Environmental Variables Research Articles

Low Mortality Rates Among Tropical Ferns

Tropical ferns are underrepresented in demographic studies, despite their ecological importance in forest ecosystems. This study investigates the mortality rates of terrestrial ferns along an elevational gradient (500–4000 m a.s.l.) in Ecuador, focusing on relationships with environmental variables, community characteristics, and plant size. Over two years (2009–2011), 3213 individuals representing 88 species were monitored in 22 permanent plots across eight elevations. Mortality rates, calculated as the percentage of individuals lost annually, averaged 0.87%, with a hump-shaped trend along the gradient and a significant negative relationship with temperature. Mortality rates were positively correlated with species richness and fern density, suggesting competition may influence community structure. Larger individuals exhibited higher mortality rates, likely due to greater resource demands and exposure to environmental stressors. These findings emphasize the interplay of abiotic factors, such as elevation and temperature, and biotic interactions, including competition and herbivory, in shaping fern population dynamics. The low mortality rates observed reflect population stability, potentially linked to unique life history traits, such as extended generation times. This study provides critical insights into the demographic strategies of tropical ferns and underscores the need for long-term research to better understand their responses to environmental and biotic pressures.

Exploring latitudinal gradients and environmental drivers of amphipod biodiversity patterns regarding depth and habitat variations

Amphipods are known as umbrella species in conservation biology that their protection indirectly protects other species. Recent hypotheses suggest a bimodal latitudinal global species richness pattern for amphipods, irrespective of species’ depth or habitat type. Phylogeographic hypotheses suggested two distinct procedures for amphipod diversification: ecological radiation and Pangea fragmentation. This study aimed to revisit the bimodal latitudinal pattern of species richness for amphipods, investigate similarities in species composition and main environmental amphipod distribution drivers regarding their depth and habitat variations. The comprehensive database was collected from open-access data and a personal sampling database from the Persian Gulf and the Gulf of Oman. After rigorous data quality controls, the final dataset comprised 1,142,416 distribution records of 6,424 accepted marine amphipod species distributed from 0 to 10,900 m depth. All the species were grouped into pelagic and benthic, shallow-water (> 200 m), and deep-sea (< 200 m). Alpha and gamma species richness and the expected number of species per 50 random samples (ES50) were calculated. Our findings showed that species richness patterns in amphipods are shaped not only by habitat types but also by depth, and they are not significantly bimodal in all groups. Also, the Beta diversity of benthic species revealed distinct diversification patterns between benthic and pelagic species. The species similarity clusters revealed a fragmentation between Gondwana and Laurasia for shallow benthic species. However, the similarity between pelagic amphipod communities is more compatible with ecological parameters. Generalized Additive Models (GAMs) highlighted that environmental variables play a significant role in species distributions, for example, temperature and chlorophyll levels were the main predictors of species richness in shallow waters. However, a complete effect of multiple environmental variables is responsible for deep-sea species gradients. These findings are crucial information to be considered when managing the species richness and establishing conservation priorities for their habitats.

Mobile measurements with bicycles: a systematic review applied to the thermal environment of urban microclimates

This study concerns in to collect, via a systematic review, papers from international academic researches that employed bicycles only to collect primary data on environmental variables for research on microclimatic thermal problems in urban contexts and others. Three international article search platforms were used. After reading the titles, abstracts, and entire works, 13 articles were chosen that employed bicycles in their mobile transects between 2011 and 2023. The analytical methodologies connected them to the thermal environment employed by the research, such as the primary characterizers of urban morphology, were visible from this. It is worth emphasizing that, while the platforms offer outstanding results in terms of article discovery, it is know that there are additional publications that employed similar technique but did not appear in the platforms' results. Six more items were discovered that were absent from the chosen search engines. It is significant to observe that, after looking over the 19 articles, the most part of them were only released in the past 25 years, with the oldest being found in 1998. Research has indicated that the use of bicycles to collect environmental data is related to the possibility that bicycles are more accessible than cars and more safety than walking along cities, moreover, this approach is less expensive. Apart from the several benefits that studies utilizing this methodology observed, it is believed that bicycles would be a more environmentally responsible and sustainable mode of transportation than alternatives. In this manner, it is hoped that additional research utilizing the effective mobile measurement technique with bicycles would be carried out in the future.

