Environmental influences on juvenile Atlantic Croaker (Micropogonias undulatus) growth in the Western Gulf of Mexico

Environmental influences on juvenile Atlantic Croaker (Micropogonias undulatus) growth in the Western Gulf of Mexico | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental influences on juvenile Atlantic Croaker ( Micropogonias undulatus ) growth in the Western Gulf of Mexico Isabelle Cummings, Joel Anderson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8116250/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spatial and environmental variation in finfish growth has important implications for fisheries management. Atlantic Croaker ( Micropogonias undulatus ) is a valuable sportfish and baitfish throughout the Gulf of Mexico and U.S. western Atlantic coast; however, growth throughout the juvenile stage, when most growth occurs, is largely understudied in the Western Gulf of Mexico. Therefore, this study aimed to model Atlantic Croaker young-of-the-year growth, determine spatial growth variation along the Texas coast, and evaluate environmental influences on growth rate. Length frequency data for juvenile Atlantic Croaker was collected from all major Texas bays as part of the Texas Parks and Wildlife Department’s long-term fishery-independent monitoring program from 1990-2023. Multiple growth models were evaluated to compare juvenile growth curves among regions, and generalized additive models assessed the influence of salinity, temperature, turbidity, and dissolved oxygen on growth. Richard’s model provided the best fit for juvenile growth and described clear spatial differences in growth among bays. Growth rate increased substantially from north to south, following trends of increasing salinity and temperature and decreasing turbidity and dissolved oxygen. Salinity and dissolved oxygen were significant predictors of growth in the generalized additive model, with higher salinity promoting faster growth in larger juveniles (approximately ≥ 70 mm). These findings suggest salinity plays an important role in the growth of juvenile Atlantic Croaker and likely contributes to spatial variation in growth along the Texas coast. As anthropogenic effects continuously alter estuarine conditions throughout the Gulf of Mexico, understanding juvenile growth dynamics will be essential for effective management. Marine and Freshwater Ecology Marine and Freshwater Biology Croaker Growth Habitat Biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The Atlantic Croaker Micropogonias undulatus is a marine finfish from the family Sciaenidae that ranges throughout the Gulf of Mexico (GOM; also known as the Gulf of America) and along the western Atlantic coast from southern Florida to Massachusetts (ASMFC, 1987). Typically, Atlantic Croaker reach maturity between ages one and two, and adults migrate from estuarine habitats during fall months to spawn offshore (White & Chittenden, 1977; Barbieri et al., 1994; ASMFC, 2010). Larvae hatch offshore and are transported back into low salinity estuarine habitats to grow (White & Chittenden, 1977). As the juvenile stage progresses, Atlantic Croaker shift into higher salinities downstream and within bays (Yakupzack et al., 1977; Lankford and Targett, 2021), where they become an important component of the ecological trophic web as both a predator for small fish and crustacean species and prey for economically valuable inshore fish species (Mercer, 1987; GSMFC, 2017). Atlantic Croaker are also economically valuable baitfish and food fish. In the 1960s and 70s, total landings and catch per unit effort (CPUE) of Atlantic Croaker began to decline, driven by the groundfish fishery, bycatch in shrimp nets, and their growing popularity as a sportfish both recreationally and commercially (Lassuy, 1983). With the collapse of the groundfish fishery in the late 1970s, Atlantic Croaker stabilized and have since been popularized in the Western GOM as a successful baitfish for catching heavily sought-after inshore and offshore species (Oritz et al., 2000; GSMFC, 2017). Atlantic Croaker currently support a dynamic and economically beneficial fishery for coastal communities throughout their entire range (ASMFC, 2010; GSMFC, 2017). Despite their recreational and commercial importance, there is limited knowledge on Atlantic Croaker, particularly on important information needed for effective management including age and growth data. Much of the research completed on this species, including most age and growth data, is from the western U.S. Atlantic coast (Haven, 1957; Warlen, 1980; Barbieri et al., 1994; GSMFC, 2017). Age and growth is an important tool for monitoring and managing fisheries, but most age and growth studies published on Atlantic Croaker in the GOM focus primarily on adults (Barger, 1985), and few are recent. Age classes of adult Atlantic Croaker in the GOM have been reported to range from 0 – 8 years with most fish between one and two years of age and most growth occurring within the first year (Barger, 1985) but less is known about growth between hatching and age 1. Some studies have examined daily larval and early juvenile growth along the western U.S. Atlantic coast and in the northern GOM (Warlen, 1980; Cowan, 1988; Nixon & Jones, 1997; Peterson, 1999). However, almost no studies have described monthly growth of juvenile Atlantic Croaker throughout the entirety of their first year of life. In Texas specifically, recent juvenile Atlantic Croaker age and growth data is limited to Anderson et al. (2018) which mostly focused on adults with brief mention of juvenile growth. Furthermore, studies of environmental impacts on juvenile growth focus primarily on growth between 0-80 days after hatching and may not provide a complete understanding of juvenile growth. Salinity has been suggested to have the greatest impact on growth with some research suggesting that high salinities reduce growth rates in juveniles between 10-20 mm total length (TL; Peterson, 1999). The negative correlation between salinity and early juvenile growth has been further supported in GOM Atlantic Croaker (Kupchik & Shaw, 2016). However, a more recent study suggests that at larger juvenile sizes (> 71 mm standard length; SL), high salinity may benefit grow rates (Lankford & Targett, 2021). Temperature had been shown to affect sciaenid growth, with higher water temperatures resulting in greater growth and survivability, but data is limited on the effect of temperature on juvenile Atlantic Croaker growth (Diaz & Onuf, 1985; Diamond et al., 2013; Kupchik & Shaw, 2016). Mortality is typically greatest for finfishes within the first year of life due to small body size and overall greater vulnerability to predators and environmental fluctuations, suggesting juvenile growth is a critical component of survivability (Currin et al., 1984; Searcy et al., 2007; Stige et al., 2019). Additionally, growth rates for Atlantic Croaker are the greatest within the first few months after hatching and tend to slow as fish near maturity around age one (Barger, 1985; Barbieri et al., 1994). Because of this, having a thorough understanding of juvenile growth, including spatial variation in growth rates and the variables that influence growth, is vital for effective fisheries management. Studies on juvenile Atlantic Croaker growth are limited to the larval stage and juveniles up to 85 mm (Lankford & Targett, 2021), but size at first maturity is at minimum 170 mm TL (Barbieri et al., 1994). To mend this notable data gap, this study aimed to describe age and growth of young-of-the-year (YOY) Atlantic Croaker in the Western GOM using length frequency data. While otoliths are typically preferred for age estimations, length frequency data have long been used to estimate growth when otolith collection is infeasible (Schnute & Fournier, 1980; Pauly, 1980). Therefore, this project used long-term (1990-2023) fishery-independent sampling data from the Texas Parks and Wildlife Department (TPWD) to 1) model monthly juvenile growth trends, 2) compare growth trends within these models across major Texas bay systems, and 3) determine effects of water quality parameters (with emphasis on salinity) on YOY Atlantic Croaker growth. Methods Field sampling All sampling was completed within the framework of fishery-independent bag seines and bay trawls conducted by TPWD along the Texas coast from 1990 through 2023. Sampling was conducted using a stratified design determined by bay and month to select randomized locations within the following major bays: Sabine Lake, Galveston Bay, East Matagorda Bay, West Matagorda Bay, San Antonio Bay, Aransas Bay, Corpus Christi Bay, and the Upper and Lower Laguna Madre (Fig. 1). Bay trawls were constructed using 38-mm stretched nylon multifilament mesh and were 6.1 m in width. In larger bays (Galveston Bay, West Matagorda Bay, San Antonio Bay, Aransas Bay, and Corpus Christi Bay), twenty trawls were performed each month, and in smaller bays (Sabine Lake, East Matagorda Bay, and Upper and Lower Laguna Madre), ten trawls were performed each month. To ensure temporal uniformity, half of each month’s trawls were conducted by the 15 th of that month, and the second half were conducted before the end of that month. All trawls were towed in a circular motion at an approximate speed of 5.556 km/h for 10 minutes. Bag seines were constructed using 13-mm stretched nylon mesh in the bag and 19-mm stretched nylon mesh in the wings with an overall width of 1.8 m and length of 18.3 m. Twenty bag seines were conducted each month for all bays except East Matagorda Bay in which only 10 bag seines were conducted a month. Similarly to bay trawls, half of each month’s bag seines were collected by the 15 th of each month with the second half collected between the 16 th and the last day of the month. All bag seines were pulled parallel to the shoreline with a total sample area of 0.03 ha. At each date and location for bag seines and trawls, environmental data was collected and included temperature (˚C), salinity (psu), turbidity (Nephelometric Turbidity Units; NTU), and dissolved oxygen (mg/L). Growth models Because bay trawls and bag seines likely contained multiple age cohorts, length frequencies based on 10-mm length classes were used to extract only YOY