Search Results (561)

Human scabies, caused by Sarcoptes scabiei var. hominis, is a neglected tropical disease with a significant public health burden worldwide. This retrospective study analyzed the spatiotemporal epidemiology of scabies in Mexico from 2004 to 2024 using national surveillance data. A total of 1,500,348 cases were reported. Children were disproportionately affected, with the highest crude incidence rates in the 0–4 age group. Females had higher incidence than males across all age groups, except in the 0–4 and ≥65 years age groups. Temporal trends were assessed using joinpoint regression on age-standardized incidence rates. Joinpoint regression identified two joinpoints in 2012 and 2017 for both sexes. Among males, the age-standardized incidence rate decreased from 2004 to 2012 (APC = −5.75%), increased from 2012 to 2017 (APC = 19.00%), and decreased from 2017 to 2024 (APC = −20.86%), with an AAPC of −6.02% (p < 0.001). Among females, a similar pattern was observed (APC = −5.94%, 18.23%, and −20.83%, respectively), with an AAPC of −6.24% (p < 0.001). Both sexes showed a significant net decrease in scabies incidence over the study period, despite a transient increase between 2012 and 2017. Spatial analyses revealed statistically significant spatial clustering, with persistent hot spots in the southeast, the Yucatán Peninsula, the Gulf of Mexico coast, and the Pacific Coast. These findings highlight the persistent burden of scabies in Mexico and the need for targeted public health interventions in high-incidence regions and among vulnerable populations. Full article

Holoplanktonic mollusks (Mollusca: Gastropoda: Pteropoda) are vital structural and functional components of marine zooplankton communities, characterized by high sensitivity to physicochemical shifts in the water column. Consequently, multidisciplinary assessments are essential to elucidate their community dynamics. This study investigated the epipelagic pteropod community structure in the deep-water basin of the southern Gulf of Mexico (sGoM) in relation to hydrographic features and circulation patterns. During the summer (September) of 2016, we collected high-resolution hydrographic data and zooplankton samples using CTD casts and oblique bongo net tows. Hydrographic data revealed intense temperature and density gradients, including a cold-dense core associated with a well-defined cyclonic eddy. The pteropod assemblage comprised 25 species from 13 genera and 10 families. Heliconoides inflatus (947.5 ind 100 m⁻³) and Limacina trochiformis (396.8 ind 100 m⁻³) were the most abundant species, whereas Cavolinia gibbosa (0.4 ind 100 m⁻³) and Cymbulia sp. (0.3 ind 100 m⁻³) were the least abundant. Horizontal distribution analyses revealed that the peak population densities occurred within the influence of the cyclonic eddy, particularly at its periphery where strong currents (>0.5 m/s) were recorded. A Canonical Correspondence Analysis identified temperature and salinity as the primary environmental drivers of community variability, while current systems significantly influenced the horizontal distribution of key species. Although pteropod research in the sGoM spans decades, most studies have been limited to taxonomic checklists, often overlooking environmental drivers and hydrographic influences. By applying a multidisciplinary approach to examine physical–biological coupling, this study advances the ecological understanding of this group within the historically underrepresented deep-water basin of the sGoM. Full article

The dryline is a sharp boundary between moist air from the Gulf of Mexico and dry air from the desert Southwest. In West Texas, this boundary often surges east during the day and retreats west at night. Understanding exactly why it moves back and forth is critical for predicting where severe thunderstorms will form. Yet the physical drivers of dryline life cycle remain poorly understood and frequently under-predicted. This study utilizes a variable-resolution Model for Prediction Across Scales (MPAS) configuration (3–60 km) with the YSU non-local planetary boundary layer (PBL) scheme to investigate a representative dryline event from April 2017. The control simulation was validated against NWS Surface Analysis, demonstrating a high spatial correlation in both synoptic-scale pressure distributions and mesoscale moisture gradients, successfully resolving a nocturnal retrogression of approximately 170 km, with the dryline retreating from its peak afternoon surge at 100.7° W to a recovery point of 102.5° W between 0000 UTC and 0600 UTC 10 April. This recovery occurred at an average speed of 28.3 km/h, consistently constrained beneath a resilient capping inversion. To decouple the environmental drivers of this motion, two targeted sensitivity experiments were conducted: (1) Mechanical Forcing: A 50% reduction in regional topography confirms that the West Texas sloping ramp acts as a “topographic pump.” Without this gradient, the hydrostatic pressure falls were insufficient to drive the nocturnal retreat, causing the boundary to stall eastward. (2) Thermodynamic Regulation: A 50% reduction in soil moisture revealed an “energy swap,” the near-total partitioning of net radiation into sensible heat drove the planetary boundary layer to a higher peak value—a 600 m increase over the control simulation. These results provide a comprehensive physical framework for dryline mobility, demonstrating that while terrain plays an important role in the extent of the diurnal oscillation, soil moisture governs the vertical structure and moisture gradient intensity. Our findings suggest that high-resolution vertical layering and accurate land-surface initialization are prerequisites for capturing the inversion layer dynamics essential for dryline forecasting. However, these findings are based on a single event and require validation across a broader range of dryline cases. Full article

