4.2 Variability among krill aggregation types
Aggregative behavior is common in krill (Hamner, 1984) and has been described using numerous classification schemes including layers, superpatches, and diffuse “clouds” (Kalinowski and Witek, 1985); discrete and irregular aggregations (Watkins and Brierley, 2002); and small, standard, and large swarms (Tarling et al., 2009). Similarly, a variety of detection methods have been used to identify and classify krill aggregations including visual classification within echograms (Kalinowski and Witek, 1985), or school detection algorithms and hierarchical clustering and ordination (Tarling et al., 2009). Despite differences in data acquisition and aggregation detection methods, the preponderance of krill aggregation classification studies all include large aggregations extending over several kilometres, scattered forms comprised of smaller aggregations, and various other classes of intermediate shapes and patterns (Ricketts et al., 1992).
There is no consensus on a universal approach to characterize krill aggregations. Studies characterizing krill aggregations have used a variety of instruments that evolved over manufacturer product generations, using different descriptive metrics, and a range of spatial and/or temporal lag periods, which may all affect comparisons of aggregation types among results. Moving and stationary platforms may differ in horizontal chord lengths of aggregations, depending on platform speeds and current velocities. While aggregation types identified from alternate platforms will be consistent internally, caution must be taken when comparing types detected from stationary and moving platforms. The combination of the SHAPES and Echometrics metric suites used to characterize krill aggregations are comprehensive but may be sensitive to variations in their values to provide a universal description of aggregation types in Bransfield Strait. The Echoview school detection module used to identify krill aggregations is designed to isolate discrete aggregations with well-defined boundaries (Burgos and Horne, 2008), which is not always the case for krill as they also form diffuse clouds not detected by the algorithm. Potential differences in data acquisition and processing methods used among studies potentially obfuscates if resulting differences are due to krill behavioral differences, location-specific environmental conditions, or to differences in measurement and analytic methodologies.
As one example, Kalinowski and Witek (1985) recorded four types of krill aggregations in the West Atlantic sector of the Southern Ocean. They described irregular aggregation shapes of several tens of meters high and several hundred thousand meters long. Off the South Shetland Islands, krill were observed to form “super aggregations” of approximately 200 m in at least one dimension and hollow domes, flat sheets, or long-thin ribbons, with the aggregations constantly changing shape and position (Hamner, 1984). More recent work identified three aggregation types in the vicinity of the South Shetland Islands (Cox et al., 2010) and in the Western Antarctic Peninsula (Bernard et al., 2017). In the latter, Type I aggregations were larger, shallower, and had higher biomass than Type II aggregations, which were deeper and had the lowest biomass. Type III aggregations were the smallest type (Bernard et al., 2017). Aggregation types ‘a’, ‘b’ and ‘c’ described in this study are similar in densities, but not in their depths, as those identified in Bernard et al. (2017) from an inflatable boat platform. Across studies, general patterns in krill aggregations can be inferred such as long layers and diffuse “clouds”, with a variety of intermediate shapes and forms. Krill are long known to form different types of aggregations depending on their habitat and environmental/biological conditions (Haury et al., 1978). Primary scales of temporal variability in krill distribution also differed between Nelson (2 h and 5.3 h) and Deception Island (2.7 h and 8 h). Since both the environmental and biological conditions differ across the Antarctic Peninsula, it seems plausible that no pair of sites would have similar aggregation patterns.
A variety of factors are known to influence the size and shapes of krill aggregations over space and time. The shape of an aggregation is impacted by organisms balancing access to oxygenated water against predation risk (Hamner, 1984; Brierley and Cox, 2010). Krill aggregation size may ultimately be constrained by oxygen availability (Johnson et al., 1984), which might explain why very large aggregations (‘super-patches’) were the least common aggregation types identified in this study. The beam diameters of the echosounders and the sampling strategy of switching the WBAT for approximately one hour may also prevent the detection of super-patches of krill. Aggregations are believed to be dynamic features that form and disperse (Hamner, 1984; Macaulay et al., 1984; Brierley and Cox, 2010). Therefore, any particular aggregation observed is a trade-off among multiple biological and environmental factors such as self-pollution by excretion (Johnson et al., 1984), nearest neighbor distance (Gueron et al., 1996; Tien et al., 2004), tidal regime, surface currents (Bernard and Steinberg, 2013; Bernard et al., 2017), and presence of predators (O’Brien, 1987). The presence of diving predators would potentially influence the thickness and density of krill aggregations due to krill escape reactions. Krill avoid predators by adopting a range of strategies such as “molting” (i.e., shedding their exoskeleton; Hamner, 1984) and darting backward (Kils, 1979) that result in the contraction or expansion of aggregations (O’Brien, 1987).
