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Article

Influence of Environmental Parameters on the Abundance of Tub Gurnard, Chelidonichthys lucerna, in the Eastern Sea of Marmara

by
Uğur Uzer
Faculty of Aquatic Sciences, Istanbul University, 34134 Istanbul, Türkiye
Fishes 2025, 10(3), 127; https://doi.org/10.3390/fishes10030127
Submission received: 19 January 2025 / Revised: 8 March 2025 / Accepted: 10 March 2025 / Published: 14 March 2025
(This article belongs to the Section Biology and Ecology)
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Abstract

:
Tub gurnard, Chelidonichthys lucerna, is a common and widely distributed species throughout the Sea of Marmara (SoM). The knowledge on the spatial distribution of tub gurnards in the SoM in association with environmental factors is limited. The relationship between tub gurnard abundance and environmental variables (depth, temperature, dissolved oxygen, pH, and spatial variability) in the eastern Sea of Marmara (ESoM) was analyzed by means of the generalized additive model (GAM) in the present study. Additionally, the size distribution of tub gurnards was examined in relation to depth and season. Data were collected over an 11-year sampling period (2014–2024) within the scope of scientific demersal trawl surveys in the ESoM. The GAM results revealed that while all the analyzed variables influenced the spatial distribution patterns of tub gurnards, temperature and depth were the most significant contributors in the ESoM. The abundance of tub gurnard exhibited a strongly nonlinear relationship with the explanatory covariates. Regarding the depth distribution patterns of tub gurnards in association with fish size, statistical tests showed significant differences between the size frequency distributions in the two depth ranges, of which the mean total lengths were 24.1 ± 6.90 and 23.5 ± 4.27 cm for depths >80 and <80 m, respectively. A preferred depth was obviously expressed, with tub gurnards moving into deeper water as they grew larger. The mean sizes (range) were 23.56 ± 6.92 cm (13.1–69.6 cm), 24.8 ± 5.35 cm (17.1–58.5 cm), 24.9 ± 8.14 cm (13.1–56.5 cm), and 23.0 ± 5.22 cm (14.2–46 cm) for winter, spring, summer, and autumn, respectively. Therefore, the observed distribution patterns highlight the influence of environmental factors on the abundance of tub gurnard species.
Keywords:
tub gurnard; distribution; abundance; GAM; Sea of Marmara
Key Contribution: Tub gurnard population varies in terms of biomass and abundance under the influence of environmental parameters. The present study reveals the effects of these parameters that influence the diversity of the tub gurnard species.

1. Introduction

Gurnards of the family Triglidae (Scorpaeniformes) are one of the most important commercial bony fish species, representing nine genera and 125 species [1]. According to Kovačić et al., (2021) [2], in the Mediterranean Sea, this family is represented by four genera (Eutrigla, Trigla, Chelidonichthys, and Lepidotrigla), and the following eight species: Chelidonichthys cuculus (Linnaeus, 1758); Chelidonichthys lastoviza (Bonnaterre, 1788); Chelidonichthys lucerna (Linnaeus, 1758); Chelidonichthys obscurus (Walbaum, 1792); Eutrigla gurnardus (Linnaeus, 1758); Lepidotrigla cavillone (Lacepède, 1801), Lepidotrigla dieuzeidei Blanc & Hureau, 1973; and Trigla lyra Linnaeus, 1758. Gurnards inhabit tropical and temperate marine areas [3], and play an important role in the demersal assemblages of both the eastern and western Mediterranean Sea, contributing to the total biomass [4,5,6].
Tub gurnard (Chelidonichthys lucerna, Linnaeus, 1758), which is also known as Mediterranean gurnard, is a significant demersal marine species of the family Triglidae. It is usually found in coastal waters to the depth of 500 m, and distribution extends from the northeastern Atlantic Ocean, from Norway to Mauritania and south Ghana, the Mediterranean Sea, the Sea of Marmara, and the Black Sea [7,8,9,10,11,12,13,14]. Characterized as a little to medium-sized species, C. lucerna is considered one of the well-recognized teleosteans of Mediterranean demersal fauna. The habitat preferences of C. lucerna range in a wide variety of substrates including detritus, muddy bottoms, rubble, and rocky reef bottoms. Tub gurnard mainly preys on epibenthic and nektobenthic organisms, which consist of crustaceans, mollusks, and also small teleosts; the diet of this species, therefore, occupies a wide niche in the benthic community [14,15,16]. Reproduction is observed throughout the year, with increased frequency during certain periods in different regions [9]. In Turkish waters, spawning occurs from December to May [17]. C. lucerna is one of the most abundant and commercially important triglid species in the Mediterranean. It was one of the target species of the MEDITS (Mediterranean International Trawl Survey) project [18], which was conducted in the Mediterranean to monitor commercially exploited fish species. ICES has identified C. lucerna as a Memorandum of Understanding (MoU) species and recommended that a survey of its biological data should be undertaken to define stock characteristics and assist in the development of management strategies for sustainable exploitation [19,20,21].
The tub gurnard is a common catch of bottom trawlers operating in the Mediterranean Sea and by small-scale fleets in the other regions [22]. The main fishing methods used to catch C. lucerna are gillnets, beam trawls, trammel nets, bottom trawls, beach seines, handlines, and longlines. These fishing techniques reflect the adaptability of fishing for this species in different marine environments [22,23]. While specific analyses of the abiotic factors influencing the distribution of C. lucerna in the SoM have not been conducted, Tserpes et al. (2002) [24] suggested that direct spatio-temporal comparisons could enhance the understanding of this species’ distribution patterns across the broader Mediterranean region. Despite its ecological importance, studies on the spatio-temporal distribution patterns of C. lucerna in the SoM are limited. Environmental factors, especially depth-dependent physico-chemical factors such as temperature, salinity, dissolved oxygen, and pH, as well as depth and biological parameters such as plankton, can have direct or indirect effects on the distribution and abundance of fish communities [25]. A comprehensive understanding of these patterns is essential for effective management and conservation strategies for this species and its habitat. Understanding the distribution patterns of C. lucerna within the SoM is essential, as it has important implications for fisheries management and conservation strategies. Species composition, distribution, and abundance of triglids in the SoM have been previously investigated in general ichthyological studies [26,27,28], as well as in the only Triglidae-specific study [29]. However, it is clear that the abundance and biomass of C. lucerna related to environmental parameters have not been studied before. In order to fill the above-mentioned gap in knowledge, the present study was carried out in the EsoM and is the first study in the region to be based on long-term scientific trawl surveys between 2014 and 2024. The present paper aims to investigate the spatio-temporal distribution of C. lucerna in the SoM and to identify the environmental factors influencing its distribution patterns. This study will model the distribution of tub gurnards in relation to environmental parameters, including dissolved oxygen, temperature, and pH, as well as bathymetry and geographical location within the SoM. The analysis will use time-series data from scientific bottom trawl surveys to provide a comprehensive understanding of the factors influencing the distribution of the species.

