The study area encompasses the northern coast of the San Matías Gulf, in northeastern Patagonia, Argentina, extending approximately 180 km from the mouth of the Negro River to the town of San Antonio Oeste (SAO) (Fig. 1). This area includes a series of villages and recreational areas, such as El Cóndor, La Lobería, Bahía Rosas, Bahía Creek, Caleta de los Loros, San Antonio Este and Oeste, and Las Grutas (Carbone et al., 2007; Kokot and Favier-Dubois, 2017). The combined permanent population of these towns was 22 205 inhabitants in 2010 (INDEC, 2010), with a significant seasonal increase due to tourism, particularly in Las Grutas, which averaged 118 000 visitors annually between 2006 and 2023 (INDEC, 2023). The area is connected by National Route No. 3 and Provincial Route No. 1, the latter running parallel to the coast. The presence of natural protected areas – Bahía de San Antonio, Punta Bermeja, and Pozo Salado–Caleta de los Loros–Punta Mejillón – highlights the region's ecological value, hosting diverse flora and fauna, as well as archaeological sites evidencing human occupation since at least 6000 years BCE (Favier-Dubois and Kokot, 2011; Marcos and Mancini, 2012; Favier-Dubois, 2013).

The climate is classified as cold semi-arid (BSk) according to the Köppen–Geiger system (Peel et al., 2007). Mean annual temperatures range from 14 to 17 °C, with maxima in January (22.2–23.6 °C) and minima in July (7.1–7.5 °C) (Fig. 2). Annual precipitation increases from west to east, averaging 292 mm in SAO and 383 mm in Viedma for the 1991–2020 period, with higher values typically recorded in late summer and autumn (Servicio Meteorológico Nacional, 2024). Annual evapotranspiration ranges from 1050 to 1485 mm, and mean air humidity was 57 % during 1961–2000 (Genchi et al., 2010; Bohn et al., 2014).

The region lies within the belt of mid-latitude westerlies, driven by the South Pacific and South Atlantic anticyclones and the subpolar low-pressure channel (del Valle et al., 2008; Rusticucci et al., 2016; Montes et al., 2017). These winds induce a mean surface circulation towards the E–ENE (Saavedra, 2011; Pisoni, 2012). Wind speeds are generally highest during the austral summer, exacerbating aridity in an already dry environment shaped by the rain-shadow effect of the Andes (Montes et al., 2017). Dominant summer wind directions are W, SW, and NW. Easterly winds, associated with sea breezes, are more frequent in winter, bringing moisture, cloud cover, and dew (López Alfonsín et al., 2012; Agosta et al., 2019).

The Southern Annular Mode (SAM) is the dominant mode of extratropical climate variability in the Southern Hemisphere. Its positive phase is associated with a poleward contraction and intensification of the westerly wind belt, leading to warmer, drier conditions at mid-latitudes (∼ 40° S). Conversely, the negative phase is characterized by increased precipitation, cooler temperatures, and a northward expansion of storm tracks (Berman et al., 2012; Fogt and Marshall, 2020). A positive trend in the SAM index has been observed since the mid-20th century, particularly in summer, linked to greenhouse gas increases and stratospheric ozone depletion (Fogt and Marshall, 2020).

The El Niño–Southern Oscillation (ENSO) also influences regional climate. El Niño events are typically associated with increased annual precipitation (up to +50 mm) and higher autumn-winter temperatures in the study area, while La Niña phases correlate with reduced precipitation and cooler summers (Servicio Meteorológico Nacional, 2014). The interplay between SAM and ENSO can amplify or attenuate their individual climatic signals (Rusticucci et al., 2016).

The tides in the San Matías Gulf are semidiurnal, with a mean range of 6.67 m at San Antonio Este and 3.35 m near the Negro River mouth. The maximum high tide reaches 9.62 m and the minimum low tide is 0.14 m (Servicio de Hidrografía Naval, 2024). Typical wave heights range from 0.5 to 1.5, reaching up to 3 m during storms, with periods of 7–10 s (Wörner et al., 2019).