Atlantic Croaker. First, all Atlantic Croaker 200 mm or greater TL (the typical maximum size at which Atlantic Croaker were no longer caught in either gear; Fig. 2) were removed from the dataset. Similar methods were used by Williford and Anderson (2025), and previous literature suggests most age 1 and older Atlantic Croaker were above 200 mm (Haven, 1957; Barbieri et al., 1994b). Monthly boxplots of TL were then examined to identify outliers that clearly belonged to a significantly larger cohort, which were later re-identified and removed stepwise per month using a quantile range outlier analysis. The extraction methods for YOY Atlantic Croaker differed slightly in November, December, January, and February from the rest of the year. In November and December, a new cohort of Atlantic Croaker with distinctively smaller sizes appeared as annual spawning began, thus introducing a new cohort. In January and February, the previous year’s cohort was evident in distinctly larger sizes than the YOY sizes expected in the first month of growth. To remove these cohorts from the targeted YOY Atlantic Croaker, all Atlantic Croaker below 88 mm (the smallest TL present in October) were removed from November and December, and all Atlantic Croaker above 135 mm (the largest TL in March) were removed from January and February. This followed the assumption that the smallest YOY fish present in October (when approximately 10 months old) would be the same size or larger in following months (when 11 or 12 months old), and the largest YOY fish present in March (approximately three months old) would have been the same size or smaller in previous months (one or two months old). Once the YOY cohort was isolated for each month, multiple growth models were fit to the data following recommendations for multi-model inference (Burnham & Anderson, 2002; Katsanevakis & Maravelias, 2008). All growth models were calculated using the “nls” and “FSA” packages in R (Ogle et al., 2023) and fit to the data using nonlinear least square regressions. The first model was the three-parameter von Bertalanffy growth curve (von Bertalanffy, 1938) with the function: L t = L ∞ [1 – e - k (t – t 0 ) ], in which L t is the expected length at t (time; month), L ∞ is the theoretical maximum TL (in millimeters), k is the growth coefficient (growth to L ∞ ), and t 0 is the hypothetical age (month) at which TL is zero. The logistic growth model (Ricker, 1979; Katsanevakis, 2006) was fit to the data using the function: L t = L ∞ / [1 + e -g (t – t 0 ) ], where g is the relative growth rate parameter and t 0 is time (month) at which growth is maximized. The Gompertz model was evaluated using the equation: L t = L ∞ [ e - k exp(-gt) ], in which g is the instantaneous growth rate and k represents the initial relative growth rate at age-0 (Ricker, 1979). The final model fit was Richard’s growth model (Tjorve & Tjorve, 2010): L t = L ∞ [1 – ae - k (t) ] b , with the slope at the inflection point ( k ), and a and b representing dimensionless parameters influencing the horizontal (age in months) and vertical positions (TL in millimeters) of the inflection point respectively. Best fit was determined by the lowest Akaike information criterion (AICc) once adjusted for possible small sample size bias (Akaike, 1973; Hurvich & Tsai, 1989). Highest Akaike weights were also considered to determine best fit (Burnham & Anderson, 2002). Once selected, the best model was separated into three regional growth curves termed the Upper Coast (Sabine Lake, Galveston Bay, and East Matagorda), Middle Coast (West Matagorda, San Antonio Bay, and Aransas Bay), and Lower Coast (Corpus Christi Bay and Upper and Lower Laguna Madre) to evaluate potential differences among regions. To specifically evaluate differences in growth among regional groups, L ∞ , ‘ a’, and ‘ b’ were constrained to coastwide values and only the growth coefficient ‘ k’ was allowed to vary. Analysis of residual variation Length frequency growth estimations result in growth functions that cannot be anchored to an age-informed t 0 parameter, which may yield unstable growth parameters particularly through the bias of the growth parameter ( k ) for one or more of the regional growth functions (Pauly, 1980b). Since this could impact direct growth comparisons, an independent approach was employed alongside growth models to confirm regional growth variation. To repeat the findings of the regional growth functions and validate statistically significant regional differences, the distribution of residual variation around the unified (coast-wide) growth function was examined. This method has the benefit of applying a single growth function to all observed specimens and using residual variation around the unified function to model spatial differences in observed size (and effectively, growth). Essentially, the bias associated with parameter estimation using this approach should impact all individuals equally, leading to higher confidence in spatial inferences than those that rely on regional model parameters. Residual variation was obtained by subtracting model-fitted size predicted by the coast-wide growth model from observed size (mm TL), such that positive residuals represent individuals that were larger than predicted by the model, and negative residuals were smaller than predicted. Residuals were checked for normality, and the Analysis of Variance (ANOVA) was used to determine differences in mean residual variation among Upper, Middle, and Lower coast regions. Tukey’s honestly significant difference test (Tukey’s HSD; Tukey, 1949) was used post hoc to compare pairwise individual means for each region. Statistical analyses Kruskal-Wallis tests were used to detect differences in water quality parameters among Upper, Middle, and Lower Coasts, and when significant ( a = 0.05), post-hoc Dunn’s tests were used to determine which regions differed. Mean monthly growth (mean increase in TL per month for each region) was calculated for Atlantic Croaker coastwide and for each region separately to compare with mean monthly water quality parameters for each year ( N = 1218). A generalized additive model (GAM) was employed using the “mgcv” package in R (Wood, 2011) to determine the effects of salinity, water temperature, turbidity, dissolved oxygen, month, and year on growth. The GAM was selected over linear models due to nonlinearity detected in salinity, temperature, and turbidity and lower AIC values than the linear models. T-tests determined when monthly growth diverged among regions. Recent literature has suggested that salinity begins to increase growth after body sizes reach 71 mm SL (Lankford & Targett, 2021); thus, the percentage of Atlantic Croaker in this project above and below this size was calculated and compared in different salinity regions to evaluate differences in salinity effects on growth at different sizes. Results Of the 503,316 Atlantic Croaker measured between 1990 and 2023, 435,640 juvenile Atlantic Croaker were included in the growth models once outliers from other yearly cohorts were removed, and YOY cohorts were clearly isolated within months. The TL ranged from 6 mm to 199 mm, with bag seines containing overall smaller Atlantic Croaker (mean ± SD: 61.0 mm ± 25.3 mm) than bay trawls (mean ± SD: 112.8 mm ± 30.4 mm). The growth model with the lowest AICc was Richard’s model, which also had the highest Akaike weight and was therefore selected as the best model (Table 1). The equation for the full model was L t = 153.4 [1 – (3.2) e - 0.337 (t) ] 1.57 . For the regional growth models in which ‘ k ’ was allowed to vary and other parameters were constrained to those from the coastwide model, growth differed among Upper, Middle, and Lower Coasts ( k = 0.262, 0.340, 0.528, respectively; Fig. 3, Table 2). The growth coefficient ( k ) increased notably from north to south with the Lower Coast k almost twice as high as the Upper Coast. The ANOVA of residual variation around the coast-wide Richard’s growth function validated the findings from the regional growth functions. The model using region to predict residual growth around the coast-wide mean was highly significant ( F 2,451854 = 15,329, p < 0.0001). The Lower Coast had a positive mean residual variation of 10.2 mm TL, the Middle Coast had a positive mean residual variation of 1.4 mm TL, and the Upper Coast had a negative mean residual variation of -6.5 mm TL (Fig. 4). All pairwise differences among regions were statistically significant based on Tukey’s HSD test. Kruskal-Wallis tests indicated there were differences in all water quality parameters among regions (all variables: p < 0.001). The only parameter between each region that was not significantly different was the Upper and Middle Coast water temperature (Dunn’s test: z = 1.11, p = 0.27; Fig. 5). Mean growth in the GAM was significantly predicted by month, dissolved oxygen, and salinity ( edf : 7.38, F = 8.34, p < 0.0001; edf : 1.00, F = 12.77, p < 0.001; edf : 4.02, F = 6.17, p < 0.0001, respectively). Growth was greatest in early months during spring, slowed in summer and was lowest in the final few months of the year. Dissolved oxygen predicted by the GAM exhibited a negative linear relationship with growth, while salinity demonstrated a strongly positive relationship with growth until salinity surpassed approximately 35 psu at which predicted growth notably decreased (Fig. 6). While demonstrating slight trends, temperature, turbidity, and year were non-significant in predicting mean monthly growth of YOY Atlantic Croaker (Fig. 6). When considering mean monthly growth, most juveniles (≈ 70%) beneath the size threshold reported for positive salinity effects on growth (71 mm SL; Lankford & Targett, 2021) were observed between January and March (March mean TL: 64.5 mm). Mean monthly growth from January to March was notably less than growth in April (t-test: t = -8.07, df = 217.0, p < 0.0001), when the mean TL was above the 71 mm SL threshold (April mean TL: 84.1 mm; Table 3). Additionally, the Lower Coast had higher salinities from January through March than the Upper Coast (mean: 31.8 psu, 13.7 psu, respectively), but growth did not significantly differ between the two regions during this time (t-test: t = -1.42, df = 118.3, p = 0.16; Table 3). Growth