Search Results (108)

Background: Ctenomys is a subterranean rodent genus known for exhibiting the highest levels of chromosome variation, both among species (2n = 10 to 70) and within species. Ctenomys minutus is particularly notable for its extensive chromosomal diversity, comprising the greatest number of described cytotypes within this genus. In contrast, Ctenomys lami presents the highest degree of karyotypic variation within a comparatively restricted geographic range. Both species inhabit the coastal plain of southern Brazil: C. minutus occurs in dunes and sandy fields extending from Laguna (Santa Catarina State) to São José do Norte (Rio Grande do Sul State), whereas C. lami is restricted to the “Coxilha das Lombas” region, which lies parallel to the distribution of C. minutus in Rio Grande do Sul State. Despite their close evolutionary relationship and the absence of external morphological differences, the mechanism underlying their karyotypic divergence remains poorly understood. Methods: In this study, we applied whole-chromosome painting using probes from Ctenomys flamarioni to investigate chromosomal evolution in C. minutus and C. lami. Results: The resulting homology maps revealed a variety of chromosomal rearrangements that differentiate cytotypes both within and between these species. Comparative analyses demonstrated substantial karyotypic divergence from C. flamarioni, although some entire chromosomes and large chromosomal segments remained conserved between C. minutus and C. lami. Our findings underscore the critical role of chromosomal rearrangements in shaping the diversification of Ctenomys. Additionally, we identified shared chromosomal rearrangements in C. minutus and C. lami, which are likely restricted to the torquatus group. Conclusions: These rearrangements provide new insights into the processes driving chromosomal evolution in genus Ctenomys. Full article

In this paper, an innovative method consisting of the construction of an artificial dune reinforced with a composite made by combining sand and seagrass wrack is presented. The performance of this reinforced dune is compared with sand-only dunes, built at the same time, through data collected during 17 field campaigns (covering a period of one year) carried out with an unmanned aerial vehicle (UAV), whose images allow digital elevation models (DEMs) to be built. The results show that, in the medium term, while the sand-only dunes lose much of their volume (up to 25% of the refilled sediment), the reinforced dune only reduces its volume by around 1.4%. In addition, the cross-shore and longitudinal profiles extracted from the DEMs of the dunes indicate that sand-only dunes greatly reduce the elevation of their crests, while the profile of the reinforced dune remains almost unchanged. This suggests that the addition of seagrass wrack can greatly contribute to increasing the resilience of restored dunes and the time between re-fillings, therefore reducing beach protection costs. However, as the results are based on a single wrack–sand dune and have not been replicated, they should be treated with caution. At the same time, this work illustrates how UAVs can acquire the data needed to map coastal restoration works in a fast and economical way. Full article

Desert regions face unique challenges under climate change, including the emerging phenomenon of sand dune expansion. This research investigates aeolian sand transport in the Seqale watershed (eastern Iran) using geostatistical and machine learning methods to model and forecast dune spread, aiming to reduce the loss of sustainability in these valuable landscapes. Predictor variables (altitude, slope, climate, land use, etc.) and wind erosion occurrence were analyzed using classification algorithms (decision tree, random forest, etc.) and bivariate methods (information value, area density) in R software 4.5.0. Risk zoning maps were created and evaluated by combining these approaches. Results indicate a higher sand dune presence in regions with specific altitude (1200–1400 m), gentle northeast-facing slopes (2–5 degrees), moderate rainfall (250–500 mm), high evaporation (2500–3000 mm), outside flood plains, and far from roads (>3000 m) and water channels (>500 m). Dune expansion maps based on density area and information value methods showed substantial areas classified as high to very high movement risk. Machine learning analysis identified the Support Vector Machine (SVM) algorithm (AUC = 0.94) as the most effective for classifying sand dune zones. The study concludes that spatial forecasts, combined with tailored physical and biological measures, are essential for effective sand dune management in the region. Full article

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments. Full article

Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. This study analyzed the distribution and temporal evolution of three fishing-related items (EPS fish boxes, fragments, and buoys) along the Bocassette spit in the northern Adriatic Sea, a region with high fishing and aquaculture activity. UAV monitoring (November 2019, June/October 2020) and structured interviews with Po Delta fishermen were conducted. The collected debris was mainly EPS, with boxes (54.8%) and fragments (39.6%). Fishermen showed strong awareness of degradation, identifying plastic as the primary litter type and reporting gear loss. Litter concentrated in active dunes and the southern sector indicates human and riverine influence. Persistent items (61%) at higher elevations suggest longer residence times. Mapped EPS boxes could generate billions of micro-particles (e.g., ~10¹³). The results reveal a complex interaction between natural processes and human activities in litter distribution. This highlights the need for integrated management strategies, like improved waste management, targeted cleanup, and community involvement, to reduce long-term impacts on vulnerable coastal ecosystems. Full article

