Study Area Research Articles

Floristic composition, life forms and phytogeography of the mountains between Damghan and Shahrud, Eastern Alborz (Iran)

Floristic study of the southern parts of Eastern Alborz in Iranian country was conducted. Encompassing an area of approximately 700 km², this region is located about 50 km north of Damghan and Shahrud (Semnan province), with a high difference in elevations ranging from 1400 to 3813 meters above sea level. According to 2400 collected herbarium specimens, a total of 674 taxa belonging to 332 genera and 71 families were identified. The most abundant families were Asteraceae (46 genera, 97 species), Brassicaceae (35 genera, 59 species), and Poaceae (33 genera, 65 species), respectively. The genus Astragalus, with 34 species, stands as the largest genus in the study area, followed by Cousinia (14 species), Bromus (13 species), Silene, Acantholimon, and Allium (each with 9 species), as well as Alyssum, Gagea, and Poa (each with 8 species). The flora of the study area has a significant influence of Irano-Turanian elements (369 taxa), followed by combinations of Irano-Turanian/Europe-Siberian (127 taxa), Irano-Turanian/Europe-Siberian/Mediterranean (47 taxa), Pluriregional (24 taxa), and Irano-Turanian/Saharo-Arabian elements (22 taxa), among others. Hemicryptophytes (43.62%) emerged as the most prevalent life forms in this region, followed by the others. Overall, 78 species were identified as Irano-Turanian endemics, with many facing threats due to overgrazing and the ongoing effects of climate change.

Determining the Moho topography using an improved inversion algorithm: a case study from the South China Sea

The Parker-Oldenburg method, as a classical frequency-domain algorithm, has been widely used in Moho topographic inversion. The method has two indispensable hyperparameters, which are the Moho density contrast and the average Moho depth. Accurate hyperparameters are important prerequisites for inversion of fine Moho topography. However, limited by the nonlinear terms, the hyperparameters estimated by previous methods have obvious deviations. For this reason, this paper proposes a new method to improve the existing Parker-Oldenburg method by taking advantage of the invasive weed optimization algorithm in estimating hyperparameters. The synthetic test results of the new method show that, compared with the trial and error method and the linear regression method, the new method estimates the hyperparameters more accurately, and the computational efficiency performs excellently, which lays the foundation for the inversion of more accurate Moho topography. In practice, the method is applied to the Moho topographic inversion in the South China Sea. With the constraints of available seismic data, the crust-mantle density contrast and the average Moho depth in the South China Sea are determined to be 0.535 g/cm3 and 21.63 km, respectively, and the Moho topography of the South China Sea is inverted based on this. The results of the Moho topography show that the Moho depth in the study area ranges from 5.7 km to 32.3 km, with more obvious undulations. Among them, the shallowest part of the Moho topography is mainly located in the southern part of the Southwestern sub-basin and the southern part of the Manila Trench, with a depth of about 6 km. Compared with the CRUST 1.0 model and the model calculated by the improved Bott’s method, the RMS between the Moho model and the seismic point difference in this paper is smaller, which proves that the method in this paper has some advantages in Moho topographic inversion.

Reservoir Static Modelling towards Safe CO2 Storage in Depleted Oil Reservoirs of ‘CRK’ Field, Niger Delta, Nigeria

Geological carbon storage (GCS) is gradually gaining acceptance as the technology of highest repute for the mitigation against greenhouse gas effect. This involves the injection and long-term or permanent storage of carbon dioxide (CO2) in subsurface geological formations. The development of a robust 3D reservoir model then becomes necessary to understand the facies changes and the petrophysical properties distribution of candidate CO2 storage reservoirs. This study attempts the use of static modelling technique to investigate the depleted oil and gas reservoirs of ‘CRK’ field in the Niger Delta sedimentary Basin as a potential CO2 storage site. Using an integrated approach of 3D seismic and a suit of well logs from the study area, two reservoir sands (D10C0 and D6200) were mapped. 3D static modelling of discrete and continuous property distribution of the reservoirs revealed the reservoirs are composed majorly of clean sands with water saturation ranging from 8 to 75%. The average porosity and permeability values are between 20 to 29% and 250 to 890mD respectively. A theoretical storage capacity of 24.85Mt is estimated for the two reservoirs combined, while the median effective storage capacity is 6.22Mt. Comparison of the results obtained in the study with recommended standard values show the reservoirs are suitable for CO2 storage. The property models constructed can serve as primary input for dynamic simulation of the oilfield in future studies.

