Climate Change Impacts on Fisheries and Aquaculture: A Global Analysis

The first comprehensive review of the current and future effects of climate change on the world’s fisheries and aquaculture operations

The first book of its kind, Climate Change Impacts on Fisheries and Aquaculture explores the impacts of climate change on global fisheries resources and on marine aquaculture. It also offers expert suggestions on possible adaptations to reduce those impacts.

The world's climate is changing more rapidly than scientists had envisioned just a few years ago, and the potential impact of climate change on world food production is quite alarming. Nowhere is the sense of alarm more keenly felt than among those who study the warming of the world's oceans. Evidence of the dire effects of climate change on fisheries and fish farming has now mounted to such an extent that the need for a book such as this has become urgent. A landmark publication devoted exclusively to how climate change is affecting and is likely to affect commercially vital fisheries and aquaculture operations globally, Climate Change Impacts on Fisheries and Aquaculture provides scientists and fishery managers with a summary of and reference point for information on the subject which has been gathered thus far.

• Covers an array of critical topics and assesses reviews of climate change impacts on fisheries and aquaculture from many countries, including Japan, Mexico, South Africa, Australia, Chile, US, UK, New Zealand, Pacific Islands, India and others
• Features chapters on the effects of climate change on pelagic species, cod, lobsters, plankton, macroalgae, seagrasses and coral reefs
• Reviews the spread of diseases, economic and social impacts, marine aquaculture and adaptation in aquaculture under climate change
• Includes special reports on the Antarctic Ocean, the Caribbean Sea, the Arctic Ocean and the Mediterranean Sea

Extensive references throughout the book make this volume both a comprehensive text for general study and a reference/guide to further research for fisheries scientists, fisheries managers, aquaculture personnel, climate change specialists, aquatic invertebrate and vertebrate biologists, physiologists, marine biologists, economists, environmentalist biologists and planners.

• Language: English
• Publisher: Wiley
• Release date: Sep 20, 2017
• ISBN: 9781119154068

Book preview

Table of Contents

Cover

Volume 1

Title Page

Copyright

List of Contributors

Acknowledgements

Chapter 1: Climate Change: The Physical Picture

1.1 Introduction

1.2 Climate

1.3 Knowledge Gaps

1.4 Climate Change

1.5 Evidence for Change Since 1880

1.6 Changes to the Ocean

1.7 Climates of the Past

References

Chapter 2: Future Physical Changes

2.1 Introduction

2.2 Short-Term Projections – 35 years

2.3 Longer Term Changes

2.4 Concluding Remarks

References

Chapter 3: Climate Change Implications for Fisheries and Aquaculture

3.1 Introduction

3.2 Historic Trends in Global Fish Production and Causes of the Trends

3.3 Global Patterns of Expected Climate Change and its Impacts

3.4 Uncertainties and Assumptions in Projections of Future Global Fish Production

3.5 Options for Adaptation and their Implications for the Future

3.6 Conclusions

References

Chapter 4: Biodiversity and Climate Change in the Oceans

4.1 Introduction

4.2 Regional Changes on Ocean Physics and Chemistry

4.3 Detection and Attribution of Climate Change Effects on Biodiversity of Fish and Fisheries

References

Chapter 5: Impacts of Climate Change on New Zealand Fisheries and Aquaculture

5.1 Introduction

5.2 The New Zealand Marine Environment

5.3 Fisheries and Fisheries Management in New Zealand

5.4 Climate Variability and Change in New Zealand

5.5 Effects of Climate Change on New Zealand Fisheries and Aquaculture

5.6 Concluding Remarks

Acknowledgements

References

Chapter 6: Impacts of Climate Change on the Marine Resources of Japan

6.1 Introduction

6.2 Climate Change Effects on Marine Environments

6.3 The Status and Adaptation to Climate Change in Japanese Fishery Species and Predictions for the Future

6.4 Climate Change Impacts for Larval Stages of Species Important for Aquaculture and Stock Enhancement

6.5 Discussion

References

Chapter 7: Impacts of Climate Change on Eastern Australia Fisheries

7.1 Introduction

7.2 North East Australia

7.3 South Eastern Australia

7.4 Conclusions

References

Chapter 8: Climate Change Impacts on Fisheries and Aquaculture of the United States

8.1 Introduction

8.2 Gulf of Alaska

8.3 California Current

8.4 Pacific Islands Region

8.5 Gulf of Mexico

8.6 Southeast US Atlantic – Climate Impacts on Fisheries and Aquaculture

8.7 Climate Change and Fisheries on the Northeast U.S. Shelf

References

Chapter 9: Impacts of Climate Change on Mexican Pacific Fisheries

9.1 Introduction

9.2 Case Studies

9.3 Discussion

References

Chapter 10: Impacts of Climate Change on Marine Fisheries and Aquaculture in Chile

10.1 Introduction

10.2 Institutional Framework in Chile for Addressing Climate Change

10.3 The Eastern South Pacific Climate System: Evidence and Projections

10.4 Case Studies

Acknowledgements

References

Chapter 11: The Pacific Island Region: Fisheries, Aquaculture and Climate Change

11.1 Introduction to the Pacific Island Region

11.2 Pacific Island Fisheries and Aquaculture

11.3 Projected Effects of Climate Change on Fisheries and Aquaculture

11.4 Implications for Economic Development, Food Security and Livelihoods

11.5 Adaptation Options

11.6 Future Research

11.7 Conclusions

References

Chapter 12: Impacts of Climate Change in the United Kingdom and Ireland

12.1 Background

12.2 Climate Change Impacts on Fisheries

12.3 Climate Change Impacts on Aquaculture

12.4 Discussion and Conclusions

Acknowledgements

References

Chapter 13: Canadian Fisheries and Aquaculture: Prospects under a Changing Climate

13.1 Introduction – The Canadian Marine Ecosystems and Fisheries

13.2 Effects of Climate Change on Ocean Conditions

13.3 Potential Climate Change Impacts on Canadian Atlantic Fisheries and Fish Stocks

13.4 Potential Climate Change Impacts on Canadian Pacific Fisheries and Fish Stocks

13.5 Potential Climate Impacts on Canadian Aquaculture Facilities and Production

13.6 Conclusions and Implications

References

Chapter 14: Potential Impacts of Climate Change in Brazilian Marine Fisheries and Aquaculture

14.1 Introduction

14.2 Projections from Global Models

14.3 Potential Impacts of Climate Change on Brazilian Marine Fisheries

14.4 Case Study: Evidence of ENSO Effects on Fishery Resources of the Patos Lagoon Region (Extreme South of Brazil) Exploited by Small-Scale Fisheries

14.5 Potential Effects of Climate Change in Marine and Brackish Aquaculture

14.6 Final Remarks

Acknowledgements

References

Volume 2

Title Page

Copyright

List of Contributors

Acknowledgements

Chapter 15: South Africa

15.1 Introduction

15.2 Climate Change Effects on the Marine Environment

15.3 Effects of Climate Change on Marine Resources and Associated Fauna

15.4 Management Response to Climate Change Effects on Marine Resources and Aquaculture

15.5 Discussion and Conclusions

References

Chapter 16: The Seychelles Tuna Fishery and Climate Change

16.1 Introduction

16.2 Observed Responses of Tuna Fisheries to Environmental Anomalies

16.3 Projected Environmental Changes and Possible Effects on Tuna Fisheries

16.4 Possible Impacts on the Seychelles Economy

16.5 Conclusions

Acknowledgements

References

Chapter 17: The Impact of Climate Change on Marine and Inland Fisheries and Aquaculture in India

17.1 Introduction

17.2 Impact on Biological Processes

17.3 Impact on Marine Ecosystems

17.4 Impact of Climate Change on Fragile Coastal Ecosystems – The Indian Sundarban as a Case Study

17.5 Impact of Climate Change on Fish Production

17.6 The Way Forward

17.7 Conclusions

References

Chapter 18: Management Adaptation to Climate Change Effect on Fisheries in Western Australia

18.1 Introduction

18.2 Climate Sensitivity of Marine Environment

18.3 Effect of Climate Change on Fish Stocks

18.4 Climate Change Effect on Aquaculture Species

18.5 Implications for Fisheries Management

18.6 Discussion and Conclusions

References

Chapter 19: Climate Change and Fisheries in the Caribbean

19.1 Introduction

19.2 Fish Production

19.3 Observed and Projected Changes

19.4 Impacts on Fisheries and Aquaculture

19.5 Challenges for Fisheries and Aquaculture

References

Chapter 20: Impacts of Climate Change on the Southern Ocean

20.1 Introduction

20.2 Southern Ocean Fisheries

20.3 Interactions Between Fisheries and Climate Forcing

20.4 Management, Monitoring and Future Perspectives

Acknowledgements

References

Chapter 21: Regional Assessment of Climate Change Impacts on Arctic Marine Ecosystems

