Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

Temporal dynamics of bacterial populations

in recirculating aquaculture systems for sea urchin production

Marisa Garcés1; https://orcid.org/0009-0005-7600-2330

Tamara Rubilar1,2; https://orcid.org/0000-0003-1728-3273

Maximiliano Cledon3 ; https://orcid.org/0000-0002-1757-7274

Cynthia Sequeiros1*; https://orcid.org/0000-0002-7563-8477

1. Centro para el Estudio de Sistemas Marinos (CESIMAR-CCT CONICET-CENPAT). Blvd. Brown 2915, U9120ACD,

Puerto Madryn, Chubut. Argentina; garces@cenpat-conicet.gob.ar, rubilar@cenpat-conicet.gob.ar, sequeiro@cenpat-

conicet.gob.ar (*Correspondence)

2. Laboratorio de Química de Organismos Marinos (LABQUIOM), Instituto Patagónico del Mar (IPAM), Facultad de

Ciencias Naturales y Ciencias de la Salud, Universidad Nacional de la Patagonia San Juan Bosco, Bv. Brown 3051,

9120U, Puerto Madryn, Chubut, Argentina

3. Centro de Investigación aplicada y transferencia tecnológica en recursos Marinos “Almirante Storni” (CIMAS

(CONICET, UnComa)). Güemes 1030, San Antonio Oeste, Argentina; mcledon@gmail.com

Received 02-VII-2023. Corrected 18-X-2023. Accepted 02-XI-2023.

ABSTRACT

Introduction: Sea urchin aquaculture is a rising industry, and in consequence, there is a need to establish optimal

culture parameters to ensure the health of the cultured animals.

Objective: To evaluate the bacterial counts in the seawater of sea urchin (Arbacia dufresnii) aquaculture recir-

culating systems (RAS).

Methods: The bacteriological water quality of two RAS containing sea urchins was determined. For approxi-

mately two months, weekly water samples were taken. The bacteriological quality was determined by counting

total aerobic heterotrophic populations, lactic acid bacteria, enterobacterias and genus Vibrio. Physicochemical

parameters were also measured.

Results: There was no presence of disease or mortality. Enterobacteria and lactic acid bacteria were not detected

from both RAS systems. The number of animals had an important effect on the observed difference in the count

of total bacteria and Vibrio spp. In RAS 1 the maximum counts of total bacteria and Vibrio spp. were 2.8 x 105

± 1.7 x 105 and 1.45 x 105 ± 3.6 x 104 UFC ml-1, respectively. In RAS 2 total bacteria and Vibrio spp. exhibited

repetitive behavior over time influenced in part by water exchange and mainly by feeding. The results indicate

that periodic water changes ensure a limited growth of bacterial strains as Vibrio and other bacteria.

Conclusions: Our results suggests that the bacterial count levels recorded in this study can be used as a threshold

or safety limit for Arbacia dufresnii aquaculture.

Key words: Arbacia dufresnii; culturable bacteria; recirculating system; bacteriological quality; physicochemical

parameters; seawater quality.

RESUMEN

Dinámica temporal de poblaciones bacterianas en sistemas de recirculación

para la producción acuícolas de erizo de mar

Introducción: La acuicultura de erizos de mar es una industria en auge, y en consecuencia, existe la necesidad de

establecer los parámetros de cultivo óptimos para garantizar la salud de los animales en cultivo.

https://doi.org/10.15517/rev.biol.trop..v72iS1.58882

SUPPLEMENT

2Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

INTRODUCTION

Commercial sea urchin aquaculture is a

growing industry in different parts of the world.

One of the main needs of an aquaculture com-

pany is to establish the optimal parameters of

the recirculating aquaculture systems (RAS) to

maintain the farmed animals healthy, prevent-

ing disease outbreaks and overuse of antibiotics

in order to have a successful business. The sea

urchins are constantly exposed to high concen-

trations of bacteria, some of which are harmful,

in addition to the microbiota derived via food.

However, this microbiota also might be crucial

for the host’s biocontrol, keeping it free of dis-

ease (Laport et al., 2018). Regular monitoring in

a recirculation system can help identify changes

in microbial composition and abundance that

may indicate the presence of harmful bacteria

or an imbalance in the ecosystem (de Bruijn

et al., 2018; Hovanec & DeLong, 1996). Bacte-

rial safety thresholds need to be established by

determining a baseline of bacterial composition

and abundance. If such threshold is reached,

measures can be taken to prevent the outbreak

of diseases, such as adjusting water quality

parameters, increasing water change rates, or

adjusting feed regimes (Dahle et al., 2023).

Since several Vibrio bacterial species are known

to infect numerous aquatic species, including

fish, shrimp, and oysters, it is vital to monitor

them in aquaculture (Dahle et al., 2023). Vibrio

species are ubiquitous in marine environments

and can be introduced into aquaculture systems

through water, food, or other sources (Austin

& Zhang, 2006; Thompson et al., 2004). The

thresholds for Vibrio bacteria in different aqua-

culture species vary depending on the species

being raised, the environmental conditions,

and the management practices used in the sys-

tem (Almeida et al., 2021; Rajeev et al., 2021;

Dahle et al., 2023). In general, the thresholds

for Vibrio bacteria in aquaculture systems are

established based on the level of risk associated

with the particular Vibrio species present, the

environmental conditions, and the susceptibil-

ity of the species being raised (Arunkumar et

al., 2020; Culot et al., 2021; Joshi et al., 2014;

Prado et al., 2020). Until today, there has been

no study focused on bacterial counts and Vibrio

counts in sea urchin aquaculture to establish a

threshold for management.

