Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico

The North American Black Terns (Chlidonias niger) are the only obligate wetland-breeding terns in the New World. Black Terns breed primarily in the Prairie Pothole Region with small populations distributed across the northern United States and southern Canada and north into the Boreal Transition Zone, then overwinter in Central and South America (Beyersbergen et al. 2004, Kudell-Ekstrum and Rinaldi 2004, Heath et al. 2020). Migratory routes may differ by breeding location and season and display individual variation (Shephard et al. 2023). It is also possible that some nonbreeding birds may occur in the Gulf of Mexico throughout the year. Throughout their annual cycle, Black Terns likely change their foraging behavior from being aerial and plunge-feeding insectivores in wetlands, marshes, and adjacent grasslands during the breeding season to being primarily piscivores in marine areas (Heath et al. 2020).

The Black Tern is identified as a Bird of Conservation Concern for multiple Bird Conservation Regions (USFWS 2021). Since the 1960s, the North American continental population of Black Terns has experienced an estimated annual rate of decline of ∼3%/year, with a total estimated decline of ∼60% (Peterjohn and Sauer 1997, Heath et al. 2020). Population declines, particularly during the 1966–1979 interval, appear to coincide with extensive wetland loss over much of the breeding range (Dahl 1990, Naugle et al. 2000). While population declines have appeared to slow or lessen since the 1980s (Peterjohn and Sauer 1997), the conversion of grasslands and drainage of wetlands accelerated dramatically in the 2010s following increased agricultural commodity prices (Doherty et al. 2013, Johnston 2014), which may subsequently have a negative impact on breeding habitat. Current data from the Breeding Bird Survey find a survey-wide trend from 1993 to 2019 of 1.01, but there is high variability between regions (confidence intervals: −1.59, 6.00) (Sauer et al. 2020).

Compared to behavior and habitat associations on the breeding grounds (e.g., Niemuth and Solberg 2003, Forcey et al. 2014, McKellar and Clements 2023), relatively little contemporary information is available for Black Tern abundance, distribution, seasonal occurrence, and habitat associations in the Gulf of Mexico (see Clapp et al. 1983, Fritts et al. 1983). Tracking data indicate that some Black Terns breeding in Saskatchewan, Ontario, and Michigan stage in the northern Gulf of Mexico (nGoM) during both fall and spring migration (Shephard et al. 2023). Historical data from vessel-based observations in marine waters in the northern portion of the Gulf of Mexico, north of the southern border of Texas and the southern end of Florida (nGoM), provide insight into occurrence in the region inconsistent with respect to spatial and temporal coverage. For example, Ribic et al. (1997) recorded the occurrence of Black Terns during vessel transects on the north-central continental shelf and slope of the nGoM but did not estimate abundance. Ribic et al. (1997) did not observe any Black Terns during surveys in winter in 42 vessel transects or spring in 56 vessel transects and only recorded individuals on 8 of 50 vessel transects during summer. In contrast, surveys on the north, central, and eastern continental shelf and slope undertaken by Davis et al. (2000) observed Black Terns during vessel-based surveys in spring (∼500 individuals), mid-summer (∼1,100), and late summer (∼10) but not during fall. Haney et al. (2019) observed ∼2,300 individuals during vessel-based surveys covering most of the continental shelf and into continental slope habitats in the central and eastern nGoM. Clapp et al. (1983) reported dates that Black Terns were observed and estimated numbers of individuals but provided only general descriptions of the location of observations (e.g., “near Tampa”). However, none of these efforts estimated the relative density of the species or attempted to model habitat associations. Subsequently, none of these studies had sufficient data to determine arrival and departure dates or residency times for this species in the nGoM. These differences in methodology make comparisons across studies challenging.

Although data collection methods were inconsistent among these studies, making it infeasible to quantitatively compare and/or combine observation data across studies, these previous studies indicate that this species is present across the nGoM during its nonbreeding season. The presence of Black Terns in the nGoM makes them susceptible to anthropogenic stressors, including but not limited to oil and gas development (Michael et al. 2022), wind energy development, and shipping traffic. For example, the estimated injury to Black Terns from the Deepwater Horizon oil spill was ∼1,000 individuals, ranking it 11th out of the 93 injured bird species detected (Trustees 2004: table 4.7-3). For these reasons and others, this species is considered a high priority for monitoring in the Gulf of Mexico (see Jodice et al. 2019).

