Prista et al: Use of SARIMA models to assess data-poor fisheries 
171 
connection to the economy and business (Vasconcellos 
and Cochrane, 2005). Commercial landings result from 
complex interactions between the environment, the fish- 
ing fleet, and the stocks, and therefore do not directly 
reflect the status of exploited populations. However, 
landing records contain valuable information that can 
be useful to managers if routine monitoring, rather 
than stock assessment, is established as a manage- 
ment objective (Scandol, 2003). In fact, even if they 
provide suboptimal indications on the status of the 
stocks, statistical analyses of landings can lead to the 
timely detection of phenomena such as sudden increases 
in fishing effort or marked population declines that 
could otherwise remain undetected (Caddy, 1999). Such 
detection is important — particularly within multispe- 
cies, budget-limited, management contexts — because it 
allows the prioritization of research and management 
actions toward the subset of fisheries and stocks most 
likely to be depleted (Scandol, 2003). 
Autoregressive integrated moving-average (ARIMA) 
models are simple time series models that can be used 
to fit and forecast univariate data such as fisheries 
landings. With ARIMA models data are assumed to 
be the output of a stochastic process, generated by un- 
known causes, from which future values can be pre- 
dicted as a linear combination of past observations and 
estimates of current and past random shocks to the 
system (Box et al., 2008). In fisheries, ARIMA models 
(and their seasonal multiplicative version, SARIMA) 
have a long record of successful application that extends 
from modeling (e.g., Hare and Francis, 1994; Fogarty 
and Miller, 2004) to short-term forecasting of a variety 
of variables and resources for both data-rich and data- 
poor fisheries (Table 1). Specifically, SARIMA models, 
which are applicable to many already-available land- 
ings data sets, have been found to provide both annual 
and monthly forecasts that are comparable to, or even 
better than forecasts from many multivariate models, 
including some with fishing effort among the predictors 
(Stergiou et al., 1997). 
The good record, flexibility, and simplicity of SARI- 
MA models have made them natural candidates for 
the modeling of data-poor fisheries (Rothschild et al., 
1996). However, to date, SARIMA models in fisheries 
have only been applied in detail on relatively long time 
series (>120 months) (Table 1), and a single study has 
provided a few (but not detailed) results from shorter 
series (Lloret et al., 2000). Such emphasis of previous 
SARIMA modeling on long time series finds little sup- 
port in statistical literature where 50 months is gener- 
ally regarded as the minimum sample size for model 
application (e.g., Pankratz, 1983; Chatfield, 1996a). Ad- 
ditionally, most literature to date has focused on SARI- 
MA models as tools to generate accurate forecasts of 
future landings. However, in addition to good forecast- 
ing, these models also possess significant capabilities 
for monitoring landings that have remained unexplored. 
These capabilities become apparent when SARIMA 
models are approached from a statistical process-control 
perspective and it is made known that SARIMA model 
forecasts include the assumption of persistence (through 
time) of the process that generated the data (Box et al., 
2008; Mesnil and Petitgas, 2009). Briefly, good land- 
ing forecasts are only attainable as long as significant 
changes do not take place in the fishery; therefore large 
forecast errors can be regarded as indications that can 
be changes in the fishery process took place that may 
require management intervention (Pajuelo and Lorenzo, 
1995; Georgakarakos et al., 2006; Box et al., 2008). 
In this study, we report the first detailed applica- 
tion of SARIMA models for monitoring of data-poor 
fisheries landings. We use data from a previously un- 
assessed Portuguese fishery on meagre (Sciaenidae: 
Argyrosomus regius ) as our example. The meagre is 
a valuable top predator from European coastal wa- 
ters but its stocks have not been analytically assessed 
because of limitations in data, personnel, and fund- 
ing existing at the national level. At the time of our 
analysis only a short time series of monthly landings 
(60 months) was available for this fishery, a situation 
that replicates conditions found in many other data- 
poor fisheries worldwide. We show that the short time 
series was not a problem for SARIMA modeling and 
forecasting and that prediction intervals from SARI- 
MA models can be used to provide this fishery with 
basic monitoring. We suggest that SARIMA models 
should be more widely considered to extend the cover- 
age of monitoring to all exploited marine resources. 
Materials and methods 
Meagre (Argyrosomus regius) and its fisheries 
Meagre is one of the world’s largest and most valuable 
sciaenids (up to 180 cm, 50 kg, and with a US$ 15 per 
kg exvessel price). It ranges from France to Senegal, and 
the largest fisheries take place off Mauritania, Morocco, 
and Egypt. In Europe, the meagre constitutes a prized 
trophy-fish for anglers and an important income for 
small-scale commercial fishermen along the Atlantic 
shores of France, Spain, and Portugal. Its biology and life 
cycle remain scarcely documented, but recent concerns 
about the overexploitation of juveniles and interests in 
aquaculture production have sparked some research. 
Currently, the fish is known to be fairly long-lived (up 
to 44 yr) (Prista et al., 2009), to present fast juvenile 
growth (Morales-Nin et al., 2010) and to spawn at 3-4 yr 
old (N. Prista, unpubl. data). Data on adult growth and 
reproduction have not been published, but preliminary 
reports indicate a life-cycle characterized by fast growth, 
high fecundity, and a long reproductive span, and that 
the estuaries of the Gironde (France), Tagus (Portugal), 
and Guadalquivir (SW Spain) rivers constitute the main 
spawning habitats (Quemener, 2002; Prista et al. 2 ; N. 
2 Prista, N., C. M. Jones, J. L. Costa, and M. J. Costa. 
2008. Inferring fish movements from small-scale fisheries 
data: the case of Argyrosomus regius (Sciaenidae) in Por- 
tugal, 19 p. ICES CM 2008/K-19, Copenhagen, Denmark. 