An Overview of Geomorphological Mapping: A Case Study of Rimbi Chhu River Basin, Sikkim, India

Geomorphology is the primary science which demonstrates the basic understanding and mapping of terrain features. A geomorphological map consists of scientific data and dynamic source of information on characteristics of landforms, their origin and evolution, which can be recorded in a map form. Basin hydrology also gets impacted by the terrain features of a basin as overland flow, stream flow and through flow is largely determined by the underneath surface. The configuration of the basin topography reflects the nature of the interaction between process and form. Profound understanding and in depth analysis of geomorphological features of a river basin can provide beneficial information for predicting, preventing and mitigating natural hazards; managing natural resources sustainably for future generation. Implementation of remote sensing and GIS techniques have fostered incredible improvements in landform recognition and accelerated the growth of geomorphological research. The river Rimbi Chhu has been selected as the study area in order to determine terrain configuration in relation with geomorphological features of the basin area. Visual representation of terrain (such as aspect, slope, channel networks, different river profiles, shaded relief and flow direction) and geomorphological (saddle, ridge line, convex-concave slope, rounded hills, fault for instance) derivatives is therefore illustrated, as well as a description of the challenges and crisis that can occur in this context.

Analysis and Simulation of Land Use Changes and Their Impact on Carbon Stocks in the Haihe River Basin by Combining LSTM with the InVEST Model

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data in the Haihe River Basin from 2000 to 2020 was studied via Mann–Kendall (MK) trend analysis, the transfer matrix, and land use dynamic attitude. Through integrating the models of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and the Long Short-Term Memory (LSTM), the results of the spatial distribution of land use and carbon stock were obtained and compared with Cellular Automation (CA-Markov), and then applied to predict the spatial distribution in 2025. The results show the following: (1) The land use and land cover (LULC) changes in the Haihe River Basin primarily involve an exchange between cultivated land, forest, and grassland, as well as the conversion of cultivated land to built-up land. This transformation contributes to the overall decrease in carbon storage in the basin, which declined by approximately 1.20% from 2000 to 2020. (2) The LULC prediction accuracy of LSTM is nearly 2.00% higher than that of CA-Markov, reaching 95.01%. (3) In 2025, the area of grassland in Haihe River Basin will increase the most, while the area of cultivated land will decrease the most. The spatial distribution of carbon stocks is higher in the northwest and lower in the southeast, and the changing areas are scattered throughout the study area. However, due to the substantial growth of grassland and forest, the carbon stocks in the Haihe River Basin in 2025 will increase by about 10 times compared with 2020. The research results can provide a theoretical basis and reference for watershed land use planning, ecological restoration, and management.

Disposal of solid waste from civil construction: a screening proposal for a suitability system and case study in Nepomuceno, Minas Gerais

ABSTRACT Most Brazilian municipalities do not have regulated areas for solid waste disposal in civil construction. Usually, residues are disposed of vacant lots and dumps, posing risks to the population health and the environment. Soils are the primary means for the disposal or recycling of waste, highlighting the importance of well-characterized soils and their respective landscape. This study aimed to establish a land suitability system for solid residues in civil construction and apply such information in a case study in Southeastern Brazil. An unprecedented digital soil map with a resolution of 30 m was created using the random forest classifier algorithm and soil field prospection information. A guide listing favorable soil-landscape attributes that most prevent soil erosion, water bodies or water table contamination was elaborated and discussed. Thus, such information was linked through a suitability system to classify areas with potential for receiving waste on a daily volume basis as follows: large size: >500 m³ day -1 , medium size: >100 m³ day -1 and <300 m³ day -1 , and small size <100 m³ day -1 . Topography and soil depth were the most limiting factors of the areas in the case study. The proposed attributes as criteria for the suitability system complement the current state legislation. A total of 236 ha closer to the urban perimeter connected by roads in good condition were classified as suitable for managing medium- and small-scale daily volume, whose destination might reduce transportation and installation costs in the study area.

