Larval Dispersal of Gray Snapper (Lutjanus griseus) on the West Florida Shelf

Abstract

Gray snapper (Lutjanus griseus) move from inshore to offshore habitats as they mature and spawn along the West Florida Shelf. The connectivity between offshore spawning sites and inshore settlement regions along the Eastern Gulf of America (formerly Gulf of Mexico, hereafter Gulf) coast is unknown, and this study therefore predicted these larval dispersal pathways. To determine larval transport, an ocean model was integrated with the Connectivity Modeling System (CMS), which is a biophysical model that allowed for the inclusion of larval behavior and updated spawning information for the gray snapper. Our larval dispersal model showed that spawning sites offshore of Tampa, in the Florida Keys, and in the Florida Middle Grounds had the highest percentages of successfully settled larvae inshore. Larvae that were spawned at the offshore Tampa Bay and offshore Southwest Florida spawning sites were mostly transported to the Tampa Bay and Southwest Florida settlement regions, showing local retention. In contrast, larvae spawned offshore in the Florida Middle Grounds were transported northwest, exclusively to the Florida Panhandle. In addition, there was no difference in the proportion of successful larval settlers between full and new moon spawning events. Since gray snapper are an important recreational fishery in the eastern Gulf, especially off the west coast of Florida, it is important to identify spawning sites that have the largest proportions of settling larvae, such as offshore Tampa Bay.

1. Introduction

The gray snapper (Lutjanus griseus), also known as the mangrove snapper, constitutes a large recreational fishery in the Southeastern USA, with ~60% of its recreational landings coming from the west coast of Florida in the Gulf of America (formerly Gulf of Mexico, and hereafter Gulf) [1]. Large, mature gray snapper usually spawn in offshore habitats, whereas juvenile gray snapper are found exclusively in a variety of inshore habitats, including polyhaline seagrass beds, mangroves, and estuaries [2,3,4,5,6]. Juveniles remain in these habitats until they mature, at which time they are known to undertake an ontogenetic movement offshore [2,7] to natural and artificial reefs, oil platforms, and other hard-bottom structures, where they spawn [3,8,9,10]. The spawning season for gray snapper in the Gulf ranges from late May to September, with a peak in the summer months of June, July, and August [8,11,12,13]. Eggs/larvae spawned at these offshore sites then disperse across the West Florida Shelf (WFS) into inshore habitats, where the larvae settle and grow. Despite research focusing on inshore juvenile gray snapper in this area [6,8], there has not been any research regarding which offshore spawning sites contribute larvae to the inshore regions.

The WFS is a wide shelf that extends up to 200 km offshore to the shelf break [14], where the Loop Current (LC), West Florida Estuarine Waters (WFEW), and Florida Bay Waters (FBW) all mix [15] (Figure 1). The circulation of the WFS is driven by winds and heat fluxes [16]. The circulation on the WFS is upwelling-favorable at a long-term average [17]. However, during the summer months that encompass gray snapper spawning, i.e., June to September, the WFS transitions to a downwelling-favorable area [18], which leads to flow on the inner area of the WFS in a northern direction [19]. In conjunction with this downwelling, there is less shoreward-directed transport near the bottom [20], which may limit larval transport inshore. Additionally, during the summer, the LC, an anti-cyclonical loop (i.e., turns clockwise), is the main driving force of the WFS’ circulation [15], but wind-driven flows still dictate the direction of flow on the inner shelf [19]. The LC originates in the Yucatan Channel and exits into the Strait of Florida, where it feeds into the Gulf Stream in the Atlantic Ocean [14,21] (Figure 1). The LC sheds eddies into the WFS [22], which influences larval dispersal on spawning sites closer to the shelf break, such as the Florida Middle Grounds (FMG) [23]. The FMG has been designated as a “habitat area of particular concern” because of its multispecies aggregations [24]. It is a high-relief, live-bottom habitat (i.e., bottom covered with a high density of benthic organisms), located ~130 km off the west coast of Florida, and one of the few areas where gray snapper are known to form spawning aggregations [13,25].Larval dispersal modeling uses a combination of physical and biological processes [26,27]. However, only 61% of larval dispersal studies from 2009 to 2017 included any larval behavior [28], and there has been a call for more biological traits to be included in larval dispersal studies [27,28]. For instance, including larval behavior in a biophysical model resulted in reduced dispersal distances and increased settlement [27]. The Connectivity Modeling System (CMS) is an individual-based Lagrangian model that incorporates both physical and biological processes [29]. The CMS is the most common particle-tracking biophysical model used [28], and it has been applied with a variety of different ocean circulation models to estimate the larval dispersal of gray snapper spawning on the Cuban Shelf [30,31,32] and other commercially important species in the Gulf, such as red snapper (Lutjanus campechanus) [33,34], red grouper (Epinephelus morio) [35], and blue crab (Callinectes sapidus) [36].

Adult spawning behavior can also affect larval dispersal. For example, many snapper species only spawn around specific lunar phases, such as the mutton snapper (Lutjanus analis), aggregating on the full moon and spawning ~1–5 days later [38]. For the gray snapper, however, previous studies have offered conflicting results on which lunar cycle cues them to spawn, with some indicating the influence of the full moon [2,12], the new moon [5,11,39], or no lunar pattern [8]. As with other fish species, knowledge of spawning aggregation sites and lunar periodicity is important because the predictability in the location and timing of the aggregations can also render them more susceptible to over-exploitation [40].

The overall goal of this study was therefore to (1) estimate the larval dispersal of gray snapper spawning in aggregations on the West Florida Shelf using the Connectivity Modeling System; (2) highlight any areas of high connectivity between offshore spawning sites and inshore settlement regions; and (3) determine any difference in the settlement success of larvae dispersed in full and new moon spawning events.

2. Materials and Methods

2.1. Biophysical Model

The CMS is a multi-scaled biophysical model that combines an individual-based model with a Lagrangian framework [29]. The CMS determines the movement of particles (e.g., eggs/larvae) using 3D ocean velocity fields, such as the zonal component (x-direction), meridional component (y-direction), and vertical component (z-direction). Using these three components, the distance traveled per user-specified time step is calculated using a 4th-order Runge–Kutta scheme [29]. The value of 15 m² s⁻¹ was used for horizontal diffusivity due to the resolution of the hydrodynamic model [41], which allows the larvae to move following the random displacement method [42]. Vertical diffusivity was not included in the biophysical model; however, the CMS has optional modules that allow for the inclusion of complex larval behavior, such as ontogenetic vertical migration [29], which was incorporated into our model. In addition, the optional orientation module (i.e., horizontal swimming behavior) was unavailable during the time of the study.

