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ORIGINAL PAPER
Reproductive dynamics of the sea urchin Paracentrotus lividuson the Galicia coast (NW Spain): effects of habitat and populationdensity
Rosana Ourens • Luis Fernandez • Marıa Fernandez-Boan •
Ines Naya • Juan Freire
Received: 19 September 2012 / Accepted: 3 April 2013 / Published online: 12 April 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract We studied the spatial variability in the size at
first maturity and the reproductive cycle of Paracentrotus
lividus in Galicia, contributing key information for the
exploitation and management of this resource. The size at
maturity varied between 20.4 (±1.2 SE) mm and
27.9 ± 1.2 mm and was smaller in areas of low population
density where sea urchins do not form patches. Using a
nonlinear model, we analysed the effect of depth, body
size, sex and population density on the temporal pattern of
the gonad index. The maximum and minimum indices were
obtained at 4 m depth in the months before and after the
spring spawning, respectively. The depth also affected the
cycle phase, and the sea urchins at 4 m depth spawned
9.4 ± 3.0 days later than the sea urchins at 8 m depth and
20.5 ± 3.0 days later than those at 12 m depth. Moreover,
the sea urchins living in patches showed a slight increase in
gonad size as a consequence of the better-quality habitat.
This shows that there is no intraspecific competition in this
area despite the high population densities reached
(18.5 kg m-2).
Introduction
Many echinoid species are traditionally eaten in various
regions of the world, and in Japan, the beginning of this
practice date backs to the ninth century (Lawrence 2007).
Asian and Mediterranean countries are the main markets
for this product, and the growing demand in the last dec-
ades has led to the expansion and development of new
fisheries and a staggering increase in the extraction rates
(Williams 2002). Consequently, the echinoid stocks have
decreased drastically on a world level and various cases of
overexploitation and collapse have been documented
(Andrew et al. 2002; Williams 2002; Micael et al. 2009).
This situation has made it urgent to revise the fisheries
management policies that govern the exploitation of these
resources searching new management strategies that guar-
antee the sustainability of these fisheries (Rogers-Bennett
et al. 2003). For this goal, both the fishing strategy and the
basic biological processes that determine the population
dynamics of the resource have to be understood. Repro-
duction is one of these processes, providing key informa-
tion for fisheries management such as size at first maturity
or spawning season. In addition, in species whose com-
mercial value resides in the gonads, such as the echinoids,
the study of the reproduction has another complementary
interest. In these cases, knowledge of the spatial and tem-
poral variations experienced by the gonad size provides
information on the expected commercial yield. This
information could be used to guide fishing strategy and
regulations, and it allows the planning of rotations and the
seasonality of the fishing operations, thus maximising
fishing profits.
The sea urchin fishery in Spain is focused on the species
Paracentrotus lividus. This echinoid is distributed along
the entire Mediterranean and NE Atlantic coasts, from
Communicated by M. Byrne.
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00227-013-2236-2) contains supplementarymaterial, which is available to authorized users.
R. Ourens (&) � L. Fernandez � M. Fernandez-Boan � I. Naya �J. Freire
Grupo de Recursos Marinos y Pesquerıas, Universidad de A
Coruna, Rua da Fraga 10, 15008 A Coruna, Spain
e-mail: [email protected]
Present Address:
J. Freire
Barrabes Next, C. Serrano 16-1, 28001 Madrid, Spain
123
Mar Biol (2013) 160:2413–2423
DOI 10.1007/s00227-013-2236-2
Ireland to Morocco, including the Canary and Azores
Islands (Boudouresque and Verlaque 2007). Several studies
on the species have investigated its reproductive cycle in a
wide range of regions, determining parameters such as the
spawning period and size at first maturity (e.g., Haya de la
Sierra 1990; Spirlet et al. 1998; Sanchez-Espana et al.
2004). Other studies, many carried out under culture con-
ditions, have also analysed the effect of certain environ-
mental variables on the mean gonad size (e.g., Guettaf and
San Martın 1995; Spirlet et al. 2000; Shpigel et al. 2004).
Ourens et al. (2011) carried out a statistical review of these
studies, and one of the most interesting results was the
latitudinal increase in the gonad index in Atlantic popula-
tions, as well as the higher values that these populations
showed in relation to the Mediterranean populations loca-
ted in similar habitats.
