importance of research in agriculture and fisheries

Advancing the Science of Climate Change (2010)

Chapter: 10 agriculture, fisheries, and food production, chapter ten agriculture, fisheries, and food production.

M eeting the food needs of a still-growing and more affluent global population—as well as the nearly one billion people who already go without adequate food—presents a key challenge for economic and human security (see Chapter 16 ). Many analysts estimate that food production will need to nearly double over the coming several decades (Borlaug, 2007; FAO, 2009). Recent trends of using food crops for fuel (e.g., corn ethanol) or displacing food crops with fuel crops, along with potential opportunities for reforesting land for carbon credits, may amplify the food security challenge by increasing competition for arable land (Fargione et al., 2008). Climate change increases the complexity of meeting these food needs because of its multiple impacts on agricultural crops, livestock, and fisheries. The potential ability of agricultural and fishery systems to limit climate change adds yet another dimension to be considered.

Questions that farmers, fishers, and other decision makers are asking or will be asking about agriculture, fisheries, and food production in the context of climate change include the following:

How will climate change affect yields?

How will climate change affect weeds and pests, and will I need more pesticides or different technology to maintain or increase yields?

Will enough water be available for my crops? Will the risk of flooding or drought increase?

Should I change to more heat-resistant or slower-growing crop varieties?

What new market opportunities should I take advantage of? How will competitors in other regions be affected?

What adjustments do I need to make to guarantee the sustainability of the fisheries under my management?

How will climate change affect my catch? Will I need new equipment and technology? Will regulations change?

How will climate change affect the availability of food in domestic and international markets? Will food become more expensive? Will food security increase or decrease?

How can changes in agricultural production and practices contribute to reduc-

tions in greenhouse gas emissions or dampen regional-scale impacts related to climate change?

The scientific knowledge summarized in this chapter illustrates how agriculture will be influenced by climate change, and it explores the less well understood impacts of climate change on fisheries. The chapter also indicates how agricultural management may provide opportunities to reduce net human greenhouse gas (GHG) emissions, and it offers insight into the science needed for adaptation in agriculture systems as well as food security issues. Finally, the chapter provides examples of a broad range of research that is needed to understand the impacts of climate change on food production systems and to develop strategies that assist in both limiting the magnitude of climate change through management practices and reducing vulnerability and increasing adaptive capacity in regions and populations in the United States and other parts of the world.

CROP PRODUCTION

Crop production will be influenced in multiple ways by climate change itself, as well as by our efforts to limit the magnitude of climate change and adapt to it. Over the past two decades, numerous experimental studies have been carried out on crop responses to increases in average temperature and atmospheric CO 2 concentrations (often referred to as carbon fertilization), and mathematical models depicting those relationships (singly or in combination) have been developed for individual crops. Fewer experiments and models have evaluated plant responses to climate-related increases in air pollutants such as ozone, or to changes in water or nutrient availability in combination with CO 2 and temperature changes. A recently published report of the U.S. Climate Change Science Program (CCSP, 2008e) summarized the results from experimental and modeling analyses for the United States. Results of experimental studies, for example, indicate that many crop plants, including wheat and soybeans, respond to elevated CO 2 with increased growth and seed yield, although not uniformly so. Likewise, elevated CO 2 also reduces the conductance of CO 2 and water vapor through pores in the leaves of some plants, with resulting improvements in water use efficiency and, potentially, improved growth under drought conditions (Leakey et al., 2009). On the other hand, studies carried out in the field under “free air CO 2 enrichment” environments indicate that growth response is often smaller than expected based on more controlled studies (e.g., Leakey et al., 2009; Long et al., 2006). The response of crop plants to carbon fertilization in field environments hence remains an important area of research (see Research Needs section at the end of the chapter).

Some heat-loving crop plants such as melons, sweet potatoes, and okra also respond positively to increasing temperatures and longer growing seasons; but many other crops, including grains and soybeans, are negatively affected, both in vegetative growth and seed production, by even small increases in temperature ( Figure 10.1 ). Many important grain crops tend to have lower yields when summer temperatures increase, primarily because heat accelerates the plant’s developmental cycle and reduces the duration of the grain-filling period (CCSP, 2008b; Rosenzweig and Hillel, 1998). In some crop plants, pollination, kernel set, and seed size, among other variables, are harmed by extreme heat (CCSP, 2008b; Wolfe et al., 2008). Studies also indicate that some crops such as fruit and nut trees are sensitive to changes in seasonality, reduced cold periods, and heat waves (Baldocchi and Wong, 2008; CCSP, 2008e; Luedeling et al., 2009).

Most assessments conclude that climate change will increase productivity of some crops in some regions, especially northern regions, while reducing production in others (CCSP, 2008b; Reilly et al., 2003), an expected result given the range of projected climate changes and diversity of food crops around the world. The Intergovernmental Panel on Climate Change (IPCC) suggests, with medium confidence, that moderate warming (1.8°F to 5.4°F [1°C to 3°C]) and associated increases in CO 2 and changes in precipitation would benefit crop and pasture lands in middle to high latitudes but decrease yields in seasonally dry and low-latitude areas (Easterling et al., 2007). This response to intermediate temperature increases would generate a situation of midlatitude “winners” in developed countries and low-latitude “losers” in developing coun-

FIGURE 10.1 Growth rates (green) and reproductive response (purple) versus temperature for corn (left) and soybean (right). The curves show that there is a temperature range (colored bars) within which the plants can optimally grow and reproduce, and that growth and reproduction are less efficient at temperatures above this range. The curves also show that, above a certain temperature, the plants cannot reproduce. SOURCE: USGCRP (2009a).

tries, thus magnifying rather than reducing existing inequities in food availability and security. The IPCC also concludes with medium to low confidence that, on the whole, global food production is likely to decrease with increases in average temperatures above 5.4°F (3°C).

Regional assessments of agricultural impacts in the United States (e.g., CCSP, 2008b, and references therein) suggest that over the next 30 years, the benefits of elevated CO 2 will mostly offset the negative effects of increasing temperature (see below for limits in modeling conducted to date). In northern regions of the country, many crops may respond positively to increases in temperature and atmospheric CO 2 concentrations. In the Midwest corn belt and more southern areas of the Great Plains, positive crop responses to elevated CO 2 may be offset by negative responses to increasing temperatures; rice, sorghum, and bean crops in the South would see negative growth impacts (CCSP, 2008b). In California, where half the nation’s fruit and vegetable crops are grown, climate change is projected to decrease yields of almonds, walnuts, avocados, and table grapes by up to 40 percent by 2050 (Lobell et al., 2007). As temperatures continue to rise, crops will increasingly experience temperatures above the optimum for growth and reproduction. Adaptation through altered crop types, planting dates, and other management options is expected to help the agricultural sector, especially in the developed world (Burke et al., 2009; Darwin et al., 1995). However, regional assessments for other areas of the world consistently conclude that climate change presents a serious risk to critical staple crops in sub-Saharan Africa, where adaptive capacity is expected to be less than in the industrialized world (Jones and Thornton, 2003; Parry et al., 2004). Parts of the world where agriculture depends on water resources from glacial melt, including the Andean highlands, the Ganges Plain, and portions of East Africa, are also at risk due to the worldwide reduction in snowpack and the retreat of glaciers (Bradley et al., 2006; Kehrwald et al., 2008; also see Chapter 8 ).

While models of crop responses to climate change have generally incorporated shifts in average temperature, length of growing season, and CO 2 fertilization, either singly or in combination, most have excluded expected changes in other factors that also have dramatic impacts on crop yields. These critical factors include changes in extreme events (such as heat waves, intense rainfall, or drought), pests and disease, and water supplies and energy use (for irrigation). Extreme events such as heavy downpours are already increasing in frequency and are projected to continue to increase (CCSP, 2008b; Rosenzweig et al., 2001). Intense rainfalls can delay planting, increase root diseases, damage fruit, and cause flooding and erosion, all of which reduce crop productivity. Drought frequency and intensity are likely (Christensen et al., 2007) to increase in several regions that already experience water stress, especially in developing

countries where investments have focused on disaster recovery more than adaptive capacity (e.g., Mirza, 2003).

Changes in water quantity and quality due to climate change are also expected to affect food availability, stability, access, and utilization. This will increase the vulnerability of many farmers and decrease food security, especially in the arid and semiarid tropics and in the large Asian and African deltas (Bates and Kundzewicz, 2008). As noted in Chapter 8 , freshwater demand globally will grow in coming decades, primarily due to population growth, increasing affluence, and the need for increased production of food and energy. Climate change is exacerbating these issues, and model simulations under various scenarios indicate that many regions face water resource challenges, especially in regions that depend on rainfall or irrigation from snowmelt (Hayhoe et al., 2007; Kapnick and Hall, 2009; Maurer and Duffy, 2005). As a result, many regions face critical decisions about modifying infrastructure and pricing policies as climate change progresses.

Many weeds, plant diseases, and insect pests benefit from warming (and from elevated CO 2 , in the case of most weed plants), sometimes more than crops; as temperatures continue to rise, many weeds, diseases, and pests will also expand their ranges (CCSP, 2008b; Garrett et al., 2006; Gregory et al., 2009; Lake and Wade, 2009; McDonald et al., 2009). In addition, under higher CO 2 concentrations, some herbicides appear to be less effective (CCSP, 2008b; Ziska, 2000; Ziska et al., 1999). In the United States, aggressive weeds such as kudzu, which has already invaded 2.5 million acres of the southeast, is expected to expand its range into agricultural areas to the north (Frumhoff, 2007). Worldwide, animal diseases and pests are already exhibiting range extensions from low to middle latitudes due to warming (CCSP, 2008b; Diffenbaugh et al., 2008). While these and other changes are expected to have negative impacts on crops, their impact on food production at regional or national scales has not been thoroughly evaluated.

Similar to crop production, commercial forestry will be affected by many aspects of climate change, including CO 2 fertilization, changes in length of growing season, changing precipitation patterns, and pests and diseases. Models project that global timber production could increase through a poleward shift in the locations where important forest species are grown, largely as a result of longer growing seasons. Enhanced growth due to carbon fertilization is also possible (Norby et al., 2005). However, experimental results and models typically do not account for limiting factors such as pests, weeds, nutrient availability, and drought; these limiting factors could potentially offset or even dominate the effects of longer growing seasons and carbon fertilization (Angert et al., 2005; Kirllenko and Sedjo, 2007; Norby et al., 2005).

LIVESTOCK PRODUCTION

Livestock respond to climate change directly through heat and humidity stresses, and they are also affected indirectly by changes in forage quantity and quality, water availability, and disease. Because heat stress reduces milk production, weight gain, and reproduction in livestock, production of pork, beef, and milk is projected to decline with warming temperatures, especially those above 5.4°F (3°C; Backlund et al., 2008) ( Figure 10.2 ). In addition, livestock losses due to heat waves are expected to increase, with the extreme heat exacerbated by rising minimum nighttime temperatures as well as increasing difficulties in providing adequate water (CCSP, 2008b).

Increasing temperatures may enhance production of forage in pastures and rangelands, except in already hot and dry locations. Longer growing seasons may also extend overall forage production, as long as precipitation and soil moisture are sufficient; however, uncertainty in climate model precipitation projections makes this difficult to determine. Although CO 2 enrichment stimulates production on many rangelands and pastures, it also reduces forage quality, shifts the dominant grass species toward those with lower food quality, and increases the prevalence of nonforage weeds (CCSP, 2008b; Eakin and Conley, 2002). In northern Sonora, Mexico, for example, buffelgrass, which was imported from Africa and improved in the United States, is increasingly planted as livestock pasture in arid conditions. However, the grass has become an

FIGURE 10.2 Percent change in milk yield from 20th-century (1850 to 1985) climate conditions to projected 2040 climate conditions made using two different models of future climate (bold versus italicized numbers) in different regions of the United States. The bold values are associated with the model that exhibits more rapid warming. SOURCE: CCSP (2008e).

aggressive invader, spreading across the Sonoran Desert landscape and into Arizona and overrunning important national parks and reserves (Arriaga et al., 2004). Overall, changes in forage are expected to lead to an overall decline in livestock productivity.

FISHERIES AND AQUACULTURE PRODUCTION

Over one billion people around the world rely on seafood as their primary source of protein, and roughly three billion people obtain at least 15 percent of their total protein intake from seafood (FAO, 2009). Global demand for seafood is growing at a rapid rate, fueled by increases in human population, affluence, and dietary shifts (York and Gossard, 2004). While demand for seafood is increasing, the catch of wild seafood has been declining slightly for 20 years (Watson and Pauly, 2001). Meeting the growth in demand has only been possible by rapid growth in marine aquaculture. The United States consumes nearly five billion pounds of seafood a year, ranking it third globally behind China and Japan. This large consumption, however, comes primarily from fish caught outside the nation’s boundary waters. Nearly 85 percent of U.S. consumption is imported, and that fraction is increasing (Becker, 2010). Therefore, consumption of food from the sea links the United States to nearly all the world’s ocean ecosystems.

Marine Fisheries

The impacts of climate change on marine-based food systems are far less well known than impacts on agriculture, but there is rapidly growing evidence that they could be severe (see Chapter 9 ). This is especially problematic given that a sizeable fraction of the world’s fisheries are already overexploited (Worm et al., 2009) and many are also subject to pollution from land or under stress from the decline of critical habitats like coral reefs and wetlands (Halpern et al., 2008; Sherman et al., 2009).

Year-to-year climate variability has long been known to cause large fluctuations in fish stocks, both directly and indirectly (McGowan et al., 1998; Stenseth et al., 2002), and this has always been a challenge for effective fisheries management (Walters and Parma, 1996). Similar sensitivity to longer time-scale variations in climate has been documented in a wide range of fish species from around the globe (Chavez et al., 2003; Steele, 1998), and this portends major changes in fish populations under future climate change scenarios. Successful management of fisheries will require an improved ability to forecast population fluctuations driven by climate change; this in turn demands significant new investments in research, including research on various management options (e.g., Mora et al., 2009). Fundamental shifts in management prac-

tices may be needed. For example, restoration planning for depleted Chinook salmon populations in the Pacific Northwest needs to account for the spatial shift in salmon habitat (Battin et al., 2007). An added complexity is that, because most of the fish catch comes from open oceans under international jurisdiction, any management regime will need to be negotiated and accepted by multiple nations to be effective.

Fished species tend to be relatively mobile, either as adults or young (larvae drifting in the plankton). As a result, their distributions can shift rapidly compared to those of land animals. In recent decades, geographical shifts toward the poles of tens to hundreds of kilometers have been documented for a wide range of marine species in different areas (Grebmeier et al., 2006; Lima et al., 2006; Mueter and Litzow, 2008; Sagarin et al., 1999; Zacherl et al., 2003). Model projections for anticipated changes by 2050 suggest a potentially dramatic rearrangement of marine life (Cheung et al., 2009). Although such projections are based upon relatively simple models and should be treated as hypotheses, they suggest that displacements of species ranges may be sufficiently large that the fish species harvested from any given port today may change dramatically in coming decades. Fishers in many Alaskan ports are already facing much longer commutes as distributions of target species have shifted (CCSP, 2009b).

