Skip to main content
Dryad logo

Data from: A global network of marine protected areas for food


Cabral, Reniel et al. (2020), Data from: A global network of marine protected areas for food, Dryad, Dataset,


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.


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 >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 <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 >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".


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. (April 18, 2020).

R. Froese, D. Pauly, FishBase. World Wide Web electronic publication. (2019).

IUCN, The IUCN Red List of Threatened Species. (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. (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).

Usage Notes

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).