Next Article in Journal
The Occurrence of Microplastics Pollution in the Surface Water and Sediment of Lake Chenghai in Southwestern China
Previous Article in Journal
Hydrological Data Projection Using Empirical Mode Decomposition: Applications in a Changing Climate
Previous Article in Special Issue
The Impact of Low Temperatures on the Hatching Success of Eurytemora pacifica (Copepoda, Calanoida) Resting Eggs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 18
10.3390/w16182670
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Ichthyoplankton Assemblages from the Coasts of Hamsilos Nature Park, Sinop, Southern Black Sea: Biodiversity, Abundance, and Relationships with Environmental Variables

by
Orçin Uygun
*,† and
Funda Üstün
†
Department of Hydrobiology, Faculty of Fisheries, Sinop University, 57000 Sinop, Türkiye
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Water 2024, 16(18), 2670; https://doi.org/10.3390/w16182670
Submission received: 19 July 2024 / Revised: 13 September 2024 / Accepted: 17 September 2024 / Published: 19 September 2024
(This article belongs to the Special Issue Implementation of Biodiversity and Ecosystem Services in Marine Ecosystem Management, 3rd Edition)
Browse Figures
Versions Notes

Abstract

:
This study was conducted using monthly data collected between July 2015 and June 2016 in the marine area of Hamsilos Nature Park, located in Sinop, Southern Black Sea. The primary objectives were to determine the diversity of ichthyoplankton assemblages, examine seasonal variations, assess spatial differences between the inner and outer parts of Hamsilos Bay, and highlight the ecological relationships of the predominant species with environmental factors. The comprehensive collection during the study resulted in a mean abundance of 2217 eggs.10 m2 and 2743 larvae.10 m2, with 28 species belonging to 21 families identified. Specifically, Engraulis encrasicolus is the most abundant in spring and summer, Gaidropsarus mediterraneus is the most abundant in autumn, and Sprattus sprattus is the most abundant in winter. A temporal analysis revealed the highest abundances in August, September, and July, with the lowest in April, May, and February. Significant seasonal differences in abundance and diversity were detected. No spatial differences were found between the inner and outer areas of Hamsilos Bay. Small pelagic species dominated the ichthyoplankton community, while demersal species, though diverse, were less abundant. According to the results of the DistLM analysis, the structure of the ichthyoplankton community is influenced by temperature and salinity. During the study, the water temperature ranged from 8.35 °C in February to 25.50 °C in August, and the salinity varied from 17.67 psu in September to 19.04 psu in May.

1. Introduction

Ichthyoplankton, made up of fish eggs and larvae, are critical for marine ecosystems and fishery management. They provide essential data on the spawning periods, distribution, abundance, and diversity of fish larvae, crucial for sustainable fisheries and stock assessment [1]. Understanding these dynamics aids in evaluating fish population health and predicting recruitment patterns [2], and studying ichthyoplankton is essential for stock assessment and fishery management [1].
Physical factors like temperature and salinity and anthropogenic influences such as pollution and fishing pressure impact ichthyoplankton [3,4]. Seasonal and long-term variations reflect the responses to environmental changes and human activities [5,6]. Ichthyoplankton’s spatial and temporal variability underscores the need for comprehensive studies [7]. In conclusion, studies on ichthyoplankton serve as vital indicators of ecosystem health, contributing to the sustainable management of marine biodiversity and fisheries [8,9]. The relationship between ichthyoplankton structure and environmental variables, particularly temperature, has been well documented in various studies [10,11]. Temperature is a crucial factor influencing the distribution and composition of ichthyoplankton [12]. Salinity and other factors like dissolved oxygen and chlorophyll-a also significantly impact ichthyoplankton [13]. Understanding the combined effects of temperature and salinity is crucial for predicting the impact of environmental changes on ichthyoplankton [5].
Türkiye is one of the leading countries in terms of fisheries in the Mediterranean and Black Sea basins [14,15]. The Black Sea is significant for its commercial fish species, making it an important fishing area for Türkiye [16]. Engraulis encrasicolus is the most harvested species in Turkish fisheries [16]. Globally, the Black Sea is notable for the high abundance of this species [17]. Located in the central part of the Southern Black Sea, Sinop Province is one of Türkiye’s major fishing centers. Sinop’s coasts are crucial for the spawning and feeding of demersal fish (such as whiting, red mullet, and turbot) and pelagic fish (anchovy, horse mackerel, sea bass, bluefish, and garfish) in the Black Sea [18,19]. In Türkiye, about 5–7% of the nationalfish catch occurs along the coasts of Sinop [20]. Despite being a significant fishing region in the Black Sea, research on ichthyoplankton within Sinop’s marine ecosystem has been limited [21,22,23]. Studies indicate that Hamsilos Bay in Sinop supports a healthy ecosystem, serving as a habitat for numerous marine organisms [24,25,26].
The composition of ichthyoplankton can vary temporally and spatially, reflecting the unique environmental characteristics of each habitat, such as the hydrodynamic structure, temperature, and nutrient availability [7,13,27]. Monitoring these changes regularly in the highly dynamic ichthyofaunal structure of the Southern Black Sea is very important. The present study aims to (1) investigate the diversity, abundance, and distribution patterns of bonefish species along the central coast of the Southern Black Sea; (2) understand the seasonal variations in ichthyoplankton composition and their ecological drivers, focusing on the influence of environmental factors on assemblage structure; and (3) provide baseline data on the ichthyoplankton before the planned construction of a nuclear facility along the Sinop coast, contributing to future research and effective fishery management in the Black Sea.