Such projected shifts in fisheries distributions are likely to be most pronounced for U.S. fisheries in the North Pacific and North Atlantic, where temperature increases are likely to be greatest and will be coupled to major habitat changes driven by reduced sea ice (CCSP, 2009b). Abrupt warming in the late 1970s, which was associated with a regime shift in the Pacific Decadal Oscillation, greatly altered the marine ecosystem composition in the Gulf of Alaska (Anderson and Piatt, 1999). Rapid reductions in ice-dominated regions of the Bering Sea will very likely expand the habitat for subarctic piscivores such as arrowtooth flounder, cod, and pollock. Because there are presently only fisheries for cod and pollock, arrowtooth flounder may experience significant population increases with broad potential consequences to the ecosystem (CCSP, 2009b).

The effects of ocean acidification from increased absorption of CO 2 by the sea (see Chapters 6 and 9 ) may be even more important for some fisheries than other aspects of climate change, although the overall impact of ocean acidification remains uncertain (Fabry et al., 2008; Guinotte and Fabry, 2008). Many fished species (e.g., invertebrates such as oysters, clams, scallops, and sea urchins) produce shells as adults or larvae, and the production of shells could be compromised by increased acidification (Fabry et al., 2008; Gazeau et al., 2007; Hofmann et al . , 2008). Many other fished species rely on shelled plankton, such as pteropods and foraminifera, as their primary food source. Projected declines in these plankton species could have catastrophic impacts

on fished species higher in the food chain. Finally, acidification can disrupt a variety of physiological processes beyond the production of shells. Hence, the potential impacts of acidification—especially in combination with other climate changes on marine fish-eries—is potentially enormous, but the details remain highly uncertain (NRC, 2010f).

Aquaculture and Freshwater Fisheries

Today, approximately a third of seafood is grown in aquaculture, and that number rises to half if seafood raised for animal feed is included. As the fastest growing source of animal protein on the planet, aquaculture is widely touted as critical for meeting growing demands for food. Although aquaculture avoids some of the climate impacts associated with wild fish harvesting, others (e.g., ocean acidification) are equally challenging. Indeed, the current predominance of aquaculture facilities in estuaries and bays may exacerbate some of the impacts of ocean acidification (Miller et al., 2009). In addition, since different forms of aquaculture may require a variety of other natural resources such as water, feed, and energy to produce seafood, there may be much broader indirect impacts of climate change on this rapidly growing industry.

Freshwater fisheries face most of the same challenges from climate change as those in saltwater, as well as some that are unique. Forecasting the consequences of warming on fish population dynamics is complicated, because details of future climate at relatively small geographic scales (e.g., seasonal and daily variation, regional variation across watersheds) are critical to anticipating fish population responses (Littell et al., 2009). Yet, as noted in Chapter 6 , regional and local aspects of climate change are the hardest to project. Expected effects include elevated temperatures, reduced dissolved oxygen (Kalff, 2002), increased stratification of lakes (Gaedke et al., 1998; Kalff, 2002), and elevated pollutant toxicity (Ficke et al., 2007). Although the consequences of some of these changes are predictable when taken one at a time, the complex nature of interactions between their effects makes forecasting change for even a single species in a single region daunting (Littell et al., 2009). In addition to altering these physical and chemical characteristics of freshwater, climate change will also alter the quantity, timing, and variability of water flows (Mauget, 2003; Ye et al., 2003; Chapter 8 ). Climate-driven alterations of the flow regime will add to the decades or even centuries of alterations of stream and river flows through other human activities (e.g., urbanization, water withdrawals, dams; Poff et al., 2007). Finally, changes in lake levels that will result from changed patterns of precipitation, runoff, groundwater flows, and evaporation could adversely affect spawning grounds for some species, depending on bathymetry. While the full ramifications of these changes for freshwater fish require further analysis, there is evidence that coldwater fish such as salmon and trout will be especially

sensitive to them. For example, some projections suggest that half of the wild trout population of the Appalachians will be lost; in other areas of the nation, trout losses could range as high as 90 percent (Williams et al., 2007).

Globally, precipitation is expected to increase overall, and more of it is expected to occur in extreme events and as rain rather than snow, but anticipated regional changes in precipitation vary greatly and are highly uncertain (see Chapter 8 ). As a result, major alterations of stream and lake ecosystems are forecast in coming decades, but the details remain highly uncertain (Ficke et al., 2007). Although freshwater fish and invertebrates are typically as mobile as their marine counterparts, their ability to shift their range in response to climate change may be greatly compromised by the challenges of moving between watersheds. In contrast to the rapid changes in species ranges in the sea (Perry et al., 2005), freshwater fish and invertebrates may be much more constrained in their poleward range shifts in response to climate change, especially in east-west stream systems (Allan et al., 2005; McDowall, 1992).

In the United States, per capita consumption of fish and shellfish from the sea and estuaries is more than 15 times higher than consumption of freshwater fish (EPA, 2002); nevertheless, freshwater fish are important as recreation and as food for some U.S. populations. Globally, however, freshwater and diadromous fish (fish that migrate between fresh- and saltwater) account for about a quarter of total fish and shellfish consumption (Laurenti, 2007) and in many locations serve as the predominant source of protein (Bayley, 1981; van Zalinge et al., 2000). Given the large uncertainty in how climate change impacts on freshwater ecosystems will affect the fisheries they support, this important source of food and recreation is at considerable risk.

SCIENCE TO SUPPORT LIMITING CLIMATE CHANGE BY MODIFYING AGRICULTURAL AND FISHERY SYSTEMS

Food production systems are not only affected by climate change, but also contribute to it. Agricultural activities release significant amounts of CO 2 , methane (CH 4 ), and nitrous oxide (N 2 O) to the atmosphere (Cole et al., 1997; Paustian et al., 2004; Smith et al., 2007). CO 2 is released largely from decomposition of soil organic matter by microorganisms or burning of live and dead plant materials (Janzen, 2004; Smith, 2004); decomposition is enhanced by vegetation removal and tillage of soils. CH 4 is produced when decomposition occurs in oxygen-deprived conditions, such as wetlands and flooded rice systems, and from digestion by many kinds of livestock (Matson et al., 1998; Mosier et al., 1998). N 2 O is generated by microbial processes in soils and manures, and the flux of N 2 O into the atmosphere is typically enhanced by fertilizer use,

especially when applied in excess of plant needs (Robertson and Vitousek, 2009; Smith and Conen, 2004). The 2007 IPCC assessment concluded, with medium certainty, that agriculture accounts for about 10 to 12 percent of total global human-caused emissions of GHGs, including 60 percent of N 2 O and about 50 percent of CH 4 (Smith et al., 2007). The Environmental Protection Agency (EPA) estimates that about 32 percent of CH 4 emissions and 67 percent of N 2 O emissions in the United States are associated with agricultural activities (EPA, 2009b).

Typically, the projected future of global agriculture is based on intensification—increasing the output per unit area or time—which is typically achieved by increasing or improving inputs such as fertilizer, water, pesticides, and crop varieties, and thereby potentially reducing agricultural demands on other lands (e.g., Borlaug, 2007). Given this projected intensification, global N 2 O emissions are predicted to increase by about 50 percent by 2020 (relative to 1990) due to increasing use of fertilizers in agricultural systems (EPA, 2006; Mosier and Kroeze, 2000). If CH 4 emissions grow in direct proportion to increases in livestock numbers, then global livestock-related CH 4 production is expected to increase by 60 percent up to 2030 (Bruinsma, 2003); in the United States, the EPA (2006) forecasts that livestock-related CH 4 emissions will increase by 21 percent between 2005 and 2020. Projected changes in CH 4 emissions from rice production vary but are generally smaller than those associated with livestock (Bruinsma, 2003; EPA, 2006).

The active management of agricultural systems offers possibilities for limiting these fluxes and offsetting other GHG emissions. Many of these opportunities use current technologies and can be implemented immediately, permitting a reduction in emissions per unit of food (or protein) produced, and perhaps also a reduction in emissions per capita of food consumption. For example, changes in feeds and feeding practices can reduce CH 4 emissions from livestock, and using biogas digesters for manure management can substantially reduce CH 4 and N 2 O emissions while producing energy. Changes in management of fertilizers, and the development of new fertilizer application technologies that more closely match crop demand—sometimes called precision or smart farming—can also reduce N 2 O fluxes. It may also be possible to develop and adopt new rice cultivars that emit less CH 4 or otherwise manage the soil-root microbial ecosystem that drives emissions (Wang et al., 1997). Alternatively, organic agriculture or its fusion into other crop practices may reduce emissions and other environmental problems. To date, however, there has been little research on the willingness of farmers and the agricultural sector in general to adopt practices that would reduce emissions, or on the kinds of education, incentives, and institutions that would promote their use.

Beyond limiting the trace gases emitted in agricultural practice, there are opportunities for offsetting GHG emissions more broadly by managing agricultural landscapes to absorb and store carbon in soils and vegetation (Scherr and Sthapit, 2009). For example, minimizing soil tillage yields multiple benefits by increasing soil carbon storage, improving and maintaining soil structure and moisture, and reducing the need for inorganic fertilizers, as well as reducing labor, mechanization, and energy costs. Such practices may also have beneficial effects on biodiversity and other ecosystem services provided by surrounding lands and can be made economically attractive to farmers (Robertson and Swinton, 2005; Swinton et al., 2006). Incorporating biochar (charcoal from fast-growing trees or other biomass that is burned in a low-oxygen environment) has also been proposed as a potentially effective way of taking carbon out of the atmosphere; the resulting biochar can be added to soils for storage and improvement of soil quality (Lehmann and Joseph, 2009), although there has been some debate about the longevity of the carbon storage (Lehmann and Sohi, 2008; Wardle et al., 2008). Shifting agricultural production systems to perennial instead of annual crops, or intercropping annuals with perennial plants such as trees, shrubs, and palms, could also store carbon while producing food and fiber. Biofuel systems that depend on perennial species rather than food crops could be an integral part of such a system. Research is needed to develop these options and to test their efficacy. Most important, a landscape approach would be required in order to plan for carbon storage in conjunction with food and fiber production, conservation, and other land uses and the ecosystem services they provide.

Land clearing and deforestation have been major contributors to GHG emissions over the past several centuries, although as fossil fuel use has grown, land use contributions have become proportionally less important. Still, tropical deforestation alone accounted for about 20 percent of the carbon released to the atmosphere from human activities from 2000 to 2005 (Gullison et al., 2007) and 17 percent of all long-lived GHGs in 2004 (Barker et al., 2007). Reducing deforestation and restoring vegetation in degraded areas could thus both limit climate change and provide linked ecosystem and social benefits (see Chapter 9 ). It is not yet clear, however, how such programs would interact with other forces operating on agriculture to affect overall land uses and emissions. Finally, as with all proposed emissions-limiting land-management approaches, it is critical that attention be paid to consequences for all GHGs, not just a single target gas (Robertson et al., 2000), and to all aspects of the climate system, including reflectivity of the land surface (Gibbard et al., 2005; Jackson et al., 2008), as well as co-benefits in conservation, agricultural production, water resources, energy, and other sectors.

SCIENCE TO SUPPORT ADAPTATION IN AGRICULTURAL SYSTEMS

The ability of farmers and the entire food production, processing, and distribution system to adapt to climate change will contribute to, and to some extent govern, the ultimate impacts of climate change on food production. Adaptation strategies may include changes in location as well as in-place changes such as shifts in planting dates and varieties; expansion of irrigated or managed areas; diversification of crops and other income sources; application of agricultural chemicals; changes in livestock care, infrastructure, and water and feed management; selling assets or borrowing credit (Moser et al., 2008; NRC, 2010a; Wolfe et al., 2008). At the broadest level, adaptation also includes investment in agricultural research and in institutions to reduce vulnerability. This is because the ability of farmers and others to adapt depends in important ways on available technology, financial resources and financial risk-management instruments, market opportunities, availability of alternative agricultural practices, and importantly, access to, trust in, and use of information such as seasonal forecasts (Cash, 2001; Cash et al., 2006a). It also depends on specific institutional arrangements, including property rights, social norms, trust, monitoring and sanctions, and agricultural extension institutions that can facilitate diversification (Agrawal and Perrin, 2008). Not all farmers have access to such strategies or support institutions, and smallholders—especially those with substantial debt, and the landless in poor countries—are most likely to suffer negative effects on their livelihoods and food security. Smallholder and subsistence farmers will suffer complex, localized impacts of climate change (Easterling et al., 2007).

Integrated assessment models, which combine climate models with crop models and models of the responses of farmers and markets, have been used to simulate the impacts of climate changes on productivity and also on factors such as farm income and crop management. Some modeling studies have included adaptations in these integrated assessments (McCarl, 2008; Reilly et al., 2003), for example by adjusting planting dates or varieties and by reallocating crops according to changes in profitability. For the United States, these studies usually project very small effects of climate change on the agricultural economy, and, in some regions, positive increases in productivity and profitability (assuming adaptation through cropping systems changes). As noted earlier with regard to climate-crop models, assessments have not yet included potential impacts of pests and pathogens or extreme events, nor have they included site- and crop-specific responses to climate change or variations. Moreover, even integrated assessment models that include adaptation do not include estimates of rates of technological change, costs of adaptation, or planned interventions (Antle, 2009). Thus, our understanding of the effects climate change will have on U.S. agriculture and on

international food supplies, distribution, trade, and food security remains quite limited and warrants further research.

As they have in the past, both autonomous adaptations by farmers and planned interventions by governments and other institutions to facilitate, enable, and inform farmers’ responses will be important in reducing potential damages from climate change and other related changes. Investments in crop development, especially in developing countries, have stagnated since the 1980s (Pardey and Beintema, 2002), although recent investments by foundations may fill some of the void. Private-sector expenditures play an important role, especially in developed countries, and some companies are engaging in efforts to develop varieties well suited for a changing climate (Burke et al., 2009; Wolfe et al., 2008).

Government investments in new or rehabilitated irrigation systems (of all sizes) and efficient water use and allocation technologies, transportation infrastructure, financial infrastructure such as availability of credit and insurance mechanisms (Barnett et al., 2008; Gine et al., 2008; World Bank, 2007), and access to fair markets are also important elements of adaptation (Burke et al., 2009). Likewise, investments in participatory research and information provision to farmers have been a keystone of past agricultural development strategies (e.g., through extension services in both developed and developing countries) and no doubt will remain so in the future. Finally, the provision of social safety nets (e.g., formal and informal sharing of risks and costs, labor exchange, crop insurance programs, food aid during emergencies, public works programs, or cash payments), which have long been a mainstay of agriculture in the developed world, will remain important (Agrawal, 2008; Agrawal and Perrin, 2008). These considerations need to be integrated into development planning.

