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Estimation of growth parameters for the exploited sea cucumber Holothuria arguinensis from South Portugal.

The economic value of sea cucumbers is well known and at least 66 species have been harvested in more than 70 countries (Purcell et al., 2012), but these fisheries usually follow a boom and bust pattern (Eriksson et al., 2012; Purcell et al., 2013; Gonzalez-Wanguemert et al., 2014; Gonzalez-Wanguemert et al., 2015). The rapid expansion of sea cucumber fisheries over the past several decades has overshadowed efforts to implement effective management, leading to overexploitation of sea cucumbers by the majority of fisheries worldwide (Purcell et al., 2013; Gonzalez-Wanguemert et al., 2016). This inadequate management is primarily due to insufficient biological and ecological knowledge of target species (Lovatelli et al., 2004; Purcell, 2010). Knowledge of growth parameters not only is essential for conservation and management of a target species but also for reducing fishing pressure on wild populations. One common fisheries management measure, for example, has been the implementation of a minimum size at capture, which is species-specific and often based on size at first maturity (Bruckner, 2006; Purcell, 2010). This management measure is important because of its applicability in the field, and together with information on the time to reach maturity, can be used to implement seasonal closures, as well as be used in guidelines for rebuilding hatchery stocks.

Estimation of growth rates of sea cucumbers is difficult because of their ability to shrink (negative growth), their flaccid body, and their propensity to shed physical tags (Conand, 1983; Uthicke et al., 2004; Purcell et al., 2008). Until recently, external tagging methods have been unsuitable for estimating growth owing to biological factors such as evisceration, necrosis, skin sloughing, changes in sea cucumber behavior, and even increases in natural mortality and predation rates (Conand, 1991; Purcell et al., 2006). The use of passive inductive transponder (PIT) tags in sea cucumbers is also problematic because of the different results obtained with different sea cucumber species. Some studies have, for example, reported poor retention of PIT tags in 2 tropical sea cucumbers, Holothuria whitmaei and Actinopyga miliaris (Purcell et al., 2008). In fact, only 8 d after release in suitable reef habitats, just one-quarter of H. whitmaei individuals tagged by Purcell et al. (2008) retained PIT tags and no A. miliaris individuals retained them. Similar results were found for the gray sea cucumber (Holothuria grisea), where the retention of the PIT tag was low, with 100% being lost by week 9 (Rodriguez-Barreras et al., 2016), but in the case of Cucumaria frondosa, results on tag retention were much improved (Gianasi et al., 2015). Implanting the tag in an oral tentacle of C. frondosa to reach the hydrovascular system of the aquapharyngeal bulb resulted in the best retention rates in full-size individuals: from a maximum of 92% after 30 d to 68% at the end of the experimental period (300 d). Efficacy was lower in smaller individuals (84% after 30 d and 42% after 300 d).

In a pioneer work, Ebert (1978) studied growth of the sea cucumber known as the lolly fish (Holothuria atra) by tagging the plates of their calcareous rings with tetracycline. The growth of the plates was used to estimate growth parameters for the Brody-Bertalanffy growth equation. Subsequently, other methods based on weight and size data have been undertaken to study growth in both wild and cultured sea cucumber populations. For example, Battaglene et al. (1999) calculated sea cucumber length from wet weight measurements, using a polynomial regression. Measurements of specific growth rates and relative weight increase have also been obtained for sea cucumbers (Yang et al., 2005; Dong et al., 2008), while other studies have adapted different growth models like Francis's model and the von Bertalanffy model to calculate weight and length data (Conand, 1988; Herrero-Perezrul et al., 1999; de la Fuente-Betancourt et al., 2001; Uthicke et al., 2004; Sulardiono and Prayitno, 2012). Interestingly, some consistent differences in growth rates have been observed between tropical species of sea cucumber, such as Stichopus vastus, the Florida sea cucumber (Holothuria floridana), the lolly fish, and the mammy fish (Actinopyga nobilis), exhibite slow growth rates (Ebert, 1978; de la Fuente-Betancourt et al., 2001; Uthicke et al., 2004; Sulardiono and Prayitno, 2012) and some temperate species, such as Australostichopus mollis and C. frondosa, that grow faster than the previously mentioned tropical species but also show some seasonal variation (Hamel and Mercier, 1996; Slater and Jeffs, 2010).

