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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 (PAEC1) and the current assessment has considered to what extent the Norwegian Sea pelagic ecosystem deviates from the reference condition2 by evaluating change in trajectories. Panel-based Assessment of Ecosystem Condition of the Norwegian Sea Pelagic Ecosystem

The Workshop on pathways to climate-aware advice (WKCLIMAD) met in the autumn of 2021 to develop a proposal for an advisory framework that accounts for the influences of climate change on aquaculture, fisheries, and ecosystems. The workshop worked through online sessions with over 40 participants.Climate-informed advice should be provided through a risk-based framework that considers magnitude and likelihood of impacts, effectiveness and feasibility of measures. A wealth of data, tools and methods exists to on-ramp the advice. However, it is important to consider how these are utilised. To provide robust climate-informed advice, there is a need to identify and rank climate impacts and the associated risks, and match adaptation measures with public policy objectives. There must be a balance between actionable advice and reporting of uncertainty.The next steps for ICES should be to evaluate these three recommended additions to the advice framework/principles:• Development of a framework for spatial knowledge and advice, that includes definitions of temporal and spatial scale of management challenges.• Proactive solicitation of experts and stakeholders in relevant fields. Co-production of knowledge with iterative feedback, accounting for the plurality of knowledge and participation mechanisms.• Formulation of a plan for outputs, and communication, from the start of process, including allocation of sufficient resources to deliver advice.Greater emphasis needs to be placed on the communication and co-creation of advice.Climate-informed advice should include an assessment of current conditions in relation to the desired state. This requires not just an evaluation of the current state of the system, but the likely and/or desired future state of the fisheries/aquaculture system. This will also require greater effort on scoping of future scenarios of ecosystem state, and potential management measures for adaptation, and some mitigation. Advice should document the expected effects of specific management actions, giving attention to the potential distribution of management costs and benefits. Advice should be produced in response to requests but also be proactively produced by ICES. WKCLIMAD provides ICES with definitions, language and terminology that align with, and build on, those of the IPCC and calls on ICES to consistently use this terminology. The workshop also provides example lists of drivers, impacts, measures and potential actions.ICES needs to attract expertise from beyond its traditional areas of ecosystem and population dynamics and oceanography. Engagement with the plurality of the knowledge base is required, as a mean to refine goals, explore trade-offs between management objectives, as well as to build a common understanding and build knowledge about the system and efficient pathways of action to governance. WKCLIMAD considered that to provide credible climate-informed advice, the evidence base needs to be strengthened in the following scientific fields:• future scenarios of management options and ecosystem state• risk, vulnerability and resilience analysis of species, ecosystems, and human communities• spatial planning information and models• trade-offs among potential actions, and incentives for best practice sharing including technological developments• carbon accounting across the system• monitoring and early-warning systems

Numerical models of ecological systems are increasingly used by scientists to address complex environmental questions. One challenge for scientists, managers, and stakeholders is to appraise the performance of these models to answer specific questions of scientific or societal relevance, that is to perform, communicate or access transparent evaluations of ecological models. While there have been substantial developments to support standardised descriptions of ecological models, less has been done to standardise and to report model evaluation practices. We present here a general protocol designed to guide the reporting of model evaluation. The protocol is organised in three major parts: the objective(s) of the modelling application, the ecological patterns of relevance and the evaluation methodology proper, and is termed the OPE (objectives, patterns, evaluation) protocol. We present the 25 questions of the OPE protocol which address the many aspects of the evaluation process and then apply them to six case studies based on a diversity of ecological models. In addition to standardising and increasing the transparency of the model evaluation process, we find that going through the OPE protocol helps modellers to think more deeply about the evaluation of their models. From this last point, we suggest that it would be highly beneficial for modellers to consider the OPE early in the modelling process, in addition to using it as a reporting tool and as a reviewing tool., The published version of this article is available at: https://doi.org/10.1016/j.ecolmodel.2022.110059

This report presents a study of possible changes in species’ spatial distribution in the Barents Sea as a result of possible future changes in the ocean climate. Species Distribution Models (SDMs) are constructed to describe and quantify the relationship between past distribution of species and environmental conditions. On the basis of simple environmental scenarios, the same models are used to project possible changes in individual species’ spatial distributions. The work was conducted under the NFR funded project BarEcoRe: Barents Sea Ecosystem Resilience under global environmental change.