Two new mesophotic species of Chrysymenia are reported for the Gulf of Mexico. Chrysymenia oliverhardyii sp. nov. is a broadly foliose species growing attached to rhodoliths (free-living carbonate nodules predominantly accreted by crustose coralline algae) at 58–66 m depth offshore Louisiana (northwestern Gulf) and at ~64–68 m depth in the vicinity of the Dry Tortugas, Florida (southeastern Gulf). A multi-marker phylogenetic analysis based on chloroplast-encoded rbcL and UPA, as well as nuclear LSU rDNA sequences, indicates that this taxon is most closely related to C. stanlaurelii sp. nov., a slender, branched species from the vicinity of the Florida Middle Grounds, Florida (northeastern Gulf), growing at 60 m depth. These two species from the eastern Gulf of Mexico share a basal most recent common ancestor with respect to the other known species of Chrysymenia. Illustrations and discussion are provided for the new species, as well as for the other Gulf of Mexico members C. planifrons, C. littleriana, C. halymenioides and C. nodulosa. A nomenclatural list, morphological figures, a dichotomous key, and a phylogenetic tree of pertinent Chrysymenia are provided. Full article

Background: Mitochondrial genomes provide valuable information on evolutionary relationships among organisms and on the selective pressures acting on energy metabolism, increasing their relevance in ecological and environmental genomics studies. Haemulon plumierii is a reef-associated fish distributed throughout the Gulf of Mexico and Caribbean Sea and has been proposed as a bioindicator species within the Mesoamerican Reef System. Methods: In this study, we present a high-quality mitochondrial genome of H. plumierii from the southeastern coast of Mexico generated using PacBio HiFi long-read sequencing. Results: The circular mitogenome is 16,823 bp long and contains the complete set of 37 canonical mitochondrial genes, including 13 protein-coding genes, 22 tRNAs, two rRNAs, and one control region (D-loop). The gene order, strand orientation, and tRNA secondary structures were consistent with the conserved vertebrate mitochondrial architecture. Comparative analyses with closely related haemulid species revealed conserved nucleotide composition patterns, negative GC skew values, strong AT enrichment within the D-loop, and highly conserved mitochondrial synteny. Phylogenetic reconstruction based on complete mitochondrial genomes placed H. plumierii firmly within the Haemulon clade. Selective pressure analyses revealed pervasive purifying selection acting on mitochondrial protein-coding genes, supported by low dN/dS ratios, high amino acid identity, constrained nucleotide diversity in cytochrome oxidase genes, and conserved codon usage patterns shaped primarily by AT-driven mutational bias. Pairwise genetic distance analyses further supported moderate interspecific divergence within Caribbean Haemulon species. Conclusions: Overall, the mitogenomic resource generated here provides an important evolutionary and functional framework for future phylogenetic, ecological, and environmental genomics studies in Caribbean reef fishes. Full article

