Prista et al: Use of SARIMA models to assess data-poor fisheries 
173 
Figure 1 
Time series of monthly meagre (Argyrosomus regius) landings, in tons, 
in the Lisboa region of the Portuguese coast (May 2002 to April 2008). 
The dashed vertical line is the forecast origin (April 2007) and separates 
the fitting period (May 2002 to April 2007, left) from the hold-out period 
(May 2007 to April 2008, right). (A) Raw data. (B) Log 10 -transformed 
mean-centered data. 
to use estuaries as nursery areas during 
the warmer months and overwinter in 
adjoining coastal grounds (Quero and 
Vayne, 1987; Quemener, 2002; Prista et 
al. 2 ; N. Prista, unpubl. data). 
Recently, substantial conservation 
risks have been identified in European 
meagre fisheries that are related to 
the overexploitation of juvenile and 
adults schools in estuaries and nearby 
coastal areas (Quemener, 2002; Prista 
et al. 2 ). To protect juveniles, precau- 
tionary management measures have 
been put in place (namely minimum 
landing size regulations) but the ac- 
tual status of the meagre stocks was 
never assessed. This lack of assess- 
ment mainly results from a lack of suf- 
ficient multivariate time-series data 
and because national assessment pri- 
orities, funding, and expertise are gen- 
erally allocated to the largest national 
and transnational fisheries instead of 
the less-significant, albeit numerous 
and regionally important, ones. The 
fish being largely absent from routine 
fishery-independent surveys (Quero 
and Vayne, 1987; F. Cardador, personal 
commun. 3 ) and difficulties related to 
its sampling at port and the estima- 
tion of fishing effort (Prista et al. 2 ’ 4 ) 
further contribute to its unassessed 
status. In this type of setting, if simple 
methods are not put in place that can, 
at least, detect the most alarming sig- 
nals in the landings data it is likely 
that stock collapses can occur without being detected. 
Data set and data transformations 
The Lisboa region in Central West Portugal (hence- 
forth termed “Lisboa region”) (38°25'N to 38°59'N lat., 
~9°15'W long.) is the main fishing area for meagre off 
the Iberian Peninsula (between 29% and 45% of annual 
landings of meagre, all gears combined, in 2001-05). 
In this region, most of the catch is associated with the 
Tagus estuary and its adjoining coastal area. The catch 
derives essentially from a small-scale artisanal fleet in 
which gillnets, trammel nets, and longlines are used to 
catch meagre during its spawning and nursery season 
(Prista et al. 2 ). To minimize overfishing of juvenile fish, 
a minimum landing size of 42 cm was established in 
2002 that complements an array of other gear-related 
3 Cardador, Fatima. 2008. INRB, I.P./IPIMAR, Av. Brasilia, 
1449-006 Lisboa, Portugal. 
4 Prista, N., J. L. Costa, M. J. Costa, and C. M. Jones. 
2007. New methodology for studying large valuable fish in 
data poor situations: commercial mark-recapture of meagre 
Argyrosomus regius in the southern coast of Portugal, 18 p. 
and effort-related management regulations that are not 
specific to meagre. 
To test SARIMA models in the monitoring of the 
Lisboa meagre landings, we obtained a time series of 
meagre monthly landings from the Portuguese General- 
Directorate for Fisheries and Aquaculture (DGPA). The 
landings data resulted from mandatory reports of fish 
sales obtained at all ports of the Lisboa region ( A7= 14 ) 
from May 2002 to April 2008 (i.e., 72 monthly values) 
as part of a routine data collection program (Fig. 1). We 
used the first 60 months to fit the SARIMA models and 
the last 12 months as a hold-out period to evaluate fore- 
casting performance and to monitor the fishery. Some 
previous data were available on this fishery, but those 
data were found to be unreliable because of contamina- 
tion with landings from Portuguese vessels operating 
off North African waters. No significant management 
interventions occurred on the fishery during the course 
of our study. 
Before fitting a SARIMA model, the time series must 
be checked for violations of the weak stationarity as- 
sumption of the models (Brockwell and Davis, 2002; Box 
et al., 2008). In SARIMA models, trend and seasonal 
nonstationarities are handled directly by the model 
