5feature MPA

LETTER doi:10.1038/nature13022 Global conservation outcomes depend on marine protected areas with five key features GrahamJ. Edgar1, RickD. Stuart-Smith1, Trevor J.Willis2, StuartKininmonth1,3, SusanC.Baker4, Stuart Banks5,Neville S. Barrett1, Mikel A. Becerro6, Anthony T. F. Bernard7, Just Berkhout1, Colin D. Buxton1, Stuart J. Campbell8, Antonia T. Cooper1, Marlene Davey1, Sophie C. Edgar9, Gu¨nter Fo¨rsterra10, David E. Galva´n11, Alejo J. Irigoyen11, David J. Kushner12, Rodrigo Moura13, P. Ed Parnell14, Nick T. Shears15, German Soler1, Elisabeth M. A. Strain16 & Russell J. Thomson1 In linewith global targets agreed under theConvention onBiological Diversity, the number ofmarine protected areas (MPAs) is increas- ing rapidly, yet socio-economic benefits generated byMPAs remain difficult topredict andunder debate1,2.MPAsoften fail to reach their full potential as a consequence of factors such as illegal harvesting, regulations that legally allowdetrimental harvesting, or emigration of animals outside boundaries because of continuous habitat or inadequate size of reserve3–5. Here we show that the conservation benefits of 87MPAs investigated worldwide increase exponentially with the accumulation of five key features: no take, well enforced, old (.10years), large (.100km2), and isolatedbydeepwateror sand. Using effective MPAs with four or five key features as an unfished standard, comparisonsofunderwater surveydata fromeffectiveMPAs with predictions based on survey data from fished coasts indicate that total fish biomass has declined about two-thirds fromhistorical baselines as a result of fishing. EffectiveMPAs also had twice as many large (.250mmtotal length) fish species per transect, five timesmore large fish biomass, and fourteen times more shark biomass than fished areas. Most (59%) of the MPAs studied had only one or two key features and were not ecologically distinguishable from fished sites. Our results show that global conservation targets based on area alonewill not optimizeprotectionofmarine biodiversity.More emphasis is needed on better MPA design, durable management and compliance to ensure thatMPAs achieve their desired conserva- tion value. A multitude of socio-economic and biological factors influence the responses of species to protection within MPA networks, adding con- siderable uncertainty when making specific predictions regarding the conservation benefits of newMPAs. Evenwithinwell-designedMPAs, populationsofmarine species canrespondquitedifferently toprohibitions on fishing as a consequence of species-specific factors such asmobility, larval dispersal, fecundity, longevity, indirect interactions among spe- cies, environmental context, and overall level of exploitation before protection5,6. To assess the extent to whichMPAs fulfil their ecological potential, we used a database unprecedented in geographic scale to inves- tigate how conservation value, characterized by ecological response of fish communities withinMPAs, is affected by the cumulative effects of five key planning and management features: (1) degree of fishing per- mitted within MPAs; (2) level of enforcement; (3) MPA age; (4) MPA size; and (5) presence of continuous habitat allowing unconstrained movementof fish acrossMPAboundaries6–10.Althoughprevious studies have considered these factors independently, this is the first study, to our knowledge, that considers them simultaneously, using data col- lected globally with standardized methods. Observations fromthe subset ofMPAs that seem towork effectively— that is, they include at least four of five ‘NEOLI’ (no take, enforced, old, large and isolated) features—are additionally used to infer ecological condition associated with unfished reefs. For this aspect, we used the global network of MPAs as a vast ecological experiment, where effec- tive no-take areas represent human predator exclusion plots within a matrix of fished coasts11. Eight community-level metrics were assessed using data from 40 nations on shallow reef fish densities and sizes provided by researchers and trained volunteer divers participating in the Reef Life Survey (RLS) programme12. A total of 964 sites in 87MPAswere surveyed (Extended Data Fig. 1a), with data aggregated into 121MPA/ecoregion groupings for analysis.MPAmeanswere comparedwith statistical predictions for fished coasts using data from 1,022 non-MPA sites surveyed in 76 of the 232Marine Ecoregions of theWorld13 (Extended Data Fig. 1b and Supplementary Data Table 1). The four community metrics investi- gated, eachwidely considered to respond toMPAdeclaration14,15, were: (1) total biomass of all fishes; (2) total biomass of large (.250mm length) fishes; (3) species richness of all fishes (number of species sighted per transect); and (4) species richness of large fishes. We also estimated the totalbiomassof three commercially important taxa (sharks, groupers and jacks), with unexploited damselfishes providing