ArticlePDF Available

Impact of technological creep on fishing effort and fishing mortality, for a selection of European fleets


Abstract and Figures

Marchal, P., Andersen, B., Caillart, B., Eigaard, Guyader, O., Hovgaard, H., Iriondo, A., Le Fur, F., Sacchi, J., and Santurtún, M. 2007. Impact of technological creep on fishing effort and fishing mortality, for a selection of European fleets–ICES Journal of Marine Science, 64, 192–209. Face-to-face interviews were conducted to identify the main changes in gear and vessel technology that may have improved the fishing efficiency of a number of French, Danish, and Basque fleets over the past few decades. Important changes include the gradual appearance of twin trawls (Danish and French trawlers) and trammel-nets (French gillnetters), and the increased polyvalence of Basque bottom trawlers. The results suggest that fishing effort descriptors that are not traditionally measured (gear type, groundrope type, length of net used per day, headline length, crew size, number of winch or net drums) may have a substantial impact on catch rates. Adjusting fishing effort using such descriptors may generally improve the relationship between fishing effort and fishing mortality.
Content may be subject to copyright.
ICES Journal of Marine Science
JAN 2007; 64 (1) 192-209
Copyright © 2006
Archive Institutionnelle de l’Ifremer
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title]
following peer review. The definitive publisher-authenticated version [insert complete citation information here] is
available online at: xxxxxxx.
Impact of technological creep on fishing effort and fishing mortality, for a
selection of European fleets
Paul Marchal 1 *, Bo Andersen 2, B. Caillart 3, Ole Eigaard 2, Olivier Guyader 4, Holger
Hovgaard 2, Ane Iriondo 5, Fanny Le Fur 3, Jacques Sacchi 6, and Marina Santurtún 5
1 IFREMER, Channel and North Sea Fisheries Department, 150 Quai Gambetta, BP 699, 62321
Boulogne sur mer, France
2 DIFRES, Charlottenlund Castle, DK2920 Charlottenlund, Denmark
3 Oceanic Developpement, ZI du Moros, 29900 Concarneau, France
4 IFREMER, Fisheries Economy Department, BP 70, 29280 Plouzané, France
5 AZTI, Fisheries Resources Department, Txatxarramendi Ugarte, Z/G, 48395 Sukarrieta, Spain
6 IFREMER, Mediterranean and Overseas Fisheries Department, Avenue Jean Monnet, BP 171,
34203 Sète Cedex, France
* To whom correspondence should be addressed.
Paul Marchal, E-mail:
Face-to-face interviews were conducted to identify the main changes in gear and vessel technology
that may have improved the fishing efficiency of a number of French, Danish, and Basque fleets over
the past few decades. Important changes include the gradual appearance of twin trawls (Danish and
French trawlers) and trammel-nets (French gillnetters), and the increased polyvalence of Basque
bottom trawlers. The results suggest that fishing effort descriptors that are not traditionally measured
(gear type, groundrope type, length of net used per day, headline length, crew size, number of winch
or net drums) may have a substantial impact on catch rates. Adjusting fishing effort using such
descriptors may generally improve the relationship between fishing effort and fishing mortality.
Keywords: catch rate; fishing effort; fishing mortality; generalized linear models; groundrope;
technological creep; twin trawls.
Commercial fishers continuously adapt their activities to the prevailing conditions by changing
the physical inputs of production (technological development) and the way these inputs are used
to harvest target species (tactical adaptation). There is evidence that the efficiency of fishing
vessels has increased over the last decades, as a result of technical creeping. Quantifying the
importance of fishermen’s reactions relies on the ability to define appropriate standardised effort
measures, which depends on the detail of data available on fishing effort. Fishing effort is
traditionally estimated by combining available physical measurements of fishing capacity (fixed
production inputs) and of fishing activity (variable production inputs). Fishing capacity is
frequently approached by some physical attribute of the operating vessel (engine power, gross
tonnage) but is also dependent on other factors, including gear technology and on-board
equipment, which are often ignored. The introduction of new gear and technology includes both
larger marked technological investments (e.g. acoustic fish finding equipment, electronic
navigation tools) and smaller stepwise improvements of the gear (e.g. stronger netting material,
changes in the design of trawl panels), which themselves do no not result in marked changes of a
vessels capacity but in conjunction give a noticeable capacity increase over time. Fishing
activity is typically estimated by the duration of fishing trips. Such a definition ignores a number
of factors which may potentially impact fishing pressure, including the number and the size of
gears deployed, or the effective time used for fishing.
A number of studies have been carried out to evaluate time variations in fishing efficiency
(Cook and Armstrong, 1985; Millischer et al., 1999; Marchal et al., 2001; Marchal et al.,
2002). However, these studies did not investigate the extent to which such variations could be
attributed to the technological development of the fishing fleets. A number of studies aimed
at getting more insights into the key processes of technical creeping. Such investigations
were often based on the analysis of variations in either catch per unit effort (CPUE), or catch
value per unit effort (VPUE), or profit, using a variety of modelling approaches ranging from
simple GLM (Robson, 1966; Gavaris, 1980; Kimura, 1981; Hilborn, 1985) to more complex
Stochastic Production Frontier (Pascoe et al., 2001) or multi-output distance functions
(Squires, 1987; Squires and Kirkley, 1996). However, the scope of such approaches was
generally restricted by vessel information available from log-books, which typically include
engine power, vessel length and/or gross tonnage.
This study investigates the technological development of fishing vessels, with the general
objective of refining measures of fishing capacity and fishing activity. New information on
historical vessel and technological developments has been collected through harbor enquiries.
The information on technological developments has been analysed so to assess their
importance for the catching efficiency of the fleets, using Generalised Linear Models. The
most important elements of technical developments will then be used to adjust fishing effort.
Finally, the benefits of adjusting fishing effort will be evaluated by examining the relationship
between fishing mortality and fishing effort, for the fleets and fish stocks under investigation.
The case studies examined in this study are based on a selection of Danish, French and
Spanish fleets and on their main target species.
Annual changes in fishing effort
The collection of data on the evolution of fishing effort has been carried out between April
and October 2004 for the French fleets, between March 2004 and April 2005 for the Danish
fleets, and between June 2003 and February 2005 for the Spanish fleets. In France, the survey
was conducted by 7 technicians who interviewed a pre-selected sample of fishermen located
on the Channel and the Atlantic coast, from Dunkerque to Bayonne. In Denmark, the survey
was conducted by student employees and aimed at complete geographical coverage within the
three vessel groups: Demersal trawlers, Gill netters and Danish seiners. In Spain, the survey
was conducted in two harbours of the Basque Country: Ondarroa and Pasaia.
In general, the first contact with the fishermen was made by telephone and an appointment
was arranged after obtaining his consent to answer the questionnaire. Interviews lasted
between 30 mn and 1 h. On some rare occasions, contacts and interviews were done upon
return of the vessel in harbour.
The questionnaires were divided into three main sections. The first part concerned the vessel
owner surveyed, the evolution of his career, of previously owned vessels and the different
métiers practiced since 1985. The second part concerned the current vessel owned, and the
evolution of key variables such as hull, engine, deck equipment, electronics, handling and
conservation of catches onboard, crew size and, for trawlers only, electronic devices used for
monitoring the gear. Finally, the last part of the questionnaire concerned the fishing gears,
their evolution and the fishing effort deployed. This study builds on the technological data
collected via the second and the third parts of the questionnaires. The key variables fishers
were asked to document are shown in Table 1 (vessel attributes) and Table 2 (gear attributes).
In France, the best questionnaire returns were achieved for vessels registered in Bay of Biscay
harbors, and belonging to four fleet segments. These fleets are otter-trawlers (12-16 m), (16-
20 m), (20-24 m) targeting Norway lobster (Nephrops norvegicus) and hake (Merluccius
merluccius), and gill-netters (>12 m) targeting sole (Solea solea), hake and anglerfishes
(Lophius spp.).
In Denmark and Spain, the questionnaires were designed in a manner very similar to the
French questionnaires although minor adjustments were made to accommodate differences in
vessel characteristics and target species among the fleets in the three countries. In Denmark,
the return quality of the questionnaires was best for the Danish otter-trawlers, and all
subsequent analyses have been based on that vessel group. The main targets of the Danish
otter-trawlers were cod (Gadus morhua), plaice (Pleuronectes platessa) and Norway lobster.
In Spain, three Basque fleets were considered: bottom-trawlers of length (20-30 m) registered
in Ondarroa, bottom-trawlers of length (30-40 m) registered in Ondarroa and bottom-trawlers
of length (30-40 m) registered in Pasaia. The main target species of these three fleets were
hake and anglerfishes. As a result of relatively low sampling levels in the surveys, the Basque
bottom trawlers (20-30 m) registered in Ondarroa and those (30-40 m) registered in Pasaia
were excluded from further analyses.
Table 3 and Figure 1 provide some details on the sampling for the French, Danish and
Spanish fleets under investigation. Effort data could be traced back to the early ‘80s (French
fleets), the early ‘70s (Spanish fleets) and even back to the late ‘50s (Danish fleets).
In the fishing effort dataset, each observation was a combination of one vessel and one year.
A number of vessels used several gears during one year. For the French fleets, it was possible
to identify which was the main gear used by each vessel throughout the year. For this fleet,
the fishing effort data set included the technological characteristics of both the fishing vessel
and of the main gear used. For the Danish and the Basque fleets, it was not possible to
determine which gear was the most important, and only the vessel characteristics were
included in the fishing effort dataset.
Annual changes in fleet production
Landings in weight and value were extracted from the Danish, French and Spanish log-books
and sales slips databases over the period 1990-2003, for all the vessels sampled during the
harbor enquiries. Data were aggregated by vessel and year, and then merged with the fishing
effort data set described above.
