ICES Journal of Marine Science (2020), 77(7-8), 2518–2528. doi:10.1093/icesjms/fsaa109 Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 Original Article Regulation strength and technology creep play key roles in global long-term projections of wild capture fisheries 1 Kim Scherrer * and Eric Galbraith1,2 1 Institut de Ciència i Tecnologia Ambientals (ICTA), Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain 2 Department of Earth and Planetary Sciences, McGill University, H3A 0E8 Montreal, Quebec, Canada *Corresponding author: tel: þ 34 682 92 38 63; e-mail: kim.jn.scherrer@gmail.com. Scherrer, K. and Galbraith, E. Regulation strength and technology creep play key roles in global long-term projections of wild capture fisheries. – ICES Journal of Marine Science, 77: 2518–2528. Received 15 January 2020; revised 29 May 2020; accepted 2 June 2020; advance access publication 12 September 2020. Many studies have shown that the global fish catch can only be sustained with effective regulation that restrains overfishing. However, the persistence of weak or ineffective regulation in many parts of the world, coupled with changing technologies and additional stressors like climate change, renders the future of global catches uncertain. Here, we use a spatially resolved, bio-economic size-spectrum model to shed light on the interactive impacts of three globally important drivers over multidecadal timescales: imperfect regulation, technology-driven catchability increase, and climate change. We implement regulation as the adjustment of fishing towards a target level with some degree of effectiveness and project a range of possible trajectories for global fisheries. We find that if technological progress continues apace, increas- ingly effective regulation is required to prevent overfishing, akin to a Red Queen race. Climate change reduces the possible upper bound for global catches, but its economic impacts can be offset by strong regulation. Ominously, technological progress under weak regulation masks a progressive erosion of fish biomass by boosting profits and generating a temporary stabilization of global catches. Our study illustrates the large degree to which the long-term outlook of global fisheries can be improved by continually strengthening fisheries regulation, despite the negative impacts of climate change. Keywords: catchability, climate change, collective action, fisheries management, future fisheries, management effectiveness, marine ecosystem modelling Introduction observations raise concerns for the future trajectory of global The world’s annual marine fish catches have stagnated since the catches. 1990s, after more than a century of astonishing growth (FAO, The future of global catches, which determines the sustained 2018; Watson and Tidd, 2018). The subsequent significant de- provision of nutrition and source of income for millions of peo- cline in the global catch rate, indicated by data reconstructions, ple worldwide (Teh and Sumaila, 2013; Golden et al., 2016), now has occurred despite a continued rise of the effective fishing depends on multiple interacting forces at play within human soci- effort (Pauly and Zeller, 2016; Rousseau et al., 2019). Syntheses eties, including fisheries regulation and governance, technological have suggested that for the world’s assessed fish stocks, the me- and economic progress, and the capacity to mitigate climate dian fishery is unsustainably fished (Costello et al., 2016), and change (Worm and Branch, 2012; Costello et al., 2016; Galbraith data to assess biomass and catch trends are lacking for at least et al., 2017; Österblom et al., 2017; Gaines et al., 2018; Free et al., half of the global catch (Hilborn et al., 2020). Together, these 2019; Lotze et al., 2019). The complexity of these interacting C International Council for the Exploration of the Sea 2020. V This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Regulation and technology in fishery projections 2519 factors and their multidecadal time horizons call for an improved overfishing in poorly regulated fisheries by allowing fishers to ob- mechanistic and quantitative understanding of the drivers that tain profits at progressively lower fish abundance, and shifts the determine long-term outcomes for global fisheries. fish biomass at the theoretical OA equilibrium to lower and lower The understanding of large and complex socio-ecological sys- levels (Smith and Krutilla, 1982; Whitmarsh, 1990; Squires and tems, such as the global fishery, agricultural, or climate system, Vestergaard, 2013a, 2015). Thus, if the productivity limits of eco- has recently been greatly advanced through the development of systems are exceeded, technology-driven catchability increase process-based numerical models. For fisheries, global ecosystem transitions from a source of increasing catches into a cause for models that allow long-term projections under both climatic and catch decline. socio-economic change are providing new insights (Lotze et al., At the same time, the impact of climate change on marine eco- Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 2019) and have the potential to evaluate the outcomes of multiple systems is intensifying. In addition to the conspicuous species interacting drivers on marine fisheries (Dueri et al., 2016; range shifts and ecosystem restructuring (Perry et al., 2005; Galbraith et al., 2017). While the coarse spatial resolution of these Poloczanska et al., 2013), climate change appears to be decreasing global approaches gives them limited accuracy for any given fish- the overall ecosystem productivity and thus the global fisheries ing region, they make it possible to perform mechanistically potential (Lotze et al., 2019; Free et al., 2019). Climate change founded long-term projections that help us understand what the effects are likely to include a net decrease in marine net primary future of global fisheries might hold. production (NPP) due to increased stratification (Bopp et al., Arguably, the main cause for the detrimental overdevelopment 2013; Kwiatkowski et al., 2019) while warmer waters will acceler- of many fisheries (Hilborn et al., 2005; Branch et al. 2006), and ate the metabolic rates of marine ectotherms, resulting in more the key reason why fisheries need to be effectively regulated (Smith rapid dissipation of energy and therefore a smaller biomass of up- and Sissenwine, 2001), is the problem of open access (OA) per trophic-level organisms (Carozza et al., 2019; Heneghan et al., (Gordon, 1954; Hardin, 1968). The OA problem can be effectively 2019). Given that climate change is acting on ecosystems that overcome through a great variety of regulatory systems, as has have already been heavily overfished in many regions, it has re- been demonstrated in diverse fisheries, from indigenous to indus- cently been suggested that the future effects of climate change can trial, throughout history (Ostrom, 1990; Berkes et al., 2000; Caddy be mitigated by improving fisheries regulation (Galbraith et al., and Cochrane, 2001; Hilborn et al., 2005). Today, regulation meas- 2017; Gaines et al., 2018). ures are improving the status (i.e. increasing the fish biomass and Many recent analyses have highlighted the potential benefits of lowering the fishing mortality rate) of the majority of the scientifi- reducing human pressures on marine ecosystems (e.g. Blanchard cally assessed fish stocks worldwide (Hilborn et al., 2020), moving et al., 2014; Dueri et al., 2016; Fulton et al., 2019), but few global beyond the earlier, more regionally limited examples of manage- studies have assessed the dynamics of fisheries regulation in combi- ment successes in places like Alaska, Australia or New Zealand nation with other human drivers of change. Studies performing (Hilborn et al., 2005). This development encourages optimism long-term global projections based on available stock assessments about the recovery of global fisheries (Duarte et al., 2020). (Costello et al., 2016; Gaines et al., 2018) are well-grounded in However, despite this progress, substantial challenges for fish- observations where assessments have been made but do not in- eries