48
3 An ERA Acute Model Overview
3.5.2 Time Factors and Recovery Modelling
In the water column a lag time is not assumed, as the impact will occur within
the annual spawning cycle and the oil in the water column will not be present the
following year as a residual contamination.
The larvae loss is calculated as described in Sect. 3.6.1, as the maximum fraction
killed summed up over all cells in the simulation to a total larval loss for that spill
simulation. The total oil-induced impact (sum of all cells) (Imp total ) on fish eggs and
larvae, representing the year class 0, is input data as a larvae loss to the restoration
model, which expresses impact on the reproductive unit (spawning stock development). Two runs of the global fish restoration model are made, with and without
oil impact to eggs/larvae, using basic parameters of population biology to calculate
expected recruitment (E Recr ) with and without oil, relative to the average recruitment
(Recr Average ). This is then used to calculate the time until the fish spawning stock is
back to pre-spill status (See Brönner et al. 2015 for more detail).
Recruitment of juvenile fish from spawning product to the adult spawning stock
is the result of many complex and interacting factors of both biological and oceanographic origin, and the fluctuation of recruitment success is high, resulting in strong
and weak year classes. Two of the best examined fish species worldwide; Barents Sea
cod (Gadus morhua) and capelin (Mallotus villosus) are used as representative for a
long-lived (cod) and a short-lived (capelin) species. Research of spawning and abundance of juveniles of these two species shows that typical mortality rates in pelagic
spawners are well above 99% already at the end of the larval stage (4–5 months)
(Marshall et al. 2006; Eriksen et al. 2009; Huse and Gjøsæter 1997). For 0-group
and juvenile fish, natural mortality continues to be high, or very high and are strongly
fluctuating.
ERA Acute uses a “gate model” in restoration modeling. The gate specifies
the number of surviving larvae to become recruits, rather than inducing an annual
mortality. The parameter Critical density (default 5%) expresses the threshold for
when a direct relationship is modelled between the size of the spawning stock and
recruitment.
If the analyzed fish stock is above critical density, recruitment is fully independent
of the size of the spawning stock (Eq. 3.13). If the analyzed fish stock is below
critical density the spawning success may be too low for adequate recruitment. The
model then calculates the expected recruitment relative to current spawning stock
size (SS current ) and the long-term average spawning stock (SS average ) (Eq. 3.14):
E recr = Recr average
(3.13)
E recr = Recr average ×
SS current
0.05
× SS average .
(3.14)
Critical oil mortality (%) represents the threshold mortality of eggs and larvae
and defines the level of conservatism for the relationship between larvae mortality
3 An ERA Acute Model Overview
3.5.2 Time Factors and Recovery Modelling
In the water column a lag time is not assumed, as the impact will occur within
the annual spawning cycle and the oil in the water column will not be present the
following year as a residual contamination.
The larvae loss is calculated as described in Sect. 3.6.1, as the maximum fraction
killed summed up over all cells in the simulation to a total larval loss for that spill
simulation. The total oil-induced impact (sum of all cells) (Imp total ) on fish eggs and
larvae, representing the year class 0, is input data as a larvae loss to the restoration
model, which expresses impact on the reproductive unit (spawning stock development). Two runs of the global fish restoration model are made, with and without
oil impact to eggs/larvae, using basic parameters of population biology to calculate
expected recruitment (E Recr ) with and without oil, relative to the average recruitment
(Recr Average ). This is then used to calculate the time until the fish spawning stock is
back to pre-spill status (See Brönner et al. 2015 for more detail).
Recruitment of juvenile fish from spawning product to the adult spawning stock
is the result of many complex and interacting factors of both biological and oceanographic origin, and the fluctuation of recruitment success is high, resulting in strong
and weak year classes. Two of the best examined fish species worldwide; Barents Sea
cod (Gadus morhua) and capelin (Mallotus villosus) are used as representative for a
long-lived (cod) and a short-lived (capelin) species. Research of spawning and abundance of juveniles of these two species shows that typical mortality rates in pelagic
spawners are well above 99% already at the end of the larval stage (4–5 months)
(Marshall et al. 2006; Eriksen et al. 2009; Huse and Gjøsæter 1997). For 0-group
and juvenile fish, natural mortality continues to be high, or very high and are strongly
fluctuating.
ERA Acute uses a “gate model” in restoration modeling. The gate specifies
the number of surviving larvae to become recruits, rather than inducing an annual
mortality. The parameter Critical density (default 5%) expresses the threshold for
when a direct relationship is modelled between the size of the spawning stock and
recruitment.
If the analyzed fish stock is above critical density, recruitment is fully independent
of the size of the spawning stock (Eq. 3.13). If the analyzed fish stock is below
critical density the spawning success may be too low for adequate recruitment. The
model then calculates the expected recruitment relative to current spawning stock
size (SS current ) and the long-term average spawning stock (SS average ) (Eq. 3.14):
E recr = Recr average
(3.13)
E recr = Recr average ×
SS current
0.05
× SS average .
(3.14)
Critical oil mortality (%) represents the threshold mortality of eggs and larvae
and defines the level of conservatism for the relationship between larvae mortality