Movement patterns of an iconic recreational fish species, mulloway (Argyrosomus japonicus), revealed by cooperative citizen-science tagging programs in coastal eastern Australia

Abstract

Information on the movements and population structure of an exploited fish species is vital for determining the appropriate spatial scale at which management should occur to ensure sustainable harvesting. However, such information exists for very few exploited species. Large-scale patterns and drivers of movement were examined for an iconic recreational sciaenid species, mulloway (Argyrosomus japonicus), in coastal eastern Australia using an angler-assisted tag-recapture dataset. Over 4300 individuals were tagged and released across 1005 km of coastline over three decades (1988–2017). Six-hundred and fifty-seven individuals were subsequently recaptured at a rate of 15.1% over the same time period. Average time at liberty was 216 (±9) days (range: 0–1954 days), with distances moved ranging from 0 to 355 km. Median movement distance was 4 km, and a large proportion of individuals (73%) were recaptured within 10 km of release locations. Thirty one percent of individuals were recaptured at release locations (< 1 km) and 81% in the same estuary. However, 7% moved distances of > 100 km. Generalised additive modelling revealed that release latitude, body size and time at liberty were significant predictors of distance moved. Greater distances moved were observed for fish tagged at lower latitudes, at larger sizes and over longer periods at liberty. Results indicate that A. japonicus are primarily restricted to small movements (< 10 km) in eastern Australia and display strong site fidelity, despite being capable of movements over larger scales (100s of km). This spatial scale of movement is also much smaller than the current ‘jurisdictional’ scale of management in this region (~1000 km). Assessment and management of A. japonicus in eastern Australia may therefore need to be re-examined considering these findings and potentially undertaken at more localised spatial scales in the future. This study also highlights the importance of citizen science in the cost-effective generation of a sufficiently broad spatio-temporal dataset required to detect the movement patterns revealed here.