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Gravity of human impacts mediates coral reef conservation gains

Gravity of human impacts mediates coral reef conservation gains Gravity of human impacts mediates coral reef conservation gains a,1 a,b a c,d a,e f Joshua E. Cinner , Eva Maire , Cindy Huchery , M. Aaron MacNeil , Nicholas A. J. Graham ,Camilo Mora , g a,h i,j a,e b,g,k a Tim R. McClanahan , Michele L. Barnes , John N. Kittinger , Christina C. Hicks , Stephanie D’Agata , Andrew S. Hoey , a l m n k o Georgina G. Gurney , David A. Feary , Ivor D. Williams , Michel Kulbicki , Laurent Vigliola , Laurent Wantiez , p p q r s t,u Graham J. Edgar , Rick D. Stuart-Smith , Stuart A. Sandin , Alison Green , Marah J. Hardt , Maria Beger , v,w x,y z aa bb cc Alan M. Friedlander , Shaun K. Wilson , Eran Brokovich , Andrew J. Brooks , Juan J. Cruz-Motta , David J. Booth , dd ee ff gg hh g Pascale Chabanet , Charlotte Gough , Mark Tupper , Sebastian C. A. Ferse , U. Rashid Sumaila , Shinta Pardede , a,b and David Mouillot a b Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD 4811, Australia; Marine Biodiversity Exploration and Conservation, UMR Institut de Recherche pour le Développement-CNRS-UM-L’Institut Français de Recherche pour l’Exploitation de la Mer 9190, University of c d Montpellier, 34095 Montpellier Cedex, France; Australian Institute of Marine Science, Townsville, QLD 4810, Australia; Department of Biology, Dalhousie e f University, Halifax, NS B3H 3J5, Canada; Lancaster Environment Centre, Lancaster University, LA1 4YQ Lancaster, United Kingdom; Department of Geography, g h University of Hawai‘i at Manoa, Honolulu, HI 96822; Global Marine Program, Wildlife Conservation Society, Bronx, NY 10460; Department of Botany, University of i j Hawai’i at Manoa, Honolulu, HI 96822; Center for Oceans, Conservation International, Honolulu, HI 96825; Center for Biodiversity Outcomes, Julie Ann Wrigley Global Institute of Sustainability, Life Sciences Center, Arizona State University, Tempe, AZ 85281; Laboratoire d’Excellence LABEX CORAIL, UMR-Institut de Recherche pour le Développement-UR-CNRS ENTROPIE, BP A5, 98848 Nouméa Cedex, New Caledonia; School of Life Sciences, University of Nottingham, NG7 2RD m n Nottingham, United Kingdom; Coral Reef Ecosystems Program, NOAA Pacific Islands Fisheries Science Center, Honolulu, HI 96818; UMR Entropie, Labex Corail, Institut de Recherche pour le Développement, Université de Perpignan, 66000 Perpignan, France; EA4243 LIVE, University of New Caledonia, BPR4 98851 p q Noumea cedex, New Caledonia; Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS 7001, Australia; Scripps Institution of r s Oceanography, University of California, San Diego, La Jolla, CA 92093; The Nature Conservancy, Brisbane, QLD 4101, Australia; Future of Fish, Bethesda, MD 20814; Australian Research Council Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, University of Queensland, u v Brisbane, St Lucia, QLD 4074, Australia; School of Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT Leeds, United Kingdom; Fisheries Ecology Research Laboratory, Department of Biology, University of Hawaii, Honolulu, HI 96822; Pristine Seas Program, National Geographic Society, Washington, DC x y 20036-4688; Department of Parks and Wildlife, Kensington, Perth, WA 6151, Australia; Oceans Institute, University of Western Australia, Crawley, WA 6009, z aa Australia; The Israel Society of Ecology and Environmental Sciences, 6775323 Tel Aviv, Israel; Marine Science Institute, University of California, Santa Barbara, CA bb cc 93106-6150; Departamento de Ciencias Marinas, Recinto Universitario de Mayaguez, Universidad de Puerto Rico, Mayaguez 00680, Puerto Rico; School of Life dd Sciences, University of Technology Sydney, NSW 2007, Australia; UMR ENTROPIE, Laboratoire d’Excellence LABEX CORAIL, Institut de Recherche pour le ee Développement, CS 41095, 97495 Sainte Clotilde, La Réunion (FR); Omnibus Business Centre, Blue Ventures Conservation, N7 9DP London, United Kingdom; ff gg Advanced Centre for Coastal and Ocean Research and Development, University of Trinidad and Tobago, Chaguaramas, Trinidad and Tobago, W.I.; Leibniz hh Centre for Tropical Marine Research, D-28359 Bremen, Germany; and Fisheries Economics Research Unit, Institute for the Oceans and Fisheries and Liu Institute for Global Studies, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Edited by David M. Karl, University of Hawaii, Honolulu, HI, and approved May 15, 2018 (received for review May 17, 2017) Coral reefs provide ecosystem goods and services for millions of Significance people in the tropics, but reef conditions are declining worldwide. Effective solutions to the crisis facing coral reefs depend in part on Marine reserves that prohibit fishing are a critical tool for sus- understanding the context under which different types of conserva- taining coral reef ecosystems, yet it remains unclear how human tion benefits can be maximized. Our global analysis of nearly impacts in surrounding areas affect the capacity of marine re- 1,800 tropical reefs reveals how the intensity of human impacts serves to deliver key conservation benefits. Our global study in the surrounding seascape, measured as a function of human found that only marine reserves in areas of low human impact population size and accessibility to reefs (“gravity”), diminishes the consistently sustained top predators. Fish biomass inside marine effectiveness of marine reserves at sustaining reef fish biomass and reserves declined along a gradient of human impacts in sur- the presence of top predators, even where compliance with reserve rounding areas; however, reserves located where human im- rules is high. Critically, fish biomass in high-compliance marine re- pacts are moderate had the greatest difference in fish biomass serves located where human impacts were intensive tended to be compared with openly fished areas. Reserves in low human- less than a quarter that of reserves where human impacts were low. impact areas are required for sustaining ecological functions like Similarly, the probability of encountering top predators on reefs with high human impacts was close to zero, even in high-compliance ma- high-order predation, but reserves in high-impact areas can rine reserves. However, we find that the relative difference between provide substantial conservation gains in fish biomass. openly fished sites and reserves (what we refer to as conservation Author contributions: J.E.C., E.M., C.H., M.A.M., N.A.J.G., C.M., T.R.M., M.L.B., J.N.K., gains) are highest for fish biomass (excluding predators) where hu- C.C.H., S.D., A.S.H., G.G.G., D.A.F., and D.M. designed research; J.E.C., E.M., N.A.J.G., man impacts are moderate and for top predators where human im- T.R.M., A.S.H., D.A.F., I.D.W., M.K., L.V., L.W., G.J.E., R.D.S.-S., S.A.S., A.G., M.J.H., M.B., pacts are low. Our results illustrate critical ecological trade-offs in A.M.F., S.K.W., E.B., A.J.B., J.J.C.-M., D.J.B., P.C., C.G., M.T., S.C.A.F., U.R.S., and S.P. per- meeting key conservation objectives: reserves placed where there formed research; J.E.C., E.M., C.H., and D.M. analyzed data; and J.E.C., E.M., C.H., M.A.M., N.A.J.G., C.M., T.R.M., M.L.B., J.N.K., C.C.H., S.D., A.S.H., G.G.G., D.A.F., I.D.W., M.K., L.V., are moderate-to-high human impacts can provide substantial conser- L.W., G.J.E., R.D.S.-S., S.A.S., A.G., M.J.H., M.B., A.M.F., S.K.W., E.B., A.J.B., J.J.C.-M., D.J.B., vation gains for fish biomass, yet they are unlikely to support key P.C., C.G., M.T., S.C.A.F., U.R.S., S.P., and D.M. wrote the paper. ecosystem functions like higher-order predation, which is more prev- The authors declare no conflict of interest. alent in reserve locations with low human impacts. This article is a PNAS Direct Submission. This open access article is distributed under Creative Commons Attribution-NonCommercial- marine reserves fisheries coral reefs social-ecological socioeconomic | | | | NoDerivatives License 4.0 (CC BY-NC-ND). Data deposition: A gridded global gravity data layer is freely available at dx.doi.org/10. he world’s coral reefs are rapidly degrading (1–3), which is 4225/28/5a0e7b1b3cc0e. Tdiminishing ecological functioning and potentially affecting To whom correspondence should be addressed. Email: Joshua.Cinner@jcu.edu.au. the well-being of the millions of people with reef-dependent This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. livelihoods (4). Global climate change and local human impacts 1073/pnas.1708001115/-/DCSupplemental. (such as fishing) are pervasive drivers of reef degradation (1, 5). In Published online June 18, 2018. E6116–E6125 | PNAS | vol. 115 | no. 27 www.pnas.org/cgi/doi/10.1073/pnas.1708001115 response to this “coral reef crisis,” governments around the world intensity of human impacts in the surrounding seascape (23, 24), yet have developed a number of reef conservation initiatives (1, 6, 7). these effects have never been quantified. Our focus here is on the efficacy of management tools that limit or Here, we use data from 1,798 tropical coral reef sites in 41 nations, states, or territories (hereafter “nation/states”) in every prohibit fishing. Management efforts that reduce fishing mortality should help to sustain reef ecosystems by increasing the abun- major coral reef region of the world to quantify how expected dance, mean body size, and diversity of fishes that perform critical conservation gains in two key ecological outcomes are mediated by the intensity of human impact, namely: (i) targeted reef fish bio- ecological functions (8–10). In practice, however, outcomes from these reef-management tools have been mixed (5, 11–13). mass (i.e., species generally caught in fisheries) and (ii) the pres- A number of studies have examined the social, institutional, ence of top predators (Materials and Methods and SI Appendix, Table S1). To quantify human impact at each site, we draw from a and environmental conditions that enable reef management to achieve key ecological outcomes, such as sustaining fish biomass long history of social science theory and practice to develop a (5, 14, 15), coral cover (16), or the presence of top predators metric referred to as “gravity” (Box 1). The concept of gravity (17). These studies often emphasize the role of: (i) types of key (also called interactance) has been used in economics and geog- management strategies in use, such as marine reserves where raphy to measure economic interactions, migration patterns, and fishing is prohibited, or areas where fishing gears and/or effort trade flows since the late 1800s (28–30). We adapt this approach are restricted to reduce fishing mortality (8, 18); (ii) levels of to examine potential interactions with reefs as a function of how compliance with management (12, 19, 20); (iii) the design large and far away the surrounding human population is (Box 1). characteristics of these management initiatives, for example the At each site, we also determined the status of reef management, size and age of reserves, and whether they are placed in remote grouped into either: (i) openly fished, where sites are largely un- versus populated areas (11, 21); and (iv) the role of social drivers, managed and national or local regulations tend to be poorly such as markets, socioeconomic development, and human de- mography that shape people’s relationship with nature (14, 22). In addition to examining when key ecological conditions are Populationx sustained, it is also crucial to understand the context under which conservation gains can be maximized (23, 24). By conservation gains, we are referring to the difference in a conservation outcome (e.g., the amount of fish biomass) when some form of management Populationy (i.e., a marine reserve or fishery restriction) is implemented relative Travel timex to unmanaged areas. These conservation gains can be beneficial for both people and ecosystems. For example, increased fish biomass inside marine reserves is not only related to a range of ecosystem states and processes (18), but can also result in spillover of adults Travel timey and larvae to surrounding areas, which can benefit fishers (25–27). The potential to achieve conservation gains may depend on the Box 1 Drawing on an analogy from Newton’s Law of Gravitation, B 20,000 the gravity concept predicts that interactions between two places (e.g., cities) are positively related to their mass (i.e., population) and inversely related to the distance between them (31). The gravity concept is often considered one of the 15,000 Gravity most successful and long-enduring empirical models in eco- nomics and geography (31), but has rarely been directly ap- plied in a natural resource management setting and holds 20 much promise in informing reef conservation and manage- 10,000 ment. Application of the gravity concept in a reef governance 0.05 context posits that human interactions with a reef are a 0.002 function of the population of a place divided by the squared time it takes to travel to the reefs (we used travel time instead 5,000 of linear distance to account for the differences incurred by traveling over different surfaces, such as water, roads, tracks) (14, 32) (see Fig. 1 and SI Appendix, Table S2). Here, we build upon previous work (14) by developing a new indicator that 2h 4h 6h 8h 10h 12h examines the cumulative human gravity of all populated Travel time (hours) places within a 500-km radius of a given reef, which aims to capture both market and subsistence pressures on reef fish Fig. 1. Operationalizing gravity. (A) Applied to coral reefs, our heuristic of biomass. We tested the predictive power of a series of gravity the gravity concept captures interactions between people and coral reef fish metrics with varying radii (50 km, 250 km, 500 km) and expo- as a function of the population of a place divided by the squared time it takes 2 3 nents of travel time (travel time, travel time ,travel time ) to travel to the reefs (i.e., travel time). (B) Gravity isoclines along gradients of (Materials and Methods and SI Appendix,Table