Age, Growth, and Mortality of the Common Pandora (Pagellus erythrinus, L. 1758) in the Central Aegean Sea: Insights into Population Dynamics

Abstract

This study investigates the age, growth, and mortality of the common pandora (Pagellus erythrinus) in the Central Aegean Sea, providing critical insights into its population dynamics and sustainability. A total of 589 specimens were analyzed, identifying nine age cohorts with mean total lengths ranging from 13.18 cm to 32.94 cm. Growth parameters, estimated using the von Bertalanffy growth model, yielded an asymptotic length (L∞) of 39.53 cm and a growth coefficient (k) of 0.16 year⁻¹, indicating moderate growth rates. The population exhibited non-isomorphic growth (b = 2.49, R² = 98.4), suggesting slower weight gain relative to length. Mortality estimates indicated natural mortality (M) at 0.321 year⁻¹, total mortality (Z) at 0.52 year⁻¹, and fishing mortality (F) at 0.2 year⁻¹, resulting in an exploitation rate (E) of 0.38. The fishing mortality at maximum sustainable yield (FMSY) was estimated at 0.33, with an exploitation rate at MSY (EMSY) of 0.51, suggesting that the population is currently harvested sustainably but close to the threshold of overexploitation. These findings provide essential reference points for fisheries management and highlight the need for continuous monitoring to ensure the long-term sustainability of P. erythrinus in Greek waters.

1. Introduction

The common pandora (Pagellus erythrinus, Linnaeus, 1758) is a valuable commercial species belonging to the Sparidae family, widely distributed in the Mediterranean Sea, the Black Sea, and the Northeast Atlantic Ocean from Norway to Cape Verde [1,2]. It is a demersal, semi-pelagic, and gregarious species inhabiting various substrates, including sand, mud, gravel, and rocky bottoms, at depths ranging from 20 to 100 m and occasionally reaching up to 320 m in some regions [3,4,5,6]. Pagellus erythrinus is a generalist predator, feeding on a variety of benthic organisms, including decapods, bivalves, polychaetes, euphausiids, teleosts, mysids, and cephalopods [7,8].

Recognized for its economic importance, P. erythrinus has been extensively studied in various regions, particularly in the Mediterranean, the Atlantic Ocean, and the Black Sea. It is prized for its taste and supports both local and export markets, contributing to income for fishermen and related industries [9,10,11]. Research has mainly focused on biological aspects and fisheries management [12,13,14,15,16,17]. This species is primarily harvested using methods such as trawls, gillnets, trammel nets, bottom longlines, and hand lines [18].

According to a report by the Hellenic Statistical Authority [19], overall fishery catches in Greece declined by 5.2% from 2022, affecting both open-sea and inshore fisheries. However, the catch of P. erythrinus increased by 10.1%, reaching 706.5 tons in 2023 compared to 641.5 tons in 2022. A continued increase in fishing pressure, without corresponding data on recruitment and mortality rates, could lead to stock depletion, ultimately affecting both the species and the fisheries that depend on it [20].

Effective fisheries management relies on a deep understanding of key life history traits, including age, growth, and mortality rates [21,22,23,24]. Research on P. erythrinus has primarily focused on its age and growth characteristics due to its high commercial value, yet data on mortality and exploitation remain scarce in Hellenic waters despite this [25,26,27,28,29]. Age-structured assessment models that evaluate exploitation levels and their effects on fish stocks rely heavily on an accurate knowledge of age and growth [30]. Although they are commonly used, traditional age assessment techniques such as the examination of scales, otoliths, and vertebrae can be labor-intensive, time-consuming, and subject to human error [31] as well as environmental factors [32]. According to Vitale et al. [33], length-frequency distribution analysis offers a faster alternative, although it is dependent on a number of assumptions [34]. Despite these limitations, otolith analysis remains one of the most reliable techniques for fisheries assessment [35].

