Table of Contents
Modelling the management of clam (Ruditapes decussatus)
exploitation in the Plentzia estuary (Basque Country, Northern
Spain
Juan Bald & Angel Borja
AZTI Foundation, Department of Oceanography and Marine Environment
Herrera Kaia Portualdea z/g, 20110 PASAIA (Gipuzkoa) SPAIN
Tel: +34 943 00 48 00
Fax: +34 943 00 48 01
www.azti.es
jbald@ pas.azti.es
Abstract
Some of the estuaries of the Basque Country (Northern Spain) have been
areas of exploitation of clam populations, both by professional and illegal
fishermen. There is a real possibility of overfishing and, therefore, the
Department of Fisheries of the Basque Government needs to: (a) understand
the situation relating to these populations; and (b) provide a tool to establish
the most adequate management for the exploitation of clams.
In order to simulate different alternatives for the exploitation, based upon
scientific data on the population, the best tool available is a system
dynamics model. Here the VENSIM® model was employed, utilising data
on clam populations: summer stock and biomass in the Plentzia estuary, in
1998, 2000; winter stock in 1999, 2000, and 2001; the area occupied by the
species; the length and weight class distribution; the number of fishermen;
mean of biomass, captured by the fishermen; natural mortality, by length
class; maturation; fertility rate; etc. This study improves previous
experiences in modelling clam exploitation in the Plentzia and Mundaka
estuaries.
Following validation of the model, after running it for 1 and 10 years, some
cases were simulated. This analysis was undertaken in order to establish the
effect of modifying the number of fishermen, the aperture-close season of
captures, the minimum sustainable biomass, the exploitation area and the
minimum legal length for shellfishing. This approach tries to establish the
sustainable exploitation of the clam populations in the Basque Country.
Key words: modelling, shellfishing, clams, management model, marine
Tesources, Vensim Software, Basque Country, Spain.
Introduction
Some of the estuaries of the Basque Country (Northern Spain), such as Plentzia and
Mundaka (Figure 1), have exploited clam populations (Ruditapes decussatus), both by
professional and illegal fishermen. There exists a very real possibility of overfishing of
this scarce resource. Therefore, the Department of Fisheries of the Basque Government
needs to know the real situation regarding these populations, together with a tool to
establish the most adequate management for the exploitation of clams.
Ap Bay otiscay
g
span
PLENTZIA CANTABRIAN SEA
MUNDAKA _
43°20°N)
.
SAN SEBASTIAN
f) 10 20
ew BILBAO Seale (km) aw]
Figure 1. Location of the Basque Country in the Northem Spain, showing the Plentzia and Mundaka
estuaries.
In order to simulate different alternatives for the exploitation, based upon scientific data
on the population, the best tool available is a model with a clear purpose (Sterman,
1988). From the system dynamics perspective, a model is developed to address a
specific set of questions and its purpose helps guide its formulation (Richardson &
Pugh, 1981).
In this particular case, the purpose of the model is to answer such questions as, for
example: how many fishermen could support this exploitation?; what happens if number
of fishermen increase strongly?; is it possible to modify the capture season, or the
minimum legal length of capture?; what happens after and during the establishment of a
closed-season?; can we modify the establishment of it?; and what are the implications in
maintaining a minimum sustainable biomass?.
A model is simply an ordered set of assumptions about a complex system (Meadows et
al., 1972). It is an attempt to understand some aspects of the infinitely varied world, by
selecting from perceptions and past experience a set of general observations applicable
to the problem.
This paper tries to solve this particular problem, by means of a system dynamics model
sensu (Forrester, 1973); this approach allows for a “whole system” analysis. System
dynamics can be employed for non-linear and dynamically complex problems,
involving disequilibrium conditions, bottlenecks, delays, stocks and flow relationships,
and realistic decision-making (Sterman, 1987). The VENSIM ® software has been used
previously in urban dynamics, demography, sustainable development, Alfred &
Graham., 1976; Barney et al., 1995) and clam exploitation (Borja & Bald, 2000a,
2000b) and Bald y Borja (2001).
