A Decadal Change in Shorebird Populations in Response to Temperature, Wind, and Precipitation at Hilton Head Island, South Carolina, USA

Simple Summary

Climate change is causing many shorebird species to decline across North America despite increased conservation efforts. This study looks at how changing weather conditions, like temperature, rainfall, and wind, affect shorebird populations over ten years at Fish Haul Beach in Hilton Head Island, South Carolina, USA. Twelve shorebird species were monitored to see how their numbers increased or decreased in response to meteorological variability. The study found that species like the Black-bellied Plover, Marbled Godwit, and Willet have significantly decreased, while others, such as the Semipalmated Plover and Piping Plover, are showing increases, possibly due to better habitats or conservation success. The temperature had a major impact on population sizes. These findings highlight how sensitive shorebirds are to meteorological variability and the importance of considering these factors in conservation planning. Understanding how weather influences shorebirds can help protect these species and maintain healthy coastal environments in South Carolina.

Abstract

Despite increasing conservation efforts for shorebirds, there are widespread declines in many shorebird species in North America. Climate change is causing significant shorebird range shifts and population declines. This study investigates the relationship between meteorological variability and shorebird population dynamics over ten years (2014–2023) at Fish Haul Beach, Hilton Head Island, South Carolina, USA. Shorebirds, reliant on specific habitats for breeding and foraging, are increasingly vulnerable to climate-driven changes, including shifts in temperature, precipitation, and wind speed. Using Generalized Additive Models with Poisson distribution, we analyzed species-specific count data for 12 shorebird species in relation to annual meteorological variables. Additionally, the Mann–Kendall test and Sen’s slope were employed to assess decadal trends in population counts. The results reveal significant declines in Black-bellied Plover (Pluvialis squatarola), Marbled Godwit (Limosa fedoa), and Willet (Tringa semipalmata). In contrast, Semipalmated Plover (Charadrius semipalmatus) and Piping Plover (Charadrius melodus) showed increasing trends, indicating potential habitat benefits or conservation success. Temperature emerged as a key driver affecting the abundance of several species, while precipitation and wind speed also played crucial roles in shaping population dynamics. Our findings underscore the sensitivity of shorebird populations to weather fluctuations, emphasizing the need for integrating meteorological variability into management strategies to ensure shorebird conservation. This study provides critical insights into the impacts of meteorological variables on migratory shorebird populations along the Atlantic Flyway. It highlights the importance of maintaining healthy coastal ecosystems in South Carolina.

1. Introduction

Climate change is causing significant species range shifts, contractions, temporal changes in migration chronology, and (local) extirpations [1,2,3]. Migratory species may be especially vulnerable to these changes as they move between multiple geographic regions and are consequently affected by environmental conditions worldwide [4,5]. In recent decades, migratory bird populations have experienced alarming declines worldwide, raising questions about the timing and locations of these declines within their annual cycles [2,6]. Recent studies suggest that shorebirds are experiencing rapid population changes caused by climate change [7,8,9,10].

Shorebirds are fascinating creatures. Each year, they undertake phenomenal migration in a north–south direction to avoid frigid weather. Nearctic–neotropical migratory shorebirds traverse thousands of kilometers across the Western Hemisphere bi-annually and rely on a network of coastal and interior wetland ecosystems [11], selecting specific wetland types to fulfill their energetic needs [12]. During migration, many species of shorebirds concentrate on a few critical sites for survival [11,13].

Shorebird populations across North America have experienced dramatic declines, with an estimated 70% reduction since 1973 [9]. Among the hardest-hit species are those that breed in the Arctic, where environmental changes, including climate change, have compounded the challenges these birds face [14,15]. A survey by the International Wader Study Group in 2003 revealed that of 207 shorebird populations with known population trajectories (out of a total of 511 known shorebird populations), almost half (48%) were in decline, whereas only 16% were increasing [16,17,18]. This concerning trend has placed shorebirds among the most endangered long-distance migratory birds globally, with three times as many populations in decline as those increasing [19,20].