It is important that agriculture be viewed as an integrated system. As noted above, the United States and the rest of the world will be simultaneously developing strategies to adapt agriculture to climate change, to utilize the potential of agricultural practices and other land uses to reduce the magnitude of climate change, and to increase agricultural production to meet rising global demands. With careful analysis and institutional design, these efforts may be able to complement one another while also enhancing our ability to improve global food security. However, without such integrated analysis, various practices and policies could easily work at cross purposes, moving the global food production system further from, rather than closer to, sustainability. For example, increased biofuel production would decrease reliance on fossil fuels but could increase demand for land and food resources (Fargione et al., 2008).

FOOD SECURITY

Food security is defined as a “situation that exists when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (Schmidhuber and Tubiello, 2007). The four dimensions of food security are availability (the overall ability of agricultural systems to meet food demand), stability (the ability to acquire food during income or food price shocks), access (the ability of individuals to have adequate resources to acquire food), and utilization (the ability of the entire food chain to deliver safe food). Climate change affects all four dimensions directly or indirectly; all can be affected at the same time by nonclimatic factors such as social norms, gender roles, formal and informal institutional arrangements, economic markets, and global to local agricultural policies. For example, utilization can be affected through the impact of warming on spoilage and foodborne disease, while access can be affected by changing prices in the fuels used to transport food. Most studies have focused on the first dimension—the direct impact of climate change on the total availability of different agricultural products. Models that account for the other three dimensions need to be developed to identify where people are most vulnerable to food insecurity (Lobell et al., 2008; see also Chapter 4 ).

Because the food system is globally interconnected, it is not possible to view U.S. food security, or that of any other country, in isolation. Where food is imported—as is the case for a high percentage of seafood consumed in the United States—prices and availability can be directly affected by climate change impacts in other countries. Climate change impacts anywhere in the world potentially affect the demand for agricultural exports and the ability of the United States and other countries to meet that demand. Food security in the developing world also affects political stability, and thereby U.S. national security (see Chapter 16 ). Food riots that occurred in many countries as prices soared in 2008 are a case in point (Davis and Belkin, 2008). Over the past 30 years, there has been dramatic improvement in access to food as real food prices have dropped and incomes have increased in many parts of the developing world (Schmidhuber and Tubiello, 2007). Studies that project the number of people at risk of hunger from climate change indicate that the outcome strongly depends on socioeconomic development, since affluence tends to reduce vulnerability by enlarging coping capacity (Schmidhuber and Tubiello, 2007). Clearly, international development strategies and climate change are inextricably intertwined and require coordinated examination.

RESEARCH NEEDS

Given the challenges noted in the previous section, it is clear that expanded research efforts will be needed to help farmers, development planners, and others engaged in the agricultural sector to understand and respond to projected impacts of climate change on agriculture. There may also be opportunities to limit the magnitude of future climate change though changes in agricultural practices; it will be important to link such strategies with adaptation strategies so they complement rather than undermine each other. Identifying which regions, human communities, fisheries, and crops and livestock in the United States and other parts of the world are most vulnerable to climate change, developing adaptation approaches to reduce this vulnerability, and developing and assessing options for reducing agricultural GHG emissions are critical tasks for the nation’s climate change research program. Focus is also needed on the developing world, where the negative effects of climate change on agricultural and fisheries production tend to coincide with people with low adaptation capacity. Some specific research areas are listed below.

Improve models of crop response to climate and other environmental changes. Crop plants and timber species respond to multiple and interacting effects—including temperature, moisture, extreme weather events, CO 2 , ozone, and other factors such as pests, diseases, and weeds—all of which are affected by climate change. Experimental studies that evaluate the sensitivity of crops to such factors, singly and in interaction, are needed, especially in ecosystem-scale experiments and in environments where temperature is already close to optimal for crops. Many assessments model crop response to climate-related variables while assuming no change in availability of water resources, especially irrigation. Projections about agricultural success in the future need to explicitly include such interactions. Of particular concern are assumptions about water availability that include consideration of needs by other sectors. The reliability of water resources for agriculture when there is competition from other uses needs to be evaluated in the context of coupled human-environment systems, ideally at regional scales. Improved understanding of the response of farmers and markets to production and prices and also to policies and institutions that affect land and resource uses is needed; incorporation of that information in models will aid in designing effective agricultural strategies for limiting and adapting to climate change.

Improve models of response of fisheries to climate change. Sustainable yields from fisheries require matching catch limits with the growth of the fishery. Climate variation already makes forecasting the growth of fish populations difficult, and future climate change will increase this critical uncertainty. Studies of connections between

climate and marine population dynamics are needed to enhance model frameworks for fisheries management. In addition, there is considerable uncertainty about differences in sensitivity among and within species to ocean acidification (NRC, 2010f). This inevitable consequence of increasing atmospheric CO 2 is poorly understood, yet global in scope. Most fisheries are subject to other stressors in addition to warming, acidification, and harvesting, and the interactions of these other stresses need to be analyzed and incorporated into models. Finally, these efforts need to be linked to the analysis of effective institutions and policies for managing fisheries.

Expand observing and monitoring systems. Satellite, aircraft, and ground-based measures of changes in crops yields, stress symptoms, weed invasions, soil moisture, ocean productivity, and other variables related to fisheries and crop production are possible but not yet carried out systematically or continuously. Monitoring of the environmental and social dynamics of food production systems on land and in the oceans is also needed to enable assessments of vulnerable systems or threats to food security. Monitoring systems will require metrics of vulnerability and sustainability to provide early warnings and develop adaptation strategies.

Assess food security and vulnerability in the context of climate change. Effective adaptation will require integration of knowledge and models about environmental as well as socioeconomic systems in order to project regional food supplies and demands, understand appropriate responses, to develop institutional approaches for adapting under climate variability and climate change, and to assess implications for food security (NRC, 2009k). Scenarios that evaluate implications of climate change and adaptation strategies for food security in different regions are needed, as are models that assess shifting demands for meat and seafood that will influence price and supply. Approaches, tools, and metrics are needed to assess the differential vulnerability of various human-environment systems so that investments can be designed to reduce potential harm (e.g., through interventions such as the development of new crop varieties and technologies, new infrastructure, social safety nets, or other adaptation measures). A concerted research effort is needed both for conducting assessments and to support the development and implementation of options for adaptation. Surprisingly, relatively little effort has been directed toward identification of geographic areas where damages to agriculture or fisheries could be caused by extreme events (hurricanes, drought, hypoxia); where there is or will be systematic loss of agricultural area due to sea level rise, erosion, and saltwater intrusion; or where there will be changes in average conditions (e.g., extent of sea ice cover, and warming of areas that are now too cold for agriculture) that could lead to broad-scale changes—positive or negative—in the type and manner of agricultural and fisheries production.

Evaluate trade-offs and synergies in managing agricultural lands. Improved integrated assessment approaches and other tools are needed to evaluate agricultural lands and their responses to climate change in the context of other land uses and ecosystem services. Planning approaches need to be developed for avoiding adaptation responses that place other systems (or other generations) at risk—for example, by converting important conservation lands to agriculture, allocating water resources away from environmental or urban needs, or overuse of pesticides and fertilizers. Integrated assessments would help to evaluate both trade-offs (e.g., conservation versus agriculture) and co-benefits (e.g., increasing soil carbon storage while also enhancing soil productivity and reducing erosion) of different actions that might be taken in the agricultural sector to limit the magnitude of climate change or adapt to its impacts.

Evaluate trade-offs and synergies in managing the sea. The oceans provide a wide range of services to humans, but conflicts over use of the oceans are often magnified because of the absence of marine spatial planning and relatively weak international marine regulatory systems. Efforts to limit the magnitude of climate change are causing society to consider the sea for new sources of energy (e.g., waves, tides, thermal gradients), while the opening of ice-free areas in the Arctic is encouraging exploration of offshore reserves of minerals and fossil fuels. Without analyses of the looming tradeoffs between these emerging uses and existing services, such as fisheries and recreation, conflicts will inevitably grow. New approaches for analyses of such trade-offs are needed as an integral component of marine spatial planning.

Develop and improve technologies, management strategies, and institutions to reduce GHG emissions from agriculture and fisheries and to enhance adaptation to climate change. Research on options for reducing emissions from the agricultural sector is needed, including new technologies, evaluation of effectiveness, costs and benefits, perceptions of farmers and others, and policies to promote implementation. Technologies such as crop breeding and new cropping systems could dramatically increase the sector’s adaptive capacity. Research on the role of entitlements and institutional barriers in influencing mitigation or adaptation responses; the effectiveness of governance structures; interactions of national and local policies; and national security implications of climate-agriculture interactions are also needed.

Climate change is occurring, is caused largely by human activities, and poses significant risks for—and in many cases is already affecting—a broad range of human and natural systems. The compelling case for these conclusions is provided in Advancing the Science of Climate Change , part of a congressionally requested suite of studies known as America's Climate Choices. While noting that there is always more to learn and that the scientific process is never closed, the book shows that hypotheses about climate change are supported by multiple lines of evidence and have stood firm in the face of serious debate and careful evaluation of alternative explanations.

As decision makers respond to these risks, the nation's scientific enterprise can contribute through research that improves understanding of the causes and consequences of climate change and also is useful to decision makers at the local, regional, national, and international levels. The book identifies decisions being made in 12 sectors, ranging from agriculture to transportation, to identify decisions being made in response to climate change.

Advancing the Science of Climate Change calls for a single federal entity or program to coordinate a national, multidisciplinary research effort aimed at improving both understanding and responses to climate change. Seven cross-cutting research themes are identified to support this scientific enterprise. In addition, leaders of federal climate research should redouble efforts to deploy a comprehensive climate observing system, improve climate models and other analytical tools, invest in human capital, and improve linkages between research and decisions by forming partnerships with action-oriented programs.

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Contribution of Fisheries to Food and Nutrition Security: Current Knowledge, Policy, and Research

Published: May 2018 Authors: Abigail Bennett, Pawan Patil, Kristin Kleisner, Doug Rader, John Virdin, and Xavier Basurto

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This report synthesizes current understanding of capture fisheries’ contributions to food and nutrition security and explores drivers of those contributions. Capture fisheries produce more than 90 million metric tons of fish per year, providing the world’s growing population with a crucial source of food. Due to the particular nutritional characteristics of fish, fisheries represent far more than a source of protein. A growing body of data and research focused specifically at the intersection of fisheries, nutrition, and food security can inform such efforts by improving understanding of fisheries’ production and distributional dimensions, consumption patterns, and nutritional aspects of fish in the context of healthy diets and sustainable food systems. This expanding body of knowledge can provide a basis for more directly considering fisheries in the food and nutrition security policy dialogue.

This report aims to synthesize information on the contribution of capture fisheries to food and nutrition security and on the potential for these food production systems to do more to help end hunger and malnutrition.  The information here comes from peer-reviewed articles, gray literature, and key reports from international organizations, including findings and data at global, national, and subnational levels. Although knowledge about fisheries’ contributions to nutrition and food security continues to increase, particularly in the wake of agreement on the United Nations Sustainable Development Goals, it has yet to sufficiently influence the policy realm, where explicit links between fisheries governance and food and nutrition security need to be amplified. For that reason, this report’s assessment of emerging data and research pays attention to the distinct challenges and opportunities facing capture fisheries and, to a lesser extent, aquaculture.

The Social Problem: Hunger and Malnutrition

The social problem targeted by this report is the continued prevalence of hunger and malnutrition worldwide and the global commitment to end this problem by 2030 (Sustainable Development Goal 2).  Between 2015 and 2016, the prevalence of hunger is estimated to have increased from 10.6 percent of the global population (777 million people) to 11 percent (815 million people). Essentially 1 in 10 people on the planet suffers from hunger. More than one in five children are stunted—have low height relative to weight—often indicating undernutrition or micronutrient deficiency. The problem has many dimensions, often intersecting with geography (i.e., the “birth lottery”). For example, the highest rates of undernourishment and child stunting are in Africa, where one in five people is undernourished (one in three in Eastern Africa); the highest absolute number of undernourished people are in Asia (519.6 million). Child wasting—low weight relative to height—occurs in 9 percent of children under the age of five in Asia and in 16 percent of such children in Southern Asia. At the same time, the prevalence of children under age five who are overweight is increasing in all regions of the world.

The Role of Capture Fisheries Food Production Systems in Helping to Solve the Problem

The world’s capture fisheries are major food production systems that could play a larger role in meeting SDG2.  Since 1945, the FAO has promoted the role of capture fisheries in ending hunger, but in the last seven years, the number of research publications on the topic has grown substantially. This trend reflects the fact that, unlike some staple foods such as rice and other grains, fish is unique in that it has the potential to address multiple dimensions of food and nutrition security simultaneously.

Capture (or wild-caught) fisheries and aquaculture (farmed fish production) together produced 167.2 million metric tons of fish in 2014.  That amount is equivalent to 20 kilograms per capita annually and to 17 percent of animal protein consumed by the global population. In 2014, the split in production between capture fisheries and aquaculture was roughly half and half, though a greater proportion of aquaculture production was destined for human consumption (e.g., some of the products from capture fisheries provide feed for aquaculture and livestock). Within capture fisheries, nearly half of production is from smallholders, or “small-scale fisheries,” which also employ an estimated 90 percent of the world’s fishers, almost all of whom live in developing countries.

This global supply of fish from both capture fisheries and aquaculture provides nearly one-fifth of the average per capita animal protein intake for more than 3.1 billion people.  Given that subsistence fishing (fishing for own consumption) and informal trade is often underreported in official statistics, this number may underestimate the contribution of fisheries. This contribution is much higher in a number of regions, countries, and communities. For example, the populations of some countries (Maldives, Cambodia, Sierra Leone, Kiribati, Solomon Islands, Sri Lanka, Bangladesh, Indonesia, and Ghana) obtain more than half of their animal protein from fish. In countries such as Iceland, Japan, Norway, the Republic of Korea, and some small island developing states (SIDS) where fish is the most available animal protein source, fish provide almost four times the global average of animal protein in terms of dietary energy. In aggregate, developing countries consume an annual per capita average of 18.8 kilograms of fish, and low-income, food-deficit countries consume 7.6 kilograms of fish, falling below the global average. Yet these countries tend to rely on fish for a greater portion of their animal protein than the global average, even when total consumption levels are lower. Fish is typically more affordable than other animal-source foods (ASFs), and it plays an especially important dietary role in countries in which access to animal protein is low and staple foods such as rice, wheat, corn, roots, and tubers predominate.