Despite all this information, little is known about the
variability in the reproductive cycle of this species on a
small scale. The habitat characteristics can lead to intra-
population variation in the spawning period or in the period
of maximum gonad production, and this information needs
to be taken into account when appropriate management and
exploitation strategies are determined for the local popu-
lation (the scale which is generally used in fisheries man-
agement, Orensanz and Jamieson 1998).
The aim of this work is to determine the variations in the
reproductive dynamics of the sea urchin P. lividus at the
fishing grounds, contributing information of interest for the
managers and fishers of this resource. At this spatial scale,
the population density and depth are potentially the most
influential parameters for the reproduction of the species.
The dense patches formed by sea urchins in certain areas
could result in intraspecific competition for food and space
and thus change the reproductive pattern. As an alternative
hypothesis, the higher quality of the habitat of the patches
(Tuya et al. 2007; Alvarado 2008) makes it possible to
maintain, or even increase, the growth and reproduction
rates despite the higher density, generating an Allee effect
(see Berec et al. 2007; Kramer et al. 2009). Similarly, depth
is the environmental variable that results in the largest
gradients in the distribution of the species. As in the pre-
vious case, this variable may indirectly affect the repro-
ductive rates given that it is linked to environmental factors
that influence the physiological state of the individuals,
such as the availability and quality of food, exposure to
waves, the temperature and the intensity of the light
(Garrabou et al. 2002). To test these hypotheses, we studied
the effect of population density on the size at first maturity
and quantified the variations in the temporal pattern of the
gonad index with certain environmental and population
parameters (population density, depth, sex and body size of
the sea urchins).
Materials and methods
Study area and sampling design
The study was carried out in Galicia (NW Spain), in the
sites of Lira and Porto do Son (Fig. 1). Samples were
collected monthly between June 2006 and May 2008 at two
fishing grounds in each site: Ardeleiro and Os Forcados in
Lira, and Son and Queiruga in Porto do Son. These areas
are exposed to waves and the habitats are similar, so we
considered them as replicates. The substrate is formed by
large flat rocks alternating with vertical walls oriented in
different directions. There is abundant algal cover, mainly
in the shallow areas and during the spring and the summer
months, and the species Saccorhiza polyschides, Cystoseira
baccata and Laminaria spp. can form large forests. Hal-
idrys siliquosa, Codium spp., Desmarestia spp. and the
invasive Sargassum muticum are also abundant (Veiga
Villar 1999; Otero-Schmitt and Perez-Cirera 2002; Casal
et al. 2011).
Ardeleiro and Os Forcados were sampled at the depths of
4, 8 and 12 m, and at each depth, we sampled two sea urchin
distribution types: patches (high density) and dispersed (low
density). Due to the aggregation behaviour of the species,
sea urchins are often distributed in patches with population
densities that vary between 24 and 684 individuals m-2.
However, in some areas it is also common to find isolated
sea urchins. We called these areas low-population-density
areas, given that this variable ranged between 0.04 and 3.32
individuals m-2. At the fishing ground of Queiruga, speci-
mens were only obtained in low-density areas, maybe as a
consequence of the fishing activity, while at the fishing
ground of Son, there were only sea urchins at 4 m depth, but
in both distribution types (Table 1).
Using a 50 9 50 cm metal frame, we collected sea
urchins located in the central part of three patches at each
depth through diving. In the low-density areas a 3-m-long
rope with a weight at one end was used to describe a circle
with a 3-m radius, and the sea urchins were collected
within the circle. Like in the patches, three replicates were
obtained using this method.
In addition, a monthly record of the sea surface tem-
perature (SST) was obtained in the study area during the
sampling period. To do this, satellite images obtained with
the AVHRR (Advanced Very High Resolution Radiome-
ter) sensor with a spatial resolution of 1.1 km were used.
The images were processed by the Centro de Recepcion,
Proceso, Archivo y Distribucion de Imagenes de Obser-
vacion de la Tierra (CREPAD) using a multichannel
algorithm (Willis et al. 1985). Each sampling site was
covered by two satellite images, and the monthly SST was
estimated as the average of the two images.
2414 Mar Biol (2013) 160:2413–2423
123
Sample processing
All of the sea urchins in the samples were counted and
measured (diameter without spines, ±0.1 mm) to estimate
the population density and for later studies on size
frequencies.