Crust and upper mantle S wave velocity structure in eastern Turkey based on ambient noise tomography

Eastern Turkey is an ideal location to study continental collision, which is manifested here by the presence of extensive volcanism and rapid tectonic uplift. To better understand the relationship between these surface phenomena and underground structures, we collect continuous waveform data from 121 seismic stations in the region to investigate the S wave velocity structure of the crust and upper mantle using ambient noise tomography. We obtain Rayleigh wave dispersion curves for periods between 5 and 100 s based on frequency-Bessel (F-J) transform dispersion analysis. We then perform quasi-Newton inversion to calculate the S wave velocity structure between 0 and 200 km. The results indicate the presence of two high-velocity anomalies: one in the southern Black Sea that may represent stable and cold lithospheric mantle and another near Lake Van that may represent the subduction remnant of the southern branch of the Neo-Tethys Ocean. In addition, the results demonstrate that the overall velocity in the study area is lower than the global average and reveal extensive upper mantle low-velocity zones (UMLVZs) which represent asthenospheric materials, and mid-crustal low-velocity zones (MCLVZs). In this study, we propose two possible formation mechanisms of the MCLVZs: one is partial melting, and the other is the formation of low-velocity compositional layering due to complex refining and differentiation processes and subsequent continent reworking. Considering that the spatial distribution of the MCLVZs and UMLVZs is well correlated with regional volcanic activity, the MCLVZs and UMLVZs may provide the driving force and are the material sources for volcanism in the area. Furthermore, the extremely thin and weak lithospheric mantle lid and the exceedingly shallow lithosphere asthenosphere boundary (LAB) indicate that the lower part of the lithosphere has delaminated and that the asthenosphere intrudes into a very shallow location, which then supports regional tectonic uplift.

Downscale Inversion of Soil Moisture during Vegetation Growth Period in Ebinur Lake Watershed

Soil moisture content is an important measure of soil health, and high-precision soil moisture trend analysis is essential for understanding regional ecological quality in the context of climate change, flood monitoring, and water cycle processes. However, in the arid regions of Central Asia, where data are severely lacking, obtaining high-spatial-resolution, continuous soil moisture data is difficult due to the scarcity of stations. Moreover, because soil moisture is easily affected by evaporation time, surface morphology, and anthropogenic factors, mature theoretical models or empirical or semiempirical models to measure soil moisture are also lacking. To investigate the distribution and trend of soil moisture in the Ebinur Lake water, in this study, microwave remote sensing and visible remote sensing data were selected as inputs, and the Global Land Data Assimilation System (GLDAS-2.2) data products were downscaled using the GTWR model, which increased the spatial scale from 27,830 m × 27,830 m to 30 m × 30 m. The phenomena involved in the soil moisture change cycle, spatial distribution, temporal variation, and internal randomness distance were analyzed in the study area through wavelet analysis, Theil–Sen trend analysis, the Mann–Kendall (MK) test, and a variogram. This study obtained high-resolution continuous soil moisture data in the arid and data-scarce region in Central Asia, thus broadening the field of multisource remote sensing analysis and providing a theoretical basis for the construction of precision agriculture in northwest China.

The effect of fire on tree-ring width and tracheid dimensions of Pinus nigra

Summary Forest fires play a significant role in the growth and development of trees because they can damage the phloem and cambium. Fire injury induces the formation of fire scars that can cause xylem dysfunction. The study area is located in Taşköprü, Kastamonu (Western Black Sea of Turkiye), where two forest fires occurred in 1957 and 2020. In this study, the effect of the 1957 fire on the tree-ring widths and tracheid dimensions of Pinus nigra trees was investigated. We measured tree-ring widths and tracheid dimensions of fire-scarred (FS) and unscarred (UF) trees during four different periods: the pre-fire period (1947–1956), fire event year (1957), and post–fire periods (1958–1967 and 2011–2020). In this study, UF trees showed almost 2.5–3 times wider tree–ring widths (TRWs) than FS trees. FS and UF trees also showed great variation in tracheid dimensions in the four different periods; the average tracheid diameter (TD), lumen area (TLA), and wall thickness (TWT) values increased significantly in UF trees compared to FS trees during the fire event and post-fire periods. The average TD, TLA, and average tracheid lumen diameter (TLD) decreased during the fire and post-fire years in the FS trees. In this study, we suggest that fire injury significantly affects the tracheid dimensional traits of FS Pinus nigra trees. Our results also suggest that although Pinus nigra trees experienced serious fire damage, they continued to grow but with a decrease in tracheid size.