Therefore, our goal was to characterize the occurrence and distribution of Black Terns and assess habitat associations in marine waters of the nGoM. We used standardized count data collected by trained seabird observers aboard National Oceanic and Atmospheric Administration (NOAA) Vessels of Opportunity (VOOs). To identify marine habitats with which Black Terns are associated, we both described their occurrence and estimated their relative density and association with environmental covariates. Our analysis of the most comprehensive Black Tern observational dataset for this region characterizes previously unknown temporal and abundance patterns, concentration areas, and pelagic habitats used by this species while staging in the nGoM. By identifying times and locations where Black Terns are in high abundance and by describing these occupied habitats, we address a knowledge gap in Black Terns’ migration behavior and further describe this Gulf of Mexico region’s role in their annual life cycle (Jodice et al. 2019). This new information identifies previously poorly described Black Tern conservation opportunity areas (i.e., USFWS 2021) as well as critical information for regulatory agencies and policy-makers in a region with ever-increasing pressures from energy (e.g., offshore oil and gas development, offshore wind energy development) and aquaculture development, all in the backdrop of climate change effects (e.g., sea-level rise, increasing sea-surface temperature).

We used vessel-based survey observations from the Gulf of Mexico Marine Assessment Program for Protected Species (GoMMAPPS). Data were collected from NOAA VOOs in nGoM waters of the U.S. Exclusive Economic Zone.

The data collected from these surveys define the study area and span continental shelf (≤200 m), continental slope (200–2,000 m), and pelagic (>2,000 m) bathymetric domains in the northern portion of the Gulf of Mexico (Fig. 1). Waters within the jurisdiction of individual U.S. states (i.e., within 3 nautical miles [5.55 km] of Louisiana, Mississippi, and Alabama coasts) or 9 nautical miles (16.66 km) from the coasts of Texas and Florida, and areas too shallow for survey vessels to safely operate, generally were not surveyed. Our analyses do not capture the distribution or associations of Black Terns in shallower habitats or behavior onshore, and Black Terns may have occurred in these areas during our surveys but are not documented here. Our data generally reflect the Outer Continental Shelf (OCS) waters of the nGoM under the Outer Continental Shelf Lands Act (43 U.S.C. §§ 1331 et seq.).

All observations followed a standardized protocol for marine fauna observations from a vessel (Tasker et al. 1984, Ballance and Force 2016) and were entered directly onto laptop computers (Panasonic Toughbook CF-30) using program SEEBIRD 4.3.7 software (Ballance and Force 2016). Briefly, an observer stood on the flying bridge facing the bow of the vessel and counted all birds within view to the lowest taxonomic level on the focal side of the vessel (generally, the side with the least glare). Distance from the ship, when the bird was first detected, was estimated and binned into 1 of 4 distance categories: 0–100, 101–200, 201–300 m, and “outside the area” (i.e., >300 m or observed on the non-focal side of the vessel). Observations were recorded in real time and included date, time, and GPS coordinates for the vessel. Relatively low densities of seabirds and good viewing conditions permitted most seabird species identification and enumeration to 500 m on both sides of the vessel (Spear et al. 2001, Ballance and Force 2016, Jodice et al. 2021). The ability to accurately record species out to 500 m on both sides of the vessel allowed us to maximize data return per unit effort without sacrificing the probability of detecting birds within the standard distance bins, given the unique combination of environmental conditions we encountered during seabird vessel surveys in the nGoM (see (Michael et al. 2023). Survey routes were predetermined and designed specifically for conducting joint NOAA GoMMAPPS marine mammal/bird surveys or NOAA SEAMAP ichthyoplankton surveys. As such, the seabird vessel survey team had no ability to influence or modify the study design related to survey track lines. Data collected here under the auspices of GoMMAPPS is considered the most spatially and temporally comprehensive seabird monitoring effort ever undertaken in the nGoM (but see Haney et al. 2019). Between April 2017 and September 2019, observations using the above protocol were made over 293 d, comprising ∼2,300 h of observation effort spanning ∼41,700 km. These observational data can be accessed through the National Centers for Environmental Information (NCEI) archives: https://www.ncei.noaa.gov/archive/accession/0247206 and https://doi.org/10.25921/afrq-h385 (Gleason et al. 2022). See the Supplemental Material for monthly date ranges for survey effort and Black Tern detections (Supplemental Table S1) and details on the number of days with Black Tern detections (Supplemental Table S2).

