Effects of sea surface temperature on tuna catch: Evidence from countries in the Eastern Pacific Ocean
Graphical abstract
Introduction
Ocean temperature is increasing over time, with some regions warming much faster than others. This trend is expected to continue under climate change conditions (Bindoff et al., 2019). As the ocean warms, fish populations adapt by moving to higher latitudes or deeper waters. The spatial redistribution of fishes may lead to rapid changes in marine ecosystems (Perry et al., 2011; Vergés et al., 2019). In turn, these changes affect fishing operations and how effective measures are for managing fisheries (Sumaila et al., 2011).
The impacts of ocean warming vary across regions. The fish catch at high latitudes is predicted to increase with ocean temperature, while production at low and mid latitudes are predicted to decrease, allowing for regional variations (Barange et al., 2014). Researchers have predicted that low-income countries will experience relatively more reductions in fish catch with climate change because these countries are concentrated in tropical and sub-tropical regions of the world. The spatial redistribution of fishes responding to warming waters would also affect employment and export earnings of economies. Government revenue would be similarly affected through fishing licenses sold to distant fishing nations (Sumaila et al., 2011).
Countries have different levels of vulnerability to climate change. The importance of fisheries to national economies is one of the factors that determines this vulnerability (Allison et al., 2009). Island nations such as Kiribati and French Polynesia depend highly on marine ecosystems, and have major pelagic or high value fisheries, such as tuna. These countries rely on revenue from fisheries or access agreements for foreign fishing (Selig et al., 2019).
Tuna are highly migratory and move between coastal ecosystems and the open ocean, and between domestic jurisdictions and international waters. As the ocean warms, tuna move towards areas with preferred habitat temperatures as a compensatory mechanism (Dizon et al. 1977). In the Pacific Ocean, the biomass of yellowfin (Thunnus albacares) and skipjack (Katsuwonus pelamis) tuna is expected to decrease in areas west of 170 °E, and to increase in exclusive economic zones (EEZ) east of 170°E (Bell et al., 2018). The Pacific Ocean is particularly important for tuna as 70 percent of the total global catch in 2010 came from this fishing ground (Lehodey et al., 2013).
In this paper, we determine the relationship between sea surface temperature (SST) and tuna catch in areas within the EEZ of countries in the Eastern Pacific Ocean (EPO). To determine this relationship, we use gridded data and follow the production function approach used in Mediodia et al. (2020). We focus on yellowfin and skipjack tuna because these are the dominant species caught by purse seine (commercial fishing nets) in the EPO. We consider the different types of purse seine sets in the analysis. We calculate for both the marginal product and marginal revenue product of 1 °C increase in SST for the countries included in this study.
We contribute to the literature by providing evidence on the impacts of ocean warming in the EPO. The whole Pacific Ocean is affected by ocean warming, but most studies focus on the effects of ocean waming in the western and central Pacific, as most of the small island developing states are in this area. Our study fills a gap in the literature by focusing on the countries in the Eastern Pacific Ocean.
We extend the method used in Mediodia et al. (2020) by expressing the marginal product of SST in international dollar terms to measure the effects of SST on catch. This method allows us to capture differences in purchasing power across countries. We also express the marginal product of SST in international dollars in relation to coastal population to highlight the differences in the dependence of countries on tuna.
Our results show that the volume of catch of skipjack and yellowfin tuna in the EEZ of countries in the Eastern Pacific Ocean increases as ocean warms. This supports the conclusions of fisheries science studies that showed that tuna dispersed towards the EPO as the ocean warms. We find that Mexico will have the highest increase in tuna catch as the ocean warms due to the size of their EEZ. However, if we adjust for coastal population, the marginal revenue product for island nations, such as Kiribati and French Polynesia, are greater compared to other countries in the EPO.
In the next section, we discuss economic impacts of climate change in fisheries focusing on countries in the Eastern Pacific Ocean. This is followed by a presentation of the model, data, and estimation procedure used in the study. We then discuss the results of the analysis, and finish with some concluding remarks.
Section snippets
Climate change and fisheries
The average ocean temperature is increasing over time due to anthropogenic influences and this trend is expected to continue in the next century (Collins et al., 2013; Rhein et al., 2013; Bindoff et al., 2019). The vulnerability of most organisms to warming is determined by their physiology, which defines their limited temperature ranges and thermal sensitivity (Pörtner et al., 2014), and biological functions such as metabolism, growth, and reproduction (Bindoff et al., 2019). The change in
Model, study area, estimation procedure, and data
We follow the fisheries production function approach used in Mediodia et al. (2020) to link SST and tuna catch. The standard static open access fishery model pioneered by Barbier and Strand (1998) is modified in to account for the relationship between carrying capacity and SST.
We estimate the following equation (Eq 1) assuming a logarithmic relationship between SST and carrying capacity1
Summary statistics
Fig. 2 shows the total area in square kilometres with reported catch and effort data per country for the two data groups. Mexico has the largest fished area for tuna followed by Ecuador, then Peru. Even though the EEZ of the United States of America is larger, only a small portion of this area is fished for tuna. The EEZ of Honduras is covered by only one grid cell, thus it is not included in the presentation of results.
The figure is also indicative of the difference in the area covered for
Marginal product of SST
Fig. 4 shows the increase in tuna catch as a result of 1 °C increase in SST for both yellowfin and skipjack tuna for countries arranged from North to South for each type of sets. The results presented are average values of MPSST computed using the four data groups. Values of MPSST for each data group are presented in Appendix Table 4 . The MPSST are positive except for some countries in which the value is zero for specific species.
If we consider all purse seine sets, the MPSST is highest for
Discussion
We show that the volume of catch of skipjack and yellowfin tuna in the EEZ of countries in the Eastern Pacific Ocean increases as ocean warms. The results of our study support the conclusion in fisheries science studies that showed that the dispersion of tuna towards the eastern part of Pacific Ocean as ocean warms. This result is in contrast with the results of studies on Western and Central Pacific Ocean where there are expected reduction in catch as a result of ocean warming.
Although all
Conclusion
Countries within the Eastern Pacific Ocean gain from the redistribution of tuna due to ocean warming. All countries experience increases in tuna catch as the SST increases. We reached this conclusion after applying a production function approach that links SST to tuna catch through the carrying capacity and intrinsic growth of tuna fisheries. The MPSST of countries in the eastern border of the IATTC agreement area is higher compared to countries in the western border. This difference in
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank Professor Ilan Noy and Dr. Viktoria Kahui for the mentorship and support provided to the author. We are also grateful to Dr. Kenny Bell and the audience at the 2020 New Zealand Agricultural and Resource Economics Society Conference for the helpful comments.
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