Predicted and measured catch volumes a and fishers’ prices b for 2001–2016. Predictions of the SEM (yellow), EDM (green), BEM (blue), and measured observations (red). Parameter values and functions are outlined in SI S1, SI Table S2 and S4. The simulations use time series inputs of mantle length (as a proxy for SST anomalies) and proportion of Pacific squid landings (grey, right hand axis). The data represents observations aggregated per year. The models were calibrated via a Monte Carlo process over the range of possible parameter values (SI Table S2). Thick curves represent the mean, and the shaded bands represent the 95% confidence intervals. c–e The effect of trend and amplitude of SST anomalies on the mean price gap and fishers’ income for SEM simulations. c The mean price gap calculated as the ratio between fishers’ prices and traders’ prices (i.e., market prices). The areas in red represent large price differences. d Mean fishers’ income. Areas in blue denote high fishers’ income. e, f Fishers and traders’ income 1990–2025 in two alternate fishery development programs with investments starting in 2005: demand development (E) and ) cooperation development (E). Simulations (e, f) use the simulated proportion of Pacific squid landings and SST anomalies (grey, right hand axis). e A program to increase domestic demand with the SEM (yellow) and BEM (blue). f A limitation of trader cooperation (SI S1 Eq. 8) using SEM (yellow)

Predicted and measured catch volumes a and fishers’ prices b for 2001–2016. Predictions of the SEM (yellow), EDM (green), BEM (blue), and measured observations (red). Parameter values and functions are outlined in SI S1, SI Table S2 and S4. The simulations use time series inputs of mantle length (as a proxy for SST anomalies) and proportion of Pacific squid landings (grey, right hand axis). The data represents observations aggregated per year. The models were calibrated via a Monte Carlo process over the range of possible parameter values (SI Table S2). Thick curves represent the mean, and the shaded bands represent the 95% confidence intervals. c–e The effect of trend and amplitude of SST anomalies on the mean price gap and fishers’ income for SEM simulations. c The mean price gap calculated as the ratio between fishers’ prices and traders’ prices (i.e., market prices). The areas in red represent large price differences. d Mean fishers’ income. Areas in blue denote high fishers’ income. e, f Fishers and traders’ income 1990–2025 in two alternate fishery development programs with investments starting in 2005: demand development (E) and ) cooperation development (E). Simulations (e, f) use the simulated proportion of Pacific squid landings and SST anomalies (grey, right hand axis). e A program to increase domestic demand with the SEM (yellow) and BEM (blue). f A limitation of trader cooperation (SI S1 Eq. 8) using SEM (yellow)

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Small-scale fisheries are critically important for livelihoods around the world, particularly in tropical regions. However, climate variability and anthropogenic climate change may seriously impact small-scale fisheries by altering the abundance and distribution of target species. Social relationships between fishery users, such as fish traders, ca...

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... During anomalously warm years, squid catch volume declines but value increases as the resource becomes scarcer (Elsler et al., 2021). For most fishers, this means catching proportionally less squid and diversifying to other species. ...
... Importantly, social processes can also influence how fishers respond to resource scarcity. For instance, in areas of high cooperation between fish buyers, fishers may receive less value for their squid catch as buyers collude to fix prices (Elsler et al., 2021). Depending on existing cooperative or competitive relationships with buyers, fishers may respond differently to squid shortages by ei-ther focusing on scarce but valuable squid or diversifying effort into other fisheries. ...
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Small-scale fisheries (SSFs) around the world are increasingly facing pressures from a range of environmental, economic, and social sources. To sustain SSFs, it is imperative to understand how fishing communities adapt to these pressures. In particular, to manage economic risks fishers often catch many different species; diversifying harvest portfolios creates multiple income sources in case one species becomes less abundant, less valuable, or otherwise unavailable. Here, we apply fisheries connectivity network analysis to assess the portfolios and potential adaptive capacity of small-scale fishing communities in the Baja California Peninsula (BCP), Mexico. We found that network metrics like modularity and density varied by region and through time. The Pacific coast region of Baja California displayed increasingly modular fisheries connectivity networks, indicating fisheries landings became increasingly asynchronous with each other and the potential adaptive capacity increased. The remaining three regions of Baja California showed the opposite trend, where the temporal covariance between fisheries increased over time. Overall, this study shows that the potential adaptive capacity of fishing communities varies substantially throughout the BCP, and highlights how fisheries connectivity networks can offer a way to quantify and advance our understanding of adaptive capacity within small-scale fishing communities.
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