Introduction

Climate change models integrate laboratory information in the form of population parameters that can be modified according to environmental trends (Doney et al., 2009). A large part of these models reflects the effect that the condition exerts on survival (Seijo et al., 2016); while others predict biomass change according to the stock structure and efficiency of recruitment (Hilborn and Walters, 1992). Tracking these changes allows linking the responses of individuals to the expected population patterns.

A group of organisms that is vulnerable to the effects of climate change and of global economic importance are sea cucumbers (Anderson et al., 2010, Lundquist and Botsford 2011; Purcell et al., 2016). Although the adult phases of these organisms have the physiological capacity and phenotypic plasticity needed to cope with the adverse conditions of climate change (Garcia-Arraras and Greenberg, 2001; Yang et al., 2006; Yuan et al., 2009; Zamora and Jeff, 2012; Gullian, 2013; Storey, 2015) larvae (if any) and the initial stages of benthic fixation are more vulnerable to changes in temperature and pH (Smiley, 1986; Asha and Muthiah, 2005; Brander, 2010; Morgan, 2008; Yuan et al., 2015). Thus, these stages represent a bottleneck for the species dynamics (Asha and Muthiah, 2005; Hamel and Mercier, 2008; Yuan et al., 2015; Koenigstein et al., 2016).

In this paper we focus on the effects of pH on the recruitment of Isostichopus badionotus, a holothurian that is distributed in the northern coast of the Yucatán peninsula, which has been subject to fishing exploitation since 2012 (Hernández-Flores et al., 2015). The effects of climate change were studied by modifying the recruitment function in an age-structured bioeconomic model (Anderson and Seijo, 2010) that used data from wild populations (Poot-Salazar et al., 2014, Hernández-Flores et al., 2018). Associated with the conditions of climate change and the modification of the recruitment, management strategies were defined to maximize the economic benefits and to compensate the adverse effects of climatic change.

Materials and Methods

Liability to pH

Published survival data for different species of holothurians and previous reports of gnomonic values of natural mortality (M) of I. badionotus indicate that the early stages of the life cycle, compared to the juvenile and adult phases, are more vulnerable to changing environment (Zacarias-Soto et al., 2013; Gullian and Terrats, 2017; Romero-Gallardo et al., 2018). This conclusion is congruent with the work that has been presented for larvae of different holothurian species (Hamel and Mercier, 1996; Asha and Muthiah, 2005; Morgan, 2008; Brander, 2010; Dupont et al., 2010; Stumpp et al., 2011; Zacarías et al., 2013; Yuan et al., 2015). Several studies conducted with sea cucumber larvae indicate that pH is a critical factor that can produce direct (Asha and Muthia, 2005; Hamel and Mercier, 2008; Yuan et al., 2015) and indirect (Morgan, 2008; Brander, 2010) effects. Within the former, there are reductions in development, growth and survival in the presence of extreme alkaline values (pH 9.00), as well as malformations, disintegration and low survival (up to 49% less in Holothuria spinifera) by reduction of the optimum (pH 8.0) by 0.5 pH units (Asha and Muthia, 2005). Regarding the indirect effects, the quality and availability of food is a key factor for the survival of the early life stages of sea cucumbers (Brander, 2010).

Recruitment restriction

The dependence that the recruitment maintained with the biomass of the breeding population was calculated through the Beverton-Holt recruitment function. (Beverton y Holt, 1957):

where Rt: recuritmen in year t, SSB_t−1: spawning stock biomass, α: máximum number of recruits, and β: spawning biomass that is required to produce a level of recruits equivalent to α/2. The parameter α was calculated from the biomass published by Hérnandez-Flores et al., (2015), assuming equilibrium conditions, while the parameter β was calculated considering the period that it takes the stock to recover from 5 to 100% of virgin biomass (Poot-Salazar et al., 2004). The impact that OA could have on larval mortality was incorporated by modifying α, using the values shown in Table I.

TABLE I
VALUES OF α FOR THE RCP THAT DESCRIBE THE INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (RCP2.0, RCP6.0 AND RCP8.5)

* RCP: Representative concentration pathways.

