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Panel-based Assessment of Ecosystem Condition of the Norwegian Sea Pelagic Ecosystem [1]

['Author S']

Date: 2024-09

The PAEC framework consists of four phases: 1) A scoping phase where new and existing indicators are evaluated for inclusion; 2) the analysis phase; 3) the assessment phase where the scientific panel meets and discusses the significance and validity of indicator analyses, and 4) the report phase where the scientific background material and conclusions from the scientific panel is written up according to the PAEC protocol.

PAEC for the Norwegian Sea has been led by the Institute of Marine Research (IMR) and been conducted by a panel consisting of 15 scientists from IMR and one other institution: Norwegian Institute for Nature Research (NINA). The work has been led by Per Arneberg in close cooperation with Bérengère Husson and Anna Siwertsson (all IMR). The work has been conducted in the period from 1 June 2022 to 15 January 2023.

For marine ecosystems in Norway, the Panel-based Assessment of Ecosystem Condition (PAEC) has been developed, in cooperation with ecologists working with similar assessments for terrestrial ecosystems, as a methodological framework to assess ecological condition. PAEC forms the basis for a structured, consolidated, evidence-based assessment of the ecological condition of an ecosystem (Jepsen et al., 2019; Jepsen et al., 2020; Jepsen et al., submitted). In 2019, a pilot version of the PAEC protocol was tested for Arctic tundra and the Arctic part of the Barents Sea (Jepsen et al., 2019). Based on lessons learned from these two ecosystems, the PAEC protocol has been improved and translated into English (Jepsen et al., 2020), now providing an easily accessible description of the method.

Mandated by the Ministry of Climate and Environment, the Norwegian Environment Agency is responsible for the development of the “System for assessment of ecological condition” of terrestrial and marine ecosystems in Norway. This report is the last of three from a project funded by the Norwegian Environment Agency on assessing the condition of marine ecosystems. It includes the first assessment of the ecological condition of the pelagic ecosystem in the Norwegian Sea. The first report focused on the Arctic and sub-Arctic parts of the Barents Sea (Siwertsson et al., 2023) and the second on the shelf ecosystem in the Norwegian sector of the North Sea (Arneberg et al., 2023).

3 In the first phase of the work with establishing a framework for assessing condition of Norwegian ecosystems, a joint decision was made for assessments that the climatic normal period 1961-1990 should be considered descriptive for climate under the reference condition (Nybø and Evju 2017, Fagsystem for fastsetting av god økologisk tilstand. Forslag fra et ekspertråd (in Norwegian)). Thus, this was done before the establishment of the Norwegian Sea scientific panel, which noted during the current assessment that climate in the 1961-1990 period cannot be considered unimpacted by anthropogenic emissions of greenhouse gases.

This assessment relies on international, long term monitoring efforts that need continued funding to support future assessments. Long time series from observational data or models need to be available to conduct robust assessments. In particular, continued monitoring of zooplankton, with better identification of species involved, and in situ measurements of primary production are important improvements for future assessments. Mesopelagic species and their link to the ecosystem are still poorly know and require more regular monitoring and dedicated studies. Overall, further research is needed on ecosystem mechanisms and cascading impacts of anthropogenic pressures.

Climate change is expected to further impact the Norwegian Sea pelagic ecosystem unless greenhouse gas emissions are cut immediately and severely. With climate change, the frequency, duration and severity of extreme climatic events such as storms and heatwaves are expected to increase. These are likely to increase the uncertainties around the ecosystems’ future conditions. If problems related to overfishing are not resolved, this might cause large changes for the ecosystem.

The ecosystem characteristic was assessed as showing evidence of limited deviation from the reference conditions. It is based on 8 indicators, which links to the drivers and to the ecosystem are mostly well understood. There is clear evidence of change in heat content and ocean acidification, in link with climate change. Short time series of the other indicators limit our understanding of the extent to which abiotic condition are affected by climate change. This introduces considerable uncertainty to the assessment of this ecosystem characteristic.

The ecosystem characteristic was assessed as showing evidence of limited deviation from the reference conditions. It is based on 7 indicators, for most of which the links to the drivers and the impact on the ecosystem are not well understood. Strong declines in herring and mackerel stocks can be linked to the strong fishing pressure, above recommended quotas, that is applied since 2013. However, there is some uncertainty as some other indicators show no evidence of impact.

The ecosystem characteristic was assessed as showing no evidence of deviation from the reference conditions. It is based on 3 indicators, two of which have well-known link to the drivers and well understood consequences on the ecosystem. There are no signs of change in annual primary production or date of start of spring bloom. The main driver of these indicators are fisheries and climate change. The main uncertainty resides in the length of the time series for both indicators, which do not cover a period of change in the main driver, climate change.

The ecosystem characteristic was assessed as showing no evidence of deviation from the reference conditions. It is based on 2 indicators, of which one has well-known link to the drivers and well understood consequences on the ecosystem. There are no signs of change in annual primary production or date of start of spring bloom. The main driver of these indicators is climate change. The main uncertainty resides in the length of the time series for both indicators, which do not cover a period of change in the main driver, climate change.

Some ecosystem characteristics show signs of human impact. However, many short time series and lack of information hinder an exhaustive assessment of the main human impacts. For two ecosystem characteristics ( Functionally important species and biophysical structures and Abiotic factors ) there is evidence of limited deviation anthropogenic impact, due to strong changes in temperature in the water column, signs of increased ocean acidification and declines in fish stock biomass partly caused by overfishing. There is some uncertainty associated with the assessment of the former ecosystem characteristic but considerable uncertainty about the assessment of the characteristic Abiotic factors due to short time series for many indicators. Some ecosystem characteristics could not be assessed because the data was considered insufficient ( Functional groups within trophic levels and Biological diversity ) and two were assessed as showing no evidence of anthropogenic impact ( Primary productivity and Biomass distribution among trophic levels ) but with large uncertainties because of short time series.

The indicator coverage was assessed as adequate for Abiotic factors , for which the selected indicators cover the important features of abiotic conditions in the ecosystem. The indicator coverage was only partially adequate for Primary production , Biomass distribution among trophic levels , and Functionally important species and biophysical structures, as some important indicators are lacking. The lack of information on Biological diversity and Functional groups within trophic levels is such that the panel believes it might affect the assessment, and their indicator coverage was assessed as inadequate. There were no indicators selected for the ecosystem characteristic Landscape ecological patterns.

Data coverage for each indicator is evaluated based on spatial and temporal coverage of used datasets relative to reference conditions and relevant dynamics of the biological compartments and was thus assessed as very good or good for most indicators. Stratification and indicators of blue whiting stock size and recruitment were assessed as having only “intermediate” or “poor” data coverage because of missing information on seasonality and poor spatial coverage.

The assessment was done by a scientific panel of 15 experts of the Norwegian Sea. In a first phase of scoping, the experts selected sets of indicators relevant to describe temporal changes in seven ecosystem characteristics for each of the ecosystem: Primary productivity, Biomass distribution among trophic levels, Functional groups within trophic levels, Functionally important species and biophysical structures, Landscape-ecological patterns, Biological diversity and Abiotic factors . The method for the assessment is based on developing time series of the indicators and assessing whether there is a trend that indicates a deviation away from the reference condition. First, the experts assigned a phenomenon for each indicator, which contains a description of the indicator under the reference condition (i.e., when largely unimpacted by human activities) and of how the indicator is expected to change with increasing human pressure. Different time periods were considered descriptive for the reference condition for different parts of the ecosystem, and as data for these periods are unavailable for most indicators (except for e.g., 1961-1990 for climate 3 ), the reference condition is generally described qualitatively. The phenomena are assessed as having a high, intermediate or low validity depending on the scientific basis supporting i) the link between changes in the indicator and the drivers and ii) consequences of the changes in the indicator’s value for the ecosystem. In the second phase of analysis, the data collected allow to build time series of the indicators in predefined geographical regions, and based on those, the evidence for the phenomena (i.e., whether the expected development away from the reference condition caused by increasing human pressure has occurred) are assessed. Next, the overall condition of each ecosystem characteristic is assessed as belonging to one of three categories with increasing deviation from the reference condition — from no to substantial deviation. Finally, it was assessed at the scale of the Norwegian Sea pelagic ecosystem. This report will be peer-reviewed to ensure the validity and robustness of its conclusions.

The scientific panel concludes that there is evidence of limited impact of human pressures on the Norwegian Sea pelagic ecosystem. There are large uncertainties about whether this means that the impact is indeed limited or that more substantial impact is not detected because important indicators are lacking and many time series are too short. The most clear evidence for climate change is the observed temperature increase, which is seen with a 70 year long time series. There are also signs of increased ocean acidification. While climate change has the potential to affect primary production and zooplankton communities, time series available for these groups are too short to assess this. Fishing above recommended quotas over several years have contributed to declines in the herring and mackerel stocks, and there are also strong signals of decline in seabird populations. With projected further climate change and possibly overfishing, it is anticipated that stronger evidence of anthropogenic impacts will emerge.

The System for Assessment of Ecological Condition, coordinated by the Norwegian Environment Agency, is intended to form the foundation for evidence-based assessments of the ecological condition of Norwegian terrestrial and marine ecosystems not covered by the EU Water Framework Directive. The reference condition is defined as “intact ecosystems”, i.e., a condition that is largely unimpacted by modern industrial activities. An ecosystem in good ecological condition does not deviate substantially from this reference condition in structure, functions or productivity. This report describes the first operational assessment of the ecological condition of the pelagic ecosystem in the Norwegian Sea. The assessment method employed is the Panel-based Assessment of Ecosystem Condition (PAEC 1 ) and the current assessment has considered to what extent the Norwegian Sea pelagic ecosystem deviates from the reference condition 2 by evaluating change in trajectories.