did not significantly differ between Upper and Lower Coasts until April (t-test: t = -3.59, df = 55.49, p < 0.001) once mean TL had surpassed the 71 mm SL threshold suggested by Lankford and Targett (2021). Discussion Although otoliths are typically preferred over length frequencies for age estimation due to the broad range of lengths that can occur within a given age class, length-frequency data have been widely used to model fish growth in the absence of age data (Schnute & Fournier, 1980; Pauly, 1980a). More recent approaches have used fishery-dependent length data to model lifetime growth in both short and long-lived species (Laslett et al., 2004; Froese et al., 2018). Additionally, Atlantic Croaker spawning is largely restricted to a specific window (October–December; White & Chittenden, 1977), suggesting that cohort variation within each monthly sample is likely limited to adjacent months. Given that age comparisons in this study are restricted to within the same dataset, supported by a large sample size, and overall findings are corroborated by a secondary analysis of regional residual variation, the observed growth patterns likely reflect true regional differences despite the inevitable inclusion of other cohorts within a given month. The selection of Richard’s model for juvenile Atlantic Croaker is supported by previous studies that describe Richard’s model as effective for modeling species with two-stanza growth (typically observed in long-lived fishes, with a fast growth rate early in life followed by slower and steadier growth in later years; Pacicco et al., 2021; Banks et al., 2024). The inclusion of the extra shape parameters ( a and b ) allows greater flexibility, meaning changes in growth rate throughout a species’ life (or in this case, changes during the first year of life) can be modeled more effectively (Katsanevakis & Maravelias, 2008; Banks et al., 2024). In this study, fitting standard growth functions to juvenile fishes using monthly length-frequency data allowed for observations of seasonal changes in growth, including the expectation that growth slows for many species at the onset of fall and winter and accelerates in the spring and summer. Thus, the flexibility of the Richard’s model may apply more broadly to juvenile fishes that are expected to transition between routine and compensatory growth in response to seasonal changes in resource availability (Schultz et al., 2002). Juvenile Atlantic Croaker growth was best modeled using Richard’s growth model with the equation L t = 153.4 [1 – (3.2) e - 0.337 (t) ] 1.57 . The L ∞ value (maximum theoretical size; 153.4 mm) was lower than GOM studies modelling adult growth, as expected due to younger ages (Barger, 1985; L ∞ = 419 mm). The growth coefficient ( k = 0.337) is similar to adult Atlantic Croaker studies throughout the GOM and Atlantic coast and supports the idea that most growth occurs before individuals reach age 1 (Barger, 1985; Barbieri et al., 1994b; k = 0.27 and 0.36, respectively). Comparisons of this data to other juvenile growth curves is impractical because most other studies used otoliths and days as age within a short time frame instead of months throughout the full first year. Additionally, other juvenile growth studies selected linear, von Bertalanffy, or Laird-Gompertz models, making direct comparisons to Richard’s model impractical (Warlen, 1980; Cowan, 1988). There is an importance in noting possible selection bias within this project, since the removal of all Atlantic Croaker greater than 200 mm and other steps applied to extract YOY fish may result in underestimations of growth parameters and completely isolating each monthly cohort is impractical using only length frequencies. However, the large sample size ( n = 435,640) and meticulous cohort removal likely mitigates most biases and prevents the alteration of overall trends, which was further supported by the residual variation analysis that demonstrated distinct regional growth variation. Constraining all parameters in the regional models except ‘ k ’ allowed for a more direct comparison of growth among regions and supported the trends observed in the residual analysis with growth rate increasing when moving from the northernmost region to the southernmost region, suggesting the importance of the changing environmental gradient. The GAM suggested the most influential parameters on growth were month, dissolved oxygen, and salinity. The importance of month is likely due to seasonal variation in growth, considering most Atlantic Croaker growth occurs during the first few months while within estuarine nurseries (Barger, 1985, GSMFC, 2017). Greatest monthly growth was observed during spring (March – May; Table 3). Spring typically exhibits high productivity in estuaries of the GOM, and growth in Atlantic Croaker is likely maximized during this time to provide a buffer for juveniles against predation and the environmental fluctuations that are characteristic of estuarine habitats (Sogard 1992, Schultz et al., 2002). While month was likely important due to seasonality and ontogeny, temperature was not significant for predicting spatial differences in growth within the model. However, higher temperatures are often correlated with increased growth and metabolism in Sciaenidae (Lanier & Scharf, 2007; Diamond et al., 2013; Williford & Anderson, 2025) and may contribute to the observed seasonal variation in growth. Additionally, temperature and growth followed similar latitudinal trends that, while not statistically significant, may suggest some physiological relevance of temperature in driving juvenile growth rate in this species. Dissolved oxygen was also found to be an important predictor of growth, with higher oxygen concentrations leading to lower growth rates. Although Atlantic Croaker often inhabit low-oxygen benthic habitats, the impact of dissolved oxygen on growth is not well understood (Diaz & Onuf, 1985). Dissolved oxygen displayed a negative relationship with salinity and water temperature, suggesting dissolved oxygen may be more correlated with salinity and seasonal variation than growth. This implies that dissolved oxygen’s significance in the model could simply be driven by interactive effects However, research has suggested many Atlantic Croaker spend their first year of life in low levels of dissolved oxygen and have demonstrated resilience against hypoxic waters. Dissolved oxygen did not often reach such low levels within this study, which may indicate a potential underestimation of oxygen effects in this model (Valenza et al., 2023). Additionally, there has been some speculation that Atlantic Croaker resilience to low dissolved oxygen may enhance foraging opportunities on environmentally stressed prey which may in turn increase growth of Atlantic Croaker in high salinity regions (Valenza et al., 2023). The clear increase in the growth coefficient ( k ) from north to south (Table 2) suggests growth to maximum theoretical size ( L ∞ ) is significantly faster in Lower Coast bays than the Upper Coast. Trends of increasing water temperature and salinity followed a highly similar latitudinal gradient from north to south to increasing growth while turbidity decreased from north to south. Of these, only salinity was significant and demonstrated a positive relationship with YOY growth. Previous studies have described the opposite trend, with early-stage juveniles showing better growth in low salinity compared to high salinity (Peterson et al., 1999; Kupchik & Shaw, 2016). However, the early-stage juveniles in the lab study of Peterson et al. (1999) were between 10-20 mm TL (< 0.5 years old), whereas the juveniles in the current study ranged from 6-199 mm TL and encompassed a wider range of juvenile ages (1-12 months). While Kupchik and Shaw (2016) reported that Atlantic Croaker growth increased when recruits encountered low-salinity habitats, this finding could simply be driven by the fact that early recruits seek out low-salinity habitats in the upper estuary that confer some nursery advantage, as individuals in that study were all less than 80 days old. More recent evidence indicates juveniles between 56 – 70 mm SL prefer and grow faster in low salinities (18 ppt; Lankford & Targett, 2021). While no studies were found describing salinity effects on growth for juveniles above 85 mm SL, approximately 73.5% of juvenile Atlantic Croaker within this project were above the 71 mm SL threshold described by Lankford and Targett (2021). Therefore, these larger juveniles may exhibit faster growth in the Lower Coast compared to the Upper Coast due to the higher salinities further south. This study, in comparison to previous studies using post-larval sized Atlantic Croaker, highlights the benefit of assessing juvenile growth over an annual scale and underscores the notion that growth rates in juvenile fishes are driven by complex, stage-specific environmental preferences that can be difficult to tease apart. Similar growth between Upper and Lower Coasts from January to March despite the large salinity difference (mean salinity: 13.7 psu, 31.8 psu, respectively) provides further evidence that salinity does not increase growth until juvenile Atlantic Croaker have surpassed a certain size (estimated 71 mm SL; Lankford & Targett, 2021). Salinity tolerances of juvenile Atlantic Croaker are known to increase with size during the emigration from low salinity estuarine nurseries to higher salinity bays during the juvenile stage (Haven, 1957; Yakupzack et al., 1977; Diaz & Onuf, 1985). High salinity may be metabolically costly to withstand for larvae and early-stage juveniles, but as size and salinity tolerance increase, energy is likely redirected from osmoregulation to growth (Bœuf & Payan, 2001; Schultz et al., 2002; Semra et al., 2013). This may drive juveniles in high salinities (e.g., Lower Coast bays) to favor compensatory growth early in the year to reach salinity tolerant sizes quickly during high productivity in spring (Table 3). Conversely, juveniles in low salinities (e.g., Upper Coast bays) likely spend less energy on osmoregulation and do not need to reach large sizes as quickly to withstand salinity; therefore, juveniles in low salinity are likely able to allocate energy to growth more consistently