Bioeconomic model

We considered a sea cucumber population with asymptotic recruitment dependent on the size of the reproductive population and the average age specific of the females, in which the effects of mortality of the larval phases affect the settlement of the organisms; this population did not present a homogenous distribution of age classes, and has the same parameter of growth and natural mortality (r and M, respectively). The reproductive capacity of the females corresponds to that observed in laboratory specimens (Zacarías-Soto et al., 2013) and is the sum of the biomasses of relevant ages, according to the recruitment function (Hilborn and Walters, 1992). It is assumed that in this population the weight of the individual is a function of its length, which changes with age, according to the von Bertalanfy’s growth function (Poot-Salazar et al., 2014). With regard to spatial allocation, it is assumed that the population allocates heterogeneously at different levels of K, implying that the density of the population grows until saturated and subsequently extends (Maunder and Deriso, 2013). Within the scope of its fishery, the age-specific catch is assumed to be derived from a catchability function that depends on the availability of the resource (Anderson and Seijo, 2010). It is also assumed that the number of boats constitutes the fishing effort and that its characteristics are kept constant over time with a selectivity that is established according to the magnitude of the catch, with a retention of 50 to 75% (Hernández-Flores et al., 2015). An illustration of the essential elements of the model, including the mathematical expressions thereof, is presented in Figure 1.

Figure 1
Schematic flow model of the equations that were employed in the construction of the bioeconomic model

Model parameters

Reported averages of age and growth of specimens collected in Celestún, Sisal and Progreso, all located in the northern coast of the Yucatan Peninsula, were used as model parameters (Poot-Salazar et al., 2014). The parameters of the von Bertalanfy growth equation used were: K= 0.6 year^-1, t₀= -0.50, L_∞= 31.6cm, with an exponential height-to-weight ratio (W_∞) of y= 0.14 (31.6 ^2.6) (Poot-Salazar et al., 2014). The maximum age was ten years, and in two years 50% maturation occurs, which coincides with the size of the first capture (22cm). The maximum biomass that can be registered in the 1702km² range of the resource was 93,000ton. It is estimated that an initial stock (206ton of breeding females) can produce 220,504,710 individuals at the end of the first reproductive cycle (Hernández-Flores et al., 2015); these values were considered at the time of parameterizing the stock recruitment ratio, resulting in α= 55,724,807 and β= 1,618, considering a coefficient of Rikhter and Effanov (M) of 0.58 (Hernández-Flores et al., 2018). The maximum allowed number of boats was 691 participating at an exit enter parameter of 0.000043 boats US$^–1. Fixed cost, transfer cost unit, and other variable costs were 860, 75, and 40 US$/day; and minimal price of the specie was 2000 US$/ton.

Age structure

Cohorts were followed over time in an age-structured model. Briefly, the number of individuals (Ni) across the different age classes (k) was plotted as a vector describing the passage of the population over time. The stock of spawning biomass (SSB) was determined considering the fertility coefficient (s_i) of each of the age classes. The changes in the dynamics of the fishing stock were determined following the modifications of the vector of the age class. The fishing mortality rate at a specific age was considered as the product of the effort and the catchability coefficient. In each year, the size of the age class zero was determined by recruitment and was related to the size of SSB. The size of any other age class was determined by the projection of the lower class by adding the effects of M and fishing mortality (F) (Anderson and Seijo 2010).

Bioeconomic impacts

The impact that price exerts on the bioeconomic balance depends on the costs, the rent and the abundance of the resource. All these were modified in turn by the restriction on recruitment which relates with OA. To determine this impact, three projections of fishing behavior were carried out using different market prices (US$ 2000, 2750 and 3500/ton). The model outputs were used to determine fishing effort intervals in which both spawning stock biomass and net present value (NPV) maintained positive values. Once these intervals were determined, the decision on the fishing effort to be applied at each of the scenarios were determined by the application of MiniMax and MaxiMin decision criteria.

The effects of climate change on NPV were analyzed in decision tables that integrated management strategies (related to fishing effort) and IPCC scenarios (related to the states of nature, θj) (Seijo et al., 1998, 2016; Villanueva et al., 2013). These tables present the results (r^ij) of each management strategy (rows) applied to each state of nature (columns) and their response in the NPV of the fishery (Eppen et al., 2001) (Table II).

TABLE II
MATRIX FOR THE DEFINITION OF THE NINE STATES OF NATURE THAT RESULT FROM THE INTERACTION OF FISHING EFFORTS AND RCP

The loss of opportunities for each decision criterion was determined by comparing the NPVs that were obtained by combining fishing effort within each climate change scenario. These comparisons made it possible to identify the maximum NPV, which was then subtracted from the lowest values. The differences were used to generate an opportunity loss matrix that minimized the loss of opportunity according to the MiniMax criterion (Seijo et al., 1998) (Table III).