S3). A key lim- population size and travel time illustrate how gravity values could be similar itation of our global gravity metric is that we are unable to for places that have large populations but are far from the reefs (e.g., pop- ulation = 15,000 people, travel time = 7 h, gravity = 306) as to those with capture local variations in efficiencies that may affect fishing x x x small populations that are close to the reef (e.g., population = 300 people, mortality per capita, such as fishing fleet technology or in- travel time = 1 h, gravity = 300). For ease of interpretation, we have illus- y y frastructure (e.g., road) quality. trated travel time here in hours, but we use minutes in the main text. Cinner et al. PNAS | vol. 115 | no. 27 | E6117 Population (people) SUSTAINABILITY PNAS PLUS SCIENCE complied with; (ii) restricted fishing, where there are actively pattern of depletion is likely related to: (i) human impacts in the enforced restrictions on the types of gears that can be used (e.g., surrounding seascape (fishing, pollution, and so forth) affecting eco- bans on spear guns) or on access (e.g., marine tenure systems that logical processes (recruitment, feeding behavior, and so forth) within restrict fishing by “outsiders”); or (iii) high-compliance marine reserves (33, 34); (ii) almost every marine reserve is likely to have reserves, where fishing is effectively prohibited (Materials and some degree of poaching, even where compliance is considered high Methods). We hypothesized that our ecological indicators would (20, 35) and the cumulative impacts from occasional poaching events decline with increasing gravity in fished areas, but that marine is probably higher in high-gravity situations; (iii) the life history of top reserves areas would be less sensitive to gravity. To test our hy- predators, such as old age of reproduction and small clutch size for potheses, we used general and generalized linear mixed-effects some (e.g., sharks), which makes then particularly susceptible to even models to predict target fish biomass and the presence of top mild levels of exploitation (36); and (iv) high-gravity marine reserves predators, respectively, at each site based on gravity and man- in our sample possibly being too young or too small to provide sub- agement status, while accounting for other key environmental and stantial conservation gains (11, 37). We conducted a supplementary social conditions thought to influence our ecological outcomes analysis to further examine this latter potential explanation. Because (14) (Materials and Methods). Based on our models, we calculated of collinearity, we could not directly account for reserve size in our expected conservation gains along a gravity gradient as the dif- model, but conducted a supplemental analysis where we separated ference between managed sites and openly fished sites. reserves into small (≤28 km )and large (Materials and Methods and SI Our analysis reveals that human gravity was the strongest predictor Appendix,Fig.S3). We found that the biomass and probability of of fish biomass (Fig. 2 and SI Appendix,Fig.S1). Fish biomass encountering top predators was higher in large compared with small consistently declined along a human gravity gradient, a trend par- reserves, but surprisingly, we found a flatter slope for small compared ticularly evident at the nation/state scale (Fig. 2 C–E). However, this with large reserves (SI Appendix,Fig.S3). However, there were no relationship can vary by management type (Fig. 2 and SI Appendix, large high-compliance reserves in high-gravity areas in our sample, Fig. S1). Specifically, we found that biomass in reserves demonstrated likely due to the social and political difficulties in establishing large a flatter (but still negative) relationship with gravity compared with reserves near people (38). Because there is little overlap between large openly fished and restricted areas (Fig. 2B). Interestingly, this dif- and small reserves along the gravity gradient in our sample, we are ferential slope between reserves and fished areas (Fig. 2B)was dueto unable to distinguish the effects of reserve size from those of gravity, a strong interaction between gravity and reserve age such that older but this is an important area for future research. Additionally, we reserves contributed more to biomass in high-gravity situations than modeled how the relationship between gravity and our ecological in low-gravity ones (SI Appendix,Fig.S1). This is likely due to fish outcomes changed with reserve age, comparing outcomes using the stocks at high-gravity sites being heavily depleted and requiring de- average reserve age (15.5 y) to those from reserves nearly twice as old cades to recover, whereas low-gravity sites would likely require less (29 y, which was the third quartile of our global distribution in reserve time to reach unfished biomass levels (8). Thus, given average reserve age). Older reserves were predicted to sustain an additional 180 kg/ha age in our sample (15.5 y), biomass in reserves did not decline as (+66%) of fish biomass at the highest levels of gravity compared with rapidly with gravity compared with fished and restricted areas (Fig. average age reserves. However, the effects of reserve age on the 2B). In the highest-gravity locations, modeled fish biomass in probability of encountering a top predator was less marked: the marine reserves was approximately five times higher than in fished modeled probability of encountering a top predator in older reserves areas (270 kg/ha compared with 56 kg/ha) (Fig. 2B). At the reef (29 y) was only 0.01, compared with <0.005 for average age (∼15 y) site scale, there was considerable variability in reef fish biomass, reserves, suggesting that small reserves common in high-gravity situ- particularly at low gravity (Fig. 2 F–H). For example, at the lowest- ations can support high levels of biomass, but are unlikely to sustain gravity locations, biomass levels in reserves spanned more than toppredators,evenwhentheyare mature. three orders-of-magnitude (Fig. 2F). Importantly, there was never Although absolute fish biomass under all management categories extremely high biomass encountered in high-gravity locations. Our declined with increasing gravity (Fig. 2B), the maximum expected estimate of target fish biomass included top predators. As a sup- conservation gains (i.e., the difference between openly fished and plemental analysis, we also examined target fish biomass with the managed) differed by management type along the gravity gradient biomass of top predators excluded, which displays a similar trend, (Fig. 4A). Interestingly, the conservation gains for restricted fishing but with lower fish biomass in reserves at low gravity compared is highest in low-gravity situations, but rapidly declines as human with when top predators are included (SI Appendix,Fig.S2). impacts increase (Fig. 4A) (39). For marine reserves, biomass A key finding from our study is that top predators were en- conservation gains demonstrated a hump-shaped pattern that countered on only 28% of our reef sites, but as gravity increases, the peaked at very low gravity when predators were included in the probability of encountering top predator on tropical coral reefs biomass estimates (Fig. 4A, solid blue line). When top predators dropped to almost zero (<0.005), regardless of management (Fig. 3). were excluded from biomass estimates, conservation gains peaked The probability of encountering top predators was strongly related at intermediate gravity levels, and were higher in high gravity to gravity and the type of management in place, as well as sampling compared with low gravity (Fig. 4A, dottedblueline). Our results methodology and area surveyed (Fig. 3 and SI Appendix,Fig.S1). At highlight how the expected differences between openly fished and low gravity, the probability of encountering a top predator was marine reserves change along a gravity gradient, given a range of highest in marine reserves (0.59) and lowest in fished areas (0.14), other social and environmental conditions that are controlled for when controlling for sampling and other environmental and social within our model (SI Appendix,Fig.S1). Thus, differences in these drivers (Fig. 3 and SI Appendix,Fig. S1). trends are relative to average conditions, and individual reserves Our study demonstrates the degree to which fish communities in- may demonstrate larger or smaller biomass build-up over time, side marine reserves can be affected by human impacts in the broader which can vary by fish groups or families (e.g., ref. 40). seascape (Figs. 2 and 3). Critically, high-compliance marine reserves in In an effort to minimize costs to users, many marine reserves, the lowest-gravity locations tended to support more than four times particularly the large ones, tend to be placed in remote locations more fish biomass than the highest-gravity reserves (1,150 vs. 270 kg/ha, that experience low human pressure (24, 41). However, critics of respectively) (Fig. 2B). Similarly, the modeled probability of en- marine reserves in remote locations suggest that limited re- countering a top predator decreased by more than 100-fold from sources could be better spent protecting areas under higher 0.59 in low-gravity reserves to 0.0046 in the highest-gravity reserves threat that could potentially yield greater conservation gains (23, (Fig. 3B). Our study design meant that it was not possible to uncover 24, 42). Our results make explicit the types of benefits—and the the mechanisms responsible for this decline of ecological condition limitations—to placing reserves in high versus low human-impact indicators within marine reserves along a gravity gradient, but this locations. We found that for nontop predator reef fishes, E6118 | www.pnas.org/cgi/doi/10.1073/pnas.1708001115 Cinner et al. Fig. 2. Model-predicted relationships between human gravity and reef fish biomass under different types of fisheries management. (A) Map of our study sites with color indicating the amount of fish biomass at each site. (B) Model-predicted relationships of how reef fish biomass declines as gravity increases by management type. Partial plots of the relationship between biomass and gravity under different types of management at the nation/state (C–E), and reef site (F–H) scale; openly fished (red), restricted (green), and high-compliance marine reserves (blue). Shaded areas represent 95% confidence intervals. Bubble size in C–E reflect the number of reef sites in each nation/state, scaled for each management type (such that the largest bubble in each panel represent the highest number of sites per nation/state for that type of management) (SI Appendix, Table S5). Nation/state name abbreviations for F–H are in SI Appendix, Table S5. substantial conservation gains can occur at even the highest- can have the substantial conservation gains for fish biomass, yet gravity locations but that optimal gains are obtained at moder- they are unlikely to support key ecosystem functions like pre- ate gravity (Fig. 4A). Our results also show that low-gravity marine dation, even with high levels of compliance. This highlights the reserves (and to a lesser extent low-gravity fisheries restrictions) importance of having clear objectives for conservation initiatives are critical to support the presence of top predators (Fig. 3). and recognizing the trade-offs involved (43, 44). Our analysis does not allow us to uncover the mechanisms behind However, the expected conservation gains for top predators de- clines rapidly with gravity in both marine reserves and restricted why we might observe the greatest differences in top predators areas (Fig. 4B). Our results illustrate a critical ecological trade-off between marine reserves and fished areas in low-gravity locations. A inherent in the placement of marine reserves: high-gravity reserves plausible explanation is that top predators, such as sharks, are Cinner et al. PNAS | vol. 115 | no. 27 | E6119 SUSTAINABILITY PNAS PLUS SCIENCE Fig. 3. Model-predicted relationships between human gravity and the probability of encountering top predators under different types of fisheries manage- ment. (A) Map of our study sites indicating the presence of top predators. (B) Model-predicted relationships of how the probability of encountering predators declines as gravity increases. Shaded areas represent 95% confidence intervals. The presence of top predators along a gravity gradient under different types of management at the nation/state (C–E) and site (F–H) scale; openly fished (red), restricted (green), and high-compliance marine reserves (blue). Bubble size in C–E reflect the number of reef sites in each nation/state, scaled for each management type (such that the largest bubble in each panel represent the highest number of sites per nation/state for that type of management) (SI Appendix, Table S5). Nation/state name abbreviations for F–H in SI Appendix, Table S5. particularly vulnerable to fishing (17) and are exposed to some low-gravity–fished areas and marine reserves. This difference fishing even in the most remote fished areas, driven by the may diminish along the gravity because top predators tend to extremely high price for shark fins [shark fins can fetch US$960/kg have large home ranges (37), and there were only small reserves in wholesale markets (45), compared with only $43/kg for in high-gravity locations (SI Appendix,Fig.S3), which may parrotfish in European supermarkets (46)]. Thus, even small mean that existing high-gravity reserves are not likely big amounts of fishing in remote openly fished areas may be de- enough to support the large home ranges of many predators pleting top predators, which creates a large difference between (37, 47). E6120 | www.pnas.org/cgi/doi/10.1073/pnas.1708001115 Cinner et al. AB 400 0.5 0.4 0.3 0.2 0.1 Fig. 4. The conservation gains (i.e., the difference between openly fished sites and managed areas) for high-compliance marine reserves (blue line) and re- 0.0 stricted fishing (green line) for (A) target fish biomass (solid lines