# The likely suspects framework: the need for a life cycle approach for managing Atlantic salmon (*Salmo salar*) stocks across multiple scales

C. D. Bull ID 1,2,*, S. D. Gregory ID 3,4,5, E. Rivot 6, T. F. Sheehan 7, D. Ensing 8, G. Woodward 9
and W. Crozier 1

The ongoing declines in Atlantic salmon populations across its range underscore the need for co-ordinated scientific-based knowledge to support management and decisions for their conservation. Current salmon management actions remain largely focused on addressing bottlenecks to production in the freshwater phase of the life-cycle, whereas the continued declines observed in the recent decades are thought to be driven primarily by constraints on the marine phase. The challenges brought by global warming and other emerging stressors require immediate actions, requiring us to re-think the methods behind stock assessment and forge stronger linkages between data, models and policies to promote more effective management actions. We outline a scientific framework that takes a wider ecosystem view, designed to evaluate holistically a suite of indicators and potential drivers of salmon mortality at key phases of the life cycle. The aims of the proposed "Likely Suspects Framework" are to enhance cross-fertilisation of ideas between assessment processes at the stock-complex scale and stock-specific focused management activities, and to develop new decision support tools to improve management efficiencies and scenario testing. Adopting such an approach provides a new way to catalyse the acquisition and deployment of both existing and new data and models that are urgently needed for assisting the conservation and future stewardship of salmon stocks on both sides of the Atlantic.

**Keywords:** ecosystem-based-management, life-cycle approach, Salmo salar, stock assessment.

## The status of Atlantic salmon stocks and their assessment

Atlantic salmon (*Salmo salar*) is one of the most economically, socially, and ecologically important fish species in both North America and Europe, reproducing in more than 2500 rivers and spending up to 7 years as juveniles in the freshwater phase and 1–4 years at sea (Thorstad *et al.*, 2011). In recent decades its stocks have been in decline, driven largely by reduced machine survival rates (Chaput, 2012; ICES, 2021) across most of its North Atlantic range. Atlantic salmon population dynamics are shaped by multiple biotic and abiotic drivers from local (stock-specific, e.g. Rosseland and Kroglund, 2011) to basin-wide (multi-stock, e.g. Olmos *et al.*, 2020) scales. These have their counterparts in current stock management measures whereby different systems operate at different geographical scales, from local rivers to regional, national, and ultimately entire ocean basin scales. This tiered system of management creates unique opportunities, as it links local expertise and data from across scales, but also considerable challenges. These include an incomplete knowledge of the

mechanisms driving population changes (Thorstad *et al.*, 2021), combined with limited acquisition and mobilisation of existing knowledge from across the life cycle (ICES, 2020). This creates the potential to allow misalignment between the scales of the management actions employed and the relevant pressures influencing stock status. Here, we present the “Likely Suspects Framework” as a vision for improving management of Atlantic salmon by co-ordinating knowledge transfer more effectively across scales and expanding our view beyond the single-species focus that has prevailed to date to more explicitly consider interactions between salmon and their ecosystems.

The growing crisis in Atlantic salmon stocks across most of their native range has been ongoing since at least the 1990s, despite widespread moratoriums on their harvest in high-seas and home-water fisheries (Beugrand and Reid, 2012; Mills *et al.*, 2013; Chaput, 2012; ICES, 2021). The mechanisms underpinning the declines in marine survival (or more generally, the smolt-to-adult return rate) are currently unknown (see Thorstad *et al.*, 2021 for review), but suggestions include

<footnote n="1">¹Atlantic Salmon Trust, Battleby House, Redgorton, Perth PH1 3EW, UK</footnote>

<footnote n="2">²Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK</footnote>

<footnote n="3">³Salmon and Trout Research Centre, Game and Wildlife Conservation Trust, Wareham, Dorset BH20 6BB, UK</footnote>

<footnote n="4">⁴The Centre for Environment, Fisheries and Aquaculture Science, Barrack Road, Weymouth, Dorset DT4 8UB, UK</footnote>

<footnote n="5">⁵Department of Life and Environmental Sciences, Faculty of Science and Technology, Bournemouth University, Poole BH12 5BB, UK</footnote>

<footnote n="6">⁶DECOD (Ecosystem Dynamics and Sustainability), Institut Agro, INRAE, IFREMER, 35042 Rennes, France</footnote>

<footnote n="7">⁷NOAA Fisheries Service, Northeast Fisheries Science Center, 166 Water Street, Woods Hole MA 02543, USA</footnote>

<footnote n="8">⁸Fisheries and Aquatic Ecosystems Branch, Agri-Food and Biosciences Institute Northern Ireland, Belfast BT9 5PX, UK</footnote>

<footnote n="9">⁹Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire SL5 7PY, UK</footnote>

<footnote>*Corresponding author: tel: +44(0) 7736644192; e-mail: colin@atlanticsalmontrust.org</footnote>
<figure><img src="image_1.png" /><figcaption>**Figure 1.** Conceptual overview of the life cycle of the Atlantic salmon showing hierarchical scaling of primary environmental drivers influencing stock dynamics across the marine and freshwater life stages. Processes leading to salmon mortality (red negative symbols) arise from multiple natural (dark blue circles) and anthropogenic (light blue circles) sources. Focused management strategies (green circles) intending to counter these losses (green positive symbols) and enhance stock status require benefit from knowledge of the processes acting across the life-cycle to optimise their effectiveness.</figcaption></figure>

mechanisms related to changes during both the freshwater and marine phases of their life cycle. The connections between these phases could be particularly critical bottlenecks, as changes in freshwater conditions have been linked to shifts in the timing and size of seaward migrating juveniles (Russell *et al.*, 2012; Thorstad *et al.*, 2012; Otero *et al.*, 2014; Armstrong *et al.*, 2018; Gregory *et al.*, 2018, 2019; Simmons *et al.*, 2021, in press). Whilst we are not looking at the imminent global extinction of the Atlantic salmon, the recent declines in abundance have rendered it a species of conservation concern and effectively non-viable commercially in some areas where it was previously numerous. There is a need to deepen our understanding of what is driving changes in salmon abundance and to prioritise the threats and management actions to mitigate them.

Altered abiotic ocean conditions have been linked to declines in the abundance, growth and survival of salmon at sea (Beaugrand and Reid, 2003; 2012; Mills et al., 2013; Friedland et al., 2014; Olmos et al., 2020; Todd et al., 2021; Trehin et al., 2021; Harvey et al., 2022; Vollset et al., 2022). Changes in biotic drivers, including the abundance and feeding of marine predators (Strom et al., 2019), prey distribution, abundance and quality (Renkawitz et al., 2015; Jansen et al., 2021; Utne et al., 2021a, b) and the broader activities of marine fisheries (ICES, 2021), have also been implicated in stock declines (Figure 1). These (and other) drivers have the potential to affect the time spent in various habitats by different life stages (Thorpe et al., 1998) and thus cumulative mortality risks (Mangel and Satterthwaite, 2008). Drivers influencing state, duration and functional traits of the different life stages can operate across multiple scales, often in interaction (Brook et al., 2008), and several may be operating

either in synchrony or asynchronously—as stressors rarely occur singly in space or time (Jackson *et al.*, 2021). Atlantic salmon stock assessment and management operates at multiple organisational scales, ranging from local through to national and international levels. Local (e.g. single river stock) management activity is largely focused on actions maximising freshwater production or adult spawners and egg deposition, using locally available and specifically compiled data. At the other end of the organisational scale, cooperative management and conservation efforts in the mixed stock fisheries at West Greenland and Faroes are coordinated internationally by the North Atlantic Salmon Conservation Organisation (NASCO), supported by stock advice from the International Council for the Exploration of the Sea (ICES). These assessments are generated by the ICES Working Group on North Atlantic Salmon (WGNAS) using outputs from three separate but linked models (run-reconstruction, stock forecast and catch-advice) underpinned by data on returning stock estimates and commercial and recreational catches (ICES, 2021). The objectives of these models are to reconstruct time series of abundance at sea (starting in the early 1970s) before any targeted marine fisheries (Pre-Fishery Abundance) take place and to forecast the annual returns of adult salmon to their homewaters. The model captures environmental stochasticity on the marine survival and the proportion of fish maturing as in the first year at sea. It assumes that ocean climate effects are random as there are no climate or environmental variables used to explain the temporal variability. On the hindcasting phase of the analysis, time series (random walk) models are fitted to the observed year-to-year variations of survival and maturation to capture the past variation. The same time series models are then used to forecast the returns 3 years
ahead while propagating uncertainty based on the amount of variance estimated from the hindcasting phase. Model outputs are incorporated into a risk analysis framework to assess the consequences of mixed-stock marine salmon fisheries on the returns, and to assess compliance of forecasted spawning escapement against conservation limits (biological reference points below which the stocks should not fall).

