These average conditions undergo medium- and long-term changes that are
reflected as significant variations of anomalies of some important parameters like
sea temperature and salinity along the depth gradient (Fig. 19.3a and b).
Since the 1980s, the variations of temperature and salinity anomalies have been
particularly important in some periods. For example, in the middle of the 1990s, sea
temperature anomalies significantly higher than two times the standard deviations
were detected in the most shallow layers of the sea. Also, this increase in the sea
surface temperature was very clear between the years 2010 and 2012. Also, anomalies significantly low were observed in several periods along all depth gradients.
This was particularly evident for some years in depths lower than 130 m (years 2002,
2008, and 2010).
A similar anomaly pattern was detected for the salinity (Fig. 19.3b). In this case
the variation of salinity in the surface layers is notable in the early 1990s and
between 2006 and 2010 where the anomalies oscillated between À3 and 3 times
the standard deviation and À 2 and 2 times the standard deviation, respectively.
19.6 Population Status (Assessment Approaches)
19.6.1 Length Cohort and Virtual Population Analyses (LCA
and VPA)
The cohort analysis or virtual population analysis (VPA) (Gulland 1965; Murphy
1965) is a widely known analysis in which it is assumed that the stock is composed
of several annual cohorts, each of which is analyzed and followed separately.
Therefore, VPA is based on backward calculations through time and ages in the
function of all ages in the last year age group for all years and estimates the total
abundance adding the number of individuals fished and lost by natural mortality
during a year to the number of individuals at the end of the year.
The length cohort analysis (LCA) assessment was attempted using the VIT
software (Lleonart and Salat 1992). The VIT program was designed to assess
exploited marine populations caught by means of different gears and basically can
obtain results based on catch data structured by ages or sizes. For this approach, the
main assumption is that the population is in a steady state (equilibrium conditions)
because the program works with pseudo-cohorts. Therefore it is not run with historic
series. As a first step, VIT rebuilds the mortality vectors and population from the
catch data. After that, the user can obtain comprehensive VPA results, including
yield per recruit analyses based on the fishing mortality vector, analyses of sensitivity, and transitional analyses—outside the equilibrium—due to changes in the
pattern of exploitation or recruitment. The stock size estimates, which include annual
recruitment estimates, allow to analyze the yield per recruit (Gil-Herrera et al. 2017).
Spawning stock biomass per recruit (SSB/R) (Gabriel et al. 1989) and yield per
recruit (Y/R) (Beverton and Holt 1957) analysis are procedures commonly used to
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J. Gil-Herrera et al.
reflected as significant variations of anomalies of some important parameters like
sea temperature and salinity along the depth gradient (Fig. 19.3a and b).
Since the 1980s, the variations of temperature and salinity anomalies have been
particularly important in some periods. For example, in the middle of the 1990s, sea
temperature anomalies significantly higher than two times the standard deviations
were detected in the most shallow layers of the sea. Also, this increase in the sea
surface temperature was very clear between the years 2010 and 2012. Also, anomalies significantly low were observed in several periods along all depth gradients.
This was particularly evident for some years in depths lower than 130 m (years 2002,
2008, and 2010).
A similar anomaly pattern was detected for the salinity (Fig. 19.3b). In this case
the variation of salinity in the surface layers is notable in the early 1990s and
between 2006 and 2010 where the anomalies oscillated between À3 and 3 times
the standard deviation and À 2 and 2 times the standard deviation, respectively.
19.6 Population Status (Assessment Approaches)
19.6.1 Length Cohort and Virtual Population Analyses (LCA
and VPA)
The cohort analysis or virtual population analysis (VPA) (Gulland 1965; Murphy
1965) is a widely known analysis in which it is assumed that the stock is composed
of several annual cohorts, each of which is analyzed and followed separately.
Therefore, VPA is based on backward calculations through time and ages in the
function of all ages in the last year age group for all years and estimates the total
abundance adding the number of individuals fished and lost by natural mortality
during a year to the number of individuals at the end of the year.
The length cohort analysis (LCA) assessment was attempted using the VIT
software (Lleonart and Salat 1992). The VIT program was designed to assess
exploited marine populations caught by means of different gears and basically can
obtain results based on catch data structured by ages or sizes. For this approach, the
main assumption is that the population is in a steady state (equilibrium conditions)
because the program works with pseudo-cohorts. Therefore it is not run with historic
series. As a first step, VIT rebuilds the mortality vectors and population from the
catch data. After that, the user can obtain comprehensive VPA results, including
yield per recruit analyses based on the fishing mortality vector, analyses of sensitivity, and transitional analyses—outside the equilibrium—due to changes in the
pattern of exploitation or recruitment. The stock size estimates, which include annual
recruitment estimates, allow to analyze the yield per recruit (Gil-Herrera et al. 2017).
Spawning stock biomass per recruit (SSB/R) (Gabriel et al. 1989) and yield per
recruit (Y/R) (Beverton and Holt 1957) analysis are procedures commonly used to
638
J. Gil-Herrera et al.