A novel approach to predictor selection among large-scale climate indices for seasonal rainfall forecasting in small catchments

ABSTRACT Utilizing identical climate indices as predictors for all climate divisions within large basins may result in unreliable rainfall forecasts at the sub-basin scale. This study aimed to develop a new approach to identify the most effective predictors among large-scale climate indices for seasonal rainfall forecasting in small areas. The proposed approach combines a selective singular value decomposition method (SSVD) with a non-linear sequential forward selection method (NLSFS). Applying the new algorithm for seasonal rainfall forecasting within two climate divisions in Karkheh basin, Iran, indicated that the climate indices identified by the SSVD differed between the study areas. The combination of these indices exhibited a correlation with seasonal rainfall approximately 11% higher than those derived from the SVD method. Moreover, NLSFS significantly enhanced the forecast accuracy compared to the frequently employed linear sequential forward selection (SFS) method, and the optimal predictors chosen by the two methods differed across all seasons.

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

Organic tracers in fine and coarse aerosols at an urban Mediterranean site: contribution of biomass burning and biogenic emissions.

The concentrations of anhydrosugars (levoglucosan, mannosan, and galactosan), polyols (inositol, xylitol, sorbitol, and mannitol), and glucose were measured in PM1 and PM10 samples collected during 1 year at a traffic site in the city of Elche (southeastern Spain). Levoglucosan, mannosan, and galactosan were mainly found in the PM1 fraction since they are mainly emitted from biomass burning (BB). Likewise, inositol, xylitol, and sorbitol were primarily distributed in the fine mode, suggesting a non-negligible contribution from anthropogenic sources (specifically BB) to the levels of these compounds. This was supported by their seasonal variations, with higher concentrations during winter, and their correlations with levoglucosan concentrations. The average contributions of biomass burning and biogenic sources to OC and PM levels were calculated using levoglucosan and mannitol, respectively, as tracers. On average, BB accounted for 12% and 16% of the OC in PM1 and PM10, while the estimated contribution of fungal spores to OC and PM10 levels was 1.2 and 0.8%, respectively. The results of the present study suggest that, at least in the study area, most sugar alcohols are not appropriate tracers of biogenic emissions.

Analysis of maritime piracy trends and patterns using spatial autocorrelation in Africa

ABSTRACT Many studies have analyzed causation factors for piracy, but failed to quantitatively examine how piracy trends in regions have changed. The research aimed to establish the changing patterns of maritime piracy in Africa. The study utilized world piracy data from the National Geospatial Intelligence Agency, which was analyzed using spatial auto-correlation tools. Getis-Ord Gi* statistic identified piracy clusters and hot spot comparison tool compared the hot spot layers in East and West Africa. Autocorrelation analysis results suggest changing frequency of piracy incidents in the study areas before and after 2012. The mean Getis-Ord Gi* values for East Africa were 0.35946 and 0.07839 before and after 2012, illustrating agreater change. However, West Africa mean values were 0.60917 and 0.43408 pre and post 2012, suggesting minimal change. The comparison analysis results generated a smaller similarity value for East Africa (0.43350) and a larger index for West Africa (0.9867). West Africa recorded many incidents in the study period; hence, piracy intensity was almost similar. The study revealed high similarities in piracy events in West Africa, and low similarities in East Africa pre and post 2012. These have implications for understanding the changing dynamics of maritime piracy in Africa, and asserting spatial analysis.

Atmospheric Air Pollution in Niamey, Niger: Assessment of Particulate Matter From Road Traffic (PM2.5 and PM10)