The most important contribution of fish are multiple micronutrients essential to addressing a variety of health issues worldwide. Fish contain vitamin A, D, and B and calcium, phosphorus, zinc, iron, and iodine. Precise nutrient profiles vary across fish species, processing and preparation techniques, and habitat. Micronutrients in fish can lead to a variety of health benefits, including lowered risk of cardiovascular disease; positive maternal health and pregnancy outcomes and increased early childhood physical and cognitive development; improved immune system function; and alleviated health issues associated with micronutrient deficiencies such as anemia, rickets, childhood blindness, and stunting. Vitamin D deficiency alone is a prevalent health issue worldwide. It can lead to rickets in children, affect bone health in adults, and is associated with increased risk of common cancers, autoimmune diseases, high blood pressure and cardiovascular disease as well as communicable diseases. For pregnant women, insufficient levels of vitamin D are associated with increased risk of preeclampsia, gestational diabetes, preterm birth, and low birth weight. Vitamin A deficiency is the leading cause of preventable childhood blindness and can also contribute to weakened immune system and anemia, given that it supports the body’s use of iron. Vitamin B is also important in combination with iron and folate to prevent anemia and a number of neurologic and cognitive problems. Similarly, the minerals available in fish can help address a number of health issues, for example, iron deficiency, which leads to anemia (estimated to affect about 800 million women and children worldwide), and zinc deficiency, which correlates with the prevalence of child stunting.

The global fish supply also provides crucial fatty acids, including omega-3 polyunsaturated fatty acids essential for cardiovascular and brain health.  The consumption of fish or fish oil has been shown to be associated with a number of benefits to coronary health, for example, lowered risk of death and sudden death from coronary heart disease, ischemic stroke, atrial fibrillation, and congestive heart failure. Worldwide, 1.4 million deaths are attributable to diets low in seafood-source omega-3 fatty acids. Fish consumption correlates with a 36 percent reduction in heart disease and heart attacks and a 12 percent reduction in mortality from all causes.

Fish consumption is particularly important to women, infants, and children who have higher demand for micronutrients and protein.  In low-income countries, malnutrition accounts for 45 percent of mortality in children under age five and half of years lived with a disability for children age four and younger. In Bangladesh, the risk of child mortality is significantly lower for children born during peak fishing seasons to mothers who have a preference for fish. One study found that a number of inland fish are capable of providing at least 25 percent of recommended nutrient intake across multiple micronutrients for infants and pregnant or lactating women in Bangladesh. In Cambodia, nutrient-rich fish, especially wild-caught fish, are an essential part of the diets of infants and children, even those under 12 months of age. A study in Tanzania found that the breastmilk of women who consumed high levels of freshwater fish had levels of DHA (an important omega-3 fatty acid) even higher than those recommended for baby formulas. When consumed by mothers, the omega-3 fatty acids DHA and EPA from fish have been linked with improved infant and child cognitive development, reduced preterm delivery, and decreased risk of asthma, food allergy, and eczema in children. However, in many countries, the low amount and frequency of fish consumption among young children and late introduction of fish in complementary feeding of infants likely limits health benefits.

Consumption of fish does carry some risk of exposure to toxic substances, such as polychlorinated biphenyls, dioxins, methylmercury, and, increasingly, microplastics.  This risk varies dramatically by type of fish consumed as well as by environment. Primary concerns are with levels of methylmercury, which can cause neurodevelopmental problems in children and which may contribute to cardiovascular disease in adults, and polychlorinated biphenyls (PCBs) and dioxins that may lead to cancer risks. These risks are an important consideration for certain groups such as high consumers of fish, the elderly, pregnant women, nursing mothers, and children. The general agreement of experts, however, is that the benefits of consuming fish outweigh the risks, even at high consumption levels for the general population and moderate consumption levels of most species for pregnant and lactating women.

Public Policies Needed for Fisheries to Meet SDG2

Although capture fisheries and aquaculture are both important food production systems to help end hunger and malnutrition, capture fisheries are distinct in that a number of processes, if not addressed, stand to undermine their food and nutrition contributions.  In particular, population growth, overfishing, climate change, and trade are likely to alter the volume and distribution of the supply from capture fisheries, potentially to the detriment of sufficient and equitable global food provisioning. The supply of fish from capture fisheries grew exponentially during the twentieth century until peaking in the 1990s, and it has essentially stagnated since that time as concerns of overfishing have grown (currently FAO characterizes 31 percent of assessed fish stocks as overexploited). A recent analysis predicts that 10 percent of the world will experience deficiencies in essential micronutrients and fatty acids as a result of declining capture fisheries and that these implications will be concentrated in low-latitude developing countries.

Even though some capture fisheries are overexploited and others at threat to join them, with the appropriate governance reforms, most could recover and contribute more to ending hunger and malnutrition.  Estimates suggest that the world’s marine capture fisheries could sustainably contribute an additional 16 million metric tons annually with governance reforms to address overfishing. Policies to enact these reforms will need to grapple with the more general challenges associated with governing common pool resources. Monitoring and enforcing rules limiting who can harvest these resources and how much they can harvest are costly and difficult. Governance reforms can be particularly challenging when fish resources are highly mobile and in contexts in which the number of fishing vessels is high and in which fishing activities are highly dispersed. Inland fisheries face unique governance challenges related to competition over alternative freshwater uses. Any reforms will need to address the tradeoffs and synergies related to reducing fishing effort (or allocating freshwater resources) while maintaining a nutritious food supply and ensuring traditional access for small-scale fishers. Accordingly, policy interventions and responses will need to take into account distributional consequences as well as geographically differentiated needs and vulnerabilities to short-term fluctuations in the supply of fish.

The potential health and nutrition payoff for recovering and sustaining these food production systems has often been missing in the global food policy dialogue.  For example, SDG2 targets spell out concrete actions that are relevant almost exclusively to terrestrial agricultural food systems. Similarly, much of the current thinking about nutrition-sensitive food systems—that is, about the design of interventions specifically to support diverse diets and improve nutrition—generally overlooks the role of sustainable fisheries.

A Research Agenda for Increasing the Contributions of Capture Fisheries to Ending Hunger and Malnutrition: The Food-Environment Nexus in the Water

Although overfishing has long been studied as one of the world’s major environmental problems, quantification of its effects on food and nutrition security is lacking.  Building a comprehensive understanding of the role that capture fisheries play in nutrition and food security entails integrating different data sources to address a number of key questions. Data on the production and distribution of fish provides a baseline for understanding the extent to which fisheries can contribute to food and nutritional needs in different places. Data on fish consumption patterns, typically collected at the household level, lend further insight into how fish supply translates into food provision. Consumption data can also serve to triangulate and add granularity to production and trade data. Knowledge about the nutrient profiles of different fish and the ways that processing, preservation, and preparation techniques affect nutritional characteristics can augment understanding of potential nutritional contributions associated with different fish consumption patterns. Dietary guidelines and recommended nutritional intakes for different populations then provide a basis for understanding the significance of nutrition from fish consumption with respect to individual nutritional requirements. Finally, these data can be situated within the broader context of global food and nutrition security, informing a more comprehensive understanding of where fish currently support good nutrition or could contribute more to alleviating particular forms of malnutrition. Although some of these data are established or emerging, they face substantial challenges related to reliability and comparability. Furthermore, these sources of information have only begun to be integrated to improve understanding of the current and future contributions of fisheries to food and nutrition security.

Fisheries policy that attends explicitly to food and nutrition security dimensions will depend on research that enhances understanding of both the magnitude of fisheries’ contributions as well as the factors that affect the distribution, access, and use of fisheries resources.  A critical need, given evidence that many capture fisheries are overexploited, is understanding of the implications of reducing fishing effort on food and nutrition security. Accurately predicting and evaluating the effects of any policy on that security is challenging from a methodological standpoint. However, the more that research explicitly attends to these effects, the better it stands to inform integrated, coherent, and equitable policy.

Key research topics include the role of gender dynamics, interactions between fisheries and aquaculture, the distributional consequences of trade, and the climate footprint of fisheries vis-à-vis other food production systems.  Understanding the contributions that capture fisheries make to food and nutrition security requires more rigorous and systematic research on multiple drivers of fish supply distribution (for example, trade and climate change), particularly because the challenge of ending hunger and malnutrition may be focused as much on distribution as on sustainably increasing food supply. Furthermore, the geographic scope of research would need to be expanded, because most of the research to date has taken place in a relatively small number of countries or regions, notably the United States, Pacific Island countries and territories, Bangladesh, and Cambodia. Although the body of research explicitly aimed at understanding linkages between capture fisheries and nutrition and food security is growing, it is nonetheless incipient. More robust evidence is needed to evaluate the multiple pathways (for example, direct consumption, income, empowerment of women, macroeconomic growth) through which fisheries contribute to nutrition and food security.

Research Findings

Explainers and infographics, fisheries, food and nutrition security, e107: fish need a stronger role in global food security planning, e131: fisheries need stronger role in food policy and food security planning, e133: measuring fish for food & nutrition security – improving metrics to advance policy.

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OECD Food, Agriculture and Fisheries Papers

The benefits from agricultural research and development, innovation, and productivity growth.

NB. No. 1 to No. 58 were released under the previous series title OECD Food, Agriculture and Fisheries Working Papers .

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Article Contents

Introduction, results and discussion, acknowledgements.

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An overview of global research effort in fisheries science

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Dag W. Aksnes, Howard I. Browman, An overview of global research effort in fisheries science, ICES Journal of Marine Science , Volume 73, Issue 4, March/April 2016, Pages 1004–1011, https://doi.org/10.1093/icesjms/fsv248

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We used bibliometric indicators to characterize recent (2010–2013) research activity in fisheries science with the objective of garnering insights into how this increased effort has been directed. Specifically, we provide an overview of the primary literature on fisheries research, including which countries are the largest contributors (USA, China, Japan, Australia, Canada, and Norway), and an assessment of the citation impact of the research conducted by different countries. The countries with the highest impact were the UK, Norway, Germany, France, Canada, and Italy. We further assessed the research topics that are most commonly studied and attempt to understand what drives that. During the past three decades, research appears to have shifted from a focus on species-related questions to processes. An analysis of how publication output is distributed at the level of fish species indicates that a small number of species (e.g. Atlantic salmon, rainbow trout, and Atlantic cod) account for a disproportionate volume of the total research effort. Interestingly, publication output is not correlated with the commercial importance of a species. Although fisheries management is purportedly based upon scientific research, our analysis reveals that hardly any research at all is conducted on several of the (commercially) most important species, at least as measured by articles appearing in international scientific journals.

Total global research output has increased significantly during the past 20–30 years ( National Science Board, 2014 ), including in fisheries science and aquaculture ( Mather et al ., 2008 ; Natale et al ., 2012 ). This has been driven by, among other things, the need for better science to inform fisheries management, particularly in the context of the ecosystem approach, and to the growing importance and prevalence of aquaculture on an ever-increasing number of species ( Link, 2010 ; Natale et al ., 2012 ; FAO, 2012 ).

We used bibliometric indicators to characterize recent (2010–2013) research activity in fisheries science with the objective of garnering insights into how this increased effort has been directed. Although a bibliometric analysis of fisheries science was reported by Jarić et al . (2012) , the analysis reported here expands upon that study by defining fisheries science more broadly, including a much larger number of journals, analysing more species, and reporting on a longer time series. Specifically, we provide an overview of the primary literature on fisheries research, including which countries are the largest contributors, and an assessment of the impact of the research conducted by different countries. We further assess the research topics that are most commonly studied and attempt to understand what drives that. An analysis of how publication output is distributed at the level of fish species is also presented, including an attempt to correlate publication output with the commercial importance of a species.

General approach

The methodological foundation for a bibliometric study such as this is that new knowledge—the principal objective of basic and applied research—is disseminated to the research community mainly through primary publications in scholarly journals; today, predominantly English language journals (see below). Publications can, therefore, be used as indirect measures of knowledge production ( van Raan, 2004 ). This means that indicators of the volume of research on a particular species, for example, can be obtained by using data on scientific publications. Although admittedly imperfect, this approach has been applied in a large number of studies analogous to this one—some examples from fisheries and related fields are: Neff and Corley (2009) , Jarić et al . (2012) , Jarić and Gessner (2012) , Natale et al . (2012) , and Jarić et al . (2015) .

The Web of Science © (WOS) database produced by Thomson Reuters was used as the source of data for assessing research output in fisheries. This is the most commonly used database for bibliometric studies, covering >12 000 scientific, and scholarly journals. Although its coverage is incomplete, this database includes all major scientific journals within the sciences, medicine, and technology and is generally regarded as providing a satisfactory representation of international mainstream scientific research ( Moed, 2005 ). The WOS database has been used as the basis for analogous bibliometric studies in fisheries and related sciences ( Neff and Corley, 2009 ; Nikolic et al ., 2011 ; Jarić et al ., 2012 , 2015 ; Jarić and Gessner, 2012 ; Natale et al ., 2012 ).

Non-English language journals are heavily underrepresented in WOS ( van Leeuwen et al ., 2001 ) . Therefore, it could be argued that research output from countries that would be expected to produce a significant volume of research in fisheries science (e.g. Russia, Spain, Latin American countries, etc.) will be underrepresented in an analysis based only of data from the WOS. However, recent evidence suggests that the underrepresentation of non-English-speaking countries in the WOS is much less than it has been historically. That appears to have been driven mainly by the addition of a large number of important regional and non-English language journals (e.g. that publish articles with bibliographic information in English but text in another language) to the WOS database, and because of the strong pressure on researchers to publish in English ( Wagner and Wong, 2012 ; López-Navarro et al ., 2015 ). A related issue is disciplinary differences in the WOS's coverage. Biology is very well covered by the WOS ( Moed, 2005 ), and this is also true of fisheries science—all the major journals in this field are in the WOS database. However, a significant volume of fisheries research is produced by national institutes, and they often publish internal reports for their governments in their country's language. The percentage of these reports that eventually make their way into the primary literature, and how that might vary nationally, is very difficult to assess as are its effect on the interpretations of a bibliometric analysis such as the one that we present here. Nonetheless, research results published in the “grey” literature and in non-WOS-indexed journals are not available to a global scientific audience and would, therefore, generally have limited international impact.

Research effort in fisheries science

The analysis that underlies the overview of global fisheries science presented here is based on aggregated WOS statistics acquired from the Centre for Science and Technology Studies at the University of Leiden for the period 1991–2013. Only regular articles, letters, and reviews were included (i.e. short contributions such as editorials, corrections, book-reviews, and meeting abstracts were not included).

“Fisheries” is a separate category in the WOS. The classification system used involves journal-based field definitions, meaning that all articles in a given journal are assigned to the journal's field. The fisheries category in the WOS is defined as, “Fisheries covers resources concerning numerous aspects of fisheries science, technology and industry, including fish pathology, fish physiology and biochemistry, fish diseases and aquaculture” ( http://ip-science.thomsonreuters.com/mjl/scope/scope_scie/ ). Thus, the category includes fisheries science as well as research relating to aquaculture and fish diseases. At the same time, there is a separate WOS category for “marine and freshwater biology”, in which journals with a broader marine biology focus are generally indexed, although some journals are found in both categories. The WOS category “fisheries” include 69 journals. These are the core journals within the field, but articles within fisheries published outside these core journals (e.g. in general ecology journals) are not included. Thus, the total volume of fisheries publications is underestimated in our analysis. This latter constraint is a general limitation of all journal-based field delimitations applied in bibliometric analyses ( Aksnes et al ., 2000 ).

