DOI: 10.25349/d9c32r
title: Data from: A global network of marine protected areas for food

description: Marine protected areas (MPAs) are conservation tools that are increasingly
 implemented, with growing national commitments for MPA expansion. Perhaps
 the greatest challenge to expanded use of MPAs is the perceived trade-off
 between protection and food production. Since MPAs can benefit both
 conservation and fisheries in areas experiencing overfishing and since
 overfishing is common in many coastal nations, we ask how MPAs can be
 designed specifically to improve fisheries yields. We assembled
 distribution, life history, and fisheries exploitation data for 1,338
 commercially important stocks to derive an optimized network of MPAs
 globally. We show that strategically expanding the existing global MPA
 network to protect an additional 5% of the ocean could increase future
 catch by at least 20% via spillover, generating 9 to 12 million metric
 tons more food annually than in a business-as-usual world with no
 additional protection. Our results demonstrate how food provisioning can
 be a central driver of MPA design, offering a pathway to strategically
 conserve ocean areas while securing seafood for the future.
description: Growth rate We use FishLife (Thorson 2020) to
 derive the population growth rate (r) of most of the modeled fish species.
 The growth rate for invertebrates and the remaining fish species were
 taken from FishBase (Froese & Pauly 2019) and SeaLifeBase
 (Palomares & Pauly 2019). See "Mobility and Growt
 Rates.csv". Species mobility We
 incorporate species mobility into our model of MPA effects on food
 provisioning to account for the adult movement of biomass across MPA
 boundaries. We use three categorizations of mobility characteristics:
 sedentary and/or highly site-attached (m=0.1), mobile and/or habitat
 associated (m=0.3), and highly mobile, transient, and/or highly migratory
 (m=0.9). Our classification combines both density dependent (i.e.,
 movement due to space limitation, territoriality, etc.) and density
 independent (i.e., random movement of fish via simple diffusion) movement,
 and we therefore use generous bounds to classify linear scales of
 movement. Our liberal definition of movement therefore includes
 relocations to new home ranges and excursions – potentially spawning
 migrations – by individuals with otherwise restricted home ranges (e.g.,
 linear movements &gt;1 km were recorded for Scarids, Acanthurids,
 Mullids, Epinephelinids, and Lethrinds over multiple long-term tracking
 studies (Starr et al. 2007, Davis et al. 2017, Chateau & Wantiez
 2009, Meyer et al. 2010)). The assignment of values to
 mobility characteristics is arbitrary, but our categorization is modeled
 around our ~55 km by 55 km planning unit. We define m=0.1 to represent
 species with maximum scales of movement &lt;1 km for adults. Species
 in the m=0.3 category have maximum adult scales of movement between 1-55
 km. Species in the m=0.9 category are wide ranging and many cross national
 jurisdictions, with maximum adult scales of movement &gt;55
 km. The mobility indices were assigned using keyword
 matching from four databases that were searched sequentially: FishBase
 (Froese & Pauly 2019), SeaLifeBase (Palomares
 & Pauly 2019), FAO (FAO 2020), and the IUCN Red List of Threatened
 Species (IUCN 2020) (Table S2). Mobility indices were assigned by three
 unique classifiers; insufficient information in reference databases and
 discrepancies between mobility classifications between scorers were
 resolved using peer-reviewed literature. All species mobility indices,
 classification keywords, source information, and relevant notes are
 presented in "Mobility and Growt Rates.csv".
 References O. Chateau, L. Wantiez, Movement
 patterns of four coral reef fish species in a fragmented habitat in New
 Caledonia: implications for the design of marine protected area
 networks. ICES J. Mar. Sci. 66, 50–55 (2009). K. Davis,
 P. M. Carlson, C. G. Lowe, R. R. Warner, J. E. Caselle, Parrotfish
 movement patterns vary with spatiotemporal scale. Mar. Ecol. Prog.
 Ser. 577, 149–164 (2017). FAO, Food and Agriculture
 Organization of the United Nations. www.fao.org (April 18,
 2020). R. Froese, D. Pauly, FishBase. World Wide Web
 electronic publication. www.fishbase.org (2019). IUCN,
 The IUCN Red List of Threatened Species. www.iucnredlist.org (April 18,
 2020). C. G. Meyer, Y. P. Papastamatiou, T. B. Clark,
 Differential movement patterns and site fidelity among trophic groups of
 reef fishes in a Hawaiian marine protected area. Mar. Biol. 157, 1499–1511
 (2010). D. Palomares, D. Pauly, SeaLifeBase. World Wide
 Web electronic publication. www.sealifebase.org (2019).
 R. M. Starr, E. Sala, E. Ballesteros, M. Zabala, Spatial dynamics
 of the Nassau grouper Epinephelus striatus in a Caribbean atoll. Mar.
 Ecol. Prog. Ser. 343, 239–249 (2007). J. T. Thorson,
 Predicting recruitment density dependence and intrinsic growth rate for
 all fishes worldwide using a data‐integrated life‐history model. Fish
 Fish. 21, 237–251 (2020).
description: Growth rate (r) and mobility categorization (m) per species used
 in our model. The common name of the species is derived from FishBase. See
 "Mobility Classification.pdf" for the mobility classification.
 Movement sources are from FishBase (FB), SeaLifeBase (SLB), FAO, IUCN, or
 from other references as cited (REF).