management still lie ahead. The scientifically assessed fish clude energetic constraints at the ecosystem level or physiological stocks make up only about 50% of the global reported fish representations of temperature response. Moreover, although real- catches, or 40% when considering global catch reconstructions istic long-term simulations should include technological progress (Pauly and Zeller, 2016; FAO, 2018; Hilborn et al., 2020). The (Galbraith et al., 2017; Palomares and Pauly, 2019), this has gener- remaining unassessed stocks are believed to be in substantially ally been lacking in previous global projections (Costello et al., worse states than the assessed stocks, with low biomass and high 2016; Gaines et al., 2018). Thus, there is a need for complementary exploitation rates (Costello et al., 2012; Hilborn et al., 2020). investigations of how imperfect regulation and continuous techno- Supporting this notion, global assessments of management effec- logical progress affect long-term global fisheries dynamics. tiveness indicate that inefficient regulation is widespread (Mora Here, we perform the first whole-ecosystem simulations of et al., 2009; Pitcher et al., 2009; Coll et al., 2013), with lax limits global fisheries that simultaneously include a variable effective- and an inability to enforce compliance with the limits both being ness of fisheries regulation, the possibility of future technological key challenges (Bundy et al., 2017; Melnychuk et al., 2017; Ye and progress, and the bio-energetic impacts of climate change. We de- Gutiérrez, 2017). This overall inefficiency in keeping fishing pres- scribe a new generalized regulation component for the dynamical, sure at sustainable levels makes it important to investigate the spatially resolved BiOeconomic mArine Trophic Size-spectrum long-term implications of imperfect regulation. model, BOATS (Carozza et al., 2016, 2017) that reflects the ten- Technological progress, or creep, in catch efficiency (Eigaard sion between the individual profit motivations and a common, et al., 2014; Palomares and Pauly, 2019), has played a tremen- socially defined fishing target. We use the model to evaluate the dously important role in the history of fisheries (Squires and theoretical importance of fisheries regulation and its effectiveness Vestergaard, 2013b) but may also pose a major future sustainabil- in the face of technological and climatic change and compare the ity challenge. The great increase in global catches seen over the in- results with observed global catches, with the aim to better under- dustrialization of fisheries in the 20th century, and the associated stand the mechanisms that will determine long-term sustainabil- nutritional and economic gains, would not have been possible ity in the global fishery. without development of better fishing gears, vessels, navigation systems and fish-finding methods (Finley, 2016). Recent model- Existing model ling work suggests that technology-driven catchability increases BOATS is a global, ecosystem-scale model of fish size distribu- explain the first-order historical development of global catch tions, coupled with an economic model of profit-driven fishing (Galbraith et al., 2017). However, while being a large potential activity. As inputs, the model uses global time-varying grids of source of increased efficiency, technological creep exacerbates sea surface temperature (SST) and NPP at 1 spatial resolution, 2520 K. Scherrer and E. Galbraith and three economic forcings (the cost of fishing per unit effort c, Mathematical representation the ex-vessel price of fish p, and the catchability parameter q), Our generalized regulation model contains two key elements: (i) each of which can be spatio-temporally varying or constant (see societal determination of a fishing target and (ii) adjustment of Model simulations for forcing specifications). The reader is re- fishing effort towards the target by societal enforcement mecha- ferred to Carozza et al. (2016, 2017) for a thorough description of nisms. Undesired behavioural responses to regulations, which ren- the original model, which we briefly summarize in the der management ineffective, is a pervasive problem in fisheries Supplementary Material. The version of the model used here dif- (Fulton et al., 2011), which we represent explicitly with a societal fers only in its inclusion of the new regulation component. enforcement strength parameter, S. Since the individual incentive to overfish under OA is the essence of the regulation challenge, we Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 New fisheries regulation component define S as the extent to which OA is eliminated. Fisheries are then modelled on a simple continuum between pure OA behaviour and Main features of regulation behaviour perfectly in line with the shared societal interest. To model the regulation of capture fisheries on a global scale and The new component is implemented by modifying equation over long time periods, we must boil the process of regulation down (Supplementary S2) so that the fishing effort exerted on a fish to the most significant features that are common through time and size group k, Ek (W m2), evolves over time as across fisheries types. Fisheries regulation can be seen as a manifesta- tion of collective action, “the action taken by a group (either directly dEk revenuek  costk S ð or on its behalf through an organization) in pursuit of members’ per- ¼ Ke e þ 1  eS Þ Ks ðEtarg;k  Ek Þ; dt Ek ceived shared interests” (Marshall, 1998), to overcome the OA prob- (1) lem (Gordon 1954; Hardin, 1968). In a renewable resource system, the shared interest is often to maintain the extraction rate at an opti- where Etarg,k (W m2) is the societal target for fishing effort, S mal level given the group’s values and interests, which could be to (dimensionless) is the societal enforcement strength (S  0), Ke is maximize food production for a society, or to maximize profit for a the fleet dynamics parameter (W2 m2 $1), and Ks (m2 s1) is fishing collective. Achieving the desired optimum generally requires the regulation response parameter. Simplified, revenuek ¼ creating rules and enforcement mechanisms that incentivize (through pkqkEkBk, where pk is the ex-vessel price of fish, qk is the catchabil- rewards and/or punishments) individual behaviour in line with the ity parameter and Bk is the selectable biomass of size group k, shared interest (Oliver, 2013). while costk ¼ ckEk, where ck is the cost of fishing per unit effort In many aspects, modern fisheries regulation systems can be (see Supplementary Material for details). The first term in (1), considered fundamentally similar to the traditional ones weighted by the exponential function eS, thus represents the in- (Lertzman, 2009) and often apply similar regulation practices fluence of individual, immediate profit incentives in a population (Gadgil and Berkes, 1991). The same universal components of of fishers. The second term, weighted by 1  eS, represents the regulation can be identified in subsistence, small-scale, and indus- influence of regulation; it will be negative if Ek > Etarg,k and posi- trial fisheries: target setting, rule design, and enforcement tive if Ek < Etarg,k. (Table 1). The target may be based on different knowledge systems In real-world fisheries, defining a target in terms of effort (in- (e.g. scientific vs. traditional knowledge), and enforcement meth- put regulation) rather than catch (output regulation) means that ods range from traditions and religious beliefs (Johannes, 1978; Etarg,k must be adjusted to account for technological progress in Gadgil and Berkes, 1991; Berkes et al., 2000) to fines and criminal catchability, known as technological creep (Walters and Martell, sanctions (Caddy, 1999; Caddy and Cochrane, 2001; Cacaud et al., 2004). This problem is addressed in the model by calculating the 2003) depending on the context. However, the basic types of rules nominal effort target Etarg,k every year depending on a fishing tend to be similar across fisheries: generally, they control access to mortality target for fish group k, Ftarg,k (s1), and the current the