An Assessment of the Population Structure and Stock Dynamics of Megalobrama skolkovii During the Early Phase of the Fishing Ban in the Poyang Lake Basin

Abstract

The ten-year fishing ban on the Yangtze River aims to restore aquatic biodiversity and rebuild fishery resources. Megalobrama skolkovii, a key species in the basin, was investigated using 2024 data to provide a preliminary assessment of its population structure, stock dynamics, and early recovery. Age analysis (n = 243) showed that 1–6-year-olds were dominated by fish aged 3 (35%), with few older than 4, indicating moderate structural truncation. Growth parameters modeled by the von Bertalanffy Growth Function yielded L_∞ = 61.89 cm and k = 0.25 year⁻¹, with a weight–growth inflection age of 4.4 years. Natural mortality (M = 0.48 year⁻¹) was estimated using Pauly’s empirical formula, and total mortality (Z = 0.55 year⁻¹) was estimated from the catch curve analysis. While fishing mortality (F) was statistically indistinguishable from zero, a plausible low-intensity fishing scenario was explored to assess potential impacts of residual activities. Length-based indicators (LBIs) showed P_mat = 46.05%, P_opt = 9.51%, and P_mega = 6.88%, suggesting reproductive recovery but incomplete structural restoration. These preliminary findings reveal an asymmetrical recovery trajectory, whereby physiological improvements and enhanced recruitment have occurred, yet full structural restoration remains incomplete. This underscores the need for continued, long-term conservation and monitoring to support population resilience.

1. Introduction

The Yangtze River is the longest river in China and the third longest in the world, with a drainage area exceeding 1.8 million km², covering approximately one-fifth of China’s territory and supporting one of the most diverse temperate freshwater ecosystems globally [1]. Historically, the basin has recorded over 400 freshwater fish species, more than half of which are endemic to China [2], underscoring its exceptional biodiversity and ecological value. As an integral part of the Yangtze River Basin, Poyang Lake is located in northern Jiangxi Province, China (115°49′–116°46′ E, 28°22′–29°45′ N), and is the largest freshwater lake in the country [3]. Situated on the southern bank of the middle–lower Yangtze River, it connects to the mainstem via a narrow outlet, forming a typical river–lake connected system and serving as a critical ecological hub within the Yangtze ecosystem [4]. With an area of 16,220 km² encompassing interconnected water bodies and wetlands, the lake plays a crucial role in regulating the Yangtze River’s water levels, conserving hydrological resources, stabilizing the regional climate, and supporting ecological balance. Due to its important strategic position in ecology, agriculture, and economics, the lake is often referred to as the “Kidney of the Yangtze”, “Paradise for Migratory Birds”, and “Land of Fish and Rice”, underscoring both ecological and socioeconomic value [3]. As a global biodiversity hotspot region, the lake’s rich aquatic resources are considered essential for maintaining regional ecological security and supporting the sustainability of fisheries in the Yangtze River Basin [5].

Megalobrama skolkovii belongs to the order Cypriniformes and the family Cyprinidae. It is taxonomically regarded as a synonym of M. mantschuricus. This species is widely distributed across major river systems in China, including the Heilong River, Yellow River, and Yangtze River, as well as in southeastern coastal regions and the Russian Far East [6]. Given its broad distribution and large body size, M. skolkovii plays a vital role in aquatic communities and holds substantial ecological value. As a herbivorous freshwater species, it primarily feeds on aquatic macrophytes. It typically reaches sexual maturity at 2–3 years of age, with spawning occurring during May to July in lotic environments. Individuals can attain a maximum standard length of up to 60 cm and a body weight exceeding 3.5 kg, with a typical lifespan of approximately 10 years [7,8,9]. Through its feeding behavior, this species helps regulate submerged macrophyte communities, enhances nutrient cycling via excretion, and serves as a primary prey species for top predators such as the Yangtze finless porpoise. In a recent survey from 2022 to 2023, M. skolkovii was identified as the most dominant fish species in the Poyang Lake Basin, indicating that its population abundance and biomass occupy a central position in the regional fishery ecosystem [10].

Over the past several decades, the Yangtze River Basin has been subjected to multiple anthropogenic stressors, including the construction of hydraulic engineering projects, overfishing, water pollution, and habitat fragmentation, leading to a marked decline in aquatic biological resources. Iconic species such as the Chinese paddlefish (Psephurus gladius) have been officially declared extinct [11], while the Yangtze River dolphin (Lipotes vexillifer) is considered functionally extinct, with no confirmed sightings since 2002 [12]. In response to the severe degradation of fishery resources and aquatic biodiversity, the Chinese government implemented a comprehensive ten-year fishing ban across the Yangtze River Basin, effective from 1 January 2020 to 1 January 2030 [13]. This policy is regarded as a forward-looking ecological restoration initiative aimed at halting further resource decline, protecting key life-history stages, and providing sufficient time for the natural recovery of fish populations and aquatic ecosystems [14,15]. Despite the lack of systematic baseline data and long-term monitoring in certain regions, the fishing moratorium reflects a precautionary and restorative governance strategy grounded in ecosystem-based management principles.

