Ontogenetic resource utilization and migration reconstruction with δ13C values of essential amino acids in the Cynoscion acoupa otolith

With the increasing anthropogenic impacts on fish habitats, it has become more important to understand which primary resources sustain fish populations. This resource utilization can differ between fish life stages, and individuals can migrate between habitats in search of resources. Such lifetime information is difficult to obtain due to the large spatial and temporal scales of fish behavior. The otolith organic matrix has the potential to indicate this resource utilization and migration with C-13 values of essential amino acids (EAAs), which are a direct indication of the primary producers. In a proof-of-concept study, we selected the Acoupa weakfish, Cynoscion acoupa, as a model fish species with distinct ontogenetic migration patterns. While it inhabits the Brazilian mangrove estuaries during juvenile stages, it moves to the coastal shelf as an adult. Thus, we expected that lifetime resource utilization and migration would be reflected in C-13(EAA) patterns and baseline values in C.acoupa otoliths. By analyzing the C.acoupa otolith edges across a size range of 12-119cm, we found that baseline C-13(EAA) values increased with size, which indicated an estuarine to coastal shelf distribution. This trend is highly correlated with inorganic C-13 values. The C-13(EAA) patterns showed that estuarine algae rather than mangrove-derived resources supported the juvenile C.acoupa populations. Around the juvenile size of 40cm, resource utilization overlapped with those of adults and mean baseline C-13(EAA) values increased. This trend was confirmed by comparing otolith core and edges, although with some individuals potentially migrating over longer distances than others. Hence, C-13(EAA) patterns and baseline values in otoliths have great potential to reconstruct ontogenetic shifts in resource use and habitats. The insight could aid in predictions on how environmental changes affect fish populations by identifying the controlling factors at the base of the food web.