2.2. Hydrodynamic Model

The Hybrid Coordinate Ocean Model (HYCOM), specifically the 1/25 ° HYCOM Gulf of Mexico Reanalysis model, with the Tendral Statistical Interpolation (T-SIS) package [43] (www.tendral.com/tsis; accessed through HYCOM on 28 August 2024), was used for this study (www.hycom.org; accessed on 28 August 2024). HYCOM is a generalized hybrid isopycnal coordinate model as it is isopycnal (density-layered) in the open ocean and transitions to a terrain-following coordinate in shallow coastal regions, which allows for the simultaneous modeling of both open and coastal oceans. The TPXO Atlas Model was used to apply tidal forcing at the surface [44,45]. The resolution of this model is ~4 km for each variable, and it has 41 coordinate surfaces. Data were used from 2021 to 2023 for the months of June to early October, at an hourly frequency, with coordinate surfaces from 0 to 80 m (16 layers). The eastward velocity and northward velocity were extracted from an Open-Source Project for a Network Data Access Protocol (OPeNDAP) server using the CMS. The eastward and northward velocities from each layer were integrated into the CMS as the zonal and meridional components to predict larval movement.

2.3. Spawning Sites

Gray snapper spawning sites were identified from three sources: (1) a reproductive study within the same sample area [13]; (2) the Gulf Fishery Independent Survey of Habitat and Ecosystem Resources (G-FISHER) program [46]; and (3) a known spawning location in the Florida Keys [47].

Spawning sites identified from [13] included locations that were known to have actively spawning gray snapper females. This resulted in 16 release points identified, ranging from latitudes of 27.6° N to 28.5° N within the “Florida Middle Grounds”, “The Elbow”, and the offshore waters of Tampa Bay (Figure 2, grids 1–15, with 2 spawning sites within grid 11).

To expand the study to include other potential spawning aggregation sites, data for gray snapper from the G-FISHER survey on the WFS [46] during June, July, and August within the years 2017–2022 were used. The G-FISHER survey is a stereo-baited remote underwater video array that is deployed at hard-bottom sites in the Gulf, which are selected using a depth-stratified random design [48]. The abundance of gray snapper at a G-FISHER survey site was based on the maximum number of individuals observed in a single video frame (MaxN) during 20 min of video. This abundance measure (MaxN) can be used as an index of the relative abundance of gray snapper among multiple sites [49,50].

Out of the 4218 G-FISHER samples available from the WFS, 1009 had a positive MaxN value for gray snapper, while the rest were zeros; most sites had a MaxN of <20 (Figure S1). Therefore, the MaxN values of gray snapper that were at the right tail end of the positive MaxN distribution were chosen as probable aggregations, since they were a magnitude higher than the average MaxN value of 6. These sites had their associated fish lengths examined, and only sites with a median length above 50% maturity (301 mm TL) [13] were chosen as probable summer spawning aggregations. Using this methodology, an additional three release points were identified at latitudes ranging from 25.6° N to 27.3° N (areas offshore of Sarasota Bay, Charlotte Harbor, and Southwest Florida) (Figure 2, grids 16–18). The potential spawning site offshore of Southwest Florida (grid 18) was later sampled during the 2024 spawning season, and one female with hydrated oocytes was collected (pers. obs.), thus confirming that spawning occurred at this location.

Lastly, the Western Dry Rocks, an area that is ~16 km southwest of Key West, was included as a spawning site (Figure 2, grid 19). Gray snapper have been observed in spawning aggregations at this location, along with mutton snapper [47]. This area has a seasonal closure from April 1st to July 31st, which is within the spawning season of gray snapper. The combination of all three methods to identify known and potential spawning aggregations for gray snapper resulted in 20 spawning sites.

The release depth for every release location was derived from the bathymetry of the HYCOM reanalysis model. Since the gray snapper is a demersal reef fish and there are no observations of spawning at the surface [2,11], all release depths were set 10 m above the seafloor to prevent any particles (i.e., eggs/larvae) from being trapped under the seafloor due to the changing bathymetry surrounding the release points.

2.4. Release Frequency

Spawning events for this study were limited to the peak spawning period of gray snapper, which is within the summer months of June, July, and August [8,11,12,13]. The spawning frequency from [13], which was a concurrent study within the same area, was used to determine the release intervals for this study. To capture the increased spawning activity throughout the peak spawning season, the release frequency changed, being every 16 days in June, every 3 days in July, and every 6 days in August [13]. This resulted in a total of 18 releases within a spawning season.

To determine whether there was a difference in the dispersal and settlement of gray snapper larvae if females spawned only during the full moon [2] versus only during the new moon [11,39,51], spawning was limited to the new and full moon periods only from June to August in the years 2021–2023. Each lunar phase was given 3 days (the day before, day of, and day after) of spawning. The release abundance and locations stayed the same in both scenarios.

2.5. Egg Release Abundance

To weight the number of eggs released at each spawning site based on an index of the relative abundance of spawning fish, the relative abundance of gray snapper at all egg release sites was estimated using the MaxN from G-FISHER samples. The spawning locations were matched with the G-FISHER sampling sites, and the highest MaxN was designated as the release site’s abundance estimate. However, due to the limited number of G-FISHER samples within the “Elbow” site, a search radius of 14 km was needed to obtain a positive abundance of gray snapper. Every spawning/egg release site therefore used a 14 km radius, and the G-FISHER sample with the highest MaxN was designated as the release site’s abundance estimate.

The abundance estimates of individual sites were then standardized by dividing the MaxN of each site (${MaxN}_i$) by the average MaxN of all sites ($\overline{{MaxN}_{ij}}$) (Equation (1)). This standardized value was then multiplied by 100 eggs/larvae to determine the absolute number of particles (i.e., eggs/larvae) released at each site for each spawning event:

(1)$\mathrm{Standardized} \mathrm{relative} \mathrm{abundance}={\displaystyle \frac{{MaxN}_i}{\overline{{MaxN}_{ij}}}}100$

The 100 eggs/larvae multiplier was determined using a sensitivity analysis that was conducted to determine the lowest number of eggs that needed to be released at every spawning site to capture a consistent settling proportion. All release sites were assigned the same egg release abundance of 50 or 100 or 200 in the year 2021. The abundances of 100 and 200 had the same settling percentages, which resulted in 100 eggs/larvae being chosen to save computational time.