The Atlantic Flyway is a crucial migratory route for numerous shorebird species, facilitating their seasonal movements between breeding and nonbreeding grounds. This flyway contains diverse habitats, including coastal wetlands, intertidal zones, and estuaries, which are essential for the survival of shorebirds during migration. Recent studies indicate alarming declines in shorebird populations across North America, with a significant proportion of these declines attributed to habitat loss, climate change, and anthropogenic disturbances [9]. In particular, South Carolina is a vital stopover site along the Atlantic Flyway, where shorebirds rely on its coastal ecosystems for refueling during their long migrations [21]. Coastal South Carolina is experiencing significant habitat degradation due to coastal development, increased recreational activities, and rising sea levels, which threaten the availability of essential foraging and nesting sites for migratory shorebirds [22,23]. The impacts of climate change further exacerbate these challenges, leading to alterations in habitat quality and availability, thereby contributing to the observed declines in shorebird abundance [9,23]. Addressing these issues is crucial for the conservation of shorebird populations in South Carolina and throughout the Atlantic Flyway.

The impact of climatic variables on shorebird populations is a critical area of research, particularly in the context of ongoing climate change. Shorebirds are increasingly vulnerable to alterations in temperature, wind speed, and precipitation patterns [14,24,25,26]. These climatic factors not only influence the immediate availability of suitable habitats but also affect the broader ecological interactions that underpin shorebird populations. Temperature changes can have profound effects on the phenology of shorebirds, impacting their breeding success and migratory patterns. Long-term datasets have been analyzed to evaluate the synchrony between shorebird hatching periods and peak invertebrate abundance, emphasizing the potential for phenological mismatches resulting from climate-induced shifts in invertebrate emergence [27]. Warming temperatures can create mismatches between the timing of shorebird migrations and the availability of food resources, mainly invertebrates, that are essential for chick development [27]. Wind speed is another climatic variable that can influence shorebird behavior and distribution. High wind speeds can deter shorebirds from foraging effectively, particularly in coastal areas where they rely on specific feeding strategies. Local and landscape variables, including wind conditions, significantly affect wetland bird habitat use during migration, indicating that adverse weather can limit access to critical resources [28]. Extensive field surveys and spatial modeling techniques have been utilized to analyze shorebird responses to wind speed and other weather factors, demonstrating that adverse weather conditions can restrict access to critical resources during migration [28]. A study utilizing long-term monitoring, remote sensing, and spatial modeling found that changes in rainfall can lead to alterations in wetland hydrology, affecting the availability of foraging habitats. Drought conditions can significantly impact the abundance and distribution of nonbreeding shorebirds, emphasizing the importance of maintaining healthy wetland ecosystems to support these populations during extreme weather events [29].

Long-term monitoring and time-series data are necessary to distinguish a directional trend from annual population fluctuations and the relative roles of climatic factors, especially where there are time lags and complex relationships among potential population drivers [30,31,32]. Our study explores the relationship between shorebird populations and meteorological variables over 10 years on Fish Haul Beach at Hilton Head Island, South Carolina, USA. By analyzing species-specific count data alongside meteorological variables such as annual mean average temperature, annual mean precipitation, and annual mean average wind speed, this research aims to uncover potential non-linear relationships that may explain how meteorological variability affects shorebird abundance. Specifically, our objectives are to (1) analyze species-specific responses to meteorological variables and (2) identify decadal population trends for each species. By examining population trends in the context of meteorological variability, this research aims to inform management strategies for shorebird conservation in the region and contribute to the broader understanding of shorebird population dynamics across coastal South Carolina and the Atlantic Flyway.

2. Study Area

Our study area for this research is located at Fish Haul Creek (32°14′13.69″ N, 80°40′37.12″ W), where it enters Port Royal Sound on Hilton Head Island, South Carolina, USA (Figure 1). The survey area is adjacent to the Atlantic Ocean, offering a mix of beach and mudflat habitats essential for shorebirds [33]. Access to the area begins at the “Hilton Head Steam Gun”, with counts initiated at the beach access point and continuing north along the creek’s confluence with the sound. The survey area spans 3.2 to 4.0 ha, covering key coastal habitats that are influenced by tidal changes and provide feeding grounds for migratory shorebirds [34,35]. Hilton Head Island is a popular tourist destination with developed coastlines containing golf courses, several resorts, and public beach access points with high levels of human and dog disturbance.