Conventionally, in Europe, conservation limits are defined as the number of spawners required to achieve egg deposition that maximises average harvest of adult fish, commonly known as the Maximum Sustainable Yield (MSY) (Prévost and Chaput, 2001). An alternative biological reference point now applied in Canada, but not yet in US or Europe, is the egg deposition needed to maximise recruitment to the marine stage (i.e. maximum smolt production) (Chaput et al., 2015). Conservation limits are produced across the national/regional stock units in each of three continental stock complexes (North America and the southern and northern North East Atlantic) and compared to Pre-Fishery Abundance forecasts for the next three years. The probability of achieving aggregate conservation requirements for each national/regional stock unit and for the three continental stock complexes are calculated and considered against current management objectives, to produce fishery catch options.

ICES' efforts are focused largely on forecasting salmon stock status to underpin advice on allowable catch in mixed-stock marine fisheries and to guide homewater fisheries advice, relying on the coordination of salmon population monitoring across a network of "index" rivers and catch statistics from commercial and recreational fisheries (ICES, 2021). In response to declining stock status in recent decades the work of WGNAS has been evolving to more fully consider how the results from new research and wider ecosystem influences may impact the productivity of Atlantic salmon stocks across the North Atlantic and how future assessment model development may better consider these factors. The current WGNAS assessment model is likely to be superseded within the next three years by a new single Bayesian Life Cycle Model to harmonise future stock assessments (hereafter Life Cycle Model; Massiot-Granier et al., 2014; Olmos et al., 2019; ICES, 2021).

## Linking stock assessment to management of Atlantic salmon populations

Stock assessment simply seeks to quantify and compare fish numbers against a desired state and (in theory) does not require any understanding of fish ecology or behaviour: it is phenomenological rather than truly mechanistic or predictive, and so it has limited forecasting ability, especially under changing conditions, such as warming or altered food supplies. Stock management, on the other hand, requires a more mechanistic understanding of why certain stock units, or wider complexes of stock units, change and how management actions affect them in a predictable manner.

The international Atlantic salmon stock assessment process is undertaken to provide catch advice for the mixed stock fisheries at West Greenland and the Faroes, as requested by NASCO, and is not designed to provide quantitative management advice at the scale of regional or individual river salmon stocks. As single-species stock advice, its provision does not currently take full advantage of the huge volumes of available data and knowledge relating to potential ecosystem controls and drivers. For instance, the assessments do not

incorporate key environmental variables or possible interactions with other marine species in the Atlantic food web. The capacity to forecast how multiple biotic and abiotic drivers shape current and future salmon stocks currently remains limited.

International Atlantic salmon management is bound by the legislative catch controls derived by NASCO, and the trends and modelled projections of stock abundance. Management planning may be further informed to varying degrees by national and regional guidance generated from individual countries. Despite these multiple levels of acquisition and input and transfer of knowledge across the management scales, some gaps remain that may be limiting success. For example, the implementation of many salmon management activities remain largely constrained by our poor understanding of the mechanisms driving temporal patterns in abundance, and how these relate to stock-specific actions. Addressing these constraints will require new cooperative research efforts. We believe these should include (but not be limited to) investigations on how altered energy flow within the North Atlantic ecosystem and changing energy densities of important Atlantic salmon prey items may be altering salmon growth and survival, how conditions experienced during the freshwater phase may effect life history during the marine phase, and on the evolutionary responses to various anthropogenic and naturogenic influences. Coupled with advances in these key research areas, an improved understanding of the marine migrations undertaken by salmon is still required to support future spatiotemporal-explicit hypothesis testing.

Developing stock-specific management strategies to address impacts of Atlantic basin-wide drivers such as climate change would benefit from a wider life-cycle context to assist with making resource allocation decisions. New knowledge on the growth and survival responses of salmon at particular life stages needs to be combined with an understanding of cumulative responses across the life-cycle, and what these mean for predicting adult population dynamics. These bottlenecks are now being recognised within the stock assessment process, with moves to address them more explicitly within life-cycle modelling approaches (e.g. Olmos et al., 2020), but these approaches will remain constrained by the sparse and incomplete data and forecasts available from the large suite of candidate environmental variables suspected to drive salmon population dynamics. An accessible framework that links information on the ecological processes governing salmon survival across the life-cycle, and across scales (e.g. global, regional and local environmental drivers: Figure 1), would be highly beneficial for delivering new knowledge to assist salmon management efforts.

We recommend targeting the gaps between the existing stock-complex focused assessment process and the requirements of single-stock focused management by utilising all available information to expedite the development of more effective Atlantic salmon management strategies. New approaches can be harnessed alongside the mobilisation of existing data to better integrate knowledge on key ecosystem processes and salmon population dynamics for building more informed management and decision-making frameworks. Pacific salmon management has already begun this transition by developing tools that span the assessment and management realms to improve knowledge flow and provide management tools (e.g. the Risk Assessment Methodology for Salmon or RAMS approach; Hyatt et al., 2017), including the
integration of life history and life cycle modelling (Hare *et al.*, 2016; Crozier *et al.*, 2019; 2021; Scheuerell *et al.*, 2006; 2021; Walsh *et al.*, 2020). Indeed, work is also underway to include spatial data on migration and catches at sea for Atlantic salmon stocks in the Baltic Sea (Whitlock *et al.*, 2021). Building this more holistic understanding of the Atlantic salmon's ecosystems for improving stock assessment and management is a considerable challenge, but it potentially offers high rewards. These include more effective management based on new insights into how the multiple pressures can lead to the nonlinear, time-lagged, and often unexpected outcomes we see in the real world, but which cannot be captured by current approaches. To improve the current situation, we need to shift our focus from single-species observations to more sophisticated systems-based approaches that offer better approximations of reality.

## Embedding Atlantic salmon within an Ecosystem-based management framework

The ecosystem-based management approach to resource use has evolved in recent years due to the growing realisation that focal species are just one component of a much larger and more complex ecosystem (Morishita, 2008; Levin *et al.*, 2009; Curtin and Prellezo, 2010; Link, 2010; Link and Browman, 2014; 2017 and references therein). This broader management view considers how biotic and abiotic components are interlinked, emphasising the need for understanding ecosystem properties and the services they deliver (Curtin and Prellezo, 2010). In the context of fisheries management, ecosystem-based approaches can provide better quality management advice for specified components of an ecosystem (e.g. a target fish species) by linking it with other dynamic components (e.g. via predator-prey relationships, environmental forcing, habitat overlaps) (Pikitch *et al.*, 2004; Link, 2010) and they also have predictive power for defining expected baselines and deviations from them at these higher organisational levels (e.g. Nicholson and Jennings, 2004). These benefits have led to ecosystem approaches becoming broadly accepted as crucial for effective marine resource management, including multi-species stock assessments and rebuilding damaged ecosystems (Levin *et al.*, 2009).

Implementing an ecosystem-based approach requires data on the likely abiotic and biotic drivers operating within the system (reviewed by the NASCO-ICES Salmon Mortality at Sea workshop in 2019; ICES, 2020a) and key indicators (e.g. abundance of prey and predators) that can provide useful proxies for stock status and ecosystem productivity (Jennings, 2005; Link, 2010; Large *et al.*, 2013, 2015; Otto *et al.*, 2018). For example, large basin-scale climatic and oceanographic indicators (e.g. the North Atlantic Oscillation) have been used to represent abiotic conditions that influence salmon survival in the ocean (e.g. Friedland *et al.*, 2014; Todd *et al.*, 2021). Regional trends in salmon population dynamics have also been linked to sea surface temperature or primary productivity (Olmos *et al.*, 2020). Typically, these large-scale indicators only weakly represent variability shown in individual salmon populations, likely because they are influenced by multiple layers of drivers operating at different scales, with some (e.g. predation and resource competition in local food webs) coming into play increasingly as spatial scale is reduced (e.g. Winship *et al.*, 2015), and also because different populations with different genetic pools can respond differently to

the same driver. Ecosystem complexity and the intricate interactions with individual populations may limit the success of some environmentally-based forecast processes (e.g. Wainwright, 2021), and the design of new management systems that rely on their outputs will need to be robust enough to accommodate uncertainties. These early insights into high-level ecosystem indicators nonetheless merit deeper investigation and eventual integration into existing modelling frameworks for Atlantic salmon, and as coverage improves, they will help to direct future ways of disentangling proximate and ultimate drivers of change. We suggest that considering these largely ignored relationships will generate important new opportunities for evaluating and refining the biological reference points used in stock status evaluation both now and in the future.