throughout the year. Another explanation for increased growth in high salinities may be a competitive advantage over less salinity tolerant estuarine species. In larval and early-stage juveniles, growth is likely optimized in upper-estuary nursery habitats with low salinity, but as fish reach larger sizes, increased predator avoidance and access to larger food items might drive Atlantic Croaker into higher-salinity bays. The benefit of fewer predators and larger food as a product of increased salinity tolerance may provide a competitive advantage over other, less salinity tolerant mesopredators in bays with more extreme salinity (e.g., Lower Coast bays). This may explain the apparent selection for juvenile Atlantic Croaker that reach large sizes faster in high salinity. Maximizing growth within the first year increases fecundity, survivability, and overall reproductive success (Sogard, 1997), which further supports the idea that greater growth in high salinity is driven by selection. This inference is also supported by the finding that a majority of age-1 Atlantic Croaker in Texas are sexually mature (Anderson et al., 2018), meaning environmental interactions that drive growth in year one of this species may have downstream impacts on survival and lifetime reproductive success. The findings of this project provide valuable and previously understudied insights into the growth of juvenile Atlantic Croaker, which have important implications for fisheries management. While Atlantic Croaker populations currently appear stable, their significance to both commercial and recreational fisheries highlights the need for a deeper understanding of their life history. Though limited to the Texas coast, the observations in this study describe environmental influences on juvenile Atlantic Croaker that may be observed throughout their entire range. Physiological processes and environmental influences throughout the juvenile stage determine the success of annual cohort recruitment and are therefore critical to the health and persistence of the fishery. Numerous studies report that juvenile mortality of marine finfish is highly indicative of year class strength, and faster growth typically increases overall survivability, further supporting the importance of understanding juvenile growth for the sake of effective management (Steele, 1997; Fromentin et al., 2001; Shima, 2001). As waters throughout the GOM continue to warm (NOAA, 2024), effectively assessing and understanding juvenile growth will be a major component of managing Atlantic Croaker fisheries throughout their entire distribution. Declarations Authors' contributions: Isabelle Cummings implemented and evaluated data analyses, interpreted data, wrote the final work, and assisted with the initial project conceptualization. Joel Anderson conceived the initial study idea and assisted in data analyses, method design, data interpretation and editing. Ethics approval and consent to participate: Fish collection and handling protocols followed the ethical guidelines described by a Federal Aid in Sport Fish Restoration grant agreement (TPWD TX-F-281-M) as well as a federal permit for the handling of endangered and threatened species issued by the U.S. Department of the Interior (Permit number TE814933-0). Consent for publication: Not applicable. Availability of data and materials: The data that support the findings of this study are available from the corresponding author upon request. Competing interests: The authors have no competing interests to declare that are relevant to the content of this article. Funding: No funding was received for conducting this study. Acknowledgements: We would like to acknowledge and offer special thanks to all the Texas Parks and Wildlife employees who performed the fieldwork and collected the data used in this study. We would also like to thank the anonymous reviewers who provided their insights to improve the manuscript. References Akaike, H. (1973). Information theory and the extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), International symposium on information theory (pp. 267–281). Academiai Kaido. Anderson, J., McDonald, D., Bumguardner, B., Olsen, Z., & Ferguson, J. W. (2018). Patterns of Maturity, Seasonal Migration, and Spawning of Atlantic Croaker in the Western Gulf of Mexico. Gulf of Mexico Science, 34 (1). https://doi.org/10.18785/goms.3401.03 Banks, K. G., Streich, M. K., & Stunz, G.W. (2024). Age, growth, and mortality of King Mackerel in the western Gulf of Mexico. Marine and Coastal Fisheries, 16 , 1-14. https://doi.org/10.1002/mcf2.10278 Barbieri, L. 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Age determination, reproduction, and population dynamics of the Atlantic croaker, Micropogonias undulatus . Fishery Bulletin , 75 , 109–123. Williford, D., & Anderson, J. (2025). Abundance, seasonality, and growth of spot ( Leiostomus xanthurus ) in the Western Gulf of Mexico. Environmental Biology of Fishes , 108 , 339-354. https://doi.org/10.1007/s10641-025-01672-0 Wood, S. N. (2011). Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models. Journal of the Royal Statistical Society Series B: Statistical Methodology , 73 , 3-36. https://doi.org/10.1111/j.1467-9868.2010.00749.x Yakupzack, P. M., Herke, W. H., Perry, W. G. (1977). Emigration of Juvenile Atlantic Croakers, Micropogon undulatus , from a Semi‐impounded Marsh in Southwestern Louisiana. Transactions of the American Fisheries Society , 106 (6), 538-544. https://doi.org/10.1577/1548-8659(1977)1062.0.CO;2 Tables Tables are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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06:51:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225203,"visible":true,"origin":"","legend":"\u003cp\u003eLength frequency for Atlantic Croaker in bay trawls (blue) and bag seines (red) for each group of months. The percentage of Atlantic Croaker within each corresponding 10 mm TL length class is located on the y-axis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/ce8a9ab79d7c2b75e46efc61.png"},{"id":96052066,"identity":"54043aae-46ec-4dc4-a2f6-93872f332e0f","added_by":"auto","created_at":"2025-11-17 06:51:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26018,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly total length (mm) boxplots and growth curves for the Upper Coast (slowest growth; green, solid line), Middle Coast (red, dotted line), and Lower Coast (fastest growth; blue, dashed line) from the final filtered YOY cohort data. Notable regional growth differences appear after mean total length has surpassed the previously proposed 71 mm length threshold for salinity effects (Lankford and Targett 2021).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/b34e1cde8ed22c93cd773b60.png"},{"id":96247323,"identity":"f587debc-1141-4d98-8759-902d6e474738","added_by":"auto","created_at":"2025-11-19 07:27:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46823,"visible":true,"origin":"","legend":"\u003cp\u003eResidual variation of growth (mm TL) produced from the coast-wide growth function for YOY Atlantic Croaker in each studied region depicting clear variation in growth from upper (slowest growth, most negative residuals) to lower coasts (fastest growth, most positive residuals).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/89724a5ea4440d6180e92794.png"},{"id":96247250,"identity":"0129d4d3-4ec5-4291-a443-ca5edc3b7d75","added_by":"auto","created_at":"2025-11-19 07:27:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35481,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean water quality parameters (dissolved oxygen (mg/L), turbidity (NTU), salinity (psu), and water temperature (C) for each region (Upper, Middle, and Lower Coast).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/b2fecc6b7e260dc2c8cfae69.png"},{"id":96052067,"identity":"46ae98e1-b8fa-4b86-828d-9ba1b6b440f2","added_by":"auto","created_at":"2025-11-17 06:51:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":33458,"visible":true,"origin":"","legend":"\u003cp\u003eThe generalized additive model threshold plot predicting growth (partial effects) in young-of-the-year Atlantic Croaker including salinity (psu), water temperature (C), turbidity (NTU), dissolved oxygen (mg/L), month of the year, and year. Green shading indicates confidence intervals of the partial effects.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/fd91857e0d0c58570a92bb7d.png"},{"id":96256165,"identity":"dd280514-7a5a-45e0-87a5-59ac528bc9e8","added_by":"auto","created_at":"2025-11-19 07:49:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/1df13fcc-ed2e-4890-b9b6-4e2e8a09c7a9.pdf"},{"id":96248016,"identity":"55eb71ba-4fdc-48fc-92e1-afb258587dcf","added_by":"auto","created_at":"2025-11-19 07:27:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21385,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8116250/v1/72875777725347421f25ee38.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnvironmental influences on juvenile Atlantic Croaker (\u003cem\u003eMicropogonias undulatus\u003c/em\u003e) growth in the Western Gulf of Mexico\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Atlantic Croaker \u003cem\u003eMicropogonias undulatus\u003c/em\u003e is a marine finfish from the family Sciaenidae that ranges throughout the Gulf of Mexico (GOM; also known as the Gulf of America) and along the western Atlantic coast from southern Florida to Massachusetts (ASMFC, 1987). Typically, Atlantic Croaker reach maturity between ages one and two, and adults migrate from estuarine habitats during fall months to spawn offshore (White \u0026amp; Chittenden, 1977; Barbieri et al., 1994; ASMFC, 2010). Larvae hatch offshore and are transported back into low salinity estuarine habitats to grow (White \u0026amp; Chittenden, 1977). As the juvenile stage progresses, Atlantic Croaker shift into higher salinities downstream and within bays (Yakupzack et al., 1977; Lankford and Targett, 2021), where they become an important component of the ecological trophic web as both a predator for small fish and crustacean species and prey for economically valuable inshore fish species (Mercer, 1987; GSMFC, 2017).