Methodology
The modelling of the exploitation of the clam in the Plentzia estuary is based upon the
methodology described in Borja & Bald (2000a, 2000b). The model was constructed
graphically; first variables and levels were defined, afterwards the relationships between
them. Six levels, corresponding to six length and weight class distribution of clams,
were established. These classes were defined from biological data of the species (Perez-
Camacho, 1979) (Figure 2). Hence, the class (0-20 mm) includes immature clams and
the other five classes are mature individuals. Taking into account that minimum legal
length for fishing clams in Spain is 40 mm, these five classes are divided further into
two non-exploited mature classes (21-30 mm and 31-40 mm) and three exploited
matures classes (41-50 mm, 51-60 mm and >60 mm).
CLAM STOCK
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til eatin Ee a, | | Taitiad | | Taitial | | Taitial
a pepulctionai-40 | | popularionai-60 | | pepuictianbi-o0 ponularicexeo
Figure 2. Stock and length class structure of the model.
iFing Wort?
hate 480 pe
Fisira Mortality) [Fi
[ Sate 31-40 II
The weight in kilograms of each level, which contributes to the final stock, is the result
of the incorporation of new individuals from a previous class, less those that mature and
pass to the next class and those who are dead. The death of clams is due both to natural
and fishing mortality. The fishing mortality is equal to zero, in classes under legal
length for fishing (Table 1); however it could be modified in order to simulate illegal
captures.
On the other hand, the “births” in clam population are the result of the sum of births on
each class bias a fertility rate (Table 1). The contribution of each size class to the total
weight of births was calculated taking in count the weight of each individual in each
size class according to Borja and Bald (2000) and Bald and Borja (2001) (Figure 3).
Table 1. Clam population data inputs to the model, obtained from field experimentation and bibliography.
LENGTH CLASSES (mm)
VARIABLES
0-20 21-30 31-40 «= 41-50-5160 >60
Initial Population (kg)” 1504 5023 333,95 33395 41,4 0
Natural Mortality Rate” 0,060 0,044 0,031 0,052 0,071 0,070
Fishing Mortality Rate” 0 0 0 0,65 0,30 0,05
Fertility Rate” 0 0,90 0,85 0,80 0,75 0,7
(1) Baldy Borja, 2001.
(2) Pérez-Camacho, 1979.
Tnitid month far birth Final menth for bi rth
Birth period
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Figure 3. Births structure of the model.
The clam population biomass is calculated on the basis of dividing the total stock, by
the shellfishing area (Figure 4). A minimum sustainable biomass is established, in order
to calculate the overfishing. Therefore, if real biomass is below established minimum
sustainable biomass, there is a deficit that corresponds to an overfishing effort.
According to relationships established in the corresponding tables (tables 3 and 3bis),
this biomass deficit correspond to a determinate number of fishermen. These Tables of
double entry relate biomass with shellfishers number in such a way that increasing the
availability of the resource (more biomass) supposes an increasing number of
shellfishers. Then, the adequate number of shellfishers is equal to the number of
shellfishers without minimum biomass control less the remaining shellfishers according
to objectives established by sustainable minimum biomass.
The total captures are the result of shellfishers capture sums. Shellfisher’s captures
depend upon three factors: biomass of clam population; aperture and close season; and
shellfishers capture day rate (Figure 5). The clam biomass modifies captures by mean of
different Tables function (Table 1 in Figure 5). These Tables of double entry relate
biomass with shellfishers captures, in such a way that increasing the availability of the
resource (more biomass) supposes an increasing number of captures.
Table 3 Initial equilibrium of shelifishers
£
| ADEQUATE NUMBER OF MINIMUM SUSTAINABLE
SHELLFTSHERS BIOMASS
| F
‘
Figure 4. Biomass and number of shellfishers structure in the model.
Avanage rurnben of daily shell fishers copturas
Average number of
menthiy shell fishers [>
captures
Find month for
coptures
% days for capture ~
+
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Shel fishers coptune day rete
Figure 5. Captures structure in the model.
Furthermore, some improvements described in Bald and Borja (2001) have been
incorporated to the model, including:
* new capabilities of modelling and analysis; and
* an easily used approach based upon different “windows”, with each one,
being capable acceding to a different part of the model.
In order to undertake these objectives, we have used a new version of VENSIM®
(version 4.1). Relating to the new capabilities of modelling and analysis, the main
improvements, as outlined below, are:
a) The capacity to establish a close season every 2 or 5 years, or the
establishment of a closed season only one selected year. This is made by
means of two variables (“Periodic close season” and “Within a year close
season” variables) that equal to cero, multiplies the adequate number of
shellfishers the year or the period selected (every 2 or 5 years) (Figure 6).
b) Beginning within a particular year, the capacity to modify the capture season
annually throughout the time of the modelling.
c) Beginning within a particular year, the capacity to increase or decrease the
number of fishermen annually throughout the time of the modelling, or in
only a selected year. This is made by means of two variables that multiplies
the adequate number of fishermen increasing or decreasing in a determinate
percentage the number of fishermen. (Figure 7).
d) Beginning in a particular year, the capacity to modify the fishing effort
throughout the time of the modelling, by modifying the fish mortality rate for
each length class.
e) Beginning in a particular year, the capacity to modify the minimum legal
length for capture throughout the time of the modelling, or in only a selected
year.
f) The capacity to modify the exploitation area and the minimum sustainable
biomass within a particular year.
In order to validate the model, some field samples were collected in the Plentzia estuary
for this particular study, using the methodology described in Borja (1989 and 1991):
summer stock and biomass, in 1998 and 2000; winter stock and biomass, in 1999, 2000
and 2001 (Table 2); area occupied by the species in 1998 (56.538 m’ ?) and the length
and weight class distribution, in 1998 (see Table 1).
The Fisheries Service of the Basque Government have provided data relating to:
shellfishing activities in the estuary, in 1998-1999: capture season dates (October-
December); percentage of work days (70%); number of shellfishers (40-50 at the
beginning of the capture season) and mean biomass captured by the fishermen (about 3
kg-day'!, at the beginning of the season; 1 kg-day"' at the end). Also, bibliographic data
on clams were obtained: fertility rate i.e.; natural mortality by length class, maturation,
etc, (Perez-Camacho, 1979). Table 1 lists the above mentioned data. The results
obtained here are valid only for the management of the stock in the Plentzia estuary.
Any other site application requires specific field data to be obtained, in order to validate
the results in that particular area.
Table 2. Clam stock and biomass in the Plentzia estuary between summer 1998 and winter 2001.
VARIABLES STOCK (kg) BIOMASS (g-m”)
Summer 1998 2760 47,08
Winter 1999 880 15,56
Winter 2000 840 14,85
Summer 2000 2990 52,88
Winter 2001 1410 24,93
All these new capabilities needs to be easily handled. For this, the model has been
structured in different “windows” as mentioned above. These windows allows an easy
access to the different parts of the model and the modelization of different cases. Then,
the model is divided into three main parts. Each part can be accessed by clicking in the
corresponding button of the first view (Figure 6a). The first contains the model structure
(Figure 6b), the second permits the query of the results of the key parameters (Figure
6c), after running the model; and the last permits the modelling of different cases
(Figure 6d).
|
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Figure 6. a) Main view of the model for the exploitation of the clam (Ruditapes decussatus) in the
Plentzia estuary; b) Model structure; c) Query of the main parameters and c) Modelling different cases
views.
The view corresponding to “Model structure” (Figure 6b) provides access to the
different parts of the model that represent the different aspects of the fisheries (captures,
open-close seasons, increasing and decreasing of the number of fishermen, etc.) and the
natural dynamics of the resource (length and age classes, births, etc). The view
corresponding to “Query of the main parameters” (Figure 6c) allows investigation of
biomass, stock, captures and number of fishermen results, by mean of Figures, Tables
and statistics relating to each parameter.
In this paper, some cases are studied. Case 1 validates and fits the model for the first
four years (1998-2001), comparing the results with field data. Case 2 nuns the validated
model over 10 years, starting in September 1998. The model allows then some of the
variables to be changed such as: number of shellfishers (Case 3); aperture-close season
(Case 4); the minimum sustainable biomass (Case 5), the exploitation area (Case 6) and
the fishing effort (Case 7). The analyses demonstrate the most adequate management
policy, according to the management objectives and based upon biological or social
requirements.
Results
CASE 1, fitting the model
The inputs to the model were based upon the actual situation of the clam fishery, in
1998-1999. The open capture season was in October, with the close season in
December: the shellfishers were auto-regulated by the existing stock each month. Table
3 compares the observed and predicted results. The fit between the data is good (+
10%), excepting for the winter stock and biomass results in 1999 and 2001. However, a
15% of fishing effort in clams below the minimum legal length (31-40 mm) provided a
better fit of the model, especially in 1999. The fact is that the “real” fishery in Plentzia
fits better to this case, but the lack of information on this particular subject makes us
model all the cases, considering only the legal length for capture (40 mm).
Table 3. Comparison between observed and predicted data relating to some variables, showing the
deviation percentage.
VARIABLES OBSERVED PREDICTED DEVIATION %
Summer 1998 2760 2715 -1,63
& | winter1999 880 1045 418,75
% | winter2000 840 902 47,38
E Summer 2000 2990 2725 -8,86
Winter 2001 1410 843 -40,21
= | Summer 1998 47,08 48 +1,95
i Winter 1999 15,56 18,4 418,25
d Winter 2000 14,85 15,9 +7,07
6 Summer 2000 52,88 48,2 -8,85
5 Winter 2001 24,93 14,9 -40,23
Figure 6 shows the annual evolution of the biomass and stock of the population,
between 1998 and 2001, together with the predicted values of the model. In autumn-
winter (October to March-April), there is a decrease in the population in response to
natural mortality. The spring and middle summer (may-august) provide a high increase
of the biomass, due to growth and settlement of the larvae and juveniles. Then,
commences a new decrease, which accelerates when the capture season opens in
October. The captures lie at between 700 to 800 kg-year! (Figure 7).
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Figure 7. Modelled captures (kg) and number of fishermen in the Plentzia estuary, between 1998 and
2001.
The most important captures are in October, due to the presence of the high biomass
and large individuals. Hence, when the season starts there are about 50 fishermen, with
a mean number during the season, of 30 shellfishers. The mean capture per day for each
shellfisher is about 3 kg during the first month, 2 kg the second one and 1 kg the last
one. All these data fit with the information provided by the Department of Fisheries of
the Basque Government validating, once again, the developed model.
CASE 2, running the model for 10 years
This case is similar to that of Case 1, but the model runs over a period of approximately
10 years. Over this period, the biomass and clam’s stock reach the equilibrium situation
with maximum values of about 50 gm? and 2500 kg, respectively, in summer and
minimum values about 10 g-m? and 500 kg in winter (Figure 8). Captures are about
1000 kg during each season, whilst the average number of shelfishers is about 30 during
each season.
CASE 3, regulating the number of professional fishermen.
In this case we, have increased by 50, 75 and 100% and decreased by 50% the number
of fishermen every season, beginning in October 2001 (2001-2002 season). In the first
of the cases (50% increase), the number of fishermen increase over 50 in October 2001.
Then, the first month of captures they finish with all the legal sized clams stock and
biomass decreasing the annual average stock and biomass until 1000 kg and 15 gm
respectively (30% less than today). According to relationship established between
biomass and number of fishermen and captures in the model (Tables 1, 3 and 3bis),
captures decrease to 600 kg per season (15% less than today) but the number of
fishermen remain high due to the influence established (50% of increase in the number
of shellfishers). Then the expected recovery of stock and biomass is not given and they
remain in the above mentioned values (Figure 9).
We find the same behaviour when the number of professional fishermen is increased by
75%. The annual average stock decreases until it is less than 1000 kg with a 12 gm?
biomass. Captures decrease strongly and only after six years do they reach 60% of the
actual captures (Figure 10). When we double the number of fishermen, stock’s decrease
strongly until 500 kg of annual average with a 10 g:m® biomass (Figure 11). Captures
do not reach 500 kg per season and the number of fishermen decrease slightly at the end
of the modelling period. This result shows that the system is not able to support this
particular level of fishing effort.
On the other hand, if we reduce the number of fishermen by 50%, we find the contrary
behaviour as described above. The decrease of the number of fishermen until an average
number of 16 persons in 2001 allows the decrease of the fishing effort and then, stock
and biomass increase until 2500 kg and 45 g-m” of biomass (Figure 12). These increase
permit great captures according to relationships established in Tables functions in the
model, but the expected parallel increase of the number of fishermen is not given due to
the control in the number of fishermen established. These allows the captures increase
until 2000 kg during each season and a better performance for fishermen (see
discussion).
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Figure
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CASE 4, regulating the aperture-close season.
In this case the capture season is reduced, increased and changed in terms of its timing. In the
first case, the capture season is reduced to 1 month (October). Due to this modification, the
capture season during the first year finish with values of stock and biomass greater than those
obtained in the precedent years allowing the recovery of the system until the next year. This
recovery is detected every year increasing stock and biomass rapidly until the 3th year were
system becomes asymptotic, whilst the summer stock reaches 3 t and biomass 50 gm”
(Figure 13). According to Tables functions established in the model (Tables 1, 3 and 3bis) the
available biomass allows a high number of fishermen, increasing to more than 80 (even if
they remain only one month). Captures increase, being asymptotic in 1600 kg each season
(60% more than period 1998-2000).
In the second case, the capture season is increased by one month, from October to January
(actually it finishes in December and its results have been explained in Cases 1 and 2). This
measure allows greater captures and consequently the decrease of the stock and biomass, until
an annual average of 1000 kg and 20 g-m” are reached, respectively (Figure 14). The number
of fishermen decrease, and becomes asymptotic, at an annual average of 20, which capture
approx. 1100 kg of clams.
The last case modifies the capture season, from May to September during the reproductive
period of clams. Consequently, the elimination of reproductive individuals decrease the birth
of new generations and the renewal of the system. Therefore, the stock and biomass decrease
strongly until an annual average below 1000 kg and 15 g-m”, respectively (Figure 15). The
number of fishermen decrease until an annual average of 10 and the captures become
asymptotic at 200 kg. This pattern indicates that the establishment of the capture season,
during the reproductive period of clams, causes strong damage to this resource.
CASE 5, increasing the minimum sustainable biomass.
On occasions, it may be interesting not only to have a high yield, but also to preserve a part of
the stock, in order to feed the wild life (birds, crustaceans, etc.), In this case, a minimum
sustainable biomass, proposed as double that of the normal biomass (60 gm”), was fixed in
the model. At the same time, the other inputs, such as capture season (October-December) or
auto-regulated number of fishermen depending the existing available biomass are maintained.
The establishment of the minimum sustainable biomass decrease strongly the number of
fishermen during 2001-2002 capture season, achieving an average value of below 10
fishermen by mean of the autoregulation described in the methodology and schematised in the
Figure 4. This sequence of events entails a parallel reduction of captures in 2001-2002 season,
decreasing to below 500 kg, allowing a great recovery of stock, and biomass during the
following year (2002) until an average value near to 3 t and 50 g-m” respectively (Figure 16).
The above mentioned minimum biomass is reached during that year.
Consequently, the following years, the number of captures and fishermen increase strongly
and especially the first month of the capture season, reaching values near 80 fishermen and
1,7 t of captures. However, this situation means a reduction of capture season to only two
months (October to November), beginning in 2002, due to the capture of all legal sized
individuals during the first two month of each season. Consequently stock and biomass
becomes asymptotic at an annual average value near to 3 t and 60 g:m” respectively. The
number of fishermen reach an annual average number near to 40th fishermen.
Annual average biomass
bs
Biomass
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1999 2000 2001 2002 2003 2004 2005 2006 2007
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13. a)
reducing the capture season to 1 month
Captures and number of fishermen evolution. c) Annual captures sum and average
Stock and biomass evolution in the Plentzia estuary,
number of fishermen evolution.
Figure
)
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x Annual average biomass
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Annual average stock —
Stock
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1999 2000 2001 2002 2003 2004 2005 2006 2007
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increasing the capture season in 1 month
)) Captures and number of fishermen evolution. c) Annual captures sum and
average number of fishermen evolution.
October to January).
(
Figure 14. a) Stock and biomass evolution in Plentzia the estuary,
9)
60
JaLUBYSY JO JeqLUNN,
x Annual average biomass
~ Biomass
Annual average stock ——
Stock
2006 2007
2005
1999 2000 2001 2002 2003 2004
) Annual captures sum and average
a) Stock and biomass evolution in the Plentzia estuary modifying the capture season from May to
1998
September. b) Captures and number of fishermen evolution. c)
number of fishermen evolution.
15.
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Annual average stock Biomass x Annual average biomass
Stock
JBWUEYSY JO JOqUIN|
(z4u.6) sseworg, Sa aly ae a
2000 2001 2002 2003 2004 2005,
b) Captures and number of fishermen evolution. c) Annual captures sum and average
1999
a) Stock and biomass evolution in the Plentzia estuary, increasing the minimum sustainable biomass
1998
number of fishermen evolution.
to 60 gm”.
2 8 BELERIRRSo Sie A ie
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Figure 16.
CASE 6, increasing the exploitation area
In some cases, it is possible to increase the exploitation area, by seeding the area with small
clams in some places. A 10 and a 20% increase in the exploitation area has been simulated. In
both cases, there are not significant differences with respect to original situation. Stock and
biomass increases slightly to 1500-2000 kg and 25-30 g-m™, respectively. The average
number of fishermen is about 27-30 persons, which capture 1200 to 1400 kg of clams each
season. This result shows the limited success of this particular measure.
CASE 7, modifying the fishing effort
In this case, a 15% of fishing effort has been applied to the clams below the minimum legal
length (specifically, to 21-30 and 31-40 mm size clams). This percentage means, for instance,
that 21-30 mm size clams comprise 15% of the total captures. The fishing effort for 41-50
mm size has been reduced in 35%.
These measure increase the fishing effort allowing the capture of one of the most reproductive
part of the system (21-30 and 31-40 mm size clams) according to fertility rates established in
Table 1. Consequently, the stock and biomass decrease dramatically, to values near 500 kg
and 8 gm”, respectively (Figure 17). Similarly, the number of fishermen and captures
decrease, reaching average values of around 10 fishermen and captures of 100 kg each season.
DISCUSSION
In order to determine if a model is valid, system dynamicists attribute many structural
changes to a shift in the loop dominance (Forrester, 1987). This pattem is created by
endogenous factors in the system; hence, analysts are comfortable in making forecasts about
model behaviour into the future and under various assumptions (Krahmer, 1997).
The model presented here has some incorporated important assumptions (Borja & Bald,
2000): (a) the initial values of some variables and levels (natural mortality, area occupied by
clams, percentage of days worked by fishers, etc.) do not change, with time; and (b) the
possible influence of the environment, on the variability of the population, is included in the
natural mortality (which is considered invariable with time), etc.
However, the results obtained for the Plentzia estuary represent very approximately the real
fishery (Bald & Borja, 2001). Auto-regulation in the number of shellfishers has been detected,
in the field area as well as in the model, when the available biomass is unprofitable. The same
behaviour has been detected with the intensive professional clam shellfishing in Galicia
(Fernandez Cortés et al., 1987a, 1987b). This model has detected that the regulation in the
shellfishers number and capture season, from October to December, can increase the biomass
and yield in the Plentzia estuary, at has been demonstrated for the Mundaka estuary, in
response to the control imposed by Fisheries Service, of Basque Government (Bald & Borja,
2001). The model show that the low stock and biomass situation can retum, if an increase in
the number of fishermen is produced; this reduces, by half, the actual stock and biomass, if
the fishing effort is doubled. A similar behaviour has been detected in Galicia, where a 25%
decrease in the fishing effort produces a 30% increase in performance, whilst fishing effort
increases reduce this performance (Fernandez Cortés et al., 1987b).
When the results of different cases are compared (Table 4), it can be detected that decreasing
by 50% the actual number of fishermen is the best management strategy. Taking in to account
the consequent social cost, this measure will produce a 24 kg:shellfisher!-month
performance, considerably less than to those produced in Galicia (3,4 kg-day’!, according to
Fernandez Cortés et al., 1987a), due to the high productivity of Galician system.
X_ Annual average biomass
Biomass
Annual average stock
Stock
(z1u6) ssewoig
3500
2000 4
f=}
S
ral
N
3000}
Q)>PaS
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Captures --—-— Number of fishermen
JeUWUEaYsy JO JOQUNN
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i
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Average number of fishermen
Annual captures sum ------
JOULYSY JO JEQUNN
8
Fr}
ee ee
888 8 8
a a 48
(6) saumde 3,
°
1999 2000 2001 2002 2003 2004 2005 2006 2007
1998
b) Captures and number of fishermen evolution. c) Annual
captures sum and average number of fishermen evolution.
Figure 17. a) Stock and biomass evolution in the Plentzia estuary, applying a 15% of fishing effort to 21-
30 and 31-40 mm size clams.
Table 4. Different modelled management measure results, for clams stock and biomass the in Plentzia
estuary. Relate to the time when the system had tended to stabilize we select a period and then the
average and total number of shellfishers and the performance is calculated for each management measure,
taking in to account the captures of each shellfisher, within the period of the capture season.
REGULATING INCREASING DECREASING
ae 3 E
Zz
4 § H £6 Bg
a é
2 z 5
4 q g 3 z¢ a:
DATA 5 :
OBTAINED 5 4 —
<
gee els G Ele ale al |
fee 3 3 8 5
Stock (t) 2,71 5.2 1,79 1,47 1,21] 5,7 2,05 1,37 | 6,08 a? 31 3,7 0,7
Biomass (g-m*) 47,9 | 92,9 31,6 26,0 21,4 |101,1 36,3 24,2] 1075 1356] 505 54,6 13,0
Shellfishers at the ’ :
Reginning ns) | 42:7 [4L3 42.2 40,5 38,3) 061 323 7.8 | 088 i104] 45,8 494 11,7
Average number of
shelifishers in the | 29,3 | 28,1 29,0 28,1 26,9] 86,1 23,1 11,1] 589 678 | 31,1 336 86
period (n")
Total number of
shelifishers in the | 87,8 | 844 86,9 84,2 80,8] 86,1 69,4 33,4]117,8 135,5] 93,3. 100,7 259
period (n!")
Captures (kg-year')| 966 | 2010 643 549 477 11636 923 312 | 1779 2334] 1233 1482 214
Performance
(kgshelisher | 11 ]238 74 65 59] 19 132 93] 151 172] 132 147 83
month")
The second best measure is to limit the capture season to one month (October), with a
performance of 19 kg:shellfisher-month” and the same social cost as the first measure
(actually, the capture season comprise 3 months, October to December).
The third best measure is to establish a minimum sustainable biomass, of 60 gm’. This
measure will produce 15 kg-shellfisher'month! of performance, but it will be
necessary to reduce the capture season and the number of shellfishers ie. only in
October the first year and October-November over the remainder of the time (with an
average number of shellfishers at below 30).
On the other hand, several measures are contrary to the sustainability of the resource
and the continuity of shellfishing activity in the estuary. Such measures are those which
modify the present regulations, such as increasing the capture season, the number of
shellfishers, and reducing the minimum legal length for capture. Figure 18 shows these
alterations.
In all cases, the proposed action takes place in 2001. The system reacts progressively
when, for example, the number of shellfishers decrease by 50% (Figure 18c) and the
exploitation area increases in response to seed sowing (Figure 18g).