In the context of sea levels rising, monitoring spatial and temporal topographic changes along coastal dunes is crucial to understand their dynamics since they represent natural barriers against coastal flooding and large sources of sediment that can mitigate coastal erosion. Different technologies are currently used to monitor coastal dune topographic changes (GNSS, UAV, airborne LiDAR, etc.). Satellites recently emerged as a new source of topographic data by providing high-resolution images with a rather short revisit time at the global scale. Stereoscopic or tri-stereoscopic acquisition of some of these images enables the creation of 3D models using stereophotogrammetry methods. Here, the Ames Stereo Pipeline was used to produce digital elevation models (DEMs) from tri-stereo panchromatic and high-resolution Pleiades images along three 19 km long stretches of coastal dunes in SW France. The vertical errors of the Pleiades-derived DEMs were assessed by comparing them with DEMs produced from airborne LiDAR data collected a few months apart from the Pleiades images in 2017 and 2021 at the same three study sites. Results showed that the Pleiades-derived DEMs could reproduce the overall dune topography well, with averaged root mean square errors that ranged from 0.5 to 1.1 m for the six sets of tri-stereo images. The differences between DEMs also showed that Pleiades images can be used to monitor multi-annual coastal dune morphological changes. Strong erosion and accretion patterns over spatial scales ranging from hundreds of meters (e.g., blowouts) to tens of kilometers (e.g., dune retreat) were captured well, and allowed to quantify changes with reasonable errors (30%). Furthermore, relatively small averaged root mean square errors (0.63 m) can be obtained with a limited number of field-collected elevation points (five ground control points) to perform a simple vertical correction on the generated Pleiades DEMs. Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography. Full article

The region of Eastern Siberia that we have been studying is situated in Yakutia in the permafrost area. We studied five lakes of various geneses, located both in the urbanized territory of Yakutsk city and its suburbs and in natural landscapes at a distance from the impacted area. All lakes were found to have high levels of ammonium nitrogen, total phosphorus and total iron. The lakes’ plankton was found to contain 92 species of algae and cyanobacteria. Cyanobacteria in most lakes accounted for 53 to 98% of the biomass. In one of the natural lakes, 95% of the total biomass was Dinoflagellata. Bioindication, statistics and ecological mapping methods revealed correlations between cyanobacterial production intensity, landscape runoff and lake trophic state. Potentially toxic cyanobacteria containing microcystin and saxitoxin synthesis genes were found in four lakes. Our previous studies established that cyanobacterial harmful algal bloom (CyanoHABs) with microcystin production are characteristic only for lakes in urbanized areas that experience the input of nutrients and organic matter due to anthropogenic runoff. This study indicates that CyanoHABs are possible in lakes in natural areas that are permafrost-dune-type lakes according to their genesis. For the first time in the region, potentially toxic cyanobacteria with saxitoxin synthesis genes have been found. Dune-type lakes do not freeze to the bottom during winter due to taliks underneath them, which provides advantages for cyanobacteria vegetation. Dune-type lakes are very common in the permafrost area, so the extent of CyanoHAB’s distribution in this region may be underestimated. Full article

