DEPARTMENT OF BIOLOGICAL AND
ENVIRONMENTAL SCIENCES
Sustainably farmed rainbow trout in the
Baltic Sea Region
A sustainable mariculture index calculation
Ginnette Flores Carmenate
Degree project for Master of Science (120 hec) with a major in Environmental Science
2016, 120 HEC
Second Cycle
Table of content
Abstract ................................................................................................................................ ii
Sammanfattning ................................................................................................................... iii
1. Introduction....................................................................................................................... 1
1.1 Problem formulation .................................................................................................... 1
1.2 Aim ............................................................................................................................. 6
1.3 Research questions....................................................................................................... 6
2. Methods ............................................................................................................................ 6
2.1 Study area .................................................................................................................... 6
2.2 Ocean Health Index ..................................................................................................... 7
2.3 Baltic Health Index .................................................................................................... 12
2.3.1 Mariculture sub-goal index calculation .................................................................... 12
3. Results ............................................................................................................................ 20
3.1 Estimating the mariculture per capita production for Sweden, Denmark and Finland.. 20
3.2 Calculating mariculture status for the BSR based on a single reference point from 2005
to 2014. ........................................................................................................................... 22
3.2.1 Mariculture current status and trend estimations for the BSR based on a single
reference point. ................................................................................................................ 24
3.3 Calculating mariculture status for the BSR based on two reference points from 2005 to
2014. ............................................................................................................................... 25
3.3.1 Mariculture current status and trend estimations for the BSR based on two reference
points............................................................................................................................... 28
4. Discussion ....................................................................................................................... 29
4.1 Mariculture status and trend analysis.......................................................................... 29
4.2 Mariculture subgoal: its improvement suggestions ..................................................... 31
4.3 Considerations on the Ocean Health Index integral assessment tool ........................... 32
5. Conclusions ..................................................................................................................... 33
Acknowledgements ............................................................................................................. 34
References .......................................................................................................................... 34
Appendix A......................................................................................................................... 39
i
Abstract
In the light of the increased demand on overexploited or depleted fish stocks worldwide
aquaculture has become a highly productive sector supplying almost half of all the fish
human-consumed worldwide. In Europe, mariculture activities (i.e. aquaculture exploitations
in marine and brackish environments) play an important role in the economic scenario,
thus preserving the healthy state and functionality of marine ecosystems is more important
today than ever. In the Baltic Sea the aquaculture production is stagnated. Among the causes
are considerable administrative burdens on the licensing procedures, an undeveloped marine
spatial planning for aquaculture practices and the associated environmental impacts that may
aggravate its current eutrophication conditions. The Ocean Health Index (OHI) is an
innovative tool created to integrate social, economic, biological and physical features of
current marine ecosystems through the evaluation of ten public goals, and by doing so tends
to assess the health status of oceans, seas and brackish waters. Based on the OHI
methodology the present work describes the current and near future status of the mariculture
subgoal for the Baltic Sea. The study only included rainbow trout species because it is the
most farmed species in the Baltic region, and exclusively concerned Sweden, Denmark and
Finland production figures from 2005 to 2014 as they were the main producers during that
period. Results portrayed a mariculture sector situation that does not seem to attain desirable
levels of sustainability when it comes to marine-farmed rainbow trout production.
Nevertheless, based on the mariculture index calculations presented here, it might be to some
extent misleading to confidently state how sustainable the mariculture activities in the Baltic
region are or even will be in the near-term future. The estimations contain a bias in favour of
the per capita production of countries. Therefore, this formulation might not be the most
accurate approach when determining if the Baltic region is (or is not) sustainably managing
its resources to pursuit the mariculture sub-goal.
ii
Sammanfattning
I takt med att världshavens fiskebestånd blir alltmer utarmade samtidigt som efterfrågan på
fisk och skaldjur ökar har vattenbruk blivit en växande sektor. I dag står odlingar av fisk och
skaldjur för nästan hälften av världskonsumtionen. Det europeiska vattenbruket är viktigt ur
ett ekonomiskt perspektiv och kraven har ökat på att de marina ekosystemen ska nyttjas på ett
långsiktigt hållbart sätt. I Östersjön har utvecklingen av vattenbruket stagnerat de senaste
åren. Bland orsakerna kan nämnas en omfattande byråkrati kring tillståndsansökningarna, att
den marina spatiala planeringen inte är anpassad för vattenbrukssektorn och att
näringsbelastningen från vattenbruket riskerar att ytterligare öka på övergödningen. Ocean
Health Index (OHI) är ett integrerat bedömningsverktyg för att utvärdera de sammanlagda
effekterna av sociala, ekonomiska, biologiska och fysiska faktorer som påverkar de marina
ekosystemen genom utvärdering av 10 delmål för havets hälsostatus. Baserat på metoden i
OHI beskriver detta master-arbete den nuvarande och framtida statusen för delmålet
vattenbruk för Östersjön. Studien är inriktad på laxfisken regnbågsforell eftersom det är den
mest odlade arten i regionen. Resultaten tyder på en vattenbruks-sektor som i viss mån
misslyckas
att
upprätthålla
en
långsiktigt
hållbar
produktion.
Dock
innehåller
utvärderingsmetoden en bias till förmån för per capita produktion per land, och är därför
förmodligen
inte
den
mest
lämpliga
metoden
för
att
beskriva
hållbarheten
i
vattenbrukssektorn i Östersjön.
iii
1. Introduction
According to FAO (Food and Agriculture Organization of the United Nations) latest
estimates, aquaculture is probably the fastest growing food-producing sector and now
accounts for nearly 50 percent of fish consumption worldwide (FAO, 2014). The steadily
increasing demand on overexploited or depleted fish stocks worldwide has paved the way for
a more productive aquaculture sector (Figure A1 in the Appendix).
Aquaculture generally includes finfish, shellfish and seaweeds raised in freshwater, marine or
brackish environments. In Europe, several regulation instruments relevant for aquaculture
practices aim to monitor the state of aquatic ecosystems and pursuit ecological, social and
economic sustainability. With nearly 50 percent of the production made up of molluscs and
crustaceans, 27 percent of marine finfish and the last 23 percent coming from freshwater
finfish, the marine aquaculture, also called mariculture, plays an important role in the EU
economic scenario (Graph A1; Science for Environment Policy, 2015). Marine resources
must be managed sustainably in order to guarantee food security not only for current but also
future world population. If aquaculture production will continue being part of the solution to
the increasing fish demand in Europe and worldwide, preserving the healthy state and
functionality of marine ecosystems is more important today than ever.
Sustainable aquaculture is definitely not an easy outcome to achieve. It requires relative large
capital investments and also generates a number of negative environmental impacts which all
challenge the innovation of environmental friendly as well as profitable technologies.
Nevertheless, the many expectations placed on the production sector as a reliable source of
food, livelihoods, income and seeds for restocking of endangered and overexploited natural
populations have positioned sustainable aquaculture as an important piece of socio-political
and economic debates (Figure A2).
1.1 Problem formulation
Even though the fishery resources are declining and the demand for seafood continues
increasing, in the Baltic Sea Region (BSR) the aquaculture production does not appear to
follow the same global production trend (AQUABEST, 2012b). Aquaculture production in
the Baltic Sea is currently stagnated. However, experts claim that the region possesses a great
1
potential to shift this situation and boost the production in a sustainable and efficient way
(AQUABEST, 2012a.).
A study conducted to assess stakeholder attitudes towards aquaculture development in the
BSR clarified some of their concerns in this regard. It concluded that the aquaculture
expansion is strongly harmed by constraints such as less supportive subsidy policy when
compared to the agriculture sector, absence of political support at national level, undeveloped
marine spatial planning for aquaculture activities, too short permits periods along with
laborious and
highly time-consuming permits application (AQUABEST,
2012c).
Environmental legal regulations have in fact been identified as major obstacles to aquaculture
development not only within the BSR but also within the European Union (European
Commission, 2013).