Increased fishing pressure has led to earlier maturation and reduced adult sizes, raising concerns about stock sustainability [36,37]. Conservation measures, such as those outlined in Council Regulation (EC) No 1967/2006 [38], set a minimum catch size limit of 15 cm for P. erythrinus. The regulation establishing a 15 cm minimum catch size for P. erythrinus aims to protect juveniles, promote reproductive success, and ensure long-term stock sustainability. It supports stock recovery by reducing fishing pressure on immature fish, contributing to ecosystem balance.

The study aims to enhance our understanding of P. erythrinus life history and resilience by bridging critical knowledge gaps related to age, growth, and mortality in Greek waters. Despite research conducted on the species in other regions, the last assessment in Greek waters was in 2002 [25]. The provision of updated data on the species will enhance the accuracy of stock assessments and support the development of sustainable fisheries management strategies for P. erythrinus in the region. The resulting insights will provide a scientific foundation for sustainable fisheries management and conservation strategies in the Aegean Sea, guiding future research aimed at preserving marine biodiversity and ensuring the species’ long-term sustainability while balancing economic benefits with population stability.

2. Materials and Methods

2.1. Study Area and Sampling Methodology

A total of 589 common pandora individuals were collected using commercial bottom trawls during the month of May for four consecutive years, between 2021 and 2024 in the western part of the Central Aegean Sea (Eastern Mediterranean) (Figure 1). Samplings were conducted at three locations: North Euboean Gulf, Pagasitikos Gulf, and the western part of the Central Aegean Sea. A traditional Greek commercial bottom trawl equipped with a square mesh codend (bar length: 28 mm stretched) was used, operating at an average speed of approximately 3 knots.

The seabed in the studied area is predominantly composed of fine sand, sandy mud, and muddy plains within the sublittoral zone [39]. The mean annual water temperature fluctuated between 14 and 16 °C, with salinity levels ranging from 37 to 39 ppt. pH values were recorded between 8.40 and 8.46, while oxygen concentrations varied between 5.3 and 5.9 mg/L. Turbidity levels varied between 17.4 and 19.8 m [40]. For each collected specimen, total length (cm) and total weight, to the nearest 0.01 g, were measured to assess growth and population dynamics.

2.2. The Length–Weight Relationship

The length–weight relationship (LWR) was estimated following the method described by Quinn and Deriso [41] by fitting the curvilinear power regression in Equation (1) to the data, where total weight (TW) is expressed in grams (g) and total length (TL) in centimeters (cm). In this equation, “a” represents the intercept of the curve (growth factor) and “b” the slope (allometry coefficient).

To assess the uncertainty of the estimated slope in the length–weight relationship, a 95% confidence interval (CI) for the slope was constructed using linear regression with the stats package in R (version 4.3.2). The standard error of the slope (SE) was extracted from the regression model’s summary, and the degrees of freedom (df) were calculated as the number of observations minus 2. The t-distribution was used to determine the critical t-value for a 95% confidence level with the corresponding degrees of freedom.

A standard Student t-test was applied to evaluate potential deviations from isometric growth (b ≠ 3) [42,43].

$$\mathrm{T}\mathrm{W}=\mathrm{a}\times {\mathrm{T}\mathrm{L}}^{\mathrm{b}}$$

(1)

2.3. Age

In total, 589 pairs of sagittal otoliths were extracted by locating the otic capsule in the retroventral portion of the neurocranium. Following removal, each otolith was rinsed with distilled water to eliminate biological impurities. The otoliths were stored in Eppendorf tubes and finally kept in dry storage for 24 h in order to eliminate the humidity according to Mejri et al. [44]. For age identification and allometric analyses, only left otoliths were used, as no significant differences in morphometric characteristics between otolith sides were observed, consistent with previous findings [45].

When necessary, a shallow incision was made in the middle part of the otic capsule, which was gently broken, to expose the sagittal otoliths [46]. To improve contrast, otoliths were submerged in glycerin before microscopic examination. Age determination was conducted by counting alternating opaque and translucent bands, with each pair representing one year of growth [45]. The dark rings correspond to the translucent growth zone (slow growth), while the opaque (white—fast growth) zone, with a dark ring, is considered to represent annual growth (annulus). The clarity of annuli was verified under transmitted or reflected light to ensure consistency and accuracy in age readings [45].