21.1 Introduction

21.2 Methods

21.3 Results

21.4 Discussion

Acknowledgements

Appendix: Barents Sea Commercial Fish Stocks

References

Chapter 22: Seagrasses and Macroalgae: Importance, Vulnerability and Impacts

22.1 Introduction

22.2 Importance for Fisheries and Aquaculture

22.3 Impact of Climate Change on Seagrass and Macroalgae

22.4 Climate-Related Loss of Seagrass and Macroalgae and their Potential Links to Fisheries and Aquaculture

22.5 Conclusions

References

Chapter 23: Impacts of Climate Change on Pelagic Fish and Fisheries

23.1 Introduction

23.2 Climate Change and the Pelagic Ocean Environment

23.3 Impacts on Physiology and Ecology of Pelagic Fishes

23.4 Future Projections for Pelagic Fishes

23.5 Significance for Fisheries

23.6 Management Responses

23.7 Conclusions

23.8 Case Study: Sardine and Anchovy in the California Current Ecosystem

References

Chapter 24: Lobsters in a Changing Climate

24.1 Introduction

24.2 Panulirus cygnus

24.3 Panulirus interruptus

24.4 Panulirus argus

24.5 Jasus edwardsii

24.6 Homarus americanus

24.7 Adaptation to Climate Change

24.8 Conclusions

Acknowledgements

References

Chapter 25: Climate Change, Zooplankton and Fisheries

25.1 Introduction

25.2 Direct Response of Zooplankton to Climatic Variables

25.3 Indirect Responses of Zooplankton to Climate Change

25.4 Implications for Fisheries

Acknowledgements

References

Chapter 26: Tropical Marine Fishes and Fisheries and Climate Change

26.1 Introduction

26.2 Tropical Currents, Propagule Transport, and Ecosystem Connectivity Under Climate Change Conditions

26.3 Climate-Change Induced Range Shifts of Tropical Fishes and Fisheries

26.4 Fish and Fisheries in the Hottest Seas, Effects of Climate Change

References

Chapter 27: The Impacts of Climate Change on Marine Phytoplankton

27.1 Introduction

27.2 Effects of Increasing Solar UV-B on Phytoplankton Caused by Stratospheric Ozone Depletion

27.3 Light Climate in the Water Column

27.4 Effects of Ocean Warming

27.5 Effects of Ocean Acidification and Changes in Seawater Chemistry

27.6 Effects of Nutrients on Phytoplankton Growth

27.7 Effects of Increasing Pollution

27.8 Conclusions and Future Work

Acknowledgements

References

Chapter 28: Socioeconomic Impacts of Changes to Marine Fisheries and Aquaculture that are Brought About Through Climate Change

28.1 Introduction

28.2 Small-Scale, Artisanal and Subsistence-Based Fisheries of the Western Indian Ocean

28.3 The Traditional Dugong and Turtle Fisheries of the Torres Strait

28.4 Commercial Fisheries of Australia

28.5 Discussion

28.6 Concluding Comments and Directions for Future Research

Acknowledgements

References

Chapter 29: Conclusions

Index

End User License Agreement

List of Illustrations

Chapter 1: Climate Change: The Physical Picture

Figure 1.1 Sunspot cycles from 1610. Data from Hoyt & Schatten (1998).

Figure 1.2 Solar radiation spectrum. From Wikipedia.

Figure 1.3 Variation in Earth's albedo (Earth image: Google Earth).

Figure 1.4 Global mean energy budget under present-day climate conditions. Numbers state magnitudes of the individual energy fluxes. Adapted from IPCC (2013: AR5 WG1 p. 181).

Figure 1.5 Spikes in atmospheric aerosols following major volcanic eruptions (after Mishchenko et al., 2007).

Figure 1.6 Earthshine spectrum and the absorbing greenhouse gases. (After Kiehl & Trenberth, 1997).

Figure 1.7 The Great Ocean Conveyor Belt. Concept sourced from World Ocean Review (2010), Maribus, Hamburg.

Figure 1.8 Global land-ocean temperature annual average (Source: NASA at http://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/GLBTSSST/).

Figure 1.9 Frequency of occurrence (y-axis) of local temperature anomalies divided by local standard deviation (x-axis) obtained by binning all local results for 11-year periods into 0.05 frequency intervals. Area under each curve is unity. Standard deviations are for the indicated base periods (after Hansen et al., 2012, qv for detail).

Figure 1.10 September Arctic sea ice extent (data: National Snow & Ice Data Center).

Figure 1.11 5-year averaged time series of ocean heat content 0–2,000 m. Shaded band is ±2 SD. (After Levitus et al., 2012).

Figure 1.12 Change in ocean partial pressure of CO2 and pH in the sub-tropical North Atlantic Ocean near Bermuda (1985–2012). Black line indicates trend, grey band covers the range of observations (simplified after Bates et al., 2012).

Figure 1.13 Pteropod shells taken at 1,000 m depth (left) and 2,000 m (right) from the Southern Ocean. The dissolution of the aragonite shell taken from deeper water is very obvious. Scale bar is 1 mm. Photos Donna Roberts.

Figure 1.14 Sea level rise since 1860. After Church & White (2011).

Figure 1.15 Geological time scale showing times of cooling (darker) and of warming (lighter). Snowball Earth is marked by the cold segment between 800 and 650 million years ago.

Figure 1.16 Diagram relating climate evidence and deduced CO2 levels in the atmosphere for the past 400 million years. Variation from today's temperature is the upper black line, taken from Zachos et al. (2001) for the period 0–65 million years ago, and from Royer et al. (2004) from 65 million years ago to 400 million years. CO2 through the ages is the broad grey band after Franks et al. (2014). Temperature and CO2 have changed roughly in step, with ice ages and coal deposits providing some of the evidence.

Chapter 2: Future Physical Changes

Figure 2.1 Representative Concentration Pathways (RCPs) under various scenarios of radiative forcing. RCP4.5 is shown as the dashed line. Cubash et al. (2013).

Figure 2.2 CMIP5 projections to 2050 relative to the baseline period of 1986–2005. Kirtman et al. (2013).

Figure 2.3 Pacific Decadal Oscillation (PDO) 5-year average compared with annual global temperature. PDO from http://jisao.washington.edu/pdo/; temperature data GISS.

Figure 2.4 Trends in annual mean surface air temperature in the Arctic and Tropical zones for the period 1960–2009; data from the National Aeronautics and Space Administration Goddard Institute for Space Sciences (NASA GISS) temperature analysis. Base period 1951–1980.

Figure 2.5 Time series for June Arctic sea-ice and for Northern Hemisphere June snow cover. Sea-ice extent provided by the National Snow and Ice Data Center. Snow cover extent provided by the Rutgers University Global Snow Laboratory. All anomalies are relative to the 1981–2010 average.

Figure 2.6 CMIP5 projection of zonal precipitation change for 2016–2035 compared to 1986–2005. The shaded band denotes the 5 to 95% range. Modified from IPCC (2013).

Figure 2.7 Global sea surface temperature (°C) since 1850. Modified by smoothing from HadSST3 from the Climatic Research Unit (University of East Anglia) in conjunction with the Hadley Centre at the UK Met Office.

Figure 2.8 Observed (solid line) and projected (dashed line) change in globally averaged surface ocean temperature based on 12 models from CMIP5 under RCP4.5. Grey band covers the range of all projections RCP 2.6, 4.5, 6.0 and 8.5. After IPCC (2013).

Figure 2.9 The main wind-driven oceanic gyres. After Centre for Multiscale Modeling of Atmospheric Processes at http://www.cmmap.org/learn/climate/oceans4.html.

Figure 2.10 Actual changes in marine chemistry since 1800 and CO2/pH predictions to 2100. Doney et al. (2012).

Figure 2.11 Contributions to sea level rise. Global mean sea level from altimetry from 2005 to 2014 (upper, black line). Ocean mass changes are shown in mid grey (as measured by Gravity Recovery and Climate Experiment (GRACE)) and thermosteric sea level changes (as measured by the Argo Project) are shown in light grey. The dotted line shows the sum of the ocean mass and thermosteric contributions. After IPCC (2013) as updated from Boening et al. (2012) and updated beyond 2012 from Yi et al. (2015).

Figure 2.12 Projected area of summer Arctic sea-ice to 2100 under IPCC RCP4.5. Liu et al. (2013).

Chapter 3: Climate Change Implications for Fisheries and Aquaculture

Figure 3.1 Global fish production (million tonnes) including invertebrates (capture – solid line; aquaculture – dashed line). Original Figure using FAO statistics.

