Multiple stakeholders’ perspectives of marine social ecological systems, a case study on the Barents Sea

The Barents Sea ecosystem components and services are under pressure from climate change and other anthropogenic impacts. Following an Ecosystem-based management approach, multiple simultaneous pressures are addressed by using integrative strategies, but regular prioritization of key issues is needed. Identification of such priorities is typically done in a ‘scoping’ phase, where the characterization of the social-ecological system is defined and discussed. We performed a scoping exercise using an open and flexible multi-stakeholder approach to build conceptual models of the Barents Sea social-ecological system. After standardizing vocabulary, a com plex hierarchical model structure containing 155 elements was condensed to a simpler model structure con taining a maximum of 36 elements. To capture a common understanding across stakeholder groups, inputs from the individual group models were compiled into a collective model. Stakeholders’ representation of the Barents Sea social-ecological system is complex and often group specific, emphasizing the need to include social scientific methods to ensure the identification and inclusion of key stakeholders in the process. Any summary or simpli fication of the stakeholders’ representation neglects important information. Some commonalities are highlighted in the collective model, and additional information from the hierarchical model is provided by multicriteria analysis. The collective conceptual stakeholder model provides input to an integrated overview and strengthens prioritization in Ecosystem-based management by supporting the development of qualitative network models. Such models allow for exploration of perturbations and can inform cross-sectoral management trade-offs and prioritiespublishedVersio

Management Strategy Evaluation: Allowing the Light on the Hill to Illuminate More Than One Species

Management strategy evaluation (MSE) is a simulation approach that serves as a “light on the hill” (Smith, 1994) to test options for marine management, monitoring, and assessment against simulated ecosystem and fishery dynamics, including uncertainty in ecological and fishery processes and observations. MSE has become a key method to evaluate trade-offs between management objectives and to communicate with decision makers. Here we describe how and why MSE is continuing to grow from a single species approach to one relevant to multi-species and ecosystem-based management. In particular, different ecosystem modeling approaches can fit within the MSE process to meet particular natural resource management needs. We present four case studies that illustrate how MSE is expanding to include ecosystem considerations and ecosystem models as ‘operating models’ (i.e., virtual test worlds), to simulate monitoring, assessment, and harvest control rules, and to evaluate tradeoffs via performance metrics. We highlight United States case studies related to fisheries regulations and climate, which support NOAA’s policy goals related to the Ecosystem Based Fishery Roadmap and Climate Science Strategy but vary in the complexity of population, ecosystem, and assessment representation. We emphasize methods, tool development, and lessons learned that are relevant beyond the United States, and the additional benefits relative to single-species MSE approaches

Difference in predicted growth (<i>g</i>⋅<i>g</i>⁻¹⋅<i>d</i>⁻¹) of juvenile walleye pollock between the bioenergetics model and the IBM for 2005 (a) and 2010 (b).

<p>Areas of positive differences indicate where maximum growth potential from the bioenergetics model was higher than predicted growth from the IBM.</p

Summary of sensitivity analyses for the IBM model in 2005 and 2010 showing the minimum (min), mean, and maximum (max) growth potential and depth (m) over all stations.

<p>Base values are predicted growth (<i>g</i>⋅<i>g</i>⁻¹⋅<i>d</i>⁻¹) and depth (m) of juvenile pollock from the base model scenarios (<i>W</i> = 2.5 g, zooplankton prey distributed according to vertical profiles). All other values are predicted changes in growth and depth. Negative changes in depth indicate a shallower distribution; positive values indicate a deeper distribution. Weight is a constant value applied across all station, so varying the parameter acts as a scalar and results in similar spatial patterns across the area. The effect of applying a uniform distribution of zooplankton prey with depth varies across stations.</p

Predicted growth (<i>g</i>⋅<i>g</i>⁻¹⋅<i>d</i>⁻¹) of juvenile walleye pollock interpolated over the range of observed temperatures and prey energy density values across both 2005 and 2010, providing a continuous scale of growth over a broad range of possible environmental and biological scenarios.

<p>The observed fish energy density was higher in 2010 (<i>v₂₀₁₀</i> = 5.29 kJ⋅g⁻¹; used in plot shown); therefore this interpolation demonstrates the range of predicted growth for fish with high energy density. Temperatures included 0–16°C to show possible range under variable climate conditions. The dashed rectangle encompasses the range of temperatures and prey energy density values observed in 2005; solid rectangle encompasses values in 2010. Points are shown for average temperature and prey energy density conditions in 2005 and 2010. Predicted growth above 15°C was not possible (black) because the bioenergetics model has a temperature threshold of 15°C.</p

Parameter definitions and values used in the bioenergetics model to estimate maximum growth potential (<i>g</i>⋅<i>g</i>⁻¹⋅<i>d</i>⁻¹) of juvenile walleye pollock.

<p>Parameters were used as inputs to the bioenergetics model described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084526#pone.0084526-Ciannelli1" target="_blank">[16]</a>.</p><p>^a<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084526#pone.0084526-Holsman1" target="_blank">[25]</a>; <i>^b</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084526#pone.0084526-Ciannelli1" target="_blank">[16]</a>.</p

Main prey taxa included in the models for 2005 and 2010.

<p>Prey items cumulatively accounting for at least 90% of the diet by % volume and individually accounting for at least 2% of the diet by % volume were included. Prey taxa common to both years are shown in <b>bold</b>.</p><p><i>Neocalanus plumchrus</i> was not identified in the 2010 bongo data, but did occur in the Juday data (small-mesh; not quantitative for large zooplankton taxa). Due to the absence in the bongo data, <i>N. plumchrus</i> was excluded from further analyses.</p

Summary of sensitivity analyses for the bioenergetics model in 2005 and 2010 showing the minimum (min), mean, and maximum (max) growth potential over all stations.

<p>Base values are predicted maximum growth potential (<i>g⋅g</i>⁻¹⋅<i>d</i>⁻¹) of juvenile pollock from the base model scenarios (<i>W</i> = 2.5 g, Temp = average temperature in upper 30 m,  = 1.0,  = prey energy density,  = 3.92 kJ⋅g⁻¹;  = 5.29 kJ⋅g⁻¹). All other values denote the change in growth rate resulting from indicated changes in inputs; therefore (−) effects indicate that varied conditions resulted in lower predicted growth and vice versa. Pooled standard deviations (SDs) for each parameter were calculated across stations after removing the annual means. <i>W</i> and are constant values applied across all station, so changes (±1 SD) act as a scalar and result in similar spatial patterns across the area. Temperature and vary across stations.</p

Predicted growth (<i>g⋅g</i>⁻¹⋅d⁻¹) of juvenile walleye pollock from the bioenergetics model.

<p>Top panel (a and b) shows growth under the base model scenarios for 2005 and 2010 (<i>W</i> = 2.5 g, Temp = average temperature in upper 30 m,  = 1.0,  = prey energy density,  = 3.92 kJ⋅g⁻¹;  = 5.29 kJ⋅g⁻¹). Middle panel (c and d) shows changes in predicted growth when temperature is increased by 1 standard deviation (SD). Predicted growth could not be estimated at one station in 2005 (c) in the inner domain under increased temperatures because the water temperature in the upper 30 m was greater than 15°C (<i>T_cm</i> = 15°C in the model). Lower panel (e and f) shows changes in predicted growth when prey energy density is increased by 1 SD. Spatial plots of predicted growth when parameters are decreased by 1 SD are not shown, but can be visualized by subtracting the anomalies (lower two panels) from the base scenario plots (top panel).</p