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Article

Spatiotemporal Dynamics of the Thermophilic Benthic Harmful Dinoflagellates in Annaba Bay (Southern Mediterranean): Influence of Environmental Factors and Macrophyte Substrates

by
Ouafa Sad Laib
1,2,
Aicha Beya Amira
1,
Hocine Frihi
1,
Mounia Aouissi
3 and
Mohamed Laabir
2,*
1
Marine Bioressources Laboratory, Faculty of Sciences, Badji, Mokhtar University, PB 12, Annaba 23000, Algeria
2
University of Montpellier, MARBEC, IRD, Ifremer, CNRS, CEDEX 5, 34095 Montpellier, France
3
Department of Biologic Sciences, Faculty of Natural and Life Sciences, University Mohamed-Cherif Messaadia, Souk Ahras 41000, Algeria
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2026, 14(4), 398; https://doi.org/10.3390/jmse14040398
Submission received: 16 December 2025 / Revised: 11 February 2026 / Accepted: 11 February 2026 / Published: 22 February 2026
(This article belongs to the Special Issue Impacts of Climate Change on Marine Life)
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Abstract

For the first time in the bay of Annaba (Southern Mediterranean), we studied the spatiotemporal distribution of potentially toxic benthic dinoflagellates: Ostreopsis cf. ovata, Prorocentrum lima, Coolia monotis, and Amphidinium carterae, hosted by the dominant macrophytes Posidonia oceanica, Padina pavonica, Codium fragile, and Halopteris scoparia. Sampling of these macrophytes was conducted weekly during spring and summer as well as bi-weekly in autumn and winter, from October 2022 to November 2023, at contrasting sites within Annaba Bay. The measured environmental parameters included temperature, pH, dissolved oxygen, salinity, ammonium, nitrate, nitrite, dissolved organic nitrogen, dissolved inorganic nitrogen, phosphate, dissolved organic phosphorus, silicate, and chlorophyll a. A proliferation of O. cf. ovata was recorded in July 2023, coinciding with a marked increase in temperature, with a maximum abundance exceeding 40 × 103 cells g−1 of fresh weight (FW) on H. scoparia and C. fragile. The maximum abundance of P. lima reached 8700 cells g−1 FW on H. scoparia during July and August 2023. Coolia monotis exhibited a peak of 2800 cells g−1 FW on H. scoparia. The abundance of A. carterae increased with temperature, reaching a maximum of 980 cells g−1 FW on P. pavonica. The distribution of epiphytic dinoflagellates varied according to the macrophyte substrate. Overall, statistical analyses indicate that benthic dinoflagellate community structure is shaped by the combined effects of temperature, nutrient availability, and ecological niche differentiation, with temperature emerging as the dominant driver. This suggests that climate-driven increases in Mediterranean Sea surface temperatures are likely to extend the seasonal window of harmful benthic algal blooms, thereby enhancing ecological disturbances and potential risks to human health. This study provides the first assessment of BHAB dynamics along the Eastern Algerian coast, highlighting the role of ongoing regional warming in shaping future bloom patterns.

1. Introduction

Benthic harmful algal blooms (BHABs) represent a major environmental concern due to their potential damaging effects on marine ecosystems [1,2,3]. Certain BHAB species produce a variety of toxins that can accumulate throughout the food web [4,5,6,7], posing serious threats to both marine organisms and human health [2,8]. Over the past decades, research on BHABs has expanded significantly [3,9,10,11]. However, studies focusing on the warm Southern Mediterranean waters remain scarce and are still urgently needed. The geographic spread of BHAB species is facilitated by various vectors, including ballast water discharge and the translocation of shellfish carrying viable cysts or vegetative cells [12,13]. The increase in seawater temperature associated with climate change, together with the artificialization of coastal areas, has been identified as a major driver of the global expansion of BHABs [14,15,16,17]. In this context, several BHAB-forming species, including those investigated in the present study, have been described in the literature as thermophilic, meaning that they exhibit optimal growth, increased abundance, or enhanced physiological performance at relatively high temperature ranges compared to temperate counterparts [16,18,19,20]. The implementation of research and monitoring programs on BHAB species remains essential for understanding their dynamics and mitigating their impacts [8,21].
The environmental parameters that regulate epibenthic dinoflagellate populations may differ from those influencing planktonic species [18]. A key distinction lies in the ecological behavior of epibenthic dinoflagellates, which typically coexist in assemblages composed of species from the genera Prorocentrum, Ostreopsis, Coolia, Amphidinium, and Gambierdiscus; the latter is a typical tropical-to-subtropical species [22]. One of the defining characteristics of these species is their ability to attach to various substrates such as macrophytes, algal turf, rocks, corals, and detritus [5,6,23]. In aquatic ecosystems, abiotic factors such as temperature, salinity, nutrients, light, and hydrodynamic are recognized as key parameters influencing the proliferation of epibenthic dinoflagellates [6,10,20,24,25,26,27,28,29]. As noted by Glibert et al. (2018) [30], increasing temperature can enhance nutrient uptake by microalgae; however, their development is constrained by species-specific temperature tolerance limits [31]. BHAB occurrences appear to be predominant in tropical and subtropical regions [32,33]. Nevertheless, a notable increase in the number of BHAB species has been reported in temperate waters over the past two decades [6,10,23,34,35,36]. Although climate change and anthropogenic activities are thought to play a role in BHAB expansion, the complex interactions between abiotic and biotic factors and their effects on BHAB development is not fully understood [6]. The diversity, abundance, and distribution of BHAB species are likely influenced by multiple interacting environmental factors. For example, the proliferation of epibenthic dinoflagellates is closely related to the specific characteristics of macrophytes, such as their structural complexity and the release of allelopathic compounds [10,22,37,38,39,40,41]. In addition, grazing pressure by herbivores have also been shown to shape and control epibenthic dinoflagellate communities [18]. Numerous studies have been conducted in the Mediterranean to investigate the dynamic of epibenthic dinoflagellates and the environmental factors driving their blooms [19,24,26,27,36,39,42,43,44,45,46,47,48,49,50,51,52].
Among epiphytic dinoflagellates, O. cf. ovata, P. lima and C. monotis often represent an important fraction of potentially toxic assemblages [53]. Ostreopsis cf. ovata emerged as one of the main species responsible for toxic blooms in the Northern Mediterranean Sea and has been linked to several human health incidents [1,19,45,54,55,56,57]. This species forms floating aggregates at the sea surface and releases toxic aerosols, which can cause respiratory disorders and skin or eye irritation. Comprehensive descriptions of the intracellular profiles of the various Palytoxins (PLTX) analogs, namely ostreocins, mascarenotoxins, and ovatoxins produced by Ostreopsis species have been synthesized in the review paper by Pavaux et al. (2020) [58]. Along Mediterranean coastlines, several ovatoxins (OVTXs) as well as isobaric PLTX have been detected in mussels, sea urchins, and omnivorous or herbivorous fish [58,59,60]. In many cases, the measured concentrations exceeded the safety alert limit of 30 μg PLTX-equivalents per kilogram of fresh tissue recommended by the European Food Safety Authority [61,62,63,64]. Prorocentrum lima is a cosmopolitan species, widely distributed and often abundant in coastal environments. This dinoflagellate is known to synthesize several toxic compounds, including okadaic acid (OA) and its analogs, dinophysistoxins (DTXs), prorocentrolide, and prorocentin [65]. Prorocentrum lima has been implicated in diarrhetic shellfish poisoning (DSP) events worldwide and has also been suspected of contributing to ciguatera poisoning. Coolia monotis is generally considered nontoxic, although earlier studies suggested the existence of toxic strains producing cooliatoxin, exhibiting hemolytic activity [66,67,68]. Subsequent taxonomic revisions demonstrated that these toxic strains were misidentified and actually corresponded to Coolia tropicalis or Coolia malayensis [69].
To date, only two studies have reported the presence of harmful benthic dinoflagellates along the Algerian coast [45,70], and high-resolution data on their seasonal dynamics and substrate preferences remain lacking. In contrast, previous research in the region has primarily focused on neurotoxic planktonic dinoflagellates, such as Alexandrium pacificum [71,72,73]. In Annaba Bay, the interactions between toxic dinoflagellate species and local macrophytes as well as the influence of physicochemical environmental parameters have not yet been investigated. Addressing these knowledge gaps is essential for establishing robust ecological baselines and for assessing the potential impacts of benthic harmful algal blooms on coastal biodiversity and public health in the Southern Mediterranean.
The present study aims to: (1) monitor the dynamics of benthic dinoflagellates colonizing macrophytes at contrasting sites within Annaba Bay and (2) investigate the relationships between principal environmental factors (temperature, salinity, and nutrients), the colonized macrophytes, and the abundance of BHAB species.