Three independent readers performed age estimations by analyzing the growth markers in the otolith sections. Measurements were taken under a stereoscope (OLYMPUS, U-TV0.5XC-3, 7H01028, Tokyo, Japan) using the digital image processing software ImageJ V1.54 (Philadelphia, PA, USA). The final age value used for model estimation was calculated as the mean value from all three independent readers [21].

Interreader variability was estimated [47] according to the index of average percentage error (IAPE) (Equation (2)) to compare the precision of age determinations [48]:

$$IAPE={\displaystyle \frac{100}{N}}\sum _{i=1}^N{\displaystyle \frac{\left|X_i-\overline{X}\right|}{\overline{X}}}$$

(2)

where:

N = Number of samples;

${\mathrm{X}}_{\mathrm{i}}$ = Age estimate from an individual reader;

$\overline{\mathrm{X}}$ = Mean age from all readers for that sample.

Chang’s coefficient of variation (CV) (Equation (3)) was further employed to test the reproducibility of the age determination [49]:

$$CV=\sqrt{{\displaystyle \frac{{{\sum }_{i=1}^N(X_i-\overline{X})}^2}{N-1}}}\times {\displaystyle \frac{1}{\overline{X}}}\times 100$$

(3)

where:

${\mathrm{X}}_{\mathrm{i}}$ = Age estimate from an individual reader;

$\overline{\mathrm{X}}$ = Mean age from all readers for that sample;

N = Number of readers.

2.4. Growth

Growth parameters were calculated using the von Bertalanffy growth equation [50] (Equation (4)).

$$\mathrm{L}\mathrm{t}=\mathrm{L}\mathrm{\infty }\times \left(1-{\mathrm{e}}^{-\mathrm{k}\times \left(\mathrm{t}-{\mathrm{t}}_0\right)}\right)$$

(4)

where k (growth coefficient) represents the rate at which the asymptotic length (L∞) is approached, $t$ is the age in years, and t₀ is the hypothetical age at which the fish has zero length. Length-at-age data were fitted to the von Bertalanffy growth function using a maximum likelihood approach, where asymptotic length and growth coefficient were constrained to be positive by exponentiation. Confidence intervals (CIs) for the estimated parameters were derived using the Hessian matrix and its generalized inverse to estimate standard errors. The goodness of fit was assessed via the coefficient of determination (R²). A growth curve with confidence intervals was plotted, incorporating uncertainty propagation through Jacobian-based error estimation. All analyses were conducted in R (version 4.3.2) using the numDeriv and MASS packages.

The growth performance index (φ′) (in length) was estimated using the von Bertalanffy parameters [51] (Equation (5)).

$${\mathrm{\phi }}^{\prime }=\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{k}+2\times \mathrm{l}\mathrm{o}\mathrm{g}\mathrm{L}\mathrm{\infty }$$

(5)

Generally, fish with φ′ > 3.0 are considered fast-growing, moderate growth is indicated by φ′ between 2.5 and 3.0, and fish with φ′ < 2.5 are classified as slow-growing [52,53].

The maximum lifespan was estimated according to Hoggarth [54] (Equation (6)).

$$\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}={\displaystyle \frac{2.9957}{\mathrm{k}}}+{\mathrm{t}}_0$$

(6)

The inflection point (time when growth rate starts to decrease) was estimated according to Ricker [55] (Equation (7)).

$$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{p}\mathrm{o}\mathrm{i}\mathrm{n}\mathrm{t}={\mathrm{t}}_0+{\displaystyle \frac{{\mathrm{l}\mathrm{n}}_3}{\mathrm{K}}}$$

(7)

2.5. Mortality and Exploitation Rate

Natural mortality (M) was calculated using the updated Hoenig_nls estimator according to Then et al. [56] (Equation (8)).

$$\mathrm{M}=4.899\times {t_{max}}^{-0.916}$$

(8)

Total mortality (Z) was calculated using the Beverton and Holt empirical equation [57] (Equation (9)).

$$\mathrm{Z}=\mathrm{K}\times (\mathrm{L}\mathrm{\infty }-{\mathrm{L}}_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}})/({\mathrm{L}}_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}}-{\mathrm{L}}^{\prime })$$

(9)

where L_mean represents the mean length of all fish individuals in a sample representing a steady-state population and L′ is the cut-off length or the lower limit of the smallest length class included in the computation.