Regular monitoring can also help produc-

ers identify patterns in bacterial communities

over time, and adjust management practices

to maintain optimal bacterial composition and

abundance (Dahle et al., 2023). Studies have

shown that the composition and abundance of

bacterial communities in recirculating aqua-

culture systems can vary over time, and can be

Objetivo: Evaluar los recuentos bacterianos en el agua de cultivo de los sistemas de recirculación acuícola (RAS)

de erizo de mar Arbacia dufresnii.

Métodos: Se determinó la calidad bacteriológica del agua de cultivo de dos RAS que contenían erizos de mar.

Durante aproximadamente dos meses, se tomaron muestras de agua semanalmente. La calidad bacteriológica se

determinó realizando recuento de las poblaciones heterótrofas aerobias totales, bacterias ácido lácticas, entero-

bacterias y bacterias del género Vibrio. También se midieron parámetros fisicoquímicos.

Resultados: No se observaron patologías ni mortalidad. No se detectaron enterobacterias ni bacterias del ácido

láctico en ninguno de los sistemas RAS. El número de animales cultivados tuvo un efecto importante en la dife-

rencia observada en el recuento de bacterias totales y Vibrio spp. En el RAS 1 los recuentos máximos de bacterias

totales y Vibrio spp. fueron 2.8 x 105 ± 1.7 x 105 y 1.45 x 105 ± 3.6 x 104 UFC ml-1, respectivamente. En RAS 2 los

recuentos de bacterias totales y Vibrio spp. exhibieron un comportamiento repetitivo en el tiempo influenciado

en parte por el recambio de agua y principalmente por la alimentación. Los resultados indican que los cambios

periódicos de agua aseguran un crecimiento limitado de cepas bacterianas como Vibrio y otras bacterias.

Conclusiones: Nuestros resultados sugieren que los niveles de recuento bacteriano registrados en este estudio

pueden usarse como umbral o límite de seguridad para la acuicultura de Arbacia dufresnii.

Palabras claves: Arbacia dufresnii; bacterias cultivables; sistemas de recirculación; calidad bacteriológica; paráme-

tros fisicoquímicos; calidad de agua de mar.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

influenced by a range of factors. These factors

include water quality parameters, management

practices, and the presence of aquatic species

(Dahle et al., 2023). Studies have shown that

the abundance of Vibrio bacteria in recircu-

lating aquaculture systems can fluctuate over

time, with peaks in abundance coinciding with

increasing temperature or lowering pH. By

maintaining a healthy and diverse bacterial

community, it is possible to reduce the risk of

disease outbreaks and minimize the need for

antibiotics (Elston et al., 2008; Purgar et al.,

2022). Therefore, the aim of this study was to

evaluate the bacterial counts in the seawater of

A. dufresnii RAS to establish bacterial patterns,

the main factors affecting those patterns, and

safety thresholds.

MATERIALS AND METHODS

Aquaculture system design: Two RAS for

commercial A. dufresnii sea urchin aquaculture

were used in this study (Start Up ERISEA SA,

a technology-based company located in Puerto

Madryn, Chubut, Argentina). Each RAS con-

sisted of one rectangular 3 000 l tank with a

surface of 2 m2 and a depth of 70 cm, mechani-

cal filters, one dark biofilter tank (1 000 l), a

UV light, a chiller, and a water pump. Of the

3 000 l of seawater present in each RAS, 15 %

was changed every 10-14 days. RAS were main-

tained at 15 °C and reared on an 8 h light/16 h

dark cycle. RAS 1 was considered an immature

system since it started without sea urchins and

individuals were incorporated gradually from

day 7 after launching the study (Fig. 1), while

RAS 2 was a mature system, since it had a stable

adult sea urchin population of 1 400 individuals

(test diameter 35–40 mm) for at least 6 months

prior to the study (Fig. 1). Animals were fed 400

mg of formulated feed twice a week, according

to Rubilar et al. (2016). The farm’s staff per-

formed daily visual inspections to assess the

health of the sea urchins.

Analyses of the RAS seawater microbiota:

500 ml water samples of each RAS were collect-

ed at three different points within each 3 000 l

tank (at the ends and the middle) using sterile

glass bottles and stored at 4 °C for 60 minutes

until microbiological analyses were performed.

The interval between one water exchange and

the next was referred to as a “cycle”. Three

moments were identified for each cycle: (M1)

the moment after the exchange of seawater,

(M2) six days after the seawater exchange, and

Fig. 1. Experimental setup of the sea urchin culture in recirculating aquaculture system. Light-grey shading shows each cycle.

The different sampling moments are indicated: (M1) moment 1; (M1) moment 2 and (M3) moment 3.

4Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

(M3) the moment before the fresh exchange.

Seawater samples were collected at those three

times; when it coincided with the feeding day,

the sample was taken before feeding.

The quantification of colony-forming units

(CFU) was carried out by the serial dilution

method, followed by plating on agar medium.

Serial dilutions of such suspensions were pre-

pared and plated on Tryptone Soya agar (TSA,

Britania) to determine total aerobic heterotro-

phic populations, MRS agar for LAB, Violet

Red Bile Dextrose Agar (VRB dextrose-agar,

Biokar, France) for viable enterobacteriaceae,

and Thiosulfate Citrate Bile Salt Sucrose agar

(TCBS, Biokar, France) for genus Vibrio, using

the spread plate method. All culture media were

supplemented with 2.0 % (w/v) NaCl. Incuba-

tion was performed at 18 °C for 48–96 h. The

counts were expressed as CFU ml-1 seawater.

Assessment of seawater quality parame-

ters: Ammonium (NH4+ mg l-1), nitrites (NO2ˉ

mg l-1), nitrates (NO3ˉ mg l-1) and pH were

measured in collected seawater samples using

a multiparameter equipment (Hanna® HI8323),

temperature (T °C) and salinity (Vernier).

Data analysis: Test results were expressed

as the mean ± SE. We performed a non-para-

metric Kruskal-Wallis test to compare median

values of total bacteria and Vibrio spp. counts.

Statistical analyses were carried out with the

SPSS 25 package (Norusis, 1997).

As a first step, a graphical exploration,

based on the scatter and residual plots of the

data, was performed to understand the rela-

tionship between the bacterial counts, physico-

chemical parameters, as well as to explain the

explanatory variables (RAS, seawater exchange,

number of animals, and type of bacteria).

To analyze the effect of seawater exchanges,

number of animals, type of bacteria, and RAS

system (mature and immature) on bacterial

count, a Generalized Linear Model (GLM) was

applied. Different replicates were included in

the model analysis to evaluate method error

and to analyze whether the replicates presented

similar values. Different models, starting with

a null model (a simpler model without any

explanatory variable), were proposed, and their

complexity resulted in a complex triple inter-

action model between the explanatory vari-

ables referred to as RAS, seawater exchange,

number of animals, and type of bacteria. The

best model was selected based on the Akaike

criterion, considering the residual analysis, the

explained variance (deviation), and the prin-

ciple of parsimony or normality (Hurvich et

al., 1998). All GLM analyses were performed

with the free software R Studio version 3.5.1

(R Core Team, 2021).

Animals and welfare of animals: sea

urchin aquaculture welfare protocol was

applied during the entire experiment according

to Crespi-Abril and Rubilar (2023).

RESULTS

RAS seawater microbiota: Through-

out the study, no enterobacteria or lactic acid

bacteria were detected in any of the rearing

seawater samples from both RAS. Instead, dif-

ferent counts of total bacteria and Vibrio spp.

were recorded.

GLM analysis can be interpreted as that

the number of animals had an important effect

on the observed difference (DIF) in the count

of total bacteria and Vibrio spp. in both RAS

(Table 1). The analysis shows that the number

of animals is important regardless of the inter-

active or additive effects; in fact, models 7 (RAS

+ N° Anim) and 8 (RAS * N° Anim) have the

lowest AKAIKE values. In addition, model 4

presented slight differences in AKAIKE value,

however, it is a simpler model, where only the

number of individuals is the important factor

for the bacterial count response. Considering

the parsimony principle, model 4 is the best

explanation for this analysis.

Immature RAS: In RAS 1, at the beginning

of the sampled period without sea urchins, the

total bacterial count in rearing seawater was 7.3

x 102 ± 2.1 x 101 CFU ml-1 of seawater, and no

count was detected for Vibrio spp. (Fig. 2). GLM

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

analyses can be interpreted as that the increase

observed in the total bacteria count (1.2 x 104 ±

6.5 x 102 UFC ml-1) and the presence of Vibrio

spp. (2.8 x 103 ± 6.6 x 102 UFC ml-1) was due to

the incorporation of sea urchin into the system

(Fig. 1, Fig. 2, Table 2). During the experiment,

the maximum counts of total bacteria and

Vibrio spp. were 2.8 x 105± 1.7 x 105 and 1.45

x 105± 3.6 x 104 UFC ml-1, respectively (Fig. 2).

On day 26, a decrease in the total bacteria and

the counts of Vibrio spp. were observed with-

out having performed a water exchange, which

could be related to the decrease in organic mat-

ter since there was no feeding for 4 days.

Mature RAS: RAS 2 had 1 400 sea urchins

during the entire experiment. At the same

period, the concentration of total bacteria and

Vibrio spp. exhibited repetitive behavior over

time. However, the bacterial counts within a

Table 1

Generalized Linear Model analysis for the bacterial count

of the RAS1 and RAS2.

Model for bacterial count DIF Akaike

1. Null model 1.82 3 586.35

2. RAS 1.36 3 585.89

3. Bac 2.75 3 587.28

4. N° Anim 0.12 3 584.65

5. RAS + Bac 2.27 3 586.80

6. RAS * Bac 4.27 3 588.80

7. RAS + N° Anim 0.00 3 584.53

8. RAS * N° Anim 0.00 3 584.53

9. Bac + N° Anim 1.03 3 585.55

10. Bac * N° Anim 2.95 3 587.48

11. RAS + N° Anim + Bac 0.88 3 585.41

12. RAS * N° Anim * Bac 4.80 3 589.33

In bold, the model with the best fit according to the Akaike

information criterion (AIC). Factors were RAS, number of

animals (N° Anim), and type of bacteria (Bac).

Fig. 2. Total bacteria (white bars) and Vibrio spp. (grey bars) counts through time in the RAS 1. The data are presented as

the mean of three repeated measures. Error bars indicate standard errors. The grey arrow indicates seawater exchange. Black

arrow indicates the time of feeding and the thin arrow with the name “sea urchin” shows the date of the introduction of

animals. Light-grey shading shows each cycle.

Table 2

Generalized Linear Model analysis for the bacterial count

of the RAS 1.

Model for bacterial count DIF Akaike

1. Null model 2.893578 1 846.372

2. SW Exchange 4.473507 1 847.952

3. Bac 4.038361 1 847.517

4. N° Anim 0.000000 1 843.478

5. SW Exchange + Bac 5.786445 1 849.265

6. SW Exchange + N° Anim+ Bac 1.889112 1 845.367

7. SW Exchange * N° Anim * Bac 7.325407 1 850.804

In bold, the model with the best fit according to the

Akaike information criterion (AIC). Factors were seawater

exchange (SW Exchange), Type of bacteria (Bac) and

number of animals (N° Anim).

6Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

seawater exchange cycle showed significant dif-

ferences at all moments (M1, M2, and M3) of

each cycle (Fig. 3).

In the cycle 1 (C1), the total bacterial count

for M1 (moment immediately after seawater

change) was 3.73 x 104 ± 2.2 x 103 UFC ml-1

(Fig. 3, Table 3), for M2 the count was sig-

nificantly higher at 1.26 x 105 ± 9.74 x 103 UFC

ml-1 (p < 0.05) (Fig. 3, Table 3), and at the end,

immediately before the median of next water

change (M3), it was lower (3.13 x 104 ± 6.06

x 103 UFC ml-1) respect to M2 ( KW p < 0.05)

(Fig. 3, Table 3). Similarly, the Vibrio spp.

counts were 2.1 x 104 ± 5.35 x 102 UFC ml-1

for M1, 1.04 x 105 ± 2.6 x 104 UFC ml-1 half-

way through (M2), and 7.2 x 102 ± 1.15 x 101

UFC ml-1 for M3. These significant differences

in the counts for the different moments of C1

were also observed for the following cycles (C2

and C3) (Fig. 3, Table 3). Throughout the evalu-

ation period, the bacterial count showed a pat-

tern that was repeated cycle by cycle. Feeding

Table 3

Bacterial count of the RAS 2 at different moments of the cycle.

Cycle Cycle time

M1 M2 M3

Total bacteria count CFU ml-1

C1 3.73 x 104 ± 2.20 x 103a 1.26 x 105 ± 9.74 x 103b 3.13 x 104 ± 6.06 x 103a

C2 5.20 x 104 ± 1.92 x 104a 2.03 x 105 ± 9.30 x 104b 2.48 x 104 ± 6.40 x 103a

C3 6.20 x 103 ± 1.01 x 103a 3.97 x 105 ± 5.24 x 104b 1.54 x 104 ± 3.20 x 103a

Average value 3.18 x 104 ± 8.74 x 103a 2.42 x 105 ± 5.10 x 104b 2.38 x 104 ± 3.50 x 103a

Vibrio spp. count CFU ml-1

C1 2.10 x 104 ± 5.55 x 102b 1.04 x 105 ± 2.60 x 104c 7.20 x 102 ± 1.15 x 101a

C2 1.22 x 104 ± 3.10 x 103a 3.93 x 105 ± 1.20 x 104b 9.20 x 103 ± 2.75 x 103a

C3 9.63 x 104 ± 8.25 x 102a 1.95 x 105 ± 2.30 x 104b 8.23 x 103 ± 1.30 x 103a

Average value 1.40 x 104 ± 1.96 x 103a 2.31 x 105 ± 4.40 x 104b 6.04 x 103 ± 1.60 x 103a

The data are presented as the mean of three repeated measures. Different lowercase letters indicate significant differences

between the mean values of the bacterial counts among the 3 moments of each cycle.

Fig. 3. Bacterial counts through time in the RAS 2 seawater. Total bacteria (white bars) and Vibrio spp. (grey bars) counts.

Data are presented as the mean of three repeated measures. Error bars indicate standard errors. The grey arrow indicates

seawater exchange. Black Arrow indicates the date of feeding. Light-grey shading shows each cycle.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

had a significant effect on the total bacteria

and Vibrio count, this is evidenced in the count

that was carried out the day after feeding (M2),

since the count increased with respect to the

previous one (M1) (Table 3). However, when

sampling was performed on M3 at each cycle,

the bacterial count decreased with respect to

the previous count (M2).

An average value for the total bacteria

count (3.18 x 104 ± 8.74 x 103 UFC ml-1) and

another for the Vibrio spp. count (1.40 x 104

± 1.96 x 103 UFC ml-1) were obtained for M1,

and were taken as basic reference values for this

cycle moment. In the same way, average values

were obtained for M2 (2.42 x 105 ± 5.1 x 104

and 2.31 x 105 ± 4.4 x 104 UFC ml-1, for total

bacteria and Vibrio spp., respectively) and M3

(2.38 x 104 ± 3.5 x 103 and 6.04 x 103 ± 1.6 x 103

UFC ml-1; respectively).

GLM analysis showed that the null model

presented a lower AKAIKE value, however,

water exchange also presented a low value,

indicating that it may not be the main factor but

that it influences the bacterial count (Table 4).

Seawater quality parameters: Physico-

chemical parameters in the seawater affect-

ed the bacterial count in both RAS. GLM

results showed that in RAS 1, the interaction

between water exchange and ammonium and

nitrite were the most important parameters

that explained the bacterial count (Table 5).

Instead, in RAS 2, water exchange along with

nitrate were the most important parameters

that explained the bacterial count; however, it is

not clear if the effects are additive or if there is

an interaction between them (Table 6).

Table 6

Generalized Linear Model analysis for bacterial count in the RAS 1.

Model for bacterial count DIF Akaike

1. Null model 260.60 1 734.66

2. SW Exchange 261.46 1 735.52

3. SW Exchange + Bac 263.09 1 737.14

4. SW Exchange + Nitrate + Bac 97.31 1 571.37

5. SW Exchange + Amm + Bac 111.81 1 585.87

6. SW Exchange * Amm * Bac 104.87 1 578.06

7. SW Exchange * Nitrate * Bac 85.72 1 559.77

8. SW Exchange + Amm + Nitrate + Bac 89.17 1 563.23

9. SW Exchange * Amm * Nitrate * Bac 0.00 1 474.05

In bold, the model with the best fit according to the Akaike information criterion (AIC). Factors were seawater exchange (SW

Exchange), Type of bacteria (Bac), Ammonium, (Amm), Nitrite and Nitrate.

Table 4

Generalized Linear Model analysis for bacterial count in

the RAS 2.

Model for bacterial count DIF Akaike

1. Null model 0.00 1 734.66

2. Bac 1.63 1 736.29

3. SW Exchange 0.86 1 735.52

4. SW Exchange + Bac 2.48 1 737.15

5. SW Exchange * Bac 2.60 1 737.26

In bold, the model with the best fit according to the Akaike

information criterion (AIC). Factors Types of bacteria (Bac)

and seawater exchange (SW Exchange).

Table 5

Generalized Linear Model analysis for bacterial count in

the RAS 1.

Model for bacterial count DIF Akaike

1. Null model 162.94 1 846.37

2. SW Exchange 164.52 1 847.95

3. Ammonium 15.33 1 698.77

4. Nitrite 7.33 1 690.77

5. Nitrate 11.92 1 695.36

6. SW Exchange + Amm + Nitrite 8.185 1 691.62

7. SW Exchange * Amm * Nitrite 0.00 1 683.43

8. SW Exchange + Amm + Nitrate 15.07 1 698.50

9. SW Exchange * Amm * Nitrate 21.13 1 704.56

In bold, the model with the best fit according to the

Akaike information criterion (AIC). Factors were seawater

exchange (SW Exchange), Ammonium (Amm), Nitrite and

Nitrate.

8Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

Throughout the present study no changes

in the behavior or general condition of the sea

urchins were observed. There was no evidence

of disease or mortality.

DISCUSSION

This is the first study conducted on sea

urchin aquaculture, measuring bacteria and

their fluctuation in recirculation systems. Sea

urchin aquaculture is a growing industry, and

as a relatively new industry (Rubilar & Cardozo,

2021), it is important to establish effective and

sustainable management plans from the begin-

ning. In this regard, measuring bacteria and

their fluctuation in recirculation systems can

provide valuable information for the develop-

ment of management plans that allow ani-

mal health and welfare while avoiding the use

of chemical treatments and antibiotics in sea

urchin production. While there are studies on

disease treatments in sea urchins (Wang et al.,

2013), these include formalin baths, benzalko-

nium chloride, and oxytetracycline; with the

use of oxytetracycline being the most effective

in controlling the disease. However, it is impor-

tant to mention that the use of antibiotics and

other chemical treatments in aquaculture can

increase bacterial resistance and produce nega-

tive effects on the intestinal microbiota, which

in turn can have negative consequences on

their health and growth, the environment, and

human health. Therefore, it is best to take pre-

ventive actions (Aly & Albutti., 2014; Chen et

al., 2020; Vignesh et al. 2011; Watts et al., 2017).

According to our study, the presence of

Vibrio spp. and other bacteria in water recir-

culation systems is related to the number of

animals. In RAS 1, before the individuals were

introduced, no bacteria of the genus Vibrio

were detected in the culture water, this would

indicate that the seawater treatment using UV

light and filters (biological and physical ) that

is carried out before introducing them to the

RAS is effective and that the vibrio spp. are

associated with sea urchins. This finding is

in line with previous studies that have shown

that the introduction of aquatic organisms in

recirculation systems increased the presence of

bacteria in the water (Blancheton et al., 2013;

Rurangwa & Verdegem, 2015; Sharrer et al.,

2005). Laport et al. (2018) isolated bacteria

belonging to genus Vibrio from the gastroin-

testinal tracts of the sea urchins. In addition,

Hakim et al. (2015) showed that the gut digesta

and egested fecal pellets of sea urchins had a

high abundance of class Gammaproteobacteria,

of which Vibrio was found to be the primary

genus. Furthermore, the production of biofilm

by Vibrio spp. may actually be a survival tactic

(Grimes, 2020; Wai et al., 1999) because cells

can utilize nutrients absorbed into the biofilm

matrix more effectively (Sampaio et al., 2022).

Although different culture conditions can

yield varied bacterial counts (Haditomo et al.,

2021), our findings demonstrate that the imple-

mented water exchanges effectively maintained

bacterial counts at levels that did not adverse-

ly impact the A. dufresnii sea urchin. These

counts were comparable to those achieved in

non-recirculating systems that require daily

water exchanges. Importantly, our study has

the advantage of being conducted without the

use of antibiotics, distinguishing it from the

research by Dang, Song et al. (2006) and Dang,

Zhang et al. (2006).

The water exchanges in RAS 1 and 2 helped

limit bacterial growth in the recirculation sys-

tems with established bacterial cycles. The

nutrients that are available to bacteria could be

diluted by these water exchanges, which would

restrict their ability to proliferate. These results

are consistent with previous studies that have

demonstrated that water exchanges can influ-

ence the composition and abundance of the

bacterial community in recirculation systems

(Blancheton et al., 2013; Cardona et al., 2016;

Eck et al., 2019).

Considering that the bacterial count

increased the day following feeding compared

to the prior counts, the feed clearly had an

impact on the bacterial count. Additionally,

the opposite result was observed when feeding

was done four days prior to the count, which

was reduced in comparison to the preceding

counts. This is in accordance with preview

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

works showing that the number of bacteria can

be affected by a number of factors, such as the

organic matter load of food (Alfiansah et al.,

2018; García-Mendoza et al., 2019; Heenatigala

& Fernando, 2016; Leonard et al., 2002; Qin et

al., 2016; Vasile et al., 2017)

Sea urchins are known to be susceptible to

poor water quality, especially when ammonia

levels are high (Basuyaux & Mathieu, 1999;

Lawrence et al., 2003; Rubilar & Crespi-Abril,

2017; Siikavuopio et al., 2004). Although some

of the physicochemical parameters in this

investigation were higher, it did not affect the

welfare of the animals. In addition, it must be

taken into account that they were commercial

crops on a large scale and not small volumes

like the aforementioned farming systems.

Despite fluctuations in the number of bac-

teria present in the recirculation systems, we

did not find values indicating a significant risk

of disease in the animal densities raised in our

study. This result suggests that the bacterial

count levels recorded in this study can be used

as a threshold or safety limit for Arbacia dufres-

nii aquaculture. By monitoring bacteria levels

within the ranges obtained in this study, it will

be feasible to prevent the occurrence of diseases

without resorting to antibiotics.

In conclusion, this study highlights the

importance of measuring bacteria and their

fluctuation in sea urchin aquaculture recircu-

lation systems to establish effective and sus-

tainable management plans. In addition, it

would also allow designing strategies to maxi-

mize recirculation and plan the rate of water

exchange. By monitoring bacteria levels and

physicochemical parameters, it is possible to

maintain the sea urchin culture in healthy con-

ditions to prevent diseases without resorting

to antibiotics and other chemical treatments.

This approach can lead to a more responsible

and environmentally friendly sea urchin aqua-

culture industry, and can also provide valuable

insights for the development of aquaculture in

other species.

Ethical statement: the authors declare that

they all agree with this publication and made

significant contributions; that there is no con-

flict of interest of any kind; and that we fol-

lowed all pertinent ethical and legal procedures

and requirements. All financial sources are fully

and clearly stated in the acknowledgments sec-

tion. A signed document has been filed in the

journal archives.

ACKNOWLEDGMENTS

This research was supported by the Con-

sejo Nacional de Investigaciones Científicas y

Técnicas PDTS Acuicultura de Erizos de Mar

con Fines Biotecnológicos 786/20. M. E. Garcés

would like to thank Consejo Nacional de Inves-

tigaciones Científicas y Técnicas (CONICET,

Argentina) for her postdoctoral fellowship.

REFERENCES

Alfiansah, Y. R., Hassenrück, C., Kunzmann, A., Taslihan,

A., Harder, J., & Gärdes, A. (2018). Bacterial abundan-

ce and community composition in pond water from

shrimp aquaculture systems with different stocking

densities. Frontiers in Microbiology, 9, 2457. https://

doi.org/10.3389/fmicb.2018.02457

Almeida, D. B., Magalhães, C., Sousa, Z., Borges, M. T.,

Silva, E., Blanquet, I., & Mucha, A. P. (2021). Micro-

bial community dynamics in a hatchery recirculating

aquaculture system (RAS) of sole (Solea senegalensis).

Aquaculture, 539, 736592. https://doi.org/10.1016/j.

aquaculture.2021.736592

Aly, S. M., & Albutti, A. (2014). Antimicrobials used in

aquaculture and their public health impact.Journal of

Aquaculture Research & Development,5(4), 1. https://

doi.org/10.4172/2155-9546.1000247

Austin, B., & Zhang, X. H. (2006). Vibrio harveyi: a signi-

ficant pathogen of marine vertebrates and invertebra-

tes. Letters in Applied Microbiology, 43(2), 119–124.

https://doi.org/10.1111/j.1472-765X.2006.01989.x

Arunkumar, M., Lewis-Oscar, F., Thajuddin, N., Puga-

zhendhi, A., & Nithya, C. (2020). In vitro and in

vivo biofilm forming Vibrio spp: a significant threat

in aquaculture. Process Biochemistry, 94, 213–223.

https://doi.org/10.1016/j.procbio.2020.04.029

Basuyaux, O, & Mathieu, M. (1999). Inorganic nitro-

gen and its effect on growth of the abalone

Haliotis tuberculata Linneaus and the sea urchin

Paracentrotus lividus Lamarck. Aquaculture Inter-

national, 174(1–2), 95–107. https://doi.org/10.1016/

S0044-8486(98)00510-9

10 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

Blancheton, J. P., Attramadal, K. J. K., Michaud, L.,

d’Orbcastel, E. R., & Vadstein, O. (2013). Insight into

bacterial population in aquaculture systems and its

implication. Aquacultural Engineering, 53, 30–39.

https://doi.org/10.1016/j.aquaeng.2012.11.009

Cardona, E., Gueguen, Y., Magré, K., Lorgeoux, B., Pique-

mal, D., Pierrat, F., Noguier, F., & Saulnier, D. (2016).

Bacterial community characterization of water and

intestine of the shrimp Litopenaeus stylirostris in a

biofloc system. BMC Microbiology, 16, 157. https://

doi.org/10.1186/s12866-016-0770-z

Chen, J., Sun, R., Pan, C., Sun, Y., Mai, B., & Li, Q. X. (2020).

Antibiotics and food safety in aquaculture. Journal

of Agricultural and Food Chemistry, 68(43), 11908–

11919. https://doi.org/10.1021/acs.jafc.0c03996

Crespi-Abril, A. C., & Rubilar, T. (2023). Ethical Con-

siderations for Echinoderms: New Initiatives in

Welfare. Preprints. https://doi.org/10.20944/pre-

prints202310.0447.v1

Culot, A., Grosset, N., Bruey, Q., Auzou, M., Giard, J.

C., Favard, B., Wakatsuki, A., Baron, S., Frouel, S.,

Techer, C. & Gautier, M. (2021). Isolation of Harveyi

clade Vibrio spp. collected in aquaculture farms: How

can the identification issue be addressed? Journal of

Microbiological Methods, 180, 106106. https://doi.

org/10.1016/j.mimet.2020.106106

Dahle, S. W., Gaarden, S. I., Buhaug, J. F., Netzer, R., Attra-

madal, K. J., Busche, T., Aas, M., Ribicic, D. & Bakke,

I. (2023). Long-term microbial community structures

and dynamics in a commercial RAS during seven pro-

duction batches of Atlantic salmon fry (Salmo salar).

Aquaculture, 565, 739155. https://doi.org/10.1016/j.

aquaculture.2022.739155

Dang, H., Song, L., Chen, M., & Chang, Y. (2006). Con-

currence of cat and tet genes in multiple antibio-

tic-resistant bacteria isolated from a sea cucumber

and sea urchin mariculture farm in China. Micro-

bial Ecology, 52, 634–643. https://doi.org/10.1007/

s00248-006-9091-3

Dang, H., Zhang, X., Song, L., Chang, Y., & Yang, G.

(2006). Molecular characterizations of oxytetra-

cycline resistant bacteria and their resistance genes

from mariculture waters of China. Marine Pollution

Bulletin, 52(11), 1494–1503. https://doi.org/10.1016/j.

marpolbul.2006.05.011

de Bruijn, I., Liu, Y., Wiegertjes, G. F., & Raaijmakers, J. M.

(2018). Exploring fish microbial communities to miti-

gate emerging diseases in aquaculture. FEMS Micro-

biology Ecology, 94(1), fix161. https://doi.org/10.1093/

femsec/fix161

Eck, M., Sare, A. R., Massart, S., Schmautz, Z., Junge, R.,

Smits, T. H., & Jijakli, M. H. (2019). Exploring bacte-

rial communities in aquaponic systems. Water, 1 1(2),

260. https://doi.org/10.3390/w11020260

Elston, R. A., Hasegawa, H., Humphrey, K. L., Polyak, I.

K., & Häse, C. C. (2008). Re-emergence of Vibrio

tubiashii in bivalve shellfish aquaculture: severity,

environmental drivers, geographic extent and mana-

gement. Diseases of aquatic organisms, 82(2), 119–

134. https://doi.org/10.3354/dao01982

García-Mendoza, M. E., Cáceres-Martínez, J., Vásquez-

Yeomans, R., & Cruz-Flores, R. (2019). Bacteriologi-

cal water quality of recirculating aquatic systems for

maintenance of yellowtail amberjack Seriola lalandi.

Journal of the World Aquaculture Society, 50(5), 934–

953. https://doi.org/10.1111/jwas.12620

Grimes, D. J. (2020). The vibrios: scavengers, symbionts, and

pathogens from the sea. Microbial Ecology, 80(3), 501–

506. https://doi.org/10.1007/s00248-020-01524-7

Haditomo, A. H. C., Yonezawa, M., Yu, J., Mino, S., Sakai,

Y., & Sawabe, T. (2021). The structure and function of

gut microbiomes of two species of sea urchins, Meso-

centrotus nudus and Strongylocentrotus intermedius, in

Japan. Frontiers in Marine Science, 8, 1895. https://doi.

org/10.3389/fmars.2021.802754

Hakim, J. A., Koo, H., Dennis, L. N., Kumar, R., Ptacek,

T., Morrow, C. D., Lefkowitz, E. J., Powell, M. L., Bej,

A. K., & Watts, S. A. (2015). An abundance of Epsi-

lonproteobacteria revealed in the gut microbiome of

the laboratory cultured sea urchin, Lytechinus varie-

gatus. Frontiers in Microbiology, 6, 1047. https://doi.

org/10.3389/fmicb.2015.01047

Heenatigala, P. P. M., & Fernando, M. U. L. (2016). Occu-

rrence of bacteria species responsible for vibriosis

in shrimp pond culture systems in Sri Lanka and

assessment of the suitable control measures. Sri Lanka

Journal of Aquatic Sciences, 21(1), 1–17. https://doi.

org/10.4038/sljas.v21i1.7481

Hovanec, T. A., & DeLong, E. F. (1996). Comparative

analysis of nitrifying bacteria associated with fres-

hwater and marine aquaria. Applied and Environ-

mental Microbiology, 62(8), 2888–2896. https://doi.

org/10.1128/aem.62.8.2888-2896.1996

Hurvich, C. M., Simonoff, J. S., & Tsai, C. L. (1998).

Smoothing parameter selection in nonparametric

regression using an improved Akaike information

criterion. Journal of the Royal Statistical Society: Series

B (Statistical Methodology), 60(2), 271–293. https://

doi.org/10.1111/1467-9868.00125

Joshi, J., Srisala, J., Truong, V. H., Chen, I. T., Nuangsaeng,

B., Suthienkul, O., Lo, C. H., Flegel, T. W., Sritun-

yalucksana, K., & Thitamadee, S. (2014). Variation

in Vibrio parahaemolyticus isolates from a single

Thai shrimp farm experiencing an outbreak of acute

hepatopancreatic necrosis disease (AHPND). Aqua-

culture, 428, 297–302. https://doi.org/10.1016/j.

aquaculture.2014.03.030

Laport, M. S., Bauwens, M., Collard, M., & George, I.

(2018). Phylogeny and antagonistic activities of

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72(S1): e58882, marzo 2024 (Publicado Mar. 01, 2024)

culturable bacteria associated with the gut microbiota

of the sea urchin (Paracentrotus lividus). Current

Microbiology, 75, 359–367. https://doi.org/10.1007/

s00284-017-1389-5

Lawrence, J. M., McBride, S. C., Plank, L. R., & Shpigel, M.

(2003). Ammonia tolerance of the sea urchins Lytechi-

nus variegatus, Arbacia punctulata, Strongylocentrotus

franciscanus, and Paracentrotus lividus. In J. P. Féral,

& B. David (Eds.), Echinoderm Research 2001 (pp.

233–236). A.A. Balkema.

Leonard, N., Guiraud, J. P., Gasset, E., Cailleres, J. P., & Blan-

cheton, J. P. (2002). Bacteria and nutrients—nitrogen

and carbon—in a recirculating system for sea bass

production. Aquacultural Engineering, 26(2), 111–

127. https://doi.org/10.1016/S0144-8609(02)00008-0

Norusis, M. J. (1997) SPSS advanced statistics 7.5 [Compu-

ter software]. SPSS.