Accurate ship trajectory prediction is vital for intelligent maritime traffic management, yet conventional hybrid models often fail to balance local feature extraction, long-term dependency capture, and flexible feature weighting when processing AIS data. This paper proposes an MSC-BiLSTM-ATTENTION model that integrates trajectory clustering and an adaptive attention mechanism into a unified framework. Its fundamental advance over existing incremental hybrid architectures is twofold. First, a K-means clustering step groups trajectories with similar motion patterns before model training, effectively reducing the impact of data heterogeneity on prediction accuracy. Second, the deep learning backbone synergizes multi-scale convolution (MSC)—which captures local features at multiple temporal granularities via parallel kernels—with a bidirectional LSTM (BiLSTM) for forward–backward dependency learning, and an adaptive self-attention mechanism that dynamically optimizes feature weights to amplify critical navigation information. Extensive experiments on AIS data from the Gulf of Mexico and the U.S. Atlantic Coast, covering four seasons, benchmark the model against attention-enhanced architectures including Transformer, CNN-BiLSTM-ATTENTION, and DenseNet-BiGRU-ATTENTION across two distinct regions. The proposed model achieves significant improvements in predicting longitude, latitude, speed over ground, and course over ground, reducing MAE by over 76.9% and RMSE by over 65.3% compared with the strongest baseline. Ablation studies confirm that the synergy of all three modules is essential. The results demonstrate the model’s effectiveness and its practical value for intelligent maritime supervision, navigation risk warning, and waterborne traffic management. Full article

Offshore oil and gas exploitation is one of the riskiest businesses to invest in and is dominated by various uncertainties: high deepwater pressure, low temperatures, remote operation, long-distance tiebacks and transportation, as well as environmental factors such as wind, waves and ocean currents. Serving as a profitability threshold, the minimum economic field size is defined as the economic recoverable reserve level that an oilfield must exceed to achieve economic returns. This paper develops an approach for determining the minimum economic field size of offshore oil and gas reservoirs. It categorizes the capital expenditure into four major components: drilling and completion costs, platform costs, pipeline costs, and subsea production system costs. The regression models of drilling costs and subsea production costs are developed respectively, with water depth and recoverable reserves as key influencing factors. The pipeline costs are estimated using the unit pipeline cost per mile and pipeline length. A profit model for the offshore field is established under the constraints of the contract, which allocates the oilfield’s production profits between the contractor and the government according to the contractual fiscal terms. Finally, taking the Lucius oilfield in the Gulf of Mexico as a case study, the paper simulates its investment, operating costs, and oilfield revenues. The minimum economic field size is calculated, accompanied by the derivation of the sensitivity boundaries for the primary parameters. Full article

Ecological risk assessment of ecosystems facing anthropogenic pressures informs coastal management. This study evaluated the ecological risk of ecosystems in two coastal municipalities in the Gulf of California, Mexico. The coastal area under study spans 175 km of coastline and includes various ecosystems, as well as the cities of Guaymas and Empalme (~160,000 inhabitants). Ecological risk was assessed by surveying the opinions of experts on local and global activities and influences (climate change), the ecological consequences of hazards, and the resilience (fragmentation) and natural recovery of ecosystems. In addition, potential synergies between human activities and the effects of climate change were identified. The results showed that the main threats are discharges of raw or poorly treated wastewater into the sea, the generation and dumping of garbage, and illegal fishing. Wastewater discharges represent the local threat that interacts most intensively with the effects of climate change. Mangroves, coastal water bodies, and rocky shores face the greatest ecological risk due to continuous exposure to anthropogenic threats, poorly planned urban growth, and industrial development. Approximately 20% of the coastal zone is estimated to correspond to the metropolitan areas of Guaymas and Empalme, where the greatest ecological risk occurs, and these represent opportunities to promote coastal management processes aimed at ecosystem restoration and planned urban development to prevent the loss of coastal ecosystem functions and the services they provide to society. Full article

The Gulf of Mexico and the Caribbean Sea are key regions of the western Atlantic, where sea-state conditions are critical for coastal safety and offshore operations. This study analyzes wave climate trends (1981–2022) using WAVEWATCH III simulations validated against buoy observations. The Mann–Kendall test and Theil–Sen estimator were employed to quantify trends in significant wave height (Hs), energy period (Te), and wave power (P), while correlation analysis was performed to explore teleconnections with the Oceanic Niño Index (ONI), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The results reveal basin-wide increases in mean Hs and P, characterized by pronounced spatial and seasonal heterogeneity. The most robust positive trends occur during winter and spring; in summer and fall, the weaker or negative tendencies, particularly in Te, suggest an intensification of seasonal contrasts rather than uniform change. Teleconnection analysis demonstrates that, among the climate indices considered in this study, ENSO is the primary driver of interannual wave variability in the Caribbean, particularly modulating wave power through remotely generated swell. While the NAO exerts regionally dependent control associated with storm-track modulation, the AMO plays a secondary role, affecting swell-dominated sectors. In contrast, the Gulf of Mexico shows limited sensitivity to large-scale climate modes, with wave variability largely governed by local wind–sea processes. These findings highlight the contrasting wave dynamics between these two basins, providing critical insights for coastal hazard assessments, maritime traffic along major shipping routes, oil spill management, and regional wave energy planning. Full article