The processes that control the spatial distribution of North Sea whiting (Merlangius merlangus) spawning adults are investigated using a statistical multi-model approach. Models of external and internal controls on the population, such as environmental conditions, spatial constraints, present or past spatial distribution, and demographic state of the population, are evaluated, compared and ranked to select those that are the best able to predict the observed distribution of spawning adults. Model selection is greatly influenced by the selection method, either based on data fitting or prediction, as well as by the threshold value used to stop the selection. Model selection based on prediction tends to select simpler models than selection based on data fitting. The hypotheses underlying the selected models are inferred to play a significant role in controlling North Sea whiting spatial distribution. The multi-model inference approach developed in this study enables comparison of several theoretical concepts and hypotheses and the results provide important clues on the processes involved in the control of the spatial distribution of whiting. We conclude that whiting has a high spatial fidelity to spawning site which can be linked to either geographical attachment or year-to-year persistence of the spatial distribution of the population. Environmental factors - temperature and salinity - appear to influence the geographical extent of spawning whiting distribution, whereas local abundance levels are primarily controlled by internal factors, i.e., population size and spatial segregation between ages.

This document reports on the results of the project 13327 on establishing methods for the surveillance of deepwater resources. After a presentation of the project mandate and background information on past surveys and their role for advice and research, a general method for survey evaluation is presented and specific aspects of survey design are evaluated with quantitative methodologies. Based on the results of these analyses, a multiannual survey strategy is proposed. It is build on three core surveys: two surveys along the continental slope approximately 62°N-75°N (Egga Sør) and 62°N-80°N (Egga Nor), and a survey in the Norwegian Sea (“open sea deepwater”). Egga Nor and Egga Sør should be conducted every two year, alternating, while the schedule of the “open sea deepwater” survey should be defined in accordance with recommendations from ICESWGRS. Additionally deepwater stations should be maintained on ecosystem survey in the Barents Sea and on shrimp survey in the North Sea (Norwegian Trench). This new survey strategy offers a 4 substantial reduction in requested annual ship-time in comparison with recent years, even for 2011 when all three surveys should be conducted.

Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology. The spatial distribution of fish populations observed in the wild results from the complex interactions of multiple controls both external or internal to the fish populations. Whilst species distribution models (SDMs) have been mostly concerned with static description of species distribution as a function of environmental constraints, models of animal movements (MAMs) have focussed on the dynamic nature of spatial distribution of groups of individuals under a number of constraints external and internal to the population. Besides SDMs and MAMs, modelling the spatial distribution of fish populations can be achieved by models that are fundamentally static (like SDMs) but can also incorporate many hypotheses on the control of fish spatial distribution (like MAMs). The hypotheses underlying these models need to make sense at the population level - rather than at the individual or species level –we term these‘population distribution models’ (PDMs). PDMs are statistical models that rely on several hypotheses, which include: (i) control through geographical attachment, (ii) environmental conditions, (iii) density- dependent habitat selection, (iv) spatial dependency, (v) population demographic structure, (vi) species interactions and (vii) population memory. We review the basis behind each of these conceptual models and we examine corresponding numerical applications. We argue that the conceptual models are complementary rather than competing, that existing numerical applications are still rarely compared and combined, and that PDMs can offer a statistical framework to achieve this. We recommend that the numerical models associated with different hypotheses be constructed within such a common general framework. This will permit evaluation, comparison and combination of the multiple hypotheses on fish spatial distribution. It will ultimately lead to a more comprehensive understanding of the factors controlling the spatial distribution of fish populations and to more accurate predictions in which model uncertainty is accounted for.