2. Materials and Methods

2.1. Description and Geographic Location of the Study Area

The Bay of Annaba is located on the eastern coast of Algeria, in the southwestern Mediterranean Sea (Figure 1). It stretches approximately 40 km between Cape Rosa (8°15′ E, 36°58′ N) and Cape de Garde (7°16′ E, 36°58′ N). The maximum depth of the bay does not exceed 65 m [74,75]. It receives freshwater input from two main rivers: the Seybouse River, which flows directly into the southwestern part of the bay, and the Mafragh River to the east. In addition to freshwater, the bay is also impacted by various sources of pollution [75]; the primary sources of pollution include agricultural runoff, industrial discharges, and urban wastewater, all of which contribute to degraded water quality and a disrupted Redfield ratio (N:P:Si).
In the context of this study, four sampling stations were selected within Annaba Bay. Station 1 (ST1: 36°56′2″ N, 7°45′50″ E) is located at Caroube Beach, near a highly urbanized and anthropized area. Station 2 (ST2: 36°94′7″ N, 7°77′46″ E) is situated at Belvedere Beach, slightly further north and more distant from direct pollution sources (see Figure 1). Site selection is based on substrate characteristics (rocky coastal systems) and local hydrodynamic conditions. Both areas are known to be particularly productive, supporting a wide diversity of marine flora and fauna (Table 1).
Furthermore, the low hydrodynamic activity in these areas [76] may provide favorable conditions for the development of benthic dinoflagellates. During the summer season, two additional monitoring stations were established, as sampling at these sites was not feasible during the rest of the year. The third station (ST3: 36°57′ N, 7°48′17″ E) was located at Cap de Garde, in a zone far from anthropogenic influences and characterized by strong currents and high hydrodynamic disturbances. In contrast, the fourth station (ST4: 36°55′01″ N, 7°46′05″ E) is situated at Fellah Rachid Beach, an urban coastal area that is easily accessible to bathers and visitors during the summer. This site is characterized by the absence of macrophytes during colder months and only a sparse presence during the warm season (Table 1).

2.2. Sampling Frequency and Collection of Macrophytes

Between October 2022 and November 2023, samples of fresh leaves and thalli from three macrophyte species Posidonia oceanica, Padina pavonica, and Halopteris scoparia were collected weekly during the spring and summer seasons as well as bi-weekly during autumn and winter. These species were consistently present throughout the year, whereas other macrophytes, such as Codium fragile and Sargassum sp., were only observed for short periods of 3 to 4 months. To access areas where macrophytes thrive, samples were collected at depths ranging from 0.5 to 0.6 m. Approximately 30 g of leaves and thalli from each target species were carefully placed into hermetically sealed 500 mL plastic containers, which were closed underwater to retain the surrounding seawater. This step was essential to preserve sample integrity and ensure the reliability of subsequent analyses. After collection, all samples were promptly transported to the laboratory for further processing.

2.3. Physicochemical Parameters

Temperature (°C), salinity, pH, and dissolved oxygen (DO) in mg L−1 were measured at each station using a HANA 9829 multiparameter probe (Hanna Instruments, Smithfield, RI, USA). Moreover, 500 mL of seawater was collected from each site for the analysis of nutrient concentrations. In the laboratory, water samples were filtered using Whatman GF/C filters (47 mm diameter, 1.2 µm pore size). All nutrient analyses were performed on the same day. The concentrations of silicates (SiO4), phosphates (H3PO4), and dissolved inorganic nitrogen (DIN), the latter comprising nitrite (NO2), nitrate (NO3), and ammonium (NH4), were determined following the procedures described in the manual by Aminot and Chaussepied (1983) [77]. The dissolved organic fraction was quantified as dissolved organic nitrogen (DON) and dissolved organic phosphorus (DOP), based on the method outlined by Rodier et al. (1966) [78]. Total dissolved nutrients in phosphorus (TDP) and nitrogen (TDN) were calculated as follows: TDP = H3PO4 + DOP, TDN = DIN + DON, where DIN is the sum of NO2 + NO3 + NH4. All the nutrients’ concentrations are expressed in µmol L−1. Seasonal fluctuations in chlorophyll a and phaeopigments concentrations (µg L−1) were measured using the Lorenzen method (1967). For this, 1000 mL of seawater was filtered through Whatman filters, and pigment extraction was performed using methanol [79].

2.4. Enumeration, Identification, and Cellular Concentration

The water from the benthic macroalgae samples was sieved to remove debris using a 200 µm filter and then placed in a container. The detachment of epiphytes was carried out by rinsing macrophyte fragments with 250 mL of pre-filtered seawater (0.2 µm) while vigorously agitating for 20 to 30 s, a duration established in the literature to ensure high detachment efficiency [1], then filtering through 200 µm. The two samples (from the container and from the box) were mixed (final volume 750 mL). Three subsamples of 50 mL were fixed with neutralized formaldehyde (4%. final concentration) and then stored, protected from light. To estimate the number of epiphytes per gram of fresh weight of macrophytes, the latter were drained and dried with absorbent paper and weighed [10]. The morphological identification of benthic dinoflagellates was based on several publications and subsequent studies [5,32,49,80,81,82,83,84,85].
Two types of sedimentation chambers were used for counting epiphytes: an Utermöhl (1958) [86] (Hydro-Bios, Altenholz, Germany) chamber of 25 mL for low cell densities and a Sedgwick–Rafter (Graticules Optics, Panningen, Netherlands) cell of 1 mL for higher densities, a minimum of 400 cells was counted per sample to ensure statistical robustness. After a sedimentation time adjusted to the volume of the counting chamber (25 mL subsamples were allowed to settle for at least 12 h in sedimentation chambers before being examined), dinoflagellates were enumerated using an inverted microscope (Exacta Optech Mod. IB., Munich, Germany) by scanning the entire bottom of the sedimentation chamber. Cellular concentrations are expressed as the number of cells per gram of fresh weight of macrophyte (cells g−1 FW).