3. Materials and Methods

3.1. Shorebird Counting Data

The survey was conducted monthly (January–December) for ten years (2014–2023) on Hilton Head Island near Fish Haul Creek at its confluence with Port Royal Sound (Figure 1). Counting was initiated at the “Hilton Head Steam Gun” beach access point and continued north to the creek–sound confluence and then west in the mud flat area on the northern side of the creek [36]. The survey area is approximately 3.2 to 4.0 ha. Birds were counted individually using the direct count method to derive a total count of birds. Shorebird counts were recorded by species using binoculars (Nikon Monarch M511, 10 × 42, Nikon Corp., Minato City, Tokyo, Japan; Swarovski NL 8 × 32, Swarovski Optics, Absam, Tyrol, Austria) and spotting scopes (Swarovski ATS-80 20-60×, Swarovski Optics, Absam, Tyrol, Austria) from distances that minimized the disturbance of birds present in the survey area [37]. Efforts were made to limit external disturbances during the count window by posting signage and engaging with beach visitors to prevent disruptions within the survey area. Although these measures were not entirely effective throughout the 10-year study period, they contributed to reducing the frequency and intensity of human and dog disturbances. Two observers used spotting scopes and binoculars, while a third observer recorded the observations and took photographs using Canon SX60 HS and SX70 HS, Melville, NY, USA. We sampled as much as possible without replacement by scanning from one end of the flock to the other. Birds that were flushed were added to the count. The average time surveying the area was approximately 90–120 min during peak population months, although survey times were considerably less during the summer months (May to August) [38]. No counting occurred on the team’s southerly return unless a previously unseen species was observed. Surveys were always initiated approximately 2 h after high tide, although this was sometimes extended to 2.5 h when tides were higher than normal [39]. Species nomenclature for scientific names, authorities, and common names follows [40,41] and four-letter species code used following Bird Banding Laboratory standards. Each species’ annual total counts were analyzed in relation to meteorological variables to investigate potential correlations with meteorological conditions and reveal abundance trends that meteorological influences may shape.

3.2. Meteorological Data

Monthly and annual mean average temperature (°C), precipitation (mm), and wind speed (Km/h) data for Hilton Head Island were collected from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) (https://www.weather.gov/) (accessed on 26 July 2024) [42] to assess their impacts on shorebird abundance. The NOAA provides comprehensive and reliable area-specific meteorological data from their NWS stations, which is crucial for understanding climatic trends and their ecological implications. This study used mean annual meteorological values to represent overall environmental conditions over time, providing a stable basis for analyzing long-term population dynamics. Selecting annual mean values over median values is a common approach in ecological studies, as means are more sensitive to population fluctuations and better capture meteorological variability that may influence shorebird abundance and behavior.

3.3. Statistical Analysis

3.3.1. Trend Analysis Using the Mann–Kendall Test and Sen’s Slope

We applied the Mann–Kendall test [43,44] to each shorebird species’ yearly total population counts to assess the temporal trends in shorebird species counts and to detect the presence of any significant monotonic trends over the 10 years. The Mann–Kendall test is a nonparametric test widely used for identifying monotonic trends in time-series data. The Mann–Kendall test evaluates whether a statistically significant trend (increasing, decreasing, or stable) exists over time without assuming normal distribution [45,46], and this method can be applied to data showing seasonal fluctuations [47]. The Mann–Kendall test for each species was performed using the Kendall () function from the “Kendall” package, and graphics were generated with the R v.4.3.3 software [48]. The test calculates a statistic S, which is based on the differences between each pair of data points. For a dataset {x₁, x₂,…,x_n}, the statistic S is calculated as

$$S=\sum _{i=1}^{n-1} \sum _{j=i+1}^n sgn\left(x_j{-x}_i\right)$$

(1)

where n is the number of data points, x_j and x_i are annual values in years j and i, j > 1, and sign (x_j − x_i) is calculated using the equation:

$$\mathrm{s}\mathrm{i}\mathrm{g}\mathrm{n}\left(x_j-x_k\right)=\left\{\begin{array}{c}1 if x_j-x_k>0\\ 0 if x_j-x_k=0\\ -1 if x_j-x_k<0\end{array}\right.$$

(2)

The variance Var(S) of the test statistic is also calculated to account for tied values in the data. The standardized test statistic, Z, is calculated as follows:

$$Z=\left\{\begin{array}{c}{\displaystyle \frac{S-1}{\sqrt{VAR \left(S\right)}}} If S>0\\ 0 if S=0\\ {\displaystyle \frac{S+1}{\sqrt{VAR \left(S\right)}}} If S<0\end{array}\right.$$

(3)

Based on this analysis, positive Z-values indicate an increasing trend, while negative Z-values indicate a decreasing trend in the time series. Statistical significance was determined by comparing the Z-statistic to the critical value from the standard normal distribution. At a 95% confidence level (α = 0.05), a trend was considered significant if ∣Z∣ > 1.96, as per the standard normal distribution table.