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. Full article

Mangroves rank among the most productive ecosystems on Earth, yet they are increasingly threatened by climate change and the expansion of agricultural land use. Among agricultural pollutants reaching coastal environments, glyphosate-based herbicide formulations (GBHFs) are of particular concern owing to their widespread application and environmental persistence. This study evaluated the phytotoxic effects of a GBHF (commercial product Velfosato, 48% active ingredient) on Rhizophora mangle L. seedlings under controlled experimental conditions simulating the intertidal regime of the collection site. Propagules were collected from the Los Petenes Biosphere Reserve (Campeche, Mexico), established in experimental tanks containing mangrove soil, and grown until uniform seedling development was achieved. Once seedlings reached uniform development, they were exposed to nominal concentrations of 0.003, 0.03, 0.3, 3.0, and 10 mg L⁻¹ of the formulation dissolved in interstitial water. The experiment followed a completely randomized design (three replicate tanks per treatment plus a triplicate control; n = 1170 seedlings total). All inferential tests used the tank as the experimental unit (n = 3 per treatment). Total chlorophyll concentration was significantly lower in treated seedlings than in the control across all tested concentrations (ANOVA F_5,12 = 4.55, p = 0.015). Height growth rates were significantly reduced at concentrations ≥ 3 mg L⁻¹ (F_5,12 = 6.84, p = 0.003). Lenticel number increased significantly at the two highest concentrations (F_5,24 = 3.63, p = 0.014). Mangrove soil exhibited significant increases in pH and decreases in redox potential across the concentration gradient (p < 0.001 and p = 0.001, respectively). These findings indicate that sublethal exposure to a GBHF is associated with alterations in key ecophysiological processes and soil physicochemical conditions in R. mangle seedlings under controlled conditions, highlighting the sensitivity of early developmental stages to GBHF exposure. Full article

This study aims to improve the process of characterizing oil on the sea surface using synthetic aperture radar (SAR) imagery, seeking to increase the accuracy of oil slick classification as natural or anthropogenic. A set of spatial attributes was obtained using semivariograms and phase-space pictures. This novel approach demonstrated potential to add value for monitoring seepage phenomena, which is of great scientific and environmental importance. The results achieved have potential for operational application as an aid in understanding active petroleum systems, reducing exploration risk in the decision-making process. Different targets display semivariograms with distinct geostatistical parameters, thus expressing contrasting models of spatial data correlation. The research results indicate that trajectories developed by the targets “sea”, “seepage slick”, and “oil spill” showed diagnostic behavior in their respective phase-space pictures. Full article