As a representative species in the lake, the conservation and recovery status of M. skolkovii is critical to the stability of the regional fishery ecosystem. However, in the Yangtze River Basin—particularly in the Poyang Lake region—studies on the population biology and stock dynamics of M. skolkovii remain notably limited. In particular, systematic assessments of its recovery status during the early stage of the fishing ban are still lacking. Addressing this research gap is essential not only for revealing the ecological responses of M. skolkovii to the fishing moratorium but also for providing a scientific basis and empirical support for future fisheries management strategies. Although positive ecological responses have been reported in some fishing ban regions, including signs of stock rebuilding [16] and improvements in fish community structure [17], in-depth analyses of the population recovery and stock dynamics of this key species remain insufficient. This limitation is largely due to the lack of long-term, species-specific historical catch and mortality data for M. skolkovii, as past fisheries statistics often aggregated such species under broad categories like “miscellaneous fishes” or “breams” [5].

Based on samples of M. skolkovii collected in 2024 in the Poyang Lake Basin, this study, for the first time, establishes a preliminary growth parameter matrix and stock dynamics model to assess the initial ecological responses of this species in the early stage of the ten-year fishing ban. By addressing critical gaps in the ecological understanding of M. skolkovii, the research contributes to filling important knowledge voids regarding post-ban recovery processes. These results contribute critical baseline data for future long-term monitoring efforts and provide empirical insights into the ecological outcomes of the Yangtze River fishing ban. They may also inform conservation and sustainable management strategies in similar freshwater ecosystems worldwide.

2. Materials and Methods

2.1. Sampling Design and Methodology

In the spring (April–May), summer (June–July), and autumn (September–November) of 2024, a total of 14 sampling sites were established along the Jiangxi section of the Yangtze River mainstem, northern Poyang Lake, the Boyang River, and the Xiu River (Figure 1). Due to the annual renewal process of fishing permits, no sampling was conducted during the winter months of January and February when permit renewal was in progress. Each site was sampled continuously for five consecutive days in each month. At each site, one set of floating gillnets and one set of sinking gillnets were deployed, while in riverine or flowing-water environments, two sets of sinking gillnets were placed nearshore (within 10–15 m of the bank). Gillnets had mesh sizes of 2.0 cm, 6.0 cm, 10.0 cm, and 14.0 cm; each net was 50 m in length and 2 m in height, resulting in a total length of 200 m. Nets were set at 18:00 each evening and then retrieved at 06:00 the following morning.

Each M. skolkovii specimen was assigned a unique identifier and measured for standard length (to the nearest 0.1 cm) and body weight (to the nearest 0.1 g). Age was estimated using the method described by Beamish and McFarlane [18], applying a three-tier classification: scales with a closed marginal annulus were designated as n; those with an additional outer growth zone were labeled as n⁺; and juvenile specimens with no closed annuli but visible growth rings were denoted as 0⁺. Age classification followed a stepwise mathematical interval model, where age was defined as the ceiling value of the annulus count, using a left-closed, right-open convention: [n⁺, (n + 1)) → age (n + 1).

2.2. Data Analysis

2.2.1. Length–Weight Relationship Estimation

The length–weight relationship of M. skolkovii was described using a power function, as proposed by Froese [19]:

$$W=a{L}^b$$

(1)

where W (g) denotes body weight, L (cm) is standard length, a is the condition factor, and b is the allometric growth exponent. A one-sample t-test was used to assess whether the estimated b value significantly differed from the theoretical value of 3.00, thereby evaluating whether the growth of M. skolkovii conforms to an isometric pattern.

2.2.2. Growth Model

The growth of M. skolkovii was characterized using the von Bertalanffy Growth Function (VBGF), which models growth patterns across developmental stages. Parameter fitting was conducted using the nonlinear least squares (nls) function in the base stats package in R 4.3.3 [20]:

$$L_t=L_{\infty }[1-{\mathrm{e}}^{-k(t-t_0)}]$$

(2)

where t is the age (years), L_t is the standard length at age t (cm), L_∞ is the asymptotic length (cm), t₀ is the hypothetical age when length is zero (usually a negative value, indicating nonzero initial length at hatching), and k (year⁻¹) is the growth coefficient indicating how rapidly the fish approaches L_∞. Based on the magnitude of k, growth rate categories are defined as follows: k > 0.2 indicates fast growth, 0.1 < k < 0.2 indicates moderate growth, and 0.05 < k < 0.1 indicates slow growth [21].

By substituting the length–weight equation W = aL^b into the VBGF, the body weight growth function is derived as

$$W_t=W_{\infty }{[1-{\mathrm{e}}^{-k(t-t_0)}]}^b$$

(3)

where W_t is the body weight at age t (g), W_∞ is the asymptotic body weight (g), and b is the allometric exponent from the length–weight relationship.