Additionally, we used Sen’s slope estimator [49] to quantify the rate of change over time. Sen’s slope is the median of the slopes calculated between all pairs of data points. Sen’s slope was calculated using the “trend” package’s sens.slope () function in R statistics software. For a given time series x_i = x₁, x₂ … x_n, with N pairs of data, the slope is calculated as follows:

$${\beta }_i={\displaystyle \frac{x_j-x_k}{j-k}}, \forall k\le j and i=\mathrm{1,2}......N$$

(4)

Median of N values of β_i gives the Sen’s estimator of the slope, β.

$$\beta =\left\{\begin{array}{c} {\beta }_{ {\displaystyle \frac{ N+1}{2}}} if N is odd\\ {\displaystyle \frac{1}{2}}\left[{\beta }_{{\displaystyle \frac{N}{2}} }+{\beta }_{{\displaystyle \frac{N+2}{2}} }\right] if N is even\end{array}\right.$$

(5)

The median of these slopes represents the overall rate of change in the dataset. A positive Sen’s slope indicates an increasing trend, while a negative slope indicates a decreasing trend.

This combined approach allowed us to detect the presence and direction of trends in the population counts of shorebird species over time, while Sen’s slope provided an estimate of the rate of change for each species.

3.3.2. Generalized Additive Models (GAMs) with Poisson Distribution and Log Link

We used Generalized Additive Models (GAMs) with a log link function under a Poisson distribution [50] to model the relationship between each shorebird species counts and meteorological variables. We used shorebird population data collected annually from 2014 to 2023. The response variable for the analysis was the annual count of each shorebird species, representing the abundance of the species. The predictor variables were annual meteorological measures, specifically annual mean average temperature (°C), annual mean precipitation (mm), and annual mean average wind speed (Km/h). The GAMs were fitted using a Poisson distribution, which is suitable for count data, and a log link function, which ensures that the predicted values are non-negative. The GAM model was fitted by using the gam() function of the “mgcv” package with restricted maximum likelihood (REML) [50]. REML was used to ensure stable and unbiased estimates of smooth terms. Model fits were checked based on the residuals with the gam.check() function of the “mgcv” package. All statistical analyses were performed, and graphics were generated with the R v.4.3.3 software [48] in the RStudio environment.

The general form of the GAM with log link is as follows:

log (E(Y_i)) = β₀ + f₁ (X₁) + f₂ (X₂) + … + f_k (X_k)

(6)

where

• log is the log link function;
• E(Y_i) is the expected count of shorebirds in year i;
• β₀ is the intercept;
• f_k (X_k) represents smooth functions of the predictor variables (X₁ = annual mean average temperature, X₂ = annual mean precipitation, X₃ = annual mean average wind speed).

Smooth terms f_k were modeled using the smoothing function s (X, k), where k = 8 controls the smoothness of the fitted curve, preventing overfitting by limiting the complexity of the model. This allows the model to account for potential non-linear relationships between the climatic variables and species abundance.

For the Poisson distribution, the relationship between the observed species count (y_i) and the expected mean count (μ_i) is defined as follows:

y_i ~ poisson(µ_i)

(7)

where the variance is equal to the mean, i.e., Var (y_i) = µ_i.

Three models were constructed to evaluate the impact of meteorological variables on shorebird abundance:

Model 1: Annual mean average temperature as the only predictor.

log (E(Y_i)) = β₀ + s(Annual.Avg.Temp, k = 8)

(8)

Model 2: Both annual mean average temperature and annual mean precipitation as predictors.

log (E(Y_i)) = β₀ + s(Annual.Avg.Temp, k = 8) + s(Annual.Precip, k = 8)

(9)

Model 3: Annual mean average temperature, annual mean precipitation, and annual mean average wind speed as predictors.

log (E(Y_i)) = β₀ + s (Annual.Avg.Temp, k = 8) + s(Annual.Precip, k = 8) + s(Annual.Avg.Wind.Speed, k = 8)

(10)

The three models were compared using the Akaike Information Criterion (AIC), defined as follows:

AIC = −2log(L) + 2k

(11)

where

• L is the likelihood of the model;
• k is the number of estimated parameters in the model.