To combat issues of scale, suites of indicators representing different scales could be used to represent different drivers of stock dynamics (Pardo *et al.*, 2021), especially as salmon move through the food web over both space and time as they grow. For example, developing marine prey species indicators could exploit datasets used by ICES for annual fish stock assessments, integrating spatial parameters from recent salmon migration studies (e.g. Gilbey *et al.*, 2021; Rikardsen *et al.*, 2021) and dietary changes. Salmon feed mostly on various fish larvae and large zooplankton during their early marine migration (Rikardsen *et al.*, 2004; Haugland *et al.*, 2006; Hvidsten *et al.*, 2009; Renkawitz and Sheehan, 2011) and larger fish prey, such as capelin (Mallotus villosus), blue whiting (Micromesistius poutassou) and sandlance (Ammodytidae spp), as they grow and mature (Jacobsen and Hansen, 2001; Rikardsen and Dempson, 2011; Renkawitz *et al.*, 2015; ICES, 2017). Species interactions (Huse *et al.*, 2012) will influence the feeding and growth of salmon both directly (e.g. metabolic constraints on individual salmon) and indirectly (e.g. metabolic constraints on their prey, competitors and predators) (Utne *et al.*, 2012; 2021a, b). Developing suitable indicators for salmon that reflect these interactions will require the consideration of population data not only from other fish species (Lacroix, 2013; Strom *et al.*, 2019), but also for other taxa, such as seabirds (e.g. Montevecchi, 2007) and marine mammals (e.g. Middlemas *et al.*, 2003).

Identifying data sources, developing suitable indicators, setting thresholds and translation into decision-making criteria are challenging, but they move us considerably closer to capturing ecosystem complexity and the multivariate reality of management issues on the ground (Link, 2005). Following from the start made in identifying potential marine data sources by NASCO-ICES (ICES, 2020), we now need to identify how best to marshal our existing data and models to better prioritise future actions to meet these goals. This will require new levels of international cooperation and integration to join the dots from across the existing knowledge base and to identify the key gaps to fill in the next phases of the process (Woodward *et al.*, 2021). A key next step will be to garner the existing data for identifying which are the best indicators across relevant temporal and spatial scales, to help prioritise the next phase of modelling, validation and subsequent targeted data collection to plug the remaining critical gaps iteratively.

## Promoting a new approach for future management of Atlantic salmon stocks

The Likely Suspects Framework for Atlantic salmon was conceived by a consortium of scientists and stakeholders to unify
past and future efforts to understand the mechanisms affect-
ing salmon survival across all life stages (Crozier *et al.*, 2017).
It is an organisational approach to provide the resources nec-
essary to test robust hypotheses about the relative strength,
direction and shape of putative drivers (i.e. the "likely sus-
pects") associated with mortality at each part of the life-cycle.
The overall objective is firmly focused on developing and pro-
viding the scientific basis for a more ecosystem-based man-
agement framework for salmon and the provision of a wider
range of decision support tools. This will be advanced via four
routes:

1. Better describe conditions facing salmon during the marine phase of their life-cycle,

2. Mobilise existing evidence and synthesise new data to test the resulting hypotheses relating to reduced ocean survival,

3. Test and rank these against competing hypotheses,

4. Assist the movement away from single-species stock status and catch advice towards a wider ecosystem management and assessment-based system.

Although these form a natural progression, in terms of the
logical order of an ascending hierarchy of complexity and
data-needs and availability (Woodward *et al.*, 2021), each of
the four areas of work can be at least partially addressed in
parallel, so it is not a single linear timeline and rapid progress
can be made in each of these areas even in the near term.
To advance the Likely Suspects Framework process, a con-
sortium of UK-based salmon conservation organisations—
the Missing Salmon Alliance (https://missingsalmonalliance.
org/)—launched a programme to work in 2019 collabora-
tively with salmon stock assessment and management groups
worldwide. The programme is providing resources, focusing
future research initiatives, assisting with the improvement of
salmon stock assessment and the generation of management
guidance. It is designed explicitly to align with other such pro-
grammes (Levin *et al.*, 2009; NMFS, 2016; Hare *et al.*, 2019;
Wells *et al.*, 2020) to maximise its transferability and begins
with identifying objectives and priorities to underpin devel-
opment. It is recursive by design, so lessons that are learned
can help refine objective(s) that enter the cycle again. The first
steps in the Likely Suspects Framework process are focused on
the marine environment, given its impact on population pro-
ductivity and the state of the current assessment methods and
processes, and it will then fan out to include drivers of salmon
survival across all life stages.

The research objectives and candidate mortality hypothe-
ses for the Likely Suspects approach have been formulated
by expert-led assessment. Examples of this type of approach
towards identifying mortality hypotheses are provided by
O'Neil et al. (2000), Cairns (2001), Peterman et al. (2010),
Beauchamp et al. (2012), Ó Maoiléidigh et al. (2018) and Ol-
mos et al. (2020). Candidate mortality hypotheses cover the
entire life cycle and represent the current focus and future vi-
sion for collaborative research.

Collating the necessary datasets and building the analyt-
ical framework to test mortality hypotheses is challenging.
There is still a paucity of data at certain spatial and tem-
poral scales that can be matched to salmon-specific datasets,
and inadequate data mobilisation frameworks and incomplete
understanding are common obstacles to advancing this ap-
proach (ICES, 2020). Facilitating improved organisation and
mobilisation of relevant information began within the Likely

Suspects Framework with the development of a central data
resource—a database that adheres to FAIR data standards
(Wilkinson et al., 2016). Since it is initially focused on collat-
ing data resources identified by NASCO-ICES (ICES, 2020)
it includes the concept of “space-time domains” occupied by
in the Atlantic salmon during the different phases of their life
cycle (Olmos et al., 2020). This approach is used as an organ-
isational structure to incorporate spatially- extensive knowl-
edge resources and assist with making them useable in testing
mortality hypotheses. This organisation is further informed
by recent studies on the ocean migration of Atlantic salmon
(e.g. Ounsley et al., 2020; Gilbey et al., 2021; Rikardsen et
al., 2021) that provide opportunities for spatial and temporal
refinement on the key space-time domains occupied.

Once organised into the database, resources can be mobilised and used to evaluate the current state of knowledge on relevant mortality hypotheses, associated mechanisms and drivers, and for identifying knowledge gaps that still need to be filled. Testing hypothesised influences on salmon survival could then follow a multidisciplinary Integrated Ecosystem Assessment approach (Levin et al., 2009; Möllmann et al., 2014) as a means of providing a coherent strategy to map onto ecosystem-based objectives and for informing decisions across sectors, scales and species. Ultimately, as the database matures over time the capacity to consider additional new types of information, such indigenous knowledge systems (e.g. Sethi et al., 2011; Reid et al., 2021), will grow as its range of coverage expands, although this is currently out of scope for the first round of iterations. Similarly, its increasing depth and breadth of temporal and spatial coverage will help to disentangle historical drivers from newly emerging threats, such as climate change and the ongoing invasion of pink salmon *Oncorhynchus gorbuscha* (Sandlund et al., 2019). It is imperative that we seek to capture these baselines and emerging trends now, whilst we still can, and before previous phenomenological approaches lose more of what limited predictive power they still have.

We have conceptualised where the phases in developing
the Likely Suspects Framework could link to the evolution of
existing international stock assessment methods and emerg-
ing space-time domains of interest to provide important new
information to guide management interventions focused at
the river stock-specific scale (Figure 2). The Likely Suspects
Framework will facilitate the mobilisation of new knowl-
edge as it evolves, drawing from a growing range of sources
(Table 1), with the potential to catalyse the development of
the stock assessment and ecosystem-based management ap-
proaches. It provides a common platform for re-synthesis, co-
operative analysis and sharing of new knowledge to support
hypothesis-testing and the development of future management
support tools.

Improved data mobilisation provided by the central data re-
source will be integral to the requirement for developing suites
of abiotic and biotic indicators of relevance to salmon pop-
ulation dynamics, and to inform on stock prospects. These
could include environmental forcing and predator and prey
dynamics, all of which could be used within life cycle mod-
elling efforts to provide more realistic stock assessments and
forecasts based on a more complete understanding of the roles
of various drivers of Atlantic salmon productivity. Developing
a weighting scheme for suitable indicators will be critical for
the successful implementation of an ecosystem-based frame-
work, and to integrate with population models, and there are
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Conceptual outline of how the components of the Likely Suspects Framework process could facilitate the development of new river stock-specific management guidance and support, linking the processes of stock assessment and catch advice for Atlantic salmon (at the stock-complex scales) and wider Integrated Ecosystem Assessments.</figcaption></figure>