For the study of the reproductive cycle, a mean of 340
sea urchins per month, and 20 per sampling site, depth and
distribution type were analysed.
The individuals were weighed (±0.01 g), and sex was
then identified with a macroscopic analysis of the gonads
and the gametes released. After removing excess water with
absorbent paper, the wet weight of the gonads (±0.001 g)
was recorded. The gonads were then placed in an oven at
60 �C for 48 h to obtain the dry weight (GDW, ±0.001 g).
Estimating the size at first maturity
To calculate the size at maturity, we chose the individuals
collected in the months of maximum gonadal development
(January to April, according to an exploratory analysis of
the data). We classified sea urchins as immature when their
gonads were not macroscopically visible or the gonad dry
weight was below the accuracy of the scales (±0.001 g).
The analysis was based on 1,386 individuals with sizes
between 5.6 and 83.7 mm, coming from the sampling
grounds of Os Forcados and Son. The other locations were
not included in the study because there were hardly any
immature individuals in these areas. Given that sea urchins
smaller than 40 mm were practically absent at 8 and 12 m,
we assume that recruitment of this species occurs in
Fig. 1 Locations of the
sampling sites
Table 1 Population density (mean ± SE) of P. lividus in each sampling area
Ardeleiro Os Forcados Queiruga Son
HD LD HD LD HD LD HD LD
4 m 76.44 ± 4.86 0.56 ± 0.03 132.70 ± 8.48 0.78 ± 0.04 – 0.69 ± 0.06 227.88 ± 15.55 0.61 ± 0.08
8 m 61.90 ± 3.74 0.46 ± 0.02 50.74 ± 2.04 0.42 ± 0.02 – 0.45 ± 0.05 – –
12 m 57.40 ± 3.33 0.42 ± 0.03 50.81 ± 2.09 0.49 ± 0.04 – 0.36 ± 0.04 – –
The patches were absent in Queiruga, and sea urchins were absent at 8 and 12 m in Son
HD high density, LD low density
Mar Biol (2013) 160:2413–2423 2415
123
shallow areas (unpubl. data) and that it was not possible to
estimate a size at maturity for each depth.
We used a logistic model with the logit function link
(Hardin and Hilbe 2007) to determine the percentage of
mature individuals in relation to size. We studied the effect
of the density (high and low) and study area on the slope of
the curve. The model was as follows:
P ¼ eaþb1�Dþb2�D�Densityþb3�D�Location
1þ eaþb1�Dþb2�D�Densityþb3�D�Location
where P is the probability of an individual being mature; a,
b1, b2 and b3 are parameters of the model; D is the diameter
of the individual; Density is the type of distribution (high or
low density); and Location is the sampling ground. The dot
symbol between two variables represents their interaction.
We defined the size at first maturity in two ways: 1)
diameter at which 50 % of the individuals in the population
are mature (D50) and 2) diameter at which 95 % of the
individuals of the population are mature (D95).
Estimating the gonad index
To avoid the disturbances produced by including juveniles
in the analysis, the study of the reproductive cycle was
based on individuals larger than the size at which 95 % of
the population was mature.
The gonadal growth of these individuals showed slight
negative allometry with respect to the somatic growth
(GDW = 1.3 9 10-5�D 2.8, n = 6,436). Therefore, to be
able to compare the gonad index of different samples, we
needed to use individuals from the same size class or use an
independent indicator of the diameter. We chose to use the
standardised gonad index (SGI) defined in Ourens et al.
(2012), which represents the residuals of the linear
regression between the logarithm of the gonad dry weight
and the logarithm of the sea urchin diameter (Fig. 2). This
indicator is centred at zero, and individuals in the phase
before spawning have larger gonads than the annual mean,
and thus their SGI will be high. However, the individuals in
the phase after spawning will have undeveloped gonads
that will result in negative SGI values. The main advantage
of this index with respect to the other indices most com-
monly used is that it allows comparisons between samples
with sea urchins of different diameters, collected at dif-
ferent locations or in different seasons of the year.
Statistical analysis
The temporal pattern of the SGI shows the periodic nature
of the reproductive cycle (Fig. 3), and therefore to analyse,
it we designed a mathematical function that reflects this
periodicity (Fig. 4):
SGIi ¼ ai þ bi � senðp=6 � t þ xiÞ þ ei
where
• SGI is the standardised gonad index for an individual
ith,
• a is the SGI value at the inflection point, and we
consider it to be the mean annual value,
• b is the semi-amplitude of the cycle and can be
interpreted as the mean deviation of the SGI over time,
• g/6�t represents the annual periodicity of the cycle,
where t is the month,
• x is a phase parameter that shifts the sine wave left or
right, and
• e is the residual error.
Nonlinear mixed-effects models were fitted using the
nlme package in R (Pinheiro and Bates 2000). The vari-
ability produced by the sampling ground and year was
included in the random structure of the model. To do this,
we created a new variable called ground-year (eight lev-
els), which is the result of combining the variables ground
(four levels) and year (two levels). Initially, the random
effects were included in the estimation of the three
parameters (a, b and x).
The objective of this analysis was to evaluate the effect
of the depth, population density, sex and diameter of the
individuals on the temporal pattern of the SGI. The depth
was included as a categorical variable to estimate the three
parameters (a, b and x), while the variable sex was only
included in the estimation of the first two parameters (a and
b) because biologically a large phase lag between males
and females would not be possible in the spawning period.
Despite the SGI being independent of the body size by
definition, the diameter of the sea urchins may affect the
reproductive cycle, altering for example the spawning
season. Therefore, the diameter was introduced into the
model to estimate b and x.
1.6 1.7 1.8 1.9
−0.5
0.0
0.5
1.0
Log D (mm)
Lo
g G
DW
(g)
y = − 4.90 + 2.83·x
Fig. 2 Linear relationship between the logarithm of the diameter
(D) and that of the GDW for the set of individuals (n = 6,436)
2416 Mar Biol (2013) 160:2413–2423
123
The high density values reached in the patches (up to
684 ind m-2) could generate intraspecific competition for
food and space, leading to low gonadal development. To
test this hypothesis, we used the biomass density (kg m-2)
as an indicator of the competition given that it is a better
estimator of the intensity of food consumption than the
population density (ind m-2). Furthermore, we observed
that the highest population density occurs in patches with
large numbers of juveniles and recruits, which hardly
compete for food or space.
To determine the total weight of the samples, we esti-
mated the weight of the individuals (WW) by means of its
power-law relationship with the diameter (SM-I, WW =
2.1 9 10-3�D2.6, n = 8,917). The biomass density ranged
between 3.6 9 10-3 and 2.3 9 10-1 kg m-2 in low-den-
sity areas, and between 1.8 and 18.5 kg m-2 in patches.
This numeric variable was introduced into the equation to
estimate a, b and x. To estimate a, we also considered the
quadratic term in order to determine the existence of non-
linear relationships in the competition for food.
Following the protocol designed by Zuur et al. (2009),
modelling was initiated by using a complex model with all
explanatory variables as fixed components. In our case, the
fixed structure of the full model was the following:
ai ¼ a0 þ a1 � Depth8 þ a2 � Depth12 þ a3 � Biomass þ a4
� Biomassð Þ2þa5 � Sexi
bi ¼ b0 þ b1 � Depth8 þ b2 � Depth12 þ b3 � Biomass þ b4
� Sexi þ b5 � Diameteri
xi ¼ x0 þ x1 � Depth8 þ x2 � Depth12 þ x3 � Diameteri
þ x4 � Biomass
where a0, b0 y x0 represent the intercepts of the parameters
to be estimated for each individual ith; and the coefficients
a1,…, 5, b1,…,5 y x1,…,4 are the main effects of the covari-
ables. Depth8 and Depth12 are dummy variables that take
the value 1 when the depth is 8 and 12 m, respectively, and
0 otherwise. Similarly, Sex is the dummy variable that
takes the value 1 with males and 0 with females.
We then determined the best variance–covariance
structure for the random effects, and finally, we used a
backward elimination approach to select the variables that
affected the fixed structure. Starting values for modelling
−1.0
−0.5
0.0
0.5
1.0A
−1.0
−0.5
0.0
0.5
1.0B
−1.0
−0.5
0.0
0.5
1.0C
−1.0
−0.5
0.0
0.5
1.0D
J F M A M J J A S O N D J F M A M J J A S O N D
J F M A M J J A S O N D J F M A M J J A S O N DMonth
SG
I
Fig. 3 Temporal pattern of the SGI
at each sampling site
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
Month
SG
I
J F M A M J J A S O N D
α
β ω
− β
Fig. 4 Explanatory drawing of the parameters that define the
temporal pattern of the SGI
Mar Biol (2013) 160:2413–2423 2417
123
were estimated from numerical summaries and graphical
representations.
The minimum adequate model was chosen by compar-
isons of the Akaike information criterion (AIC, Akaike
1974), the Bayesian information criterion (BIC, Schwarz
1978), and, when models were nested, testing the
improvement in the likelihood ratio using a X2 test.
We used graphical methods (e.g., residual diagnostic
plots, and the observed trends augmented with the fitted
trends plots) to assess the adequacy of the final fitted
model.
Results
Size at sexual maturity
The sea urchins of Os Forcados matured before those of
Son (p \ 0.01), and maturity was reached earlier in the
low-population-density areas than in the patches
(p = 0.03). In accordance with this, the D50 varied a
maximum of 7.5 (±2.4 SE) mm between samples and was
20.4 ± 1.2 mm in low-density areas in Os Forcados and
27.9 ± 1.2 mm in the patches in Son (Fig. 5). Similarly,
the D95 also varied, and in these two extreme habitats,
95 % of the population matured with 29.6 ± 1.9 mm and
40.5 ± 2.1 mm, respectively.
Reproductive cycle
Description of the nonlinear model
To analyse the reproductive cycle, we used individuals
with a diameter larger than 40.5 mm, the size corre-
sponding to the D95 in the area where individuals reached
maturity latest (high-density areas in Son). Given that there
is no available information from Queiruga and Ardeleiro,
we had to assume that at least 95 % of the individuals from
these areas reached maturity at 40.5 mm.
The nonlinear mixed model fitted to these organisms
was simplified with respect to the full model, given that the
AIC of the two models was similar (-4,336.8 for the full
model and -4,337.5 for the final model) and there were no
significant differences in the likelihood ratio (p = 0.12).
The fixed structure of the final model was the following:
ai ¼ a0 þ a1 � Depth8 þ a2 � Depth12 þ a3 � Biomass þ a4
� Sexi
bi ¼ b0 þ b1 � Depth8 þ b2 � Depth12 þ b3 � Sexi
xi ¼ x0 þ x1 � Depth8 þ x2 � Depth12 þ x3 � Diameteri
The random effects were included in the three parameters
(a, b and x) and showed a general symmetrical positive-
definite variance–covariance matrix. A correction for het-
eroscedasticity was applied by allowing a variance struc-
ture with different spread per month and ground-year
(varIdent function in R). A correlation between the indi-
viduals belonging to the same sample was also observed,
and the error structures were assumed to be first-order auto-
regressive processes.
Diagnostic plots suggest that the residuals of the final
model meet all of the assumptions of normality and
homoscedasticity (SM-II). As the fitted values matched the
observed values closely, we concluded that the model fit is
satisfactory.
Spatial variability of the reproductive cycle
Although environmental and demographic conditions have
resulted in variations in the reproductive cycle, it is pos-
sible to observe a general temporal pattern with three dis-
tinct stages (Fig. 6). The largest SGI values were obtained
in the three or four first months of the year. The SGI then
started to decrease gradually with the spring, reaching the
lowest values in July or August, and then increasing again
at the start of autumn. This annual cycle was the opposite
0.0
0.2
0.4
0.6
0.8
1.0 A
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40 50 60 70 80
0 10 20 30 40 50 60 70 80
Low density
High density
B
D (mm)
Pro
po
rtio
n o
f m
atu
re s
pec
imen
s
Fig. 5 Proportion of mature individuals for each size (± SE) in areas
of high and low population densities and at the Os Forcados (a) and
Son (b) fishing grounds. The lines on the top and bottom axes indicate
the number of mature and immature individuals observed,
respectively
2418 Mar Biol (2013) 160:2413–2423
123
of that described for the SST, which showed temperatures
higher than 15 �C from April to October (Fig. 6a).
The depth affected the three parameters estimated in the
reproductive cycle (Table 2). The semi-amplitude of the
cycle (b) was greatest at 4 m, so that the differences in the
mean monthly SGI were larger in this area, reaching a
value of 0.52 ± 0.03 g (the maximum differences in the
monthly SGI at 8 and 12 m were 0.43 ± 0.06 and
0.44 ± 0.06 g, respectively). However, the mean annual
SGI (a) was slightly higher in the 8-m area (p = 0.03) as
the result of a more stable temporal pattern. The parameters
a and b did not show significant differences between the
individuals located at 8 and 12 m depth (p = 0.10 and
p = 0.55, respectively). In parallel, the sea urchins located
in the deep areas showed an earlier reproductive cycle with
respect to the sea urchins in shallower areas (p \ 0.01).