The risk status, signatures of adaptation, and environmental suitability of village-based indigenous chickens from certain regions of Limpopo and KwaZulu-Natal provinces of South Africa

Indigenous chickens are an important Farm Animal Genetic Resource (FAnGR) in South Africa as they alleviate poverty and are a source of protein. Climate change and market demand for high-performing exotic breeds threaten and undermine locally adapted village chickens. The current study explored the risk status and signatures of adaptation of village-based indigenous chickens from two provinces and mapped their environmental suitability across the country. A total of 244 village chickens from rural areas of the Capricorn (n = 85) and Sekhukhune (n = 113) districts of Limpopo province; the Harry Gwala (n = 21) and uMzinyathi (n = 25) districts of KwaZulu-Natal province were genotyped using the Illumina 60K BeadChip. The conservation flock comprised Ovambo (OV; n = 10), Potchefstroom KoeKoek (PK; n = 20), and Venda (VD; n = 20). Naked Neck (NN; n = 20), New Hampshire (NH; n = 10), White Leghorn (WL; n = 10), and White Plymouth Rock (WR; n = 10) from the Agricultural Research Council Poultry Breeding Unit were used as reference populations and representative of flocks under conservation. The effective population size (Ne) in village chickens and conserved flocks ranged from 18 to 53 and 26 to 38 at 12 generations ago, respectively. PC1 and PC2 explained 5.64% of the total variation, which resulted in five clusters with the Venda, Naked Neck, and White Leghorn being separated from village chickens. The first three redundancy analysis (RDA) axes capture 46.8% of the total genetic variation used to detect significant outlier SNPs. A total of 386 outlier SNPs associated with all 10 environmental variables were detected. Using ecological niche modeling, chickens from Dipakakeng, Mgababa, and Podu villages, Limpopo, had a localized predicted suitability probability, while chickens originating from Nhlonga village, KwaZulu-Natal, had a broader distribution of predicted suitability habitats with elevation and BIO6 being important variables. The results of this study provide insight into the risk status, geographic suitability, and contributing environmental factors of indigenous chickens that can be used to influence conservation and improvement decisions.

Spatial Prediction of Soil Total Phosphorus in a Karst Area: Comparing GWR and Residual-Centered Kriging

Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg−1, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg−1, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R2 (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments.

Bacterial Community Structure and Environmental Driving Factors in the Surface Sediments of Six Mangrove Sites from Guangxi, China

Mangroves, as blue carbon reservoirs, provide a unique habitat for supporting a variety of microorganisms. Among these, bacteria play crucial roles in the biogeochemical processes of mangrove sediments. However, little is known about their community composition, spatial distribution patterns, and environmental driving factors, particularly across the large geographical scales of mangrove wetlands. In this study, the composition and spatial distribution of the bacterial community structure and its response to fifteen physicochemical parameters (including temperature, pH, salinity, moisture, clay, silt, sand, organic carbon (OC), total nitrogen (TN), total phosphorus (TP), inorganic phosphorus (IP), organic phosphorus (OP), δ13C, δ15N, and carbon/nitrogen ratio (C/N ratio)) were characterized in 32 sampling locations of six different mangrove habitats from Guangxi, China, applying 16S rRNA gene high-throughput sequencing technology and correlation analysis. Our results indicated that the spatial distribution patterns in bacterial communities were significantly different among the six different mangrove sites, as evidenced by NMDS (non-metric multidimensional scaling), ANOSIM (analysis of similarity), and LDA (linear discriminant analysis) analysis. Composition analysis of bacterial communities showed that overall, Chloroflexi (8.3–31.6%), Proteobacteria (13.6–30.1%), Bacteroidota (5.0–24.6%), and Desulfobacterota (3.8–24.0%) were the most abundant bacterial phyla in the mangrove surface sediments. Redundancy analysis (RDA) further highlighted that salinity, δ13C, temperature, δ15N, and silt were the most critical environmental variables influencing the composition of bacterial communities across the whole mangrove samples. Notably, Chloroflexi, one of the most abundant bacterial phyla in the mangrove wetlands, displayed a significantly positive correlation with OC and a negative correlation with δ13C, suggesting its essential role in the degradation of terrestrial-derived organic carbon. These findings support the current understanding of the roles of the bacterial communities and their interactions with environmental factors in diverse mangrove ecosystems.