<table><caption>**Table 1.** Exemplar datasets and compiled model estimates drawn from the Likely Suspects Framework database along with their relevance to hypothesis testing and construction of a panel of ecosystem indicators to integrate with the development of ecosystem-based management for Atlantic salmon.</caption><thead><tr><th>Resource title</th><th>Description</th><th>url</th><th>Relevance for developing a more life-cycle approach to salmon management</th></tr></thead><tbody><tr><td>Freshwater habitat quality assessment</td><td>River ecological quality assessment indices (Scotland)</td><td>https://www.sepa.org.uk/da ta-visualisation/water-cla ssification-hub/</td><td>Extraction of variables that describe ranges and temporal change on drivers of growth and survival in freshwater stages of development</td></tr><tr><td>Smolt migration phenology</td><td>Daily Fish Counts at Burrishoole index river, Newport (Ireland)</td><td>https://erddap.marine.ie/erd dap/tabledap/newport_dai ly_fish_counts.html</td><td>Emigration timing reflects variation in previous growth opportunity during freshwater stage. Timing of migration may inform the development of suitable marine ecosystem indicators</td></tr><tr><td>Annual marine survival rates</td><td>Monitoring abundance of migratory salmon in the River Scorff salmon index river (France)</td><td>https://doi.org/10.15468/yvc w8n</td><td>Underlying temporal change in the stage survival reflecting outcome of multiple influences including environmental drivers in marine domains</td></tr><tr><td>Plankton abundance and composition</td><td>Continuous Plankton Recorder distribution, seasonal cycles and changes in abundance of plankton over the Northern Hemisphere.</td><td>https://www.gbif.org/dataset /67c54f85-7910-4cbf-8d e4-6f0b136a0e34</td><td>Temporal and spatial variation at key points along migration routes and main feeding grounds in response to basin-scale water temperature change. Important factor when considering developing future marine phase ecosystem indicators due to influence on salmon growth, maturation, stage duration and cumulative mortality</td></tr><tr><td>Oceanographic conditions</td><td>The Scottish Shelf Waters Reanalysis Service is a 26 year hind cast of one year climatology</td><td>https://sites.google.com/vie w/ssw-rs/home</td><td>Access to multiple-scales of environmental control data reflecting drivers of growth and survival (both direct and indirect) during entire marine phase</td></tr><tr><td>Annual stock abundance estimates of pelagic fish species</td><td>ICES Working Group on Widely Distributed Stocks compiles annual data on large stocks of pelagic species, as well as other widely distributed and highly migratory species.</td><td>https://www.ices.dk/comm unity/groups/pages/wgwid e.aspx</td><td>Patterns of abundance and distribution of competitors, prey and alternative forage for predators that may inform development of indicators of salmon growth and survival</td></tr><tr><td>Coastal seabird abundance</td><td>Seabird 2000 Census Data 1998–2002. Joint Nature Conservation Committee (UK)</td><td>https://webarchive.nationalarchives.gov.uk/201903011 35521/http://jncc.defra.gov.uk/page-4 460-theme = default</td><td>Temporal variation in the abundance and distribution of potential predators of salmon during critical early marine migration stage</td></tr></tbody></table>
several examples of marine fish species for which this has been
done. For example, Bedford et al. (2018) illustrate the value
of surveying the planktonic community to indicate changing
oceanic conditions for informing statutory reporting under
the Marine Strategy Framework Directive. Indicators have
also been applied in ecosystem-based modelling (e.g. Linde-
gren et al., 2011) to improve Baltic cod (Gadus morhua) stock
assessment (Gardmark et al., 2013). Bils et al. (2017) also
combined metrics of larval Atlantic herring (Clupea harengus)
with that of mesoplankton, highlighting the potential for sur-
veying small planktonic organisms as a means of understand-
ing the factors affecting larval fish feeding, growth and sur-
vival. Bentley et al. (2017) provide tantalising insights into the
potential for linking species models within wider frameworks
for identifying proximate drivers and indicators that could
also be useful for salmon management in marine ecosystems.

Discussion about what indicators may be of use, how they
will be weighted, and what determines “Ecosystem condi-
tions” (Figure 2) for critical space-time domains during the
marine phase could be informed by analyses of ecosystem con-
ditions before versus after the sudden Atlantic regime shift in
the 1990s (Chaput et al., 2005). For instance, the tempera-
ture dependence of growth and survival (Litzow et al., 2019;
Tillotson et al., 2021) could ultimately be accounted for by de-
veloping a suitably weighted panel of environmental and bio-
logical indicators (e.g. Harvey et al., 2020). These could help
to identify potential ecological thresholds (Large et al., 2013;
Satterthwaite et al., 2020) and early warning signals (Litzow
and Hunsicker, 2016) of future reduced survival at sea.

Directing efforts to identify and interpret signal shifts within the food web around these major transitions could help to unearth new ecological indicators that could also be used for hindcasting to explain past growth challenges (Todd et al., 2020; Tillotson et al., 2021; Trehin et al., 2021; Harvey et al., 2022; Vollset et al., 2022), as well as for forecasting responses to future environmental change.

In the freshwater environment, examination of the pri-
mary indicators influencing growth and survival have helped
guide management strategies towards maximising the num-
ber and resilience of pre-smolts (Thorstad et al., 2021) and
while this is logical, the newly emerging science is highlight-
ing new tools (e.g. Bell-Tilcock et al., 2021) that can address
the often neglected linkages between pre-smolt performance in
fresh waters and their growth and survival as post-smolts at
sea (McLennan et al., 2018; Gregory et al., 2018, 2019; Aus-
tad et al., 2021; Tian et al., 2021; Simmons et al., in press).
By integrating new and emerging science outputs into the de-
velopment of the more holistic Likely Suspects Framework
approach there is increasing potential to uncover new miti-
gating actions for freshwater-based salmon management that
ultimately fan out across the full life cycle.

As the Likely Suspects Framework approach becomes es-
tablished it can consider suitable initial suites of indicators
for salmon conditions in freshwater domains, and during
these critical marine-freshwater transitions, extracted from
the decades of work by government agencies in creating in-
dices of Good Ecological Status for legislative frameworks
(e.g. EU Water Framework Directive: Kallis and Butler, 2001).
In addition, there is an extensive complementary knowledge
base of the processes leading to variability in marine re-
cruitment (i.e. smolt production from the freshwater phase,
Crozier et al., 2003) with adult stock characteristics, egg de-
position abundance, cohort survival influenced by predation,

prey abundance, intra and interspecific competition and fresh-
water habitat quality all being important (Jonsson and Jon-
son, 2004).

Once an initial suite of suitable indicators has been agreed
across salmon domains, an analytical framework needs to be
constructed to test competing mortality hypotheses. A range
of possible population modelling approaches could be em-
bedded within this more integrative, interdisciplinary future
framework and this could mirror what has been used to im-
prove Baltic cod stock assessment (Gardmark et al., 2013)
and ecosystem modelling tools such as Atlantis (Audzijonyte
et al., 2019) or EcoPath-Ecosim and Ecospace (Pauly et al.,
2000) that interweave ecological sub-models and approaches.
We envisage new opportunities arising in the development of
agent-based modelling of the dynamics of salmon and other
ecosystem components, and how stocks could respond to cli-
mate change and varying management scenarios. Spatially
explicit agent-based models have already been developed for
several species that might be linked to salmon survival, includ-
ing mackerel (Scomber scomber) (Boyd et al., 2018, 2020),
herring (Hufnagl and Peck, 2011) and North Sea cod (Ro-
magnoni et al., 2020).

Ecopath with Ecosim EwE models have been used to sim-
ulate and evaluate changes to climate scenarios in the Nor-
wegian and Barents seas ecosystem, suggesting warming will
lead to increased biomass of pelagic species like blue whit-
ing and herring (potential competitors with salmon) and de-
creases in boreal species of redfish (Sebastes spp), prawns
(Pandalus borealis) and capelin (potential prey for salmon)
(Haughland et al., 2006; ICES, 2017). The current hierar-
chical Bayesian life cycle model for Atlantic salmon (Olmos
et al., 2019; ICES, 2021) could potentially be extended to
include more ecological realism (e.g. multi-species and food
web approaches) and combined within an Integrated Ecosys-
tem Assessment framework to gauge ecosystem status and
trends. Individual components can be designed to address spe-
cific mortality hypotheses targeting Atlantic salmon, or to in-
vestigate the influence of the wider ecosystem or communi-
ties within particular salmon domains (Olmos et al., 2019).
Lessons could be learned from the development of individ-
ual based models (Piou and Prévost, 2012) to test hypothesis
on individual process affecting salmon growth, survival, and
life history, and from recent Pacific salmon (Onchoryhnchus
spp.) decision-making processes that have partially fused mod-
elling outputs (biogeochemical, individually based) and expert
opinion to provide lay guidance for managers. For instance,
the Climate Vulnerability Assessment methodology (NOAA,
2020) uses a variety of data on species sensitivities and distri-
bution to assess vulnerability to communicate risks to salmon
managers. Similarly, the Risk Assessment Method for Salmon
(Hyatt et al., 2017)—adapted from Ecological Risk Assess-
ment methodology (Hobday et al., 2011)—can be modified to
help identify limiting factors to salmon production across the
various phases of life cycle. A Priority Threat Management
framework (Gregory et al., 2012) has also been adapted to
aid decision-making in assigning limited resources for max-
imising Pacific salmon population recovery potential (Walsh
et al., 2020). In this vein, recent collaborative research to un-
derstand mortality drivers for salmon in the California Cur-
rent (Wells et al., 2017) has identified proximate drivers of
survival as variation in prey abundance and associated preda-
tor responses (Friedman et al., 2019) and has prioritised man-
agement options (Wells et al., 2020). The Likely Suspects
Framework programme will evaluate these past efforts and work to develop a management framework that builds from these early successes, but also is tailored to the unique challenges of managing Atlantic salmon across the Atlantic basin.