The aim of this study was to characterise the air based on atmospheric particles in the city of Niamey. To do this, a mass concentration detector type PCE-RCM 11 and station type Davis Pro were used as collection equipment. Particulate matter with diameters of less than 2.5 μm and 10 μm, PM2.5 and PM10, respectively, as well as meteorological parameters, temperature and humidity were determined. The study was carried out over 15 days in 5 districts of Niamey between 7.30 am and 10.10 pm at the main roundabouts in each district. According to the results obtained, the average concentrations of PM2.5 in the air measured ranged from 75.34 μg/m3 to 162.34 μg/m3, whereas the WHO recommends 15 μg/m3 for PM2.5 for daily exposure, which would cause cardiovascular or pulmonary disease in those exposed. The PM10 values recorded ranged from 100.43 μg/m3 to 221.90 μg/m3, well in excess of the WHO standards published in 2021, which recommend 45 μg/m3 for PM10, indicating a probable risk of throat and respiratory disorders. Average temperature values ranged from 23.051 °C ± 0.1 °C to 34.186 °C ± 0.1 °C. The average relative humidity measured ranged from 17.164% ± 1% to 26.910% ± 1%. The results of the correlation analysis showed a strong correlation between PM2.5 and PM10. The latter has a strong positive correlation with temperature. PM2.5 is strongly negatively correlated with relative humidity, while PM10 is moderately negatively correlated with relative humidity. Temperature and humidity show a weak negative correlation with each other. Each of the particles studied is present in the communes of Niamey at the 5 roundabouts representing the study area. It should be confirmed that these particles emitted into the air could have come from urban transport. The correlation circle obtained from the PCA explains the relationship between the variables measured (PM and meteorological parameters). The first two dimensions explain 97.55% of the total information, with 81.62% for dimension 1% and 15.93% for dimension 2.

The green window view index: automated multi-source visibility analysis for a multi-scale assessment of green window views

ContextProviding accessible urban green spaces is crucial for planning and ensuring healthy, resilient, and sustainable cities. The importance of visually accessible urban green spaces increases due to inner urban development processes.ObjectivesThis article proposes a new index, the Green Window View Index (GWVI) for analyzing and assessing visible vegetation, that promotes an integrated planning of urban green spaces and buildings at different scales and levels. It is defined as the proportion of visible vegetation area in a field of view when looking out of a specific window with a defined distance to the window.MethodsThe method for estimating GWVI consists of three steps: (a) the modeling of the three-dimensional environment, (b) the simulation of the two-dimensional window views using modern rendering engines for three-dimensional graphics, (c) the computation of the GWVI. The method is proposed and tested through a case study of the urban area of Bonn, Germany, using a Digital Terrain Model (DTM), CityGML-based semantic 3D City Model at level of detail (LoD) 2, airborne Light Detection and Ranging (LiDAR) data, and 2D land use data from the official German property cadaster information system (ALKIS).ResultsWith an average processing time of 0.05 s per window view, an average GWVI of 26.00% could be calculated for the entire study area and visualized in both 2D and 3D.ConclusionThe proposed engine generates multi-scale visibility values for various vegetation shapes. These values are intended for use in participatory citizenship and decision-making processes for analysis by architects, real-estate appraisers, investors, and urban as well as landscape planners.

Spatio-temporal urban land surface temperature variations and heat stress vulnerability index in Thiruvananthapuram city of Kerala, India

ABSTRACT Land use/Land cover (LU/LC) modifications leads to rise in surface temperature is a global concern. This study investigates the effects of LU/LC changes in Land Surface Temperature (LST) variations and effects of Urban Heat Island (UHI) in Thiruvananthapuram city. Furthermore, the study proposed a novel heat stress vulnerability index (HSVI) using Analytical Hierarchy Process (AHP) method. Results shows that transformation of all land uses is to built-up areas, which shows an increase of +118.46% where as others such as vegetation (-24.27%), open area (-64.315) and waterbody (-43.88%) shows drastic decrease over 34 years. The relationship between Normalized Difference Vegetation Index and LST shows a significant change with R2 values between 0.223 in 1988, 0.382 in 2000 and 0.253 in 2022. Moreover, this also contributed to the increase of the mean LST of 26.96 ºC in 1988 to 28.52 ºC in 2022. HSVI of the study indicates that 30.83% of the study area is categorized as very low heat stress vulnerable area followed by 37.22% area in low heat stress vulnerability, 17.88% in moderate heat stress vulnerability, 8.87% in high heat stress vulnerability and 5.19% of the area falling in very high heat stress Vulnerability class.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

Taxonomic and functional-trait metrics track recovery of demersal fish and shrimp communities following system collapse