The growth performance index (φ′) is a dimensionless indicator used to compare growth performance across species or populations [22]:

$${\phi }^{\prime }=\lg k+2\lg L_{\infty }$$

(4)

Standard length and body weight growth rate equations:

$$dL/dt=L_{\infty }k{\mathrm{e}}^{-k(t-t_0)}$$

(5)

$$dW/dt=b{W}_{\infty }k{\mathrm{e}}^{-k(t-t_0)}{[1-{\mathrm{e}}^{-k(t-t_0)}]}^{b-1}$$

(6)

Growth acceleration equations:

$$d^{2}L/d{t}^2=-L_{\infty }{k}^2{\mathrm{e}}^{-k(t-t_0)}$$

(7)

$$d^{2}W/d{t}^2=b{W}_{\infty }{k}^2{\mathrm{e}}^{-k(t-t_0)}{[1-{\mathrm{e}}^{-k(t-t_0)}]}^{b-2}[b{\mathrm{e}}^{-k(t-t_0)}-1]$$

(8)

Inflection age t_i (year):

$$t_i=\ln b/k+t_0$$

(9)

2.2.3. Mortality Coefficients and Exploitation Rate

Length class intervals were determined by averaging the results obtained from the Snedecor and Sturges methods [23,24]:

$$I\left(\mathrm{Snedecor}\right)=\mathrm{Range}/(\mathrm{Range}/\mathrm{SD}\times 4)$$

(10)

$$I\left(\mathrm{Sturges}\right)=\mathrm{Range}/(1+3.322\times \lg N)$$

(11)

where I is the class interval width, Range is the difference between the maximum and minimum lengths, SD is the standard deviation of length, and N is the total number of individuals.

Mortality parameters include total mortality (Z), natural mortality (M), and fishing mortality (F), which are related by the following equation: Z = M + F. Due to the lack of species-specific catch or fishing mortality data for M. skolkovii prior to the fishing ban, the model does not incorporate pre-ban F estimates. All parameters and assessments are based on empirical data collected in 2024. Consequently, total mortality (Z) was estimated using the length-converted catch curve module in FiSAT II [25]. To minimize estimation bias, individuals not yet fully recruited to the stock and those approaching the asymptotic length (L_∞) were excluded. This exclusion helped avoid potential bias caused by incomplete recruitment in juveniles and growth retardation in older fish. The remaining data were transformed into relative age using the VBGF, and a linear regression of the natural logarithm of catch per unit time [ln(N/dt)] against relative age was conducted. The slope of the regression line corresponds to the value of Z. Natural mortality (M) was estimated using the empirical formula proposed by Pauly [26]:

$$\lg \left(M\right)=-0.0066-0.279\lg \left(L_{\infty }\right)+0.6543\lg \left(k\right)+0.4634\lg \left(T\right)$$

(12)

where T is the mean annual water temperature of Poyang Lake, assumed to be 18 °C [27,28,29,30,31].

The fishing mortality (F) and exploitation rate (E) were calculated as follows [32]:

$$F=Z-M$$

(13)

$$E=F/Z$$

(14)

The length at optimal yield (L_opt) was estimated based on Beverton’s formula [33]:

$$L_{opt}=L_{\infty }[3/(3+M/k)]$$

(15)

2.2.4. Length-Based Indicators (LBIs)

Three length-based indicators of fishery sustainability—P_mat, P_opt, and P_mega—were estimated following the method of Froese [34].

$$P_{mat}={\displaystyle \sum _{L_{mat}}^{L_{\max }}{P}_L}$$

(16)

$$P_{opt}={\displaystyle \sum _{0.9L_{opt}}^{1.1L_{opt}}{P}_L}$$

(17)

$$P_{mega}={\displaystyle \sum _{1.1L_{opt}}^{L_{\max }}{P}_L}$$

(18)

where P_L is the proportion of individuals in a given length range. P_mat indicates the proportion of fish longer than the length at maturity (L_m), reflecting the degree of protection afforded to mature individuals. P_opt indicates the proportion of fish within ± 10% of the optimal length (L_opt), representing favorable conditions for both growth and harvest. P_mega denotes the percentage of individuals exceeding 1.1 L_opt, also known as “mega spawners” due to their high reproductive capacity [34]. Effective management requires P_mega to remain below 20% to avoid the overharvesting of highly fecund individuals, while P_mat and P_opt should ideally reach 100% to maintain a mature and sustainable population structure [34,35].

To enhance the integrative assessment of LBIs, P_mat, P_opt, and P_mega were combined into a single composite metric, P_obj, reflecting stock status under varying management scenarios. The composite indicator was developed based on a decision tree framework proposed by Cope and Punt [36], which uses deterministic population dynamics simulations under fixed biological parameters (e.g., birth rate, mortality, and growth) to assess the influence of selectivity patterns, compensation capacities, and life-history traits on LBI outputs. Through simulation of stock responses under different strategies, P_obj provides a robust reference for fishery assessment and decision support.

2.2.5. Selectivity and Yield-per-Recruit Analysis

Capture probabilities were estimated in FiSAT II using logistic selectivity curves based on length–frequency data. The lengths at 25%, 50%, and 75% capture probabilities were defined as L_0.25, L_0.5, and L_0.75 [25]. Among them, L_0.5 is often used to represent the length at first capture (L_c).