The second decision criterion was the MiniMax, which included a regret matrix obtained from the payoff matrix. Again, the headings regret matrix were the states of nature in the columns and fishing mortality levels in the rows. The data were now obtained by selecting the highest average annual catch in each column and subtracting the outputs from that column (loss of opportunities). Therefore, each column contains a zero that corresponds to the maximum value. Then, the highest loss of opportunity was selected in each column of the regret matrix, and the final decision corresponds to the fishing mortality that produced the lowest loss of opportunities (Table III).

TABLE III
DECISION-MAKING MATRIX WITH INTERACTION D_I AND θ_j AND ITS EFFECTS ON THE PERFORMANCE OF THE FISHING VARIABLE, THIS CASE NPV*

* NPV: Net present value.

The performance variables that were used to measure the impact of OA (α) and F were recruitment, biomass, and fishing effort, as well as the proportion of biomass for a specific year (B₁₀) compared to the initial biomass (B₀), assuming B₀= K.

Results

The impacts of ocean acidification (OA) on the population structure of I. badionotus were determined using different values of the parameter α. These values were assigned according to the concentration trends representative of the IPCC (Table I). The responses were analyzed in an age-structured growth model. In each scenario of acidification, the price stimulated the fishing effort in terms of the gains that were obtained from the total catch volume. The increase in the adverse effects of OA (reduction of the value of α) decreased the profit margin regardless of prices (Table IV).

TABLE IV
MAXIMIZATION OF ECONOMIC BENEFITS ACCORDING TO MARKET PRICES AND OA EXPRESSED IN TERMS OF NUMBER OF BOATS AND (NPV IN 10⁶ US $)

The number of vessels that maximize NPV according to the trends of representative concentration and prices is presented in Table IV. The best economic benefit under normal conditions without OA (θ₁) was obtained with US$ 3,500 using 182 boats. The reduction of α concentrated fishing effort from 167 to 115 boats with US$ 2,750 and from 182 to 137 boats with US$ 3,500. Under the conditions of RCP6.0 and RCP8.5, the maximum NPV was obtained at US$ 3,500 with 163 and 137 boats, respectively. The decision considered to define the number of boats that maximized the capture utility was obtained by applying the criteria MaxiMin and MiniMax (Tables V and VI). The marginal yields that were obtained by affecting the fishery with less recruitment and, consequently, higher operating costs, were low, regardless of price and fishing effort. Precautionarily, the fishing effort that could be applied in case maximum OA and presence of different market prices were: 95, 115 and 137 boats for US$ 2,000; 2,750 and 3,500; respectively (Table V).

TABLE V
OUTPUTS OF THE MODEL (NPV IN 10⁶ US$) THAT WERE OBTAINED BY RESTRICTING THE NUMBER OF BOATS PARTICIPATING IN THE FISHERY

The MaxiMin matrix shows three states of OA for each market prices.

The reduction of fishing effort from 182 to 137 was the strategy that most reduced losses in the presence of strong effects of OA (θ₃) and maximum resource price. Interme-diate values of OA (θ₂) and intermediate prices allowed to operate with an effort in the range of 167 to 115 boats, with 147 being the effort that most reduced the marginal yield losses. Absence of OA (θ₁) in the presence of low price, allowed to operate in the range of 144 to 95 boats, being 144 the optimal effort (Table VI).

TABLE VI
OPPORTUNITY LOSS MATRIX (10⁶ US$) OF THE MINIMAX CRITERION FOR THREE STATES OF OA AND DIFFERENT MARKET PRICE

The management strategies identified by the MaxiMin and MiniMax criteria coincide in that a greater impact of OA, and increases the restriction of the number of participating boats, which explains that the best management option, given the increase in gravity of OA, is the strengthening of the precautionary approach.

The way in which the fishery could behave given current management conditions and considering possible OA scenarios, as well as the effect of market prices, shows that the increase in the restriction in α produces less spawning biomass and recruitment. Although the market price encourages participation in the fishery, the increase in adverse effects due to OA and consequently the reduction of exploitable biomass restricts fishing effort to maximize the economic benefits of the participants (Figure 2).

At all prices, fishing activity was greater during the first three years of exploitation; furthermore, between years 4 and 10, there were significant decreases in biomass, which significantly limits fishing effort. These effects were most evident when considering the impact of climate change. Subsequent monitoring shows that there were no major changes in fishing, indicating that activity remains profitable at a bioeconomic equilibrium, albeit at different levels of performance, even in adverse climate change scenarios (Figure 2).