Holothuria (Rowethuria) arguinensis has a restricted distribution in the northeast Atlantic Ocean, from Portugal to the Canary Islands and Mauritania in northwestern Africa, although recently, it was recorded in the Southwestern Mediterranean Sea (Alicante, Spain) and in Algerian waters, implying that it is colonizing the Mediterranean Sea (Gonzalez-Wanguemert and Borrero-Perez, 2012; Mezali and Thandar, 2014). Holothuria arguinensis is one of the new target species of NE Atlantic and Southwestern Mediterranean fisheries, where catches driven by its high commercial value, are increasing (Gonzalez-Wanguemert and Godino, 2016; Gonzalez-Wanguemert et al., 2016; Roggatz et al., 2016). Illegal harvest of this species in Portugal and Spain, is a threat to the sustainability of the fishery, especially in the Ria Formosa lagoon, at Portimao and Albufeira (South Portugal), and at the coast of Cadiz (South Spain) (Gonzalez-Wanguemert and Godino, 2016). Also, this species is targeted for aquaculture development for both commercial production and restocking (Dominguez-Godino et al., 2015).

The species has been linked to the presence of seaweed and seagrass meadows of Cymodocea nodosa and Zostera noltii, with reported densities up to 563 individuals/ha (Faro, South Portugal) (Gonzalez-Wanguemert et al., 2013; Siegenthaler et al., 2015). The home range of H. arguinensis individuals has been estimated to average 35 [m.sup.2], and individual movements are close to 10 m/d (Siegenthaler et al., 2015).

An average size of 255 mm was reported for this species in Gran Canaria (Canary Islands, Spain) and a maximum size of 370 mm (Navarro et al., 2013). In Ria Formosa (South Portugal), Gonzalez-Wanguemert et al. (2013) found individuals ranging between 70 to 360 mm, and having an average size of 221 mm and a multimodal size-frequency distribution. Three years later, Gonzalez-Wanguemert et al. (2016), considering H. arguinensis populations throughout the geographical range of this species, estimated a positive allometric growth. In that study, individuals ranged from 90 to 360 mm in length and had significant differences in mean size between sampled areas, such that individuals from Sagres (SW Portugal) were significantly longer than those from Ria Formosa.

Our aim was to obtain growth parameters from length-frequency data of the species H. arguinensis inhabiting the Ria Formosa (South Portugal), by fitting different growth models: the nonseasonal von Bertalanffy and the Hoenig seasonal von Bertalanffy models.

Material and methods

Study area

Ria Formosa is a coastal lagoon located in the Algarve region (South Portugal) (Fig. 1). Its area is approximately 105 [km.sup.2], half of which is intertidal and covered by sand, mud and marshes (Duarte et al., 2007). It is composed of a system of barrier islands with small ponds, inlet deltas, and barrier platforms (Pacheco et al., 2010). The water temperature varies between 12[degrees]C and 13[degrees]C in winter and 27[degrees]C and 28[degrees]C in summer. Variations in salinity are small due to the low freshwater input into the lagoon, ranging between 35.5 and 36.9 throughout the year (Ribeiro et al., 2008), with the exception of surface waters for brief periods after heavy winter rainfall (Ribeiro et al., 2008; Rodrigues et al., 2015).

Ria Formosa, was declared a Natural Park in 1987 (Decreto-Lei No. 373/87, available from website) and it plays an important role in the local economy by supporting commercial fisheries, bait harvesting, tourism, aquaculture, port activities, and providing a recreational fishing area near human settlements. The lagoon is classified as coastal waters within the scope of the European Water Framework Directive (EC/2000/60, available from website).