The length converted catch curve [58] was further employed to estimate Z and the result was used to verify the Z estimate obtained through the Beverton and Holt method.

The annual fishing mortality rate (F) was obtained by subtracting natural mortality from total mortality, following Sparre et al. [59] (Equation (10)).

$$\mathrm{F}=\mathrm{Z}-\mathrm{M}$$

(10)

The exploitation rate (E), a measure of the number of fish that are caught from a population each year, was calculated as the ratio of F to Z [58] (Equation (11)).

$$\mathrm{E}=\mathrm{F}/\mathrm{Z}$$

(11)

The exploitation rate is a key metric for assessing overfishing. Generally, an exploitation rate of $E\le 0.5$ is considered sustainable, indicating that no more than 50% of the fishable population is removed annually. Rates exceeding 0.5 often signal overfishing, risking population decline [60,61].

The length class with the highest biomass (Le) (eumetric length) was calculated according Froese and Binohlan, Hoggarth [54,62] (Equation (12)).

$$\mathrm{L}\mathrm{e}={\displaystyle \frac{3\times \mathrm{L}\mathrm{\infty }}{3+\frac{\mathrm{M}}{\mathrm{K}}}}$$

(12)

The length at first capture (Lc) was defined as the length of the smallest individual captured. The probability of capture was estimated at 25%, 50%, and 75% levels by the linear regression derived from the ascending data points of the selectivity curve [63].

2.6. Relative Y/R and B/R Analysis: Knife-Edge Selection

The relative yield per recruit (Y′/R) was estimated using the knife-edge method of Beverton and Holt’s model [59,64] (Equation (13)).

$${\displaystyle \frac{{\mathrm{Y}}^{\prime }}{\mathrm{R}}}=\mathrm{E}\times {\mathrm{U}}^{\mathrm{M}/\mathrm{K}}\times (1-{\displaystyle \frac{3\times \mathrm{U}}{\left(1+\mathrm{m}\right)}}+{\displaystyle \frac{3\times {\mathrm{U}}^2}{\left(1+2\mathrm{m}\right)}}+{\displaystyle \frac{{\mathrm{U}}^3}{\left(1+3\mathrm{m}\right)}})$$

(13)

where:

$$\mathrm{U}=1-\left({\displaystyle \frac{{\mathrm{L}}_{\mathrm{c}}}{{\mathrm{L}}_{\mathrm{i}\mathrm{n}\mathrm{f}}}}\right)$$

$$\mathrm{m}={\displaystyle \frac{1-\mathrm{E}}{\frac{\mathrm{M}}{\mathrm{K}}}}=\mathrm{K}/\mathrm{Z}$$

Fishing mortality (F) and exploitation rate (E) were varied systematically to estimate their effects on Y/R and B/R. The total mortality (Z) was computed as the sum of natural mortality (M) and F, and the corresponding relative yield per recruit (Y′/R) and relative biomass per recruit (B′/R) values were derived based on exploitation patterns. The analysis was conducted for the current state of the fishery as well as for ±10% variation in natural mortality (M) to assess sensitivity under different mortality scenarios. Reference points, including the fishing mortality at maximum sustainable yield (FMSY) and the corresponding biomass (BMSY), were identified. All computations were performed in R (version 4.3.2) using the dplyr package.

3. Results

3.1. Population Parameters and Length–Weight Relationship

In total, 589 individuals were analyzed, with mean length 21.55 ± 6.16 cm and mean weight 148.7 ± 97.16 gr. The length distribution was slightly right-skewed and near-symmetrical whereas the weight distribution was strongly right-skewed (Figure 2).

The length–weight relationship demonstrated a strong fit of the model to the data, with the resulting equation explaining approximately 98.4% of the variance in weight based on length. The relationship exhibited significant negative allometry (Figure 3) (t-value −31.13, p < 0.001), indicating that, as the organism grows in length, its weight increases at a proportionally slower rate than expected under isomorphic growth, implying that individuals become relatively lighter or less robust as they grow longer. The slope of the length–weight relationship was found to be 2.48518, with the 95% confidence interval for the slope calculated to range from 2.464516 to 2.505844.

