170 
Abstract — Research on assessment 
and monitoring methods has primar- 
ily focused on fisheries with long 
multivariate data sets. Less research 
exists on methods applicable to data- 
poor fisheries with univariate data 
sets with a small sample size. In this 
study, we examine the capabilities of 
seasonal autoregressive integrated 
moving average (SARIMA) models to 
fit, forecast, and monitor the landings 
of such data-poor fisheries. We use a 
European fishery on meagre (Sciaeni- 
dae : Argyrosomus regius), where only 
a short time series of landings was 
available to model (;; = 60 months), as 
our case-study. We show that despite 
the limited sample size, a SARIMA 
model could be found that adequately 
fitted and forecasted the time series 
of meagre landings (12-month fore- 
casts; mean error: 3.5 tons (t); annual 
absolute percentage error: 15.4%). We 
derive model-based prediction inter- 
vals and show how they can be used 
to detect problematic situations in 
the fishery. Our results indicate that 
over the course of one year the meagre 
landings remained within the predic- 
tion limits of the model and therefore 
indicated no need for urgent man- 
agement intervention. We discuss 
the information that SARIMA model 
structure conveys on the meagre life- 
cycle and fishery, the methodological 
requirements of SARIMA forecasting 
of data-poor fisheries landings, and 
the capabilities SARIMA models pres- 
ent within current efforts to monitor 
the world’s data-poorest resources. 
Manuscript submitted 8 March 2010. 
Manuscript accepted 20 January 2011. 
Fish. Bull. 109:170-185(2011). 
The views and opinions expressed 
or implied in this article are those 
of the author (or authors) and do not 
necessarily reflect the position of the 
National Marine Fisheries Service, 
NOAA. 
Use of SARIMA models to assess 
data-poor fisheries: a case study 
with a sciaenid fishery off Portugal 
Nuno Prista (contact author ) 1 - 2 
Norou Diawara 3 
Maria Jose Costa 1 - 2 
Cynthia Jones 4 
Email address for contact author: nmprista@ipimar.pt 
1 Centro de Oceanografia 
Faculdade de Ciencias da Universidade de Lisboa 
Campo Grande, 1749-016 
Lisboa, Portugal 
Present address for contact author: Unidade de Recursos Marinhos e Sustentabilidade 
Instituto Nacional de Recursos Biologicos (INRB, I.P./IPIMAR) 
Avenida de Brasilia, 1449-006 
Lisboa, Portugal 
2 Departamento de Biologia Animal 
Faculdade de Ciencias da Universidade de Lisboa 
Campo Grande, 1749-016 
Lisboa, Portugal 
3 Department of Mathematics and Statistics 
Old Dominion University 
Norfolk, Virginia 23529-0077 
4 Center for Quantitative Fisheries Ecology 
Old Dominion University 
800W 46 th Street 
Norfolk, Virginia 23508-2099 
Research, assessment, and manage- 
ment have traditionally focused on 
fisheries with the greatest landings 
and revenues (Scandol, 2005; Vas- 
concellos and Cochrane, 2005). Such 
fisheries are generally data-rich and 
have available the funds and exper- 
tise required to complete stock assess- 
ments and provide state-of-the-art 
advice to management. However, that 
is not the case for the vast majority 
of fisheries worldwide, which remain 
subjected to limited (if any) assess- 
ment and management (Vasconcel- 
los and Cochrane, 2005). The latter 
have been collectively termed “data- 
poor fisheries” and are character- 
ized by a low diversity and quantity 
of data, limitations in funding and 
expertise, and an overall shortage of 
assessment methods (Mahon, 1997; 
Scandol, 2005). Among the world’s 
data-poorest fisheries are nearly all 
fisheries in developing countries, 
but also most fisheries in developed 
countries, namely the smaller-scale 
or less valuable commercial and rec- 
reational ones (NRC, 1998; Berkes et 
al., 2001; EEA, 2005; Vasconcellos and 
Cochrane, 2005; Worm et al., 2009; 
OSPAR, 2010; ICES 1 ). 
Assessment of data-poor fisheries 
requires a significantly different ap- 
proach from their data-rich counter- 
parts. For data-poor fisheries, many 
deterministic multivariate stock as- 
sessment models cannot be used (e.g., 
NRC, 1998) and more pragmatic as- 
sessment methods must be put in 
place, particularly when fishery-in- 
dependent data are not available and 
fishing effort cannot be quantified 
(Berkes et al., 2001; Scandol, 2003; 
ICES 1 ). In many countries, the most 
readily available fisheries data are 
commercial landings because of their 
1 ICES (International Council for the 
Exploration of the Sea). 2008. Report 
of the study group on management strat- 
egies (SGMAS), 74 p. ICES CM 2008/ 
ACOM:24, Copenhagen, Denmark. 