2. Materials and Methods

2.1. Study Area and Methodology

The Sinop Peninsula, located in northern Türkiye, is also situated in the central part of the Southern Black Sea. Hamsilos Bay lies approximately 11 km northwest of this peninsula. Hamsilos Bay, within the Hamsilos Nature Park, is a natural protected area with a ria-type coastline where the sea intrudes into the land like an estuary [28]. Its location, far from nearby settlements and lacking direct pollution sources, ensures that Hamsilos Bay faces no environmental threats to its local biodiversity. Additionally, its sheltered geographic structure, unaffected by waves and winds, and its sandy bottom covered with Zostera meadows [24] provide essential reproduction and nursery areas for numerous marine organisms [25,26].
Ichthyoplankton were collected monthly during the daytime from July 2015 to June 2016 at six designated stations within Hamsilos Bay, Sinop, and Türkiye. The inner stations (St 1, St 2, St 3) ranged from 5 to 15 m, with a mean depth of 10 m. The mean depth of the outer stations (St 4, St 5, St 6) was 30 m (Table 1). To enhance the comprehensibility of the study data, the six stations were divided into two groups based on their location and depth: inner stations and outer stations.
The temperature (°C), salinity (psu), and dissolved oxygen (mg/L) values were measured using a YSI 6600 MDS model multiparameter probe throughout the entire water column at each station during the sampling surveys. The analyses used the mean values of all environmental parameters measured throughout the water column. The concentration of surface seawater chlorophyll-a values (μg/L) was calculated according to the methods of Parsons et al. [29]. Zooplankton biomass values were calculated according to the methodology of Üstün [26].
Ichthyoplankton samples were gathered from the sea bottom to the surface using vertical tows with a plankton net with a mesh size of 112 μm and a mouth area of approximately 0.2 m2. Two hauls were performed at each station. Following collection, the zooplankton samples were promptly preserved in a 4% formalin–seawater solution for subsequent quantitative and qualitative taxonomic analyses.
The primary references utilized for identifying the species of collected eggs and larvae include D’Ancona [30], Dekhnik [31], Russell [32], Mater and Çoker [33], and Rodríguez et al. [34]. Each species’ name was meticulously checked and compared against the taxonomic reference list from the World Register of Marine Species [35]. Abundance values at each station were standardized to 10 m2 of the vertical water column [36]. The frequency of occurrence (FO%) for each species was calculated by dividing the number of samples in which the total number of samples collected detected the species. The dominance index assessed the prevalence of each species within the assemblage [37]. A log transformation was applied to mitigate the influence of major species in the ichthyoplankton abundance data [38,39]. Rare species, contributing less than 16.7% to the overall FO% among all taxa, were removed from the analysis [38]. The alpha diversity indices, including Margalef’s richness index (d) [40], Shannon–Wiener diversity index (H’) [41], and Pielou’s evenness index (J’) [42], were calculated using Primer v.7 to represent the ichthyoplankton species diversity of Hamsilos Bay. Ocean Data View v5.6 software was employed to generate distribution maps depicting the stations within the study area [43]. See Figure 1.

2.2. Statistical Analyses of Data

Spatial and temporal variations were assessed using a multivariate PERMANOVA with two fixed factors: location (inner and outer) and season (winter, spring, summer, and autumn). PERMANOVA detected differences in spatial and temporal variations in log-transformed total abundance data. The Kruskal–Wallis test was applied to identify temporal and spatial differences in each dominant species. As the assumptions of parametric analyses were not met, non-parametric analyses (PERMANOVA and Kruskal–Wallis) were used [44]. Normality was assessed using the Shapiro–Wilk test, with its significance considered. A p-value less than 0.05 was considered statistically significant for all statistical analyses. Kruskal–Wallis and Shapiro–Wilk tests were conducted using the “stats” package from the R software (version 4.3) [45].
The effect of environmental variables (temperature, dissolved oxygen, salinity, zooplankton biomass, and chlorophyll-a) on the ichthyoplankton assemblage was examined using distance-based linear modeling (DistLM). DistLM identifies the primary patterns of dependence between the Bray–Curtis similarity matrix of ichthyoplankton abundance and environmental data, highlighting the most influential variables shaping the observed diversity structures. The procedure was applied to fourteen abundant species (frequency of occurrence > 16.7%). Relationships between environmental data and taxa abundance were visualized in the ordination space using dbRDA (distance-based redundancy analysis), a component of the DistLM approach. Shannon–Wiener and Pielou indices, cluster analyses, DistLM, dbRDA, and PERMANOVA were conducted using the PRIMER 7 statistical package [46] with the PERMANOVA+ add-on [47].

3. Results

3.1. Abiotic and Biotic Environmental Factors

During the study period in Hamsilos Bay, the water temperature ranged from 8.35 to 25.50 °C, with the lowest temperature recorded in February and the highest in August. The average temperature in the inner part of the study area was calculated as 15.36 ± 0.98 °C, and in the outer part, it was 15.23 ± 0.90 °C. The salinity values ranged from a minimum of 17.67 psu in September to a maximum of 19.04 psu in May. In the inner area, the average salinity was 18.49 ± 0.05 psu, while in the outer area, it was 18.53 ± 0.05 psu. The dissolved oxygen levels ranged from a minimum of 4.97 mg/L in May to a maximum of 9.91 mg/L in February, with averages of 7.93 ± 0.21 mg/L in the inner area and 8.24 ± 0.15 mg/L in the outer area. The chlorophyll-a values ranged from a minimum of 0.01 µg/L in March to a maximum of 0.95 µg/L in October, with averages of 0.35 ± 0.03 µg/L in the inner area and 0.42 ± 0.04 µg/L in the outer area. The zooplankton biomass ranged from a minimum of 1.23 mg.m−3 in January to a maximum of 87.19 mg.m−3 in September, with averages of 24.18 ± 3.77 mg.m−3 in the inner area and 29.56 ± 3.31 mg.m−3 in the outer area (Figure 2).

3.2. Ichthyoplankton Assemblage Structure

During the study, thorough collection efforts resulted in an average of 2217 eggs per 10 m2 and 2743 larvae per 10 m2. In total, 28 species from 21 families of Osteichthyes were captured during the surveys, with 15 species identified in the egg stage and 21 in the larval stage. The family with the highest species richness in the study was Gobiidae (four species), followed by Sparidae (two), Blennidae (two), Labridae (two), and Mugilidae (two). The dominant families included Engraulidae (72.6%), Clupeidae (7.7%), and Gobiidae (4.6%). Among the species, Engraulis encrasicolus [3601 individuals (ind).10 m2; 72.6%], Sprattus sprattus (382 ind.10 m2; 7.7%), Gobius niger (195 ind.10 m2; 3.9%), and Gaidropsarus mediterraneus (178 ind.10 m2; 3.6%) were the most abundant (Table 1).
Small pelagic species in the study area (E. encrasicolus, S. sprattus, T. mediterraneus) accounted for 10.7% of the total ichthyoplankton species diversity, yet they comprised 82.7% of the abundance. In contrast, demersal species contributed 89.3% to the species richness but only 17.3% to the abundance.

3.2.1. Temporal Variations

When examined on a temporal scale, the highest total abundance was observed in August (11,873 ind.10 m−2: 40.1%), followed by September (8968 ind.10 m−2: 30.3%) and July (4382 ind.10 m−2: 14.8%). The lowest abundance was recorded in April, May, and February, with values of 51, 204, and 255 ind.10 m−2, respectively. The monthly species analysis revealed that G. niger was the most abundant in June, E. encrasicolus from July to September, G. mediterraneus in October and November, S. sprattus from December to April, and E. encrasicolus in May (Figure 3a).
Regarding the biodiversity indices, the highest species diversity and richness were observed in July (S: 13, d: 1.43), with the highest Shannon index in June (H’: 1.48). The highest Pielou index was recorded in May (J’: 0.95). Despite August having the highest egg and larval abundance, the high dominance of anchovies resulted in lower biodiversity indices for that month. Conversely, although the abundance was lower in June, the more homogeneous distribution of the species led to higher index values (Figure 3b).
When the ichthyoplankton abundance and species diversity in the study area were examined by season, the average abundance and biodiversity values were higher in the summer months compared to those in other months (Figure 3b). However, the PERMANOVA analysis showed that these seasonal differences were not statistically significant (df = 3, Pseudo-F = 5.18, p > 0.05).

