Anchovy recruitment with a bio-physical model in
the Peru current system
ICES 2010
SESSION L
A reanalysis of the 1992-2008 period
†
†
Timothée Brochier*, Vincent Echevin*, Ricardo Oliveros Ramos , Christophe Hourdin*, Jorge Tam
* : Laboratoire d'Océanographie et du Climat: Expérimentations et approches numériques (LOCEAN) Unité Mixte de Recherche 7159 CNRS / IRD / Université Pierre et Marie Curie/MNHN. Institut
Pierre Simon Laplace (IPSL), Paris, France, [email protected]
† : Centro de Investigaciones en Modelado Oceanografico y Biologico Pesquero (CIMOBP). Instituto del Mar del Peru (IMARPE), Callao, Peru, [email protected]
The northern Humboldt current system, or Peru current system, sustain the world biggest fisheries. The main exploited species is the anchovy
Engraulis encrasicolus. As it is common for small pelagic fishes in upwelling ecosystem, the peruvian anchovy present a huge variability in annual
recruitment, making difficult the fisheries management. Bakun (1996) proposed a conceptual framework based on physical processes that may
determine the recruitment. In this context we present here an assemblage of (1) an hydrodynamic model forcing (2) a biogeochemical model, and
(3) an anchovy early life stage individual based model. We then compare the result to another recruitment series obtained by a conventional method
using different information input for the 1992-1999 period.
Material and Methods
2.1 - Model
2.2 Input data
The input data for the biological part of the model was the observed
spawning patterns, used as initial conditions for release of virtual eggs.
IMARPE field observations were aggregated to construct a monthly
time-series of spatiotemporal spawning patterns (Fig. 2). For periods
with no observations we complete the time-series with a monthly
climatology built with observations from 1961 to 2008.
N/m2
We used the Regional Ocean Modeling System
(ROMS), providing 3D currents, temperature and
salinity fields. The grid covered most of the western
south America coast with an horizontal resolution of
1/6° (~15 km) and 32 vertical levels using scoordinate (following bathymetry). The interannual
boundary conditions were provided by the global
model ORCA, and the surface forcing (wind and
heat fluxes) were derived from NCEP reanalysis for
the 1992-1999 period. A biogeochemical model,
PISCES, resolve nutrients, oxygen, phytoplankton
production (Fig. 1) and zooplankton dynamics in
each grid cell. Finally, the individual based model
Ichthyop was forced by the archived ROMS-PISCES
output (every 5 days) and simulate the anchovy
spawning and early life stages, including 3D-current
advection, egg buoyancy and larval vertical diurnal
migration from surface to 50m.
1 2 3 4 5 6 7 8 9 10 11 12
Figure 1: Vertically integrated chlorophyll a distribution (ROMS-PISCES) and 30
days ichthyoplankton drift, August 1993. «*» and «o» : initial and final positions.
Dotted line : 1000m isobat.
Figure 2 : Anchovies eggs distribution (IMARPE, 1963-2002)
2.3 Simulations
Each simulation consist in 8-year virtual spawning during the 1992-1999 period. A constant number (500) of virtual eggs was released each day over the observed eggs spatial distribution and
tracked for one month. Each egg is a super-individual that represents the observed spawning intensity at each location. In order to separate the effect of spawning intensity seasonality and spatial
variability on the recruitment, both results (with or without super-individuals) are showed. Three different recruitment criterion were tested :
Simulation 1 : retention over the continental shelf (500m)
Simulation 2 : retention within the area where surface chlorophyll a is greater than 1mg.L-1 (SeaWiFS climatology from 1997 to 2005)
Simulation 3 : retention within the area where vertically integrated chlorophyll a is greater than 20g.m-2 (ROMS-PISCES model)
•
2.4 Validation data
The results of each simulation are compared with a classical reanalysis of the recruitment based on fisheries catch and acoustic campaign for stock evaluation. This method provided time-series of
pre-recruitment for (1) 2-6 month old individuals and (2) an estimation of reproductive success for the two main spawning periods, summer and winter. As the bio-physical model tracked
individuals for only one month, the results were transformed to fit the classical method by considering a constant mortality during the non-simulated period.
Results & Discussion
Simulation 3
Simulation 2
0.8
0.6
0.4
0.2
Feb91 Aug91 Feb92 Aug92 Feb93 Aug93 Feb94 Aug94 Feb95 Aug95 Feb96 Aug96 Feb97 Aug97 Feb98 Aug98 Feb99 Aug99 Feb00 Aug00
0.4
0.2
Jan91 Jul91 Jan92 Jul92 Jan93 Jul93 Jan94 Jul94 Jan95 Jul95 Jan96 Jul96 Jan97 Jul97 Jan98 Jul98 Jan99 Jul99 Jan00 Jul00
Jan91 Jul91 Jan92 Jul92 Jan93 Jul93 Jan94 Jul94 Jan95 Jul95 Jan96 Jul96 Jan97 Jul97 Jan98 Jul98 Jan99 Jul99 Jan00 Jul00
Edad0 (spawning success) for summer and winter: Bioïphysical Vs IMARPE
1
0.8
0.6
0.4
0.2
Edad0 (spawning success) for summer and winter: Bioïphysical Vs IMARPE
1
0.8
0.6
0.4
0.2
Feb91 Aug91 Feb92 Aug92 Feb93 Aug93 Feb94 Aug94 Feb95 Aug95 Feb96 Aug96 Feb97 Aug97 Feb98 Aug98 Feb99 Aug99 Feb00 Aug00
Feb91 Aug91 Feb92 Aug92 Feb93 Aug93 Feb94 Aug94 Feb95 Aug95 Feb96 Aug96 Feb97 Aug97 Feb98 Aug98 Feb99 Aug99 Feb00 Aug00
Seasonnality PreREC (2ï6 month old)
0.4
0.3
0.2
2
1
0.1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
4
0.4
3
0.3
2
0.2
1
0.1
5
0.7
4
0.6
0.5
3
0.4
2
0.3
0.2
1
0.1
0
ICHTHYOP
IMARPE
0
- Serie : no fit; shelf retentio increase in 97-98.
