Ecopath with Ecosim, födovävsinteraktioner, samverkande drivkrafter, regimskifte, trofisk kontroll

List of papers

Paper I: Tomczak, M. T., Niiranen, S., Hjerne, O. and Blenckner, T. (2012) Ecosystem flow dynamics in the Baltic Proper – Using a multi-trophic dataset as a basis for food-web modelling. Ecological Modelling, 230, 123-147. My contribution: The model presented in this paper resulted from equal contribution by myself and M. T. Tomczak. I had an active role in the writing process.

Paper II: Niiranen, S., Blenckner, T., Hjerne, O., Tomczak, M. T. (2012) Uncertainties in a Baltic Sea Food-Web Model Reveal Challenges for Future Projections. Ambio, 41, 613-625. My contribution: I designed the study, executed necessary model runs, as well as was responsible for analyzing the results and the writing process.

Paper III: Gårdmark, A., Lindegren, M., Neuenfeldt, S., Blenckner, T., Heikinheimo, O., Müller-Karulis, B., Niiranen, S., Tomckzak, M. T., Aro, E., Wikström, A. and Möllmann, C. (2013) Biological ensemble modeling to evaluate potential futures of living marine resources. Ecological Applications, 23(4), 742-754. My contribution: I actively participated in the discussion and writing process.

Paper IV: Niiranen, S., Yletyinen, J., Tomczak, M. T., Blenckner, T., Hjerne, O., MacKenzie, B. R., Müller-Karulis, B., Neumann, T. and Meier, H. E. M. (2013, published online) Combined effects of global climate change and regional ecosystem drivers on an exploited . Global Change Biology, doi: 10.1111/gcb.12309. My contribution: I designed the study from the food web perspective and restructured the food web model to be a suitable tool to use in a multi-model study. I was responsible for the future food web model runs, data analysis, as well as the writing process.

Paper V: Niiranen, S., Blenckner, T., Yletyinen, J., Otto, S., Meier, H. E. M., Hjerne, O. and Tomczak, M. T. (manuscript) The potential risk of regime shifts and changes in ecosystem dynamics in the future Baltic Sea. My contribution: I designed the study, ran the necessary model runs and analyzed the results. I was responsible for the writing process.

Papers I-IV are reprinted with the kind permission from Elsevier (I), Springer (II), Ecological Society of America (III) and John Wiley and Sons Ltd (IV).

Table of Contents

Introduction ...... 2

Marine under pressure ...... 2 Factors controlling marine food web dynamics ...... 2 Regime shifts and ecosystem resilience ...... 3 Marine ecosystems facing climate change ...... 4 Study area - The Baltic Sea ...... 5 Ecosystem modeling in the Baltic Sea ...... 7 The aim of this thesis ...... 8

Material and methods ...... 10

Modeling approach ...... 10 External forcing and Ecosim model calibration ...... 10 Model sensitivity and uncertainty ...... 11 Food web model in future projections ...... 11 Ensemble approaches ...... 13 Projecting regime shifts ...... 13

Results and discussion ...... 15

BaltProWeb model and past food web dynamics ...... 15 Baltic Sea food web under future climate conditions ...... 16 Potential risk of regime shifts under future conditions ...... 17 Using models to support future ecosystem-based management ...... 18

Conclusions ...... 19

Acknowledgements ...... 20

References ...... 21

Introduction

Marine ecosystems under pressure

Marine ecosystems have undergone large-scale changes during the past decades, and events such as fish stock collapses, benthic , jellyfish outbreaks, as well as large-scale ecosystem reorganizations are documented in increasing numbers worldwide (Lees et al. 2006, Daskalov et al. 2007, Blenckner and Niiranen 2013). Many of the changes have been observed concomitant to variations in climate conditions, and hence indicate a close coupling between function and climate variation and change (Francis et al. 1998, Beaugrand et al. 2008, Alheit and Bakun 2010). In addition to being affected by climate, marine systems are under great anthropogenic stress due to their function as food providers and the end- recipients of land-based pollution and excess nutrients. Halpern et al. (2008) estimate that currently nearly half of the world’s marine area is strongly affected by multiple drivers and that no marine area is unaffected by human activities. Coastal, enclosed and transitional waters (e.g., estuaries, fjords and lagoons) in particular are often subject both to great fluctuations in climate-related environmental conditions as well as to intensive human activities (Jackson et al. 2001, Elliott 2011, Basset et al. 2012, published online).