Meeting the goals of the Likely Suspects Framework to improve the linkages amongst processes underpinning salmon management will require careful consideration to ensure the most appropriate integration of knowledge, the correct use of inputs (Dickey-Collas et al., 2014) and minimising the potential misuse of outputs in management actions. For the process to capitalise on the full spectrum of opportunities scientists, managers, and stakeholders will need to worktogether (DePiper et al., 2021). Whilst plenty of scientific tools have been developed for implementing the general approach (Smith et al., 2007), many have been considered “too complex” for adoption by non-technical audiences (Patrick and Link, 2015) and can fail on translation into management practices. Co-design from the outset, which is central to the Likely Suspects Framework premise, should help to make the transition of outputs into management actions as smooth and rapid as possible.

## Addressing challenges of the future

Modifying current salmon management systems and providing new ecosystem indicators should neither immediately detract from already identified pressures nor should they supersede existing “good guidance” (e.g. Hansen et al., 2012; Thorstad et al., 2021). Rather, they offer a broader view of prospects across the life cycle for prioritising actions and potential trade-offs in areas over which managers may be able to exert some control. The value of advancing this approach is not simply in providing management prescriptions for any particular course of action to address a mortality pressure (although it can), but in presenting new guidance in terms of risk and expectation.

By developing resources that can bring greater biological realism to stock modelling and improved forecasting of salmon survival to inform management, the central questions posed within the Likely Suspects Framework can be revised, including: what data resources exist already, what are the appropriate scales at which to assess salmon survival, and what higher-level metrics are best able to predict performance of salmon across the lifecycle under different management and environmental conditions? Thus, the development of the data resources and mobilisation programme together with future workshops focused on salmon mortality at sea can be steered by feeding back iteratively as these gaps and challenges are addressed over time.

In addition, we also need to review how this information can be communicated and deployed: too often the highly technical outputs from salmon research, and from stock assessment outputs, are not translated into positive actions because they are not readily accessible to salmon managers, and the *ad hoc* and patchy uptake of the current scientific state of the art can embed biases in salmon management strategies, leading to ineffective resource allocation. For the Likely Suspects Framework approach to succeed, communication tools must be appropriate and accessible for integration into future salmon management. Viable decision or assessment support tools (Hyatt et al., 2017; Crozier et al., 2019; 2021; Scheuerell et al., 2006; 2021; Walsh et al., 2020) must also deliver

scientific outputs that address “what-if” questions and future scenarios that are realistic for specific stock or river management operations. Key considerations in designing suitable applications for salmon management will be user-friendliness, engaging recognised formats to describe risk levels and survival expectation, and the use of simple messaging and tools to offer guidance in terms of balancing risks and expectations.

Progress from single-species fisheries management towards a more ecosystem-based methodology has generally been slower than might be hoped for or expected, reflecting historical inertia among both the individuals and institutions involved and project management path dependencies (Fulton, 2021). Progress towards accommodating ecosystem assessments in the context of the Atlantic salmon management will be shaped by the rate at which these hurdles can be overcome, and how effectively it can improve our still limited understanding of ecosystem resilience under future scenarios.

In summary, we have outlined a new process that starts to build the long-term approach and data infrastructure we need urgently for improving knowledge, understanding and prediction of future Atlantic salmon stocks across their range. The Likely Suspects Framework will facilitate and focus future cooperative research, and integrate the results into the development of improved management guidance. Allowing managers access to more comprehensive support tools when weighing the effectiveness of their actions within the context of the salmons' life-cycle will help them to meet the myriad of challenges posed by climate change and other pressures that salmon face, both now and in the future. The challenge now is to harness the huge potential of the salmon science and the management communities and channel our collective resources towards building an ecosystem based approach fit for the 21st century and beyond.

## Authors' contributions

WC led on the conceptualization of the Likely Suspects Framework, and CB led on writing the paper. All authors contributed to drafting and revising the manuscript.

## Data availability

No new data were generated or analysed in support of this research.

## Acknowledgements

This work was supported largely by funding from the UK Missing Salmon Alliance. The authors wish to thank J. Gillson and A. Walker from CEFAS, UK, for useful comments on the manuscript and three anonymous referees.

## References

* Armstrong, J. D., McKelvey, S., Smith, G. W., Rycroft, P., and Fryer, R. J. 2018. Effects of individual variation in length, condition and run-time on return rates of wild-reared atlantic salmon *salmo salar* smolts. Journal of Fish Biology, 92: 569–578.

* Audzijonyte, A., Pethybridge, H., Porobic, J., Gorton, R., Kaplan, I., and Fulton, E. A. 2019. AtlAntis: a spatially explicit end-to-end marine ecosystem model with dynamically integrated physics, ecology and socio-economic modules. Methods in Ecology and Evolution, 10: 1814–1819.
Austad, B., Vollestad, L. A., and Foldvik, A. 2021. Frequency of vateritic otoliths and potential consequences for marine survival in hatchery-reared atlantic salmon. Journal of Fish Biology, 98: 1401–1409.

Beauchamp, D., Berejikian, B., Chamberlin, J., Chapman, A., Crewson, M., Ellings, C. et al. 2012. Marine survival of salmon & steelhead in the salish sea. https://marinesurvivalproject.com/resources/research-plans/

Beaugrand, G., and Reid, P. C. 2003. Long-term changes in phytoplankton, zooplankton and salmon related to climate. Global Change Biology, 9: 801–817.

Beaugrand, G., and Reid, P. C. 2012. Relationships between north atlantic salmon, plankton, and hydroclimatic change in the northeast atlantic - ICES Journal of Marine Science, 69: 1549–1562.

Bedford, J., Johns, D., Greenstreet, S., and McQuatters-Gollop, A. 2018. Plankton as prevailing conditions: a surveillance role for plankton indicators within the marine strategy framework directive. Marine Policy, 89: 109–115.

Bell-Tilcock, M., Jeffres, C. A., Rypel, A. L., Sommer, T. R., Katz, J. V., Whitman, G., and Johnson, R. C. 2021. Advancing diet reconstruction in fish eye lenses. Methods in Ecology and Evolution, 12(3): pp. 449–457.

Bentley, J. W., Serpetti, N., and Heymans, J. J. 2017. Investigating the potential impacts of ocean warming on the norwegian and barents seas ecosystem using a time-dynamic food-web model. Ecological Modelling, 360: 94–107.

Bils, F., Moyano, M., Aberle, N., Hufnagl, M., Alvarez-Fernandez, S., and Peck, M. A. 2017. Exploring the microzooplankton-ichthyoplankton link: a combined field modelling study of atlantic herring (*Clupea harengus*) in the irish sea. Journal of Plankton Research, 39: 147–163.

Boyd, R., Roy, S., Sibly, R., Thorpe, R., and Hyder, K. 2018. A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to atlantic mackerel. Ecological Modelling, 382: 9–17.

Boyd, R., Walker, N., Hyder, K., Thorpe, R., Roy, S., and Sibly, R. 2020. SEASIM-NEAM: a spatially-explicit Agent-based SIMulator of north east atlantic mackerel population dynamics. MethodsX, 7: 101044.

Brook, B. W., Sodhi, N. S., and Bradshaw, C. J. A. 2008. Synergies among extinction drivers under global change. Trends in Ecology & Evolution, 23: 453–460

Cairns, D. K. (ed) 2001. An evaluation of possible causes of the decline in pre-fishery abundance of North American Atlantic salmon. Canadian Technical Report of Fisheries and Aquatic Sciences No 2358, 67 pp.

Chaput, G. 2012. Overview of the status of atlantic salmon (*Salmo salar*) in the north atlantic and trends in marine mortality. ICES Journal of Marine Science, 69: 1538–1548.

Chaput, G., Legault, C. M., Reddin, D. G., Caron, F., and Amiro, P. G. 2005. Provision of catch advice taking account of non-stationarity in productivity of atlantic salmon (*Salmo salar* L.) in the northwest atlantic. ICES Journal of Marine Science, 62: 131–143.

Chaput, G., Prévost, E., Dempson, B., Dionne, M., Jones, R., Levy, A., Robertson, M. et al. 2015. Hierarchical bayesian modelling of atlantic salmon egg to smolt time series from monitored rivers of eastern canada to define and transport reference points. Fisheries and oceans canada, department of fisheries and oceans canada. https://hal.archives-ouvertes.fr/hal-01603753

Crozier, W. W., Whelan, K., Buoro, M., Chaput, G., Daniels, J., Grant, S., Hyatt, K. et al. 2017. Atlantic salmon mortality at sea: developing an evidence based ‘likely suspects’ framework. Atlantic Salmon Trust Report https://atlanticsalmontrust.org/wp-content/uploads/2020/07/LSF-Blue-Book-June-2018-June-2018-copy-2.pdf.

Crozier, W. W., Potter, E. C. E., Prévost, E., Schön, P.-J., and Ó Maoiléidigh, N. 2003 A Coordinated Approach towards the Development of a Scientific Basis for Management of Wild Atlantic Salmon in the North East Atlantic (SALMODEL). Queens University of Belfast, Belfast.