Data collection

The data were collected by 2 methods: visual census and random sampling. During the volunteer program linked with the research project "Sea cucumbers: the new resources for a hungry fishery" (CUMFISH) conducted by the Centro de Ciencias do Mar, Universidade do Algarve, monthly visual censuses of sea cucumbers were carried out in the Ria Formosa lagoon during low tides from November 2012 to March 2014 in 3 locations: 2 sites (west and east) at Praia do Faro and 1 site at Fuzeta (Fig. 1). In each locality, 6 transects (each 100x2 m) were surveyed. Three transects were surveyed by walking along the coast "outside" the water and 3 transects were surveyed by snorkeling "inside" sea water and by following the edge of the low tide level. All sea cucumber individuals were identified to species and their total length registered to the nearest 0.1 cm. Measurements of length were recorded in situ without disturbing the animals, which were not touched or removed from the water. More details of the methods can be found in Gonzalez-Wanguemert et al. (2013).The random sampling was conducted in 6 different locations inside the Ria Formosa--Quinta do Lago, Barinha, Armona, Tavira, Culatra, and Cacela (Fig. 1)--during February and March 2014. Presence (in absolute geographical coordinates [datum: World Geodetic System 1984]) and total length (in centimeters) of all sea cucumbers were recorded by 2 researchers walking along the intertidal zone during low tide and following a route with registered geographical coordinates. Again, sea cucumbers were measured in situ. In this way, it was possible to sample over a wide area and later to calculate the area sampled. The sampling was undertaken during tides lower than 0.7 m, when the seagrass meadows were always exposed in the sampling locations.

Data analysis

All length data obtained from visual census and random sampling were pooled together and grouped in 4-cm classes. Two different growth models--the non-seasonal von Bertalanffy growth model (Pauly, 1987) and the Hoenig seasonal von Bertalanffy model (Hoenig and Hanumara, 1982)--were fitted to the lengthfrequency data by using the electronic length frequency analysis (ELEFAN) system implemented in the length frequency distribution analysis (LFDA) software, vers. 5.0 (Kirkwood (1)). The best estimators of asymptotic length ([L.sub.[infinity]]) and the growth coefficient (K) were selected on the basis of maximum value of the score function and the best fit of the curve to the observed data (Kirkwood1).

The ELEFAN method is based on fitting the von Bertalanffy model to length- frequency data (Pauly, 1987). It works by restructuring the length-frequency data, emphasizing the peaks and troughs in the data set, and calculating score functions for growth curves generated for different combinations of von Bertalanffy growth parameters. Every time the growth curves passed through the peaks in the restructured data it accumulates points, resulting in a score function for each curve. The Hoenig function takes into account seasonality in growth, including a parameter that fits the beginning of the sinusoidal function (Ts), and the parameter C that expresses the relative amplitude of the seasonal oscillation in growth (Pauly et al., 1992).


Length data of 1198 individuals of H. arguinensis from 9 different localities inside the Ria Formosa were used for length-frequency analysis (Table 1).

For the nonseasonal growth model, estimates of K=1.99 and [L.sub.[infinity]]=40 cm were obtained with a score function of 0.460. However, the growth curve does not adequately fit the data (Suppl. Fig.).

Considering these results, we then fitted the Hoenig model for seasonal growth. This provided estimates of K=0.88 and [L.sub.[infinity]]= 69.9 cm, and a score function of 0.565. The average length was 23.1 cm, sizes ranged from 4 to 48 cm, and approximately 98% of the individuals ranged from 11 to 35 cm (Fig. 2). The growth curve showed a higher score function than that of the non-seasonal growth model (Fig. 3). The value of C=1 indicates the existence of a period of no growth during the year for the species.