a control group for effects evidenton targeted fishery groups. Effect sizewas calculatedusing the log ratio of measured values in MPAs relative to values predicted using global models for fished coasts. Among14 environmental and socio-economiccovariates used in ran- dom forest models16 to develop predictions for fished coasts, mean sea surface temperature, annual temperature range, photosynthetically active radiation, and latitude consistently exerted the strongest influence on the global distribution of species richness and biomassmetrics (Extended Data Fig. 2). Biomass of groupers and jackswas also greatly influenced by human population density, and the biomass of sharks and groupers was influenced by phosphate concentration. Fish species richness along fished coasts peaked in the southeast Asian ‘coral triangle’ region (Fig. 1a), as expected12,17. However, when only the number of large fishes sighted along transects was considered (Fig. 1b), the global centre of species richness shifted to more isolated locations within the Indo-Pacific region. Overfishing of large predatory fishes presumably contributed to these geographical patterns. Sharks, groupers and other large fishes were present within the coral triangle 1Institute for Marine and Antarctic Studies, University of Tasmania, GPO Box 252-49, Hobart, Tasmania 7001, Australia. 2Institute of Marine Sciences, School of Biological Sciences, University of Portsmouth, FerryRoad, PortsmouthPO49LY, UK. 3StockholmResilienceCentre, StockholmUniversity, Kra¨ftriket 2B, SE-10691Stockholm, Sweden. 4School of Plant Science, University of Tasmania,GPO Box 252, Hobart, Tasmania 7001, Australia. 5Charles Darwin Foundation, Puerto Ayora, Galapagos, Ecuador. 6The Bites Lab, Natural Products and Agrobiology Institute (IPNA-CSIC), 38206 La Laguna, Tenerife, Spain. 7Elwandle Node, South African Environmental Observation network, Private Bag 1015, Grahamstown 6140, South Africa. 8Wildlife Conservation Society, Indonesia Marine Program, Jalan Atletik No. 8, Bogor Jawa Barat 16151, Indonesia. 9Department of Water, Perth, Western Australia 6000, Australia. 10Facultad de Recursos Naturales, Escuela de Ciencias del Mar, Pontificia Universidad Catolica de Valparaıso, Valparaıso, Chile. 11Centro Nacional Patagonico, Consejo Nacional de Investigaciones Cientificas y Tecnicas, Bvd Brown 2915, 9120 Puerto Madryn, Argentina. 12Channel Islands National Park, United States National Park Service, 1901 Spinnaker Dr., Ventura, California 93001, USA. 13Instituto de Biologia, Universidade Federal do Rio de Janeiro, Av. Carlos Chagas Filho 373, Rio de Janeiro21941-902,Brazil. 14Scripps InstitutionofOceanography,UCSanDiego,Mail Code0227,9500GilmanDr., La Jolla, California92093-0227,USA. 15LeighMarine Laboratory, University ofAuckland, 160 Goat Island Road, Leigh 0985, New Zealand. 16Dipartimento di Scienze Biologiche, Geologiche ed Ambientali, Universita` di Bologna, Via San Alberto, Ravenna 163-48123, Italy. 2 1 6 | N A T U R E | V O L 5 0 6 | 1 3 F E B R U A R Y 2 0 1 4 Macmillan Publishers Limited. All rights reserved©2014 region but had been exploited to near absence on most reefs, and so were rarely recorded on transects; consequently, observed species rich- ness of large fishes was relatively low. Our predictive models indicated that total fish and large fish biomass werehighest inFrenchPolynesia and thenearbyLine Islands (Figs1c, d), and sharks, groupers and jacks also had disproportionally high biomass in that region (ExtendedData Figs 3a–c). Shark biomass on fished coasts was also very high off the Pitcairn Island group, and northeastern and northwesternAustralia. Reassuringly, high shark and grouper biomass was accurately predicted for Galapagos, regardless that no data from fished sites in the oceanic tropical eastern Pacific region were used to generate the predictivemodels. At the time of the surveys, all islands in the region (Galapagos, Cocos and Malpelo) were within MPAs; how- ever, data obtained before fishing restrictions in Galapagos indicate anomalously high shark and grouper biomass for fished coasts in that archipelago (S.B. andG.J.E., unpublished data). Damselfishes occurred in relatively high abundance in all tropical ocean basins (Extended Data Fig. 3d). Across all 87MPAs investigated, species richness of large fishes was 36% greater inside MPAs compared to fished areas (95% confidence interval (CI), 16–60% increase), biomass of large fisheswas 35%greater (CI 3–78% increase) and sharks 101% greater (CI 17–239% increase). Nevertheless, for species richness of all fishes and the other four bio- massmetrics investigated, no significant difference (P. 0.05)was found between levels observed inMPAs and those predicted for fished coasts. Moreover, many MPAs possessed fish biomass well below predicted regional averages, as indicated by the large percentage of MPAs with negative log ratios for total biomass, ranging from 25% of MPAs for large