3.2.2. Spatial Variations

The distribution of the major species obtained in the study is shown in Figure 4a–f. The abundance values of each dominant species were examined between the inner and outer localities, and a statistically significant difference was found only for the anchovy species (df: 1, χ2: 3.86, p: 0.0495). Although the Kruskal–Wallis test result was at the borderline of significance, the Bonferroni multiple comparison tests confirmed a significant difference between the localities (p: 0.0248). When the overall distribution of the ichthyoplankton community was examined (Figure 4a,b), no significant difference was found between the average abundance and biodiversity values of the inner and outer localities (PERMANOVA, df: 1, Pseudo-F: 1.58, p > 0.05).

3.2.3. Relationships of Ichthyoplankton Assemblages with Environmental Variables

The spatially and temporally sorted ichthyoplankton assemblages were subjected to a comparative analysis via a cluster analysis, which revealed five distinct cluster groups (Figure 5a). “Group A”, “Group B”, and “Group C” represent the spring and summer months when water temperatures are high, while “Group D” and “Group E” represent the autumn months when water temperatures are low. These groups are statistically significant (ANOSIM Global R: 0.965, p < 0.001). The shade plot distribution of the abundance (log) of the main species in the study is according to the groups formed in the cluster analysis (Figure 5b). Accordingly, the abundance and diversity in group c (during July, August, and September) are higher than those in the other groups.
The analysis indicates that temperature and salinity are the most significant environmental factors affecting ichthyoplankton assemblages. Other variables, such as dissolved oxygen, chlorophyll-a, and zooplankton biomass, show limited or no significant impact.
The DistLM model revealed that the first two axes captured 90.2% of the variance in the fitted model and 58.1% of the total variation in the dataset (Table 2). The dbRDA diagram demonstrated a seasonal separation of the ichthyoplankton community. The months characterized by high water temperatures tended to cluster towards the left side of the graph. In contrast, the months with lower water temperatures were more frequently positioned toward the right side of the plot (Figure 6). The results of the DistLM indicate that temperature and salinity are the most significant environmental factors affecting the ichthyoplankton assemblages. Temperature emerged as the predominant factor, accounting for 40.9% of the relationship (p ≤ 0.001). Specifically, temperature showed a high negative correlation with the first axis (r = −0.875). Among the other environmental factors, salinity was identified as statistically significant in the DistLM model, accounting for 9.8% of the variability (p < 0.05). In the dbRDA diagram, salinity was correlated with the second axis (r = −0.809). Other variables, such as the dissolved oxygen, chlorophyll-a, and zooplankton biomass, show limited or no significant impact (p > 0.05) (Figure 6a).
Among the dominant species, E. encrasicolus showed a positive relationship with temperature along the first axis, whereas S. sprattus displayed a negative relationship with temperature on the same axis (Figure 6b). Diplodus annularis and G. niger also showed some relationship with temperature, but their correlation with the first axis was weak (r < ±0.4). On the second axis, G. mediterraneus exhibited a negative correlation with salinity.

4. Discussion

4.1. Ichthyoplankton Composition

This study along the Southern Black Sea’s central coast identified 28 bonefish species, which aligns with the species diversity found in other regional studies [13,21,23,48].
The structure of the ichthyoplankton composition obtained in this study shows seasonal clustering, with corresponding differences observed in the ichthyoplankton biodiversity indices across seasons. The reproductive cycles of fish species are intricately linked with seasonal changes, with spawning seasons typically occurring at specific times of the year [49]. The seasonal changes in the ichthyoplankton assemblages reflect the distinct oceanographic conditions observed during different seasons [50]. Studies have demonstrated significant seasonal changes in the composition and abundance of ichthyoplankton species, with the spawning season’s duration, intensity, and activity closely related to temperature [51,52]. Most fish populations in the Mediterranean, including the Black Sea, show heightened spawning activity in late spring and early summer, corresponding with rising water temperatures. This timeframe generally overlaps with the peak abundance of zooplankton in coastal waters following the spring phytoplankton bloom [12,52,53,54,55]. The seasonal rhythm of the ichthyoplankton community is an adaptation seen in the distribution of dominant species to avoid intense competition and fully utilize food resources [56,57].
Engraulis encrasicolus was most abundant in spring and summer, G. mediterraneus in autumn, and S. sprattus in winter. These patterns are consistent with other studies showing that anchovy dominates during warmer months, while sprat dominates during colder months in the Black Sea [13,21,23,48,52].
The pelagic taxa, such as E. encrasicolus, S. sprattus, and T. mediterraneus, dominate the ichthyoplankton community in our study. In contrast, demersal species account for most species diversity but a smaller portion of the abundance. This dominance of pelagic species aligns with fishery data from the southern coast of Türkiye’s Black Sea. [16] and other studies in the Black Sea [13,21,23,48,52]. The pelagic fish species play a crucial role in marine ecosystems, connecting lower and upper trophic levels [58,59,60,61].
E. encrasicolus is the most fished species in Turkish fisheries [16], and the Black Sea leads globally for its abundance of this species [17]. Anchovies exhibit high ichthyoplanktonic dominance along the Black Sea coasts [13,21,22,23,48,52], likely due to their batch spawning strategy and high fecundity [62,63]. Anchovies’ multi-batch spawning, extended spawning periods, repeated spawning events, and widespread distribution facilitate their adaptation to fluctuating environmental conditions [52].
Our study collected anchovies between May and September, with peak abundance in August when the water temperatures ranged from 21.5 to 25.5 °C. This positive correlation between temperature and anchovy distribution aligns with other studies in the Black Sea [31,48,64,65,66,67]. In regions with high surface temperatures, the egg stage of anchovies lasts less than two days due to the inverse relationship between temperature and egg stage duration [62,68]. Encountering suitable planktonic food during the critical period when fish larvae begin feeding can reduce mortality rates and increase recruitment success [69,70,71]. While temperature can expedite this process, it may also increase egg mortality [72].
Anchovies were predominantly found in the deeper outer region of Hamsilos Bay. Studies across the Mediterranean have shown that anchovy eggs and larvae significantly influence ichthyoplankton assemblages from coastal to shelf break zones [12,73,74,75]. Research along the Southern Black Sea’s eastern coasts similarly indicated higher densities of anchovies offshore [48,76]. The maximum abundance of anchovy eggs and larvae is typically associated with the continental shelf, decreasing closer to the coast [52,77,78]. This pattern may be due to anchovies exploiting the high productivity of pre-shelf areas, particularly during peak spawning season [12,79].
The demersal species in the study exhibit low dominance, with the most abundant species including G. niger, G. mediterraneus, and D. annularis. These species are predominantly found in shallow waters, although no significant difference was observed between the inner and outer zones of the study area. Other Black Sea studies highlight dominant demersal species predominantly in shallower areas [23,48,52].

