Bottom Trawl Surveys - Proceedings of a Workshop Held at Ottawa ...

have also been examined to determine if they
provide measures of these same parameters for
pelagic fish stocks and squid. Distributional
and hydrographic data have occasionally been
utilized in provision of stock management
advice, as have small parts of the biological
data generated by these surveys. Distributional
data, in particular, have been of substantial
interest to those involved in environmental
impact assessment in relation to offshore
industrial development. Most of the
"ancillary" data collected on surveys, however,
has not yet been adequately examined for its
value to be assessed. This review concentrates
on those survey usages which have had a direct
impact on stock management advice.
Several papers describe the surveys and
estimates of finfish biomass derived from them
(Halliday and Kohler,, MS 1971; Grosslein and
Halliday, MS 1972; Halliday, MS 1974a; Koeller,
MS 197g, MS 1980a), and these are essentially
overviews of general biomass trends. Others
describe species distribution and the
hydrographic regime (Scott, MS 1976a, b). These
papers played an important role in devising the
location of the "small mesh gear line" on the
Scotian Shelf, an important regulatory measure
reducing the bycatches of juvenile cod and
haddock. An investigation of age at sexual
maturity for silver hake from survey data
(Doubleday and Halliday, MS 1975; Doubleday et
al., MS 1976), which showed the first maturity
for 75% of females (on average) occurred at age
2 and for the remainder at age 3, strengthened
arguments for an increase in mesh size for this
fishery which depended heavily on age 2 fish.
Abundance estimates utilized have almost
exclusively been in the form of biomass and
population number (or the directly proportional
stratified mean catch-per-standard-tow
calculation on the basis of the equation 5)
without any sort of transformation of the data.
Occasionally, the stratification has been
discarded and unstratified mean catch per tow
utili zed (e.g. , Winters and Moores, MS 1979;
Maguire, MS 197g; Metuzals, MS 1979, MS 1980a
and b). On rare occasion, the ln (x + 1)
transformation has been used (Winters and
Moores, MS 1979, MS 1980; Maguire, MS 1979). It
may be noteworthy that it was herring and
mackerel data which were treated this way. Hare
and Kohler (MS 1974) utilized a logarithmic
transformation in developing a recruitment index
for 4T cod from fixed station surveys. Later
work by Lett (MS 1977) discarded the
transformation as unnecessary. Clay (MS 1980a)
developed a method whereby observations were
removed and replaced by 3-year stratum means
when that observation generated 90% or more of
the stratum biomass estimate and over 20% of the
NAFO Subdivision total estimate. Application to
Division 4VWX redfish data for 1970-79 greatly
reduced annual variability of estimates and
showed trends not obvious in the "uncorrected"
estimates. There has been no critical
examination to determine "best estimators" from
these data.
Variability in the data has quite
frequently been addressed by smoothing, usually
with a 3-year running average (e.g., Gray, MS
1979a; Beacham et al., MS 1980), although a
2-year running average gave the best correlation
with population biomass from cohort analysis for
mackerel (Maguire, MS 1979). The first and last
points when calculating 3-year running averages
have usually been calculated by averaging the
last and second last (or first and second)
points. Sometimes each point is given equal
weight but some analysts prefer to give a
one-third, two-thirds weighting to the second
last and last point, respectively. In cases of
extreme variability, points have been dropped
from the analysis (e.g., Maguire, MS 1980) or
some tows on which the estimate from a
particular survey is based have been deleted to
give a revised estimate (Gray, MS 1979a).
Smoothing techniques have frequently been
used in deriving recruitment estimators. One
method has been an extension of the 3-year
averaging technique which is first applied to
the total population estimate and the adjusted
estimate for each year is then prorated over the
age-group estimates within that year (e.g.,
O'Boyle, MS 1980). Averaging estimates of
year-class size in adjacent years (e.g., mean
numbers of year-class x at age 1 in year t and
at age 2 in year t+1) has been a fairly common
practice (e.g., Cleary, MS 1978, MS 1979;
Metuzals, MS 1979, MS 1980b; Waldron, MS 1980).
Beacham (MS 1980) standardized abundance
estimates at ages 1, 2 and 3 to the mean for
that age, then averaged these relative abundance
estimates at age for ages one to three for each
year-class. Where these techniques have failed,
ratios of mean year-class size estimates from
surveys to the mean year-class size estimated by
cohort (or virtual population) analysis for the
same year-classes have been calculated for some
early period in the data series and this ratio
has been used to adjust current survey indices
of year-class size to absolute estimates (Gray,
MS 1g79a; Maguire, MS 1980).
Historical agreement between survey
abundance estimates and independent estimates,
such as commercial catch rates and population
estimates from cohort or virtual population
analysis, could be taken as validation that
surveys provide estimates of real population
abundance with some (variable) degree of
reliability. A number of statistically good
relationships have been demonstrated,
particularly between survey estimates and cohort
analysis estimates of abundance. These,
however, represent short time series (10 years)
and most of the major changes in abundance
estimates have occurred in the latter part of
the data series - the period for which the
cohort analysis is most sensitive to input
parameters. While these high corre 1 at ions are
encouraging, analysts are essentially assuming
that surveys give valid estimators of
abundance. Possibly because of the limitations
of the data, most analysts have considered only
linear relationships between survey and other
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