The relative yield per recruit (Y′/R) was analyzed using the Beverton–Holt model to explore the theoretical effects of different exploitation levels and size limits. This analysis operates under a simplified knife-edge selectivity assumption, a standard approach in which fishing mortality is assumed to be zero below L_c and constant for all fish at or above L_c [33]. The relevant equations are

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

(19)

$$U=1-L_c/L_{\infty }$$

(20)

$$m=(1-E)/(M/k)=k/Z$$

(21)

where Y′/R is the relative yield per recruit, representing the amount of yield an average recruit can contribute over its entire lifetime. The biological parameters (L_∞, k, M), mortality rate (Z), and selectivity (L_c) were empirically estimated from our 2024 data. The exploitation rate (E) was derived from the parameters of our hypothetical low-intensity fishing scenario. The model was then used to simulate Y′/R values over a range of theoretical E and L_c values to generate a yield isopleth diagram for evaluating different exploitation scenarios.

Y′/R and B′/R values in this study are calculated per recruit and do not require an estimate of the absolute number of recruits. These values are interpreted as relative indicators for comparative and management purposes.

2.3. Terminology and Definitions

In this study, we use the terms population recovery and stock rebuilding to describe different aspects of the recovery process. Population recovery refers to biological and demographic improvements, such as increased growth rates, a more balanced age structure, and enhanced reproductive capacity. Stock rebuilding denotes structural signs of improvement and reduced exploitation pressure that suggest the potential for future increases in biomass, but without implying a confirmed rise in absolute abundance.

This distinction facilitates more accurate evaluation of recovery trajectories in data-limited contexts, thereby enhancing interpretive clarity.

3. Results

3.1. Population Structure

3.1.1. Standard Length, Body Weight, and Age

In 2024, a total of 1597 specimens of M. skolkovii were collected, with a combined body weight of 614.19 kg (see

Table 1. Sample distribution of M. skolkovii collected in different seasons and regions in the Poyang Lake Basin in 2024.

Category	Subcategory	Sample Size (n)
Season	Spring	312
	Summer	496
	Autumn	789
Region	Jiangxi section of the Yangtze River mainstem	699
	Northern Poyang Lake	706
	Xiu River	79
	Boyang River	113
Total	\	1597

for sample distribution by season and region). Among them, 243 individuals were randomly selected for age determination. To reduce potential bias in age estimation, the subset of 243 individuals selected for age determination was systematically stratified to encompass the entire observed range of standard lengths. This approach ensured proportional representation of all length classes, thereby enhancing the robustness and reliability of growth parameter estimation and subsequent population structure analyses. The specimens were aged from 1 to 6 years (Figure 2). Individuals aged 3 were the dominant group, accounting for 85 individuals or 35.0% of the total. Individuals aged 1 accounted for 26.3%, whereas individuals aged 2 constituted only 14.8%, indicating a marked decrease. The proportion of individuals aged 4 to 6 years decreased progressively.

Based on the age structure and age-specific abundance (Figure 2), the standard length and body weight of the specimens were categorized accordingly. The standard length of M. skolkovii in 2024 ranged from 3.72 to 53.51 cm, with a mean ± SD of 20.58 ± 11.82 cm. The dominant length class was 25–35 cm, accounting for 33.74% of the total sample (Figure 3a). Body weight ranged from 1.30 to 3777.20 g, with a mean ± SD of 383.61 ± 611.77 g. The dominant body weight class was 400–1200 g, comprising 40.74% of all samples (Figure 3b).

In addition, analysis of covariance (ANCOVA) was performed to examine differences in body weight among sampling regions after controlling for the effect of standard length. The results indicated a significant effect of region on body weight after adjusting for standard length (p < 0.001). Pairwise comparisons showed that individuals from the Jiangxi section of the Yangtze River mainstem and northern Poyang Lake had significantly higher body weight than those from the Xiu River (p < 0.01), while no significant differences were observed among the Jiangxi section of the Yangtze River mainstem, northern Poyang Lake, and the Boyang River (p > 0.3). The difference in body weight between the Xiu River and the Boyang River approached significance (p = 0.104). These findings suggest that fish growth characteristics vary among regions, potentially reflecting spatial heterogeneity in habitat conditions.

3.1.2. Length–Weight Relationship

The relationship between standard length (L, cm) and body weight (W, g) in M. skolkovii was described by a power function (Figure 4). The derived regression equation was as follows: W = 1.46 × 10⁻²L^3.094 (R² = 0.982, n = 1597). In this equation, the condition factor a was 1.46 × 10⁻², and the allometric growth exponent b was 3.094. The high coefficient of determination (R² = 0.982) indicated a strong fit, suggesting that the equation reliably reflects the length–weight relationship. A one-sample t-test indicated no significant difference between the estimated b value and the theoretical isometric value of 3.0 (p = 0.61 > 0.05), suggesting that M. skolkovii exhibits isometric growth. This result supports the applicability of both the von Bertalanffy growth model and the Beverton–Holt yield-per-recruit model [37,38].