To describe the habitat of Black Terns in the nGoM, we selected environmental covariates previously used in seabird habitat models in the Gulf of Mexico (Poli et al. 2017, Jodice et al. 2021, Michael et al. 2023). Three daily sea-surface covariates were used and obtained from the Hybrid Coordinate Ocean Model (HYCOM; Chassignet et al. 2009, Metzger et al. 2017): sea-surface temperature, sea-surface salinity (associated with water mass), and sea-surface height (indicating hydrographic features including convergence and divergence). Monthly chlorophyll-a data from Modis Aqua 4 km L3 SMI were used as a proxy for primary productivity (OBPG 2018). Depth data, which is associated with bathymetric domain, was obtained from the SMRT30+ version 6.0 30 arc-second dataset (Becker et al. 2009). Although we considered including distance to the nearest coastline as a covariate, a strong inverse correlation between bathymetry (depth) and distance to the nearest coastline (Spearman’s rho: −0.80) suggested that including both covariates would provide redundant information. Depth data were transformed by multiplying by −1 and then taking the natural log to reduce the range of the covariate values. As such, distance to the nearest coastline was excluded from further consideration in models, and depth was retained. Correlations between the remaining covariates were > −0.5 and < 0.5 with the following exceptions: bathymetry and latitude (0.58), bathymetry and chlorophyll-a (0.64), and temperature and month (0.74). These covariates were retained as each correlated pair captures unique habitat gradients (e.g., spatial gradient, static physical feature, dynamic physical features, temporal gradient). All covariates used in the analyses are summarized in Table 1.

To standardize the area surveyed, survey data were filtered for vessel speed, between 6 and 12 nautical miles per h (11.11–22.22 km/h), and interpolated into 10–15 min bins along each survey line (Ainley et al. 2009, Kinlan et al. 2016, Winship et al. 2018). Filtering the speed of the vessel reduces the chance of over- or underestimating birds per unit area due to observers spending a disproportionate amount of time documenting observations in one location versus another location. Interpolating observations into bins makes it easy to compare the content of observations made at different locations or times. Given the coarse resolution of the distance intervals associated with each observation recorded in Program SEEBIRD (0–100, 101–200, 201–300, and 301–500 m), we were not able to account for imperfect detection (Buckland et al. 2015). Herein, we thus refer to the resulting seabird observations as relative abundance (Michael et al. 2023). Our characterization of Black Terns in the nGoM is not behavior-specific; our analyses do not distinguish between observations of birds exhibiting different behaviors, such as “sitting” and “flying.” Thus, our analyses describe Black Tern occurrence and habitat associations in the nGoM in the broadest sense, including potential searching, foraging, and transiting areas, and do not explicitly model habitat associated with an exclusive behavior.

The covariates described above (Table 1) were associated with each 10–15 min bin at a daily resolution, and the location of the bin was described as the midpoint of the bin. To standardize the spatial resolution of the data, we aggregated the midpoint locations into 10 × 10 km cells across the study area (Renner et al. 2012, Smith et al. 2014). This spatial resolution has been found to minimize spatial autocorrelation in oceanographic variables (Renner et al. 2012). Each survey bin was associated with the cell in which the bin’s midpoint overlapped. To account for the number of kilometers surveyed in each 10–15 min bin, we divided the relative abundance of Black Terns by the number of kilometers surveyed multiplied by the strip width (2 × 500 m). This calculation produced an estimate of Black Terns/km², hereinafter referred to as relative density, in each cell × day combination (hereinafter “cell-day”). When more than 1 bin occurred within a given cell on the same day, we calculated the mean relative density and mean of each environmental covariate.