Figure 3.2 Food fish production (million tonnes) in mariculture (coastal brackish and marine environments) and inland aquaculture in 2013. Original Figure using FAO statistics.

Figure 3.3 Kobe plot of potential catch change and national dependency on fisheries per national EEZ. National dependency on fisheries combines the effects of food, economic and employment provision. Circles correspond to the regional centroid, scaled by the expected population in the regions by 2050.

Chapter 4: Biodiversity and Climate Change in the Oceans

Figure 4.1 Summary of the physical and morphological effects of ocean warming and acidification on marine species depending on life stage. Figure drawn by the authors.

Figure 4.2 A common coral reef fish, Pseudochromis fuscus, becomes more active on average under acidified ocean conditions (mid: 600 µatm and high: 900 µatm) than current-day conditions (400 µatm). Significance from planned comparisons (control vs. high) at P <0.05 = *. From Cripps et al. (2011).

Figure 4.3 Egg area (A), clutch size (B) and reproductive output (C; mean egg area × clutch size) produced by pairs of a reef damselfish (Acanthochromis polyacanthus) developed under current-day and future warmer temperature treatments (+1.5 and +3.0°C). Values are mean ± SE. Letters represent significant differences between treatments (P <0.05). Without full development water temperature reduces all reproductive traits (Donelson et al., 2010). Figure from Donelson et al. (2014).

Chapter 5: Impacts of Climate Change on New Zealand Fisheries and Aquaculture

Figure 5.1 Undersea topography of New Zealand (red shallow to blue deep). White dashed line shows the EEZ boundary.

Figure 5.2 Main oceanographic features of New Zealand showing the three main frontal features: Tasman Front (TF), Subtropical Front (STF) and the Subantarctic Front (SAF)/Antarctic Circum-Polar Current (ACC). These separate the main water masses of the New Zealand oceans: Subtropical Water (STW), Subantarctic Water (SAW) and Circumpolar Surface Water (CSW). Also shown are important coastal currents and fronts: West Auckland Current (WAUC), East Auckland Current (EAUC), D'Urville Current (DC), Westland Current (WC), Southland Current (SC), Southland Front (SF), East Cape Current (ECC). Three persistent eddies are important to the east of North Island: North Cape Eddy (NCE), East Cape Eddy ECE, and the Wairarapa Eddy (WE).

Figure 5.3 Major fishing areas in the New Zealand region. Shading indicates the average number of bottom trawls per year (dark colors indicate more fishing effort). The main areas for bottom trawling are the Chatham Rise, Challenger Plateau and Campbell Plateau. Two important areas for aquaculture are also shown: Hauraki Gulf and the Marlborough Sounds. (Data used courtesy of the New Zealand Ministry for Primary Industries, and visualized by M. Pinkerton, © NIWA.)

Figure 5.4 The concentration of chlorophyll-a (mg/m³) as a proxy for ocean primary productivity in the New Zealand region. (a) Median annual chl-a from merged SeaWiFS and MODIS-Aqua observations between 1997 and 2016. (b) An example of an annual chl-a anomaly (difference from the long-term mean) for 2009 showing large positive anomaly in the western Subtropical Front. The boundary of the New Zealand EEZ is also shown. (Data used courtesy of NASA GSFC, and visualized by M. Pinkerton, © NIWA, after Pinkerton, 2016, Figure 3). (See color plate section for the color representation of this figure.)

Figure 5.5 Chlorophyll-a anomalies (differences from typical monthly concentrations, mg/m³) for the New Zealand EEZ. Monthly anomalies between September 1997 and February 2016 showing data from: SeaWiFS (version R2014.0), MODIS-Aqua (R2014.0), MERIS (R2012.1), and the merged (SeaWiFS and MODIS-Aqua) dataset. Data are not shown where there was less than 80% data coverage for a given month in the region. The thick black line is the merged dataset smoothed with a 4-year running mean. (Image produced by M. Pinkerton, © NIWA, after Pinkerton, 2016, Figure 5a.)

Figure 5.6 Food-web model flow diagram for the Hauraki Gulf (present day). Arrows show the direction of organic carbon flow. Bacterial and detrital groups are omitted for clarity. Bigger boxes mean more biomass, and boxes are positioned vertically according to trophic level. Thick/dark lines show higher flows in or out of the group. Groups are: 1 Birds; 2 Cetaceans; 3 Crayfish; 4 Crabs; 5 Seastars; 6 Urchins; 7 Gastropods (carnivorous); 8 Gastropods (grazing); 9 Sea cucumbers; 10 Bivalves; 11 Sponges; 12 Encrusting invertebrates; 13 Macrobenthos; 14 Meiobenthos; 15 Snapper; 16 Jack mackerels; 17 Blue mackerel; 18 Gurnard; 19 Leatherjacket; 20 Tarakihi; 21 Kahawai; 22 Rig; 23 Flatfish; 24 Trevally; 25 Barracouta; 26 Skipjack; 27 Reef fish (large); 28 Reef fish (small); 29 Demersal fish; 30 Sharks; 31 Pelagic fish (large); 32 Pelagic fish (small); 33 Squid; 34 Octopus; 35 Gelatinous zooplankton; 36 Macrozooplankton; 37 Mesozooplankton; 38 Microzooplankton; 39 Nanozooplankton; 40 Phytoplankton; 41 Macroalgae; 42 Mangrove and seagrass; 43 Microphtyes. (Image produced by M. Pinkerton, © NIWA, after Pinkerton et al., 2015a, Figure 3.)

Figure 5.7 Food-web model flow diagram for the Chatham Rise. Other information as for Figure 5.6 except for groups: 1 Birds; 2 Toothed whales; 3 Baleen whales; 4 Seals; 5 Hoki; 6 Orange roughy; 7 Oreos; 8 Warehous; 9 Large javelinfish guild; 10 Small javelinfish guild; 11 Hake guild; 12 Rattails & ghost sharks guild; 13 Ling guild; 14 Demersal fish (small); 15 Mesopelagic fish; 16 Cephalopods; 17 Macrozooplankton; 18 Gelatinous zooplankton; 19 Corals; 20 Encrusting invertebrates; 21 Seastars & brittlestars; 22 Echinoids; 23 Holothurians; 24 Arthropods; 25 Large benthic worms; 26 Shelled megabenthos; 27 Macrobenthos; 28 Meiobenthos; 29 Mesozooplankton; 30 Ciliates; 31 Heterotrophic flagellates; 32 Phytoplankton. (Image produced by M. Pinkerton, © NIWA, after Pinkerton 2014), Figure 3.)

Chapter 6: Impacts of Climate Change on the Marine Resources of Japan

Figure 6.1 Map showing the geography and hydrography in and around the Japanese Archipelago. The Oyashio cold current flows from subarctic sea area of North Pacific and Kuroshio warm current flows from equatorial sea area as a part of subtropical gyre of North pacific. In the Sea of Japan side, the Tsushima warm current, a branch of the Kuroshio warm current, flows from south western Japan and the Liman cold current flows from North Russian sea area.

Figure 6.2 The catch of Japanese sardine (Sardinops melanostictus) from 1965 to 2012. The clear climate regime shift occurred in the late 1970s and late 1980s. Data obtained from the homepage of the ministry of Agriculture, Forestry and Fisheries, Japan (http://www.e-stat.go.jp/SG1/estat/List.do?bid=000001024930&cycode=0).

Figure 6.3 The increasing trend of catch of five-ray yellowtail (Seriola quinqueradiata) (A) and decreasing trend of walleye pollock (Theragra chalcogramma), (B) in Hokkaido, Northern Japan. Data obtained from the official homepage of Hokkaido Prefecture, Japan (http://www.pref.hokkaido.lg.jp/sr/sum/03kanrig/sui_toukei/ruinen_gensei.pdf). The scientific relationship between these trends and the global warming has not been proven.

Chapter 7: Impacts of Climate Change on Eastern Australia Fisheries

Figure 7.1 Bathymetry of north-eastern Australia. Depths are based on the GEBCO 2014 Digital Atlas published by the British Oceanographic Data Centre on behalf of Intergovernmental Oceanographic Commission (IOC) of UNESCO and International Hydrographic Organization (IHO). Australian coastline data used courtesy of NOAA National Centers for Environmental Information (NCEI) Geophysical Data System - Next Generation (GEODAS-NG). Image produced by M. Pinkerton, NIWA, New Zealand .

Figure 7.2 Time series of observed and predicted north eastern Australian barramundi catch adjusted for effort (CAE). The predictive model used July–Sept rainfall and annual evaporation two years prior to capture. Reproduced from: Balston (2009).