3.1.3. Growth Parameters and Model Results

The growth of M. skolkovii was modeled using the VBGF, based on standard length and body weight data (Figure 5). The derived equations were as follows:

Length growth equation: L_t = 61.89[1 − e^{−0.25(t+0.129)}]

Weight growth equation: W_t = 5106.07[1 − e^{−0.25(t+0.129)}]^3.094

The fitted parameters indicated an asymptotic length (L_∞) of 61.89 cm and an asymptotic weight (W_∞) of 5106.07 g. The theoretical age at zero length (t₀) was estimated at −0.129 year, and the growth coefficient (k) was 0.25 year⁻¹. The growth performance index (φ′), derived from L_∞ and k, was estimated at 2.98.

Growth rate and acceleration equations for both standard length and body weight are presented in Figure 6.

Growth rate:

dL/dt = 15.47e^{−0.25(t+0.129)}, dW/dt = 3949.55e^{−0.25(t+0.129)}[1 − e^{−0.25(t+0.129)}]^2.094

Growth acceleration:

d²L/dt² = −3.87e^{−0.25(t+0.129)}, d²W/dt² = 987.39e^{−0.25(t+0.129)}[1 − e^{−0.25(t+0.129)}]^1.094[3.094e^{−0.25(t+0.129)} − 1]

No inflection point was observed in the standard length growth rate or acceleration curves (Figure 6a). The acceleration of length growth was negative at onset and increased with age, while the growth rate declined continuously. Both metrics approached zero as fish aged. In contrast, the body weight growth curve exhibited a typical sigmoid (S-shaped) pattern (Figure 6b). During the early growth stages, the acceleration was 214.4 g/year² and the growth rate was 157.8 g/year. With increasing age, acceleration decreased, while the growth rate increased, reaching a maximum at the inflection point (t_i = 4.4 years) (Figure 6b). At this point, body weight was 1515.01 g, and the corresponding standard length was 41.79 cm. Beyond this point, acceleration became negative, and the growth rate began to decline, with the rate of decline progressively increasing. At age 7.6 years, the standard length reached 52.82 cm, and body weight was 3125.47 g, at which point growth acceleration reached its minimum. With further aging, both length and weight gradually approached their respective asymptotic values, while the growth rate and acceleration tended toward zero.

3.2. Mortality Parameters

A 3 cm length class interval, derived as the average of the Snedecor and Sturges methods, was adopted for subsequent population analyses. Length frequency data were subsequently entered into FiSAT II, and linear regression was performed using the length-converted catch curve module (Figure 7). The fitted regression equation was ln(N/dt) = 6.85 − 0.55t (R² = 0.924); the slope of the regression line (−0.55) indicated a total mortality coefficient (Z) of 0.55 year⁻¹, with a 95% confidence interval of 0.406–0.686. Natural mortality (M = 0.48 year⁻¹) was estimated using Pauly’s temperature-based empirical formula, based on inputs of 18 °C, L_∞ = 61.89 cm, and k = 0.25. Crucially, the 95% confidence interval of Z fully encompasses the point estimate of M, meaning that fishing mortality (F) is statistically indistinguishable from zero. However, assuming F = 0 would be ecologically unrealistic in a system where residual fishing activities (e.g., illegal catch) are known to occur. To provide a more precautionary assessment, we therefore conducted an exploratory analysis of a hypothetical low-intensity fishing scenario. For this purpose, we used an illustrative F value of 0.07 year⁻¹ (the difference between the point estimates of Z and M) to parameterize this scenario, yielding a conceptual exploitation rate (E) of 0.13. We emphasize that these values are hypothetical modeling inputs only, and not empirical estimates.

Based on the growth parameters and natural mortality, the optimal length at first capture (L_opt) was estimated at 37.78 cm using Equation (15), offering a theoretical biological benchmark under model assumptions.

3.3. Population Assessment Based on Length-Based Indicators (LBIs)

According to previous studies, the age at first maturity for M. skolkovii in Poyang Lake has been reported as approximately 2 to 3 years [8,9]. Based on this initial maturity age, the length at first maturity (L_m) was calculated using Equation (2) as approximately 25.53 cm. This value was used to estimate three length-based indicators: P_mat, P_opt, and P_mega (

Table 2. Length-based stock status indicators and risk-based reference point evaluation for M. skolkovii.

Lm (cm)	Lopt (cm)	Pmat	Popt	Pmega	Pobj	Stock Condition	Probability of BeingSB < RP
25.53	37.78	46.05%	9.51%	6.88%	62.44%	SB > RP	4% for TRP and 2% for LRP

Note: SB: spawning biomass; RP: reference point; TRP: target reference point, defined as RP = 0.4 SB₀; LRP: limit reference point, defined as RP = 0.25 SB₀; SB₀: unfished (virgin) spawning biomass.

).