San Matías Gulf covers approximately 18 000 km² with a maximum depth of 160 m (Isla, 2013). The gulf was flooded around 11 000 years BCE, with the post-glacial marine transgression peak (∼ 6000 years BCE) reaching about 6 m above present sea level, flooding depressions that now form coastal inlets and tidal flats (Isla, 1989; Mancini et al., 2005; Favier-Dubois and Kokot, 2011). A subsequent sea-level drop of 2–4 m, coupled with abundant sand supply and persistent westerlies, favored the development of extensive transgressive dunefields (Isla, 2013, 2017; Sander et al., 2018). Sand availability has also been linked to humidity fluctuations over the last 7500 years (Marcos et al., 2014), consistent with typical formation mechanisms for transgressive coastal dunefields (Hesp, 2013).

The northern coast of the gulf features active sea cliffs fronted by beaches, backed by cliff-top dunes where cliffs are present, and transgressive dunefields where cliffs are absent (Toffani, 2020). Major dunefields include Bahía Rosas (largely stabilized), Bahía Creek, Bajo la Quinta, San Antonio Este/Oeste, and El Cóndor-Villa 7 de Marzo (Table 1, Fig. 3) (Carbone et al., 2007; Cortizo and Isla, 2012; Kokot and Favier-Dubois, 2017; Vergara Dal Pont et al., 2017; Sander et al., 2018).

The cliffs are composed of sedimentary formations ranging from Oligocene to Holocene age, including the Gran Bajo del Gualicho, Río Negro, Baliza San Matías, Tehuelche, and San Antonio formations (Andreis, 1965; Angulo et al., 1978; Lizuain and Sepúlveda, 1978; Sepúlveda, 1983). These deposits, representing continental and marine facies, are the primary sediment source for the dunes, via cliff erosion, beach/tidal flat reworking, and dune recycling (Gelós et al., 1990; Zavala and Freije, 2005; Fucks et al., 2012; Toffani et al., 2020). Sandstone compositions indicate a dominant volcaniclastic contribution from the North Patagonian Cordillera, mixed with quartz-rich sand derived from the Holocene transgression and fluvial input from the Negro and Colorado rivers (Gelós et al., 1990).

The dunefields exhibit significant spatial variability in morphology, size, and activity (Table 1, Fig. 3):

San Antonio Oeste/Este (SAO/SAE): The dunefield extends up to 4 km in width, featuring barchanoid and transverse ridges, and blowouts. Dune heights reach up to 10 m, composed of fine to medium sand, migrating at an average rate of 4 m yr⁻¹ towards the E–NE (Carbone et al., 2007; Kokot and Favier-Dubois, 2017).

Climatic data were obtained from the Viedma Aero and San Antonio Oeste Aero (SAO) meteorological stations operated by the SMN (Fig. 1). The study period was 1991–2020, during which at least hourly records were collected from 06:00 to 23:00 (local time/GMT-3). This time frame corresponds to a standard reference period for regional climatic studies, as recommended by the World Meteorological Organization (Wang, 2005). In Viedma, the data were recorded 7 m above the ground, while in SAO, they were recorded 20 m above the ground. Both datasets were normalized to 10 m standard acquisition data height (Touma, 1977; Robeson and Shein, 1997; Klink, 1999), according to the following Eq. (1) (Guevara Díaz, 2013): 1VZ/Vref=(Z/Zref)a, where VZ is the wind speed to be estimated at the height (Z), where the measurement was taken; Vrefis the measured wind speed; Zrefis the standardized height above the ground; and “a” is the wind shear exponent (a=0.2 in this study, corresponding to crops and bushes).

Hourly wind speed and direction (grouped monthly), total monthly precipitation, and mean monthly temperature data were analyzed. The dataset provided one measure per hour for each variable: wind speed (km h⁻¹), precipitation (mm), and temperature (°C). In addition, values for the SAM and the Southern Oscillation Index (SOI) were analyzed, obtained from the Natural Environment Research Council–British Antarctic Survey (NERC-BAS, 2025) and the National Oceanic and Atmospheric Administration (NOAA, 2025, https://www.noaa.gov/, last access: 26 August 2025), respectively. Wave height data were downloaded from a Copernicus reanalysis, while cattle population was obtained from several censuses from the Instituto Nacional de Estadística y Censos (INDEC) between 1960 and 2018.