Factors controlling marine food web dynamics

What controls the production and food web dynamics in different ecosystems is one of the fundamental questions of marine ecosystem studies (Hunter and Price 1992, Menge et al. 1997). Marine ecosystems can be classified in three main categories, i.e., bottom-up (resource-driven), top-down (-driven) and wasp- waist (intermediate -driven) controlled systems. The bottom-up control processes have long been studied in marine ecology and are often the best known. In these systems changes in primary production are positively reflected in the production of higher trophic level groups (Pace et al. 1999). In the top-down controlled systems changes at the higher trophic levels will cascade down the food web, such that biomass time series of predators and prey will display a negative correlation (Shurin et al. 2002, Worm and Myers 2003). The awareness of top-down processes is more recent, and largely a result of commercial fish stock collapses that have taken place during the past decades both in coastal and offshore ecosystems worldwide (Jackson et al. 2001, Hutchings and Reynolds 2004, Frank et al. 2006). Wasp-waist control features properties from both the bottom-up and top-down control, and is least recorded in marine ecosystems up to date. In wasp-waist control a species or functional group of intermediate trophic level (e.g., planktivorous fish) has a controlling role in the food web, such that changes in its biomass are negatively reflected in its prey and positively in its predators (Cury and Shannon 2004). According to the study by Frank et al. (2006) bottom-up control is a typical feature in the lower latitude ecosystems with high temperatures and , while the higher latitude, cold marine systems with low species richness tend to be more top-down controlled. Wasp-waist control has mainly been described in ecosystems (Cury et al. 2000, Cury et al. 2003). In the Northern Benguela, for example, the cyclically switching between sardine and anchovy is suggested to shape the marine ecosystem structure (Cury and Shannon 2004). However, wasp-waist control may not be unique to upwelling systems. Fauchald et al. (2011) suggest that in the North Sea, e.g., herring (Clupea harengus) is able to exert top-down control on zooplankton as well as bottom-up control on the seabird populations. Also, in the Baltic Sea the highly abundant sprat (Sprattus sprattus) has been shown to exert trophic control on copepod Pseudocalanus acuspes. However, unlike in the other systems, sprat may have a negative effect on its main predator cod by preying on cod eggs and competing for P. acuspes with cod larvae (Köster and Möllmann 2000). Currently most studies recognize that marine ecosystems that are under great pressure may display characteristics of several trophic control mechanisms simultaneously, i.e., mixed control (see for example Jaschinski and Sommer 2008). Chassot et al. (2007) found that the European production has over the past 30 years correlated mainly with changes in primary production on regional (100 000 km2) as well as sub-regional scales (10 000 km2). However, their data also indicated differences in the level of fish production between ecosystems with alike primary production levels, such as 4-5 times higher fish production in the Faroe waters and the Baltic Sea than in the eastern Mediterranean and the Barents Sea. Consequently, Chassot et al. (2007) suggest that top-down control or environmental forces, may be responsible for the short-term changes in fish biomass. Furthermore, temporal variations in the strength of different types of trophic control are well documented (e.g., Litzow and Ciannelli 2007; first described in lakes and intertidal ecosystems (Menge 1976)).

Regime shifts and ecosystem resilience

An increasing number of changes observed in marine systems are defined as ecosystem regime shifts (Lees et al. 2006, Blenckner and Niiranen 2013). Some of the most well-known examples include a change from a piscivore-dominated system into a planktivore- and further jellyfish-dominated one in the Black Sea (Daskalov et al. 2007), a change from cod dominance into sprat dominance in the Baltic Sea (Casini et al. 2008, Möllmann et al. 2009), and the climate-induced change in the North Pacific ecosystem (Hare and Mantua 2000). According to the original definition by Holling (1973) an ecological regime shift takes place when a system is pushed from one stable state (later identified also as dynamic states, e.g., in Scheffer and Carpenter (2003)) to an alternative one, i.e., the ecosystem structure, functioning and feed-backs change. Bakun (2005) has rephrased the definition as “a persistent radical shift in typical levels of or of multiple important components of marine biological structure, occurring at multiple trophic levels and on a geographical scale that is at least regional in extent”, with focus on marine ecosystems. Regime shifts are most often caused by changes in external drivers. Scheffer et al. (2001) classify ecosystem response to changes in external conditions into three types: 1) smooth, or linear, response, 2) abrupt, or non-linear response and 3) discontinuous response with characteristic of hysteresis, i.e., for ecosystem recovery it is not sufficient to restore the external conditions to right-before-the-collapse values, but greater effort is

3 required. Several statistical methods have been employed in regime shift detection using long-term oceanographic and biological time series (Hare and Mantua 2000, Beaugrand 2004, Möllmann et al. 2009). However, Collie et al. (2004) and Thrush et al. (2009) note that due to the prerequisite of major changes in the ecosystem function it is not sufficient to base one’s analysis only on statistical methods, but deeper process understanding is required. Ecosystem resilience is an ecosystem property that defines the ecosystem capacity to absorb external without changing its structure, function and (Folke et al. 2004, Walker et al. 2004). For example species richness, high response diversity within functional groups, large benthic-to- pelagic and biogenic structure (Folke et al. 2004, Thrush et al. 2009, Blanchard et al. 2011) are suggested to enhance ecosystem resilience. However, defining and measuring resilience of a particular ecosystem has proven challenging. Climate is often identified as the main driver or triggering factor causing regime shifts, particularly in large offshore marine systems (Hare and Mantua 2000, Benson and Trites 2002, Link et al. 2009). Concomitantly, regime shifts were detected in several marine systems worldwide in the late 1980s, following changes in the Northern Atlantic Oscillation (NAO) (e.g., Alheit et al. 2005, Beaugrand et al. 2008). In the more coastal or enclosed ecosystems, such as the Baltic Sea (Möllmann et al. 2009) or Black Sea (Daskalov et al. 2007), combinations of external drivers are often documented as causes for ecological regime shifts.

Marine ecosystems facing climate change

The current climate models project accelerating atmospheric warming toward the end of the 21st century (IPCC 2007). High latitude areas are expected to warm faster than low latitude areas. Such pattern is supported by the past observations, and in Europe for example higher rates of warming were observed in the Northern than Southern seas (Belkin 2009). Belkin et al. (2009) also show that the semi-enclosed and enclosed seas have experienced higher rates of warming than the open seas at corresponding latitudes. Even if different marine ecosystems are likely to differ in their response to global warming, some more general climate-related ecosystem responses have been observed. Such generic responses include poleward species range expansions, changes in local species compositions due to physiological intolerance to new conditions and arrival of non-indigenous species (Beaugrand et al. 2002, Drinkwater et al. 2010, Doney et al. 2012). The ecosystem-specific response to changes in climate is defined by its interplay with other, often local, anthropogenic drivers and system characteristics. Intensive fishing in a certain area, for example, may increase the sensitivity of this marine ecosystem to changes in climate (Ottersen et al. 2006, Planque et al. 2010, Rouyer et al. 2012). Furthermore, food web structure or functional diversity may affect the strength or occurrence of the indirect climate effects that take place as a result of climate-induced feedbacks, such as trophic cascades (Drinkwater et al. 2010, Planque et al. 2010, Philippart et al. 2011). Food webs with higher functional diversity are often considered more stable (McCann 2000). On the other hand, if climate change affects in the system, even small or moderate changes can affect the ecosystem function (Power et al. 1996).