3.2. Age Composition and Growth

A total of nine age classes were identified (

Table 1. Mean length, standard error (SE), standard deviation (StDev), minimum (min) and maximum (max) for each age class of the common pandora captured from Central Aegean Sea.

Age	Mean	SE	StDev	Min	Max
1	13.181	0.140	1.531	10.9	16.7
2	17.187	0.126	1.336	14.8	20.0
3	20.457	0.133	1.167	18.3	22.7
4	23.368	0.115	0.985	21.5	25.3
5	25.245	0.102	0.937	23.2	27.4
6	27.246	0.126	0.808	25.7	28.8
7	29.380	0.137	0.879	27.2	30.7
8	31.393	0.185	1.014	29.1	32.8
9	32.942	0.241	0.836	31.4	34.0

) with the smallest individual measuring at 10.9 cm and the largest at 34.0 cm in total length. IAPE was calculated at 3.35% and CV was calculated at 4.54%.

Figure 4 illustrates the age structure of P. erythrinus, showing the percentage of individuals within each age class from 1 to 9 years. The distribution shows a relatively young population, with the highest percentage of individuals (20%) belonging to the youngest age class (1 year). Across all years, younger age classes (1–3 years) exhibit the highest number of individuals, with a progressive decline in abundance as age increases.

Table 2. Annual variations in size, weight, and age distribution of the common pandora between 2021 and 2024 in the Central Aegean Sea (N = number of individuals, min = minimum, max = maximum).

Year	N	Min Length (cm)	Max Length (cm)	Min Weight (g)	Max Weight (g)	Age Range
2021	145	11.3	34.0	25.01	420.56	1–9
2022	148	11.0	31.9	23.37	342.63	1–8
2023	144	10.9	33.8	19.32	383.21	1–9
2024	152	11.0	33.6	23.17	379.21	1–9

shows the age distribution of individuals across the four consecutive years (2021–2024). The average length fluctuated between 10.9 and 11.3 cm, while the maximum recorded weight varied between 342.63 and 420.56 grams across the years. The age range spanned between 1 and 9 years across most years, except for 2022, where the maximum age was recorded at 8 years.

Asymptotic length (L∞) was estimated as 39.53 cm with CI of (37.03–42.19), growth coefficient (k) was estimated as 0.16 year⁻¹ with CI of (0.14–0.18), and the hypothetical age at which the fish had zero length (t₀) was estimated as −1.53 years with CI of (−1.72, −1.34). The von Bertalanffy growth curve (Figure 5) showed a strong fit, explaining approximately 95% of the variance in length-at-age.

The growth performance index (φ′) was estimated at 2.39, indicating a relatively slow growth rate. The maximum lifespan (t_max) was estimated at 17 years and the inflection point (the age at which the growth rate begins to decline) was estimated at 5.3 years.

3.3. Mortality and Exploitation Rate

Natural mortality (M) was estimated at 0.321, while total mortality (Z) was estimated at 0.52. Fishing mortality (F) was estimated at 0.2. The exploitation rate (E), calculated as 0.38, indicates that the population is underexploited.

To achieve an exploitation rate (E) of 0.5, the corresponding fishing mortality (F) would need to be 0.32. However, the current F in the studied population is 0.2, which is 37.5% lower than the level required to remove half of the biomass. Additionally, the length class with the highest biomass (Le) was estimated at 23.7 cm.

The probability of capture was estimated at 25% (LC₂₅), 50% (LC₅₀), and 75% (LC₇₅) levels as 15.21, 19.18, and 21.74 cm, respectively (Figure 6). The age at which there is a 50% probability of capture (t₅₀) was estimated at 2.63 years.

The yield per recruit (Y/R) against the fishing mortality and exploitation rate for different levels of natural mortality are shown in Figure 7 and Figure 8, respectively.