2. Materials and Methods

2.1. Study Area

The Sea of Marmara is the northeastern extension of the Mediterranean ecosystem and is defined as a geographical sub-area (GSA) 28 [30]. The SoM is a semi-enclosed basin with an area of 11,500 km2 and a maximum depth of 1390 m [31] (Figure 1). A less saline but highly productive Black Sea origin water (~18 psu) enters the Marmara Sea via the Istanbul Strait and floats with increased salinity (seasonally around ~23 psu in summer and ~28 psu in winter) over a highly saline (~38.5 psu) Mediterranean Sea origin water [32]. Due to its unique physical, chemical, and biological characteristics, it has been identified as a marine biodiversity hotspot [33,34]. Its hydrobiological uniqueness makes the SoM an important site for various marine species [35]. The SoM supports a diverse and rich ecosystem, with coastal areas and shallow bays providing a variety of habitats including seagrass beds, reefs, and rocky formations that support biodiversity and act as nurseries for many fish species [36]. SoM has historically been considered one of the most productive fishing grounds in Türkiye [37]. The ichthyofauna of the SoM consists of 277 species [38]. Although the permanent lack of oxygen in waters below the halocline is a well-known oceanographic feature of the SoM [31], the significant drop in oxygen concentrations below the threshold of hypoxia (even anoxia) in the water column has been threatening marine life for the past 40 years mainly because of terrestrial discharges and anthropogenic impacts [39].

2.2. Characterization of Tub Gurnard Fishery in the SoM

Along the coasts of Türkiye, the tub gurnard is a highly valued commercial species, especially in the SoM. Triglids are an important group in the local fisheries and play an important role in the marine ecosystem of the region. Their presence contributes to the economic vitality of the fisheries along these coasts [40]. In the SoM, fishermen typically use gillnets, set nets, fishing lines, and longlines to catch C. lucerna. Additionally, there is incidental catch of this species in beam trawl fisheries, although bottom trawling is prohibited in the region. The tub gurnard catch amount ranges from 5 to 30 tonnes per year over the last decade in the SoM [41] (Figure 2). The trends observed in by-catch species, including gurnards, closely mirror those of the primary target species, suggesting significant levels of exploitation and a decline in stock biomass during the second half of the 20th century. This correlation underscores the broader impacts of fishing practices on marine ecosystems and the sustainability of fish populations [42].

2.3. Data Collection

As part of the “Estimation of Demersal Fish Biomass in the Eastern Sea of Marmara” (MARSTOK) project, seasonal sampling was carried out in the ESoM from December 2014 to February 2024. The samplings were carried out at a total of (ten) stations in the eastern part of the SoM (Table 1). Bottom trawl surveys were conducted during daylight hours at depths ranging from 30 to 140 m. A total of 177 trawl operations of 30 min duration were conducted at a constant speed of 2.5–3 nautical miles·h−1 at the stations. During the trawl operation, vessel speed and the start and end coordinates were determined using the echosounder and GPS satellites on board the vessel. The sampling protocol was used for all 25 surveys conducted with the research vessel R/V Yunus S (31.85 m long and 510 hp) during the study. The distance between trawl net wings was 12 m, the headline length of the net was 24 m, and the codend mesh size of the trawl net used in the study was 16 mm. Due to various factors such as weather conditions, net damage, and other logistical reasons, it was not possible to conduct every survey at all the stations in every season. For each sampling haul, all the tub gurnard species were sorted, weighed, and counted, and the total length (cm) of all the individuals was measured without subsampling. Depth (m), bottom water temperature (°C), bottom salinity, pH, conductivity, and dissolved oxygen (mg·L−1) profiles were measured at each haul using the CTD system (conductivity, temperature, and dissolved oxygen recorder). Total length (TL) was measured to the nearest 0.1 mm for each examined individual for the length-stratified analysis and presented as minimum, maximum, and mean (±standard deviation) TLs.