4 Results from previous modeling studies imply risk of unfavorable ecosystem response due to future climate change. In the Mediterranean Sea, which is classified as a climate vulnerability hotspot by IPCC (2007), some fish species are projected to go extinct due to disappearance of cold-water in combination to limited possibility for northwards movement (Ben Rais Lasram et al. 2010). In the Northeast Pacific, Ainsworth et al. (2011) project overall declining fisheries landings as a response to changes in several climate related parameters. In the study area of this thesis, the Baltic Sea, Lindegren et al. (2010) project a risk for cod stock collapse due to decreasing salinities, in case fishing pressure is not adjusted to worsening cod reproduction conditions. However, all studies highlight that results from the future scenarios need to be considered with care. For example, there are several unknown aspects when it comes to ecosystem responses to multiple effects of climate in combination with other drivers, the potential existence of ecological thresholds, non- linear responses or unforeseen events, such as the invasion by new species.

Study area - The Baltic Sea

The Baltic Sea is one of the world’s largest brackish water ecosystems. It has a semi- enclosed shape with a narrow connection to the North Sea, from where inflows of saline and oxygen rich water intermittently enter the Baltic influencing water salinity, stratification and deep-water oxygen concentration (Leppäranta and Myrberg 2009). The Baltic Sea environmental conditions, e.g., salinity and temperature, have pronounced spatial gradients due to the large North-South climatic gradient, high riverine input and salt-water inflows from the South. The large (1.7 million km2) catchment area has over 85 million inhabitants and large parts of the Baltic Sea suffer from human-induced eutrophication and long-term hypoxia (Zillén and Conley 2010, Conley et al. 2011). Cod (Gadus morhua), herring (Clupea harengus membras) and sprat (Sprattus sprattus) form approximately 80% of the Central Baltic Sea (CBS) fish biomass and the small pelagic planktivore sprat has dominated the food web since the late 1980s (Casini et al. 2009). Simultaneously, the abundances of cod and herring have decreased and are currently relatively low, regardless the recent recovery of the cod stock (Eero et al. 2012). Fishing on these species is intensive and has had a particularly negative effect on the Eastern Baltic cod. The main mesozooplankton groups present are the copepods Acartia spp., Temora longicornis and Pseudocalanus acuspes, all important prey items of sprat, herring and young cod (Möllmann et al. 2000, Möllmann et al. 2004). This study focuses on the open areas (minimum depth 20m) of the CBS (Fig. 1). Here, a permanent halocline at approximately 70 m depth separates the surface water from the more saline bottom water. At present, the CBS surface salinity ranges between 6 psu in the North, and 10 psu in the South.

5

Fig. 1 Map of the Baltic Sea and the Central Baltic Sea study area (shaded).

Climate, nutrient loads and fishing are the main external forces driving the CBS ecosystem (Savchuk and Wulff 2007, Möllmann et al. 2008). Möllmann et al. (2008, 2009) and Casini et al. (2008) describe an ecological regime shift in the CBS at the end of the 1980s. In this shift the ecosystem changed from a cod-dominated into a sprat-dominated state (Casini et al. 2009). Intensive fishing on cod in combination with decreasing cod reproductive volume (RV), i.e, the volume of water where cod eggs can survive (salinity >11psu and oxygen content >2mg l-1, MacKenzie et al. (2000)), resulted in sudden decrease of the cod stock. Meanwhile, sprat was released from the predation pressure by cod. Sprat was further favored by increasing water temperature resulting in sudden increase in sprat biomass. In addition, changes in zooplankton composition were observed. The abundance of P. acuspes, an important prey item for sprat and juvenile cod, decreased rapidly due to increased predation by sprat and lower salinity. Casini et al. (2009) also observed a change in control of P. acuspes, from salinity control before the shift to sprat predation control in the sprat- dominated state, when P. acuspes did not increase in biomass despite salinity returned to pre-shift values. Opposite to P. acuspes, Acartia spp. and T. longicornis increased in abundance following the increase in water temperature (Möllmann et al. 2009). Despite some cod recovery has been observed as a response to significantly lowered fishing effort during the previous years (Eero et al. 2012), and several climate variables have returned to pre-shift values, the ecosystem has not returned to its previous state. Consequently, some mechanisms have been suggested to preserve the current ecosystem state. First, sprat and cod larvae compete for the same prey item P. acuspes, which is currently at low abundance (Möllmann et al. 2009).

6 Secondly, in diet studies cod eggs have been found in sprat stomachs (Köster and Möllmann 2000). Finally, van Leeuwen et al. (2008) suggest an , i.e., that the release of sprat from cod predation pressure results in a stunted sprat size distribution and lower quality prey for cod. Österblom et al. (2007) also suggest a change from a seal-dominated oligotrophic, into a cod-dominated eutrophied system earlier in the 20th century. The Baltic Sea is one of the marine systems most affected by global warming. In 1982-2006, the Baltic Sea surface temperature (SST) increased by 1.35 C, which is a clearly higher rate than the global average (Belkin 2009). Furthermore, the newest projections of the future conditions of the Baltic Sea show that the SST values may by 2100 be higher, and the salinity and oxygen concentrations lower, than ever measured since the 1850s (Meier et al. 2012).