Wind speeds exceeding 6.17 m s⁻¹ were considered sufficient for sediment transport, based on Toffani (2020)'s calculations, which considered local grain size, environmental conditions, and Bagnold (1954)'s equations for the friction speed threshold, when sediment transport begins and the minimum velocity threshold for grains to travel by saltation. Annual and seasonal DP were calculated according to the formula developed by Fryberger and Dean (1979): 2Q∝V2V-Vt×t, where Q represents transported sediments within a certain time t, V is the mean wind speed during t time, Vt is the minimum speed for wind saltation transport, and t is the wind blow time as a percentage. The Fryberger and Dean (1979)'s method was applied using wind velocity data expressed in knots, with 16 directional classes based on geographic coordinates (each 22.5°). DP indicates wind energy environments classified as low (< 200), mean (200–400), and high (> 400). From these, the RDP and RDD were calculated. This approach is commonly used to assess regional wind intensity and sand transport in areas receiving more than 50 mm of annual rainfall (Levin et al., 2014).

The Lancaster (1988) and Tsoar (2005) mobility indices were calculated to estimate a measure of dune migration: 3M=DP1000-750RDPDP4M=W/(P/PE)

Equation (3) represents the Tsoar index, which has been widely used to study dunefields across diverse environments worldwide, subjected to different climatic conditions. This index assumes that wind intensity is the most important factor in dune activity and is valid for sites with annual rainfall greater than 50 mm. Values below 1 represent fixed or vegetated dunes, whereas values above 1 correspond to dunes that are fully active or lack vegetation growth (Tsoar, 2005). Equation (4) represents the Lancaster index, where W is the wind percentage above the sand transport threshold, P is precipitation, and PE represents potential evapotranspiration, obtained using the Thornthwaite equation (Thornthwaite, 1948). This equation has also been successfully applied to assess dune behavior across different environments. According to Hugenholtz and Wolfe (2005), dunes are classified as inactive when M<50, as active crest dunes when 50 < M < 100, as active dunes except the interdune area when 100 < M < 200, and as fully active dunes when M > 200. For this study, dunes were considered active only when both the Tsoar index exceeded 1 and the Lancaster index exceeded 50 at the same time. This new index is a combination of the previous ones and is referred to here as the TsoLa index, which provides values that reinforce the characteristic of fixed or mobile dunes for every observation and also works better for the proposed model. In this case, 0 values refer to stabilized dunes, while 1 values refer to active dunes.

For the statistical analysis, RStudio software (R Core Team, 2024, version 2024.12.0), including the boot, broom, car, ggeffects, ggplot2, glm2, glm.predict, glmtoolbox, mosaic, ResourceSelection, ROCR, sjPlot, and vcd packages, was used to perform a multiple binary logistic regression, following the guidelines proposed by Hosmer and Lemeshow (1980), Kalil et al. (2010), and Stoltzfus (2011). This statistical method is used to establish the relationship between a set of continuous independent predictor variables and a binary categorical variable to model the probability of occurrence of an event, for which variables with p < 0.05 were considered significant, represented as Pr(>|z|) in the software calculations (Zhu, 2016).

To perform the logistic regression, the independence of the predictor variables was considered through statistical correlation tests using the “cor.test()” function, with significance defined as p < 0.05 (Schober et al., 2018). Collinearity was evaluated to exclude correlated variables, seeking values closer to 1 (Daoud, 2017). Likewise, the less frequent results concerning the number of independent variables were considered to avoid overfitting (ratio equal to or greater than 10) (Kalil et al., 2010). The outliers were calculated based on the third quartile plus 1.5 times the interquartile range. However, they were retained in the analysis as they were interpreted as valid extreme environmental values rather than measurement errors. Once the individual variables met the selection criteria, they were combined in the model, which was tested using the “stats::step(model)” function to identify if the overall model performed better with or without each variable. Thus, the final models included monthly values (1991–2020) of the SAM index, total monthly precipitation, and monthly average wind speed as independent predictor variables. Temperature, potential evapotranspiration, and soil moisture were excluded due to multicollinearity, while wave height, cattle population, and SOI and NDVI indices were excluded to improve the model performance. Moreover, monthly data for cattle and NDVI were considered unreliable due to limited availability and minimal temporal variability. The binary categorical variable was defined using a combination of the Tsoar and Lancaster indices and RDD values. A value of 0 was assigned when the dunes behaved as fixed in at least one index and/or when the RDD values ranged between 181 and 360°. Conversely, a value of 1 was assigned when they behaved as active in both indices and the RDD values ranged from 1 to 180°, consistent with the general drift direction for dunes along the coast.