2.4. Data Evaluation

The swept area (A) for each haul was estimated as follows [43]:
A = D × h × X2
D = V × t
A = swept area (km2), D = distance, V = velocity of the trawl over the ground during trawling (knots), t = the time spent trawling (h), h = the length of the headline (m), and X2 = wingspread constant.
CPUA = CW/A (kg·km−2)
The formula above was used. In the formula, CW is catch weight (kg), A is swept area, and CPUA is Catch Per Unit Area [43].
Assuming nonlinearity, generalized additive models (GAMs) using the Poisson distribution with a log link function [44,45] were applied to examine the relationship between the tub gurnard Chelidonichthys lucerna (Linnaeus, 1758) distribution and environmental and spatio-temporal factors. GAM is a nonparametric regression technique that generalizes multivariate linear regression by relaxing the assumptions of linearity and normality, replacing regression lines with smooths [46]. A smoother is a tool for summarizing the trend of a dependent variable Y as a function of one or more independent variables. This tool produces an estimate of the trend that is less variable than Y itself, hence the name smoother [47]. Given the constraint that the smoother is linear at boundaries [48], a natural cubic spline was applied to represent the relationship [49]. The variables included in the analysis were longitude, latitude, water depth, bottom temperature (SBT), bottom pH, and dissolved oxygen (DO).
The “mgcv” package (version 1.30-27) and ggpilot2 of the R program was used [50]. Deviance explained (analogous to variance in linear regression; the higher the better), adjusted R2, Akaike information criterion (AIC), and UBRE (Unbiased Risk Estimator; generated by the Poisson family) were calculated as a measurement of the goodness-of-fit [51]. Models with the lowest AIC and UBRE scores were selected [52]. The UBRE criteria was as follows:
d/n + 2 × s × df/n − s
where d is the deviance, n is the number of data, s is the scale parameter, and df is the effective degrees of freedom of the model [45]. AIC is a function of both the log-likelihood function and the effective number of parameters being estimated. The AIC in the stepwise GAM was calculated:
AIC = d + 2 df × φ
where d = deviance (residual sums of squares), df = effective degrees of freedom, and φ = dispersion parameter (variance) [53].
A correlation matrix was deployed to determine the collinearity between variables and which combinations of predictors could be incorporated as independent variables. Because several variables were highly correlated (>0.3 correlation coefficients), only some sub-models with 3 or 4 variables were assessed. To compare the total length distribution by depth and season, the nonparametric Kruskal–Wallis test was used. The Mann–Kendall test for trend, which was performed by means of the statistical software PAST, version 4.03 [54], was used to detect annual trends in the biomass, abundance, and TL data for the respective MD stations [55]. The test could not be performed at the MD3 and MD6 stations because the number of years for which measurements could be made was less than 4. A positive and a negative value of Z indicates an upward and a downward trend, respectively [55]. A p-value of 0.05 was defined as statistical significance [56].

3. Results

3.1. Size Analysis

A total of 1412 gurnards were sampled, of which 996 were sampled <80 m depth and the remaining 416 gurnards were sampled in deep water (>80 m). The general size distribution (TL) ranged from 13.1 cm to 69.6 cm (n = 1412), with a mean length of 23.9 ± 6.55 cm. The results showed that in most cases, more than 92.7% of the catches consisted of individuals with a total length of less than 30 cm, mostly between 19 and 21 cm. Two distinct depth–length clusters were observed in the statistical and graphical results (Figure 3). The Kruskal–Wallis test showed statistically significant differences between the size frequency distributions in the two depth ranges (p < 0.05). The mean total lengths were 24.1 ± 6.90 and 23.5 ± 4.27 cm for depths >80 and <80 m, respectively. There was an apparent preference for depth, with gurnards moving into deeper water as they grew larger. Mean sizes (range) were 23.56 ± 6.92 cm (13.1–69.6 cm), 24.8 ± 5.35 cm (17.1–58.5 cm), 24.9 ± 8.14 cm (13.1–56.5 cm), and 23.0 ± 5.22 cm (14.2–46 cm) for winter, spring, summer, and autumn, respectively. The seasonal distribution of size was also significantly different for all seasons except spring and winter (Kruskal–Wallis test, p < 0.001) (Figure 4). No trends over the years were identified regarding the minimum, maximum, and mean lengths. The highest total length (TL) obtained at depths between 0 and 80 m was 69.6 cm, while the highest TL individual obtained at depths between 80 and 140 m was 42.1 cm. The highest total lengths of individuals (TL: 42.1–69.6 cm) were mainly obtained in coastal areas, particularly at the MD9 and MD10 stations, where the depth varied between ca. 30 and 51 m (Table 1), and the increasing trend of TL at the shallow stations was also observed in the Mann–Kendall trend test (Table 2). The Mann–Kendall trend test showed an increasing trend for the TLs of individuals captured at the MD1, MD9, and MD10 stations and a decreasing trend at deeper (62.1 to 139.5 m depth) the MD2, MD4, MD7, and MD8 stations. With the exception of MD8, where a statistically significant decreasing trend was observed, there is no statistically significant trend at the other MD stations (Table 2).