The legal framework affecting aquaculture practices in the Baltic countries is mainly defined
at national level (AQUABEST, 2013a). Nevertheless, there are several key European legal
instruments that influence the aquaculture performance in these countries by regulating the
management of the natural resources involved in the production activity and the
environmental impacts associated to it (AQUABEST, 2013a). Applicable European
legislation on this matter includes the Marine Strategy Framework Directive, the Water
Framework Directive, Birds and Habitats Directives (in combination with Natura 2000),
Regulation on the use of alien and locally absent species in aquaculture, Regulation on the
prevention and management of the introduction and spread of invasive alien species,
Environmental Impact Assessment, Strategic Environmental Assessment and the Directive on
Maritime Spatial Planning and the Common Fisheries Policy (Science for Environment
Policy, 2015). Besides these legal mechanisms, there are a few regional plans or guidelines
that directly relate to the Baltic Sea Region in terms of the correct management of its natural
resources and anthropogenic impacts that the region is currently facing, namely, a Sustainable
Blue Growth Agenda for the Baltic Sea Region (2014), the European Union Strategy for the
Baltic Sea Region (2009) or the Baltic Sea Action Plan by HELCOM (2007).
Aquaculture as a way of farming aquatic species at really large scales is a relatively new
industry in most countries (FAO, 1987). Yet, many negative environmental impacts
associated to this type of seafood production are well known nowadays and, as a result, it is
portrayed as a highly problematic mean for food production (CCB, 2014). In the BSR the
current major environmental concerns linked to aquaculture are the discharge of nutrients into
2
a system that is already dealing with eutrophication problems, feeding with fishmeal and fish
oil which increases the pressure on the wild fish stocks, the release of chemicals and the
spreading of diseases and alien species to the wild ecosystem (CCB, 2014).
Aquaculture applications in the BSR include food production, restocking and water quality
improvement by mussel farming (AQUABEST, 2014a). Some of the aquaculture
technologies that are being used to date include mussel farms in marine coastal waters and
fish farms in open cage systems in marine and brackish coastal waters; open cage systems in
fresh waters (rivers and reservoirs) are mainly exploited in Sweden and Finland
(AQUABEST, 2014a). On the other hand, fish farms in land-based systems operate through
ponds with natural food production, raceways, tanks, pond systems and recirculating
aquaculture systems (RAS) (Table A1; AQUABEST, 2014a). Due to meteorological
conditions (sea ice, strong winds and wave action) offshore marine aquaculture in the BSR is
currently a challenge that has impeded the production in the open sea areas (AQUABEST,
2013b)
Inevitably, the Baltic Sea environmental issues call for a sustainable and responsible
aquaculture production that must be both profitable and respectful of the ecological system
that is at stake (AQUABEST, 2012b; FAO, 2010). Innovative approaches are presented as a
way to deal with the adverse effects of conventional techniques in the production of
aquacultured species. One example is the so-called integrated multi-trophic aquaculture
(IMTA) technology which combines fed species (e.g. finfish) with organic and inorganic
extractive species (e.g. shellfish and seaweed respectively) (AQUAFIMA, 2014). In this
manner the organic waste from the finfish is assimilated by the algae and shellfish, thus
potentially reducing the nutrient loading to the Baltic Sea (Figure A3; AQUAFIMA, 2014).
Land-based RAS technology is also another example of successfully reducing environmental
impacts from aquaculture activities in the BSR by treating the water used before it is either
recycled or discharged (AQUABEST, 2014b). However, the technique is not yet feasible to
implement in brackish or marine environments (AQUABEST, 2014b). Finding the most
suitable strategies of assessing the current expansion potential of the Baltic Sea aquaculture
constitutes a fresh discussion involving all the Baltic Sea countries and has already borne
some fruit. Four main areas that demand special attention have been identified (AQUABEST,
2014b). In brief, a first block includes reducing the administrative burden affecting the
licensing procedures without compromising the environmental standards and promoting the
3
competiveness of the sector. This would be achieved by implementing the same institutional
framework conditions as for the rest of the production sectors in market in terms of public
subsides, transparency and societal acceptance (AQUABEST, 2014b). A second block of
suggestions claims for a better spatial planning management that allows for identifying the
best suitable locations for aquaculture exploitations with regard to environment carrying
capacity, good conditions for operations and potential environmental impacts. It would also
aim to minimize conflicts by including stakeholder and incorporate spatial planning
information into the licensing procedures (AQUABEST, 2014b). The third block address the
development and incentive of local sources to produce fish meal employing not only fish or
mussels but also plants and microbial proteins in order to create a more nutrient-balanced
aquaculture in the BSR (AQUABEST, 2014b). However, this is not easy to achieve since
highly protein demanding species (e.g. salmon, trout) would require nutrient supplements that
increases the cost considerably (EUCC, 2014). Ole Torrissen, who is principal research
scientist at Institute of Marine Research in Norway, comments: “In my eyes you should put it
on a company-level. The ones releasing nutrients also should be responsible for getting out e.
g. nitrogen and phosphor. This is the only possibility to deal with this, because you cannot
transfer this responsibility to others. It is a political question to get something like this on the
way”. Finally, a fourth block of measures is directed to the development and implementation
of feasible advanced recirculation technologies. Nowadays, the Danish model “trout farm
system” (an innovative recirculation farming concept) is commercially succeeding by
removing up to 50% of the nitrogen content in the RAS waste water treatment devices and at
the same time increasing the fish production (AQUABEST, 2014c). Experimental studies
have shown that further nitrogen reductions can be potentially achieved along with some
other environmental benefits like biogas generation from surplus RAS sludge (AQUABEST,
2014c). In sum, for advanced RAS technologies to expand and be viable, facilities must
operate at large scales with really competitive production prices (AQUABEST, 2014b).
Therefore, comprehensive feasibility studies of RAS technologies should be conducted and
their capital investment boosted (AQUABEST, 2014b).
It would have been helpful to support the facts here presented with a definition of what is
meant by sustainable aquaculture. Actually, there is not a general accepted definition of
sustainable aquaculture; the task is complicated as the industry is diverse regarding farmed
species, technologies used and impacts generated (Seas at Risk, 2015). Moreover, “a
sustainable aquaculture should be (also) environmentally acceptable, economically viable,
4
and socially equitable” (Simard, 2011), the same three fundamental pillars that underpin
sustainable development. On one hand, the environmental acceptability aspect requires the
formulation of the question “acceptable by whom?”(Simard, 2011). To gain understanding of
what could be environmentally acceptable the functions and services of the ecosystem that
receives the activity have to be comprehended in detail as much as possible (Simard, 2011).
On the other hand, the economical viability and the social equity would more directly depend
on the economic development and the societal elements that describe the society in question
(Simard, 2011).
Because of the complex interactions among all the principles previously introduced
defining sustainable aquaculture certainly does not seem as a straightforward exercise. Even
the Aquaculture Stewardship Council prefers the term “responsible aquaculture” (ASC,
2013). Despite of not being possible to walk through a robust definition, at least some
approaches could be somehow successfully implemented to attain some desired status of
sustainability. The Ecosystem Approach to Aquaculture could be an example of it (FAO,
2010): “An ecosystem approach to aquaculture is a strategy for the integration of the activity
within the wider ecosystem such that it promotes sustainable development, equity and
resilience of interlinked social-ecological systems.”(FAO, 2010)
Nevertheless, in order to suggest a more concrete vision of what sustainable aquaculture
could imply, the following accomplishment is a good attempt to conceptualize and gather all
the significant processes involved.
“The sustainability of aquaculture is decided by how the inputs (e.g. kind of species, type of
fodder, density of species, production type) generate outputs (emissions and social and
economic impacts). The emissions impact the surrounding ecosystem depending on the
carrying capacity of the (water) ecosystem (e.g. water circulation, the size and depth of the
water course, level of nutrients in the water) and the receptivity of the species in the
ecosystem. The social and economical impacts can be both positive in form of income but
also negative in form of conflicts between different stakeholders” (Berggren, 2007).
The present Master thesis is based on a regional assessment that evaluates the health of the
Baltic Sea. The assessment is being conducted by applying the method of the Ocean Health
Index by Halpern et al. (2012a), which encompasses ten public goals that measure the ability
of oceans to deliver ecosystem goods and services valued by people. One of those indicators
5
is food provision and since the index only focuses on marine and brackish environments the
food provision goal is then divided into two subgoals: wild-caught fisheries and mariculture.
1.2 Aim
With the problem formulation in mind, the overall aim of the study was to assess the
mariculture performance of rainbow trout in the BSR, following the same procedures and
criteria as for the Ocean Health Index framework.
1.3 Research questions
i.
How sustainable is current and near-future mariculture performance in the BSR based
on its own potential?
ii.
Is the mariculture subgoal indicator an effective mean of measuring the sustainable
harvest of seafood in the BSR?
