Conclusions, Stock Status - All five modeling approaches used in this assessment show that fishing mortality rates declined sharply after the 1995 net-ban and have remained at low levels in recent years in both coasts of Florida. This is consistent with significant drops in landings and fishing effort since the 1995 net-ban. Commercial landings have declined from an annual average of 28 million pounds during 1930-1994 to an annual average of 8.8 million pounds during 1995-2013, a 70% reduction in landings after the net-ban. The fishing effort (number of one-day trips) declined from an annual average of 59,399 trips during 1985-1994 to an annual average of 25,288 trips during 1995-2014, a 57% reduction in fishing effort after the net-ban. Presently, the Florida Fish and Wildlife Conservation Commission, has adopted a target spawning potential ratio (SPR) of 35% (FSPR35%) for striped mullet in Florida. The estimates of current fishing mortality rates relative to FSPR35% (F/FSPR35%) or relative to FMSY (F/FMSY) were below 1 from all five models, suggesting that overfishing is not occurring in either coast of Florida (Table 8). Estimates of current biomass relative to the biomass level at FSPR35% or relative to BMSY (B/BMSY) were above 1 from all five models, suggesting that mullet stocks are not considered to be overfished in Florida (Table 8). Estimates of the SPR from the SS3 model showed that SPR varied between 15% and 25% prior to the 1995 net-ban and then increased sharply to above the 35% target level after 1995, varying around 0.45 on the east coast and around 0.5 on the west coast (Figure 44A and Figure 52A). Phase plots from the delay difference and SS3 models (Figure 31, Figure 34, Figure 44B, and Figure 52B) indicated that mullet stocks are considered healthy in both coasts of Florida given recent fishing mortality rates and biomass levels. The SSRA, ASPIC, delay difference, and SS3 models assumed that the standardized CPUE were a reliable index of adult population abundance, although greater weighting was allowed for the catch. Results from these models were heavily dependent on the trends observed in the catch and CPUE indices given lack of strong signals in the length and age composition data. Because of limited direct aging data from the fishery, the catch-at-age matrices were constructed primarily based on age length keys (ALK) derived from biological studies and fishery-independent samplings. Consequently, the age structures to which the models were fit for this assessment may not be representative of a particular year because the pooled ALK would tend to minimize apparent differences among years. In addition, the pooled ALK would underestimate apparent decline in older age over time and or the influence of weak or strong year classes.