4. Discussion

This study investigated the age, growth, and mortality of the common pandora (Pagellus erythrinus, L. 1758) in the Central Aegean Sea, providing a comprehensive assessment of its population dynamics in a region of high ecological and economic significance. The length–weight relationship (LWR) slope exhibited a significant departure from 3, indicating that the P. erythrinus population exhibits non-isomorphic growth in this region. In the present study, gender was not evaluated. While gender can influence the length–weight relationship and growth dynamics in some fish species, previous studies on Pagellus erythrinus have shown minimal sexual dimorphism in these parameters. For instance, a study in the Northern Aegean Sea found no significant differences between sexes in the length–weight relationships for P. erythrinus [65]. Similarly, Kondylatos et al. [66] argue that estimated parameters for the total population of each species are significant for fisheries management since no fishing gear is sex-selective, at least for fish, and all fisheries restrictions apply to the entire stock or population. Additionally, because the samples were collected over a long period rather than in a specific season, the data represent annual values rather than season-dependent variations. Our results are in contrast with previous studies conducted across both the Atlantic and Mediterranean regions [25,26,66,67,68,69,70,71,72,73,74,75,76], which reported isometric growth patterns. Such discrepancies may be attributed to differences in sampling methodologies, regional differences in environmental factors, including water temperature, food availability, and habitat conditions, as well as variations in fishing pressure that can influence growth dynamics [43,77,78,79,80,81,82].

Age determination is a fundamental aspect of fisheries management, as it provides critical insights into growth rates, longevity, and population structure, which are essential for assessing stock sustainability [83]. The present study identified nine cohorts, based on the number of otolith growth rings, in contrast with [16], who reported a maximum age of 4 years in the Mediterranean, and [6], who identified six age classes. Similarly, between five and eight age classes were also reported by [84,85] in the Mediterranean. In contrast, other studies have documented a higher number of age classes, with 10-year-old individuals identified [18] in the Mediterranean and 21-year-old individuals identified [13] in Atlantic populations. The observed differences in age structure across regions may be attributed to several factors, including ecological conditions such as food availability and competition, environmental variability, and fishing pressure [86]. Competition for resources can affect individual growth, potentially leading to slower maturation or higher mortality rates in more competitive environments. Environmental variability, such as changes in temperature, salinity, and oxygen levels, can impact both growth and the longevity of individuals [86]. Regions experiencing intense fishing activity often exhibit truncated age structures, with fewer older individuals due to high exploitation rates. In areas with high fishing pressure, older fish are often disproportionately removed from the population, resulting in a shift toward younger individuals. This can alter the natural age distribution and affect the reproductive potential of the stock [87]. Additionally, differences in age determination methods, and particularly variations in reading otolith growth increments, can contribute to discrepancies in reported age estimates [41,88,89,90,91]. Accurate age assessments are essential for developing effective fisheries management strategies, as they inform stock assessment models, growth parameter estimation, and mortality calculations.

The annual age structure suggests a typical population structure where recruitment is strong, but survival decreases with age. Notably, the highest number of individuals is consistently observed in the youngest age class each year, implying continuous recruitment into the population. Age distribution indicated a relatively young population, with the highest percentage of individuals (20%) belonging to the youngest age class (1 year). The proportion of individuals decreased steadily with increasing age, suggesting high mortality rates or significant fishing pressure on older age classes. This pattern is typical of exploited fish populations, where younger individuals dominate due to the removal of older, larger fish by fisheries [23,24]. The low representation of older age classes (e.g., 7–9 years) may indicate overfishing or natural mortality pressures, highlighting the need for sustainable management practices to ensure the long-term viability of the population [92,93]. Understanding the age composition of commercially exploited populations enables for the establishment of appropriate conservation measures, such as size limits and fishing quotas, to ensure long-term sustainability and prevent overexploitation.