Dunes are key geomorphological features in aeolian environments, and their automated mapping is essential for ecological management and sandstorm disaster early warning in desert regions. However, the diversity and complexity of the dune morphology present significant challenges when using traditional classification methods, particularly in feature extraction, model parameter optimization, and large-scale mapping. This study focuses on the Gurbantünggüt Desert in China, utilizing the Google Earth Engine (GEE) cloud platform alongside multi-source remote sensing data from Landsat-8 (30 m) and Sentinel-2 (10 m). By integrating three deep learning models—DeepLab v3, U-Net, and U-Net++—this research evaluates the impact of the batch size, image resolution, and model structure on the dune segmentation performance, ultimately producing a high-precision dune type map. The results indicate that (1) the batch size significantly affects model optimization. Increasing the batch size from 4 to 12 improves the overall accuracy (OA) from 69.65% to 84.34% for Landsat-8 and from 89.19% to 92.03% for Sentinel-2. Increasing the batch size further to 16 results in a slower OA improvement, with Landsat-8 reaching OA of 86.63% and Sentinel-2 reaching OA of 92.32%, suggesting that gradient optimization approaches saturation. (2) The higher resolution of Sentinel-2 greatly enhances the ability to capture finer details, with the segmentation accuracy (OA: 92.45%) being 5.82% higher than that of Landsat-8 (OA: 86.63%). (3) The U-Net model performs best on Sentinel-2 images (OA: 92.45%, F1: 90.45%), improving the accuracy by 0.13% compared to DeepLab v3, and provides more accurate boundary delineation. However, DeepLab v3 demonstrates greater adaptability to low-resolution images. This study presents a dune segmentation approach that integrates multi-source data and model optimization, offering a framework for the dynamic monitoring and fine-scale mapping of the desert’s geomorphology. Full article

This study reconstructs the agricultural landscape of the southern coastal plain of late Ottoman and British Mandatory Palestine (today southwestern Israel) utilizing late 19th and early 20th century cartographic materials and aerial photographs. Immense human effort and ingenuity were required to maintain sustainable agricultural on the fringes of the desert. Given today’s increasingly severe climate crisis, the lessons drawn from these historical agricultural practices have particular resonance. The agricultural land use described in this work extended into the coastal dunes of the region where the shallow water table was exploited to create complex agricultural systems that enabled the growth of citrus trees, grapes, and other crops for export and trade. Aerial photos and maps reveal the critical aspects of this region’s neglected agricultural history. The stability and resilience of these systems, some of which are still in existence 76 years or more after they were abandoned, as seen in the survey conducted for this study, point to the importance of understanding and preserving this chapter of the region’s agricultural heritage. The unique varieties of fruit trees adapted to the local climate of the western Negev still have significant economic value and are threatened with extinction from rapid urban encroachment. The remnants of this tradition serve as historical testimony of a bygone agricultural era which was replaced by mechanized monoculture. The discussion centers on the ways n which the study of heritage agriculture in rapidly changing areas can contribute to the broader field of historical geography by reconstructing landscapes that preserve the knowledge and societal patterns of behavior of past communities for future generations. Full article

Machine Learning Techniques (MLTs) and accurate geographic mapping are crucial for managing natural hazards, especially when monitoring the movement of sand dunes. This study presents the integration of MLTs with geographic information systems (GIS) and “R” software to monitor sand dune movement in Najran City, Saudi Arabia (KSA). Utilizing Linear Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN) with nine dune-related variables, this study introduces a new Drifting Sand Index (DSI) for effectively identifying and mapping dune accumulations. The DSI incorporates multispectral sensors data and demonstrates a robust capability for monitoring sand dune dynamics. Field surveys and spatial data analysis were used to identify about 100 dune locations, which were then divided into training (70%) and validation (30%) sets at random. These models produced a thorough dune encroachment risk map that divided areas into five hazard zones: very low, low, medium, high, and very high risk. The results show an average sand dune movement of 0.8 m/year towards the southeast. Performance evaluation utilizing the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) approach revealed AUC values of 96.2% for SVM, 94.2% for RF, and 93% for ANN, indicating RF (AUC = 96.2%) as the most effective MLTs. This crucial information provides valuable insights for sustainable development and environmental protection, enabling decision-makers to prioritize regions for mitigation techniques against sand dune encroachment. Full article

Although dune systems play a crucial ecological role and offer various ecosystem services, they are listed among the habitat types of community interest in the European Union that are undergoing the most severe conservation challenges. The subject of this study was the monitoring of habitat types protected under Directive 92/43/EEC (Habitats Directive) along the coastal dune systems of the Taranto Ionian Arc. Vegetation sociological surveys, GIS mapping, landscape metrics, NBR and dNBR indices were employed to assess the conservation status of the dune system and the impact of disturbance factors. Special attention was given to habitat 2250* (Coastal dunes with Juniperus spp.), revealing that it expanded from 2006 to 2019 but then significantly reduced between 2019 and 2022, with increasing fragmentation, mainly due to wildfires. The study also highlighted the impact of invasive species such as Acacia saligna and Carpobrotus acinaciformis, which compete for space and vital resources. These findings provide scientific evidence for the management and restoration of coastal dune ecosystems, emphasizing the need for targeted conservation strategies to mitigate the effects of these disturbances. Full article