4.2. Environmental Relationship of Ichthyoplankton

In the present study, temperature and salinity exert the most pronounced influence on the ichthyoplankton community structure. These pivotal variables predominantly shape fish reproductive behavior and the spatiotemporal distribution and composition of ichthyoplankton assemblages [80,81,82]. The temperature significantly affects fish growth, reproduction, behavior, and physiology [10,27,75,83,84], making seasonal temperature variation crucial in ichthyoplankton community dynamics [85]. In particular, this factor influences the duration of the larval stage, the distance larvae disperse, and their survival. Consequently, changes in water temperature can directly impact population connectivity, community structure, and regional biodiversity patterns [86]. In our study conducted in the Southern Black Sea, temperature was identified as a primary environmental variable affecting the diversity and abundance of ichthyoplankton. These results are consistent with findings from similar studies conducted in the Aegean Sea [12,61], the eastern Mediterranean [75], the western Mediterranean, and the northern Pacific [10,84,87].
Salinity also influences ichthyoplankton growth, development, reproduction, and distribution [57,87,88]. A similar study conducted in the eastern Black Sea emphasized that salinity is one of the leading environmental variables affecting the ichthyoplankton structure [13]. Decreasing salinity levels can increase fish egg and larval numbers, significantly affecting their hatching and development in low-salinity coastal systems. This has been shown to affect the early stages of life in fish, with lower salinity levels often correlating with higher abundances of certain species [82,89]. For example, a study in the Aegean Sea found a negative relationship between salinity and Sardina pilchardus eggs and larvae abundance, indicating the species’ preference for low-salinity spawning areas [90]. These findings align with the results of the present study, which observed a higher abundance of ichthyoplankton during periods of reduced salinity.
Briefly, the distribution of fish eggs and larvae is regulated by spatiotemporal variations in temperature and salinity, with global warming contributing to changes in ocean conditions that impact the abundance and distribution of marine species stocks [91]. These factors determine the success of hatching and larval development and have implications for future stock assessments and conservation efforts in marine environments [11,92].

5. Conclusions

This study identified 28 bonefish species along the central coast of the Southern Black Sea, aligning with previous research in the region. The ichthyoplankton composition showed distinct seasonal clustering, with biodiversity indices varying across seasons, highlighting the significant role of seasonal variations in fish species’ reproductive success. Small pelagic species, especially anchovies, dominated the ichthyoplankton community, reflecting their high fecundity and batch spawning strategy. Anchovy abundance peaked during the warmer months, aligning with broader Black Sea trends.
Temperature and salinity were the most significant factors influencing ichthyoplankton community structure. Temperature affects various aspects of the reproductive process, from spawning timing to gonadal maturation, and can have intergenerational effects on reproductive traits. Therefore, ecological-based ichthyoplankton community studies are needed to understand the impacts of climate change on fish reproductive strategies. Understanding these environmental influences is vital for practical fishery management in the Black Sea. Identifying critical spawning and nursery areas and monitoring seasonal patterns are crucial for sustaining fish populations in this region. Despite being conducted a decade ago, this study also serves as an essential reference point for evaluating possible ecological variations in the area. It provides crucial baseline data on the status of the ichthyoplankton community before the planned construction of a nuclear facility along the Sinop coast. This baseline is essential for future research and assessing the impacts of ongoing climate change, thus contributing to effective fishery management in the Black Sea.

Author Contributions

Conceptualization, O.U. and F.Ü.; methodology, O.U. and F.Ü.; software, O.U. and F.Ü.; validation, O.U. and F.Ü.; formal analysis, O.U.; investigation, O.U. and F.Ü.; data curation, O.U.; writing—original draft preparation, O.U. and F.Ü.; writing—review and editing O.U. and F.Ü.; visualization, O.U.; project administration, F.Ü. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