The estimated probability for each model, known as the Logit function (p^), is: 5p^=eβ0+β1X1+…+βnX/1+eβ0+β1X1+…+βnX=ln⁡p^/(1-p^)=β0+β1X1+…+βnX, where X1, X2, …, Xn are the different predictors and β1, β2, …, βn are their associated coefficients (Herrera Briones, 2023). If the regression coefficient βi is positive (negative), an increase in the explanatory variable Xi increases (decreases) the probability of the event (p^) (Sperandei, 2014). The inverse function of the Logit is the cumulative distribution function, also known as the sigmoid function, which is used to plot the probabilities of occurrence of an event (Jansche, 2005).

To build the statistical model, the dataset was randomly split, allocating 70 % for training and 30 % for testing. The accuracy of the models for both subsets was evaluated by calculating the proportion of correct predictions made by the model relative to the total number of predictions made. To separate these classes, a threshold value was determined that maximized or minimized a specific metric (between 0 and 1). This optimal cut-off point was identified using the “opt.cut” function. In this study, priority was given to minimizing false negatives, which correspond to cases where dunes mobility is not predicted despite the actual movement, given the potential risks this poses to nearby coastal villages and for coastal management applications based on coastal dunes dynamics. To achieve this, the cut-off value that maximizes sensitivity (true positive rate) was selected. Additionally, the receiver operating characteristic curve (ROC) and the area under the curve (AUC) value were used to evaluate the model's performance. An AUC value greater than 0.5 indicates that the model performs better than random guessing. However, the model is overfitted when this value is close to 1 (Wei and Dunbrack, 2013).

The goodness of fit of the model was evaluated, i.e., how well it can predict whether a dune is fixed or mobile, using the chi-square regression test (with p < 0.05 considered statistically significant) (Healy, 2006) and the Hosmer–Lemeshow test, where p > 0.05 indicates adequate fit (Hosmer and Lemeshow, 1980). Finally, internal validation of the predictive values was also performed using a bootstrapping method (Steyerberg et al., 2001).

Satellite and aerial images were processed to estimate dune migration rates between 1985 and 2023. Visible-spectrum satellite images with three spectral bands and a spatial resolution of 1.05 to 1.25 m (except one Landsat image from 1985 from the Viedma area), were obtained from Google Earth for the years 1985, 2003, 2004, 2009, 2010, 2013, 2016, 2019, 2020, and 2023 (Table 2). In addition, a 1986 aerial photograph with a spatial resolution of 2.35 m and three spectral bands was obtained from the Instituto Geográfico Nacional for the San Antonio area (Table 2). Most images from the Viedma area were acquired during spring, whereas those from San Antinio correspond to both spring and autumn. The spatial resolution was determined by measuring individual pixels in ArcGIS Pro 3.4.0 software and verified using the image properties in QGIS 4.0.2. Due to cloud cover, portions of the dune fronts could not be identified in the 2009 image from Viedma. The images were processed using ArcGIS Pro 3.4.0, georeferenced using ground control points, primarily located in the towns of San Antonio Este and Villa 7 de Marzo, orthorectified, and projected to the WGS 84/UTM Zone 20 S coordinate system.

Dune fronts and selected slipface toes of unvegetated dunes were manually digitalized where the boundary between bare sand and vegetation was clearly identifiable. The extent of the dune surface was delineated based on dune morphology and pixel color, with bare sand appearing lighter than the surrounding vegetated areas. Only images in which these features could be reliably distinguished were included in the analysis; for example, the September 2019 image from San Antonio Este was excluded because the spectral similarity between bare sand and adjacent sand sheets prevented accurate delineation. Migration rates were estimated following an approach similar to that of Tsoar and Blumberg (2002), Yao et al. (2007), and Ding et al. (2020). Reference points were automatically placed at 50 m intervals along each digitalized dune front (slipface toe), with lengths ranging from 500 to 1450 m. Orthogonal distances between each reference point and the corresponding dune front position in the subsequent image were manually measured in the GIS to calculate migration rates, obtaining a mean value from all of measurements for each time period (Fig. 4). To ensure that distances were measured perpendicular to the dune front, a short line segment was drawn at each reference point, and the measurement was taken at 90° to this segment. If there was retreat at a certain point, the measured value was negative. The root mean square error (RMSE) values were always below the spatial resolution of the images, indicating minimal position error. Meteorological data from the SAO station were compared with dune activity in the San Antonio Este dunefield, located 13 km away. Likewise, data from the Viedma meteorological station were compared to the El Cóndor-Villa 7 de Marzo dunefield, located 26 km away, distances equal to or less than those considered acceptable by Hugenholtz and Wolfe (2005).