A lower AIC value indicates a better model fit, striking a balance between goodness of fit and model complexity. The model with the lowest AIC value was selected as the best model.

The significance of the smooth terms for each predictor variable was assessed using p-values from the model summary, and residual diagnostics were checked using “gam.check()” to ensure model adequacy. Model summaries were obtained using the “summary()” function, which provides detailed information about the smooth terms and their contribution to the model.

4. Results

Over the survey period, a total of 20 shorebird species were recorded at the survey location (Appendix A). Twelve species were selected for analysis due to their regular annual presence and relative abundance (

Table 1. Total count of selected 12 shorebird species from 2014 to 2023 from Port Royal Beach at Hilton Head Island, South Carolina.

Species	Scientific Name	Total Count
Black-bellied Plover	Pluvialis squatarola	1709
Dunlin	Calidris alpina	18,376
Least Sandpiper	Calidris minutilla	975
Marbled Godwit	Limosa fedoa	2207
Piping Plover	Charadrius melodus	558
Red Knot	Calidris canutus	1578
Ruddy Turnstone	Arenaria interpres	709
Sanderling	Calidris alba	5992
Semipalmated Plover	Charadrius semipalmatus	16,279
Short-billed Dowitcher	Limnodromus griseus	1682
Western Sandpiper	Calidris mauri	696
Willet	Tringa semipalmata	1207

).

4.1. Mann–Kendall Test and Sen’s Slope for Trend

4.1.1. Significant Trends

The results of the Mann–Kendall test and Sen’s slope analysis revealed significant trends in population counts for several shorebird species (

Table 2. Summary of Mann–Kendall trend analysis along with Sen’s slope for shorebird yearly count. The light green color indicates a statistical significance trend. Red indicates a decreasing trend, and blue indicates an increasing trend over the study period.

Species	Mann–Kendall Z	Trend	Sen’s Slope
Black-bellied Plover	−1.97	Decreasing	−23.00
Dunlin	1.07	No trend	154.66
Least Sandpiper	−0.18	No trend	−0.33
Marbled Godwit	−1.97	Decreasing	−23.33
Piping Plover	2.78	Increasing	9.00
Red Knot	0.36	No trend	6.12
Ruddy Turnstone	1.17	No trend	3.33
Sanderling	−0.18	No trend	−8.00
Semipalmated Plover	2.68	Increasing	295.50
Short-billed Dowitcher	−0.63	No trend	−10.83
Western Sandpiper	−0.36	No trend	−3.40
Willet	−2.68	Decreasing	−13.00

, Figure 2) over the study period. Notably, Black-bellied Plover (Z = −1.97) and Marbled Godwit (Z = −1.97) showed statistically significant decreasing trends, with Sen’s slope values of −23.00 and −23.33, respectively, indicating notable declines in these species populations. Similarly, Willet (Z = −2.68) exhibited a significant decrease with a Sen’s slope of −13.00, further suggesting a downward trend in population numbers. On the other hand, Piping Plover (Z = 2.78) and Semipalmated Plover (Z = 2.68) showed significant increasing trends, with Piping Plover showing a moderate increase with a slope of 9.00 and Semipalmated Plover demonstrating a substantial population rise with a slope of 295.50, suggesting potential habitat benefits or successful conservation measures for these species.

4.1.2. Non-Significant Trends

Several species displayed non-significant trends, indicating that changes in their populations were not statistically meaningful during the study period (Table 2). For instance, Dunlin (Z = 1.07), Ruddy Turnstone (Z = 1.17), and Red Knot (Z = 0.36) exhibited increasing trends with Sen’s slope estimates, but the results were not statistically significant, with slopes of 154.66, 3.33, and 6.12, respectively. On the other hand, species such as Least Sandpiper (Z = −0.18), Short-billed Dowitcher (Z = −0.63), Western Sandpiper (Z = −0.36), and Sanderling (Z = −0.18) exhibited decreasing trends with Sen’s slope estimates. However, these trends were also non-significant, with slopes of −0.33, −10.83, −3.40, and −8.00, respectively. These non-significant results suggest variability and random fluctuations in population counts over time, but they do not indicate a consistent decadal trend.