Figure 7.3 Map of the north-western Coral Sea showing the major current patterns that influence the eventual recruitment of Panulirus ornatus larvae. Circles indicate areas of plankton sampling conducted during May 1997 and hatched areas indicate the known breeding grounds. Reproduced from: Dennis et al. (2001).

Figure 7.4 Bathymetry of south-eastern Australia. Depths are based on the GEBCO 2014 Digital Atlas published by the British Oceanographic Data Centre on behalf of Intergovernmental Oceanographic Commission (IOC) of UNESCO and International Hydrographic Organization (IHO). Australian coastline data used courtesy of NOAA National Centers for Environmental Information (NCEI) Geophysical Data System - Next Generation (GEODAS-NG). Image produced by Matt Pinkerton, NIWA, New Zealand .

Chapter 8: Climate Change Impacts on Fisheries and Aquaculture of the United States

Figure 8.1 The Gulf of Alaska. Circulation within the Gulf generally flows east to west near shore with the cyclonic (counter-clockwise) Alaska Gyre offshore.

Figure 8.2 Composition of small-mesh trawl catches in the Gulf of Alaska from 1953–1997.

Figure 8.3 Map of the California Current System depicting the major regions (northern, central, southern) and highly simplified direction of flow (large arrow).

Figure 8.4 The Hawaii-based pelagic longline fishery is centered in the North Pacific Subtropical Gyre (grey ellipse) and spans from roughly 5°–40°N, 180°–130°W, as outlined. Historically, the swordfish fishery has been centered near 30°N (striped area) and the bigeye tuna fishery has been concentrated closer to the Main Hawaiian Islands (hatched area).

Figure 8.5 Schematic of major climate-related pressures on the Gulf of Mexico. Overlaying the bathymetry data (GEBCO; www.gebco.net) are: tracks of all major hurricanes for the period 1980–2010 (IBTrACS; www.ncdc.noaa.gov/ibtracs), the position of the Loop Current at 10 m depth (HYCOM; www.hycom.org), the area in which hypoxia generally occurs (interpolated measures of bottom oxygen; SEAMAP surveys for the period 1990–2010), and major rivers of North America (www.naturalearthdata.com).

Figure 8.6 The southeast United States Atlantic coastal region, with arrows showing the general location and direction of the Gulf Stream. Gray points show locations of SERFS seafloor temperatures, and each black × shows locations of SEAMAP-SA bottom temperatures (note that these symbols often overlap). The large black dot shows the location of a moored CO2 probe at Gray's Reef National Marine Sanctuary. Black contour lines show the 30-, 50-, and 100-m deep isobaths.

Figure 8.7 Standardized seafloor water temperatures (°C) based on data from the Southeast Reef Fish Survey and the Southeast Area Monitoring and Assessment Program-South Atlantic Coastal Trawl Survey. Standardization of water temperatures was completed using a generalized additive model that accounted for variability associated with season, depth, and geographic location. Black dots indicate mean annual values, dashed lines show 95% confidence intervals, and the solid line shows a linear model fit through mean values (slope = 0.01; P = 0.63).

Figure 8.8 Map of the Northeast U.S. Shelf Ecosystem showing place names used in the text.

Figure 8.9 Annual average temperature for the Northeast U.S. Shelf based on the NOAA Extended Reconstructed SST v3b (http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.html). The multi-decadal variability (high temperatures in the early 1860s, 1950s, and early 2010) is related to the Atlantic Multi-decadal Oscillation. The long-term temperature increase (straight line) is related to anthropogenic climate change. The shading of the symbols is equivalent to the annual average temperature (e.g., dark is cold, light is warm).

Figure 8.10 A yield-per-recruit analysis for Atlantic croaker comparing conditions as of the 2005 stock assessment (observed) and as projected for the 2050–2100 period under three emission scenarios (commit – 380 ppm CO2 by 2100, B1 – 550 ppm CO2 by 2100, A1B – 720 ppm CO2 by 2100, lines). Recent global CO2 concentrations have reached 400 ppm (http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html).

Chapter 9: Impacts of Climate Change on Mexican Pacific Fisheries

Figure 9.1 Mexican Pacific surface ocean currents: California Current (CC), North Equatorial Countercurrent (NECC), North Equatorial Current (NEC). Lines and Stars: mesoscale phenomena of biological, ecological and fishing importance for the region. Baja California Peninsula (BCP); Gulf of California (GC) .

Figure 9.2 Local explained variance (%) and temporal patterns of the first EOF mode of sea surface temperature anomalies. Filled arrows: warming (dark gray) and cooling (light gray) trend changes in climate regime; unfilled vertical arrows: years with conditions of strong and moderate El Niño (dark gray) and moderate and strong conditions of La Niña (light gray)

Figure 9.3 Annual catches (thousands of tons) of jumbo squid (right axis) and sardines (left axis), the arrows indicate the negative effect on catches of different El Niño events within the past three decades.

Figure 9.4 Contraction (left) and expansion (right) of California sardine populations in the Northeast Pacific at the end of cooling and warming periods respectively

Figure 9.5 Historical change and future projections of the anomalies of sea surface temperature (SSTA) of the California Current; annual time series of observed SSTA (line with circles); historical model of the SSTA obtained from major cycles present in the historical series (Light gray); projections of expected changes in the SSTA taking into account the historical variation (medium dark line); projections of expected changes in the SSTA taking into account historical and changes in slope of the monotonic increase of SST based on climate change scenarios adopted by the INECC (dark grey) (

Chapter 10: Impacts of Climate Change on Marine Fisheries and Aquaculture in Chile

Figure 10.1 Institutional framework for climate change. Adapted from the Spanish version of National Adaptation Plan to Climate Change of 2014 which is adopted in the new PANCC-II of 2016. Draw by the authors based on http://portal.mma.gob.cl/.

Figure 10.2 Climatological winds and currents, austral winter and summer. Winds are from NCEP reanalysis at 1,000 mb, averaged over austral summer and winter. The primary currents are the West Wind Drift (WWD), the Peru Current (PC), the Peru–Chile Countercurrent (PCCC), the Poleward Undercurrent (PUC), the Peru Coastal Current (PCC), the Coastal Current (CCC) and the Cape Horn Current (CHC).

Figure 10.3 Vertical distribution of water mass percentages: (a) Longitudinal section off Peru and Chile (10°–52°S; KRILL, SCORPIO, PIQUERO expeditions); (b) latitudinal section off 28°S (SCORPIO expedition); (c) latitudinal section off 43°S (SCORPIO expedition). STW = Subtropical Water; SAAW = Sub Antarctic Water; ESSW = Equatorial Subsurface Water; AAIW = Antarctic Intermediate Water; PDW = Pacific Deep Water.

Figure 10.4 Average global atmospheric temperatures as deviations from the global mean.

Figure 10.5 Sea surface temperature (annual means) measured in the piers (a–d). The solid lines depict linear regression fits for Callao (>1950: –0.02 ± 0.01°C y–1, p < 0.05), Pisco (>1976, no data before: –0.04 ± 0.02°C y–1, p < 0.05), San Juan (>1976: –0.04 ± 0.02°C y–1, p < 0.05), and Ilo (>1950: –0.02 ± 0.01°C y–1, p < 0.05). Reprinted from Environmental Development, 17, Gutiérrez et al., Productivity and sustainable management of the Humboldt Current Large Marine Ecosystem under climate change, pp. 126–144, Copyright (2011), with permission from Elsevier. Below: Annual average SST in Arica, Iquique, Valparaíso, and Talcahuano.

Figure 10.6 Pacific Decadal Oscillation Index, 1950–2015.

Figure 10.7 Average annual sea level in Arica, Iquique, Valparaiso, and Talcahuano.

Figure 10.8 Oxygen trends at Ocean Station P on the 26.5 (X), 26.7 (◊), 26.9 (+) and 27 (□) isopycnal surfaces and at station P4 (∆) on the 26.7 surface. O2 declining at 1.22 µmol kg–1 y–1.

Figure 10.9 Time series of annual mean 200–700 m dissolved oxygen concentrations (x) since 1975 (µmol kg–1) with linear fits. Analyzed between 3°S and 3°N at (a) 165°–175°W, (b) 135°–145°W, (c) 105°–115°W, (d) 90°–100°W. (a) Includes time series of 26.2–27.8 kg m–3 isopycnal layer.

Figure 10.10 Time series of the monthly average depth of the upper limit of the oxygen minimum layer off Arica.

Figure 10.11 Average kinetic energy (EKE; cm–2 s–2) calculated from the sea level extracted from the IPSL-CM4 model given the most extreme 4CO2 event.