This research was supported by Sinop University through the Scientific Research Project (grant number SÜF-1901-14-04), entitled ’Determination of Zooplankton Composition of Hamsilos Bay, Sinop. We thank Zeynep Hasançavuşoğlu and Mehmet Bahtiyar for their help. Also, we would like to thank Genuario Belmonte for his contributions to this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Macedo-Soares, L.; Garcia, C.; Freire, A.; Muelbert, J. Large-scale ichthyoplankton and water mass distribution along the south Brazil shelf. PLoS ONE 2014, 9, e91241. [Google Scholar] [CrossRef] [PubMed]
  2. Duke, E.; Burton, R. Efficacy of metabarcoding for identification of fish eggs evaluated with mock communities. Ecol. Evol. 2020, 10, 3463–3476. [Google Scholar] [CrossRef] [PubMed]
  3. Costa, M.; Possingham, H.; Muelbert, J. Incorporating early life stages of fishes into estuarine spatial conservation planning. Aquatic Conserv. Mar. Freshw. Ecosyst. 2015, 26, 1013–1030. [Google Scholar] [CrossRef]
  4. Nynatten, A.; Gallage, K.; Lujan, N.; Mandrak, N.; Lovejoy, N. Ichthyoplankton metabarcoding: An efficient tool for early detection of invasive species establishment. Mol. Ecol. Resour. 2023, 23, 1319–1333. [Google Scholar] [CrossRef]
  5. Zhang, H.; Xian, W.; Liu, S. Seasonal variations of the ichthyoplankton assemblage in the Yangtze estuary and its relationship with environmental factors. PeerJ 2019, 7, e6482. [Google Scholar] [CrossRef]
  6. Jiang, Y.; Lin, B.; He, H.; Ding, G.; Yan, L.; Zhang, G.; Liu, M.; Zheng, L. Species composition and assemblages of ichthyoplankton in Sansha Bay, Fujian Province, China. Front. Mar. Sci. 2021, 8, 758089. [Google Scholar] [CrossRef]
  7. Govoni, J.J. Fisheries oceanography and the ecology of early life histories of fishes: A perspective over fifty years. Sci. Mar. 2005, 69, 125–137. [Google Scholar] [CrossRef]
  8. Frantine-Silva, W.; Sofia, S.; Órsi, M.; Almeida, F. Dna barcoding of freshwater ichthyoplankton in the neotropics as a tool for ecological monitoring. Mol. Ecol. Resour. 2015, 15, 1226–1237. [Google Scholar] [CrossRef]
  9. Koslow, J.A.; Wright, M. Ichthyoplankton sampling design to monitor marine fish populations and communities. Mar. Policy 2016, 68, 55–64. [Google Scholar] [CrossRef]
  10. Wang, Y.; Liang, C.; Chen, Z.; Liu, S.; Zhang, H.; Xian, W. Spring ichthyoplankton assemblage structure in the Yangtze Estuary under environmental factors. Front. Mar. Sci. 2021, 8, 806096. [Google Scholar] [CrossRef]
  11. Zhang, H.; Xian, W.; Liu, S. Autumn ichthyoplankton assemblage in the Yangtze Estuary shaped by environmental factors. PeerJ 2016, 4, e1922. [Google Scholar] [CrossRef] [PubMed]
  12. Uygun, O.; Hoşsucu, B. Temporal dynamics of the ichthyoplankton assemblages in the central Aegean Sea during one year and the effects of ecological factors. Reg. Stud. Mar. Sci. 2024, 72, 103439. [Google Scholar] [CrossRef]
  13. Öztürk, R.Ç. Genetic identification of ichthyoplankton in the Black Sea and their abundance and community assemblages. Turk. J. Fish. Aquat. Sci. 2023, 23, TRJFAS23514. [Google Scholar] [CrossRef]
  14. Bat, L.; Şahin, F.; Satılmış, H.H.; Üstün, F.; Özdemir, Z.B.; Kıdeys, A.E.; Shulman, G.E. The changed ecosystem of the Black Sea and its impact on anchovy fisheries. J. Fish. Sci. 2007, 4, 191–227. [Google Scholar] [CrossRef]
  15. Demirel, N.; Zengin, M.; Ulman, A. First large-scale eastern Mediterranean and Black Sea stock assessment reveals a dramatic decline. Front. Mar. Sci. 2020, 7, 103. [Google Scholar] [CrossRef]
  16. Turkstat. Turkish Statistical Institute, Fishery Statistics. Available online: http://www.turkstat.gov.tr/ (accessed on 28 June 2024).
  17. FAO. FishStat, License: CC BY-NC-SA 3.0 IGO. Available online: https://www.fao.org/fishery/statistics-query/en/capture/capture_quantity (accessed on 4 July 2024).
  18. Erdem, Y.; Özdemir, S.; Özsandıkçı, U.; Büyükdeveci, F. Fishery infrastructures of Sinop Province. Turk. J. Marit. Mar. Sci. 2018, 4, 20–32. (In Turkish) [Google Scholar]
  19. Erdem, Y.; Özdemir, S.; Özsandıkçı, U.; Büyükdeveci, F. Technical features of nets used industrial fisheries in the western Black Sea (Sinop Province). Turk. J. Marit. Mar. Sci. 2019, 5, 74–87. (In Turkish) [Google Scholar]
  20. Korkmaz, A.Ş.; Çoskun, T. Socioeconomic indicators of industrial fishing vessels:A case study of Sinop Province. J. Aquacult. Eng. Fish. Res. 2016, 2, 208–216. (In Turkish) [Google Scholar] [CrossRef]
  21. Satılmış, H.H.; Gordina, A.D.; Bat, L.; Bircan, R.; Çulha, M.; Akbulut, M.; Kıdeyş, A.E. Seasonal distribution of fish eggs and larvae off Sinop (the Southern Black Sea) in 1999–2000. Acta Oecol. 2003, 24, 275–280. [Google Scholar] [CrossRef]
  22. Satılmış, H.H.; Bat, L.; Birinci Özdemir, Z.; Üstün, F.; Şahin, F.; Kıdeyş, A.E.; Erdem, Y. Composition of eggs and larvae fish and macrogelatinious zooplankton in Sinop Region (the Central Black Sea) during 2002. EU J. Fish. Aquat. Sci. 2006, 23, 135–140. (In Turkish) [Google Scholar]
  23. Satılmış, H.H.; Mavruk, S.; Bat, L.; Avşar, D. Seasonal changes of ichthyoplankton assemblages of Sinop coasts in southern of the Black Sea, Turkey. Turk. J. Fish. Aquat. Sci. 2014, 14, 403–411. [Google Scholar] [CrossRef] [PubMed]
  24. Ersoy Karaçuha, M. Crustacea (Arthropoda) Fauna of Zostera Facies in Sinop Peninsula Coasts and Their Bioecological Features. Master’s Thesis, Ondokuz Mayıs University, Samsun, Türkiye, 2006. (In Turkish). [Google Scholar]
  25. Uygun, O. Composition of Fish Larvae in Sinop-Akliman Coast of the Black Sea. Master’s Thesis, Sinop University, Sinop, Türkiye, 2015. (In Turkish). [Google Scholar]
  26. Üstün, F. Seasonal cycle of zooplankton abundance and biomass in Hamsilos Bay, Sinop, Southern Black Sea. Turkey. J. Nat. Hist. 2019, 53, 365–389. [Google Scholar] [CrossRef]
  27. Santos, R.V.S.; Severi, W. Dynamics of early life-history stages of fish along an estuarine gradient. Fish. Oceanogr. 2019, 28, 402–418. [Google Scholar] [CrossRef]
  28. MAF. Ministry of Agriculture and Forestry, Sinop-Hamsilos Natural Park. Available online: https://bolge10.tarimorman.gov.tr/Menu/48/Sinop-Hamsilos-Tabiat-Parki (accessed on 3 March 2023). (In Turkish)
  29. Parsons, T.R.; Maita, Y.; Lalli, C.M. A Manual of Chemical and Biological Methods for Seawater Analysis; Pergamon Press: Oxford, UK, 1984; pp. 101–111. [Google Scholar] [CrossRef]