Monitoring programs that integrate both structural and functional ecosystem components play integral roles in ecosystem management and conservation planning. In the early 1990’s, the marine ecosystem of the waters surrounding Newfoundland and Labrador (NL) underwent a regime shift. Several demersal and pelagic fish stocks collapsed simultaneously, and this had significant ecological and socioeconomic consequences. As this regime shift impacted numerous commercial and non-commercial species, assessments based on individual species would be insufficient. We explored a variety of metrics that capture different facets of diversity across multiple species to provide a more robust ecosystem assessment. These were species richness, evenness, community-weighted means of maximum body length and trophic level (i.e., the mean maximum body size or trophic level of the species present in a community), and functional dispersion (FDis). The objectives of this study were 1) to assess trends in community structure of the NL demersal community during the post-collapse period (1995-2018), 2) explore how the various community-level metrics differ or are redundant, and 3) investigate how these metrics are associated with important covariates. Several metrics were redundant and displayed strongly positive, temporal trends being consistent with expectations for a recovery encompassing the entire demersal community. In particular, unweighted community-weighted means of body length and trophic level displayed nearly equal temporal patterns, showing increasing trends throughout the study period which were most prominent in the northern study area and within a limited depth range at the upper shelf break. Corresponding biomass-weighted metrics were also correlated with each other but only showed similar increasing tendencies after the first decade. In contrast, species richness did not show any temporal increase. Evenness and biomass-weighted FDis showed similar temporal patterns, decreasing during the first decade followed by strong increases during subsequent years, patterns that were directly linked to variation in Northern shrimp and Atlantic cod biomass. This study demonstrates how a variety of community metrics can provide insight into different aspects of the post-collapse recovery of the demersal community and help us better understand the complexity of the changes the ecosystem is undergoing.

Prevalence of surgical affections of sheep in Mymensingh division of Bangladesh

Background: This study aimed to investigate the occurrence of surgical conditions in sheep and analyze their distribution based on age, sex, sheep, and seasonal variation. Methods: Retrospective data on surgical conditions in sheep were compiled from Upazila Livestock Offices and Veterinary Hospitals within Mymensingh division between January 2021 and December 2021. Simple descriptive statistics were used for data analysis. Results: A total of 788 surgical cases in sheep were documented. The predominant surgical conditions observed in sheep included wounds (29.18%), myiasis (15.10%), bloat (14.08%), subcutaneous cysts (12.56%), and foot rot (8.24%). In addition, there were instances of abscesses (6.47%), horn fractures (4.56%), atresia ani (3.93%), dermoid cysts (1.64%), dog bites (1.90%), overgrown hooves (1.52%), and gangrenous mastitis (0.63%). Surgical affections were more prevalent in adult sheep (73.35%) compared to young ones (26.64%). Among adult sheep, myiasis, bloat, subcutaneous cysts, and foot rot showed higher prevalences. In females, myiasis, subcutaneous cysts, bloat, and abscesses occurred more frequently than in male sheep. The occurrence of surgical affections was relatively higher in adult sheep during the rainy season, followed by summer, autumn, and winter during the rainy season followed by summer, autumn, and winter. Conclusion: The findings of this study highlight the variable demographic distribution of surgical affections in sheep. The observed patterns suggest that the surgical disorders pose a substantial threat to sheep rearing in the study areas.

An Assessment of Metal Pollution in the Surface Sediments of an East Antarctic Lake

ABSTRACT This study discusses the metal concentrations and sedimentological data from Lake L55, a dried, land-locked lake from Schirmacher Oasis, East Antarctica. Toxic metals in sediments are a major threat to the environment. Twenty-five surface sediment samples were analyzed for the presence of fifteen metals. The sedimentological data indicates that the sediments are predominantly sandy with low concentrations of silt and clay. The chemical weathering index for the sediments reflects low degree of weathering. Negligible concentrations of organic content were present in the sediments owing to the cold and dry conditions of the study area. The environmental pollution indices indicate elevated concentrations of Ba, Zn, As and Pb in the sediments. Barium could be supplied by the weathering of the quartzofeldspathic catchment rocks, while Zn, As and Pb could be supplied from the fuel burning for power generation and logistical activities, paint residue, and battery waste. The sediment quality guidelines were calculated for the sediments, and they indicate that the sediments have a 21% risk of being toxic. The pollution load index, potential ecological risk index (PERI) and toxic risk index (TRI) values suggest low toxic risk in the sediments of the lake.