Regional Classification Research Articles

Geographical classification of population: Analysis of amino acid in fingermark residues using UHPLC-QQQ-MS/MS combined with machine learning

ObjectiveTo determine the living regions of individuals based on amino acids in fingermark residues and to establish a rapid and accurate regional classification method using machine learning. Methods: A total of 71 fingermark donors from six different provinces in various regions of China were selected. The content of 18 amino acids in their fingermarks was detected using UHPLC-QQQ-MS/MS. Classification models were established using various machine learning algorithms, and the cross-validation accuracy of 72 combinations, including feature engineering, classification algorithms, and optimization algorithms, was compared. Results: UHPLC-QQQ-MS/MS successfully quantified 16 amino acids. Significant differences in the relative content of amino acids were found between the fingermarks from the eastern and western regions of China, as well as among neighboring provinces. The combination of SFS+SVM+BO was identified as the optimal classification model, achieving an accuracy of 90.14%. Conclusion: The study found regional differences in the relative content of amino acids in fingermarks and established a regional classification method combining UHPLC-QQQ-MS/MS and machine learning. The method developed in this study can be applied to incomplete or distorted fingermarks, and the experimental results can be directly used in police investigations. This research uncovers the multidimensional information carried by fingerprint substances, demonstrating innovation and application value. It not only saves and shortens investigation time and provides investigative leads, but also enables previously unusable physical evidence to play a role again, enhancing the profiling of suspects.

Estimation of the Proportion of Vulnerable Households and Its Determinants in Lampung Province in 2022

Aims: The purpose of this research to determine the profile of poverty vulnerability in Lampung Province and the factors that influence it. This is one of the efforts to achieve the ultimate goal of determining targeted policies as a preventive effort to prevent poverty from occurring in vulnerable households in Lampung Province in 2022. Study Design: Carrying out poverty grouping then carries out testing to determine poverty vulnerability. Place and Duration of Study: The data sources used in this study are secondary data in the form of raw data from the National Socio-Economic Survey (SUSENAS) KOR and Module for March 2022. In addition, this study also uses various other data sourced from the BPS Lampung Website and the Lampung Province Publication in Figures 2022. Methodology: The first step is to estimate poverty vulnerability using the Vulnerability as Expected Poverty (VEP) method. The study continued by analyzing the determinants of poverty vulnerability. The analysis of vulnerability determinants was carried out using the logit regression method. Results: Analysis of the level of vulnerability to poverty in the Lampung region shows that areas with high vulnerability, such as East Lampung, Tanggamus, and South Lampung, are influenced by various structural factors such as limited access to infrastructure and public services, minimal economic opportunities, and dependence on the agricultural sector. These factors are exacerbated by low levels of education and limited access to credit, which further worsen the economic conditions of the community. The number of household members, age of the head of household, education, gender, regional classification, access to credit, savings, employment status, and non-cash food assistance all contribute to the level of vulnerability; increasing the number of household members and the age of the head of household increase the likelihood of vulnerability, while higher education is also associated with increased risk. Male heads of households and urban areas are less likely to experience poverty vulnerability, while access to credit, savings, employment status, and non-cash food assistance can reduce the risk of poverty. Conclusion: The government needs to prioritize infrastructure development in vulnerable areas, such as East Lampung, Tanggamus, and South Lampung, to improve connectivity and public accessibility to basic services, in line with the Lampung Provincial Government's program to improve production efficiency and regional connectivity.

Spatial Expansion Characteristics and Nonlinear Relationships of Driving Factors in Urban Agglomerations: A Case Study of the Yangtze River Delta Urban Agglomeration in China

Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta urban agglomeration and explores its driving factors using a Geographically Weighted Random Forest model. The results demonstrate a transition from “point expansion” to “infill development”, while also revealing a gradual southward shift in the developmental focus of the Yangtze River Delta urban agglomeration. Although expansion intensity has decreased, spatial clustering has intensified. Based on the expansion patterns of impervious surface areas, we propose a novel regional classification method, dividing the Yangtze River Delta urban agglomeration into three zones: “A-Development Decline Zone”, “B-Development Core Zone”, and “C-Development Ascendance Zone”. Socio-economic factors are the primary drivers of this expansion, followed by science and education, and then the ecological environment, while physical geography factors have the least impact. The study reveals differentiated regional development characteristics and further refines the sub-regions within the urban agglomeration, providing a new perspective for future regional coordinated development policies.