\u003c/p\u003e\n\u003cp\u003eAtlantic Croaker are also economically valuable baitfish and food fish. In the 1960s and 70s, total landings and catch per unit effort (CPUE) of Atlantic Croaker began to decline, driven by the groundfish fishery, bycatch in shrimp nets, and their growing popularity as a sportfish both recreationally and commercially (Lassuy, 1983). With the collapse of the groundfish fishery in the late 1970s, Atlantic Croaker stabilized and have since been popularized in the Western GOM as a successful baitfish for catching heavily sought-after inshore and offshore species (Oritz et al., 2000; GSMFC, 2017). Atlantic Croaker currently support a dynamic and economically beneficial fishery for coastal communities throughout their entire range (ASMFC, 2010; GSMFC, 2017).\u003c/p\u003e\n\u003cp\u003eDespite their recreational and commercial importance, there is limited knowledge on Atlantic Croaker, particularly on important information needed for effective management including age and growth data. Much of the research completed on this species, including most age and growth data, is from the western U.S. Atlantic coast (Haven, 1957; Warlen, 1980; Barbieri et al., 1994; GSMFC, 2017). Age and growth is an important tool for monitoring and managing fisheries, but most age and growth studies published on Atlantic Croaker in the GOM focus primarily on adults (Barger, 1985), and few are recent. Age classes of adult Atlantic Croaker in the GOM have been reported to range from 0 \u0026ndash; 8 years with most fish between one and two years of age and most growth occurring within the first year (Barger, 1985) but less is known about growth between hatching and age 1. Some studies have examined daily larval and early juvenile growth along the western U.S. Atlantic coast and in the northern GOM (Warlen, 1980; Cowan, 1988; Nixon \u0026amp; Jones, 1997; Peterson, 1999). However, almost no studies have described monthly growth of juvenile Atlantic Croaker throughout the entirety of their first year of life. In Texas specifically, recent juvenile Atlantic Croaker age and growth data is limited to Anderson et al. (2018) which mostly focused on adults with brief mention of juvenile growth.\u003c/p\u003e\n\u003cp\u003eFurthermore, studies of environmental impacts on juvenile growth focus primarily on growth between 0-80 days after hatching and may not provide a complete understanding of juvenile growth. Salinity has been suggested to have the greatest impact on growth with some research suggesting that high salinities reduce growth rates in juveniles between 10-20 mm total length (TL; Peterson, 1999). The negative correlation between salinity and early juvenile growth has been further supported in GOM Atlantic Croaker (Kupchik \u0026amp; Shaw, 2016). However, a more recent study suggests that at larger juvenile sizes (\u0026gt; 71 mm standard length; SL), high salinity may benefit grow rates (Lankford \u0026amp; Targett, 2021). Temperature had been shown to affect sciaenid growth, with higher water temperatures resulting in greater growth and survivability, but data is limited on the effect of temperature on juvenile Atlantic Croaker growth (Diaz \u0026amp; Onuf, 1985; Diamond et al., 2013; Kupchik \u0026amp; Shaw, 2016).\u003c/p\u003e\n\u003cp\u003eMortality is typically greatest for finfishes within the first year of life due to small body size and overall greater vulnerability to predators and environmental fluctuations, suggesting juvenile growth is a critical component of survivability (Currin et al., 1984; Searcy et al., 2007; Stige et al., 2019). Additionally, growth rates for Atlantic Croaker are the greatest within the first few months after hatching and tend to slow as fish near maturity around age one (Barger, 1985; Barbieri et al., 1994). Because of this, having a thorough understanding of juvenile growth, including spatial variation in growth rates and the variables that influence growth, is vital for effective fisheries management.\u003c/p\u003e\n\u003cp\u003eStudies on juvenile Atlantic Croaker growth are limited to the larval stage and juveniles up to 85 mm (Lankford \u0026amp; Targett, 2021), but size at first maturity is at minimum 170 mm TL (Barbieri et al., 1994). To mend this notable data gap, this study aimed to describe age and growth of young-of-the-year (YOY) Atlantic Croaker in the Western GOM using length frequency data. While otoliths are typically preferred for age estimations, length frequency data have long been used to estimate growth when otolith collection is infeasible (Schnute \u0026amp; Fournier, 1980; Pauly, 1980). Therefore, this project used long-term (1990-2023) fishery-independent sampling data from the Texas Parks and Wildlife Department (TPWD) to 1) model monthly juvenile growth trends, 2) compare growth trends within these models across major Texas bay systems, and 3) determine effects of water quality parameters (with emphasis on salinity) on YOY Atlantic Croaker growth.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u0026lt;B\u0026gt;Field sampling\u003c/p\u003e\n\u003cp\u003eAll sampling was completed within the framework of fishery-independent bag seines and bay trawls conducted by TPWD along the Texas coast from 1990 through 2023. Sampling was conducted using a stratified design determined by bay and month to select randomized locations within the following major bays: Sabine Lake, Galveston Bay, East Matagorda Bay, West Matagorda Bay, San Antonio Bay, Aransas Bay, Corpus Christi Bay, and the Upper and Lower Laguna Madre (Fig. 1).\u003c/p\u003e\n\u003cp\u003eBay trawls were constructed using 38-mm stretched nylon multifilament mesh and were 6.1 m in width. In larger bays (Galveston Bay, West Matagorda Bay, San Antonio Bay, Aransas Bay, and Corpus Christi Bay), twenty trawls were performed each month, and in smaller bays (Sabine Lake, East Matagorda Bay, and Upper and Lower Laguna Madre), ten trawls were performed each month. To ensure temporal uniformity, half of each month\u0026rsquo;s trawls were conducted by the 15\u003csup\u003eth\u003c/sup\u003e of that month, and the second half were conducted before the end of that month. All trawls were towed in a circular motion at an approximate speed of 5.556 km/h for 10 minutes.\u003c/p\u003e\n\u003cp\u003eBag seines were constructed using 13-mm stretched nylon mesh in the bag and 19-mm stretched nylon mesh in the wings with an overall width of 1.8 m and length of 18.3 m. Twenty bag seines were conducted each month for all bays except East Matagorda Bay in which only 10 bag seines were conducted a month. Similarly to bay trawls, half of each month\u0026rsquo;s bag seines were collected by the 15\u003csup\u003eth\u003c/sup\u003e of each month with the second half collected between the 16\u003csup\u003eth\u003c/sup\u003e and the last day of the month. All bag seines were pulled parallel to the shoreline with a total sample area of 0.03 ha. At each date and location for bag seines and trawls, environmental data was collected and included temperature (˚C), salinity (psu), turbidity (Nephelometric Turbidity Units; NTU), and dissolved oxygen (mg/L).\u003c/p\u003e\n\u003cp\u003e\u0026lt;B\u0026gt;Growth models\u003c/p\u003e\n\u003cp\u003eBecause bay trawls and bag seines likely contained multiple age cohorts, length frequencies based on 10-mm length classes were used to extract only YOY Atlantic Croaker. First, all Atlantic Croaker 200 mm or greater TL (the typical maximum size at which Atlantic Croaker were no longer caught in either gear; Fig. 2) were removed from the dataset. Similar methods were used by Williford and Anderson (2025), and previous literature suggests most age 1 and older Atlantic Croaker were above 200 mm (Haven, 1957; Barbieri et al., 1994b). Monthly boxplots of TL were then examined to identify outliers that clearly belonged to a significantly larger cohort, which were later re-identified and removed stepwise per month using a quantile range outlier analysis. The extraction methods for YOY Atlantic Croaker differed slightly in November, December, January, and February from the rest of the year. In November and December, a new cohort of Atlantic Croaker with distinctively smaller sizes appeared as annual spawning began, thus introducing a new cohort. In January and February, the previous year\u0026rsquo;s cohort was evident in distinctly larger sizes than the YOY sizes expected in the first month of growth. To remove these cohorts from the targeted YOY Atlantic Croaker, all Atlantic Croaker below 88 mm (the smallest TL present in October) were removed from November and December, and all Atlantic Croaker above 135 mm (the largest TL in March) were removed from January and February. This followed the assumption that the smallest YOY fish present in October (when approximately 10 months old) would be the same size or larger in following months (when 11 or 12 months old), and the largest YOY fish present in March (approximately three months old) would have been the same size or smaller in previous months (one or two months old).