Asymptotic length was estimated at 39.53 cm for the studied population which is considerably lower than the values reported for Atlantic populations in Portugal [94] (47.1 cm), the Canary Islands [26] (41.8 cm) and the Northern Tyrrhenian Sea [95] (54.3 cm), suggesting that individuals in Hellenic waters may exhibit slower growth and attain smaller maximum sizes. However, our estimation was in agreement with the value reported from the Sicilian Channel [96] (40.0 cm). In contrast, several studies from the Eastern Mediterranean have reported lower asymptotic length, including the Cretan Shelf [25] (27.8 cm), Central Aegean Sea [18] (30.67 cm), Egypt [85] (33.4 cm), and Southwestern Mediterranean [97] (29.55 cm).

The growth coefficient (i.e., the rate at which the asymptotic length is approached) was estimated at 0.16 year⁻¹ which is higher than that reported for the Atlantic population [13] (0.08 year⁻¹), similar to the Central Aegean Sea [18] (0.165 year⁻¹), and lower than values observed in the Atlantic [98] (0.33 year⁻¹), the Cretan Shelf [25] (0.32 year⁻¹), and Egypt [85] (0.37 year⁻¹). The growth performance index (φ′) estimated in the present study was 2.39, slightly lower but comparable to the value reported for the South Levant Sea [99] (2.44). Similar variations were observed in other regions, such as the Castellon coast (2.57) [100] and Lyon Bay (2.59) [98], which exhibited slightly higher growth performance indices, further indicating variations in growth dynamics among regions.

The von Bertalanffy growth function (VBGF) effectively described the growth of P. erythrinus in the Central Aegean Sea, but its estimates may be influenced by interannual growth variation, as sampling occurred only in May across four years. Environmental fluctuations (e.g., temperature, prey availability) can alter growth rates annually [21], with Mediterranean sparids like Dentex dentex demonstrating measurable growth shifts linked to climatic and fishing pressures [101]. Such variability may bias VBGF parameters (L∞, k) and subsequent mortality estimates [56]. To address this, future studies should integrate multi-season sampling and environmental covariates [30] to refine growth models and ensure robust stock assessments in this dynamic region. Furthermore, studies on P. erythrinus could focus on reproductive biology (spawning periods, fecundity, size at maturity), diet and feeding ecology (trophic interactions, diet composition), and habitat preferences (depth distribution, habitat selection). Investigating recruitment dynamics and socioeconomic assessments could further facilitate adaptive, sustainable fisheries management [21,60].

The Greek population, exhibiting a relatively moderate asymptotic length (L∞) and growth coefficient (k), may reflect a life-history strategy adapted to local environmental conditions or it may be a consequence of increased fishing mortality leading to earlier maturation at smaller sizes. The scarcity of mortality data for Pagellus erythrinus has long hindered robust stock assessments and the formulation of effective management strategies for this important commercial species. It is important to note that the mortality rate estimations presented in this study are derived using indirect methods based on growth parameters and empirical models. While these approaches are widely used in fisheries research [56,58], they rely on assumptions that may introduce uncertainty. Future studies should consider incorporating direct cohort-based survival data or mark–recapture studies to validate these findings and provide more robust mortality estimates. Such approaches would enhance the accuracy of stock assessments and support the development of more effective fisheries management strategies.

One of the few studies available on the Eastern Mediterranean coastline of Turkey, in İskenderun, reported a total mortality (Z) of 0.97 year⁻¹, with natural mortality (M) at 0.36 year⁻¹ and fishing mortality (F) at 0.61 year⁻¹, yielding an exploitation rate (E) of 0.63 [102]. The total mortality (Z) reported in the Southwestern Mediterranean study was 0.891 year⁻¹, with natural mortality (M) at 0.32 year⁻¹ and fishing mortality (F) at 0.571 year⁻¹ [97]. In contrast, our study estimated a lower total mortality of 0.52 year⁻¹, comparable natural mortality (M) at 0.321 year⁻¹, and considerably lower fishing mortality (F) at 0.2 year⁻¹, resulting in a much lower exploitation rate (E) at 0.38.