fishery, protect vulnerable life stages, and limit the allowed catchability, qk, according to catch (Johannes 1978; Acheson, 1997; Gullestad et al., 2017). Thus, despite great diversity, the regulation measures applied Ftarg;k in fisheries have a universal aim to align fisher behaviour to Etarg;k ðt Þ ¼ ; (2) qk ðt Þ maintain a broadly desired state of the fish resource. At the same time, the degree to which regulations succeed varies widely so that Etarg,k varies inversely with qk to maintain a constant fish- (Melnychuk et al., 2017). We use these fundamental features to ing mortality. Thus, although nominal effort is the regulated create a generalized model of regulated fisheries. Table 1. Examples of universal components of regulation systems in three diverse fisheries. Fishery Type Basis for target Rules Enforcement Source Maine lobster Small-scale Fisher experience Limited access, seasonal and spatial Social sanctions, Acheson and interests closures, protection of vulnerable moral obligations (1997) life stages Norwegian Large-scale Scientific model Catch limits, limited access, spatial Fines, criminal Gullestad fisheries closures, gear restrictions, sanctions et al. (2017) protection of vulnerable life stages Oceania island Subsistence Traditional Limited access, seasonal and spatial Taboos, religion Johannes (1978) ecological closures, escapement, protection knowledge of vulnerable life stages Regulation and technology in fishery projections 2521 variable in our model, the real target is actually Ftarg,k, which time tro,k. This guarantees that regulation is only initiated after lo- makes it equally applicable to output targets (e.g. quotas) or well- cal overfishing has occurred: designed adaptive input targets (e.g. access, fishing time, or en-  gine size restrictions). In reality, insufficient knowledge and eco- Hk ðt Þ < hro Hmax;k Initiate regulation if : (5) system variability may prevent accurate estimation of a fishing qðt ÞEk ðt Þ > Ftarg;k target (Mace, 2001), and strong trade-offs between objectives may result in biologically unsustainable targets (Pascoe et al., 2017). Once initiated, regulation forces the nominal effort towards The impact of such uncertainty in target setting could be explored Etarg,k. The value of Ks determines the rate of effort change due to in our model framework, but we do not model this aspect of im- regulations. For example, the abrupt establishment of well- Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 perfect regulation here (as explained in Regulation target). enforced marine protected areas and fishing moratoria can result The diverse mechanisms by which regulation is enacted (e.g. in rapid and substantial effort decreases for individual stocks or those listed in Table 1) cannot feasibly be explicitly modelled at whole ecosystems over short time periods, as would be repre- the global scale. Thus, we treat regulation systems implicitly, sented by a large value of Ks. We here choose a value (Ks ¼ 4 meaning that we consider only the extent to which OA is elimi- 108) that allows the nominal effort to respond on a timescale of a nated and not the mechanisms by which this is achieved (whether few years, so that the model can stabilize at the fishing target it is through quotas, seasonal closures, or licensing). The fact that when S is high in our scenarios. some structures are more effective than others (e.g. Hilborn et al., 2005; Ostrom, 2009; Fulton et al., 2011) is captured by variations Model simulations in S, which could reflect the effect of diverse enforcement mecha- We explore the emergent dynamics of the new global regulation nisms, like local, governmental, satellite, or divine surveillance, or model through a suite of hindcasts and future scenarios that focus social, monetary, or religious sanctions, that promote compliance on the interactions with technological progress and climate (Table 1). We do not treat these factors implicitly because they change. Following Galbraith et al. (2017), the simulations are are unimportant or uninteresting, but rather as a useful simplifi- performed by forcing the model with constant c (1.8  104 cation to generate tractable global models of regulation. $ kW1) and constant p (1.1 $ kg1) for all grid cells and size Behavioural change is often hindered by structural and psycho- groups, reflecting global average values. The possible effects of fu- logical barriers (Amel et al., 2017). In fisheries, uncertainty and ture changes in average fish prices or fishing costs are discussed in conflicting values contribute to making regulation reactive rather Additional economic drivers. The scenarios for regulation, tech- than proactive (Rosenberg, 2003). Therefore, we assume that reg- nological progress, and climate change are described below and ulation will not begin before a substantial decline in catch of a summarized in Table 2. We also describe a simulation protocol given fish size group occurs at a given location. We define the for comparing the model with observed fisheries in Alaska. time of regulation onset for a size group, tro,k, as the time when catch declines below a certain fraction, hro, of the observed maxi- mum historical catch, Hmax,k. We here use hro ¼ 0.75, reflecting a Regulation target relatively rapid reaction to declining catch. As long as the fishing We here use the maximum sustainable yield (MSY) as an illustra- mortality (qkEk) is larger than Ftarg,k, regulation is initiated at tive target for regulation. We define the target as FMSY,k (2), the fishing mortality associated with maximum catch from a long- term simulation in which catchability increases very slowly, Table 2. Overview of model forcing variables used in scenarios. Forcing variable Meaning Values applied Motivation Domain Technology q Embodied and I. 5% increase year1 1950–2100 I. Exogenous technology adoption, Equal for all grid cells disembodied II. 5% increase year1 until 2020, economic incentives and fish size fishing technology then stable II. Exogenous technology groups and skill stagnation, no incentives in fisheries Regulation Ftarg Fishing mortality I. FMSY I. Maximized food production, Specific for each cell target for II. 0.3  FMSY SDG 13 and fish size group regulation II. Precautionary target S Regulation I. S ¼ 0 I. Open access fishing Equal for all grid cells strength II. S ¼ 3 II. Weak regulation and fish size III. S ¼ 10 III. Strong regulation groups Climate NPP Net primary I. Time varying (1950–2100) I. Upper range of projected climate Specific for each grid production, upper according to IPSL RCP 8.5 change impact cell 75 m II. Stabilizing at 2015–2020 II. Comparison scenario with no levels according to IPSL further climate change SST Average sea surface I. Time varying (1950–2100) I. Upper range of projected Specific for each grid temperature, according to IPSL RCP 8.5 climate change impact cell upper 75 m II. Stabilizing at 2015–2020 levels II. Comparison scenario with no according to IPSL further climate change 2522 K. Scherrer and E. Galbraith approximating steady state (see Galbraith et al., 2017). Experiments were run using the FMSY,k at each simulation year for each fish size group, corresponding to the respective tempera- ture and NPP conditions given by the Institute Pierre Simon Laplace (IPSL) Earth System Model. Since there are no explicit interactions between the three size groups (only within each of the three size spectra), the MSY represents a size group maximum in an idealized ecosystem where small, medium, and large fish occupy independent niches. We emphasize that, although our Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 main simulations use an MSY target for illustration, a more pre- cautionary target than MSY is generally recommended given real- world uncertainties (Mace, 2001; UN General Assembly, 2015). Societal enforcement strength scenarios S represents the strength with which the effort dynamics of a pure OA fishery are opposed. While this definition is theoretically useful, S lacks a directly measurable counterpart. For illustration, Figure 1. Modelled and empirical fisheries reference points in a well we use three values to represent no