  30. D’Ancona, U. Clupeoidei, in Uova, Larve e Stadi Giovanili di Teleostei, Fauna Flora Golfo di Napoli; Pub. Stazione Zoologica Di Napoli: Monographia, Napoli, 1931–1956; p. 1064. [Google Scholar]
  31. Dekhnik, T.V. Ichthyoplankton of the Black Sea; Cernova Moria Haukova: Kyiv, Ukraine, 1973; p. 234. (In Russian) [Google Scholar]
  32. Russell, F.S. The Eggs and Planktonic Stages of British Marine Fishes; Academic Press: London, UK, 1976; p. 524. [Google Scholar]
  33. Mater, S.; Çoker, T. Türkiye denizleri ihtiyoplankton atlası; Ege University Press: İzmir, Türkiye, 2004; p. 210. (In Turkish) [Google Scholar]
  34. Rodríguez, J.M.; Alemany, F.; Garcia, A. A Guide to the Eggs and Larvae of 100 Common Western Mediterranean Sea Bony Fish Species; FAO: Rome, Italy, 2017; p. 256. [Google Scholar]
  35. WoRMS Editorial Board, 2024, World Register of Marine Species. Available online: https://www.marinespecies.org/about.php#cite_worms (accessed on 4 May 2024). [CrossRef]
  36. Smith, P.E.; Richardson, S. Standard techniques for pelagic. FAO Fish. Tech. Pap. 1977, 175, 27–73. [Google Scholar]
  37. Bellan-Santini, D. Influence des pollutions sur le peuplement des amphipodes dans la biocénose des algues photophiles. Tethys 1981, 10, 185–194. [Google Scholar]
  38. Legendre, P.; Gallagher, E.D. Ecologically meaningful transformations for ordination of species data. Oecologia 2001, 129, 271–280. [Google Scholar] [CrossRef]
  39. Legendre, P. Species associations: The Kendall coefficient of concordance revisited. J. Agric. Biol. Environ. Stat. 2005, 10, 226–245. [Google Scholar] [CrossRef]
  40. Margalef, R. Information theory in ecology. Gen. Syst. 1958, 3, 36–71. [Google Scholar]
  41. Wilhm, J.L. Use of biomass units in Shannon’s formula. Ecology 1968, 49, 153–156. [Google Scholar] [CrossRef]
  42. Pielou, E.C. Ecological Diversity; Wiley: New York, NY, USA, 1975. [Google Scholar]
  43. Schlitzer, R. Ocean Data View. 2024. Available online: https://odv.awi.de (accessed on 10 April 2024).
  44. Zar, J.H. Biostatistical Analysis, 4th ed.; Prentice-Hall Int.: Upper Saddle River, NJ, USA, 1999. [Google Scholar]
  45. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2024; Available online: https://www.R-project.org (accessed on 15 May 2024).
  46. Clarke, K.R.; Gorley, R.N. PRIMER v7: User Manual/Tutorial; PRIMER-E: Plymouth, UK, 2015; p. 296. [Google Scholar]
  47. Anderson, M.J.; Gorley, R.N.; Clarke, K.R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods; PRIMER-E: Plymouth, UK, 2008; p. 214. [Google Scholar]
  48. Şahin, A.; Düzgüneş, E. Spatial and temporal variation in the distribution and abundance of pelagic fish eggs and larvae off Giresun, south-eastern Black Sea, Turkey. Acta Ichthyol. Piscat. 2019, 49, 159–169. [Google Scholar] [CrossRef]
  49. Almatar, S.; Lone, K.; Abu-Rezq, T.; Yousef, A. Spawning frequency, fecundity, egg weight and spawning type of silver pomfret, Pampus argenteus (Euphrasen) (Stromateidae), in Kuwait waters. J. Appl. Ichthyol. 2004, 20, 176–188. [Google Scholar] [CrossRef]
  50. Muhling, B.; Beckley, L. Seasonal variation in horizontal and vertical structure of larval fish assemblages off south-western Australia, with implications for larval transport. J. Plankt. Res. 2007, 29, 967–983. [Google Scholar] [CrossRef]
  51. Joyeux, J.; Pereira, B.; Almeida, H. The flood-tide ichthyoplanktonic community at the entrance into a Brazilian tropical estuary. J. Plankt. Res. 2004, 26, 1277–1287. [Google Scholar] [CrossRef]
  52. Klimova, T.; Podrezova, P. Seasonal distribution of the Black Sea ichthyoplankton near the Crimean Peninsula. Reg. Stud. Mar. Sci. 2018, 24, 260–269. [Google Scholar] [CrossRef]
  53. Gaudy, R.; Champalbert, G. Space and time variations in zooplankton distribution south of Marseilles. Oceanol. Acta 1998, 21, 793–802. [Google Scholar] [CrossRef]
  54. de Puelles, M.F.; Grás, D.; Hernández-León, S. Annual cycle of zooplankton biomass, abundance and species composition in the neritic area of the Balearic Sea, Western Mediterranean. Mar. Ecol. 2003, 24, 123–139. [Google Scholar] [CrossRef]
  55. Tsikliras, A.C.; Antonopoulou, E.; Stergiou, K.I. Spawning period of Mediterranean marine fishes. Rev. Fish Biol. Fisher. 2010, 20, 499–538. [Google Scholar] [CrossRef]
  56. Shan, X.J.; Xian, W.W.; Wu, Y.F. Progress of studies on ichthyoplankton ecology of Changjiang River Estuary. Trans. Oceanol. Limnol. 2004, 4, 87–93. [Google Scholar]
  57. Zhang, H.; Wang, Y.; Liang, C.; Liu, S.; Xian, W. Estuarine ichthyoplankton studies—A review. Front. Mar. Sci. 2022, 9, 794433. [Google Scholar] [CrossRef]
  58. Rice, J. Food web theory, marine food webs and what climate changes may do to northern marine fish populations. In Climate Change and Northern Fish Populations; Beamish, R.J., Ed.; Canadian Special Publication Fish Aquatic Science: Sidney, Australia, 1995; Volume 121, pp. 561–568. [Google Scholar]
  59. Bakun, A. Patterns in the Ocean: Ocean Processes and Marine Population Dynamics; California Sea Grant College System: La Jolla, CA, USA, 1996; p. 323. [Google Scholar]
  60. Cury, P.; Bakun, A.; Crawford, R.J.; Jarre, A.; Quinones, R.A.; Shannon, L.J.; Verheye, H.M. Small pelagics in upwelling systems: Patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 2000, 57, 603–618. [Google Scholar] [CrossRef]
  61. Koutrakis, E.T.; Kallianiotis, A.A.; Tsikliras, A.C. Temporal patterns of larval fish distribution and abundance in a coastal area of northern Greece. Sci. Mar. 2004, 68, 585–595. [Google Scholar] [CrossRef]
  62. Somarakis, S.; Palomera, I.; Garcia, A.; Quintanilla, L.; Koutsikopoulos, C.; Uriarte, A.; Motos, L. Daily egg production of anchovy in European waters. ICES J. Mar. Sci. 2004, 61, 944–958. [Google Scholar] [CrossRef]
  63. Sabatés, A.; Olivar, M.P.; Salat, J.; Palomera, I.; Alemany, F. Physical and biological processes controlling the distribution of fish larvae in the NW Mediterranean. Prog. Oceanogr. 2007, 74, 355–376. [Google Scholar] [CrossRef]
  64. Demir, N. Notes on the variations of the eggs of anchovy (Engraulis encrasicolus Cuv.) from Black, Marmara, Aegean and Mediterranean Seas. Istanb. Univ. Hidrobiyoloji Mecmuası 1959, 4, 180–187. [Google Scholar]