Ecosystem modeling in the Baltic Sea

In the Baltic Sea ecosystem models have been traditionally used for two purposes: to simulate the short- and long-term development of summer algal blooms and their possible mediation (biogeochemical models, e.g., Roiha et al. (2010) and Savchuk and Wulff (2007)), and to estimate the size of commercially important fish stocks for setting annual quotas (single species fish models, e.g., Darby and Flatman 1994). The more recent International Council for the Exploration of the Sea (ICES) projections of sprat and herring stock include input from a multi-species fish model (MSVPA, Vinthern et al. 1998). Furthermore, Lindegren et al. (2010) use a multivariate autoregressive multi-species model of upper trophic levels to project the combined climate and fishing effects on the Eastern Baltic cod and Van Leuween et al. (2008) describe an emergent allee effect between cod and sprat using a stage- structured multi-species model. To describe both the direct and indirect multiple driver effects, it is necessary to use food web modeling that includes both lower and higher trophic level groups, and can also incorporate environmental forcing. Harvey et al. (2003) have studied the past fishery effects (1974-2000) on the CBS ecosystem with an Ecopath with Ecosim (EwE) model (Christensen and Pauly 1992), which was also used in Österblom et al. (2007) and Hansson et al. (2007) to explore combined effects of fishing, eutrophication and seal management. Fennel (2010), on the other hand, describes the Baltic Sea food web and its dynamics with a three-dimensional nutrient to fish (i.e., nutrient-phytoplankton-zooplankton--fish (NPZDF)) model, which adds fish (herring, sprat and cod) to the NPZD-model (ERGOM, Neumann et al. 2002). The EwE model of the CBS food web (BaltProWeb) used in this PhD thesis is developed based on the Harvey et al. (2003) model. EwE is a globally well-known and extensively used modeling approach to describe trophic flows in aquatic ecosystems (Fulton 2010). Ecosystem models have proven to be powerful tools, but at the same time they are, like all quantitative models, always subject to uncertainties in model structure and parameterization (Beck 1987, Li and Wu 2006, Turley and Ford 2009). Comprehensive stochastic methods, such as the Monte Carlo random parameter search procedure (Waller et al. 2003) are available to study the uncertainty resulting from model parameterization. Often, however, such methods are time and resource intensive. Meta-modeling approaches offer an alternative with reduced computational costs (Scavia et al. 1981, Ratto et al. 2007). In addition, Bayesian modeling

7 approaches working in units of probability have more recently been applied, also in marine ecosystem studies (Mäntyniemi et al. 2009). The structural model uncertainty, on the other hand, can be addressed by comparing models with differing configurations, such as model complexity (Vichi et al. 2003, Brown et al. 2010). However, model uncertainties are not standardly addressed in complex ecosystem models (Planque et al. 2011). Recently, also Link et al. (2012) called after new methods capable of presenting model uncertainties without overriding the usability of results.

The aim of this thesis

The aim of this PhD thesis is to provide new information about the mechanisms and forces that control the CBS food web under different environmental conditions. The most recent studies indicate that the environmental variables that have previously induced large-scale ecosystem change in the Baltic Sea, e.g., salinity and temperature, are likely to change significantly by the end of 21st century (Meier et al. 2012). Hence, particular focus is directed to study the potential ecosystem change under future climate conditions. State-of-the-art climate and biogeochemical projections were provided by the three-year-long international ECOSUPPORT project (Meier et al. 2012, Wake 2012) and their ecosystem effects were studied in combination with different nutrient load and cod fishing scenarios. As human influence is great in the Baltic Sea, evaluating the interactive effects of climate and other main stressors is necessary from the perspective of ecosystem-based management (Brander 2007, Cury et al. 2008). Concomitantly, the potential of using models, such as the BaltProWeb food web model, to aid future management is addressed throughout the thesis. In Paper I, a new EwE food web model of the Baltic Proper (BaltProWeb) is presented, and used to study the past (1974-2006) ecosystem dynamics including the CBS regime shift (Casini et al. 2008, Möllmann et al. 2009). In Paper II, a new simplified model sensitivity and uncertainty analysis approach for EwE is suggested, and the sensitivity of the BaltProWeb model to uncertainties in input data and climate forcing is tested in the past and future projections. Papers III, IV and V, have focus on analyzing future food web projections. In Paper III, future projections of cod biomass are studied in an ensemble of higher trophic level models in different cod fishing and climate scenarios, focusing on the usability of an ensemble approach for ecosystem-based management. In Paper IV the effects of multiple drivers, i.e., climate, nutrient loads, and cod fishing, on the future Baltic Sea food web are studied in six nutrient load - cod fishing scenarios. Finally, the Paper V addresses the possible risk of ecological regime shifts in the future Baltic Sea, and identifies food web properties that may contribute to ecosystem resilience in the face of climate change. The flow of this PhD thesis, including the relationships between different papers, is presented in Fig. 2.

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9 Material and methods

Modeling approach

An Ecopath with Ecosim (EwE, Christensen and Pauly 1992) food web model for the CBS (BaltProWeb) was built and further developed during this PhD study. As a difference from the previous CBS food web model by Harvey et al. (2003), the new model was built to describe changes in the entire food web instead of having focus on fish and fisheries. By having a more detailed model structure at lower trophic levels, using calibration data across trophic levels, and including more environmental effects the new model was able to describe changes in the food web function described for example by Möllmann et al. (2008). BaltProWeb model and its properties are described in detail in Paper I. The Ecopath mass-balance and the Ecosim 1D simulation models of EwE were used. The Ecopath model builds on the mass-balance equation (Eq. 1), in which the annual biomass production (P) of group i equals the annual biomass (B) lost from group i via n C predation ( ( ) j " Bj " dietij , where C/Bj is the annual consumption rate of ! j=1 B predators j (j=1,…, n), and dietij is the proportion of i in the predator j diet), fishing (F=fishing mortality rate) and other mortality (M0=other mortality rate) (Fulton 2010). Also annual biomass accumulation (BA), immigration (I) and emigration (E) can be included in the Ecopath equation.

P n C Bi !( )i = ( ) j ! Bj ! dietij + Fi ! Bi + M 0i ! Bi + BAi # Ii + Ei Eq. 1 B " j=1 B

The Ecosim simulation model describes changes in the biomass of each functional group by coupled differential equations (Eq. 2) that are derived from the Ecopath master equation (Eq. 1). dB i Eq. 2 = gi #Qji !#Qij !(Fi + M 0i + ei )" Bi + Ii dt j j

Where, gi is the net growth efficiency, ei the emigration rate, ΣjQji the total food consumption of i. ΣjQij is the total biomass of i predated. In Ecosim, the dynamics of trophic interactions follow a arena theory (Polovina 1984, Christensen and Pauly 1992). In other words, each prey population is split into a component that is vulnerable and a component that is invulnerable to predation (Walters et al. 1997).