To assess the publication output for individual countries, all articles were classified according to the nationality of all authors affiliated with the article (i.e. the country of their institutional addresses). Many papers are multi-authored, with an international list of authors. In such cases, all countries were fully credited for the article, i.e. with no fractional attribution of credit. The sum of all countries' publication numbers was used as the denominator to calculate any one country's relative contributions to fisheries science. The main indicators of research output at the country level are the number of publications in the WOS “fisheries” category and the relative citation index (see below) of those articles.

The WOS database also includes information on how many times the articles have been referred to or cited in the subsequent scientific literature. The number of citations can be seen as an indirect measure of the scientific impact of the results reported in the article ( Moed, 2005 ). In absolute counts, the countries with the largest number of articles would, of course, also receive the highest number of citations simply because these countries have more papers that can be cited. It is, however, common to use a size-independent measure to assess whether a country's articles have been highly or poorly cited. One such indicator is the relative citation index. This index, which is calculated yearly, shows whether a country's scientific publications have been cited above or below the world average (a score >1.00 means that the country is above the world average). To calculate this indicator, the average citation count of a country's articles in fisheries science is divided by the world average for all articles within fisheries science. Publications co-authored by scientists in more than one country will appear in the calculations of several countries. Since such publications are generally cited more often than articles for which all the authors are from one country, many countries will have a citation index that is higher than the world average.

Research effort on selected species

We analysed the research output for selected individual fish species (see below). Publication searches were conducted in the WOS database in January 2013 (i.e. publication data for 2013 were not yet available), however, unlike the overall analysis, the species-specific assessment was not limited to journals within the WOS fisheries category; publications in all journals, regardless of field affiliation, were included. There is no straightforward and widely accepted method to identify the volume of research on a particular species. Here, we assumed that if a particular species is mentioned in either the title or the abstract of an article, then that article is focused on that species. Applying this approach, we searched the titles and abstracts of all the publications in the entire WOS database. We used the English and Latin species names, including spelling variants, as search terms. Some species have several different common names: although we used the most common one, this is a potential source of error that would manifest as an underrepresentation in the searches.

The bibliographic details of the publications identified were downloaded and further analysed using software developed for this kind of analysis ( Leydesdorff, 1989 ). This software transforms the bibliographic data of each article into a format permitting quantitative bibliometric analyses.

The number of different species of fish on which research has been conducted is enormous, and therefore we limited the analysis to species with the greatest commercial importance. Although the volume of research on any given species or topic is determined by a large number of factors other than commercial importance, it is reasonable to hypothesize that there is a relationship between the commercial value of a species and the amount of research conducted on it. Commercial importance was decided based upon catch production drawn from the Food and Agriculture Organization of the United Nations' (FAO) statistics. The FAO database contains capture production statistics (in tonnes) by country, fishing area, and species ( Garibaldi, 2012 ). The 2010 statistics, published in 2012, were used ( FAO, 2012 ; Garibaldi, 2012 ). We focused on the species with the largest capture production. We recognize that capture production in tonnes does not always equate directly with commercial economic value. However, far less data are available on the commercial value of landings, and therefore we used capture production as our proxy for the importance of the fishery. Although farming production was not included, we also analysed three species of major importance in aquaculture: Atlantic Salmon ( Salmo salar ), Rainbow trout ( Oncorhynchus mykiss ), and Nile tilapia ( Oreochromis niloticus ).

Research effort in fisheries science over time

Although the main focus of the study was to characterize the contemporary publication output in fisheries science, we also assessed changes over time using a snapshot approach. To accomplish this, searches for publications (which followed the same methods as described in Research effort in fisheries science) were conducted over discrete periods within a 20-year span: 1991–1992, 2001–2002, and 2008–2012. We assumed that this approach would be sufficient to identifying overall trends/changes, although any particular brief temporal burst in publication volume (if any) would not have been identified.

The calculation of relative article proportions at the country level, and citation indicators, differs slightly from the analysis described above for fisheries science overall because the data were in different formats. In the analyses of relative proportions, each country was assigned their respective fraction of articles (see e.g. Moed, 2005 ). For example, if an article had one author address from France and one from Germany, each country received a value of 0.5. Obviously, not every participant makes the same contribution to a study, but at the aggregated level over long time frames and thousands of articles such differences tend to normalize. Thus, the publication measures are taken to reflect the contribution of individual countries to international research. Here, we calculated the citation indicator at the article level and each article is compared with the average article in the respective area of research and year. On this basis, an overall index is calculated (for a further description and assessment of this method, see Waltman and van Eck, 2013) . We calculated citation indices for each article separately and this is the basis for the average normalized citation score ( Lundberg, 2007 ).

To detect whether the frequency of the words most commonly appearing in the titles of articles has changed during the past three decades, principal component analysis (PCA) was applied on word counts for all fisheries articles appearing in the years 1993, 2003, and 2013. The word dataset was first distilled by retaining only the words that were listed a minimum of 15 times in all 3 years. Words that were present in both singular and plural form, and nouns and adjectives (e.g. “Australia” and “Australian”, or “coast” and “coastal”) were considered as one word.

Global production of publications in fisheries research has increased significantly during the past few decades. For example, 4750 articles were published in 2013, compared with 2000 in 1991. In relative terms, the publication volume has risen by 136% during the period; this is higher than the growth in the total global research output (all fields), which was 108% (Figure 1 ). This reflects an overall increase in global research activity, in which an increasing number of scientists are involved ( OECD, 2014 ). An increased focus on aquaculture species such as Atlantic salmon ( Salmo salar ), Rainbow trout ( Oncorhynchus mykiss ), and Nile tilapia ( Oreochromis niloticus ), coupled with the rapid expansion of aquaculture in general, has contributed significantly to the growth of publication output in fisheries science (see below). The analysis also reveals that the increase has been strongest for countries in the Asian region, both in absolute and relative terms. It should be noted that the coverage of the WOS database—in terms of the number of journals indexed—has grown during the period analysed; that is, the database covers a larger part of the research literature today than it did in the past. At least part of the observed increase is due to this expanded coverage.

Absolute number of articles within fisheries science (world total = solid black line through the grey-filled circular points) from 1991 to 2013 and relative increase (%) (1991 is normalized to 100 = solid black line). The relative increase in the number of publications in the entire Web of Science database (all fields) is included as a reference (= solid grey line).

The number of publication in fisheries science for two recent 3-year periods—2008–2010 and 2011–2013—were compiled (Figure 2 ). As in most other research fields, the USA is the largest contributor ( National Science Board, 2014 ), with a proportion of 18.3% of the world total during the most recent period. China has shown a remarkable overall growth in research output during the last decade ( Aksnes et al ., 2014 ); this growth is also evident in fisheries science. From 2011 to 2013, China was the second largest fisheries research nation, accounting for 8.4% of the global total, compared with the fifth largest during the previous period (Figure 2 ). Japan, Australia, and Canada follow, with 5.4–6.0% of the global production. Non-Western countries show the strongest relative growth between these two periods: Iran, India, China, and Chile have all increased their publication output by >50%. On the other hand, publication output declined for seven countries (most notably, Japan and France). In terms of the total publication output (all fields), the relative growth during the two periods has also been strongest for non-Western countries (e.g. China, 53%, Iran, 68%, Malaysia, 95%, Saudi-Arabia, 186%). However, the confounding reality of the expansion in database coverage—which was partly composed of journals from non-Western countries—must also be kept in mind. For example, the Indian Journal of Fisheries was added to the WOS database in 2009 and the Iranian Journal of Fisheries Sciences in 2007—as a result of this, and other additions, the relative numbers of articles coming from these countries that appear in the WOS database has increased substantially.

The absolute number of articles in fisheries science, and the proportion of world production, for the 20 countries with the highest publication volume (during the period 2011–2013) for 2008–2010 and 2011–2013. The proportion of the world production was calculated using the total publication output of all countries as the denominator.

The number of citations (i.e. how many times a paper has been referred to or cited in the subsequent scientific literature) is a common indicator of the scientific impact of the research reported in an article. It is generally accepted that frequently cited papers have been more useful or important than publications that are hardly cited at all. The same reasoning can be used for aggregated levels of articles: the more citations that they accrue, the greater their influence. Citation rates vary significantly between countries, with the largest research nations in terms of publication output not necessarily being the most highly cited (Figure 3 ). Generally, Western countries perform much better than countries from other parts of the world ( National Science Board, 2014 ). The extent to which a country is engaged in international collaboration influences its citation rates. Generally, publications that are coauthored by scientists from many countries are cited more often than articles for which all the authors are from the same country ( Jarić et al ., 2012 ).

The relative citation index in fisheries science for selected countries (those with the largest output in terms of number of articles). The analysis is based on publication output for the period 2008–2012 and accumulated citations to these publications through 2013. A relative citation index >1.00 means that a country's publications have been cited above the world average, and vice versa.

Two countries stand out with citation indices that are >50% above the world average—the UK and Norway (Figure 3 )—indicating that the fisheries research conducted by these nations has a disproportionate impact. Then follows Germany, France, and Canada with citation indices ∼1.4. The largest nation in terms of research output, the USA, has an index of 1.10. Publications from Iran, Turkey, and Japan typically have lower scientific impact when measured by citations (citation indices of 0.52–0.59). The latter is likely related to the relatively low global profile of research from these countries, particularly when published in regional journals that often have only abstracts in English. For example, the journals Nippon Suisan Gakkaishi ( Bulletin of the Japanese Society of Scientific Fisheries ), Turkish Journal of Fisheries and Aquatic Science , and Iranian Journal of Fisheries Sciences are among the least cited journals in the WOS category. The articles appearing in Nippon Suisan Gakkaishi are sometimes published in Japanese (more so in former times than now) and, therefore, are not easily accessible to the worldwide scientific community. Similar negative “language effects” for citation rates have been reported for journals published in German and French ( van Raan et al ., 2011 ).

We focused on the species (fish, crustaceans, molluscs, etc.) with the largest capture production in 2010, according to FAO data, and also included Atlantic salmon and Rainbow trout (Figure 4 , and see Supplementary Table S1 for a more complete overview). Anchoveta (Peruvian anchovy, Engraulis ringens ) has by far the highest capture production (>4 million tonnes). Interestingly, there appears to be relatively little research on this species (Figure 4 ), which is used mainly for the production of fishmeal: only 24 articles on the species were identified during the period 2010–2012. Next follows Alaska Pollock ( Theragra chalcogramma ), Skipjack tuna ( Katsuwonus pelamis ), and Atlantic herring ( Clupea harengus ), with capture production between 2 and 3 million tonnes. Of these species, Atlantic herring has the highest number of articles: 175 during the 3-year period. Only 31 articles concern Skipjack tuna. The aquaculture production of Atlantic salmon and Rainbow trout amounts to 2.2 and 0.7 million tonnes, respectively. The research efforts on these species, measured in number of articles (2540 and 1780, respectively), far exceed those of other species. At least part of this pattern may relate to the relative ease of studying a species widely available in culture vs. the relative difficulty of studying species that are not cultured in the laboratory. In addition, more research funds may be available for studying these species.

Capture production by species and the number of articles during the period 2010–2012. The species presented are those with capture production of 700 000 tonnes or more in 2010, excluding aquaculture. Nonetheless, Atlantic salmon ( Salmo salar ) and Rainbow trout ( Oncorhynchus mykiss ) are also presented because of the very large number of articles on these farmed species.

The rank order in terms of scientific article production is very different from the one based upon capture production (Figure 4 , Supplementary Table S1 ): the highest number of articles by far are on Atlantic salmon and Rainbow trout, as well as another species with significant aquaculture production, Nile tilapia ( Oreochromis niloticus , 695 articles, not show in Figure 4 ). These findings are consistent with Jarić et al . (2012) , who reported that the most frequently studied group of species was the Salmonidae. Next follow Atlantic cod ( Gadus morhua , 669 articles), European pilchard ( Sardina pilchardus , 401 articles), and Giant tiger prawn ( Penaeus monodon , also an aquaculture species, 312 articles). Surprisingly, for many of the commercially important species, there is hardly any research at all, at least as measured by articles in scientific journals (11 species with <5 articles each). As a result, there is no correlation ( R 2 = 0.01) between capture production and number of articles (Figure 5 ). Jarić et al . (2012) argued that the most commercially important species were the most studied. However, the basis for such a general conclusion is unclear and is not supported by the observation that there is significant research on several commercially important species (particularly intensively farmed ones) but not on others.

Relationship between the number of articles published during 2010–2012 and capture production in 2010. Each point corresponds to the data for a single species (Cf. data in Supplementary Table S1 ).

Fisheries management is purportedly based upon scientific research. Our findings cast doubt about the extent to which this is generally the case. However, the management of exploited stocks is still based primarily on stock assessments. Since much of the research conducted in support of such assessments is not published in the primary literature, it is not included in the preceding analysis.

To provide more insight into the distribution of research effort in fisheries science between nations, we analysed (by country) the profiles of a selection of species that are important either in terms of volume of capture production (again, excluding aquaculture) or number of articles ( Supplementary Table S2 ). As expected, the country profiles reflect, to a large degree, the geographical distribution of the species. For example, the major contributor to research on Atlantic cod ( Gadus morhua ) is Norway, USA to research on Alaska Pollock ( Theragra chalcogramma ), USA and Japan to the research on Sockeye salmon ( Oncorhynchus nerka ), and Thailand and India to research on Giant tiger prawn ( Penaeus monodon). The national profiles also reflect that there is little research on species that are mainly fished by less developed countries although these may be important for the global food supply. Most of the global fishery research effort is carried out by developed countries and concerns species fished by those countries. Further, there is little co-variation between the volume of capture production at the national level and scientific research in terms of published articles (Figure 6 ). China has by far the largest capture production, with >15 million tonnes landed (although there are concerns about the reliability if these statistics, Watson and Pauly, 2001 ; Pauly et al ., 2014 ) but only a moderate scientific production of 325 articles during 2010–2012. However, several of the highest producing fishery countries have hardly any research at all, at least as reflected in journal articles, for example, Indonesia, Peru, Myanmar, and Viet Nam (Figure 6 ). Of course, these are developing countries with small research systems both generally and in terms of fishery research. Nevertheless, it is clear that there may be research activity in these countries that are not captured by the bibliometric indicators that we report. For example, Peru has a large marine research institute that regularly publishes reports concerning fisheries science, but most of this material appears to be published in Spanish and/or as grey literature, neither of which are captured by our analysis.

Capture production in 2010 of fish, crustaceans, molluscs, etc. (excluding aquaculture), by country and the number of articles during the period 2010–2012. The countries displayed are those with capture production of 2 million tonnes or more in 2010. The number of articles refers to articles about the species included in Supplementary data, Table S1 (those with capture production of 150 000 tonnes or more in 2010).