Immature individuals (<L_m) made up 53.95% of the catch, while mature individuals (P_mat) constituted 46.05%. The proportion of fish within the optimal length at first capture ±10% (P_opt) was 9.51%, and large spawners (P_mega) accounted for only 6.88% (Figure 8). Based on the decision tree evaluation (Table 2), the length structure of the M. skolkovii population in Poyang Lake was generally considered to be healthy. However, there remained a 4% probability of falling below the target reference point (TRP) and a 2% probability of falling below the limit reference point (LRP). To support future explorations of size-based management, the L_opt range (estimated at 34–42 cm) may serve as a theoretical reference point for assessing optimal capture lengths under sustainable harvest scenarios.

3.4. Stock Dynamics Modeling

Based on the length-at-capture probability curve (Figure 9), the length at 50% probability of capture (L_0.5) for M. skolkovii was estimated at 20.43 cm, corresponding to a body weight of 165.36 g. The lengths at 25% and 75% probability of capture (L_0.25 and L_0.75) were 13.52 cm and 27.34 cm, corresponding to body weights of 46.08 g and 407.32 g, respectively.

A knife-edge selectivity model under the Beverton–Holt framework was used to examine the relationship among the exploitation rate (E), the ratio of length at capture to asymptotic length (L_c/L_∞), and the relative yield per recruit (Y′/R) (Figure 10). Under the assumption of M/k = 1.92, contour lines depict the response of Y′/R to variations in both E and L_c/L_∞. Point A illustrates a hypothetical low-exploitation scenario, parameterized with E ≈ 0.13 (derived from an illustrative F of 0.07). We emphasize that this point represents a modeling scenario only and does not reflect an empirical estimate of the current stock status. Under the same M/k assumption, point B corresponds to the theoretical maximum Y′/R (0.037), achieved at L_c/L_∞ ≈ 0.7 and E ≈ 0.9, reflecting a heavily exploited status not aligned with current conservation goals. These comparative simulations highlight the trade-offs between size-at-capture and exploitation intensity within the theoretical yield landscape. Based on biological parameters, the optimal length at first capture (L_opt) was estimated at approximately 34–42 cm (L_opt ± 10%). This may serve as a theoretical reference point for future stock assessments or simulation-based evaluations. However, given the absence of pre-ban data and inherent model assumptions, these outputs should not be interpreted as prescriptive management recommendations, but rather as exploratory tools to support ongoing scientific inquiry into post-ban recovery dynamics.

4. Discussion

4.1. Appropriate Population Structure and Assessment Methods

A healthy fish population should exhibit a complete spectrum of age and size classes, characterized by a balanced distribution across cohorts and a sufficient proportion of sexually mature and large individuals. Such a structure is essential for supporting natural recruitment and maintaining key ecological functions [34,39]. In well-structured populations, biomass typically declines across age classes due to cumulative natural mortality and energy allocation trade-offs [37,40]. By contrast, an excessive proportion of juveniles or a deficiency of large individuals may signal overfishing or environmental stress, potentially impairing reproductive capacity and causing structural discontinuities within the population [41]. Therefore, accurate assessment of population structure is fundamental to effective fisheries management and the long-term sustainability of aquatic resources [42,43].

Methods for evaluating population structure include length–frequency and age composition analyses [32,44], whereas stock dynamics are typically assessed using length-based indicators (LBIs) and other data-limited approaches [45,46]. These complementary methods integrate structural assessment with dynamic modeling to establish a multidimensional evaluation framework. When applied in conjunction with key biological parameters (e.g., L_∞, k, φ′) and mortality rates (e.g., Z, M), they enable the quantification of exploitation intensity, recovery trajectories, and stock resilience [36]. Under the fishing ban, this integrative approach substantially improves the precision of stock assessments and is particularly effective for long-lived, economically important species such as M. skolkovii. It provides a robust scientific basis for adaptive management aimed at achieving long-term fishery sustainability.

4.2. Age Structure and Growth Characteristics of M. skolkovii

Following the implementation of the Yangtze River’s “ten-year fishing ban”, the M. skolkovii population in Poyang Lake has exhibited early signs of recovery; however, the population structure remains incomplete. A high proportion of individuals aged 1 and 2 reflects a strong recruitment capacity, which supports both population growth and stabilization of the age structure. However, the scarcity of individuals aged four years and older suggests a persistent structural discontinuity. Similar patterns have been observed in Atlantic salmon (Salmo salar), where prolonged overfishing reduced the abundance of older, larger fish, leading to disruptions in population structure [47]. Similar structural discontinuities have also been reported in other long-lived commercial species, such as Atlantic cod (Gadus morhua) [48] and Pacific halibut (Hippoglossus stenolepis) [49], underscoring the long-term impacts of historical overexploitation. Commercial fishing often targets individuals with large-bodied genotypes, typically late-maturing and vital for population sustainability [50]. The removal of these individuals results in juvenile-dominated populations, reduced recruitment, and long-term instability in the population structure. This aligns with the observed structural discontinuity in the current study, representing a typical legacy of historical overfishing pressure [39].

Despite this structural gap, the M. skolkovii population shows substantial recovery potential. Compared with data from 2017 to 2019 and 2021 to 2022 [51], the 2024 data show increases in both the standard length range and mean length (

Table 3. Changes in standard length of M. skolkovii population in the Poyang Lake Basin.