regulation (S ¼ 0, OA), weak managed fishery. Lines show stock assessments (blue) and regulation (S ¼ 3), and strong regulation (S ¼ 10). To better in- corresponding modelled fish populations in BOATS under different terpret the meaning of these S values, we also compare the mod- regulation effectiveness (grey) in US Alaska. If S  10, the F is el’s performance with some well-regulated stocks (see Model maintained at 30% of FMSY over the historical period like in comparison with observed Alaskan fisheries). Although it should observations. For lower, constant S, technological creep in the model be feasible to use global proxies to estimate the variations in en- makes F diverge from the target level. Shaded areas show uncertainty ranges: 1 SD among stock assessments, and model ensemble members. forcement strength between jurisdictions and over time, such as the World Governance Index or estimates of fisheries manage- ment effectiveness (Mora et al., 2009; Pitcher et al., 2009; Climate change scenarios Melnychuk et al., 2017), these qualitative estimates are not di- To investigate global fisheries dynamics under climate change, we rectly translatable to numerical values of S. Thus, as a simple first used gridded monthly NPP and SST output from the IPSL Earth step, we here simulate global catches under spatially and tempo- System Model as input for BOATS. We use Representative rally constant S. Concentration Pathway (RCP) 8.5 for the upper-range baseline scenario with no climate mitigation and provide a comparison simulation where the average present day (2015–2020) green- house gas levels are kept constant into the future. These two ide- Technology scenarios alized scenarios span a wide range of possible futures in a way Technological improvements that increase catch efficiency can be that is consistent with the scenarios for technology. Separate sim- modelled by increasing the catchability parameter, q (1 and ulations were performed using monthly climatological fields of Supplementary S1), reflecting both embodied and disembodied empirical NPP and SST [as in Carozza et al. (2016)], and are used aspects of technology (Pauly and Palomares, 2010; Squires and in Different global catch trajectories. Vestergaard, 2013b). Empirical studies have estimated an average rate of increase of 2–8% year1 in diverse fisheries and time peri- ods (Wilberg et al., 2009; Pauly and Palomares, 2010; Squires and Model comparison with observed Alaskan fisheries Vestergaard, 2013b; Eigaard et al., 2014; Palomares and Pauly, To provide a real-world example, we compare the model output 2019). Most of these estimates consider only a subset of techno- to stock assessments in Alaskan fisheries, which have a long his- logical aspects and therefore would be expected to underestimate tory of effective regulation (Hilborn and Ovando, 2014), mainly the overall rate of catchability increase (Scherrer and Galbraith, through strict quota systems (Worm et al., 2009). Technological 2020). Consistent with this expectation, the rate of q increase in progress (for example improvements in fish finding, navigation BOATS that best reproduces the observed global catches is a rela- and processing) has undoubtedly raised the catchability of the tively high value of 5% year1 (see Galbraith et al., 2017 for Alaskan fleet during the past decades. Yet, a survey of all the stocks model sensitivity to different rates). in the “US Alaska” region in the RAM legacy database (version 4.4) Technological progress often undergoes local hiatuses, and its shows that the average biomass weighted fishing mortality (F) has future rate will undoubtedly vary, but the underlying mechanisms been maintained near 30% of the fishing mortality corresponding are difficult to untangle, making predictions highly uncertain to MSY (FMSY) since the late 1980s (Figure 1), testament to the (Nagy et al., 2013). We therefore choose two simple model high degree of regulation effectiveness. scenarios that bracket the likely range: one with a continued We applied a similar precautionary target of 30% of FMSY, as- constant catchability growth rate of 5% year1 throughout the sumed a 5% year1 catchability increase with the RCP 8.5 climate 21st century and the other stagnating, with catchability increasing scenario, and tested multiple values for S (¼3, 5, 10) to investi- only until the year 2020 value after which it is held constant. We gate which level of enforcement strength would best recreate the impose the change in catchability homogenously across all grid historical trends in Alaskan stocks. The RAM legacy data show cells. that about 80% of the total catch during 1980–2014 was from fish Regulation and technology in fishery projections 2523 Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 Figure 2. Simulated and empirical global catch (1950–2050). Two end-member scenarios with continued technological progress and either open access (S ¼ 0, light grey) or strong regulation (S ¼ 10, Figure 3. Simulated fisheries trajectories under continued blue) are compared with historical catch data (1950–2014) from the technological progress. Aggregated global (a) catch, (b) effort, (c) SAUP (dark grey) and Watson and Tidd (black; 2018). Solid and dashed profit (in year 2000 US$), and (d) biomass under no (S ¼ 0, dark blue lines show results with (RCP 8.5) and without future climate grey), weak (S ¼ 3, red), or strong (S ¼ 10, blue) regulation. Weak change effects (no CC), respectively. Shaded areas show uncertainty regulation prolongs the period of high catch and profit and slows ranges (1 SD among model ensemble members) of simulated catches. down biomass loss, but to achieve perpetual sustainability, open With technological progress of 5% year-1, regulation plays a larger role access must be strongly offset (S > 10). The dashed blue line shows than climate, indicated by the grey and blue arrows, respectively. the development without future climate change. Triangles mark the start of divergence between the weak and strong regulation scenarios. stocks with an asymptotic size corresponding to that of the large fish size group modelled here (<8.5 kg or 90 cm; Carozza et al., globally strong enforcement (S ¼ 10, solid blue line), the catch 2016; Pauly and Zeller, 2016; FishBase, 2020), while vessel track- plateaus at 150 6 50 Mt wB year1 by year 2050 under climate ing indicates that fishing mainly takes place in highly productive change scenario RCP 8.5, with potential for an 8% increase in waters surrounding Alaska (Kroodsma et al., 2018). We therefore catches if the climate was stabilized. On the other end of the spec- show BOATS results for the large fish group, averaged over all trum, pure OA fishing (S ¼ 0) with climate change results in a grid cells of the Bering Sea and Gulf of Alaska large marine eco- 60% catch decrease by the middle of the century relative to the systems that have higher NPP than the regional average. peak catch in the early 2000s, a loss of about 90 Mt year1 com- pared to the strongly regulated case. Results Comparison with observed Alaskan fisheries Global outcomes of variable regulation strength Figure 1 compares the simulated BOATS historical trajectories of Figure 3 shows global trajectories of four key fisheries variables fishing mortality in the US Alaskan fisheries with different levels under three different regulation strengths and continued techno- of S with the historical fishing mortality obtained from stock logical progress. The catch projections in Figure 3a carry on from assessments. The modelled trajectory of F/FMSY for this subset of those shown in Figure 2, with the addition of a scenario with fish populations in BOATS coincided with the observations when weak regulation (S ¼ 3). For the latter, global catches remain high S approached 10 (Figure 1). We therefore take a value of S ¼ 10 and close to the strong regulation case until mid-century but then as representing highly effective, yet achievable, regulation. decline to 50 6 20 Mt year1 in 2100 (Figure 3a). This result is qualitatively robust to the choice of regulation target; a long-term Range of possible future catches catch decline under weak regulation also occurs