Due to the absence of any G-FISHER samples for the Western Dry Rocks (Florida Keys) site, but with a known large spawning aggregation, the abundance estimate assigned was equal to the MaxN of offshore Charlotte Harbor (120), which was the highest MaxN among the G-FISHER sampling sites for gray snapper. This relative abundance weighting factor led to a range of eggs released per spawn of 5–308 eggs, with the Elbow site having the lowest and offshore Charlotte Harbor/Dry Rocks having the highest (Table 1). The total eggs released per spawning season was calculated by the number of eggs released per spawning event multiplied by the number of spawning events (Table 1).

2.6. Larval Behavior

The larval behavior incorporated into this model was derived from a literature review and incorporated as a probability matrix. The time steps of the probability matrix were defined using the different stages of larval development, including hatching at 0–1 days, pre-flexion at 2–9 days, flexion at 10–14 days, and post-flexion at 15–24 days [52] and settlement from 24 to 42 days (Figure 3). The probability matrix of the ontogenetic vertical migration of gray snapper detailed in Karnauskas et al. [33,41] was used for this study, which was estimated from ichthyoplankton observations from SEAMAP surveys [53]. In addition, the larval stage durations and the depth distribution for the larval settlement phase given for red snapper in Karnauskas et al. [33,41] were readjusted for gray snapper specifically (Figure 3). The simulations in [33] determined that the inclusion of the hatching stage (i.e., 0–1 days) had a negligible effect on dispersal and was omitted; therefore, the hatching stage was also omitted from this study. The pelagic larval duration of gray snapper is 24–42 days [5,8,54]; therefore, larvae were not able to settle before day 24 of the simulation, and those larvae that did not settle by day 42 were deemed unsuccessful.

Table 1

Description of G-FISHER gra snapper abundance (MaxN) and release values related to release sites used in this study.

Location Name	Grid #	G-FISHER Collection Year	G-FISHER MaxN	Eggs Released Per Spawning Event	Total Eggs Released PerSpawning Season
Middle Grounds	1	2020	45	116	2088
Middle Grounds	2	2020	45	116	2088
Middle Grounds	3	2020	28	72	1296
Middle Grounds	4	2021	5	13	234
Middle Grounds	5	2020	14	36	648
Middle Grounds	6	2020	20	51	918
Middle Grounds	7	2017	33	85	1530
Middle Grounds	8	2022	5	13	234
Middle Grounds	9	2017	33	85	1530
The Elbow	10	2020	3	8	144
The Elbow	11	2020	17	44	792
The Elbow	11	2020	17	44	792
The Elbow	12	2022	2	5	90
The Elbow	13	2020	9	23	414
The Elbow	14	2020	9	23	414
Offshore Tampa Bay	15	2021	70	180	3240
Offshore Charlotte Harbor	16	2021	120	308	5544
Offshore Sarasota Bay	17	2022	107	275	4950
Offshore Southwest Florida	18	2019	76	195	3510
Florida Keys (Dry Rocks)	19	N/A	120 a	308	5544

^a Used Charlotte Harbor estimate as the G-FISHER abundance value due to no G-FISHER samples in the Florida Keys.

2.7. Settlement Habitat

The gray snapper has a wide range of settlement habitats, from seagrasses to mangroves, and is euryhaline [2,3,7]. To capture the settlement habitats of the gray snapper, the coasts of Florida, Alabama, Mississippi, and Louisiana were gridded into 5 km × 5 km habitat polygons that had depths of 2–20 m using ArcGIS Pro 3.1.1 (Figure 4). Bathymetry data from the HYCOM reanalysis model were used to determine settlement habitats. This depth range was chosen as it contained depths suitable for all inshore habitats and it allowed for the settlement of larvae from all release sites. Similar simulations in the region using HYCOM also used deeper settlement habitat criteria (e.g., [35]). This was mainly due to the slow current speeds produced by the HYCOM reanalysis model on the WFS, which were near 0.0 m/s, compared to the speeds near the LC being up to 1.65 m/s.

The settlement habitat polygons were then assigned into their respective geographical regions of Louisiana, Alabama, Mississippi, the Florida Panhandle, Cedar Key, Tampa Bay, Sarasota Bay, Charlotte Harbor, Southwest Florida, the Florida Keys, and Eastern Florida (Figure 4). Different subsections of the settlement habitats within the state of Florida were divided based on FWRI inshore sampling areas [46].

2.8. Post-Processing Analysis

All post-processing analyses on the outputs from the CMS were computed using the R statistical software [55]. A connectivity matrix was used to determine the connectivity between spawning sites (sources) and settlement regions (sinks). The CMS saved the information on successfully settling larvae (hereafter “settlers”), which was used to create a connectivity matrix (C_ij), where the rows represented the spawning sites (i) and the columns represented the settlement regions (j). Connecting nodes with higher values indicated higher connectivity between the spawning sites and settlement regions.

To estimate the probability of settlers from a spawning site i reaching a settlement region j (P_ij), the connectivity matrix was normalized by column (i.e., by the total number of settlers from each spawning site i, where the sum of each column is 1) [34,56]. The main diagonal of the matrix represents the probability that larvae settled within the same settlement region where they were spawned [56].

To estimate the relative contribution of each spawning site i for each settlement region j (P_ji), the connectivity matrix was normalized by row (i.e., by the total number of settlers by each settlement region j, where the sum of each row is 1) [34]. The main diagonal of this matrix shows the probability of self-recruitment or the settlement of larvae from local spawning sites. This indicates whether a settlement region is reliant on larvae from spawning sites that are not locally sourced.

A one-way analysis of variance (ANOVA) [55] was used to determine whether there were any differences in the proportion of successful settlers among the spawning sites for the 3 years used in the study. The data were arc-sin-transformed and the residuals of the one-way ANOVA were tested for normality using a Shapiro–Wilk test [55] and for homoscedasticity using Levene’s test (R package car, Vers. 3.1-1; [57]). Student–Newman–Keuls (SNK) pairwise tests were used post hoc to test for any differences within the main effects of the spawning site (R package agricolae, Vers. 1.3-7; [58]).

Differences in settlement age as a function of the spawning site or settlement region were tested using a Kruskal–Wallis test [55] since the data failed to demonstrate normality. Differences in the settlement age within the main effect of the spawning site or settlement region were tested post hoc using Dunn’s test (R package FSA, Vers. 0.9.5; [59]). In addition, differences in the distance traveled by larvae among the spawning sites were also tested using a Kruskal–Wallis test, followed by Dunn’s test.