External forcing and Ecosim model calibration

The Ecosim model can be simultaneously driven with multiple time series of external forcing. In Papers I-III, the model was forced with catch rates of juvenile and adult

10 sprat, juvenile and adult herring and small and adult cod. In addition, spring temperature was signed to have a positive effect on Acartia spp. and T. longicornis, and the extent of oxygenated area on macrozoobenthos and mysids. Cod reproductive volume and August temperature were used to force cod and sprat egg production, respectively. Herring egg production was forced by time-series of herring . In addition to forcing used in Papers I-III, in Papers IV-V salinity was signed a positive effect on P. acuspes and phytoplankton production was forced with primary production estimates from biogeochemical models. All environmental forcing together with their application, data sources and relevant references are presented in respective papers (I-V). Ecosim model calibration was carried out using the automatic time series fitting routine of EwE. This routine searches for the vulnerability values between predator and prey that result in the best model fit, i.e., total least sum of squares (SS), between simulated and observed biomass and catch data, while accounting for the environmental forcing and fishing mortality predefined by the user. Calibration data was available for 13 functional groups (data sources presented in Paper I), and the same calibration data was used in all papers.

Model sensitivity and uncertainty

In Paper II the BaltProWeb model sensitivity to input data uncertainty was studied using a simplified model uncertainty and sensitivity analysis. In this analysis the food web key groups, i.e., the groups that have the largest effect on the food web function, were first identified based on the Relative Total Impact index, which is calculated from the Mixed Trophic Impact index in EwE (Ulanowicz 1990). After cod, sprat, macrozoobenthos, P. acuspes and other mesozooplankton were identified as the groups the model was most sensitive to, the natural variability or uncertainty in their biomass data was estimated. Based on this variability, the Ecosim model was recalibrated changing one by one the Ecopath input biomasses of the respective groups to reflect the minimum and maximum values of the uncertainty range. Results from these ten new food web realizations were then considered as representative of the model uncertainty range caused by variability in key group biomass data. Next, future projections were run on each ten new model realizations, as well as the original model, to test how the assumed uncertainties in the input data translate in model uncertainties in two different cod fishing and a cod fishing - future climate change scenario. In addition, the effect of changing the intensity of environmental forcing was tested.

Food web model in future projections

The BaltProWeb model was used to run future food web projections in Papers III-V. Slightly different model scenarios and environmental forcing was used in each of the three papers. In Paper III, an earlier unpublished version of the food web model was used, but the main features were intact. Also, in Papers IV and V, small changes were made to the original model. These changes are described in detail in Paper IV. Differing from Paper III, in Papers IV and V the food web model was calibrated using environmental forcing data from Baltic Sea biogeochemical models that were

11 driven by atmospheric forcing from ERA-40 reanalysis data (details and data sources in Paper IV) for 1974-2006. In Paper III, two future climate scenarios were used: a) no further climate change in comparison to 1973-2005 conditions, and b) climate change, with a surface water temperature increase of 3.5 °C and a salinity decrease of 0.8 psu by 2100 (time- slice results from regionally downscaled projections given the A2 emission scenario; Meier et al. 2006, IPCC 2007, BACC 2008). For both scenarios time series for climate variables were constructed for 2006-2100 based on the past climate variability. In Papers IV and V, climate trajectories (1974-2098) were available. In both papers the climate scenarios were based on the dynamically downscaled output of the ECHAM5/MPI-OM Global Circulation Model (GCM, Uppala et al. 2005) given the IPCC emission scenarios A1B and A2 (Nakićenović 2000). Further, to investigate the impact of natural climate variability in the A1B scenario two initial realizations, i.e., ECHAM5-r1-A1B (A1B1) and ECHAM5-r3-A1B (A1B3), of the GCM were used resulting in three climate scenarios: a) AIB1, b) A1B3, c) A2. In Papers IV and V, no future climate change was considered as an unlikely scenario and was not included in the study. In all papers the climate scenarios were combined with two different cod fishing scenarios: a) intensive cod fishing (F1.1), with a constant cod fishing mortality (F) at 1.1, and b) the cod recovery plan (F0.3), with a constant cod F at 0.3 following EU’s cod management plan (EC 2007). In addition in Paper IV, the focus of which is on multiple driver effects, three nutrient load scenarios were studied: a) business as usual (BAU), with increasing nutrient loads, b) reference conditions (REF), with no change from current nutrient loads, and c) Baltic Sea Action Plan (BSAP), with decreasing nutrient loads following the BSAP (HELCOM 2007). BAU and BSAP nutrient load scenarios were also used in Paper V. An example of the scenario modeling approach in Paper IV is presented in Fig. 3.

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Fig. 3 The scenario modeling approach in Paper IV. (BGMs: biogeochemical models, FWM: food web model, F: fishing mortality, EwE: Ecopath with Ecosim, prim.prod.:primary production; environmental forcing that is affected by both climate and nutrient loads is indicated with gray color).