when the target Figure 2 compares historical global catch estimates to the range is 30% of FMSY,k ðSupplementary Figure S1). of model ensemble trajectories, including technological progress, Regulation places a limit on the nominal fishing effort, reduc- two levels of regulation, and two climate projections (Table 2). ing it relative to OA by about 30% in 2020 and by 35–70% in The empirical estimates suggest that global catches have either de- 2100 depending on the scenario (Figure 3b). If the fishery is clined (Pauly and Zeller, 2016) or reached a plateau (Watson and unregulated, the simulated effort continues to increase after the Tidd, 2018) over the past decades (Figure 2). Neither of the esti- catch peak despite stagnating catches, as is the case in effort mates are consistent with the short peak and rapid global catch reconstructions (Rousseau et al., 2019). The reduction in effort decline that the model simulates under global OA, but the esti- achieved by regulation greatly improves the projected global mate by Pauly and Zeller (2016) is clearly inconsistent with the profit (Figure 3c), with the strongly regulated fishery yielding a stable plateau of globally strong regulation. continuously increasing profit over time thanks to the In contrast to the agreement between the two simulated histor- technology-driven increase in catch efficiency. Under weak regu- ical regulation trajectories, the future regulation scenarios diverge lation, profit remains high in the short term and is continually dramatically if technological progress continues apace. Under positive throughout the rest of the century. 2524 K. Scherrer and E. Galbraith While weak regulation significantly increases profits compared Abrupt technological stagnation to the corresponding OA scenarios, it does relatively little to miti- The importance of regulation strength is greatly diminished in gate the loss of fish biomass (Figure 3d). Although S ¼ 3 slows the the scenarios where catchability abruptly stops increasing at the decline in global biomass in the near future, biomass diverges year 2020 level (Figure 4). In this case, the largest gains that can from the strong regulation scenarios early in the 21st century be achieved through stronger regulation are about 30% more (marked by the empty triangle in Figure 3d). In the long term, catch than OA by end-of-century if climate change continues, weak regulation fails to fulfil conservation objectives; by simula- with roughly twice as much global fish biomass, and the differ- tion year 2100, the global biomass has been fished down to <3% ence between strong and weak regulation is small (Figure 4a and of the pristine biomass (5 6 2 Gt), and profit also dwindles. d). Moreover, without technologically driven improvements in Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 Under strong regulation, the global biomass initially stabilizes at catch efficiency, the decreasing catch per unit effort under climate about 30% of the pristine biomass, the model estimate of biomass change results in a long-term decrease in global profits. In con- associated with global average MSY (BMSY). However, in the final trast, a stabilizing climate in this case leads to completely stabiliz- decades, biomass begins to decline even under S ¼ 10 (Figure 3d). ing biomass, catch, and profit, which means that these three factors are considerably higher than under scenarios with climate change by the end of the century. Discussion Different global catch trajectories We find that under continued technological progress, different levels of regulation strength generate qualitatively different global catch curves with very different implications. In the absence of regulation (Figure 5a), the global catch curve increases, peaks, and declines, reflecting the sum of catch trajectories in individual regions throughout the world (grey lines). Under weak regula- tion, the global catch passes through a temporary plateau (Figure 5b), as regulation slows down the post-peak catch decline in each region, until technological progress overcomes the socie- tal enforcement strength. If strong regulation pushes all regional catches to approach their local MSY targets (Figure 5c), rebuild- ing efforts and new exploitations lead to a global increase prior to stabilization at the global MSY (which in this illustration is unaf- fected by climate change). Figure 4. Simulated fisheries trajectories under stabilizing technology. Aggregated global (a) catch, (b) effort, (c) profit (in year A “Red Queen race” in regulated fisheries 2000 US$), and (d) biomass under no (S ¼ 0, dark grey), weak (S ¼ 3, Our simulations illustrate a persistent challenge that arises under red), or strong (S ¼ 10, blue) regulation. The importance of imperfect regulation. Increasing catchability ultimately leads to regulation strength is greatly diminished if technology stabilizes and higher instantaneous individual profit, strengthening the profit the race between catchability and regulation strength ceases. Still, incentive even as stocks are depleted and yields fall. From (1), it regulated fisheries yield substantially higher benefits. The dashed blue line shows the development without future climate change. can be inferred that if S is constant, this applies regardless of the exact rate of technological progress. Thus, although the time Figure 5. Illustration of the impact of regulation strength. Three types of global catch curves (thick red line) arise under different regulation strengths and continued catchability increase (5% year1). Grey lines show underlying catches in each of the world’s large marine ecosystems, and shade of grey indicates the timing of the catch peak. In contrast with the unregulated case, S ¼ 0 (a), weaker regulation, S ¼ 1.2 (b) results in a temporary global catch plateau, while strong regulation, S ¼ 10 (c) results in growing and stabilizing global catches. Illustrations are representative of a stable climate. Regulation and technology in fishery projections 2525 horizon depends on S and q, long-term sustainability eventually depends on a race between improving catchability and improving regulation—in line with the arguments of Whitmarsh (1990). We call this the “Red Queen race” of fisheries regulation, analogous to the ecological Red Queen hypothesis that organisms must con- tinuously evolve to keep up with the evolution of competitors and predators. This Red Queen race occurs even though we here assume that the effort target is perfectly adjusted for technological creep [by continuously adjusting the nominal effort target Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 according to (2)], a correction that can be very difficult to make input-regulated fisheries (Branch et al., 2006; Eigaard et al., 2014). The role of technological creep is here shown using a model in which regulation reduces OA behaviour in favour of a societal target, but similar dynamics should arise under alternative for- malizations of fisheries regulation. In models of compliance, reg- ulation can be represented as additional costs of fishing, either Figure 6. Hidden losses under weak regulation. The percent change monetary (fines/taxes) or non-monetary (social/moral) (see in ensemble average global profit, catch, and biomass is shown for Sutinen and Kuperan, 1999; Nøstbakken, 2008). Adding regula- 2050 and 2100 relative to year 2020, for the simulation with weak tory costs in the original effort equation (Supplementary regulation (S ¼ 3), continued technological progress, and climate S2) would also give rise to a Red Queen race; as increasing q change. In year 2050, weak regulations uphold relatively high profit increases potential revenues, the costs (i.e. fines, taxes, or fear of and stable catch, masking a substantial biomass decline that sanctions) would have to increase continuously at a rate that ultimately leads to a large decline of catch and profit by 2100. counteracts catchability increases, if catches are to be sustainable. The realized impact of the Red Queen race will depend on the aquaculture growth (which also requires feed from capture fisher- rate of continued technological progress, as illustrated