Annual changes in fishing mortality
Total international landings and estimated fishing mortality by stock were derived from the
ICES advice (ICES, 2003). The stocks investigated are Celtic Sea and Bay of Biscay
anglerfishes, North Sea cod, Northern hake, Bay of Biscay Norway lobster, Celtic Sea
Norway lobster, North Sea plaice, Bay of Biscay sole, Celtic Sea sole and North Sea sole.
Separate F estimates were given for the two anglerfish species (Lophius budegassa and
Lophius piscatorius). An overall anglerfish fishing mortality was calculated by averaging the
landings-weighted F of each of these two species.
Data exploration
The data collated during our enquiries were first examined to check for missing values.
Poorly documented fishing effort descriptors were excluded from subsequent analyses. The
annual trends of the remaining variables were then inspected visually. Special consideration
was given to variables exhibiting substantial trends over the study period.
Modeling catch rates
Catch rates (CPUE, catch per unit effort), calculated for each vessel and each year, are
modeled using Generalized Linear Models (Mc Cullagh and Nelder, 1989). Two models are
considered. In model 1, the explained variable is CPUE, which is assumed to follow a gamma
distribution. In model 2, the explained variable is Log(CPUE), which is assumed to follow a
normal distribution. In order to choose between these 2 models, the distribution of CPUE is
tested visually against a gamma distribution, using QQ-plots, and the distribution of
Log(CPUE) is similarly tested against a normal distribution. The most appropriate
combination of explained variable and probability distribution (CPUE/gamma distribution,
model type 1, or log-transformed CPUE/normal distribution, model type 2) is selected. The
link function is either Logarithm (model type 1) or Identity (model type 2).
The explanatory variables are year and the different descriptors of fishing effort. Some of
these descriptors are discrete factors (e.g. gear unit), while others are continuous variables
(e.g. soaking time). Assuming that technical creeping is described by the fishing effort
variables, the “Year” effect may indicate annual abundance changes for the species (or the
combination of species) under consideration. Each observation cell is a combination of vessel
and year. A general formulation of the model is:
(1a) Model 1:
+++= NI
(1b) Model 2:
+++= kk
where α is the intercept, β is the year effect, ε is the effect of the discrete effort descriptors, θk
is the regression coefficient associated to ek, e is the vector of the continuous fishing effort
The model is preliminarily parameterized using the outcomes of the data exploration, which
allows the a priori selection of the most appropriate model (1 or 2). The model chosen is
validated with regards to residual plots resulting from the analysis. Residuals are plotted
against predicted values and are tested for normal distribution (QQ-plot, Kolmogorov-
Smirnov test). Once an appropriate model type (1 or 2) is selected, the goodness of fit of the
model is evaluated using the model’s scaled deviance and Pearson Chi-square and also two
criteria, the Akaike Information Criterion (AIC) and the Schwarz Bayesian Information
Criterion (BIC). If the model chosen fits reasonably well the data, both AIC and BIC should
be as low as possible. In addition, both scaled deviance and Pearson chi-square should have a
chi-square distribution, with degrees of freedom equal to the number of observations minus
the number of parameters estimated. It follows that the ratio between scaled deviance and
degrees of freedom, and also the ratio between Pearson chi-square and degrees of freedom
should be close to 1. Finally, only the most contributive explanatory variables are retained in
the final model (Type III analysis).
Adjusting fishing effort
The method is adapted from the traditional approach (Kimura, 1981). The adjustment factors
are the effects of the different variables characterizing fishing effort, estimated by either (1a)
or (1b). If ε* is the effect of the reference effort factor, the relationship between the adjusted
(or effective) log fishing effort ln_Ee and the nominal (or untransformed) log fishing effort
ln_En may be expressed as
++= NI
v,yv,y e
_En_Ee 1,,*
Evaluating the benefits of adjusting fishing effort
The benefits of adjusting fishing effort are evaluated by scrutinizing the relationship between
fishing mortality and fishing effort, where effort is defined either as nominal or adjusted
effort. Partial fishing mortality was calculated, for each fishing vessel, by weighting the total
annual F using the ratio of the vessel’s and landings to the total international landings for the
stock under consideration. The relationship between F and effort was examined for the main
stocks harvested by the fleets under investigation, and for which a stock assessment was
available. A linear regression between log-transformed F and effort will be tested with effort
defined as nominal or adjusted. The goodness of fit of the regression will be appraised by, (i)
eye-balling the plots between Log(F) and Log(effort), (ii) comparing the values of R-square
and, (iii) testing using the t-statistic the value of the regression slope, which should be close to
1 if the regression model (2) is appropriate.
As a result of data availability, subsequent analyses were applied to four French fleets fishing
in the Bay of Biscay (otter-trawlers of length (12-16 m), (16-20 m), (20-24 m), and gill-
netters (>12 m)), one Danish otter-trawling fleet and one Basque fleet (bottom-trawlers (30-
40 m) registered in Ondarroa). The methods developed in this study were mainly
implemented using SAS/STAT (1999) procedure GENMOD.
Data exploration
Gear types have changed considerably over time for most of the fleets under investigation
(Figure 2). For the French trawlers (Figure 2a) and the Danish trawlers (Figure 2c), the main
feature has been the emergence of twin trawls in the ‘80es, which is associated with Nephrops
fishing. For the French gill-netters (Figure 2b), the main feature has been the increasing
importance of trammel nets, which is associated to sole fishing. Trammel nets have been the
main gear on-board since 1996. For the Basque trawlers registered in Ondarroa of length (30-
40 m) (Figure 2d), the proportion of the two main gear types (single otter-trawls and pair
trawls with “Very High Vertical Opening”) has remained stable over the period 1990-2003.
Since 1995 however, this fleet appeared to be increasingly polyvalent, as reflected by the
emergence of an other trawl type (“Bou” otter-trawls) and increasing use of static gears (fixed
nets and long-lines).
There has been an emergence of electronics on-board (GPS or computers) for the different
fleets. In particular, GPS appeared in the 60-70es’ (Danish fleets, Figure 3c) or in the 80es’
(French fleets, Figures 3a-b). All the Basque trawlers were equipped with GPS and
computers appeared on board around 1990. In 2004, all French and Basque vessels were
equipped with GPS, while 10-30% of the Danish vessels were not equipped with the device.
The horsepower of the small French otter-trawlers (Figure 4a) and of the Danish (Figure 4c)
has increased over time, while the horsepower of the Basque (Figure 4b) and of the larger
French trawlers (Figure 4a) has either remained constant or decreased. The decrease in the
horse power of the Spanish fleet results from the emergence of new vessels working as pair-
trawlers. Such vessels do not need as much horsepower as the traditional single-trawl vessels.
Bollard pull for the Danish fleets appeared to increase over time, along with horsepower.
For the small and large French otter-trawlers (Figures 5a and 5c), the headline length has
increased slightly over the study period. Otter-trawlers equipped with twin trawls had a
longer headline than those equipped with single trawls. For the medium French otter-trawlers
(Figure 5b), the headline length has decreased over time. Otter-trawlers equipped with twin
trawls had a similar headline length than those equipped with single trawls. For the Basque
fleet, both the headline length (Figure 5d) and the vertical opening (Figure 5f) have increased
over time. Trawlers equipped with “VHVO” trawls had larger headline and vertical opening
than those equipped with single trawls. The vertical opening of Danish trawlers (Figure 5e)
has increased over time. Danish trawlers equipped with single trawls had the largest vertical
opening, while those equipped with multi-rig trawls had the smallest.
Modeling catch rates and adjusting fishing effort
Model 2 was more appropriate than model 1 in all cases. The GLM residuals diagnostics are
shown in Tables 4-7 and Figure 6. The outcomes of the Kolmorov-Smirnov tests indicate that
the assumption of normal distribution is not rejected only for a few cases. However, the
inspection of the QQ-plots indicates that, except in few cases where outliers make the
observed plot slightly deviate from the reference line (Figures 6b, 6e, 6g, 6i, 6m) the
distribution of residuals is close to normal.
Results of the Generalized Linear Models are summarized in Tables 4-7 and Figures 7 and 8.
In the case of French gill-netters, the highest catch rates of hake were achieved with fixed
nets, while the highest catch rates of sole and anglerfishes were reached with trammel nets
(Table 4 and Figure 7). Net length had a positive effect on catch rates of both hake and sole
while the effect of soaking time was unclear. Vessel length only had an effect on the catch
rates for hake.
A gear type variable was created by combining the gear unit with the type of groundrope for
the French trawlers. The effect of gear type was dominant for all combinations of fleets and
species, but it was also fleet- and species-dependent (Table 5 and Figure 8). For the small
trawlers (12-16 m) the highest Nephrops catch rates were achieved with twin trawls using
metallic spheres, while chains were better for larger trawlers (20-24 m). The highest catch
rates for hake by both small and large French trawlers were achieved with single trawls
equipped with the diabolos. Single trawls equipped with chains had also high catch rates of
both Nephrops and hake for large trawlers. The effect of gear type was not so clear for the
medium trawlers (16-20 m). Headline length generally had a positive effect on catch rates for
all fleets. Short hauls (reflecting either a relatively high towing speed or a short haul
duration) had often a positive impact on catch rates, except for the large trawlers harvesting
hake, where the effect was negative. Finally, the effect of on-board electronics and of engine
power was unclear and/or limited.
The smallest Danish trawlers equipped with the largest number of winch drums had the
highest catch rates for all species (Table 6). Other technological factors had a positive impact
on the CPUE for some species under investigation: the crew size on cod and plaice, the
number of net drums on Norway lobster and plaice, the number of sounders on plaice.