12 Ensemble approaches

The main focus of Paper III was treating the ensemble results of higher trophic level models. In Paper IV, on the other hand, one food web model was forced with results from an ensemble of climate scenarios and biogeochemical models. In Paper III, an ensemble of results from seven different higher trophic level models that were capable of projecting cod biomass was studied. These seven models included three single-species models, three multi-species models and one food web model. All models were run with the same forcing data in future projections, and to make results comparable, the future projections of cod spawning stock biomass (SSB) were analyzed based on the relative change from the 2009 SSB of the respective model. The effect of structural differences in the ecological models was studied by comparing the simulated cod SSB between models in each combination of climate and fishing scenarios. To assess the effect of the statistical uncertainty of future climate, the variability (i.e, coefficient of variation [CV]) in simulated cod SSB was calculated across ten climate realizations in each scenario and within each model. The model-specific mean, ranges and CVs were then contrasted among models to compare the simulated cod biomass and their sensitivity to climate variation. In Paper IV, the food web model was forced by data from three different biogeochemical models, i.e, the BAltic sea Long-Term large-Scale Eutrophication Model (BALTSEM, Gustafsson 2003), the Ecological Regional Ocean Model (ERGOM, Neumann et al. 2002), and the Swedish Coastal Ocean Biogeochemical model coupled to Rossby Centre Ocean circulation model (RCO-SCOBI, Meier et al. 2003, Eilola et al. 2009), which were each driven by three different climate scenarios or their realizations (A1B1, A1B3, A2) in combination with three nutrient load scenarios (BAU. REF, BSAP; Fig. 3). The food web model was run with output from each individual biogeochemical model run, but before final analysis of the results, they were averaged across all climate scenarios and biogeochemical models, such that only six different (2 fishing times 3 nutrient) scenarios were used to compare with each other. However, the entire range of biomass projections per scenario was presented and considered to represent uncertainty in the multi-model approach, in which model results were used to drive other models. In addition to the ensemble approach, uncertainties in the future projections were studied, by partly implementing the simplified model sensitivity and uncertainty routine introduced in Paper II.

Projecting regime shifts

In Paper V the potential risk of ecological regime shifts in the Baltic Sea food web was tested in four different nutrient load – cod fishing scenarios, i.e., BAU-F1.1, BAU- F0.3, BSAP-F1.1 and BSAP-F0.3. Differing from the Paper IV, the food web model was driven in every scenario by the output from biogeochemical model RCO-SCOBI given the A1B1 climate scenario. We defined an ecological regime shift as an abrupt and profound change in biological variables that resulted in changes in trophic control, similar to Daskalov et al. (2007). Principal Component Analysis (PCA) was run on the projections of functional group biomasses and the first principal component (PC1bio) was used as an indicator of the ecosystem state (Choi et al. 2005, Möllmann et al. 2008). To detect abrupt changes in biological variables, the Sequential Regime Shift Detection Method

13 (STARS, Rodionov 2004, Rodionov and Overland 2005) was run on the PC1bio. In addition, STARS was run on individual environmental forcing and functional group biomass time series to identify if timing of abrupt changes in any of these variables coincided with the abrupt changes in the PC1bio. To separate abrupt changes with little effect on food web function from ecological regime shifts (see our definition above) changes in food web control were analyzed. It was assumed that negative predator-prey correlation implicates of predator (or top-down) control and positive correlation prey/resource (or bottom-up) control (Frank et al. 2007). The Spearman’s non-parametric multivariate correlation was used to test correlation strength and type between environmental forcing and targeted functional groups, as well as predators and prey. To study the ecosystem properties that may affect the resilience, and thereby the buffer capacity of the CBS food web, a selection of resilience indicators, i.e., piscivore-planktivore ratio (Litzow and Ciannelli 2007), the ratio between benthic and pelagic energy flow reaching the top predator cod (Blanchard et al. 2011), and species evenness (Elmqvist et al. 2003, Folke et al. 2004), were calculated and analyzed in the context of the suggested regime shift frequency and timing. In addition, changes in trophic control dynamics preceding the regime shifts, or absence of them in some cases, were studied.

14 Results and discussion

BaltProWeb model and past food web dynamics

The new Ecopath with Ecosim model for the Central Baltic Sea (CBS; BaltProWeb) introduced in Paper I was able to reproduce the main features of CBS food web dynamics and explained 51% of the variation in observed data in 1974-2006. As earlier shown by Möllmann et al. (2008), environmental conditions and fishing were indicated to control the past food web dynamics. Accounting for fishing improved the model fit by 20% and environmental forcing, having effects on predator-prey dynamics and fish egg production, by approx. 40%. Hence, using holistic modeling methods that can incorporate multiple driver interactions is necessary when the CBS food web dynamics are described (see also Bakun 2006). This is different from some other marine systems, where bottom-up dynamics, or trophic interactions alone, can explain most ecosystem changes (e.g., Beaugrand 2004, Shannon et al. 2004, Stenseth et al. 2006, Coll et al. 2008). The BaltProWeb model was also capable of reproducing the main features of the CBS regime shift, i.e., sudden decrease in cod in the mid-1980s, as well as increase in sprat and decrease in P. acuspes in the late 1980s. Concomitantly, the energy flow from benthos to cod decreased from being > 40% of total cod energy intake in the mid- 1980s to < 10% by the end of 20th century following the increase in pelagic cod prey sprat (see also Uzars 1994). Simultaneously, the flow of P. acuspes to sprat increased and exceeded the flow to herring. The quantity of fish extracted by fishermen exceeded the total natural mortality from the late 1990s onwards, making humans the most significant higher trophic level predator of the system. For example, fisheries extracted on average approximately half of the annual production of cod, while the estimates for predation by seals were a fraction (0.1-0.5%) of this. Also, MacKenzie et al. (2011) found that seal predation is likely to have much lower impact than future changes in salinity and fishing on cod recovery. The key species analysis in Paper II indicated that cod, sprat, macrozoobenthos, P. acuspes and other mesozooplankton (mainly cladocerans) were CBS key groups in 1974-2006. Previous studies (Casini et al. 2009, Möllmann et al. 2009) confirm the central roles of cod, sprat and P. acuspes for the CBS ecosystem function. Furthermore, macrozoobenthos is an important food item for cod (Uzars 1994) and cladocerans are an important prey for clupeids, particularly in summer (Flinkman et al. 1992, Möllmann et al. 2004, Casini et al. 2009). The simplified model sensitivity and uncertainty analysis showed that the food web model is sensitive to model uncertainties in the Ecopath input data as well as environmental forcing. The model uncertainties were particularly high in the future projections. However, the good model fit in 1974-2006 and the extensive work done on data- collection and model testing increase confidence in model projections. Furthermore, the model was most sensitive to changes in cod and sprat biomass, both groups that we have reliable long-term data for. For macrozoobenthos, limited data was available in the CBS. Hence, directing future effort on parameterization of this group has potential to improve the model performance.