According to the established procedures (Sect. 3.2), i.e., the independence of the predictor variables, collinearity, and model test performance, the optimal model for each location included the Southern Annular Mode (SAM), precipitation, and wind speed as predictor variables. A total of 360 data measurements per variable were used for each meteorological station to develop the models. Overfitting was assessed based on the frequency of the least common outcome relative to the number of independent variables (n=3), resulting in at least 30 events of each possible outcome (0 and 1). Variance Inflation Factor (VIF) values were below 1.08 in SAO and below 1.16 in Viedma, indicating no multicollinearity. Precipitation ranged from 122 to 578 mm in SAO and from 214 to 653 mm in Viedma, while SAM index values varied between -5.77 and 4.92. For wind, a more exhaustive analysis was performed. Winds exceeding the minimum threshold accounted for 33.7 % of the total observations for the 1991–2020 period in SAO and 30.6 % in Viedma, mainly blowing from the NW and SW in both cases (Fig. 5). According to the Tsoar index, dunes were identified as active in 119 months (33.1 %) in SAO and in 268 months (74.4 %) in Viedma. Using the Lancaster index, dunes were classified as mobile during 254 months (70.6 %) in SAO and during 247 months (68.6 %) in Viedma. When both indices were considered simultaneously, active dune conditions were recorded in 82 months (22.8 %) in SAO and in 160 months (44.4 %) in Viedma. Monthly DP values in SAO ranged from 30 to 1976, averaging 549. In Viedma, DP values ranged from 55 to 6203, with a mean of 1176 (Fig. 6). Overall, higher DP values were recorded during the austral summer, with NW, SW, and SE as main components, while lower values were typical in autumn. During the remainder of the year, most records correspond to the NW component, with the addition of the SW component during spring in Viedma, where in general stronger winds were registered (Fig. 7). The RDD values suggest a general trend of dune migration toward the east. Over the full study period, the mean RDD was 90 ° in SAO and 89 ° in Viedma. A decreasing trend in DP values was observed in Viedma over time (Fig. 8). In contrast, although fluctuations were recorded in SAO, DP values remained relatively stable over the study period (Fig. 8). RDP values generally follow these tendencies.

For both study areas, the logistic regression model identified three significant predictors of dune migration: SAM, precipitation (P), and wind speed (Wind) (Table 3). In the SAO region, significance levels were p=0.01 for SAM, p=0.001 for precipitation, and p < 0.001 for wind speed. In Viedma, all three predictors also showed statistical significance (p=0.04 for SAM, p < 0.001 for precipitation, and p < 0.001 for wind speed). The estimated coefficients indicate that SAM and precipitation have a negative effect on dune migration probability, while wind speed has a positive effect. Considering the binary outcome included in the model from the TsoLa index, values of 1 refer to mobile dunes, while 0 values correspond to fixed dunes. This suggests that the probability of dune migration (i.e., the event coded as 1) increases when wind speeds are higher or if precipitation and SAM index values decrease. Coefficient values further support this interpretation: in the SAO region, estimates were -0.3 for SAM, -0.04 for precipitation, and 0.64 for wind speed; in Viedma, the values were -0.2, -0.04, and 0.32, respectively. The combined influence of these three variables determines the final predicted probability of dune migration for each observation (Table 3). A response curve was generated to visualize these relationships (Fig. 9), illustrating that dune migration is unlikely when precipitation exceeds 150 mm, wind speeds fall below 20 km h⁻¹, or SAM values are high. The final equations for this model are: 6p=11+e-(-14.52-0.30(SAM)-0.04(Precipitation)+0.64(Wind))7p=11+e-(-5.15-0.20(SAM)-0.04(Precipitation)+0.32(Wind)) Where p represents the probability of occurrence of dune migration. Precipitation, wind speed, and SAM index variables can be replaced with actual values to evaluate the final result (outcome). Equation (6) is for SAO, and Eq. (7) is for Viedma. If it is > 0.2 (SAO) or > 0.4 (Viedma), dunes will probably migrate, while if it is < 0.2 (SAO) or < 0.4 (Viedma), dunes will probably not migrate.

