Article: Luo J, Ault JS, Shay LK, Hoolihan JP, Prince ED, et al. (2015) Ocean Heat Content Reveals Secrets of Fish Migrations. PLoS ONE 10(10): e0141101. Doi: 10.1371/journal.pone.0141101
Many people who fish will tell you that many species of fish prefer certain water temperatures or certain areas. One type of preferred fish area is a front, a swirling boundary between two water masses of differing temperatures or salinities, and fish seem to like it there because the front often enhances biological productivity. However, most of the limited evidence for fish inhabiting these fronts is pulled from fisheries data or ecological theory. Few methods have provided quantitative evidence to support the theory, in part because fish are hard to track.
One method of tracking fish consists of tagging them with a device that measures temperature, depth, and light levels, as well as a GPS location if the fish is at the surface. The tag eventually pops off the fish and surfaces to transmit all the data it has gathered; hence the name pop-up satellite archival tag (PSAT). The reasons for measuring temperature and depth are clear enough, but why measure light levels? Based on the light measured by the tag, scientists can determine the time of sunrise and sunset, and from this calculate a rough location of the fish, even while it is underwater.
In the past, these rough locations have been refined using the temperature data recorded by the tag. If the fish nears the surface, the temperature can be compared to satellite measurements of sea surface temperature to correct the track to a more likely location. The researchers of this study noticed that sea surface temperature maps are often uniform and featureless in some areas, and may not provide the best check for improving fish tracks (Fig. 1a). Instead, they turned to ocean heat content, a measurement of the total heat contained in the upper layers of the ocean, as a way to map the fronts and eddies of the ocean in more detail (Fig. 1b) and better refine fish locations.
First, the study team followed a previously used procedure for refining the 137 light-based fish location tracks available. Starting with the sunrise/sunset times measured along a track, they applied an algorithm known as a Kalman filter to compare the measured temperature to the sea surface temperature measured from satellites. The algorithm adjusted the rough track locations to better align the tag temperature with the satellite measurement, as well as moved points that were too shallow or on land (Figure 2a). At this point, the tracks could be further refined using ocean heat content measurements.
Ocean heat content is a measurement of the heat contained in the upper layer of the ocean and is usually measured so that we can better track the pathways of hurricanes. The upper layer extends from the surface to where the ocean temperature is 26°C, which is the part of the ocean most important to hurricane formation, and therefore has been well-studied and documented (Figure 3). The heat energy contained in that upper layer is calculated, which gives an estimate of how much thermal energy is available for various processes. Since some of the fish species studied prefer cooler temperatures, the layer was sometimes expanded to lower temperatures (below 26°C), but the process remains the same.
Maps of ocean heat content were generated from an ocean model that uses a variety of near real-time information to predict ocean temperatures at all depths. These maps show more structure and variability than the satellite sea surface temperature maps.
The ocean heat content for each fish track was estimated using the temperature reading from the tag, and then another algorithm was used to further adjust the tracks to better match the ocean heat content maps (Fig. 2b).
The study found that using the more detailed ocean heat content map increased the resolution of the fish tracks. This higher level of detail revealed that most of the fish did tend to follow fronts and eddies and stay on their edges, just as ecological theory predicts (Fig. 3). When the tags were active, these frontal areas made up about 18% of the total habitat available to the fishes. Despite this small fraction, more than half the fish spent over 60% of their tracked time in a front. The authors point out that these results are sensible, since using ocean heat content (which includes temperatures at depth) rather than surface temperatures better reflects the way a fish interacts with the ocean, moving up and down through temperature layers (not just remaining near the surface).
These results, which have not been attained with any other method, show that using ocean heat content to refine the fish tracks greatly enhances the accuracy of the light-based locations. It also finally provides a quantifiable measurement of where fish like to spend their time, affirming what was known based on somewhat vague fisheries data.
Many of the tagged fish were species very popular in fisheries, such as yellowfin tuna, blue marlin, white marlin, and sailfish. Getting a more detailed idea of where these fish like to hang out or where they migrate to breed can help improve fisheries management and conservation efforts, as well as inform fishers where the best catch may be. This research also suggests that using improved fish tag data to help predict ocean heat content in models may be beneficial, as the fish may stay in these fronts even under hurricanes, where ships and other instruments have trouble going. Finally, other ecological studies of all types in the ocean may want to investigate the roles of ocean heat content in other applications, as it appears to have been an influential factor here.
Have you noticed any patterns that wildlife near you tend to follow? How would you go about studying and quantifying them?
Austen Blair is a MS candidate at the University of Rhode Island Graduate School of Oceanography. While his current research focuses on the influences of wave fields in a hurricane-wave-ocean model, he enjoys the many interdisciplinary opportunities the field of oceanography provides. When not doing research, you can find him on the water, rock climbing, or on his bike.