Crozier, L. G., McClure, M. M., Beechie, T., Bograd, S. J., Boughton, D. A., Carr, M., Cooney, T. D. et al. 2019. Climate vulnerability assessment for pacific salmon and steelhead in the california current large marine ecosystem. Plos One 14: e0217711.

Crozier, L. G., Burke, B. J., Chasco, B. E., Widener, D. L., and Zabel, R. W. 2021. Climate change threatens chinook salmon throughout their life cycle. Communications Biology, 4: 1–14.

Curtin, R., and Prellezo, R. 2010. Understanding marine ecosystem based management: a literature review. Marine policy, 34: 821–830.

DePiper, G., Gaichas, S., Muffley, B., Ardini, G., Brust, J., Coakley, J., Dancy, K. et al. 2021. Learning by doing: collaborative conceptual modelling as a path forward in ecosystem-based management. ICES Journal of Marine Science, 78: 1217–1228.

Dickey-Collas, M., Payne, M. R., Trenkel, V. M., and Nash, R. D. 2014. Hazard warning: model misuse ahead. ICES Journal of Marine Science, 71: 2300–2306.

Friedland, K. D., Shank, B. V., Todd, C. D., McGinnity, P., and Nye, J. A. 2014. Differential response of continental stock complexes of atlantic salmon (*Salmo salar*) to the atlantic multidecadal oscillation. Journal of Marine Systems, 133: 77–87.

Friedman, W. R., Martin, B. T., Wells, B. K., Warzybok, P., Michel, C. J., Danner, E. M., and Lindley, S. T. 2019. Modeling composite effects of marine and freshwater processes on migratory species. Ecosphere, 10: e02743.

Fulton, E. A. 2021. Opportunities to improve ecosystem-based fisheries management by recognizing and overcoming path dependency and cognitive bias. Fish and Fisheries, 22: 428–448.

Gårdmark, A., Lindegren, M., Neuenfeldt, S., Blenckner, T., Heikinheimo, O., Müller-Karulis, B., Niiranen, S. et al. 2013. Biological ensemble modelling to evaluate potential futures of living marine resources. Ecological applications, 23: 742–754.

Gilbey, J., Utne, K. R., Wennevik, V., Beck, A. C., Kausrud, K.,ндар, К., Garcia de Leaniz, C. et al. 2021. The early marine distribution of atlantic salmon in the North-east atlantic: a genetically informed stock-specific synthesis. Fish and Fisheries, 22: 1274–1306.

Gregory, R., Failing, L., Harstone, M., Long, G., McDaniels, T., and Ohlson, D. 2012. Structured Decision Making: A Practical Guide to Environmental Management Choices. Wiley, Germany.

Gregory, S. D., Armstrong, J. D., and Britton, J. R. 2018. Is bigger really better? Towards improved models for testing how atlantic salmon *salmo salar* smolt size affects marine survival. Journal of Fish Biology, 92: 579–592.

Gregory, S. D., Ibbotson, A. T., Riley, W. D., Nevoux, M., Lauridsen, R. B., Russell, I. C., Britton, J. R. et al. 2019. Atlantic salmon return rate increases with smolt length. ICES Journal of Marine Science, 76: 1702–1712.

Hansen, L. P., Hutchinson, P., Reddin, D. G., and Windsor, M. L. 2012. Salmon at sea: scientific advances and their implications for management: an introduction. ICES Journal of Marine Science, 69: 1533–1537.

Hare, J. A., Kocik, J. F., and Link, J. S. 2019. Atlantic Salmon Recovery Informing and Informed by Ecosystem-Based Fisheries Management. Fisheries, 44(9): 403–411.

Hare, J. A., Morrison, W. E., Nelson, M. W., Stachura, M. M., Teeters, E. J., Griffis, R. B., Michael, A. et al. 2016. A vulnerability assessment of fish and invertebrates to climate change on the northeast U.S. continental shelf. Plos One 11: e0146756.

Harvey, A., Skaala, Ø., Borgstrøm, R., Fjeldheim, P. T., Christine Andersen, K., Rong Utne, K., Askeland Johnsen, I. et al. 2022. Time series covering up to four decades reveals major changes and drivers of marine growth and proportion of repeat spawners in an atlantic salmon population. Ecology and evolution, 12: e8780.

Harvey, C., Garfield, N. T., Williams, G., Tolimieri, N., Andrews, K., Barnas, K., Bjorkstedt, E. et al. 2020. Ecosystem status report of the california current for 2019-20: a summary of ecosystem indicators compiled by the california current integrated ecosystem assessment team (CCIEA). NOAA Technical Memorandum NMFS-NWFSC 160.
Haugland, M., Holst, J. C., Holm, M., and Hansen, L. P. 2006. Feed-
ing of atlantic salmon (*Salmo salar* L.) post-smolts in the northeast
atlantic. ICES Journal of Marine Science, 63: 1488–1500.

Hobday, A. J., Smith, A. D. M., Stobutzki, I. C., Bulman, C., Daley, R., Dambacher, J. M., Deng, R. A. et al. 2011. Ecological risk assessment for the effects of fishing. Fisheries Research, 108: 372–384.

Hufnagl, M., and Peck, M. A. 2011. Physiological individual-based modelling of larval atlantic herring (*Clupea harengus*) foraging and growth: insights on climate-driven life-history scheduling. ICES Journal of Marine Science, 68: 1170–1188.

Huse, G., Holst, J. C., Utne, K., Nøttestad, L., Melle, W., Slotte, A., Ottersen, G. et al. 2012. Effects of interactions between fish populations on ecosystem dynamics in the norwegian Sea- results of the INFERNO project. Marine Biology Research, 8: 415–419.

Hvidsten, N. A., Jensen, A. J., Rikardsen, A. H., Finstad, B., Aure, J., Ste-
fansson, S., Fiske, P. et al. 2009. Influence of sea temperature and ini-
tial marine feeding on survival of atlantic salmon salmo salar post-
smolts from the rivers orkla and hals, Journal of Fish Biology, 74:
1532–1548.

Hyatt, K., Pearsall, L., and Luedke, W. 2017. A risk assessment method-
ology for salmon (RAMS) population conservation and manage-
ment within an ecosystem context. Department of Fisheries and
Oceans, Science Branch, Pacific Biological Station, Nanaimo, British
Columbia.

ICES. 2017. Report of the Working Group on North Atlantic Salmon (WGNAS), 29 March-7 April 2017, Copenhagen, Denmark. ICES CM 2017/ACOM.20.

ICES. 2020. NASCO workshop for north atlantic salmon at-sea mor-
tality (WKSalmon, outputs from 2019 meeting). ICES Scientific Re-
ports. 2(69): 175pp. http://doi.org/10.17895/ices.pub.5979.

ICES. 2021. Working group on north atlantic salmon (WGNAS).
ICES Scientific Reports. 3:29. 407pp. https://doi.org/10.17895/
ices.pub.7923

Jackson, M. C., Pawar, S., and Woodward, G., 2021. The temporal dy-
namics of multiple stressor effects: from individuals to ecosystems.
Trends in Ecology & Evolution. 36, 402–410.

Jacobsen, J. A., and Hansen, L. P. 2001. Feeding habits of wild and es-
caped farmed atlantic salmon, *salmo salar* L., in the northeast at-
lantic. ICES Journal of Marine Science, 58: 916–933.

Jansen, T., Hansen, F. T., and Bardarson, B. 2021. Larval drift dynam-
ics, thermal conditions and the shift in juvenile capelin distribution
and recruitment success around iceland and east greenland. Fisheries
Research, 236: 105845.

Jennings, S., 2005. Indicators to support an ecosystem approach to fish-
eries. Fish and Fisheries, 6: 212–232.

Jonsson, B., and Jonsson, N. 2004. Factors affecting marine production of atlantic salmon (*Salmo salar*). Canadian Journal of Fisheries and Aquatic Sciences, 61: 2369–2383.

Kallis, G., and Butler, D. 2001. The EU water framework directive: mea-
sures and implications. Water Policy, 3: 125–142.

Lacroix, G. L. 2013. Population-specific ranges of oceanic migration for adult atlantic salmon (*Salmo salar*) documented using pop-up satellite archival tags. Canadian Journal of Fisheries and Aquatic Sciences, 70: 1011–1030.

Large, S. I., Fay, G., Friedland, K. D., and Link, J. S. 2013. Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures. ICES Journal of Marine Science, 70: 755–767.

Large, S. I., Fay, G., Freidland, K. D., and Link, J. S. 2015. Quantifying patterns of change in marine ecosystems response to multiple pressures. Plos One 10: e0119922.

Levin, P. S., Fogarty, M. J., Murawski, S. A., and Fluharty, D. 2009. Integrated ecosystem assessments: developing the scientific basis for ecosystem-based management of the ocean. PLoS Biology, 7: e1000014.

Lindegren, M., Östman, Ö., and Gårdmark, A. 2011. Interacting trophic forcing and the population dynamics of herring. Ecology, 92: 1407–1413.