2. Methods
2.1 Study area
The Baltic Sea constitutes a semi-enclosed shallow sea surrounded by nine countries, namely,
Sweden, Finland, Russian Federation, Estonia, Latvia, Lithuania, Poland, Germany, and
Denmark (Helcom, 2010). The sea is one of the largest brackish water bodies in the world,
comprising a surface area of 415 000 km2 (BalticSTERN secretariat, 2013a). It extends from
the Bothnian Bay (northern end) to the Danish Sounds through which saline and oxygen-rich
water inflows from the North Sea and sporadically aerates the deep layers of the Baltic Sea
(Figure A4; BalticSTERN secretariat, 2013a; Helcom, 2010).
Salinity, temperature and oxygen content are fundamental physical parameters for marine
biodiversity and water quality in an almost entirely closed basin such as the Baltic Sea
(HELCOM, 2007; Håkanson et al. 2003). Salinity concentration, for example, especially
impacts on number of species, their reproduction and growth as well as on the sedimentation
of particles (Håkanson et al. 2010). The water exchange in this region is rather small which
means that it takes between 25 and 40 years for water masses to be renewed, hence
facilitating a poor dilution of pollutants (Håkanson et al. 2003; BalticSTERN secretariat,
2013b). The contribution of freshwater inflows (coming from rivers, land runoff and
precipitations) and salt water inputs through the Danish Sounds lead to a strong stratification
6
of the water column produced by a great difference in salt content (Helcom, 2010). This
gradient is called the halocline and restrains the transport of oxygen from the upper brackish
water segment to the saltier bottom water layers (Andersen et al. 2015). A permanent and
deeper halocline is especially noticeable in the central part of the Baltic Sea (Baltic Proper)
(Andersen et al. 2015). Figure A5 shows how the saline concentration varies both
horizontally and vertically in the system.
Owing to its geographical, climatological and oceanographic features the Baltic Sea is highly
vulnerable to the environmental impacts resulting from anthropogenic activities at the sea and
in the catchment area (which is currently sustaining 85 million people) (BalticSTERN
secretariat, 2013b; Helcom, 2010). It is a relatively young and still shifting marine ecosystem
consisting not of a uniform mass of water but a succession of sub-basins that gradually vary
their physic-chemical and biological attributes (Andersen et al. 2015; Helcom, 2010).
Consequently, those aspects will influence each sub-basin response threshold to different
anthropogenic pressures on the marine ecosystem such as discharges of nutrients and
hazardous substances, overfishing, oil spills, invasive species and climate-change-related
impacts, among others (BalticSTERN secretariat, 2013a; Helcom, 2010). Furthermore,
environmental changes such as oxygen deficiency, a decrease in salinity and water
temperature increases might also increase the vulnerability of the system to all the stresses
referred above (Helcom, 2010; Störmer, 2011; Niemelä et al. 2015).
2.2 Ocean Health Index
Measurable targets and indicators are essential to monitor the performance and development
of a given system as a whole or of the components that such system encompasses (e.g.
forests, lakes, rivers, terrestrial and marine biodiversity etc.). The Ocean Health Index (OHI)
was created with the aim of assessing the state of marine ecosystems by integrating
ecological and socioeconomic aspects specifically relevant for this environment (Halpern et
al. 2012a). It constitutes an innovative tool of integral assessment that combines biological,
physical, economic and social dimensions of the marine ecosystem and quantifies the overall
status of the ecosystem (Lowndes et al. 2014).
7
2.2.1 Conceptual tools
According to the OHI philosophy, a healthy ocean is one that can sustainably deliver a range
of benefits to people both now and in the future (Halpern et al. 2012a). However, to date,
there is no robust and accepted definition on what a healthy ocean should imply (Samhouri et
al. 2012). Discrepancies arise from applying (or not) human community values in relation to
the structural and functional integrity of the marine ecosystem.
The OHI tool is based on 10 broadly held public goals that are meant to capture the benefits
and services human societies expect of healthy ocean ecosystems at any scale (Lowndes et al.
2015), namely, food provision, artisanal fishing opportunities, natural products, carbon
storage, coastal protection, coastal livelihood, tourism and recreation, sense of place, clean
waters and biodiversity (Halpern et al. 2012a).
The goal indices are calculated at the scale of the assessment unit called region within the
OHI framework. Afterwards, the overall index value is estimated as a linear weighted sum of
the ten public goal scores for that region (or country) and it ranges from 0 to 100 (Elfes et al.
2014; Lowndes et al. 2014 ):
where αi is the goal-specific weight and Ii is the average of present and likely future status for
each goal i (Halpern et al. 2012a).
The present Status is calculated by comparing the current value of the goal with its
sustainable reference point while the likely future status is based on trend, cumulative
pressures and resilience measures (Halpern et al. 2012b). The picture below shows the
contributions and descriptions of the dimensions that form one goal score (Ocean Health
Index 2015, methodology).
8
pressures
trend
status will rise if resilience exceeds pressures
status will fail if pressures exceeds resilience
resilience
future
status
5 years
ago
(50 % of goal score)
goal’s present value
(represented by the
most recent data
available) compared to
a goal-specific
reference point.
(50 % of goal score)
Trend (67%)
average percentage
change in Status
shown by the most
recent five years of
data.
Pressure (16.5%)
sum of the
ecological and
social pressures
likely to depress
near-Future scores
for a goal.
Resilience (16.5%)
Sum of ecological
factors (if any) and social
initiatives (policies, laws,
etc.) enacted that can
reduce pressures and
therefore increase nearFuture scores for a goal.
Figure 1. OHI framework dimensions: present status, likely future status, trend, pressures and
resilience. Figure taken from http://www.oceanhealthindex.org/methodology.
Each reference point is goal-specific and it is measured by using one of the following
approaches: functional relationship, time series approach and spatial reference points
(Samhouri et al. 2012). The table below shows the type of reference point used in the current
Status calculation of each socio-ecological objective and their sub-goals components.
9
Table 1. List of the ten goals that compose the OHI framework and the respective reference points
used. Sub-goal components of a single goal are combined in a weighted average (Halpern et al.
2012b; Lowndes et al. 2014) (*e.g. % of marine waters that should be qualified as protected areas or
to have all species at a risk status of Least Concern).
Goals and their description
Sub-goal:
component
Reference point
type
Food Provision measures the amount of seafood sustainably Fisheries
harvested for human consumption.
Mariculture
Artisanal Fishing Opportunities measure whether or not local
communities who need to fish in coastal waters have the
opportunity to do so.
Functional
relationship
Spatial comparison
Functional
relationship
Natural Products measure how sustainably people harvest non- Ornamental fish,
food products from the oceans.
shells, seaweeds &
plants, sponges,
corals
Fish oil
Temporal comparison
Carbon Storage measures the carbon stored and sequestration
rates in coastal habitats: mangroves, seagrasses and salt
marshes.
Functional
relationship
Temporal comparison
Coastal Protection measures the degree of protection provided
by marine and coastal habitats against storm waves and
flooding.
Temporal comparison
Coastal Livelihoods & Economies measure the direct benefits Livelihoods: jobs
the society obtains from marine-related industries (i.e. jobs and
revenue) as well as indirect benefits people gain from Livelihoods: wages
community identity, tax revenue and other indirect economic Economies: revenue
and social impacts of a stable coastal economy.
Temporal comparison
Tourism & Recreation measure the proportion of the total labor
force engaged in the coastal tourism and travel sector,
factoring in unemployment and sustainability.
Spatial comparison
Sense of Place measures the state of iconic species and the Iconic Species
percentage of protected coastline as intangible resources Lasting Special
provided by the oceans.
Places
Known target*
Established target*
Clean Waters measure the contamination of waters by
nutrients, chemicals, pathogens and floating trash.
Known target*
Biodiversity measures the condition of species and key habitats
that support richness and marine diversity.
10
Habitats
Species
Spatial comparison
Temporal comparison
Temporal comparison
Known target*
In functional relationships a target is set based on the connection between the indicator of
ocean conditions for a goal and natural or human pressures (e.g. the maximum sustainable
yield associated to an intermediate level of fishing pressure) (Samhouri et al. 2012). In case a
functional relationship is unavailable temporal or spatial comparisons are the main ways to
determine a reference point. In the time series approach the current ocean conditions at a
particular location is compared to the conditions in that location at a previous time period
(e.g. historical Caribbean coral extent can be used to evaluate their current conditions)
(Samhouri et al. 2012). Spatial comparison approach describes the current ocean conditions
relative to a reference location. It uses the location with the maximum observed benefit to
measure the status of that benefit in other locations (e.g. country with the highest amount of
sustainably seafood production per capita) (Lowndes et al. 2014).