Structurally, PAEC is conducted in a hierarchical manner and consists of four phases: 1) Scoping , 2) Analysis , 3) Assessment, and 4) Reporting and peer review (Fig. 1). Key to the Scoping Phase , is the selection of relevant indicators within a set of ecosystem characteristics covering structural and functional components (biotic and abiotic) of the ecosystem as well as the formulation of specific formalised expectations (termed Phenomena ) describing expected directional changes in a given indicator or state variable as a result of relevant drivers acting on the system. Phenomena are thus the equivalent of a scientific hypothesis formulated prior to a scientific study. The Analysis Phase consists of a statistical analysis of the underlying data to permit an assessment of the level of evidence for each phenomenon. This is based on evaluating whether rates of change seen in indicator time series can, as described above, be attributed to anthropogenic impact as described in the phenomena. The Assessment Phase consists of a plenary session where the assessment panel scrutinises and assesses the knowledge base underlying the assessment, assesses the condition of each ecosystem characteristic, and finally assesses the condition of the entire ecosystem. An independent Peer review of the final assessment report will be undertaken, with the aim of continuous improvements, and is seen to be a fundamental step in PAEC. An assessment according to PAEC is primarily a scientific exercise, and the scientific assessment panel should consist of a group of scientists with in-depth knowledge of the focal ecosystem characteristics, as well as relevant quantitative methodologies (study design, statistical modelling and ecosystem modelling). However, PAEC is also envisioned to be a tool for adaptive management of ecosystems, or specific ecosystem components. Thus, the protocol allows for the integration of a stakeholder group (consisting for instance of representatives from management agencies responsible for the specific ecosystem) into the assessment process (Fig. 1). This is not mandatory but may serve to broaden PAEC, from a purely scientific assessment to an operational and policy-relevant tool for developing management goals and adaptive management strategies for the implementation and assessments of specific management actions. Depending on the type of process in which the protocol is used, the level of stakeholder involvement in the assessment phase may vary across the different phases. For the assessments of marine ecosystems, the Advisory Group on Monitoring at IMR (“Overvåkingsgruppen” in Norwegian, The Royal Norwegian Ministry of the Environment (2006)), which is established to support the ocean management plans, has been informed about the work regularly (4 times yearly), throughout all phases of the work, with possibilities to provide feedback.

The overall question the current assessment aimed to answer, was whether there has been a change away from the defined reference condition (“intact nature”, see chapter 2), which can be attributed to anthropogenic impacts. Anthropogenic impact on climate is commonly measured relative to the 1850-1900 period (IPCC, 2021), while over-harvesting of marine mammal stocks started even earlier. Observational time series covering these time periods do not exist in the marine realm and, as a consequence, the current assessment did not include quantitative estimates of indicators for when the ecosystem was not significantly impacted by humans (reference values). In other assessment frameworks, lack of data for the reference condition has been dealt with by assigning values for the reference condition using expert judgement, observations from least impacted sites or modelling (e.g., Pedersen et al. (2016); Direktoratsguppen vanndirektivet (2018); Pedersen et al. (2018)). Values for the current state is then compared with these estimates, setting a threshold value for substantial deviation from the reference condition as for example 60% of the reference values (Nybø and Evju, 2017; Nybø et al., 2019; Jakobsson et al., 2021). There are several major shortcomings with this approach, including high uncertainty in expert-based reference values (Morgan, 2014; Pedersen et al., 2018) and low robustness of the threshold values set for deviation from the reference condition (Mupepele et al., 2016; Jepsen et al., 2019). PAEC has therefore been developed as an alternative to the requirement of reference and threshold values, instead focusing on the direction and rate of change (trajectories). The use of expert-based reference and threshold values is replaced by first describing how we expect an indicator to develop as a result of anthropogenic drivers acting on the ecosystem and then use time series data to assess whether this development has indeed taken place. This involves qualitatively describing each indicator under the reference condition (to help describing how we expect indicators to change from anthropogenic impact) but only to the extent that information from published literature allows.

The background for developing PAEC is an increasing demand for integrated assessments of the condition of entire ecosystem units under intensified anthropogenic pressures. PAEC is inspired by approaches used in several national and international bodies, including the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2020), Intergovernmental Panel on Climate Change (IPCC, 2020) and the French national ecosystem assessment (EFESE, 2020). These bodies share the common belief that the condition or state of complex systems (e.g., climate systems, ecosystems), and the level of evidence for change in the condition of such systems as a result of anthropogenic and natural drivers, is best assessed by broad scientific panels following stringent and structured protocols. PAEC is a structured protocol for a panel-based assessment of the condition of an ecosystem relative to a specific reference condition (Jepsen et al., 2020; Jepsen et al., submitted). A principal goal of PAEC is that it should provide a framework for making reproducible qualitative assessments based on quantitative analyses of the underlying data.

Based on a mandate from the Norwegian Ministry of Climate and Environment, the System for Assessment of Ecological condition was developed with the aim — for each of the major terrestrial and marine ecosystems not covered by the EU Water Framework Directive in Norway — to 1) define criteria for what could be considered good ecological condition and 2) develop methods for assessing the degree of deviation from “good condition” (Nybø and Evju, 2017). The results will be used to follow up the national action plan for biodiversity (Minstry of Climate and Environment, 2015) and holistic ecosystem-based ocean management plans (Ministry of Climate and Environment, 2020). For the latter, results from the assessments will have a central role in the description and evaluation of status of the marine environment, a key part of the scientific advisory work established for the management plans.

PAEC requires that the assessment of temporal representativity (Fig. 7.1, Tab 7.1) includes an evaluation of the extent to which data underlying the indicators are overlapping with any “temporally defined reference period” used. Following the arguments above about different time periods being representative for the reference condition for different components of the ecosystem, this period has been set differently across indicators.

Systematic monitoring of the Norwegian Sea shelf ecosystem used for this assessment generally started after the periods that can be considered descriptive for the reference condition, with monitoring of the herring stock as an exception (Toresen and Østvedt, 2000). This has two important implications for how the assessment is done (Jepsen et al., submitted). First, it is not possible to describe the reference condition quantitatively, and this has here therefore been done qualitatively to the extent possible from literature sources. Second, the assessment is based on phenomena . In short, and as described in the introduction, this is done by describing the direction we expect an indicator to change away from the (qualitatively described) reference condition with increasing pressure from the most important anthropogenic drivers (i.e. describe a phenomenon), and then assessing whether this development has indeed occurred using analyses of time series data (i.e. assess the evidence that a phenomenon has occurred, see the protocol (Jepsen et al., 2020) for details). Descriptions of the reference condition for each indicator are found in the phenomena descriptions (chapter 5).

For climate, the period that should be considered descriptive for the reference condition has been pre-set to 1961-1990 for all assessments of ecological condition in Norway (Nybø and Evju (2017), Box 1). It should be noted that this period is already part of the strong increase in global temperatures after 1950 (IPCC, 2021) and therefore is not pre-industrial, i.e., it is already and increasingly impacted anthropogenically. IPCC AR6 (IPCC, 2021) uses 1850-1900 as their reference period as a compromise between a climate state that can still be considered pre-industrial, but that has a reasonable coverage of reliable climate records.

Whaling and seal hunting have a long history in the Norwegian Sea (Rørvik and Jonsgård, 1981). Thus, any recent period may not be considered descriptive for the reference condition for the marine mammal community. It should be noted that data on marine mammals were not used in the current assessment for capacity reasons.

Industrial scale fisheries in the Norwegian Sea developed after the Second World War as a consequence of development of more efficient gear. This resulted in over-fishing of the stock of Norwegian spring spawning herring, leading to collapse of the stock in the early 1970s (Toresen and Østvedt, 2000). Thus, the conditions from the second half of the 20 th century and onwards may not be considered descriptive for the reference condition for fish.

Human influence, under the definition of the reference condition, can be present but not pervasive or dominating. The current assessment focuses on the extent to which an ecosystem deviates from a condition that is under little or no influence from anthropogenic pressures. When operationalising this, an obvious question is how human pressures have historically changed the Norwegian Sea pelagic ecosystem, and related to this, how the recent time periods for which we have data from systematic monitoring can be considered descriptive for the reference condition.

Primary productivity : The primary productivity does not deviate substantially from the productivity in an intact ecosystem. Reason: Elevated or decreased primary productivity indicates a system impacted for instance by eutrophication, overgrazing or drought. Biomass distribution among trophic levels: The distribution of biomass among trophic levels does not deviate substantially from the distribution in an intact ecosystem. Reason: Substantial shifts in biomass distribution between trophic levels indicate a system impacted for instance by removal of top predators. Functional groups within trophic levels: The functional composition within trophic levels does not deviate substantially from the composition in an intact ecosystem. Reason: Substantial changes in the functional composition within trophic levels indicate a system impacted for instance by to loss of functional groups (e.g., pollinators), loss of open habitat species due to encroachment, or super-dominance of certain functional groups or species (e.g., jellyfish in marine habitats). Functionally important species and biophysical structures: The functions of functionally important species, habitat building species and biophysical structures do not deviate substantially from the functions in an intact ecosystem. Reason: Functionally important species (e.g., small rodents), habitat building species (e.g., coral reefs, kelp forest), and biophysical structures (e.g., dead wood) have vital importance for the population size of a number of species, and changes in their occurrence will hence have functional implications for the ecosystem. Landscape-ecological patterns: Landscape-ecological patterns are compatible with the persistence of species over time, and do not deviate substantially from an intact ecosystem. Reason: Human influences can lead to changes in landscape-ecological patterns which have implications for the population size and population structure of native species, for instance through habitat fragmentation. Fragmented habitats may not be sufficiently large or connected to permit long-term survival of native species. Climate change, altered area use, pollution and invasive or introduced species may also influence landscape-ecological patterns with implications for population size and composition of native species. Biological diversity: The genetic diversity, species composition and species turnover do not deviate substantially from an intact ecosystem. Reason: Loss of biological diversity can cause the ecosystem to be less resilient towards pressures and disturbances, and influence the structure, functions and productivity of the ecosystem. Changes in rates of species turnover, due to extinction or colonisation can indicate a modified system. Abiotic factors: Abiotic conditions (physical and chemical) do not deviate substantially from an intact ecosystem. Reason: Human influences* (e.g., environmental toxins, fertilization, changed hydrology or acidification) can lead to substantial changes in the physical/chemical structure and function of the ecosystem, which in turn will impact the species composition, function and dynamics of the ecosystem.