\u003c/p\u003e\n\u003cp\u003eOnce the YOY cohort was isolated for each month, multiple growth models were fit to the data following recommendations for multi-model inference (Burnham \u0026amp; Anderson, 2002; Katsanevakis \u0026amp; Maravelias, 2008). All growth models were calculated using the \u0026ldquo;nls\u0026rdquo; and \u0026ldquo;FSA\u0026rdquo; packages in R (Ogle et al., 2023) and fit to the data using nonlinear least square regressions. The first model was the three-parameter von Bertalanffy growth curve (von Bertalanffy, 1938) with the function:\u003c/p\u003e\n\u003cp\u003eL\u003csub\u003et\u0026nbsp;\u003c/sub\u003e= \u003cem\u003eL\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e [1 \u0026ndash; \u003cem\u003ee\u003c/em\u003e \u003csup\u003e-\u003cem\u003ek\u003c/em\u003e (t \u0026ndash; t\u003c/sup\u003e\u003csub\u003e0\u003c/sub\u003e\u003csup\u003e)\u003c/sup\u003e],\u003c/p\u003e\n\u003cp\u003ein which L\u003csub\u003et\u0026nbsp;\u003c/sub\u003eis the expected length at \u003cem\u003et\u0026nbsp;\u003c/em\u003e(time; month),\u003cem\u003e\u0026nbsp;L\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eis the theoretical maximum TL (in millimeters), \u003cem\u003ek\u003c/em\u003e is the growth coefficient (growth to\u003cem\u003e\u0026nbsp;L\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e), and t\u003csub\u003e0\u003c/sub\u003e is the hypothetical age (month) at which TL is zero. The logistic growth model (Ricker, 1979; Katsanevakis, 2006) was fit to the data using the function:\u003c/p\u003e\n\u003cp\u003eL\u003csub\u003et\u0026nbsp;\u003c/sub\u003e=\u003cem\u003e\u0026nbsp;L\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e / [1 + \u003cem\u003ee\u003c/em\u003e \u003csup\u003e-g (t \u0026ndash; t\u003c/sup\u003e\u003csub\u003e0\u003c/sub\u003e\u003csup\u003e)\u003c/sup\u003e],\u003c/p\u003e\n\u003cp\u003ewhere g is the relative growth rate parameter and t\u003csub\u003e0\u003c/sub\u003e is time (month) at which growth is maximized. The Gompertz model was evaluated using the equation:\u003c/p\u003e\n\u003cp\u003eL\u003csub\u003et\u0026nbsp;\u003c/sub\u003e=\u003cem\u003e\u0026nbsp;L\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e [\u003cem\u003ee\u003c/em\u003e \u003csup\u003e-\u003cem\u003ek\u003c/em\u003e exp(-gt)\u003c/sup\u003e],\u003c/p\u003e\n\u003cp\u003ein which g is the instantaneous growth rate and \u003cem\u003ek\u003c/em\u003e represents the initial relative growth rate at age-0 (Ricker, 1979). The final model fit was Richard\u0026rsquo;s growth model (Tjorve \u0026amp; Tjorve, 2010):\u003c/p\u003e\n\u003cp\u003eL\u003csub\u003et\u0026nbsp;\u003c/sub\u003e= \u003cem\u003eL\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e [1 \u0026ndash; \u003cem\u003eae\u003c/em\u003e \u003csup\u003e-\u003cem\u003ek\u003c/em\u003e (t)\u003c/sup\u003e]\u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e,\u003c/p\u003e\n\u003cp\u003ewith the slope at the inflection point (\u003cem\u003ek\u003c/em\u003e), and \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u0026nbsp;\u003c/em\u003erepresenting dimensionless parameters influencing the horizontal (age in months) and vertical positions (TL in millimeters) of the inflection point respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Best fit was determined by the lowest Akaike information criterion (AICc) once adjusted for possible small sample size bias (Akaike, 1973; Hurvich \u0026amp; Tsai, 1989). Highest Akaike weights were also considered to determine best fit (Burnham \u0026amp; Anderson, 2002). Once selected, the best model was separated into three regional growth curves termed the Upper Coast (Sabine Lake, Galveston Bay, and East Matagorda), Middle Coast (West Matagorda, San Antonio Bay, and Aransas Bay), and Lower Coast (Corpus Christi Bay and Upper and Lower Laguna Madre) to evaluate potential differences among regions. To specifically evaluate differences in growth\u003cem\u003e\u0026nbsp;\u003c/em\u003eamong regional groups, \u003cem\u003eL\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e, \u0026lsquo;\u003cem\u003ea\u0026rsquo;,\u0026nbsp;\u003c/em\u003eand \u0026lsquo;\u003cem\u003eb\u0026rsquo;\u0026nbsp;\u003c/em\u003ewere constrained to coastwide values and only the growth coefficient \u0026lsquo;\u003cem\u003ek\u0026rsquo;\u0026nbsp;\u003c/em\u003ewas allowed to vary.\u003c/p\u003e\n\u003cp\u003e\u0026lt;B\u0026gt; Analysis of residual variation\u003c/p\u003e\n\u003cp\u003eLength frequency growth estimations result in growth functions that cannot be anchored to an age-informed \u003cem\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e parameter, which may yield unstable growth parameters particularly through the bias of the growth parameter (\u003cem\u003ek\u003c/em\u003e) for one or more of the regional growth functions (Pauly, 1980b). Since this could impact direct growth comparisons, an independent approach was employed alongside growth models to confirm regional growth variation. To repeat the findings of the regional growth functions and validate statistically significant regional differences, the distribution of residual variation around the unified (coast-wide) growth function was examined. This method has the benefit of applying a single growth function to all observed specimens and using residual variation around the unified function to model spatial differences in observed size (and effectively, growth). Essentially, the bias associated with parameter estimation using this approach should impact all individuals equally, leading to higher confidence in spatial inferences than those that rely on regional model parameters.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Residual variation was obtained by subtracting model-fitted size predicted by the coast-wide growth model from observed size (mm TL), such that positive residuals represent individuals that were larger than predicted by the model, and negative residuals were smaller than predicted. Residuals were checked for normality, and the Analysis of Variance (ANOVA) was used to determine differences in mean residual variation among Upper, Middle, and Lower coast regions. Tukey\u0026rsquo;s honestly significant difference test (Tukey\u0026rsquo;s HSD; Tukey, 1949) was used \u003cem\u003epost hoc\u003c/em\u003e to compare pairwise individual means for each region.\u003c/p\u003e\n\u003cp\u003e\u0026lt;B\u0026gt;Statistical analyses\u003c/p\u003e\n\u003cp\u003eKruskal-Wallis tests were used to detect differences in water quality parameters among Upper, Middle, and Lower Coasts, and when significant (\u003cem\u003ea\u0026nbsp;\u003c/em\u003e= 0.05), post-hoc Dunn\u0026rsquo;s tests were used to determine which regions differed. Mean monthly growth (mean increase in TL per month for each region) was calculated for Atlantic Croaker coastwide and for each region separately to compare with mean monthly water quality parameters for each year (\u003cem\u003eN\u003c/em\u003e = 1218). A generalized additive model (GAM) was employed using the \u0026ldquo;mgcv\u0026rdquo; package in R (Wood, 2011) to determine the effects of salinity, water temperature, turbidity, dissolved oxygen, month, and year on growth. The GAM was selected over linear models due to nonlinearity detected in salinity, temperature, and turbidity and lower AIC values than the linear models. T-tests determined when monthly growth diverged among regions. Recent literature has suggested that salinity begins to increase growth after body sizes reach 71 mm SL (Lankford \u0026amp; Targett, 2021); thus, the percentage of Atlantic Croaker in this project above and below this size was calculated and compared in different salinity regions to evaluate differences in salinity effects on growth at different sizes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 503,316 Atlantic Croaker measured between 1990 and 2023, 435,640 juvenile Atlantic Croaker were included in the growth models once outliers from other yearly cohorts were removed, and YOY cohorts were clearly isolated within months. The TL ranged from 6 mm to 199 mm, with bag seines containing overall smaller Atlantic Croaker (mean \u0026plusmn; SD: 61.0 mm \u0026plusmn; 25.3 mm) than bay trawls (mean \u0026plusmn; SD: 112.8 mm \u0026plusmn; 30.4 mm). The growth model with the lowest AICc was Richard\u0026rsquo;s model, which also had the highest Akaike weight and was therefore selected as the best model (Table 1). The equation for the full model was L\u003csub\u003et\u0026nbsp;\u003c/sub\u003e= 153.4 [1 \u0026ndash; (3.2)\u003cem\u003e\u0026nbsp;e\u003c/em\u003e \u003csup\u003e-\u003cem\u003e0.337\u003c/em\u003e (t)\u003c/sup\u003e]\u003cem\u003e\u003csup\u003e1.57\u003c/sup\u003e\u003c/em\u003e. For the regional growth models in which \u0026lsquo;\u003cem\u003ek\u003c/em\u003e\u0026rsquo; was allowed to vary and other parameters were constrained to those from the coastwide model, growth differed among Upper, Middle, and Lower Coasts (\u003cem\u003ek\u0026nbsp;\u003c/em\u003e= 0.262, 0.340, 0.528, respectively; Fig. 3, Table 2). The growth coefficient (\u003cem\u003ek\u003c/em\u003e) increased notably from north to south with the Lower Coast \u003cem\u003ek\u0026nbsp;\u003c/em\u003ealmost twice as high as the Upper Coast.\u003c/p\u003e\n\u003cp\u003eThe ANOVA of residual variation around the coast-wide Richard\u0026rsquo;s growth function validated the findings from the regional growth functions. The model using region to predict residual growth around the coast-wide mean was highly significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,451854 \u0026nbsp;\u003c/sub\u003e= 15,329, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). The Lower Coast had a positive mean residual variation of 10.2 mm TL, the Middle Coast had a positive mean residual variation of 1.4 mm TL, and the Upper Coast had a negative mean residual variation of -6.5 mm TL (Fig. 4). All pairwise differences among regions were statistically significant based on Tukey\u0026rsquo;s HSD test.\u003c/p\u003e\n\u003cp\u003eKruskal-Wallis tests indicated there were differences in all water quality parameters among regions (all variables: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The only parameter between each region that was not significantly different was the Upper and Middle Coast water temperature (Dunn\u0026rsquo;s test: \u003cem\u003ez\u0026nbsp;\u003c/em\u003e= 1.11, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.27; Fig. 5). Mean growth in the GAM was significantly predicted by month, dissolved oxygen, and salinity (\u003cem\u003eedf\u003c/em\u003e: 7.38, \u003cem\u003eF\u003c/em\u003e = 8.34, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001; \u003cem\u003eedf\u003c/em\u003e: 1.00, \u003cem\u003eF\u003c/em\u003e = 12.77, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001; \u003cem\u003eedf\u003c/em\u003e: 4.02, \u003cem\u003eF\u003c/em\u003e = 6.17, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001, respectively). Growth was greatest in early months during spring, slowed in summer and was lowest in the final few months of the year. Dissolved oxygen predicted by the GAM exhibited a negative linear relationship with growth, while salinity demonstrated a strongly positive relationship with growth until salinity surpassed approximately 35 psu at which predicted growth notably decreased (Fig. 6). While demonstrating slight trends, temperature, turbidity, and year were non-significant in predicting mean monthly growth of YOY Atlantic Croaker (Fig. 6).