Intraspecific variation in aerobic and anaerobic locomotion: gilthead sea bream (Sparus aurata) and Trinidadian guppy (Poecilia reticulata) do not exhibit a trade-off between maximum sustained swimming speed and minimum cost of transport

Abstract

Intraspecific variation and trade-off in aerobic and anaerobic traits remain poorly understood in aquatic locomotion. Using gilthead sea bream (Sparus aurata) and Trinidadian guppy (Poecilia reticulata), both axial swimmers, this study tested four hypotheses: (1) gait transition from steady to unsteady (i.e., burst-assisted) swimming is associated with anaerobic metabolism evidenced as excess post exercise oxygen consumption (EPOC); (2) variation in swimming performance (critical swimming speed; Ucrit) correlates with metabolic scope (MS) or anaerobic capacity (i.e., maximum EPOC); (3) there is a trade-off between maximum sustained swimming speed (Usus) and minimum cost of transport (COTmin); and (4) variation in Usus correlates positively with optimum swimming speed (Uopt; i.e., the speed that minimizes energy expenditure per unit of distance traveled). Data collection involved swimming respirometry and video analysis. Results showed that anaerobic swimming costs (i.e., EPOC) increase linearly with the number of bursts in S. aurata, with each burst corresponding to 0.53 mg O2 kg−1. Data are consistent with a previous study on striped surfperch (Embiotoca lateralis), a labriform swimmer, suggesting that the metabolic cost of burst swimming is similar across various types of locomotion. There was no correlation between Ucrit and MS or anaerobic capacity in S. aurata indicating that other factors, including morphological or biomechanical traits, influenced Ucrit. We found no evidence of a trade-off between Usus and COTmin. In fact, data revealed significant negative correlations between Usus and COTmin, suggesting that individuals with high Usus also exhibit low COTmin. Finally, there were positive correlations between Usus and Uopt. Our study demonstrates the energetic importance of anaerobic metabolism during unsteady swimming, and provides intraspecific evidence that superior maximum sustained swimming speed is associated with superior swimming economy and optimum speed.

Field swimming performance of bluegill sunfish, Lepomis macrochirus: implications for field activity cost estimates and laboratory measures of swimming performance

“…The maximal aerobically supported swimming speed of bluegills sunfish is 2.14 L/s (Kendall et al, 2007; D. Ellerby unpublished data, average fish mass 0.104 kg), above which some metabolic power must be supplied anaerobically. This is indicated by excess postexercise oxygen consumption (EPOC), elevated energy expenditure associated with a range of physiological processes including lactate clearance and glycogen synthesis (Milligan, 1996;Peake & Farrell, 2004;Svendsen, Tirsgaard, Cordero, & Steffensen, 2015;Svendsen et al, 2010). The cost-speed relationship being applied was obtained across a speed range designed to exclude anaerobic effort (Kendall et al, 2007;Ellerby & Gerry 2011).…”

“…The cost-speed relationship being applied was obtained across a speed range designed to exclude anaerobic effort (Kendall et al, 2007;Ellerby & Gerry 2011). However, if EPOC costs are included with aerobic energy expenditure during swimming, the total cost continues to follow the curvilinear cost-speed relationship obtained for purely aerobic activity (Lee et al, 2003;Svendsen et al, 2015). Swimming costs for velocities above the aerobic speed limit for bluegills were therefore estimated by extrapolation of the aerobic cost-speed relationship to the measured velocity.…”

Convergent evolution of locomotor morphology but not performance in gymnotiform swimmers

“…The minimum cost of transport (MinCOT, mgO 2 kg −B m −1 and J kg −B m −1 ) and the optimal swimming speed ( U opt ) are derived from the MO 2 versus swimming speed relationship following previous studies (Beecham, Pearson, LaBarre, & Minchew, ; Svendsen, Tirsgaard, Cordero, & Steffensen, ) and provide single numbers that may be used more readily in statistical analyses of species comparisons and may be more readily related to other factors like ecology (Tudorache et al., ). The cost of transport is the amount of energy required to move a unit mass a unit distance and was determined by dividing MO 2 data by swimming speed, U (Svendsen et al., ).…”

“…The minimum cost of transport (MinCOT, mgO 2 kg −B m −1 and J kg −B m −1 ) and the optimal swimming speed ( U opt ) are derived from the MO 2 versus swimming speed relationship following previous studies (Beecham, Pearson, LaBarre, & Minchew, ; Svendsen, Tirsgaard, Cordero, & Steffensen, ) and provide single numbers that may be used more readily in statistical analyses of species comparisons and may be more readily related to other factors like ecology (Tudorache et al., ). The cost of transport is the amount of energy required to move a unit mass a unit distance and was determined by dividing MO 2 data by swimming speed, U (Svendsen et al., ). We then fit the relationship between MO 2 and swimming speed to a quadratic function to expand the range of velocities over which we can predict cost of transport: and again compared a mixed model to a fixed model, in this case, the fixed model was preferred for each species (∆AIC c < 2, results not shown).…”

“…MorphoPC1 explained 62% of the variation in the data and described species with long but shallow anal fin as exhibited by gymnotiform swimmers or a long dorsal fin exhibited by the Aba Aba (Gymnarchus niloticus). MorphoPC2 explained 24% of the variation in the data set and was largely driven by body size, with all traits loading in the same direction (see Supporting Information Table S6) The minimum cost of transport (MinCOT, mgO 2 kg −B m −1 and J kg −B m −1 ) and the optimal swimming speed (U opt ) are derived from the MO 2 versus swimming speed relationship following previous studies (Beecham, Pearson, LaBarre, & Minchew, 2009;Svendsen, Tirsgaard, Cordero, & Steffensen, 2015) and provide single numbers that may be used more readily in statistical analyses of species comparisons and may be more readily related to other factors like ecology (Tudorache et al, 2008). The cost of transport is the amount of energy required to move a unit mass a unit distance and was determined by dividing MO 2 data by swimming speed, U (Svendsen et al, 2015).…”