The common pandora fishery in Greece experiences distinct mortality pressures due to localized variations in fishing intensity, habitat quality, and environmental conditions. While fishing mortality in Greek waters is currently lower than in other Mediterranean regions, the population remains vulnerable to overexploitation, particularly in heavily fished areas. The observation that both total mortality (Z) and fishing mortality (F) are higher in certain populations of Pagellus erythrinus, while natural mortality (M) remains relatively constant, can be attributed to the selective pressures exerted by fishing activities [92]. In many fish populations, fishing often targets specific age classes, typically adults, leading to an increase in fishing mortality. This selective removal disrupts the age structure of the population, potentially leaving behind individuals less susceptible to fishing gear, thereby maintaining a stable natural mortality rate. However, elevated fishing mortality cumulatively increases total mortality, especially under intense fishing pressure. This dynamic underscores the importance of considering both natural and fishing-induced mortality rates in fisheries management to ensure sustainable population levels.

The sensitivity analysis of yield-per-recruit (Y/R) and biomass-per-recruit (B/R) indicates that the common pandora in the Central Aegean Sea can sustain its population at optimal levels when fishing mortality (F) is maintained at 0.33 and the exploitation rate (E) at 0.51. At these levels, the species achieves its maximum sustainable yield (MSY) of 0.0245, suggesting that this fishing mortality rate allows the population to produce the highest possible yield without compromising long-term stock stability. The corresponding biomass-per-recruit (BMSY) of 0.0742 reflects the optimal biomass available for future recruitment under these conditions. Sensitivity scenarios involving ±10% changes in natural mortality (M) revealed that decrease in M results in an increased Y/R, whereas an increase in M leads to a decrease in Y/R, highlighting the importance of accurately estimating M for effective fisheries management.

The current fishing mortality (F = 0.2) is below FMSY, suggesting that the fishery is underexploited and has the potential for increased yield without compromising sustainability. This interpretation is consistent with general fisheries management principles, where fishing at or below FMSY allows for sustainable yield increases without overexploiting the stock [93]. While fishing mortality appears to be within sustainable limits, continuous monitoring is essential to prevent fishing effort from exceeding sustainable thresholds, particularly in response to economic or environmental changes [103]. The relatively low F may result from effective management strategies, fishing regulations, or natural constraints such as fleet capacity and gear restrictions. Understanding these factors is crucial for balancing conservation efforts with economic interests while maintaining a healthy fishery.

The reported 5.2% decline in catches from 2022, affecting both open-sea and inshore fisheries [19], was followed by a 10.1% increase the following year, which may reflect increased fishing effort rather than an actual rise in population size. In the absence of updated stock assessments, it remains unclear whether current harvest levels are sustainable or pose a risk of overexploitation. However, even minor changes in natural mortality or fishing pressure could significantly impact yield and biomass, underscoring the importance of precautionary management. To ensure the long-term sustainability of P. erythrinus, it is essential to implement adaptive fisheries management strategies that account for fluctuations in natural mortality and environmental variability. By ensuring that fishing efforts remain within sustainable limits, policymakers can help maintain the resilience of the stock while supporting the livelihoods that depend on it.

5. Conclusions

The population of Pagellus erythrinus in Greek waters exhibits moderate growth rates, with an exploitation rate of 0.38, suggesting it is currently harvested below the maximum sustainable yield threshold. However, the proximity of the exploitation rate to the EMSY (0.51) highlights the risk of overexploitation with increased fishing pressure. Based on these findings, we recommend maintaining sustainable fishing practices and monitoring the population closely to avoid reaching the overexploitation threshold. The observed negative allometric growth pattern (b = 2.49, R² = 98.4) suggests that larger individuals in the population exhibit thinner body shapes relative to their length. This pattern may reflect ecological or fishing-induced influences on population structure, with potential effects driven by factors such as hydrodynamics or nutritional availability. These findings highlight the need to consider both intrinsic and extrinsic factors when assessing the health and dynamics of the population. While future research will be essential to refining mortality estimates, assess recruitment patterns, and evaluate the impact of environmental variability on P. erythrinus populations, this study provides a crucial baseline for stock assessments. The insights gained into growth rates, exploitation rate, and population structure in Greek waters serve as a valuable foundation for informing future management strategies and policy development aimed at ensuring the long-term sustainability of this commercially important species in the Aegean Sea. By providing key data on the status of the population, our study plays an important role in guiding fisheries management and further research.