The [L.sub.[infinity]] and K for H. arguinensis were estimated by using 2 different functions. The first function, the nonseasonal von Bertalanffy model, did not provide realistic results. However, when seasonality was included (with the Hoenig model), more reliable values were obtained, which confirmed the seasonality in the growth of H. arguinensis. The observed growth rate (K=0.88) indicates that this species achieves asymptotic size quickly, even faster than tropical commercially valuable holothurians, such as the lolly fish (K=0.11), Thelenota ananas (K=0.20), Stichopus chloronotus (K=0.45), or Isostichopus fuscus (K=0.18) (Ebert, 1978; Conand, 1988; Herrero-Perezrul et al., 1999). Herrero-Perezrul et al. (1999) discussed the difficulties of comparing growth parameters derived from von Bertalanffy-like models of different sea cucumbers species because not all model assumptions are fulfilled. The 3 general assumptions for these models are the following: 1) species reproduce and recruit once per year; 2) species reach a specific size; and 3) species grow isometrically. Most commercial sea cucumber species fulfill the first 2 assumptions, but usually they do not exhibit isometric growth. In the case of H. arguinensis, allometric growth has also been reported (Gonzalez-Wanguemert et al., 2016). For this reason, comparisons of growth rates from different commercial species must be regarded with caution; most of them do not show the isometric growth assumed by the model.

Faster growth rates may have important and positive effects on the survival of a species, primarily under high stress conditions and in disturbed environments (Morgan, 2012). Achieving a larger size rapidly could be an adaptive advantage: larger sea cucumbers would be capable of moving and stabilizing themselves in more mobile sediments, such as sandy substrates, and they would also be more likely to escape predation (Wiedemeyer, 1993). In the Salomon Islands, the growth rate, as well as the daily distance covered by the sand fish (Holothuria scabra), was found to be related to substrate type, and the sandy areas were occupied only when individuals reached a specific size (Mercier et al., 2000). Inside the Ria Formosa, individuals of H. arguinensis move through a patchy environment; despite the relatively large percent coverage of Z. noltii (46% of the intertidal area) within the Ria Formosa, distribution of this seagrass species is highly fragmented, dominated by small patches from 0 to 5 ha (Guimaraes et al., 2012). These seagrass patches are separated by sand, mud, and marshes that sea cucumbers must pass through when searching for more suitable feeding areas, or probably for areas where they can avoid direct sunlight and desiccation (Gonzalez-Wanguemert et al., 2013). However, the results obtained by Siegenthaler et al. (2015) in a small-scale capture-recapture experiment with H. arguinensis in the Ria Formosa, revealed that mean length was not a significant factor in determining the distribution of this species, and its movement was independent of habitat type (sand and seagrass). In fact, movement of H. arguinensis was better explained by the need of specimens for shelter against UV-radiation, when individuals remain outside water during low tides (Siegenthaler et al., 2015). Further studies should be considered to evaluate whether the same pattern exists at larger spatial and temporal scales than those considered by Siegenthaler et al. (2015), and also including a greater size range of animals for the experiment.

A faster growth rate could also be the result of a decrease in predation rate, mainly during the early life history stages. Although few predators are known to feed on sea cucumbers, several antipredation mechanisms has been described for holothurians (Francour, 1997) and some of these mechanisms seems to be linked to animal size. For example, in the lolly fish, the strength of the toxin present on its skin significantly increases with the size of an individual (Castillo (2)).

On the other hand, the high growth rates of H. arguinensis in the Ria Formosa could be linked to the high productivity of this ecosystem, where a rich phytobenthos, of macrophytic and microphytic organisms (seagrass and diatoms) is located (Brito et al., 2009). In some mesoscosm experiments carried out on the Great Barrier Reef in Australia with lolly fish, it was found that higher food availability per individual increased growth rate (Lee et al., 2008). Therefore, further studies of individuals from areas outside Ria Formosa, and with varying levels of natural productivity, should be carried out to confirm the growth rates we have reported here.