4.3 Predator-prey interactions
Potential predator candidates creating the ‘U’, ‘V’ or ‘W’-shaped dives observed on echograms at E2 and E4 may be seals, whales, penguins and/or other seabirds (Charrassin et al., 2002; Calambokidis et al., 2007; Godø et al., 2013; Viviant et al., 2014; Fais et al., 2016). These predators were sighted by observers in Bransfield Strait during the survey and penguins were breeding within the vicinity of the study sites (Krafft et al., 2019). The ‘V’-shaped dives with no bottom phase over diffuse aggregations or acoustically “empty” water can be further interpreted as search or foraging dives where the predator was unable to locate prey (Chappell et al., 1993). “Wiggles” observed in some of the dive profiles at E2 and E4 and may be linked to predators pursuing and capturing prey from above (e.g., Viviant et al., 2014; Cimino et al., 2016). The greater percentages of predator dive profiles within the first 50 m of the water column are consistent with preferred dive depths of Antarctic fur seals, Chinstrap, Adélie, Gentoo and Macaroni penguins (Chappell et al., 1993; Wilson, 2010; Blanchet et al., 2013). Previous studies have shown that although these penguins can dive down to 100 m, more than 50% of their dives ended at depths less than 40 m (Wilson, 2010). Given the characteristics of whale dive profiles (Croll et al., 2001; Calambokidis et al., 2007; Goldbogen et al., 2011; Nowacek et al., 2011; Fais et al., 2016), they would be seen in lower numbers within our upward-looking acoustic beams.
In addition to predator foraging strategies, prey distribution patterns strongly influence predator foraging behavior (Alonzo et al., 2003, Cutter Jr. et al., 2022), with groups of predators feeding more effectively on swarming prey (Hamner, 1984). Chinstrap and Adélie penguins were observed to synchronously dive and forage on krill aggregations (Hamner, 1984; Hinke et al., 2021). Group foraging behavior (i.e., multiple dive profiles observed over a particular krill aggregation) was observed at Deception and Nelson Islands but is less common than recordings of individual dive profiles. Among detected krill aggregations, type ‘a’ (which had high densities and were evenly spread) had the highest percentage and occurrences of multiple dive profiles compared to the other aggregation types. Type ‘b’ aggregations, which were low density, recorded greater percentages of zero dive profiles than types ‘a’ and ‘c’. Groups of predators appear to preferentially dive over dense krill aggregations at Nelson and Deception Islands.
While shallow krill aggregations are predicted to attract diving penguins (Chappell et al., 1993; Bernard et al., 2017), we observed that aggregation types with the highest densities, even if not found at the surface, attracted diving predators. Once a dense aggregation type is detected, predators may forage individually and in groups on those aggregations and return to areas where encounter rates with that particular aggregation type was highest (e.g., Santora et al., 2009). The large number of predator dives recorded over diffuse rather than discrete aggregations at Nelson Island is consistent with the observation that most krill predators are believed to target individuals (Brierley and Cox, 2010) rather than aggregations, or, that these dives are search rather than foraging dives. At Deception Island, a greater percentage of predator dive profiles occurred over empty water compared to detected krill aggregations. The inferred low successful foraging rates can be attributed to spatial segregation among predators within their foraging ranges (Trivelpiece et al., 1987), that result in longer travel distances within Bransfield Strait to reduce intra- and inter- specific predator competition. If true then predators would only pass through the E4 acoustic beam close Deception Island during their transit to other prey locations (Hunt et al., 1992).
Biological responses to climate-induced changes in the Southern Ocean may impact both krill dynamics and their predators’ foraging success. Future ocean warming is predicted to decrease oxygen levels by 3% for every 1°C of warming, resulting in krill aggregations becoming smaller or less densely packed (Brierley and Cox, 2010), which may further impact predator foraging. Future work will associate predator dive profiles with known predator foraging dive patterns (e.g., Chappell et al., 1993; Schreer et al., 2001; Viviant et al., 2004; Calambokis et al., 2007). Rapid and consistent krill aggregation and predator classifications from acoustic data can then be used in simulation models such as the Krill-Predator-Fishery Model (Watters et al., 2013) or the Spatial Multi-Species Operating Model (Plagányi and Butterworth, 2006) to predict the krill–predator–fishery interactions in krill management units defined by CCAMLR. When combined with knowledge of regional ocean dynamics, predator-krill surveys would allow critical predator foraging grounds to be identified, and the distribution of catch and fishing effort to be adapted to changes in krill distributions and predator foraging patterns.
Summary
Aggregation patterns of Antarctic krill, Euphausia superba, were characterized in two environmentally contrasting sites in Bransfield Strait using 70 kHz data from moored echosounders. Echometrics (Urmy et al., 2012) and hierarchical clustering were used to characterize and classify recorded aggregations. This study showed that krill aggregations respond to environmental and biological factors such as current velocities, directions, and presence of predators, with aggregations that are temporally heterogeneous in density and mean thickness. Detection of predator dive profiles within stationary echogram records illustrated diverse dive shapes and potential foraging strategies. Predator dive profiles and krill aggregations are closely linked, with the predator dives influencing the size and shape of aggregations and the krill densities influencing the predator foraging strategies.