Geosmin Events Associated with Dolichospermum circinale Abundance Promoted by Nitrogen Supply in a Chinese Large Tropical Eutrophic Reservoir

Taste and odor (T/O) compounds are a global threat in drinking water, mainly produced by cyanobacteria in freshwater environments. Temperature plays a crucial role in regulating geosmin dynamics in temperate and subtropical lakes, while its influence may be lower in tropical waters. To better understand the factors affecting geosmin occurrence in tropical waters, a dataset from a field investigation conducted in a large tropical reservoir was analyzed. The water temperature varied between 16 °C and 32 °C, with geosmin concentration ranging from below the detection limit (3 ng/L) to as high as 856 ng/L. Elevated geosmin levels exceeding &gt; 10 ng/L were observed over the whole year except for in September, suggesting that the annual temperature was suitable for geosmin production. Among the diverse cyanobacteria, Dolichospermum circinale was identified as the main producer of geosmin in the reservoir, both by correlation analysis and cells’ geosmin measurements. Geosmin concentration was also significantly related to the abundance of D. circinale. None of the environmental variables (temperature, pH, transparency and nutrients) were significantly directly correlated with geosmin concentration. But the high total nitrogen significantly explained the increase in D. circinale abundance associated with geosmin elevation. Our results suggest that nutrients, particularly nitrogen, directly affected the competitive advantage and abundance of key geosmin producers and thus modified geosmin levels in this tropical reservoir. Our study thus hints at the possible management of the geosmin problem through nutrient reduction in tropical reservoirs.

ECOSYSTEM INDICATORS TO ASSESS THE SUSTAINABILITY OF MULTISPECIFIC ARTISANAL FISHERIES IN FACE OF ENVIRONMENTAL CHANGES IN THE AMAZON BASIN SOUTH

The construction of hydroelectric dams causes several impacts on the aquatic environment and affects its structure and functions with direct consequences for the provision of ecosystem services. Despite its importance, since 1970, fisheries in the Amazon region, especially small-scale artisanal fisheries practiced by traditional populations, have been facing serious problems arising from human and climatic actions. In this study, we evaluated the impacts of dam construction and the environmental responses on fisheries production and fishing sustainability indexes in the Madeira River, which is the largest tributary of the Amazon River, between 2000 and 2019. Fisheries landing data in the city of Porto Velho and large-scale environmental variables were collected for each of the years in the period and were compared using general linear models. We found that dams, as well as changes in hydrological levels, affect fisheries production. The sustainability indexes showed a decline after the completion of the two dams. Moreover, catch-per-unit-of-effort showed a significant decline after the implementation of the dam. We therefore highlight the importance of understanding the factors that affect the sustainability of fisheries in large rivers in order to mitigate the impacts of large dams and other anthropogenic actions.

Unclouding the Correlations: A Principal Component Analysis of Convective Environments

AbstractIn this study, we leverage 25 years of observations from spaceborne radars, along with coincident reanalysis data, to determine how the depth and width of precipitating convective storms are related to the large‐scale environments in which they are observed. We find that the deepest convective features are observed in environments markedly different from the environments of other convective features, including organized convection. Deep storms co‐occur with relatively dry, unstable conditions, while wide storms are observed in moist, relatively stable environments. We identify eight large‐scale environmental variables that serve to distinguish between storm modes, and then show that principal component analysis can be used to condense this information into just two scalar variables. The methodology presented offers a succinct way to describe a storm's environment and will allow us to better relate a storm's initial environment to its dynamical characteristics.

