Editing IPCC:AR6/WGII/Chapter-5 (section)

== 5.9 Ocean-Based and Inland Aquaculture Systems ==

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Global aquaculture provides more fish for human consumption than wild capture fisheries, with projected provisioning of 60% by 2030 ( [[#FAO--2018c|FAO, 2018c]] ). Aquaculture can contribute to SDGs by reducing poverty and food insecurity, filling increasing aquatic food demand shortages from declining capture fisheries production ( ''medium confidence'' ) (Figure 5.13a and c, [[#World%20Bank--2013|World Bank, 2013]] ; [[#Béné--2016|Béné et al., 2016]] ; [[#Hambrey--2017|Hambrey, 2017]] ; [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Belton--2020|Belton et al., 2020]] ) and improving social inequities for poor rural communities ( [[#Béné--2016|Béné et al., 2016]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Vannuccini--2018|Vannuccini et al., 2018]] ; [[#Pongthanapanic--2019|Pongthanapanic et al., 2019]] ). Global aquaculture production reached 82 million tonnes (Mt) of food fish, crustaceans, molluscs and other aquatic animals from inland (51 Mt) and marine (31 Mt) systems, and 32 Mt of aquatic plants in 2018 ( [[#FAO--2020d|FAO, 2020d]] ). China, India, Indonesia, Vietnam, Bangladesh, Egypt, Norway and Chile are major production regions ( [[#FAO--2020d|FAO, 2020d]] ). The range of species, farming methods and environments makes aquaculture the most diverse, long-standing farming practice in the world, with an estimated global sectoral value of USD 250 billion in 2018 (Figure 5.13b and 5.14d, [[#Bell--2019|Bell et al., 2019]] ; [[#Harland--2019|Harland, 2019]] ; [[#FAO--2020d|FAO, 2020d]] ; [[#Houston--2020|Houston et al., 2020]] ; [[#Metian--2020|Metian et al., 2020]] ), but it is dominated by 20 finfish, 9 mollusc and 6 crustacean species (FAO, 2020). Inland aquaculture in freshwater and coastal ponds accounts for 85–90% of farmed production ( [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Naylor--2021|Naylor et al., 2021]] ). Globally, 20.5 million people are engaged in aquaculture ( [[#FAO--2020d|FAO, 2020d]] ), where marine finfish farming is primarily conducted by high-income countries and inland production is dominated by small-scale producers in lower-middle-income countries ( [[#Vannuccini--2018|Vannuccini et al., 2018]] ).

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[[File:1c8841eb3972fd86b867855f6d84cf55 IPCC_AR6_WGII_Figure_5_013.png]]

'''Figure 5.13 |''' '''Global and regional aquaculture production.''' '''(a)''' World wild capture fisheries and aquaculture inland (freshwater and brackish) and marine production from 1950 to 2018; '''(b)''' diversity of aquaculture groups cultured in 2016; '''(c)''' regional aquaculture share of total fisheries production; and '''(d)''' global aquaculture species production in 2018 by region and type (freshwater, brackish or marine) on a logged scale ( [[#FAO--2018c|FAO, 2018c]] ; [[#FAO--2020c|FAO, 2020c]] ; [[#FAO--2020d|FAO, 2020d]] ).

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=== 5.9.1 Observed Impacts ===

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Marine aquaculture food production is being impacted directly and indirectly by climate change ( ''high confidence'' ) ( [[#Bindoff--2019|Bindoff et al., 2019]] ). Ocean pH and oxygen levels are declining, whereas global warming, sea level rise and extreme events are increasing (Cross-Chapter Box SLR in Chapter 3, [[#Canadell--2021|Canadell et al., 2021]] ; [[#Eyring--2021|Eyring et al., 2021]] ; [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ; Lee et al., 2021;). Marine heatwaves have been increasing in both incidence and longevity over the past century ( [[#Frolicher--2018|Frolicher and Laufkotter, 2018]] ; [[#Oliver--2018|Oliver et al., 2018]] ; [[#Bricknell--2021|Bricknell et al., 2021]] ), with productivity consequences for marine aquaculture (mariculture), carbon sequestration and local species extinctions ( ''high confidence'' ) ( [[#Weatherdon--2016|Weatherdon et al., 2016]] ; [[#Smale--2019|Smale et al., 2019]] ). Temperature increases related to El Niño climatic oscillations have caused mass fish mortalities either through warming waters (e.g., Pacific threadfin in Hawaii ( [[#McCoy--2017|McCoy et al., 2017]] )) or associated HABs (e.g., 12% loss of Atlantic salmon as well as other fish and shellfish in Chile in 2016, with estimated USD 800 million in losses ( ''high confidence'' ) ( [[#Clement--2016|Clement et al., 2016]] ; [[#Apablaza--2017|Apablaza et al., 2017]] ; [[#Leon-Munoz--2018|Leon-Munoz et al., 2018]] ; [[#Trainer--2020|Trainer et al., 2020]] )). Increases in sea lice parasite infestations on salmon are related to higher salinity and warmer waters ( ''medium confidence'' ) ( [[#Groner--2016|Groner et al., 2016]] ; [[#Soto--2019|Soto et al., 2019]] ). Ocean acidification is having negative impacts on the sustainability of mariculture production ( ''high confidence'' ) ( [[#Bindoff--2019|Bindoff et al., 2019]] ), with observed impacts on shellfish causing significant production and economic losses for regions, estimated at losses of nearly USD 110 million by 2015 in the Pacific Northwest ( [[#Barton--2015|Barton et al., 2015]] ; [[#Ekstrom--2015|Ekstrom et al., 2015]] ; [[#Waldbusser--2015|Waldbusser et al., 2015]] ; [[#Zhang--2017b|Zhang et al., 2017b]] ; [[#Doney--2020|Doney et al., 2020]] ). Ocean oxygen levels are declining due to climate change ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#IPCC--2021|IPCC, 2021]] ), and decreased oxygen (hypoxia) has negative impacts on fish physiology ( [[#Cadiz--2018|Cadiz et al., 2018]] ; [[#Hvas--2019|Hvas and Oppedal, 2019]] ; [[#Martos-Sitcha--2019|Martos-Sitcha et al., 2019]] ; [[#Perera--2021|Perera et al., 2021]] ), fish growth, behaviour and sensitivity to concurrent stressors ( ''high confidence'' ) ( [[#Stehfest--2017|Stehfest et al., 2017]] ; [[#Abdel-Tawwab--2019|Abdel-Tawwab et al., 2019]] ).

