Energy content of anchovy and sardine using surrogate calorimetry methods

European anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) are crucial species for the marine ecosystem of the Northwestern Mediterranean Sea. They account for a high percentage of fish landings and they represent an important economic income for the fishery sector. Concerns over their stock status are rising in recent years as biomass, growth, reproductive capacity, and body condition of both species are declining, with latitudinal variations. Therefore, there is an urgent need for a body condition monitoring scheme. Energy storage variability has important implications for both fish recruitment and population structure. Direct condition indices, such as energy density (ED) with bomb calorimetry, are highly reliable for measuring the energy content, but time-consuming. Alternatively, fatmeter analysis and relative condition index (Kn) have been proposed as effective indirect methods. The aim of this study is to test the application of fatmeter as a surrogate of bomb calorimetry to infer the energy content of sardine and anchovy. To validate its use, fatmeter values were compared with both ED and Kn values. Individuals of both species were sampled monthly for a year in order to assess seasonal variations in energy content. Our results highlight that fatmeter measurements are strongly correlated with calorimetry ED for sardine, while a weaker but significant correlation was found for anchovy. The observed differences between the two species are related to their breeding strategies. Based on this study, Kn cannot be considered a good proxy of the energy density of sardine, in particular during the resting period. By contrast, fatmeter analysis appears to be a faster and suitable method to evaluate the energy content of both species routinely. In addition, we provide a linear model to infer ED from fatmeter values for both small pelagic fish. Eventually, these findings could be used to implement body condition monitoring protocols and boost continuous large-scale monitoring.