Year	Standard Length Range (cm)	Mean Standard Length ± SD (cm)
2017–2019	4.1–78.9	17.8 ± 10.9
2021–2022	5.8–56.3	20.0 ± 6.3
2024	3.7–53.5	20.6 ± 11.9

), suggesting a mitigation of previous dwarfing trends. The estimated growth coefficient (k = 0.25 year⁻¹) and growth performance index (φ′ = 2.98), along with other growth parameters, exceeded those reported for most conspecific populations in other habitats (Table 4), indicating strong somatic growth in the early post-ban period. This growth advantage may result from a combination of favorable environmental conditions in Poyang Lake, including high primary productivity, abundant benthic resources, and elevated cumulative thermal units [20]. Moreover, the condition factor a derived from the length–weight relationship was significantly higher than that of conspecifics in the Beijiang River, Xujiahe Reservoir, and Nanshui Reservoir, further reflecting improved nutritional status and habitat quality (Table 4).

The inflection age (t_i = 4.4 years) was notably later than the age at sexual maturity (2–3 years), indicating continued somatic growth after maturation. This trait may confer adaptive advantages, enabling individuals to accumulate energy reserves and maintain resilience following initial reproduction [52]. However, the long-term stability of the population also depends on successful recruitment. The extreme drought in 2022, which caused sharply reduced water levels, degraded water quality, and lower primary productivity, likely suppressed spawning success and larval survival. This may explain the markedly lower abundance of individuals aged 2 relative to those aged 1, suggesting that extreme climatic events have emerged as a significant constraint on stock rebuilding [53].

This emerging ecological dominance of M. skolkovii may represent a compensatory response to the moratorium, driven by a combination of ecological mechanisms. Specifically, the release from harvest pressure [54], competitive advantage over slower-recovering species [55], and increased macrophyte availability [56] may have jointly facilitated its population expansion. These findings highlight the species’ ecological responsiveness and reflect broader community-level shifts following the fishing ban.

Table 4. Comparison of growth parameters of Megalobrama species across different water bodies.

Species	Sampling Site	a	k (Year−1)	ti (Year)	L∞ (cm)	W∞ (g)	Inflection Length (cm)	Inflection Weight (g)	Apparent Growth Index
M. skolkovii	Poyang Lake	1.46 × 10−2	0.25	4.40	61.89	5106.07	41.80	1515.00	2.98
M. skolkovii	Nanshui Reservoir [57]	1.14 × 10−2	0.21	5.40	49.29	2875.63	33.81	902.15	2.71
M. skolkovii	Beijiang River [58]	4.00 × 10−5	0. 23	4.38	48.05	3052.92	31.76	900.22	2.73
M. skolkovii	Xujiahe Reservoir [9]	2.23 × 10−5	0.18	5.90	45.83	2197.30	30.47	665.68	2.58
M. terminalis	Pearl River [59]	1.55 × 10−2	0.18	5.91	47.20	2388.12	31.70	712.21	2.60
M. terminalis	Xijiang River [60]	7.00 × 10−5	0.22	4.70	42.01	1602.76	28.03	476.16	2.59

Table 4. Comparison of growth parameters of Megalobrama species across different water bodies.

Species	Sampling Site	a	k (Year−1)	ti (Year)	L∞ (cm)	W∞ (g)	Inflection Length (cm)	Inflection Weight (g)	Apparent Growth Index
M. skolkovii	Poyang Lake	1.46 × 10−2	0.25	4.40	61.89	5106.07	41.80	1515.00	2.98
M. skolkovii	Nanshui Reservoir [57]	1.14 × 10−2	0.21	5.40	49.29	2875.63	33.81	902.15	2.71
M. skolkovii	Beijiang River [58]	4.00 × 10−5	0. 23	4.38	48.05	3052.92	31.76	900.22	2.73
M. skolkovii	Xujiahe Reservoir [9]	2.23 × 10−5	0.18	5.90	45.83	2197.30	30.47	665.68	2.58
M. terminalis	Pearl River [59]	1.55 × 10−2	0.18	5.91	47.20	2388.12	31.70	712.21	2.60
M. terminalis	Xijiang River [60]	7.00 × 10−5	0.22	4.70	42.01	1602.76	28.03	476.16	2.59

4.3. Stock Dynamics and Resource Status of M. skolkovii

In the absence of long-term species-specific data, population dynamics were assessed using mortality estimates and length–frequency information as proxies for stock status.

Understanding population dynamics is fundamental to effective fisheries management, with accurate estimation of mortality rates and stock status serving as a core components [25]. In this study, natural mortality (M = 0.48 year⁻¹) was obtained from Pauly’s temperature-based empirical formula. The resulting M/K ratio (1.92) lies within the generally accepted range of 1.5–2.5, supporting the biological plausibility of the estimate [37]. Under the current fishing ban on the Yangtze River, natural mortality remains the dominant driver of population change. A key consideration is that the point estimate of M falls within the 95% confidence interval of total mortality Z (0.406–0.686), meaning that the fishing mortality (F) cannot be statistically distinguished from zero. Despite this statistical uncertainty, assuming F = 0 would be ecologically unrealistic given documented reports of illegal or incidental fishing [61,62]. Therefore, we used the value of F ≈ 0.07 year⁻¹ to parameterize an exploratory, low-intensity fishing scenario. This scenario is intended to illustrate the potential implications of residual fishing pressure, rather than to inform direct management decisions. Similar patterns have been documented in other closed fishing systems, such as Kaptai Lake in Bangladesh and the Azores in Portugal [63,64], suggesting that even minimal fishing activity may still hinder full stock rebuilding and should be considered when evaluating post-ban recovery dynamics.