Abstract 4130622: Neonatal Outcomes of Hypertensive Women Across US Races: 2016-2022

Background: Maternal hypertension, a major pregnancy complication, can adversely affect newborn health. We aim to evaluate the racial and ethnic disparities in neonatal outcomes among hypertensive pregnant women in the United States. Methods: Using data from the CDC WONDER Natality database, we conducted a retrospective cohort study focusing on live births to hypertensive mothers from 2016 to 2022. Neonatal outcomes, including NICU admissions, low birth weight, infant mortality, and assisted ventilation, were analyzed by calculating rates [95% CIs] per 1000 live births across maternal racial/ethnic groups and the US Census Bureau's regional classification: Northeast, Midwest, South, and West. Results: We analyzed 2,336,679 live births to hypertensive women (23% pre-pregnancy; 77% gestational). This cohort comprised: 0.2% infants born to Native Hawaiian/other Pacific Islander women (NH/PI), 0.9% to American Indian/Alaska Native women (AI/AN), 3.8% to Asian American women (AA), 19.6% to Hispanic/Latino women (H/L), 20.0% to Black/African American women (BA), and 55.4% to White American women (WA). Infant mortality rates were highest in BA (3.6 [3.5, 3.8]), followed by AA (2.1 [1.8, 2.4]), AI/AN (2.1 [1.6, 2.7]), H/L (1.9 [1.8, 2.0]), NH/PI (1.9 [1.0, 3.3]), and lowest in WA (1.5 [1.4, 1.6]). Similarly, NICU admission rates were highest in BA (217.2 [216.1, 218.4]) and lowest in WA (156 [155.4, 156.6]). Assisted ventilation rates were highest in NH/PI (131.8 [123.4, 140.7]) and lowest in AA (87.8 [86.0, 89.7]). Low birth weight rates were highest in BA (235.1 [233.9, 236.3]) and lowest in WA (121.8 [121.2, 122.4]). The Midwest had the highest rates for NICU admissions in NH/PI (254.2 [217.2, 295.2]), assisted ventilation in AI/AN (143.8 [133.2, 155.1]), and mortality in BA (4.1 [3.7, 4.5]). The South had the highest rate of low birth weight for BA (244.4 [242.9, 246.0]). Conclusion: Significant racial and regional disparities exist in neonatal outcomes among hypertensive women in the US. BA mothers consistently experienced the poorest outcomes, while WA mothers had the best. Further research is crucial to understand the reasons behind these disparities and develop targeted interventions for high-risk populations.

Feature Intensification Using Perception-Guided Regional Classification for Remote Sensing Image Super-Resolution

In recent years, super-resolution technology has gained widespread attention in the field of remote sensing. Despite advancements, current methods often employ uniform reconstruction techniques across entire remote sensing images, neglecting the inherent variability in spatial frequency distributions, particularly the distinction between high-frequency texture regions and smoother areas, leading to computational inefficiency, which introduces redundant computations and fails to optimize the reconstruction process for regions of higher complexity. To address these issues, we propose the Perception-guided Classification Feature Intensification (PCFI) network. PCFI integrates two key components: a compressed sensing classifier that optimizes speed and performance, and a deep texture interaction fusion module that enhances content interaction and detail extraction. This network mitigates the tendency of Transformers to favor global information over local details, achieving improved image information integration through residual connections across windows. Furthermore, a classifier is employed to segment sub-image blocks prior to super-resolution, enabling efficient large-scale processing. The experimental results on the AID dataset indicate that PCFI achieves state-of-the-art performance, with a PSNR of 30.87 dB and an SSIM of 0.8131, while also delivering a 4.33% improvement in processing speed compared to the second-best method.

Awareness and preparedness of health systems and emergency medicine systems to the climate change challenges and threats: an international survey.