include biomass of top predators, dotted lines exclude top predator biomass as per SI Appen- dix, Fig. S2), and (B) the probability of encountering 0 10 100 1000 10000 0 10 100 1000 10000 Gravity Gravity top predators change along a gradient of gravity. Successful conservation also depends on a range of social populations and the accessibility of reefs change (50). Demographic considerations (48). For example, gear restrictions often have projections of high migration and fertility rates in some countries greater support from local fishers (49) and are usually imple- suggest substantial increases in coastal human populations in de- mented over greater reef areas than marine reserves. We show veloping countries, where the majority of coral reefs are located (5, 51– here that there are conservation gains produced by gear re- 53). Development projects that address high rates of fertility through strictions, although they are low relative to marine reserves (Fig. improvements in women’s education, empowerment, and the expan- 4). Thus, in locations where a lack of support makes establishing sion of family-planning opportunities have successfully reduced fertility marine reserves untenable, gear restrictions may still provide rates (54, 55). Such initiatives, when partnered with resource man- incremental gains toward achieving some conservation goals (8), agement, have the potential to be beneficial to both people and reefs. particularly for specific fish groups and families (39). Demographic changes, such as increased migration in coastal areas, As a supplemental analysis, we examined the conservation gains are also expected to be coupled with coastal development and road for biomass of nontarget species (SI Appendix, Figs. S1D and S4). building that will increase the accessibility of reefs. For example, This supplemental analysis addresses whether the effects of gravity previously uninhabited areas have become more accessible, as evi- on reef fish communities are from fishing or other impacts, such as denced by China’s recent Belt and Roads Initiative and island-building sedimentation or pollution. We found very different patterns for enterprise in the South China Sea (56–58). Investments in sustainable nontarget species compared with target species, suggesting the planning of coastal development and road building could help to relationship between target fish biomass and gravity (SI Appendix, minimize unnecessary increases in reef accessibility. Importantly, Fig. S1) is primarily driven by fishing pressure. stemming increases in gravity is only part of the potential solution Overall, our results demonstrate that the capacity to not space: it will also be important to dampen the mechanisms through only sustain reef fish biomass and the presence of top pred- which gravity operates, such that a given level of gravity can have a ators, but also the potential to achieve conservation gains, may lesser impact on reef systems (1). People’s environmental behavior is be highly dependent on the level of human impact in the fundamentally driven by their social norms, tastes, values, practices, surrounding seascape. It is therefore essential to consider the and preferences (59), all of which can be altered by policies, media, global context of present and future human gravity in coral and other campaigns in ways that could change the local relationship reef governance. Consequently, we calculated gravity of hu- between gravity and reef degradation. man impacts for every reef cell globally using a 10- × 10-km Gravity Future Directions grid across the world’s coral reefs (Fig. 5). Critically, the distribution of gravity varies substantially among regions, with Our gravity index (Materials and Methods and Box 1) makes the central and eastern Indo-Pacific demonstrating lower- several key assumptions that could potentially be refined in gravity values. Even within a region, there can be substantial further applications. First, our application of gravity held friction variability in gravity values. For example, the Central Indo- constant across each specific type of surface (i.e., all paved roads Pacific has highly contrasting gravity patterns, with Southeast had the same friction value). Future applications of more lo- Asian reefs (Fig. 5 A, 3) generally showing extremely high- calized studies could vary travel time to reflect the quality of road networks, topographic barriers to access (such as cliffs), and gravity values while Australian and Melanesian reefs (Fig. the availability of technology. Similarly, future applications could 5 A, 4) are dominated by relatively low-gravity values. The ways in which gravity will increase over time, and how the also aim to incorporate local information about fishing fleet ef- impacts of gravity on reef systems can be reduced, is of substantial ficiency. Second, our adaptation of the gravity model (31) is concern for coral reef governance. The potential benefits of protecting unidirectional, assuming a constant level of attraction from any locations that are currently remote could increase over time as human reef (i.e., gravity varies based on human population size, but not Cinner et al. PNAS | vol. 115 | no. 27 | E6121 Difference in expected fish biomass (kg/ha) relative to openly fished areas Difference in expected probability of encountering top predators relative to openly fished areas SUSTAINABILITY PNAS PLUS SCIENCE A Gravity 4 70 12 3 0.3 Western Indo-Pacific Central Indo-Pacific Eastern Indo-Pacific 0.2 Tropical Atlantic 0.1 0.0 0 1 10 100 1000 10000 Gravity Fig. 5. Distribution of gravity on the world’s coral reefs. (A) Map of gravity calculated for every coral reef in the world ranging from blue (low gravity) to red (high gravity). The four coral reef realms (70) are delineated. Insets highlight gravity for key coral reef regions of the world: (1) Red Sea, (2) Western Indian Ocean, (3) Southeast Asia, (4) Great Barrier Reef of Australia and the South Pacific, (5) Caribbean. For visual effect, gravity values in Inset maps are also given vertical relief, with higher relief indicating higher gravity values. (B) Distribution of gravity values per coral reef realm. on the quality or quantity of fish on a specific reef). Reefs with ture applications could examine whether ecological recovery in more fish, or higher fish value, could be more attractive and reserves (8) depends on the level of gravity present. To this end, exert a higher pull for exploitation (60). Likewise, societal values we provide a global dataset of gravity for every reef pixel globally and preferences can also make certain fish more or less attrac- (Materials and Methods). tive. Our adaptation of gravity was designed to examine the We demonstrate that human impacts deplete reef fish stocks observed conditions of reefs as a function of potential interac- and how certain types of management can mediate but not tions with markets and local settlements, so our modification of eliminate these pressures. In an era of increasing change, the the concept for this application was appropriate. However, fu- global network of marine reserves may not safeguard reef fish ture applications wishing to predict where reefs may be most communities from human impacts adequately enough to ensure vulnerable might wish to consider incorporating fish biomass or key ecological functions, such as predation, are sustained. Efforts composition (i.e., potential market price of reef fish) in the must be made to both reduce and dampen key drivers of change gravity equation. Third, our database was not designed to look at (1, 61), while maintaining or improving the well-being of reef- ecological changes in a single location over time. However, fu- dependent people. Importantly, we find evidence that both E6122 | www.pnas.org/cgi/doi/10.1073/pnas.1708001115 Cinner et al. Density of reefs Road network data were extracted from the Vector Map Level 0 (VMap0) remote and human-surrounded reserves can produce different from the National Imagery and Mapping Agency’s (NIMA) Digital Chart of types of conservation gains. Ultimately, multiple forms of man- the World (DCW). We converted vector data from VMap0 to 1 km agement are needed across the seascape to sustain coral reef fishes resolution raster. and the people who depend upon them. Land cover data were extracted from the Global Land Cover 2000 (64). Materials and Methods To define the shorelines, we used the GSHHS (Global Self-consistent, Hi- Scales of Data. Our data were organized at three spatial scales: reef site (n = erarchical, High-resolution Shoreline) database v2.2.2. 1,798), reef cluster (n = 734), and nation/state (n = 41). These three friction components (road networks, land cover, and shore- Reef site is the smallest scale, which had an average of 2.4 surveys lines) were combined into a single friction surface with a Behrmann map (transects), hereafter referred to as “reef.” projection (an equal area projection). We calculated our cost-distance models For reef cluster (which had an average of 2.4 ± 2.4 reef sites), we in R using the accCost function of the “gdistance” package. The function clustered reefs together that were within 4 km of each other, and used the uses Dijkstra’s algorithm to calculate least-cost distance between two cells centroid to estimate reef cluster-level social and environmental covariates. on the grid taking into account obstacles and the local friction of the To define reef clusters, we first estimated the linear distance between all landscape (65). Travel time estimates over a particular surface could be af- reef sites, then used a hierarchical analysis with the complete-linkage fected by the infrastructure (e.g., road quality) and types of technology used clustering technique based on the maximum distance between reefs. We (e.g., types of boats). These types of data were not available at a global scale set the cut-off at 4 km to select mutually exclusive sites where reefs cannot but could be important modifications in more localized studies. be more distant than 4 km. The choice of 4 km was informed by a 3-y study Gravity computation. To compute gravity, we calculated the population of of the spatial movement patterns of artisanal coral reef fishers, corre- the cell and divided that by the squared travel time between the reef site and sponding to the highest density of fishing activities on reefs based on GPS- the cell. We summed the gravity values for each cell within 500 km of the reef derived effort density maps of artisanal coral reef fishing activities (62). site to get the “total gravity” within 500 km. We used the squared distance This clustering analysis was carried out using the R functions “hclust” (or in our case, travel time), which is relatively common in geography and and “cutree.” economics, although other exponents can be used (31) (Table S3). A larger scale in our analysis was “nation/state” (nation, state, or territory, We also developed a global gravity index for each 10- × 10-km grid of reef which had an average of 44 ± 59 reef clusters), which are jurisdictions that in the world (Box 1), which we provide as an open-access dataset. The pro- generally correspond to individual nations (but could also include states, cedure to calculate gravity was similar to above with the only difference territories, overseas regions), within which sites were nested for analysis. being in the precision of the location; the former was a single data point Targeted fish biomass. Reef fish biomass estimates were based on visual counts in (reef site), while the latter was a grid cell (reef cell). For the purpose of the 1,798 reef sites. All surveys used standard belt-transects, distance sampling, or analysis, gravity was log-transformed and standardized. point-counts, and were conducted between 2004 and 2013. Where data from We also explored various exponents (1–3) and buffer sizes (50, 250, and multiple years were available from a single reef site, we included only data from 500 km) to build nine gravity metrics. The metric providing the best model, so the year closest to 2010. Within each survey area, reef-associated fishes were with the lowest Akaike Information Criterion (AIC), was that with a squared identified to species level, their abundance counted, and total length (TL) es- exponent for travel time and a 500-km buffer (SI Appendix,Table S3). timated, with the exception of one data provider who measured biomass at the Management. For each observation, we determined the prevailing type of family level. To make estimates of targeted biomass from these transect-level management, including the following. (i) Marinereserve (whether thesitefell data comparable among studies, we did the following. (i) We retained fami- within the borders of a no-take marine reserve): we asked data providers to lies that were consistently studied, commonly targeted, and were above a further classify whether the reserve had high or low levels of compliance. For this minimum size cut-off. Thus, we retained counts of >10 cm diurnally active, analysis, we removed sites that were categorized as low-compliance reserves (n = noncryptic reef fish that are resident on the reef (14 families), excluding sharks 233). (ii) Restricted fishing: whether there were active restrictions on gears (e.g., and semipelagic species (SI Appendix,Table S1). We calculated total biomass of bans on the use of nets, spearguns, or traps) or fishing effort (which could have targeted fishes on each reef using standard published species-level length– included areas inside marine protected areas that were not necessarily no take). weight relationship parameters or those available on FishBase (63). When Or (iii) openly fished: regularly fished without effective restrictions. To determine length–weight relationship parameters were not available for a species, we these classifications, we used the expert opinion of the data providers, and tri- used the parameters for a closely related species or genus. For comparison, we angulated this with a global database of marine reserve boundaries (66). We also 2 2 also calculated nontarget fish biomass (SI Appendix,Table S1). (ii)Wedirectly calculated size (median = 113.6 km ,mean = 217,516 km ,SD = 304,417) and age accounted for depth and habitat as covariates in the model (see Environmental (median = 9, mean = 15.5 y, SD = 14.5) of the no-take portion of each reserve. Drivers,below).