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; When considering mean monthly growth, most juveniles (\u0026asymp; 70%) beneath the size threshold reported for positive salinity effects on growth (71 mm SL; Lankford \u0026amp; Targett, 2021) were observed between January and March (March mean TL: 64.5 mm). Mean monthly growth from January to March was notably less than growth in April (t-test: \u003cem\u003et\u003c/em\u003e = -8.07, \u003cem\u003edf\u003c/em\u003e = 217.0, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001), when the mean TL was above the 71 mm SL threshold (April mean TL: 84.1 mm; Table 3). Additionally, the Lower Coast had higher salinities from January through March than the Upper Coast (mean: 31.8 psu, 13.7 psu, respectively), but growth did not significantly differ between the two regions during this time (t-test: \u003cem\u003et\u003c/em\u003e = -1.42, \u003cem\u003edf\u003c/em\u003e = 118.3, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.16; Table 3). Growth did not significantly differ between Upper and Lower Coasts until April (t-test: \u003cem\u003et\u003c/em\u003e = -3.59, \u003cem\u003edf\u003c/em\u003e = 55.49, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) once mean TL had surpassed the 71 mm SL threshold suggested by Lankford and Targett (2021).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough otoliths are typically preferred over length frequencies for age estimation due to the broad range of lengths that can occur within a given age class, length-frequency data have been widely used to model fish growth in the absence of age data (Schnute \u0026amp; Fournier, 1980; Pauly, 1980a). More recent approaches have used fishery-dependent length data to model lifetime growth in both short and long-lived species (Laslett et al., 2004; Froese et al., 2018). Additionally, Atlantic Croaker spawning is largely restricted to a specific window (October\u0026ndash;December; White \u0026amp; Chittenden, 1977), suggesting that cohort variation within each monthly sample is likely limited to adjacent months. Given that age comparisons in this study are restricted to within the same dataset, supported by a large sample size, and overall findings are corroborated by a secondary analysis of regional residual variation, the observed growth patterns likely reflect true regional differences despite the inevitable inclusion of other cohorts within a given month.\u003c/p\u003e\n\u003cp\u003eThe selection of Richard\u0026rsquo;s model for juvenile Atlantic Croaker is supported by previous studies that describe Richard\u0026rsquo;s model as effective for modeling species with two-stanza growth (typically observed in long-lived fishes, with a fast growth rate early in life followed by slower and steadier growth in later years; Pacicco et al., 2021; Banks et al., 2024). The inclusion of the extra shape parameters (\u003cem\u003ea\u0026nbsp;\u003c/em\u003eand \u003cem\u003eb\u003c/em\u003e) allows greater flexibility, meaning changes in growth rate throughout a species\u0026rsquo; life (or in this case, changes during the first year of life) can be modeled more effectively (Katsanevakis \u0026amp; Maravelias, 2008; Banks et al., 2024). In this study, fitting standard growth functions to juvenile fishes using monthly length-frequency data allowed for observations of seasonal changes in growth, including the expectation that growth slows for many species at the onset of fall and winter and accelerates in the spring and summer. \u0026nbsp;Thus, the flexibility of the Richard\u0026rsquo;s model may apply more broadly to juvenile fishes that are expected to transition between routine and compensatory growth in response to seasonal changes in resource availability (Schultz et al., 2002).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Juvenile Atlantic Croaker growth was best modeled using Richard\u0026rsquo;s growth model with the equation L\u003csub\u003et\u0026nbsp;\u003c/sub\u003e= 153.4 [1 \u0026ndash; (3.2)\u003cem\u003e\u0026nbsp;e\u003c/em\u003e \u003csup\u003e-\u003cem\u003e0.337\u003c/em\u003e (t)\u003c/sup\u003e]\u003cem\u003e\u003csup\u003e1.57\u0026nbsp;\u003c/sup\u003e\u003c/em\u003e. The \u003cem\u003eL\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evalue (maximum theoretical size; 153.4 mm) was lower than GOM studies modelling adult growth, as expected due to younger ages (Barger, 1985; \u003cem\u003eL\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e= 419 mm). The growth coefficient (\u003cem\u003ek\u0026nbsp;\u003c/em\u003e= 0.337) is similar to adult Atlantic Croaker studies throughout the GOM and Atlantic coast and supports the idea that most growth occurs before individuals reach age 1 (Barger, 1985; Barbieri et al., 1994b; \u003cem\u003ek\u0026nbsp;\u003c/em\u003e= 0.27 and 0.36, respectively). Comparisons of this data to other juvenile growth curves is impractical because most other studies used otoliths\u0026nbsp;and days as age within a short time frame instead of months throughout the full first year. Additionally, other juvenile growth studies selected linear, von Bertalanffy, or Laird-Gompertz models, making direct comparisons to Richard\u0026rsquo;s model impractical (Warlen, 1980; Cowan, 1988). There is an importance in noting possible selection bias within this project, since the removal of all Atlantic Croaker greater than 200 mm and other steps applied to extract YOY fish may result in underestimations of growth parameters and completely isolating each monthly cohort is impractical using only length frequencies.\u0026nbsp;However, the large sample size (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 435,640) and meticulous cohort removal likely mitigates most biases and prevents the alteration of overall trends, which was further supported by the residual variation analysis that demonstrated distinct regional growth variation. Constraining all parameters in the regional models except \u0026lsquo;\u003cem\u003ek\u003c/em\u003e\u0026rsquo; allowed for a more direct comparison of growth among regions and supported the trends observed in the residual analysis with growth rate increasing when moving from the northernmost region to the southernmost region, suggesting the importance of the changing environmental gradient. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The GAM suggested the most influential parameters on growth were month, dissolved oxygen, and salinity. The importance of month is likely due to seasonal variation in growth, considering most Atlantic Croaker growth occurs during the first few months while within estuarine nurseries (Barger, 1985, GSMFC, 2017). Greatest monthly growth was observed during spring (March \u0026ndash; May; Table 3). Spring typically exhibits high productivity in estuaries of the GOM, and growth in Atlantic Croaker is likely maximized during this time to provide a buffer for juveniles against predation and the environmental fluctuations that are characteristic of estuarine habitats (Sogard 1992, Schultz et al., 2002). While month was likely important due to seasonality and ontogeny, temperature was not significant for predicting spatial differences in growth within the model. However, higher temperatures are often correlated with increased growth and metabolism in Sciaenidae (Lanier \u0026amp; Scharf, 2007; Diamond et al., 2013; Williford \u0026amp; Anderson, 2025) and may contribute to the observed seasonal variation in growth. Additionally, temperature and growth followed similar latitudinal trends that, while not statistically significant, may suggest some physiological relevance of temperature in driving juvenile growth rate in this species.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dissolved oxygen was also found to be an important predictor of growth, with higher oxygen concentrations leading to lower growth rates. Although Atlantic Croaker often inhabit low-oxygen benthic habitats, the impact of dissolved oxygen on growth is not well understood (Diaz \u0026amp; Onuf, 1985). Dissolved oxygen displayed a negative relationship with salinity and water temperature, suggesting dissolved oxygen may be more correlated with salinity and seasonal variation than growth. This implies that dissolved oxygen\u0026rsquo;s significance in the model could simply be driven by interactive effects However, research has suggested many Atlantic Croaker spend their first year of life in low levels of dissolved oxygen and have demonstrated resilience against hypoxic waters. Dissolved oxygen did not often reach such low levels within this study, which may indicate a potential underestimation of oxygen effects in this model (Valenza et al., 2023). Additionally, there has been some speculation that Atlantic Croaker resilience to low dissolved oxygen may enhance foraging opportunities on environmentally stressed prey which may in turn increase growth of Atlantic Croaker in high salinity regions (Valenza et al., 2023). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The clear increase in the growth coefficient (\u003cem\u003ek\u003c/em\u003e) from north to south (Table 2) suggests growth to maximum theoretical size (\u003cem\u003eL\u003cstrong\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003eis significantly faster in Lower Coast bays than the Upper Coast. Trends of increasing water temperature and salinity followed a highly similar latitudinal gradient from north to south to increasing growth while turbidity decreased from north to south. Of these, only salinity was significant and demonstrated a positive relationship with YOY growth. \u0026nbsp;Previous studies have described the opposite trend, with early-stage juveniles showing better growth in low salinity compared to high salinity (Peterson et al., 1999; Kupchik \u0026amp; Shaw, 2016). However, the early-stage juveniles in the lab study of Peterson et al. (1999) were between 10-20 mm TL (\u0026lt; 0.5 years old), whereas the juveniles in the current study ranged from 6-199 mm TL and encompassed a wider range of juvenile ages (1-12 months). While Kupchik and Shaw (2016) reported that Atlantic Croaker growth increased when recruits encountered low-salinity habitats, this finding could simply be driven by the fact that early recruits seek out low-salinity habitats in the upper estuary that confer some nursery advantage, as individuals in that study were all less than 80 days old.