Climate change is widely recognised as a critical public health challenge. The objective of this study was to assess the awareness, preparedness and mitigation plans for climate change threats. A cross-sectional observational study targeting emergency medical societies in different countries was conducted between 15 February and 15 March 2024. The survey featured 16 closed questions on climate change awareness, preparedness and risks. Focus groups of 4-6 members were organised by country. Results were correlated to income levels, United Nations (UN) regional classification and the World Risk Index. The questions were ranked using a Likert-like scale from 0 to 9 (9 being the highest). Descriptive statistics used central tendency estimators, and inferential analysis used chi-square and Kruskal-Wallis tests, with the significance level set at P < 0.05. Forty-two focus groups responded, representing 36 countries: 21 (50%) high-income, seven (16.7%) low-income, five (11.9%) lower middle-income and nine (21.4%) upper middle-income countries, representing 31 of the 22 UN regions. According to the World Risk Index, the respondent countries belonged to the different categories as follows: very low risk, 6 (14%); low risk, 8 (19%); medium risk, 5 (12%); high risk, 8 (19%) and very high risk, 14 (34%). The estimated impact of climate change on national health systems had a mean score of 6.75 (SD = 2.16), while on Emergency Medical Systems was 6.96 (SD = 2.05). Overall, assessment and preparedness measures were reported by just 21.4 and 37.6% of respondents, respectively. Analysis by income did not show significant differences, with the exception of food supply. The main differences in the analysis by region were the risks of extreme weather events, vector-borne diseases and wildfires, whereas the World Risk Index was food and chain of supplies. Education and integration of health services were indicated by all as the main mitigation actions. Geographical position and country risk index influence risk perception among focus groups more than income economy, with vector-borne diseases, extreme weather events and food shortages being the threats with the most variability. The most important actions identified to mitigate Climate Change effects are educational and strategic plans.

Novel comprehensive unified classification system toward smart standardized built environment knowledge and low-risk international collaborations: UniCCC

The Construction Industry (CI) data is acknowledged to still be unstructured and high-dimensional. Efforts were undertaken to tackle this problem using regional Unified Classification Systems (UCSs). The study emphasizes the necessity for a comprehensive international UCS to manage the growing trend of international collaborations within the CI and associated data risks. Existing UCSs exhibit significant inconsistencies, such as regional omissions, terminological disparities, and diverse data structuring, underscoring the need for a unified approach. Accordingly, using a 3-stage mixed method, the novel UCS ‘Unified Classification of Construction Components_ UniCCC’ was developed, comprising 4 phyla, 9 divisions, and 54 classes, and was shown to have good adaptability/scalability, structure/breakdown, clarity, codifiability, and inclusivity of the 11 key CI aspects. By addressing inconsistencies and smartly structuring CI knowledge from North America, Europe, Africa, and the Middle East, using a faceted classification scheme aligned with ISO 12,006, and being developed using a conventional modeling language, UniCCC represents a significant advancement in UCSs, with promising implications for global CI practices and research, paving the way for new avenues in CI performance, innovation, and sustainability. It demonstrated efficiency in various CI aspects, including risk reduction, automation, and sustainability enhancement. As evidence of its performance, using UniCCC for scheduling might decrease up to 58.17%, 66.85%, and 31.74% of the related time, request for information, and omissions, respectively. The study suggests future research perspectives to explore UniCCC's applicability in different regions, its integration with emerging technologies, its proficiency metrics for different purposes, and strategies for organizational implementation.

The Urban Geo-climate Footprint approach: Enhancing urban resilience through improved geological conceptualisation

Urban resilience is critical to allow cities to withstand the challenges of the 21st Century. One factor that is often overlooked in such assessments is the role of the subsurface. A novel methodology called the Urban Geo-climate Footprint (UGF) has been developed to classify cities quickly and comprehensively from geological and climatic perspectives. The method operates on the fundamental assumption that cities with similar geological-geographical settings will face similar challenges, due to both common geological issues and associated climate impacts. The UGF approach has been applied to 41 European cities in collaboration with 17 Geological Surveys of Europe, the results of the UGF analysis are presented along with a regional classification of the geological resilience indicators. The UGF tool provides a semi-quantitative representation of the pressures driven by geological and climatic complexity for the cities presented, providing for a first time such classification of the urban environment. The advantage of this methodology lies in increasing awareness among non-experts and decision-makers of the interplay between geological settings, climate change pressures, and anthropogenic activities. Furthermore, it facilitates the exchange best practices among city planners to increase resilience, supporting knowledge based decision making to promote actions and policies, that enhance geoscience-informed climate justice.