A two-sample t-test was used to determine any differences in the proportion of successful settlers that were spawned on either the new moon or the full moon for the 3 years used in the study. The data were tested for normality using the Shapiro–Wilk test and homogeneity of variance using the F-test [55].

2.9. Model Validation

To validate the results from the CMS model, estimates of the proportion of larvae settling in the various coastal settlement regions were compared to indices of the age-0 and age-1 gray snapper abundance obtained from FWRI’s Fisheries-Independent Monitoring (FIM) sampling program. Data were available from the following estuaries: Apalachicola Bay, Cedar Key, Tampa Bay, and Charlotte Harbor. The FIM program collected gray snapper using a 21.3 m center-bag seine, a 183 m center-bag seine, and a 6.1 m otter trawl. The effort of gear types was converted to a sampling area of 100 m². All sets of gears were included from the years 2017 to 2022. This period was chosen to capture trends occurring during the larval dispersal model’s timeframe; data for 2023 and 2024 were not available for this study.

The catch per unit effort (CPUE) for each estuary was standardized by the gear type used for collection, temperature, salinity, average depth, and different time interactions. Due to the zero-inflated nature of the data, a suite of CPUE standardization models was used, including zero-inflated models and delta-log hurdle models using the R package glmmTMB (Vers. 1.18; [60]). A delta-log hurdle model is a two-part model that specifies one model to predict the number of zeros (“Zero”), while the other model predicts the positive (non-zero) component (“Positive”). Then, the two models are combined and use the specified covariates to predict values. The best model chosen was the one with the lowest Akaike Information Criterion (AIC) score for both the “Positive” and “Zero” models within the delta-hurdle model [61], with models within a delta AIC of 2 being considered equally viable. Based on these AIC criteria, the delta-log hurdle model with a year–month-structured random effect was the model that best fit the abundance data (Table 2). Including random effects and a monthly interaction helped to lower the AIC score for the binomial part (Zero) of the delta-log hurdle model. This was due to the increase in the catch of age-0 gray snapper in the fall/winter (Figure 5). This could have been due to settled age-0 larvae being more susceptible to capture in the sampling gear.

3. Results

3.1. Differences in the Loop Current Among Years

The Loop Current intrusion presented high variability during the gray snapper’s peak spawning season (June to August) among the 3 years considered in the study. In 2021, the Loop Current intrusion was the greatest, reaching 28° N and 273° W in June 2021 (Figure S2A). In 2022, the intrusion of the LC extended to 28° N and 273° W in May, which was 2 months earlier than in 2021 and 1 month before the peak spawning season, and lasted until the end of July (Figure S2B). In 2023, the LC was much less prominent in the Gulf, and the maximum extent of intrusion occurred in April and reached 26° N and 272° W; therefore, eddies were the dominant circulation patterns during the peak spawning season (Figure S2C).

3.2. Differences Among Spawning Sites

The residuals from the one-way ANOVA between the spawning sites and the average proportion of settled larvae were normally distributed (W = 0.956, p = 0.432) and homogenous (F = 0.53, p = 0.779). There was a significant difference in the average proportion of successful settlers among the spawning sites (F = 22.71, p < 0.0001), and the average proportion of settlers was greater from the offshore Tampa Bay spawning site than all other spawning sites (SNK, all p < 0.05) (Figure 6). The Florida Keys spawning site contributed the next largest proportion of successful settlers, followed by the offshore Southwest Florida spawning site. The Middle Grounds, Elbow, offshore Sarasota Bay, and offshore Charlotte Harbor spawning sites contributed the smallest proportions of successful settlers. There was also interannual variability in the settlement proportions among the spawning sites, with 2021 having the largest settlement proportion in the Middle Grounds, the Elbow, offshore Tampa Bay, and offshore Southwest Florida (Figure 6), while 2023 had the smallest settlement proportion for the Middle Grounds, offshore Tampa Bay, offshore Charlotte Harbor, and the Florida Keys (Figure 6). The proportion of successfully settled larvae from all spawning sites combined was 0.15 in 2021, 0.09 in 2022, and 0.09 in 2023.

3.3. Connectivity Patterns Based on Spawning Sites

Larvae spawned at the offshore Tampa Bay spawning site had the highest probability (74%) of being transported into the Tampa Bay settlement region, with most of the remaining larvae being transported south into the Sarasota Bay settlement region (Figure 7). Similarly, larvae spawned at the offshore Southwest Florida spawning site had a higher probability (66%) of settling in the Southwest Florida settlement region, with a 34% probability of settling in the Florida Keys. Most other spawning sites did not transport larvae to the closest settlement region. The majority of the larvae spawned in the Florida Keys settled in Eastern Florida (58%), with the remainder self-recruiting to the Florida Keys (42%). Larvae spawned at the offshore Charlotte Harbor spawning site were transported mostly to Sarasota Bay (39% probability) or to Charlotte Harbor (32%). Most of the larvae spawned at the offshore Sarasota spawning site were transported to Tampa Bay. Spawning sites at the Elbow transported settlers to the Florida Panhandle (80%) and less so (20%) south to the Florida Keys.

The only spawning site that transported larvae exclusively to a single settlement region was the Florida Middle Grounds (Figure 7). Due to oceanographic features, the only larval pathway for successful settlement was northwest to the Florida Panhandle (Figure 8), despite the Tampa Bay settlement region being closer. Larvae that were transported south were not able to reach the settlement habitat within the required 42 days of the assigned PLD. Additionally, despite Cedar Key having one of the largest settlement regions in this study, there was no transport of larvae from the Florida Middle Grounds northeast into Cedar Key (Figure 8).

3.4. Connectivity Patterns Based on Settlement Regions

Settlers in the Tampa Bay and Southwest Florida settlement regions were primarily locally sourced, with a 97% and 98% probability of the settlers being contributed by spawning sites off Tampa Bay and Southwest Florida, respectively (Figure 9). There was also a high probability (69%) that settlers in the Florida Keys settlement region were spawned at the Florida Keys spawning site, with a 25% probability that the rest of the Florida Keys settlers were contributed by the spawning site offshore of Southwest Florida. Settlers in the Charlotte Harbor and Sarasota Bay settlement regions were most likely to have originated from the spawning site offshore of Tampa Bay (64% and 82% probability), with the remaining settlers being contributed by the spawning site offshore of Charlotte Harbor (36% and 18%, respectively). Interestingly, settlers in the Cedar Key settlement region were most likely to be contributed by the spawning sites offshore of Tampa Bay and offshore of Sarasota Bay (71% and 24% probability, respectively), both south of the Cedar Key settlement region. The Florida Panhandle settlement region received its settlers exclusively from the Florida Middle Grounds and the Elbow (97% and 3% probability, respectively). There was no larval transport to the Florida Middle Grounds or the Elbow because they had no appropriate settlement habitats. In addition, the settlers in the Eastern Florida settlement region were contributed by the Florida Keys spawning site exclusively.