- Clim : 2-6 month pre-recruitment predicted was too weak from
Septembre to December, because shelf retention was much lower
during austral winter than in summer (right).
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.5
0.8
6
0.7
5
0.6
4
0.5
0.4
3
0.3
2
0.2
3
2.5
2
1.5
1
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Seasonal cycle of surface chlorophyll a over
the continental shelf, from 4°S to 14°S
(SeaWiFS 1997-2005)
1
0.1
0
4
Chlorophyll a [mg.mï3]
0.8
JanïFebïMar
JulïAugïSep 7
0.9
0.6
x 10
8
ICHTHYOP
IMARPE
0
- Serie : Retention in SeaWiFS Chla increase in 97-98
- Clim : The recruitment seasonality is accentuated by the combination
of SeaWiFS Chla variability and retention seasonality.
PreREC ICHTHYOP pondere
3
0.5
6
11
AGE 0 IMARPE
0.5
5
1
0.9
6
0.6
x 10
spawning success ICHTHYOP
4
0.7
Reproduction success (age 0)
11
PreREC RICARDO
0.6
5
PreREC RICARDO
PreREC ICHTHYOP pondere
0.7
1
JanïFebïMar
JulïAugïSep 7
0.8
0.8
Seasonnality PreREC (2ï6 month old)
PreREC ICHTHYOP pondere
6
11
x 10
8
AGE 0 IMARPE
0.9
Reprodusction success (age 0)
11
x 10
spawning success ICHTHYOP
Seasonnality PreREC (2ï6 month old)
0.5
0.4
0.3
Reproduction success (age 0)
11
x 10
6
4
3
0.2
2
0.1
1
JanïFebïMar
JulïAugïSep 7
0.7
5
11
x 10
8
0.6
6
0.5
5
0.4
4
0.3
3
0.2
2
0.1
1
90
80
70
60
50
40
30
1992
1993
1994
1995
1996
1997
1998
1999
Integrated chlorophyll a over the continental shelf, from 4°S to
14°S (ROMS-PISCES)
75
70
65
60
0
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
100
Integrated chlorophyll a [mg.mï2]
Jan91 Jul91 Jan92 Jul92 Jan93 Jul93 Jan94 Jul94 Jan95 Jul95 Jan96 Jul96 Jan97 Jul97 Jan98 Jul98 Jan99 Jul99 Jan00 Jul00
Ichthyop simple
Edad0 (spawning success) for summer and winter: Bioïphysical Vs IMARPE
IMARPE
Ichthyop pondere
1
0.2
0.6
Integrated chlorophyll a [mg.mï2]
0.2
0.4
0.8
Ichthyop simple
IMARPE
Ichthyop pondere
AGE 0 IMARPE
0.4
0.6
1
spawning success ICHTHYOP
0.6
0.8
Ichthyop simple
IMARPE
Ichthyop pondere
Preïrecruits (2ï6 month): Bioïphysical Vs IMARPE
PreREC RICARDO
0.8
1
Index of preïrecruitment
1
Preïrecruits (2ï6 month): Bioïphysical Vs IMARPE
Index of spawning success
Index of preïrecruitment
Preïrecruits (2ï6 month): Bioïphysical Vs IMARPE
Index of spawning success
Index of spawning success
Index of preïrecruitment
Simulation 1
0
ICHTHYOP
IMARPE
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Seasonal cycle of integrated chlorophyll
a over the continental shelf, from 4°S to
14°S (ROMS-PISCES 1992-1999)
- Serie : The recruitement drop in 97-98 as PISCES does reproduce ENSO. The
IMARPE serie suggest however a recruitment drop before ENSO, in 96.
- Clim : The recruitment seasonality is correctly reproduced by the bio-physical
model. Last figure consider an uniform spawning over the shelf instead of
observed one but doesn’t fit so good the IMARPE recruitment seasonality.
Variability is in the range of IMARPE when using observed spawning patterns
Conclusion:
- The bio-physical model successfully reproduce the anchovy pre-recruitment seasonnal cycle at 2-6 month and the relative reproduction success in winter
and summer was also reproduced when using a dynamical nursery area provided by the biogeochemical model (simulation 3). However, no simulations
reproduced the interannual variability of the recruitment.
- The 97-98 El Niño induce an increase in retention due to the hydrodynamics (simulations 1 & 2), compensated by a strong decrease in production
(simulation 3), resulting in a low recruitment.
- Including both spatial egg distribution and abundance was necessary to obtain the best fit with IMARPE (pre)-recruitment series.
Perspectives: we plan to extend this study to the 1960-2010 period in order to detect the known regime shift in the decadal seasonal cycle of the
recruitment
Acknowlegdement : we thanks Patricia Ayon for providing observation of anchovy eggs, and Olga Hernandez for the data processing