Global assessments of the index are based on the coastal Exclusive Economic Zones (EEZ) of
221 nations and territories (the last assessment included 15 High Seas and Antarctica)
(Lowndes et al. 2015). EEZ were defined at the Third United Nations Conference on the Law
of the Sea (1982), whereby a coastal State assumes jurisdiction over the exploration and
exploitation of marine resources in its adjacent section of the continental shelf, taken to be a
band extending 200 miles from the shore (OECD, 2003).
Several assessments of the index have also been undertaken at national and sub-national
scales up to date (Lowndes et al. 2015). Within the OHI context they are called regional
assessments (studies conducted at smaller spatial scales than the global ones) (Lowndes et al.
2014). The adaptability of the OHI systematic framework has made possible to use the same
global model assessment not only at different spatial scales but also in different study
conditions, e.g. developing and developed countries, information-limited and -rich areas
(Lowndes et al. 2015). Nevertheless, when conducting these types of analyses, substitution of
the global-based information for more refined data will be favoured as much as possible
(Elfes et al. 2014).
To sum up, the index addresses connections between human societies and ocean ecosystems,
it provides a single measure after combining the interactions between the different goals and
the reference points used in the scoring procedure are explicit and easy to implement
(Lowndes et al. 2014).
11
2.3 Baltic Health Index
The Baltic Health Index (BHI) implements the OHI tools already presented to assess the
overall status of the Baltic Sea. However, this thesis will only cover the methodology
foundations of the Status and Trend dimensions for mariculture sub-goal (one of the two subgoals of the food provision index).
2.3.1 Mariculture sub-goal index calculation
The mariculture sub-goal indicator measures the capacity of a certain region to attain a
maximum seafood yield out of farm-raised facilities without compromising the ocean ability
to continue providing fish for human consumption in the future (Lowndes et al. 2014). The
Status of mariculture sub-goal (XMAR) is defined as production of exclusively marine species
from marine and brackish waters and it ranges from 0 to 100 (after it is scaled) (aquatic plants
are excluded because they do not greatly contribute as a food source) (Halpern et al. 2012b).
Mariculture present Status (XMAR)
Mariculture status is computed as the current sustainably-harvested yield (YC) within the
region relative to its reference point:
XMAR =
where the reference point (Yr) is: Yr = P95_region (Max{
}) with all regions above that value
scoring 100 for the mariculture status;
and
YC =
,
where Yk is the 4-year moving window average of mariculture production for all k fish and
invertebrate species that are currently cultured within the region, SM,k is the sustainability
score for each k marine species and PC is the coastal population within 25 km of coastline for
a fixed year (Kleisner et al. 2013). Here, the coastal population density is intended to be a
proxy for the mariculture potential development of the region given that coastal communities
12
are the logistic support behind the mariculture production through the labor force engaged in
the sector, the needed infrastructures and the local demand of seafood (Kleisner et al. 2013).
Trend (t)
Trend calculations measure the change in the Status (slope) over the most recent five years
(2010, 2011, 2012, 2013, and 2014). The annual rate of change (i.e. slope) is then multiplied
by 5 in order to get an approximation of the mariculture Status performance in the near-term
future (i.e. 5 years). Because Status values range from 0 to 1, Trend naturally ranges from -1
to 1 and any value oddly falling outside this range is constrained to these range end points
(Halpern et al. 2012b).
Mariculture production (Yk)
Several premises tailored the mariculture Status and Trend estimations for the entire BSR.
Firstly, only Germany, Denmark Finland, Sweden and Estonia produce seafood from
mariculture activities. However, not all of them harvest with the same intensity (see Graphs
A2, A3, A4, A5, A6) and given that trend calculations cover the most recent five years of
data Estonia and Germany figures were excluded from the assessment; their comparatively
small fraction of tonnes harvested per year was not significant for the study. Consequently,
the BSR mariculture sub-goal will exclusively concern Denmark, Finland and Sweden
performances. Secondly, although the OHI framework does not specifically indicate that
marine-farmed species with very low production levels should be excluded from the
assessment, the Baltic Health Index Work Team understood that species other than rainbow
trout (Oncorhynchus mykiss) did not contribute significantly to the total mariculture
production in those countries (and in the BSR in general) (see Graphs A2, A3, A4, A5, A6),
therefore, the amount of species combined in the SM,k coefficient estimation was reduced to
one.
13
Statistics on rainbow trout harvested tonnes for consumption were extracted from annually
published reports or online query services from national statistic websites from 2005 to 2014
(Denmark:
http://agrifish.dk/fisheries/fishery-statistics/aquaculture-statistics/#c32851
Finland:
http://statdb.luke.fi/PXWeb/pxweb/fi/LUKE/LUKE__06%20Kala%20ja%20riista__02%20R
akenne%20ja%20tuotanto__10%20Vesiviljely/?tablelist=true&rxid=5211d344-451e-490d8651-adb38df626e1
and
Sweden:
http://www.scb.se/sv_/Hitta-statistik/Statistik-efter-
amne/Jord--och-skogsbruk-fiske/Vattenbruk/Vattenbruk/#_).
The most accurate data for computing mariculture trend and current status would have
included number of farms, their location and annual rainbow trout yields of each mariculture
facility that is operating in Denmark, Finland and Sweden coastlines but unfortunately this
information is not publicly available. However, it was possible to estimate mariculture
regional production based on the available information of each country.
For this purpose, the Baltic Health Index Work Team divided the Baltic Sea into 42 coded
regions (hereafter BHI regions) resulting from the intersection between each country EEZ
and HELCOM sub-basins (the HELCOM 17 assessment units of the Baltic Sea). The
countries, in turn, are divided according to the European Nomenclature of Territorial Units
for Statistics (NUTS3) (Figure 2). Based on the available statistical data, this spatial
visualization made it possible to locally monitor and manage cultured rainbow trout
production numbers at national and subnational levels throughout Sweden, Finland and
Denmark coastlines. Rainbow trout production was from national reporting units to BHI
regions by manually identifying all BHI regions associated with a national reporting unit (the
total production of each national reporting unit was divided by the respective number of BHI
regions). The area of national reports varied among countries. Tables 2, 3 and 4 show how
the countries regionally disaggregated their marine-farmed rainbow trout production figures
and also the corresponding BHI regions.
Mariculture Status and Trend calculations for the BSR were carried out for each of the BHI
regions relevant for Finland, Denmark and Sweden (22 BHI regions in total).
14
Table 2. Marine water codes relevant to each coastline subdivision that Sweden employs to submit
annual mariculture production of rainbow trout (see Figure 2) (*for the last 10 years, the only rainbow
trout farm facility unsteadily operating in Uppsala might be a land-based one, therefore the county
was conveniently moved to the North-Eastern segment: information kindly provided by The Swedish
Board of Agriculture).
Coastline
North-Eastern coastline
South-Eastern coastline
South-Western coastline
Counties
BHI regions
Gävleborg, Västernorrland,
Västerbotten and Norrbotten
Stockholm, Uppsala*,
Södermanland, Östergötland and
Kalmar
Blekinge, Skåne, Halland and
Västra Götaland
41, 39, 37
35, 29, 26
14, 11, 5, 1
Table 3. BHI codes relevant to those Danish regions that have produced rainbow trout by the
mariculture industry from 2005 to 2014 (see Figure 2).
Regions
Hovedstaden
BHI regions
2, 6, 12, 15
Midtjylland
2, 3
Sjælland
3, 7, 9, 12
Syddanmark
3
Table 4. BHI codes relevant to those Finnish regions that have produced rainbow trout by the
mariculture industry from 2005 to 2014 (see Figure 2).
Regions
Kaakkois-Suomi
BHI regions
32
Uusimaa
32
Åland
36
Varsinais-Suomi
36, 38
Pohjanmaa
38, 40, 42
Kainnuu
42
15
Figure 2. NUTS3 subdivisions of the Baltic countries and boundaries of the 42 coded regions of the
Baltic Sea (map prepared and kindly provided by the Baltic Health Index Work Team).