Analysis of health behavior changes among residents in depopulation areas in Korea: a cross-sectional study based on Community Health Survey data from 2010 to 2019

Objectives: The total population of Korea began to decline in 2019; in particular, the population in rural areas has been rapidly decreasing and is aging. Therefore, the government has designated depopulation areas and is seeking ways to support them. To assess whether health disparities exist between areas with population decline and those without, this study used community health survey data to observe temporal changes in health behaviors between the two types of areas. Methods: The analysis used Community Health Survey data from 2010 to 2019, and regional classification was divided by depopulation areas designated by the Ministry of the Interior and Safety. Trends in health behavior and chronic disease prevalence between depopulation and non-depopulation areas were analyzed. All analyses were conducted using complex sample analysis procedures in SAS 9.4 software. Results: The smoking rate steadily decreased in both depopulation and non-depopulation areas, whereas the high-risk drinking rate increased slightly. The walking practice rate did not improve in depopulation areas compared to non-depopulation areas. Furthermore, nutritional labeling usage rate was consistently lower in depopulation areas than in non-depopulation areas, with the gap being the largest. The prevalence of obesity, diabetes, and hypertension showed that the gap between depopulation and non-depopulation areas is continuously increasing. Conclusions: Health behaviors in depopulation areas have not improved, and the prevalence of chronic diseases is increasing rapidly. Therefore, the demand for health care services that support healthy lifestyle practices and chronic disease management in these areas is expected to increase.

Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests

Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.

Analysis and extreme event prediction of waves in the eastern shelf seas of China based on a 44-year hindcast

Analysis and extreme event prediction of regional waves are crucial for the design, spatial layout and protection of ocean engineering structures. In this study, the regional wave characteristic is analysed in the eastern shelf seas of China based on a 44-year wave hindcast. The spatial variation of peak period is smaller than that of significant wave height, which may be affected by swell propagation, and the seasonal distribution differences of these parameters can generally be explained by changes in the East Asian monsoon and are significantly affected by tropical cyclones. Both local wind wave and remotely generated swell should be considered when designing ocean structures. In addition, the joint distribution characteristics of wave period and height reveal a natural regional classification of representative wave states, which can be used to avoid deploying structures near classification boundaries. The extreme wave events are characterized by their number, duration and intensity, highlighting areas susceptible to extreme waves. Meanwhile, the extreme event is predicted, and its return patterns are highly consistent with weather system characteristics such as tropical cyclone tracks. The results can provide a significant basis for the design and protection of ocean engineering structures.

A Common Climate–Yield Relationship for Wheat and Barley in Japan and the United Kingdom

Wheat and barley yields in Japan are considerably lower than those in the UK, even where similar Climate Zones (CZs) of relatively cold and humid nature are shared. In order to understand this difference, it is first necessary to find out if any common climate–yield relationship exists between the two countries. The Climate Zonation Scheme (CZS) developed in the Global Yield Gap Atlas (GYGA) was used to analyse actual yield (Ya) with three climatic factors of the GYGA-CZS, i.e., growing degree days (GDD), aridity index (AI) and temperature seasonality (TS). A significant relationship was found between AI scores and Ya values across the two countries. Ya values decreased with an increase in AI scores; in other words, lower yields are associated with higher AI scores. In addition, the degree of yield reduction with the rise in AI scores was greater in Japan than in the UK. The present study also proposed a novel method to link CZs of the GYGA-CZS to regional classification units, especially for countries where statistical crop yield data are available only at a coarse scale.

Land Cover and Land Use Ontology—Evolution of International Standards, Challenges, and Opportunities

Knowledge of land is of central importance to manage the impact of mankind upon the environment. The understanding and treatment of land vary greatly across different regions and communities, making the description of land highly specific to each locality. To address the larger global issues, such as world food production or climate change mitigation, one needs to have a common standardized understanding of the biosphere cover and use of land. Different governments and institutions established national systems to describe thematic content of land within their jurisdictions. These systems are all valid and tuned to address various national needs. However, their integration at regional or global levels is lacking. Integrating data from widely divergent sources to create world datasets not only requires standards, but also an approach to integrate national and regional land cover classification systems. The ISO 19144 series, developed through the collaboration between the Food and Agriculture Organization of the United Nations (FAO) and the International Organization for Standardization (ISO), offers a meta-language for the integration of disparate land classification systems, enhancing interoperability, data sharing, and national to global data integration and comparison. This paper provides an overview of classification system concepts, different stages for the development of standards in ISO and the status of different standards in the ISO 19144 series. It also explores the critical role of the ISO 19144 series in standardizing land cover and land use classification systems. Drawing on practical case studies, the paper underscores the series’ potential to support global sustainable development goals and lays out a path for its future development and application. Using these standards, we gain not only a tool for harmonizing land classification, but also a critical level for advancing sustainable development and environmental stewardship worldwide.