Some settlement regions had relatively consistent annual recruitment from specific spawning sites (i.e., weak variation in the proportion of settlers contributed by the various spawning sites) (Figure 10). For example, the Tampa Bay settlement region consistently had larval recruitment from the offshore Tampa Bay spawning site, whereas the Cedar Key settlement region had variable recruitment on an annual basis from the offshore Tampa Bay spawning site.

3.5. Comparison of Settlement Rates

The age at settlement was different among the spawning sites (X² = 5301, df = 6, p < 0.0001). Offshore Charlotte Harbor, offshore Sarasota Bay, and the Middle Grounds had similar settlement ages (p > 0.05). All other sites were significantly different from each other (p < 0.05). The average age of successfully settled larvae was 29 ± 0.05 days (Figure 11). The spawning site with the shortest settlement dates were the Florida Keys (25 days) and offshore Tampa Bay (27 days ± 5). All other spawning sites had a settlement age of over 30 days, with the Elbow having the highest mean settlement age (39 days ± 2) (Figure 11).

The age at settlement was significantly different among the settlement regions (X² = 3984, df = 7, p < 0.0001). Settlers at Cedar Key and the Florida Panhandle had similar settlement ages (p = 0.54), and those at the Florida Keys and Tampa Bay had similar settlement ages (p = 0.59) (Figure 12). All other settlement regions had significantly different settlement ages (p < 0.05). The shortest settlement times were for settlers at Eastern Florida (25 days ± 3), Sarasota Bay (27 days ± 5), Tampa Bay (28 days ± 5), and the Florida Keys (28 days ± 6), with all other settlement regions with settlement times over 30 days; Cedar Key had the oldest settlers (37 days ± 4).

The age at settlement was different among the 3 years used in this study (X² = 189, df = 2, p < 0.0001), with 2021 having an older settlement age than 2022 (p < 0.0001) and 2023 (p < 0.0001) (Figure 13). The settlement ages in 2022 and 2023, however, were not different from one another (p = 0.67). The average settlement age was 30 days ± 0.08 in 2021, 28 days ± 0.10 in 2022, and 28 days ± 0.10 in 2023. The distances traveled by settled larvae were significantly different among the spawning sites (p < 0.05). Successful settlers from the offshore Charlotte Harbor spawning site traveled a similar distance to those from the offshore Sarasota Bay spawning site (p = 0.06), and the settlers from the Middle Grounds and offshore Sarasota Bay spawning sites also had similar distances traveled (p = 0.25), along with the Elbow and the Florida Keys (p = 0.66). Settlers from other spawning sites presented different distances traveled (p < 0.05). The average distance traveled by successful settlers was 247 km (± 207 km). The spawning sites with the longest distances traveled by larvae were the Florida Keys (534 km ± 233), primarily due to the settlement of larvae along the east coast of Florida, and the Elbow (396 km ± 164), primarily due to the settlement of some larvae in the Florida Keys. All other sites had an average distance traveled by larvae of under 250 km, with Tampa Bay having the lowest at 120 km (±38).

3.6. Lunar Cycle Release Frequencies

The proportions of settled larvae were normally distributed for both the new moon (W = 0.965, p = 0.644) and the full moon (W = 0.999, p = 0.932) and homogenous (F = 0.14, p = 0.239). There was no significant difference in the proportion of successful settlers spawned during the new moon versus the full moon (t = -0.56, df = 4, p = 0.607). On average, over the 3 years used in the study, the proportion of successfully settled larvae that were spawned during the full moon was 0.16 ± 0.02 and that for the new moon was 0.18 ± 0.04. The proportion of settlers was the most variable for the new moon, with the largest proportion of settlers for the new moon in 2021 (0.26), as well as the smallest in 2023 (0.11).

3.7. Model Validation

Compared among the different estuaries, Charlotte Harbor had the highest mean annual abundance of age-0 and age-1 gray snapper and was the only estuary that had a higher CPUE than the average Gulf-wide nominal CPUE (Figure 14). In addition, there was a latitudinal trend between the age-0 and age-1 gray snapper abundance, with a higher abundance as the latitude decreased, as seen with Apalachicola Bay, having the lowest abundance of age-0 and age-1 gray snapper, whereas Charlotte Harbor had the highest abundance (Figure 14).

4. Discussion

In this study, a modern biophysical model was incorporated [29] with updated spawning aggregation sites for gray snapper on the WFS to highlight the connectivity between offshore spawning sites and inshore habitats where larvae settle. The model’s flexibility enabled the inclusion of larval behavior, including the PLD and ontogenetic vertical migration. Our dispersal model highlighted that the spawning site offshore of Tampa Bay had the largest proportion of successful settlers and was an important source of larvae for estuaries ranging from Cedar Key south to Charlotte Harbor. This was despite offshore Tampa Bay having a smaller number of eggs released than other spawning sites, such as offshore Charlotte Harbor and offshore Sarasota Bay. The oceanographic features within offshore Tampa Bay also allowed the area to disperse its larvae widely, rather than only transporting larvae into one estuary. This emphasizes the importance of the area for gray snapper spawning aggregations.

Additionally, the Florida Middle Grounds, an area with multispecies aggregations [24], was identified as an important source of larvae for estuaries in the Florida Panhandle. In all 3 years of the study, larvae from the Florida Middle Grounds dispersed northward and settled within Apalachicola Bay (Florida Panhandle). This was an unknown larval pathway before this study and highlights the importance of finding more possible spawning aggregations within the WFS, since there was high connectivity between the offshore habitats and inshore habitats within the WFS.

4.1. Differences in Settlement Among Years

The extent of the Loop Current’s position on the WFS appears to influence the ability of gray snapper larvae in settling. In our study, 2021 was the year with the largest settlement proportion (Figure 6) and the year when the Loop Current reached the northernmost position in the Gulf (Figure S2). In 2021, a cyclonic pattern in the northern region appeared to enhance settlement from the Florida Middle Grounds towards the Florida Panhandle (Figure 6). Vaz and Karnauskas [62] described a similar larval transport pattern, with red snapper larvae being transported from the east to the central region of the Gulf via a similar cyclonic pattern, despite using a different hydrodynamic model (HYCOM + NCODA Gulf of Mexico 1/25° Analysis). In addition, Vaz and Karnauskas [62] found that, in years when the Loop Current was further south, there was lower larval transport from the eastern region to the central region, which was similar to the smaller proportion of settlement from the Florida Middle Grounds to the Florida Panhandle in 2023 (Figure 6).