Link, J. S. 2005. Translating ecosystem indicators into decision criteria. ICES Journal of Marine Science, 62: 569–576.

Link, J. S. 2010. Ecosystem-Based Fisheries Management: Confronting Tradeoffs. Cambridge University Press, Cambridge, UK.

Link, J. S., and Browman, H. I. 2014. Integrating what? Levels of marine ecosystem-based assessment and management. ICES Journal of Marine Science, 71: 1170–1173.

Link, J. S., and Browman, H. I. 2017. Operationalizing and implementing ecosystem-based management. ICES Journal of Marine Science, 74: 379–381.

Litzow, M. A., Ciannelli, L., Cunningham, C. J., Johnson, B., and Puerta, P. 2019. Nonstationary effects of ocean temperature on pacific salmon productivity. Canadian Journal of Fisheries and Aquatic Sciences, 76: 1923–1928.

Litzow, M. A., and Hunsicker, M. E. 2016. Early warning signals, nonlinearity, and signs of hysteresis in real ecosystems. Ecosphere, 7: 1923–1928.

McLennan, D., Armstrong, J. D., Stewart, D. C., McKelvey, S., Boner, W., Monaghan, P., and Metcalfe, N. B. 2018. Links between parental life histories of wild salmon and the telomere lengths of their offspring. Molecular Ecology, 27: 804–814.

Mangel, M., and Satterthwaite, W. H. 2008. Combining proximate and ultimate approaches to understand life history variation in salmonids with application to fisheries, conservation, and aquaculture. Bulletin of Marine Science, 83: 107–130.

Massiot-Granier, F., Prévost, E., Chaput, G., Potter, T., Smith, G., White, J., Mäntyniemi, S. et al. 2014. Embedding stock assessment within an integrated hierarchical bayesian life cycle modelling framework: an application to atlantic salmon in the northeast atlantic. ICES Journal of Marine Science, 71: 1653–1670.

Middlemas, S. J., Armstrong, J. D., and Thompson, P. M. 2003. The significance of marine mammal predation on salmon and sea trout. In Salmon at the Edge, pp. 43–60. Ed by D. Mills Wiley, Germany.

Mills, K. E., Pershing, A. J., Sheehan, T. F., and Mountain, D. 2013. Climate and ecosystem linkages explain widespread declines in north american atlantic salmon populations. Global Change Biology, 19: 3046–3061.

Möllmann, C., Lindegren, M., Blenckner, T., Bergström, L., Casini, M., Diekmann, R., Flinkman, J. et al. 2014. Implementing ecosystem-based fisheries management : from single-species to integrated ecosystem assessment and advice for baltic sea fish stocks. ICES Journal of Marine Science, 71: 1187–1197.

Montevecchi, W. A. 2007. Binary dietary responses of northern gannets sula bassana indicate changing food web and oceanographic conditions. Marine Ecology Progress Series, 352: 213–220.

Morishita, J. 2008. What is the ecosystem approach for fisheries man-
agement? Marine Policy, 32: 19–26.

National Oceanic and Atmospheric Administration Fisheries. (Decem-
ber 28, 2020). Climate vulnerability assessments. Retrieved from
https://www.fisheries.noaa.gov/.

National Marine Fisheries Service (NMFS) 2016. Ecosystem-based fish-
eries management policy of the national oceanic and atmospheric
administration NOAA fisheries policy directive 01–120.

Nicholson, M. D., and Jennings, S. 2004. Testing candidate indicators to support ecosystem-based management: the power of monitoring surveys to detect temporal trends in fish community metrics. ICES Journal of Marine Science, 61: 35–42

Olmos, M., Massiot-Granier, F., Prévost, E., Chaput, G., Bradbury, I.R., Nevoux, M., and Rivot, E. 2019. Evidence for spatial coherence in time trends of marine life history traits of atlantic salmon in the north atlantic. Fisheries Research, 20: 322–342.

Olmos, M., Payne, M. R., Nevoux, M., Prévost, E., Chaput, G., Du Pon-
tavice, H., Guitton, J. et al. 2020. Spatial synchrony in the response
of a long range migratory species (*Salmo salar*) to climate change in
the north atlantic ocean. Global Change Biology, 26: 1319–1337.

Ó Maoiléidigh, N., White, J., Hansen, L. P., Jacobsen, J. A., Potter, T., Russell, I., Reddin, D. et al. 2018. Fifty years of marine tag recoveries from atlantic salmon. ICES Cooperative Research Report No. 343. http://doi.org/10.17895/ices.pub.4542.
O'Neil, S., Ritter, J., and Robichaud-Leblanc, K. 2000. Proceedings of a workshop on research strategies into the causes of declining Atlantic salmon returns to North American rivers: 12–14 June 2000, Dalhousie University, Halifax, Nova Scotia.

Otero, J., L'Abe-Lund, J. H., Castro-Santos, T., Leonardsson, K., Storvik, G. O., Jonsson, B., Dempson, B. et al. 2014. Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of atlantic salmon (*Salmo salar*). Global Change Biology, 20: 61–75.

Otto, S. A., Kadin, M., Casini, M., Torres, M. A., and Blenckner, T. 2018. A quantitative framework for selecting and validating food web indicators. *Ecological Indicators*, 84: 619–631.

Ounsley, J. P., Gallego, A., Morris, D. J., and Armstrong, J. D. 2020. Regional variation in directed swimming by atlantic salmon smolts leaving scottish waters for their oceanic feeding grounds—a modelling study. ICES Journal of Marine Science, 77: 315–325.

Pardo, S. A., Bolstad, G. H., Dempson, J. B., April, J., Jones, R. A., Raab, D., and Hutchings, J. A. 2021. Trends in marine survival of atlantic salmon populations in eastern canada. ICES Journal of Marine Science, 78: 2460–2473.

Patrick, W. S., and Link, J. S. 2015. Myths that continue to impede progress in ecosystem-based fisheries management. Fisheries, 40: 155–160.

Pauly, D., Christensen, V., and Walters, C. 2000. Ecopath, ecosim, and ecospace as tools for evaluating ecosystem impact of fisheries. ICES Journal of Marine Science, 57: 697–706.

Peterman, R. M., Marmorek, D., Beckman, B., Bradford, M., Mantua, N., Riddell, B. E., Scheuerell, M. et al. 2010. Synthesis of evidence from a workshop on the decline of Fraser River sockeye. A report to the Pacific Salmon Commission, Vancouver.

Piou, C., and Prévost, E. 2012. A demo-genetic individual-based model for atlantic salmon populations : model structure, parameterization and sensitivity. *Ecological Modelling*, 231: 3752.

Pikitch, E. K., Santora, C., Babcock, E. A., Bakun, A., Bonfil, R., Conover, D. O., Dayton, P., Doukakis, P. et al. 2004. Ecosystem-based fishery management. *Science*, 305: 346–347.

Prévost, E., and Chaput, G. 2001. Stock, recruitment and reference points assessment and management of Atlantic salmon. INRA Editions, Paris, France.

Reid, A. J., Eckert, L. E., Lane, J. F., Young, N., Hinch, S. G., Darimont, C. T., Cooke, S. J. et al. 2021. Two-Eyed seeing": an indigenous framework to transform fisheries research and management. Fish and Fisheries, 22: 243–261.

Renkawitz, M. D., and Sheehan, T. F. 2011. Feeding ecology of early marine phase atlantic salmon (*Salmo salar* L.) postsmolts in penobscot bay, Maine USA. *Journal of Fish Biology*, 79: 356–373.

Renkawitz, M. D., Sheehan, T. F., Dixon, H. J., and Nygaard, R., 2015. Changing trophic structure and energy dynamics in the northwest atlantic: implications for Atlantic salmon feeding at west greenland. *Marine Ecology Progress Series*, 538: 197–211.

Rikardsen, A. H., and Dempson, J. B. 2011. Dietary life-support: the marine feeding of Atlantic salmon. *In* Atlantic Salmon Ecology. Eds. by Ø. Aas, S. Einum, A. Klemetsen, and J. Skurdal Wiley-Blackwell, Oxford, UK.

Rikardsen, A. H., Haugland, M., Bjørn, P. A., Finstad, B., Knudsen, R., Dempson, J. B., Holst, J. C. et al. 2004. Geographical differences in early marine feeding of Atlantic salmon post-smolts in norwegian fjords. *Journal of Fish Biology*, 64: 1655–1679.

Rikardsen, A. H., Righton, D., Strøm, J. F., Thorstad, E. B., Gargan, P., Sheehan, T., Økland, F. et al. 2021. Redefining the oceanic distribution of Atlantic salmon. *Scientific Reports*, 11: 1–12.

Romagnoni, G., Kvile, K. Ø., Dagestad, K. F., Eikeset, A. M., Kristiansen, T., Stenseth, N. C., and Langangen, Ø. 2020. Influence of larval transport and temperature on recruitment dynamics of north sea cod (*Gadus morhua*) across spatial scales of observation. Fisheries Oceanography, 29: 324–339.