3.2. Abundance and Biomass Index

When catches of all the hauls were pooled to obtain absolute numbers, a total of 44,669 individuals were calculated as the total abundance (number/km2) during the sampling period. In total, 77.6% of the tub gurnards were found in stations MD1, MD2, MD5, MD8, and MD9. The catch variability was very high in all the stations and seasons. The maximum abundance in N per km2 (>10,000) was recorded at stations MD1, MD2, and MD8. The mean tub gurnard abundance had one seasonal peak in winter (Figure 5), but no statistically significant changes among seasons (Kruskal–Wallis test, p > 0.05). The fish occupied the whole of the depth range investigated. However, the species was not very abundant in terms of the total number of tub gurnards caught in the bottom trawls, calculated per km2. The fluctuations in the index of abundance have been characterized by constant catches, but not by very abundant catches. The mean biomass values are high in the summer and winter seasons at 180.18 kg/km2 and 156.08 kg/km2, respectively. The highest mean biomass value is in the summer and winter seasons in the MD5 and MD9 stations (Figure 6, Table 3). Despite the insignificant increasing trend of biomass and the decreasing trend of abundance observed at the MD5 and MD10 stations (Mann–Kendall test Z scores 1 and 5 for biomass, and −17 and −7 for abundance, respectively), an increasing trend of biomass and abundance was observed at the stations MD1, MD2, MD4, MD7, MD8, and MD9 (Table 2).

3.3. GAM Analysis

The environmental parameters, such as dissolved oxygen, sea bottom temperature (SBT), pH, and depth, effects were the most important predictors explaining the spatial distribution of the tub gurnard, with 27% and 18% of the deviance explained, respectively (models 6 and 13) (Table 4). The effects of single predictors on the distribution of tub gurnards were mostly nonlinear. With the models, the CPUA numbers peaked between depths of <40 m and approximately 30 m (Figure 7A). For oxygen, the distribution was positively related to low oxygenated waters (DO < 3.16 mg·L−1; Figure 7B). A tendency for high numbers was observed in waters with SBT values between 14 and 18 °C (Figure 7C). The corresponding spline plots of latitude–longitude, which were used as a measure of spatial location, suggest the presence of local maxima, with the highest maxima between approximately 40.3 and 41° N and 28.6 and 29.4° E (Figure 7E,F).
The correlations among predictor variables revealed high positive correlations between oxygen and depth (>0.3 correlation coefficients; Figure 8). Hence, the models with more than one variable were evaluated by avoiding the correlation between predictors and according to the explained deviance and AIC results. Model 13 best explained the distribution, including the effect of oxygen, SBT, and pH (Table 4). The overall performance of the model was adequate, with an R2 = 0.21, and the resulting model explained 27% of the total variance. The effect of the no-variable setting was slightly positive or negative and displayed nonlinear responses to the covariates. Of the variables included in the models, the “SBT” and the “Depth” were the most important environmental variables, explaining up to 21% and 18% of the total variance, respectively. In contrast to model 6, abundance exhibited the maximum effect, with peaks below 40 m (Figure 7A).
The results indicate that the inclusion of dissolved oxygen, pH, and temperature sequentially improves the model’s explanatory power. Higher DE values suggest that these environmental covariates capture significant variability in abundance. This supports the importance of dissolved oxygen, pH, and temperature in predicting abundance. The overall significance of the covariates in the models (p < 0.001) indicates that these environmental factors have a statistically significant effect on abundance. These results highlight the critical role of environmental variables in shaping abundance and suggest that changes in dissolved oxygen, pH, and temperature could significantly impact the spatio-temporal distribution of the tub gurnard.