Intact ecosystems Intact, natural and semi-natural, ecosystems are characterised by the maintenance of fundamental structures, functions and productivity. Intact ecosystems are further characterised by having complete food webs, and element cycles. The majority of the food web consists of native species which dominate at all trophic levels and in all functional groups. The species composition, population structure and genetic diversity of native species is a result of natural processes occurring through the ecological and evolutionary history of the ecosystem. Intact ecosystems possess characteristics which are not changing systematically over time but vary within the boundaries of the natural dynamics of the system. Human influences can be present, but should not be pervasive or dominating, or be a factor which changes the structure, function or productivity of the ecosystem. This means that human influences should not be at a scale which exceeds the impacts of natural pressures (e.g., disturbance) or dominating species (e.g., top predators) in the ecosystem. Further, human influences should not lead to changes which are more rapid or more pervasive than natural pressures in the ecosystem. In semi-natural ecosystems, the human activities which define the system (e.g., grazing, hay cutting) are considered an integral part of the ecosystem.

Below, the complete definitions from Nybø and Evju (2017) of what constitutes “intact ecosystems” is given first. This includes the climatic reference on which the assessment should be based (Box 1). We further reiterate their normative description of the condition of each ecosystem characteristic under the reference condition (Box 2) before going on to describe how these definitions have been incorporated into the current assessment.

All assessments of ecological condition done to follow up Norway's national biodiversity action plan (Minstry of Climate and Environment, 2015) apply the methodological framework described in the System for Assessment of Ecological Condition (Nybø and Evju, 2017). Note that this includes both marine and terrestrial ecosystems. The reference condition in this framework is defined as “intact ecosystems”, and the assessment should consider the extent to which the current condition of the ecosystem deviates from this reference condition. The term “good ecological condition” is used herein to characterise a condition in which the structure, functions and productivity of an ecosystem do not deviate substantially from the reference condition.

We also attempted to identify important parameters of the ecosystem that are currently missing from the monitoring programs. Thus, issues of data availability or responsiveness to anthropogenic pressure (with the exception of biological diversity indicators) were not considered in the first part of the scoping exercise. A list of additional indicators to consider are presented in Table 4.2. This also includes indicators that were attempted to include in the assessment, but where it was realized that the quality was not sufficient. The indicators that were finally used were those for which direct measurements or proxies with sufficient quality were available.

Three of the seven ecosystem characteristics– “Biomass distribution among trophic levels”, “Functional groups within trophic levels” and “Biological diversity”- are more complex than the other characteristics and require integrating data over ecosystem compartments. This is challenging in the marine environment as the different components of the ecosystem are observed and sampled following different strategies and methods. Therefore, resulting biomass estimates are not comparable. For “Biomass distribution among trophic levels”, we thus decided to select indicators to describe biomass distribution of different trophic level within each ecosystem component for phytoplankton and seabirds and have developed an indicator for the relative biomass of mesozooplankton and pelagic fish. The assessment of if and how the biomass distribution has changed among trophic levels was done by integrating all this information when doing the ecosystem characteristic assessment. Future reiterations of the assessment, however, should try to find a way to further combine different indicators to describe the overall variation in biomass across trophic levels. For “Functional groups within trophic levels”, important functions performed by each ecosystem component are identified by experts. Finally, for “Biological diversity”, classical biodiversity indices were difficult to link to anthropogenic drivers. Instead, the selected indicators represent species or groups of species that are known to be sensitive to, or to benefit from, certain anthropogenic pressures and can thus act as “indicator” species. For “Functional groups within trophic levels” and “Biological diversity”, indicators are only available for copepods, thus severely limiting the assessment.

To assess the status of the ecosystem through its seven ecosystem characteristics, we have grounded our choice of indicators in the panel’s knowledge on the ecosystem’s key components and functions. Researchers have based their selection of indicators on a compromise between parsimony in the number of indicators and their relevance and importance, supported by the scientific literature.

It should be noted that the Norwegian Sea is less data rich than the Barents Sea (see Siwertsson et al. (2023)) and the North Sea (see Arneberg et al. (2023)), as there are no ecosystem cruises that produces both biomass data and taxonomic information for large groups of ecosystem components, like the ecosystem survey in the Barents Sea (Eriksen et al., 2018) and the international bottom trawl survey in the North Sea (ICES, 2020). Also, the time series from CPR are much shorter than in the North Sea and other core areas for the CPR.

The main data sources for the current assessment are the international ecosystem survey in the Nordic seas (ICES (2021a), hereafter IESNS) and the international ecosystem summer survey in the Nordic seas (Nøttestad et al. (2021), hereafter IESSNS). These provide data on mesozooplankton biomass and contributes with data for the assessments of the large pelagic commercial fish stocks and estimation of oceanographic parameters (ICES, 2019a). Data from Argo buoys have also contributed to estimation of oceanographic parameters. Satellite monitoring of ocean colour from which chlorophyll a concentration is inferred has contributed with data for estimation of net primary production (Behrenfeld and Falkowski, 1997). Monitoring of seabird colonies along the Norwegian mainland coast through the SEAPOP programme has contributed with data on seabird abundance (SEAPOP, 2022) and dedicated monitoring for estimation of pH and aragonite saturation with data for indicators related to ocean acidification (Jones et al., 2020). The continuous plankton recorder survey (hereafter CPR), which provides estimates of relative abundance of a large number of zooplankton and phytoplankton taxa (Richardson et al., 2006) has been extended into the Norwegian Sea in recent years, and has provided data for estimation of changes in biological diversity. There are also data on marine mammals from the Norwegian Sea (Øien, 2009; Leonard and Øien, 2020b; Leonard and Øien, 2020a), but due to capacity constraints, these have not been used in the current assessment.

Vertically, the assessment of the pelagic ecosystem in the Norwegian Sea is limited to the upper 800 meters of the deeper parts of the Norwegian Sea. Horizontally, the area is limited to the east by the 1000 meters isobath, and as the assessment focuses mainly on areas dominated by Atlantic water masses, the delineation to the north-west follows Mohn’s ridge and in the west and south the Norwegian basin (Fig. 3.1)

• For time series with less than 50 data points, autoregressive models are not robust (Hardison et al., 2019), and only the TREC analysis was applied then. As support information, a linear trend without autocorrelation in the residuals was applied to give an idea of the direction of the trend, but the interpretation should be done carefully as the trend coefficients and confidence interval may be erroneous. For supplementary information, we also applied a moving average smoother. All plots are available in Appendix 8.1.

• For time series with at least 50 data points, four autoregressive linear models taking into account effects of autocorrelation in time series data were fitted (without autocorrelation or with autocorrelation from first to third order, see Pedersen et al. (2021) for an example of application of such models in PAEC), and the best fitted model was selected based on the AIC criteria. Then, to highlight potential nonlinear trends, the first step of a TREC analysis (Solvang and Planque, 2020; Solvang and Ohishi, 2022) was applied to the standardized time series to compare the fit of polynomial models (from degree 1 to 3). The best fitted model was also selected based on AIC criteria.

The objective of the method used to assess deviation from the reference conditions is to fit a trend to each indicator’s data and to compare it to the phenomena stated by the experts (see chapter 5.1) and development of relevant anthropogenic drivers (see Appendix 8.2). Depending on the length of the indicator’s time series, two types of trend analyses were done:

The CPR is operated using ships of opportunity and data has been collected for a portion of the Norwegian Sea (figure 4.1). Some of the observations occur outside the assessment area, and all these data were used here.

This chapter describes the methods for calculation of indicator values based on the datasets described in chapter 3 and the analytical framework for estimating rates of change in the resulting time series. First, we give a general description on how the dataset from the Continuous Plankton Recorder Survey (CPR) (Continuous Plankton Recorder Survey, 2022) has been treated (chapter 4.1). This is followed by a description of the framework for estimating rates of change (chapter 4.2). Brief description of the specific methods for each indicator is given in Table 4.1. Additional descriptions of the methods are given in appendix 8.1, which also includes graphical representation of all indicator values and results from statistical analyses. Statistical analyses were conducted in R (R Core Team, 2019).

5 - Methods used to assess deviation from the reference condition

Deviation from the reference condition was assessed by comparing the expected variation in an indicator’s value with increasing human pressure (phenomenon, see short titles in Table 5.1 and full descriptions in section 5.1) to observed trend in the indicator’s data (see 4.2). If the fitted trend on the observed data was similar to what is expected given the observed variation in the relevant anthropogenic drivers (see Appendix 8.2), then there is evidence for deviation from the reference condition.

Indicator [ID] Phenomenon [ID] Anthropogenic drivers Approach Annual primary productivity [NwI01] Decreasing primary production [NwP01] Climate change 2) and 3) Timing of the spring bloom [NwI02] Change in timing of spring bloom [NwP02] Climate change 2) and 3) Mesozooplankton biomass relative to pelagic fish biomass [NwI03] Change in relative biomass of mesozooplankton to pelagic fish OR change in biomass ratio of mesozooplankton and pelagic fish [NwP03] Fisheries and climate change 2) and 3) High trophic level seabirds [NwI04] Decreasing populations of pelagic high TL seabirds [NwP04] Fisheries and climate change 2) and 3) Copepod body size [NwI05] Decreasing average copepod body size [NwP05] Climate change 2) and 3) Mackerel stock size [NwI06] Decreasing mackerel stock size [NwP06] Fisheries and climate change 2) and 3) Mackerel recruitment [NwI07] Change in mackerel recruitment [NwP07] Fisheries and climate change 2) and 3) Herring stock size [NwI08] Decreasing herring stock size [NwP08] Fisheries 2) and 3) Herring recruitment [NwI09] Decreasing herring recruitment [NwP09] Fisheries 2) and 3) Blue whiting stock size [NwI10] Decreasing blue whiting stock size [NwP10] Fisheries 2) and 3) Blue whiting recruitment [NwI11] Decreasing blue whiting recruitment [NwP11] Fisheries 2) and 3) Calanus finmarchicus productivity [NwI12] Decreasing Calanus finmarchicus production [NwP12] Climate change 2) and 3) Copepod species vulnerable to climate warming [Nw13] Decreasing number of copepod species sensitive to higher temperatures [NwP13] Climate change 2) and 3) Copepod species benefitting from climate warming [NwI14] Increasing number of copepod species benefitting from higher temperatures [NwP14] Climate change 2) and 3) Heat content [NwI15] Increasing heat content [NwP15] Climate change 2) and 3) Freshwater content [NwI16] Increasing freshwater content [NwP16] Climate change 2) and 3) Inflow of Arctic water [NwI17] Change in volume of Arctic Water [NwP17] Climate change 2) and 3) Stratification [NwI18] Increasing stratification [NwP18] Climate change 2) and 3) Inflow of Atlantic water [NwI19] Decreasing inflow of Atlantic Water [NwP19] Climate change 2) and 3) Nutrients [NwI20] Change in concentrations of nutrients [NwP20] Climate change 2) and 3) pH [NwI21] Decreasing pH [NwP21] Global increase in CO 2 2) and 3) Aragonite saturation [NwI22] Decreasing aragonite saturation [NwP22] Global increase in CO 2 2) and 3) Table 5.1 List of phenomena including overall approach used to determine the extent to which each phenomenon has occurred in the pelagic ecosystem in the Norwegian Sea. Approach refers to methods used to determine the extent to which the phenomenon has occurred. (1) For quantitative phenomena: The values of the indicator relative to an estimated quantitative threshold value (category not used in the current assessment) (2) For qualitative phenomena: The value of the indicator relative to variation estimated from the indicator time series or other qualitative or quantitative information about a reference state (3) For all phenomena: Observed and expected effects of changes in the indicator on other components of the ecosystem (i.e. ecosystem significance)