fishes to 31% for sharks to 47% for groupers. These negative values indicate considerable site-scale variability in fish densities, with some MPA sites exhibiting low fish biomass due to local habitat vari- ability between survey sites and, in other cases, a bias resulting from stakeholder consultation processes before MPA declaration aimed at minimizing lost fishing opportunity18. The poor overall performance ofMPAsworldwide in terms of recov- ery of fish biomass relative to fished sites was due to a high frequency of ineffectiveMPAs and high spatial variability in fish densities, rather than an absence of recovery in all MPAs. The efficacy of MPAs was strongly influenced by the five NEOLI planning andmanagement fea- tures (no take, enforced, old, large and isolated),withMPAs that scored highly with multiple NEOLI features typically having highly elevated biomass of exploitable fishes compared to fished sites (Fig. 2). MPAs with at least four NEOLI features were distributed across six countries in three oceans (Extended Data Fig. 1a) and a range of environmental conditions, indicating that model outputs and conclusions were not strongly regionally biased. No significant differences were evident between fished sites (zero features) and MPAs with one or two NEOLI features; however, effect sizes rose rapidly when the number of features increased from three to five (Fig. 2 and Extended Data Fig. 4). For example, the measured rises in mean values within MPAs relative to fished areas for total fish bio- mass, total large fish biomass and shark biomass with three NEOLI features were 30%, 66% and 104%, respectively. These increases were, however, modest compared to values when all five NEOLI features were present, with large increases of 244%, 840% and 1,990%, respec- tively. Similar marked increases in biomass were evident for groupers (882%) and jacks (864%). Non-fished damselfishes showed a smaller mean increase of 111%atMPAswith fiveNEOLI features. This increase was on themargins of statistical significance, lying outside the 95% con- fidence interval (Extended Data Fig. 4) but nonsignificant (P, 0.05) when assessed with a t-test, which adjusts for small sample size. All four MPAs with five NEOLI features were small oceanic islands (Cocos,CostaRica;Malpelo,Colombia;Kermadec Islands,NewZealand; and Middleton Reef, Australia), raising a potential concern that calcu- lated effect sizes were biased by plankton and pelagic fish subsidies that enlarge food webs at isolated oceanic locations. ‘Oceanic island’ was, however, included as a categorical covariate in random forest models, therefore model predictions should accommodate small island effects. Regardless, further investigation into the contribution of external sub- sidies to food webs at isolated MPAs is warranted. Alternative expla- nations for elevated damselfish numbers in the most effective MPAs comparedwith poorly protectedMPAs include reduced fishing-related habitat deterioration such as dynamite damage to coral, and trophic cascades involving smaller predators that consume damselfishes and are prey to sharks and groupers. No-take regulations, efficient enforcement, large area (.100 km2) and old age (.10 years) each contributed similar increases in fish bio- masswithinMPAs (Fig. 2).However, isolation, a categorical factor that distinguishedMPAswith reef habitat surroundedbydeep (.25m)water or large expanses of sand fromMPAswith shallow reef habitat extend- ing to fished areas, seemed to exert a stronger influence for community- level biomass and richness metrics than the other four features. For example, the mean increase (95% CI) for total fish biomass associated withMPAswith threeNEOLI featureswas 100% (14–252%)when one of the three features was isolation, compared to 14% (218%–58%) for three NEOLI MPAs when isolation was not included. Compliance All fish Species 55 40 30 22 16 11 7 5 4 3 2 1 Large fish Species 4.2 3.0 2.2 1.6 1.1 0.8 0.6 0.4 0.3 0.2 0.1 0 All fish Biomass 68.4 55.0 35.6 23.0 14.5 9.2 5.7 3.5 2.1 1.2 0.6 0.3 Large fish Biomass 18.0 12.0 8.0 5.3 3.5 2.3 1.5 1.0 0.6 0.3 0.2 0.1 a b c d Figure 1 | Predicted global distribution of four community metrics for fishes associated with coral and rocky reefs outside ofMPAs. Predictions are from random forest models developed using data from 1,022 sites in fished locations worldwide. a, Species richness of all fishes (number of species sighted per 250m2). b, Species richness of large (.250mm total length) fishes (per 250m2). c, Total biomass of all fishes (kg per 250m2). d, Total biomass of large fishes (kg per 250m2). LETTER RESEARCH 1 3 F E B R U A R Y 2 0 1 4 | V O L 5 0 6 | N A T U R E | 2 1 7 Macmillan Publishers Limited. All rights reserved©2014 may have contributed to the isolation effect, in that isolatedMPAs are generally well demarcated for control purposes. They are readily recog- nized by fishers andmore easily policed than coastlineswith complicated mosaics