The characterization of natural hazards in coastal environments is of great necessity, especially in the current context of global climate change and increasing population concentrations. This research focuses on a multi-hazard analysis of the main geotechnical, geomorphological, hydrological, and lithological risks in the southeastern margin of the Ría de Arosa using Geographic Information System techniques. The integration of geotechnical characterization maps and natural hazard maps has allowed for the identification of areas with a high susceptibility to natural disasters, which is crucial for territorial planning and management in the context of growing urban pressure and global climate change. The results indicate that poorly consolidated surface formations, especially in transitional areas such as dunes and marshes, are particularly vulnerable. Additionally, areas with higher lithological competence have been identified, where slope changes contribute to ground instability. This analysis provides valuable tools for decision-making and the implementation of risk management policies, promoting sustainable development, the protection of coastal ecosystems, and the prevention of risks from urban planning and civil engineering activities in the Ría de Arosa. Full article

The state of research into acarofauna in Polish national parks is very uneven. One of the least examined areas in this regard is Bory Tucholskie National Park (BTNP), established in 1996. The aim of the current research was to explore the species diversity and community structure of mites from the suborder Uropodina (Acari: Mesostigmata), inhabiting different forest, open, and unstable microhabitats in the area of BTNP. Based on the analysis of over 300 samples collected in BTNP between 2004 and 2024, 29 taxa of Uropodina were identified, with 3839 specimens found in the analyzed material. The highest species diversity has been observed in different types of pine forests (19 species), in transformed alder and alder forests (15 species, each), and in reeds (12 species), while the lowest diversity occurred in peat bog areas (8 species) and inland dunes (5 species). The spatial distribution analyses for Uropodina in the area of BTNP have been made and distribution maps for each species have been drawn. Moreover, the Maturity Index (MI) was also calculated to compare the species diversity of the Uropodina communities in BTNP with those in other Polish national parks. The Uropodina community in BTNP ranked eighth in terms of species richness among 13 national parks explored in Poland so far. Finally, the comparative analysis of the MI for the selected Polish national parks has revealed that BTNP could be ranked fourth in terms of the faunistic value for the discussed mite group. Full article

This study investigates the possibility of utilising a drone equipped with a thermal camera to monitor the spatial distribution of red deer (Cervus elaphus) and to determine their behavioural patterns, as well as preferences for vegetation types in a moor in Denmark. The spatial distribution of red deer was mapped according to time of day and vegetation types. Reed deer were separated manually from fallow deer (Dama dama) due to varying footage quality. Automated object detection from thermal camera footage was used to identification of two behaviours, “Eating” and “Lying”, enabling insights into the behavioural patterns of red deer in different vegetation types. The results showed a migration of red deer from the moors to agricultural fields during the night. The higher proportion of time spent eating in agricultural grass fields compared to two natural vegetation types, “Grey dune” and “Decalcified fixed dune”, indicates that fields are important foraging habitats for red deer. The red deer populations were observed significantly later on grass fields compared to the natural vegetation types. This may be due to human disturbance or lack of randomisation of the flight time with the drone. Further studies are suggested across different seasons as well as the time of day for a better understanding of the annual and diurnal foraging patterns of red deer. Full article

This study analyses land use changes in the Sado Estuary (West-Central Portugal) based on a multi-temporal analysis of 19th century cartographic data and 21st century remote sensing land use maps, updated by fieldwork. A GIS plot of land use evolution is summarized in a quantitative table. The comparison shows the changes in land use, with increasing occupation by human economic activities, including extensive agriculture and forestry, as well as localized urbanization and industrialization. The main elements of the landscape impacted by anthropogenic uses were (i) hydrography—river dams affected the flow dynamics and sedimentary processes in the estuary; (ii) vegetation—increasing agriculture and forestry reduced the area of native vegetation, which is now mostly occupied by vineyards, pine forests and cork oaks; (iii) wetlands—tidal and alluvial plains are being occupied by rice cultivation, aquaculture, industries, and ports; (iv) coastal dunes—new developments are occupying large areas of Holocene coastal dunes; and (v) natural environment—mining and dredging have affected some habitats and biodiversity. This analysis is intended to help the territorial organization of present and future economic activities, as well as to reduce environmental and social problems, thus promoting the long-term sustainability of this rapidly evolving region. Full article