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;More recent evidence indicates juveniles between 56 \u0026ndash; 70 mm SL prefer and grow faster in low salinities (\u0026lt;10 ppt), but juveniles between 71\u0026ndash; 85 mm SL tend to prefer and grow faster in higher salinities (\u0026gt;18 ppt; Lankford \u0026amp; Targett, 2021). While no studies were found describing salinity effects on growth for juveniles above 85 mm SL, approximately 73.5% of juvenile Atlantic Croaker within this project were above the 71 mm SL threshold described by Lankford and Targett (2021). Therefore, these larger juveniles may exhibit faster growth in the Lower Coast compared to the Upper Coast due to the higher salinities further south. This study, in comparison to previous studies using post-larval sized Atlantic Croaker, highlights the benefit of assessing juvenile growth over an annual scale and underscores the notion that growth rates in juvenile fishes are driven by complex, stage-specific environmental preferences that can be difficult to tease apart.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Similar growth between Upper and Lower Coasts from January to March despite the large salinity difference (mean salinity: 13.7 psu, 31.8 psu, respectively) provides further evidence that salinity does not increase growth until juvenile Atlantic Croaker have surpassed a certain size (estimated 71 mm SL; Lankford \u0026amp; Targett, 2021). Salinity tolerances of juvenile Atlantic Croaker are known to increase with size during the emigration from low salinity estuarine nurseries to higher salinity bays during the juvenile stage (Haven, 1957; Yakupzack et al., 1977; Diaz \u0026amp; Onuf, 1985). High salinity may be metabolically costly to withstand for larvae and early-stage juveniles, but as size and salinity tolerance increase, energy is likely redirected from osmoregulation to growth (B\u0026oelig;uf \u0026amp; Payan, 2001; Schultz et al., 2002; Semra et al., 2013). This may drive juveniles in high salinities (e.g., Lower Coast bays) to favor compensatory growth early in the year to reach salinity tolerant sizes quickly during high productivity in spring (Table 3). Conversely, juveniles in low salinities (e.g., Upper Coast bays) likely spend less energy on osmoregulation and do not need to reach large sizes as quickly to withstand salinity; therefore, juveniles in low salinity are likely able to allocate energy to growth more consistently throughout the year.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Another explanation for increased growth in high salinities may be a competitive advantage over less salinity tolerant estuarine species. In larval and early-stage juveniles, growth is likely optimized in upper-estuary nursery habitats with low salinity, but as fish reach larger sizes, increased predator avoidance and access to larger food items might drive Atlantic Croaker into higher-salinity bays. The benefit of fewer predators and larger food as a product of increased salinity tolerance may provide a competitive advantage over other, less salinity tolerant mesopredators in bays with more extreme salinity (e.g., Lower Coast bays). This may explain the apparent selection for juvenile Atlantic Croaker that reach large sizes faster in high salinity. Maximizing growth within the first year increases fecundity, survivability, and overall reproductive success (Sogard, 1997), which further supports the idea that greater growth in high salinity is driven by selection. This inference is also supported by the finding that a majority of age-1 Atlantic Croaker in Texas are sexually mature (Anderson et al., 2018), meaning environmental interactions that drive growth in year one of this species may have downstream impacts on survival and lifetime reproductive success.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The findings of this project provide valuable and previously understudied insights into the growth of juvenile Atlantic Croaker, which have important implications for fisheries management. While Atlantic Croaker populations currently appear stable, their significance to both commercial and recreational fisheries highlights the need for a deeper understanding of their life history. Though limited to the Texas coast, the observations in this study describe environmental influences on juvenile Atlantic Croaker that may be observed throughout their entire range. Physiological processes and environmental influences throughout the juvenile stage determine the success of annual cohort recruitment and are therefore critical to the health and persistence of the fishery. Numerous studies report that juvenile mortality of marine finfish is highly indicative of year class strength, and faster growth typically increases overall survivability, further supporting the importance of understanding juvenile growth for the sake of effective management (Steele, 1997; Fromentin et al., 2001; Shima, 2001). As waters throughout the GOM continue to warm (NOAA, 2024), effectively assessing and understanding juvenile growth will be a major component of managing Atlantic Croaker fisheries throughout their entire distribution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIsabelle Cummings implemented and evaluated data analyses, interpreted data, wrote the final work, and assisted with the initial project conceptualization. Joel Anderson conceived the initial study idea and assisted in data analyses, method design, data interpretation and editing.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFish collection and handling protocols followed the ethical guidelines described by a Federal Aid in Sport Fish Restoration grant agreement (TPWD TX-F-281-M) as well as a federal permit for the handling of endangered and threatened species issued by the U.S. Department of the Interior (Permit number TE814933-0).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge and offer special thanks to all the Texas Parks and Wildlife employees who performed the fieldwork and collected the data used in this study. We would also like to thank the anonymous reviewers who provided their insights to improve the manuscript.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eAkaike, H. (1973). Information theory and the extension of the maximum likelihood principle. In B. N. Petrov \u0026amp; F. Csaki (Eds.), \u003cem\u003eInternational symposium on information theory\u003c/em\u003e (pp. 267\u0026ndash;281). Academiai Kaido.\u003c/li\u003e\n\u003cli\u003eAnderson, J., McDonald, D., Bumguardner, B., Olsen, Z., \u0026amp; Ferguson, J. W. (2018). Patterns of Maturity, Seasonal Migration, and Spawning of Atlantic Croaker in the Western Gulf of Mexico. \u003cem\u003eGulf of Mexico Science,\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e (1). https://doi.org/10.18785/goms.3401.03\u003c/li\u003e\n\u003cli\u003eBanks, K. G., Streich, M. K., \u0026amp; Stunz, G.W. (2024). 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Emigration of Juvenile Atlantic Croakers, \u003cem\u003eMicropogon undulatus\u003c/em\u003e, from a Semi‐impounded Marsh in Southwestern Louisiana. \u003cem\u003eTransactions of the American Fisheries Society\u003c/em\u003e, \u003cstrong\u003e106\u003c/strong\u003e (6), 538-544. https://doi.org/10.1577/1548-8659(1977)106\u0026lt;538:EOJACM\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Texas Parks and Wildlife Department","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Croaker, Growth, Habitat, Biology","lastPublishedDoi":"10.21203/rs.3.rs-8116250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8116250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatial and environmental variation in finfish growth has important implications for fisheries management. Atlantic Croaker (\u003cem\u003eMicropogonias undulatus\u003c/em\u003e) is a valuable sportfish and baitfish throughout the Gulf of Mexico and U.S. western Atlantic coast; however, growth throughout the juvenile stage, when most growth occurs, is largely understudied in the Western Gulf of Mexico. Therefore, this study aimed to model Atlantic Croaker young-of-the-year growth, determine spatial growth variation along the Texas coast, and evaluate environmental influences on growth rate. Length frequency data for juvenile Atlantic Croaker was collected from all major Texas bays as part of the Texas Parks and Wildlife Department’s long-term fishery-independent monitoring program from 1990-2023. Multiple growth models were evaluated to compare juvenile growth curves among regions, and generalized additive models assessed the influence of salinity, temperature, turbidity, and dissolved oxygen on growth. Richard’s model provided the best fit for juvenile growth and described clear spatial differences in growth among bays. Growth rate increased substantially from north to south, following trends of increasing salinity and temperature and decreasing turbidity and dissolved oxygen. Salinity and dissolved oxygen were significant predictors of growth in the generalized additive model, with higher salinity promoting faster growth in larger juveniles (approximately ≥ 70 mm). These findings suggest salinity plays an important role in the growth of juvenile Atlantic Croaker and likely contributes to spatial variation in growth along the Texas coast. As anthropogenic effects continuously alter estuarine conditions throughout the Gulf of Mexico, understanding juvenile growth dynamics will be essential for effective management.\u003c/p\u003e","manuscriptTitle":"Environmental influences on juvenile Atlantic Croaker (Micropogonias undulatus) growth in the Western Gulf of Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:51:27","doi":"10.21203/rs.3.rs-8116250/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24693795-8497-4aad-8dc5-1f0646c010e7","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58086251,"name":"Marine and Freshwater Ecology"},{"id":58086252,"name":"Marine and Freshwater Biology"}],"tags":[],"updatedAt":"2025-11-17T06:51:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 06:51:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8116250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8116250","identity":"rs-8116250","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}