4. Discussion

Current stock assessments for Mediterranean and Black Sea fisheries resources reveal that 58% of the stocks are overexploited, with varying levels of exploitation across different subregions [57]. The Eastern Mediterranean is generally characterized as a data-poor area, lacking comprehensive or complete information on various ecological, physical, and chemical parameters that are essential for comprehending and managing the marine environment. The marine ecosystem of the SoM presents significant challenges to researchers, conservationists, and policy-makers as they have a highly variable and dynamic structure to make informed decisions about resource management. The SoM represents a relatively less-studied region within the Mediterranean basin. Notable ecological contrasts between the SoM and adjacent ecosystems, such as the Black Sea [58] and the Mediterranean Sea, warrant further discussion [59,60]. Many countries lack detailed long-term data sets for fish stocks which are needed for the evaluation of proper stock assessment practice, and SoM stocks are defined in the “data poor stocks” category [61].
Gurnards are a key component of demersal assemblages in Mediterranean ecoregions, occupying soft and detrital bottoms across a range of habitats, from the shallow depths of the continental shelf to the upper slope. Tub gurnard is an important commercial species for the demersal fisheries in the SoM, and the present study provides the effects of dissolved oxygen, temperature, and pH in relation to the spatio-temporal data of tub gurnard as part of the “Estimation of Demersal Fish Biomass in the Eastern Sea of Marmara” (MARSTOK) project with seasonal sampling carried out in the ESoM. Environmental conditions, especially the bathymetry factor and geographical location, were found to be significant covariates influencing the variation in the abundance and distribution of the gurnard population in the SoM.
Environmental factors (e.g., temperature, pH, salinity, and dissolved oxygen) can profoundly influence the physiology, behavior, and population dynamics of fish [62]. Therefore, assessing the impact of these factors on fish is essential for effective fisheries management and conservation efforts. Alterations in fish behavior, reproduction, growth, and survival can significantly influence predator–prey interactions, nutrient cycling, and overall biodiversity within aquatic ecosystems [63]. Numerous studies have shown relationships between fish diversity and water conditions such as salinity, temperature, depth, and tidal ranges [64].
Temperature plays an important role in shaping various aspects of fish biology, including metabolic processes, growth rates, reproductive success, and overall physiological performance. Profound effects of acute and chronic temperature variations on fish physiology and behavior can be translated: high temperatures often accelerate metabolic processes, increase energy demands, and exacerbate oxygen limitations, while reduced temperatures may slow metabolic rates, disrupt enzyme activities, and compromise immune function. Temperature fluctuations which can alter the distribution and abundance of fish species have the potential to reshape the ecosystem dynamics and interactions [65]. On the other hand, in terms of the metabolic effects of salinity, fish species exhibit differing tolerances to salinity variations, and fluctuations in salinity can significantly disrupt reproductive functions, ionic balance, and osmotic homeostasis [66].
Breitburg et al. (2018) [67] underlined the role of dissolved oxygen (DO), which is a critical component of aquatic ecosystems, essential for respiration and aerobic metabolism, as follows: It directly influences the metabolic rate of fish and plays a vital role in sustaining aquatic life; thus, insufficient DO levels, often resulting from eutrophication or pollutant inputs, can lead to hypoxic or anoxic conditions, posing significant threats to fish survival, health, and overall fitness. Low DO levels can adversely affect fish growth, reproduction, and immune function, potentially leading to population declines or even mass mortality of the stocks [67]. Since gradients in DO levels significantly influence fish habitat selection and distribution patterns, many fish species exhibit distinct preferences for specific DO ranges, actively selecting habitats that offer optimal oxygen conditions to meet their physiological and survival requirements [68]. The substantial decline in dissolved oxygen concentrations below hypoxic thresholds, and in some cases reaching anoxic levels, has posed a significant threat to marine life over the past four decades [39]. Keskin et al. (2011) [69] stated that dissolved oxygen (DO), depth, and temperature significantly influence the distribution of fish species, with DO identified as the most critical factor affecting fish distribution in the SoM, and reported that dissolved oxygen concentrations ranged from 4 to 9 mg/L at depths of 0–20 m, 2 to 5 mg/L at depths of 20–50 m, and 3 to 5 mg/L at depths of 50–70 m in the southwestern part of the SoM. In the present study, the average DO levels are about 2.3 mg/L at depths of 20–50 m, and 1.77 mg/L at depths of 50–70 m in the eastern part of the SoM. In this case, it can be said that latitude and longitude are one of the important effects on DO levels in the SoM.
The rapid and ongoing warming of the Mediterranean Sea represents a crucial factor influencing the temporal trends of species distribution and abundance. This environmental change may lead to a progressive shift in gurnards’ distribution and abundance as a result of climate forcing. Additionally, this warming indirectly affects the distribution of gurnards by altering the availability of their prey, thereby influencing their overall distribution patterns [70]. The observed affinity of small-sized prey species and smaller individuals of larger species for hypoxic conditions may be interpreted as a sheltering strategy, as larger, potentially predatory species tend to avoid hypoxic areas [71].
Colloca et al. (2019) [72] conducted spatio-temporal studies on gurnard species in the Mediterranean Sea using 22 years of bottom trawl data from the International Bottom Trawl Survey in the Mediterranean (MEDITS) program (1994–2015). Their findings revealed a significant decline in the abundance of C. lucerna on the continental shelf of the western Mediterranean, particularly in the General Fisheries Commission for the Mediterranean (GFCM) Geographical Sub-Areas (GSAs) 6 and 9. Conversely, an increasing trend in abundance was observed in the Aegean Sea (GSA 22). In other regions, C. lucerna exhibited fluctuations at low abundance levels without any discernible temporal trend. During the 2013–2015 period, the highest biomass of C. lucerna was recorded on the continental shelf of the Aegean Sea, reaching 8.3 kg/km2 in 2014, and in the western Ionian Sea (GSA 19), where biomass peaked at 2.8 kg/km2. In the study, the biomass of C. lucerna was notably high in the waters surrounding the Balearic Islands, Maltese waters, the Gulf of Lions, eastern Corsica, and the eastern Ionian Sea. Conversely, the lowest biomass levels were recorded in the Alboran Sea, Tyrrhenian Sea, and western Ionian Sea. This low biomass is attributed to factors such as intense fishing pressure, the ongoing warming of the Mediterranean Sea, and reduced habitat suitability, as explained by the authors.