A
+ Annual average stock
srock
srock
stock «t
STOCK (
a. --Annual average biomass Performance
1600
B
hema average stock
me Pesomance «ge Annual average biomass
100
1400 3 2600 a
ag 3%
200 zu :
3 a» n§
100 ae 2
£ fow Hy
00 rea ry
z £m :
ca w Py +to®
“ - 5
53
20 PR oe . a0] a aye
° 0 0 0 8
zoie "99 2000” 2001” 2002” 2008” 2004” 2008 "2006 "2007 ioe ” tee” 2000” 2001” gama 2002 gone” 2005 ” 006 2007
—o—Anmuatavenge sock —¢—Peromace .--a---Anual average biomass Annual average sock —gg-—Peromarce Aral average Biomass
2200 ‘0 3000 7
cata tas B
s oe 3 m0 so
= ow 8
3
2000 a o
wb y
1500 aE & 1500 = x
a gS
1000 “4 £1000 x0
s =
seo 2 2 sto »
s § ee
° 0 ° o
1soe 1999” 2000 "2001 "2002 "2002 "2004 "2005 "2006 "2007 ese" 1889" 2000 " 2001 ” 2002” 2003” 2008” 2005 2006 ” 2007
—+~ Annual average stock —=— Performance
a -- Annual average biomass
BIOMASS (gm2) and PERFORMANCE (
19981999” 2000 2001 2002” 2003” 20042005” 2008 2007
E
+2 Annual average stock —m=— Performance
H
4000
3800
3000
2500
2000
1800
1000
500
1998” 1999” 2000" 2001 2002
2003 2004” 2008” 2006 "2007
BIOMASS (gr2)and PERFORMANCE (i
‘Annual average biomass
BIOMASSig) and PERFORMANCE (k
—e— Annual average stick —a—Peromance ...a---Annuslavengebiomass 5 annua average stack —e— Peformance ..-ar-- Annual average bomass
2000 2, 2000
Beg so as
1500 ww i 1500
208
1000 we & a0
wd
s00 3 so0
4
ee:
Q
° °
age 1999° 2000 "2001 2002 2003” 2004” 2008 "2006 2007
9981999" 2000 2001 2002” 2003” 2008 2005 2006 2007
Figure 18. Stock, biomass and performance evolution, depending upon the management measures taken
in 2001 for the Plentzia estuary: a) increasing the number of shellfishers by 50%; b) increasing the
number of shellfishers by 100%; c) decreasing the number of shellfishers by 50%; d) reducing the capture
season to one month (October); e) establishing the capture season May to September; f) establishing a
minimum sustainable biomass of 60 g-m”; g) increasing by 20%, the exploitation area; h) reducing the
minimum legal length for capture, from 40 to 20 mm size clams.
BIOMASS 4-2) and PERFORMANCE (k
On the other hand, the system reacts suddenly when, for example, the capture season is
established during the reproductive months (Figure 18e), when the minimum legal
length for capture is reduced (Figure 18h) and the number of shellfishers is increased
(Figures 18a and b). In other cases, even if the stock and biomass recovery is detected,
there is not an increase in the capture performance; for example, when captures are
reduced to one month (Figure 18d) or a minimum sustainable biomass is established
(Figure 18f). It is illustrative to note that for shellfishers, the more small clams that are
captured the performance will decrease progressively; for example, when the minimum
legal length is reduced (Figure 49h).
This model could be improved in the future, incorporating new items such as: the
influence of an increasing vigilance of the fishers; the influence of the environment; and
growth effects; etc. However, the results of the model are now reasonably reproducible,
by way of validation of a model (Sterman, 1988), however, they have to be interpreted
as a guide, rather than as an exact prediction of the future, even if model fitting is within
the correct predetermined limits (5-15%).
This analysis provides an interesting approach to the management of a minority fishery,
in the Basque Country. Its limited economic importance prevents other scientific
approaches, which require large amounts of financial support (from the Administration).
In summary, this second approach to the modelling of clam exploitation and
management demonstrates the most adequate policy, according to the management
objectives; this is based not only upon biological requirements, but also upon social
considerations.
Acknowledgements
This study was supported by a contract undertaken between the “Departamento de
Agricultura y Pesca” of the Basque Government and AZTI. We thank the staff of
Fisheries Service for the provided data. We wish to thank also Professor Michael
Collins (School of Ocean and Earth Science, University of Southampton, UK) for
kindly advising us on some details of this paper and Professor Juan Martin (Universidad
Politécnica de Catalunya) for advising us on details about developing the model.
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