16
Sustainability coefficient (SM,k )
The sustainability coefficient (SM,k) is a country- and species- specific indicator based on the
Mariculture Sustainability Index (MSI) of Trujillo (2008). The index ranges from 0 to 10 by
combining thirteen sub-indices covering ecological and socioeconomic aspects that relate to
the impacts of mariculture activities on those systems (Table 5). The integrated assessment
incorporated 86 species and 64 major countries over a period of 10 years (1994-2003) and
generated a matrix revealing production-weighted socio-ecological scores for each countryspecies combination (Trujillo, 2008).
Under the OHI framework SM,k coefficient combines a subset of the thirteen-list that aims to
better account for long-term renewability of the mariculture performance in each assessed
region, namely, waste treatment, fishmeal and larvae and seed provenance. Table 6 shows the
scoring scheme and rationale that was followed to quantify these attributes (Kleisner et al.
2013).
Table 5. Type of data used to produce the ecological and social indicators within the MSI assessment
(Trujillo, 2008).
Indicator
Native or introduced
Fishmeal use
Description
Two way response (native or none native).
-Use or non-use; and if use, how much of it.
-Farm diet information.
-Industrial feed composition information.
Stocking density
-Stocking capacity.
Better practice protocols, and maximum production carrying
capacity.
Seed and larvae origin
Habitat impacts
Origin, provider, hatchery implementation.
Direct and indirect effects on the surrounding environments;
biodiversity change biomass changes, eutrophication, etc.
Waste treatment
Use of filter and waste disposal systems, re-use and recycling
systems.
Destination market and secondary markets.
Usage and quantities.
Use of GMOs, (farmed species and/or feeds.
Implemented or not; also: which code and standards.
Market and product control and monitoring.
Equity, fair trade, number of employees per farm.
Protein content.
Product destination
Chemical and drug use
Genetic manipulation
Code of practice
Traceability
Employment
Nutrition, protein ratio
17
Table 6. Scoring scheme detailing the rationale used for the three attributes included in the SM,k
coefficient estimation (Trujillo, 2008).
Ecologic-wise criteria
Waste water treatment
Fish meal, oil use
Larvae and seed provenance
(hatchery vs wild)
Scoring basis
1= high discharges with no waste treatment whatsoever; 3= high discharges with
some waste treatment; 5= moderate treatment operating at carrying capacity; 7=
adequate treatment or none needed with minor seasonal variations; 10=complete
isolation of waste discharge and more than adequate treatment implementation,
or no treatment needed.
1= usage; 3= relatively less usage; 5= usage and non fish based diet substitute
used; 7= almost no usage; 10= none.
1= indiscriminate wild capture with depleting consequences; 3= indiscriminate
wild capture when population is stressed; 5= unknown origin but hatchery
production and/or larvae importation exist; 7= mostly hatchery stocking with
somewhat unknown seed/larvae provenance; 10= predominantly hatchery
stocking with adequate wild broodstock provenance.
Unfortunately in this case it was not possible to a assign a unique SM,k coefficient to each
country (only for Sweden). According to Trujillo (2008), country and species combinations
covered by the MSI assessment represent over 95% of global mariculture industry because
the study did not include species with very low production level. That condition can explain,
for example, why rainbow trout-Germany or -Estonia combinations were not assessed by the
MSI, however, neither was it for Finland and Denmark even when both countries are
harvesting marine-farmed rainbow trout species at higher intensities than Sweden since the
late 1970s (FAO FishStatJ, 2016).
It was not a purpose of the present paper to analyze in depth the reasons behind this absence
of information, Swedish rainbow trout sustainability scores from waste treatment, fishmeal
and hatchery vs wild indicators were averaged and applied to Denmark and Finland after
assuming similar conditions for production for all of them (SM,k = 5,3; rescaled to 0,53 by the
OHI code).
18
Coastal population (PC)
The 25km strip of coastal population was calculated for all the 22 BHI regions for the year
2005 based on a 10km2 gridded population density map produced by the Baltic Health Index
Team Work, see Graph A7. Population raw figures were collected from The Netherlands
Environmental
Assessment
Agency:
http://themasites.pbl.nl/tridion/en/themasites/hyde/download/index-2.html.
Reference point (Yr)
As already mentioned the reference point is the region which is at the 95th percentile of
production across all BHI regions with all regions above that value scoring 100 for the
mariculture Status. Two approaches were used in order to incorporate geographical
differences into the assessment.
One method evaluates the Baltic Sea area as a whole and calculates a single reference point
for the entire Baltic region. The other one divides the Baltic Sea into two large areas of study
based on its eutrophication levels and calculates a reference point for each area of study (i.e.
two reference points in total). Figure A6 shows the Baltic Sea spatial variations in
eutrophication status from a recently Helcom assessment. One area (“basin 2 in the BHI
code”) includes all BHI regions that fall into the Gulf of Bothnia (the Bothnian Sea, the
Quark and the Bothnian Bay). The other area (“basin 1 in the BHI code”) comprises the BHI
regions that correspond to the rest of the Baltic Sea basins (Åland Sea, Archipelago Sea,
Baltic Proper and Danish Straits waters).
Tool of statistical analysis
Analyses were conducted in R (R Core Team 2015) using the package dplyr (Wickham and
Francois 2015) as well as the code developed by the OHI team (available on Github,
https://github.com/OHI-Science/ohicore). The analysis code for the BHI project is also
available on Github (https://github.com/OHI-Science/bhi). Results are illustrated by graphs
and tables generated in Microsoft Office Excel format.
19
3. Results
Table 7 below shows the constant values used in the calculations of the mariculture current
Status and Trend estimations for the BSR. Per capita production (YC) and mariculture Status
(XMAR) are hereafter presented by country over the time period of study; current mariculture
Status and Trend dimensions are displayed by BHI regions. Country coded regions are
consistent with code numbers in Figure 2.
Table 7. Settings of constants for mariculture Status and Trend calculations.
Parameters
Rainbow trout mariculture production: 4-year running mean
Rainbow trout Sustainability coefficient (SM,k): 0,53
Trend calculation: 5-year regression length
Near-term likely future status: 5 years
Time period: 2005-2014
3.1 Estimating the mariculture per capita production for Sweden, Denmark and Finland.
Graph below shows that, in Sweden, the greatest rainbow trout production is concentratetd in
the Gulf of Bothnia (Sw_41, Sw_39 and Sw_37). Different coastal population densities
accounted for different yields among regions that fall into the same coastal segment (see
Tonnes per capita
Graph A7, A8 and Table 2).
4,00E-03
Sw_1
3,50E-03
Sw_5
3,00E-03
Sw_11
2,50E-03
Sw_14
2,00E-03
Sw_26
Sw_29
1,50E-03
Sw_35
1,00E-03
Sw_37
5,00E-04
Sw_39
0,00E+00
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Sw_41
Graph 1. Per capita production of sustainably-harvested rainbow trout yields from 2005 to 2014 along
Swedish coastal regions (Sw_codes = BHI coded regions, see Figure 2).
20
The largest Danish mariculture production is detected in southern Sjælland (Dk_7 and Dk_9,
see Graph 2). Different coastal population densities account for different sustainable yields
among BHI areas with same regional production (see Graph A7, A9 and Table 3). For
example, regions Dk_7 and Dk_9 share the same productivity figures but they slightly differ
in coastal population densities (PC_7 < PC_9, see Graph A7). Because BHI regions 6 and 15
lacked harvesting data in years 2009, 2010, 2011, 2012 and 2013, there was not enough data
Tonnes per capita
to perform the 4-year running mean production for years 2010 and 2011 in those regions.
0,05
Dk_2
0,04
Dk_3
Dk_6
0,03
Dk_7
0,02
Dk_9
0,01
Dk_12
0
Dk_15
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph 2. Per capita production of sustainably-harvested rainbow trout yields from 2005 to 2014 along
the Baltic coastlines of Denmark (Dk_codes = BHI coded regions, see Figure 2).
Graph 3 below presents Åland (Fn_36) as the Finnish region with highest productivity, and
again different coastal population densities among BHI areas from one reporting region are
expected to cause different patterns in their sustainable production (see Graph A7, A10 and
Table 4).
Tonnes per capita
0,01
Fn_32
0,008
Fn_36
0,006
Fn_38
0,004
Fn_40
0,002
Fn_42
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph 3. Per capita production of sustainably-harvested rainbow trout yields from 2005 to 2014 along
Finnish coastal regions (Fn_codes = BHI coded regions, see Figure 2).