(iii) We accounted for differences among census methods by Reserve size was strongly related to our metric of gravity and could not be di- including each census method (standard belt-transects, distance sampling, or rectly included in the analysis. We conducted a supplemental analysis where we 2 2 point-counts) as a covariate in the model. (iv) We accounted for differences in separated reserves into small (≤28 km )and large (>65 km ) based on a natural sampling area by including total sampling area for each reef (m )asacovariate break in the data to illustrate: (i) how biomass and the presence of top predators in the model. might differ between small and large reserves; and (ii) how large reserves are Top predators. We examined the presence/absence of eight families of fish absent in our sample in high gravity. considered top predators (SI Appendix, Table S1). We considered presence/ absence instead of biomass because biomass was heavily zero inflated. Other Social Drivers. To account for the influence of other social drivers that Gravity. We first developed a gravity index for each of our reef sites where we are thought to be related to the condition of reef fish biomass, we also had in situ ecological data. We gathered data on both population estimates included the following covariates in our model. and a surrogate for distance: travel time. Local population growth. We created a 100-km buffer around each site and used Population estimates. We gathered population estimates for each 1- by 1-km this to calculate human population within the buffer in 2000 and 2010 based cell within a 500-km radius of each reef site using LandScan 2011 database. on the Socioeconomic Data and Application Centre gridded population of the We chose a 500-km radius from the reef as a likely maximum distance fishing world database. Population growth was the proportional difference between activities for reef fish are likely to occur. the population in 2000 and 2010. We chose a 100-km buffer as a reasonable Travel time calculation. The following procedure was repeated for each range at which many key human impacts from population (e.g., land-use and populated cell within the 500-km radius. Travel time was computed using a nutrients) might affect reefs (67). cost–distance algorithm that computes the least “cost” (in minutes) of Human development index. Human development index (HDI) is a summary traveling between two locations on a regular raster grid. In our case, the two measure of human development encompassing a long and healthy life, being locations were the centroid of the reef site and the populated cell of in- knowledgeable, and having a decent standard of living. In cases where HDI terest. The cost (i.e., time) of traveling between the two locations was de- values were not available specific to the state (e.g., Florida and Hawaii), we termined by using a raster grid of land cover and road networks with the used the national (e.g., United States) HDI value. cells containing values that represent the time required to travel across them Population size. For each nation/state, we determined the size of the human (32) (SI Appendix, Table S2), we termed this raster grid a “friction-surface” population. Data were derived mainly from the national census reports CIA fact (with the time required to travel across different types of surfaces analogous book (https://www.cia.gov/library/publications/the-world-factbook/rankorder/ to different levels of friction). To develop the friction-surface, we used 2119rank.html), and Wikipedia (https://en.wikipedia.org/wiki/Main_Page). For global datasets of road networks, land cover, and shorelines: the purpose of the analysis, population size was log-transformed. Cinner et al. PNAS | vol. 115 | no. 27 | E6123 SUSTAINABILITY PNAS PLUS SCIENCE Environmental Drivers. AIC values, so we chose the interaction with reserve age for consistency. All con- Depth. The depth of reef surveys was grouped into the following categories: tinuous covariates were standardized for the analysis, and reserve age was then <4m, 4–10 m, >10 m to account for broad differences in reef fish community normalized such that nonreserves were0and theoldestreserveswere 1.In structure attributable to a number of interlinked depth-related factors. Cat- summary, our models thus predicted target fish biomass or probability of top egories were necessary to standardize methods used by data providers and predators being observed at thereef sitescale with an interaction between gravity were determined by preexisting categories used by several data providers. and reserve age, while accounting within the random factors for two bigger scales Habitat. We included the following habitat categories. (i) Slope: the reef slope at which the data were collected (reef cluster, and nation/state) (SI Appendix), and habitat is typically on the ocean side of a reef, where the reef slopes down into key social and environmental characteristics expected to influence the biomass of deeper water. (ii) Crest: the reef crest habitat is the section that joins a reef reef fish (14). In addition to coefficient plots (SI Appendix,Fig. S1), we conducted a slope to the reef flat. The zone is typified by high wave energy (i.e., where the supplemental analysis of relative variable importance (SI Appendix, Table S4). waves break). It is also typified by a change in the angle of the reef from an We ran the residuals from the models against size of the no-take areas of inclined slope to a horizontal reef flat. (iii) Flat: the reef flat habitat is typically the marine reserves and no patterns were evident, suggesting it would ex- horizontal and extends back from the reef crest for tens to hundreds of me- plain no additional variance in the model. Trend lines and partial plots ters. (iv) Lagoon/back reef: lagoonal reef habitats are where the continuous (averaged by site and nation/state) are presented in the figures (Figs. 2 B–H reef flat breaks up into more patchy reef environments sheltered from wave and 3H). We plotted the partial effect of the relationship between gravity energy. These habitats can be behind barrier/fringing reefs or within atolls. and protection on targeted fish biomass and presence of top predators (Figs. Back reef habitats are similar broken habitats where the wave energy does not 2 B–G and 3 B–G) by setting all other continuous covariates to 0 because they typically reach the reefs and thus forms a less continuous “lagoon style” reef were all standardized and all categorical covariates to their most common habitat. Due to minimal representation among our sample, we excluded other category (i.e., 4–10 m for depth, slope for habitat, standard belt transect for less-prevalent habitat types, such as channels and banks. To verify the sites’ census method). For age of reserves, we set this to 0 for fished and restricted habitat information, we used the Millennium Coral Reef Mapping Project hi- areas, and to the average age of reserves (15.5 y) for reserves. erarchical data (68), Google Earth, and site depth information. To examine the expected conservation gains of different management Productivity. We examined ocean productivity for each of our sites in milligrams strategies, we calculated: (i) the difference between the response of openly −2 −1 of Cper square meter per day (mgCm d )(www.science.oregonstate.edu/ fished areas (our counterfactual) and high-compliance marine reserves to ocean.productivity/). Using the monthly data for years the 2005–2010 (in hdf gravity; and (ii) the difference between the response of openly fished areas format), we imported and converted those data into ArcGIS. We then calcu- and fisheries restricted areas to gravity. For ease of interpretation, we lated yearly average and finally an average for all these years. We used a plotted conservation gains in kilograms per hectare (kg/ha; as opposed to 100-km buffer around each of our sites and examined the average productivity log[kg/ha]) (Fig. 4A). A log-normal (linear) model was used to develop the within that radius. Note that ocean productivity estimates are less accurate slopes of the biomass (i) fished, (ii) marine reserve, and (iii) fisheries re- for nearshore environments, but we used the best available data. For the stricted areas, which results in the differences between (i) and (ii) and be- purpose of the analysis, productivity was log-transformed. tween (i) and (iii) being nonlinear on an arithmetic scale (Fig. 4A). Climate stress. We included an index of climate stress for corals, developed by We plotted the diagnostic plots of the general linear-mixed model to check that Maina et al. (69), which incorporated 11 different environmental conditions, the model assumptions were not violated. To check the fit of the generalized linear- such as the mean and variability of sea-surface temperature. mixed model, we used the confusion matrix (tabular representation of actual versus predicted values) to calculate the accuracy of the model, which came to 79.2%. Analyses. We first looked for collinearity among our covariates using bivariate To examine homoscedasticity, we checked residuals against fitted values. We correlations and variance inflation factor estimates. This led to the exclusion checked our models against a null model, which contained the model structure of several covariates (not described above): (i) Biogeographic Realm (Tropical (i.e., random effects), but no covariates. We used the null model as a baseline Atlantic, western Indo-Pacific, Central Indo-Pacific, or eastern Indo-Pacific); against which we could ensure that our full model performed better than a (ii) Gross Domestic Product (purchasing power parity); (iii) Rule of Law model with no covariate information. In all cases our models outperformed our (World Bank governance index); (iv) Control of Corruption (World Bank null models by more than 2 AIC values, indicating a more parsimonious model. governance index); (v) Voice and Accountability (World Bank governance All analyses were undertaken using R (3.43) statistical package. index); (vi) Reef Fish Landings; (vii) Tourism arrivals relative to local pop- ulation; (viii) Sedimentation; and (ix) Marine Reserve Size. Other covariates Data Access. A gridded global gravity data layer is freely available at dx.doi. had correlation coefficients 0.7 or less and Variance Inflation Factor scores org/10.4225/28/5a0e7b1b3cc0e. The ecological data used in this report are less than 5 (indicating multicollinearity was not a serious concern). Care must owned by individual data providers. Although much of these data (e.g., be taken in causal attribution of covariates that were significant in our NOAA CRED data, and Reef Life Surveys) are already open access, some of model, but demonstrated collinearity with candidate covariates that were these data are governed by intellectual property arrangements and cannot be removed during the aforementioned process. Importantly, the covariate of made open access. Because the data are individually owned, we have agreed interest in this study, gravity, was not strongly collinear with candidate upon and developed a structure and process for those wishing access to the covariates except reserve size (r = −0.8, t = 3.6, df = 104, P = 0.0004). data. Our process is one of engagement and collaboration with the data To quantify the relationships between gravity and target fish biomass, we providers. Anyone interested can send a short (one-half to one page) proposal developed a general linear-mixed model in R, using a log-normal distribution for for use of the database that details the problem statement, research gap, biomass. To quantify the relationships between gravity and presence/absence of research question(s), and proposed analyses to the Principle Investigator and top predators, we developed a generalized linear-mixed model with a binomial database administrator Joshua.cinner@jcu.edu.au, who will send the proposal family and a logit link function. For both models, we set reef cluster nested to the data providers. Individual data providers can agree to make their data within nation/state as a random effect to account for the hierarchical nature of available or not. They can also decide whether they would like to be con- the data (i.e., reef sites nested in reef clusters, reef clusters nested in nation/ sidered as a potential coauthor if their data are used. The administrator will states). We included an interaction between gravity and reserve age, as well as then send only the data that the providers have agreed to make available. all of the other social and environmental drivers and the sampling method and total sampling area as covariates. We also tested interactions between gravity ACKNOWLEDGMENTS. We thank J. Zamborian Mason and A. Fordyce for and management and used AIC to select the most parsimonious model. For fish assistance with analyses and figures, and to numerous scientists who biomass, the interaction between gravity and reserve age had AIC values >2lower collected data used in the research. The Australian Research Council Centre than the interaction between gravity and management (and a combination of of Excellence for Coral Reef Studies and The Pew Charitable Trusts funded both interactions). For the top predator models, both interactions were within 2 working group meetings. 1. Hughes TP, et al. 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O’Leary BC, et al. (2018) Addressing criticisms of large-scale marine protected areas. 69. Maina J, McClanahan TR, Venus V, Ateweberhan M, Madin J (2011) Global gradients Bioscience 68:359–370. of coral exposure to environmental stresses and implications for local management. 42. Ferraro PJ, Pressey RL (2015) Measuring the difference made by conservation initia- PLoS One 6:e23064. tives: Protected areas and their environmental and social impacts Introduction. Philos 70. Spalding MD, et al. (2007) Marine ecoregions of the world: A bioregionalization of Trans R Soc Lond B Biol Sci 370:20140270. coastal and shelf areas. Bioscience 57:573–583. Cinner et al. PNAS | vol. 115 | no. 27 | E6125 SUSTAINABILITY PNAS PLUS SCIENCE http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the National Academy of Sciences of the United States of America Pubmed Central