4.2. Differences Among Ages of Larvae at Settlement

A notable aspect where our simulation results differed from previous research on gray snapper larvae was the longer PLD time for larvae settling in the northern WFS (e.g., Florida Panhandle and Cedar Key). Allman and Grimes [8] sampled grass beds from St. Andrews Bay (Florida Panhandle) to Ft. Meyers (Southwest Florida), and their PLD was an average of 25 days, with no obvious differences between the northern and southern regions. In our biophysical model, the PLD for settled gray snapper in the Florida Panhandle was mainly over 29 days. This was likely due to the fact that the Florida Panhandle in this study received larvae mostly from the Florida Middle Grounds (i.e., far offshore). Additionally, the lower PLD in [8] could have been due to differences in oceanographic features between the years of their study (1996 and 1997) compared to ours (2021–2023), which allowed for their earlier settlement age, such as instances when the LC intruded further north and east. Years with a higher PLD would also lead to higher mortality and lower settlement numbers.

While the distance from the settling habitat is important, the current direction and speed have a considerable influence on the settlement age. This was confirmed by the average PLD for settlers in Eastern Florida being, on average, 3 days shorter than in the Florida Keys, despite the spawning site being in the Florida Keys. The speed of the LC when it enters the Florida Strait transports settlers faster to Eastern Florida, rather than retaining them in the Florida Keys. In addition, spawning sites such as offshore Charlotte Harbor and offshore Sarasota Bay, which were only located between 45 and 50 km from a settlement region, had most of their larvae settled after 29 days. This shows that, while the distance from the settlement region can contribute to lowering the PLD within the model, the most important factor is the current speed at the spawning site.

Additionally, the settlement habitat in our simulations was defined by the 20 m isobath, which, in certain regions of the Gulf, such as Tampa Bay, can extend to 27 km offshore or over 80 km offshore in areas just north of Cedar Key. If the settlement habitats were constrained to seagrass beds and mangroves (i.e., 1–5 m depth), the mean modeled PLD would be longer. The settlement ages being older in our larval dispersal model compared to the settlement ages observed from the otoliths of age-0 fish in the same region [8] suggests that there must be some populations of spawning gray snapper closer inshore than our known spawning sites. However, fish collected inshore (within estuaries) in [13] showed that fish above the 50% maturity length of 292 mm TL were still immature (i.e., not spawning-capable during the spawning season). This suggests that fish within inshore habitats do not spawn, but it indicates that there are probably spawning populations of gray snapper within nearshore habitats (i.e., between inshore and offshore habitats). The observed PLD in the region could have been different from the modeled PLD due to larvae being transported from spawning sites that were outside the domain of the study.

4.3. Differences in the Lunar Cycle

The output from our larval dispersal model showed no difference in the proportion of successful larval settlement between the full and new moons, and our biophysical model therefore did not directly support the theory that gray snapper spawn only in one lunar phase [2,11,39,51]. This, however, does not negate the importance of the lunar cycle for larval settlement, as the lunar cycle may be a cue for larval fish to ingress inshore to settlement habitats [4,5,8]. Previous research has shown evidence of the higher settlement of gray snapper larvae within the new moon phase [4,5], possibly due to the combination of both lower light levels and higher tidal amplitudes during a new moon, potentially lowering the predation of larval gray snapper as they enter settlement habitats [5].

This relationship of settlement within the new moon phase lowers as the distances between spawning and settlement regions increase. Denit et al. [5] stated that the lack of gray snapper larval settlement with the new moon at their North Carolina (East Coast, US) sites may have been due to larval sources being transported from distant areas, which can cause variability in the settlement age/date. Higher variability in the settlement age was observed in our larval dispersal model output when comparing the settlement ages of gray snapper by spawning location, such as the Florida Middle Grounds, which had higher variability in the settlement age than spawning sites closer to shore (i.e., offshore Tampa Bay). This could result in less settlement during new moons, therefore depriving larvae of the benefits associated with settling in lower-light conditions. To determine whether the moon phase influences gray snapper settlement, future studies could use light traps to quantify larvae settlement among all lunar phases. Unfortunately, light trap studies on the larvae of benthic and seagrass-associated species, such as gray snapper, have been limited to the Florida Keys [63].

4.4. Discrepancies Between Model Output and Model Validation

In Leis’ [27] review of the biophysical modeling of larval dispersal, he emphasized the importance of validating the biological portion of the connectivity model, although only one study had done so at the time of the review. Validating our larval dispersal model proved difficult because there were no available data on the abundance of gray snapper larvae along the entire west coast of Florida, and we therefore used the abundance of 0- and 1-year old juveniles as a proxy. However, there is an overall paucity of information on the timing and the habitats of larval gray snapper at settlement on the west coast of Florida. Therefore, using the juvenile abundance as a proxy for larval abundance, we must assume that larvae settle in the same habitats where the age-0 and age-1 juveniles are available for capture, but this is unproven to date.

The output from our larval dispersal model determined that the estuaries with the greatest proportions of settlers should be, in decreasing order, Tampa Bay, Apalachicola Bay, Sarasota Bay, Charlotte Harbor, and Cedar Key. However, when compared to the standardized age-0 and age-1 gray snapper CPUE model from inshore FWRI data, there were major differences. Charlotte Harbor presented the highest CPUE of any of the estuaries, and there was an inverse relationship between the latitude and CPUE of juvenile gray snapper (i.e., higher-latitude estuaries had a lower CPUE). These discrepancies could potentially be attributed to a number of factors, including differences between larval and juvenile habitats that were mentioned previously, as well as differential mortality. Although marine fish larval mortality is often assumed to be extremely high (e.g., 99.9%, [64]), mortality was not incorporated into our biophysical model because we would have had to apply it at the same rate at all release sites, since there is no information on the differential larval mortality rates for gray snapper based on latitude in the Gulf. However, larval mortality increases with increasing PLD [27], and so larvae that settled within the Florida Panhandle region would presumably have had higher mortality, since their PLD was longer than those of larvae that settled in Tampa Bay, Sarasota Bay, or Charlotte Harbor. There was also an unknown mortality rate for age-0 fish in each of the estuaries before they were recruited to the sampling gear used by FWRI, which may have also contributed to the mismatch. Gray snapper also appear to synchronize their spawning events over a short period of time [13], making it difficult to observe them in an actively spawning reproductive phase. It is therefore probable that there are more spawning aggregation sites for gray snapper than reported here, especially off the southern portion of the WFS, which would then possibly lead to more larvae settling within the Charlotte Harbor region.