21
3.2 Calculating mariculture status for the BSR based on a single reference point from 2005 to
2014.
The reference point is the smallest YC value (per capita production) that is above the estimated
95th percentile (i.e. region 9 in 2011; see Table 8 below). Estimations indicate that those
regions with the maximum score are also the regions with the largest mariculture production
of the entire BSR, (i.e. southern Sjælland, Dk_7 and Dk_9 regions).
Table 8. List of region-year combinations scoring 100 for mariculture Status within the BSR. The 95th
percentile statistically computed in R was 0,02866789.
BHI
region
9
9
9
7
9
7
7
7
7
7
7
Year
2011
2013
2012
2008
2014
2009
2010
2011
2013
2012
2014
YC
0,02880535
0,02953614
0,02969472
0,02976495
0,02979587
0,03489874
0,03938616
0,03963837
0,04064399
0,04086221
0,0410014
Status values for Sweden, Denmark and Finland from 2005 to 2014 (Graphs 4, 5 and 6
below, respectively) followed the same pattern as their sustainable per capita production (see
Graph 1, 2 and 3). Which means that regions with larger sustainable rainbow trout production
scored higher too.
22
Status (0-100)
14
Sw_1
12
Sw_5
10
Sw_11
Sw_14
8
Sw_26
6
Sw_29
4
Sw_35
2
Sw_37
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Sw_39
Sw_41
Graph 4. Status of marine-farmed rainbow trout industry from 2005 to 2014 along Swedish coastal
regions (Sw_codes = BHI coded regions, see Figure 2). BHI regions in the Gulf of Bothnia exhibited
the higher scores for mariculture status.
In Denmark, status scores for BHI regions 6 and 15 are missing in years 2010 and 2011
because, as was stated before, there was a data gap for harvesting data in years 2009, 2010,
2011, 2012 and 2013, therefore it was not enough data to perform the 4-year running mean
production for years 2010 and 2011 in those regions (see Graph 5 below).
Status (0-100)
100
Dk_2
80
Dk_3
60
Dk_6
40
Dk_7
20
Dk_9
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Dk_12
Dk_15
Graph 5. Status of marine-farmed rainbow trout industry from 2005 to 2014 along the Baltic
coastlines of Denmark (Dk_codes = BHI coded regions, see Figure 2). BHI regions in southern
Sjælland stood out for scoring higher than the rest of the Dk_regions. The representation is consistent
with the information shown in Table 8.
23
Status (0-100)
35
30
Fn_32
25
Fn_36
20
Fn_38
15
Fn_40
10
Fn_42
5
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph 6. Status of marine-farmed rainbow trout industry from 2005 to 2014 along Finnish coastal
regions (Fn_codes = BHI coded regions, see Figure 2). Åland (Fn_36) was the region that scored
higher for the mariculture status in Finland.
3.2.1 Mariculture current status and trend estimations for the BSR based on a single
reference point.
Most of the BHI regions stand out for having either very low rainbow trout production or
high coastal population in some cases, leading to poor sustainability in the mariculture sector
(see Graph 7 below). Regions 7 and 9 may be considered as a single one because of their
spatial proximity and similar features. As mentioned before, mariculture status will be
directly affected by the sustainable per capita production (e.g. both status and production in
regions 36, 38 and 39: Fn_36 > Fn_38 > Sw_39).
7
BHI regions
9
Current Status
100
80
60
36
40
20
1 2 3
5 6
1112 1415
26
29
32
3839
41
40 42
35 37
0
Graph 7. Mariculture Status for the BSR in 2014 (present conditions). Scoring range: 0-100.
24
It was not possible to estimate trend scores for BHI regions 6 and 15 due to their data gap in
years 2010 and 2011, as was mentioned before (see Graph 8). Regions 36 and 38 revealed a
very weak tendency towards a positive shift in the near-term future status while regions 40
and 42 tend to worsen, although to a lesser extent. The rest of the regions remained on zero or
very close to it.
BHI regions
36
0,08
Trend
0,06
38
0,04
0,02
1 2 3
5
7
9
11 12
14
26
29
32
35
37
39
41
40
0
42
-0,02
-0,04
Graph 8. Mariculture trend for the BSR based on most recent five years of data: from 2010 to
2014. Scoring range from -1 to 1.
3.3 Calculating mariculture status for the BSR based on two reference points from 2005 to
2014.
BHI regions included in “basin 1”: 1, 2, 3, 5, 6, 7, 9, 11, 12, 14, 15, 26, 29, 32, 35 and 36.
BHI regions included in “basin 2”: 37, 38, 39, 40, 41 and 42.
Each basin reference point is the smallest YC value (per capita production) that is above the
estimated 95th percentile (i.e. region 7 in 2008 and region 38 in 2005; see Table 9 below).
Those regions with the maximum score are also the regions with the largest mariculture
production in “basin 1” and “basin 2”: Dk_7 and Dk_9 regions (southern Sjælland) and Fn_38
region (the Bothnian Sea) respectively. The reference point for the “basin 1” scaled up three
positions in the list of the reference values obtained in the first assessment (see Table 8).
25
Table 9. List of region-year combinations scoring 100 for mariculture Status within the BSR.
The 95th percentiles statistically computed in R for basins 1 and 2 were 0,029712277 and
0,004052536 respectively.
basin_id
1
1
1
1
1
1
1
1
2
2
2
BHI region
7
9
7
7
7
7
7
7
38
38
38
Year
2008
2014
2009
2010
2011
2013
2012
2014
2005
2006
2014
YC
0.029764952
0.029795874
0.034898741
0.039386156
0.039638366
0.040643991
0.040862206
0.041001404
0.004242543
0.004244716
0.004268668
As occurred before, status values for Sweden, Denmark and Finland from 2005 to 2014 under
this second approach of analysis (see Graphs 9, 10 and 11 respectively) followed the same
pattern as their sustainable per capita production (see Graph 1, 2 and 3). Which means that
regions with larger sustainable rainbow trout production scored higher too. However, some
differences will be noticeable as a result of estimating mariculture status based on two new
Status (0-100)
reference regions.
Sw_1
100
90
80
70
60
50
40
30
20
10
0
Sw_5
Sw_11
Sw_14
Sw_26
Sw_29
Sw_35
Sw_37
Sw_39
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Sw_41
Graph 9. Status of marine-farmed rainbow trout industry from 2005 to 2014 along Swedish coastal
regions (Sw_codes = BHI coded regions, see Figure 2). In this analysis with two reference points the
regions 41, 39 and 37 scored much higher compared to the first assessment while the rest of the
regions remained pretty much the same.
26
In Denmark, status scores for BHI regions 6 and 15 are missing in years 2010 and 2011
because of the data gap for harvesting figures in years 2009, 2010, 2011, 2012 and 2013,
therefore it was not enough data to perform the 4-year running mean production for years
Status (0-100)
2010 and 2011 in those regions (see Graph 10 below).
100
Dk_2
80
Dk_3
Dk_6
60
Dk_7
40
Dk_9
20
Dk_12
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Dk_15
Graph 10. Status of marine-farmed rainbow trout industry from 2005 to 2014 along the Baltic
coastlines of Denmark (Dk_codes = BHI coded regions, see Figure 2). Not much change was
expected under this new approach of study as Table 9 showed before.
The Graph 11 below shows how, depending on the region, mariculture status scores for
Finland were affected by the two different reference values (i.e. Fn_38 and Dk_7). While
Fn_38, Fn_40 and Fn_42 regions clearly scored higher than in the previous assessment,
Fn_32 and Fn_36 areas barely changed their status conditions (as occurred with Sweden).
Status (0-100)
100
80
Fn_32
Fn_36
60
Fn_38
40
Fn_40
20
Fn_42
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph 11. Status of marine-farmed rainbow trout industry from 2005 to 2014 along Finnish coastal
regions (Fn_codes = BHI coded regions, see Figure 2).
27
3.3.1 Mariculture current status and trend estimations for the BSR based on two reference
points.
For “basin 1” sustainable rainbow trout production conditions of all regions are extremely
poor (with status scores over zero), except for region 36 ( Åland). “Basin 2”, however,
indicated comparatively better conditions in attaining sustainable benefits from rainbow trout
farming in those regions (see Graph 12 below). Here it is also noted that mariculture status is
directly influenced by the sustainable per capita production in each “basin” (see also Graphs
A11, A12, A13 and A14). As occurred before, many of the BHI regions stand out for having
either very low rainbow trout production or high coastal population densities leading to
overall poor sustainability in the mariculture sector.