The value obtained for the parameter C (Hoenig model) indicates a seasonal cycle of growth for H. arguinensis, which may cease at least during part of the year. According to Pauly (1987), Ts + 0.5 is the time of the year when growth is slower, and where Ts denotes the beginning of the sinusoidal growth oscillation. This winter point, suggests a reduction in the growth rate of H. arguinensis at 0.19 time units (the time unit for this study was a month, where a 0 time unit represents the month in which the sampling started [November 2012] and 1 time unit corresponds to the 12th month of sampling) from the beginning of the sampling year, which coincides with the months of January and February, when waters may have brackish conditions and the lowest temperatures occur in Ria Formosa (Newton and Mudge, 2003). Feeding experiments on H. arguinensis, in laboratory tanks, could confirm this reduction in growth rate during winter months because under rearing conditions, the species is stationary and decreases feeding rate when water temperature is lower than 14[degrees]C (Dominguez-Godino and Gonzalez-Wanguemert (3)). These low temperatures are common during winter months in Ria Formosa (Ramos et al., 2013). Another temperate sea cucumber species inhabiting the Aegean Sea, Holothuria tubulosa, exhibits similar behavior, with a negative specific growth rate at 15[degrees]C. At this temperature, individuals enter a hibernation phase and have a corresponding drop in metabolic rate, which directly affects their feeding activity (Gunay et al., 2015).

This pattern of a complete shutdown in growth during winter is also found in other temperate species such as A. mollis (Morgan, 2012), C. frondosa (Hamel and Mercier, 1996), Parastichopus californicus (Paltzat et al., 2008) and Holothuria theeli (Sonnenholzner, 2003). Sea cucumbers, seem to have failed to adapt to large fluctuations in water temperature (Dong et al., 2008), clearly showing an optimal temperature for growth which is close to the optimal temperature for food intake (Yang et al., 2005). Conversely, high water temperatures can also induce aestivation in some sea cucumber species, such as has been observed in Apostichopus japonicus and Australostichopus mollis (Yang et al., 2005). These results show that water temperature has an effect on the physiological performance of sea cucumbers and that their growth is largely influenced by environmental conditions and food supply (Hamel and Mercier, 1996; Sonnenholzner, 2003).


This research was supported by the CUMFISH (PTDC/ MAR/119363/2010) and "Sea cucumber as new marine resource: potential for aquaculture" (CUMARSUR) (PTDC/MAR-BI0/5948/2014) projects funded by Fundacao para a Ciencia e a Tecnologia (FCT, Portugal), and was performed as a dissertation for the International Master in Marine Biodiversity and Conservation. Authors thank the field support of all volunteers implementing and carrying out the visual censuses. The first author was supported by Fundacion para el Futuro de Colombia (Colfuturo) and M. Gonzalez-Wanguemert by FCT Investigator Programme-Career Development (IF/00998/2014).

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Julian Olaya-Restrepo [1,2]

Karim Erzini [1]

Mercedes Gonzalez-Wanguemert [contact author] [1]

Email address for contact author:

[1] Centro de Ciencias do Mar Universidade do Algarve Campus de Gambelas 8005-139 Faro, Portugal

[2] Departamento de Zoologia Setor de Ciencias Biologicas Universidade Federal do Parana Avenida Coronel Francisco H. dos Santos, 100 19020 Curitiba, Brazil

Manuscript submitted 18 January 2017. Manuscript accepted 21 September 2017. Fish. Bull. 116:1-8 (2018). Online publication date: 10 October 2017. doi: 10.7755/FB.116.1.1

The views and opinions expressed or implied in this article are those of the author (or authors) and do not necessarily reflect the position of the National Marine Fisheries Service, NOAA.

(1) Kirkwood, G. 2001. Enhancement and support of computer aids for fisheries management. Final technical report, 65 p. MRAG, Ltd., London. [Available from website.]

(2) Castillo, J. A. 2006. Predator defense mechanisms in shallow water sea cucumbers (Holothuroidea). UCB Moorea Class: Biol. Geomorphol. Trop. Isl., Univ. Calif. Berkeley, Berkeley, CA. [Available from website.]

(3) Dominguez-Godino, J., and M. Gonzalez-Wanguemert. In review. Centro de Ciencias do Mar, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal.