Research on some species tends to be much more cited than that on other species (Figure 7 ). For example, the citation indices are high for publications about Sockeye salmon (1.39) and Atlantic cod (1.22), while those on Nile tilapia, Giant tiger prawn, and European pilchard are significantly lower (0.64–0.88). Although the reasons for this are difficult to assess, the former species are of relevance to developed nations with a long history of research in fisheries and aquaculture while the latter are mainly of relevance to developing countries (see below). Further, as discussed above, there are significant national variations in citation indices that confound the by-species interpretations. Specifically, the Western European and North American countries generally have citation indices that are significantly higher than the world average, while those from other countries are below average (Figure 3 ). The citation indices for articles on individual species (Figure 7 ) at least partly reflect this. It should be noted that the number of articles underlying this analysis is small for some of the species and, in such cases, the citation index is vulnerable to the presence or absence of a small number of highly cited articles.

The relative citation index by species based on publications during 2008–2011 and accumulated citations to these publications through 2012. Only species with >60 articles during this period are presented. A relative citation index >1.00 means that a country's publications have been cited above the world average, and vice versa.

Several issues should be considered when interpreting these data. For example, the commercial value of the capture production varies significantly across species and is not strongly related to tonnage captured. Commercial value might be a more relevant driver for research effort than capture production measured in tonnes. Nonetheless, the list includes many high-value species on which there has been very little research. The large research focus on aquaculture species may reflect their high commercial value and increasing importance in human food consumption (see FAO, 2012 , p. 26). There are surely many other reasons why some species are heavily researched while others are not. A large knowledge base is already available for species such as Atlantic cod for which there is a very long historical tradition of research, while less is known about species for which there has been little research in the past. As a result, species such as Atlantic cod have become model organisms for studying various more general biological and ecological questions. In addition, there is large annual variability in the landings of some of the species, for example, Anchoveta, while stocks of other species have collapsed. It is possible that a significant research effort is applied to species that formerly had very large landings but which were overfished and then collapsed. Under that scenario, there is suddenly a lot of research on them, but little or no landings. The reverse is also possible—when landings fall to low levels, research support disappears.

The relative increase in research output on individual species is highly variable ( Supplementary Table S3 ). Among the species with the largest research volume in terms of publication output, Nile tilapia stands out with a particularly strong growth. In 1991–1992, 64 articles could be identified for this species compared with 449 in 2011–2012. This growth probably reflects the increasing importance of this species in aquaculture. Atlantic cod also shows a large increase, from 121 articles in 1991–1992 to 455 articles in 2011–2012. This is likely due to the collapse of the cod fishery in Canada in the early 1990s. The growth in output has been weaker for the next ranked species on the list, European pilchard, Giant tiger prawn, and Saithe (Pollock, Pollachius virens ).

As science evolves, activity on some research questions expands while that on others contracts. Neff and Corley (2009) proposed that the frequency of words appearing in articles could provide insights into the development of a scientific field and changes in research priorities. The frequency of words most commonly appearing in the titles of articles about fisheries has changed during the past three decades (Figure 8 ). The highest word counts were 259 in 1993 (“fish”), 379 in 2003 (“growth”), and 532 in 2013 (“fish”). After PCA, all 3 years had similar negative scores on the first principal component (PC), while they were more scattered along the second PC. Correlations between year and PC2 were 0.71, 0.001, and −0.71 for 1993, 2003, and 2013, respectively. Words with positive scores on PC1 (i.e. that had a low word count = 149 words) were removed from Figure 8 to ease visualization of the words that contributed most to the analysis. The general trend from 1993 to 2013 is that there was a decrease in the use of species names and an increase in words descriptive of more general processes and concepts (Figure 8 ). Interestingly, the number of times that the word "first" (as in, “… for the first time” …) appeared was 2 in 1993, 29 in 2003, and 79 in 2013. Since it is doubtful that the number of “firsts” (entirely new discoveries) has actually increased by a factor of 40 in 20 years, we propose that this increase reflects the intense pressure on researchers to promote the importance of their work.

Usage frequency of words appearing in fisheries articles published in 1993, 2003, and 2013. The usage frequency is represented by the factorial scores (row scores on the second principal component, PC2) of each word calculated by PCA. The correlation between the years (column scores) and PC2 was 0.71, 0.001, and −0.71 for 1993, 2003, and 2013, respectively. Therefore, 1993 was characterized by words with positive scores while 2013 was characterized by words with negative scores. Words with lower scores appeared mainly during the intermediate period (2003).

This overview of worldwide research effort in fisheries science was intended to assess how the field has changed during the past 20 years, and to identify current trends and priorities. In some cases, the outcome was counterintuitive (e.g. seemingly low level of effort on economically important species), demonstrating how a bibliometric analysis such as this one can inform the scientific community, funding agencies, and policy-makers.

HIB's contribution to this article was supported by Project no. 83741 (“Scientific Publishing and Editing”) from the Institute of Marine Research, Norway.

We thank Emory Anderson, Geir Ottersen, and Kostas Stergiou for comments on earlier drafts of the manuscript and Caroline Durif for help with the figures and the word frequency analysis.

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• Published: 05 July 2022

Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities

• Joshua E. Cinner   ORCID: orcid.org/0000-0003-2675-9317 1 ,
• Iain R. Caldwell   ORCID: orcid.org/0000-0001-8148-8762 1 ,
• Lauric Thiault   ORCID: orcid.org/0000-0002-5572-7632 2 , 3 ,
• John Ben 4 ,
• Julia L. Blanchard   ORCID: orcid.org/0000-0003-0532-4824 5 , 6 ,
• Marta Coll   ORCID: orcid.org/0000-0001-6235-5868 7 ,
• Amy Diedrich 8 , 9 ,
• Tyler D. Eddy   ORCID: orcid.org/0000-0002-2833-9407 10 ,
• Jason D. Everett   ORCID: orcid.org/0000-0002-6681-8054 11 , 12 , 13 ,
• Christian Folberth   ORCID: orcid.org/0000-0002-6738-5238 14 ,
• Didier Gascuel   ORCID: orcid.org/0000-0001-5447-6977 15 ,
• Jerome Guiet   ORCID: orcid.org/0000-0002-2146-5160 16 ,
• Georgina G. Gurney 1 ,
• Ryan F. Heneghan   ORCID: orcid.org/0000-0001-7626-1248 17 ,
• Jonas Jägermeyr 18 , 19 , 20 ,
• Narriman Jiddawi 21 ,
• Rachael Lahari 22 ,
• John Kuange 23 ,
• Wenfeng Liu   ORCID: orcid.org/0000-0002-8699-3677 24 ,
• Olivier Maury   ORCID: orcid.org/0000-0002-7999-9982 25 ,
• Christoph Müller   ORCID: orcid.org/0000-0002-9491-3550 20 ,
• Camilla Novaglio   ORCID: orcid.org/0000-0003-3681-1377 5 , 6 ,
• Juliano Palacios-Abrantes   ORCID: orcid.org/0000-0001-8969-5416 26 , 27 ,
• Colleen M. Petrik   ORCID: orcid.org/0000-0003-3253-0455 28 ,
• Ando Rabearisoa   ORCID: orcid.org/0000-0001-5371-7695 29 ,
• Derek P. Tittensor 30 , 31 ,
• Andrew Wamukota 32 &
• Richard Pollnac 33 , 34  

Nature Communications volume  13 , Article number:  3530 ( 2022 ) Cite this article

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• Climate change
• Climate-change ecology

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

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Introduction.

Climate change is expected to profoundly impact key food production sectors, with the tropics expected to suffer losses in both fisheries and agriculture. For example, by 2100 tropical areas could lose up to 200 suitable plant growing days per year due to climate change 1 . Likewise, fishable biomass in the ocean could drop by up to 40% in some tropical areas 2 , 3 . While understanding the magnitude of losses that climate change is expected to create in key food production sectors is crucial, it is the social dimensions of vulnerability that determine the degree to which societies are likely to be affected by these changes 4 , 5 , 6 , 7 , 8 . Vulnerability is the degree to which a system is susceptible to and unable to cope with the effects of change. It is comprised of exposure (the degree to which a system is stressed by environmental or social conditions), the social dimensions of sensitivity (the state of susceptibility to harm from perturbations), and adaptive capacity (people’s ability to anticipate, respond to, and recover from the consequences of these changes) 4 , 9 . Together, the exposure and sensitivity domains are referred to as “potential impacts”, which are the focus of this article.

Incorporating key social dimensions of vulnerability is particularly important because many coastal communities simultaneously rely on both agriculture and fisheries to varying degrees 10 , yet assessments of climate change impacts and the policy prescriptions that come from them often consider these sectors in isolation 1 , 5 , 11 , 12 , 13 , 14 . Recently, studies have begun to look at the simultaneous impacts of climate change on both fisheries and agriculture at the national level 15 , 16 , but this coarse scale does not capture whether people simultaneously engage with- and are likely to be affected by- changes in these sectors. Indeed, whether households engage in both fisheries and agriculture 10 will determine whether people have the knowledge, skills, and capital to substitute sectors if one declines, or alternatively, make them particularly susceptible to the potential double burden of a combined decline across sectors 15 . Thus, more localised analyses incorporating key social dimensions of vulnerability are required to better understand how combined impacts to fisheries and agriculture may affect coastal communities.

Here, we combine a measure of exposure based on model projections of losses to exploitable marine biomass (here dubbed fisheries catch potential) and agriculture from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 dataset with a measure of sensitivity based on survey data about material wealth and engagement in fisheries, agriculture, and other occupational sectors from >3,000 households across 72 tropical coastal communities in five countries (see Supplementary Data  file ). We answer the following questions: 1) What are the potential impacts of projected changes to fisheries catch potential and agriculture on coastal communities?, 2) How much will mitigation measures reduce these potential impacts?, and 3) Are lower socioeconomic status coastal communities facing more potential impacts from climate change than their wealthier counterparts? We show that: fisheries tend to be more impacted than agriculture although there is substantial within-country variability; climate change mitigation can reduce the number of locations experiencing a double burden (i.e. losses to both fisheries and agriculture); and communities with lower socioeconomic status will experience the most severe climate change impacts.

Our study has three key results. First, we find that overall possible impacts on fisheries catch potential is higher than possible impacts on agriculture, but there can be substantial within-country variability in both exposure and sensitivity (Fig.  1 ). Specifically, exposure under the high-emissions Shared Socioeconomic Pathway 8.5 scenario (which has tracked historic cumulative CO 2 emissions 17 , but has been recently critiqued for over-projecting CO 2 emissions and economic growth 18 ) indicates substantive losses by mid-century to fisheries catch potential [Fig.  1 ; 14.7% +/− 4.3% (SE) mean fisheries catch potential loss]. To put these projected losses in perspective, Sala et al 19 . found that strategically protecting 28% of the ocean could increase food provisioning by 5.9 million tonnes, which is just 6.9% of the 84.4 million tons of marine capture globally in 2018 20 . Thus, the mean expected fisheries catch potential losses are approximately double that which could be buffered by strategic conservation. Model run agreement about the directionality of change for projected impacts to fisheries catch potential was high (SSP5-8.5: 84.7 + /− 4.5% (SE); SSP1-2.6: 89.2 + /− 4.06% (SE)). Interestingly, crop models projected that agricultural productivity (based on rice, maize, and cassava- see methods) is expected to experience small average gains across the 72 sites (1.2% +/− 1.5% (SE) mean agricultural gain), with a large response range between sites and crops (Supplementary Fig.  1 ). However, the average gains are not significantly different from zero ( t  = −0.80, df = 5.0, p  = 0.46), and model run agreement about directionality of change was lower for agriculture (SSP5-8.5: 69.1 + /− 4.82% (SE); SSP1-2.6: 70.4 +/− 3.27% (SE)). These projected agricultural gains are driven exclusively by rice (Supplementary Fig.  1 ), which has particularly large model disagreement 14 , 21 . Excluding rice shows an average decline in agricultural production by mid-century, since maize and cassava show consistent median losses under both SSP1-2.6 and SSP5-8.5 climate scenarios (Supplementary Fig.  1 ). Significantly greater losses in fisheries catch potential compared to agriculture productivity are apparent not only for our study sites (i.e. 15.9 + /− 5.6% (SE) greater; t  = 2.81, df = 4.97, p  = 0.0379), but also for a random selection of 4746 (10% of) coastal locations in our study countries with populations >25 people per km 2 (Fig.  2 ). Among those random sites, fisheries catch potential losses are an average of 15.6 + /− 5.1% (SE) greater than agriculture productivity changes ( t  = 3.06, df = 5.00, p  = 0.0282). Differences between expected losses at our sites and the randomly selected sites are small for agriculture (Cohen’s D for SSP5-8.5 = -0.31, SSP1-2.6 = −0.35) and negligible for fisheries catch potential (Cohen’s D for SSP5-8.5 = -0.02, SSP1-2.6 = -0.03), indicating that our sites are not particularly biased towards high or low exposure for the study region. Not only is the level of exposure generally higher in fisheries compared to agriculture, but the sensitivity is on average nearly twice as high (Fig.  1a, b ; 0.077 + /− 0.007 mean fisheries sensitivity; 0.04 +/− 0.01 mean agricultural sensitivity; t  = 3.0, df = 2.26, p value =0.0815).

Potential impacts comprise the exposure (y-axis, measured in potential losses, with error bars showing 25th and 75th percentiles) and sensitivity (x-axis, measured as level of dependence by households). Model run agreement (shown as colour gradient) highlights the proportion of ( a ) crop model runs ( n  = 20), ( b ) fisheries model runs ( n  = 16), and ( c ) average of agriculture and fisheries model runs that agree about the direction of change per site. Point shapes indicate country of each community. Inset map in Supplementary Fig.  9 .

Black dots, histograms, and dotted lines (for mean exposures) represent our study sites ( n = 72). Grey dots, histograms, and dotted lines represent a random selection of 10% of coastal cells with population densities >25 people/km 2 from our study countries ( n = 4746).

Our analysis also reveals high within-country variability in potential impacts (i.e. both exposure and sensitivity), particularly for fisheries (Fig.  1 ) - a finding that may be masked in studies looking at national-level averages 15 , 16 . Looking only at the mean expected losses can obscure the more extreme fisheries catch potential losses projected for many communities (Figs.  1 , 2 ). For example, under SSP5-8.5, our Indonesian sites are projected to experience very close to the average fisheries catch potential losses among our study sites (15.9 + /− 2.1%SE), but individual sites range from 6.5-32% losses (Fig.  1b ). There is also substantial within-country variation in how communities are likely to experience climate change impacts, based on their sensitivity (Fig.  1a, b ). For example, in the Philippines, exposure to fisheries is consistently moderate (range 8.9-12.6% loss), but sensitivity ranges from our lowest (0.001) to our highest recorded scores (0.32). There is also within-country variability in model agreement, particularly for the agricultural models in Indonesia, where agricultural model agreement ranges from 50-85% and fisheries model agreement ranges from 56-100% for SSP5-8.5, and 50-80% and 50-94%, respectively, for SSP1-2.6.