by the two ies; FAO, 2018). If such a development was to drive up real ex- contrasting scenarios (Figures 3 and 4). Predicting how future vessel prices of wild-caught fish, profitability would be increased, technology will progress is difficult, but fisheries technologies enhancing the profit incentive and weakening the effect of regula- generally originate from exogenous fields (e.g. echo location, po- tion, all else being equal (as shown under OA conditions in sitioning systems, material development, robotics, ocean model- Galbraith et al., 2017). Thus, rising prices would have a dynamical ling, or artificial intelligence), while also responding to economic impact similar to that of technological progress. Similarly, subsi- incentives (Hilborn et al., 2005; Squires and Vestergaard, 2013b). dies that reduce the cost of fishing, or encourage technology up- This suggests that continued technological progress in fisheries is take (Sumaila et al., 2016), would also exacerbate biomass likely as long as the overall global rate of technological progress depletion and catch losses. Conversely, higher cost per unit effort, does not stagnate. We underline, however, that the future tech- e.g. due to rising oil prices in response to carbon pricing, would nology scenarios used here are exploratory, intended to help illus- abate the profit incentive, making a given strength of regulation trate mechanisms. more effective. However, explicitly modelling price dynamics would be a possible avenue for future work. Hidden losses Our simulations show that, under weak regulation, technological progress helps to maintain a relatively high global profit and Real-world variability in regulation extends the period of relatively stable catches, hiding a steadily Since this study focused on mechanistic understanding and since declining biomass (Figure 6). These hidden losses are in line with variability in regulation effectiveness is difficult to quantify glob- theoretical work on economic optimality under technological ally, S was held spatially homogeneous and constant in our sce- progress in fisheries (Squires and Vestergaard, 2015). Because narios. In reality, regulation effectiveness varies between regions, catch and profit are easier to measure than biomass, technological countries, or even individual fish stocks, as it depends on com- progress could thus give a false sense of security, especially by cre- plex interactions between socio-economic and ecological factors ating a temporary plateau in total catches (Figure 5c). These hid- (Hilborn et al., 2005; Ostrom 2009; Fulton et al. 2011). Different den losses would be expected to render fish conservation management solutions, tailored to the local context, are therefore particularly difficult and would become more severe if ex-vessel required to achieve effective regulation for different target species, prices increase in future (rather than staying constant as in our fishing techniques, and socio-economic circumstances (Duarte simulations). et al., 2020). Although all these nuances are unfeasible to include into a global model, some general patterns of regulation effective- Additional economic drivers ness are suggested by global studies. Generally, developed and The effects of many additional economic factors and develop- high-latitude regions have higher regulation effectiveness, likely ments, not explicitly included in our scenarios, can be discerned due to their higher capacity to assess fish stocks and enforce regu- from the effort equation (1). For example, the FAO projects that lations (Ye and Gutiérrez, 2017; Melnychuk et al. 2017; Hilborn the global demand for fish will rise faster than the supply in the et al., 2020). To provide more detailed projections, future work upcoming decade due to the growth of both the human popula- could find ways to translate such knowledge into regionally vary- tion and their incomes, and the expected slowdown of ing values for S. 2526 K. Scherrer and E. Galbraith Potential future benefits from the global fishery Our model scenarios suggest that, under continued technologi- The results from our global, whole-ecosystem modelling ap- cal progress, weak fisheries regulation results in hidden biomass proach strongly corroborate stock-assessment-based estimates in losses and fails to ensure long-term sustainability due to what we predicting large benefits of strong regulation and fishery rebuild- term the “Red Queen race” of fisheries regulation. Rising demand ing (Worm et al., 2009; Costello et al., 2016). The analysis by for fish would further exacerbate this race. As a result, regulation Costello et al. (2016) projected biomass, catch, and profit to be effectiveness must be continually improved to sustain the global about 0.8 Gt, 70 Mt year1, and 50 B$ year1, respectively, in fishery. Optimally, under strong regulation and technological 2050 for a subset of global stocks under a perfectly implemented progress, simulated global catches, biomass, and profit approach global MSY strategy. Our mean estimates in 2050 under S ¼ 10 180 6 40 Mt year1, 1.7 6 0.7 Gt, and 170 6 50 B$ year1, respec- Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 and no future climate change are consistently about twofold for tively. Unmitigated climate change is likely to decrease the maxi- all three measures (1.7 Gt, 160 Mt year1, and 120 B$ year1; mum catch potential (MSY) and fish biomass, but global catches Figure 3). The higher values arise because BOATS is designed to can largely be maintained at present levels throughout the 21st simulate all global catch, including an estimate of unreported century if regulations are effective and technological progress catches (Pauly and Zeller, 2016), as well as possible future expan- continues. sions in targeted fish, such as a greater exploitation of small fish The dynamics that arise in our regulated fisheries model out- in the deep sea (Carozza et al., 2017). Given the fact that our ap- line key long-term challenges for global fisheries. We find that proach models the flow of energy through the whole ecosystem, global fisheries regulations must continue to be strengthened as while that of Costello et al. (2016) uses logistic growth models for long as catchability in the fishery continues to increase. This rein- individual fish stocks, we find the remarkably strong agreement forces the great importance of initiatives that strengthen regula- of the relative impacts on biomass, catch, and profit arrived at by tions, from the revitalization of traditional community-based the two approaches to be very encouraging. The finding that management (Johannes 2002; Ostrom, 2009), improved leader- strong regulation can more than offset climate-driven productiv- ship and community cohesion (Gutiérrez et al., 2011), and imple- ity declines is also in line with perfectly regulated simulations mentation of catch share systems (Costello et al., 2008), to with the same model (Galbraith et al., 2017) as well as with a technologically aided monitoring, control, and surveillance thermal-niche-based approach (Gaines et al., 2018). (Caddy, 1999; McCauley et al., 2016; Bradley et al., 2019). The de- The model suggests that the maximum possible global catch is gree to which technological improvements can empower regula- larger than the observed historical maximum. If effort was tion may play a critical role in determining the outcome of the strongly regulated to achieve MSY and if the climate was stabi- Red Queen race of fisheries regulation. If successful, such regula- lized, simulated catches and profits continue to increase towards tory advances might prevent a dramatic decline in global biomass 180 6 40 Mt year1 and 170 6 50 B$ year1 throughout the 21st and catches over the 21st century and ensure an indefinite supply century. However, in line with previous work, unmitigated cli- of wild-caught fish to support human nutrition and well-being. mate change decreases the MSY by almost 30% by 2100 (the dif- ference between the blue dashed and solid lines in Figure 4a). Supplementary data Thus, the sustainable future catch may yield somewhat less fish Supplementary material is available at the