Finally, the newest vessels appeared to be the least efficient at catching both Norway lobster
and plaice.
The availability of variable pitch propellers increased the catch rates of both hake and
anglerfish by Basque trawlers (Table 7). The number of net drums had a positive effect on
anglerfish CPUEs, but a negative effect on hake CPUEs.
Evaluating the benefits of adjusting fishing effort
The relationships between Log(effort) and Log(F) were investigated in situations where
reliable stock assessments were available (Table 8, Figures 9 and 10). Adjusting fishing
effort generally led to an improvement of the relationship between Log-transformed fishing
effort and mortality, except for the French medium trawlers (16-20 m) harvesting Northern
hake and the French gill-netters harvesting Bay of Biscay sole. In two cases (French gill-
netters harvesting Bay of Biscay sole and Bay of Biscay/Celtic Sea anglerfishes), the slope of
the relationship was not significantly different from zero, and the model was clearly not
appropriate, whatever the measure of fishing effort. The average slope of the regression with
adjusted effort was not significantly different from 1 in the case of French gill-netters
harvesting Northern hake and North Sea/Western Scotland anglerfish, Danish otter-trawlers
harvesting North Sea cod and plaice, and Basque bottom-trawlers harvesting hake and both
anglerfish stocks. For these combinations of fleets and species, the assumption that fishing
mortality is directly proportional to fishing effort is not unreasonable.
An important feature revealed by the data exploration is the gradual appearance of twin trawls
since the early eighties, for both Danish and French trawlers, which is clearly associated with
the gradual emergence of Norway lobster as target species. For the French trawlers, the
emergence of twin trawls is accompanied by the appearance of new groundropes (diabolos,
metallic spheres), which allow fishing on harder grounds, on areas which could hardly be
exploited before. A similar change in fishing technologies is observed for the French gill-
netters, where the increased importance of trammel nets is associated with sole becoming a
dominant target species. These shifts are likely to be due to both Norway lobster and sole
having a high market value, and by the low abundance level of hake, the traditional target
species of both fleets. For the Basque bottom-trawlers, the main feature is the increased
polyvalence of fishing vessels, which may reflect the greater opportunism of skippers in
recent years.
The analysis of the effects of vessel and gear properties on fishing efficiency for the six fleets
clearly shows that collecting non trivial information on fine-scale technological changes
would allow more insight into the factors affecting fishing power. For the four French fleets,
where both vessel and gear information was compiled in the fishing effort dataset, the gear
effect appeared to be dominant over the vessel effect. This result bears out the high plasticity
of these fleets’ fishing strategies. In the case of the French gill-netters, trammel nets were
clearly designed to target sole during the night, when the fish is swimming in the water
column, while fixed nets have traditionally been used to target hake. Therefore, it could be
anticipated that vessels equipped with trammel and fixed nets would be more efficient with
regards sole and hake fishing respectively. Other characteristics of gill nets, such as twine
thickness, are thought to have a substantial effect on fishing power (Holst et al., 2002), but
information could not be consistently made available on such attributes. Also, the length of
net being towed had a positive effect on fishing efficiency for the main target species (sole
and hake), and could henceforth be considered as an useful measure of fishing capacity. Soak
time, which is sometimes evoked as a measure of the fishing activity of gill-netters (Marchal
et al., 2001; Marchal et al., 2002), did not have a clear effect on catch rates. One could
anticipate that increasing soak time would allow more fish to be caught in the net. However,
discussion with skippers who participated with the inquiry indicated that leaving fish more
than 24 h in the net would adversely alter the quality of the flesh, and hence make it
unmarketable. Therefore, it is likely that soak time has a non-linear effect on catching
efficiency, which would require further investigations.
We had anticipated that, within each groundrope category, French otter trawlers using twin
trawls would have a greater efficiency than single trawls when fishing Norway lobster but a
lower efficiency when fishing hake (Sangster and Breen, 1998). This expectation was
fulfilled for the small (12-16 m) and the large (20-24 m) otter-trawlers, but not for the
intermediate vessels (16-20 m). The reason why medium trawlers did not have the expected
efficiency when fishing for Norway lobster and hake could be the result of vessels targeting
other benthic (e.g. flatfish, anglerfish) or demersal species (e.g. cod, whiting), which were not
included in our analysis. French trawlers chose different groundropes depending on the type
of ground visited. For 8 out of 12 combinations of fleet, species and gear type categories,
vessels equipped with hard bottom groundropes (e.g. diabolos, metallic spheres) had a greater
efficiency than those equipped with soft bottom groundropes (e.g. plain wires, chains, rubber),
irrespective of the target species. Before diabolos and metallic spheres could be used, fishing
on hard bottom was more risky (gear breakage, etc.). The emergence of such devices made it
possible for vessels to have an easier access to alternative fishing grounds, which were
probably less exploited than the traditional ones. This higher local stock density could be the
reason why higher efficiency was observed when trawls were equipped with diabolos and
metallic spheres.
The effect of gear size on trawl selectivity and catching efficiency has been investigated in
past studies (e.g. Rose and Nunnallee, 1998; Dahm et al., 2002). One would expect that
increasing the trawl opening would enhance its efficiency. However, Rose and Nunnallee
(1998) found that restricting the trawl opening did not necessarily lead to decreased catch
rates. In our study, we found that trawl size, as reflected by the headline length, had a
positive effect on catch rates for hake by all French trawlers, and on catch rates for Norway
lobster by the smallest trawlers. Such results seem to be in accordance with expectations. It
is however difficult to compare our results, which are based on interviews, with those of Rose
and Nunnallee (1998), which are based on field experiments.
One would expect that towing speed has an effect on catching mobile species (e.g. hake) but
not on catching sedentary species (e.g. Norway lobster). Our results seem to confirm this
hypothesis. However, whether increasing towing speed results in an increase or a decrease in
catching efficiency is clearly fleet-dependent, and would require further investigations.
For the Danish and the Basque trawling fleets, gear information could not be used to adjust
fishing effort, and only vessel characteristics were examined in relation to fishing efficiency.
Small and old Danish trawlers generally appeared to be more efficient than large and new
vessels, which was unexpected.
With regards the vessel size effect on catch rates, a plausible explanation could be that larger
vessels periodically targeting other species (e.g. pelagics) than those included in our analysis.
The negative effect of the date of construction on fishing efficiency may indicate that vintage
is a misleading descriptor of fishing effort. Because vessels can be continuously rebuilt, older
vessels may in fact have more up-to-date equipment and technologies, and hence be more
efficient, than newer vessels. Also, although we cannot demonstrate it with the data available,
one cannot exclude in principle that more experienced skippers fish on older vessels.
The major contributors to fishing power of the Danish and Basque fleets appeared to be
mainly the crew size, the number of winch drums and the number of net drums. Bollard pull,
which is sometimes put forward as an appropriate metric of fishing power, had no appearant
effect on catching efficiency. As for Danish trawlers, the number of net drums on Basque
trawlers had an impact on fishing efficiency, but the effect was species-dependent. In fact,
the main factor with a positive effect on fishing efficiency was the availability of a variable
pitch propeller. In itself, this result is not surprising, since variable pitch propellers allow a
more optimal transfer of energy from the engine to the propeller, especially during trawling,
thereby enhancing fishing efficiency. We had not anticipated that this would be the only
vessel attribute to positively impact fishing efficiency. The results obtained for the Danish
and Basque fleets should however be treated cautiously, as the gear effect could not be
included in the analyses.
The effect of on-board electronics and of technical efficiency was overall unclear and/or
limited for the six French, Danish and Basque fleets under investigation. This unexpected
result bears out findings from Kirkley et al. (2004), who suggested that the adoption of
electronics (e.g. GPS) could be associated with other types of unmeasured output-dampening
impacts, such as stock or regulation changes, that are being picked up as part of the
electronics effect.
The CPUE analysis has been carried out using a GLM. Although it is a standard procedure in
that field of research (Robson 1966, Kimura 1981, Hilborn 1985, Marchal et al. 2002), it has a
number of limitations.
First, the dataset used in this investigation is unbalanced (not all vessels are present over all
the time series). Not explicitly accounting for the vessel effect by a fixed effects or random
effects model may lead to biased and inconsistent parameter estimates. A fixed or random
effect specification could help to explain unobserved heterogeneity between vessels, including
the skipper effect. In that context, one may consider using GLMMs (Generalized Linear
Mixed Models) as an alternative to GLMs (Venables and Dichmont 2004). GLMMs make it
possible to include both fixed and random terms in the linear predictor. Although still a
research topic, this method has recently been applied in the field of fisheries research (Squires
and Kirkley 1999).
Second, the model used here is fully linear. To allow for a broader use of our approach, more
general models could be contemplated. For instance, the GLM model used in our study is
consistent with the Cobb-Douglas function used by fisheries economists to model production
in relation economic inputs (, labour, fuel) and various dummy variables (e.g.
accounting for spatial and annual effects). A Cobb-Douglas function has thus been used by
Kirkley et al. (2004) to evaluate the effect of technological effects on the production of the
Sète trawl fishery (Kirkley et al. 2004). The Cobb-Douglas function is in fact a simplification
of the trans-log production function which includes, in addition to the linear explanatory
variables, a quadratic functional term. This quadratic term could in principle be used to
account for elasticities of substitution between the fishing effort descriptors and also, to some
extent, non-linear effects of the explanatory variables. However, given the relatively large
number of explanatory variables, a quadratic functional form might be intractable due to
multi-collinearity. A more general approach could be to account for non-linear effects of
explanatory variables (e.g. the effect of soak time on the catch rates of gill-netters) using
GAMs (Generalized Additive Models). GAMs may extend the scope of GLMs, by
substituting the linear predictor by a generalized additive (and possibly non-linear) predictor
(Maunder and Punt, 2004).