Regional Classification of Courtyard Houses across Maharashtra

Sensitivity to the regional context and an inductive approach is essential to realise the intentions behind the Maharashtrian traditional house forms. This research focuses on development of the House form in Maharashtra, aiming to establish the principle behind the generic form and the typical variations in 5 traditional regions that comprises the present day state of Maharashtra, that is Desh, Khandesh, Marathwada, Vidharbha and Konkan. Spatial and distinct architectural styles of the WADA has been taken under here along the exploration of technology adopted for construction. However, this would not be an exhaustive account of all aspects related to this issue which includes sociocultural and political determinants as well. Keywords: Wada Architecture, Vaastu Shastra, Indian traditional architecture, context, cardinal orientation, sustainability.

THE RELATIONSHIP OF LOW BIRTH WEIGHT WITH STUNTING INCIDENTS IN ACEH IN 2022

Background: Stunting is a failure in growth and development experienced by children due to insufficient nutritional intake, infectious diseases over a long period of time which is characterized by length or height that is not appropriate for their age. Stunting is still a major health problem in Aceh Province because its prevalence is still high and is ranked 5th nationally. Objective: to determine the determinants of stunting in toddlers in Aceh Province. Research method: Descriptive quantitative research with a cross sectional design with a sample size of 16865 toddlers obtained from total sampling based on inclusion and exclusion criteria. The data used is 2022 SSGI data belonging to the Ministry of Health's BKPK. The independent variables in this study include Early Breastfeeding Initiation (IMD), completeness of immunization, ISPA, diarrhea, ownership of health insurance, ownership of KIA books, drinking water facilities, ownership of toilets, mother's education, gender of toddler, birth weight, number of families, and regional classification. Data analysis includes univariate and bivariate using chii square and multivariate using multiple logistic regression. Results: The prevalence of stunting in children aged 0 – 59 months is 31.7% in Aceh Province in 2022. Bivariate results are low birth weight (p=0.033). Conclusion: the high prevalence of stunting in Aceh Province in 2022 is caused by multiple and interrelated factors, both specific and sensitive. To reduce the stunting rate, preventive and promotive measures need to be taken early on.by carrying out routine check-ups during pregnancy and consuming a variety of foods. Short birth length babies need to receive growth and development monitoring, additional food and nutrition and health Information Communication and Education (KIE) interventions for mothers of toddlers.

A Portrait of Multidimensional Poverty of Indonesian Agricultural Households

The problem of poverty is not only measured by purchasing power but also by access to fulfillment of needs such as education, health, and quality of life. Poverty measured from this perspective is referred to as multidimensional poverty. This study aims to identify the multidimensional poverty conditions of households in the agricultural sector, which is the sector that contributes the highest monetary poverty rate in Indonesia. The data used is data from the National Socio-Economic Survey (SUSENAS) from 2017 to 2021. This research was conducted on 34 provinces in Indonesia. The portrait of multidimensional poverty is processed using the Alkire-Foster method using three dimensions, namely the dimensions of education, health, and quality of life. The three dimensions are measured using thirteen indicators that have been adjusted to the indicators in SDG'S 2030. The results of this study state that the deprivation of agricultural households is still high in the indicators of years of schooling, ownership of health insurance and availability of proper sanitation. The province with the highest Multidimensional Poverty Index (MPI) value is Papua, followed by four other provinces, namely, East Nusa Tenggara, Banten, West Nusa Tenggara, and West Java. In addition, this study also identifies multidimensional poverty in the agricultural sector by regional classification and gender of the household head.

Ecological Aspects and Methodology for Assessing the Forests of the Ural Floodplain

The floodplain forests in West Kazakhstan’s Urals are challenging to study due to complex growth patterns. Existing tables estimate the trunk volume and wood size but lack comprehensive data for effective forest management. A developed research methodology focuses on creating growth and productivity models for forest-forming species across diverse forest types. Multidimensional linear growth models, with dummy variables for species and forest types, offer reliable insights into the average height and diameter changes at different ages. These models facilitate statistical analyses of asynchronous growth with high reliability. Three-level growth models detail regression lines for individual forest-forming species within specific forest types. This article illustrates the construction of a model for black poplar stands in a central floodplain’s medium-level conditions. Furthermore, it highlights the potential for a regional planting classification based on average height gradients at the base age of stands. The presented methodology aims to address the challenges in forest management and forestry in the region.