2.5. Statistical Analyses

Seasonal and spatial variations in physicochemical and biological variables were assessed using two-way analysis of variance (ANOVA), with months and stations as fixed factors. Importantly, this analysis was restricted to stations ST1 and ST2 to ensure a balanced temporal design across the complete sampling period (October 2022–November 2023). Stations ST3 and ST4, which were sampled exclusively during the summer, were included only for spatial and substrate-preference comparisons during the peak bloom period. When significant differences were detected, Tukey’s post hoc tests were applied to identify pairwise differences between groups. To explore the relationships between cell densities of epibenthic dinoflagellates (Ostreopsis, Prorocentrum, Coolia, and Amphidinium), physical parameters (temperature, salinity, dissolved oxygen, and pH), nutrients, and biological variables (chlorophyll a and phaeopigments), pairwise linear relationships among environmental variables were examined using Pearson’s correlation coefficient. Environmental controls on benthic dinoflagellate community structure were further investigated using redundancy analysis (RDA), implemented with the vegan package. The significance of each environmental variable in explaining the variability of the biological data was subsequently tested. All statistical analyses were performed using R software (version 4.2.2; R Core Team, Vienna, Austria, 2022).

3. Results

3.1. Seasonal Variation in Physicochemical Parameters

Figure 2 shows the variations in the measured physicochemical parameters. Overall, environmental variables exhibited pronounced seasonal variability (Figure 2), as confirmed by tow-way ANOVA (Table A1) and Tukey’s HSD post hoc tests (Figure A2). In particular, temperature showed a strong seasonal variation (p < 0.001), increasing from mean values of 21.72 ± 4.07 °C at Station 1 and 21.86 ± 4.10 °C at Station 2 to 25.43 ± 2.41 °C and 26.17 ± 2.22 °C at Stations 3 and 4, respectively (Table 2). Consistent with this pattern, the highest seasonal mean temperatures were recorded in summer (25–27 °C), whereas the lowest values occurred in spring and winter (15–18 °C) (Figure 2).
Similarly, salinity (Sal) varied significantly among seasons (p < 0.001), although within a relatively narrow range (Table A1). Mean salinity values ranged from 36.84 ± 0.77 at Station 1 to 36.98 ± 0.82 at Station 2 (Table 2), with a marked decrease during spring and higher values during summer and autumn (Figure 2).
In contrast, dissolved oxygen (DO) exhibited an inverse seasonal pattern (p < 0.001) (Figure A2). Mean DO concentrations were highest during autumn and winter, reaching 9.64 ± 1.25 mg L−1 at Station 2, while lower values were observed during summer, with a minimum of 7.22 mg L−1. Concurrently, pH displayed significant seasonal variation (p < 0.001), increasing from mean values of 8.42–8.50 in autumn/winter to 8.61–8.65 during spring and summer (Figure 2).

3.2. Seasonal Dynamics of Nutrients

Figure 3 shows the seasonal variation in nutrient concentrations in the studied stations. The two-way ANOVA indicated that seasonal variability was the primary driver of physicochemical conditions in Annaba Bay, with significant temporal effects observed for most variables (Table A1 and Figure A1). In contrast, spatial variability among stations was limited and significant only for ammonium, silicate, and chlorophyll a. Phosphate and DOP remained stable across both temporal and spatial scales. Overall, these results highlight the dominance of seasonal forcing over spatial heterogeneity in structuring environmental dynamics in the study area.
Regarding nitrogen, distinct seasonal trends were evident. Nitrite varied significantly across seasons (p < 0.001) (Table A1), with summer mean concentrations reaching 3.58 µmol L−1 at Station 2 compared with values of below 1 µmol L−1 during autumn and spring. In addition, ammonium showed significant seasonal differences (p = 0.0057), with higher mean concentrations in autumn and winter (up to 2.30 µmol L−1 at Station 1) and markedly lower values in summer, particularly at Stations 3 and 4 (0.27–0.59 µmol L−1). By contrast, nitrate did not exhibit significant seasonal variation (p = 0.66), despite spatial differences in mean concentrations ranging from 1.18 µmol L−1 at Station 3 to 3.58 µmol L−1 at Station 2 (Figure 2). Similarly, DIN remained statistically stable throughout the year (p = 0.21), with mean values between 2.22 and 5.35 µmol L−1 (Figure 3).
Nutrient analyses revealed a clear predominance of organic forms over inorganic fractions throughout the study period (Figure 3). Dissolved organic nitrogen (DON) constituted the major component of the total dissolved nitrogen (TDN) pool, accounting for 59.98–84.20% of TDN, while dissolved inorganic nitrogen (DIN) remained at or near detection limits during the summer bloom peaks. This dominance of organic nitrogen was particularly pronounced at Stations 1 and 4. Similarly, dissolved organic phosphorus (DOP) represented 55.93–77.83% of total dissolved phosphorus across all stations. Overall, these findings indicate that organic nutrient forms contributed substantially more to total nutrient availability than inorganic fractions.
In parallel, phosphate showed significant seasonal variation (p = 0.0053), with mean summer concentrations reaching 0.79 µmol L−1 at Station 4, compared with values close to zero during spring and winter (Figure 2). Furthermore, silicate varied markedly (p < 0.001), increasing from mean spring values of ~1.5–2.0 µmol L−1 to summer and winter maxima exceeding 7.0 µmol L−1 (Figure 3).

3.3. Biological Indicators

Biological variables also reflect seasonal forcing (Table 1 and Figure A1). Chlorophyll a displayed strong seasonality (p < 0.001), with mean concentrations increasing from approximately 1.0 µg L−1 in winter to more than 3.0 µg L−1 in summer, indicating enhanced phytoplankton biomass during warmer periods (Figure 4). Conversely, Phaeopigments did not differ significantly among seasons (p = 0.49), with mean values remaining consistently below 2.0 µg L−1.