Rosseland, B. O., and Kroglund, F. 2011. Lessons from acidification and pesticides. *In* Atlantic Salmon Ecology. Eds. by Ø. Aas, S. Einum, A. Klemetsen, and J. Skurdal Wiley-Blackwell, Oxford, UK.

Russell, I.C., Aprahamian, M.W., Barry, J., Davidson, I.C., Fiske, P., Ibbotson, A.T., Kennedy, R.J. et al. 2012. The influence of the freshwater environment and the biological characteristics of atlantic salmon smolts on their subsequent marine survival. ICES Journal of Marine Science, 69: 1563–1573.

Sandlund, O. T., Berntsen, H. H., Fiske, P., Kuusela, J., Muladal, R., Niemelä, E., Uglem, I. et al. 2019. Pink salmon in norway: the reluctant invader. *Biological Invasions*, 21: 1033–1054.

Satterthwaite, W. H., Andrews, K. S., Burke, B. J., Gosselin, J. L., Greene, C. M., Harvey, C. J., Munsch, S. H. et al. 2020. Ecological thresholds in forecast performance for key united states west coast chinook salmon stocks. *ICES Journal of Marine Science*, 77: 1503–1515.

Scheuerell, M. D., Hilborn, R., Ruckelshaus, M. H., Bartz, K. K., Lagueux, K. M., Haas, A. D., and Rawson, K. 2006. The shiraz model: a tool for incorporating anthropogenic effects and fish-habitat relationships in conservation planning. Canadian Journal of Fisheries and Aquatic Sciences, 63: 1596–1607.

Scheuerell, M. D., Ruff, C. P., Anderson, J. H., and Beamer, E. M. 2021. An integrated population model for estimating the relative effects of natural and anthropogenic factors on a threatened population of steelhead trout. *Journal of Applied Ecology*, 58: 114–124.

Sethi, S. N., Sundarav, J. K., Panigrahi, A., and Chand, S. 2011. Prediction and management of natural disasters through indigenous technical knowledge, with special reference to fisheries. Indian Journal of Traditional Knowledge 10: January 2011, 167–172.

Simmons, O. M., Gregory, S. D., Gillingham, P. K., Riley, W. D., Scott, L. J., and Britton, J. R. 2021. Biological and environmental influences on the migration phenology of Atlantic salmon *salmo salar* smolts in a chalk stream in southern england. *Freshwater Biology*, 66: 1581–1594.

Simmons, O. M., Britton, J. R., Gillingham, P. K., Nevoux, M., Riley, W. D., Rivot, E., and Gregory, S. D. In press. Predicting how environmental conditions and smolt body length when entering the marine environment impact individual atlantic salmon *salmo salar* adult return rates. *Journal of Fish Biology*.

Smith, A. D. M., Fulton, E. J., Hobday, A. J., Smith, D. C., and Shoulder, P. 2007. Scientific tools to support the practical implementation of ecosystem-based fisheries management. ICES Journal of Marine Science, 64: 633–639.

Strøm, J. F., Rikardsen, A. H., Campana, S. E., Righton, D., Carr, J., Aarestrup, K., Stokesbury, M. J. et al. 2019. Ocean predation and mortality of adult atlantic salmon. *Scientific Reports*, 9: 1–11.

Thorpe, J. E., Mangel, M., Metcalfe, N. B., and Huntingford, F. A. 1998. Modelling the proximate basis of salmonid life-history variation, with application to Atlantic salmon, *salmo salar* L. *Evolutionary Ecology*, 12: 581–599.

Thorstad, E. B., Whoriskey, F. G., Rikardsen, A. H., and Aarestrup, K. 2011. Aquatic nomads: the life and migrations of the atlantic salmon. *In* Atlantic Salmon Ecology. Eds. by Ø. Aas, S. Einum, A. Klemetsen, and J. Skurdal Wiley-Blackwell, Oxford, UK.

Thorstad, E. B., Whoriskey, F., Uglem, I., Moore, A., Rikardsen, A. H., and Finstad, B. 2012. A critical life stage of the atlantic salmon *salmo salar*: behaviour and survival during the smolt and initial post-smolt migration. *Journal of Fish Biology*, 81: 500–542.

Thorstad, E. B., Bliss, D., Breau, C., Damon-Randall, K., Sundt-Hansen, L. E., Hatfield, E. M., Horsburgh, G. et al. 2021. Atlantic salmon in a rapidly changing environment—Facing the challenges of reduced marine survival and climate change. *Aquatic Conservation: Marine and Freshwater Ecosystems*, 31: 2654–2665.

Tian, Z., Zhao, H., Peter, K. T., Gonzalez, M., Wetzel, J., Wu, C., Hu, X. et al. 2021. A ubiquitous tire rubber-derived chemical induces acute mortality in coho salmon. *Science*, 371: 185–189.

Tillotson, M. D., Sheehan, T. F., Ellingson, B., Haas-Castro, R. E., Olmos, M., and Mills, K. E. 2021. Non-stationary effects of growth on the survival of north american atlantic salmon (*Salmo salar*). ICES Journal of Marine Science, 78: 2967–2982.
Todd, C. D., Hanson, N. N., Boehme, L., Revie, C. W., and Marques, A. R. 2021. Variation in the post-smolt growth pattern of wild one sea-winter salmon (*Salmo salar* L.), and its linkage to surface warming in the eastern north atlantic ocean. Journal of Fish Biology, 98: 6–16.

Trehin, C., Rivot, E., Lamireau, L., Meslier, L., Besnard, A-L., Gregory, S. D., and Nevoux, M. 2021. Growth during the first summer at sea modulates sex-specific maturation schedule in Atlantic salmon. Canadian Journal of Fisheries and Aquatic Sciences, 78: 659–669.

Utne, K. R., Huse, G., Ottersen, G., Holst, J. C., Zabavnikov, V., Jacobsen, J. A. et al. 2012. Horizontal distribution and overlap of planktivorous fish stocks in the norwegian sea during summers 1995–2006. Marine Biology Research, 8: 420–441.

Utne, K. R., Thomas, K., Jacobsen, J. A., Fall, J., Maoiléidigh, N. Ó, Broms, C. T., and Melle, W. 2021a. Feeding interactions between atlantic salmon (*Salmo salar*) postsmolts and other planktivorous fish in the northeast Atlantic. Canadian Journal of Fisheries and Aquatic Sciences, 78: 255–268.

Utne, K. R., Pauli, B. D., Haugland, M., Jacobsen, J. A., Maoileidigh, N. O., Melle, W. R., Broms, C. et al. 2021b. Poor feeding opportunities and reduced condition factor for salmon post-smolts in the northeast Atlantic ocean. ICES Journal of Marine Science, 78: 2844–2857

Vollset, K. W., Urdal, K., Utne, K., Thorstad, E. B., Sægrov, Raunsgard, A., Skagseth, Ø. et al. 2022. Ecological regime shift in the northeast atlantic ocean revealed from the unprecedented reduction in marine growth of atlantic salmon. Science Advances 8: eabk2542

Wainwright, T. C. 2021. Ephemeral relationships in salmon forecasting: a cautionary tale. Progress in Oceanography, 193: 102522.

Walsh, J. C., Connors, K., Hertz, E., Kehoe, L., Martin, T. G., Connors, B., Bradford, M. J. et al. 2020. Prioritising conservation actions for pacific salmon in canada. Journal of Applied Ecology, 57: 1688–1699

Wells, B. K., Huff, D. D., Burke, B. J., Brodeur, R. D., Santora, J. A., Field, J. C., Richerson, K. et al. 2020. Implementing ecosystem-based management principles in the design of a salmon ocean ecology program. Frontiers in Marine Science, 7: 342.

Wells, B. K., Santora, J. A., Henderson, M. J., Warzybok, P., Jahncke, J., Bradley, R. W., Huff, D. D. et al. 2017. Environmental conditions and prey-switching by a seabird predator impact juvenile salmon survival. Journal of Marine Systems, 174: 54–63.

Whitlock, R. E., Pakarinen, T., Palm, S., Koljonen, M. L., Östergren, J., and Dannewitz, J. 2021. Trade-offs among spatio-temporal management actions for a mixed-stock fishery revealed by bayesian decision analysis. ICES Journal of Marine Science, 78: 3625–3638.

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N. et al. 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3: 160018.

Winship, A. J., O'Farrell, M. R., Satterthwaite, W. H., Wells, B. K., and Mohr, M. S. 2015. Expected future performance of salmon abundance forecast models with varying complexity. Canadian Journal of Fisheries and Aquatic Sciences, 72: 557–569.

Woodward, G., Morris, O., Barquín Ortiz, J., Belgrano, A., Bull, C., de Eyto, E., Friberg, N. et al., 2021 Using food webs and metabolic theory to monitor, model, and manage atlantic salmon – a keystone species under threat. Frontiers in Ecology and Evolution, 9: 675261.

Handling Editor: Wesley Flannery