5.1 Scientific evidence base for the phenomena in the Norwegian Sea

Annual primary productivity [NwI01]

Phenomenon: Decreasing primary production [NwP01]

Ecosystem characteristic: Primary productivity

Under the reference condition annual primary production is high enough to sustain a food web of naturally occurring species. I n winter the surface water mixing is 100 to 200 meters deep in the Norwegian Sea. As insolation increases in spring, primary production increases accordingly, while thermal stratification develops. As described below, climate change is the main driver. In the Norwegian Sea, the mean surface temperature has not increased during the last 100 years, because the region is dominated by circulation and advection of Arctic waters versus Atlantic Waters (Xu et al., 2021). The present situation in this respect can therefore be assumed to mirror the reference condition.

The most important anthropogenic driver of change in the indicator is climate change. Global warming of surface waters has resulted in a worldwide increased stabilization of stratification and reduction of vertical advection of mineral nutrients to the euphotic zone (Yamaguchi and Suga, 2019; Kwiatkowski et al., 2020). As described above, mean surface temperature has not increased in the Norwegian Sea during the last 100 years (Xu et al., 2021). Future warming is expected to result in higher surface temperature, increased stratification and reduced primary production (Moore et al., 2018). While modelling work for the Norwegian Sea has suggested warming may give a weak increase in primary production (Kjesbu et al., 2022), the understanding of the link between climate change and change in the indicator is still rated as certain .

Both models and observations show that primary production is generally positively related to fisheries yield (Iverson, 1990; Ware and Thomson, 2005; Chassot et al., 2007; Chassot et al., 2010), thus providing strong evidence that changes in primary production have substantial impacts on other parts of marine ecosystems. The understanding of the importance of changes in the indicator for the rest of the ecosystem is thus rated as good .

Knowledge gaps include a need for in situ measurements and measurements of the ratio of new to regenerated production. It also includes a need for more information on changes in phytoplankton bloom phenology and the impact of changes in phytoplankton species composition on annual primary production and trophic transfer, as well as high spatial resolution biogeochemical models with high-quality atmospheric forcing which can consider changes in species composition.

Timing of the spring bloom [NwI02]

Phenomenon: Change in timing of spring bloom [NwP02]

Ecosystem characteristic: Primary productivity

In winter the surface water mixing is 100 to 200 meters deep in the Norwegian Sea and insolation is very low. As insolation increases in spring, primary production increases accordingly, while thermal stratification develops. Spring bloom dynamics are governed by a variety of factors and has been thoroughly reviewed and discussed by Lindemann and St. John (2014). Insolation, stratification, and grazing are recognized as main drivers of variability, and light and surface water mixing are influenced by cloudiness and storms. Thus, good weather in April and early May is conducive to early spring bloom, but a successful population of grazers may slow the accumulation of phytoplankton biomass. In the literature, the start of the spring bloom has been defined as the point in time when phytoplankton biomass reaches a certain threshold (Siegel et al., 2002). The biomass accumulation will be a function of both phytoplankton growth and water column stratification, which are influenced by temperature.

The most important anthropogenic driver of change in the indicator is climate change. Increasing sea surface temperature is predicted to stabilize stratification, but on the other hand climate change may increase storminess and influence timing of storms (Landgren et al., 2019). As the development of future storminess may be harder to predict, the understanding of the link between climate change and the indicator is rated as less certain .

The spring bloom is a major event in temperate marine ecosystems, and the success of many grazers depends on the high food availability at the height of the bloom. Many species have synchronized their spawning with the expected bloom for optimized food conditions. Changes in the timing of spring bloom may have negative consequences for these grazers (Edwards and Richardson, 2004; Durant et al., 2019; Yamaguchi et al., 2022). In temperate regions of the ocean, global warming has been shown to have influenced the onset of the bloom to earlier dates (Racault et al., 2012), and this prolongs the growing season. The effect of global warming on temperature has been detectable since the late 1980s, but the start spring bloom in the open ocean can only be precisely determined from the global ocean color satellite programs which started in 1996. The understanding of the importance of changes in the indicator for the ecosystem is rated as less good .

Research is needed to understand and quantify temporal changes and variability in the start of the spring bloom, as well as studies on the impact of climate change on the multiple controls at work (Lindemann and St. John, 2014). Other knowledge gaps include a need for more in situ measurements, high spatial resolution biogeochemical models, and analysis and interpretation of remote sensing data. Too little is known about the relationship between magnitude of spring bloom timing shift and effects on the ecosystem to evaluate how large changes should be for effects with ecosystem significance to occur.

Mesozooplankton biomass relative to pelagic fish biomass [NwI03]

Phenomenon: Change in relative biomass of pelagic fish [NwP03]

Ecosystem characteristic: Biomass distribution among trophic levels

Mesozooplankton and pelagic fish are dominant components of the Norwegian Sea pelagic ecosystem (Skjoldal, 2004). Mesozooplankton make up a large part of the diet of the three pelagic fish stocks; mackerel, Norwegian spring-spawning herring and blue whiting (Dalpadado et al., 2000; Langøy et al., 2012; Bachiller et al., 2016; Mousing et al., Submitted). Thus, important aspects of the overall distribution in biomass among trophic levels in the ecosystem can be observed by looking at the biomass of these two groups. While there is limited information about zooplankton and pelagic fish biomass variation under the reference condition in the Norwegian Sea, information do exist for one of the stocks, Norwegian spring-spawning herring (see phenomenon for herring stock size [NwP08]), showing that the size of the stock may vary over nearly an order of magnitude for periods that can be considered descriptive for the reference condition for this stock (i.e., pre WWII, Toresen and Østvedt (2000)). Large variation in stock size has been observed also for mackerel and blue whiting for more recent periods, and although some of this variation can be attributed to fishing, large parts of it is clearly due to variation in recruitment (ICES, 2021d; ICES, 2021c; ICES, 2022d), which may be more loosely linked to anthropogenic drivers and thus possibly to a large extent represent natural variation (see phenomena on herring recruitment [NwP09], mackerel recruitment [NwP07] and blue whiting recruitment [NwP11]). Related to this, it should be noted that several orders of magnitude variation in stock size has been demonstrated for pelagic fish stocks in other systems under periods of little anthropogenic impact (Baumgartner et al., 1992). Thus, pelagic fish biomass should be considered to be highly variable under the reference condition. Information on mesozooplankton biomass is more limited compared with what we know about pelagic fish, but estimates from 1995 and onwards from the Norwegian Sea shows that there was a drop of about a third from the mid-2000s to around 2010 linked to reduced inflow of Arctic water and an increase of a similar magnitude linked to increased inflow of Arctic water a decade later (Skagseth et al., 2022). This suggests that there is considerable natural variation also in mesozooplankton biomass under the reference condition.

The indicator is represented by an index on the ratio of overall biomass of the three pelagic fish stocks to mesozooplankton biomass. The most important anthropogenic driver of change in this indicator is fisheries, with climate change also having a possible role. The link between these drivers and biomass of the three pelagic fish stocks is described in the phenomena for these stocks (phenomena for mackerel stock size [NwP06], herring stock size [NwP08] and blue whiting stock size [NwP10]). In short, fisheries have the potential to cause declines in biomass of all the three stocks, whereas links to climate change are more uncertain. In addition, it should be noted that predation by marine mammals is estimated to be 3 times greater than removals from the fisheries (Skern-Mauritzen et al., 2022), thus having a potential for introducing considerable natural variation in the indicator. For mesozooplankton biomass, there are indications of a link to inflow of Arctic water to the ecosystem, with increasing inflow causing increased biomass (Skagseth et al., 2022). While there is a possible link between Arctic water inflow and climate change, there are large uncertainties associated with this (phenomena #Arctic water). There is also a fishery on mesozooplankton, but the quota is small and the effect on the stock is considered to be negligible (Broms et al., 2016; Hansen et al., 2021a). Although the understanding of the link between fisheries and pelagic fish stock biomass is rated as certain (see phenomena for fish stocks, NwP06, NwP08 and NwP10, i.e., capturing the effects on both pelagic fish and mesozooplankton biomass), is rated as less certain .