Thus, the temporal pattern of the SGI at 4 m showed a
delay of 9.4 ± 3.0 days with respect to the reproductive
cycle of the sea urchins located at 8 m, and
20.5 ± 3.0 days with respect to those located at 12 m
depth (Fig. 6b).
The sea urchin diameter led to the opposite effect to that
of depth in the gonadal development cycle, so that there
was a delay of 5.1 ± 1.3 days for each 10 mm that the sea
urchin increased in size (Fig. 6c; Table 2). That is, the
difference in the reproductive cycle between the smallest
individuals (40.8 mm) and the largest (96.0 mm) was
28.3 ± 7.1 days.
The biomass density only affected the parameter a of the
model (p = 0.02, Table 2), increasing the mean annual
SGI 2.2 9 10-3 ± 9.2 9 10-4 for each kilogram of sea
urchin biomass (Fig. 6d). This positive linear effect of the
J F M A M J J A S O N D
12
16
20A
SS
T (
ºC)
1 2 3 4 5 6 7 8 9 10 11 12
−0.2
0.0
0.2
4 m8 m12 m
B
1 2 3 4 5 6 7 8 9 10 11 12
−0.2
0.0
0.2
45−50 mm65−70 mm85−90 mm
C
−0.2
0.0
0.2D
0−2 kg m−2
5−7 kg m−2
10−13 kg m−2
J F M A M J J A S O N D J F M A M J J A S O N D
−0.2
0.0
0.2
FemalesMales
E
Month
SG
I
Fig. 6 Seasonal SST (sea surface temperature) pattern and its
confidence interval of 95 % (a). Variations predicted by the model
for the temporal pattern of the SGI (standardised gonad index) with
the depth (b), sea urchin diameter (c), biomass density (d), and sex
(e). The curves were predicted by the fitted model, varying in each
plot a single factor. The reference values were: depth = 8 m,
diameter = 60 mm, density = 3 kg m-2, sex = female
Mar Biol (2013) 160:2413–2423 2419
123
density indicates that there is no intraspecific competition
between the individuals in patches, at least at the popula-
tion densities studied.
Finally, the mean SGI for males was 0.035 ± 0.004 g
higher than for females (p \ 0.01), perhaps as a conse-
quence of a more stable temporal pattern, which did not
reach minima as low as those reached for females in the
summer (Fig. 6e).
Discussion
We have studied the reproductive dynamics of P. lividus
through the size at first maturity and the reproductive cycle,
analysing the factors that cause spatial variations at local
scale.
The size at maturity of a population is frequently defined
as the size at which 50 % of the individuals are mature.
However, this is not the only definition (Williams and
Babcock 2005), and there are also various possible meth-
ods for calculating it. For example, classifying individuals
into mature or immature can be based on a macroscopic
analysis of the gonads, histological cuts or variations in the
gonad index (Haya de la Sierra 1990; Saborido-Rey and
Junquera 1998; Corgos and Freire 2006), and the statistical
procedures for estimating the size can also vary (Trippel
and Harvey 1991; Roa et al. 1999; Zhu et al. 2011). Due to
this and the spatial variability in the size at maturity
(Lozano et al. 1995), the studies on P. lividus show a
variation of up to 2 cm in the diameter at onset of sexual
maturity, which ranges between 20 and 40 mm (Semroud
and Kada 1987; Haya de la Sierra 1990; Sanchez-Espana
et al. 2004). According to this information, the size at
maturity estimated for P. lividus in this study is consistent
with the information previously published.
We have observed spatial variability in the size at
maturity related to the sea urchin distribution type. Various
studies on echinoids have documented the advantages
offered by patches to the individuals that constitute them,
such as protection against predators or wave action (Pearse
and Arch 1969; Freeman 2003; Vega-Suarez and Romero-
Kutzner 2011). In addition, patches are usually formed in
areas with good habitat quality, where food is abundant
(Vadas et al. 1986; Unger and Lott 1994; Alvarado 2008).