Observations were not uniformly distributed (Fig. 1). Uneven survey effort in space and time may result in a biased characterization of the distribution and relative density of Black Terns. This could produce a model more sensitive to observations in frequently surveyed areas or periods relative to areas or periods with fewer observations. By standardizing the data to a 10 × 10 km daily resolution, we can account for different levels of effort in each 10 × 10 km cell on a given day. This reduces the potential bias from uneven nonuniform spatial or temporal survey effort within and across days but does not remove it. This standardization also enables comparisons and maximizes the number of cell-day combinations included in the analysis. The data processing procedure described here is similar to that of Michael et al. (2023).

Our analysis involved characterizing patterns in the occurrence and relative density of Black Terns. Occurrence indicates the prevalence of Black Terns across all cell-day observations, while relative density reflects the concentration of Black Terns when and where this species was observed.

Using the cell-day data filtered to standardize the area surveyed, we described temporal patterns in the occurrence of Black Terns by identifying the months they were observed. We then calculated the monthly minimum, maximum, range, mean, median, and standard error associated with observations of Black Terns. To visualize differences in Black Terns observed across the annual cycle, we plot the sum of the relative density of Black Terns observed each month.

We developed 2 separate models characterizing the habitat associations of Black Terns in the nGoM. One model describes the prevalence of Black Terns through presence and absence data (“occurrence model”). The other model describes the concentration of Black Terns when present, expressed as relative density (“relative density model”). This approach is particularly useful when modeling the distribution of species that have relatively low occurrence rates. These 2 approaches are used here to describe Black Terns’ use of the nGoM; we consider them complementary, and together, they provide a more robust characterization than either would on its own.

Both models included the environmental covariates listed above: sea-surface temperature, sea-surface salinity, sea-surface height, chlorophyll-a, and bathymetry. As we were interested in the potential for seasonal patterns in occurrence and relative density, we also included month as a covariate. While Black Terns do not experience seasons as strict categories, for ease of discussion, seasons are often discussed by grouping months as spring: March–May, summer: June–August, fall: September–November, winter: December–February. Year was not included as a covariate in the model. The 2 models (occurrence and relative density) differ in how space is characterized. In addition to the covariates described above, the occurrence model includes longitude (i.e., an east–west spatial gradient) and latitude (i.e., a north–south spatial gradient) as individual covariates. We considered including the interaction between longitude and latitude in this model, but their interactions are challenging to interpret in a linear model. In the relative density model, we incorporated a spatial smooth parameter as a covariate, i.e., the interaction between latitude and longitude creating a 2-dimensional spatial surface that accounts for potential spatial structure in the data (Grüss et al. 2019, Barnes et al. 2020, Michael et al. 2023). Our models retained all environmental covariates since our primary interest was to assess the relationship of Black Terns with all environmental covariates rather than identifying the most parsimonious model from a set of competing models (Anderson and Burnham 2002). We assessed the relationship between Black Tern occurrence and environmental covariates (Table 1) using a generalized linear model (GLM) with a logit link function for binomial data, hereinafter “occurrence model” (Faraway 2006).

We split the data into training and testing data with 75% and 25% of the original observations, respectively (e.g., Jodice et al. 2021). Training data were used to create the occurrence model, and the testing data were used to assess the model’s ability to predict Black Tern occurrence. We then estimated the area under the receiver operating characteristics curve (AUC) to assess the predictive power of the occurrence model. An AUC of 0.5 indicates no predictive power, while an AUC of 1 indicates perfect discrimination (Bradley 1997). To distinguish presence versus absence predictions, we used the value where sensitivity (accurate prediction of presence rate) is equal to specificity (accurate prediction of absence rate) in the AUC as the threshold value (Swets 1988, Pearce and Ferrier 2000). Predicted values equal to or greater than the threshold value indicate presence, whereas predicted values smaller than the threshold value indicate absence. We report the percentage of presence and absence observations correctly identified by the model using the testing data.