Figure 2
Behavior of the study variables (recruitment, biomass and effort) obtained at different prices (US$ 2,000; 2,750 and 3,500) in three different states of climate change (IPCC 2.6, 6.0 and 8.5)

The proportion of the initial biomass (B₀) that remains at B₁₀, shows that OA and fishing effort had no significant adverse effects on the conservation of the population, maintaining the B₁₀/B₀ ratio above 0.30 units (Figure 3a). Even when maintenance effects did not occur within the first ten years of exploitation, it might be possible that further exposure under similar condition could significantly affect the population (Figure 3b). Proportion of biomass at year 15 (B₁₅) with regard to year 0 (B₀), showed that extending the effect of OA in the long run and increasing the price of the product could reduce the resource (B₁₅/B₀ ratio) under 0.30 units.

Figure 3
Fishery impact for the three scenarios of OA and prices: a) B₁₀/B₀; b) B₁₅/B₀

Discussion

In this study, we focused directly on the effect that OA could exert on recruitment of I. badionotus by combining future scenarios (RCP) with probabilistic epistemic (α) prediction to build an approach that determine the possible consequences in a fishery. To avoid ambiguity, we presented a background that sustains evidence based on the possibility that such events could occur. The approach was further reinforced by decisions making under uncertainty conditions applying the criteria MiniMax and MaxiMin (Seijo et al., 2016).

The decision to include the adverse effect of OA in the recruitment function is consistent with previous reports that exist for this and other marine invertebrate species, in which pH reduction significantly increased the harmful conditions for organisms and reduced survival (Tomanek et al., 2011; Matozzo et al., 2013; Hu et al., 2015). Since some of these reports also indicated that such effects were greater in larvae than in juveniles and adults (Asha and Muthiah 2005, Morgan 2008, Yuan et al., 2015) we conceived, in agreement with Hamel and Mercier (1996) and Zacarías-Soto et al., (2013), that these stages of the life cycle represented a bottleneck for the organisms’ survival.

Approaching the effects of climate change on recruitment is not new, and several studies that have included different methodological proposals have addressed the issue from an ecosystemic point of view. Previous work with barnacles (Balanus glandula and Chthamalus dalli) and oysters (Mytilus spp.) on the west coast of the USA, have shown that variation in large-scale climatic conditions such as El Niño-Southern Oscillation (ENSO), Decadal Oscillation (PDO) and the North Pacific Turnaround Oscillation (NPGO), and mean scale variations such as coastal upwelling, strongly affected the magnitude of the recruitment, accounting for up to 40% of the observed variance, representing a high explanatory power considering the great source of varition for recruitment (Menge et al., 2011). Similar results were reported in the sense of recruitment affected by climates for the sardine Sardinops sagax by Galindo-Cortes et al.,(2010).

Although the prolific nature of the species could compensate for larval mortality (Zacarías-Soto et al., 2013), the yield that was generated in terms of biomass exploitable under OA conditions was able to impact spawning stock biomass, producing differences that were reflected in lower yields in RCP8.5 as compared to RCP2.0; the effect of increasing recruitment constraint on performance reduction showed the above. Under these conditions, the best management option, given the increase in OA, is the strengthening of the precautionary approach; this allows to maximize income and offset the quasi-rent that are related to catch reduction (Anderson and Seijo 2010; González-Olivares and Flores 2015).

It should be noted that regardless of the OA condition and price, all monitoring indicators stabilized over time, suggesting that activity remains profitable because it achieves a bioeconomic equilibrium. This stability could be related to the incentives that fishermen need in order to maintain themselves in the fishing activity. In neither case, within the first ten years, the combination of price and climate change managed to collapse the resource, keeping B₁₀/B₀ index above 0.30. Nevertheless, when the effects were considered over a longer period of time, the drop of the ratio B₁₅/B₀ indicates that in the long run the combination of OA and price exert a more negative effect on the availability of the resource.

Conclusions

The decrease in catch, consistent with recruitment, dissipated rent and reduced fishing effort. These effects were greater as the price of the product increased. The best management strategy that compensates for the adverse effects of OA is the reduction of fishing effort.

REFERENCES

Anderson L, Seijo JC (2010) Bioeconomics of Fisheries Management. Wiley-Blackwell. Oxford, RU. 305 pp.

