Marine Biology, in press

Oxygen consumption of the semiterrestrial crab Pachygrapsus marmoratus in relation to body mass and temperature - an information theory approach

 Stelios Katsanevakis, John Xanthopoulos, Nikos Protopapas, George Verriopoulos

 Department of Zoology-Marine Biology, School of Biology,  University of Athens, Panepistimioupolis, 15784 Athens, Greece 



Pachygrapsus marmoratus is a semi-terrestrial crab and the most common grapsid crab in the intertidal belt of rocky shores throughout the Mediterranean Sea, Black Sea and northeastern Atlantic. In this study, the combined effects of temperature (T), body mass (M), and sex (S) on the routine oxygen consumption rate (R) in P. marmoratus were quantified. The blotted wet body mass of the specimens ranged between 43 mg and 18.0 g, and five test temperatures were used between 13.5 oC and 28.0 oC. Six candidate models that reflected different assumptions regarding the dependence of R on S and T were compared. Model selection was based on Kullback-Leiblerís information theory and Akaikeís information criterion (AIC).  The model  had the highest support by the data (E is the activation energy, B = 8.618x10-5 eV K-1 is Boltzmannís constant, Ta is the absolute temperature in degrees Kelvin, and b the allometric scaling exponent); for P. marmoratus it was found that . No sex dependence of R was supported by the data. Following a multi-model inference approach, the mean (Ī SE) allometric exponent  was 0.750 (Ī 0.013) having a 95% (bootstrap) confidence interval of 0.726 to 0.774. Thus, it was established that P. marmoratus follows Kleiberís ¾ law, as seems to be generally true for intertidal crabs. The allometric exponent was independent of temperature as has also been reported for many other marine invertebrates (at normal temperatures). Q10 values were relatively low, indicating wide thermal tolerance of the species. Model selection based on information theory is recommended for respiration studies, as an effective method in finding a parsimonious approximating model. Multi-model inference by model averaging, based on Akaike weights, is an effective way to make robust parameter estimations and deal with model selection uncertainty.