We then used a generalized additive model (GAM) to assess the relationship between the relative density of Black Terns when present with associated environmental covariates (“relative density model”; Table 1; Wood 2011). We assumed a Tweedie distribution with a log-link function, thin-plate splines to covariates, and estimated the scale parameter when fitting the model (Wood 2003, 2017). We tested model performance using other distributions, but model fit diagnostics (q–q plots, residuals, restricted marginal likelihood, Akaike’s information criterion) indicated that the Tweedie distribution produced a better overall fit to the data. We also assessed the degree of concurvity in the model. Concurvity occurs within a model when the smooth term of 1 covariate can be approximated by the smooth term of another covariate in the model. Covariates indicating time or space often show concurvity with 1 or more covariate smooths. Concurvity estimates ≥0.5 are discussed (Ramsay et al. 2003).

We then used the relationships identified in the model to project the distribution and relative density of Black Terns on the mean values of the environmental covariates used in the model. This created a spatial surface of predicted relative densities, which we compared to the observed relative densities of Black Terns. Overlap of areas with elevated predicted relative densities with high observed relative densities indicates the model has some predictive capacity. Covariates in occurrence and relative density models are described as significant at P ≤ 0.05. Analyses were performed in R 4.2.3 (R Development Core Team 2023). The GLM of Black Tern occurrence was implemented using base-package stats, the GAM of relative density was implemented using the mgcv package, and the AUC was estimated using the ROCR package (Sing et al. 2005, Wood and Wood 2015).

The number of cell-days with survey effort was not uniform across months (Fig. 2). The most cell-days occurred in September (1,295), followed by August (960) and May (946). February, July, and March had 425, 261, and 240 cell-days, respectively, while the remaining months each had fewer than 200 cell-days of survey effort: April (182), January (172), October (89), and July (40). The number of observation days (cell-days) with effort was not uniform spatially (Fig. 1). Cells with multiple days of survey effort tended to occur near the Port of Pascagoula, Mississippi (often used as a port of departure and return for NOAA VOOs) and near the mouth of the Mississippi River (MoM), Louisiana. Most cells (73.5%; 1,679/2,283) had 1 and 2 d with effort, but a single cell next to the Port of Pascagoula had 19 d with survey effort (Fig. 1).

Before filtering the data, we tallied 726 detections of Black Terns, with 12,109 individuals observed within those detections. Black Terns were observed from March to May and July to October (Supplemental Fig. S1, Tables S1, S2). After data were filtered, processed, and aggregated into cell-days, Black Terns were limited to only ∼6% (258) of the 4,610 total cell-days with survey effort. Although Black Terns were observed in March (Supplemental Fig. S1, Table S2), filtering the data to standardize survey effort removed these observations from subsequent analyses.

When present, ∼60% of Black Terns’ relative density per cell-day was <1 Black Tern/km². The mean relative density of Black Terns was lowest in April at 0.21 Black Terns/km² and greatest in September at 8.09 Black Terns/km² (Table 2). Comparatively high mean monthly relative densities occurred from late summer through mid-fall (Fig. 3, Table 2).

Regarding broad-scale spatiotemporal patterns, Black Terns were observed predominantly on the continental shelf (<200 m; Fig. 4). The greatest relative densities within each season occurred near the MoM (Fig. 4). In the summer and fall, the southwestern portion of the study area, offshore of Corpus Christi, Texas, had seasonally moderate to high relative densities (Fig. 4). Lower relative densities of Black Terns occurred along the continental slope (>200 to ≤2,000 m) and into pelagic (>2,000 m) waters in the spring and summer. The maximum relative density observed within a season increased from spring through fall (Fig. 4), but the months with the 3 greatest densities were, in descending order, September, August, May, October, July, and April (Table 2).

In the occurrence model, the AUC was 0.841 for the training dataset and 0.819 for the test dataset, indicating very good performance by both models (Duan et al. 2014). Applying a threshold of sensitivity equal to specificity (0.060, to distinguish modeled presence from absence) correctly predicted 56 of 70 (80%) true presences and 851 of 1,082 (79%) true absences using the test data.