Observed impacts on inland systems have generally been site and region specific ( ''high confidence'' ) ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Sainz--2019|Sainz et al., 2019]] ; [[#Lebel--2020|Lebel et al., 2020]] ). Salinity intrusions into freshwater aquaculture systems have changed oxygen and water quality of inland ponds, resulting in mortalities in areas such as India and Bangladesh ( ''medium confidence'' ) ( [[#Dubey--2017|Dubey et al., 2017]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ). Rapid changes in temperature, precipitation, droughts, floods and erosion have created significant production losses for aquatic farmers in Cambodia, Laos, Myanmar, Thailand, Viet Nam and Ghana ( ''medium confidence'' ) ( [[#Asiedu--2017|Asiedu et al., 2017]] ; [[#Pongthanapanic--2019|Pongthanapanic et al., 2019]] ; [[#Lebel--2020|Lebel et al., 2020]] ). Algal blooming and inland lake browning related to warming was found to negatively affect fish biomass ( [[#van%20Dorst--2018|van Dorst et al., 2018]] ). Observed indirect effects of climate change on aquaculture include extreme weather events that damage coastal aquaculture infrastructure or enable flooding, both leading to animal escapees (e.g., fish, shrimp), damaged livelihoods and interactions with wild species ( ''high agreement'' , ''medium evidence'' ) ( [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#Kais--2018|Kais and Islam, 2018]] ; [[#Pongthanapanic--2019|Pongthanapanic et al., 2019]] ; [[#Ju--2020|Ju et al., 2020]] ).

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=== 5.9.2 Assessing Vulnerabilities ===

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Aquaculture vulnerability assessments have shown that countries from both high and low latitudes are highly vulnerable to climate change, where vulnerability is driven by particular exposures, economic reliance, type of production sector (freshwater, brackish, marine) and adaptive capacity ( ''high confidence'' ) ( [[#Handisyde--2017|Handisyde et al., 2017]] ; [[#Soto--2018|Soto et al., 2018]] ). Regional aquaculture vulnerabilities and risk mitigation potentials for the major FAO reporting regions are shown in Figure 5.14. Best practice guidelines for assessments exist ( [[#Brugère--2019|Brugère et al., 2019]] ; [[#FAO--2020d|FAO, 2020d]] ), but in practice most only cover some climatic drivers ( ''medium agreement'' , ''limited evidence'' ) ( [[#Soto--2018|Soto et al., 2018]] ). Holistic vulnerability assessments include ecosystem services ( [[#Custódio--2020|Custódio et al., 2020]] ; [[#Gentry--2020|Gentry et al., 2020]] ) and farming practices which can exacerbate production pressures (stocking densities, eutrophication, fish stress) ( [[#Soto--2018|Soto et al., 2018]] ; [[#Sainz--2019|Sainz et al., 2019]] ). Common vulnerabilities to inland and marine aquaculture include increasing incidence and toxicity of HABs related to warming waters, causing fish kills and product consumption risks, negatively impacting the productivity and stability of production sectors and reliant communities ( ''high confidence'' ) ( [[#Soto--2018|Soto et al., 2018]] ; [[#Aoki--2019|Aoki et al., 2019]] ; [[#Bannister--2019|Bannister et al., 2019]] ).

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[[File:a0c65172291b805ac6a0e50a7da9ad38 IPCC_AR6_WGII_Figure_5_014.png]]

'''Figure 5.14 |''' '''Assessment of inland freshwater and brackish aquaculture''' ''(salinities of &lt;10 ppm and/or no connection to the marine environment)'' '''(a)''' ''and marine aquaculture vulnerabilities and mitigation potential per major FAO production zones'' '''(b).''' See SM5.6 (Tables SM5.5, 5.6, 5.9, 5.10) for assessment methodologies.

There is ''high confidence'' that inland aquaculture in Southeast Asia is highly vulnerable to climate change, due to fluctuations in water resources either through climatic variability in precipitation, flooding or salinity inundation or through competition ( [[#Handisyde--2017|Handisyde et al., 2017]] ; [[#Nguyen--2018|Nguyen et al., 2018]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#Islam--2019|Islam et al., 2019]] ; [[#Nguyen--2019b|Nguyen et al., 2019b]] ; Prakoso et al., 2020). Studies in Bangladesh and Indonesia highlighted regional and species-specific vulnerabilities (Prakoso et al., 2020) and roles of governance in vulnerability reduction ( [[#Islam--2019|Islam et al., 2019]] ).

In the marine sector, vulnerability models ( [[#Brugère--2015|Brugère and De Young, 2015]] ; [[#Handisyde--2017|Handisyde et al., 2017]] ) have been adapted and applied to semi-quantitative spatial risk assessments for Chilean Atlantic salmon, where analysis of exposure threat coupled with mortality and temperature farm data could enhance salmon production ( [[#Soto--2019|Soto et al., 2019]] ). Vulnerability assessments in Korea (RCP8.5 temperature increase of 4–5°C by 2100) ( [[#Kim--2019a|Kim et al., 2019a]] ) and the USA (ocean acidification, [[#Barton--2015|Barton et al., 2015]] ; [[#Ekstrom--2015|Ekstrom et al., 2015]] ) found major exposure-related vulnerabilities for seaweeds and shellfish, with reduced vulnerabilities under higher production control and adaptive capacity. Global bivalve vulnerability assessments (RCP8.5 by 2100) show high vulnerabilities for major producing countries related to cyclones (China, Japan, South Korea, Thailand, Viet Nam and North Korea), regional risk of high sensitivity and low adaptive capacity (Chile, Peru, Spain, Italy), with few major producers (France, the Netherlands and USA) anticipated to remain moderately vulnerable by 2100 ( [[#Stewart-Sinclair--2020|Stewart-Sinclair et al., 2020]] ).

Climate uncertainty and data limitations hinder vulnerability assessments ( ''high confidence'' ), so broader vulnerabilities and qualitative assessments can be used ( [[#Brugère--2015|Brugère and De Young, 2015]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#Brugère--2019|Brugère et al., 2019]] ; [[#Cochrane--2019|Cochrane et al., 2019]] ). Filling data gaps with monitoring ( ''high confidence'' ), increasing governmental support to assist particularly vulnerable small- and medium-scale farmers with increased costs associated with risk management and uncertainty ( ''medium confidence'' ) and the early inclusion of community stakeholders ( ''high agreement'' , ''medium evidence'' ) can reduce vulnerabilities ( [[#Handisyde--2017|Handisyde et al., 2017]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ; [[#Cochrane--2019|Cochrane et al., 2019]] ).

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==== 5.9.2.1 Gender and other social vulnerability and roles in aquaculture ====

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There are regional differences in women’s roles, responsibilities and involvement in adaptation strategies in the aquaculture sector. Women comprise 14% of the 2018 global aquaculture workforce of 20.5 million ( [[#FAO--2020c|FAO, 2020c]] ), representing up to 42% of the salmon workforce in Chile ( [[#Chávez--2019|Chávez et al., 2019]] ), predominantly in processing roles ( [[#Gopal--2020|Gopal et al., 2020]] ). In the majority of lower-middle-income countries, seaweed culture is dominated by women in family-owned businesses as in Zanzibar and the Philippines ( [[#Brugere--2020|Brugere et al., 2020]] ; [[#Ramirez--2020|Ramirez et al., 2020]] ), where women are not always paid directly but contribute to family incomes ( ''high confidence'' ) ( [[#Msuya--2017|Msuya and Hurtado, 2017]] ; [[#Brugere--2020|Brugere et al., 2020]] ; [[#Ramirez--2020|Ramirez et al., 2020]] ). In India, women collect stocking juveniles and assist in pond construction; in Bangladesh, women do the same tasks as men; and in Ghana, women undertake post-harvest fishing activities ( [[#Lauria--2018|Lauria et al., 2018]] ). Women employed in aquaculture cooperatives gained adaptive capacity, which reduced gender inequities ( ''medium confidence'' ) ( [[#Farquhar--2018|Farquhar et al., 2018]] ; [[#Gonzal--2019|Gonzal et al., 2019]] ), but lack of financial access for women can create gender inequity at larger commercial scales ( [[#Gurung--2016|Gurung et al., 2016]] ; [[#Call--2019|Call and Sellers, 2019]] ). Women in aquaculture experience competing roles between employment, childcare and home duties ( ''high confidence'' ) ( [[#Morgan--2015|Morgan et al., 2015]] ; [[#Lauria--2018|Lauria et al., 2018]] ; [[#Chávez--2019|Chávez et al., 2019]] ; see Cross-Chapter Box GENDER in Chapter 18) and differ from men in terms of perceptions of environmental risk, climate change and adaptation behaviour, with limited contributions to decision making ( ''medium confidence'' ) ( [[#Barange--2018|Barange and Cochrane, 2018]] ). Therefore, effective climate aquaculture adaptation options need to address gender inequity, such as suitable technology designs that fit with social norms and access to credit to facilitate independent uptake ( ''medium evidence'' , ''high agreement'' ) ( [[#Morgan--2015|Morgan et al., 2015]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). Generalised best practices for gender-sensitive approaches to adaptation are relevant for aquaculture ( [[#UNFCCC--2013|UNFCCC, 2013]] ).

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=== 5.9.3 Projected Impacts ===

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Projected impacts on regional inland and marine aquaculture production are summarised in Figure 5.15.

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[[File:95107a8a3adb28c82f0d09e5d2c9fa20 IPCC_AR6_WGII_Figure_5_015.png]]

'''Figure 5.15 |''' '''Assessment of projected impacts of climate change on inland freshwater and brackish aquaculture''' ''(salinities of &lt;10 ppm and/or no connection to the marine environment)'' '''(a)''' ''and marine aquaculture'' '''(b)''' ''per major FAO production zones.'' See SM5.6 (Tables SM5.7, 5.11) for assessment methodologies.

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==== 5.9.3.1 Inland freshwater and brackish aquaculture ====

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Predicted sea level and temperature rise will result in coastal inundation into brackish and inland aquaculture systems ( ''high confidence'' ) ( [[#Mehvar--2019|Mehvar et al., 2019]] ; [[#Nhung--2019|Nhung et al., 2019]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ; [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ), with negative impacts on aquaculture production in Viet Nam, East Africa and Jamaica ( ''medium confidence'' ) ( [[#Lebel--2018|Lebel et al., 2018]] ; [[#Nguyen--2018|Nguyen et al., 2018]] ; [[#Bornemann--2019|Bornemann et al., 2019]] ). Precipitation and temperature changes will cause drought and flooding, negatively affecting near-shore fishpond productivity ( ''limited evidence'' ) ( [[#Canevari-Luzardo--2019|Canevari-Luzardo et al., 2019]] ), but provide competitive advantages to non-native shrimp in Australia ( ''limited evidence'' ) ( [[#Cerato--2019|Cerato et al., 2019]] ). Warming and acidification will increase HAB toxicity in freshwater systems, but responses may be strain-specific ( [[#Griffith--2020|Griffith and Gobler, 2020]] ; [[#Hennon--2020|Hennon and Dyhrman, 2020]] ). As for molluscs in marine systems, projected climate change in freshwater and brackish systems may limit the availability of wild-sourced juveniles from fisheries (Beveridge et al., 2018). Projected impact studies for the inland and small-scale aquatic sectors are very limited ( [[#Halpern--2019|Halpern et al., 2019]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ); therefore, this is a noted knowledge gap.

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==== 5.9.3.2 Marine Aquaculture ====

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===== 5.9.3.2.1 Finfish culture =====

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Global projections of ocean warming, primary productivity and ocean acidification predict suitable habitat expansions and short-term growth benefits for finfish aquaculture for some regions ( ''medium confidence'' ) (see Figure 5.15) until thermal tolerances or productivity constraints are exceeded by 2090 ( [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#Froehlich--2018a|Froehlich et al., 2018a]] ; [[#Catalán--2019|Catalán et al., 2019]] ; [[#Thiault--2019|Thiault et al., 2019]] ; [[#Falconer--2020a|Falconer et al., 2020a]] ). Sensitivities for marine finfish may be high even under +1.5–2.0°C ( ''medium confidence'' ) ( [[#Gattuso--2018|Gattuso et al., 2018]] ), resulting in finfish farms moving northward to maintain productivity (e.g., Arctic ( [[#Troell--2017|Troell et al., 2017]] )). Downscaled projections of regionally specific tolerances ( [[#Klinger--2017|Klinger et al., 2017]] ) may be particularly useful for management and planning; a 0.5°C rise is predicted for Chilean salmon aquaculture ( [[#Soto--2019|Soto et al., 2019]] ), and potential negative impacts on productivity in Norway by 2029 have been projected ( ''limited evidence'' ) ( [[#Falconer--2020a|Falconer et al., 2020a]] ). Marine heatwaves are predicted to increase in occurrence, intensity and persistence under RCP4.5 or RCP8.5 by 2100 ( [[#Oliver--2019|Oliver et al., 2019]] ; [[#Bricknell--2021|Bricknell et al., 2021]] ), with risk partly mitigated by husbandry ( ''medium confidence'' ) ( [[#McCoy--2017|McCoy et al., 2017]] ). Generally, negative impacts are predicted for marine species, with residual risk increasing with level of exposure ( [[#Sara--2018|Sara et al., 2018]] ; [[#Smale--2019|Smale et al., 2019]] ), where warming will affect oxygen solubility and reduce salmon culture capacity ( ''limited evidence'' ) ( [[#Aksnes--2019|Aksnes et al., 2019]] , Chapter 3) and combine with increasing incidence of HABs ( ''high confidence'' ) resulting in negative impacts for food security and nutrition and health ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ; [[#Colombo--2020|Colombo et al., 2020]] ; [[#Glibert--2020|Glibert, 2020]] ; [[#Raven--2020|Raven et al., 2020]] ). Climate change is predicted to affect the incidence, magnitude and virulence of finfish disease such as ''Vibriosis'' ( [[#Barber--2016|Barber et al., 2016]] ; [[#Mohamad--2019a|Mohamad et al., 2019a]] ; [[#Mohamad--2019b|Mohamad et al., 2019b]] ), but specific host–pathogen–climate relationships are not yet established ( ''high confidence'' ) ( [[#Slenning--2010|Slenning, 2010]] ; [[#Marcogliese--2016|Marcogliese, 2016]] ; [[#Montanchez--2019|Montanchez et al., 2019]] ; [[#Bandin--2020|Bandin and Souto, 2020]] ; [[#Behringer--2020|Behringer et al., 2020]] ; [[#Filipe--2020|Filipe et al., 2020]] ; [[#Montanchez--2020|Montanchez and Kaberdin, 2020]] ). Projected climate change will also increase competition for feed ingredients between aquatic and terrestrial animal production systems (see [[#5.13.2|Section 5.13.2]] .).

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===== 5.9.3.2.2 Shellfish culture =====

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Globally, there is overall ''high confidence'' that suitable shellfish aquaculture habitat will decline by 2100 under projected warming, ocean acidification and primary productivity changes, with significant negative impacts for some regions and species before 2100 (Table 5.9, [[#Froehlich--2018a|Froehlich et al., 2018a]] ; [[#Ghezzo--2018|Ghezzo et al., 2018]] ). Shellfish growth will increase with warming waters until tolerances are reached, such as through extreme El Niño events ( ''high confidence'' ) ( [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#Liu--2018b|Liu et al., 2018b]] ; [[#Liu--2020|Liu et al., 2020]] ). Rising temperatures and ocean acidification will result in losses of primary productivity and farmed species from tropical and subtropical regions, and gains in higher latitudes ( ''high confidence'' ) ( [[#Froehlich--2018a|Froehlich et al., 2018a]] ; [[#Aveytua-Alcazar--2020|Aveytua-Alcazar et al., 2020]] ; [[#Chapman--2020|Chapman et al., 2020]] ; [[#Des--2020|Des et al., 2020]] ; [[#Oyinlola--2020|Oyinlola et al., 2020]] ), but net marine production gains could be achieved under strong mitigation ( [[#Thiault--2019|Thiault et al., 2019]] ). Shellfish ''Vibrio'' infections will increase with warming waters and extreme events, increasing shellfish mortalities ( ''medium confidence'' ) ( [[#Green--2019|Green et al., 2019]] ; [[#Montanchez--2019|Montanchez et al., 2019]] ), with ocean acidification impairing immune responses ( ''limited evidence'' ) ( [[#Cao--2018b|Cao et al., 2018b]] ). Bivalve larvae are known to be highly vulnerable to ocean acidification ( ''high confidence'' ) (see [[IPCC:Wg2:Chapter:Chapter-3#3.3|Section 3.3]] , [[#Bindoff--2019|Bindoff et al., 2019]] ), with projected regional and species-specific levels of impact ( ''high confidence'' ) ( [[#Ekstrom--2015|Ekstrom et al., 2015]] ; [[#Zhang--2017b|Zhang et al., 2017b]] ; [[#Mangi--2018|Mangi et al., 2018]] ) ( [[#Greenhill--2020|Greenhill et al., 2020]] ). Ocean acidification is also projected to weaken shells, affecting productivity and processing ( ''high confidence'' ) ( [[#Martinez--2018|Martinez et al., 2018]] ; [[#Cummings--2019|Cummings et al., 2019]] ) and dependent livelihoods ( [[#Doney--2020|Doney et al., 2020]] ).

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===== 5.9.3.2.3 Aquatic plant culture =====

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There is ''medium confidence'' that cultivated seaweeds are predicted to suffer habitat loss resulting in population declines and northward shifts (Table 5.11).

'''Table 5.11 |''' Projected impacts of climate on specific inland, brackish and marine culture systems and species.

{| class="wikitable"
|-
! '''Exposure'''

! '''Scenario'''

! '''Region'''

! '''Production system'''

! '''Species'''

! '''Impact'''

! '''Reference'''

|-
| Temperature increase

| RCP4.5 and RCP8.5 by 2050

| Northern Thailand

| Inland

| Nile tilapia

| Reduced productivity

| [[#Lebel--2018|Lebel et al. (2018)]]

|-
| Precipitation change (drought, hurricane, heavy rainfall)

| –

| Jamaica

| Inland

| Tilapia

| Reduced productivity, infrastructure damage

| Canevari-Luzardo et al. (2019)

|-
| Temperature increase

| 4°C increase, B2, A1B by 2100

| Australia

| Inland

| Freshwater shrimp

| Increased production in non-native zones

| [[#Cerato--2019|Cerato et al. (2019)]]

|-
| Temperature increase, ocean acidification, primary productivity declines

| CMIP5 RCP8.5 in 20-year increments to 2090

| Global

| Marine

| Finfish species

| Increased suitable habitat expansion for regions (Russia, Norway, USA Alaska, Denmark, Canada). By 2100, reduction in productivity for major producers (Norway, China)

| Froehlich et al. (2018a),

[[#Thiault--2019|Thiault et al. (2019)]]

|-
| Temperature increase

| 2–5°C increase under RCP8.5

| Europe

| Marine

| Atlantic salmon

| Increased growth

| [[#Catalán--2019|Catalán et al. (2019)]]

|-
| Temperature increase

| RCP4.5 to 2029

| Norway

| Marine

| Atlantic salmon

| Growth threshold reached by 2029

| [[#Falconer--2020a|Falconer et al. (2020a)]]

|-
| Temperature increase

| Downscaled CM2.6 by 2050

| Global

| Marine

| Atlantic salmon, cobia and sea bream

| Increased or decreased growth rates depending on region

| Klinger et al. (2017)

|-
| Temperature increase, ocean acidification, primary productivity declines

| CMIP5 RCP8.5 in 20-year increments to 2090

| Global

| Marine

| Shellfish

| Overall declines in suitable habitat globally, up to 50–100% reductions in regions in China, Thailand and Canada

| Froehlich et al. (2018a)

|-
| Temperature increase

| CMIP5 RCP8.5 by 2050, 2100

| Italy

| Marine

| Clams

| Negative impacts for juvenile timing, spatial distribution, and quality

| [[#Ghezzo--2018|Ghezzo et al. (2018)]]

|-
| Temperature increase

| CMIP5 RCP2.6 and RCP8.5 by 2035, 2070

| France

| Marine

| Oysters

| Increase incidence of oyster mortality; increase by 2035 to annual occurrence by 2070

| [[#Thomas--2018|Thomas et al. (2018)]]

|-
| Temperature increase

| RCP2.6 and RCP8.5 by 2050

| Global

| Marine

| Shellfish

| Species reduction (10–40%) in tropical and subtropical regions, with increase (40%) in higher latitudes

| [[#Oyinlola--2020|Oyinlola et al. (2020)]]

|-
| Temperature increase, ocean acidification

| Ecopath with RCP8.5 by 2100 (2.8°C warming and pH 7.89)

| USA

| Marine

| Shellfish

| Reduction primary productivity and subsequent bivalve carrying capacity

| [[#Chapman--2020|Chapman et al. (2020)]]

|-
| Temperature increase, stratification change

| RCP8.5 by 2088–2099

| Spain

| Marine

| Mussels

| Decline in mussel optimal culture conditions of 60% in upper and 30% in deeper waters by 2099

| [[#Des--2020|Des et al. (2020)]]

|-
| Temperature increase, ocean acidification

| RCP2.6 and 8.5 by 2070–2090

| Global

| Marine

| Shellfish

| Under RCP8.5, a decline in shellfish production due to primary productivity reduction in tropical regions and gains in high latitudes. Under RCP2.6, marine production will have net gain

| [[#Thiault--2019|Thiault et al. (2019)]]

|-
| Temperature increase

| 4°C increase

| Global

| Marine

| ''Vibrio'' spp. (mortality causative agent)

| Increased virulence

| [[#Montanchez--2019|Montanchez et al. (2019)]]

|-
| Temperature increase (marine heatwave)

| 5°C increase

| Global

| Marine

| Oysters

| Increased oyster mortality

| [[#Green--2019|Green et al. (2019)]]

|-
| Ocean acidification

| ~2000 ppm CO 2

| Global

| Marine

| Oysters

| Impaired immune function

| [[#Cao--2018b|Cao et al. (2018b)]]

|-
| Ocean acidification

| RCP8.5 in 20-year increments to after 2099

| USA

| Marine

| Shellfish

| Regional projected vulnerabilities; southern Alaska and Pacific Northwest at more immediate risk

| [[#Ekstrom--2015|Ekstrom et al. (2015)]]

|-
| Ocean acidification

| A1B and RCP8.5 by 2100

| UK

| Marine

| Shellfish

| Regional projected vulnerabilities; Wales and England at more immediate risk

| [[#Mangi--2018|Mangi et al. (2018)]]

|-
| Ocean acidification

| RCP2.6 and RCP8.5 by 2300

| East China

| Marine

| Shellfish

| Carbonate saturation projected to decrease by 13% and 72% under RCP2.6 and RCP8.5 respectively, projecting decreased shellfish productivity

| RCP2.6 and RCP8.5 by 2300 ( [[#Zhang--2017b|Zhang et al., 2017b]] )

|-
| Increased temperature

| RCP2.6 and RCP8.5 by 2100

| North Sea

| Marine

| Seaweed

| Northward population shift by 110–163 km and 450–635 km under RCP2.6 and RCP8.5, respectively

| [[#Westmeijer--2019|Westmeijer et al. (2019)]]

|-
| Increased temperature

| RCP4.5 and RCP8.5 by 2090

| Japan

| Marine

| Kelp

| Habitat decline to 30–51% and 0–25% under RCP4.5 and RCP8.5, respectively

| [[#Sudo--2020|Sudo et al. (2020)]]

|}

<div id="5.9.3.2.4" class="h4-container"></div>

<span id="societal-impacts-within-the-production-system-1"></span>
===== 5.9.3.2.4 Societal impacts within the production system =====

<div id="h4-10-siblings" class="h4-siblings"></div>

Marine aquaculture provides distinct ecosystem services through provisioning (augmenting wild fishery catches), regulating (coastal protection, carbon sequestration, nutrient removal, improved water clarity), habitat and supporting (artificial habitat) and cultural (livelihoods and tourism) services ( [[#Gentry--2020|Gentry et al., 2020]] ), which vary with species, location and husbandry ( [[#Alleway--2019|Alleway et al., 2019]] ). Projected thermal increases of 1.5°C will reduce ecosystem services, further reduced under 2°C warming, with associated increases in acidification, hypoxia, dead zones, flooding and water restrictions ( ''medium confidence'' ) ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Sudden production losses from extreme climate events can exacerbate food security challenges across production sectors, including aquaculture, increasing global hunger ( ''high confidence'' ) ( [[#Cottrell--2019|Cottrell et al., 2019]] ; [[#Food%20Security%20Information%20Network--2020|Food Security Information Network, 2020]] ). While aquaculture provides positive influences such as food security and livelihoods, there are negative concerns over environmental impacts (including high nutrient loads from sites) and socioeconomic conflicts ( [[#Alleway--2019|Alleway et al., 2019]] ; [[#Soto--2019|Soto et al., 2019]] ), and adoption of ecosystem approaches is dependent on particular user groups and regions ( [[#Gentry--2017|Gentry et al., 2017]] ; [[#Brugère--2019|Brugère et al., 2019]] ; [[#Gentry--2020|Gentry et al., 2020]] ). In coastal Bangladesh, projected saline inundation to wetland ecosystem services will result in ecosystem services losses of raw materials and food provisioning, ranging from USD 0 to 20.0 million under RCP2.6 to RCP8.5 scenarios ( [[#Mehvar--2019|Mehvar et al., 2019]] ). Mangrove deforestation for shrimp farming in Asia negatively impacts ecosystem services and reduces climate resilience ( ''medium confidence'' ) ( [[#Mehvar--2019|Mehvar et al., 2019]] ; [[#Nguyen--2019|Nguyen and Parnell, 2019]] ; [[#Reid--2019|Reid et al., 2019]] ; [[#Custódio--2020|Custódio et al., 2020]] ), while mangrove reforestation efforts may have some effectiveness in re-creating important nursery grounds for aquatic species ( ''low confidence)'' ( [[#Gentry--2017|Gentry et al., 2017]] ; [[#Chiayarak--2019|Chiayarak et al., 2019]] ; [[#Hai--2020|Hai et al., 2020]] ). Families are highly vulnerable to climate change where nutritional needs are being met by self-production, such as in Mozambique, Namibia ( [[#Villasante--2015|Villasante et al., 2015]] ), Zambia ( [[#Kaminski--2018|Kaminski et al., 2018]] ) and Bangladesh ( ''high confidence'' ) ( [[#Pant--2014|Pant et al., 2014]] ). Climate change will therefore affect multiple ecosystem services where ultimately decisions on balance or trade-offs will vary with regional perceptions of service value ( ''high confidence'' ).

<div id="5.9.4" class="h2-container"></div>

<span id="aquaculture-adaptation"></span>
=== 5.9.4 Aquaculture Adaptation ===

<div id="h2-30-siblings" class="h2-siblings"></div>

<div id="5.9.4.1" class="h3-container"></div>

<span id="adaptation-planning"></span>
==== 5.9.4.1 Adaptation planning ====

<div id="h3-42-siblings" class="h3-siblings"></div>

Aquaculture is often viewed as an adaptation option for fisheries declines, thereby alleviating food security from losses of other climate change impacts ( [[#Sowman--2018|Sowman and Raemaekers, 2018]] ; [[#Johnson--2020|Johnson et al., 2020]] ) such as Pacific Islands freshwater aquaculture, Bangladesh crop-aquaculture systems or Viet Nam rice–fish cultivations ( [[#Soto--2018|Soto et al., 2018]] ). Many adaptations are specific to regions, countries or sectors, implemented on a regional to national scale ( [[#FAO--2018c|FAO, 2018c]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ). Adaptation likelihood (potential), effectiveness and risk of maladaptation was assessed per major FAO production region for inland, brackish and marine aquaculture (Figure 5.16) production systems. Potential adaptation measures to reduce production loss can be built upon existing adaptation planning and guidelines, to reduce the risk of maladaptation including feedback loops (e.g., [[#FAO--2015|FAO, 2015]] ; [[#Bueno--2017|Bueno and Soto, 2017]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Poulain--2018|Poulain et al., 2018]] ; [[#Brugère--2019|Brugère et al., 2019]] ; [[#Pham--2021|Pham et al., 2021]] ; [[#Soto--2021|Soto et al., 2021]] ). Large climate change adaptation strategies for the aquaculture sector exist, such as in the USA ( [[#Link--2015|Link et al., 2015]] ), Australia ( [[#Hobday--2017|Hobday et al., 2017]] ) and South Africa ( [[#Department%20of%20Environmental%20Affairs--2016|Department of Environmental Affairs, 2016]] ). Lower-income countries often lack financial, technical or institutional capacity for adaptation planning ( [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ), but examples include Bangladesh and Myanmar ( [[#FAO--2018c|FAO, 2018c]] ), with programmes offering adaptation funding ( [[#Dabbadie--2018|Dabbadie et al., 2018]] ). Early participation of stakeholders in adaptive planning has promoted action and ownership of results ( ''high confidence'' ), such as in India and the USA ( [[#Link--2015|Link et al., 2015]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Soto--2018|Soto et al., 2018]] ) Early outreach, education and knowledge gap assessments raise awareness, where utilisation of local knowledge and Indigenous knowledge and scientific involvement support informed adaptive planning and uptake for all stakeholders ( ''high confidence'' ) ( [[#Cooley--2016|Cooley et al., 2016]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Rybråten--2018|Rybråten et al., 2018]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#McDonald--2019|McDonald et al., 2019]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ), as perceptions of climate risk and capacity will vary ( [[#Tiller--2018|Tiller and Richards, 2018]] ). Supporting the active involvement of women helps address gender inequity and perceived risk, particularly for smallholder farmers ( ''high confidence'' ) ( [[#Morgan--2015|Morgan et al., 2015]] ; [[#Barange--2018|Barange and Cochrane, 2018]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Avila-Forcada--2020|Avila-Forcada et al., 2020]] ). However, regional and national political influences, financial and technical capacity, governance planning and policy development will ultimately support or hinder adaptation for aquaculture ( ''high confidence'' ) ( [[#Cooley--2016|Cooley et al., 2016]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; [[#Greenhill--2020|Greenhill et al., 2020]] ).

<div id="_idContainer063" class="Figure"></div>

[[File:38a48987bdb010dfd623e34bb7638ac3 IPCC_AR6_WGII_Figure_5_016.png]]

'''Figure 5.16 |''' '''Assessment of the likelihood and effectiveness of a range of adaptation options for potential implementation in the near term (next decade) for inland freshwater and brackish aquaculture''' ''(salinities of &lt;10 ppm and/or no connection to the marine environment)'' '''(a)''' ''and marine aquaculture systems'' '''(b)''' ''per major FAO production zone.'' See SM5.6 (Tables SM5.8, 5.12) for assessment methodologies.

<div id="5.9.4.2" class="h3-container"></div>

<span id="species-selections-and-selective-breeding"></span>
==== 5.9.4.2 Species selections and selective breeding ====

<div id="h3-43-siblings" class="h3-siblings"></div>

Adaptation options at the operational level include species selections, such as cultivation of brackish species (shrimp, crabs) during dry seasons, and rice-finfish in wetter seasons in Thailand ( [[#Chiayarak--2019|Chiayarak et al., 2019]] ), use of salt-tolerant plants in Viet Nam ( [[#Nhung--2019|Nhung et al., 2019]] ; [[#Paik--2020|Paik et al., 2020]] ), converting inundated rice paddies into aquaculture, rotating shrimp, and rice culture ( ''high confidence'' ) ( [[#Chiayarak--2019|Chiayarak et al., 2019]] ). Species diversification through co-culture, integrated aquaculture–agriculture (e.g., rice–fish) or integrated multi-trophic culture (e.g., shrimp–tilapia–seaweed or finfish–bivalve–seaweed) may maintain farm long-term performance and viability by: creating new aquaculture opportunities; promoting societal and environmental stability; reducing GHG emissions through reduced feed usage and waste; and carbon sequestration ( ''medium confidence'' ) (see [[#5.10|Section 5.10]] , [[#Ahmed--2017|Ahmed et al., 2017]] ; [[#Bunting--2017|Bunting et al., 2017]] ; [[#Gasco--2018|Gasco et al., 2018]] , [[#Soto--2018|Soto et al., 2018]] ; [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Dubois--2019|Dubois et al., 2019]] ; [[#FAO--2019c|FAO, 2019c]] ; [[#Li--2019|Li et al., 2019]] ; [[#Freed--2020|Freed et al., 2020]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; Prasko et al., 2020; [[#Tran--2020|Tran et al., 2020]] ). In practice, most aquaculture operations concentrate on single-species systems ( [[#Metian--2020|Metian et al., 2020]] ), and barriers such as land availability, freshwater resources and lack of credit access may limit the uptake and success of integrated adaptation approaches to climate change ( [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Tran--2020|Tran et al., 2020]] ; [[#Kais--2021|Kais and Islam, 2021]] ).

Selective breeding can promote climate resilience ( ''medium confidence'' ) ( [[#Klinger--2017|Klinger et al., 2017]] ; [[#Fitzer--2019|Fitzer et al., 2019]] ), and operations have already intentionally, or unintentionally, selected for production traits for changing conditions ( [[#de%20Melo--2016|de Melo et al., 2016]] ; [[#Tan--2020|Tan and Zheng, 2020]] ). Exposure of broodstock to future climate conditions may or may not confer advantages to offspring ( ''moderate evidence'' , ''low agreement'' ) ( [[#Parker--2015|Parker et al., 2015]] ; [[#Griffith--2017|Griffith and Gobler, 2017]] ; [[#Thomsen--2017|Thomsen et al., 2017]] ; [[#Durland--2019|Durland et al., 2019]] ). Traditional pedigree developments require extensive phenotypic data, but genomic selections can rapidly select for robust climate-associated traits ( [[#Sae-Lim--2017|Sae-Lim et al., 2017]] ; [[#Gutierrez--2018|Gutierrez et al., 2018]] ; [[#Zenger--2018|Zenger et al., 2018]] ; [[#Houston--2020|Houston et al., 2020]] ; [[#Tan--2020|Tan and Zheng, 2020]] ). Genomic resources are available for salmon, rainbow trout, coho, carp, tilapia, seabass, bream, turbot, flounder, catfish, yellow drum, scallops, oysters and shrimp, but have been developed for disease and growth selections rather than climate resistance ( [[#Dégremont--2015a|Dégremont et al., 2015a]] ; [[#Dégremont--2015b|Dégremont et al., 2015b]] ; [[#Abdelrahman--2017|Abdelrahman et al., 2017]] ; [[#Gjedrem--2018|Gjedrem and Rye, 2018]] ; [[#Gutierrez--2018|Gutierrez et al., 2018]] ; [[#Guo--2018|Guo et al., 2018]] ; [[#Liu--2018a|Liu et al., 2018a]] ; [[#FAO--2019d|FAO, 2019d]] ; [[#Houston--2020|Houston et al., 2020]] ), although bivalve selections for ocean acidification and warming resiliency are underway ( [[#Tan--2020|Tan and Zheng, 2020]] ). Targeted genome editing could modify phenotypes of major aquaculture species ( [[#Li--2014|Li et al., 2014]] a; [[#Elaswad--2018|Elaswad et al., 2018]] ; [[#Yu--2019|Yu et al., 2019]] ; [[#Houston--2020|Houston et al., 2020]] ), but uptake is dependent upon national regulatory and public approvals. Local adaptations within species with higher climate resiliencies may assist in selections ( [[#Thomsen--2017|Thomsen et al., 2017]] ; [[#Falkenberg--2019|Falkenberg et al., 2019]] ; [[#Scanes--2020|Scanes et al., 2020]] ; [[#Toomey--2020|Toomey et al., 2020]] ), but highlight the need to consider specific farming environments for selective processes ( [[#Houston--2020|Houston et al., 2020]] ). Projections of climate on aquaculture production traits are not well understood ( [[#Lhorente--2019|Lhorente et al., 2019]] ); therefore, genetic diversity needs to be maintained to ensure population fitness ( ''high confidence'' ) ( [[#Bitter--2019|Bitter et al., 2019]] ; [[#Lhorente--2019|Lhorente et al., 2019]] ; [[#Visch--2019|Visch et al., 2019]] ; [[#Houston--2020|Houston et al., 2020]] ; [[#Mantri--2020|Mantri et al., 2020]] ).

<div id="5.9.4.3" class="h3-container"></div>

<span id="farm-site-selection-infrastructure-and-husbandry"></span>
==== 5.9.4.3 Farm site selection, infrastructure and husbandry ====

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Land-based aquaculture systems including hatcheries may reduce exposure to climatic extremes (due to better control of the culture environment), limit water usage, reduce juvenile reliance and buffer climate effects using optimal diets ( ''high confidence'' ) ( [[#Barton--2015|Barton et al., 2015]] ; [[#Reid--2019|Reid et al., 2019]] ; [[#Cominassi--2020|Cominassi et al., 2020]] ). However, land-based aquaculture requires large capital and operational costs and use of land, increasing conflicts between land and water use, have increased energy demands (increasing GHG if fossil fuels are the primary energy source), require necessary expertise and will not reduce outgrowing exposures ( ''high confidence'' ) (see [[#5.13|Section 5.13]] , [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#Tillotson--2019|Tillotson et al., 2019]] ; [[#Costello--2020|Costello et al., 2020]] ; Prakoso et al., 2020).

Geographical selection of marine farm sites may prevent climate productivity declines ( ''medium confidence'' ) ( [[#Froehlich--2018a|Froehlich et al., 2018a]] ; [[#Sainz--2019|Sainz et al., 2019]] ; [[#Oyinlola--2020|Oyinlola et al., 2020]] ), particularly for temperature-related mortality hotspots ( [[#Garrabou--2019|Garrabou et al., 2019]] ), HAB occurrences ( [[#Dabbadie--2018|Dabbadie et al., 2018]] ) or extreme events ( [[#Liu--2020|Liu et al., 2020]] ; [[#Wu--2020|Wu et al., 2020]] ). However, while downscaled climate forecasts facilitate localised adaptation planning ( [[#Falconer--2020a|Falconer et al., 2020a]] ), such projections are rare ( [[#Whitney--2020|Whitney et al., 2020]] ). GIS can be used for climate adaptive planning along with routine site assessments ( [[#Falconer--2020b|Falconer et al., 2020b]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; [[#Jayanthi--2020|Jayanthi et al., 2020]] ). Building coastal protection, stronger cages and mooring systems, and deeper ponds and using sheltered bays can reduce escapees and mortalities related to flooding, increased storms and extreme events ( ''medium confidence'' ) ( [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#Bricknell--2021|Bricknell et al., 2021]] ; [[#Kais--2021|Kais and Islam, 2021]] ). Inshore aquaculture in low-lying areas prone to sea level salinity intrusion (e.g., Mekong delta and Viet Nam) have already implemented adaptation measures, such as conversion of land to mixed plant–animal systems ( [[#Nguyen--2019a|Nguyen et al., 2019a]] ), conversion of freshwater ponds to brackish or saline aquaculture ( [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ), building of dams and dykes ( [[#Renaud--2015|Renaud et al., 2015]] ) and intensification of shrimp or fish pond culture to reduce water and land usage ( [[#Nguyen--2019b|Nguyen et al., 2019b]] ; [[#Johnson--2020|Johnson et al., 2020]] ). Other adaptation options for limited water supply are government equitable water allocations and water storage ( ''high confidence'' ) ( [[#Bunting--2017|Bunting et al., 2017]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ).

Feed formulations and improved feed conversion can reduce climate-associated stress for freshwater species, significantly reducing waste and increase sustainability ( ''medium confidence'' ) ( [[#FAO--2018c|FAO, 2018c]] ; [[#Gasco--2018|Gasco et al., 2018]] ; [[#Chen--2019|Chen and Villoria, 2019]] ). Projected decreases in fish meal and global targets of limiting warming to under 2°C may increase the ratio of plant-based diets but reduce fish nutritional content (see Sections 5.10 and 5.13, [[#Hasan--2017|Hasan and Soto, 2017]] ; [[#Johnson--2020|Johnson et al., 2020]] ). Companies provide insurance in major production areas, but aquaculture is considered high risk with large levels of small claims ( [[#Secretan--2007|Secretan et al., 2007]] ). Insurance covers natural disasters and disease, helping to reduce and cope with climate-induced risk, enabling faster livelihood recoveries and preventing poverty ( ''high agreement'' , ''limited evidence'' ) ( [[#Xinhua--2017|Xinhua et al., 2017]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Soto--2018|Soto et al., 2018]] ). For example, small-scale shrimp farmers were willing to pay higher premiums to manage risk, after participation in government pilot insurance schemes, ensuring greater pay-outs if a mortality event occurred ( [[#Nyguyen--2016|Nyguyen and Pongthanapanic, 2016]] ; [[#Pongthanapanic--2019|Pongthanapanic et al., 2019]] ). Technological innovations are more widely implemented in larger operations, with Internet access promoting adoption at the farm site ( [[#Joffre--2017|Joffre et al., 2017]] ; [[#Salazar--2018|Salazar et al., 2018]] ). Improved farm management is a key opportunity ( ''high confidence'' ) to reduce climate risks on aquaculture, where Best Management Practices can increase resiliency ( [[#Soto--2018|Soto et al., 2018]] ) and lower additional risk from non-climatic stressors ( [[#Gattuso--2018|Gattuso et al., 2018]] ; [[#Smith--2020|Smith and Bernard, 2020]] ), and decision-tree frameworks can provide adaptation choices when events occur ( [[#Nguyen--2016|Nguyen et al., 2016]] ).

<div id="5.9.4.4" class="h3-container"></div>

<span id="early-warning-and-monitoring-systems"></span>
==== 5.9.4.4 Early-warning and monitoring systems ====

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Globally, monitoring is increasing to fill scientific uncertainties ( [[#Goldsmith--2019|Goldsmith et al., 2019]] ) but is not often at spatial scales which facilitate farm or regional adaptation management ( [[#Whitney--2020|Whitney et al., 2020]] ) or data complexities prevent direct uptake by operators, resource managers and policymakers ( ''medium confidence'' ) ( [[#Soto--2018|Soto et al., 2018]] ; [[#Gallo--2019|Gallo et al., 2019]] ). Specialised industry portals (Pacific shellfish) and government-established monitoring programmes (Chilean salmon) and other observational networks (e.g., Global Ocean Acidification Observing Network (GOA-ON)) can provide real-time monitoring and early-warning event alerts and facilitate aquaculture decision making ( ''medium confidence'' ) ( [[#Cross--2019|]] [[#Cross--2019|Cross et al., 2019]] ; [[#Farcy--2019|Farcy et al., 2019]] ; [[#Soto--2019|Soto et al., 2019]] ; [[#Tilbrook--2019|Tilbrook et al., 2019]] ; [[#Bresnahan--2020|Bresnahan et al., 2020]] ; [[#Peck--2020|Peck et al., 2020]] ). Seasonal forecasting, downscaled models and early-warning systems provide valuable regional or farm site risk information ( [[#Hobday--2018|Hobday et al., 2018]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; [[#Whitney--2020|Whitney et al., 2020]] ), but monitoring will need to be useful for farmers, involve farmers, and be accurate, timely, cost-effective, reviewed and maintained in order to ensure uptake ( ''high confidence'' ) ( [[#Soto--2018|Soto et al., 2018]] ). Early-warning systems for HABs enable rapid decision making and risk mitigation ( ''medium confidence'' ), such as ocean colour monitoring in South Africa ( [[#Smith--2020|Smith and Bernard, 2020]] ), where early harvesting and additional husbandry were used to minimise production and economic losses ( [[#Pitcher--2019|Pitcher et al., 2019]] ). New tools, strategies and observations are needed to predict HAB occurrences and range shifts with changing climate ( ''high confidence'' ) ( [[#Schaefer--2019|Schaefer et al., 2019]] ; [[#Tester--2020|Tester et al., 2020]] ), as there is uncertainty on drivers of incidence and toxicity ( [[#Wells--2020|Wells et al., 2020]] ).

<div id="5.9.5" class="h2-container"></div>

<span id="contributions-of-indigenous-traditional-and-local-knowledge"></span>
=== 5.9.5 Contributions of Indigenous, Traditional and Local Knowledge ===

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Indigenous mariculture practices, such as intertidal clam gardens, have been occurring for thousands of years, providing knowledge of traditional practices still applicable to mariculture ( [[#Deur--2015|Deur et al., 2015]] ; [[#Jackley--2016|Jackley et al., 2016]] ; [[#Poulain--2018|Poulain et al., 2018]] ; [[#Bell--2019|Bell et al., 2019]] ; [[#Toniello--2019|Toniello et al., 2019]] ). Indigenous groups differ in opinions on aquaculture acceptability, implications for coastal management and territorial rights ( ''high confidence'' ) ( [[#Young--2019|Young et al., 2019]] ). Such perceptions may determine culturally appropriate types and benefits of aquaculture (employment, food diversification, income, building autonomy and skillsets), such as in Australia ( [[#Petheram--2013|Petheram et al., 2013]] ) and Canada ( [[#Young--2010|Young and Liston, 2010]] ). Marginalised people, like small-scale aquaculture farmers in lower-income and lower-middle-income countries, are often overlooked and are not represented at a governance level ( [[#Barange--2014|Barange et al., 2014]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ). Therefore policy, economic, knowledge and other support must ensure representation with traditional and other stakeholder ecological knowledge at national, regional and local levels to facilitate climate change adaptation and safeguard human rights for poor and vulnerable groups ( ''high confidence'' ) ( [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Poulain--2018|Poulain et al., 2018]] ).

<div id="5.10" class="h1-container"></div>

<span id="mixed-systems"></span>