The second key result from our integrated assessment reveals that some locations will bear a double burden of losses to fisheries and agriculture simultaneously, but mitigation efforts that reduce greenhouse gas emissions could curb these losses. Specifically, under SSP5-8.5, 64% of our study sites are expected to lose productivity in fisheries and agriculture simultaneously (Fig.  3a ), but this would reduce to 37% of sites under the low emissions scenario SSP1-2.6 (Fig.  3b ). Again, the effect of mitigation is consistent in the random selection of 4746 sites (Supplementary Fig.  2 ), with 70% of randomly selected sites expected to experience a double burden under SSP5 8.5, and 47% under SSP1 2.6. Many of the sites expected to experience the highest losses to both fisheries catch potential and agriculture have moderate to high sensitivity (Fig.  3a , Supplementary Fig.  3 ), which means the impacts of these changes could be profoundly felt by coastal communities.

a Under SSP5-8.5 agricultural losses (y-axis) plotted against fisheries losses (x-axis), with bubble size revealing the overall sensitivity and colour revealing the fisheries-agricultural relative sector dependency of each community’s sensitivity. b Potential benefits of mitigation shown by the potential losses for each community change going from the high emissions scenario (SSP5-8.5 in red) to a low emissions scenario (SSP1-2.6 in yellow).

Over a third of our sites (36% under SSP5-8.5) are expected to experience increases in agriculture (due to CO 2 fertilization effects that fuel potential increases particularly in rice yields) while experiencing losses in fisheries catch potential. For these sites, a question of critical concern is whether the potential gains in agriculture could help offset the losses in fisheries catch potential. The answer to this lies in part in the degree of substitutability between sectors. Our survey of 3,008 households reveals high variation among countries, and even within some countries in the degree of household occupational multiplicity incorporating both agriculture and fisheries sectors (Table  1 ). 31% of households in our study engaged in both fishing and agriculture, though this ranged from 10% of households in the Philippines to 77% of households in Papua New Guinea. This means that the degree to which agricultural gains might possibly offset some fisheries losses at the household scale is very context dependent. Our survey also revealed that 17% of households were involved in agriculture but not fisheries, ranging from 33% in Madagascar to 3% in our Papua New Guinean study communities. Alternatively, more than a third of households surveyed in Indonesia and Philippines were involved in fisheries but not agriculture (36% and 37% respectively), compared to a low value of 16% in Madagascar. In 12% of the Philippines communities surveyed (n = 3), not a single household was engaged in agriculture. Thus, for 32% of households across our sample, including some entire communities, potential agricultural gains will not offset potential fisheries losses. In these locations building adaptive capacity to buffer change will be critical 9 .

Our third key result is that coastal communities with lower socioeconomic status are more likely to experience potential impacts than communities of higher socioeconomic status across the climate mitigation scenarios (SSP1-2.6 and SSP5-8.5; Fig.  4 ). Specifically, we examined the relationship between the average material style of life (a metric of wealth based on material assets; see methods) in a community and the relative potential impacts of simultaneous fisheries catch potential and agriculture losses (measured as the Euclidean distance of sensitivity and exposure from the origin). Importantly, socioeconomic status is related to both sensitivity and exposure (Supplementary Fig.  4 ). In other words, low socioeconomic status communities tend to have higher sensitivity to fisheries and agriculture than the wealthy, and are significantly more likely to be exposed to climate change impacts. Our findings regarding the relationship between socioeconomic status and sensitivity are consistent with a broad body of literature that shows how people tend to move away from natural resource-dependent occupations as they become wealthier 10 , 22 , 23 , 24 , 25 . One potential interpretation of our findings is that alternative livelihood programs (e.g. jobs outside the fisheries or agricultural sectors, such as the service industry) could reduce sensitivity in lower socioeconomic status communities. However, decades of research on livelihood diversification has highlighted a multitude of reasons why alternative livelihood projects frequently fail 26 , including that they do not provide high levels of non-economic satisfactions (e.g., social, psychological, and cultural) 27 , 28 , 29 , as well as cultural barriers to switching occupations (e.g. caste systems) 30 , and attachment to identity and place 31 . Alternative occupations need to provide some of the same satisfactions, including basic needs (safety, income), social and psychological needs (time away from home, community in which you live, etc.), and self-actualization (adventure, challenge, opportunity to be own boss, etc.). For example, fishing attracts individuals manifesting a personality configuration referred to as an externalizing disposition, which is characterized by a need for challenges, adventure, and risk. Fishing can be extremely satisfying for people with this personality complex, while many alternative occupations can lead to job dissatisfaction, which has negative social and psychological consequences 32 , 33 . Research has shown that recreational fishing captain or guide jobs produce some of the same satisfactions as fishing and have been successfully introduced as alternative occupations 33 . Despite these limited successes, alternative livelihood programs frequently fail and are not a viable substitute for mitigating climate change for the ~6 million coral reef fishers globally 34 .

Black lines are predictions from linear mixed-effects models (with country as random effect) and grey bands are standard errors. Statistical significance ( p ) and fit ( R 2 ) of the mixed-effects models are also shown: (m) = marginal R 2 , (c) = conditional R 2 . Point shape and colour indicate country.

Our study is an important first step in examining the potential simultaneous impacts to fisheries catch potential and agriculture in coastal communities, but has some limitations, some of which could be addressed in future studies. First, our measure of exposure was dynamic (i.e., it was projected into the future), while our measures of sensitivity and material wealth were static (i.e., from a single point in time) and did not consider potential changes over time. Although there are projections of how national-scale measures of wealth (e.g. gross domestic product; GDP) may change in the future, there are no reliable projections for household- or community-scale changes to material wealth or livelihoods. As an additional analysis, we examined observed changes in sensitivity and material wealth over 15 and 16 years, respectively, in two Papua New Guinean coastal communities (Fig.  5 ). We found that, over the observed time frame (2001-2016), which is approximately half that of the predicted time frame of exposure, sensitivity scores were extremely stable, particularly in Ahus (Fig.  5 ). Similarly, material wealth was also reasonably stable over time, but did reflect a shift in both communities toward more houses being built out of sturdier material (e.g., wood plank walls and floor, metal roofs). Importantly, while there were absolute changes to material wealth in both communities, the relative position stayed very similar. Although these data do not allow us to make inferences about what will happen into the future, they do highlight that, at least in decadal timeframes, these indicators are reasonably stable. One alternative approach may have been to assume that projected national-scale changes to GDP would apply evenly across each coastal community within a country (i.e., adjust the intercept of both material wealth and correlated sensitivity for each country relative to the projected changes in GDP). However, given the wide spread of material wealth and sensitivity scores within countries, we ultimately were less comfortable with the assumptions inherent in the approach (i.e., that national-scale changes would affect all communities in a country equally) than with the caveat that our metrics were static.

b shows how the communities change along the first two axes of a principal component analysis (i.e., PC1 and PC2), based on 16 household-scale material items, with black text and grey lines indicate the relative contribution of each material item to principal components.

Second, there are key limitations and assumptions to the models we used. For example, many tropical small-scale fisheries target seagrass 35 and coral reef habitats 34 , which are not represented in the global ensemble models. Additionally, the ensemble models were developed at relatively low spatial resolution (e.g. 1° cells), and are not designed to capture higher-resolution structures and processes. Our approach for dealing with this was to make transparent the degree of ensemble model run agreement about the direction of change, which relies on the assumption that we have greater confidence in projections that have higher model run agreement. Another limitation is that there may be discrepancies between the total consumer biomass (see method) in the absence of fishing that is outputed by the models used here and what would actually be harvested by fishers since total consumer biomass can include both target and non-target species as well as other taxa entirely. Despite these limitations, we assumed that total consumer biomass is directly related to potential fisheries yields 11 . Likewise, we included just three crops in the agricultural models (rice, maize, and cassava), which are key in the study region, with many study countries growing 2 or more of these crops. For example, in 2020 Indonesia was the 4th largest producer of rice in the world, the 5th largest producer of cassava, and the 8th largest producer of maize 36 . However, subsistence agriculture in Papua New Guinea is dominated by banana and yams, for which agricultural yield projections were not available. We used an unweighted average of projected changes in these three crops to represent a portfolio of small-scale agriculture, with a sensitivity test based on agricultural projections weighted by current yields/production area proportions of current yields (Supplementary Fig.  1 ). Finally, it is important to keep key model assumptions in mind when interpreting these data. For example, the agricultural models assumed no changes in farm management or climate change adaptation over time, while the fisheries models do not explicitly resolve predation impacts from higher trophic levels on phytoplankton.

Third, our sensitivity metric examined a somewhat narrow aspect of what makes people sensitive to climate change. Sensitivity is thought to contain dimensions of economic, demographic, psychological, and cultural dependency 37 . Our metric was based on people’s engagement in natural resource-based livelihoods, which primarily captures the economic dimensions (although livelihoods do provide cultural and psychological contributions to people 26 , 28 , 29 , 31 , 38 ).

Fourth, our study explicitly focused on the potential impacts of climate change in 72 Indo-Pacific coastal communities by examining their sensitivity and exposure, but our methodology did not enable us to incorporate adaptive capacity. Adaptive capacity is a latent trait that enables people to adapt to and take advantage of the opportunities created by change 39 , 40 , and is critically important in determining the fate of coastal communities under climate change. Adaptive capacity is thought to consist of dimensions of assets, flexibility, social organisation, learning, socio-cognitive, and agency 9 , 41 , 42 . Unfortunately, indicators of these dimensions of adaptive capacity were not collected in a standardised manner across all of the different projects comprising this study.

Fifth, we investigated the potential impacts of climate change on two key food production sectors, but there may be other climate change impacts which have much more profound impacts on people’s wellbeing. For example, sea level rise may destroy homes and other infrastructure 43 , while heat waves may result in direct mortality 44 . Last, we used shared socioeconomic pathway exploratory scenarios that bracket the full range of scenario variability (SSP5-8.5 and SSP1-2.6). At the time of publication, these were the only scenarios available for both fisheries and agriculture using the ISIMIP Fastrack Phase 3 dataset. Future publications may wish to explore additional scenarios.

Our study quantifies the potential impacts of climate change on key food production sectors in tropical coastal communities across a broad swath of the Indo-Pacific. We find that both exposure and sensitivity to fisheries is generally higher than to agriculture, but some places may experience losses from both sectors simultaneously. These losses may be compounded by other drivers of change, such as overfishing or soil erosion, which is already leading to declining yields 45 , 46 . Simultaneous losses to both fisheries catch potential and agriculture will limit people’s opportunity to adapt to changes through switching livelihoods between food production sectors 9 . This will especially be the case in lower socioeconomic status communities where dependence on natural resources is higher 10 . Together, our integration of model projections and socioeconomic surveys highlight the importance of assessing climate change impacts across sectors, but reveals important mismatches between the scale at which people will experience the impacts of climate change and the scale at which modelled projections about climate change impacts are currently available.

Sampling of coastal communities

Here, we integrated data from five different projects that had surveyed coastal communities across five countries 47 , 48 , 49 , 50 . Between 2009 and 2015, we conducted socioeconomic surveys in 72 sites from Indonesia ( n  = 25), Madagascar ( n  = 6), Papua New Guinea ( n  = 10), the Philippines ( n  = 25), and Tanzania (Zanzibar) ( n  = 6). Site selection was for broadly similar purposes- to evaluate the effects of various coastal resource management initiatives (collaborative management, integrated conservation and development projects, recreational fishing projects) on people’s livelihoods in rural and peri-urban villages. Within each project, sites were purposively selected to be representative of the broad range of socioeconomic conditions (e.g., population size, levels of development, integration to markets) experienced within the region. We did not survey strictly urban locations (i.e., major cities). Because our sampling was not strictly random, care should be taken when attempting to make inferences beyond our specific study sites.

We surveyed between 13 and 150 households per site, depending on the population of the communities and the available time to conduct interviews per site. All projects employed a comparable sampling design: households were either systematically (e.g., every third house), randomly sampled, or in the case of three villages, every household was surveyed (a census) (see Supplementary Data  file ). Respondents were generally the household head, but could have been other household members if the household head was not available during the study period (i.e. was away). In the Philippines, sampling protocol meant that each village had an even number of male and female respondents. Respondents gave verbal consent to be interviewed.

The following standard methodology was employed to assess material style of life, a metric of material assets-based wealth 48 , 51 . Interviewers recorded the presence or absence of 16 material items in the household (e.g., electricity, type of walls, type of ceiling, type of floor). We used a Principal Component Analysis on these items and kept the first axis (which explained 34.2% of the variance) as a material wealth score. Thus, each community received a mean material style of life score, based on the degree to which surveyed households had these material items, which we then scaled from 0 to 1. We also conducted an exploratory analysis of how material style of life has changed in two sites in Papua New Guinea (Muluk and Ahus villages) over fifteen and sixteen-year time span across four and five-time periods (2001, 2009, 2012, 2016, and 2002, 2009, 2012, 2016, 2018), respectively, that have been surveyed since 2001/2002 52 . These surveys were semi-panel data (i.e. the community was surveyed repeatedly, but we did not track individuals over each sampling interval) and sometimes occurred in different seasons. For illustrative purposes, we plotted how these villages changed over time along the first two principal components.

Sensitivity

We asked each respondent to list all livelihood activities that bring in food or income to the household and rank them in order of importance. Occupations were grouped into the following categories: farming, cash crop, fishing, mariculture, gleaning, fish trading, salaried employment, informal, tourism, and other. We considered fishing, mariculture, gleaning, fish trading together as the ‘fisheries’ sector, farming and cash crop as the ‘agriculture’ sector and all other categories into an ‘off-sector’.

We then developed three distinct metrics of sensitivity based on the level of dependence on agriculture, fisheries, and both sectors together. Each metric incorporates the proportion of households engaged in a given sector (e.g., fisheries), whether these households also engage in occupations outside of this sector (agriculture and salaried/formal employment; referred to as ‘linkages’ between sectors), and the directionality of these linkages (e.g., whether respondents ranked fisheries as more important than other agriculture and salaried/formal employment) (Eqs.  1 – 3 )

where \({{{{{{\rm{S}}}}}}}_{{{{{{\rm{A}}}}}}}\) , \({{{{{{\rm{S}}}}}}}_{{{{{{\rm{F}}}}}}}\) and \({{{{{{\rm{S}}}}}}}_{{{{{{\rm{AF}}}}}}}\) are a community’s sensitivity in the context of agriculture, fisheries and both sectors, respectively. A, F and AF are the number of households relying on agriculture-related occupations within that community, fishery-related and agriculture- and fisheries-related occupations within the community, respectively. NA, NF and NAF are the number of households relying on non-agriculture-related, non-fisheries-related, and non-agriculture-or-fisheries-related occupations within the community, respectively. N is the number of households within the community. \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{a}}}}}}}\) , \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{f}}}}}}}\) and \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{af}}}}}}}\) are the number of times agriculture-related, fisheries-related and agriculture-and-fisheries-related occupations were ranked higher than their counterpart, respectively. \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{na}}}}}}}\) , \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{nf}}}}}}}\) and \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{naf}}}}}}}\) are the number of times non-agriculture, non-fisheries, and non-agriculture-and-fisheries-related occupations were ranked higher than their counterparts. As with the material style of life, we also conducted an exploratory analysis of how joint agriculture-fisheries sensitivity has changed over time in a subset of sites (Muluk and Ahus villages in Papua New Guinea) that have been sampled since 2001/2002 52 . Although our survey methodology has the potential for bias (e.g. people might provide different rankings based on the season, or there might be gendered differences in how people rank the importance of different occupations 53 ), our time-series analysis suggest that seasonal and potential respondent variation do not dramatically alter our community-scale sensitivity metric.