ICESJMS online ver- than at the historical peak, though it could be far more profitable. sion of the manuscript. Furthermore, the results imply that a gradual catch decline fol- lowing global peak [as found by Pauly and Zeller (2016)] is con- Acknowledgements sistent with globally weak fisheries regulation, potentially The authors thank Jérôme Guiet and Sarah Eggleston for contrib- exacerbated by climate change effects. uting to the model development, and thank four anonymous Finally, we underline that mesopelagic fish are not well repre- reviewers whose comments helped improve and clarify this sented by our model since they have not been targeted by fisheries manuscript. and therefore were not included in the model tuning (Carozza et al., 2017). If the mesopelagic fish biomass is as large as recently suggested (Proud et al., 2019), and if future technological prog- Data availability statement ress enables efficient catch methods, they may support large addi- The model code and output supporting the findings of this study tional catches beyond those estimated here. This would however are available from the corresponding author on request. not alter our results for currently exploited species, and mesope- lagic fisheries would also be subject to the Red Queen race of Funding regulation. This work is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innova- tion programme under grant agreement 682602. Conclusion Fisheries regulation includes a diverse array of collective actions References that counteract detrimental OA fishing, all of which define a fish- Acheson, J. M. 1997. The politics of managing the Maine lobster in- dustry: 1860 to the present. Human Ecology, 25: 3–27. ing target and implement practices to achieve it. We have de- Amel, E., Manning, C., Scott, B., and Koger, S. 2017. Beyond the scribed a new, simple mathematical formulation to represent roots of human inaction: fostering collective effort toward ecosys- these universal features in a global bio-economic model, and used tem conservation. Science, 356: 275–279. it to explore how variable regulation effectiveness, technological Berkes, F., Colding, J., and Folke, C. 2000. Rediscovery of traditional progress, and climate change may shape the future of global ecological knowledge as adaptive management. Ecological fisheries. Applications, 10: 1251–1262. Regulation and technology in fishery projections 2527 Blanchard, J. L., Andersen, K. H., Scott, F., Hintzen, N. T., Piet, G., FAO. 2018. The State of World Fisheries and Aquaculture 2018. and Jennings, S. 2014. Evaluating targets and trade-offs among Meeting the Sustainable Development Goals. FAO, Rome. fisheries and conservation objectives using a multispecies size Finley, C. 2016. The Industrialization of Commercial Fishing, spectrum model. Journal of Applied Ecology, 51: 612–622. 1930–2016. Oxford Research Encyclopedia of Environmental Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, Science, doi: 10.1093/acrefore/9780199389414.013.31. M., Halloran, P. et al. 2013. Multiple stressors of ocean ecosys- FishBase. 2020. http://fishbase.org/ (last accessed February 2020). tems in the 21st century: projections with CMIP5 models. Free, C. M., Thorson, J. T., Pinsky, M. L., Oken, K. L., Wiedenmann, Biogeosciences, 10: 6225–6245. J., and Jensen, O. P. 2019. Impacts of historical warming on ma- Bradley, D., Merrifield, M., Miller, K. M., Lomonico, S., Wilson, J. R., rine fisheries production. Science, 363: 979–983. and Gleason, M. G. 2019. Opportunities to improve fisheries Fulton, E. A., Punt, A. E., Dichmont, C. M., Harvey, C. J., and Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 management through innovative technology and advanced data Gorton, R. 2019. Ecosystems say good management pays off. Fish systems. Fish and Fisheries, 20: 564–583. and Fisheries, 20: 66–96. Branch, T. A., Hilborn, R., Haynie, A. C., Fay, G., Flynn, L., Griffiths, Fulton, E. A., Smith, A. D. M., Smith, D. C., and van Putten, I. E. J., Marshall, K. N. et al. 2006. Fleet dynamics and fishermen be- 2011. Human behaviour: the key source of uncertainty in fisheries haviour: lessons for fisheries managers. Canadian Journal of management. Fish and Fisheries, 12: 2–17. Fisheries and Aquatic Sciences, 63: 1647–1668. Gaines, S. D., Costello, C., Owashi, B., Mangin, T., Bone, J., Molinos, Bundy, A., Chuenpagdee, R., Boldt, J. L., Borges, M. D. F., Camara, J. G., Burden, M. et al. 2018. Improved fisheries management M. L., Coll, M., Diallo, I. et al. 2017. Strong fisheries management could offset many negative effects of climate change. Science and governance positively impact ecosystem status. Fish and Advances, 4: eaao1378. Fisheries, 18: 412–439. Gadgil, M., and Berkes, F. 1991. Traditional Resource Management Cacaud, P., Kuruc, M., and Spreij, M. 2003. Administrative Sanctions Systems. Resource Management and Optimization, 8: 127–141. in Fisheries Law. FAO Legislative Study 82. Food and Agriculture Organization, Rome. Galbraith, E. D., Carozza, D. A., and Bianchi, D. 2017. A coupled human-Earth model perspective on long-term trends in the global Caddy, J. F. 1999. Fisheries management in the twenty-first century: marine fishery. Nature Communications, 8: 14884. will new paradigms apply? Reviews in Fish Biology and Fisheries, 9: 1–43. Golden, C. D., Allison, E. H., Cheung, W. W. L., Dey, M. M., Halpern, B. S., McCauley, D. J., Smith, M. et al. 2016. Nutrition: Caddy, J. F., and Cochrane, K. L. 2001. A review of fisheries manage- fall in fish catch threatens human health. Nature News, 534: ment past and present and some future perspectives for the third 317–320. millennium. Ocean & Coastal Management, 44: 653–682. Gordon, H. S. 1954. The economic theory of a common-property re- Carozza, D. A., Bianchi, D., and Galbraith, E. D. 2016. The ecological source: the fishery. Journal of Political Economy, 62: 124–142. module of BOATS-1.0: a bioenergetically constrained model of marine upper trophic levels suitable for studies of fisheries and Gullestad, P., Abotnes, A. M., Bakke, G., Skern-Mauritzen, M., ocean biogeochemistry. Geoscientific Model Development, 9: Nedreaas, K., and Søvik, G. 2017. Towards ecosystem-based fish- 1545–1565. eries management in Norway—practical tools for keeping track of relevant issues and prioritising management efforts. Marine Carozza, D. A., Bianchi, D., and Galbraith, E. D. 2017. Formulation, Policy, 77: 104–110. general features and global calibration of a bioenergetically-constrained fishery model. PLoS One, 12: Gutiérrez, N. L., Hilborn, R., and Defeo, O. 2011. Leadership, social e0169763. capital and incentives promote successful fisheries. Nature, 470: 386–389. Carozza, D. A., Bianchi, D., and Galbraith, E. D. 2019. Metabolic impacts of climate change on marine ecosystems: implications for Hardin, G. 1968. The tragedy of the commons. Science, 162: fish communities and fisheries. Global Ecology and Biogeography, 1243–1248. 28: 158–169. Heneghan, R. F., Hatton, I. A., and Galbraith, E. D. 2019. Climate Coll, M., Libralato, S., Pitcher, T. J., Solidoro, C., and Tudela, S. change impacts on marine ecosystems through the lens of the size 2013. Sustainability implications of honouring the code of con- spectrum. Emerging Topics in Life Sciences, 3: 233–243. duct for responsible fisheries. Global Environmental Change, 23: Hilborn, R., Amoroso, R. O., Anderson, C. M., Baum, J. K., Branch, 157–166. T. A., Costello, C., de Moor, C. L. et al. 2020. Effective fisheries Costello, C., Gaines, S. D., and Lynham, J. 2008. Can catch shares management instrumental in improving fish stock status. prevent fisheries collapse? Science, 321: 1678–1681. Proceedings of the National Academy of Sciences of the United States of America, 117: 2218–2224. Costello, C., Ovando, D., Clavelle, T., Strauss, C. K., Hilborn, R., Melnychuk, M. C., Branch, T. A., et al. 2016. Global fishery pros- Hilborn, R., Orensanz, J. M., and Parma, A. M. 2005. Institutions, pects under contrasting management regimes. Proceedings of the incentives and the future of fisheries. Philosophical Transactions National Academy of Sciences of the United States of America, of the Royal Society B: Biological Sciences, 360: 47–57. 