Monthly landings and effort data from the Barcelona trawl fleet (NW Mediterranean) were selected to analyse and standardize the landings per unit effort (LPUE) of the red shrimp (
Se llevó a cabo un análisis del volumen de desembarcos por unidad de esfuerzo (LPUE) de la gamba roja (
Deepwater red shrimp is one of the main resources in Mediterranean fisheries in terms of landings and economic value (
The deepwater distribution of stocks extends to below 2000 m depth (
The catches of
Despite the commercial importance of
Here, we evaluate the landings per unit effort from the daily sale slips provided by the Barcelona Fishers’ Association from 1994 to 2008, corresponding to all the commercial transactions involving
The main objective of this study was to establish the factors influencing the LPUE (see e.g.
Trawlers from the Barcelona port operate on the continental shelf and slope fishing grounds (501000 m depth) located between 1°50’ and 2°50’ longitude east and 40°50’ and 41°30’ latitude north (
We used the daily sale slips containing all transactions of red shrimp (
The response variable,
$$lpue=\frac{lands}{trips}$$
hence, lpue is the monthly average landings of one vessel in each trip, corresponding to one fishing day (kg boat^{–1} day^{–1}), which will form the basis for providing a standardized abundance index.
To assess the individual effect of each vessel, a numeric variable
Variable  description (units) 

landings per unit effort derived from 

total monthly landings per vessel (kg month^{–1})  
time index of months, 

number of trips performed by each vessel during the time 

engine power of vessels  
gross registered tonnage of vessels  
red shrimp exvessel price (€ kg^{–1})  
fuel price one month before the observed lands (10^{–3} € L^{–1})  
mean annual NAO index, 

12 categories corresponding to months  
2 categories: 

21categories corresponding to a code assigned to each vessel  
3 categories corresponding to groups of vessel 
Differences between pairs of categories of the variable
The same procedure was applied for the variable
We used generalized additive models, GAM, as described by
where
The model building process consists of the following steps: 1) selection of the underlying distribution of the response (see Section 2.2.2 for more details); 2) selection of predictors building independent models for each covariant deleting insignificant effects in the final model; 3) selection between correlated predictors through the Pearson correlation coefficient (threshold value:
All analyses were performed in R3.0.1 (mgcvRpackage:
Landings per unit effort are usually modelled following Gaussian or gamma distribution functions, often without formal justifications (
The empirical cumulative distribution function,
where
$${\epsilon}_{n}=\sqrt{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{1ex}{$2n$}\right.{\mathrm{log}}_{e}\left(\raisebox{1ex}{$2$}\!\left/ \!\raisebox{1ex}{$\alpha $}\right.\right)}$$
The hypothesis tested is H_{0}:
for
Selection criteria and explained deviance
Both AIC (
The ED for each variable was also calculated in order to determine their relative importance in the final model. The residual deviance of the full model and the deviances of reduced models (i.e. the model excluding variable
DE
where
LPUE standardization
The model used for standardization was built in order to avoid dependency on fleet variables, maintaining environmental variables, which are expected to be related to the natural abundance of the species. The standardized LPUE is then

(2) 
where
Finally we compared our standardized LPUE with an alternative abundance index derived from fisheriesindependent data, available in the technical report SGMED1211 (
Characteristics of
The model building process is summarized in
N  model  
RD  ED (%)  AIC 

1  2  863.8  0  13544  
2  3  844.4  2.2  13501  
2.1  3  815.3  5.6  13432  
3  5  741.0  14.2  13250  
4  6  734.7  15  13235  
5  8.9  627.8  27.3  12936  
6  12.4  614.0  28.9  12900  
6.1  24.8  527.0  39  12632  
7  27.7  493.5  43.0  12512  
8  28.7  493.3  43.0  12513  
9  28.7  493.5  43.0  12514  
10  28.7  493.4  43.0  12513  
11  9  626.4  27.5  12932  
12  24.75  608.7  29.5  12910 
The final model for
log 
(3) 
where
(a) Linear terms  mean  std  

ED (%) 

1.746  0.103  16.980  <2e16  0  
–0.152  0.035  –4.383  1.24e05  0.72  
0.281  0.081  3.449  5.75e04  3.58  
0.637  0.086  7.404  2.02e13  
0.010  0.001  16.205  <2e16  5.62  
(b) Smooth terms  



ED (%) 
15.239  0.011  24.82  <2e16  12.40  
2.457  30.007  71.01  <2e16  11.38  
4.026  0.059  26.36  <2e16  9.30  
(c) Global  
R^{2}(adj)  AIC  GCV  ED tot (%)  
27.722  0.274  0.488  12512  0.282  43.00 
The model used to standardize
(4) 
where
Finally, LPUE index and the SGMED in 20022008 with their confidence intervals after normalization of variables are plotted in
This study presents models for relative abundance of
We incorporated effort, temporal, economic and environmental variables into a global regression model to evaluate their relative importance. Model 7 captures LPUE variability with a total deviance of 43% explained by six predictors. In order to quantify the different sources of LPUE variability, we found that the set of fisheryrelated variables (
Interannual variable (
The partial effect of exvessel prices,
Obtaining information on deepsea species population dynamics is notoriously difficult, but our analysis suggests that the peculiarity of red shrimp fishery makes it possible to use fisherydependent data to accurately describe the relative abundance of this resource. There are no discards for this fishery and the bycatch fraction, represented for example by
In turn, the definition of the relative importance of explanatory variables enables their impact on LPUE to be understood and makes intervention on the relevant variables possible from a management perspective. Fisheryrelated variables tend to have a significant effect on LPUE (ED=21% in our case), and management measures aiming to reduce fishing mortality in this heavily harvested stock (
In addition, to evaluate the impact of predictors on the LPUE, the regression analysis could be the basis to provide a standardized index for assessing species stocks. Standardization of landings data allows an index of the real species abundance to be developed, assuming that the explanatory variables available remove (or explain) most of the variation in the data that is not attributable to natural changes (
Trends in CPUE (and LPUE) are usually assumed to reflect changes in the abundance of marine stocks (
During the study period, the fleet was practically constant, making monthly trips a good indicator of fishing effort and landing ability, and remained almost constant despite potential technological creep (
Studies of deepwater systems, where harsh conditions limit methods for evaluating ﬁsheries, often suffer from a lack of data in order to assess stock status. Although the goal of ﬁsheries managers is to promote sustainable production of ﬁsh stocks through formal stock assessment, it is often impractical to collect ﬁsheryindependent data in isolated or harsh environments. In these cases the information collected by a ﬁshery is the main (or only) source of abundance data available (
The authors would like to express their sincere thanks to the Fisheries Directorate of the Government of Catalonia for giving access to the sales data of the Barcelona Fishers’ Association, as well as to fishers of Barcelona. The first author is also very grateful to A. Rodríguez Casal and C. Cadarso Suárez for transmitting their knowledge in nonparametric statistics, to A. Gallen for revising the English and to M. Reyes for his suggestions on the manuscript. G. Aneiros is partly supported by Grant number MTM201122392 from the Ministerio de Ciencia e Innovación (Spain). Finally, this study was financed by the Spanish National Research Council (CSIC) through the JAEpredoc grant programme.