The occurrence of Black Terns had a significant, negative association with longitude indicating occurrence tended to be greater in western versus eastern portions of the study area (Table 3). The positive association of the occurrence of Black Terns with latitude indicates greater occurrence in northern versus southern portions of the study area (Table 3). Shallower depths and greater chlorophyll-a were also significantly associated with the occurrence of Black Terns (Table 3).

Our model of relative density explained 56.6% of the deviance in the data. As is common (Ramsay et al. 2003), concurvity ≥0.5 only occurred in pairs of terms that involved a spatial or temporal covariate: month and sea-surface temperature and spatial smooth with log-transformed bathymetry. An association between these covariates makes sense ecologically, but retaining these covariates allows insight into the features they serve as a proxy for (Table 1), which would not be indicated if only 1 covariate from each pair was retained (e.g., month and sea-surface temperature; Fig. 5a, e).

Five of the 7 covariates in the model of relative density were statistically significant (Fig. 5). Relative densities were significantly associated with the spatial smooth, i.e., the interaction between longitude and latitude, indicating a spatial structure and potential spatial autocorrelation in the relative density of Black Terns. Greater relative densities also occurred during late summer and fall, August–October, and were greater on the mid-continental shelf (Fig. 5a, b). Greater relative densities were associated with sea-surface temperatures <29 °C and slightly greater relative densities at chlorophyll-a values of 5–6 mg/m³ (Fig. 5e, f).

Based on habitat associations from the models mentioned above, the predicted relative densities of Black Terns were greatest near the MoM and Corpus Christi, Texas (Fig. 6). These areas are colocated with the greatest observed relative densities of Black Terns. Moderate predicted relative densities occurred on the continental shelf. Moderate relative densities were also predicted in pelagic areas, particularly in the southcentral portion of the study area (Fig. 6). Modeled relative density of Black Terns was low overall in continental slope waters (>200 to ≤2,000 m; Fig. 6).

Our observations confirm that Black Terns are present in the offshore waters of the nGoM for a minimum of 7 months of the year, March–May and July–October, with no strong seasonal patterns associated with occurrence. Using a standardized data-collection protocol, our observations build upon those previous studies using mixed data-collection methodologies (e.g., Ribic et al. 1997, Davis et al. 2000). Specifically, Black Terns were observed by Ribic et al. (1997) from late August to early September. Black Terns were observed in spring and fall by Davis et al. (2000) and in the present study but not in spring by Ribic et al. (1997). Vessel-based surveys to support the post-spill (Deepwater Horizon oil spill) Natural Resources Damage Assessment also observed Black Terns in June (USFWS 2015), indicating that Black Terns are indeed present in offshore, oceanic waters of the nGoM in March through October. eBird, a citizen-based bird observation network, also indicates that Black Terns are present, likely in nearshore or onshore environments where we did not have survey effort for much of the year (Sullivan et al. 2009, Fink et al. 2022). Black Terns were not observed during 34 d of winter survey effort in this study (11 d in Jan 2018, 23 in Feb 2018; Supplemental Table S1), nor were they observed by Ribic et al. (1997) in 16 d of winter survey effort (Feb 1993). Tracking data from adult individuals tagged on breeding grounds indicate that the species occupies areas of the Caribbean Sea and Central and South American waters during winter (Shephard et al. 2023). These patterns may explain, in part, why we did not observe individuals during the core winter months, but it is possible that nonbreeders or juveniles overwinter in the nGoM. Continued study of the occurrence of Black Terns via vessel-based observations and movements via tracking data of multiple life stages (e.g., nonbreeders, juveniles) could improve confidence in the seasonal characterization of Black Terns in the nGoM.