Although the present study is the first to focus solely on the abundance and biomass of the ESoM population of tub gurnard, the results of the previous stock assessment studies of demersal fish species occurring in the region also provide an overview of the long-term trend of C. lucerna stocks. In a previous bottom trawl survey conducted in the SoM during the autumn of 1992, at depths ranging from 20 to 100 m, the tub gurnard biomass was reported as 17.5 kg/km2 [73]. Between 1992 and 1995, Gözenç et al. (1997) [74] conducted a research survey in the SoM at depths ranging from 20 to 110 m for the demersal fish stock estimation. In the Northwestern Marmara Sea region (Mürefte), the tub gurnard biomass was reported as 278 kg/km2, whereas in region 4A (Southwestern Marmara Sea), it was 51 kg/km2. The highest biomass values were observed in region 4 (Western Marmara Sea), while the lowest values were recorded in region 1 (Eastern Marmara Sea). Overall, the average tub gurnard biomass in the SoM decreased significantly, from 67.7 kg/km2 in 1990 to 16 kg/km2 in 1995. Karakulak et al. (2004) [75], using bottom trawl surveys at depths of 200 m, reported the biomass of tub gurnard as 209 kg/mile2 in the SoM. In the present study, the biomass values are consistent with the other studies, as the mean biomass values are 84.94 kg/km2 in the winter, 53.69 kg/km2 in the spring, 81.27 kg/km2 in the summer, and 60.64 kg/km2 in the autumn seasons in the ESoM.
Moreover, the biomass of C. lucerna in the SoM was also investigated from the perspective of catch per unit effort (CPUE) in several studies. İşmen et al. (2018) [27] conducted bottom beam trawl surveys in the SoM and reported the catch per unit effort (CPUE) for C. lucerna as 2.09 kg/h in autumn, 2.11 kg/h in winter, 2.01 kg/h in spring, and 28.60 kg/h in summer. Their study identified the highest densities in the Tekirdağ (north SoM) region. Daban et al. (2021) [76] conducted bottom trawl surveys for the fish fauna in the SoM, and reported a CPUE of 15.6 kg/km2, with a mean CPUE of 18.6 kg/km2 for depths between 20 and 100 m. In comparison, Gözenç et al. (1997) [74] recorded a CPUE of 46.9 kg/km2 in the SoM. In the present study, the average CPUE was found to be significantly higher, at 73.2 kg/km2. As bottom trawling is prohibited in the SoM, beam trawling is intensively used in some regions of the SoM continental shelf and the main target species are rose shrimp, Parapenaeus longirostris, and bony fishes such as tub gurnard and European hake reported as the main commercial by-catch species. Therefore, biodiversity and species biomass and abundance of demersal fish species such as tub gurnard show differences in various regions of the SoM due to intensive beam trawl fishing activities, especially in the northwestern SoM. In contrast, our findings indicate that the highest densities of tub gurnard species were observed in the northeastern SoM region. Biodiversity can be influenced by various factors, including declines in water quality [77], fishing pressure [78,79], and hypoxia [80]. Additionally, fishing exploitation can alter the size structure of fish communities by selectively removing larger individuals, thereby increasing the relative abundance of smaller individuals within populations [81]. The SoM serves as a critical area for the nursery and reproduction activities of both demersal and pelagic species [82].
The SoM experiences significant anthropogenic pressure due to multiple uses (intense marine traffic, a high concentration of home ports, and illegal trawling activities) and the effects of climate change, all of which exert pressure on demersal stocks. Notably, the coasts of SoM host the densest residential and industrial areas in Turkiye, contributing heavily to pollution. This sea is subjected to substantial pollution from domestic and industrial wastewater discharges, agricultural activities, and ship wastewater [83]. This has led to various environmental issues, such as significant deoxygenation and leads to habitat contraction in the water column [84] and biodiversity loss [85].
An examination of the biomass and abundance data across stations reveals that the abundance and biomass of tub gurnards increased following the intense mucilage phenomenon in 2021 compared to previous years of the study in the SoM. After this significant mucilage event, tub gurnards, particularly larger individuals, were predominantly found in coastal zones rather than deeper areas although Serena et al. (1990) [86] and Colloca et al. (1994) [15] noted that the juvenile gurnard species tend to occur in shallower waters than the adults. This shift can be attributed to the low dissolved oxygen levels caused by the intense mucilage, leading to anoxic conditions in the demersal zones of the SoM. These findings support the hypothesis that the observed spatial differences in gurnard abundance and biomass may be linked to varying oxygen levels across the region. Another possible explanation is that certain demersal species, which could serve as prey for tub gurnards, may have migrated toward coastal areas due to the mucilage phenomenon which caused a lack of prey. In the present study, Mann–Kendall trend test results for abundance and biomass during the period 2014–2024 showed an increasing trend for these parameters, supporting the expected vertical habitat compression for the C. lucerna population in the shelf waters of the ESoM. Furthermore, the decreasing trend in the mean TL of C. lucerna in the ESoM since 2014 also suggests the growth overfishing of tub gurnard due to vertical habitat compression, associated with increasing fishing impacts on the species. A similar case of vertical habitat compression of a deepsea species in association with bathyal deoxygenation and habitat deterioration was reported for the sixgill shark, Hexanchus griseus (Bonnaterre, 1788), in the SoM [87]. Long-term observations revealed that the depth of occurrence of large specimens (TL ranging from 350 to 550 cm) has begun to restrict in the shallows (<100 m) of the shelf waters mainly due to hypoxia in deeper shelf and slope waters since the late 1990s [87].
Understanding the distribution and abundance patterns of fish species is important for the effective management and conservation of fish stocks in the SoM. Additionally, studying the distribution of fish species provides valuable information about the ecological processes that contribute to biodiversity in the region. The distribution of Triglidae species in the SoM is influenced by various factors [88]. As Yalçin and Gurbet (2012) [25] previously explained in detail, depth-dependent physico-chemical variables such as temperature, salinity, dissolved oxygen, and pH, along with depth and biological parameters such as plankton, can have direct or indirect effects on the fish communities’ distribution and abundance. However, the specific interactions between these factors and their relative importance in shaping the distribution of gurnard species in the SoM is an important issue.
The present study contributes to the knowledge of the ecology of C. lucerna in the SoM, providing valuable information for fisheries management and conservation efforts, and further data collected from surveys provide valuable insights into the spatial distribution of the species. The results derived from GAM analysis can serve as essential criteria for establishing and managing fishing-restricted areas, conducting stock assessments, or evaluating species distribution. In conclusion, the findings of this study support the hypothesis that the distribution of tub gurnards is closely linked to environmental conditions, with depth emerging as a more significant factor compared to the other abiotic variables. The results further demonstrate that the diverse approaches employed in this research provide valuable insights into the distribution of tub gurnard species in the study area and their relationships with environmental conditions, provide valuable insights into the ecology of tub gurnard species in the SoM, and contribute to the development of sustainable management strategies for its conservation. A solid understanding of species–environment relationships and their spatio-temporal dynamics is needed to identify and locate critical areas for the survival of C. lucerna, such as nursery grounds. It is, therefore, of great importance to protect areas of high biodiversity in the SoM ecosystem.