of no take, restricted take and fishing zones. Although very important, the effect of isolation was similar in magnitude—rather than clearly superior—to other MPA features for biomass of sharks, groupers and jacks (Extended Data Fig. 4). WhenMPAs that are no take and well enforced are considered, dif- ferences were evident in how the other MPA features affect different components of the fish community (Fig. 3 and Extended Data Fig. 5). Total fish biomass increased significantly from low to high levels for all fiveMPAfeatures, and these same trendsweremagnified for large fishes (Fig. 3). Regardless of general concerns that large pelagic species move such great distances that few individuals are fully protected within MPAs19, sharks and jacks seem to receive considerable protection from fishing mortality within the large, well-enforced, no-take MPAs studied here.Thebiomass of sharks andgroupers rose exponentiallywhenMPAs were fully isolated, and also greatly increased with area and age. The biomass of jacks showed little isolation and age effects, but rose greatly in MPAs that were large, well enforced and no take. Damselfish bio- mass did not increase significantly with the accumulation of individ- ual NEOLI features. The large number of MPAs investigated here has allowed relatively subtle and higher order interactive MPA effects to be detected. Previ- ous studies of MPAs have shown, for example, negligible or weak pat- terns associated with MPA size6,9,14,15,20, and those detected here were only evident forMPAswith at least three of the NEOLI features. How- ever, MPA size was very important for such metrics as jack biomass, which showed a stronger response to MPA area than to other metrics (ExtendedData Fig. 5). This response probably resulted from time spent by actively-swimming fishes outside park boundaries, which increases probability of capture for fishes associated with small MPAs. Species richness of large fishes exhibited a highly significant differ- ence between MPAs with five NEOLI features and fished locations (115% increase relative to predicted, CI 95–137%; t-test, P, 0.0001; Fig. 2). By contrast, MPAs with five NEOLI features did not differ sig- nificantly in total species richness (6% increase relative to predicted) 0 200 400 0 500 1,000 P er ce nt ag e of p re d ic te d 100 200 Number of NEOLI features 0 0 200 400 Biomass Biomass (>25 cm) Species Species (>25 cm) No take Enforced Old Large Isolated 0 793 1 2 3 4 5 1 2 3 4 5 a b Figure 2 | Mean response ratios for MPAs with different numbers of NEOLI (no take, enforced, old, large, isolated) features. Mean ratio values have been back transformed from logs and expressed as percentages with 95% confidence intervals, with 100% equivalent to fished coasts. Sites on fished coasts have 0 NEOLI features. a, Mean response ratios for four community metrics. b, Mean response ratios for community metrics where each NEOLI feature was included within the set examined. The ‘no-take’ plot with two features, for example, depicts themean response for no-takeMPAswith a single other NEOLI feature. 95% confidence limits that lie off-scale are shown by number. Samples sizes are shown in Extended Data Table 1. 100 200 Biomass Biomass (>25 cm) Species Species (>25 cm) 0 100 200 R egulations E nforcem ent A ge P er ce nt ag e of p re d ic te d 0 200 400 A rea 0 200 400 Isolation Level of MPA feature Lo w M ed ium Hi gh 0 300 600 Lo w M ed ium Hi gh Lo w M ed ium Hi gh Lo w M ed ium Hi gh 1,193 792 Figure 3 | Mean response ratios with 95% confidence intervals for four community metrics and low, medium and high levels of five MPA features. Values have been back transformed to per cent scale, with 100% equivalent to fished coasts. The feature ‘regulations’ was analysed using data from 82 MPAs with high enforcement; the feature ‘enforcement’ was analysed using data from 75MPAs that are no-take; and the features ‘isolation’, ‘age’ and ‘area’ were analysed using data from 52 MPAs that are both no take and well enforced. 95% confidence limits that lie off-scale are shown by number. Samples sizes are shown in Extended Data Table 1. RESEARCH LETTER 2 1 8 | N A T U R E | V O L 5 0 6 | 1 3 F E B R U A R Y 2 0 1 4 Macmillan Publishers Limited. All rights reserved©2014 from fished locations (t-test, P5 0.42; Fig. 2), nor did any of the five features individually have a clear effect on species richness (Fig. 3). Thus, total species richness along transects did not detectably increase in effec- tiveMPAs, despite the presence of additional large fish species, perhaps because of food web changes in the form of reduced presence of small fish species that comprised prey of the larger predatory species5,21,22. Regardless of these transect-scale effects, species richness at regional scales probably increased in areas with a mosaic of fished and effective MPAs because of the additional presence of large fishery-targeted spe- cies within the seascape18. Of th