3.4. Epibenthic Assemblage

During the study period (October 2022–November 2023), a diverse assemblage of microalgal taxa was identified. Although various diatoms and dinoflagellates were sporadically observed, these groups were recorded on a qualitative basis only. In accordance with a targeted monitoring strategy, quantitative analyses were focused on the four dominant and potentially harmful benthic dinoflagellates (identified morphologically as Ostreopsis cf. ovata, Prorocentrum lima, Coolia monotis, and Amphidinium carterae), as these taxa represent the primary ecological and public health concerns in Annaba Bay. These species were found in association with various macrophytes hosts, including Posidonia oceanica, Padina pavonica, Halopteris scoparia, Codium fragile, and Sargassum sp.; they exhibited variable cell abundances throughout the monitoring period (Figure 5, Figure 6, Figure 7 and Figure 8). For the first time, the four recorded benthic dinoflagellates in field samples (O. cf. ovata, P. lima, C. monotis and A. carterae) were isolated from Annaba Bay and monoclonal cultures were established. Ribotyping analyses confirmed the morphological identification of the three species (Sad Laib et al., in preparation).
The results suggest that temperature plays a key role in influencing the distribution and occurrence of BHAB.
Ostreopsis cf. ovata exhibited the highest cell abundance, exceeding 40 × 103 cells g−1 fresh weight (FW) on H. scoparia and C. fragile in July 2023 at Stations 1 and 4, respectively (Figure 5a,c). This species was absent in January, when water temperatures were at their lowest, and reappeared in June (Figure 5). This seasonal pattern reflects a strong and highly significant positive correlation between O. cf. ovata abundance and temperature (r = 0.75, p = 0.01) (Figure A2). In contrast, P. lima was present throughout the sampling period, reaching a peak abundance of 8700 cells g−1 FW on H. scoparia at Station 1, coinciding with the maximum abundance of O. cf. ovata (Figure 6c). The lowest cell abundance for P. lima (65 cells g−1 FW) was recorded in February on P. pavonica (Figure 6b).
In the case of C. monotis, April favored its development, with densities exceeding 2800 cells g−1 FW on H. scoparia (Figure 7c). Amphidinium carterae reached its highest abundance in June at station 1, with 980 cells g−1 FW recorded on P. pavonica (Figure 8b).
The results show that the abundances of BHAB species observed in Annaba Bay reached their highest values during the summer season, with O. cf. ovata being the dominant dinoflagellate exceeding 40 × 103 cells g−1 fresh weight (FW) on H. scoparia and C. fragile. Similarly, the other dinoflagellates, namely P. lima, C. monotis, and A. carterae, reached their maximum abundance on the macrophyte H. scoparia. To better understand the spatiotemporal distribution and diversity of the BHAB species, two additional stations (ST3 Cap La Garde and ST4 Fellah Rachid Beach) were sampled, each characterized by a distinct macrophyte habitat. The data indicate that the highest abundances were recorded at Station 4 on the macrophyte C. fragile (Figure 5a and Figure 9). Similarly to ST1 and ST2, the two additional stations exhibited the same trend in dinoflagellate distribution, with a marked dominance of O. cf. ovata on both C. fragile and Sargassum sp. Across all macroalgal substrates, O. cf. ovata exhibited the highest cell abundances particularly on C. fragile and Sargassum sp. (Figure 9). Amphidinium carterae and C. monotis consistently showed lower and more stable abundances, with more than 2800 cells g−1 fresh weight (FW) and 980 cells g−1 fresh weight (FW), respectively. Prorocentrum lima displayed intermediate abundances, with higher values on P. pavanica, P. oceanica and Sargassum sp. compared to other macrophytes. Overall, the results highlight a strong substrate-dependent pattern in BHAB species distribution, with Ostreopsis clearly dominating epiphytic assemblages.

3.5. Influence of Biotic and Abiotic Parameters on BHAB Dynamics

Redundancy analysis (RDA) revealed significant relationships between environmental variables and BHAB community structure (Figure 10 and Figure 11). Temperature was the strongest explanatory variable, accounting for approximately 5.3% of the total variance in community composition and showing a highly significant effect (p < 0.001). Among nutrient variables, NO2 and NO3 explained approximately 1.1% and 0.9% of the variance, respectively, and were also highly significant (p < 0.001) (Figure 10 and Figure 11). Pheophytin a and salinity each accounted for about 0.6% of the explained variance (p < 0.01), while DIN explained approximately 0.5% (p < 0.01). Dissolved organic phosphorus and TDS contributed more modestly, each explaining around 0.4% of the variance (p < 0.05). Chlorophyll a accounted for approximately 0.3% of the variance and was marginally significant (p < 0.05) (Figure 8). In contrast, DON explained less than 0.3% of the variance and showed only a weak association with community structure, while DO, PO4, SiO4, NH4, and pH each explained less than 0.2% of the variance and were not statistically significant (p > 0.05). Overall, the RDA results indicate that temperature- and nitrogen-related variables are the dominant drivers of community structure, whereas other physicochemical factors play comparatively minor roles (Figure 10 and Figure 11).
The combined interpretation of the correlation analysis and the RDA provides a coherent understanding of the environmental controls governing the distribution of the studied epibenthic dinoflagellates (O. cf. ovata, P. lima, C. monotis and A. carterae) (Figure A2). The correlation matrix highlights strong positive relationships among these genera, particularly between O. cf. ovata and P. lima (r ≈ 0.74) as well as with C. monotis (r ≈ 0.61) and A. carterae (r ≈ 0.51), indicating a shared ecological niche and common responses to environmental forcing. A direct correlation was observed between rising temperatures and an increase in bloom events for this species (Figure 10). Among abiotic factors, temperature shows consistent positive correlations with all four genera (r ≈ 0.32–0.47), underscoring its central role in promoting epibenthic dinoflagellate proliferation. This pattern is further supported by the RDA results, where temperature emerges as the most influential driver of community structure, explaining the largest proportion of variance (≈ 5–6%, p < 0.001), thus confirming its dominant regulatory effect (Figure 10 and Figure 11).
Additionally, chlorophyll a was positively correlated with temperature (r = 0.59, p < 0.001), indicating enhanced phytoplankton biomass under warmer conditions. Moreover, chlorophyll a exhibited significant positive correlations with nitrogen-related nutrients, including dissolved inorganic nitrogen (DIN; r = 0.47, p < 0.001), nitrate (NO3; r = 0.28, p = 0.011), and ammonium (NH4; r = 0.34, p = 0.0017) (Figure A2). Taken together, these relationships suggest that seasonal warming, coupled with increased nitrogen availability and regeneration, jointly promoted phytoplankton growth. In addition to temperature, nutrient-related variables contribute significantly, although with differentiated roles. Nitrogen species such as NO2 and NO3 display moderate correlations with dinoflagellate abundance (r generally < 0.4), yet they appear as significant predictors in the RDA, with NO2 and NO3 explaining notable fractions of variance (≈0.8–1.2%, p < 0.01). This suggests that while inorganic nitrogen alone does not directly control blooms, its seasonal availability modulates community composition. Moreover, DOP contributed more modestly, explaining around 0.4% of the variance (p < 0.05). Chlorophyll a accounted for approximately 0.3% of the variance and was marginally significant (p < 0.05). In contrast, DON explained less than 0.3% of the variance and showed only a weak association with community structure, while DO, PO43, SiO4, NH4, and pH each explained less than 0.2% of the variance and were not statistically significant (p > 0.05). Overall, the RDA results indicate that temperature- and nitrogen-related variables are the dominant drivers of community structure, whereas other physicochemical factors play comparatively minor roles (Figure 10 and Figure 11).
Consequently, the co-occurrence and proliferation of Ostreopsis, Prorocentrum, Coolia, and Amphidinium appear to result from the combined effect of warm conditions, moderate nutrient availability, and stable coastal environments, a pattern characteristic of epibenthic harmful algal assemblages in temperate to subtropical coastal systems.