Overall, although the GLM may oversimplify the processes underlying the dynamics of
fishing effort, the diagnostics and residuals analyses suggest that for our case studies, the
main outcomes of this investigation are fairly robust to the assumptions made.
The link between fishing mortality and effort was investigated for a number of combinations
of fleets and stocks. In most case studies, adjusting fishing effort led to, (i) a gain in the
precision of the relationship between fishing mortality and fishing effort (10 out of 12 case
studies) and, (ii) fishing mortality being directly proportional to fishing effort (7 out of 12
case studies). However, the results also indicated that the linkage between fishing mortality
and effort could still be enhanced. This could be done by both revisiting some of the
assumptions and refining the scale of the investigation.
First, it has been assumed in the GLMs that the “Year” effect is indicative of the annual
abundance changes of the stocks, while technical creep is embodied in the different fishing
effort descriptors. This assumption could be violated for several reasons. Thus, there may be
factors contributing to improve technical efficiency which have not been captured by the
survey. In particular, gear-related factors of the Danish and the Basque fleets could not be
used in this study. In these cases, the annual effect may reflect a combination of both stock
fluctuations and improved gear efficiency. In addition, an implicit assumption made in this
study was that the skipper’s effect is captured by the different fishing effort descriptors in the
GLM. It has been demonstrated that skipper skill was an important determinant in explaining
catch rates (e.g. Houghton 1977, Hilborn 1985, Hilborn and Ledbetter 1985, Squires and
Kirkley 1999). Skipper skill may be reflected by e.g. choice of fishing grounds (Marchal et al.
2006), experience and education levels (Kirkley et al. 1998). Shifts in target species observed
for the fleets under investigation have required an adaptation of technologies, but also of
skippers’ skills from year to year. Moreover, it is likely that vessels’ skippers have changed
over time during the period examined. Not accounting for the skipper effect can likely lead to
omitted variable bias for the parameter estimates. Information of skippers’ skill and on
comings and goings of skippers on different vessels over time was not available to our study.
It is therefore likely that part of the skippers’ effect has been embedded in the “Year” effect.
Finally, the “Year” effect may pick up other excluded factors that are correlated with time,
including changes in the environment, along with changes in institutions and markets (Pascoe
et al. 2001).
Second, an improvement in our results could be expected with more appropriate F estimates.
F estimates from stock assessments have high uncertainty, and estimates for the most recent
years of VPA assessments may not have converged.
Third, the linkage between fishing effort and fishing mortality could be enhanced by refining
both the time (month or fishing trip instead of year) and spatial scales of this analysis.
Another unsettled issue pertaining the modeling of CPUE and, more generally, of any
production functions, is that of endogeneity. Some researchers have claimed that endogeneity
bias may arise if input (or output) quantities are not exogenous to the dependent, left-hand-
side variable, in turn leading to biased and inconsistent estimates of the parameters. Others,
however, have suggested that the stochastic nature of catch levels and composition (due to
weather conditions, the “luck” component of fishing and imperfect gear selectivity) implies
that errors in input choice based on expected profits will be uncorrelated with the error terms
associated with estimation (Bjorndal 1989, Campbell 1991, Kirkley et al. 1998, Pascoe and
Coglan 2002). Zellner et al. (1966) show more formally the conditions under which such bias
will not arise.
Overall, despite some limitations, this study provided good insights into the key processes of
technical creeping. The results suggest that fishing effort descriptors which are not
traditionally measured (gear type, length of net used per day, headline length, number of
winch and net drums) may have a substantial impact on catch rates. Such variables are
currently not routinely recorded in log-books. The results of this analysis suggest that they
should be.
This work was funded through the TECTAC project by the European Union (DG Fisheries,
study no. QLRT-2001-01291). This support is gratefully acknowledged. We are also
indebted to skippers and vessels owners for their kind cooperation during harbor enquiries.
Finally, we thank ICES for providing fishing mortality estimates.
Bjorndal, T. 1989. Production in a Schooling Fishery: The Case of the North Sea Herring
Fishery. Land Economics, 65: 49-56.
Campbell, H.F. 1991. Estimating the Elasticity of Substitution between Restricted and
Unrestricted Inputs in a Regulated Fishery: A Probit Approach. Journal of
Environmental Economics and Management, 20: 262-274.
Cook, R.M., and Armstrong, D.W. 1985. Changes in catchability of cod, haddock, and
whiting associated with the Scottish seine-net fishery. Journal du Conseil international
pour l’Exploration de la Mer, 42 : 171-178.
Dahm, E., Wienbeck, H., West, C.W., Valdemarsen, J.W., and O’Neill, F.G. 2002. On the
influence of towing speed and gear size on the selective properties of bottom trawls.
Fisheries Research, 55: 103-119.
Gavaris, S. 1980. Use of a multiplicative model to estimate catch rate and effort from
commercial data. Canadian Journal of Fisheries and Aquatic Sciences, 37: 2272-2275.
Hilborn, R. 1985. Fleet dynamics and individual variation: why some people catch more fish
than others. Canadian Journal of Fisheries and Aquatic Sciences, 42: 2-13.
Hilborn, R., and Ledbetter, M. 1985. Determinants of catching power in the British Columbia
Salmon Purse Seine Fleet. Canadian Journal of Fisheries and Aquatic Sciences, 40:
Holst, R., Wileman, D., and Madsen, N. 2002. The effect of twine thickness on the size
selectivity and fishing power of Baltic cod gill nets. Fisheries Research, 56: 303-312.
Houghton, R. 1977. The fishing power of trawlers in the Western English Channel between
1965 and 1968. Journal du Conseil Inernational pour l’Exploration de la Mer, 37:
ICES 2003. Report of the Advisory Committee on Fishery Management, October 2003.
Kimura, D.K. 1981. Standardized measures of relative abundance based on modelling
log(c.p.u.e.), and their application to Pacific ocean perch (Sebastes alutus). Journal du
Conseil International pour l’Exploration de la Mer, 39 : 211-218.
Kirkley, J., Morrison Paul, C.J., Cunningham, S., and Catanzano, J. 2004. Embodied and
disembodied technical change in fisheries: an analysis of the Sète trawl fishery, 1985-
1999. Environmental and Resource Economics, 29: 191-217.
Kirkley, J., Squires, D., Strand, I.E. 1998. Characterizing managerial skill and technical
efficiency in a fishery. Journal of Productivity Analysis, 9: 145-160.
Mc Cullagh, P., and Nelder, J.A. 1989. Generalized Linear Models. Chapman & Hall, New
Marchal, P., Andersen, B., Bromley, D., Iriondo, A., Mahévas, S., Quirijns, F., Rackham, B.,
Santurtun, M., Tien, N., and Ulrich, C. 2006. Improving the definition of fishing
effort for important European fleets by accounting for the skipper effect. Canadian
Journal of Fisheries and Aquatic Sciences, 63: 510-533.
Marchal, P., Nielsen, J.R., Hovgaard, H., and Lassen, H. 2001. Time changes in fishing
power in Baltic Sea cod fisheries. ICES Journal of Marine Science, 58: 298-310.
Marchal, P., Ulrich, C., Korsbrekke, K., Pastoors, M., and Rackham, B. 2002. A comparison
of three indices of fishing power on some demersal fisheries of the North Sea. ICES
Journal of Marine Science, 59: 604-623.
Maunder, M.N., and Punt, A.E. 2004. Standardizing catch and effort data: a review of recent
approaches. Fisheries Research, 70: 141-159.
Millischer, L., Gascuel, D., and Biseau, A. 1999. Estimation of the overall fishing power : a
study of the dynamics and fishing strategies of Brittany’s industrial fleets. Aquatic
Living Resources, 12: 89-103.
Pascoe, S., Andersen, J.L., and de Wilde, J.W. 2001. The impact of management regulation
on the technical efficiency of vessels in the Dutch beam trawl fishery. European
Review of Agricultural Economics, 28: 187-206.
Pascoe, S., Coglan., L. 2002. The contribution of unmeasurable inputs to fisheries production:
an analysis of technical efficiency of Fishing vessels in the English Channel.
American Journal of Agricultural Economics, 84: 585- 597.
Robson, D.S. 1966. Estimation of the relative fishing power of individual ships. ICNAF
Research Bulletin, 3: 5-14.
Rose, C.S., and Nunnallee, E.P. 1998. A study of changes in groundfish trawl catching
efficiency due to differences in operating width, and measures to reduce width
variation. Fisheries Research, 36: 139-147.
Sangster, G.I., and Breen, M. 1998. Gear performance and gear comparison between a single
trawl and a twin rigged gear. Fisheries Research, 36: 15-26.
SAS/STAT 1999. SAS Institute Inc., SAS/STAT User’s Guide, Version 8, Cary, NC, 3884
Squires, D. 1987. Fishing Effort: Its Testing, Specification, and Internal Structure in Fisheries
Economics and Management. Journal of Environmental Economics and Management,
14, 268-282.
Squires, D., and Kirkley, J. 1996. Individual Transferable Quotas in a Multiproduct Common
Property Industry. The Canadian Journal of Economics, 29: 318-342.
Squires, D., and Kirkley, J. 1999. Skipper skill and panel data in fishing industries. Canadian
Journal of Fisheries and Aquatic Sciences, 56: 2011-2018.
Venables, W.N., and Dichmont, C.M. 2004. GLMs, GAMs and GLMMs: an overview of
theory for applications in fisheries research. Fisheries Research, 70: 319-337.
Zellner, A., Kmenta, J., and Dreze, J. 1966. Specification and Estimation of Cobb-Douglas
Production Function Models. Econometrica, 34: 784-795.
Table 1. Variables describing vessel attributes collected during the harbor enquiries for the
French and Danish fleets.
Type Variable Unit
General characteristics Date of construction DD/MM/YYYY
(hull, equipment) Date of acquisition DD/MM/YYYY
Date of sale DD/MM/YYYY or NA
Overall length m
Tonnage GT
Main engine power HP
Number of rotations per minute
Date of acquisition of engine DD/MM/YYYY
Maximal speed knots
Bollard pull tonnes
Crew size Number
Hull type (displacement, surfing, catamaran) D/S/C
Hull material (steel / Alu / GRP / wood) S/A/G/W
Knozzle YES/NO
Storing room capacity m3
Freezer room capacity m3
Ice making machine Y/N
Deck surface m2
Variable pitch propeller YES/NO
Winch (or net hauler) capacity (power) KW
Winch (loading) capacity m (of cable)
Winch speed (or net hauler) m/s or r/mn
Number of winch drums number
Number of net drums number
Net disentangling machine YES/NO
Net washing machine YES/NO
Electronics GPS YES/NO
Radar YES/NO
Shore / ship confidential communication YES/NO
Computer YES/NO
Charting software (dedicated plotter or computer) YES/NO
Number of sounders number
Sounder 1 frequency kHz
Computer interface of sounder 1 YES/NO
Sounder 2 frequency kHz
Computer interface of sounder 2 YES/NO
Number of sonars number
Sonar frequency kHz
Computer interface of sonar YES/NO
Catch handling Conveyor YES/NO
RSW system YES/NO
Container / Boxes on-board YES/NO
Deck crane YES/NO
Table 2. Variables describing gear attributes collected during the harbor enquiries for the
French and Danish fleets.
Type Variable Unit
All gears Gear unit
Number of fishing trips per year number
Number of days per fishing trip days
Number of fishing days per fishing trip days
Trawls Number of warps 2, 3 or NA if not trawl
Number of panels 2, 4, 6 or NA if not trawl
Yarn material
Yarn diameter in codend mm
Vertical opening m or NA if not trawl
Horizontal opening m or NA if not trawl
Mesh size of codend mm or NA if not trawl
Mesh size of wings mm or NA if not trawl
Length of headline m or NA if not trawl
Length of groundrope m or NA if not trawl
Type of groundrope
Scanmar sensors Y/N or NA if not trawl
Trawleye (or Netsonde) Y/N or NA if not trawl
Number of otter boards 0, 2, 4 or NA if not trawl
Weight of an otter board kg or NA if not trawl
Average trawling speed knots or NA if not trawl
Selectivity device
Volume of water filtered per time unit m3/s
Number of hauls per fishing day number or NA if not trawl
Mean duration of one haul hours or NA if not trawl
Nets Number of panels number
Smallest stretched mesh size mm or NA if not net
Stretched mesh size of the external panel mm or NA if not net
Net material
Total length of net set per fishing trip m or NA if not net
Total length of net set per fishing day m or NA if not net
Total height of net m or NA if not net
Soaking time of nets hours or NA if not net
Seines Diameter of the seine rope mm or NA if not seine
Length of the seine rope m or NA if not seine
Number of panels 2, 4, 6 or NA if not seine
Yarn material
Yarn diameter in codend mm
Vertical opening m or NA if not seine
Horizontal opening m or NA if not seine
Mesh size of codend mm or NA if not seine
Mesh size of wings mm or NA if not seine
Length of headline m or NA if not seine
Length of groundrope m or NA if not seine
Type of groundrope
Tickler chain Y/N or NA if not seine
Selectivity device
Number of hauls per fishing day number or NA if not seine
Mean duration of one haul hours or NA if not seine
Table 3. Details on the sampling procedure for the harbor enquiries for the French, Danish and Basque fleets.
Country Fleet Population (2003) Sample Sampling rate
France Gill-netters 99 21 21%
Otter-trawlers (12-16 m) 125 35 28%
Otter-trawlers (16-20 m) 87 19 22%
Otter-trawlers (20-24 m) 106 26 25%
Denmark Otter-trawlers 531 76 14%
Gill-netters 459 36 8%
Danish Seiners 81 8 10%
Spain Bottom-trawlers (Ondarroa), (20-30 m) 5 4 80%
(Basque Country) Bottom-trawlers (Ondarroa), (30-40 m) 27 25 93%
Bottom-trawlers (Pasaia), (30-40 m) 9 9 100%
Table 4. Summary of the results of the analysis of CPUE by Generalised Linear Models for French gill-netters targeting hake (Merluccius
merluccius), sole (Solea solea) and anglerfishes (Lophius sp.). The statistics include the degrees of freedom (DF), ratio of scaled Pearson
chi-square to DF (SCC/DF) and the values of the coefficients associated to the significant fishing effort descriptors (p<0.05). Gear types
are fixed nets (GNS) or trammel nets (GTR). A ‘*’ indicates that the hypothesis that residuals are normally distributed is not rejected by
based on the Kolmogorov-Smirnov test (p<0.05).
Species DF SCC/DF Gear type Net length Soaking time Vessel length
Hake 136 1.04 2.15 0.00 0.04 -0.05 0.001
Sole 113 1.04 -3.06 0.00 0.04 0.04
Anglerfishes* 137 1.02 -0.78 0.00
Table 5. Summary of the results of the analysis of CPUE by Generalised Linear Models for French otter-trawlers targeting hake (Merluccius
merluccius) and Norway lobster (Nephrops norvegicus). The statistics include degrees of freedom (DF), ratio of scaled Pearson chi-
square to DF (SCC/DF) and the values of the coefficients associated to the significant fishing effort descriptors (p<0.05). Gear types are
single-trawls (OTB) or twin-trawls (TTB), combined to different groundropes: diabolo (1), chains (3), spheres (4), rubber (5), plain wire
(6). A ‘*’ indicates that the hypothesis that residuals are normally distributed is not rejected by based on the Kolmogorov-Smirnov test
Length (m) Species DF SCC/DF Gear type Headline Towing Haul Computer Vessel
OTB1 OTB3 OTB4 OTB5 OTB6 TTB1 TTB3 TTB4 TTB5 TTB6 Length Speed duration Yes No HP
(12-16) Norway lobster 176 1.14 1.23 0.48 0.33 1.22 1.30 1.55 0.32 1.85 0.92 0.00 0.03
Hake* 176 1.15 0.11 0.16 1.03 0.51 0.58 -0.01 -0.42 0.19 -0.40 0.00 0.04 0.62 0.00 0.49
(16-20) Norway lobster 107 1.18 0.38 -2.96 -0.23 -0.38 0.33 0.00
Hake 96 1.24 0.53 1.67 0.37 0.46 1.24 0.00 0.04 1.29 0.00 -0.44 -0.01
(20-24) Norway lobster 88 1.22 -0.77 0.14 -0.65 0.38 0.00 -1.87
Hake* 160 1.13 0.97 0.74 -0.53 -0.21 0.00 0.02 -0.56 0.23
Table 6. Summary of the results of the analysis of CPUE by Generalised Linear Models for Danish otter-trawlers targeting cod (Gadus morua),
Norway lobster (Nephrops norvegicus) and plaice (Pleuronectes platessa). The statistics include degrees of freedom (DF), ratio of scaled
Pearson chi-square to DF (SCC/DF) and the values of the coefficients associated to the significant fishing effort descriptors (p<0.05). A
‘*’ indicates that the hypothesis that residuals are normally distributed is not rejected by based on the Kolmogorov-Smirnov test (p<0.05).
Species DF SCC/DF Date of construction Crew size Vessel length No. winch drums No. net drums No. sounders
Cod 208 1.09 0.70 -0.27 1.00
Norway lobster 180 1.11 -5.0 10-4 -0.25 2.55 1.87
Plaice* 178 1.12 -3.0 10-4 0.77 -0.31 1.38 1.38 0.74
Table 7. Summary of the results of the analysis of CPUE by Generalised Linear Models for Basque bottom-trawlers, registered in Ondarroa, of
length (30-40 m), targeting hake (Merluccius merluccius) and anglerfishes (Lophius spp.). The statistics include degrees of freedom
(DF), ratio of scaled Pearson chi-square to DF (SCC/DF) and the values of the coefficients associated to the significant fishing effort
descriptors (p<0.05).
Species DF SCC/DF Variable pitch propeller Number of net drums
Yes No
Hake 114 1.10 0.86 0.00 -0.37
Anglerfishes 114 1.10 0.83 0.00 0.79
Table 8. Outputs comparison of, (a) the regression between Log fishing mortality (LF) and Log nominal fishing effort (LEn) and, (b) the
regression between Log fishing mortality (LF) and Log adjusted fishing effort (LEe). The standard error of the slope of regression (b) is
provided, and marked with a “*” when the slope is not significantly different from 1 (p<0.05). BB: Bay of Biscay, CS: Celtic Sea, NS:
North Sea, WS: Western Scotland.