7
BHI regions
9
38
Current status
100
39
80
60
41
36
37
40
20
1 2 3
5 6
1112 1415
26
29
32
35
40 42
0
Graph 12. Mariculture Status for the BSR (“basin 1” and “basin 2”) in 2014 (present conditions).
Scoring range: 0 - 100.
Again, it was not possible to estimate the trend for BHI regions 6 and 15 due to their data gap
in years 2010 and 2011 (see Graph 13 below). Few trend values tend to be either slightly
more positive or negative than in the previous approach. Regions 38, 9 and 39 showed a weak
tendency towards a positive shift in the near-term future status while regions 40 and 42 tend
to worsen. The rest of the regions remained on zero or close to it.
28
BHI regions
0,20
38
0,15
Trend
0,10
0,05
36
9
1 2 3
5
7
39
1112 14
26
29
32
35 37
41
0,00
40
-0,05
-0,10
42
-0,15
-0,20
Graph 13. Mariculture Trend for the BSR (“basin 1” and “basin 2”) based on most recent five years of
data: from 2010 to 2014. Scoring range: from -1 to 1.
4. Discussion
4.1 Mariculture status and trend analysis
Two approaches were used for examining mariculture status and trend components for the
BSR, namely the “one-basin” and the “two-basin” approach. Under the first one, mariculture
status and trend estimations were carried out for the entire Baltic Sea basin as a whole. The
common denominator of both assessments was that higher sustainable per capita production
accounts for better score in the status. Country-wise, under the one-basin approach southern
Sjælland (Denmark), Åland region (Finland) and the Swedish coastline of the Gulf of Bothnia
scored higher for the mariculture status from 2005 to 2014 than the other regions. Consistent
with this, the 2014 state of the Baltic mariculture revealed that southern Sjælland and Åland
regions held the two highest status scores of the entire BSR. Generally, trend estimations did
not seem to detect any significant change in the status within the near-term future which
might imply certain stagnation of production in the mariculture sector, however, given that
the differences in production figures among countries are so great, even if a region increased
its production it could still remain very far away from the maximum regional producer. Yet,
few regions like Åland showed a slightly positive tendency to increase its status while others
such as the Finnish coastlines of the Bothnian Bay showed a weakly negative trend meaning
a lower mariculture status within the next five years (see mariculture status scores for
Sweden, Denmark and Finland from 2010 to 2014, Graphs 4, 5 and 6). It is worth mentioning
29
that trend estimates for southern Sjælland remained on zero or close to it because for the most
part of the trend period (2010-2014) the regions scored the maximum (or very close to it) for
mariculture status (i.e. -almost- no change was perceived; this clarification is also valid for
the two-basin approach). Nevertheless, Halpern et al. (2012b) argue that estimating the
trajectory of a goal status into the future is clearly more complex than a linear regression
from status estimates as it will be affected by, for example, the existence of nonlinear patterns
in physical and biological system responses (stochasticity) or socio-ecological pressures and
resilience mechanisms inherent to the system in question. However, direct measurements of
trend are expected to impact more directly upon the near future conditions of a the given goal
than indirect measurements of pressures and resilience (Halpern et al. 2012b). Hence the
trend is given a greater relative importance in the OHI framework than pressures and
resilience when the likely future status is calculated (Figure 1).
The two-basin approach demonstrated the adaptability of the OHI methodology. Because the
Baltic Sea is not a homogeneous system, some local or regional measures/polices that might
arise as part of environmental solutions (i.e. socio-economic resilience instruments) may
(should) be site-specific. In the case at stake, the focus was on the identification and
delimitation of areas highly susceptible to organic discharges from mariculture activities.
Therefore, taking into account the eutrophication conditions throughout the Baltic Sea, the
whole region was primarily divided into two largest areas (“basin 1” and “basin 2”) each of
them individually evaluated following the same procedures as in the previous analysis and
during the same period of time (from 2005 to 2014) (read epigraph 3.2 to identify the BHI
regions included in both basins). Under this second approach the Swedish coastlines of the
Gulf of Bothnia scored much higher because the newly estimated reference point for that
basin was not only different but also smaller. However, the rest of the BHI regions for
Sweden scored fairly similar to the one-basin study because the reference value applied to
them barely changed; which is also why all Danish scores suffered comparatively minimal
variations. The same pattern was observed in Finland scores where the Finnish coastlines of
the Gulf of Bothnia computed higher for mariculture status while the southern regions of the
country remained practically unchanged. Mariculture trend calculations under this approach,
although not very significantly (as occurred before), indicated that, within the next five years,
the Finnish coastlines of the Bothnian Sea might improve its status to a small extent whereas
the coastal region of the Bothnian Bay might slightly get worse (see mariculture status scores
for Sweden, Denmark and Finland from 2010 to 2014, Graphs 9, 10 and 11).
30
4.2 Mariculture subgoal: its improvement suggestions
Regional assessments of the Ocean Health Index context like the Baltic Health Index are
expected to contribute with valuable understanding of system tradeoffs and more
comprehensive ecosystem-based managements through reiterated evaluations over time.
Furthermore, because the index defines goals and sub-goals status by a single number it
makes it easier to be handled by managers, policy makers and the public.
The results here presented portray a mariculture sector situation that does not seem to attain
desirable levels of sustainability when it comes to marine-farmed rainbow trout production in
the BSR. Nevertheless, based on the procedures and assumptions described in the present
work it could be to some extent misleading to certainly state how sustainably the mariculture
activities are actually being managed in the BSR or even will be in the near-term future
Estimations have shown that mariculture status calculations contain a bias in favour of the
sustainable per capita production of regions. Therefore, this formulation might not be the
most accurate approach when determining if a Baltic region is (or is not) sustainably
managing its resources to pursuit the mariculture sub-goal. Different results from those
presented in this paper might derive from adding other attributes to the analysis and thus
potentially help describe and assess more accurately how the system may be assimilating the
impacts from mariculture activities. For example, taking water retention rates into account
would identify those regions more suitable for mariculture activities partly due to naturally
higher water fluxes thus preventing exceedances of nutrient critical loads in the farm
surroundings (e.g. Danish Straits). Furthermore, it might not only be relevant to estimate the
mariculture production of a region, but also its extension. For example, southern Sjælland
(Denmark) turned out to be a reference place for sustainable mariculture production (under
both studied approaches) despite having the largest per capita production within such a small
portion of space. Additionally, it would have been more informative to use a country-specific
sustainability score for rainbow trout species in each BHI region-analysis or using a reference
point reflecting policy goals for each region instead of an arbitrary comparison across space.
31
4.3 Considerations on the Ocean Health Index integral assessment tool
As every integrated environmental assessment (IEA), the Ocean Health Index draws upon the
biological, physical and social sciences in order to address environmental problems through
policy-relevant and multidisciplinary analysis. Although IEA is sometimes seen as the most
satisfactory way to evaluate and integrate policy-oriented knowledge, it still possesses
applicability limitations inherent to the nature of its proceedings from their conception to
their use.
Firstly, defining the scope of the assessment is a crucial and difficult task since it will shape
the further steps within the evaluation. The complexity of the systems makes the integrating
processes at different stages even more challenging (e.g. is the BHI assessment more useful
as a whole or are the public goals more effective and clarifying individually). Limitation of
data, lack of system bio-physical understanding, type of key stakeholders and interests
involved, focus only on quantifiable constituents, etc. are some of the factors that might
affect the significance of an integrated assessment. Setting the objectives that the IEA wants
to pursuit would engage other kinds of constraints that will definitely define its essence. Is the
IEA embracing objectives more achievable, more recommended or more convenient? (e.g.
environmental pressures that hamper the delivery of a goal vs environmental pressures that
alter the natural state of the ecosystem). Values, assumptions and beliefs underlying
subjective proceedings should at least be explicit and justified (e.g. pressures and resilience
weighting methodologies).
Communicating the outcomes to the different stakeholders (policy managers/makers,
scientists and public) constitutes the last stage before any strategic guideline is conceived and
implemented. Apart from using the appropriate language of communication which will
depend on the target audience, the information should never be misleading or sensationalist,
like for example stating that southern Sjælland (Denmark) is the region that most sustainably
performs in the mariculture industry of the entire BSR. The message must be also accurate in
terms of findings, assumptions and uncertainties hence the suitability and robustness of the
environmental policies could be guaranteed as much as possible.