Global Overview of the Application of the Braun-Blanquet Approach in Research

Environmental classifications are of paramount importance for assessing the impacts of land-use changes, for prioritizing conservation efforts, and for developing effective management strategies to mitigate the negative impacts of human activities. The aim of our research was to provide as complete an analysis as possible of the studies that have been carried out using the Braun-Blanquet approach. The global review of studies based on the Braun-Blanquet approach includes 1168 papers and was conducted using the PRISMA 2009 methodological recommendations, strict criteria for the selection/quality of papers, and modern methods of data analysis and visualization using VOSviewer software developed by Nees Jan van Eck and Ludo Waltman (Centre for Science and Technology Studies (CWTS) of Leiden University in the Netherlands) (version 1.6.18), which ensures a representative sample, minimization of subjective judgements, and reliability of conclusions. It was noted that the number of publications on Braun-Blanquet is growing exponentially. This is an indication of the scientific interest in this methodology and its continuous further development. Based on a detailed analysis of the keywords, the main research directions and challenges are identified. These include improving the conceptual and methodological foundations of the Braun-Blanquet approach; improvement in regional vegetation classifications, synthesizing them and producing a comprehensive classification for large areas as a basis for biodiversity conservation and sustainable land use; expansion of the geography; compilation and updating of databases of phytosociological data; management of dynamics and vegetation; discussion of the important problem of continuity and discreteness of vegetation in the context of ecological classifications; and vegetation mapping. The top 20 journals publishing the most cited articles were identified, as well as the top 20 most cited journals whose high citation rate is due to the large number of high-quality articles. The analysis of the bibliographic network of papers in dynamics has shown that the structure of relationships is not constant and has changed significantly. The analysis of the authors’ publication activity showed that the vast majority of researchers have a low publication activity and have published only one to three papers. A peculiarity also emerges: if all the most cited authors are concentrated in Eurasia, then most of the most actively published authors are outside Eurasia. The importance of the Braun-Blanquet approach for the study and classification of forest vegetation should be emphasized. In this case, the Braun-Blanquet approach is integrated into forest typologies, increasing their ecological validity and environmental relevance.

Vertebral chemistry distinguishes regional populations of Blacktip Sharks in the northern Gulf of Mexico

AbstractObjectiveUnderstanding the spatial connectivity of elasmobranch populations is critical for regional fisheries management. The Blacktip Shark Carcharhinus limbatus is abundant in coastal waters of the Gulf of Mexico (GoM) and important in recreational and commercial fisheries. Based on genetic and tagging studies, GoM Blacktip Sharks are currently managed under separate quotas between the eastern and western GoM (divided at 88°W), but no studies have used vertebral chemistry to assess the population structure of adult Blacktip Sharks.MethodsWe compared vertebral elemental signatures (barium [Ba], magnesium [Mg], manganese [Mn], strontium [Sr], and zinc [Zn]) for the first year of life with last occupied habitats (vertebral edges) in Blacktip Sharks collected from the western (Texas and Louisiana) and eastern (Alabama and Florida) coastal waters of the northern GoM.ResultWe found significant regional differences in Ba, Mg, Mn, and Sr vertebral edge signatures, suggesting ecological separation of Blacktip Sharks. Significant correlation between first‐year and edge signatures suggested a high degree of residency between life stages. Cross‐validated discriminant function analyses yielded highest regional classification accuracies when Florida sharks were grouped separately west of 88°W (90%), demonstrating the unique elemental signatures of eastern versus western GoM Blacktip Sharks under current management delineations.ConclusionCombined, these findings demonstrate that trace element markers can distinguish regional populations of Blacktip Sharks and provide a complimentary approach in addition to genetics and physical tagging to support current stock management efforts.

Utilizing HS-SPME-GC-MS for Regional Classification of Ethiopian Green Coffee Beans: An In-Depth Analysis of Volatile Compounds

Utilizing HS-SPME-GC-MS for Regional Classification of Ethiopian Green Coffee Beans: An In-Depth Analysis of Volatile Compounds