The extent to which mesozooplankton affects pelagic fish biomass or vice versa has been subject of several studies. For example, based on negative relationships between fish individual growth and biomass, Huse et al. (2012) suggested that there are clear indications of intra- and interspecific competition over food, that the biomass of pelagic planktivorous fish had been above the carrying capacity in the years preceding the study and that reduction in zooplankton biomass seen in the early 2000s was caused by fish predation. Similarly, based on a study of herring abundance, herring feeding and mesozooplankton abundance, Olsen et al. (2007) suggested that there was a top-down effect from herring on mesozooplankton biomass acting in the western part of the Norwegian Sea. Planque et al. (2022), on the other hand, quantified trophic interactions in the Norwegian Sea pelagic ecosystem using inverse modelling and found no support for top-down control on planktonic prey biomass and little support for the hypothesised competition for resources between the three small pelagic species. Thus, the knowledge about consequences of change in the indicator for the ecosystem as a whole is rated as less good .

High trophic level seabirds [NwI04]

Phenomenon: Decreasing populations of pelagic seabirds [NwP04]

Ecosystem characteristic: Biomass distribution among trophic levels

Under the reference condition, large breeding colonies hosting around 1.8 million breeding pairs of pelagic seabirds are found along the outer Norwegian coast bordering the Norwegian Sea. This rich pelagic seabird community is dominated by Atlantic puffin (Fratercula arctica) followed by black-legged kittiwake (Rissa tridactyla) and common guillemot (Uria aalge) (Brun, 1979). Fauchald et al. (2015) estimated the total population of these species to about 1.8 mill. pairs in 1980. The populations of puffin and common murre declined while the population of kittiwake increased during the 1970s (Brun, 1979), and assuming that populations of other pelagic seabirds is negligible, a conservative reference abundance (“pre-industrial” level) of pelagic seabirds in the eastern Norwegian Sea is about 1.8 - 2 mill. breeding pairs. Other pelagic seabirds that breed in the area include Northern fulmar (Fulmarus glacialis), European storm petrel (Hydrobates pelagicus), Leach's storm petrel (Hydrobates leucorhous), Northern gannet (Morus bassanus), great skua (Stercorarius skua), Arctic skua (Stercorarius parasiticus) and razorbill (Alca torda). Combined, these species counted around 20,000 breeding pairs, or about 3% of the pelagic seabirds in the eastern Norwegian Sea in an estimate from 2015 (Anker-Nilssen et al., 2015). Due to lack of monitoring they are excluded from the present analyses. The major food resource supporting the large colonies of pelagic seabirds in the eastern Norwegian Sea is juvenile fish drifting and residing in the Norwegian coastal current in an area from the spawning sites along the coast to the nursery and feeding areas in the Norwegian and Barents Seas (Anker-Nilssen, 1992; Sætre et al., 2002; Durant et al., 2003; Sandvik et al., 2016). Most notably, the pelagic seabirds prey upon juveniles of the large pelagic fish stocks inhabiting the Norwegian and Barents Seas, including the Norwegian spring spawning (NSS) herring stock (Clupea harengus) and the stocks of Northeast Arctic (NEA) saithe (Pollachius virens), haddock (Melanogrammus aeglefinus) and cod (Gadus morhua) (Anker-Nilssen, 1987; Anker-Nilssen and Øyan, 1995; Barrett et al., 2002; Anker-Nilssen and Aarvak, 2006). In addition, local stocks of sandeels (Ammodytes spp.) are important (Anker-Nilssen and Aarvak, 2006; Christensen-Dalsgaard et al., 2018). These food resources are seasonal with a peak in availability during spring and summer. Accordingly, pelagic seabirds are mainly present in the eastern part of the Norwegian Sea during pre-breeding and breeding (March-July), while they are mostly absent during autumn and winter (Fauchald et al., 2021).

The most important anthropogenic drivers of change in this indicator are fisheries and climate change. Following overfishing in the 1960s, the collapse of the NSS herring in 1970 (Dragesund et al., 2008) resulted in a series of years with low spawning output, low abundance of 0-group herring, and as a consequence, breeding failures and subsequent population declines in Atlantic puffin as well as other pelagic seabirds (Anker-Nilssen and Øyan, 1995; Sætre et al., 2002; Anker-Nilssen and Aarvak, 2006; Cury et al., 2011). The link between overfishing and the indicator is well documented in the Norwegian Sea (ibid.) and similar impacts have been described elsewhere (Cury et al., 2011; Grémillet et al., 2018). In addition to overfishing, climate change affects the seabird populations in a number of more indirect and subtle ways. Importantly, climate change impacts the timing and location of fish spawning and the subsequent production, distribution and survival of juvenile fish. As a result, the fine-tuned interrelationship between the availability of 0-group fish and the breeding cycle of seabirds is disrupted, causing a mismatch between prey availability and seabird reproduction (Durant et al., 2003; Durant et al., 2004). Rapid climate change does therefore negatively affect the productivity of pelagic seabirds breeding in colonies bordering the Norwegian Sea, and this mechanism is partly responsible for the current decline in the populations (Durant et al., 2003). The link between climate change and the indicator is well documented in the Norwegian Sea (ibid.) and similar impacts have been described elsewhere (see e.g., Piatt et al. (2020); Hansen et al. (2021b)). The understanding of the link between the indicator and fisheries and climate change is assessed as certain .

Atlantic puffin, black-legged kittiwake and common guillemot are, together with marine mammals and predatory fish, important predators on juvenile and pelagic fish and constitute a significant part of the top predator guild in the eastern part of the Norwegian Sea (Sætre et al., 2002; Skjoldal, 2004). A large relative drop in the abundance of these species could impact their role as top predators in the ecosystem and would signal negative changes at lower trophic levels. The understanding of the importance of changes in the abundance of pelagic seabirds is assessed as good .

Decreasing abundance of pelagic seabirds can be considered of ecosystem significance if, for example i) there is a sudden drop in the populations caused by a mass die-off of birds following a collapse in the availability of prey due to climate extremes or overfishing, ii) there is a significant gradual long-term (> 10 years) decrease in the populations associated with climate warming and/or decrease in the availability of prey.

Monitoring and research have highlighted the impacts of overfishing and climate change on seabird populations dynamics in the eastern Norwegian Sea. It is, however, difficult to discern the relative importance of the different drivers. Moreover, the negative impact of predation from a growing population of white-tailed eagles has probably also contributed to the decline in the populations of black-legged kittiwakes and common guillemots in the eastern Norwegian Sea (Hipfner et al., 2012).

Copepod body size [NwI05]

Phenomenon: Decreasing average copepod body size [NwP05]

Ecosystem characteristic: Functional groups within trophic levels

Under the reference condition, copepod body size is considered as a key trait in zooplankton as it is related to numerous physiological and ecological processes, e.g., individual growth, metabolic rates, feeding behavior and life history strategies (Pope et al., 1995; Kiørboe, 2011; McGinty et al., 2021). In planktonic communities, body size is of particular importance, because food webs are comprised of regular and progressively increasing size spectra (Sheldon et al., 1972). Copepod b ody size is affecting grazing efficiency, predator prey interactions, and trophic energy transfer and thereby determining the trophic structure and dynamics of pelagic communities (Gorokhova et al., 2013). Zooplankton body size varies with latitude and species tend to be larger in colder, higher latitudes compared to its congeners found in warmer regions (Bergmann's temperature-size rule).

The most important anthropogenic driver of change in zooplankton body size is climate change, in terms of increasing temperature. Higher temperatures cause elevated metabolic rates and energy costs, resulting in smaller body sizes both within species (Record et al., 2012) as well as at the community level (Beaugrand et al., 2002b). Ecological theory and observations suggest that climate warming is expected to favor small copepods over large copepods (Daufresne et al., 2009). This suggests that an increase in temperature should result in a decrease in the proportion of larger-sized individuals and species in a community.

As ocean temperatures increase over the next century, these changes are likely to shift communities into states where smaller phytoplankton and zooplankton species dominate. Significant shifts in zooplankton community structure and size-spectra towards the dominance of the small-sized copepod Oithona similis relative to large-bodied calanoid copepods have already been observed across the global ocean such as in the Arctic (Balazy et al., 2021), the North Sea (Nielsen and Sabatini, 1996; Bedford et al., 2018), the North Atlantic and the Mediterranean Sea (Beaugrand et al., 2003; Goberville et al., 2014; Castellani et al., 2015).

The replacement of large copepods with small ones has also been suggested as an indicator of eutrophication in the Baltic region (Gorokhova et al., 2013; HELCOM, 2018) but in marine systems the causal link between eutrophication and body size is ambiguous (Ndah et al., 2022).

Given the solid evidence described above the understanding of the link between temperature and zooplankton body size is rated as certain .

Changes in the average copepod body size are expected to alter the food web structure and the carbon transfer between trophic levels. Zooplankton communities composed of large-bodied copepods have a higher capacity for transfer of energy from primary producers (phytoplankton) to fish, i.e., higher energy transfer efficiency. By contrast, a dominance of small-bodied copepods is usually associated with lower energy transfer efficiency, due to higher losses (Lewandowska and Sommer, 2010). Thus, a reduction in the mean copepod body size represents unfavorable fish feeding conditions and less efficient utilization of primary production. According to ecological theories, this would represent a less efficient food web (HELCOM, 2018).

As ocean temperatures increase over the next century, these changes are likely to shift communities into states where smaller phytoplankton and zooplankton species dominate. This will result in a less productive system, with decreased trophic efficiency and reduced fecal carbon flux (Hébert et al., 2016).

A reduction in the zooplankton body size will have direct negative impact on fish feeding conditions, fish larval survival and recruitment (Beaugrand, 2005; Pitois et al., 2012). Pitois et al. (2021) found strong correlations between herring distribution and larger copepod mean sizes rather than high copepod abundances, confirming that copepod mean size has the potential to reflect food web and ecosystem health status as well as highlight climatic impacts on marine ecosystems. In the Norwegian Sea the herring selected the larger copepodite stages and adults of C. finmarchicus and C. hyperboreus during its feeding migration (Dalpapado et al. 2000).

Given the substantial evidence described above the understanding of the importance of change in the indicator for other parts of the ecosystem is rated as good .

Decreasing zooplankton body size can be considered of ecosystem significance if i) it causes massive declines in the production and recruitment of fish stocks, and ii) it causes reduced vertical carbon flux (carbon pump).

Knowledge gaps in monitoring and research: In the CPR data set, copepods are classified into two size groups: as Small” (< 2 mm) and “Large” (2> mm). However, dataset including species specific copepod sizes are available (Razouls et al., 2005-2022; Brun et al., 2017).

