# function used below to report mean rates meanrate <- function(f, y) { model$discard %>% dplyr::filter(Fleet == f, Yr %in% y) %>% dplyr::select(Obs) %>% apply(., MARGIN = 2, FUN = mean) %>% magrittr::multiply_by(100) %>% round(0) %>% paste0(.,"\\%") }
Discard rates were modeled for the commercial fisheries only (TW and FG)
using data from the West Coast Groundfish Observer Program (WCGOP).
This program is part of the NWFSC and has been recording discard observations
starting in 2002. Since 2011, when the IFQ program was implemented,
observer coverage rates increased to nearly 100 percent for all the
limited entry trawl vessels in the program and discard rates
for the trawl fishery declined compared to pre-2011 rates.
Discard rates were obtained for both the
IFQ catch-share (observed and electronic monitored vessels) and
the non-catch share sector for r spp
. A single aggregated annual
discard rate for each fleet was calculated by weighting discard rates
from three sectors within each gear group:
catch-shares, non-catch-shares, and electronic monitoring, where
the weights were based on the commercial landings by each sector.
The variances of the total discard estimates were calculated for the non-catch shares sector and pre-catch share years by bootstrapping vessels within ports because the observer program randomly chooses vessels within ports to be observed. Post trawl rationalization, all catch-share vessels (including electronic monitoring) have 100 percent observer coverage and discarding from the catch-share subset of the fleet is assumed to be known.
The variance of the aggregated annual discard amount was calculated as the sum of the variances of the total annual discard for each sector under the assumption that the variances are independent. This variance of the total discard amount was then converted to a standard error of the aggregated discard rate.
``{asis, echo = TRUE, eval = ifelse(params$area == "North", TRUE, FALSE)}
The resulting aggregated discard rates for the commercial trawl fishery
show high values (
r meanrate(1, 2002:2006))
during the first five years of data: 2002-2006,
then declining to an average of
r meanrate(1, 2011:2019)` during the
catch-shares period (2011-onward).
The rates from the catch-shares period are more precise due to the
100\% observer coverage during this period.
The commercial fixed-gear discard rates were never as high as the
trawl rates and did not fall as low during the catch-shares period
(the catch-shares management only applied to a subset of the fixed-gear
catches). After a lower initial value of r meanrate(2, 2002)
in 2002,
the rates were stable over the period 2003-2010, with a mean of
r meanrate(2, 2003:2010)
and then dropped to a mean of
r meanrate(2, 2011:2019)
for the period 2011-onward.
The precise trawl discard rates from 2011 onward were found in initial model runs to be overly influential so a value of 0.05 was added to the standard error of all discard rates to account for unmodeled variability in the retention process rather than add additional parameters representing that annual variability.
```{asis, echo = TRUE, eval = ifelse(params$area == "South", TRUE, FALSE)} The resulting aggregated discard rates for the commercial trawl fishery show high values (`r meanrate(1, 2002:2005)`), during the first four years of data: 2002-2005, a low rate of `r meanrate(1, 2006)` in 2006, three years of moderate rates, and then a lower period with mean `r meanrate(1, 2010:2019)` starting in 2010, beginning prior to the implementation of catch shares in 2011. The contrast before and after 2011 is smaller for the south than the north. The rates from the catch-shares period are more precise due to the 100\% observer coverage during this period. The commercial fixed-gear discard rates were showed little contrast over time, with a mean of `r meanrate(2, 2002:2019)` over the full 2002-2019 range of data. The precise trawl discard rates from 2011 onward were found in initial model runs to be overly influential so a value of 0.05 was added to the standard error of all discard rates to account for unmodeled variability in the retention process rather than add additional parameters representing that annual variability.
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