113: 5125–5129. Hilborn, R., and Ovando, D. 2014. Reflections on the success of tradi- Costello, C., Ovando, D., Hilborn, R., Gaines, S. D., Deschenes, O., tional fisheries management. ICES Journal of Marine Science, 71: and Lester, S. E. 2012. Status and solutions for the world’s unas- 1040–1046. sessed fisheries. Science, 338: 517–520. Johannes, R. E. 1978. Traditional marine conservation methods in Duarte, C. M., Agusti, S., Barbier, E., Britten, G. L., Castilla, J. C., Oceania and their demise. Annual Review of Ecology and Gattuso, J.-P., Fulweiler, R. W. et al. 2020. Rebuilding marine life. Systematics, 9: 349–364. Nature, 580: 39–51. Johannes, R. E. 2002. The renaissance of community-based marine Dueri, S., Guillotreau, P., Jiménez-Toribio, R., Oliveros-Ramos, R., resource management in Oceania. Annual Review of Ecology and Bopp, L., and Maury, O. 2016. Food security or economic profit- Systematics, 33: 317–340. ability? Projecting the effects of climate and socioeconomic Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, changes on global skipjack tuna fisheries under three management K., Ferretti, F., Wilson, A., .. 2018. Tracking the global footprint strategies. Global Environmental Change, 41: 1–12. of fisheries. Science, 359: 904–908. Eigaard, O. R., Marchal, P., Gislason, H., and Rijnsdorp, A. D. 2014. Kwiatkowski, L., Aumont, O., and Bopp, L. 2019. Consistent trophic Technological development and fisheries management. Reviews in amplification of marine biomass declines under climate change. Fisheries Science & Aquaculture, 22: 156–174. Global Change Biology, 25: 218–229. 2528 K. Scherrer and E. Galbraith Lertzman, K. 2009. The paradigm of management, management sys- Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J., and Brierley, tems, and resource stewardship. Journal of Ethnobiology, 29: A. S. 2019. From siphonophores to deep scattering layers: uncer- 339–358. tainty ranges for the estimation of global mesopelagic fish bio- Lotze, H. K., Tittensor, D. P., Bryndum-Buchholz, A., Eddy, T. D., mass. ICES Journal of Marine Science, 76: 718–733. Cheung, W. W. L., Galbraith, E. D., Barange, M. et al. 2019. Rosenberg, A. A. 2003. Managing to the margins: the overexploitation Global ensemble projections reveal trophic amplification of ocean of fisheries. Frontiers in Ecology and the Environment, 1: 102–106. biomass declines with climate change. Proceedings of the Rousseau, Y., Watson, R. A., Blanchard, J. L., and Fulton, E. A. 2019. National Academy of Sciences of the United States of America, Evolution of global marine fishing fleets and the response of 116: 12907–12912. fished resources. Proceedings of the National Academy of Mace, P. 2001. A new role for MSY in single-species and ecosystem Sciences of the United States of America, 116: 12238–12243. Downloaded from https://academic.oup.com/icesjms/article/77/7-8/2518/5905013 by UAB 03 Humanities Lib user on 14 April 2021 approaches to fisheries stock assessment and management. Fish and Fisheries, 2: 2–32. Scherrer, K. J. N., and Galbraith, E. D. 2020. The risk of underesti- mating long-term fisheries creep. Ecology & Society, 25:: 18. Marshall, G. 1998. A Dictionary of Sociology. Oxford University Press, New York. Smith, V. K., and Krutilla, J. 1982. Reformulating the role of natural McCauley, D. J., Woods, P., Sullivan, B., Bergman, B., Jablonicky, C., resources in economic models. In Explorations in Natural Roan, A., Hirshfield, M. et al. 2016. Ending hide and seek at sea. Resource Economics. Ed. by V. K. Smith and J. Krutilla. Johns Science, 351: 1148–1150. Hopkins University Press, Baltimore. Melnychuk, M. C., Peterson, E., Elliott, M., and Hilborn, R. 2017. Smith, T. P., and Sissenwine, M. P. 2001. Fishery management. In Fisheries management impacts on target species status. Encyclopedia of Ocean Sciences, 2nd edn, pp. 513–521. Ed. by J. Proceedings of the National Academy of Sciences of the United H. Steele. Academic Press, Oxford. States of America, 114: 178–183. Squires, D., and Vestergaard, N. 2013a. Technical change and the Mora, C., Myers, R. A., Coll, M., Libralato, S., Pitcher, T. J., Sumaila, commons. The Review of Economics and Statistics, 95: R. U., Zeller, D. et al. 2009. Management effectiveness of the 1769–1787. world’s marine fisheries. PLoS Biology, 7: e1000131. Squires, D., and Vestergaard, N. 2013b. Technical change in fisheries. Nagy, B., Farmer, J. D., Bui, Q. M., and Trancik, J. E. 2013. Statistical Marine Policy, 42: 286–292. Basis for Predicting Technological Progress. PLoS ONE, 8: Squires, D., and Vestergaard, N. 2015. Productivity growth, catchabil- e52669. ity, stock assessments, and optimum renewable resource use. Nøstbakken, L. 2008. Fisheries law enforcement—A survey of the Marine Policy, 62: 309–317. economic literature. Marine Policy, 32: 293–300. Sumaila, U. R., Lam, V., Le Manach, F., Swartz, W., and Pauly, D. Oliver, P. 2013. Selective incentives. In The Wiley-Blackwell Encyclopedia 2016. Global fisheries subsidies: An updated estimate. Marine of Social and Political Movements. Ed. by D. A. Snow, D. della Porta, Policy, 69: 189–193. B. Klandermans, and D. McAdam. Wiley, Malden, MA. Österblom, H., Crona, B. I., Folke, C., Nyström, M., and Troell, M. Sutinen, J., and Kuperan, K. 1999. A socio-economic theory of regu- 2017. Marine ecosystem science on an intertwined planet. latory compliance. International Journal of Social Economics, 26: Ecosystems, 20: 54–61. 174–193. Ostrom, E. 1990. Governing the Commons: The Evolution of Teh, L. C. L., and Sumaila, U. R. 2013. Contribution of marine fisher- Institutions for Collective Action. Cambridge University Press, ies to worldwide employment. Fish and Fisheries, 14: 77–88. Cambridge. UN General Assembly. 2015. Transforming our World: The 2030 Ostrom, E. 2009. A general framework for analyzing sustainability of Agenda for Sustainable Development. A/RES/70/1. social-ecological systems. Science, 325: 419–422. Walters, C. J., and Martell, S. J. D. 2004. Tactics for effective harvest Palomares, M., and Pauly, D. 2019. On the creeping increase of ves- regulation. In Fisheries Ecology and Management. Ed. by C. J. sels’ fishing power. Ecology and Society, 24:31. Walters and S. J. D. Martell. Princeton University Press, New Pascoe, S. D., Plagányi, É. E., and Dichmont, C. M. 2017. Modelling Jersey. multiple management objectives in fisheries: Australian experien- Watson, R. A., and Tidd, A. 2018. Mapping nearly a century and a ces. ICES Journal of Marine Science, 74: 464–474. half of global marine fishing: 1869–2015. Marine Policy, 93: Pauly, D., and Palomares, M. D. 2010. An empirical equation to pre- 171–177. dict annual increases in fishing efficiency. Fisheries Centre Whitmarsh, D. 1990. Technological change and marine fisheries de- University of British Columbia. Working Paper Series, 2010–07. velopment. Marine Policy, 14: 15–22. Pauly, D., and Zeller, D. 2016. Catch reconstructions reveal that Wilberg, M. J., Thorson, J. T., Linton, B. C., and Berkson, J. 2009. global marine fisheries catches are higher than reported and de- Incorporating time-varying catchability into population dynamic clining. Nature Communications, 7: 10244. stock assessment models. Reviews in Fisheries Science, 18: 7–24. Perry, A. L., Low, P. J., Ellis, J. R., and Reynolds, J. D. 2005. Climate change and distribution shifts in marine fishes. Science, 308: Worm, B., and Branch, T. A. 2012. The future of fish. Trends in 1912–1915. Ecology & Evolution, 27: 594–599. Pitcher, T., Kalikoski, D., Pramod, G., and Short, K. 2009. Not hon- Worm, B., Hilborn, R., Baum, J. K., Branch, T. A., Collie, J. S., ouring the code. Nature, 457: 658–659. Costello, C., Fogarty, M. J. et al. 2009. Rebuilding global fisheries. Poloczanska, E. S., Brown, C. J., Sydeman, W. J., Kiessling, W., Science, 325: 578–585. Schoeman, D. S., Moore, P. J., Brander, K. et al. 2013. Global im- Ye, Y., and Gutiérrez, N. L. 2017. Ending fishery overexploitation by print of climate change on marine life. Nature Climate Change, 3: expanding from local successes to globalized solutions. Nature 919–925. Ecology & Evolution, 1: 0179. Handling editor: Raúl Prellezo