4.5. Future Steps to Improve the Larval Dispersal Model

One of the largest sources of uncertainty in our larval dispersal model was that it relied on user-specified spawning sites. As seen by the differences between our dispersal model results and the validation, it is likely that not all spawning sites were captured, or, alternatively, the weighting for the egg release abundances based on the relative abundance of spawning fish using the G-FISHER sampling could have been biased.

While the G-FISHER survey was used to identify previously uncharacterized spawning sites, other surveys were also explored. For instance, the Southeast Area Monitoring and Assessment Program’s (SEAMAP) trawl data were explored to help identify spawning aggregations. However, SEAMAP trawls are used over primarily the sand bottom, which excluded hard-bottom habitats, where reef fish are more prevalent. Additionally, there were no SEAMAP trawl samples identified that had both a large count of gray snapper and an average weight that would indicate sexually mature fish.

The CMS does offer a back-tracking module [29], which retraces the movement of the larvae back to hatching. However, as the days from hatching increase, it can lead to more uncertainty in identifying potential spawning locations. Two sources of gray snapper larvae collected on the WFS were explored in an attempt to identify other potential spawning sites. One source was the SEAMAP plankton trawls performed throughout the Gulf, but only 30 gray snapper larvae were collected from 2017 to 2021 [65]. We also explored data from a survey of fish eggs along the WFS, carried out by the University of South Florida (Tampa, Florida) [66]. However, this survey only collected eggs at the surface of the ocean [67], and only 13 gray snapper eggs were collected over 6 years (2014 to 2019) [66]. The larval pathways identified in this study do not preclude pathways from outside the domain of this study region; however, larval input from other areas of the Gulf, such as Campeche Bank (Southern Gulf), may be limited [68,69]. In addition, our results do not account for mortality, which might influence the relative importance of the simulated transport pathways for gray snapper.

Thus, the only consistent relative abundance index for spawning gray snapper available was the G-FISHER video survey data; however, this survey is used to acquire abundance estimates for all reef species within the Gulf and not designed to specifically target gray snapper or spawning habitats. Therefore, the sampling areas may not necessarily capture all spawning aggregations of gray snapper. This was the case observed in areas such as the Florida Middle Grounds, where the highest MaxN was 45, despite this being an area that is known to have spawning aggregations that sustain large headboat catches (i.e., over 400 fish per trip, pers. obs.). Additionally, the MaxN could be biased towards low values due to (1) physical limitations in capturing the maximum number of fish within one video frame; (2) other species that may “crowd around the camera”, which lowers the MaxN of other species; and (3) hard-bottom habitats being too complex to be fully captured, which lowers the MaxN for all species in the sample.

4.6. Model Uncertainty

Another major source of uncertainty in our larval dispersal simulations was the ocean velocity fields on the WFS, where low velocities in inshore areas required the expansion of the settlement habitats towards mid-depth, nearshore areas. Since our settlement regions were so broad, and larvae were considered settled once inside the region, it was not possible to parse out the importance of the tidal cycle for larval transport into inshore habitats. However, fish larvae are known to ingress into settlement habitats using selective tidal stream transport, which is when larvae move into settlement habitats at specific tidal phases (e.g., Atlantic menhaden Brevoortia tyrannus during flood tide at night) [70]. This larval behavior could not be captured in our simulations; thus, this important process was not fully resolved in our larval dispersal model for gray snapper.

Other ocean models have been used to study larval transport in the region; indeed, the West Florida Coastal Ocean Model (WFCOM), which is a model that combines a Finite Volume Coastal Ocean Model (FVCOM) with HYCOM, was used when simulating gag grouper (Mycteroperca microlepis) larval dispersal on the WFS and was able to capture transport from offshore spawning areas to the surf zone of Tampa [20]. However, Weisberg et al. [20] modeled gag larval dispersal during the winter/spring, when the speeds on the WFS are faster than during the summer, when gray snapper spawn. Thus, it is possible that only considering a different oceanographic model (such as the WFCOM) may not be adequate for gray snapper larval transport. In addition, at the time of the study, oceanographic data from the WFCOM could not be integrated with the CMS. However, it is worth exploring additional models, such as the higher-resolution HYCOM-TSIS 1 km, the Coupled Northwest Atlantic Prediction System (CNAPS), or the WFCOM. Testing individual larval orientations and swimming capabilities could also lead to new insights [71], and adding horizontal swimming behavior into larval dispersal models is known to lower the PLD and increase the settlement of larvae [72,73].

4.7. Future Directions

Our gray snapper larval dispersal model could benefit from exploring other forms of model validation, such as the use of genetics and otolith microchemistry, which was beyond the scope of our study. Genetic techniques, such as the use of kinship probabilities from parent–offspring and half-sibling relationships [74,75], could be used to identify relationships between adult gray snapper and juveniles in specific settlement regions. Additionally, otolith microchemistry can be used to identify the nursery habitats of juvenile gray snapper due to the differences in the elements within the water masses of their nursery habitats [76]. This methodology could be applied in the future so that juvenile gray snapper from different inshore settlement regions (i.e., Charlotte Harbor, Sarasota Bay, Tampa Bay) can be collected, and their otolith signatures could be used as a library/catalog. Adult gray snapper can then be collected from offshore spawning sites and the cores of their otoliths analyzed and compared to this catalog/library to determine from which nursery habitats they originated. The use of these alternative methodologies may provide more insights into the validation of a biophysical model for gray snapper larvae.

Our simulation results showed a degree of connectivity between the Gulf and the US South Atlantic. The Florida Keys are a boundary region between the designation of the Gulf resources (under the management of the Gulf Fishery Management Council, GMFMC) and the South Atlantic (under the management of the South Atlantic Fishery Management Council, SAFMC). Gray snapper, however, are managed by the GSMFC throughout the Florida Keys [77]. Our model showed that there was larval transport from the Gulf to the Atlantic, with just under 10% of all gray snapper larvae spawned in the Florida Keys transported to Eastern Florida. If additional spawning aggregations are found in the southern WFS, this could potentially lead to higher settlement within the South Atlantic management area. This provides some preliminary evidence of the connectivity between the two different management zones, with the Florida Keys being a dynamic transition area.