4. Discussion

4.1. Occurrence of Benthic Harmful Algal Blooms (BHABs) in Annaba Bay

The present study investigated the dynamics of benthic dinoflagellates colonizing dominant macrophytes across contrasting sites in Annaba Bay (Southern Mediterranean Sea) and examined their relationships with environmental variables. Several of the recorded taxa are known producers of bioactive or toxic compounds, with O. cf. ovata considered to be among the most harmful benthic dinoflagellates due to its ability to produce volatile toxic compounds that can cause respiratory disorders in humans [19,61,62,87,88]. In addition to O. cf. ovata, the assemblage included P. lima a producer of lipophilic shellfish toxins, C. monotis, and Amphidinium carterae, all of which have been associated with toxin production or cytotoxic activity in previous studies [67,89,90,91,92]. Nevertheless, it is important to note that toxicity can vary markedly among species, strains, and regions, and that the presence of potentially toxic taxa does not necessarily imply toxin production or health risk in situ.

4.2. Spatial and Seasonal Variability of Ostreopsis cf. ovata Abundance

The seasonal pattern observed in Annaba Bay is consistent with previous Mediterranean studies reporting summer–autumn peaks of Ostreopsis spp. [10,36,44,93]. In July 2023, O. cf. ovata reached a maximum abundance of 4.4 × 105 cells g−1 FW on C. fragile at ST4 (Fellah R Beach, Annaba Bay), coinciding with elevated water temperatures. These abundances fall within the range reported for several Mediterranean regions, including Morocco (2.7 × 105 cells g−1 FW) [36] and Italy (5.28 × 105 cells g−1 FW) [44], although they remain lower than peak values exceeding 106 cells g−1 FW reported in parts of the Northwestern Mediterranean [27]. Conversely, lower abundances have been reported in Tunisian waters [10,49,50,94], highlighting substantial spatial heterogeneity across the Mediterranean basin. The higher abundances compared to Tunisian lagoons (e.g., Bizerte) likely result from the prevalence of rocky substrates and macroalgae like C. fragile and H. scoparia in Annaba, which provide higher surface-to-volume ratios for attachment than the seagrass-dominated or sandy environments often found in Southern Mediterranean lagoons. Furthermore, the localized anthropogenic pressure at ST1 and ST4, characterized by high nutrient inputs from urban runoff, creates a localized eutrophication that supports higher biomass than the more oligotrophic open waters of the Algerian coast. Such variability likely reflects site-specific combinations of hydrodynamics, substrate availability, nutrient regeneration, and anthropogenic pressure rather than uniform regional drivers.

4.3. Dynamics of Prorocentrum lima, Coolia monotis, and Amphidinium carterae

Unlike O. cf. ovata, P. lima was present throughout the sampling period and exhibited two peaks in July and August, with abundances of up to 8 × 103 cells g−1 FW. This year-round persistence is likely due to its broad thermal tolerance and mixotrophic capabilities, allowing it to survive in colder, nutrient-limited winter waters where more specialized species fail to thrive. Our values for P. lima are comparable to those reported from Morocco [36], Tunisia [10,51], Italy [44,95,96,97], and France [25,48], confirming the widespread seasonal persistence of this species in Mediterranean coastal systems. Coolia monotis reached maximum abundances of 2.8 × 103 cells g−1 of fresh wight of H. scoparia in ST2 during April 2023, comparable to observations from the Aegean Sea but notably higher than those reported from Tunisia and Egypt, with 8.4 × 102 cells g−1 FW on Cymodocea nodosa in autumn 2001 in the Bay of Marsa (Tunisia) [94] and 1.1 × 103 cells g−1 FW of the same dinoflagellate on C. nodosa in May in the Bay of Bizerte, as documented by Ben-Gharbia et al. (2019) [10]. Ismael et al. (2014) [47] recorded densities of 4.54 × 102 cells g−1 FW in summer 2005 along the coast of Alexandria (Mediterranean Sea). Such elevated abundances in Annaba Bay are likely favored by the local hydrodynamic conditions of the study sites. Coolia monotis is known to thrive in sheltered environments characterized by low hydrodynamic conditions and high organic matter accumulation [1,34], such as Stations 1 and 2, which may provide a particularly stable and suitable niche for this species. Although the genus Coolia has historically been associated with toxicity, recent taxonomic revisions indicate that toxic strains are largely confined to Coolia tropicalis and Coolia malayensis [67,98,99]. Consequently, the ecological significance of C. monotis in Annaba Bay is more likely related to benthic community structure than to direct health risks. Amphidinium carterae was observed at comparatively low abundance (maximum 8.5 × 103 cells g−1 FW), consistent with previous reports suggesting that this genus often plays a secondary role in Mediterranean BHAB assemblages. This species has been associated with biological activity [92,100,101].

4.4. Role of Temperature in Shaping BHAB Dynamics

In the Mediterranean Sea, temperature has emerged as the environmental variable most strongly associated with BHAB abundance. This pattern was corroborated by our study, which revealed a highly significant positive correlation between Ostreopsis cf. ovata abundance and seawater temperature, with maximum cell densities coinciding with exceptionally high temperatures (30.1 °C) recorded in July 2023. Although this temperature exceeds the laboratory-determined optimal growth range of approximately 25 °C [88,102], it is important to distinguish between instantaneous physiological optima and ecologically observed peak abundances. Moreover, the relationship between temperature and benthic harmful algal bloom (BHAB) species is often species- and strain-specific and strongly modulated by local environmental conditions. The maximum cell density recorded at 30.1 °C likely reflects the cumulative effects of a prolonged warming period combined with high water column stability, conditions that favor cell accumulation while limiting physical dispersion. In semi-enclosed systems such as Annaba Bay, high summer temperatures may also enhance metabolic activity or coincide with peak irradiance levels, thereby sustaining high standing stocks even as intrinsic growth rates begin to plateau [103]. Consequently, these exceptionally high temperatures may enhance bloom intensity through environmental forcing and water column stratification rather than acting as a sole physiological driver. However, several studies have reported weak or inconsistent temperature BHAB species abundance relationships [104], suggesting that temperature interacts with additional environmental and biological factors.

4.5. Nutrient Availability and Benthic Dinoflagellate Responses

The apparent decoupling between nutrient concentrations and BHAB species abundance found in our study likely reflects fundamental ecological differences between planktonic and benthic microalgae, particularly in their nutrient acquisition strategies and habitat dependence. Benthic species are sessile and often rely on localized nutrient regeneration at the substrate–water interface rather than ambient water column concentrations. Specifically, host macrophytes actively contribute to this nutrient pool through the physiological leaching of DON and DOP or by passively trapping organic detritus within their complex architectural structures [1]. In our study, the dominance of organic nutrient fractions, particularly DON, which represented the largest portion of the total nitrogen pool, further supports the hypothesis that internal recycling and the remineralization of organic matter by the associated bacterial community play a key role in sustaining BHAB populations [30]. This mechanism allows benthic dinoflagellates to thrive even when inorganic nutrients are depleted in the surrounding water column [105].