Fleet Stock N R2 (a) R2 (b) Standard error of slope (b) Equation (b)
French otter-trawlers (12-16 m) Northern hake 246 0.29 0.39 0.05 LF = -23.73 + 0.59*LEe
French otter-trawlers (16-20 m) Northern hake 246 0.63 0.31 0.07 LF = -25.46 + 0.76*LEe
French otter-trawlers (20-24 m) Northern hake 246 0.00 0.07 0.05 LF = -19.38 + 0.22*LEe
French gill-netters Northern hake 194 0.00 0.43 0.07* LF = -21.51 + 0.90*LEe
BB sole 49 0.21 0.03 0.21 NS
BB/CS anglerfishes 130 0.01 0.01 0.29 NS
NS/WS anglerfishes 45 0.00 0.09 0.50* LF = -23.45 + 1.06*LEe
Danish otter-trawlers NS cod 64 0.03 0.51 0.14* LF = -13.92 + 1.14*LEe
NS plaice 73 0.06 0.79 0.07* LF = -22.11 + 1.14*LEe
Basque bottom-trawlers (30-40 m) Northern hake 170 0.11 0.19 0.17* LF = -14.76 + 1.06*LEe
BB/CS anglerfishes 95 0.03 0.35 0.14* LF = -18.35 + 0.98*LEe
NS/WS anglerfishes 47 0.01 0.35 0.24* LF = -19.03 + 1.16*LEe
(a) (b)
(c) (d)
Figure 1. Number of vessels, by year, for which fishing effort data were recorded: (a) French
otter-trawlers (black dot: (12-16 m), circle: (16-20 m), square: (20-24 m)), (b) French
gill-netters, (c) Danish fleets (black dot: gill-netters, square: Danish seiners, diamond:
trawlers, circle: others) and, (d) Basque bottom-trawlers (black dot: (20-30 m)
registered in Ondarroa, circle: (30-40 m) registered in Ondarroa, square: (30-40 m)
registered in Pasaia).
(a) (b)
(c) (d)
Figure 2. Annual changes in gear types for: (a) French otter-trawlers, all length classes
(white: single otter-trawls, black: twin trawls), (b) French gill-netters (white: drift nets,
black: fixed nets, double hashed: trammel nets), (c) Danish otter-trawlers (white:
multi-rig trawls, black: pelagic trawls, double hashed: single trawls, single hashed:
twin trawls) and, (d) Basque bottom-trawlers (30-40 m) registered in Ondarroa in 2003
(white: fixed nets, black: long-lines, double hashed: “Bou” otter-trawls, thick single
hashed: single otter-trawls, thin single hashed: “Very High Vertical Opening” bottom-
(a) (b)
Figure 3. Annual changes in (a, b, c) GPS availability and in (d) computer availability for:
(a) French otter-trawlers, all length classes confounded, (b) French gill-netters, (c)
Danish otter-trawlers, and, (d) Basque bottom-trawlers (30-40 m) registered in
Ondarroa. White bars represent the absence of electronic devices (GPS or computers),
black bars represent their presence.
(a) (b)
(c) (d)
Figure 4. Annual changes in average (a, b, c) horse power (HP) and (d) bollard pull (t) for:
(a) French otter-trawlers (black dot: (12-16 m), circle: (16-20 m), square: (20-24 m)),
(b) Basque bottom-trawlers (30-40 m) registered in Ondarroa and, (c, d) Danish otter-
(c) (d)
Figure 5. Annual changes in (a, b, c, d) headline length (m) and (e, f) vertical opening (m)
for: (a) French otter-trawlers (12-16 m) (black dot: single trawls, circle: twin trawls),
(b) French otter-trawlers (16-20 m) (black dot: single trawls, circle: twin trawls), (c)
French otter-trawlers (20-24 m) (black dot: single trawls, circle: twin trawls), (d, f)
Basque bottom-trawlers (30-40 m) registered in Ondarroa (black dot: single trawls,
circle: “Very High Vertical Opening” trawls) and, (e) Danish otter-trawlers (black dot:
multi-rig trawls, circle: pelagic trawls, square: single trawls, diamonds: twin trawls).
Figure 6. GLM residuals inspection through QQ plots. French gill-netters harvesting (a)
hake, (b) sole, (c) anglerfishes; French otter-trawlers (12-16 m) harvesting (d) hake,
(e) Norway lobster; French otter-trawlers (16-20 m) harvesting (f) hake, (g) Norway
lobster; French otter-trawlers (20-24 m) harvesting (h) hake, (i) Norway lobster,
Danish otter-trawlers harvesting, (j) cod, (k) Norway lobster, (l) plaice; Basque
bottom-trawlers (30-40 m) registered in Ondarroa harvesting, (m) hake, (n)
Log (fishing power) Log (fishing power)
Log (fishing power)
Figure 7. Relationships between log-transformed catch per unit effort (CPUE) and
fishing power by net type (black dot: fixed nets, circle: trammel nets), as derived from
the Generalized Linear Models. French gill-netters harvesting, (a) hake (Merluccius
merluccius), (b) (Solea solea) and, (c) anglerfishes (Lophius sp.).
(a) (b)
(c) (d)
(e) (f)
Figure 8. Relationships between log-transformed catch per unit effort (CPUE) and fishing
power by trawl type trawl type and groundrope type: single trawl equipped with
diabolos (dot), chains (circle), metallic spheres (square), rubber (diamond), plain wire
(triangle); twin trawl equipped with diabolos (plus), chains (cross), metallic spheres
(star), rubber (hash), plain wire (encircled plus), as derived from the Generalized
Linear Models. French otter-trawlers of length range (a, b) (12-16 m), (c, d) (16-20
m), (e, f) (20-24 m) harvesting, (a, c, e) hake (Merluccius merluccius) and, (b, d, f)
Norway lobster (Nephrops norvegicus).
(a) (b)
Log(fishing mortality)
(c) (d)
(e) (f)
(g) (h)
Log(fishing mortality)
Log(fishing mortality)Log(fishing mortality)
Log(nominal fishing effort) Log(adjusted fishing effort)
Figure 9. Relationships between log-transformed; (a, c, d, g) partial fishing mortality,
Log(F), and nominal fishing effort, Log(En); (b, d, f, h) partial fishing mortality,
Log(F), and adjusted fishing effort, Log(Ee). French (a, b) gillnetters, (c, d) otter-
trawlers (12-16 m), (e, f) otter-trawlers (16-20 m) and, (g, h) otter-trawlers (20-24 m)
harvesting Northern hake (Merluccius merluccius).
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Log(fishing mortality)
Log(fishing mortality)
Log(fishing mortality)
Log(fishing mortality)
Log(nominal fishing effort) Log(adjusted fishing effort)
Figure 10. Relationships between log-transformed; (a, c, d, g) partial fishing mortality,
Log(F), and nominal fishing effort, Log(En); (b, d, f, h) partial fishing mortality,
Log(F), and adjusted fishing effort, Log(Ee). (a, b) Danish otter-trawlers harvesting
North Sea cod (Gadus morhua), (c, d) Danish otter-trawlers harvesting North Sea
plaice (Pleuronectes platessa), (e, f) Basque bottom-trawlers harvesting Northern hake
(Merluccius merluccius), (g, h) Basque bottom-trawlers harvesting Celtic Sea and Bay
of Biscay anglerfish (Lophius spp.).
... All of this has substantially changed not only stock assessments (through the impact of novel technology on catch rates overtime), but also the way fisheries are managed. In short, as technological innovations creep into a sector, assessment and management have to adapt (Marchal et al. 2006). ...
... In general, there are many research needs (Holder et al. 2020) and very little empirical research on the effects of technology on recreational fishing outcomes (but see Feiner et al. 2020 for example). There is evidence of technology creep that enhances fish capture success along with tools that could influence the selectivity of a given fishery-something observed in the commercial realm for decades (see Marchal et al. 2006;Eigaard et al. 2014). An important message here is that resource management agencies need to share their experiences and that scientists should more intensively study the impact of innovations in recreational fishing. ...
Full-text available
Technology that is developed for or adopted by the recreational fisheries sector (e.g., anglers and the recreational fishing industry) has led to rapid and dramatic changes in how recreational anglers interact with fisheries resources. From improvements in finding and catching fish to emulating their natural prey and accessing previously inaccessible waters, to anglers sharing their exploits with others, technology is completely changing all aspects of recreational fishing. These innovations would superficially be viewed as positive from the perspective of the angler (aside from the financial cost of purchasing some technologies), yet for the fisheries manager and policy maker, technology may create unintended challenges that lead to reactionary or even ill-defined approaches as they attempt to keep up with these changes. The goal of this paper is to consider how innovations in recreational fishing are changing the way that anglers interact with fish, and thus how recreational fisheries management is undertaken. We use a combination of structured reviews and expert analyses combined with descriptive case studies to highlight the many ways that technology is influencing recreational fishing practice, and, relatedly, what it means for changing how fisheries and/or these technologies need to be managed—from changes in fish capture, to fish handling, to how anglers share information with each other and with managers. Given that technology is continually evolving, we hope that the examples provided here lead to more and better monitoring of technological innovations and engagement by the management and policy authorities with the recreational fishing sector. Doing so will ensure that management actions related to emerging and evolving recreational fishing technology are more proactive than reactive.
... The latter includes the technical features of the gear and the fish accessibility [24]. The fishing capacity of trawlers is typically used in regulatory measures and is defined by the engine power of vessels, without considering the specifics of the gear used [25][26][27]. By ignoring the gear in fishing capacity measurements, the actual impact of fishing pressure (fishing mortality) may significantly change with modifications in gear and technological improvements (e.g., fish-finding sonar, stronger netting, changes in trawl panel design) [26]. ...
... Some differences are possible due to the skippers' behavior during trawling (fine tuning of the speed, gear depth, etc.) and the availability of information (more accurate sonar, trawl monitoring systems), which can lead to different decisions in fine-tuning the gear. The kilowatt power of the vessel's main engine is widely used as a proxy to characterize fishing capacity, however, this is not the most accurate measure of a vessel's ability to catch fish [25][26][27]45]. Catchability depends on several factors. ...