Many of the general patterns we observed in the spatial distribution of Black Terns agree with those reported by Davis et al. (2000). For example, the majority of Black Terns observed by Davis et al. (2000) were over the continental shelf, with few observations occurring in waters >200 m depth. Furthermore, Davis et al. (2000) also noted that Black Terns were encountered more frequently than expected near the Mississippi River outflow. The association of Black Terns with cool, productive waters and their occurrence in the vicinity of river mouths such as the Mississippi River, as well as off the coast from Corpus Christi, Texas (Laguna Madre, Matagorda Bay), may reflect increased prey availability associated with these productive areas. Other river outflows may also support high numbers of Black Terns in shallower waters. With the exception of the MoM near Port of Pascagoula, Mississippi, river mouths were not extensively sampled by NOAA VOOs during our study nor that of Davis et al. (2000). We hypothesize that staging Black Terns may be foraging on schools of bay anchovy (Anchoa mitchilli), a small, ∼75 mm, schooling fish which is one of the most abundant coastal forage fish species in the western Atlantic Ocean (Hildebrand 1963, Houde et al. 2014), and also has a relatively high level of energy density (Lamb et al. 2017). Different age classes and sizes of bay anchovies migrate to and from relatively fresh water (river mouths) to relatively saline (oceanic) habitats at different times in their annual cycle (Morton 1989). The spatial distribution and habitat associations of Black Terns in the Gulf may somewhat reflect the distribution of bay anchovy, which could serve as a seasonally and spatially predictable prey source.

The increase in relative density of Black Terns in the Gulf in late summer through fall appears to coincide with post-breeding migration from breeding grounds in the continental interior to wintering grounds in the Caribbean and Central and South America (Heath et al. 2020, Shephard et al. 2023). For example, tracking data from birds tagged on breeding grounds in Saskatchewan, Michigan, and Ontario in the late summer/early fall demonstrated that Black Terns have a relatively broad temporal migration window and use the nGoM as a stopover on their southward migration (Shephard et al. 2023). Black Terns may arrive in “pulses” around the observed peak in fall, presumably with early nesters or failed breeders arriving first, followed by successful breeders, sub-adults, and lastly, young-of-the-year birds. Before continuing their migration, individuals remained in the Gulf of Mexico for ∼2–6 weeks, with concentrated use occurring near Corpus Christi, Texas, and the Florida Panhandle (Shephard 2023).

Fall also appears to coincide with a spatial shift in the distribution of bay anchovy. Larval and juvenile bay anchovy move from inland areas to brackish water in estuaries, bays, or river mouths in the fall, then move into more saline, oceanic waters in winter, resulting in a predominantly saltwater distribution by late November (Morton 1989). Large schools of larval and juvenile bay anchovy may have attracted the particularly large flocks of Black Terns that we observed near river mouths in the fall. As tracking data from birds tagged on their breeding grounds also indicate the use of the MoM during Black Terns’ northward spring migration (Shephard et al. 2023), the Black Terns we observed this season may have been staging in the nGoM prior to returning to their breeding grounds.

The greater use of the nGoM by tagged Black Terns on their northward spring migration, particularly birds originating from colonies in Michigan and Ontario, versus southward fall migration, contrasts the greater total relative densities observed in the fall versus the spring observed in the GoMMAPPS survey. This difference in apparent seasonal use could relate to low vessel survey coverage in March and April relative to August and September (Shephard et al. 2023). Additional tracking of birds migrating and overwintering could refine the understanding of migration patterns and the degree to which they differ by breeding location, season, and the extent of individual variation. For example, is the observed greater relative use of the Gulf of Mexico on the northward migration by Black Terns tagged in relatively eastern (Michigan, Ontario) colonies consistent over time (Shephard et al. 2023)? Insights from our observations and recent geolocator data confirm spring–fall use of the nGoM by Black Terns, particularly near river mouths and areas of mixing brackish water, further underscoring the important role of the nGoM as a stopover or staging habitat for this long-distance migrant.

As Black Terns do not breed until they are 2 years old (Servello 2000), and the prevalence of nonbreeding adults in the overall population is not thoroughly characterized, the spatial and temporal patterns of distribution and/or abundance of a large portion of the Black Tern population are yet to be understood. While tracking data have documented seasonal movements of Black Terns into and out of the nGoM as a part of their annual migration to more southerly latitudes (Shephard et al. 2023), it is plausible that some segment of the continental Black Tern population may not complete the southbound migration, instead remaining in the nGoM. Instead, some individuals may become seasonal “residents” outside of their core southern wintering area(s), in this case, utilizing areas within the nGoM (or adjacent coastal habitats) until the following northbound spring migration. Our observations did not detect Black Terns during our surveys in January and February, and no observations were made in November and December. Increased vessel-survey effort in the nGoM during the winter could provide insight into the degree that Black Terns occur in and use the nGoM in the winter.