The interpretation of this phenomenon may be demanding due to top-down effects. Size-selective predation on zooplankton by predators (top-down) will affect the size composition of zooplankton and may counteract climate -induced effects (bottom-up). Future studies should try to disentangle the interaction between top-down and bottom-up control.

Alternative metrics related to copepod size should be investigated further, e.g., “Copepod community body size” as the abundance weighted mean prosome length (Evans et al., 2020). R elative metrics, including both size and abundance may be more informative, e.g. Normalized Biomass Size-Spectra (NBSS) and the Abundance-Size Spectrum of zooplankton, referring to the relative abundance or biomass of zooplankton organisms of different size classes (Thompson et al., 2013).

The MSTS (Zooplankton Mean Size and Total Stock) is a core indicator in the Baltic region, where the lengths of individuals are measured for each species (HELCOM, 2018). A similar indicator exists in the OSPAR area (FW6; Ndah et al. (2022)). However, a major limitation is that zooplankton sizes are not regularly measured in marine monitoring and are usually estimated using mean values from the literature. The lack of in-situ size information will mask any potential long-term change in species-specific size structure. Future monitoring should aim at including size measurements of zooplankton, by the use of laboratory image analyzing methods (e.g., FlowCam) or, preferably, by in situ methods (e.g., VPR).

Mackerel stock size [NwI06]

Phenomenon: Decreasing mackerel stock size [NwP06]

Ecosystem characteristic: Functionally important species and biophysical structures

Under the reference condition, the mackerel stock is one of the largest pelagic stocks in the Norwegian Sea, which is its main feeding area. Over the last 100 years, the perception of the NEA mackerel stock structure and migration patterns has changed considerably (Iversen, 2002). For a long time, the stock was assumed to consist of three populations with distinct migration patterns: Southern, Western and North Sea (Iversen, 2002). However, recent studies indicated no clear evidence of structuring within the populations (Jansen, 2013; Jansen and Gislason, 2013; Gíslason et al., 2020), but rather suggested that the stock is a single population with a dynamic migration pattern, spawning over a large area from the coast of Portugal to the North Sea (Brunel et al., 2017; ICES, 2019b); feeding in North-Sea, Norwegian Sea and adjacent areas (Nøttestad et al., 2016) and wintering around Shetland (Jansen et al., 2012). Since around 2005, the stock has experienced changes in growth, condition, and distribution pattern associated with an increase in recruitment and spawning stock biomass (Jansen et al., 2015; Olafsdottir et al., 2015; Nøttestad et al., 2016; ICES, 2021b). Such dynamics in the distribution of a commercially important stock extending over different exclusive economic zones (EEZs) is clearly a challenge for an effective management (Baudron et al., 2020). Hence, considerable research efforts have been put into understanding the reasons behind observed changes, both regarding the spawning (Bruge et al., 2016; Brunel et al., 2017; dos Santos Schmidt et al., In prep.), feeding (Astthorsson et al., 2012; Jansen et al., 2016; Pacariz et al., 2016; Nikolioudakis et al., 2018; Olafsdottir et al., 2019), and wintering distribution (Jansen et al., 2012). This includes attempts to develop new modeling approaches to study mackerel migrations (Heinänen et al., 2018; Boyd et al., 2020; Payne et al., 2022). The general conclusion from these studies is that the observed changes in distributions are driven by both the density dependent effects of changes in size of the stock as well as oceanographic, environmental, and ecological dynamics. In addition, recent research reveal that the NEA mackerel is highly dependent on its energetic status and feed heavily during the spawning period (Jansen et al., 2021), which ultimately may have significant influence on migration choices during spawning, and between spawning and feeding areas, for a large population under strict competition for prey. Furthermore, a new large scale tagging program have shown that the mackerel undertake size dependent spawning and feeding migrations with the wintering area in Northern North Sea as basis, and that recruits growing up in the North Sea migrates out of the area to feed far west into Icelandic Waters and spawn west of British Isles and farther south as they grow older and larger (ICES, 2021b; Ono et al., 2022). This suggest that any fidelity for a mackerel growing up in the North Sea or other nursery areas to maintain spawning there is low. Furthermore, one should expect that any dynamics in growth and condition, which typically fluctuates with stock size (Olafsdottir et al., 2015), may be an important driver for the migration and distribution of the stock.

The most important anthropogenic drivers of change in abundance of mackerel as a whole, and therefore also for the abundance in the Norwegian Sea feeding area, are fisheries and climate change. Under the recent condition with warming of NEA waters from 2000 onwards (Asbjørnsen et al., 2020; ICES, 2021c; Kjesbu et al., 2022), the NEA mackerel has had series of large year classes leading to high stock levels (ICES, 2021b), but at the same time the fishing pressure has been high with overshooting quotas around 40% since 2010. Under the continuous high fishing pressure with no large recent year classes this has ultimately led to the decreasing stock size after 2014. Our understanding of the link between fisheries and changes in the stock size inside the Norwegian Sea is therefore rated as certain , whereas the link to climate change and recruitment variation leading to more mackerel in the Norwegian Sea as well as the actual dynamics in distribution inside the area is less certain , i.e. although, as described above, scientists do agree that there are climate effects, there is still need for more research to understand actual underlying processes.

Decreasing stock size of mackerel feeding in the Norwegian Sea area would potentially affect the ecosystem. The diet and consumption of mackerel in the area is well known to overlap with the other larger fish stocks in the area Norwegian spring spawning herring and blue whiting (Bachiller et al., 2016; Bachiller et al., 2018), and it has been proposed that mackerel may interact with the other two stocks due to competition for prey, and that there may be top-down effects on zooplankton levels (Huse et al., 2012). Still, it is not confirmed yet that decreasing mackerel stock may have positive effects on the other pelagic fish stocks, or if bottom-up effects are more important for the dynamics. Salmon is another species proposed to potentially suffer from competition with mackerel overlapping during feeding in Norwegian Sea, but recent research have concluded that there is little evidence for this hypothesis (Utne et al., 2021). Instead it is suggested that the salmon is suffering from bottom-up effects and ecological regime shifts leading to changes in zooplankton availability (Utne et al., 2021; Vollset et al., 2022). In the North Sea region mackerel has been proved to feed heavily on 0-1 group of various fishes (ICES, 1997), whereas fish is a minor part of the diet in the Norwegian Sea as a whole as this is not a nursery area for fish in general (Bachiller 2016). However, when mackerel enters the more coastal areas off Norway it demonstrated that it potentially may wipe out local abundance of herring larvae when overlapping in time and space, but the effect on total recruitment is not known (Skaret et al., 2015; Allan et al., 2021). To sum up, our understanding of the importance of change in the indicator for other parts of the system is therefore rated as less good .

Mackerel recruitment [NwI07]

Phenomenon: Change in mackerel recruitment [NwP07]

Ecosystem characteristic: Functionally important species and biophysical structures

Following the argumentation for decreasing stock size above, decreasing recruitment of mackerel into the Norwegian Sea is also directly linked to the changes in the total stock.

The most important anthropogenic drivers in the indicator are fisheries and climate change. The effect of fisheries as a whole is reduced stock, and there is a relation between stock size and recruitment. More spawners produce more eggs, but also expanding distribution that ultimately may affect survival of progeny (ICES, 2019b; ICES, 2021b), so drivers affecting the stock size also indirectly affect the recruitment. Under the recent condition with warming of NEA waters from 2000 onwards (Asbjørnsen et al., 2020; ICES, 2021c; Kjesbu et al., 2022), the NEA mackerel has had series of large year classes. This has happened simultaneously with a north and westward shift in spawning (Brunel et al., 2017; ICES, 2019b; dos Santos Schmidt et al., In prep.) towards Iceland and the Norwegian Sea respectively, which ultimately also has led to more progeny ending up in the Norwegian Sea area visible as 1-2 year olds in the international trawl survey (Nøttestad et al., 2021). It is uncertain whether the shift in spawning areas is related to temperature (Brunel et al., 2017), or more related to migration potential following the size structure and condition of the stock as such and the need for feeding while spawning (Jansen et al., 2012). Both factors may play a role. It is also uncertain whether the more north-western spawning also has resulted in higher survival due to improved environmental conditions for progeny, which indirectly leads to more recruits in the Norwegian Sea area. In conclusion the knowledge about effects of fisheries and climate change for recruitment of mackerel into the Norwegian Sea is regarded less certain .

When interpreting potential ecosystem effects of decreasing recruitment into the Norwegian Sea, i.e., fewer young fish ages 1-2, the geographic distribution is to be taken into account. Here it is evident that the youngest fish is found more south and centrally or closer to Norwegian coast than the older fish (Nøttestad et al., 2021; Bjørdal et al., 2022), presumably due to reduced migration potential (Ono et al., 2022) and the fact that recruits feed closer to their nursery areas (Bjørdal et al., 2022). So, any ecosystem effect of large new year classes in the area would not have an impact over large areas prior to reaching the adult stages. With regard to diet and consumption the knowledge for recruits relative to adults is that they have similar diet (ICES, 1997; Bjørdal et al., 2022). So, the knowledge on potential effects on the ecosystem is similar described above for the total stock. The conclusion is that there is no quantitative evidence of actual ecosystem effects of decreasing abundance of recruits in the area, although this potentially may be the case. Hence, the impact of the indicator recruitment in the Norwegian Sea is rated as less good .

