Assessing Trade-Offs in Large Marine Protected Areas

Large marine protected areas (LMPAs) are increasingly being established and have a high profile in marine conservation. LMPAs are expected to achieve multiple objectives, and because of their size are postulated to avoid trade-offs that are common in smaller MPAs. However, evaluations across multiple outcomes are lacking. We used a systematic approach to code several social and ecological outcomes of 12 LMPAs. We found evidence of three types of trade-offs: trade-offs between different ecological resources (supply trade-offs); trade-offs between ecological resource conditions and the well-being of resource users (supply-demand trade-offs); and trade-offs between the well-being outcomes of different resource users (demand trade-offs). We also found several divergent outcomes that were attributed to influences beyond the scope of the LMPA. We suggest that despite their size, trade-offs can develop in LMPAs and should be considered in planning and design. LMPAs may improve their performance across multiple social and ecological objectives if integrated with larger-scale conservation efforts. © 2018 Davies et al

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Managing small-scale commercial fisheries for adaptive capacity: Insights from dynamic social-ecological drivers of change in Monterey Bay

Globally, small-scale fisheries (SSFs) are driven by climate, governance, and market factors of social-ecological change, presenting both challenges and opportunities. The ability of small-scale fishermen and buyers to adapt to changing conditions allows participants to survive economic or environmental disturbances and to benefit from optimal conditions. This study presented here identifies key large-scale factors that drive SSFs in California to shift focus among targets and that dictate long-term trends in landings. We use Elinor Ostrom’s Social-Ecological System (SES) framework to apply an interdisciplinary approach when identifying potential factors and when understanding the complex dynamics of these fisheries. We analyzed the interactions among Monterey Bay SSFs over the past four decades since the passage of the Magnuson Stevens Fisheries Conservation and Management Act of 1976. In this region, the Pacific sardine (Sardinops sagax), northern anchovy (Engraulis mordax), and market squid (Loligo opalescens) fisheries comprise a tightly linked system where shifting focus among fisheries is a key element to adaptive capacity and reduced social and ecological vulnerability. Using a cluster analysis of landings, we identified four modes from 1974 to 2012 that were dominated by squid, sardine, anchovy, or lacked any dominance, enabling us to identify external drivers attributed to a change in fishery dominance during seven distinct transition points. Overall, we show that market and climate factors drive the transitions among dominance modes. Governance phases most dictated long-term trends in landings and are best viewed as a response to changes in perceived biomass and thus a proxy for biomass. Our findings suggest that globally, small-scale fishery managers should consider enabling shifts in effort among fisheries and retaining existing flexibility, as adaptive capacity is a critical determinant for social and ecological resilience

Adaptive Capacity of the Monterey Bay Wetfish Fisheries: Proactive Responses to the 2015–16 El Niño Event

Natural resource sustainability depends on adaptive capacity: the latent ability of a social-ecological system to rely on or implement effective strategies to cope with disturbances. We propose and implement a new three-step framework for studying a social-ecological system's adaptive capacity that consists of assessment, strategy identification, and activation. We apply this framework to analyze the adaptive capacity of the Monterey Bay wetfish fisheries community in response to a significant stressor in the system: El Niño Southern Oscillation (ENSO). We conducted 49 semi-structured interviews with participants engaged in these fisheries and/or providing ENSO information. Of the 67 social-ecological strategies identified through a literature review, we found 42 contribute to high adaptive capacity to ENSO. The most influential strategies were matching formal and informal rules to system dynamics, diversifying livelihoods, enhancing economic safety nets, social learning, and accessing early warning systems. Our findings suggest approaches for enhancing adaptive capacity in other resource systems

Visual representations of how the three conceptual trade-offs (as identified by Mouchet <i>et al</i>. (23)) may appear across the seven outcomes assessed in our study.

<p>Each example radar plot (A,B,C) shows all five focal outcomes (ecosystem health, migratory species, fishery resources, well-being of user groups (e.g., fishers), and well-being of users of the ecosystem (e.g., coastal residents, tourists), with the inner-most band representing a decline and the outside line representing an increase (indicated with ‘worst’ to ‘best’ on the radar plot). Key outcome trade-offs have been circled to aid understanding of the trade-off typology and how it applies to our data. Outcome abbreviations used in radar plot: Eco = ecosystem health change; WB_Eco = well-being change of the user of the ecosystem health indicator; WB_Fish = well-being change of the user of the fisheries indicator; Mig = migratory species change; Fish = fisheries change. <b>A</b>: Supply trade-off: ecosystem health improving, but fisheries declining (or vice versa; conservation versus use). <b>B</b>: Supply-demand trade-off: fisheries improving, but well-being of a user (fisher) declining (or vice versa). <b>C</b>: Demand trade-off: differentiated impacts in the well-being of different users, with a well-being decline of a user dependent on fisheries, and a well-being improvement of a user dependent on ecosystem health (e.g. tourism) (or vice versa).</p

The three main components of the social-ecological system and the five outcomes measured in this study.

<p>The three main components of the social-ecological system and the five outcomes measured in this study.</p

Visual representations of how the three conceptual trade-offs (as identified by Mouchet <i>et al</i>. (23)) may appear across the seven outcomes assessed in our study.

<p>Each example radar plot (A,B,C) shows all five focal outcomes (ecosystem health, migratory species, fishery resources, well-being of user groups (e.g., fishers), and well-being of users of the ecosystem (e.g., coastal residents, tourists), with the inner-most band representing a decline and the outside line representing an increase (indicated with ‘worst’ to ‘best’ on the radar plot). Key outcome trade-offs have been circled to aid understanding of the trade-off typology and how it applies to our data. Outcome abbreviations used in radar plot: Eco = ecosystem health change; WB_Eco = well-being change of the user of the ecosystem health indicator; WB_Fish = well-being change of the user of the fisheries indicator; Mig = migratory species change; Fish = fisheries change. <b>A</b>: Supply trade-off: ecosystem health improving, but fisheries declining (or vice versa; conservation versus use). <b>B</b>: Supply-demand trade-off: fisheries improving, but well-being of a user (fisher) declining (or vice versa). <b>C</b>: Demand trade-off: differentiated impacts in the well-being of different users, with a well-being decline of a user dependent on fisheries, and a well-being improvement of a user dependent on ecosystem health (e.g. tourism) (or vice versa).</p

Mechanisms that may give rise to trade-offs, including description and examples from the literature.

<p>Mechanisms that may give rise to trade-offs, including description and examples from the literature.</p

Radar plots of all outcomes for each case study.

<p>Inner line is declining status, middle line is same or mixed effects, and outer line is increasing status. Missing data (either where there was no user so an outcome was not appropriate, or no data present) were not plotted as points on the radar chart and the lines connect the points where data were present. Outcome abbreviations used in radar plot: Eco = ecosystem health change; WBEco = well-being change of the user of the ecosystem health indicator; WBFish = well-being change of the user of the fisheries indicator; Migratory = migratory species change; Fish = fisheries change.</p