5. Conclusions

The present study revealed distinct seasonal variations in the tub gurnard species abundance and spatial distribution in the SoM. The variations in distribution are attributed to the species’ life history characteristics, including reproductive behavior, feeding ecology, and environmental preferences. The GAM analysis demonstrated that in the EsoM, the distribution and abundance of C. lucerna are impacted mainly by sea bottom temperature, DO, and depth. As shown in model 6 and model 13, SBT and depth were the most important environmental variables, explaining up to 21% and 18% of the total variance, respectively. In contrast to model 6, abundance had the largest effect, with peaks below the 40 m depth. For oxygen, the distribution was positively related to low oxygenated waters (DO < 3.16 mg·L−1), and a tendency for high numbers was observed in waters with SBT values between 14 and 18 °C. C. lucerna is among the most abundant and largest gurnard species, making it a commercially significant bony fish, particularly for European fishing nations bordering the Atlantic Ocean and Mediterranean Sea [89]. It is currently classified as “Least Concern” in the IUCN Red List [90]. Current fisheries management primarily focuses on regulations such as time and area closures, minimum landing sizes, and fishing gear restrictions. However, to implement an effective management plan, it is crucial to understand parameters such as biomass, distribution, stock recruitment, and fishing mortality rates. The SoM, part of the Mediterranean basin, faces significant challenges due to the overexploitation of fish stocks and rapid warming. This region is particularly vulnerable to the negative impacts of both anthropogenic and environmental factors. Therefore, it is essential to develop a fisheries management plan tailored to the SoM incorporating ecosystem-based fisheries management principles that account for the effects of climate change and habitat sustainability. The spatio-temporal distribution patterns identified in this research can serve as a baseline for future studies investigating the impacts of environmental changes and anthropogenic activities on the population dynamics of tub gurnard species.

Funding

This research was funded by Istanbul University, Project #51922, Faculty of Aquatic Sciences.

Institutional Review Board Statement

Animal-related experiments were conducted in accordance with the NIH Guidelines for the care and use of laboratory animals (http://oacu.od.nih.gov/regs/index.htm, accessed on 13 April 2024). All the applicable international, national, and/or institutional guidelines for the care and use of animals were followed. Tub gurnards were captured during scientific bottom trawl surveys performed within the scope of the ongoing Stock Identification of Demersal Fishes in the Eastern Marmara Sea project, and the relevant inspections of the captured specimens were carried out in accordance with the ethics committee. The approval granted by the Local Ethics Committee of Istanbul University Animal Experiments also covers the present study (Project ID: 51922; Approval granted 17 April 2015).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within this article.

Acknowledgments

This study was supported by the Scientific Research Projects Coordination Unit of Istanbul University (Project #51922). The author thanks the crew of R/V Yunus S of İstanbul University, Faculty of Aquatic Sciences, for their hard and friendly efforts during the field survey. The author also thanks Saadet Karakulak and Hakan Kabasakal for their valuable contributions.

Conflicts of Interest

The author declares no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MDPIMultidisciplinary Digital Publishing Institute
SoMSea of Marmara
ESoMEastern Sea of Marmara
DOdissolved oxygen
GAMgeneralized additive model
MoUMemorandum of Understanding