4.6. Influence of Macrophyte Substrates

Macrophyte diversity emerged as a critical factor structuring BHAB distribution. Across stations, Ostreopsis and Coolia together accounted for approximately 80% of total benthic dinoflagellate abundance, with H. scoparia identified as the most favorable substrate at Stations 1, 2 and 3. At Station 4, the invasive macroalga C. fragile supported the highest abundances of O. cf. ovata, P. lima, and C. monotis. The pilose and spongy morphology of C. fragile likely enhances cell attachment and retention, providing a structurally complex microhabitat. In addition, allelopathic interactions between macrophytes and epiphytic microalgae may further influence colonization success [38,40].

4.7. Environmental Gradients Structuring Benthic Dinoflagellate Communities Revealed by Redundancy Analysis

The redundancy analysis ordination reveals a structured response of benthic dinoflagellate communities to interacting physicochemical and nutrient gradients with RDA1 and RDA2 explaining 49.44% and 17.23% of the total variance, respectively. The first canonical axis (RDA1) represents a dominant environmental gradient primarily driven by thermal conditions and salinity, whereas the second axis (RDA2) captures variation associated with nutrient speciation and organic matter dynamics.
Temperature and salinity exhibit strong, collinear vectors along RDA1, indicating their primary role in constraining community composition. Samples collected during summer cluster distinctly along the negative side of RDA1, reflecting the influence of elevated temperatures on community structure. Ostreopsis cf. ovata aligns closely with this thermal–salinity gradient, demonstrating a strong positive association with warm, saline conditions. This spatial configuration supports the interpretation that O. cf. ovata functions as a thermophilic, seasonally restricted taxon, whose proliferation is favored under high-temperature regimes rather than by nutrient enrichment alone. In contrast, P. lima is positioned along the positive side of RDA1 and shows close association with NH4, DIN and pH, indicating a distinct ecological strategy characterized by sensitivity to regenerated nitrogen forms.
The co-occurrence of winter and spring samples within this ordination sector suggests that P. lima may exploit periods of enhanced nutrient recycling and reduced thermal stress, thereby maintaining elevated abundances outside peak summer conditions. However, the mechanisms by which nutrient availability influences epibenthic dinoflagellate bloom dynamics remain incompletely understood [6,14,20,24,25,30]. In oligotrophic environments, mixotrophic behavior may provide a competitive advantage, allowing species such as O. cf. ovata, P. lima and C. monotis to supplement inorganic nutrient uptake through the assimilation of organic dissolved or particulate nutrient sources, notably via phagotrophy [6,20].
Along RDA2, C. monotis and Amphidinium carterae segregate toward vectors associated with DOP, NO3, and NO2, highlighting their linkage to organic nutrient pools and micro-scale benthic nutrient regeneration [18,30,106]. Their proximity to the ordination origin indicates broader ecological tolerance and reduced dependence on any single environmental driver, consistent with opportunistic or substrate-mediated life strategies. Seasonal structuring is clearly expressed in the ordination space. Summer samples are primarily structured by temperature forcing, whereas autumn and winter samples exhibit stronger associations with nutrient-related variables, underscoring a seasonal shift from temperature-driven to biogeochemically mediated controls. Importantly, the relatively short length of most nutrient vectors, compared with temperature and salinity, indicates that nutrients act as secondary modulators rather than primary drivers of community structure at the scale of observation.
Altogether, the RDA results demonstrate that benthic dinoflagellate community organization arises from the interaction of thermal forcing, nutrient form, and ecological niche differentiation. These findings reinforce the view that BHAB dynamics are governed by multifactorial controls, in which temperature sets the environmental window for bloom development, while nutrient input and regeneration and substrate-associated processes modulate species-specific responses.

5. Conclusions

Potentially harmful benthic dinoflagellates were recorded for the first time in Annaba Bay, with a marked dominance of O. cf. ovata and P. lima. The presence of potentially toxic species underscores a risk to public health, necessitating the implementation of regular monitoring for both cell densities and emerging biotoxins.
Our findings identify temperature as the overarching factor governing BHAB dynamics. It exerted a strong positive influence on the proliferation of O. cf. ovata. Regarding nutrients, the results suggest that these dinoflagellates are well-adapted to oligotrophic coastal waters, with some species maintaining populations even when inorganic nutrient levels are low. While opportunistic species are temperature-dependent, others like C. monotis and A. carterae persist as a more stable and constant component of the epiphytic community. The distribution of the found BHAB species was substrate-dependent, with the highest densities recorded on the native Halopteris scoparia and the invasive Codium fragile. The successful colonization of C. fragile by all four genera suggests that this invasive macroalga may act as a favorable permanent substrate, potentially facilitating the expansion of BHABs in the bay. Further research is required to explore host-preference trends and potential allelopathic interactions between these toxic dinoflagellates and their macrophyte substrates.

Author Contributions

Conceptualization, H.F.; methodology, H.F., O.S.L. and M.A.; software, O.S.L. and A.B.A.; validation, O.S.L., H.F. and M.L.; formal analysis, O.S.L. and A.B.A.; investigation, O.S.L.; resources, M.L. and H.F.; data curation, O.S.L.; writing—original draft preparation, O.S.L.; writing—review and editing, M.L. and A.B.A.; visualization, O.S.L.; supervision, M.L., A.B.A. and H.F.; project administration, H.F.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Faculty of Sciences, Badji Mokhtar University Annaba, Algeria. The PhD project was funded partially by CIBSEEA (MUSE Montpellier University) and EXALTOX (EXPOSUM Montpellier University). We would like to express our sincere gratitude to the Laboratory of Marine Biodiversity, Exploitation and Conservation (MARBEC) for partially funding Sad Laib Ouafa’s doctoral research.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

Thanks to the French Research Institute for Development (IRD) for the valuable support of Sad Laib. We also thank the Marine Bioresources Laboratory (BIOMAR) of Badji Mokhtar University for providing the necessary facilities and resources for field sampling.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A