15 Baltic Sea food web under future climate conditions

Cod is the most valuable commercial fish of the Baltic Sea (Quaas et al. 2013). The future projections in Papers III-V clearly show that fishing is the main driver defining cod biomass. Very low cod biomasses were projected in high F scenarios

(F1.1), while low cod fishing scenarios (F0.3) resulted in cod stock recovery (Fig. 4, note: results between papers are not fully comparable due to differences in forcing data (see material and methods)). In Papers IV and V changing climate conditions had a clear negative effect on cod biomass, such that even in the F0.3 scenarios cod biomasses decreased after 2040. The model ensemble in Paper III did not show as consistent a response to climate change. However, in the F1.1 scenarios the extinction risk was slightly higher given future climate change. Combined negative effects of fishing and future climate are also described in Lindegren et al. (2010).

Fig. 4 Comparison of the future cod spawning stock biomass (SSB) projections between Paper IV and the model ensemble in Paper III under climate change conditions and a) high cod fishing (F1.1) and b) low cod fishing (F0.3). The results are presented as relative changes from the observed cod SSB in 2009. The shaded area represents the range of simulations across all models in Paper III. Due to differences in environmental forcing, and because the nutrient load effect is not tested in Paper III, the results between papers are not fully comparable. The past (1974-2008) estimates are from ICES (BAU: business as usual, BSAP: Baltic Sea Action Plan).

In Papers IV and V, the dynamics of the entire food web were studied under future climate and different nutrient load - cod fishing scenarios. While cod was mainly controlled by the fishery, phytoplankton, Acartia spp. and T. longicornis responded mainly to changes in nutrient conditions and climate, and these groups were projected to increase across scenarios. The intermediate trophic level groups, sprat, herring and P. acuspes, were clearly affected by both bottom-up and top-down forces. Hence, when describing these groups a food web approach that can accommodate for both top-down and bottom-up drivers is needed. This is particularly important as both sprat and P. acuspes are indicated as key species of the CBS food web (Paper II, Möllmann et al. 2009). Concomitantly, in addition to predation effects by cod, a clear indirect positive nutrient response was observed in the simulations of clupeid fish. Previously, published model results by Österblom et al. (2007) have indicated that the

16 onset of eutrophication around the 1950s initiated an increase in the Baltic Sea fish biomass. Ware and Thomson (2005), Frank et al. (2007) and Chassot et al. (2010) have described bottom-up control of fish stocks in several marine regions. Furthermore, Brown et al. (2010) projected climate-induced increases in phytoplankton biomass to cascade to small pelagic fish in their study of Australian marine ecosystems. Thus, even if the top-down control mechanisms have been in focus in the recent Baltic Sea studies (e.g., Casini et al. 2009), potential response to changes in bottom-up forces should not be forgotten when studying the possible futures of the sea. Large inter-scenario differences were found, indicating that the choice of fishery and nutrient load management may largely define the future of the Baltic Sea food web regardless of changing climate conditions. The worst-case (BAU-F1.1) scenario resulted in a eutrophicated and strongly sprat-dominated ecosystem, while in the best-case scenario (BSAP-F0.3) cod domination and eutrophication levels close to present were projected. Also, in both BAU scenarios the total food web biomass projected for the far future (2070-98) was twice the biomass projected in the BSAP- scenarios, or during the reference period (1974-2006), indicating the importance of bottom-up control for total system production. However, some direct effects of future climate change were not fully compensated by changes in nutrient loads or fishing. For example, positive direct effects of increasing temperatures translated in biomass increases of sprat and thermophile zooplankton species Acartia spp. and T. longicornis across nutrient load - cod fishing scenarios. Cod recruitment, on the other hand, was disfavored by climate-mediated worsening of reproduction conditions and P. acuspes by the freshening of the sea, and their biomasses decreased from 2050s onwards in every scenario.

Potential risk of regime shifts under future conditions

Hughes et al. (2013) state, “…there is no reason to expect that planetary-scale regime shifts will not continue in the future”. Correspondingly, the results of Paper V indicate that there is a risk for ecological regime shifts in the future Baltic Sea. These findings are supported by the projections of cod extinction in Paper III and the biomass projections that exceeded the past observations in Paper IV. We defined an ecological regime shift as a profound change in biological variables that results in a change in food web control mechanisms. Based on this definition, two of the seven abrupt changes detected by statistics across the nutrient load - cod fishing scenarios were suggested as ecological regime shifts. The results imply that statistical shift detection methods are not always an indication of change in ecosystem mechanisms (Thrush et al. 2009, Bestelmeyer et al. 2011). One of the regime shifts was detected in the high nutrient load and cod fishing scenario (BAU- F1.1) in 2023, and resulted in a switch from top-down controlled into a bottom-up controlled state with high sprat biomass. The other regime shift was detected in the low nutrient load and cod fishing scenario (BSAP-F0.3). In this case cod biomass was high both before and after the suggested shift, but resource limitation, i.e., bottom-up control, became more important after 2033. All in all, the near proximity of regime shifts to changes in cod fishing imply that changes in top-down control in combination to bottom-up variation may be necessary to trigger an ecological regime shift as earlier suggested by Litzow and Ciannelli (2007). Furthermore, the results