Full-text available
The Gulf of Riga stock of Baltic herring (Clupea harengus membras L.) has been managed through several management tools. One of them has been the restriction of vessels´ main engine power (< 221 kW). This restriction was implemented in the early 1990s based on the vessel types available in the area and on assumption that the gear size used in trawl fishery depends on vessel size (power). In the current study, we compared vessels with different engine powers using same gear as currently allowed in the gulf to identify whether vessel power has any relation to the catch structure. The results showed that the engine power did not explain the differences in catch structure, which were more dependent on season and depth of water. Easing the power restriction of the trawl vessels in the Gulf of Riga will most likely not have a major negative impact on the sustainable management of the herring population in the gulf. However, the vessels with higher engine power should not use larger trawl gear than currently used in the gulf.
... Our findings add to the increasing evidence that such factors are also important in the dynamics of fishing activities, especially in small-scale fisheries (Salas and Gaertner, 2004). Our findings on fine-scale variation in fishers' behaviour regulating their chances of catching fish can be transferrable to other small-scale fisheries in which fishers adjust gear, effort, timing, and movement to increase fishing success (see Marchal et al., 2007). However, individual differences in behaviour, skills, and performance are often masked in management, as fisheries science typically relies on aggregated data (Bockstael and Opaluch, 1983). ...
Understanding the dynamics of small-scale fisheries requires considering the diversity of behaviours and skills of fishers. Fishers may have different abilities and tactics that can translate into different fishing outcomes. Here, we investigate variation in fishing behaviours among traditional net-casting fishers that are assisted by wild dolphins, and how this variation interacts with environmental conditions and influences fishing success. By combining in situ environmental sampling with fine-scale behavioural tracking from overhead videos, we found a higher probability of catching fish among fishers well-positioned in the water and that cast their nets wide-open and closer to dolphins. These differences in net-casting performance affect their chance of catching any fish over and above environmental conditions related to fish availability. This finding suggests that fishers’ success may not be simply an outcome of variations in resource availability, but also result from subtle variations in fishing behaviours. We discuss how such behavioural variations can represent skills acquired over the years, and how such skills can be crucial for fishers to benefit and keep interacting with dolphins. Our study demonstrates the role of behavioural variation in the dynamics of a century-old fishery and highlights the need to consider fishers’ behaviours in co-management of small-scale fisheries.
... Whereas such information is often available in large-scale industrial fisheries (e.g. trawling and long-line) [6][7][8] , it is usually lacking in data-limited fisheries, notably small-scale coastal fisheries using gear such as trap; often with limited or no reporting requirements. This contributes to a lack of data and monitoring in many coastal systems that are among the most productive yet depleted marine ecosystems 9,10 . ...
Full-text available
Fishery-dependent data are frequently used to inform management decisions. However, inferences about stock development based on commercial data such as Catch-Per-Unit-Effort (CPUE) can be severely biased due to a phenomenon known as technological creep, where fishing technology improves over time. Here we show how trap improvement over nine decades has driven technological creep in a European lobster (Homarus gammarus) fishery. We combined fishing data, experimental fishing with contemporary and older trap types, and information on depletion effects during fishing seasons. The resulting standardized CPUE time series indicates a 92% decline in lobster abundance between 1928 and 2019 compared to 70% if technological creep is not corrected for. Differences are most pronounced within the last 40 years when the most substantial shift in gear technology occurred: an uncorrected CPUE index suggests an 8% increase in lobster abundance during this period, while the corrected CPUE index declined by 57%. We conclude that technological creep has masked a continuous stock decline, particularly in recent decades and largely driven by the shift from one- to two-chambered traps, as well as the ability of newer trap designs to capture larger lobsters. Our study confirms the importance of adequate standardization, including technological development, when using fishery dependent CPUE for monitoring and management of data-limited fisheries.
... Fisheries management based on catch quotas imply adaptive management because quotas are set on a time-defined basis (usually annually), while effort-based regimes are more difficult to adapt to the available fishing opportunities. Additionally, when effort restrictions are implemented, fisheries management must face the risk of increasing fishing mortality by means of investment in technology (''technological creep'') (Marchal et al., 2007). ...
Full-text available
The exploitation of mixed fisheries leads to trade-offs between fisheries rent, production (landings) and resource conservation because harvest rent cannot be optimized simultaneously for all species. Additionally, the exploitation of mixed fisheries by heterogeneous fleets complicates their management because of the necessity to allocate catch or effort quotas, under some criterion of efficiency or equitability. The allocation of fishing opportunities impacts directly on the availability of jobs in fisheries. To analyse the trade-offs between employment and profits in mixed fisheries, an optimization bioeconomic model was built for the three bottom-trawl fleet segments operating in the Catalonia demersal fishery (NW Mediterranean Sea). The fishery is subject to a multiannual management plan to align fishing effort with the fisheries mortality that would produce the maximum sustainable yield. The optimal effort allocation among the three fleet segments were compared subject to alternative fisheries management policies: (i) maximum sustainable yield, (ii) maximum economic yield, (iii) maximum labour remuneration, (iv) pretty good yield, and (v) equilibrium biomass larger than biomass at maximum sustainable yield, taking into account the multispecies nature of the fishery. The results show that all management policies provide higher profits than current. In the first three scenarios, high profitability can be made compatible with a lower number of better paid jobs, because the optimal allocation of effort in most scenarios would imply a reduction in the number of vessels. The results also show that the current number of vessels and effort distribution (which are the result of a historical process, rather than the results of a management strategy) are far from any optimum.
... Technological change has been a driving force behind increases in fishing efficiency worldwide for many decades now. For example, several studies have highlighted the substantial impact of new technology on catch rates of demersal fishes (Marchal et al., 2007), tunas, and billfishes caught by pelagic longlines (Ward, 2008) and on the impact of global positioning systems (GPS) and plotter systems on the relative fishing power in a prawn fishery (Robins et al., 1998). If the increase due to this new technology is not properly quantified and integrated into stock assessments, they will bias stock size estimates based on raw catch per unit effort (CPUE) derived from captain's logbooks or observer data towards larger stock size, potentially leading to cryptic overfishing and hyperstability Maunder and Punt, 2004). ...
Numerous pelagic species are known to associate with floating objects (FOBs), including tropical tunas. Purse seiners use this behaviour to facilitate the capture of tropical tunas by deploying artificial drifting fish aggregating devices (dFADs). One major recent change has been the integration of echosounders in satellite-tracked GPS buoys attached to FOBs, allowing fishers to remotely estimate fishable biomass. Understanding the effects of this new technology on catch of the three main tuna species (yellowfin tuna, Thunnus albacares; bigeye tuna, Thunnus obesus; and skipjack tuna, Katsuwonus pelamis) is important to accurately correct for this change in catch-per-unit-effort (CPUE) indices used for stock assessments. We analysed catch data from the French purse seine fleet for the period 2010–2017 in the Indian Ocean to assess the impact of this fleet’s switch to echosounder buoys around 2012. Results indicate that echosounders do not increase the probability a set will be succesful, but they have a positive effect on catch per set, with catches on average increasing by ≈2−2.5 tonnes per set (≈10%) when made on the vessel's own dFADs equipped with an echosounder buoy. Increases were due to a decrease in sets below ≈25 tonnes and an increase in those greater than ≈25 tonnes, with a non-linear transition around this threshold. This increase explains the considerable investment of purse seiners in echosounder buoys, but also raises concerns about bias in stock size estimates based on CPUE if we do not correct for this fishing efficiency increase.
... If we are to meet the stated Common Fisheries Policy [68] goals of 'long-term environmental, economic, and social sustainability', and 'protection of the marine environment, to the sustainable management of all commercially exploited species, and in particular to the achievement of good environmental status by 2020', then we may need to reconsider some of the wider questions of how fisheries opportunities are allocated. TAC allocations on the basis of historical political decision-making does not take into account improved scientific knowledge, understanding, and technological change over that time period [69,70], nor the changing distributions and abundances of species over recent decades, in response to climate change [71][72][73][74]. RTI provides an opportunity to manage to the close to real-time temporal and spatial distributions of species, using 'smart' technologies, whilst improving data flow, providing opportunities to incentivise a wide range of desirable behaviours, avenues to include fisher ecological knowledge [54,75], protecting vulnerable ecosystem components from the ecosystem effects of fishing (e.g. ...
In the face of political and environmental uncertainty, adaptive management methods are needed to respond to variable natural resources and changing management frameworks. Based on close to real-time information updates , and harnessing modern technology, Real-Time Incentive (RTI) fisheries management is designed to co-evolve with the resource, enabling efficient responses to management issues as they arise, without the need for major structural changes. Through spatio-temporal management, and the use of a credit system, incentives can be incorporated as rewards for desirable actions. In order for such a new system to be useful and accepted, stakeholders must be involved in the development and design process. This paper details the consultative process carried out with Irish demersal fishery stakeholders in an effort to identify their likes and dislikes of the system, and work towards tailoring the RTI system into a practical solution that works for them. In this process, we achieved a detailed understanding of the fishery, the complexity of the system, and the challenges faced by the stakeholders, all of which must be considered when attempting to implement a new system such as RTI. A range of solutions were proposed, including new ideas for the future development of the RTI system. Most striking were the numerous ideas and approaches to tackling key issues currently facing the industry, many of which have relevance to existing fisheries management. Given the freedom and support to do so, fishing industry stake-holders appear eager to contribute to solving many of their own problems.
... Future efforts should build, in order to record, model and effectively manage these misreported or unreported fishing fleets, that possibly have great ecological and economic impacts in a broader scale and practically, remain unknown to date. In addition, restricting the days at sea using an effort management regime is likely to ignore possible technical improvements in vessel catching power and gear improvement that is very tempting for fishers (Marchal et al., 2007). Such an increase in effective effort for the same nominal effort will mechanically increase the fishing mortality of some species over time (Eigaard et al., 2014). ...