The individuals that live isolated from others do not have
these advantages and may initially invest more energy in
reproduction as an adaptation to high mortality rates,
leading to earlier maturity with respect to the sea urchins in
patches. This type of response to stressful situations has
been documented in other echinoids (Dix 1970; Kenner
and Lares 1991) as well as in P. lividus (Lozano et al.
1995).
Meanwhile, the reproductive cycle of P. lividus was
studied through the gonad index. Most published studies
analyse the spatial and temporal variability of this index
with linear statistical models, such as GLMs or ANOVAs
(e.g., Sellem and Guillou 2007; Gonzalez-Irusta 2009;
Hernandez et al. 2011). Given that the temporal pattern of
the gonad index is a cycle, studying it with these kinds of
models requires a categorical time variable with a certain
number of levels (e.g., the months). If the objective is also
to study the spatial variability of the temporal pattern (as in
our case), it is necessary to include a large amount of
interactions between the spatial variables and each of the
levels of the time variable. As a result, the output of the
model is difficult to interpret, and most studies limit
themselves to determining the effect of these variables on
the mean annual gonad index.
Using a nonlinear model is a good alternative for
studying the temporal pattern of the gonad index. Thus,
Ebert et al. (2012) developed a complex equation to rep-
resent mathematically the reproductive cycle of the sea
urchin S. purpuratus, using a gonad index different from
the one used in the present study. While the SGI corrects
potential biases produced by the allometric growth of the
gonad (Ourens et al. 2012), Ebert et al. (2012) incorporated
the effect of the allometry in the equation. In any case, both
models allow interpreting the effect of the independent
variables on the different components of the reproductive
Table 2 Parameters of the reproductive cycle of P. lividus estimated
with nonlinear mixed-effects models and their standard errors (SE)
Fixed effects Value SE t-value p value
a -0.014 0.013 -1.068 0.286
Depth 8 m 0.017 0.008 2.116 0.034
Depth 12 m 0.004 0.008 0.432 0.666
Males 0.035 0.004 8.996 \0.001
Biomass 0.002 0.001 2.385 0.017
b 0.258 0.017 15.171 \0.001
Depth 8 m -0.043 0.011 -3.861 \0.001
Depth 12 m -0.037 0.011 -3.190 0.001
Males -0.038 0.005 -6.884 \0.001
x 1.291 0.158 8.174 \0.001
Depth 8 m 0.162 0.052 3.129 0.002
Depth 12 m 0.353 0.052 6.842 \0.001
Diameter -0.009 0.002 -4.015 \0.001
Random effects SE Correlation
a 0.033 a b
b 0.043 0.053
x 0.203 -0.068 -0.463
Residual 0.220
The deviation associated with the random effects and the correlation
matrix are also shown
2420 Mar Biol (2013) 160:2413–2423
123
cycle, that is, the mean annual gonad index, amplitude and
the cycle phase.
The seasonality of the SGI in our study area reflects an
annual reproductive cycle in which the maximum gonadal
development occurs in winter before spawning takes place.
In the summer, the gonads have already released the
gametes, which results in low SGI values. The SGI begins to
increase again from September when the gonads start to store
new nutrients for the following gametogenesis. Similar
results have also been obtained in other regions of Galicia
(Urgorri et al. 1994; Catoira 1995; Montero-Torreiro and
Garcıa-Martınez 2003) and also in Europe and Africa (Byrne
1990; Gonzalez-Irusta 2009; Garmendia et al. 2010; Arafa
et al. 2012).
This general reproduction pattern can vary slightly
depending on the environmental and population conditions
in which the individuals live. Among the factors studied
here, depth may be one of the most influential in the
reproductive cycle. Although the mean annual SGI hardly
changed with the depth, individuals located in the shallow
areas seem to invest more energy in reproduction than
those sea urchins situated in deep areas, given the large
variation experienced by the size of their gonads along the
cycle. According to Walker et al. (2007), the gonads reach
their maximum size when most of the gonadal tissue is
constituted by phagocytic cells, which accumulate the
nutrients necessary for gametogenesis. Consequently,
gonads will be larger in those habitats where food is
plentiful and of high nutritional quality, as in the shallower
areas (Keats et al. 1984; Rogers-Bennett et al. 1995;
Guettaf et al. 2000; Ourens et al. 2011). Similarly, the
higher food availability in the 4 m depth area could lead to
the formation of a greater number of gametes or higher-
quality gametes that have large nutrient reserves. This
would explain the smaller gonad residuals observed at 4 m
depth after spawning. Following the same reasoning, the
different composition of the gametes (Unuma et al. 2003;
Walker et al. 2007; Hagen et al. 2008) and their different
contribution to the gonadal weight would justify the dif-
ferences that are observed between the SGI of males and
females after spawning.
