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Protected areas are an effective tool for reducing biodiversity loss. Current legislation distinguishes various types of marine protected areas, each allowing different levels of resource extraction. However, almost all of the theory for spatial conservation planning is focused on identifying no‐take reserves. The current approaches to zoning for multiple types of protected areas could result in suboptimal plans in terms of protecting biodiversity and minimizing negative socioeconomic impacts. We overcame these limitations in the first application of the multizone planning tool, Marxan with Zones, to design a network of four types of protected areas in the context of California's Marine Life Protection Act. We have produced a zoning configuration that entails mean value losses of less than 9% for every fishery, without compromising conservation goals. We also found that a spatial numerical optimization tool that allows for multiple zones outperforms a tool that can identify one zone (ie marine reserves) in two ways: first, the overall impact on the fishing industry is reduced, and second, a more equitable impact on different fishing sectors is achieved. Finally, we examined the tradeoffs between representing biodiversity features and impacting fisheries. Our approach is applicable to both marine and terrestrial conservation planning, and delivers an ecosystem‐based management outcome that balances conservation and industry objectives.

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We present a novel method for designing marine reserves that trades off three important attributes of a conservation plan: habitat condition, habitat representation, and socioeconomic costs. We calculated habitat condition in four ways, using different human impacts as a proxy for condition: all impacts; impacts that cannot be managed with a reserve; land-based impacts; and climate change impacts. We demonstrate our approach in California, where three important tradeoffs emerged. First, reserve systems that have a high chance of protecting good condition habitats cost fishers less than 3.1% of their income. Second, cost to fishers can be reduced by 1/2-2/3 by triaging less than 1/3 of habitats. Finally, increasing the probability of protecting good condition habitats from 50% to 99% costs fishers an additional 1.7% of their income, with roughly 0.3% added costs for each additional 10% confidence. Knowing exactly what the cost of these tradeoffs are informs discussion and potential compromise among stakeholders involved in protected area planning worldwide.

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