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Figure 1. Map showing the location of the study area and sampling sites, with the red dots indicating the sampling stations.
Figure 1. Map showing the location of the study area and sampling sites, with the red dots indicating the sampling stations.
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Figure 2. Catch trend of tub gurnard in the Sea of Marmara (TUIK, 2023).
Figure 2. Catch trend of tub gurnard in the Sea of Marmara (TUIK, 2023).
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Figure 3. C. lucerna. Cumulative density function for total length (cm) distribution in two depth ranges (<80 m; >80 m).
Figure 3. C. lucerna. Cumulative density function for total length (cm) distribution in two depth ranges (<80 m; >80 m).
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Figure 4. Boxplot of total length (cm) distribution of tub gurnards by season. The dashed line indicates the general mean of total length for the whole population analyzed. (signif. codes: p < 0.001: ***; p < 0.05: *; NS: non-significant).
Figure 4. Boxplot of total length (cm) distribution of tub gurnards by season. The dashed line indicates the general mean of total length for the whole population analyzed. (signif. codes: p < 0.001: ***; p < 0.05: *; NS: non-significant).
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Figure 5. Boxplot of seasonal transformed (log x+1) abundances (number/km2) with mean values.
Figure 5. Boxplot of seasonal transformed (log x+1) abundances (number/km2) with mean values.
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Figure 6. Boxplot of seasonal transformed (log x+1) biomass (number/km2) with mean values.
Figure 6. Boxplot of seasonal transformed (log x+1) biomass (number/km2) with mean values.
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Figure 7. Coefficients of the GAM for tub gurnards against additive terms of (A) depth, (B) oxygen, (C) SBT (sea bottom temperature), (D) pH, (E) latitude, and (F) longitude used as single predictor variables.
Figure 7. Coefficients of the GAM for tub gurnards against additive terms of (A) depth, (B) oxygen, (C) SBT (sea bottom temperature), (D) pH, (E) latitude, and (F) longitude used as single predictor variables.
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Figure 8. C. lucerna. Pairplot of the total abundance of tub gurnard and explanatory covariates. The upper diagonal panel shows the (absolute) correlation coefficient, the mid-diagonal line shows the plots (as bar graphs), and the lower diagonal part shows the pairwise scatterplots with a smoothing line. (signif. codes: *: p < 0.05; **: p < 0.01: *; ***: p < 0.001).
Figure 8. C. lucerna. Pairplot of the total abundance of tub gurnard and explanatory covariates. The upper diagonal panel shows the (absolute) correlation coefficient, the mid-diagonal line shows the plots (as bar graphs), and the lower diagonal part shows the pairwise scatterplots with a smoothing line. (signif. codes: *: p < 0.05; **: p < 0.01: *; ***: p < 0.001).
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Table 1. Chelidonichthys lucerna. Coordinates and depths (m) in the Sea of Marmara.
Table 1. Chelidonichthys lucerna. Coordinates and depths (m) in the Sea of Marmara.
StationLatitudeLongitudeDepth (m)N
MD140.86305° N29.16417° E83.9205
MD240.87603° N29.03711° E69.8195
MD340.32128° N29.39347° E94.74
MD440.78083° N29.26221° E85.999
MD540.94250° N29.40250° E62.1152
MD640.58356° N28.83618° E139.557
MD740.93639° N29.41667° E85.267
MD840.82806° N29.49528° E44.6335
MD940.95035° N 28.98361° E30.9189
MD1041.01758° N28.61443° E52.9109
N: the total number of tub gurnard samples.
Table 2. Results of Mann–Kendall test for trend of biomass, abundance, and TL data for respective MD stations. *: statistically significant trend. Z(+), increasing trend; Z(−), decreasing trend.
Table 2. Results of Mann–Kendall test for trend of biomass, abundance, and TL data for respective MD stations. *: statistically significant trend. Z(+), increasing trend; Z(−), decreasing trend.
AbundanceBiomassMean TL
StationZP (Trend)ZP (Trend)ZP (Trend)
MD1260.0029 *260.0029 *80.238
MD2230.023 *210.036 *−100.136
MD4130.0083 *110.028 *−70.136
MD5−170.07810.5
MD740.24220.408−20.408
MD8160.06100.179−220.012 *
MD9110.028 *90.06890.068
MD10−70.19150.281110.068
Table 3. The mean biomass values of C. lucerna in the Sea of Marmara (kg/km2).
Table 3. The mean biomass values of C. lucerna in the Sea of Marmara (kg/km2).
Season/Year20142015201620172018201920202021202220232024
Winter70.30 65.4849.54 83.31156.08
Spring 54.7826.5628.9067.0731.54 133.31
Summer 180.1874.3457.6470.3120.5139.4551.99155.81
Autumn 51.8032.6850.36 64.01 104.39
Table 4. Analysis of deviance explained (DE), UBRE, AIC, and R2 for GAM covariates and their interactions for the best GAM fitted by adding the covariates stepwise. p < 0.001: ***.
Table 4. Analysis of deviance explained (DE), UBRE, AIC, and R2 for GAM covariates and their interactions for the best GAM fitted by adding the covariates stepwise. p < 0.001: ***.
ModelModelsSigDEUBREAICR2
M1s(SBT)***21192,94314880.1759
M2s(O)***0.3228,9871505−0.007
M3s(pH)***5.2222,13715020.0326
M6s(Depth)***18211,17114960.1221
M5Lat***0.7229,6161505−0.006
M4Lon***0.5228,5841505−0.006
M7s(SBT) + s(O)***21196,75414890.1691
M8s(SBT) + Lat***21196,85514890.1684
M9s(SBT) + s(pH)***26189,61414860.209
M10s(SBT) + Lon***21196,73214890.1684
M11s(pH) + Lat***5.4226,42315040.0242
M12s(pH) + Lon***5.5225,96615030.026
M13s(SBT) + s(O) + s(pH)***27192,35314870.2122
M14s(SBT) + s(O) + s(pH) + Lon***27196,55514890.2033
M15s(Depth) + s(pH)***20210,68914960.1369
M16s(Depth) + Lon***18215,42114980.1131
M17s(Depth) + s(pH) + Lon***20214,98314980.1273
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Uzer, U. Influence of Environmental Parameters on the Abundance of Tub Gurnard, Chelidonichthys lucerna, in the Eastern Sea of Marmara. Fishes 2025, 10, 127. https://doi.org/10.3390/fishes10030127

AMA Style

Uzer U. Influence of Environmental Parameters on the Abundance of Tub Gurnard, Chelidonichthys lucerna, in the Eastern Sea of Marmara. Fishes. 2025; 10(3):127. https://doi.org/10.3390/fishes10030127

Chicago/Turabian Style

Uzer, Uğur. 2025. "Influence of Environmental Parameters on the Abundance of Tub Gurnard, Chelidonichthys lucerna, in the Eastern Sea of Marmara" Fishes 10, no. 3: 127. https://doi.org/10.3390/fishes10030127

APA Style

Uzer, U. (2025). Influence of Environmental Parameters on the Abundance of Tub Gurnard, Chelidonichthys lucerna, in the Eastern Sea of Marmara. Fishes, 10(3), 127. https://doi.org/10.3390/fishes10030127

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