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Abstract

Gravity of human impacts mediates coral reef conservation gains a,1 a,b a c,d a,e f Joshua E. Cinner , Eva Maire , Cindy Huchery , M. Aaron MacNeil , Nicholas A. J. Graham ,Camilo Mora , g a,h i,j a,e b,g,k a Tim R. McClanahan , Michele L. Barnes , John N. Kittinger , Christina C. Hicks , Stephanie D’Agata , Andrew S. Hoey , a l m n k o Georgina G. Gurney , David A. Feary , Ivor D. Williams , Michel Kulbicki , Laurent Vigliola , Laurent Wantiez , p p q r s t,u Graham J. Edgar , Rick D. Stuart-Smith , Stuart A. Sandin , Alison Green , Marah J. Hardt , Maria Beger , v,w x,y z aa bb cc Alan M. Friedlander , Shaun K. Wilson , Eran Brokovich , Andrew J. Brooks , Juan J. Cruz-Motta , David J. Booth , dd ee ff gg hh g Pascale Chabanet , Charlotte Gough , Mark Tupper , Sebastian C. A. Ferse , U. Rashid Sumaila , Shinta Pardede , a,b and David Mouillot a b Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD 4811, Australia; Marine Biodiversity Exploration and Conservation, UMR Institut de Recherche pour le Développement-CNRS-UM-L’Institut Français de Recherche pour l’Exploitation de la Mer 9190, University of c d Montpellier, 34095 Montpellier Cedex, France; Australian Institute of Marine Science, Townsville, QLD 4810, Australia; Department of Biology, Dalhousie e f University, Halifax, NS B3H 3J5, Canada; Lancaster Environment Centre, Lancaster University, LA1 4YQ Lancaster, United Kingdom; Department of Geography, g h University of Hawai‘i at Manoa, Honolulu, HI 96822; Global Marine Program, Wildlife Conservation Society, Bronx, NY 10460; Department of Botany, University of i j Hawai’i at Manoa, Honolulu, HI 96822; Center for Oceans, Conservation International, Honolulu, HI 96825; Center for Biodiversity Outcomes, Julie Ann Wrigley Global Institute of Sustainability, Life Sciences Center, Arizona State University, Tempe, AZ 85281; Laboratoire d’Excellence LABEX CORAIL, UMR-Institut de Recherche pour le Développement-UR-CNRS ENTROPIE, BP A5, 98848 Nouméa Cedex, New Caledonia; School of Life Sciences, University of Nottingham, NG7 2RD m n Nottingham, United Kingdom; Coral Reef Ecosystems Program, NOAA Pacific Islands Fisheries Science Center, Honolulu, HI 96818; UMR Entropie, Labex Corail, Institut de Recherche pour le Développement, Université de Perpignan, 66000 Perpignan, France; EA4243 LIVE, University of New Caledonia, BPR4 98851 p q Noumea cedex, New Caledonia; Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS 7001, Australia; Scripps Institution of r s Oceanography, University of California, San Diego, La Jolla, CA 92093; The Nature Conservancy, Brisbane, QLD 4101, Australia; Future of Fish, Bethesda, MD 20814; Australian Research Council Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, University of Queensland, u v Brisbane, St Lucia, QLD 4074, Australia; School of Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT Leeds, United Kingdom; Fisheries Ecology Research Laboratory, Department of Biology, University of Hawaii, Honolulu, HI 96822; Pristine Seas Program, National Geographic Society, Washington, DC x y 20036-4688; Department of Parks and Wildlife, Kensington, Perth, WA 6151, Australia; Oceans Institute, University of Western Australia, Crawley, WA 6009, z aa Australia; The Israel Society of Ecology and Environmental Sciences, 6775323 Tel Aviv, Israel; Marine Science Institute, University of California, Santa Barbara, CA bb cc 93106-6150; Departamento de Ciencias Marinas, Recinto Universitario de Mayaguez, Universidad de Puerto Rico, Mayaguez 00680, Puerto Rico; School of Life dd Sciences, University of Technology Sydney, NSW 2007, Australia; UMR ENTROPIE, Laboratoire d’Excellence LABEX CORAIL, Institut de Recherche pour le ee Développement, CS 41095, 97495 Sainte Clotilde, La Réunion (FR); Omnibus Business Centre, Blue Ventures Conservation, N7 9DP London, United Kingdom; ff gg Advanced Centre for Coastal and Ocean Research and Development, University of Trinidad and Tobago, Chaguaramas, Trinidad and Tobago, W.I.; Leibniz hh Centre for Tropical Marine Research, D-28359 Bremen, Germany; and Fisheries Economics Research Unit, Institute for the Oceans and Fisheries and Liu Institute for Global Studies, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Edited by David M. Karl, University of Hawaii, Honolulu, HI, and approved May 15, 2018 (received for review May 17, 2017) Coral reefs provide ecosystem goods and services for millions of Significance people in the tropics, but reef conditions are declining worldwide. Effective solutions to the crisis facing coral reefs depend in part on Marine reserves that prohibit fishing are a critical tool for sus- understanding the context under which different types of conserva- taining coral reef ecosystems, yet it remains unclear how human tion benefits can be maximized. Our global analysis of nearly impacts in surrounding areas affect the capacity of marine re- 1,800 tropical reefs reveals how the intensity of human impacts serves to deliver key conservation benefits. Our global study in the surrounding seascape, measured as a function of human found that only marine reserves in areas of low human impact population size and accessibility to reefs (“gravity”), diminishes the consistently sustained top predators. Fish biomass inside marine effectiveness of marine reserves at sustaining reef fish biomass and reserves declined along a gradient of human impacts in sur- the presence of top predators, even where compliance with reserve rounding areas; however, reserves located where human im- rules is high. Critically, fish biomass in high-compliance marine re- pacts are moderate had the greatest difference in fish biomass serves located where human impacts were intensive tended to be compared with openly fished areas. Reserves in low human- less than a quarter that of reserves where human impacts were low. impact areas are required for sustaining ecological functions like Similarly, the probability of encountering top predators on reefs with high human impacts was close to zero, even in high-compliance ma- high-order predation, but reserves in high-impact areas can rine reserves. However, we find that the relative difference between provide substantial conservation gains in fish biomass. openly fished sites and reserves (what we refer to as conservation Author contributions: J.E.C., E.M., C.H., M.A.M., N.A.J.G., C.M., T.R.M., M.L.B., J.N.K., gains) are highest for fish biomass (excluding predators) where hu- C.C.H., S.D., A.S.H., G.G.G., D.A.F., and D.M. designed research; J.E.C., E.M., N.A.J.G., man impacts are moderate and for top predators where human im- T.R.M., A.S.H., D.A.F., I.D.W., M.K., L.V., L.W., G.J.E., R.D.S.-S., S.A.S., A.G., M.J.H., M.B., pacts are low. Our results illustrate critical ecological trade-offs in A.M.F., S.K.W., E.B., A.J.B., J.J.C.-M., D.J.B., P.C., C.G., M.T., S.C.A.F., U.R.S., and S.P. per- meeting key conservation objectives: reserves placed where there formed research; J.E.C., E.M., C.H., and D.M. analyzed data; and J.E.C., E.M., C.H., M.A.M., N.A.J.G., C.M., T.R.M., M.L.B., J.N.K., C.C.H., S.D., A.S.H., G.G.G., D.A.F., I.D.W., M.K., L.V., are moderate-to-high human impacts can provide substantial conser- L.W., G.J.E., R.D.S.-S., S.A.S., A.G., M.J.H., M.B., A.M.F., S.K.W., E.B., A.J.B., J.J.C.-M., D.J.B., vation gains for fish biomass, yet they are unlikely to support key P.C., C.G., M.T., S.C.A.F., U.R.S., S.P., and D.M. wrote the paper. ecosystem functions like higher-order predation, which is more prev- The authors declare no conflict of interest. alent in reserve locations with low human impacts. This article is a PNAS Direct Submission. This open access article is distributed under Creative Commons Attribution-NonCommercial- marine reserves fisheries coral reefs social-ecological socioeconomic | | | | NoDerivatives License 4.0 (CC BY-NC-ND). Data deposition: A gridded global gravity data layer is freely available at dx.doi.org/10. he world’s coral reefs are rapidly degrading (1–3), which is 4225/28/5a0e7b1b3cc0e. Tdiminishing ecological functioning and potentially affecting To whom correspondence should be addressed. Email: Joshua.Cinner@jcu.edu.au. the well-being of the millions of people with reef-dependent This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. livelihoods (4). Global climate change and local human impacts 1073/pnas.1708001115/-/DCSupplemental. (such as fishing) are pervasive drivers of reef degradation (1, 5). In Published online June 18, 2018. E6116–E6125 | PNAS | vol. 115 | no. 27 www.pnas.org/cgi/doi/10.1073/pnas.1708001115 response to this “coral reef crisis,” governments around the world intensity of human impacts in the surrounding seascape (23, 24), yet have developed a number of reef conservation initiatives (1, 6, 7). these effects have never been quantified. Our focus here is on the efficacy of management tools that limit or Here, we use data from 1,798 tropical coral reef sites in 41 nations, states, or territories (hereafter “nation/states”) in every prohibit fishing. Management efforts that reduce fishing mortality should help to sustain reef ecosystems by increasing the abun- major coral reef region of the world to quantify how expected dance, mean body size, and diversity of fishes that perform critical conservation gains in two key ecological outcomes are mediated by the intensity of human impact, namely: (i) targeted reef fish bio- ecological functions (8–10). In practice, however, outcomes from these reef-management tools have been mixed (5, 11–13). mass (i.e., species generally caught in fisheries) and (ii) the pres- A number of studies have examined the social, institutional, ence of top predators (Materials and Methods and SI Appendix, Table S1). To quantify human impact at each site, we draw from a and environmental conditions that enable reef management to achieve key ecological outcomes, such as sustaining fish biomass long history of social science theory and practice to develop a (5, 14, 15), coral cover (16), or the presence of top predators metric referred to as “gravity” (Box 1). The concept of gravity (17). These studies often emphasize the role of: (i) types of key (also called interactance) has been used in economics and geog- management strategies in use, such as marine reserves where raphy to measure economic interactions, migration patterns, and fishing is prohibited, or areas where fishing gears and/or effort trade flows since the late 1800s (28–30). We adapt this approach are restricted to reduce fishing mortality (8, 18); (ii) levels of to examine potential interactions with reefs as a function of how compliance with management (12, 19, 20); (iii) the design large and far away the surrounding human population is (Box 1). characteristics of these management initiatives, for example the At each site, we also determined the status of reef management, size and age of reserves, and whether they are placed in remote grouped into either: (i) openly fished, where sites are largely un- versus populated areas (11, 21); and (iv) the role of social drivers, managed and national or local regulations tend to be poorly such as markets, socioeconomic development, and human de- mography that shape people’s relationship with nature (14, 22). In addition to examining when key ecological conditions are Populationx sustained, it is also crucial to understand the context under which conservation gains can be maximized (23, 24). By conservation gains, we are referring to the difference in a conservation outcome (e.g., the amount of fish biomass) when some form of management Populationy (i.e., a marine reserve or fishery restriction) is implemented relative Travel timex to unmanaged areas. These conservation gains can be beneficial for both people and ecosystems. For example, increased fish biomass inside marine reserves is not only related to a range of ecosystem states and processes (18), but can also result in spillover of adults Travel timey and larvae to surrounding areas, which can benefit fishers (25–27). The potential to achieve conservation gains may depend on the Box 1 Drawing on an analogy from Newton’s Law of Gravitation, B 20,000 the gravity concept predicts that interactions between two places (e.g., cities) are positively related to their mass (i.e., population) and inversely related to the distance between them (31). The gravity concept is often considered one of the 15,000 Gravity most successful and long-enduring empirical models in eco- nomics and geography (31), but has rarely been directly ap- plied in a natural resource management setting and holds 20 much promise in informing reef conservation and manage- 10,000 ment. Application of the gravity concept in a reef governance 0.05 context posits that human interactions with a reef are a 0.002 function of the population of a place divided by the squared time it takes to travel to the reefs (we used travel time instead 5,000 of linear distance to account for the differences incurred by traveling over different surfaces, such as water, roads, tracks) (14, 32) (see Fig. 1 and SI Appendix, Table S2). Here, we build upon previous work (14) by developing a new indicator that 2h 4h 6h 8h 10h 12h examines the cumulative human gravity of all populated Travel time (hours) places within a 500-km radius of a given reef, which aims to capture both market and subsistence pressures on reef fish Fig. 1. Operationalizing gravity. (A) Applied to coral reefs, our heuristic of biomass. We tested the predictive power of a series of gravity the gravity concept captures interactions between people and coral reef fish metrics with varying radii (50 km, 250 km, 500 km) and expo- as a function of the population of a place divided by the squared time it takes 2 3 nents of travel time (travel time, travel time ,travel time ) to travel to the reefs (i.e., travel time). (B) Gravity isoclines along gradients of (Materials and Methods and SI Appendix,Table S3). A key lim- population size and travel time illustrate how gravity values could be similar itation of our global gravity metric is that we are unable to for places that have large populations but are far from the reefs (e.g., pop- ulation = 15,000 people, travel time = 7 h, gravity = 306) as to those with capture local variations in efficiencies that may affect fishing x x x small populations that are close to the reef (e.g., population = 300 people, mortality per capita, such as fishing fleet technology or in- travel time = 1 h, gravity = 300). For ease of interpretation, we have illus- y y frastructure (e.g., road) quality. trated travel time here in hours, but we use minutes in the main text. Cinner et al. PNAS | vol. 115 | no. 27 | E6117 Population (people) SUSTAINABILITY PNAS PLUS SCIENCE complied with; (ii) restricted fishing, where there are actively pattern of depletion is likely related to: (i) human impacts in the enforced restrictions on the types of gears that can be used (e.g., surrounding seascape (fishing, pollution, and so forth) affecting eco- bans on spear guns) or on access (e.g., marine tenure systems that logical processes (recruitment, feeding behavior, and so forth) within restrict fishing by “outsiders”); or (iii) high-compliance marine reserves (33, 34); (ii) almost every marine reserve is likely to have reserves, where fishing is effectively prohibited (Materials and some degree of poaching, even where compliance is considered high Methods). We hypothesized that our ecological indicators would (20, 35) and the cumulative impacts from occasional poaching events decline with increasing gravity in fished areas, but that marine is probably higher in high-gravity situations; (iii) the life history of top reserves areas would be less sensitive to gravity. To test our hy- predators, such as old age of reproduction and small clutch size for potheses, we used general and generalized linear mixed-effects some (e.g., sharks), which makes then particularly susceptible to even models to predict target fish biomass and the presence of top mild levels of exploitation (36); and (iv) high-gravity marine reserves predators, respectively, at each site based on gravity and man- in our sample possibly being too young or too small to provide sub- agement status, while accounting for other key environmental and stantial conservation gains (11, 37). We conducted a supplementary social conditions thought to influence our ecological outcomes analysis to further examine this latter potential explanation. Because (14) (Materials and Methods). Based on our models, we calculated of collinearity, we could not directly account for reserve size in our expected conservation gains along a gravity gradient as the dif- model, but conducted a supplemental analysis where we separated ference between managed sites and openly fished sites. reserves into small (≤28 km )and large (Materials and Methods and SI Our analysis reveals that human gravity was the strongest predictor Appendix,Fig.S3). We found that the biomass and probability of of fish biomass (Fig. 2 and SI Appendix,Fig.S1). Fish biomass encountering top predators was higher in large compared with small consistently declined along a human gravity gradient, a trend par- reserves, but surprisingly, we found a flatter slope for small compared ticularly evident at the nation/state scale (Fig. 2 C–E). However, this with large reserves (SI Appendix,Fig.S3). However, there were no relationship can vary by management type (Fig. 2 and SI Appendix, large high-compliance reserves in high-gravity areas in our sample, Fig. S1). Specifically, we found that biomass in reserves demonstrated likely due to the social and political difficulties in establishing large a flatter (but still negative) relationship with gravity compared with reserves near people (38). Because there is little overlap between large openly fished and restricted areas (Fig. 2B). Interestingly, this dif- and small reserves along the gravity gradient in our sample, we are ferential slope between reserves and fished areas (Fig. 2B)was dueto unable to distinguish the effects of reserve size from those of gravity, a strong interaction between gravity and reserve age such that older but this is an important area for future research. Additionally, we reserves contributed more to biomass in high-gravity situations than modeled how the relationship between gravity and our ecological in low-gravity ones (SI Appendix,Fig.S1). This is likely due to fish outcomes changed with reserve age, comparing outcomes using the stocks at high-gravity sites being heavily depleted and requiring de- average reserve age (15.5 y) to those from reserves nearly twice as old cades to recover, whereas low-gravity sites would likely require less (29 y, which was the third quartile of our global distribution in reserve time to reach unfished biomass levels (8). Thus, given average reserve age). Older reserves were predicted to sustain an additional 180 kg/ha age in our sample (15.5 y), biomass in reserves did not decline as (+66%) of fish biomass at the highest levels of gravity compared with rapidly with gravity compared with fished and restricted areas (Fig. average age reserves. However, the effects of reserve age on the 2B). In the highest-gravity locations, modeled fish biomass in probability of encountering a top predator was less marked: the marine reserves was approximately five times higher than in fished modeled probability of encountering a top predator in older reserves areas (270 kg/ha compared with 56 kg/ha) (Fig. 2B). At the reef (29 y) was only 0.01, compared with <0.005 for average age (∼15 y) site scale, there was considerable variability in reef fish biomass, reserves, suggesting that small reserves common in high-gravity situ- particularly at low gravity (Fig. 2 F–H). For example, at the lowest- ations can support high levels of biomass, but are unlikely to sustain gravity locations, biomass levels in reserves spanned more than toppredators,evenwhentheyare mature. three orders-of-magnitude (Fig. 2F). Importantly, there was never Although absolute fish biomass under all management categories extremely high biomass encountered in high-gravity locations. Our declined with increasing gravity (Fig. 2B), the maximum expected estimate of target fish biomass included top predators. As a sup- conservation gains (i.e., the difference between openly fished and plemental analysis, we also examined target fish biomass with the managed) differed by management type along the gravity gradient biomass of top predators excluded, which displays a similar trend, (Fig. 4A). Interestingly, the conservation gains for restricted fishing but with lower fish biomass in reserves at low gravity compared is highest in low-gravity situations, but rapidly declines as human with when top predators are included (SI Appendix,Fig.S2). impacts increase (Fig. 4A) (39). For marine reserves, biomass A key finding from our study is that top predators were en- conservation gains demonstrated a hump-shaped pattern that countered on only 28% of our reef sites, but as gravity increases, the peaked at very low gravity when predators were included in the probability of encountering top predator on tropical coral reefs biomass estimates (Fig. 4A, solid blue line). When top predators dropped to almost zero (<0.005), regardless of management (Fig. 3). were excluded from biomass estimates, conservation gains peaked The probability of encountering top predators was strongly related at intermediate gravity levels, and were higher in high gravity to gravity and the type of management in place, as well as sampling compared with low gravity (Fig. 4A, dottedblueline). Our results methodology and area surveyed (Fig. 3 and SI Appendix,Fig.S1). At highlight how the expected differences between openly fished and low gravity, the probability of encountering a top predator was marine reserves change along a gravity gradient, given a range of highest in marine reserves (0.59) and lowest in fished areas (0.14), other social and environmental conditions that are controlled for when controlling for sampling and other environmental and social within our model (SI Appendix,Fig.S1). Thus, differences in these drivers (Fig. 3 and SI Appendix,Fig. S1). trends are relative to average conditions, and individual reserves Our study demonstrates the degree to which fish communities in- may demonstrate larger or smaller biomass build-up over time, side marine reserves can be affected by human impacts in the broader which can vary by fish groups or families (e.g., ref. 40). seascape (Figs. 2 and 3). Critically, high-compliance marine reserves in In an effort to minimize costs to users, many marine reserves, the lowest-gravity locations tended to support more than four times particularly the large ones, tend to be placed in remote locations more fish biomass than the highest-gravity reserves (1,150 vs. 270 kg/ha, that experience low human pressure (24, 41). However, critics of respectively) (Fig. 2B). Similarly, the modeled probability of en- marine reserves in remote locations suggest that limited re- countering a top predator decreased by more than 100-fold from sources could be better spent protecting areas under higher 0.59 in low-gravity reserves to 0.0046 in the highest-gravity reserves threat that could potentially yield greater conservation gains (23, (Fig. 3B). Our study design meant that it was not possible to uncover 24, 42). Our results make explicit the types of benefits—and the the mechanisms responsible for this decline of ecological condition limitations—to placing reserves in high versus low human-impact indicators within marine reserves along a gravity gradient, but this locations. We found that for nontop predator reef fishes, E6118 | www.pnas.org/cgi/doi/10.1073/pnas.1708001115 Cinner et al. Fig. 2. Model-predicted relationships between human gravity and reef fish biomass under different types of fisheries management. (A) Map of our study sites with color indicating the amount of fish biomass at each site. (B) Model-predicted relationships of how reef fish biomass declines as gravity increases by management type. Partial plots of the relationship between biomass and gravity under different types of management at the nation/state (C–E), and reef site (F–H) scale; openly fished (red), restricted (green), and high-compliance marine reserves (blue). Shaded areas represent 95% confidence intervals. Bubble size in C–E reflect the number of reef sites in each nation/state, scaled for each management type (such that the largest bubble in each panel represent the highest number of sites per nation/state for that type of management) (SI Appendix, Table S5). Nation/state name abbreviations for F–H are in SI Appendix, Table S5. substantial conservation gains can occur at even the highest- can have the substantial conservation gains for fish biomass, yet gravity locations but that optimal gains are obtained at moder- they are unlikely to support key ecosystem functions like pre- ate gravity (Fig. 4A). Our results also show that low-gravity marine dation, even with high levels of compliance. This highlights the reserves (and to a lesser extent low-gravity fisheries restrictions) importance of having clear objectives for conservation initiatives are critical to support the presence of top predators (Fig. 3). and recognizing the trade-offs involved (43, 44). Our analysis does not allow us to uncover the mechanisms behind However, the expected conservation gains for top predators de- clines rapidly with gravity in both marine reserves and restricted why we might observe the greatest differences in top predators areas (Fig. 4B). Our results illustrate a critical ecological trade-off between marine reserves and fished areas in low-gravity locations. A inherent in the placement of marine reserves: high-gravity reserves plausible explanation is that top predators, such as sharks, are Cinner et al. PNAS | vol. 115 | no. 27 | E6119 SUSTAINABILITY PNAS PLUS SCIENCE Fig. 3. Model-predicted relationships between human gravity and the probability of encountering top predators under different types of fisheries manage- ment. (A) Map of our study sites indicating the presence of top predators. (B) Model-predicted relationships of how the probability of encountering predators declines as gravity increases. Shaded areas represent 95% confidence intervals. The presence of top predators along a gravity gradient under different types of management at the nation/state (C–E) and site (F–H) scale; openly fished (red), restricted (green), and high-compliance marine reserves (blue). Bubble size in C–E reflect the number of reef sites in each nation/state, scaled for each management type (such that the largest bubble in each panel represent the highest number of sites per nation/state for that type of management) (SI Appendix, Table S5). Nation/state name abbreviations for F–H in SI Appendix, Table S5. particularly vulnerable to fishing (17) and are exposed to some low-gravity–fished areas and marine reserves. This difference fishing even in the most remote fished areas, driven by the may diminish along the gravity because top predators tend to extremely high price for shark fins [shark fins can fetch US$960/kg have large home ranges (37), and there were only small reserves in wholesale markets (45), compared with only $43/kg for in high-gravity locations (SI Appendix,Fig.S3), which may parrotfish in European supermarkets (46)]. Thus, even small mean that existing high-gravity reserves are not likely big amounts of fishing in remote openly fished areas may be de- enough to support the large home ranges of many predators pleting top predators, which creates a large difference between (37, 47). E6120 | www.pnas.org/cgi/doi/10.1073/pnas.1708001115 Cinner et al. AB 400 0.5 0.4 0.3 0.2 0.1 Fig. 4. The conservation gains (i.e., the difference between openly fished sites and managed areas) for high-compliance marine reserves (blue line) and re- 0.0 stricted fishing (green line) for (A) target fish biomass (solid lines include biomass of top predators, dotted lines exclude top predator biomass as per SI Appen- dix, Fig. S2), and (B) the probability of encountering 0 10 100 1000 10000 0 10 100 1000 10000 Gravity Gravity top predators change along a gradient of gravity. Successful conservation also depends on a range of social populations and the accessibility of reefs change (50). Demographic considerations (48). For example, gear restrictions often have projections of high migration and fertility rates in some countries greater support from local fishers (49) and are usually imple- suggest substantial increases in coastal human populations in de- mented over greater reef areas than marine reserves. We show veloping countries, where the majority of coral reefs are located (5, 51– here that there are conservation gains produced by gear re- 53). Development projects that address high rates of fertility through strictions, although they are low relative to marine reserves (Fig. improvements in women’s education, empowerment, and the expan- 4). Thus, in locations where a lack of support makes establishing sion of family-planning opportunities have successfully reduced fertility marine reserves untenable, gear restrictions may still provide rates (54, 55). Such initiatives, when partnered with resource man- incremental gains toward achieving some conservation goals (8), agement, have the potential to be beneficial to both people and reefs. particularly for specific fish groups and families (39). Demographic changes, such as increased migration in coastal areas, As a supplemental analysis, we examined the conservation gains are also expected to be coupled with coastal development and road for biomass of nontarget species (SI Appendix, Figs. S1D and S4). building that will increase the accessibility of reefs. For example, This supplemental analysis addresses whether the effects of gravity previously uninhabited areas have become more accessible, as evi- on reef fish communities are from fishing or other impacts, such as denced by China’s recent Belt and Roads Initiative and island-building sedimentation or pollution. We found very different patterns for enterprise in the South China Sea (56–58). Investments in sustainable nontarget species compared with target species, suggesting the planning of coastal development and road building could help to relationship between target fish biomass and gravity (SI Appendix, minimize unnecessary increases in reef accessibility. Importantly, Fig. S1) is primarily driven by fishing pressure. stemming increases in gravity is only part of the potential solution Overall, our results demonstrate that the capacity to not space: it will also be important to dampen the mechanisms through only sustain reef fish biomass and the presence of top pred- which gravity operates, such that a given level of gravity can have a ators, but also the potential to achieve conservation gains, may lesser impact on reef systems (1). People’s environmental behavior is be highly dependent on the level of human impact in the fundamentally driven by their social norms, tastes, values, practices, surrounding seascape. It is therefore essential to consider the and preferences (59), all of which can be altered by policies, media, global context of present and future human gravity in coral and other campaigns in ways that could change the local relationship reef governance. Consequently, we calculated gravity of hu- between gravity and reef degradation. man impacts for every reef cell globally using a 10- × 10-km Gravity Future Directions grid across the world’s coral reefs (Fig. 5). Critically, the distribution of gravity varies substantially among regions, with Our gravity index (Materials and Methods and Box 1) makes the central and eastern Indo-Pacific demonstrating lower- several key assumptions that could potentially be refined in gravity values. Even within a region, there can be substantial further applications. First, our application of gravity held friction variability in gravity values. For example, the Central Indo- constant across each specific type of surface (i.e., all paved roads Pacific has highly contrasting gravity patterns, with Southeast had the same friction value). Future applications of more lo- Asian reefs (Fig. 5 A, 3) generally showing extremely high- calized studies could vary travel time to reflect the quality of road networks, topographic barriers to access (such as cliffs), and gravity values while Australian and Melanesian reefs (Fig. the availability of technology. Similarly, future applications could 5 A, 4) are dominated by relatively low-gravity values. The ways in which gravity will increase over time, and how the also aim to incorporate local information about fishing fleet ef- impacts of gravity on reef systems can be reduced, is of substantial ficiency. Second, our adaptation of the gravity model (31) is concern for coral reef governance. The potential benefits of protecting unidirectional, assuming a constant level of attraction from any locations that are currently remote could increase over time as human reef (i.e., gravity varies based on human population size, but not Cinner et al. PNAS | vol. 115 | no. 27 | E6121 Difference in expected fish biomass (kg/ha) relative to openly fished areas Difference in expected probability of encountering top predators relative to openly fished areas SUSTAINABILITY PNAS PLUS SCIENCE A Gravity 4 70 12 3 0.3 Western Indo-Pacific Central Indo-Pacific Eastern Indo-Pacific 0.2 Tropical Atlantic 0.1 0.0 0 1 10 100 1000 10000 Gravity Fig. 5. Distribution of gravity on the world’s coral reefs. (A) Map of gravity calculated for every coral reef in the world ranging from blue (low gravity) to red (high gravity). The four coral reef realms (70) are delineated. Insets highlight gravity for key coral reef regions of the world: (1) Red Sea, (2) Western Indian Ocean, (3) Southeast Asia, (4) Great Barrier Reef of Australia and the South Pacific, (5) Caribbean. For visual effect, gravity values in Inset maps are also given vertical relief, with higher relief indicating higher gravity values. (B) Distribution of gravity values per coral reef realm. on the quality or quantity of fish on a specific reef). Reefs with ture applications could examine whether ecological recovery in more fish, or higher fish value, could be more attractive and reserves (8) depends on the level of gravity present. To this end, exert a higher pull for exploitation (60). Likewise, societal values we provide a global dataset of gravity for every reef pixel globally and preferences can also make certain fish more or less attrac- (Materials and Methods). tive. Our adaptation of gravity was designed to examine the We demonstrate that human impacts deplete reef fish stocks observed conditions of reefs as a function of potential interac- and how certain types of management can mediate but not tions with markets and local settlements, so our modification of eliminate these pressures. In an era of increasing change, the the concept for this application was appropriate. However, fu- global network of marine reserves may not safeguard reef fish ture applications wishing to predict where reefs may be most communities from human impacts adequately enough to ensure vulnerable might wish to consider incorporating fish biomass or key ecological functions, such as predation, are sustained. Efforts composition (i.e., potential market price of reef fish) in the must be made to both reduce and dampen key drivers of change gravity equation. Third, our database was not designed to look at (1, 61), while maintaining or improving the well-being of reef- ecological changes in a single location over time. However, fu- dependent people. Importantly, we find evidence that both E6122 | www.pnas.org/cgi/doi/10.1073/pnas.1708001115 Cinner et al. Density of reefs Road network data were extracted from the Vector Map Level 0 (VMap0) remote and human-surrounded reserves can produce different from the National Imagery and Mapping Agency’s (NIMA) Digital Chart of types of conservation gains. Ultimately, multiple forms of man- the World (DCW). We converted vector data from VMap0 to 1 km agement are needed across the seascape to sustain coral reef fishes resolution raster. and the people who depend upon them. Land cover data were extracted from the Global Land Cover 2000 (64). Materials and Methods To define the shorelines, we used the GSHHS (Global Self-consistent, Hi- Scales of Data. Our data were organized at three spatial scales: reef site (n = erarchical, High-resolution Shoreline) database v2.2.2. 1,798), reef cluster (n = 734), and nation/state (n = 41). These three friction components (road networks, land cover, and shore- Reef site is the smallest scale, which had an average of 2.4 surveys lines) were combined into a single friction surface with a Behrmann map (transects), hereafter referred to as “reef.” projection (an equal area projection). We calculated our cost-distance models For reef cluster (which had an average of 2.4 ± 2.4 reef sites), we in R using the accCost function of the “gdistance” package. The function clustered reefs together that were within 4 km of each other, and used the uses Dijkstra’s algorithm to calculate least-cost distance between two cells centroid to estimate reef cluster-level social and environmental covariates. on the grid taking into account obstacles and the local friction of the To define reef clusters, we first estimated the linear distance between all landscape (65). Travel time estimates over a particular surface could be af- reef sites, then used a hierarchical analysis with the complete-linkage fected by the infrastructure (e.g., road quality) and types of technology used clustering technique based on the maximum distance between reefs. We (e.g., types of boats). These types of data were not available at a global scale set the cut-off at 4 km to select mutually exclusive sites where reefs cannot but could be important modifications in more localized studies. be more distant than 4 km. The choice of 4 km was informed by a 3-y study Gravity computation. To compute gravity, we calculated the population of of the spatial movement patterns of artisanal coral reef fishers, corre- the cell and divided that by the squared travel time between the reef site and sponding to the highest density of fishing activities on reefs based on GPS- the cell. We summed the gravity values for each cell within 500 km of the reef derived effort density maps of artisanal coral reef fishing activities (62). site to get the “total gravity” within 500 km. We used the squared distance This clustering analysis was carried out using the R functions “hclust” (or in our case, travel time), which is relatively common in geography and and “cutree.” economics, although other exponents can be used (31) (Table S3). A larger scale in our analysis was “nation/state” (nation, state, or territory, We also developed a global gravity index for each 10- × 10-km grid of reef which had an average of 44 ± 59 reef clusters), which are jurisdictions that in the world (Box 1), which we provide as an open-access dataset. The pro- generally correspond to individual nations (but could also include states, cedure to calculate gravity was similar to above with the only difference territories, overseas regions), within which sites were nested for analysis. being in the precision of the location; the former was a single data point Targeted fish biomass. Reef fish biomass estimates were based on visual counts in (reef site), while the latter was a grid cell (reef cell). For the purpose of the 1,798 reef sites. All surveys used standard belt-transects, distance sampling, or analysis, gravity was log-transformed and standardized. point-counts, and were conducted between 2004 and 2013. Where data from We also explored various exponents (1–3) and buffer sizes (50, 250, and multiple years were available from a single reef site, we included only data from 500 km) to build nine gravity metrics. The metric providing the best model, so the year closest to 2010. Within each survey area, reef-associated fishes were with the lowest Akaike Information Criterion (AIC), was that with a squared identified to species level, their abundance counted, and total length (TL) es- exponent for travel time and a 500-km buffer (SI Appendix,Table S3). timated, with the exception of one data provider who measured biomass at the Management. For each observation, we determined the prevailing type of family level. To make estimates of targeted biomass from these transect-level management, including the following. (i) Marinereserve (whether thesitefell data comparable among studies, we did the following. (i) We retained fami- within the borders of a no-take marine reserve): we asked data providers to lies that were consistently studied, commonly targeted, and were above a further classify whether the reserve had high or low levels of compliance. For this minimum size cut-off. Thus, we retained counts of >10 cm diurnally active, analysis, we removed sites that were categorized as low-compliance reserves (n = noncryptic reef fish that are resident on the reef (14 families), excluding sharks 233). (ii) Restricted fishing: whether there were active restrictions on gears (e.g., and semipelagic species (SI Appendix,Table S1). We calculated total biomass of bans on the use of nets, spearguns, or traps) or fishing effort (which could have targeted fishes on each reef using standard published species-level length– included areas inside marine protected areas that were not necessarily no take). weight relationship parameters or those available on FishBase (63). When Or (iii) openly fished: regularly fished without effective restrictions. To determine length–weight relationship parameters were not available for a species, we these classifications, we used the expert opinion of the data providers, and tri- used the parameters for a closely related species or genus. For comparison, we angulated this with a global database of marine reserve boundaries (66). We also 2 2 also calculated nontarget fish biomass (SI Appendix,Table S1). (ii)Wedirectly calculated size (median = 113.6 km ,mean = 217,516 km ,SD = 304,417) and age accounted for depth and habitat as covariates in the model (see Environmental (median = 9, mean = 15.5 y, SD = 14.5) of the no-take portion of each reserve. Drivers,below).