Anderson S, Flemming J, Watson R, Lotze H (2010) Serial exploitation of global sea cucumber fisheries. Fish Fisher. 12: 317-339.

Asha PS, Muthiah P (2005) Effects of temperature, salinity and pH on larval growth, survival and development of the sea cucumber Holothuria spinifera Theel. Aquaculture 250: 823-829.

Brander K (2010) Impacts of climate change on fisheries. J Mar. Syst. 79: 389-402.

Doney S, Fabry V, Feely R, Kleypas J (2009) Ocean acidification: The other CO₂ problem. Annu. Rev. Mar. Sci. 1: 169-192.

Dupont S, Ortega-Martínez O, Thorndyke M (2010) Impact of near-future ocean acidification on echinoderms. Ecotoxicology 19: 449-462.

Eppen G, Gould F, Schimiet C (2001) Investigación de Operaciones en la Ciencia Administrative. Prentice-Hall. México. 489 pp.

Galindo-Cortes G, De Anda-Montañez J, Arreguín-Sánchez F, Salas S, Balart F (2010) How do environmental factors affect the stock-recruitment relationship? The case of the Pacific sardine (Sardinops sagax) of the northeastern Pacific Ocean. Fisher. Res. 102: 173-183.

Garcia-Arrarás JE, Greenberg MJ (2001) Visceral regeneration in holothurians. Microsc. Res. Tech. 55: 438-451.

González-Duran E, Hernández-Flores A, Seijo-Gutierrez JC, Cuevas-Jimenez A, Moreno-Enriquez A (2018) Bioeconomics of the Allee effect in fisheries targeting sedentary resources. ICES J. Mar. Sci. 4: 1362-1373.

González-Olivares E, Flores J (2015) Consequences of multiple Allee effect in an open Access fishery model. J. Biol. Syst. 23: S101-S121.

Gullian M (2013) Physiological and immunological condition of the sea cucumber Isostichopus badionotus (Selenka, 1867) during dormancy. J. Exp. Mar. Biol. Ecol. 444: 31-37.

Gullian M, Terrats M (2017) Effect of pH on temperature controlled degradation of reactive oxygen species, heat shock protein expression, and mucosal immunity in the sea cucumber Isostichopus badionotus. Plos One 12(4): e0175812. https://doi.org/10.1371/journal.pone.0175812.

Hamel J, Mercier A (1996) Early development, settlement, growth, and spatial distribution of the sea cucumber Cucumaria frondosa (Echinodermata: Holothuroidea). Can. J. Fisher. Aquat. Sci 53: 253-271.

Hamel J, Mercier A (2008) Precautionary management of Cucumaria frondosa in New-foundland and Labrador, Canada. In Toral-Granda V, Lovatelli A, Vasconcellos M (Eds.) Sea Cucumbers. A Global Review of Fisheries and Trade. Fisheries and Aquaculture Technical Paper 516. FAO. p. 293-306.

Hernández-Flores A, Condal A, Poot-Salazar A, Espinosa-Méndez J (2015) Geostatistical analysis and spatial modeling of population density for the sea cucumbers Isostichopus badionotus and Holothuria floridana on the Yucatan Peninsula, Mexico. Fisher. Res. 172: 114-124.

Hernández-Flores A, Cuevas-Jiménez A, Poot-Salazar A, Condal A, Espinoza-Méndez JC (2018) Bioeconomic modelling for a small-scale sea cucumber fishery in Yucatan, Mexico. PLoS ONE 13(1): e0190857.

Hilborn R, Walters CJ (1992) Quantitative Fisheries Stock Assessment: Choice, Dynamics & Uncertainty. Chapman and Hall. 570 pp.

Hu M, Li L, Sui Y, Li J, Wang Y, Lu W, Dupont S (2015) Effect of pH and temperature on antioxidant responses of the thick Shell mussel Mytilus coruscus. Fish Shellfish Immunol. 46: 573-583.

Koenigstein S, Mark F, Göbling-Reisemann S, Reuter H, Poertner H (2016) Modelling climate change impacts on marine fish populations: process-based integration of ocean warming, acidification and other environmental drivers. Fish Fisher. 17: 972-1004.

Lundquist CJ, Botsford LW (2011) Estimating larval production of a broadcast spawner: the influence of density, aggregation, and the fertilization Allee effect. Can. J. Fisher. Aquat. Sci. 68: 30-42.