Caption: Figure 1

Map of the study area showing the 9 locations, indicated by areas shaded in black, where Holothuria arguinensis were sampled between November 2012 and March 2014 inside the Ria Formosa, a coastal lagoon in southern Portugal. Sampling was conducted by visual census at 3 sites: Praia de Faro to the west of the main bridge (PFW), Praia de Faro to the east of the main bridge (PFE), and Fuzeta (FUZ). Random sampling was conducted at 6 sites: Quinta do Lago (QTL), Barinha (BAR), CUL (Culatra), Armona (ARM), Tavira (TAV), and Cacela (CAC).

Caption: Figure 2

Length-frequency distribution of Holothuria arguinensis sampled inside the Ria Formosa lagoon in southern Portugal between November 2012 and March 2014.

Caption: Figure 3

Growth curve from a von Bertalanffy model with a Hoening function for seasonal growth fitted, by using electronic length-frequency analysis (ELEFAN), to length-frequency data of Holothuria arguinensis sampled inside the Ria Formosa lagoon in southern Portugal between November 2012 and March 2014. The gray bars indicate running average frequencies (Pauly, 1987). Results for parameters of the Hoenig seasonal von Bertalanffy growth model were 0.88 for the growth coefficient (K), -0.30 for the time when the average length was zero ([T.sub.0]), 1.00 for parameter C, which expresses the relative amplitude of the seasonal oscillation in growth, and 70.00 for the asymptotic length ([L.sub.[infinity]]). Sampling time units are the relative time units (months) during data collection. A time unit of 0 represents the month in which sampling started (November 2012), and a value of 1 corresponds with the 12th month of sampling.

Caption: Supplementary Figure

Von Bertalanffy growth curve for nonseasonal growth fitted by using electronic length-frequency analysis (ELEFAN) to length-frequency data of Holothuria arguinensis sampled inside the Ria Formosa lagoon in southern Portugal between November 2012 and March 2014. The gray bars indicate running average frequencies (Pauly, 1987). Results for parameters of the nonseasonal von Bertalanffy growth model were 40.00 for the asymptotic length ([L.sub.[infinity]]), 1.99 for the growth coefficient (K), and -0.42 for the time when the average length was zero ([T.sub.0]). Sampling time units are the relative time units (months) during data collection. A time unit of 0 represents the month in which sampling started (November 2012), and a value of 1 corresponds to the 12th month of sampling.
Table 1

Number and mean lengths (with standard deviations [SD]) for
individuals of the sea cucumber Holothuria arguinensis sampled by
visual census and random sampling inside the Ria Formosa, a coastal
lagoon in southern Portugal, between November 2012 and March 2014.
The 9 localities sampled (see Fig. 1) were Armona (ARM), Barinha
(BAR), Cacela (CAC), Culatra (CUL), Fuzeta (FUZ), Praia de Faro to
the west of the main bridge (PFW), Praia de Faro to the east of the
main bridge (PFE), Quinta do Lago (QTL), and Tavira (TAV). N
signifies number of sampled individuals.

                    Mean length           Minimum    Maximum
Locality      N         (cm)        SD      (cm)       (cm)

ARM          222       26.51       6.42    10.00      45.00
BAR           78       17.54       3.31    12.00      23.00
CAC           1        23.00        --     23.00      23.00
CUL           12       23.42       5.47    13.00      34.00
FUZ           78       23.38       6.29    10.00      38.00
PFE          433       22.46       6.27     4.00      35.00
PFW          332       21.63       5.18    10.00      48.00
QTL           25       17.48       4.78    15.00      25.00
TAV           17       17.18       6.27     9.00      30.00
Total        1198      23.10       6.28     4.00      48.00
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Author:Olaya-Restrepo, Julian; Erzini, Karim; Gonzalez-Wanguemert, Mercedes
Publication:Fishery Bulletin
Article Type:Report
Date:Jan 1, 2018
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