Author Contributions

Conceptualization, E.B. and D.J.M.; methodology, E.B. and A.C.V.; formal analysis, E.B.; investigation, E.B., D.J.M. and A.C.V.; data curation, E.B. and D.J.M.; writing—original draft preparation, E.B.; writing—review and editing, E.B., D.J.M. and A.C.V.; visualization, E.B.; supervision, D.J.M. and A.C.V.; project administration, D.J.M.; funding acquisition, D.J.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The biophysical modeling data presented in this study are available on request from E. Bovee. Data for spawning site locations are available from D. Murie.

Acknowledgments

We thank Don Behringer for sharing his HiPerGator account necessary to run the CMS model and comments on a previous version of the manuscript. Thanks also to Andy Kough for compiling the CMS on the HiPerGator and providing instructions on the use of the CMS and an in-depth review of a previous version. Ashley Wechsler shared the location data for spawning sites offshore, and Ted Switzer of FWC provided the G-FISHER and FWRI inshore sampling data, which were all greatly appreciated.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Generalized summer circulation patterns in the Eastern Gulf of America (formerly the Gulf of Mexico) and Eastern Florida, adapted from [15] with permission from the Florida Academy of Sciences, showing the general location of the Loop Current, the West Florida Estuarine Waters (WFEW), and the Florida Bay Waters (FBW) of the West Florida Shelf. Bathymetry data are from [37]. FMG and polygon indicate the location of the Florida Middle Grounds. Inset shows general study area in the Southeastern USA.

Figure 2. Locations of egg/larvae release sites used in the Connectivity Modeling System, gridded in 10 km × 10 km squares. Color indicates the spawning site name, and numbers within colored squares refer to the spawning grid numbers (see Table 1). The yellow box in the inset shows the general study area within the Southeastern USA. MG = Florida Middle Grounds, TB = Tampa Bay, SB = Sarasota Bay, CH = Charlotte Harbor, and FL KEYS = Florida Keys.

Figure 3. Ontogenetic vertical migration (OVM) matrix developed by Karnauskas (latest access: github.com/Mandy-Karnauskas-NOAA on 10 April 24), but modified for gray snapper larvae, and the HYCOM reanalysis model and shallow settlement depth. Larval age and stage length in days are given above each stage based on [52].

Figure 4. Settlement habitat designation for gray snapper larvae. Settlement habitat extended up to 20 m depth indicated by black contour line. Settlement regions were categorized by state or FWC sampling area within the state of Florida.

Figure 5. Comparison of random effects between year and month. The blue line and points indicate the random effect of the catch of age-0 and age-1 gray snapper. The cyclic pattern of the blue line and points indicate that the presence of age-0 and age-1 gray snapper changes within a year.

Figure 6. The proportion of settled gray snapper larvae from each spawning site per year, with the annual mean based on all years. Differences in the mean proportion of settled larvae (black bar) among spawning sites are indicated by different letters; sites with the same letter were not significantly different from one another.

Figure 7. Connectivity matrix of successful settlers normalized by spawning sites of gray snapper from 2021 to 2023. Numbers in each box represent the proportion of successful settlers out of the total settlers from that spawning site (i.e., the sum of each row is 1). Blank boxes indicate proportions of <0.01, and gray boxes indicate no larval transport. Spawning sites transporting settlers into the same region are given by the diagonal dashed line (self-recruitment).

Figure 8. Random subset of simulated larval trajectories for gray snapper spawning at the Florida Middle Grounds from 2021 to 2023. Spawning sites are represented by the red points (randomly offset to protect fishing locations) and settlement locations are represented by the green points; yellow lines are the trajectories for successfully settled larvae and black lines are trajectories for unsuccessfully settled larvae. Points and lines are transparent; therefore, darker colors represent the greater presence of spawners, settlers, and more common pathways. The polygon indicates the location of the Florida Middle Grounds.

Figure 9. Connectivity matrix of successful settlers normalized by settlement region of gray snapper larvae from 2021 to 2023. Numbers in each box represent the proportion of successful settlers out of the total settlers in that settlement region (i.e., the sum of each column is 1). Blank boxes indicate proportions [less than] 0.01, and gray boxes indicate no larval transport. The diagonal dashed line represents self-recruitment.

Figure 10. Average annual proportion of settlers contributed by each spawning site per settlement region for gray snapper for 2021–2023. Colors indicate spawning sites. Vertical bars are ± 1 SE.

Figure 11. Distribution of the age (days) at settlement of gray snapper larvae for each spawning site. Dashed line indicates the average settlement age at 29 days ± 3.

Figure 12. Distribution of the age (days) at settlement for gray snapper larvae for each settlement region. Dashed line indicates the average settlement age at 29 days.

Figure 13. Larval trajectories for successfully settled gray snapper larvae in 2021, 2022, and 2023 at days 14, 28, and 42. Hollow squares indicate spawning sites; colored circles indicate moving larvae; and colored squares indicate settled larvae. The 20 m depth interval is indicated by the gray contour line, which is the settlement boundary. Links to animated larval trajectories are given in Figures S3 (2021), S4 (2022), and S5 (2023).

Figure 14. Mean catch per unit effort of age-0 and age-1 gray snapper per 100 m2 (±1 SE) for four estuaries sampled by FWRI’s Fisheries-Independent Monitoring Program.

Table 2

Comparison of count and continuous models to predict CPUE of age-0 and age-1 gray snapper inshore. All environmental and gear covariates are the same in all models, with the gear type, temperature, salinity, and average depth. Models 1 and 2 are zero-inflated (ZI) count models and Models 3–7 are continuous delta-log hurdle (DLH) models. Model 7 (bold) was chosen as the best model and was used to predict the abundance of gray snapper.

Model	Name	AIC Positive	AIC Zero
1	ZI-Poisson	45,052.62	45,052.62
2	ZI-Negative Binomial	28,473.20	28,473.20
3	DLH-Year (Fixed)	7482.28	16,629.67
4	DLH-Year (Random)	7485.77	16,643.43
5	DLH-Year×Month (Random)	7482.98	16,185.80
6	DLH-Year (Structured Random)	7486.18	16,645.38
7	DLH-Year×Month (Structured Random)	7483.68	16,143.61

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/oceans6010012/s1, Figure S1: Frequency of MaxN gray snapper values from G-FISHER samples on the West Florida Shelf (2017–2022), excluding zero values; Figure S2: Eastward and northward water velocities and directions on the WFS; Video S1: Animated larval trajectory GIF for 2021; Video S2: Animated larval trajectory GIF for 2022; Video S3: Animated larval trajectory GIF for 2023.

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