Nonstationary Spatial Modeling of Massive Global Satellite Data

Earth-observing satellite instruments obtain a massive number of observations every day. Despite their size, such data sets are incomplete and noisy, necessitating spatial statistical inference to obtain complete, high-resolution fields with quantified uncertainties. Such inference is challenging due to the high computational cost and the nonstationary behavior of environmental processes on a global scale. Classical methods for constructing complete maps of environmental variables can be overly simplistic and result in opaque uncertainties, thereby limiting the scientific application of these data. In this work, we address the need to construct complete, high resolution maps of geophysical processes (Level 3 data) from incomplete, noisy, georeferenced observations (Level 2 data). We develop a multi-resolution approximation (M-RA) of a Gaussian process (GP) whose nonstationary, global covariance function is obtained using local fits. We consider sea surface temperature (SST) as an application to illustrate the workflow and our computational implementation. The M-RA requires domain partitioning, which can be set up application-specifically. In the SST case, we partition the domain purposefully to account for and weaken dependence across land barriers. Our M-RA implementation is tailored to distributed-memory computation in high-performance-computing environments. We analyze a Moderate Resolution Imaging Spectroradiometer (MODIS) SST data set consisting of more than 43 million observations, to our knowledge the largest dataset ever analyzed using a probabilistic GP model. We show that our nonstationary model based on local fits provides substantially improved predictive performance relative to a stationary approach, which already improves upon current methods.

Effects of Pittosporum undulatum Vent. invasion on forest diversity and structure in Southern Brazil

ABSTRACT Invasive alien species are considered the second largest cause of biodiversity loss in the world. Pittosporum undulatum Vent., is a native species to Australia and was introduced to Brazil, currently being considered invasive in several regions. Here we analysed the floral and structural composition of forest fragments with varying degress of biological invasion by P. undulatum and assess the relationship between its presence and community diversity. A phytosociological survey was conducted in disturbed and reference forests within two forest fragments in Southern Brazil, assessing tree species diversity, structural and environmental variables between communities across all ontogenetic stages. We recorded 7554 individuals from 35 botanical families and 87 species at all ontogenetic stages, including adults, saplings, and seedlings. P. undulatum showed the highest importance in the adult and sapling stages. The presence of P. undulatum in the forest community is not affecting the richness and diversity of the fragments when the communities were analysed separately (as disturbed and reference forests), but we may see their effect when all forests were analysed together in different ontogenetic stages. In addition, there was a reduction in basal area with advancing successional stages, contrary to typical patterns found in mature forests. The presence of P. undulatum in the reference forest suggests that this species is invading older, more established forests.

Integrating Environmental and Socioeconomic Factors for a Sustainable Circular Economy in Thailand

This study fills a significant research gap by examining the factors influencing the circular economy in Thailand, particularly within the context of developing countries. Utilizing regression and correlation analysis, we investigate key environmental and socioeconomic variables: greenhouse gas emissions, forest area percentage, income levels among the poorest 20% of the population, access to electricity, and income inequality. The novelty of this work lies in its comprehensive integration of these dimensions to identify barriers and sustainable circular economy. Key findings reveal that higher greenhouse gas emissions correlate with increased recycling, underscoring the need for stricter emission controls and cleaner technologies. Conversely, greater forest coverage is associated with reduced recycled waste, highlighting the importance of forest conservation and sustainable land use. Economic disparities significantly impact recycling efforts, necessitating supportive policies for lower-income groups. Increased access to electricity correlates with higher waste generation, emphasizing the need for sustainable consumption practices. Although income inequality correlates with recycling rates, it is not a significant predictor, indicating the necessity for broader economic and environmental policies. This study offers novel, comprehensive recommendations for advancing Thailand’s circular economy. Strategies include implementing emission controls, enhancing forest conservation, promoting economic empowerment, encouraging sustainable consumption, and developing integrated policies. These recommendations aim to address identified challenges and support sustainable growth in alignment with circular economy goals.