Matozzo V, Chinellato A, Munari M, Bressan M, Marin M (2013) Can the combination of decreased pH and increased temperature values induce oxidative stress in the clam Chamelea gallina and the mussel Mytilus galloprovincialis? Mar. Pollut. Bull. 72: 34-40.

Maunder M, Deriso R (2013). A stock-recruitment model for highly fecund species based on temporal and spatial extent of spawning: short communication. Fisher. Res. 146: 96-101.

Menge B, Gouhier T, Freidenburg T, Lubchenco J (2011) Linking long-term, large-scale climatic and environmental variability to patterns of marine invertebrate recruitment: Toward explaining “unexplained” variation. J. Exp. Mar. Biol. Ecol. 400: 236-249.

Morgan A (2008) The effect of food availability on phenotypic plasticity in larvae of the temperate sea cucumber Australostichopus mollis. J. Exp. Mar. Biol. Ecol. 363: 89-95.

Poot-Salazar A, Hernández-Flores A, Ardisson P (2014). Use of the SLW index to calculate growth function in the sea cucumber Isostichopus badionotus. Scient. Rep. 4(5151): 1-7.

Purcell W, Conand C, Uthicke S, Byrne M (2016) Ecological roles of exploited sea cucumbers. Oceanogr. Mar. Biol. 54: 367-386.

Romero-Gallardo S, Velázquez-Abunader J, Rocha J, Garza-Gisholt E (2018) Natural mortality estimates throughout the life history of the sea cucumber Isostichopus badionotus (Holothuroidea: Aspidochirotida). PeerJ 6. 10.7717/peerj.5235.

Seijo J, Defeo O, Salas S (1998) Fisheries Bioeconomics: Theory, Modelling and Management. Fisheries Technical Paper 368. FAO. 108 pp.

Seijo J, Villanueva-Poot R, Charles A (2016) Bioeconomics of ocean acidification effects on fisheries targeting calcifier species: A decision theory approach. Fisher. Res. 176: 1-14.

Smiley S (1986) Metamorphosis of Stichopus californicus (Echinodermata: Holothuroidea) and its phylogenetic implications. Biol. Bull. 171: 611-631.

Storey K (2015) Regulation of hypometabolism: insigths into epigenetic controls. J. Exp. Biol. 218: 150-159.

Stumpp M, Dupont S, Thorndyke M, Melzner F (2011) CO₂ induced seawater acidification impacts sea urchin larval development II: Gene expression patterns in pluteus larvae. Comp. Biochem. Physiol. A 160: 320-330.

Tomanek L, Zuzow M, Ivanina A, Beniash E, Sokolova I (2011) Proteomic response to elevated PCO₂ level in eastern oysters, Crassotrea virginica: evidence for oxidative stress. J. Exp. Biol. 214: 1836-1844.

Villanueva R, Araneda M, Vela M, Seijo JC (2013) Selecting stocking density in different climatic seasons: A decision theory approach to intensive aquaculture. Aquaculture 384-387: 25-34.

Yang H, Zhou Y, Zhang T, Yuan X, Li X, Liu Y, Zhang F (2006) Metabolic characteristic of sea cucumber Apostichopus japonicus (Selenka) during aestivation. J. Exp. Mar. Biol, Ecol. 330: 505-510.

Yuan X, Yang H, Wang L, Zhou Y, Gaber H (2009) Bioenergetic responses of sub-adult sea cucumber Apostichopus japonicus (Selenka) (Echinodermata: Holothuroidea) to temperature with special discussion regarding its southernmost distribution limit in China. J. Therm. Biol. 34: 315-319.

Yuan X, Shao S, Dupont S, Meng L, Liu Y, Wang L (2015) Impact of CO₂-driven acidification on the development of the sea cucumber Apostichopus japonicus (Selenka) (Echinodermata: Holothuroidea). Mar. Pollut. Bull. 95: 195-199.

Zacarías-Soto M, Olvera-Novoa M, Pensamiento-Villarauz S, Sánchez-Tapia I (2013) Spawning and larval development of the four-sided sea cucumber, Isostichopus badionotus (Selenka 1867), under controlled conditions. J. World Aquacult. Soc. 44: 694-705.

Zamora L, Jeffs A (2012) Feeding, metabolism and growth in response to temperature in juveniles of the Australasian sea cucumber, Australostichopus mollis. Aquaculture 358: 92-97.

Author notes

Alternative link

https://www.interciencia.net/wp-content/uploads/2018/10/689-GONZALEZ-43_10.pdf (pdf)