The logistic regression models demonstrated a good fit, as indicated by the likelihood ratio test (p < 0.001) and the Hosmer–Lemeshow goodness-of-fit test (p > 0.05). These results suggest that the models adequately represent the relationship between the predictor variables and dune migration probability. Model discrimination was further assessed using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), which yielded values of 0.77 for SAO and 0.78 for Viedma, indicating acceptable predictive performance. The true positive rate (sensitivity) was plotted against the false positive rate (1 - specificity) across a range of classification thresholds. The ROC curve, therefore, illustrates the trade-off between correctly identifying positive cases and incorrectly classifying negative cases as positives. The area under the ROC curve (AUC) provides a threshold-independent measure of discriminatory performance, with higher AUC values indicating a greater ability of the model to distinguish between positive and negative outcomes (Fig. 10). The optimal classification thresholds (cut-points) were determined to be 0.2 for SAO and 0.4 for Viedma (Fig. 10).

The models also showed consistent classification performance, i.e., how many predictions are coincident with observations, considering 0 as fixed dunes (negative) and 1 as mobile dunes (positive). In SAO, the model correctly classified 77 % of the training data and 73 % of the test data, while in Viedma, 75 % of the training and 74 % of the test observations were correctly predicted (Figs. 11–12). A breakdown of classification performance shows that, for the training data, in SAO, predictions included 57 % true negatives, 20 % true positives, 19 % false positives, and 4 % false negatives (Fig. 11). In Viedma, the distribution was 39 % true negatives, 36 % true positives, 17 % false positives, and 8 % false negatives (Fig. 11). For the test data in SAO, dunes were correctly classified as active in 15 % of cases and as fixed in 58 %. False positives accounted for 21 %, and false negatives for 5 % (Fig. 12). In Viedma, dunes were correctly predicted as active in 39 % of cases and fixed in 35 %. False positives represented 19 %, while false negatives reached 7 % (Fig. 12).

A bootstrap analysis with 1000 resample attempts with replacement was performed to validate the stability of the fitted logistic regression model. The model coefficients were estimated in each iteration. In SAO, the coefficient associated with the variable Wind had a mean value of 0.635, with a 95 % confidence interval (percentile method) of [0.421, 0.806], suggesting a positive and consistent effect on the probability of TsoLa occurrence. In contrast, SAM and Precipitation showed negative effects, with average bootstrap coefficients of -0.296 and -0.037, and respective 95 % confidence intervals of [-0.553, -0.035] and [-0.060, -0.017]. In Viedma, the coefficient associated with the variable Wind had a mean value of 0.318, with a 95 % confidence interval (percentile method) of [0.218, 0.427]; the coefficient associated with the variable SAM had a mean value of -0.196, with a 95 % confidence interval of [-0.379, -0.035]; and the coefficient related to the variable Precipitation showed a mean value of -0.044, with a 95 % confidence interval of [-0.059, -0.029]. This non-parametric approach enabled a robust assessment of predictor significance without relying on classical model assumptions.

To estimate dune migration over time, different dune fronts from the San Antonio area (1986–2021) and the Viedma/El Cóndor-Villa 7 de Marzo area (1985–2023) were studied (Fig. 13). The dune precipitation ridge was measured at a minimum of 10 points per dune/dunefield for each specific time period. In San Antonio, average migration rates ranged from 2.51 to 6.01 m yr⁻¹ (Table 4), with no significant correlation to RDP values, except for one dune (R = 0.71). In contrast, in Viedma, the average migration ranged from 2.92 to 10.12 m yr⁻¹ (Table 4), showing a general decreasing trend over time and a positive correlation with RDP values for the same periods, R = 0.84 (Table 4). The RDP values were selected because they better represent the wind intensity in relation to the direction of dune migration.