NSS herring stock size [NwI08]

Phenomenon: Decreasing herring stock size [NwP08]

Ecosystem characteristic: Functionally important species and biophysical structures

Under the reference condition, Norwegian spring spawning herring (NSSH, Clupea harengus) is one of the large pelagic fish stocks in the Norwegian Sea (Skjoldal, 2004). Spawning occurs along the Norwegian coast and larvae drift with the Norwegian coastal current into the Barents Sea where they stay till age 3-4 years, before migrating to the Norwegian Sea and join the adult stock there. After spawning in spring, the adult stock migrates into the Norwegian Sea to feed. Overwintering occurs in fjords or close to the coast in northern Norway. NSSH is an important predator of zooplankton, with the calanoid copepods (especially Calanus finmarchicus) as a dominant prey item (Dalpadado et al., 2000; Bachiller et al., 2016). It is itself an important prey species for marine mammals (Skern-Mauritzen et al., 2022), cod, saithe and other demersal species, in addition to seabirds (Holst et al., 2004). The population dynamics of the NSSH is highly influenced by the recruitment dynamics which is characterized by infrequent strong year classes (Fiksen and Slotte, 2002; Sætre et al., 2002; Skagseth et al., 2015; Huse, 2016). NSSH is one of the few species across all Norwegian ecosystems for which there are robust data on population size and variation under the reference condition. The period prior to 1945 can be considered representative for the reference condition as fishing mortality was at a low level and likely not significantly influencing stock size (Toresen and Østvedt, 2000). Data on the stock has been collected since 1907, and virtual population analyses suggests that stock size varied between 2 and 16 million tons between 1907 and 1945 (Toresen and Østvedt, 2000).

The most important anthropogenic driver of change in this indicator is fisheries. Because of implementation of new fishing technology, fishing mortality increased to high levels in the 20 years following 1945, resulting in serious overfishing and collapse of the stock in the late 1960’ies, when the estimated stock size had declined to 0.05 million tons (Dragesund, 1970; Toresen and Østvedt, 2000). Thus, without proper management, fisheries have a potential to cause serious declines in the NSSH stock. Climate change is another anthropogenic driver that should be discussed. An assessment looking at how sensitive NSHH is to impact from climate change based on general life history and ecological interactions as well as climate projections up to 2041, suggests that NSSH will be positively affected by climate change for this period (Kjesbu et al., 2022). It should be noted that the latter study has not addressed directly climate induced changes in herring prey or predators, such as for example changes in zooplankton species composition, which has been shown to be important for fish stock dynamics in the North Sea (Beaugrand et al., 2003). This contributes to uncertainty associated with the assessment of a positive impact of near future climate change on NSHH. In addition, several studies indicate that recruitment is affected by temperature, with a dome shaped relationship between the two variables, suggesting an optimal temperature below and above which recruitment is negatively affected (Toresen and Østvedt, 2000; Toresen et al., 2019), although there are uncertainties with this result (Garcia et al., 2020). A possible negative effect on NSSH recruitment from future temperature increases would contribute further to the uncertainty of the assessment of a positive effect on the stock, and climate change is therefore not considered as an important driver for near future changes in NSHH stock size here. The understanding of the link between fisheries and NSS herring stock size is rated as certain .

Several studies have addressed the ecological interactions between herring and other components of the Norwegian Sea pelagic ecosystem. The influence of interspecific competition as well as competition with the two other large plankton-feeding pelagic fish stocks, mackerel (Scomber scombrus) and blue whiting (Micromesistius poutassou) has been studied by looking at the relationship between length at age and intraspecific and interspecific biomass of these species. While some evidence of intraspecific competition was found for mackerel, no evidence was seen for competition with the other species, suggesting variation in herring biomass do not have strong effects on mackerel. For blue whiting, strong evidence for interspecific competition was found and the vertical distribution between herring and blue whiting appears to be linked so that herring occurs shallower when the abundance of blue whiting is high, indicating interaction between these species (Huse et al., 2012). Based on these findings and observations of a decline in zooplankton biomass concurrent with an increase in pelagic fish biomass, it has also been speculated that the reduction in zooplankton biomass was caused by pelagic fish and that the system is subject to top-down control (Huse et al., 2012). Subsequent modelling work based on the principle of chance and necessity, which allows reconstruction of a large number of possible ecosystem trajectories (Planque and Mullon, 2020) suggest on the other hand that there is some support for bottom-up control, no support of top-down control and weak support for competition between pelagic fish species (Planque et al., 2022). Results related to mackerel are associated with high degree of uncertainty (ibid.). Based on these partly conflicting results, the understanding of importance of change in NSS herring stock size for other parts of the ecosystem is rated as less good .

While the collapse in the herring stock probably allowed high levels of capelin to be sustained in the Barents Sea for many years (Gjøsæter and Bogstad, 1998; Hjermann et al., 2004; Gjøsæter et al., 2009), there is no evidence of similarly strong effects on components of the Norwegian Sea ecosystem.

Knowledge gaps: There are several surveys covering the stock at different times of the year, in addition to a tagging program. However, there is no coverage of the stock during the autumn. It has been shown that there is an extension of the feeding into autumn in later years (Homrum et al., 2022). This study was based on data from the fishery, and a survey coverage of herring and its prey in the Norwegian Sea during autumn would fill an important knowledge gap.

NSS herring recruitment [NwI09]

Phenomenon: Decreasing herring recruitment [NwP09]

Ecosystem characteristic: Functionally important species and biophysical structures

Key aspects of the NSS herring stock under the reference condition are described above for the indicator for stock size. Analyses using data from 1907 suggest that recruitment of the stock is highly variable between years for a period that can be considered descriptive for the reference condition (1907-1945, (Fiksen and Slotte, 2002)).

The most important anthropogenic driver of change in this indicator is fisheries in the sense that high fishing pressure causing severe depletion in stock size can impair recruitment (Fiksen and Slotte, 2002). As described in the phenomenon for NSSH stock size above, future temperature increase may affect recruitment negatively (Toresen et al., 2019), but there are considerable uncertainties associated with this (Garcia et al., 2020), and climate change is therefore not considered as an important anthropogenic driver here. The understanding of the link between fisheries and NSS herring recruitment is rated as less certain , as clear effects can be assumed only after long periods of unsustainable fishing.

As herring larvae and younger age stages reside outside the Norwegian Sea, as described in the phenomena for herring stock size, changes in recruitment does not have other effects on the Norwegian Sea ecosystem than the indirect effects acting through herring stock size. As described above, we have a poor understanding of the consequences of changes in herring stock size for the ecosystem, and consequently the understanding of importance of change in NSS herring recruitment for other parts of the ecosystem is rated as less good.

Knowledge gaps: Investigate how climate variability impacts top-down processes (predation) during the early life stages of NSSH.

Blue whiting stock size [NwI10]

Phenomenon: Decreasing blue whiting stock size [NwP10]

Ecosystem characteristic: Functionally important species and biophysical structures

Under the reference condition, blue whiting is one of the large pelagic fish stocks in the Norwegian Sea (Skjoldal, 2004). The species is most common at 100–600 m depth but is also found close to the surface in parts of the day and close to the bottom in shallow waters. It is observed as deep as 900 meters. Adult blue whiting migrates every winter to the spawning areas west of the British Isles. Eggs and larvae are transported by currents, and the drift pattern varies from year to year. Larvae from the spawning west of Ireland can end both in the Norwegian Sea and in Bay of Biscay. The most important feeding and nursery area is the Norwegian Sea. The food of blue whiting consists mainly of euphausiids, amphipods and copepods (Pinnegar et al., 2015; Bachiller et al., 2016) and they are prey for piscivorous fish (Dolgov et al., 2009) and cetaceans (Hátún et al., 2009). Stock size is strongly influenced by variation in recruitment.

The most important anthropogenic driver of change in this indicator is fisheries. Around year 2000, there was considerable overfishing of the stock (Standal, 2006), causing worries about stock collapse, illustrating the potential of fisheries to cause decline in stock size. The understanding of the link between fisheries and blue whiting stock size is rated as certain .

Adult blue whiting carries out active feeding and spawning migrations in the same area as herring. Blue whiting consequently has an important role in the pelagic ecosystems of the area, both by consuming zooplankton and small fish, and by providing a food resource for larger fish and marine mammals (ICES, 2009a). Several studies have addressed the ecological interactions between blue whiting and other components of the Norwegian Sea pelagic ecosystem. The influence of interspecific competition as well as competition with the two other large plankton-feeding pelagic fish stocks, mackerel (Scomber scombrus) and Norwegian spring spawning herring (Clupea harengus) has been studied by looking at the relationship between length at age and intraspecific and interspecific biomass of these species. While some evidence of intraspecific competition was found for mackerel, no evidence was seen for competition with the other species, suggesting variation in blue whiting biomass do not have strong effects on mackerel. For herring, strong evidence for interspecific competition was found and the vertical distribution between herring and blue whiting appears to be linked so that herring occurs shallower when the abundance of blue whiting is high, indicating interaction between these species (Huse et al., 2012). Based on these findings and observations of a decline in zooplankton biomass concurrent with an increase in pelagic fish biomass, it has also been speculated that the reduction in zooplankton biomass was caused by pelagic fish and that the system is subject to top-down control (Huse et al., 2012). Subsequent modelling work based on the principle of chance and necessity, which allows reconstruction of a large number of possible ecosystem trajectories (Planque and Mullon, 2020) suggests on the other hand that there is some support for bottom-up control, no support of top-down control and weak support for competition between pelagic fish species. Results related to mackerel are associated with high degree of uncertainty (Planque et al., 2022). Based on these partly conflicting results, the understanding of importance of change in blue whiting stock size for other parts of the ecosystem is rated as less good .

Knowledge gaps: In 2014 the ICES Stock Identification Methods Working Group (SIMWG) reviewed the evidence of separate stocks based on the new scientific evidence (ICES, 2014b) and concluded that the perception of blue whiting in the NE Atlantic as a single‐stock unit is not supported by the best available science. SIMWG further recommended that blue whiting be considered as two units. However, there is currently no information available that can be used as the basis for generating advice on the status of the individual stocks. There is still a need for more information regarding population structure in these stocks.