Figure 10.12 Average sea surface temperature simulated with ROMS using: (a) historic forcings (1984–2007), and (b) IPSL-CM4 forcings (2000–2100). Solid black line corresponds to the 18°C isotherm.

Figure 10.13 Administrative and environmental zonification relevant to the benthic fishery of the Chilean coast. (a) Major and persistent upwelling regions; (b) Superior administrative division of the Chilean territory; regions XIV and XV were created in 2007; (c) Sub-division for administration in fisheries zones; in brackets are shown the number of official territorial users rights for fisheries (TURFs) in each zone. Classification of the coastal ecosystem in: (d) provinces; (e) eco-regions. Schematic latitudinal gradients, (f) ΔCO2 fluxes (pCO2 atmosphere – pCO2 seawater), where (+) and (–) indicate CO2 net outgassing and sequestration, respectively. (g) Oxygen minimum zone; horizontal arrows in SST and DCO2 indicate the occurrence of significant anomalies associated to upwelling regions and the inner sea of the Chiloe Island. ¹Strub et al. (1998); ²Thiel et al. (2007); ³SERNAPESCA (2014); ⁴Sullivan et al. (1997); ⁵Ramajo et al. (2013); ⁶Ramajo et al. (2015); ⁷Lardies et al. (2014); ⁸Torres et al. (2011); ⁹Mayol et al. (2012); ¹⁰Fuenzalida et al. (2009).

Figure 10.14 Evolution of Concholepas concholepas landings from 1966 to 2014 are related to changes in the Chilean economic and political models along with regulations of the benthic fisheries. Overexploitation and decline of stocks resulted in a total closure (ban) of the C. concholepas fishery during 1988 to 1992.

Figure 10.15 Sea surface temperature; NOAA monthly records and projections until 2065 for the A2 climate change scenario for (a) anchovy and common sardine (1983–2014); (b) jack mackerel (1973–2014).

Figure 10.16 Sea surface temperature; NOAA monthly records and projections until 2065 for the 4×CO2, climate change scenario for (a) anchovy and common sardine (1983–2014); (b) jack mackerel (1973–2014).

Figure 10.17 Climate change impacts on the physical ecosystem and relevant fisheries off Chile by 2065. Data shown are absolute and relative differences between future A2 high CO2 emissions in 2065 and the base period (2001–2012) for (a) sea surface temperature (SST); (b) swordfish catch per unit effort (CPUE); (c) common sardine CPUE. Source: Reprinted from Progress in Oceanography, 154, Silva et al., Forecasts of swordfish (Xiphias gladius) and common sardine (Strangomera bentincki) off Chile under the A2 IPCC climate change scenario, pp. 343–355, Copyright (2015), with permission from Elsevier .

Figure 10.18 Evolution of Chilean aquaculture harvests by group of resources.

Figure 10.19 Species distribution of Chilean aquaculture export value in USD (a) and volume in t (b), 2014.

Figure 10.20 Evolution of Chilean aquaculture export value (Million USD base 2014) by species, for the period 2003–2014.

Figure 10.21 Evolution of Chilean aquaculture volume by species, for the period 2003–2014.

Chapter 11: The Pacific Island Region: Fisheries, Aquaculture and Climate Change

Figure 11.1 Map of the tropical Pacific Island region showing the five ecological provinces and Pacific Island countries and territories.

Figure 11.2 Annual catch (mt) of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), bigeye (Thunnus obesus) and South Pacific albacore (Thunnus alalunga) tuna in the Western and Central Pacific Convention Area since 1960.

Figure 11.3 Production of: (a) all black-lipped pearl oysters (Pinctada margaritifera) products in French Polynesia; (b) penaeid shrimp in New Caledonia; and (c) seaweed (Kappaphycus alvarezii) in Kiribati between 1998 and 2008. The line represents the value of production.

Figure 11.4 Generalized oceanic food web supporting all species of tuna and other large pelagic fish.

Figure 11.5 Projected distributions of: skipjack tuna (Katsuwonus pelamis); yellowfin tuna (Thunnus albacares); bigeye tuna (Thunnus obesus); and South Pacific albacore tuna (Thunnus alalunga) biomass across the tropical Pacific Ocean under a high emissions scenario; simulations for 2005 and projections for 2050 and 2100 are derived from SEAPODYM, including projected average percentage changes for the outlined areas east and west of 170 E.

Figure 11.6 Example coastal habitats and the fisheries they support: (a) Mangrove-coral connectivity in Western Province, Solomon Islands; (b) a fish market in Papua New Guinea (PNG) selling coastal fish; (c) a fish market in Manus, PNG; and (d) invertebrates for sale in Tongatapu, Tonga.

Figure 11.7 Changes in the state of Pacific coral reef ecosystems caused by climate change: Structurally-complex coral reef habitats that support high species diversity (A); once bleached (B); become overgrown with algae (C); and then collapse to form rubble banks (D); leading to declines in fish diversity and abundance of coral-dependent species (C and D). Predatory fish are expected to decline due to reductions in coral-dependent prey species, while generalist species (e.g., herbivores) may become more abundant on algae-dominated reefs due to access to more food, at least in the short- to medium-term.

Figure 11.8 Aragonite saturation state for the periods: (a) 1986–2005 (based on a multi-model median from the CMIP5 historical simulations); and (b) 2040–2060 (based on a high emissions scenario, with 3 and 3.5 contour lines superimposed. Black dots indicate location of coral reefs.

Chapter 12: Impacts of Climate Change in the United Kingdom and Ireland

Figure 12.1 Projections of sea surface temperature: present day (1960–1989; upper row), future (2070–2098, middle row) and difference (bottom row) for each season.

Figure 12.2 Long-term distribution shifts, from the early 20th to early 21st century, for three North Sea fish species of commercial significance: (a, d) cod (north-eastwards, deepening); (b, e) plaice (north-westwards, deepening); and (c, f) sole (south-westwards, shallowing).

Chapter 13: Canadian Fisheries and Aquaculture: Prospects under a Changing Climate

Figure 13.1 Maps of the Canadian Atlantic (left) and Pacific (right) coasts with major currents and bathymetric features. For the Atlantic, SS is Scotian Shelf, GSL is Gulf of St Lawrence, and GB is the Grand Bank.

Figure 13.2 Reconstructed catches of Atlantic cod from 1800 to 1990, taken from Lear 1998. Catches since 1991 were either under moratorium or strictly regulated recovery plans, and variation in stock status is not reflected in those recent catch records.

Figure 13.3 Reported catch for catches identified as coming from Canadian NAFO zones 2–5 (eastern Canada excluding Arctic). Groundfish includes the most common commercial species. Non-groundfish catches are almost completely mixed invertebrate species. Data from Statlant 21a database (www.nafo.int).

Figure 13.4 Biomass estimates of Pacific halibut from 1888 to 2016, in thousands t. Data provided by International Pacific Halibut Commission, based on 2016 assessment.

Figure 13.5 Pacific Herring – (upper) catches during era of reduction fishery and (below) catches from roe and human consumption fishery (data from http://www.pac.dfo-mpo.gc.ca/science/species-especes/pelagic-pelagique/herring-hareng/herspawn/tabcfram-eng.html). Note the difference in scale of the Y axes between panels (upper) and (below).

Figure 13.6 (b) Change in Realized Thermal Habitat Index of individual species for long-term (2060) scenario. Each boxplot represents the distribution of net change calculated from 10 models. Dashed lines are at 10%. Net change is considered neutral if median net change among 10 subsamples is between dashed lines. Source: Shackell et al. 2014.Figure 13.6 (a) Catches of Pacific salmon from 1925–2014 (Data from North Pacific Anadromous Fish Commission). Lower catches in the 2000s reflect effects of a more restrictive management regime as well as any effects of climate change, so the absence of occasional larger catches is not necessarily indicative of absence of larger runs of salmon in some years.

Figure 13.7 Fit of a dome-shaped capelin biomass model. Open triangles denote capelin acoustic biomass estimates prior to 1991 and filled squares during the post-1990 period. Bars denote 95% confidence intervals. Note that y-axis is in logarithmic scale. Source: Buren et al. (2014b).

Chapter 14: Potential Impacts of Climate Change in Brazilian Marine Fisheries and Aquaculture

Figure 14.1 Location of the Patos Lagoon estuary in Southern Brazil strongly influenced by rainfall patterns and the two main fishery resources – the mullet and pink-shrimp – studied under ENSO events. Right picture from MODIS Aqua Satellite Image NASA.