  65. Gordina, A.D.; Nikolskiy, V.N.; Niermann, U.; Bingel, F.; Subbotin, A.A. New data on the morphological differences of anchovy eggs (Engraulis encrasicolus L.) in the Black Sea. Fish. Res. 1997, 31, 139145. [Google Scholar] [CrossRef]
  66. Niermann, U.; Bingel, F.; Gorban, A.; Gordina, A.D.; Gücü, A.C.; Kideys, A.E.; Konsulov, A.; Radu, G.; Subbotin, A.A.; Zaika, V.E. Distribution of anchovy eggs and larvae (Engraulis encrasicolus Cuv.) in the Black Sea in 1991–1992. ICES J. Mar. Sci. 1994, 51, 395–406. [Google Scholar] [CrossRef]
  67. Kideys, A.E.; Gordina, A.D.; Bingel, F.; Niermann, U. The effect of environmental conditions on the distribution of eggs and larvae of anchovy (Engraulis encrasicolus L.) in the Black Sea. ICES J. Mar. Sci. 1999, 56, 58–64. [Google Scholar] [CrossRef]
  68. Somarakis, S.; Koutsikopoulos, C.; Machias, A.; Tsimenides, N. Applying the daily egg production method (DEPM) to small stocks in highly heterogeneous seas. Fish. Res. 2002, 55, 193–204. [Google Scholar] [CrossRef]
  69. Hjort, J. Fluctuations in the great fisheries of northern Europe. Rapp. Proces Verbaux La Comm. Int. Pour L’exploration Sci. La Mer 1914, 20, 1–228. [Google Scholar]
  70. Houde, E.D. Recruitment variability. In Fish Reproductive Biology. Implications for Assessment and Management, 1st ed.; Jakobsen, T., Fogarty, M.J., Megrey, B.A., Moksness, E., Eds.; Wiley Blackwell UK: West Sussex, UK, 2009; pp. 91–171. [Google Scholar]
  71. Mavruk, S.; Avşar, D. Distribution dynamics of ichthyoplankton and recruitment hypotheses. EU J. Fish. Aquat. Sci. 2017, 34, 355–361. [Google Scholar] [CrossRef]
  72. Somarakis, S.; Schismenou, E.; Siapatis, A.; Giannoulaki, M.; Kallianiotis, A.; Machias, A. High variability in the daily egg production method parameters of an eastern Mediterranean anchovy stock: Influence of environmental factors, fish condition and population density. Fish. Res. 2012, 117, 12–21. [Google Scholar] [CrossRef]
  73. Zarrad, R.; Alemany, F.; Rodriguez, J.M.; Jarboui, O.; Lopez-Jurado, J.L.; Balbin, R. Influence of summer conditions on the larval fish assemblage in the eastern coast of Tunisia (Ionian Sea, Southern Mediterranean). J. Sea Res. 2013, 76, 114–125. [Google Scholar] [CrossRef]
  74. Patti, B.; Zarrad, R.; Jarboui, O.; Cuttitta, A.; Basilone, G.; Aronica, S.; Placenti, F.; Tranchida, G.; Armeri, G.M.; Buffa, G.; et al. Anchovy (Engraulis encrasicolus) early life stages in the Central Mediterranean Sea: Connectivity issues emerging among adjacent sub-areas across the Strait of Sicily. Hydrobiologia 2018, 821, 25–40. [Google Scholar] [CrossRef]
  75. Mavruk, S.; Bengil, F.; Yüksek, A.; Özyurt, C.E.; Kiyağa, V.B.; Avşar, D. Intra-annual patterns of coastal larval fish assemblages along environmental gradients in the northeastern Mediterranean. Fish. Oceanogr. 2018, 27, 232–245. [Google Scholar] [CrossRef]
  76. Şahin, C.; Hacımurtazaoğlu, N. Abundance and distribution of eggs and larvae of anchovy (Engraulis encrasicolus, Linnaeus, 1758) and horse mackerel (Trachurus mediterraneus, Steindachner, 1868) on the coasts of the eastern Black Sea. Turk. J. Zool. 2013, 37, 773–781. [Google Scholar] [CrossRef]
  77. Palomera, I.; Sabatés, A. Co-occurrence of Engraulis encrasicolus and Sardinella aurita eggs and larvae in the western Mediterranean. Sci. Mar. 1990, 54, 61–67. [Google Scholar]
  78. Palomera, I. Spawning of anchovy Engraulis encrasicolus in the Northwestern Mediterranean relative to hydrographic features in the region. Mar. Ecol. Prog. Ser. 1992, 79, 215–223. [Google Scholar] [CrossRef]
  79. Olivar, M.P.; Salat, J.; Palomera, I. Comparative study of spatial distribution patterns of the early stages of anchovy and pilchard in the NW Mediterranean Sea. Mar. Ecol. Prog. Ser. 2001, 217, 111–120. [Google Scholar] [CrossRef]
  80. Whitfield, A.K. Ichthyofaunal assemblages in estuaries: A South African case study. Rev. Fish. Biol. Fish. 1999, 9, 151–186. [Google Scholar] [CrossRef]
  81. Fuiman, L.A. Special consideration of fish eggs and larvae. In Fishery Science: The Unique Contributions of Early Life Stages; Fuiman, L.A., Werner, R.G., Eds.; Blackwell Science: Hoboken, NJ, USA, 2003; pp. 1–32. [Google Scholar]
  82. Feng, Y.; Yao, L.; Zhao, H.; Yu, J.; Zhao, L. Environmental effects on the spatiotemporal variability of fish larvae in the western Guangdong waters, china. J. Mar. Sci. Eng. 2021, 9, 316. [Google Scholar] [CrossRef]
  83. Sabatés, A.N.A.; Martín, P.; Lloret, J.; Raya, V. Sea warming and fish distribution: The case of the small pelagic fish, Sardinella aurita, in the western Mediterranean. Glob. Chang. Biol. 2006, 12, 2209–2219. [Google Scholar] [CrossRef]
  84. Li, F.; Zhu, M.; Chen, W.; Su, B.; Yang, Y.; Wang, B. Interannual variation of ichthyoplankton community structure in the Yellow River estuary, China. Water 2023, 15, 1040. [Google Scholar] [CrossRef]
  85. Demirel, N. Ichthyoplankton dynamics in a highly urbanized estuary. Mar. Biol. Res. 2015, 11, 677–688. [Google Scholar] [CrossRef]
  86. O’Connor, M.I.; Bruno, J.F.; Gaines, S.D.; Halpern, B.S.; Lester, S.E.; Kinlan, B.P.; Weiss, J.M. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. USA 2007, 104, 1266–1271. [Google Scholar] [CrossRef] [PubMed]
  87. Jiang, M.; Shen, X.Q.; Li, L.; Quan, W.M. Annual variability of ichthyoplankton in the Yangtze River estuary of China from August 2002 to 2009. Oceanol. Hydrobiol. Stud. 2013, 42, 59–69. [Google Scholar] [CrossRef]
  88. Lima, A.R.A.; Ferreira, G.V.B.; Barletta, M. Estuarine ecocline function and essential habitats for fish larvae in tropical south western Atlantic Estuaries. Mar. Environ. Res. 2019, 151, 104786. [Google Scholar] [CrossRef]
  89. Jiang, M.; Shen, X.Q.; Chen, L.F. Relationship between with abundance distribution of fish eggs, larvae and environmental factors in the Changjiang Estuary and vicinity waters in spring. Mar. Environ. Sci. 2006, 25, 37–39. [Google Scholar] [CrossRef]
  90. Uygun, O.; Hoşsucu, B. The abundance and distribution of the early life stages of Sardine (Sardina pilchardus) in central of the Aegean Sea (Sığacık Bay) and their interactions with certain environmental factors. Thalassas 2020, 36, 507–515. [Google Scholar] [CrossRef]
  91. Lamia, D.; Soukri, A.; Abdelouahab, H.; Mohamed, M.; Baibai, T. The Moroccan ichtyoplankton—A general overview. Annu. Res. Rev. Biol. 2020, 35, 89–94. [Google Scholar] [CrossRef]