17 indicate that bottom-up forces are dominant in structuring the ecosystem in the absence of large perturbations (Frank et al. 2006, Litzow and Ciannelli 2007). Clearly more abrupt changes were suggested in the high than low nutrient load scenarios suggesting that eutrophication may make the ecosystem more variable in general. The CBS food web was projected to be more resilient to climate variation and increasing nutrient loads when stronger top-down control was present. For example, Planque et al. (2010) have suggested that intensive fishing may make marine systems more vulnerable to climate effects. However, in the low nutrient scenarios high top-down control by cod was needed for nutrient reductions to result in a regime shift. The species evenness, a resilience indicator (Elmqvist et al. 2003), decreased over time in every scenario and may eventually indicate a negative effect of climate change on the CBS food web resilience. Defining the nature of the abrupt change detected, e.g., in biological variables, is particularly important for ecosystem-based management. If a regime shift takes place, the new regime may respond differently to management measures, such as fishing, than the old regime (Folke et al. 2004). Also hysteresis effects are possible (Scheffer et al. 2001), but are not studied in this thesis due to directionality of climate change. In the case of no change in the food web function the control mechanisms should not change, but time lags in adjusting the management to the new biomass levels may result in unwished outcomes (Brown et al. 2012). In comparison to more theoretical modeling work, where often a single driver is affecting a single variable (Beisner et al. 2003), the interpretation of results in Paper V was more complex, but the setup was also closer to that found in natural systems. For example, Thrush et al. (2009) point out that ecosystem resilience is often tested using highly theoretical and simplified model setups and its validity in nature, with complex interactions, is difficult to prove.

Using models to support future ecosystem-based management

Results from Papers III-IV show that some climate effects can be compensated by regional management actions, such as the choice of nutrient loads and fishing (see also Lindegren et al. 2010). This finding supports the view that the future ecosystem management needs to re-evaluate targets and management actions taking the changing environmental conditions into account (Cheung et al. 2012). It also emphasizes the need for ecosystem models that can integrate multiple drivers into different management scenarios (Brander 2007). This thesis introduces a modeling approach that allows the study of combined direct and indirect, i.e., via species interactions, effects of future climate, nutrient loads and cod fishing on the CBS food web (Paper IV). Furthermore, Papers II and III, address the usability of this particular model, as well as models in general, in the ecosystem-based management. Paper II suggests that large model uncertainties can arise from uncertainties in the model input data and that these uncertainties can increase with projection length. In Paper III, the ensemble of higher trophic level (HTL) models resulted in a large range of future cod biomass projections. Findings from both papers contribute to the discussion of the usability of model results in ecosystem-based management (e.g., Link et al. 2009). However, in Paper III it is suggested that more focus should be directed into finding the general conclusions that all models agree on, or model extremes. For example, no model in the ensemble

18 projected cod biomass recovery in the intensive fishing scenario. This approach differs from the ensemble mean approach used in the climate studies (also in Eilola et al. (2011)) and is thought of more appropriate to describe the unpredictable response to extremes in biological systems (Verdonschot et al. 2012). In addition the increasing uncertainty with the projection length observed in Paper II, emphasizes the importance of re-evaluating the management plans based on which of the possible model projections has realized. The projections of climate and biological conditions that exceed the past observations (Paper IV) and detecting a potential risk for abrupt ecosystem change (Paper V) both indicate the possibility of unprecedented future change that the uncertainty analysis do not account for. Hence, independent of model uncertainties, flexible and adaptive management that continuously monitors and evaluates the effects of alternative management actions is needed.

Conclusions

Results of this thesis strongly support the previous findings about the Baltic Sea food web dynamics being greatly influenced by environmental and anthropogenic forcing. The future projections suggested the Central Baltic Sea food web state and dynamics to be mainly defined by the compounded effects of nutrient loads and fishing also under future climate conditions. Depending on the management scenario, the range of potential futures was large and spanned from a sprat-dominated and highly eutrophied to a cod-dominated ecosystem with eutrophication levels close to present. Some groups, however, were clearly favored (e.g., sprat and Acartia spp.), or unfavored, (e.g., cod and P. acuspes) by the future climate. Results showed a risk of abrupt changes under future climate conditions, particularly when the ecosystem was highly eutrophied. However, only few of these changes were identified as regime shifts with changes in food web control. Ensemble modeling approaches were proven important, both from the perspective of ecosystem management as well as for studying the importance of different mechanisms in the ecosystem response. Results of this thesis suggest some future research directions. The high importance of macrozoobenthos for the model function implies that including benthic-pelagic dynamics has potential to greatly improve ecosystem models in the CBS. The ensemble modeling approach showed that models with different structures differ in their future projections. To further explore causes and implications continued collaboration between ecosystem modelers is essential. Finally, the study on the risk of future regime shifts and extreme projections of future environmental conditions suggest that building threshold-like dynamics into ecosystem models may be of high importance, in particular for ecosystem-based management evaluating different management actions under future conditions.

19 Acknowledgements

There are many people who have contributed to this thesis and without many of you we wouldn’t be holding “the book” in our hands today.

My supervisors, Thorsten Blenckner and Olle Hjerne, have provided me with all support a PhD student can wish for. Thorsten, particular thank you for your dedication, interesting research opportunities and capability of making sense out of my science. Olle, thank you for your analytical eye that was often needed.

My friends, colleagues and brilliant scientists Martina, Saskia and Johanna you have brought plenty into both my private and work life. Your help was irreplaceable in finalizing this thesis. I also wish to thank my colleagues at the Baltic Nest Institute, Stockholm Resilience Centre and ECOSUPPORT-project for an inspiring working environment.

My family I wish to thank for telling, and showing, me that anything is possible if one puts one’s mind into something. My friends, near and far, for reminding me about what really matters in life (like climbing!).

Finally, thank you Jens for making the past half a year about so much more than this thesis.

My PhD study is part of ECOSUPPORT (Advanced modelling tool for scenarios of the Baltic Sea ECOsystem to SUPPORT decision making) project and has received funding from the European Community’s Seventh Framework Programme (FP/2007- 2013) under grant agreement no. 217246 made with BONUS, the joint Baltic Sea research and development programme. Funding was also provided by the Baltic Nest Institute.

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