Table A1. Results of ANOVA two-way testing the effects of months (temporal variability) and stations (Stations 1 and 2) (spatial variability) on physicochemical and biological variables measured in Annaba Bay. Values represent p-values for each factor. Significance levels are indicated as follows: *** p < 0.001; ** p < 0.01; * p < 0.05; ns = non-significant (p ≥ 0.05). The ANOVA p-values are shown in red (ns: not significant).
Table A1. Results of ANOVA two-way testing the effects of months (temporal variability) and stations (Stations 1 and 2) (spatial variability) on physicochemical and biological variables measured in Annaba Bay. Values represent p-values for each factor. Significance levels are indicated as follows: *** p < 0.001; ** p < 0.01; * p < 0.05; ns = non-significant (p ≥ 0.05). The ANOVA p-values are shown in red (ns: not significant).
MonthsStations
T°0.000 ***0.175 ns
S0.000 ***0.972 ns
OD0.005 **0.183 ns
TDS0.002 **0.421 ns
PH0.000 ***0.762 ns
NO20.003 **0.213 ns
NO30.028 *0.377 ns
NH40.076 ns0.045 *
DON0.002 **0.069 ns
DIN0.004 **0.341 ns
DOP0.797 ns0.420 ns
PO40.095 ns0.680 ns
SiO40.000 ***0.003 **
Chl a0.000 ***0.007 **
Jmse 14 00398 g0a1
Figure A1. Monthly variation in environmental variables during the months where BHAB species proliferate (June, July, August). Boxplots show median, interquartile range, and extreme values. Differences among months were tested using one-way ANOVA, followed by Tukey’s HSD post hoc test when significant. Letters (a, b, ab) indicate statistical groupings: months sharing the same letter are not significantly different, while different letters indicate significant differences (p < 0.05).
Figure A1. Monthly variation in environmental variables during the months where BHAB species proliferate (June, July, August). Boxplots show median, interquartile range, and extreme values. Differences among months were tested using one-way ANOVA, followed by Tukey’s HSD post hoc test when significant. Letters (a, b, ab) indicate statistical groupings: months sharing the same letter are not significantly different, while different letters indicate significant differences (p < 0.05).
Jmse 14 00398 g0a1
Jmse 14 00398 g0a2
Figure A2. Pearson correlation heatmap of environmental variables and cell abundances, showing correlation coefficients (r) and p-values in each cell.
Figure A2. Pearson correlation heatmap of environmental variables and cell abundances, showing correlation coefficients (r) and p-values in each cell.
Jmse 14 00398 g0a2

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Figure 1. Map of the sampling stations. Station 1: Caroube Beach; Station 2: Belvedere Beach; Station 3: Cap de Garde; and Station 4: Fellah Rachid Beach located in the Annaba Bay (Northeastern Algeria).
Figure 1. Map of the sampling stations. Station 1: Caroube Beach; Station 2: Belvedere Beach; Station 3: Cap de Garde; and Station 4: Fellah Rachid Beach located in the Annaba Bay (Northeastern Algeria).
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Figure 2. Spatiotemporal variations in physicochemical parameters: (a) temperature (°C), (b) salinity, (c) dissolved oxygen (mg L−1), and (d) pH from October 2022 to November 2023 in Station 1 (La Caroube Beach) and 2 (Belvedere Beach) and during the summer season in Station 3 (Cap de Garde) and Station 4 (Fellah Rachid Beach).
Figure 2. Spatiotemporal variations in physicochemical parameters: (a) temperature (°C), (b) salinity, (c) dissolved oxygen (mg L−1), and (d) pH from October 2022 to November 2023 in Station 1 (La Caroube Beach) and 2 (Belvedere Beach) and during the summer season in Station 3 (Cap de Garde) and Station 4 (Fellah Rachid Beach).
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Figure 3. Spatio-temporal variations in chemical parameters (μmol L−1): (a) dissolved inorganic nitrogen (DIN), (b) dissolved organic nitrogen (DON), (c) silicate (Si(OH)4), (d) dissolved organic phosphorus (DOP), and (e) phosphate (PO4), from October 2022 to November 2023 in Stations 1 (La Caroube Beach) and 2 (Belvedere Beach) and during the summer season in Station 3 (Cap de Garde) and Station 4 (Fellah Rachid Beach).
Figure 3. Spatio-temporal variations in chemical parameters (μmol L−1): (a) dissolved inorganic nitrogen (DIN), (b) dissolved organic nitrogen (DON), (c) silicate (Si(OH)4), (d) dissolved organic phosphorus (DOP), and (e) phosphate (PO4), from October 2022 to November 2023 in Stations 1 (La Caroube Beach) and 2 (Belvedere Beach) and during the summer season in Station 3 (Cap de Garde) and Station 4 (Fellah Rachid Beach).
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Figure 4. Spatio-temporal variations in chlorophyll a (μg L−1) from October 2022 to November 2023 in Stations 1 (La Caroube Beach) and 2 (Belvedere Beach) and during the summer season in Station 3 (Cap de Garde) and Station 4 (Fellah Rachid Beach).
Figure 4. Spatio-temporal variations in chlorophyll a (μg L−1) from October 2022 to November 2023 in Stations 1 (La Caroube Beach) and 2 (Belvedere Beach) and during the summer season in Station 3 (Cap de Garde) and Station 4 (Fellah Rachid Beach).
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Figure 5. Ostreopsis cf. ovata densities expressed as cells g−1 of fresh weight of (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4; (c) Halopteris scoparia at ST1, ST2, ST3, sampled in the Annaba Bay during October 2022–November 2023.
Figure 5. Ostreopsis cf. ovata densities expressed as cells g−1 of fresh weight of (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4; (c) Halopteris scoparia at ST1, ST2, ST3, sampled in the Annaba Bay during October 2022–November 2023.
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Figure 6. Prorocentrum lima densities expressed as cells g−1 of fresh weight of (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4. Samples were taken in Annaba Bay between October 2022–November 2023. (c) H. scoparia at ST1, ST2, ST3, sampled in Annaba Bay during October 2022–November 2023.
Figure 6. Prorocentrum lima densities expressed as cells g−1 of fresh weight of (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4. Samples were taken in Annaba Bay between October 2022–November 2023. (c) H. scoparia at ST1, ST2, ST3, sampled in Annaba Bay during October 2022–November 2023.
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Figure 7. Coolia monotis assemblage expressed as cells g−1 of fresh weight of (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4; (c) Halopteris scoparia at ST1, ST2, ST3, sampled in Annaba Bay during October 2022 and November 2023.
Figure 7. Coolia monotis assemblage expressed as cells g−1 of fresh weight of (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4; (c) Halopteris scoparia at ST1, ST2, ST3, sampled in Annaba Bay during October 2022 and November 2023.
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Figure 8. Amphidinium carterae densities expressed as cell g−1 of fresh weight (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4; (c) Halopteris scoparia at ST1, ST2, ST3, sampled in Annaba Bay during October 2022 and November 2023.
Figure 8. Amphidinium carterae densities expressed as cell g−1 of fresh weight (a) Posidonia oceanica at ST1, ST2, ST3 and Codium fragile at ST4; (b) Padina pavonica at ST1, ST2, ST3 and Sargassum sp. at ST4; (c) Halopteris scoparia at ST1, ST2, ST3, sampled in Annaba Bay during October 2022 and November 2023.
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Figure 9. Distribution of phytoplankton abundance (cells g−1 FW of the macroalgae) across different macrophyte substrates. Boxplots show the abundance of dominant phytoplankton genera (Amphidinium carterae, Coolia monotis, Ostreopsis cf. ovata, Prorocentrum lima) associated with five macrophytes (Codium fragile, Halopteris scoparia, Padina pavanica, Posidonia oceanica, Sargassum sp.). Boxes represent the median and interquartile range, whiskers indicate data dispersion, and points denote outliers.
Figure 9. Distribution of phytoplankton abundance (cells g−1 FW of the macroalgae) across different macrophyte substrates. Boxplots show the abundance of dominant phytoplankton genera (Amphidinium carterae, Coolia monotis, Ostreopsis cf. ovata, Prorocentrum lima) associated with five macrophytes (Codium fragile, Halopteris scoparia, Padina pavanica, Posidonia oceanica, Sargassum sp.). Boxes represent the median and interquartile range, whiskers indicate data dispersion, and points denote outliers.
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Figure 10. Significant environmental drivers of community structure identified by redundancy analysis (RDA). Bars represent the proportion of explained variance for each environmental variable. Variables are ordered by decreasing explained variance. Asterisks indicate statistical significance: * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 10. Significant environmental drivers of community structure identified by redundancy analysis (RDA). Bars represent the proportion of explained variance for each environmental variable. Variables are ordered by decreasing explained variance. Asterisks indicate statistical significance: * p < 0.05, ** p < 0.01, and *** p < 0.001.
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Figure 11. Redundancy analysis (RDA) ordination showing the relationships between environmental variables and phytoplankton taxa across seasons (autumn, winter, spring, summer). Points represent samples colored by season. Red arrows indicate environmental variables, with arrow length and direction reflecting their relative contribution and correlation with the ordination axes (RDA1 and RDA2). Black arrows represent dominant phytoplankton genera, illustrating their associations with environmental gradients.
Figure 11. Redundancy analysis (RDA) ordination showing the relationships between environmental variables and phytoplankton taxa across seasons (autumn, winter, spring, summer). Points represent samples colored by season. Red arrows indicate environmental variables, with arrow length and direction reflecting their relative contribution and correlation with the ordination axes (RDA1 and RDA2). Black arrows represent dominant phytoplankton genera, illustrating their associations with environmental gradients.
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Table 1. Comparative characteristics of the sampling stations in Annaba Bay.
Table 1. Comparative characteristics of the sampling stations in Annaba Bay.
StationLocationGPS CoordinatesAnthropogenic ImpactHydrodynamicsDominant SubstrateSampling Frequency
ST1La Caroube Beach36°55′22.42″ N; 7°46′59.45″ EHigh (densely urbanized area)Low (Sheltered)Rocky systems (H. scoparia, P. pavonica)Monthly (October 2022–November 2023)
ST2Belvedere Beach36°55′25.59″ N; 7°46′12.19″ EModerate (Residential area)Low (Sheltered)Rocky systems (P. oceanica, P. pavonica)Monthly (October 2022–November 2023)
ST3Cap de Garde36°58′3.59″ N; 7°47′1.39″ ELow (Remote/Natural site)High (Strong currents)Rocky (Sargassum sp.)Seasonal (June–July–
August 2023)
ST4Fellah Rachid Beach36°54′11.45″ N; 7°45′32.61″ EHigh (High summer tourism)Low (Sheltered beach area)Sandy/Rocky (C. fragile)Seasonal (June–July–
August 2023)
Table 2. Physicochemical parameters in the sampled stations from October 2022 to November 2023 in Stations 1 and 2 and during the summer season in Stations 3 and 4.
Table 2. Physicochemical parameters in the sampled stations from October 2022 to November 2023 in Stations 1 and 2 and during the summer season in Stations 3 and 4.
TSalDOpHNO3NH4DONDINDOPPO4SiO4TDNTDP
C° mg L−1 µmol L−1µmol L−1µmol L−1µmol L−1µmol L−1µmol L−1µmol L−1µmol L−1µmol L−1
St 1
Mean21.7236.848.918.492.462.3020.425.312.320.663.4925.732.98
Max26.9837.8110.588.896.507.4439.0010.599.382.106.9043.8110.37
Min15.2935.637.718.140.800.602.941.860.000.000.108.560.07
Sd4.070.770.720.211.821.7313.012.672.940.572.0713.503.14
% 9.578.9279.3720.6377.8322.17
St 2
Mean21.8636.989.648.543.581.3728.535.351.100.493.4433.881.59
Max27.3037.9012.078.9210.743.2566.4914.064.271.157.2369.435.01
Min15.1035.777.228.320.170.153.370.610.000.000.746.780.11
Sd4.100.821.250.213.351.0222.633.741.330.392.3522.781.51
% 10.574.0484.2015.8068.9531.05
St 3
Mean25.4336.949.358.611.180.275.232.220.870.617.407.451.47
Max26.9337.509.968.652.520.467.353.931.570.709.2311.282.21
Min22.6536.048.738.590.060.093.870.880.160.476.095.340.63
Sd2.410.790.610.031.250.191.861.560.700.121.643.320.79
% 15.793.6070.1629.8458.9341.07
St 4
Mean26.1736.928.618.501.790.595.143.431.000.797.828.571.79
Max27.8337.189.378.553.130.675.465.202.070.9010.2310.302.96
Min23.6536.598.008.430.060.524.871.460.030.575.346.920.60
Sd2.220.300.700.061.570.080.301.881.020.192.451.691.18
% 20.826.9059.9840.0255.9344.07
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MDPI and ACS Style

Sad Laib, O.; Amira, A.B.; Frihi, H.; Aouissi, M.; Laabir, M. Spatiotemporal Dynamics of the Thermophilic Benthic Harmful Dinoflagellates in Annaba Bay (Southern Mediterranean): Influence of Environmental Factors and Macrophyte Substrates. J. Mar. Sci. Eng. 2026, 14, 398. https://doi.org/10.3390/jmse14040398

AMA Style

Sad Laib O, Amira AB, Frihi H, Aouissi M, Laabir M. Spatiotemporal Dynamics of the Thermophilic Benthic Harmful Dinoflagellates in Annaba Bay (Southern Mediterranean): Influence of Environmental Factors and Macrophyte Substrates. Journal of Marine Science and Engineering. 2026; 14(4):398. https://doi.org/10.3390/jmse14040398

Chicago/Turabian Style

Sad Laib, Ouafa, Aicha Beya Amira, Hocine Frihi, Mounia Aouissi, and Mohamed Laabir. 2026. "Spatiotemporal Dynamics of the Thermophilic Benthic Harmful Dinoflagellates in Annaba Bay (Southern Mediterranean): Influence of Environmental Factors and Macrophyte Substrates" Journal of Marine Science and Engineering 14, no. 4: 398. https://doi.org/10.3390/jmse14040398

APA Style

Sad Laib, O., Amira, A. B., Frihi, H., Aouissi, M., & Laabir, M. (2026). Spatiotemporal Dynamics of the Thermophilic Benthic Harmful Dinoflagellates in Annaba Bay (Southern Mediterranean): Influence of Environmental Factors and Macrophyte Substrates. Journal of Marine Science and Engineering, 14(4), 398. https://doi.org/10.3390/jmse14040398

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