  92. Nielsen, J.M.; Rogers, L.A.; Brodeur, R.D.; Thompson, A.R.; Auth, T.D.; Deary, A.L.; Duffy-Anderson, J.T.; Galbraith, M.; Koslow, J.A.; Perry, R.I. Responses of ichthyoplankton assemblages to the recent marine heatwave and previous climate fluctuations in several northeast pacific marine ecosystems. Glob. Chang. Biol. 2020, 27, 506–520. [Google Scholar] [CrossRef]
Water 16 02670 g001
Figure 1. Map of the sampling stations along the coasts of Sinop, Southern Black Sea. The red square indicates the study area.
Figure 1. Map of the sampling stations along the coasts of Sinop, Southern Black Sea. The red square indicates the study area.
Water 16 02670 g001
Water 16 02670 g002
Figure 2. Temporal variation in abiotic and biotic environmental factors in the study area.
Figure 2. Temporal variation in abiotic and biotic environmental factors in the study area.
Water 16 02670 g002
Water 16 02670 g003
Figure 3. Temporal distribution of ichthyoplankton species occurrence rates (a) and biodiversity index values (b) (species with dominance below 1% in figure (a) are categorized as ‘others’; S: species count, N: total abundance, d: Margalef index, J’: Pielou index, H’: Shannon index).
Figure 3. Temporal distribution of ichthyoplankton species occurrence rates (a) and biodiversity index values (b) (species with dominance below 1% in figure (a) are categorized as ‘others’; S: species count, N: total abundance, d: Margalef index, J’: Pielou index, H’: Shannon index).
Water 16 02670 g003
Water 16 02670 g004
Figure 4. Distribution maps of dominant species in the survey ((a) all of the eggs, (b) all of the larvae, (c) E. encrasicolus, (d) S. sprattus, (e) G. niger, (f) G. mediterraneus).
Figure 4. Distribution maps of dominant species in the survey ((a) all of the eggs, (b) all of the larvae, (c) E. encrasicolus, (d) S. sprattus, (e) G. niger, (f) G. mediterraneus).
Water 16 02670 g004
Water 16 02670 g005
Figure 5. Cluster analysis results of ichthyoplankton assemblages (a) and shade plot of mean logarithmic abundances of ichthyoplankton assemblages by groups (b).
Figure 5. Cluster analysis results of ichthyoplankton assemblages (a) and shade plot of mean logarithmic abundances of ichthyoplankton assemblages by groups (b).
Water 16 02670 g005
Water 16 02670 g006
Figure 6. The dbRDA ordination based on distance-based linear modeling (DistLM) shows the relationship between environmental variables (a) and the dominant species (b), with a vector overlay illustrating among correlations of taxa (restricted to those having >±0.4).
Figure 6. The dbRDA ordination based on distance-based linear modeling (DistLM) shows the relationship between environmental variables (a) and the dominant species (b), with a vector overlay illustrating among correlations of taxa (restricted to those having >±0.4).
Water 16 02670 g006
Table 1. Ichthyoplankton species recorded in Sinop from 2015 to 2016 (Ab: abundance, ind: individual, D: dominance, FO: frequency of occurrence, E: eggs, L: larvae, B: both).
Table 1. Ichthyoplankton species recorded in Sinop from 2015 to 2016 (Ab: abundance, ind: individual, D: dominance, FO: frequency of occurrence, E: eggs, L: larvae, B: both).
FamilySpeciesCodeMean Ab. (Ind.10 m−2)D%FO %E/L/BMonths (Peak)
Blenniidae Parablennius tentacularisPten80.216.7LJul
Salaria pavoSpav80.216.7LJun
Callionymidae Callionymus spp.Calsp250.533.3BJul
Carangidae Trachurus mediterraneusTmed1192.483.3BJul–Sep (Aug)
EngraulidaeEngraulis encrasicolusEenc360172.6100.0BMay–Sep (Aug)
ClupeidaeSprattus sprattusSspr 3827.7100.0BNov–Apr (Jan)
SparidaeDiplodus annularisDann851.783.3BJun–Aug (Jul)
Spicara spp.Ssma80.216.7LJul
LabridaeSymphodus tincaStin80.216.7LJul
Symphodus ocellatusSymsp80.216.7LJun
Lotidae Gaidropsarus mediterraneusGmed1783.6100.0EOct–Feb (Nov)
GadidaeMerlangius merlangusMmer250.550.0ESep
Gobiesocidae Gobiesocidae spp.Lepsp250.550.0LJul, Sep (Sep)
Gobiidae Gobius cobitisGcob80.216.7LJun
Gobius nigerGnig1953.9100.0LMay–Oct (Sep)
Gobius spp.Gobsp170.333.3LJun, Sep
Pomatoschistus minutusPmin80.216.7LMay
Mugilidae Mugil cephalusMcep80.216.7LAug
Chelon saliensCsal80.216.7ESep
MullidaeMullus barbatusMbar511.050.0BJul–Sep (Aug)
Ophidiidae Ophidion rocheiOroc250.533.3BSep
Ammodytidae Gymnammodytes cicerelusGcic80.216.7LNov
Scorpaenidae Scorpaena porcusSpor170.333.3EJul–Aug
SerranidaeSerranus scribaSscr170.316.7EAug
TrachinidaeTrachinus dracoTdra80.216.7ESep
Bothidae Arnoglossus laternaAlat511.016.7BJul
Soleidae Pegusa lascarisPlas80.216.7LSep
SciaenidaeSciaena umbraSumb420.950.0EJul–Sep
Table 2. Summary of the DistLM analysis on ichthyoplankton–environment interaction: Sequential test.
Table 2. Summary of the DistLM analysis on ichthyoplankton–environment interaction: Sequential test.
Environmental FactorsAdj. R2SS(trace)Pseudo-Fp%Cumulative
Temperature0.37928,02513.8280.001 **0.4090.409
Salinity0.4556739.63.7890.016 *0.0980.507
Dissolved oxygen0.4833463.82.0550.1260.0510.558
Chlorophyll-a0.5133288.12.0670.1190.0480.606
Zooplankton biomass0.5322625.11.7200.1910.0380.644
Note(s): (*) p < 0.05; (**) p < 0.005.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Uygun, O.; Üstün, F. Ichthyoplankton Assemblages from the Coasts of Hamsilos Nature Park, Sinop, Southern Black Sea: Biodiversity, Abundance, and Relationships with Environmental Variables. Water 2024, 16, 2670. https://doi.org/10.3390/w16182670

AMA Style

Uygun O, Üstün F. Ichthyoplankton Assemblages from the Coasts of Hamsilos Nature Park, Sinop, Southern Black Sea: Biodiversity, Abundance, and Relationships with Environmental Variables. Water. 2024; 16(18):2670. https://doi.org/10.3390/w16182670

Chicago/Turabian Style

Uygun, Orçin, and Funda Üstün. 2024. "Ichthyoplankton Assemblages from the Coasts of Hamsilos Nature Park, Sinop, Southern Black Sea: Biodiversity, Abundance, and Relationships with Environmental Variables" Water 16, no. 18: 2670. https://doi.org/10.3390/w16182670

APA Style

Uygun, O., & Üstün, F. (2024). Ichthyoplankton Assemblages from the Coasts of Hamsilos Nature Park, Sinop, Southern Black Sea: Biodiversity, Abundance, and Relationships with Environmental Variables. Water, 16(18), 2670. https://doi.org/10.3390/w16182670

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop