Banner
Current IssueEditorial BoardOnline First ArticlesArchive
Current IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Antibiotic Resistance of Pathogenic and Bacterial Contaminants Isolated from Fish Pond Wastewater in Bechar Province, Southwestern Algeria

E
Elhassan Benyagoub1,2,*
https://orcid.org/0000-0002-2276-471X
A
Afaf Radi2
A
Ahlam Benachour2
M
Manal Boutayeb2
1Architecture and Environmental Heritage Laboratory (Archipel), Mohammed Tahri University of Bechar (08000), Bechar, Algeria.
2Department of Biology, Faculty of Natural and Life Sciences, Mohammed Tahri University of Bechar (08000), Bechar, Algeria.

ABSTRACT

Background: This study investigates bacterial contaminants and pathogenic species isolated from fish pond wastewater (FPWW) in Bechar province, Southwestern Algeria and assesses their antibiotic resistance propfile.

Methods: A total of 21 FPWW samples were analyzed using different culture media to isolate bacterial contaminants via the membrane filter technique. Following purification, all bacterial isolates were identified through biochemical tests and subjected to susceptibility testing by the Kirby-Bauer disc diffusion method on Mueller-Hinton (MH) agar and the broth microdilution technique. Antimicrobial residue detection was also carried out using the microbiological tube test method (MTT).

Result: We successfully isolated 68 bacterial isolates, classified into six bacterial groups: non-Enterobacteriaceae (41.18%), Enterobacteriaceae (38.24%), Streptococcaceae (8.82%), Pseudomonadaceae (7.35%), Erwiniaceae (2.94%) and Neisseriaceae (1.47%). According to the Chi-Square Goodness-of-Fit Test, this distribution was highly significant (p < 0.001). The isolated species included Aeromonas hydrophila hydrophila, Streptococcus spp., Enterobacter spp., Serratia spp., Escherichia coli, Citrobacter spp., Klebsiella spp., Plesiomonas shigelloides, Kluyvera spp., Pseudomonas aeruginosa, Pantoea spp., Chromobacterium violaceum, Salmonella choleraesuis and Vibrio spp. Susceptibility testing of the bacterial contaminants revealed variable responses among the strains, with increased resistance to β-lactam antibiotics. Multidrug resistance results showed that 17 strains (25%) exhibited resistance to three to six different antibiotic classes, with Multiple Antibiotic Resistance (MAR) indices ranging from 0.28 to 0.68. The variability in MAR indices among bacterial groups was statistically significant (ANOVA, p = 0.0498). Furthermore, no growth-inhibiting substances were detected in the wastewater samples using the MTT method.

INTRODUCTION

Aquaculture, the practice of farming fish and other aquatic organisms, is a rapidly growing industry that significantly contributes to global food security and is among the fastest-growing food production sectors (FAO, 2021). With the rising global population, increasing income levels and growing urbanization, aquaculture is expected to surpass traditional fishing by 2030. However, its environmental footprint varies depending on the species being farmed, management practices, location and local conditions (Cretu et al., 2016). One of the major environmental issues in aquaculture is the deterioration of water quality. This degradation can be caused by factors like leftover feed, animal waste and the use of chemical treatments (Nabbou and Benyagoub, 2025). These pollutants negatively impact the water environment, influencing various components such as the water’s physicochemical characteristics, sediment composition, plankton and bottom-dwelling organisms (Naylor et al., 2000; Wang et al., 2010). Contamination of both freshwater and saline water by pathogenic bacteria presents a threat to aquatic ecosystems and public health through the food chain (Pandey et al., 2014).
       
The overuse of antimicrobial agents in aquaculture further exacerbates this issue by promoting the emergence of antibiotic-resistant bacteria (Abedin et al., 2020).
       
Despite growing concern, data on bacterial contamination and antibiotic resistance in aquaculture wastewater remain limited, especially in arid regions like Southwestern Algeria.
       
This study addresses this gap by identifying bacterial contaminants and pathogenic species in wastewater from three fish farms in Bechar and evaluating their antibiotic resistance profiles. To our knowledge, this is the first study in this region to link aquaculture wastewater contaminants with antibiotic resistance patterns, providing insights critical for environmental monitoring and public health management.

MATERIALS AND METHODS

All experiments were conducted at the University of Bechar (Algeria), over a period of eight months, from January to August 2024.
 
Study area
 
This study focused on two fish farms located in Boukais and Taghit and Nif Rhaa-Ouakda, which are situated 50km and 90 km and 15 km, respectively, from the center of Bechar (Fig 1).

Fig 1: Map showing the geographical locations of Boukais (yellow), Taghit (orange), Bechar city center (blue) and the entire Bechar province (red) in Southwestern Algeria (GADM, 2024).


       
These farms are dedicated to cultivating tilapia species in concrete and plastic ponds (Fig 2).

Fig 2: Fish ponds in Bechar province (Southwestern Algeria) (Original, 2024)-Photo by E. Benyagoub.


 
Sampling conditions
 
Fish pond wastewater (FPWW) samples were collected in sterile glass flasks, following ISO 5667-1 (2023). The frequency and dates of wastewater sampling are provided in Table 1.

Table 1: FPWW sampling frequency.


 
Bacterial isolation
 
The bacterial contaminants and pathogenic bacterial species, such as Enterobacteriaceae, Pseudomonadaceae and Streptococcaceae, were isolated using the membrane filter technique on MacConkey agar, Cetrimide agar and Salmonella-Shigella agar by filtering 250 mL of fish pond wastewater, as described by Benyagoub et al. (2018), Lipps et al. (2022) and Benyagoub (2023a, 2023b).
       
The isolates were identified following the standard microbiological procedures outlined by Tille (2018).
 
Biofilm formation
 
In this study, biofilm formation by Pseudomonas spp. strains -recognized as opportunistic pathogens-was assessed using the tube method, as described by Christensen et al. (1985) and Kirmusaoglu (2019). This qualitative assay detects biofilm-producing microorganisms based on the formation of a visible film lining the inner surface of a polystyrene test tube containing tryptic soy broth (TSB) medium inoculated with the test isolate and incubated under appropriate conditions.
 
Detection of antimicrobial residues in fish pond wastewater samples
 
Detection of antimicrobial residues were carried out using the microbiological tube test method (MTT), a qualitative technique, as outlined by Akshaya et al. (2023). Nutrient broth containing the pH indicator bromocresol purple was prepared and an equal volume of fish pond wastewater and bacterial suspension (v/v) was added. The reference bacterial strains tested were Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Staphylococcus aureus ATCC 25923 and Bacillus cereus ATCC 14579. The tubes were then incubated at 37oC for 18-24 hours. A positive reaction was indicated by tubes that remained purple, as compared to the positive and negative controls.
 
Antibiotic susceptibility testing (AST)
 
The antibiotic resistance profile of bacterial contaminants, including members of Enterobacteriaceae, Pseudomona-daceae and Streptococcaceae, were determined using the Kirby-Bauer disc diffusion method, following the protocols outlined by Kirby et al. (1956), Bauer et al. (1959), Benyagoub et al. (2020a, 2020b), Benyagoub (2022) and Benyagoub (2024). For non-Enterobacteriaceae strains, resistance profiles were assessed using the broth microdilution technique, with triphenyltetrazolium chloride (TTC) serving as a viability indicator (Lee et al., 2007; Seghir et al., 2023). Inhibition zones (mm) on agar plates and MIC values on microplates were measured and categorized as susceptible (S) or resistant (R), according to the Clinical and Laboratory Standards Institute (CLSI, 2020) guidelines. Species-specific breakpoints for Aeromonas spp. were applied where available.
       
All isolated strains were tested for antibiotic susceptibility. In this study, the following antibiotics were tested against the bacterial contaminants: Gentamicin (CN 10 µg), oxacillin (OX 1 µg), cefoxitin (CX 30 µg), chloramphenicol (C 30 µg), sulfamethoxazole-trimethoprim (SXT 25 µg), erythromycin (E 15 µg), ciprofloxacin (CIP 5 µg), ampicillin (AMP 10 µg), ofloxacin (OF 5 µg), amoxicillin-clavulanic acid (AUG 30 µg), amikacin (AK 30 µg), amoxicillin (AML 2 µg), tetracycline (TE 30 µg), trimethoprim (TM 2,5 µg), aztreonam (ATM 30 µg), cefotaxime (CTX 30 µg), ceftazidime (CAZ 10 µg), piperacillin (PRL 100 µg), ticarcillin-clavulanic acid (TTC 85 µg), fosfomycin (FOS 50 µg), nalidixic acid (NA 30 µg), cefazolin (CZ 30 µg), tobramycin (TOB 10 µg), vancomycin (VA 30 µg), azithromycin (AZM 15 µg) and imipenem (IMI 10 µg).
       
Multi-drug resistant (MDR) isolates were defined as those exhibiting resistance to more than three antimicrobial classes tested (Benyagoub et al., 2021a).
 
Calculation of the MAR index
 
The MAR index was determined using the formula below (Benyagoub et al., 2021a; Benyagoub, 2024):


Where,
MAR = Multiple antibiotic resistance.
a = Count of antibiotics to which the bacterium shows resistant.
b = Total number of antibiotics that were tested.
 
Statistical analysis
 
Statistical analyses were conducted using IBM SPSS Statistics (version 25, IBM Corp., Armonk, NY, USA) to evaluate differences in the distribution and antibiotic resistance profiles of bacterial groups isolated from fish pond wastewater. A Chi-Square Goodness-of-Fit Test was used to assess the distribution of bacterial groups, while a one-way analysis of variance (ANOVA) was performed to compare the Multiple Antibiotic Resistance (MAR) index among the different groups. A significance level of p<0.05 was considered statistically significant.

RESULTS AND DISCUSSION

Bacterial isolation

The bacterial species isolated from wastewater of the fish ponds are categorized based on their Gram stain characteristics (Fig 3) and bacterial groups (Fig 4).

Fig 3: Distribution of isolated bacteria according to Gram type (own elaboration).



Fig 4: Distribution of bacterial isolates by bacterial group (own elaboration).


       
The results of the distribution are consistent with previous findings (Sule et al., 2016; Adebami et al., 2020), which also reported a dominance of Gram-negative bacteria in aquaculture wastewater environments.
       
According to the figure above, the results showed that the bacterial isolates from the wastewater of the fish ponds are categorized into six groups: Enterobacteriaceae, non-Enterobacteriaceae, Streptococcaceae, Pseudomonadaceae,  Erwiniaceae and Neisseriaceae, with isolation rates of 41.18%, 38.24%; 8.82%, 7.35%, 2.94% and 1.47%, respectively.
       
Fish and water are the most reliable sampling sites for pathogen detection in aquaculture environments (Dronen et al., 2022). The predominance of Entero-bacteriaceae indicates fecal contamination, likely originating from runoff, contaminated water used in fish farming, fish feed, livestock manure, fish excreta, or direct anthropogenic inputs. This suggests inadequate biosecurity and poor waste management practices, which are commonly observed in local fish farms (Degefu et al., 2011; Njoku et al., 2015; Sule et al., 2016; Wisnu et al., 2019). Moreover, the failure to implement proper fishpond management practices can pose health risks to fish by facilitating the spread of harmful pathogens, which may sebsequently impact human health (Ajayi and Okoh, 2014; Sule et al., 2016; Opiyo et al., 2018).
       
While similar bacterial groups-belonging to families such as Staphylococcaceae, Streptococcaceae, Pseudomon- adaceae, Enterobacteriaceae, non-Enterobacteriaceae, Morganellaceae, Neisseriaceae and Bacillaceae-have been reported in studies conducted in other parts of Africa (e.g., Nigeria and Zambia) by Ntengwe and Edema (2008), Ajayi and Okoh (2014), Njoku et al. (2015), Sule et al. (2016), Eghomwanre et al. (2019) and Adebami et al. (2020), our findings provide region-specific microbiological insights from southwestern Algeria, a region where such baseline surveillance remains limited.

Bacterial identification
 
The results of the identification of the fish pond wastewater isolates are presented in Table 2.

Table 2: Identities of bacteria isolated from fish pond wastewater.


       
At least 21 unique bacterial species were isolated. These included 14 species from the Enterobacteriaceae family, 3 species from non-Enterobacteriaceae families and 1 species each from the Pseudomonadaceae, Erwiniaceae and Neisseriaceae families, as well as unidentified species from the Streptococcaceae group.
       
According to the Chi-Square Goodness-of-Fit Test, the overall distribution of bacterial groups was highly non-uniform (p-value < 0.001), indicating significant variation in prevalence among the groups. This reinforces the dominance of Enterobacteriaceae and Aeromonadaceae and underlines the selective pressures present in fishpond environments-potentially influenced by temperature, nutrient load and organic matter accumulation.
 
Antibiotic susceptibility testing (AST)
 
Streptococcaceae
 
The results of the antibiotic susceptibility testing for bacterial isolates belonging to the Streptococcaceae family are presented in Fig 5.

Fig 5: Antibiotic susceptibility test of bacterial isolates belonging to the Streptococcaceae family (own elaboration).


       
The antibiotic susceptibility test results of Strepto-coccaceae isolates showed 100% resistance to oxacillin, tetracycline and erythromycin, 75% resistance to ampicillin and 50% resistance to vancomycin. Meanwhile, they were 100% sensitive to chloramphenicol.
 
Enterobacteriaceae
 
The results of the antibiotic susceptibility testing for bacterial isolates belonging to the Enterobacteriaceae family are presented in Fig 6.

Fig 6: Antibiotic susceptibility test of bacterial isolates belonging to the Enterobacteriaceae family (own elaboration).


       
As shown in Fig 6, all isolates exhibited 100% resistance to cefoxitin, cefazolin and ampicillin, with variable resistance to amoxicillin-clavulanic acid. Conversely, all isolates were fully sensitive to ciprofloxacin, ofloxacin, gentamicin, azithromycin, amikacin and tobramycin.
       
Previous studies, such as those by Sule et al., (2016), Adebami et al., (2020) and Abedin et al., (2020), have similarly reported resistance to amoxicillin–clavulanic acid, amoxicillin and ceftazidime among bacterial isolates from aquaculture wastewater, while sensitivity to ciprofloxacin and ofloxacin was maintained-findings that align with our results. These patterns underscore the role of aquaculture environments as reservoirs of bacteria carrying mobile resistance genes (Del Castillo et al., 2013), which pose a risk of horizontal gene transfer to human pathogens or direct infection (Apenteng et al., 2017). Notably, multidrug resistance-including to critical antibiotics such as cephalos- porins and carbapenems-has been detected in Entero-bacteriaceae from aquaculture systems (Hamza et al., 2020; Custodio et al., 2023), highlighting the potential for interspecies gene transfer within these settings.
 
Non-enterobacteriaceae (Aeromonadaceae)
 
The results of the antibiotic susceptibility testing for bacterial isolates belonging to the non-Enterobacteriaceae family (Aeromonadaceae) are presented in Fig 7.

Fig 7: Antibiotic susceptibility test of bacterial isolates belonging to the non-Enterobacteriaceae (Aeromonadaceae) family (own elaboration).


       
The results obtained showed that Aeromonadaceae strains were 100% resistant to ticarcillin-clavulanic acid and, to a lesser extent, to imipenem, ceftazidime and cefotaxime. However, they were 100% sensitive to ciprofloxacin, amikacin, tobramycin, ofloxacin, piperacillin and gentamicin and showed 96% sensitivity to aztreonam and chloramphenicol.

Non-enterobacteriaceae (Vibrionaceae)
 
The results of the antibiotic susceptibility testing for bacterial isolates belonging to the non-Enterobacteriaceae family (Vibrionaceae) are presented in Fig 8.

Fig 8: Antibiotic susceptibility test of bacterial isolates belonging to the non-Enterobacteriaceae (Vibrionaceae) family (own elaboration).


       
The results obtained showed that the Vibrionaceae strains exhibited resistant ranging from 40% to 80% to amikacin, cefotaxime, ceftazidime, aztreonam, imipenem and ticarcillin-clavulanic acid, while the isolates remained 100% sensitive to ciprofloxacin, tetracycline, sulfamethoxazole- trimethoprim, chloramphenicol and gentamicin.
       
Previous studies have documented varying antibiotic resistance patterns in aquaculture environments. For example, while Manjusha et al. (2005) found that Vibrio spp. isolated from coastal and tissue samples (shrimp, mussels and cuttlefish) exhibited higher resistance than those from aquaculture farms-likely due to greater antimicrobial use and pollution-our results indicate a similar trend, in that bacterial isolates from inland fish ponds display comparatively lower resistance levels.
       
In addition, the resistance pattern observed for Aeromonas spp. in our study is consistent with earlier reports (Fosse et al., 2003; Henriques et al., 2006; Daood, 2012), which attributed β-lactam resistance to the presence of β-lactamase genes such as blaTEM, despite maintained efficacy for antibiotics like ciprofloxacin, gentamicin, amikacin and trimethoprim-sulfamethoxazole. This suggests that reduced antimicrobial application and lower environmental pressures in inland aquaculture may mitigate the development of resistance. Moreover, the widespread presence of mobile genetic elements (MGEs), reported in previous studies (Lamy, 2012; Del Castillo et al., 2013), supports our hypothesis that horizontal gene transfer is an important driver in the dissemination of resistance determinants in these ecosystems.
 
Pseudomonadaceae
 
The results of the antibiotic susceptibility testing for bacterial isolates belonging to the Pseudomonadaceae family are presented in Fig 9.

Fig 9: Antibiotic susceptibility test of bacterial isolates belonging to the Pseudomonadaceae family (own elaboration).


       
The results obtained showed that the Pseudomona-daceae strains were 100% resistant to ticarcillin-clavulanic acid and to a lesser degree, resistant to aztreonam and ceftazidime, with resistance rates of 80% and 40%, respectively. However, they were 100% sensitive to ciprofloxacin, amikacin, tobramycin, ofloxacin, piperacillin and gentamicin.
       
All P. aeruginosa isolates formed biofilms (Fig 10), which enhances resistance and survival in aquatic environments. This biofilm-forming capacity may explain their persistence in low-flow, sediment-rich pond systems.

Fig 10: Detection of biofilm formation using the tube method (Original, 2024)-Photo by E. Benyagoub.


       
The Pseudomonas aeruginosa strains isolated were 100% sensitive to 6 out of 10 (i.e., 60%) antibiotics tested and exhibited lower resistance compared to the P. aeruginosa strains isolated in our previous study (Benyagoub, 2023a), which also found that all isolated strains were capable of forming biofilms, a characteristic that enhances the pathogen’s adaptability, survival and resistance in various environments (Moradali et al., 2017).
       
However, due to the absence of standardized guidelines for antibiotic selection and interpretation, we did not include the AST results for Pantoea spp. and Chromobacterium violaceum strains in this study.
 
Multidrug-resistant bacterial strains
 
The strains exhibiting resistance to more than three classes of antibiotics, along with the antibiotic resistance patterns categorized by bacterial group, are presented in Table 3 and Fig 11.

Table 3: Multidrug-resistant profiles of bacterial strains isolated from fish ponds wastewater.



Fig 11: Antibiotic resistance patterns by bacterial group (own elaboration).


       
Out of 68 bacterial isolates, 17 (25%) were resistant to more than three classes of antibiotics and exhibited high MAR indices. The variability in MAR indices among bacterial groups was statistically significant (ANOVA, p = 0.0498), suggesting that resistance levels differ across bacterial families. The elevated MAR values observed in Enterobacteriaceae (above 0.4) suggest sustained antibiotic pressure and fecal contamination, potentially originating from farm runoff, livestock waste, or human sources (Sarkar et al., 2019). Similarly, Rezaul Karim et al. (2023) reported non-Enterobacteriaceae isolates with MAR indices above 0.2, indicative of antibiotic misuse in both human and veterinary contexts. These findings highlight the urgent need for the implementation of best practice codes among fish farmers and routine monitoring of fish pond wastewater prior to environmental discharge, in order to prevent the transmission of potential pathogens (Sule et al., 2016).
       
Comparable results have been reported elsewhere. Snoussi et al. (2011) found that Aeromonas hydrophila and Vibrio alginolyticus strains isolated from marine farm environments were resistant to at least three antimicrobial agents, with MAR indices of 0.71 and 0.68, respectively. Likewise, Abedin et al. (2020), identified Aeromonas spp., Pseudomonas spp. and Vibrio spp. as dominant bacteria in various fish species in Bangladesh, reinforcing concerns over antibiotic-resistant pathogens in aquaculture systems.
       
These results point to a concerning level of resistance to several antibiotic classes, which, according to Adebami et al. (2020), could increase the prevalence of infections in fish and potentially trigger disease outbreaks.
       
Gram-positive bacteria can develop resistance through two main strategies: one involves the enzymatic breakdown of the antibiotic via β-lactamase production, while the other reduces the binding affinity of the antibiotic to its target, the penicillin-binding protein (PBP) (Benyagoub et al., 2020b; Jubeh et al. 2020; Benyagoub et al., 2022; Benyagoub, 2024). However, Gram-negative bacteria can resist antibiotics through various mechanisms, such as reducing drug uptake, altering the drug target, inactivating the drug and actively pumping it out. Additionally, these bacteria can acquire resistance factors from other microorganisms (Reygaert, 2018; Benyagoub et al., 2020a; Benyagoub et al., 2021a, 2021b; Gauba and Rahman, 2023).

Search for antibiotic residues
 
The qualitative test (Fig 12) indicated the absence of detectable antibiotic residues in the samples.

Fig 12: Qualitative detection of antimicrobial residues in fish pond wastewater (Original, 2024)-Photo by E. Benyagoub.


       
This may reflect either the absence of recent antibiotic use or the limited sensitivity of the test method. However, indirect selection pressures-such as low-dose residues from reused water sources or contamination from surrounding agricultural activities-cannot be excluded.
       
Manjusha et al. (2005) and Thiang et al. (2021) underscore the threat posed by low-level antibiotic exposure, which can still drive the propagation of resistance genes. The lack of residues should not be interpreted as the absence of selective pressure, especially considering the prevalence of multidrug-resistant bacteria. Effective management of aquaculture wastewater is crucial to reduce antibiotic pollution and prevent the transfer of resistance genes to humans through the food chain. This highlights the need for a deeper understanding of how drug resistance determinants are spread and transmitted, as well as the importance of enhancing antimicrobial stewardship (Del Castillo et al., 2013, Schar et al., 2020).

CONCLUSION

This study reveals the presence of diverse bacterial communities, including multidrug-resistant and biofilm-forming strains, in fish pond wastewater. The dominance of Gram-negative bacteria and Enterobacteriaceae reflects potential fecal contamination and poor waste management practices common in the study area.
       
Despite no detectable antibiotic residues, high MAR indices and resistance patterns suggest ongoing selection pressures, likely from environmental or anthropogenic sources. These findings raise concerns about the potential transmission of resistance genes to human pathogens via water and food chains.
       
To address this risk, we recommend the following practical steps:
• Implement closed-loop or recirculating aquaculture systems (RAS) to reduce water contamination and limit the spread of antibiotic-resistant bacteria.
• Encourage the use of probiotics or approved chemical disinfectants as alternatives to antibiotics for disease prevention and microbial control in aquaculture operations.
• Educate fish farmers on the risks of indiscriminate antibiotic use and encourage adherence to good aquaculture practices (GAqPs).
• Establish local monitoring frameworks for antimicrobial resistance and residue detection.
• Treat aquaculture wastewater before environmental discharge, especially in regions near populated or agricultural zones.
       
Such measures are essential to enhance antimicrobial stewardship, protect public health and ensure sustainable aquaculture development in Algeria and similar settings globally.

ACKNOWLEDGEMENT

Special thanks to Mr. Salhi L. and Mr. Touati T, owners of the aquaculture farms in Taghit and Nif Rhaa-Ouakda, Bechar (Algeria), respectively and to Mr. Boudani M., Manager of CNRDPA-Boukais (Bechar) for their support of this study by granting access to the farms.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
Not applicable.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest. No financial support was received for this research.

REFERENCES


  1. Abedin, M.Z., Sadiqur Rahman, Md., Hasan, R., Shathi, J.H., Jarin, L., Sifat Uz Zaman, Md. (2020). Isolation, identification and antimicrobial profiling of bacteria from aquaculture fishes in pond water of Bangladesh. American Journal of Pure and Applied Bioscience. 2(3): 39-50. DOI: 10. 34104/ajpab.020.039050 

  2. Adebami, G.E., Fasiku, S.A., Solomon, O.D., Babalola, B.A. (2020). Physicochemical and microbial evaluations of different fish ponds’ wastewaters and the antibiotics profiles of isolated bacteria. Ethiopian Journal of Environmental Studies and Management. 13(4): 509-521. DOI: https:// ejesm.org/doi/v13i4.11.

  3. Ajayi, A.O. and Okoh, A.I. (2014). Bacteriological study of pond water for aquaculture purposes. Journal of Food, Agriculture and Environment. 12(2): 1260-1265. DOI: 10.1234/4.2014. 5325.

  4. Akshaya, L., Sarathchandra, G., Shanmugam, S.A., Felix, N., Kaliyamurthi, V. (2023). Microbial screening of antibiotic residues in soil and water of cultured fish farms in Tamil Nadu. Indian Journal of Animal Research. 57(4): 511- 516. doi: 10.18805/IJAR.B-5063.

  5. Apenteng, J.A, Osei-Asare, C., Oppong, E.E., Amihere, I., Hafiz, M.Y. (2017). Antibiotic sensitivity patterns of microbial isolates from fish ponds: A study in the greater Accra region of Ghana. African Journal of Pharmacy and Pharmacology. 11(28): 314-320. DOI: 10.5897/AJPP2017. 4789.

  6. Bauer, A.W., Perry, D.M., Kirby, W.M. (1959). Single disc antibiotic sensitivity testing of Staphylococci. AMA Archives of Internal Medicine. 104(2): 208-216. DOI : 10.1001/archinte. 1959.00270080034004.

  7. Benyagoub E., Nabbou, N., Bouchikhi F., Milioudi M.N. (2022). Chromatographic analyses and in vitro screening of the antimicrobial activity of tannins extract of fenugreek seeds grown in the Taghit region (Southwest of Algeria). Nutrafoods. 2022(1): 397-409. DOI: 10.17470/NF-022-0049.

  8. Benyagoub, E. (2022). Technological properties and antagonistic effect of lactic acid bacteria isolated from fermented goat milk flavoured with juniper leaves- a product from the Southwest region of Algeria-against some microbial contaminants. Nutrafoods. 2022(2): 419-437. DOI: 10. 17470/NF-022-0051. 

  9. Benyagoub, E. (2023a). Antibiotic susceptibility patterns and search for ESBL-producing Pseudomonas aeruginosa isolated from the feed water tank of a dairy unit located in Bechar province (Algeria). Fresenius Environmental Bulletin. 32(9): 3037-3049. 

  10. Benyagoub, E. (2023b). Tank water quality used for milk processing of a private dairy unit located in Ouakda-Bechar (South- western Algeria): Physicochemical analysis and micro- biological risk assessment. Fresenius Environmental Bulletin. 32(10): 3145-3156. 

  11. Benyagoub, E. (2024). Methicillin, β-lactams and clindamycin resistance profiles of Staphylococcus aureus strains isolated from patients with UTI in Bechar province (Algeria). Anti-infective Agents. 22(1): e220823220161.  DOI: 10.2174/2211352521666230822104016.

  12. Benyagoub, E., Alkhudhairy, M.K., Benchaib, S.M., Zaalan, A., Mekhfi, Y., Teyebi, N., Razni, D., Bendada, F. (2021a). Isolation frequency of uropathogenic strains and search for ESBL producing Enterobacteriaceae isolated from patients with UTI in Bechar (Algeria). Anti-Infective Agents. 19(3): 303-316. DOI: 10.2174/2211352518999201224102209.

  13. Benyagoub, E., Nabbou, N., Aguid, A., Alkhudhairy, M.K., Bendada, F. (2021b). In vitro antibacterial activity of funugreek seeds’ phytoconstituents from Taghit region (Southwest of Algeria) against the bacterial strains responsible for UTI. Current Bioactive Compounds. 17(4): 339-355. DOI: 10.2174/1573407216999200703123154.

  14. Benyagoub, E., Nabbou, N., Bendada, F. (2018). Characterization of microbial risk level of liquid waste from a private dairy factory in Bechar (Southwest of Algeria). Indian Journal of Dairy Science. 71(5): 524-529.

  15. Benyagoub, E., Nabbou, N., Boukhalkhel, S., Dehini, I. (2020b). The in vitro evaluation of the antimicrobial activity of Quercus robur L. methanolic and aqueous leaves’ extracts, from the Algerian high plateaus against some uro-pathogenic microbial strains. Phytothérapie. 18(5): 262-274. DOI: 10. 3166/phyto-2019-0133.

  16. Benyagoub, E., Nabbou, N., Dine, A. (2020a). Antimicrobial effect of Quercus robur L. leaves’ selective extracts from Mezi mountain of Djeniene Bourezg (West of Algeria). Current Bioactive Compounds. 16(8): 1181-1190. DOI: 10. 2174/1573407216666191226141609.

  17. Christensen, G.D., Simpson, W.A., Younger, J.J., Baddour, L.M., Barrett, F.F., Melton, D.M., Beachey, E.H. (1985). Adherence of coagulase-negative staphylococci to plastic tissue culture plates: A quantitative model for the adherence of staphylococci to medical devices. Journal of Clinical Microbiology. 22(6): 996-1006. DOI: 10.1128/jcm.22.6. 996-1006.1985.

  18. Clinical and Laboratory Standards Institute (CLSI) (2020). Performance standards for antimicrobial susceptibility testing M100, 30th edn. CLSI, Wayne Pennsylvania, US.

  19. Cretu, M., Dediu, L., Cristea, V., Zugravu, A., Turek Rahoveanu, M.M., Bandi, A-C., Turek Rahoveanu, A., Mocuta, D.N. (2016). Environmental impact of Aquaculture: A literature review. Proceedings of the 27th International Business Information Management Association (BIMA) conference on Innovation Management and Education Excellence Vision 2020: Regional Development to Global Economic Growth. May 4-5, 2016, Italy.

  20. Custodio, M., Peñaloza, R., Ordinola-Zapata, A., Peralta-Ortiz, T., Sánches-Suárez, H., Vieyra-Peña, E., De la Cruz, H., Alvarado- Ibáñez, J. (2023). Diversity of enterobacterales in sediments of lagoons with fish farming activity and analysis of antibiotic resistance. Toxicology Reports. 10: 235-244. DOI: 10.1016/j.toxrep.2023.02.002

  21. Daood, N. (2012). Isolation and antibiotic susceptibility of Aeromonas spp. from freshwater fish farm and farmed carp (Dam of 16 Tishreen, Lattakia). Journal for Basic Sciences. 28(1): 27-39.

  22. Degefu, F., Mengistu, S., Schagerl, M. (2011). Influence of fish cage farming on water quality and plankton in fish ponds: A case study in the Rift Valley and North Shoa reservoirs, Ethiopia. Aquaculture. 316(1-4): 129-135. DOI : 10.1016/ j.aquaculture.2011.03.010

  23. Del Castillo, C.S., Hikima, J-I., Jang, H-B., Nho, S-W., Jung, T-S., Wongtavatchai, J., Kondo, H., Hirono, I., Takeyama, H., Aokia, T. (2013). Comparative sequence analysis of a multidrug- resistant plasmid from Aeromonas hydrophila. Antimicrobial Agents and Chemotherapy. 57(1): 120-129. DOI: 10. 1128/AAC.01239-12.

  24. Dronen, K., Roalkvam, I., Nilsen, H., Olsen, A.B., Dahle, H., Wergeland, H. (2022). Presence and habitats of bacterial fish pathogen relatives in a marine salmon post-smolt RAS. Aquaculture Reports. 26: 101312. DOI: 10.1016/j.aqrep.2022.101312.

  25. Eghomwanre, A.F., Obayagbona, O.N., Imarhiagbe E.E. (2019). Bacteriological and physicochemical assessment of fish pond waters collected from Idogbo community, Edo state, Nigeria. FUW Trends in Science and Technology Journal.  4(2): 582-585. 

  26. FAO (2021). FAO’s contribution of fisheries and aquaculture towards achieving the 2030 agenda. 34th session, FAO committee on fisheries (COFI), Rome-Italy, 21.

  27. Fosse, T., Giraud-Morin, C., Madinier, I. (2003). β-lactam-resistance phenotypes in the genus Aeromonas. Pathologie Biologie. 51(5): 290-296. DOI: 10.1016/S0369-8114(03)00027-0.

  28. GADM maps and data (2024). Geographical map of Algeria.

  29. Gauba, A. and Rahman, K.M. (2023). Evaluation of antibiotic resistance mechanisms in Gram-negative bacteria. Antibiotics. 12(11): 1590. DOI: 10.3390/antibiotics12111590.

  30. Hamza, D., Dorgham, S., Ismael, E., Abd El-Moez, S.I., Elhariri, M., Elhelw, R., Hamza, E. (2020). Emergence of β-lactamase- and carbapenemase- producing Enterobacteriaceae at integrated fish farms. Antimicrobial Resistance and Infection Control. 9(1): 67. DOI : 10.1186/s13756-020- 00736-3.

  31. Henriques I.S., Fonseca F., Alves A., Saavedra M.J., Correia A. (2006). Occurrence and diversity of integrons and beta-lactamase genes among ampicillin-resistant isolates from estuarine waters. Research in Microbiology. 157(10): 938-947. DOI: 10.1016/j.resmic.2006.09.003.

  32. ISO 5667-1 (2023). Water quality-Sampling-Part 1: Guidance on the design of sampling programmes and sampling techniques. 

  33. Jubeh, B., Breijyeh, Z., Karaman, R. (2020). Resistance of Gram- positive bacteria to current antibacterial agents and overcoming approaches. Molecules. 25(12): 2888. DOI: 10. 3390/molecules25122888.

  34. Kirby, W.M., Yoshihara, G.M., Sundsted, K.S., Warren, J.H. (1956). Clinical usefulness of a single disc method for antibiotic sensitivity testing. Antibiotics Annual. 1956: 892-897.

  35. Kirmusaoglu, S. (2019). The methods for detection of biofilm and screening antibiofilm activity of agents. Ed. Intechopen, 152. DOI: 10.5772/intechopen.78751.

  36. Lamy, B. (2012). Aeromonas et aéromonoses en médecine humaine.  EMC- Biologie Médicale. 7(4): 1-7. DOI : 10.1016/S2211- 9698(12)57775-7.

  37. Lee, D.D., Lee, E.Y., Jeong, S.H., Chang, C.L. (2007). Evaluation of a colorimetric broth microdilution method for antimicrobial susceptibility testing using 2,3,5-Triphenyltetrazolium chloride. Korean Journal of Clinical Microbiology. 10(1): 49-53. 

  38. Lipps, W.C., Braun-Howland, E.B., Baxter, T.E. (2022). Standard methods for the examination of water and wastewater, 24th Ed., American Public Health Association (APHA), American Water Works Association (AWWA) and Water Environment Federation (WEF), New York-USA. pp: 1624.

  39. Manjusha, S., Sarita, G.B., Elyas, K.K., Chandrasekaran, M. (2005). Multiple antibiotic resistances of Vibrio isolates from coastal and brackish water areas. American Journal of Biochemistry and Biotechnology. 1(4): 193-198. DOI : 10.3844/ajbbsp. 2005.193.198.

  40. Moradali, M.F., Ghods, S., Rehm, B.H.A. (2017). Pseudomonas aeruginosa lifestyle: A paradigm for adaptation, survival and resistance. Frontiers in Cellular and Infection Microbiology. 7: 39. DOI: 10.3389/fcimb.2017.00039.

  41. Nabbou, N. and Benyagoub, E. (2025). Water quality of borehole and shallow wells feeding artificial fish ponds in Bechar province, Southwestern Algeria: Bacteriological and physicochemical aspects. Indian Journal of Agricultural Research. doi: 10.18805/IJARe.AF-928.

  42. Naylor, R.L., Goldbury, R.J, Primavera, J.H., Kautsky, N., Beveridge, M.C.M., Clay, J., Folke, C., Lubchenco, J., Mooney, H., Troell, M. (2000). Effect of aquaculture on world fish supplies. Nature. 405: 1017-1024. DOI: 10.1038/35016500.

  43. Njoku, O.E., Agwa, O.K., Ibiene, A.A. (2015). An investigation of the microbiological and physicochemical profile of some fish pond water within the Niger Delta region of Nigeria. African Journal of Food Science. 9(3): 155-162. DOI: 10. 5897/AJFS2014.1208.

  44. Ntengwe, F.W. and Edema, M.O. (2008). Physico-chemical and microbiological characteristics of water for fish production using small ponds. Physics and Chemistry of the Earth, Parts A/B/C. 33(8-13): 701-707. DOI: 10.1016/j.pce.2008. 06.032.

  45. Opiyo, M.A., Marijani, E., Muendo, P., Odede, R., Leschen, W., Charo- Karisa, H. (2018). A review of aquaculture production and health management practices of farmed fish in Kenya.  International Journal of Veterinary Science and Medicine. 6(2): 141-148. DOI : 10.1016/j.ijvsm.2018.07.001.

  46. Pandey, P.K., Kass, P.H., Soupir, M.L., Biwas, S., Singh, V.P. (2014). Contamination of water resources by pathogenic bacteria.  AMB Express. 4: 51. DOI: 10.1186/s13568-014-0051-x.

  47. Reygaert, W.C. (2018). An overview of the antimicrobial resistance mechanisms of bacteria. AIMS Microbiology. 4(3): 482- 501. DOI : 10.3934/microbiol.2018.3.482.

  48. Rezaul Karim, Md., Zakaria, Z., Hassan L., Mohd Faiz, N., Indah Ahmad, N. (2023). Antimicrobial resistance profiles and co-existence of multiple antimicrobial resistance genes in mcr-harbouring colistin-resistant Enterobacteriaceae isolates recovered from poultry and poultry meats in Malaysia. Antibiotics. 12(6): 1060. DOI: 10.3390/antibiotics 12061060.

  49. Sarkar, M., Chakraborty, S., Kundu, D., Ghosh, S., Khan, A., Karmakar, D., Ali, S., Mandal, S. (2019). Isolation and characterization of bacteria from sewage and pond water, Malda, India. Acta Scientific Microbiology. 2(11): 28-34. DOI: 10.31080/ ASMI.2019.02.0394.

  50. Schar, D., Klein, E.Y., Laxminarayan, R., Gilbert, M., Van Boeckel, T.P. (2020). Global trends in antimicrobial use in aquaculture. Scientifc Reports. 2020(10): 21878. DOI: 10.1038/s41598- 020-78849-3.

  51. Seghir, A., Abdenbi, A., Saad, A., Benyagoub, E. (2023). Antioxidant and antimicrobial activity of Moringa oleifera extracts from South West of Algeria. Phytothérapie. 21(4): 184- 193. DOI: 10.3166/phyto-2022-0366.

  52. Snoussi, M., Noumi, E., Lajnef, R., Belila, A., Yazidi, N., Bakhrouf, A. (2011). Phenotypic characterization and enterobacterial repetitive intergenic consensus polymerase chain reaction (eric-pcr) of Aeromonas spp. and Vibrio spp. strains isolated from Sparus aurata fish farm (Khenis, Tunisia). African Journal of Microbiology Research. 5(19): 2920- 2928.

  53. Sule, I.O., Agbabiaka, T.O., Ahmed, R.N., Saliu, B.K., Olayinka, K.J. (2016). Bacteriological and physicochemical analysis of waste water from fish ponds. Ethiopian Journal of Environmental Studies and Management. 9(2): 167-178. DOI: 10.4314/ejesm.v9i2.5.

  54. Thiang, E.L., Weng Lee, C., Takada, H., Seki, K., Takei, A., Suzukie, S., Wang, A., Bong, C.W. (2021). Antibiotic residues from aquaculture farms and their ecological risks in Southeast Asia: A case study from Malaysia. Ecosystem Health and Sustainability. 7(1): 1-18. DOI: 10.1080/ 20964129.2021.1926337.

  55. Tille, M.P. (2018). Bailey and Scott’s diagnostic microbiology, 14th Ed. Elsevier. pp: 1136.

  56. Wang, G., Xie, J., Yin, G., Yu, D., Yu, E., Wang, H., Gong, W. (2010). Influences of aquaculture on ecological environment. International Journal of Biology. 2(2): 158-164. DOI: 10. 5539/ijb.v2n2p158.

  57. Wisnu, R.P., Karuniasa, M., Moersidik, S.S. (2019). The effect of fish aquaculture on water quality in Lake Cilala, Bogor Regency. IOP Conference Series: Earth and Environmental Science. 399: 012111. DOI: 10.1088/1755-1315/399/1/012111.
Published In
Agricultural Science Digest
Article Metrics
Metrics Icon
0
Views
0
Citations
Reviewed By
Share Article
Download Article
In this Article
    Contact us
    Follow us

    Full Research Article

    Antibiotic Resistance of Pathogenic and Bacterial Contaminants Isolated from Fish Pond Wastewater in Bechar Province, Southwestern Algeria

    E
    Elhassan Benyagoub1,2,*
    https://orcid.org/0000-0002-2276-471X
    A
    Afaf Radi2
    A
    Ahlam Benachour2
    M
    Manal Boutayeb2
    1Architecture and Environmental Heritage Laboratory (Archipel), Mohammed Tahri University of Bechar (08000), Bechar, Algeria.
    2Department of Biology, Faculty of Natural and Life Sciences, Mohammed Tahri University of Bechar (08000), Bechar, Algeria.

    ABSTRACT

    Background: This study investigates bacterial contaminants and pathogenic species isolated from fish pond wastewater (FPWW) in Bechar province, Southwestern Algeria and assesses their antibiotic resistance propfile.

    Methods: A total of 21 FPWW samples were analyzed using different culture media to isolate bacterial contaminants via the membrane filter technique. Following purification, all bacterial isolates were identified through biochemical tests and subjected to susceptibility testing by the Kirby-Bauer disc diffusion method on Mueller-Hinton (MH) agar and the broth microdilution technique. Antimicrobial residue detection was also carried out using the microbiological tube test method (MTT).

    Result: We successfully isolated 68 bacterial isolates, classified into six bacterial groups: non-Enterobacteriaceae (41.18%), Enterobacteriaceae (38.24%), Streptococcaceae (8.82%), Pseudomonadaceae (7.35%), Erwiniaceae (2.94%) and Neisseriaceae (1.47%). According to the Chi-Square Goodness-of-Fit Test, this distribution was highly significant (p < 0.001). The isolated species included Aeromonas hydrophila hydrophila, Streptococcus spp., Enterobacter spp., Serratia spp., Escherichia coli, Citrobacter spp., Klebsiella spp., Plesiomonas shigelloides, Kluyvera spp., Pseudomonas aeruginosa, Pantoea spp., Chromobacterium violaceum, Salmonella choleraesuis and Vibrio spp. Susceptibility testing of the bacterial contaminants revealed variable responses among the strains, with increased resistance to β-lactam antibiotics. Multidrug resistance results showed that 17 strains (25%) exhibited resistance to three to six different antibiotic classes, with Multiple Antibiotic Resistance (MAR) indices ranging from 0.28 to 0.68. The variability in MAR indices among bacterial groups was statistically significant (ANOVA, p = 0.0498). Furthermore, no growth-inhibiting substances were detected in the wastewater samples using the MTT method.

    INTRODUCTION

    Aquaculture, the practice of farming fish and other aquatic organisms, is a rapidly growing industry that significantly contributes to global food security and is among the fastest-growing food production sectors (FAO, 2021). With the rising global population, increasing income levels and growing urbanization, aquaculture is expected to surpass traditional fishing by 2030. However, its environmental footprint varies depending on the species being farmed, management practices, location and local conditions (Cretu et al., 2016). One of the major environmental issues in aquaculture is the deterioration of water quality. This degradation can be caused by factors like leftover feed, animal waste and the use of chemical treatments (Nabbou and Benyagoub, 2025). These pollutants negatively impact the water environment, influencing various components such as the water’s physicochemical characteristics, sediment composition, plankton and bottom-dwelling organisms (Naylor et al., 2000; Wang et al., 2010). Contamination of both freshwater and saline water by pathogenic bacteria presents a threat to aquatic ecosystems and public health through the food chain (Pandey et al., 2014).
           
    The overuse of antimicrobial agents in aquaculture further exacerbates this issue by promoting the emergence of antibiotic-resistant bacteria (Abedin et al., 2020).
           
    Despite growing concern, data on bacterial contamination and antibiotic resistance in aquaculture wastewater remain limited, especially in arid regions like Southwestern Algeria.
           
    This study addresses this gap by identifying bacterial contaminants and pathogenic species in wastewater from three fish farms in Bechar and evaluating their antibiotic resistance profiles. To our knowledge, this is the first study in this region to link aquaculture wastewater contaminants with antibiotic resistance patterns, providing insights critical for environmental monitoring and public health management.

    MATERIALS AND METHODS

    All experiments were conducted at the University of Bechar (Algeria), over a period of eight months, from January to August 2024.
     
    Study area
     
    This study focused on two fish farms located in Boukais and Taghit and Nif Rhaa-Ouakda, which are situated 50km and 90 km and 15 km, respectively, from the center of Bechar (Fig 1).

    Fig 1: Map showing the geographical locations of Boukais (yellow), Taghit (orange), Bechar city center (blue) and the entire Bechar province (red) in Southwestern Algeria (GADM, 2024).


           
    These farms are dedicated to cultivating tilapia species in concrete and plastic ponds (Fig 2).

    Fig 2: Fish ponds in Bechar province (Southwestern Algeria) (Original, 2024)-Photo by E. Benyagoub.


     
    Sampling conditions
     
    Fish pond wastewater (FPWW) samples were collected in sterile glass flasks, following ISO 5667-1 (2023). The frequency and dates of wastewater sampling are provided in Table 1.

    Table 1: FPWW sampling frequency.


     
    Bacterial isolation
     
    The bacterial contaminants and pathogenic bacterial species, such as Enterobacteriaceae, Pseudomonadaceae and Streptococcaceae, were isolated using the membrane filter technique on MacConkey agar, Cetrimide agar and Salmonella-Shigella agar by filtering 250 mL of fish pond wastewater, as described by Benyagoub et al. (2018), Lipps et al. (2022) and Benyagoub (2023a, 2023b).
           
    The isolates were identified following the standard microbiological procedures outlined by Tille (2018).
     
    Biofilm formation
     
    In this study, biofilm formation by Pseudomonas spp. strains -recognized as opportunistic pathogens-was assessed using the tube method, as described by Christensen et al. (1985) and Kirmusaoglu (2019). This qualitative assay detects biofilm-producing microorganisms based on the formation of a visible film lining the inner surface of a polystyrene test tube containing tryptic soy broth (TSB) medium inoculated with the test isolate and incubated under appropriate conditions.
     
    Detection of antimicrobial residues in fish pond wastewater samples
     
    Detection of antimicrobial residues were carried out using the microbiological tube test method (MTT), a qualitative technique, as outlined by Akshaya et al. (2023). Nutrient broth containing the pH indicator bromocresol purple was prepared and an equal volume of fish pond wastewater and bacterial suspension (v/v) was added. The reference bacterial strains tested were Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Staphylococcus aureus ATCC 25923 and Bacillus cereus ATCC 14579. The tubes were then incubated at 37oC for 18-24 hours. A positive reaction was indicated by tubes that remained purple, as compared to the positive and negative controls.
     
    Antibiotic susceptibility testing (AST)
     
    The antibiotic resistance profile of bacterial contaminants, including members of Enterobacteriaceae, Pseudomona-daceae and Streptococcaceae, were determined using the Kirby-Bauer disc diffusion method, following the protocols outlined by Kirby et al. (1956), Bauer et al. (1959), Benyagoub et al. (2020a, 2020b), Benyagoub (2022) and Benyagoub (2024). For non-Enterobacteriaceae strains, resistance profiles were assessed using the broth microdilution technique, with triphenyltetrazolium chloride (TTC) serving as a viability indicator (Lee et al., 2007; Seghir et al., 2023). Inhibition zones (mm) on agar plates and MIC values on microplates were measured and categorized as susceptible (S) or resistant (R), according to the Clinical and Laboratory Standards Institute (CLSI, 2020) guidelines. Species-specific breakpoints for Aeromonas spp. were applied where available.
           
    All isolated strains were tested for antibiotic susceptibility. In this study, the following antibiotics were tested against the bacterial contaminants: Gentamicin (CN 10 µg), oxacillin (OX 1 µg), cefoxitin (CX 30 µg), chloramphenicol (C 30 µg), sulfamethoxazole-trimethoprim (SXT 25 µg), erythromycin (E 15 µg), ciprofloxacin (CIP 5 µg), ampicillin (AMP 10 µg), ofloxacin (OF 5 µg), amoxicillin-clavulanic acid (AUG 30 µg), amikacin (AK 30 µg), amoxicillin (AML 2 µg), tetracycline (TE 30 µg), trimethoprim (TM 2,5 µg), aztreonam (ATM 30 µg), cefotaxime (CTX 30 µg), ceftazidime (CAZ 10 µg), piperacillin (PRL 100 µg), ticarcillin-clavulanic acid (TTC 85 µg), fosfomycin (FOS 50 µg), nalidixic acid (NA 30 µg), cefazolin (CZ 30 µg), tobramycin (TOB 10 µg), vancomycin (VA 30 µg), azithromycin (AZM 15 µg) and imipenem (IMI 10 µg).
           
    Multi-drug resistant (MDR) isolates were defined as those exhibiting resistance to more than three antimicrobial classes tested (Benyagoub et al., 2021a).
     
    Calculation of the MAR index
     
    The MAR index was determined using the formula below (Benyagoub et al., 2021a; Benyagoub, 2024):


    Where,
    MAR = Multiple antibiotic resistance.
    a = Count of antibiotics to which the bacterium shows resistant.
    b = Total number of antibiotics that were tested.
     
    Statistical analysis
     
    Statistical analyses were conducted using IBM SPSS Statistics (version 25, IBM Corp., Armonk, NY, USA) to evaluate differences in the distribution and antibiotic resistance profiles of bacterial groups isolated from fish pond wastewater. A Chi-Square Goodness-of-Fit Test was used to assess the distribution of bacterial groups, while a one-way analysis of variance (ANOVA) was performed to compare the Multiple Antibiotic Resistance (MAR) index among the different groups. A significance level of p<0.05 was considered statistically significant.

    RESULTS AND DISCUSSION

    Bacterial isolation

    The bacterial species isolated from wastewater of the fish ponds are categorized based on their Gram stain characteristics (Fig 3) and bacterial groups (Fig 4).

    Fig 3: Distribution of isolated bacteria according to Gram type (own elaboration).



    Fig 4: Distribution of bacterial isolates by bacterial group (own elaboration).


           
    The results of the distribution are consistent with previous findings (Sule et al., 2016; Adebami et al., 2020), which also reported a dominance of Gram-negative bacteria in aquaculture wastewater environments.
           
    According to the figure above, the results showed that the bacterial isolates from the wastewater of the fish ponds are categorized into six groups: Enterobacteriaceae, non-Enterobacteriaceae, Streptococcaceae, Pseudomonadaceae,  Erwiniaceae and Neisseriaceae, with isolation rates of 41.18%, 38.24%; 8.82%, 7.35%, 2.94% and 1.47%, respectively.
           
    Fish and water are the most reliable sampling sites for pathogen detection in aquaculture environments (Dronen et al., 2022). The predominance of Entero-bacteriaceae indicates fecal contamination, likely originating from runoff, contaminated water used in fish farming, fish feed, livestock manure, fish excreta, or direct anthropogenic inputs. This suggests inadequate biosecurity and poor waste management practices, which are commonly observed in local fish farms (Degefu et al., 2011; Njoku et al., 2015; Sule et al., 2016; Wisnu et al., 2019). Moreover, the failure to implement proper fishpond management practices can pose health risks to fish by facilitating the spread of harmful pathogens, which may sebsequently impact human health (Ajayi and Okoh, 2014; Sule et al., 2016; Opiyo et al., 2018).
           
    While similar bacterial groups-belonging to families such as Staphylococcaceae, Streptococcaceae, Pseudomon- adaceae, Enterobacteriaceae, non-Enterobacteriaceae, Morganellaceae, Neisseriaceae and Bacillaceae-have been reported in studies conducted in other parts of Africa (e.g., Nigeria and Zambia) by Ntengwe and Edema (2008), Ajayi and Okoh (2014), Njoku et al. (2015), Sule et al. (2016), Eghomwanre et al. (2019) and Adebami et al. (2020), our findings provide region-specific microbiological insights from southwestern Algeria, a region where such baseline surveillance remains limited.

    Bacterial identification
     
    The results of the identification of the fish pond wastewater isolates are presented in Table 2.

    Table 2: Identities of bacteria isolated from fish pond wastewater.


           
    At least 21 unique bacterial species were isolated. These included 14 species from the Enterobacteriaceae family, 3 species from non-Enterobacteriaceae families and 1 species each from the Pseudomonadaceae, Erwiniaceae and Neisseriaceae families, as well as unidentified species from the Streptococcaceae group.
           
    According to the Chi-Square Goodness-of-Fit Test, the overall distribution of bacterial groups was highly non-uniform (p-value < 0.001), indicating significant variation in prevalence among the groups. This reinforces the dominance of Enterobacteriaceae and Aeromonadaceae and underlines the selective pressures present in fishpond environments-potentially influenced by temperature, nutrient load and organic matter accumulation.
     
    Antibiotic susceptibility testing (AST)
     
    Streptococcaceae
     
    The results of the antibiotic susceptibility testing for bacterial isolates belonging to the Streptococcaceae family are presented in Fig 5.

    Fig 5: Antibiotic susceptibility test of bacterial isolates belonging to the Streptococcaceae family (own elaboration).


           
    The antibiotic susceptibility test results of Strepto-coccaceae isolates showed 100% resistance to oxacillin, tetracycline and erythromycin, 75% resistance to ampicillin and 50% resistance to vancomycin. Meanwhile, they were 100% sensitive to chloramphenicol.
     
    Enterobacteriaceae
     
    The results of the antibiotic susceptibility testing for bacterial isolates belonging to the Enterobacteriaceae family are presented in Fig 6.

    Fig 6: Antibiotic susceptibility test of bacterial isolates belonging to the Enterobacteriaceae family (own elaboration).


           
    As shown in Fig 6, all isolates exhibited 100% resistance to cefoxitin, cefazolin and ampicillin, with variable resistance to amoxicillin-clavulanic acid. Conversely, all isolates were fully sensitive to ciprofloxacin, ofloxacin, gentamicin, azithromycin, amikacin and tobramycin.
           
    Previous studies, such as those by Sule et al., (2016), Adebami et al., (2020) and Abedin et al., (2020), have similarly reported resistance to amoxicillin–clavulanic acid, amoxicillin and ceftazidime among bacterial isolates from aquaculture wastewater, while sensitivity to ciprofloxacin and ofloxacin was maintained-findings that align with our results. These patterns underscore the role of aquaculture environments as reservoirs of bacteria carrying mobile resistance genes (Del Castillo et al., 2013), which pose a risk of horizontal gene transfer to human pathogens or direct infection (Apenteng et al., 2017). Notably, multidrug resistance-including to critical antibiotics such as cephalos- porins and carbapenems-has been detected in Entero-bacteriaceae from aquaculture systems (Hamza et al., 2020; Custodio et al., 2023), highlighting the potential for interspecies gene transfer within these settings.
     
    Non-enterobacteriaceae (Aeromonadaceae)
     
    The results of the antibiotic susceptibility testing for bacterial isolates belonging to the non-Enterobacteriaceae family (Aeromonadaceae) are presented in Fig 7.

    Fig 7: Antibiotic susceptibility test of bacterial isolates belonging to the non-Enterobacteriaceae (Aeromonadaceae) family (own elaboration).


           
    The results obtained showed that Aeromonadaceae strains were 100% resistant to ticarcillin-clavulanic acid and, to a lesser extent, to imipenem, ceftazidime and cefotaxime. However, they were 100% sensitive to ciprofloxacin, amikacin, tobramycin, ofloxacin, piperacillin and gentamicin and showed 96% sensitivity to aztreonam and chloramphenicol.

    Non-enterobacteriaceae (Vibrionaceae)
     
    The results of the antibiotic susceptibility testing for bacterial isolates belonging to the non-Enterobacteriaceae family (Vibrionaceae) are presented in Fig 8.

    Fig 8: Antibiotic susceptibility test of bacterial isolates belonging to the non-Enterobacteriaceae (Vibrionaceae) family (own elaboration).


           
    The results obtained showed that the Vibrionaceae strains exhibited resistant ranging from 40% to 80% to amikacin, cefotaxime, ceftazidime, aztreonam, imipenem and ticarcillin-clavulanic acid, while the isolates remained 100% sensitive to ciprofloxacin, tetracycline, sulfamethoxazole- trimethoprim, chloramphenicol and gentamicin.
           
    Previous studies have documented varying antibiotic resistance patterns in aquaculture environments. For example, while Manjusha et al. (2005) found that Vibrio spp. isolated from coastal and tissue samples (shrimp, mussels and cuttlefish) exhibited higher resistance than those from aquaculture farms-likely due to greater antimicrobial use and pollution-our results indicate a similar trend, in that bacterial isolates from inland fish ponds display comparatively lower resistance levels.
           
    In addition, the resistance pattern observed for Aeromonas spp. in our study is consistent with earlier reports (Fosse et al., 2003; Henriques et al., 2006; Daood, 2012), which attributed β-lactam resistance to the presence of β-lactamase genes such as blaTEM, despite maintained efficacy for antibiotics like ciprofloxacin, gentamicin, amikacin and trimethoprim-sulfamethoxazole. This suggests that reduced antimicrobial application and lower environmental pressures in inland aquaculture may mitigate the development of resistance. Moreover, the widespread presence of mobile genetic elements (MGEs), reported in previous studies (Lamy, 2012; Del Castillo et al., 2013), supports our hypothesis that horizontal gene transfer is an important driver in the dissemination of resistance determinants in these ecosystems.
     
    Pseudomonadaceae
     
    The results of the antibiotic susceptibility testing for bacterial isolates belonging to the Pseudomonadaceae family are presented in Fig 9.

    Fig 9: Antibiotic susceptibility test of bacterial isolates belonging to the Pseudomonadaceae family (own elaboration).


           
    The results obtained showed that the Pseudomona-daceae strains were 100% resistant to ticarcillin-clavulanic acid and to a lesser degree, resistant to aztreonam and ceftazidime, with resistance rates of 80% and 40%, respectively. However, they were 100% sensitive to ciprofloxacin, amikacin, tobramycin, ofloxacin, piperacillin and gentamicin.
           
    All P. aeruginosa isolates formed biofilms (Fig 10), which enhances resistance and survival in aquatic environments. This biofilm-forming capacity may explain their persistence in low-flow, sediment-rich pond systems.

    Fig 10: Detection of biofilm formation using the tube method (Original, 2024)-Photo by E. Benyagoub.


           
    The Pseudomonas aeruginosa strains isolated were 100% sensitive to 6 out of 10 (i.e., 60%) antibiotics tested and exhibited lower resistance compared to the P. aeruginosa strains isolated in our previous study (Benyagoub, 2023a), which also found that all isolated strains were capable of forming biofilms, a characteristic that enhances the pathogen’s adaptability, survival and resistance in various environments (Moradali et al., 2017).
           
    However, due to the absence of standardized guidelines for antibiotic selection and interpretation, we did not include the AST results for Pantoea spp. and Chromobacterium violaceum strains in this study.
     
    Multidrug-resistant bacterial strains
     
    The strains exhibiting resistance to more than three classes of antibiotics, along with the antibiotic resistance patterns categorized by bacterial group, are presented in Table 3 and Fig 11.

    Table 3: Multidrug-resistant profiles of bacterial strains isolated from fish ponds wastewater.



    Fig 11: Antibiotic resistance patterns by bacterial group (own elaboration).


           
    Out of 68 bacterial isolates, 17 (25%) were resistant to more than three classes of antibiotics and exhibited high MAR indices. The variability in MAR indices among bacterial groups was statistically significant (ANOVA, p = 0.0498), suggesting that resistance levels differ across bacterial families. The elevated MAR values observed in Enterobacteriaceae (above 0.4) suggest sustained antibiotic pressure and fecal contamination, potentially originating from farm runoff, livestock waste, or human sources (Sarkar et al., 2019). Similarly, Rezaul Karim et al. (2023) reported non-Enterobacteriaceae isolates with MAR indices above 0.2, indicative of antibiotic misuse in both human and veterinary contexts. These findings highlight the urgent need for the implementation of best practice codes among fish farmers and routine monitoring of fish pond wastewater prior to environmental discharge, in order to prevent the transmission of potential pathogens (Sule et al., 2016).
           
    Comparable results have been reported elsewhere. Snoussi et al. (2011) found that Aeromonas hydrophila and Vibrio alginolyticus strains isolated from marine farm environments were resistant to at least three antimicrobial agents, with MAR indices of 0.71 and 0.68, respectively. Likewise, Abedin et al. (2020), identified Aeromonas spp., Pseudomonas spp. and Vibrio spp. as dominant bacteria in various fish species in Bangladesh, reinforcing concerns over antibiotic-resistant pathogens in aquaculture systems.
           
    These results point to a concerning level of resistance to several antibiotic classes, which, according to Adebami et al. (2020), could increase the prevalence of infections in fish and potentially trigger disease outbreaks.
           
    Gram-positive bacteria can develop resistance through two main strategies: one involves the enzymatic breakdown of the antibiotic via β-lactamase production, while the other reduces the binding affinity of the antibiotic to its target, the penicillin-binding protein (PBP) (Benyagoub et al., 2020b; Jubeh et al. 2020; Benyagoub et al., 2022; Benyagoub, 2024). However, Gram-negative bacteria can resist antibiotics through various mechanisms, such as reducing drug uptake, altering the drug target, inactivating the drug and actively pumping it out. Additionally, these bacteria can acquire resistance factors from other microorganisms (Reygaert, 2018; Benyagoub et al., 2020a; Benyagoub et al., 2021a, 2021b; Gauba and Rahman, 2023).

    Search for antibiotic residues
     
    The qualitative test (Fig 12) indicated the absence of detectable antibiotic residues in the samples.

    Fig 12: Qualitative detection of antimicrobial residues in fish pond wastewater (Original, 2024)-Photo by E. Benyagoub.


           
    This may reflect either the absence of recent antibiotic use or the limited sensitivity of the test method. However, indirect selection pressures-such as low-dose residues from reused water sources or contamination from surrounding agricultural activities-cannot be excluded.
           
    Manjusha et al. (2005) and Thiang et al. (2021) underscore the threat posed by low-level antibiotic exposure, which can still drive the propagation of resistance genes. The lack of residues should not be interpreted as the absence of selective pressure, especially considering the prevalence of multidrug-resistant bacteria. Effective management of aquaculture wastewater is crucial to reduce antibiotic pollution and prevent the transfer of resistance genes to humans through the food chain. This highlights the need for a deeper understanding of how drug resistance determinants are spread and transmitted, as well as the importance of enhancing antimicrobial stewardship (Del Castillo et al., 2013, Schar et al., 2020).

    CONCLUSION

    This study reveals the presence of diverse bacterial communities, including multidrug-resistant and biofilm-forming strains, in fish pond wastewater. The dominance of Gram-negative bacteria and Enterobacteriaceae reflects potential fecal contamination and poor waste management practices common in the study area.
           
    Despite no detectable antibiotic residues, high MAR indices and resistance patterns suggest ongoing selection pressures, likely from environmental or anthropogenic sources. These findings raise concerns about the potential transmission of resistance genes to human pathogens via water and food chains.
           
    To address this risk, we recommend the following practical steps:
    • Implement closed-loop or recirculating aquaculture systems (RAS) to reduce water contamination and limit the spread of antibiotic-resistant bacteria.
    • Encourage the use of probiotics or approved chemical disinfectants as alternatives to antibiotics for disease prevention and microbial control in aquaculture operations.
    • Educate fish farmers on the risks of indiscriminate antibiotic use and encourage adherence to good aquaculture practices (GAqPs).
    • Establish local monitoring frameworks for antimicrobial resistance and residue detection.
    • Treat aquaculture wastewater before environmental discharge, especially in regions near populated or agricultural zones.
           
    Such measures are essential to enhance antimicrobial stewardship, protect public health and ensure sustainable aquaculture development in Algeria and similar settings globally.

    ACKNOWLEDGEMENT

    Special thanks to Mr. Salhi L. and Mr. Touati T, owners of the aquaculture farms in Taghit and Nif Rhaa-Ouakda, Bechar (Algeria), respectively and to Mr. Boudani M., Manager of CNRDPA-Boukais (Bechar) for their support of this study by granting access to the farms.
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    Not applicable.

    CONFLICT OF INTEREST

    The authors declare that they have no conflicts of interest. No financial support was received for this research.

    REFERENCES


    1. Abedin, M.Z., Sadiqur Rahman, Md., Hasan, R., Shathi, J.H., Jarin, L., Sifat Uz Zaman, Md. (2020). Isolation, identification and antimicrobial profiling of bacteria from aquaculture fishes in pond water of Bangladesh. American Journal of Pure and Applied Bioscience. 2(3): 39-50. DOI: 10. 34104/ajpab.020.039050 

    2. Adebami, G.E., Fasiku, S.A., Solomon, O.D., Babalola, B.A. (2020). Physicochemical and microbial evaluations of different fish ponds’ wastewaters and the antibiotics profiles of isolated bacteria. Ethiopian Journal of Environmental Studies and Management. 13(4): 509-521. DOI: https:// ejesm.org/doi/v13i4.11.

    3. Ajayi, A.O. and Okoh, A.I. (2014). Bacteriological study of pond water for aquaculture purposes. Journal of Food, Agriculture and Environment. 12(2): 1260-1265. DOI: 10.1234/4.2014. 5325.

    4. Akshaya, L., Sarathchandra, G., Shanmugam, S.A., Felix, N., Kaliyamurthi, V. (2023). Microbial screening of antibiotic residues in soil and water of cultured fish farms in Tamil Nadu. Indian Journal of Animal Research. 57(4): 511- 516. doi: 10.18805/IJAR.B-5063.

    5. Apenteng, J.A, Osei-Asare, C., Oppong, E.E., Amihere, I., Hafiz, M.Y. (2017). Antibiotic sensitivity patterns of microbial isolates from fish ponds: A study in the greater Accra region of Ghana. African Journal of Pharmacy and Pharmacology. 11(28): 314-320. DOI: 10.5897/AJPP2017. 4789.

    6. Bauer, A.W., Perry, D.M., Kirby, W.M. (1959). Single disc antibiotic sensitivity testing of Staphylococci. AMA Archives of Internal Medicine. 104(2): 208-216. DOI : 10.1001/archinte. 1959.00270080034004.

    7. Benyagoub E., Nabbou, N., Bouchikhi F., Milioudi M.N. (2022). Chromatographic analyses and in vitro screening of the antimicrobial activity of tannins extract of fenugreek seeds grown in the Taghit region (Southwest of Algeria). Nutrafoods. 2022(1): 397-409. DOI: 10.17470/NF-022-0049.

    8. Benyagoub, E. (2022). Technological properties and antagonistic effect of lactic acid bacteria isolated from fermented goat milk flavoured with juniper leaves- a product from the Southwest region of Algeria-against some microbial contaminants. Nutrafoods. 2022(2): 419-437. DOI: 10. 17470/NF-022-0051. 

    9. Benyagoub, E. (2023a). Antibiotic susceptibility patterns and search for ESBL-producing Pseudomonas aeruginosa isolated from the feed water tank of a dairy unit located in Bechar province (Algeria). Fresenius Environmental Bulletin. 32(9): 3037-3049. 

    10. Benyagoub, E. (2023b). Tank water quality used for milk processing of a private dairy unit located in Ouakda-Bechar (South- western Algeria): Physicochemical analysis and micro- biological risk assessment. Fresenius Environmental Bulletin. 32(10): 3145-3156. 

    11. Benyagoub, E. (2024). Methicillin, β-lactams and clindamycin resistance profiles of Staphylococcus aureus strains isolated from patients with UTI in Bechar province (Algeria). Anti-infective Agents. 22(1): e220823220161.  DOI: 10.2174/2211352521666230822104016.

    12. Benyagoub, E., Alkhudhairy, M.K., Benchaib, S.M., Zaalan, A., Mekhfi, Y., Teyebi, N., Razni, D., Bendada, F. (2021a). Isolation frequency of uropathogenic strains and search for ESBL producing Enterobacteriaceae isolated from patients with UTI in Bechar (Algeria). Anti-Infective Agents. 19(3): 303-316. DOI: 10.2174/2211352518999201224102209.

    13. Benyagoub, E., Nabbou, N., Aguid, A., Alkhudhairy, M.K., Bendada, F. (2021b). In vitro antibacterial activity of funugreek seeds’ phytoconstituents from Taghit region (Southwest of Algeria) against the bacterial strains responsible for UTI. Current Bioactive Compounds. 17(4): 339-355. DOI: 10.2174/1573407216999200703123154.

    14. Benyagoub, E., Nabbou, N., Bendada, F. (2018). Characterization of microbial risk level of liquid waste from a private dairy factory in Bechar (Southwest of Algeria). Indian Journal of Dairy Science. 71(5): 524-529.

    15. Benyagoub, E., Nabbou, N., Boukhalkhel, S., Dehini, I. (2020b). The in vitro evaluation of the antimicrobial activity of Quercus robur L. methanolic and aqueous leaves’ extracts, from the Algerian high plateaus against some uro-pathogenic microbial strains. Phytothérapie. 18(5): 262-274. DOI: 10. 3166/phyto-2019-0133.

    16. Benyagoub, E., Nabbou, N., Dine, A. (2020a). Antimicrobial effect of Quercus robur L. leaves’ selective extracts from Mezi mountain of Djeniene Bourezg (West of Algeria). Current Bioactive Compounds. 16(8): 1181-1190. DOI: 10. 2174/1573407216666191226141609.

    17. Christensen, G.D., Simpson, W.A., Younger, J.J., Baddour, L.M., Barrett, F.F., Melton, D.M., Beachey, E.H. (1985). Adherence of coagulase-negative staphylococci to plastic tissue culture plates: A quantitative model for the adherence of staphylococci to medical devices. Journal of Clinical Microbiology. 22(6): 996-1006. DOI: 10.1128/jcm.22.6. 996-1006.1985.

    18. Clinical and Laboratory Standards Institute (CLSI) (2020). Performance standards for antimicrobial susceptibility testing M100, 30th edn. CLSI, Wayne Pennsylvania, US.

    19. Cretu, M., Dediu, L., Cristea, V., Zugravu, A., Turek Rahoveanu, M.M., Bandi, A-C., Turek Rahoveanu, A., Mocuta, D.N. (2016). Environmental impact of Aquaculture: A literature review. Proceedings of the 27th International Business Information Management Association (BIMA) conference on Innovation Management and Education Excellence Vision 2020: Regional Development to Global Economic Growth. May 4-5, 2016, Italy.

    20. Custodio, M., Peñaloza, R., Ordinola-Zapata, A., Peralta-Ortiz, T., Sánches-Suárez, H., Vieyra-Peña, E., De la Cruz, H., Alvarado- Ibáñez, J. (2023). Diversity of enterobacterales in sediments of lagoons with fish farming activity and analysis of antibiotic resistance. Toxicology Reports. 10: 235-244. DOI: 10.1016/j.toxrep.2023.02.002

    21. Daood, N. (2012). Isolation and antibiotic susceptibility of Aeromonas spp. from freshwater fish farm and farmed carp (Dam of 16 Tishreen, Lattakia). Journal for Basic Sciences. 28(1): 27-39.

    22. Degefu, F., Mengistu, S., Schagerl, M. (2011). Influence of fish cage farming on water quality and plankton in fish ponds: A case study in the Rift Valley and North Shoa reservoirs, Ethiopia. Aquaculture. 316(1-4): 129-135. DOI : 10.1016/ j.aquaculture.2011.03.010

    23. Del Castillo, C.S., Hikima, J-I., Jang, H-B., Nho, S-W., Jung, T-S., Wongtavatchai, J., Kondo, H., Hirono, I., Takeyama, H., Aokia, T. (2013). Comparative sequence analysis of a multidrug- resistant plasmid from Aeromonas hydrophila. Antimicrobial Agents and Chemotherapy. 57(1): 120-129. DOI: 10. 1128/AAC.01239-12.

    24. Dronen, K., Roalkvam, I., Nilsen, H., Olsen, A.B., Dahle, H., Wergeland, H. (2022). Presence and habitats of bacterial fish pathogen relatives in a marine salmon post-smolt RAS. Aquaculture Reports. 26: 101312. DOI: 10.1016/j.aqrep.2022.101312.

    25. Eghomwanre, A.F., Obayagbona, O.N., Imarhiagbe E.E. (2019). Bacteriological and physicochemical assessment of fish pond waters collected from Idogbo community, Edo state, Nigeria. FUW Trends in Science and Technology Journal.  4(2): 582-585. 

    26. FAO (2021). FAO’s contribution of fisheries and aquaculture towards achieving the 2030 agenda. 34th session, FAO committee on fisheries (COFI), Rome-Italy, 21.

    27. Fosse, T., Giraud-Morin, C., Madinier, I. (2003). β-lactam-resistance phenotypes in the genus Aeromonas. Pathologie Biologie. 51(5): 290-296. DOI: 10.1016/S0369-8114(03)00027-0.

    28. GADM maps and data (2024). Geographical map of Algeria.

    29. Gauba, A. and Rahman, K.M. (2023). Evaluation of antibiotic resistance mechanisms in Gram-negative bacteria. Antibiotics. 12(11): 1590. DOI: 10.3390/antibiotics12111590.

    30. Hamza, D., Dorgham, S., Ismael, E., Abd El-Moez, S.I., Elhariri, M., Elhelw, R., Hamza, E. (2020). Emergence of β-lactamase- and carbapenemase- producing Enterobacteriaceae at integrated fish farms. Antimicrobial Resistance and Infection Control. 9(1): 67. DOI : 10.1186/s13756-020- 00736-3.

    31. Henriques I.S., Fonseca F., Alves A., Saavedra M.J., Correia A. (2006). Occurrence and diversity of integrons and beta-lactamase genes among ampicillin-resistant isolates from estuarine waters. Research in Microbiology. 157(10): 938-947. DOI: 10.1016/j.resmic.2006.09.003.

    32. ISO 5667-1 (2023). Water quality-Sampling-Part 1: Guidance on the design of sampling programmes and sampling techniques. 

    33. Jubeh, B., Breijyeh, Z., Karaman, R. (2020). Resistance of Gram- positive bacteria to current antibacterial agents and overcoming approaches. Molecules. 25(12): 2888. DOI: 10. 3390/molecules25122888.

    34. Kirby, W.M., Yoshihara, G.M., Sundsted, K.S., Warren, J.H. (1956). Clinical usefulness of a single disc method for antibiotic sensitivity testing. Antibiotics Annual. 1956: 892-897.

    35. Kirmusaoglu, S. (2019). The methods for detection of biofilm and screening antibiofilm activity of agents. Ed. Intechopen, 152. DOI: 10.5772/intechopen.78751.

    36. Lamy, B. (2012). Aeromonas et aéromonoses en médecine humaine.  EMC- Biologie Médicale. 7(4): 1-7. DOI : 10.1016/S2211- 9698(12)57775-7.

    37. Lee, D.D., Lee, E.Y., Jeong, S.H., Chang, C.L. (2007). Evaluation of a colorimetric broth microdilution method for antimicrobial susceptibility testing using 2,3,5-Triphenyltetrazolium chloride. Korean Journal of Clinical Microbiology. 10(1): 49-53. 

    38. Lipps, W.C., Braun-Howland, E.B., Baxter, T.E. (2022). Standard methods for the examination of water and wastewater, 24th Ed., American Public Health Association (APHA), American Water Works Association (AWWA) and Water Environment Federation (WEF), New York-USA. pp: 1624.

    39. Manjusha, S., Sarita, G.B., Elyas, K.K., Chandrasekaran, M. (2005). Multiple antibiotic resistances of Vibrio isolates from coastal and brackish water areas. American Journal of Biochemistry and Biotechnology. 1(4): 193-198. DOI : 10.3844/ajbbsp. 2005.193.198.

    40. Moradali, M.F., Ghods, S., Rehm, B.H.A. (2017). Pseudomonas aeruginosa lifestyle: A paradigm for adaptation, survival and resistance. Frontiers in Cellular and Infection Microbiology. 7: 39. DOI: 10.3389/fcimb.2017.00039.

    41. Nabbou, N. and Benyagoub, E. (2025). Water quality of borehole and shallow wells feeding artificial fish ponds in Bechar province, Southwestern Algeria: Bacteriological and physicochemical aspects. Indian Journal of Agricultural Research. doi: 10.18805/IJARe.AF-928.

    42. Naylor, R.L., Goldbury, R.J, Primavera, J.H., Kautsky, N., Beveridge, M.C.M., Clay, J., Folke, C., Lubchenco, J., Mooney, H., Troell, M. (2000). Effect of aquaculture on world fish supplies. Nature. 405: 1017-1024. DOI: 10.1038/35016500.

    43. Njoku, O.E., Agwa, O.K., Ibiene, A.A. (2015). An investigation of the microbiological and physicochemical profile of some fish pond water within the Niger Delta region of Nigeria. African Journal of Food Science. 9(3): 155-162. DOI: 10. 5897/AJFS2014.1208.

    44. Ntengwe, F.W. and Edema, M.O. (2008). Physico-chemical and microbiological characteristics of water for fish production using small ponds. Physics and Chemistry of the Earth, Parts A/B/C. 33(8-13): 701-707. DOI: 10.1016/j.pce.2008. 06.032.

    45. Opiyo, M.A., Marijani, E., Muendo, P., Odede, R., Leschen, W., Charo- Karisa, H. (2018). A review of aquaculture production and health management practices of farmed fish in Kenya.  International Journal of Veterinary Science and Medicine. 6(2): 141-148. DOI : 10.1016/j.ijvsm.2018.07.001.

    46. Pandey, P.K., Kass, P.H., Soupir, M.L., Biwas, S., Singh, V.P. (2014). Contamination of water resources by pathogenic bacteria.  AMB Express. 4: 51. DOI: 10.1186/s13568-014-0051-x.

    47. Reygaert, W.C. (2018). An overview of the antimicrobial resistance mechanisms of bacteria. AIMS Microbiology. 4(3): 482- 501. DOI : 10.3934/microbiol.2018.3.482.

    48. Rezaul Karim, Md., Zakaria, Z., Hassan L., Mohd Faiz, N., Indah Ahmad, N. (2023). Antimicrobial resistance profiles and co-existence of multiple antimicrobial resistance genes in mcr-harbouring colistin-resistant Enterobacteriaceae isolates recovered from poultry and poultry meats in Malaysia. Antibiotics. 12(6): 1060. DOI: 10.3390/antibiotics 12061060.

    49. Sarkar, M., Chakraborty, S., Kundu, D., Ghosh, S., Khan, A., Karmakar, D., Ali, S., Mandal, S. (2019). Isolation and characterization of bacteria from sewage and pond water, Malda, India. Acta Scientific Microbiology. 2(11): 28-34. DOI: 10.31080/ ASMI.2019.02.0394.

    50. Schar, D., Klein, E.Y., Laxminarayan, R., Gilbert, M., Van Boeckel, T.P. (2020). Global trends in antimicrobial use in aquaculture. Scientifc Reports. 2020(10): 21878. DOI: 10.1038/s41598- 020-78849-3.

    51. Seghir, A., Abdenbi, A., Saad, A., Benyagoub, E. (2023). Antioxidant and antimicrobial activity of Moringa oleifera extracts from South West of Algeria. Phytothérapie. 21(4): 184- 193. DOI: 10.3166/phyto-2022-0366.

    52. Snoussi, M., Noumi, E., Lajnef, R., Belila, A., Yazidi, N., Bakhrouf, A. (2011). Phenotypic characterization and enterobacterial repetitive intergenic consensus polymerase chain reaction (eric-pcr) of Aeromonas spp. and Vibrio spp. strains isolated from Sparus aurata fish farm (Khenis, Tunisia). African Journal of Microbiology Research. 5(19): 2920- 2928.

    53. Sule, I.O., Agbabiaka, T.O., Ahmed, R.N., Saliu, B.K., Olayinka, K.J. (2016). Bacteriological and physicochemical analysis of waste water from fish ponds. Ethiopian Journal of Environmental Studies and Management. 9(2): 167-178. DOI: 10.4314/ejesm.v9i2.5.

    54. Thiang, E.L., Weng Lee, C., Takada, H., Seki, K., Takei, A., Suzukie, S., Wang, A., Bong, C.W. (2021). Antibiotic residues from aquaculture farms and their ecological risks in Southeast Asia: A case study from Malaysia. Ecosystem Health and Sustainability. 7(1): 1-18. DOI: 10.1080/ 20964129.2021.1926337.

    55. Tille, M.P. (2018). Bailey and Scott’s diagnostic microbiology, 14th Ed. Elsevier. pp: 1136.

    56. Wang, G., Xie, J., Yin, G., Yu, D., Yu, E., Wang, H., Gong, W. (2010). Influences of aquaculture on ecological environment. International Journal of Biology. 2(2): 158-164. DOI: 10. 5539/ijb.v2n2p158.

    57. Wisnu, R.P., Karuniasa, M., Moersidik, S.S. (2019). The effect of fish aquaculture on water quality in Lake Cilala, Bogor Regency. IOP Conference Series: Earth and Environmental Science. 399: 012111. DOI: 10.1088/1755-1315/399/1/012111.
    In this Article
      Contact us
      Follow us
      Published In
      Agricultural Science Digest

      Editorial Board

      View all (0)