The mathematical model that we used to represent the
reproductive cycle assumes symmetrical cycles, in which
the concave part of the curve is the convex mirror. In these
cases, the parameter x represents the phase shift between
cycles, as shown in Fig. 4. However, if the cycles were
asymmetrical, the parameter x may be indicative of the
changes that occur between cycles in the duration of the
period of accumulation of nutrients or of the spawning
period. This could be the cause of the variability produced
by the depth and the diameter in the parameter x in our
analysis. For example, the high availability of food in the
shallow zone could lead to a period of accumulation of
nutrients more prolonged than in deep areas, and therefore
a phase lag between cycles. Similarly, the metabolic rate of
echinoids decreases as the size of the individual increases
(Fuji 1967; McPherson 1968), which means that the
nutrients necessary for gametogenesis are accumulated
more slowly, resulting in a delayed spawning.
Finally, the population density, which in this case is
related to the type of spatial distribution of the individuals
(aggregated vs. dispersed), increased the mean size of the
gonads. Although this increase was very slight, it shows
that there is not intraspecific competition for food or space
at high densities (up to 18.5 kg m-2). The particular
environmental characteristics that occur in the sea urchin
patches (e.g., higher food availability and protection from
predators and waves) may increase the carrying capacity of
the habitat. Thus, high densities do not promote the inverse
relationships that have been observed between the gonad
index and the population density in other studies where
densities were manipulated (Tomas et al. 2005). Further-
more, the dense aggregations of sea urchins not only
increase the gonad size, but also enhance the reproductive
success by decreasing the distance between individuals
(Pennington 1985; Levitan et al. 1992). In this sense,
Levitan (1991) found that fertilisation rates of Diadema
antillarum increased from 7 to 40 % when the density of
males was increased from 1 to 16 ind m-2. Estimating the
minimum aggregation level that would ensure the fertil-
isation of P. lividus is therefore a challenge we set for the
future.
In view of our results, the lower size at maturity of sea
urchins located in unfavourable habitats could be an
adaptation to high mortality rates. However, once indi-
viduals mature, they will develop greater gonads in areas
with higher habitat quality. We have also observed that sea
urchins located at 4 m invest more energy in reproduction
than individuals at greater depths, possibly as a response to
differences in food availability. This pattern contrasts to
that described by Ourens et al. (2013) for somatic growth in
the study area, who found larger individuals in deeper
areas. Inverse patterns between reproduction and growth of
P. lividus were also found by Turon et al. (1995), although
in that case the investment in reproduction was higher in a
habitat with food shortages and high hydrodynamics.
The information obtained in this study gains particular
interest for the managers and fishers of echinoids, as it
would allow them to adapt the harvesting strategy to the
spatial and seasonal variability in the reproductive
dynamics, thus optimising the productivity of the fishery.
Based on the results obtained, the minimum commercial
size (55 mm) and the closed season for P. lividus in our
study area (May–September) are two appropriate measures
that favour the reproduction of the species. The objective
of establishing a minimum harvesting size is to make sure
Mar Biol (2013) 160:2413–2423 2421
123
all individuals spawn at least once before becoming part of
the exploitable biomass. This objective is fulfilled if we
consider that more than 95 % of the population reaches
sexual maturity at 40.5 mm in all of the areas sampled.
Similarly, the species spawns at the end of spring, which
coincides with the end of the fishing season.
The maximum fishery productivity (in terms of gonad
harvest) could be obtained in the months before spawning
and at the shallow aggregations. However, probably a
management strategy that protects the shallow breeding
stock could be more adequate, due to high reproductive and
low growth rates of sea urchins in this habitat.
Acknowledgments This work was funded by the Spanish Ministry
of Education and Science and by the European Regional Develop-
ment Fund (ERDF). The authors wish to thank the other members of
the research team, especially G. Casal, N. Sanchez and M. J. Juan
Jorda, for their collaboration in processing the samples, estimating
SST data, and statistical advice.
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