The management of fisheries requires well-planned approaches that consider the socio-ecological costs and benefits of management options while minimising conflicts among fishing practices. We developed a framework to anticipate the cost-effectiveness of several fisheries management options, including space-time closures, gear selectivity improvements, and fishing effort reduction of eastern Ionian Sea fisheries. We also examined to what extent these fisheries could be influenced by placing new aquaculture sites into specific areas. We used a dynamic space-time model considering the effect of possible fishing effort displacement on alternative marine areas. A fine-scale distribution of 6 species of high commercial importance, for the eastern Ionian fisheries (central Mediterranean), was used together with the fishing effort distribution of trawlers, purse-seines, and small-scale fisheries to track the implications on several bio-economic indicators. The study revealed that the stocks and the fisheries economics benefited from a 10% reduction in fishing effort for all fishery sectors, while the unwanted fish catch was slightly higher. Thus, although there are notable advantages, this management option is not sufficient to support an EU regulatory framework aiming to promote more selective fishing practices and mitigate unwanted catches. However, we showed that the protection of juveniles, by imposing selectivity improvements or space-time closures on trawlers, slightly reduced the unwanted catch. Nevertheless, the benefit for the majority of stocks and fisheries economics, during the five-year simulation period, was limited. In the case of space-time closures, this is attributed to the offset due to the fishing effort displacement towards other areas. While the benefit on fish populations by improving trawl selectivity, it depends on the species. Finally, the establishment of new aquaculture units could lead to a slight reallocation of fishing effort along the border of the new sites without substantially affecting the profit of small-scale fisheries. Such findings are useful for fisheries management and broader spatial planning in the Ionian Sea. They will provide insight to policymakers and different fishing sectors, thus enabling a more transparent and participatory decision-making process.
Full-text available
Dans les pays en développement, la pêche artisanale est cruciale pour la sécurité alimentaire, l’emploi local et l’utilisation durable des ressources marines. Paradoxalement, la pêche artisanale est beaucoup moins étudiée que la pêche industrielle. Ce manque de données peut conduire à une sous-estimation des captures et en conséquence, à une surexploitation des ressources halieutiques et à une marginalisation des communautés de pêcheurs. La gestion spatiale est devenue un outil important pour les gestionnaires de la pêche qui cherchent à préserver les habitats et à restaurer les stocks de poissons, ainsi que ceux qui souhaitent optimiser le rendement de la pêche. Grâce au suivi satellitaire, les déplacements des pêcheurs sont connus, mais il est difficile d’identifier les zones où la ressource halieutique est réellement extraite du milieu. Pour pallier cette imprécision, de nombreuses études se tournent vers la modélisation du mouvement, des méthodes qui permettent d’inférer les comportements sous-jacents des pêcheurs, à travers des bases de données de trajectoires très volumineuses. Grâce à ces méthodes, il est possible de mettre en relation la distribution spatiale de la pêche avec les autres utilisations de l’espace maritime et en particulier avec les aires marines protégées, afin d’évaluer la pertinence d’un zonage et le respect de la législation. Cette thèse s’appuie sur l’exemple du Gabon pour illustrer ces enjeux et défis propres à la pêche artisanale, en fournissant des outils pour caractériser la pêche artisanale maritime, géolocaliser de l’effort nominal de la pêche et mettre cela en perspective avec le réseau d’aires marines protégées.
In order to ensure sustainable maritime safety, studies based on unreported maritime accidents in maritime transport are necessary. Such studies allow the causes of accidents that have not come to light, to be identified and addressed. In this study, the data of unreported occupational accidents on Turkish fishing vessels with a full length of 12 m and above was analysed using both Bayesian network (BN) and Association Rule Mining (ARM) methods. A network structure that summarizes the occurrence of occupational accidents on fishing vessels with the BN method was put forward. The network structure makes it possible to analyse the latent factors, active failures and operational conditions that cause the accident qualitatively and quantitatively. The Predictive Apriori algorithm was used to establish rules for the occurrence of occupational accidents on fishing vessels, taking variables such as day condition, length, sea condition, and ship type into account. These rules provide an understanding of how occupational accidents occur on fishing vessels. In other words, these rules define the minimum requirements for the occurrence of accidents on fishing boats. The developed hybrid model can be used for analysing unreported occupational accidents on fishing vessels.
Full-text available
Sea trials were carried out on a Danish commercial vessel measuring the size selectivity and fishing power of gill nets used to catch Baltic cod (Gadus morhua). A comparison was made of two different twine thicknesses at two different times of the year. Nominal mesh sizes of 70–130mm were used. Method of capture, condition factor and girths were measured for sub-samples of the cod caught. A model of the size selectivity of the gill nets was adapted to the experimental conditions where two gears were fished on the same population. This model was fitted to the catch data for each set. Subsequently a model was fitted for the mean selectivity taking between-set variation into account. The selectivity curve that fitted the data best was given by the sum of two normal distributions. It was found that twine thickness and trials period had relatively little effect upon the shape of the selectivity curve. Twine thickness had a substantial effect upon the fishing power of the nets.
The technical efficiency over time of the Dutch beam trawl fleet is examined using a stochastic production frontier. Factors increasing efficiency are found to be vessel size and improved quota transferability, whereas vessel age, gear restrictions and total allowable catch (implying a higher discard rate) have a negative impact. Average technical efficiency falls with decreases in stock abundance relative to the EU fleet size, but improves with fishing area restrictions, possibly through the reduction in crowding arising from a more dispersed fishing activity. The results suggest that EU fleet reduction programmes could result in a less than proportional decrease in harvesting capacity and that fleet replacement programmes associated with the reduction programmes may, to a large extent, offset the capacity reductions. © Oxford University Press and Foundation for the European Review of Agricultural Economics 2001.
The scope of this study is to identify temporal dynamics in fishing power, by deriving three different indices (IFP1, IFP2, IFP3) based on three independent methods. IFP1 is derived from the GLM analysis of the relationship between fishing mortality and fishing effort, assuming that total fishing mortality estimates from XSA (eXtended Survivors Analysis) are accurate. IFP2 is derived from the GLM analysis of the difference between the Log-CPUE of a vessel and the average Log-CPUE of a set of reference vessels, which are chosen with regards to the stability of their Log-CPUE over time. IFP3 is derived from the GLM analysis of the Log-CPUE of a vessel relative to some external survey abundance index. Particular attention is paid to the horsepower and year effects in IFP1, IFP2, and IFP3. This methodology is applied to the Danish, Dutch, English and Norwegian demersal fisheries of the North Sea. The fishing power estimated by all indices increases with horsepower, particularly in relation to target species. Despite less consensus in the estimation of annual variations in fishing power, some important features are highlighted. First, there are cases where fishing power has consistently increased over the period of investigation, possibly through an overall increase in fishing efficiency. Second, there are examples where fishing power has increased relative to one species, and remained constant or even decreased in relation to another one. In the context of mixed-species fisheries, this feature might reveal a shift in fishing tactics. Copyright 2002 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd. All rights reserved.
In this paper we consider the specification and estimation of the Cobb-Douglas production function model. After reviewing the "traditional" specifying assumptions for the model which are based on deterministic profit maximization, we develop a model in which profits are stochastic and in which maximization of the mathematical expectation of profits is posited. "Sampling theory" and Bayesian estimation techniques for this model are presented.
The catchability coefficient, Q, of cod, haddock, and whiting associated with the Scottish seine-net fleet is examined. Cod and haddock show increasing catchability for the period 1963–1979. The rise in Q is related to the spatial distribution of the fleet as vessels increasingly fish in areas of higher fish abundance. Additionally, for haddock, catchability is related to year-class strength. The utility of catchability trends in the prediction of fishing mortality is discussed.
Sixty trawlers of over 12·2 m in length landed demersal fish at Brixham, Devon, between 1965 and 1968. Their gross tonnage ranged from 6 to 123 tons, length from 12·2 to 28 m, brake horsepower from 30 to 360 BHP and date of building from 1901 to 1968. During the four year period the average tonnage, length and age of the fleet decreased slightly whereas the average engine power increased from 101 to 147·8 BHP. The fishing power of each vessel was calculated from its catchrate of total demersal species and of plaice by comparison with a standard group using a modification of Gulland's (1956) method. Significant positive correlations between fishing power and engine power were found. The only other significant correlation was that of total demersal fishing power on tonnage; this was negative and accounted for only 6% of the variance. It was shown that the fishing power of individual vessels varied with time (the average trend was upwards) as a result of a factor (or factors) which had not been included in the analyses. This meant that if the regression equations were used to predict fishing power the result would be biassed. One of the factors responsible was thought to be skipper ability. The published regression coefficients relating fishing power and brake horsepower of motor trawlers in European waters were compared and were found to have a coefficient of variation of 27%.
Among the multitude of factors possibly affecting the cod-end selectivity of bottom trawls, in the commercial fishery gear speed and gear size are thought to be of high importance. A binational experiment, funded by the EU, carried out with a German and a Norwegian ship in the period between 1994 and 1997 tested this opinion.Of four data sets collected only two demonstrated a relationship between speed through water and cod-end selectivity. A decrease of haddock selectivity by increased towing speed in one data set was equalled by an increase of cod selectivity in another. Hence, the results of this experiments do not support the hypothesis of an essential effect of trawl speed on the selectivity of the cod-end.Likewise, no significant difference could be detected between the selectivity of the same cod-end attached to one trawl on one hand and to a scaled down version of it on the other.An increase of cod-end selectivity related to catch size was observed in three of six series of hauls. Though being in contradiction to results of other authors a possible explanation is the magnitude of the catches made during the experiments. At this range it is rather probable that with the building up of the catch in the cod-end, the meshes open increasingly, thus improving the selectivity, until a point is reached where it either levels out or begins to decrease.