4.2. Poisson Generalized Additive Models (GAMs) with a Log Link Function

Out of the 12 shorebird species analyzed, eight species were best explained by Model 2, which included temperature and precipitation as predictors (

Table 3. Summary of GAMs with log link for individual shorebird species, including the best-fitting model and covariates. The table presents the Akaike Information Criterion (AIC) values for the best-fitting models for each species. Additionally, the effective degrees of freedom (edf), reference degrees of freedom (Ref.df), chi-squared values (Chi.sq), and p-values for each covariate are provided. These metrics indicate the significance and contribution of temperature (Temp), precipitation (Precip), and wind speed (Wind) in explaining the variation in shorebird counts.

Species	Model	AIC	Covariant	edf	Ref.df	Chi.sq	p-Value
Black-bellied Plover	Model 2: Temp + Precip	88.01	Temp	5.23	5.24	11.05	0.0291
			Precip	3.74	3.76	2.47	0.7102
Dunlin	Model 3: All Predictors	112.44	Temp	3.13	3.31	608.39	<2 × 10−16
			Precip	3.52	3.84	164.93	<2 × 10−16
			Wind	1.50	1.59	92.58	<2 × 10−16
Least Sandpiper	Model 2: Temp + Precip	71.16	Temp	1.71	1.83	15.60	0.0016
			Precip	4.12	4.24	7.09	0.1524
Marbled Godwit	Model 3: All Predictors	88.97	Temp	1.00	1.00	13.77	0.0002
			Precip	6.01	6.10	290.67	<2 × 10−16
			Wind	1.00	1.00	6.93	0.0084
Piping Plover	Model 2: Temp + Precip	75.36	Temp	6.37	6.51	120.21	<2 × 10−16
			Precip	2.33	2.43	10.38	0.0242
Red Knot	Model 2: Temp + Precip	77.40	Temp	7.00	7.00	98.00	<2 × 10−16
			Precip	1.84	1.98	1.57	0.4750
Ruddy Turnstone	Model 2: Temp + Precip	77.08	Temp	2.06	2.34	3.47	0.2383
			Precip	4.94	5.71	27.42	0.0008
Sanderling	Model 2: Temp + Precip	101.25	Temp	6.77	6.97	232.82	<2 × 10−16
			Precip	1.00	1.00	33.98	<2 × 10−16
Short-billed Dowitcher	Model 2: Temp + Precip	86.44	Temp	5.06	5.07	11.76	0.0200
			Precip	3.93	3.94	1.81	0.7830
Semipalmated Plover	Model 3: All Predictors	107.86	Temp	6.94	6.96	3514.23	<2 × 10−16
			Precip	1.01	1.02	16.62	0.0001
			Wind	1.02	1.02	16.33	<2 × 10−16
Western Sandpiper	Model 2: Temp + Precip	75.92	Temp	6.24	6.24	24.87	0.0002
			Precip	1.74	1.75	1.72	0.4751
			Wind	1.01	1.01	0.46	0.5002
Willet	Model 3: All Predictors	85.29	Temp	2.28	2.37	1.22	0.4833
			Precip	1.42	1.45	21.86	0.0004
			Wind	4.89	5.12	29.61	0.0003

). These species were Black-bellied Plover, Least Sandpiper, Piping Plover, Red Knot, Ruddy Turnstone, Sanderling, Short-billed Dowitcher, and Western Sandpiper. The remaining four species were best explained by Model 3 (Table 3), which incorporated all three predictors, which include temperature, precipitation, and wind speed. These species included Dunlin, Marbled Godwit, Semipalmated Plover, and Willet (Table 3).

In terms of the statistical significance (p < 0.05) of meteorological variables, five species, Black-bellied Plover, Least Sandpiper, Red Knot, Ruddy Turnstone, and Western Sandpiper, had only one significant climatic variable influencing their abundance, with temperature being the most prominent factor (Figure 3). Another four species, Dunlin, Piping Plover, Sanderling, and Short-billed Dowitcher, were significantly affected by two climatic variables. Specifically, Dunlin was influenced by temperature and wind speed (Figure 3), while Piping Plover, Sanderling, and Short-billed Dowitcher were influenced by temperature and precipitation (Figure 3). Lastly, three species, Marbled Godwit, Semipalmated Plover, and Willet, had all three meteorological variables (temperature, precipitation, and wind speed) showing statistically significant impacts on their abundance (Figure 3).