To evaluate the exposure of communities to the impact of future climates on their agriculture and fisheries sectors, we used projections of production potential from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 experiment dataset of global simulations. Production potential of agriculture and fisheries for each of the 72 community sites and 4746 randomly selected sites from our study countries with coastal populations >25 people/km 2 were projected to the mid-century (2046–2056) under two emission scenarios (SSP1-2.6, and SSP5-8.5) and compared with values from a reference historical period (1983–2013).

For fisheries exposure (E F ), we considered relative change in simulated total consumer biomass (all modelled vertebrates and invertebrates with a trophic level >1). For each site, the twenty nearest ocean grid cells were determined using the Haversine formula (Supplementary Fig.  5 ). We selected twenty grid cells after a sensitivity analysis to determine changes in model agreement based on different numbers of cells used (1, 3, 5, 10, 20, 50, 100; Supplementary Figs.  6 – 7 ), which we balanced off with the degree to which larger numbers of cells would reduce the inter-site variability (Supplementary Fig.  8 ). We also report 25th and 75th percentiles for the change in marine animal biomass across the model ensemble. Projections of the change in total consumer biomass for the 72 sites were extracted from simulations conducted by the Fisheries and marine ecosystem Model Intercomparison Project (FishMIP 3 , 54 ). FishMIP simulations were conducted under historical, SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions) scenarios forced by two Earth System Models from the most recent generation of the Coupled Model Intercomparison project (CMIP6); 55 GFDL-ESM4 56 and IPSL-CM6A-LR 57 . The historical scenario spanned 1950–2014, and the SSP scenarios spanned 2015–2100. Nine FishMIP models provided simulations: APECOSM 58 , 59 , BOATS 60 , 61 , DBEM 2 , 62 , DBPM 63 , EcoOcean 64 , 65 , EcoTroph 66 , 67 , FEISTY 68 , Macroecological 69 , and ZooMSS 11 . Simulations using only IPSL-CM6A-LR were available for APECOSM and DBPM, while the remaining 7 FishMIP models used both Earth System Model forcings. This resulted in 16 potential model runs for our examination of model agreement, albeit with some of these runs being the same model forced with two different ESMs. Thus, the range of model agreement could range from 8 (half model runs indicating one direction of change, and half indicating the other) to 16 (all models agree in direction of change). Model outputs were saved with a standardised 1° spatial grid, at either a monthly or annual temporal resolution.

For agriculture exposure (E A ), we used crop model projections from the Global Gridded Crop model Intercomparison Project (GGCMI) Phase 3 14 , which also represents the agriculture sector in ISIMIP. We used a window of 11×11 cells centred on the site and removed non-land cells (Supplementary Fig.  5 ). The crop models use climate inputs from 5 CMIP6 ESMs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), downscaled and bias-adjusted by ISIMIP and use the same simulation time periods. We considered relative yield change in three rain-fed and locally relevant crops: rice, maize, and cassava, using outputs from 4 global crop models (EPIC-IIASA, LPJmL, pDSSAT, and PEPIC), run at 0.5° resolution. These 4 models with 5 forcings generate 20 potential model runs for our examination of model agreement. Yield simulations for cassava were only available from the LPJmL crop model. All crop model simulations assumed no adaptation in growing season and fertilizer input remained at current levels. Details on model inputs, climate data, and simulation protocol are provided in ref. 14 . At each site, and for each crop, we calculated the average change (%) between projected vs. historical yield within 11×11 cell window. We then averaged changes in rice, maize and cassava to obtain a single metric of agriculture exposure (E A ).

We also obtained a composite metric of exposure (E AF ) by calculating each community’s average change in both agriculture and fisheries:

Potential Impact

We calculated relative potential impact as the Euclidian distance from the origin (0) of sensitivity and exposure.

Sensitivity test

To determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4746 sites that were randomly selected from 47,460 grid cells within 1 km of the coast of the 5 countries we studied which had population densities >25 people/km 2 , based on the SEDAC gridded populating density of the world dataset ( https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download ).

We used Cohen’s D to determine the size of the difference between our sites and the randomly selected sites.

Validating ensemble models

We attempted a two-stage validation of the ensemble model projections. First, we reviewed the literature on downscaling of ensemble models to examine whether downscaling validation had been done for the ecoregions containing our study sites.

While no fisheries ensemble model downscaling had been done specific to our study regions, most of the models of the ensemble have been independently evaluated against separate datasets aggregated at scales down to Large Marine Ecosystems (LMEs) or Exclusive Economic Zones (EEZs) (see 11 ). For example, the DBEM was created with the objective of understanding the effects of climate change on exploited marine fish and invertebrate species 2 , 70 . This model roughly predicts species’ habitat suitability; and simulates spatial population dynamics of fish stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield 2 , 62 , 71 . Compared with spatially-explicit catch data from the Sea Around Us Project (SAUP; www.seaaroundus.org ) 70 there were strong similarities in the responses to warming extremes for several EEZs in our current paper (Indonesia and Philippines) and weaker for the EEZs of Madagascar, Papua New Guinea, and Tanzania. At the LME level, DBEM MCP simulations explained about 79% of the variation in the SAUP catch data across LMEs 72 . The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% confidence interval of the linear regression relationship 62 . Another example, BOATS, is a dynamic biomass size-spectrum model parameterised to reproduce historical peak catch at the LME scale and observed catch to biomass ratios estimated from the RAM legacy stock assessment database (in 8 LMEs with sufficient data). It explained about 59% of the variability of SAUP peak catch observation at the LME level with the Agulhas Current, Bay of Bengal, and Indonesian Sea catches reproduced within +/-50% of observations 61 . The EcoOcean model validation found that all four LMEs included in this study fit very close to the 1:1 line for overserved and predicted catches in 2000 64 , 65 . DBPM, FEISTY, and APECOSM have also been independently validated by comparing observed and predicted catches. While the models of this ensemble have used different climate forcings when evaluated independently, when taken together the ensemble multi-model mean reproduces global historical trends in relative biomass, that are consistent with the long term trends and year-on-year variation in relative biomass change (R 2 of 0.96) and maximum yield estimated from stock assessment models (R 2 of 0.44) with and without fishing respectively 11 .

Crop yield estimates simulated by GGCMI crop models have been evaluated against FAOSTAT national yield statistics 14 , 73 , 74 . These studies show that the models, and especially the multi-model mean, capture large parts of the observed inter-annual yield variability across most main producer countries, even though some important management factors that affect observed yield variability (e.g., changes in planting dates, harvest dates, cultivar choices, etc.) are not considered in the models. While GCM-based crop model results are difficult to validate against observations, Jägermeyr et al 14 . show that the CMIP6-based crop model ensemble reproduces the variability of observed yield anomalies much better than CMIP5-based GGCMI simulations. In an earlier crop model ensemble of GGCMI, Müller et al. 74 show that most crop models and the ensemble mean are capable of reproducing the weather-induced yield variability in countries with intensely managed agriculture. In countries where management introduces strong variability to observed data, which cannot be considered by models for lack of management data time series, the weather-induced signal is often low 75 , but crop models can reproduce large shares of the weather-induced variability, building trust in their capacity to project climate change impacts 74 .

We then attempted to validate the models in our study regions. For the crop models, we examined production-weighted agricultural projections weighted by current yields/production area (Supplementary Fig.  1 ). We used an observational yield map (SPAM2005) and multiplied it with fractional yield time series simulated by the models to calculate changes in crop production over time, which integrates results in line with observational spatial patterns. The weighted estimates were not significantly different to the unweighted ones (t = 0.17, df = 5, p = 0.87). For the fisheries models, our study regions were data-poor and lacked adequate stock assessment data to extend the observed global agreement of the sensitivity of fish biomass to climate during our reference period (1983-2013). Instead, we provide the degree of model run agreement about the direction of change in the ensemble models to ensure transparency about the uncertainty in this downscaled application.

To account for the fact that communities were from five different countries we used linear mixed-effects models (with country as a random effect) for all analyses. All averages reported (i.e. exposure, sensitivity, and model agreement) are estimates from these models. In both our comparison of fisheries and agriculture exposure and test of differences between production-weighted and unweighted agriculture exposure we wanted to maintain the paired nature of the data while also accounting for country. To accomplish this we used the differences between the exposure metrics as the response variable (e.g. fisheries exposure minus agriculture exposure), testing whether these differences are different from zero. We also used linear mixed-effects models to quantify relationships between the material style of life and potential impacts under different mitigation scenarios (SSP1-2.6 and 8.5), estimating standard errors from 1000 bootstrap replications. To further explore whether these relationships between the material style of life and potential impacts were driven by exposure or sensitivity, we conducted an additional analysis to quantify relationships between the material style of life and: 1) joint fisheries and agricultural sensitivity; 2) joint fisheries and agricultural exposure under different mitigation scenarios. We present both the conditional R 2 (i.e., variance explained by both fixed and random effects) and the marginal R 2 (i.e., variance explained by only the fixed effects) to help readers compare among the material style of life relationships.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The de-identified exposure, sensitivity, and material style of life data generated in this study for each community can be accessed through Zenodo 76 [ https://doi.org/10.5281/zenodo.6496413 ]. All outputs from the FishMIP model ensemble are available via ISIMIP [ https://www.isimip.org/gettingstarted/data-access/ ]. Raw social survey data are not available because our verbal informed consent made it clear that only aggregated data would be published. The sample sizes and proportions of each community included in the social surveys can be found in the Supplementary Data  file . Base layer map data in Fig.  1c and Supplementary Figures  5 , 8 , and 9 is from Natural Earth, which is freely available through their website (naturalearthdata.com). The SEDAC gridded populating density of the world dataset used to identify a subset of random locations can be found at the following: https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download .

Code availability

Code used to analyse and visualize results is available through Zenodo 76 [ https://doi.org/10.5281/zenodo.6496413 ].

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Acknowledgements

J.E.C. is supported by the Australian Research Council (CE140100020, FT160100047, DP110101540, and DP0877905). This work was undertaken as part of the Consultative Group for International Agricultural Research (CGIAR) Research Program on Fish Agri-Food Systems (FISH) led by WorldFish. T.D.E acknowledges support from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2021-04319). M.C. and J.S. acknowledge support from the Spanish project ProOceans (RETOS-PID2020-118097RB-I00) and the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC). G.G.G. acknowledges support from an Australian Research Council Discovery Early Career Research Award (DE210101918). C.M.P. acknowledges support from NOAA grants NA20OAR4310441 and NA20OAR4310442. M.C. acknowledges the financial support of Ministerio de Ciencia e Innovación, Proyectos de I+D+I (RETOS-PID2020-118097RB-I00, ProOceans) and the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).

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Joshua E. Cinner, Iain R. Caldwell & Georgina G. Gurney

National Center for Scientific Research, PSL Université Paris, CRIOBE, USR 3278, CNRS-EPHE-UPVD, Maison des Océans, 195 rue Saint-Jacques, 75005, Paris, France

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Didier Gascuel

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Jerome Guiet

School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia

Ryan F. Heneghan

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Contributions

J.E.C. conceived of the study and hosted a workshop with G.G.G., A.D., and R.P. to operationalise the concept. J.E.C., G.G.G., R.P., J.K., N.J., A.R., R.L., A.W., and A.D. provided socioeconomic data. J.J., C.M., C.F., W.L. contributed crop model simulations. J.B., M.C., J.S., T.E., J.E., D.G., J.G., R.F.H., C.N., J.P.A., C.P., and D.T. contributed fisheries model simulations. L.T., J.J., R.F.H., T.E., and I.R.C. analysed the data and all authors contributed to the writing of the manuscript.

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Cinner, J.E., Caldwell, I.R., Thiault, L. et al. Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities. Nat Commun 13 , 3530 (2022). https://doi.org/10.1038/s41467-022-30991-4

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DOI : https://doi.org/10.1038/s41467-022-30991-4

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Transforming fisheries' decision-making processes from dependent on historical observations to more a forward-looking process will require interdisciplinary research efforts that advance our knowledge and understanding …

Integrating Climate Impacts into Atlantic Bluefin Tuna Stock Assessment

Our project is examining atmospheric and oceanographic changes that may have impacted the distribution of giant bluefin tuna in the Gulf of Maine. This information …

The Open Knowledge Network to Meet Ocean Decision Challenges (OceanOKN)

Throughout history, people have been able to rely on their past experience to inform their decisions about the future. We are now entering a period …

Evaluating Alternative Harvest Control Rules for New England Groundfish

The New England Fishery Management Council (NEFMC) initiated a groundfish harvest control rule review so that fishery management can be sure they are prescribing the …

Snap-a-Striper

Striped bass catches have declined dramatically in recent years — with landings down by 90%, according to some estimates — leaving both scientists and fishermen …

Groundfish Management Strategy Evaluation

The impacts of climate change on marine fisheries resources are increasing. Some groundfish stocks, such as Georges Bank cod, have declined to record-low biomass in …

Evaluating Age Structure, Aging Bias and Mixed Stock Composition of Atlantic Bluefin Tuna in the Northwest Atlantic

Our project sets up long-term biological sample collections to fill in life history gaps, including age structure and stock mixing. We do this by using …

Evaluating the Importance of Chub Mackerel in the Diet of Highly Migratory Species

Our project seeks to investigate the foraging ecology of marlins (blue, white, round-scale spearfish) and tunas (bigeye, yellowfin) along the East Coast to identify the …

Assessing Allocation Strategies for Fisheries Affected by Climate Change

Our project aims to develop guidance and adaptive strategies for fishery managers grappling with climate change induced allocation challenges.

Windjamming on the Warming Gulf of Maine - Eos

Press Clips

Bluefin Tuna Milestone

Dr. Walt Golet and his Pelagic Fisheries Lab celebrate a significant sampling milestone.

Determining Where Bluefin Tuna Come From | On The Water

Convening climate experts.

In April, GMRI scientists hosted a modeling workshop for over 30 leading climate, oceanography, socio-economic, and fisheries experts. The group convened to discuss a question …

INTEGRATING FISHERIES AND

AGRICULTURE TO ENHANCE FISH

PRODUCTION AND FOOD SECURITY