32
5. Conclusions
The Ocean Health Index is a pioneering tool which tends to evaluate the general health of
marine ecosystems and describes the state of their sustainable exploitation by one number.
The present work presented an analysis of the mariculture subgoal indicator including its
current status and future trend, the latter based on the most recent five years of data. On the
basis of the OHI framework and its criteria, the Baltic Sea is currently farming rainbow trout
species in a non-sustainable manner (i.e. the BHI regions assessed are not maximizing their
potential to sustainably harvest rainbow trout). The same trend is expected in the near-term
future (i.e. within 5 years approximately).
One of the OHI assessment purposes is to create an index that can be easily communicated to
politicians, resource managers and the public. Nevertheless, the mariculture subgoal
calculations do not perfectly define the sustainable character of rainbow trout production in
the Baltic Sea because final scores are undesirably influenced by per capita production
figures. A more accurate description of how Baltic marine ecosystems may be responding to
impacts from mariculture activities would benefit from calculation improvements tailored to
the physical and ecological nature of the Baltic Sea and thus enhancing the mariculture
indicator reliability. Beyond that, future policy implementations that might be supported on
those outcomes while regulating, boosting and/or managing Baltic mariculture production
(e.g. Marine Strategy Framework Directive, Sustainable Blue Growth Agenda for the Baltic
Sea Region, etc.) would be more effective.
33
Acknowledgements
First and foremost, I would like to thank my advisor Dr. Hanna Corell for her patience, her
great support and for giving me the possibility to write this thesis. Furthermore, I really
appreciate and would like thank you for continuing to take your time to help me even though
you have had less available time lately. I wish you all the best in your new position.
My thanks go also to Dr. Jennifer Griffiths and Dr. Lena Viktorsson for their valuable help
with R and their good advices. I thank you, Dr. Jennifer Griffiths, for listening and handling
each and every one of my questions.
Lastly, but not least, I want to thank my mom and my grandma for being my role models
throughout my entire life. My appreciation goes also to my partner, Alberto, who has learned
to love me no matter what.
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Appendix A
Figure A1. World capture fisheries and aquaculture production (FAO, 2014)
2500000
Tonnes
2000000
Mariculture (sea and brackish
waters)
1500000
Freshwater
1000000
500000
0
2008
2009
2010
2011
Years
2012
2013
2014
Graph A1. Production from aquaculture excluding hatcheries and nurseries in Europe (from
2008 to 2014) (EUROSTAT, 2016).
39
Figure A2. Applications of sustainable fish aquaculture (Figure taken
http://www.submarinerproject.eu/index.php?option=com_content&view=article&id=93&Itemid=230).
from
Table A1. Some aquaculture technologies that are implemented in the Baltic aquaculture
today (FAO, 2003).
Aquaculture technologies
Definitions
Tanks and raceways
Artificial units constructed above or below
ground level capable of high rates of water
interchange or with a high water turnover rate
and highly controlled environment but without
water recirculation.
Ponds
Relatively shallow and usually small bodies of
still water or water with a low refreshment rate,
most frequently artificially formed, but can also
apply to natural pools or small lakes.
Cages
Open or covered enclosed structures constructed
with net, mesh or any porous material allowing
natural water interchange. These structures may
be floating, suspended, or fixed to the substrate
but still permitting water interchange from
below.
Recirculation systems
Systems where the water is reused after some
form of treatment (e.g. filtering)
40
Seawater
Producing
ammonia,
phosphates, CO2
and organic
matter
Consume O2
Feed
Fish
Valuable
biomass
Uptake of
organic matter
Mussels
Producing
ammonia and
CO2
Consume O2
Valuable
biomass
Uptake of
ammonia,
phosphates and
CO2
Produce O2
Light
Seaweeds
Valuable
biomass
Figure A3. Integrated Multi-Trophic Aquaculture system. (Information adapted from
http://www.kombiopdraet.dk/media/6241/poster_g__marinho-_grenaa_conf.pdf).
Figure A4. Baltic Sea basins (Andersen, J. H., et al. 2015).
41
Figure A5. Salinity distribution in the Baltic Sea in parts per thousand (Håkanson, L. et al. 2003).
Graph A2. Germany mariculture production in the Baltic Sea from 1995 to 2013 (FAO
FishStatJ, 2016).
42
Graph A3. Denmark mariculture production in the Baltic Sea from 1995 to 2013 (FAO
FishStatJ, 2016).
Graph A4. Estonia mariculture production in the Baltic Sea from 1985 to 2013 (FAO
FishStatJ, 2016).
43
Graph A5. Finland mariculture production in the Baltic Sea from 1995 to 2013 (FAO
FishStatJ, 2016).
Graph A6. Sweden mariculture production in the Baltic Sea from 1983 to 2013 (FAO
FishStatJ, 2016).
44
2500
Coastal population (in thousands)
29
3
2000
2
1500
1000
32
6
1
12
5
37
500
11
36
42
14 26
41
38
39
35
40
7
9
15
0
BHI regions
Graph A7. Coastal population densities from 2005 employed in the mariculture Status
calculations (Sweden: 1, 5, 11, 14, 26, 29, 35, 37, 39, and 41; Denmark, 2, 3, 6, 7, 9, 12 and
15; Finland: 32, 36, 38, 40 and 42).
45
Figure A6. Eutrophication status in the Baltic Sea based on the BALTSEM model (Baltic sea
Long-Term large Scale Eutrophication Model) (HELCOM, 2013).
Sustainable tonnes
600
Sw_1
500
Sw_5
400
Sw_11
300
Sw_14
Sw_26
200
Sw_29
100
Sw_35
Sw_37
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Years
Sw_39
Graph A8. Sustainably cultured rainbow trout tonnes harvested in the coastlines of Sweden
from 2005 to 2014 (
).
46
Sustainable tonnes
2500
2000
Dk_2
Dk_3
1500
Dk_6
Dk_7
1000
Dk_9
500
Dk_12
Dk_15
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Years
Sustainable tonnes
Graph A9. Sustainably cultured rainbow trout tonnes harvested in the coastlines of Denmark
from 2005 to 2014 (
).
4500
4000
3500
3000
2500
2000
1500
1000
500
0
Fn_32
Fn_36
Dk_38
Dk_40
Dk_42
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph A10. Sustainably cultured rainbow trout tonnes harvested in the coastlines of Finland
from 2005 to 2014 (
).
47
4,50E-02
1
2
3
5
6
7
9
11
12
14
15
26
29
32
35
36
Tonnes per capita
4,00E-02
3,50E-02
3,00E-02
2,50E-02
2,00E-02
1,50E-02
1,00E-02
5,00E-03
0,00E+00
2005
2006
2007
2008
2009
2010
Years
2011
2012
2013
2014
Graph A11. Per capita production of sustainably-harvested rainbow trout yields from 2005 to 2014
along coastal regions in “basin 1” (BHI coded regions, see Figure 2 in the text). Different coastal
population densities account for different sustainable yields among BHI areas with same regional
production.
1
2
3
5
6
7
9
11
12
14
15
26
29
32
35
36
2008
2009
Status (0-100)
100
80
60
40
20
0
2005
2006
2007
2010
2011
2012
2013
2014
Years
Graph A12. Status of marine-farmed rainbow trout industry from 2005 to 2014 along coastal regions
in “basin 1” (BHI coded regions, see Figure 2 in the text). It followed the same pattern as the
sustainable per capita production (see Graph A11). Regions with larger sustainable mariculture
production scored higher.
48
0,0045
Tonnes per capita
0,004
0,0035
0,003
37
0,0025
38
0,002
39
0,0015
40
0,001
41
0,0005
42
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph A13. Per capita production of sustainably-harvested rainbow trout yields from 2005 to 2014
along coastal regions in “basin 2” (BHI coded regions, see Figure 2 in the text). Different coastal
population densities account for different sustainable yields among BHI areas with same regional
production.
100
90
Status (0-100)
80
70
37
60
38
50
39
40
40
30
41
20
42
10
0
2005
2006
2007
2008
2009 2010
Years
2011
2012
2013
2014
Graph A14. Status of marine-farmed rainbow trout industry from 2005 to 2014 along coastal regions
in “basin 2” (BHI coded regions, see Figure 2 in the text). It followed the same pattern as the
sustainable per capita production (see Graph A13). Regions with larger sustainable mariculture
production scored higher.
49
50