5. Discussion

The results of this study present critical insights into how meteorological change influences the population dynamics of shorebird species on Hilton Head Island, South Carolina. The combination of decadal trend analysis using the Mann–Kendall test and Sen’s slope estimation, along with the application of GAMs, has revealed both long-term trends and species-specific responses to meteorological variability.

5.1. Population Trends over Time

Conservation action to maintain healthy shorebird and other waterbird populations requires a basic understanding of trends and threats to populations at various spatial scales [51,52,53]. The observed decadal trends in our study reveal a complex interplay between positive and negative population changes, exhibiting both the challenges and successes of conservation efforts in the region.

Our study identified significant decreasing trends for three species, Black-bellied Plover, Marbled Godwit, and Willet, suggesting that these species experienced substantial population declines over the study period. Mainly, Black-bellied Plover and Marbled Godwit showed sharp declines, highlighting the severity of the population decline. These declines could be attributed to a combination of factors such as habitat degradation due to urban development, beach renourishment, meteorological change, and increased human activity, particularly in sensitive coastal habitats [54,55,56,57].

Two species, Semipalmated Plover and Piping Plover, exhibited statistically significant increasing trends. These positive trends could indicate successful conservation efforts or favorable environmental conditions over the last decade. The increase in Piping Plover populations is particularly noteworthy, as this species is listed as threatened under the U.S. Endangered Species Act [58]. An increase of 93% in Piping Plover abundance by 2018 was observed in Long Island, New York, which serves as a critical nesting habitat for many shorebird species in the Atlantic Flyway [56]. Research has highlighted ongoing challenges for Piping Plovers in high-disturbance areas like Hilton Head Island, where individuals tend to exhibit lower body mass and survival rates compared to those in less disturbed sites [54]. These findings suggest that human and animal stressors may impact body condition and population stability, emphasizing the need to manage disturbance levels to ensure the long-term viability of Piping Plovers.

Other species, such as Dunlin, Red Knot, and Ruddy Turnstone, showed positive trends in population counts. Still, these trends were not statistically significant, suggesting that the observed increases may be due to short-term fluctuations rather than consistent decadal growth. However, despite lacking statistical significance, the presence of species like Red Knot, which is listed as threatened under the U.S. Endangered Species Act, is important and highlights the inherent fluctuations in their population that can occur due to a variety of ecological factors, including food availability and climatic conditions [59]. Species such as Least Sandpiper, Sanderling, Short-billed Dowitcher, and Western Sandpiper showed non-significant declines, indicating variability without clear decadal trends. These species may be experiencing local fluctuations in response to transient environmental changes or stochastic events.

5.2. Meteorological Drivers of Population Changes

The application of GAMs has revealed that meteorological variables, specifically temperature, precipitation, and wind speed, significantly affected the abundance of various shorebird species in the study area. Out of the 12 species analyzed, eight were best explained by a model incorporating temperature and precipitation, while the remaining four species showed significant relationships with all three meteorological variables. This nuanced understanding of species responses to meteorological factors is crucial for informing conservation strategies aimed at mitigating the impacts of climate change on shorebird populations.

The significant influence of temperature on the abundance of species such as the Black-bellied Plover, Dunlin, Least Sandpiper, Marbled Godwit, Piping Plover, Red Knot, Sanderling, Short-billed Dowitcher, Semipalmated Plover, and Western Sandpiper highlights the importance of monitoring and managing habitat conditions that are favorable to their survival. Recent studies have highlighted the sensitivity of shorebirds to temperature fluctuations, which can affect their foraging efficiency, reproductive success, and migration patterns [60,61]. For instance, as temperatures rise, the availability of prey species may shift, leading to decreased foraging success for shorebirds that rely on specific food sources during critical migratory stopovers [62]. Some shorebird species are highly reliant on American Horseshoe Crab (Limulus polyphemus) eggs for refueling during northward migration stopovers [63,64]. If climate change affects the timing of Horseshoe Crab breeding, this would disrupt synchronicity between Horseshoe Crab egg laying and spring migration. This relationship emphasizes the need for habitat management practices that consider the thermal dynamics of coastal environments.