Extreme sea levels along the Mexican coasts pose an increasing risk to coastal infrastructure and communities, particularly under the combined influence of tropical cyclones and ongoing sea-level rise. This study analyzes tide-gauge records from the Mexican Pacific and Gulf of Mexico–Caribbean coasts to characterize the statistical behavior and seasonal modulation of extreme sea-level residuals. Astronomical tides were removed through harmonic analysis to isolate the meteorological residual associated with storm-driven processes. Extreme events were evaluated using complementary extreme-value frameworks, including Generalized Extreme Value (GEV) distributions applied to monthly maxima and a Peaks-Over-Threshold (POT) approach applied to the continuous residual series with temporal declustering and Generalized Pareto Distribution (GPD) fitting. While both approaches consistently capture regional patterns, the POT–GPD framework is adopted as the primary basis for return-level estimation due to its explicit representation of event-scale extremes. The results reveal marked regional variability. Pacific stations exhibit bounded or near-Gumbel behavior (ξ ≈ −0.30 to −0.02) and a strong seasonal concentration of extremes during the tropical cyclone season. In contrast, Gulf of Mexico–Caribbean stations display higher absolute extremes and a broader seasonal footprint, with Veracruz showing a tendency toward heavier-tailed behavior (ξ ≈ 0.13). Return levels for a 25-year return period range from approximately 0.85–0.95 m in the Pacific to about 1.7 m in Veracruz. Longer return periods (e.g., 100 years) exceed 2.2 m in Veracruz but are associated with substantial uncertainty due to record-length limitations. The analysis of ENSO variability indicates that ENSO acts primarily as a secondary modulator of background sea-level variability rather than a deterministic driver of extreme events, with the largest anomalies typically associated with tropical cyclone activity. Overall, the results demonstrate that extreme sea levels along the Mexican coasts are governed by region-specific forcing and tail behavior requiring localized extreme-value modeling strategies. The proposed framework provides a robust and reproducible baseline for coastal hazard assessment and supports the integration of sea-level rise into future risk and design analyses. Full article

This study examines how assessments of coastal extreme sea levels depend on the separability and reconstructability of the astronomical tide in hourly sea-level records. Using a global tide-gauge network, it proposes an ensemble-learning correction framework that integrates a physical-baseline threshold with multi-criteria consistency testing to determine whether machine-learning enhancement is genuinely effective across stations and time windows. The analysis uses hourly records from 528 UHSLC tide gauges, with 31-day short sequences used to reconstruct 180-day sea-level variability. Taking the physical tidal model as the baseline, residuals are corrected using Extremely Randomized Trees, Random Forest, and Gradient Boosting. To avoid false improvement driven solely by error reduction, a hierarchical decision framework is established. Baseline model quality is first screened using NSE and the coefficient of determination, after which mathematical artefacts are identified through diagnostics of peak suppression and variance shrinkage. A five-level classification is then derived from the convergent evidence of twelve performance metrics and four statistical significance tests. The results show a consistent global pattern across all three algorithms. Approximately 57% of stations meet the criterion for genuine improvement, whereas about 42% are associated with an unreliable physical baseline, indicating that the dominant source of failure arises not from the ensemble-learning algorithms themselves, but from spatially varying limitations in the underlying physical baseline. Spatially, the credibility of machine-learning correction is strongly conditioned by baseline quality: stations with effective correction are more continuous along the eastern North Atlantic and European coasts, whereas stations with ineffective correction are more concentrated in the Gulf of Mexico, the Caribbean, and the marginal seas and archipelagic regions of the western Pacific. These results indicate that the observed spatial heterogeneity primarily reflects geographically varying physical and dynamical conditions that control baseline reliability and residual learnability, rather than a standalone difference in the intrinsic capability of ensemble learning itself. Full article

The Veracruzan biogeographic province is a central part of the Gulf of Mexico slope and serves as an interface between the Neotropical Region, the Mexican Transition Zone, and the Nearctic Region. We provide an overview of amphibian and reptile diversity in the province, focusing on species richness, endemism, conservation status, and faunal similarity to neighboring biogeographic provinces. In the Veracruzan biogeographic province there are 343 native species of amphibians and reptiles, encompassing nearly one quarter of the Mexican herpetofauna, with over 85% of the families and over 90% of the genera found in Mexico represented. The province therefore possesses exceptional taxonomic richness. It has the fifth highest richness among Mexican biogeographic provinces. The herpetofauna comprises several Neotropical taxa and locally endemic species found among amphibians of montane and cloud forest fauna. Richness of amphibians and reptiles generally increases with province area. Regions of the Mexican Transition Zone exhibit a relatively higher species richness than their Neotropical neighbors. Analyses of faunal similarities between the Veracruzan province and its neighboring provinces and highlight the importance of geographic proximity, environmental continuity, and historical processes for assemblage composition. Amphibians are more threatened than reptiles, with high levels of endemism and vulnerability to habitat loss and emerging diseases, whereas reptiles are more threatened by habitat degradation, exploitation, and invasive species. Our findings show that the Veracruzan biogeographic province is an important reservoir of herpetofaunal diversity and a priority region for conservation in Mexico. Full article