Blue whiting recruitment [NwI11]

Phenomenon: Decreasing blue whiting recruitment [NwP11]

Ecosystem characteristic: Functionally important species and biophysical structures

Key aspects of the blue whiting stock under the reference condition are described above for the indicator for stock size. Recruitment of the stock is highly variable between years. However, there have been periods with different recruitment regimes; a period with low recruitment before 1996, a high level regime in the period 1996-2005, low for 2006-2009 and variable thereafter (ICES, 2021d), but even rather low spawning stock biomasses have resulted in good recruiting year-classes. Spatial distribution of spawning varies between years due to variation in oceanographic conditions, with spawning under fresher and colder conditions in the spawning region occurring mainly along the European Continental Shelf edge west of Ireland, in particular on Porcupine Bank, while during more saline and warmer conditions, spawning expands further westward across Rockall Trough onto Rockall Plateau and shifts northward along the European Continental Shelf Spawning (Miesner and Payne, 2018). Shifts in oceanographic conditions between warm/saline and cold/fresh is linked to variation in the sub-polar gyre, with a stronger gyre producing colder and fresher conditions (Hatun et al., 2009). The eggs and larvae spawned on the Porcupine Bank area (west of Ireland) can drift both towards the south and towards the north, depending on the spawning location, oceanographic conditions, and the effects from wind force, while the spawning products from the northern spawning area west of the Hebrides always drift northwards. The northward drift spreads a major part of the juvenile blue whiting to the Norwegian Sea and adjacent areas from Iceland, Faroes and North Sea to the Barents Sea. The larvae usually settle on the deeper areas of the various shelf edges in autumn and stay more or less associated with bottom the first winter or more, gradually becoming part of the mature stock after two or three years.

The most important anthropogenic driver of change in the indicator is fisheries in the sense that high fishing pressure causing severe depletion in stock size can impair recruitment. While there was a considerable fishery on 0 group on the beginning of the 2000s, thus directly affecting recruitment, fisheries are now performed in the first and second quarter on the spawning grounds, i.e., mainly on mature fish, and thus having a potential to affect recruitment only indirectly through the size of the spawning stock. Although spawning distribution is clearly influenced by variations in the physical environment (Hatun et al., 2009; Miesner and Payne, 2018), this does not give reason to assume a link to climate change as an important driver, as this is connected to variation in the sub-polar gyre (Hatun et al., 2009) and not long term changes in temperature or other parameters that are projected to change as a consequence of anthropogenic impact on the climate. Thus, climate change is not considered an important anthropogenic driver for near future changes in blue whiting recruitment. The understanding of the link between fisheries and blue whiting recruitment is rated as less certain , as clear effects can be assumed only after long periods of unsustainable fishing on the spawning stock.

The understanding of importance of change in blue whiting recruitment for other parts of the ecosystem is rated as less good.

Knowledge gaps: Focus on potential mechanisms that may account for the hypothesized links between the oceanographic climate and the recruitment dynamics.

The predation hypothesis

This hypothesis examines the role of mackerel predation and changes in the spawning distribution of blue whiting. Changes in the spawning distribution leads to changes in the mackerel–blue whiting larvae overlap, and therefore the degree of predation.

The food hypothesis

This hypothesis is based on the amount and availability of food to the larvae and juveniles. Changes in the oceanographic conditions may change the food availability and ultimately impact larval/juvenile growth, survival and recruitment. More research is required to examine these topics (ICES, 2009b).

Calanus finmarchicus production [NwI12]

Phenomenon: Decreasing Calanus finmarchicus production [NwP12]

Ecosystem characteristic: Functionally important species and biophysical structures

Calanus finmarchicus is a key species in the Norwegian Sea. It is the dominant herbivore in Atlantic water masses, but it also occurs in high numbers on the cold side of the Arctic front (Broms and Melle, 2007; Broms et al., 2009; Melle et al., 2014; Kristiansen et al., 2019). It is the main food of herring, mackerel and young blue whiting, and the main initial prey for larvae of cod, herring, saith and haddock (e.g. Ellertsen et al. (1977); Dalpadado et al. (2000); Gislason and Astthorsson (2002); Dommasnes et al. (2004); Broms et al. (2012); Langøy et al. (2012); Bachiller et al. (2016)).

The most important anthropogenic driver of change in the indicator is climate change. Temperature affects the abundance, distribution and phenology of plankton populations. Rising temperatures are causing species to expand at the northern edge of their distribution and retreat at the southern edge (Beaugrand et al., 2009). It may be difficult to disentangle the effect of temperature and advection, as species assemblages are typically associated to a given water-mass (e.g. Melle et al. (2004)). During times with increasing temperature, the abundances of C. finmarchicus and other Sub-Arctic zooplankton species are expected to decrease (see phenomenon for copepod species vulnerable to higher temperature [NwP13]). To the extent that water temperature rise is associated with reduced transport of Arctic water into the Norwegian Sea, this will probably have the most instantaneous effect. Other studies have shown a negative development of mesozooplankton biomass in the Norwegian Sea since about year 2000 (Kristiansen et al., 2019; ICES, 2022d; Skagseth et al., 2022; Utne et al., 2022), but we do not know which species being part of the biomass, are responsible for the decrease. In the waters west of the British Isles, (Planque and Fromentin, 1996) showed a negative relationship between the abundance of C. finmarchicus and temperature, while the opposite was the case for the more temperate species C. helgolandicus. Although these waters are not part of the Norwegian Sea it is worth mentioning since it indicates that in the southern range of its distribution C. finmarchicus will respond negatively to a temperature increase. The Svinøy Standard Section, heading NW from the Svinøy Island off the coast at Møre has been sampled with vertical WP2 nets from 200m depth to the surface. The abundance of C. finmarchicus in the Atlantic region showed a decline from 1996 to 2012. In this study they made no attempts linking the changes in abundance to environmental factors (Dupont et al., 2017), but others have reported an increase in water temperatures in the section over the same time period (Skagseth and Mork, 2012). Given the lack of scientific studies, the understanding of the link between climate change and the indicator is rated as less certain .

C. finmarchicus is the major prey of pelagic fish species like herring, mackerel, and to some extent blue whiting and salmon (Dalpadado et al., 2000; Langøy et al., 2012; Bachiller et al., 2016; Melle et al., 2020; Utne et al., 2022). Eggs and nauplii of C. finmarchicus are also the major prey for fish larvae, especially herring and cod. Therefore, decreased abundance of C. finmarchicus likely will have adverse effect on the individual growth, recruitment, production and distribution and phenology of key commercial fish stocks. Given the mostly circumstantial evidence described above the understanding of the consequences from change in the indicator for the rest of the ecosystem is rated as less good.

Knowledge gaps: There is a lack of knowledge about the effect of increased temperature on the abundance of cold-water species themselves and the processes involved in the numerical regulations of the populations. The effect of reduced abundance of cold-water species on key pelagic fish stocks and carbon sequestration await quantification. Combined time-series analyses and process studies based on data sampled on the same temporal and spatial scale across trophic levels probably are the way forward.

Copepod species vulnerable to higher temperature [NwI13]

Phenomenon: Decreasing number of species sensitive to higher temperatures [NwP13]

Ecosystem characteristic: Biological diversity

The Norwegian Sea contains two main water masses (e.g., Blindheim 2004). In the central and eastern Norwegian Sea, surface waters down to approximately 500 m is dominated by relatively warm and saline Atlantic water. In the west, the East Icelandic Current brings cold and less saline water of Arctic origin into the basin. Towards east this water mass dives under the Atlantic water and forms what Blindheim (2004) referred to as intermediate Arctic water. The Arctic and Atlantic water masses are separated by the Arctic front and the whole region is characterised as Sub-Arctic by Longhurst (1998) and later by Beaugrand et al. (2002a). Arctic and Atlantic water masses contain unique collections of zooplankton species as described by Wiborg (1954), Østvedt (1955), Hirche (1991), Dalpadado et al. (1998), Aßmus et al. (2009), Melle et al. (2004)(and references therein), and Strand et al. (2020). A sampling line for CPR (Continuous Plankton Recorder) was operated during the years from 1948 to 1982, going from the Norwegian coast to Weather Station Mike. Over the years copepods with smaller body size showed great variability in abundance while larger species like C. finmarchicus and Metridia longa varied far less, showing no long-term trend (Aßmus et al., 2009). Only wind conditions showed any relationship to the variations in abundance (Aßmus et al., 2009). A key species in the Norwegian Sea is the copepod Calanus finmarchicus. Being an Atlantic species, it is the dominant herbivore in Atlantic water masses, but it also occurs in high numbers on the cold side of the Arctic front (Broms and Melle, 2007; Broms et al., 2009; Melle et al., 2014; Kristiansen et al., 2019). It is the main food of herring, mackerel and young blue whiting, and the main initial prey for larvae of cod, herring, saith and haddock (e.g. Ellertsen et al. (1977); Dalpadado et al. (2000); Gislason and Astthorsson (2002); Dommasnes et al. (2004); Broms et al. (2012); Langøy et al. (2012); Bachiller et al. (2016)). An Arctic complement to C. finmarchicus is Calanus hyperboreus (Hirche, 1991). While C. finmarchicus typically feature a one-year lifecycle, spawning at the surface closely linked to the phytoplankton spring bloom, C. hyperboreus spawn at several hundred meters during winter while the nauplii move to the surface to feed when the phytoplankton bloom occurs, a behaviour developed in response to unpredictable Arctic blooms (Hirche, 1991). Melle et al. (2004) gives an overview of species common to the Atlantic and Arctic water masses of the Norwegian Sea. The Atlantic copepod community: Calanus finmarchicus, Metridia longa, Paraeuchaeta norvegica. Their Arctic equivalents often from the same genus: Calanus hyperboreus and Paraeuchaeta glacialis. By statistical analyses of the CPR data from 1958-1999, Beaugrand et al. (2002a) grouped copepod species into 9 species assemblages ranging from Subtropical and warm-temperate assemblage to Arctic assemblage with their associated geographical distribution (Table 5.2).

The most important anthropogenic driver of change in the indicator is temperature increase. Temperature affects the abundance, distribution and phenology of plankton populations. Rising temperatures are causing species to expand at the northern edge of their distribution, while they are retreating at the southern edge (Beaugrand et al., 2009). It may be difficult to disentangle the effect of temperature and advection, as species assemblages are typically associated to a given water mass (e.g. Melle et al. (2004)). During times with increasing temperature the abundances of copepods, being members of the Sub-Arctic and Arctic species assemblages (Table 5.2), are expected to decrease. To the extent that water temperature rise is associated with reduced transport of Arctic water into the Norwegian Sea, this will probably have the most instantaneous effect. Other studies

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