(iii) We accounted for differences among census methods by Reserve size was strongly related to our metric of gravity and could not be di- including each census method (standard belt-transects, distance sampling, or rectly included in the analysis. We conducted a supplemental analysis where we 2 2 point-counts) as a covariate in the model. (iv) We accounted for differences in separated reserves into small (≤28 km )and large (>65 km ) based on a natural sampling area by including total sampling area for each reef (m )asacovariate break in the data to illustrate: (i) how biomass and the presence of top predators in the model. might differ between small and large reserves; and (ii) how large reserves are Top predators. We examined the presence/absence of eight families of fish absent in our sample in high gravity. considered top predators (SI Appendix, Table S1). We considered presence/ absence instead of biomass because biomass was heavily zero inflated. Other Social Drivers. To account for the influence of other social drivers that Gravity. We first developed a gravity index for each of our reef sites where we are thought to be related to the condition of reef fish biomass, we also had in situ ecological data. We gathered data on both population estimates included the following covariates in our model. and a surrogate for distance: travel time. Local population growth. We created a 100-km buffer around each site and used Population estimates. We gathered population estimates for each 1- by 1-km this to calculate human population within the buffer in 2000 and 2010 based cell within a 500-km radius of each reef site using LandScan 2011 database. on the Socioeconomic Data and Application Centre gridded population of the We chose a 500-km radius from the reef as a likely maximum distance fishing world database. Population growth was the proportional difference between activities for reef fish are likely to occur. the population in 2000 and 2010. We chose a 100-km buffer as a reasonable Travel time calculation. The following procedure was repeated for each range at which many key human impacts from population (e.g., land-use and populated cell within the 500-km radius. Travel time was computed using a nutrients) might affect reefs (67). cost–distance algorithm that computes the least “cost” (in minutes) of Human development index. Human development index (HDI) is a summary traveling between two locations on a regular raster grid. In our case, the two measure of human development encompassing a long and healthy life, being locations were the centroid of the reef site and the populated cell of in- knowledgeable, and having a decent standard of living. In cases where HDI terest. The cost (i.e., time) of traveling between the two locations was de- values were not available specific to the state (e.g., Florida and Hawaii), we termined by using a raster grid of land cover and road networks with the used the national (e.g., United States) HDI value. cells containing values that represent the time required to travel across them Population size. For each nation/state, we determined the size of the human (32) (SI Appendix, Table S2), we termed this raster grid a “friction-surface” population. Data were derived mainly from the national census reports CIA fact (with the time required to travel across different types of surfaces analogous book (https://www.cia.gov/library/publications/the-world-factbook/rankorder/ to different levels of friction). To develop the friction-surface, we used 2119rank.html), and Wikipedia (https://en.wikipedia.org/wiki/Main_Page). For global datasets of road networks, land cover, and shorelines: the purpose of the analysis, population size was log-transformed. Cinner et al. PNAS | vol. 115 | no. 27 | E6123 SUSTAINABILITY PNAS PLUS SCIENCE Environmental Drivers. AIC values, so we chose the interaction with reserve age for consistency. All con- Depth. The depth of reef surveys was grouped into the following categories: tinuous covariates were standardized for the analysis, and reserve age was then <4m, 4–10 m, >10 m to account for broad differences in reef fish community normalized such that nonreserves were0and theoldestreserveswere 1.In structure attributable to a number of interlinked depth-related factors. Cat- summary, our models thus predicted target fish biomass or probability of top egories were necessary to standardize methods used by data providers and predators being observed at thereef sitescale with an interaction between gravity were determined by preexisting categories used by several data providers. and reserve age, while accounting within the random factors for two bigger scales Habitat. We included the following habitat categories. (i) Slope: the reef slope at which the data were collected (reef cluster, and nation/state) (SI Appendix), and habitat is typically on the ocean side of a reef, where the reef slopes down into key social and environmental characteristics expected to influence the biomass of deeper water. (ii) Crest: the reef crest habitat is the section that joins a reef reef fish (14). In addition to coefficient plots (SI Appendix,Fig. S1), we conducted a slope to the reef flat. The zone is typified by high wave energy (i.e., where the supplemental analysis of relative variable importance (SI Appendix, Table S4). waves break). It is also typified by a change in the angle of the reef from an We ran the residuals from the models against size of the no-take areas of inclined slope to a horizontal reef flat. (iii) Flat: the reef flat habitat is typically the marine reserves and no patterns were evident, suggesting it would ex- horizontal and extends back from the reef crest for tens to hundreds of me- plain no additional variance in the model. Trend lines and partial plots ters. (iv) Lagoon/back reef: lagoonal reef habitats are where the continuous (averaged by site and nation/state) are presented in the figures (Figs. 2 B–H reef flat breaks up into more patchy reef environments sheltered from wave and 3H). We plotted the partial effect of the relationship between gravity energy. These habitats can be behind barrier/fringing reefs or within atolls. and protection on targeted fish biomass and presence of top predators (Figs. Back reef habitats are similar broken habitats where the wave energy does not 2 B–G and 3 B–G) by setting all other continuous covariates to 0 because they typically reach the reefs and thus forms a less continuous “lagoon style” reef were all standardized and all categorical covariates to their most common habitat. Due to minimal representation among our sample, we excluded other category (i.e., 4–10 m for depth, slope for habitat, standard belt transect for less-prevalent habitat types, such as channels and banks. To verify the sites’ census method). For age of reserves, we set this to 0 for fished and restricted habitat information, we used the Millennium Coral Reef Mapping Project hi- areas, and to the average age of reserves (15.5 y) for reserves. erarchical data (68), Google Earth, and site depth information. To examine the expected conservation gains of different management Productivity. We examined ocean productivity for each of our sites in milligrams strategies, we calculated: (i) the difference between the response of openly −2 −1 of Cper square meter per day (mgCm d )(www.science.oregonstate.edu/ fished areas (our counterfactual) and high-compliance marine reserves to ocean.productivity/). Using the monthly data for years the 2005–2010 (in hdf gravity; and (ii) the difference between the response of openly fished areas format), we imported and converted those data into ArcGIS. We then calcu- and fisheries restricted areas to gravity. For ease of interpretation, we lated yearly average and finally an average for all these years. We used a plotted conservation gains in kilograms per hectare (kg/ha; as opposed to 100-km buffer around each of our sites and examined the average productivity log[kg/ha]) (Fig. 4A). A log-normal (linear) model was used to develop the within that radius. Note that ocean productivity estimates are less accurate slopes of the biomass (i) fished, (ii) marine reserve, and (iii) fisheries re- for nearshore environments, but we used the best available data. For the stricted areas, which results in the differences between (i) and (ii) and be- purpose of the analysis, productivity was log-transformed. tween (i) and (iii) being nonlinear on an arithmetic scale (Fig. 4A). Climate stress. We included an index of climate stress for corals, developed by We plotted the diagnostic plots of the general linear-mixed model to check that Maina et al. (69), which incorporated 11 different environmental conditions, the model assumptions were not violated. To check the fit of the generalized linear- such as the mean and variability of sea-surface temperature. mixed model, we used the confusion matrix (tabular representation of actual versus predicted values) to calculate the accuracy of the model, which came to 79.2%. Analyses. We first looked for collinearity among our covariates using bivariate To examine homoscedasticity, we checked residuals against fitted values. We correlations and variance inflation factor estimates. This led to the exclusion checked our models against a null model, which contained the model structure of several covariates (not described above): (i) Biogeographic Realm (Tropical (i.e., random effects), but no covariates. We used the null model as a baseline Atlantic, western Indo-Pacific, Central Indo-Pacific, or eastern Indo-Pacific); against which we could ensure that our full model performed better than a (ii) Gross Domestic Product (purchasing power parity); (iii) Rule of Law model with no covariate information. In all cases our models outperformed our (World Bank governance index); (iv) Control of Corruption (World Bank null models by more than 2 AIC values, indicating a more parsimonious model. governance index); (v) Voice and Accountability (World Bank governance All analyses were undertaken using R (3.43) statistical package. index); (vi) Reef Fish Landings; (vii) Tourism arrivals relative to local pop- ulation; (viii) Sedimentation; and (ix) Marine Reserve Size. Other covariates Data Access. A gridded global gravity data layer is freely available at dx.doi. had correlation coefficients 0.7 or less and Variance Inflation Factor scores org/10.4225/28/5a0e7b1b3cc0e. The ecological data used in this report are less than 5 (indicating multicollinearity was not a serious concern). Care must owned by individual data providers. Although much of these data (e.g., be taken in causal attribution of covariates that were significant in our NOAA CRED data, and Reef Life Surveys) are already open access, some of model, but demonstrated collinearity with candidate covariates that were these data are governed by intellectual property arrangements and cannot be removed during the aforementioned process. Importantly, the covariate of made open access. Because the data are individually owned, we have agreed interest in this study, gravity, was not strongly collinear with candidate upon and developed a structure and process for those wishing access to the covariates except reserve size (r = −0.8, t = 3.6, df = 104, P = 0.0004). data. Our process is one of engagement and collaboration with the data To quantify the relationships between gravity and target fish biomass, we providers. Anyone interested can send a short (one-half to one page) proposal developed a general linear-mixed model in R, using a log-normal distribution for for use of the database that details the problem statement, research gap, biomass. To quantify the relationships between gravity and presence/absence of research question(s), and proposed analyses to the Principle Investigator and top predators, we developed a generalized linear-mixed model with a binomial database administrator Joshua.cinner@jcu.edu.au, who will send the proposal family and a logit link function. For both models, we set reef cluster nested to the data providers. Individual data providers can agree to make their data within nation/state as a random effect to account for the hierarchical nature of available or not. They can also decide whether they would like to be con- the data (i.e., reef sites nested in reef clusters, reef clusters nested in nation/ sidered as a potential coauthor if their data are used. The administrator will states). We included an interaction between gravity and reserve age, as well as then send only the data that the providers have agreed to make available. all of the other social and environmental drivers and the sampling method and total sampling area as covariates. We also tested interactions between gravity ACKNOWLEDGMENTS. We thank J. Zamborian Mason and A. Fordyce for and management and used AIC to select the most parsimonious model. For fish assistance with analyses and figures, and to numerous scientists who biomass, the interaction between gravity and reserve age had AIC values >2lower collected data used in the research. The Australian Research Council Centre than the interaction between gravity and management (and a combination of of Excellence for Coral Reef Studies and The Pew Charitable Trusts funded both interactions). For the top predator models, both interactions were within 2 working group meetings. 1. Hughes TP, et al. 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