Prado, P., Carrasco, N., Catanese, G., Grau, A., Cabanes, P.,

Carella, F., García-March, J. R., Tena, J., Roque, A.,

Bertomeu, E., Gras, N., Caiola, N., Furones, M. D., &

Andree, K. B. (2020). Presence of Vibrio mediterranei

associated to major mortality in stabled individuals of

Pinna nobilis L.Aquaculture,519, 734899. https://doi.

org/10.1016/j.aquaculture.2019.734899

Purgar, M., Kapetanović, D., Geček, S., Marn, N., Haberle,

I., Hackenberger, B. K., Gavrilovic, A., Pečar Ilić, J.,

Hackenberger, D. K., Djerd, T., Caleta, B., & Klanj-

scek, T. (2022). Investigating the Ability of Growth

Models to Predict In Situ Vibrio spp. Abundances.

Microorganisms, 10(9), 1765. https://doi.org/10.3390/

microorganisms10091765

Qin, Y., Hou, J., Deng, M., Liu, Q., Wu, C., Ji, Y., & He, X.

(2016). Bacteria abundance and diversity in pond

water supplied with different feeds. Scientific Reports,

6(35232), 1–13. https://doi.org/10.1038/srep35232

R Core Team (2021). R: A language and environment for

statistical computing [Computer software]. R Foun-

dation for Statistical Computing, Vienna, Austria.

http://www.r-project.org/index.html

Rajeev, R., Adithya, K. K., Kiran, G. S., & Selvin, J. (2021).

Healthy microbiome: a key to successful and sustai-

nable shrimp aquaculture. Reviews in Aquaculture,

13(1), 238–258. https://doi.org/10.1111/raq.12471

Rubilar, T., Epherra, L., Deias-Spreng, J., De Vivar, M. E. D.,

Avaro, M., Lawrence, A. L., & Lawrence, J. M. (2016).

Ingestion, absorption and assimilation efficiencies,

and production in the sea urchin Arbacia dufresnii fed

a formulated feed. Journal of Shellfish Research, 35(4),

1083–1093. https://doi.org/10.2983/035.035.0431

Rubilar, T., & Crespi-Abril, A. (2017). Does echino-

derm research deserve an ethical consideration?

Revista de Biología Tropical, 65(S1), S11–S22. https://

doi.org/10.15517/rbt.v65i1-1.31662

Rubilar, T., & Cardozo, D. (2021). Blue Growth: Sea Urchin

Sustainable Aquaculture, Innovative Approaches.

Revista de Biología Tropical, 69(S1), 474–486. http://

dx.doi.org/10.15517/rbt.v69isuppl.1.46388

Rurangwa, E., & Verdegem, M. C. (2015). Microorga-

nisms in recirculating aquaculture systems and their

management. Reviews in Aquaculture, 7(2), 117–130.

https://doi.org/10.1111/raq.12057

Sampaio, A., Silva, V., Poeta, P., & Aonofriesei, F. (2022).

Vibrio spp.: Life strategies, ecology, and risks in a

changing environment. Diversity, 14(2), 97. https://

doi.org/10.3390/d14020097

Sharrer, M. J., Summerfelt, S. T., Bullock, G. L., Gleason, L.

E., & Taeuber, J. (2005). Inactivation of bacteria using

ultraviolet irradiation in a recirculating salmonid

culture system. Aquacultural Engineering 33, 135–149.

https://doi.org/10.1016/j.aquaeng.2004.12.001

Siikavuopio, S. I., Dale, T., Foss, A., & Mortensen, A. (2004).

Effects of chronic ammonia exposure on gonad

growth and survival in green sea urchin Strongylocen-

trotus droebachiensis. Aquaculture, 242(1–4), 313–320.

https://doi.org/10.1016/j.aquaculture.2004.08.042

Thompson, F. L., Iida, T., & Swings, J. (2004). Biodiver-

sity of vibrios. Microbiology and Molecular Biolo-

gy Reviews, 68(3), 403–431. https://doi.org/10.1128/

mmbr.68.3.403-431.2004

Vasile, M. A., Metaxa, I., Plăcintă, S., Mogodan, A., Petrea,

Ş. M., & Platon, C. (2017). Preliminary study on

bacteriological and physicochemical water profile of

cyprinid fish ponds. Aquaculture, Aquarium, Conser-

vation & Legislation, 10(1), 103–112.

Vignesh, R., Karthikeyan, B. S., Periyasamy, N., & Deva-

nathan, K. (2011). Antibiotics in aquaculture: an

overview. South Asian Journal of Experimental Biolo-

gy, 1(3), 114–120. https://doi.org/10.38150/sajeb.1(3).

p114-120

Wai, S. N., Mizunoe, Y., & Yoshida, S. I. (1999). How

Vibrio cholerae survive during starvation. FEMS

Microbiology Letters, 180(2), 123–131. https://doi.

org/10.1111/j.1574-6968.1999.tb08786.x

Wang, Y. N., Chang, Y. Q., & Lawrence, J. M. (2013).

Disease in sea urchins. In J. M. Lawrence (Ed.), Deve-

lopments in Aquaculture and Fisheries Science (Vol.

38, pp. 179–186). Elsevier. https://doi.org/10.1016/

B978-0-12-396491-5.00012-5

Watts, J. E., Schreier, H. J., Lanska, L., & Hale, M. S. (2017).

The rising tide of antimicrobial resistance in aqua-

culture: sources, sinks and solutions. Marine Drugs,

15(6), 158. https://doi.org/10.3390/md15060158