A crucial aspect of our scenario analysis is the practical interpretation of the hypothetical fishing mortality. An instantaneous rate, such as the illustrative F = 0.07 year⁻¹, cannot be converted to the absolute catch (in tonnes) without a reliable estimate of the total stock biomass (B). Obtaining such a biomass estimate for a large, dynamic, and open ecosystem like Poyang Lake is a monumental challenge that requires extensive, dedicated surveys (e.g., large-scale hydroacoustic or mark–recapture studies) and was beyond the scope of this study [65]. Furthermore, quantitative data on illegal or incidental catches are notoriously difficult to obtain [66]. Therefore, a more scientifically sound and feasible approach is to interpret the potential impact through relative metrics. The F/M ratio for our hypothetical scenario is approximately 0.15, indicating a relatively low level of fishing pressure compared to natural mortality under the model assumptions. This provides a scale-independent perspective for considering the implications of even minimal residual fishing on the stock’s recovery trajectory.

Complementing the mortality analysis, length-based indicators (LBIs) offer a rapid, age-independent means of assessing the stock status [67,68]. The decision tree model based on LBIs in this study indicated a general trend of stock rebuilding in M. skolkovii. Nonetheless, the continued scarcity of large individuals points to a risk of structural truncation. Compared with traditional assessment approaches, LBIs reveal deeper structural issues—specifically, a demographic shift toward smaller and younger fish—which may reduce the long-term resilience and ecological functionality of the population [35]. The incorporation of uncertainty analysis further strengthens the precautionary value of these findings, highlighting the value of continued stock monitoring and scientific observation to support future understanding of recovery trajectories, even during moratoria.

4.4. Management Strategies

The “ten-year fishing ban” policy has provided a crucial opportunity for the recovery of M. skolkovii stocks in Poyang Lake. However, fishing bans alone cannot fully reverse resource degradation or restore ecosystem functions [13], especially in the face of ongoing stressors such as illegal fishing, habitat fragmentation, declining hydrological connectivity, and climate variability [13,61]. Thus, follow-up policy design is crucial to avoid premature harvesting [69].

To support sustainable population recovery, an integrated management approach is needed. While this study does not propose direct management actions, theoretical benchmarks—such as length at maturity (L_m) and optimal length at first capture (L_opt)—can serve as biological reference points for informing future discussions. These parameters highlight the importance of protecting reproductive cohorts and maintaining structural integrity within fish populations. However, their application requires validation through comprehensive stock assessments and monitoring.

Habitat restoration, particularly of spawning grounds, is equally critical to support the species’ full life cycle and enhance reproductive success and early survival [70]. Additionally, establishing effective enforcement mechanisms to ensure compliance. Beyond local actions, the establishment of a regional, ecosystem-based co-management framework is recommended to foster cross-scale governance and enhance the long-term resilience of aquatic ecosystems [71].

Finally, strengthening ecological monitoring systems is imperative. Integrating indicators such as genetic diversity, reproductive behavior, and energy allocation efficiency will allow a shift from conventional abundance-based assessments to evaluations focused on functional recovery. This multidimensional, multi-scale management approach offers a scientifically grounded and operationally feasible foundation for the sustainable utilization of freshwater fishery resources.

5. Conclusions

This study provides the first integrated assessment of the population structure, growth dynamics, and mortality profile of M. skolkovii in the Poyang Lake Basin during the initial phase of the Yangtze River fishing ban. While preliminary signs of recovery were observed—including improved growth potential, reduced fishing pressure, and a younger age structure—the population has yet to regain structural integrity and full reproductive capacity. The continued scarcity of large individuals, heightened ecological vulnerability, and environmental uncertainty reveal a characteristic “two-phase recovery” pattern, in which individual physiological improvement precedes population-level structural restoration.

It should be noted, however, that the present findings are based solely on data from a single year (2024) and lack historical baseline information prior to the fishing ban. Therefore, the results should be interpreted as preliminary. Sustaining the observed recovery trajectory will require timely scientific intervention and adaptive management informed by ongoing monitoring. Future efforts should incorporate multi-year and spatially extensive sampling, along with complementary methods such as hydroacoustic surveys, tagging studies, and genetic assessments, to build a more comprehensive understanding of population dynamics.

By combining ecological, biological, and environmental analyses, this study emphasizes the critical need to restore functional population integrity. A transition from static protection to dynamic, evidence-based regulation—grounded in long-term data and multi-scale assessment—will be essential to ensure the sustainable management of freshwater fishery resources in Poyang Lake and the broader Yangtze River Basin.