Figure 14.2 Variation in chlorophyll-a level in the farming site related to cold front events (before and after cold front) in four oyster (Crassostrea gigas) crop time series, and comparison with survival in the seed-juvenile grow out phase in the sheltered farming site of South Bay, Santa Catarina Island, Brazil (modified from Mizuta et al., 2012). Symbols: diamond: chlorophyll a variation in farm waters, horizontal dotted line: total oyster survival.

Chapter 15: South Africa

Figure 15.1 (a) The complexity and variability of the marine environment around southern Africa is partly due to the latitude and associated weather. In summer, the oceanic high-pressure cells either side of southern Africa dominate the wind field, causing south-easterly winds on the west coast and north-easterly winds on the eastern Agulhas Bank and east coast. In winter, the westerly wind belt migrates north, moving cold fronts and strong westerly winds onto southern Africa. (b) The oceanography is also dominated by the warm Agulhas and cold Benguela Currents. These drive many of the physical processes and key features on the shelf.

Figure 15.2 The Benguela Current Large Marine Ecosystem (BCLME) (a) satellite-derived chlorophyll image from GlobColour archive using 8-days composite AVW Chlorophyll-a case I water, merged L3 product. (See color plate section for the color representation of this figure.) (b) The BCLME comprises four subsystems: Angola Subtropical, Northern Benguela, Southern Benguela and South Benguela South Coast. The Angola Benguela Front (ABF) off the Cunene River separates the northern subsystems, as does Cape Point between the Southern Benguela West Coast from the South Coast. Courtesy of John Wiley and Sons, United Kingdom.

Figure 15.3 Annual catches of sardine, anchovy and west coast round herring landed by the South African small pelagic fishery, 1950–2014.

Figure 15.4 Time-series of total biomass (histograms; tonnes) and recruitment strength (lines; billions of fish) of (a) anchovy, (b) sardine, and (c) west coast round herring over the period 1984–2014 estimated from bi-annual hydro-acoustic surveys off South Africa.

Figure 15.5 Time-series of the distribution of relative (% of total) biomass west of Cape Agulhas (WoCA) and east of Cape Agulhas (EoCA) for (a) anchovy and (c) sardine, 1984–2014. (b) scatterplot of relative (% of total) anchovy biomass EoCA and cross-shelf SST difference EoCA (CABO-CABC) for the period 1984–2011 (data for 1984–2005 shown as grey circles are from Roy et al. (2007) and black circles represent data for 2006–2011) and the regression line and equation parameters are shown for the full period. (d) time-series showing changes in the longitude (°E) and latitude (°S) of the centre of gravity (CoG) of adult sardine catches, 1987–2014 (the dashed line indicates the position of Cape Agulhas at 20°E and the approximate position of Cape Town at 34°S).

Figure 15.6 (a) Stylized map of South Africa illustrating the cool-temperate, warm-temperate and subtropical biogeographical regions and stock separation of Pomadasys commersonnii. (b) Proportion of fish in each gonad developmental stage from one inactive to six ripe and running to seven spent in two different time periods. (c) Seasonal proportion of reproductively active fish for two different time periods 1995–2000 and 2012–2013 respectively.

Figure 15.7 (a) Traditional linefish boats on the slipway in Arniston, a small fishing village 20km east of Cape Agulhas. (b) Change in proportion of days with wind speeds above the climatological average (6.68 m/s) over the time series 1985 to 2012. (c) The average daily wind speed is a significant predictor of the mean daily proportion of outings of Arniston. (d) Difference in mean proportion of outings of Arniston fisher boats predicted by wind strength for low wind years (1994, average daily wind speed 5.94 m/s) and high wind years (2008, average daily wind speed 7.48 m/s).

Figure 15.8 (a) Distribution of Loligo reynaudii from hake-directed demersal research trawl surveys. The species is found from Port Alfred in the east to Orange River up the west coast of South Africa to depths of about 300 m, and (b) as far north as Namibia and southern Angola on the south west coast of Africa. (c) Known spawning sites are mainly inshore in shallower than 60 m, along the south east coast of Africa. Adapted from Roberts et al. (2005) with permission of Oxford University Press.

Figure 15.9 Annual tonnage of Loligo reynaudii landed as by-catch by the demersal trawl industry from 1971 to 2014 (black bars); annual catches from the commercial jig fishery from 1985 to 2014 (grey bars).

Figure 15.10 Annual biomass survey abundance indices with 95% confidence intervals, of Loligo reynaudii from hake-directed demersal trawl research surveys conducted off the south coast of South Africa between 1985 and 2014. Surveys in austral autumn and austral spring.

Figure 15.11 Generalized Additive Model (GAM) smooths of the effects of predicted variables (region, bottom temperature, dissolved bottom oxygen, bottom turbidity, bottom depth, total trawl catch, season, region and longitude) on catch rates of (from top to bottom) adult, juveniles and combined adult + juvenile Loligo reynaudii.

Figure 15.12 Map of the South African Coast showing West Coast, Dassen Island and Southern Coast fishing regions. Reproduced from Cockcroft et al. (2008) with permission of NISC.

Chapter 16: The Seychelles Tuna Fishery and Climate Change

Figure 16.1 Average longline tuna catches by species and by 5° square at two periods: 1954–1978 (a) and 2000–2013 (b). Circles denote the level of catches, and pies indicate the relative contribution of yellowfin tuna (Thunnus albacares), bigeye tuna (Thunnus obesus) and the other tuna and billfish species pooled together. Original map designed by the author, from the Indian Ocean Tuna Commission database (http://www.iotc.org/data/datasets).

Figure 16.2 Sketch of a pelagic longline gear. Radio beacons are set at both ends to locate the longline after several hours drifting. Reprinted from Bach et al. (2009) with permission of Elsevier.

Figure 16.3 Tuna and swordfish catches by longliners in the West Indian Ocean and total longline catches for the whole Indian Ocean. Original Figure designed by the author from the Indian Ocean Tuna Commission database.

Figure 16.4 Average purse seine catches by tuna species and by 1° square, 1983–2008. Reprinted from Marsac et al. (2014) with permission of IRD Editions.

Figure 16.5 Sketch of a purse seine deployed around a school of tunas. Reprinted by Marsac et al. (2014) with permission of IRD Editions.

Figure 16.6 Tuna catches by purse seiners in the West Indian Ocean and total Indian Ocean purse seine catches. The item Other refers to neritic tuna species caught by small purse seiners in coastal areas. Original Figure designed by the author from the Indian Ocean Tuna Commission database.

Figure 16.7 Distribution range of three species of tropical tunas (yellowfin, skipjack and bigeye) in the global ocean, based on the catch distribution of purse seine and longline fleets over 1980–2003. Catch data were extracted from the open access databases of four regional fisheries management organizations, the ICCAT for the Atlantic, the IOTC for the Indian Ocean, the SPC (preceding WCPFC) for the Western Pacific and the IATTC for the Eastern Pacific. For yellowfin and skipjack, the two shading patterns denotes catch below (light) or above (dark) 5,000 t. For bigeye tuna, the catch level separating the two shading patterns is 1,000 t. The 18°C and 12°C isotherms plotted on the maps are from the open access database of the World Ocean Atlas 2013 (1° resolution). Original maps designed by the author.

Figure 16.8 Relative density (in %) of tag recoveries (grey shading) for yellowfin skipjack, yellowfin and bigeye tunas tagged during the Indian Ocean Tuna Tagging programme. The boxes indicate the major tagging areas. N is the number of recoveries for each species. Original map designed by the author from data of the open access tag-recovery database of the Indian Ocean TunaCommission.

Figure 16.9 Schematic of surface circulation during the north-east or winter (a) and south-west or summer (b) monsoons, including some choke point transport numbers (Sv = 10⁶ m³ s–1). Legend : South Equatorial Current (SEC), South Equatorial Countercurrent (SECC), Northeast and Southeast Madagascar Current (NEMC and SEMC), East African Coast Current (EACC), Somali Current (SC), Southern Gyre (SG) and Great Whirl (GW) and associated upwelling wedges, Socotra Eddy (SE), Ras al Hadd Jet (RHJ) and upwelling wedges off Oman, West Indian Coast Current (WICC), Laccadive High and Low (LH and LL), East Indian Coast Current (EICC), Southwest and Northeast Monsoon Current (SMC and NMC), South Java Current (JC) and Leeuwin Current (LC). Reprinted from Schott & McCreary (2001) with permission from Elsevier.

Figure 16.10 Monthly sea surface temperature (SST) anomalies in the Indian Ocean, 30°N–30°S, 1880–2015 (data source: NOAA_ERSST_V3 data provided by the National Oceanographic and Atmospheric Administration, Boulder, Colorado, USA, http://www.esrl.noaa.gov/psd/). Anomalies are relative to a monthly-based climatology calculated by the author for 1971–2000. Original Figure designed by the author.

Figure 16.11 Dipole mode and El Niño events, 1950–2015. The Dipole mode index (DMI) shown in grey shading and El Niño sea surface temperature anomalies (black line), representing El Niño events, show different patterns, with compelling lack of correlation during the 1961 and 1994 extreme positive dipoles. Series have been normalized and smoothed using a 5-month running mean. Original Figure designed by the author.

Figure 16.12 Monthly mean chlorophyll (left) and corresponding anomalies (right) for the tropical Indian Ocean observed by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) at the peak of the 1997–1998 El Niño/positive IOD event (November 1997 to February 1998). Climatology is based on SeaWiFS data for 1999–2006 and anomalies are departures from this climatology. Stippling marks denote non-significant anomalies about the mean (±0.02 mg m–3). Spatial grid resolution is 1° longitude by 1/3° latitude. Original Figure designed by the author.

Figure 16.13 Purse seine CPUE (t day–1) on free swimming schools in January (circles) and 20°C isothermal depth (m, color shaded). Data sources: purse seine catch and effort data are available freely on the Indian Ocean Tuna Commission web site (http://www.iotc.org/data/datasets); the 20°C isothermal depth is calculated by interpolation between consecutive depth levels from the Global Ocean Data Assimilation System (GODAS) model outputs of the National Centers for Environmental Prediction (NCEP) (http://www.cpc.noaa.gov/products/GODAS/). (a) Climatological 20°C isothermal depth in January (1980–2005) and average CPUE for 1991–2002. Fishing days: 970. CPUE: 8.5 t day–1, (b) 20°C isothermal depth and CPUE in January 1998. Fishing days: 1,260. CPUE: 2.0 t day–1, (c) 20°C isothermal depth and CPUE in January 2007. Fishing days: 1,367. CPUE: 2.8 t day–1. Original Figure designed by the author .

Figure 16.14 Purse seine catch rates (t day–1) on free swimming schools (black circles) and surface chlorophyll anomalies (mg m–3, color shaded) as measured by SeaWifs (in 1997, a) and Modis (in 2007, b). Stippling marks denote non-significant anomalies about the mean. Spatial grid resolution for chlorophyll is 1° longitude by 1/3° latitude. Original Figure designed by the author.

Figure 16.15 Longline catch rates of the Japanese fleet for yellowfin (in tons per hundred hooks), from September to November, averaged over 1970–1990 (left) and during the growing phase of the 1997–1998 Indian Ocean Dipole (right). The size of the circles is proportional to the CPUE. Reference sizes for CPUEs of 2 and 6 t/100 hooks are represented in the legend. A spatial shift of the core longline fishing zone to the central Indian Ocean occurred during the IO dipole event. Original Figure designed by the author from the Indian Ocean Tuna Commission database.

Figure 16.16 Response of free-school yellowfin purse seine CPUE in the core of the purse seine fishery (0°–10°S) in relation to (a) the 20°C isothermal depth anomaly (z20a) and to (b) the Dipole Mode Index (DMI). The plots are the partial residuals of non-linear terms estimated by means of generalized additive model with smoothing splines. Z20a (in m) is the anomaly relative to 1980–2005 monthly means, by 1°longitude/0.33° latitude. Negative anomalies denote shoaling, positive anomalies denote deepening. Deep thermocline anomalies and high DMI, denoting positive dipoles, are conducive to low tuna CPUE. Data sources: Indian Ocean Tuna Commission and Global Ocean Data Assimilation System of the National Centers for Environmental Prediction. Original Figure designed by the author.

Figure 16.17 Change in stressor intensity in 2090–2099 relative to 1990–1999 under RCP8.5 (highest CO2 scenario). Multi-model mean of (a) sea surface warming (°C), (b) subsurface dissolved oxygen change (average 200–600 m, mmol m–3), (c) surface pH change (pH unit), (d) vertically integrated NPP change (gC m–2 yr–1). Stippling marks high robustness. Robustness is estimated from inter-model standard deviation for SST and pH, from agreement on sign of changes for O2 and NPP. Dark color shading is used to mark the change in stressor that is detrimental for the marine environment (reproduced by courtesy of Laurent Bopp) .

Figure 16.18 Change in stressor intensity in 2090–2099 relative to 1990–1999 under RCP2.6 (high mitigation scenario). Multi-model mean of (a) sea surface warming (°C), (b) subsurface dissolved oxygen change (average 200–600 m, mmol m−3), (c) surface pH change (pH unit), (d) vertically integrated NPP change (gC m−2 yr−1). Stippling marks high robustness. Robustness is estimated from inter-model standard deviation for SST and pH, from agreement on sign of changes for O2 and NPP. Dark color shading is used to mark the change in stressor that is detrimental for the marine environment (reproduced by courtesy of Laurent Bopp) .

Figure 16.19 Change in multiple stressor intensity (defined as the change in the magnitude of the considered variable) in 2090–2099 relative to 1990–1999 under the highest CO2 (RCP8.5) scenario. Dark grey indicates where sea surface warming exceeds its threshold TSST (+3.64°C), thick horizontal marks indicate where subsurface (200–600 m) oxygen concentrations decrease by more than TO2 (–19.9 mmol m–3), and thin horizontal marks indicate where vertically integrated NPP decreases by more than TNPP (–79.8 gC m–2 yr–1). Hatch marks indicate present-day low-oxygen (<50 mmol m–3) subsurface (200–600 m) waters. Reprinted with permission of Laurent Bopp, in Bopp et al. (2013).

Figure 16.20 Expansion of unsuitable thermal habitats for tropical tunas (SST >31°C) from years 2040 to 2099, through a range of RCP scenarios (left to right: from reduced to high radiative forcing scenarios) (adapted from Figure 6 in Dueri et al., 2014).

Figure 16.21 Synoptic sketch of possible spatial shifts of the purse seine fleets operating for tropical tuna in the West Indian Ocean by 2100, under the highest CO2 scenario (RCP8.5). The main shift would occur towards the south-east Indian Ocean, and would result in a relocation of the purse seine fishing effort. The Mozambique Channel would remain partly suitable for purse seine fishing and new area would extend to the Southern tip of Madagascar. This sketch is purely qualitative, resulting from an expert-system based analysis combining the projected change of stressors (temperature, dissolved oxygen, net primary production), tuna habitat suitability and operational requirements of purse seine fishing with regards to the environment. Note that the wind at the sea surface is not accounted for in this qualitative analysis. Original Figure designed by the author.

Chapter 17: The Impact of Climate Change on Marine and Inland Fisheries and Aquaculture in India

Figure 17.1 Map of India and its Exclusive Economic Zone.

Figure 17.2 Atri hot spring, Odisha: the Atri hot spring located in a small village called Atri is about 42 km from Bhubaneswar, and is famed for its hot sulfur water spring (a). The temperature of the spring water is 58°–60°C which always remains steady (b), and is believed to have medicinal properties for curing skin diseases. The bathing complex, located close to the spring provides steam bathing facilities for the tourists. For this, water of the hot spring is collected in a reservoir with a depth of around 15 feet and a circumference of 10 feet (c), and it is provided to the tourist with outlets to prevent stagnation (d). This outlet which carries the hot-spring run-off water, connects to a nearby rivulet, a branch of the river Rananadi (e). The temperature of the confluence and immediate periphery remains at about 36°–38°C and fish are present in this hot water (f).

Figure 17.3 Trends in hsp gene expression in liver tissues of Channa striatus in response to heat stress. The hsps have been grouped into three clusters (a), (b), and (c) based on their similarity/near similarity in response to the heat stress. (a) hsp70, hsp78 and hsp60; (b) hsp90 and hsp110; (c) hsp27 and hsp47. Atri-fish were collected from the Atri hot spring runoff. hsp90 and hsp110, besides hsp70, are required for immediate survival of the fish at high temperature; hsp60, hsp70 and hsp78 are needed for long-term survival at high temperature in Channa.

Chapter 18: Management Adaptation to Climate Change Effect on Fisheries in Western Australia

Figure 18.1 Western Australia map with bioregions and key locations and the mesoscale ecosystems based on the IMCRA4.0 boundaries

Figure 18.2 Monthly values of the Southern Oscillation Index (black), the anomaly of Fremantle sea level (blue) and the anomaly of sea-surface temperature (SST) at the Abrolhos Islands (red) between 1980 and early 2013. The anomalies have been derived by subtracting the long-term mean annual cycle from the individual monthly values, and have been smoothed by a 3-month moving average to reduce small-scale variability and highlight the dominant relationships. (Reproduced with permission of Department of Fisheries, Western Australia) .

Figure 18.3 Linear trend of sea surface temperature in the Indian Ocean during 1950–2012, derived from Hadley Centre sea-surface temperature (SST). Reproduced with permission of Department of Fisheries, Western Australia .

Figure 18.4 (a) Summer water temperatures from the Reynolds SST dataset for the 1-degree blocks off Ningaloo, Shark Bay, the Abrolhos Islands, Rottnest Island, the Capes region, Albany and Esperance. (b) Summer temperature anomalies from the long-term annual cycle. Summer is defined as December–January–February (updated from Caputi et al., 2016), Alan Pearce, pers. comm. Used under CCBY http://creativecommons.org/licenses/by/4.0/).

Figure 18.5 The peak period (February 2011) of the marine heat wave in the summer of 2010/11 from November to March with yellow and brown representing 2 and 3°C above average (modified from Pearce & Feng, 2013). (Reproduced with permission of Department of Fisheries, Western Australia) .

Figure 18.6 The seasonally-averaged sea surface temperature (°C) in summer (months DJF), autumn (MAM), winter (JJA), and spring (SON) from OFAM and ROMS for 1990s and 2060s. The thick black lines are the 20°C, 25°C, and 30°C isotherms. The 100 m isobath (pink line) is also shown (reproduced with permission of Department of Fisheries, Western Australia) (see color plate section for the color representation of this figure).

Figure 18.7 Annual puerulus settlement of the western rock lobster fishery at various locations throughout the fishery (reproduced with permission of Department of Fisheries, Western Australia).

Figure 18.8 Catch, effort and catch rates in the western rock lobster fishery highlighting the effect of catch and effort reductions since 2008 (reproduced with permission of Department of Fisheries, Western Australia).

Figure 18.9 Correlations of average monthly sea surface temperature (SST) in Shark Bay over 2 years (t–1 and t) with (log-transformed) annual commercial standardized trap crab catch rate (July year t to June year t+1) for the period 2000/01 to 2011/12 (reproduced with permission of Department of Fisheries, Western Australia).

Figure 18.10 Relationship between standardized annual commercial trap crab catch rate (year t/t+1) and mean sea-surface temperature (SST) during January–March (t) and April–August (t–1). The year shown indicates that of the commercial catch rate with the January–March SST also plotted. The P next to 12/13 and 13/14 indicated predicted catch rates for these years and the arrow indicates April–August SST for 2013 for catch rate prediction of season 2014/15 (reproduced with permission of Department of Fisheries, Western Australia) .

Figure 18.11 Correlations between the annual scallop recruitment index (log transformed) in northern Shark Bay undertaken in November year t and the monthly sea surface temperature (SST) in the previous 2 years (reproduced with permission of Department of Fisheries, Western Australia).

Chapter 19: Climate Change and Fisheries in the Caribbean

Figure 19.1 The Caribbean region. States integrated in the Caribbean Community and Common Market (CARICOM) are showed in Table 19.2.

Figure 19.2 Coral bleaching in the Caribbean. In 2005, Caribbean coral reefs experienced massive coral bleaching followed by high levels of coral mortality throughout the region.

Chapter 20: Impacts of Climate Change on the Southern Ocean

Figure 20.1 CCAMLR Convention Area with all statistical areas, subareas and divisions (courtesy of CCAMLR).

Figure 20.2 Catch history of current of target species (Source: CCAMLR, 2015b). (a) Antarctic krill (Euphausia superba); (b) Patagonian toothfish (Dissostichus eleginoides); (c) Antarctic toothfish (Dissostichus mawsoni); (d) Mackerel icefish (Champsocephalus gunnari). Note: different scales on y-axis.

Figure 20.3 Shares of different fishing nations in total catches from fishing season 2005/06 to 2013/14 (Data source: CCAMLR, 2015b). (a) Antarctic krill (Euphausia superba); (b) Patagonian toothfish (Dissostichus eleginoides); (c) Antarctic toothfish (Dissostichus mawsoni); (d) Mackerel icefish (Champsocephalus gunnari). (See color plate section for the color representation of this figure.)

Figure 20.4 Some of the finfish species that are or have been the target of Southern Ocean fisheries. (a) Antarctic toothfish Dissostichus mawsoni (courtesy of Emilio Riginella, University of Padova). (b) Mackerel icefish Champsocephalus gunnari (courtesy of Anabela Zavateri, INIDEP, Mar del Plata). (c) Marbled rockcod Notothenia rossii (courtesy of Wolf E. Arntz, Alfred Wegener Institute, Bremerhaven). (See color plate section for the color representation of this figure.)

Chapter 21: Regional Assessment of Climate Change Impacts on Arctic Marine Ecosystems

Figure 21.1 Grids used for estimation of exposure factors by region. Red = Northern Bering Sea (170°E–160°W longitude; 60°–66°N; inflow shelf domain). Blue = Chukchi Sea (180°–155°W; 66°–75°N; inflow shelf domain. Green = Beaufort Sea (155°–125°W; 68°–77°N; interior shelf domain. Light blue = Canadian Archipelago/North Greenland Sea (125°W–0°E; 73°–83°N; outflow shelf domain). Yellow = Barents Sea (18°–60°E; 67°–82°N; inflow shelf domain). Purple = Kara Sea. (See color plate section for the color representation of this figure.)

Chapter 22: Seagrasses and Macroalgae: Importance, Vulnerability and Impacts

Figure 22.1 Fish use (a) seagrass meadows (photo credit: Peter Macreadie) and (b) kelp forests as a nursery grounds (photo credit: Shutterstock).

Figure 22.2 Basic life history patterns of macroalgae. Underlined stages are haploid, and bold stages are diploid. Note gametes can be produced by meiosis (diplontic) or mitosis (haplontic and diplohaplontic). Note: syngamy is the fusion of gametes analogous to fertilization. (Source: modified and granted from Clayton (1990)).

Figure 22.3 Macroalgae (B, C, F) tend to grow on hard substrates, whereas seagrasses (A, D, E) tend to grow on soft sediments. Examples of common macroalgal and seagrass species: (A) Halodule wrightii (shoalweed), (B) Hormosira banksii (Neptune's necklace), (C) Codium fragile (Dead man's fingers), (D) Posidonia oceanica, (E) Halophila ovalis, and (F) Macrocystis pyrifera (giant kelp). Figure created by Peter Macreadie using the Integration and Application Network, University of Maryland Center for Environmental Science (ian.umces.edu/symbols/).

Chapter 23: Impacts of Climate Change on Pelagic Fish and Fisheries

Figure 23.1 A simplified conceptual model of some of the major impacts of climate change on pelagic fish and fisheries.

Figure 23.2 (a) A conceptual model of Pacific sardine and northern anchovy responses to coastal, wind-driven and offshore, curl-driven upwelling and the associated changes favoring small or large phyto- and zooplankton species, respectively (Source: Rykaczewski & Checkley, 2008: Copyright (2008) National Academy of Sciences, USA). (b) Long-term paleorecords of sardine and anchovy scale deposition rates from the Santa Barbara Basin showing pronounced stock fluctuations during the past two millennia

Figure 23.3 (a): Probability of sardine collapse. (b) The mean number of years until collapse and subsequent recovery above a threshold value of 0.09 million metric tons for each combination of sea surface temperature (SST) (±1 °C change relative to observed SST) and catch ratios. Dashed lines show the observed maximum catch ratios (black) before the collapse, and mean catch ratios during the 1980s (gray)

Chapter 24: Lobsters in a Changing Climate

Figure 24.1 Map showing the average annual change in sea surface temperatures (SST) from March until July from 1960 to 2013. SST values used were developed by NOAA and accessed June 2014 (http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html and Smith et al., 2008). Locations identified in the map correspond to the case study fisheries: (A) Panulirus cygnus; (B) P. interruptus; (C) P. argus; (D) Jasus edwardsii; (E) Homarus americanus .

Figure 24.2 Total commercial landings of lobsters of the lobsters covered by the five case studies, Panulirus cygnus, P. argus, P. interruptus, Jasus edwardsii and Homarus americanus. Catches were derived from FAO (2014). Figure drawn by authors.

Figure 24.3 Mean carapace length of the 10% smallest mature female lobsters recorded within the Panulirus cygnus fishery each year, standardized for location and month of capture (updated from Melville-Smith & de Lestang, 2006). Figure drawn by authors.

Figure 24.4 Relationship between mean sea surface temperatures (SST) and normalized commercial landings of Panulirus interruptus. The year of commercial catch is shown on the plot and the corresponding SST values are lagged nine years previously. The area where the mean SST values were derived for is identified by the rectangle in the Pacific Ocean off the coast of Baja in the insert map with the shading of the ocean representing the R² value between the two