Similarly, not all age-one birds may complete their northward spring migration to the breeding areas from the Gulf during summer. For example, a subspecies of the Red Knot (Calidris canutus rufa), a long-distance migratory shorebird (Baker et al. 2020), appears to exhibit such a migratory strategy, with the Southeast supporting an estimated 25% of the rufa Red Knot population during the nonbreeding period (sensu Tuma and Powell 2021). Including different life-cycle statuses (breeding, nonbreeding, failed breeder, successful breeder) and age classes of marked Black Terns and increasing the level of winter seabird survey effort from VOOs would provide a more comprehensive picture of Black Terns’ use of the nGoM and movements throughout their spatial range and complete annual cycle (e.g., Hostetler et al. 2015, Rushing et al. 2016).

Our research, in combination with historical vessel-based observations (Ribic et al. 1997, Davis et al. 2000) and contemporary geolocator data (Shephard et al. 2023), demonstrate that the nGoM consistently functions as an important migratory staging area for some proportion of the continental population of Black Terns. The frequent occurrence of Black Terns in the nGoM and their association with river outflows and areas of mixing fresh, brackish, and saline waters coinciding with major ports and continental shelf waters results in exposure to multiple anthropogenic threats. High nutrient loads and contamination associated with agricultural production in the Mississippi River have created “dead zones”: oxygen depletion in waters, causing damage to fish and other aquatic species (NRC 2009). Chemical contamination in rivers draining agricultural and port activities and contaminants derived from oil and gas extraction and production can result in bioaccumulation of toxins, potentially impacting Black Terns via trophic pathways (Kuo et al. 2022). Population growth rate and the viability of Black Tern populations are sensitive to adult survival and their migratory behavior can expose them to threats beyond the breeding season (Servello 2000, Matteson et al. 2012, Davis et al. 2023). With high seasonal and spatial exposure to oiling (Michael et al. 2022) and recently observed injury due to oiling events (e.g., Deepwater Horizon spill; Trustees 2016: table 4.7-3), understanding their spatial and temporal distribution and abundance could inform the preparation and response to future events. The installation of offshore wind farms (BOEM 2021) and aquaculture (NOAA 2022) will introduce novel structures and additional infrastructure with the potential to affect the distribution and behavior of Black Terns. As additional energy and aquaculture development continues in the nGoM, the avian conservation community and regulatory agencies will need more data to better inform decisions and better understand the potential effects of development on observed patterns of distribution and abundance of this priority species.

We are grateful to the crews and NOAA field party chiefs of the R/V Gordon Gunter, Oregon II, and Pisces for their logistical support. We also thank the many seabird observers who participated in GoMMAPPS: JM Andrew, D Bauer, PJ Blank, D Breese, ET Hug, M Love, M McDowell, N Metheny, M Oberle, J Panaccione, and S Paxton. We thank N Niemuth for feedback on an early version of the manuscript and 2 anonymous reviewers. All feedback furthered the development of this manuscript. Funding for GoMMAPPS surveys was provided by the U.S. Department of the Interior, Bureau of Ocean Energy Management through Intra-Agency Agreement M17PG00011 with the U.S. Department of Interior, Fish and Wildlife Service via an Intra-Agency Agreement 4500108172-F17IA00005 with the U.S. Geological Survey, South Carolina Cooperative Fish and Wildlife Research at Clemson University. The South Carolina Cooperative Fish and Wildlife Research Unit is jointly supported by the U.S. Geological Survey, South Carolina DNR, and Clemson University. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The findings and conclusions in this paper are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service. The observational data used in the analyses are publicly available and can be accessed through the National Centers for Environmental Information (NCEI) archives: https://www.ncei.noaa.gov/archive/accession/0247206 and DOI https://doi.org/10.25921/afrq-h385 (Gleason et al. 2022).