Our findings indicate that precipitation plays a significant role in influencing the abundance of several shorebird species, including the Dunlin, Marbled Godwit, Piping Plover, Ruddy Turnstone, Sanderling, Semipalmated Plover, and Willet. Changes in precipitation patterns can impact the availability of suitable foraging habitats, particularly in coastal areas where wetland dynamics are closely tied to rainfall and where drought conditions can significantly affect the abundance and distribution of nonbreeding shorebirds [29]. Drier overall conditions may be likely and may reduce food availability during the breeding season [65]. A previous study highlights the vital link between precipitation and survival in long-distance migratory shorebirds, emphasizing the importance of managing and conserving staging habitats, such as stopover sites vulnerable to drying due to climate change [66]. Our findings and these studies highlight the importance of maintaining healthy wetland ecosystems to support these populations during extreme weather events.

The inclusion of wind speed as a significant predictor for species such as Dunlin, Marbled Godwit, Semipalmated Plover, and Willet further illustrates the complexity of factors affecting shorebird populations. Wind conditions can influence migratory behavior and energy expenditure during flight, which may ultimately affect survival and reproductive success [67,68]. Several studies have demonstrated that high migratory activity among birds often coincides with favorable winds [69,70,71]. Many Arctic-breeding shorebirds fly incredibly long distances between their breeding areas in the north and stopover and wintering sites further south [72,73,74], and they predominantly migrate on occasions and at altitudes with favorable winds [75,76,77,78,79,80]. Our study further contributes to this knowledge by providing insights into the effects of monthly and annual wind variability on shorebird abundance within the study area. While previous studies have examined wind influences on broad migration patterns, our findings offer localized insights that are crucial for region-specific conservation planning. Understanding these dynamics is critical for developing effective conservation strategies that incorporate wind conditions into habitat management efforts, ensuring that shorebird populations have access to suitable stopover sites under varying wind conditions that enhance conservation planning in the Atlantic Flyway.

The strong relationships between meteorological variables and species counts underscore the sensitivity of shorebirds to environmental fluctuations. As coastal ecosystems become increasingly vulnerable to the impacts of climate change, including rising sea levels and changes in precipitation patterns, shorebird populations may face greater challenges in maintaining stable numbers. The observed non-linear effects of temperature, precipitation, and windspeed highlight the complexity of these dynamics, as species may respond differently depending on their habitat requirements and foraging strategies.

Overall, this study highlights the importance of integrating meteorological variability into shorebird conservation efforts, emphasizing the need for long-term monitoring to understand population trends and inform adaptive management strategies, particularly for threatened and endangered species. The annual fluctuations in weather conditions can significantly influence shorebird population dynamics, affecting habitat availability and food resources. Effectively addressing these fluctuations requires proactive conservation measures that account for both long-term climatic trends and short-term meteorological variability. As climate change continues to reshape coastal ecosystems, implementing strategic measures will be essential to mitigate its effects and enhance the resilience of shorebird populations in the face of environmental uncertainty.

While this study provides valuable insights into the relationship between meteorological variability and shorebird population dynamics, several limitations should be acknowledged. The relatively small spatial scale of the study area and the ten-year observational period may limit the generalizability of the findings to broader geographic regions and longer-term climatic trends. Additionally, unmeasured factors such as habitat quality changes, predation pressures, and human disturbances could have influenced shorebird populations. Future research should expand the spatial scale of observations, incorporate additional environmental and anthropogenic factors, and utilize advanced remote sensing techniques or drone-based aerial surveys to better understand habitat dynamics. Long-term monitoring programs and interdisciplinary approaches integrating ecological and social factors will be crucial to developing effective conservation strategies in response to climate change.

6. Conclusions

This study highlights the significant influence of meteorological variability on shorebird populations at Hilton Head Island, South Carolina, over a decade. Our findings indicate that temperature, precipitation, and wind speed play crucial roles in shaping shorebird abundance trends, with species responding differently to these meteorological factors. Notable declines in Black-bellied Plover, Marbled Godwit, and Willet underscore the need for targeted conservation measures, while increases in Piping Plover and Semipalmated Plover suggest potential management successes. The study underscores the importance of long-term monitoring and adaptive conservation strategies to mitigate climate change impacts and ensure the resilience of shorebird populations in the Atlantic Flyway. Future research should focus on integrating additional environmental and anthropogenic factors to enhance conservation efforts.