Predicting the potential distribution of Astragali Radix in China under climate change adopting the MaxEnt model

IntroductionAstragali Radix is the dried root of Astragalus mongoliae or Astragalus membranaceus, a leguminous plant. Since ancient times, Astragali Radix has been widely used in Chinese traditional Chinese medicine. As people become more health-conscious, the market demand for Astragali Radix grows and its popularity is increasing in the international market. As an important medicinal plant, the growth of Astragali Radix is strongly influenced by environmental conditions. In order to meet the market demand for high quality Astragali Radix herbs, it is necessary to search and find areas suitable for the growth of Astragali Radix.MethodsIn this study, we assessed the potential impacts of climate change on the distribution of the Chinese medicinal plant Astragali Radix using the maximum entropy (MaxEnt) model in combination with a geographic information system(GIS). Distribution data and environmental variables were analyzed to predict suitable areas for Astragali Radix under the SSP126, SSP245 and SSP585 scenario for current and future (2041-2060, 2061-2080, 2081-2100). Jackknife is used to assess the importance of environmental variables, and environmental variables with a model contribution greater than 5% were considered to be the main drivers.ResultsThe results showed that the current area of suitable area for Astragali Radix is 188.41 km2, and the three climate scenarios show an increasing trend in the future, with a maximum of 212.70 km2. North China has always been the main suitable area, while the area of suitable area in Southwest China is decreasing, and Xinjiang will be developed as a new suitable area in the future. Annual precipitation (41.6%), elevation (15.9%), topsoil calcium carbonate (14.8%), annual mean temperature (8.3%), precipitation seasonality (8%) and topsoil pH (6%) contributed more to the model and were the main environmental influences on the distribution of Astragali Radix. In addition, the centroids of the suitable areas shifted northward under all three climate scenarios, indicating a migratory response to global warming.DiscussionOur study found that suitable area of Astragali Radix has been expanding for most of the time in each period of the three climate scenarios compared with the current situation. In the future, humans can focus on enhancing the cultivation techniques of Astragali Radix in these suitable areas. This study provide a scientific basis for the development of planting strategies and spatial distribution management of Astragali Radix. It helps to optimize the selection of planting areas and resource conservation of Chinese herbs.

Roles of host and environment in shift of primary anthrax host species in Kruger National Park.

Environmental and climatic factors, as well as host demographics and behaviour, significantly influence the exposure of herbivorous mammalian hosts to pathogens such as Bacillus anthracis, the causative agent of anthrax. Until the early 1990s in Kruger National Park (KNP), kudu (Tragelaphus strepsiceros) was the host species most affected by anthrax, with outbreaks occurring predominantly in the dry season, particularly during drought cycles. However, the most affected host species has shifted to impala (Aepyceros melampus), with more frequent anthrax outbreaks during the wet season. This study investigates the roles of environmental variation and other host species in this shift. Temporal trends in environmental variables such as precipitation, soil moisture, temperature, and normalised difference vegetation index (NDVI) were analyzed in relation to anthrax occurrence (presence/ absence and counts). Additionally, correlations between host species' densities and anthrax mortalities over time were examined. Anthrax cases in 1990 were concentrated in the central and northern regions of KNP(excluding Pafuri), primarily affected kudus; while subsequent mortalities affected mostly impala and were restricted to the far north, in Pafuri. Significant correlations were found between kudu anthrax mortality and a decrease in NDVI, average temperature, SPI-6 and SPI-12 (Standardised Precipitation Index in various time intervals. Conversely, anthrax occurrence in impalas was associated with a decline in SPI-3, and temperature rise, with increased mortality during the rainy season. Elephant density correlated negatively with kudu mortality, but a positive correlation with both impala mortality and impala density. The study concludes that environmental variables and species' densities may alter the diversity and frequency of hosts exposed to B. anthracis. Climate extremes and alterations therein may exacerbate anthrax severity by modifying species susceptibility and their probability of exposure over time.

Advances and Challenges in Species Ecological Niche Modeling: A Mixed Review

Species distribution modeling (SDM) is a vital tool for ecological and biogeographical research, allowing precise predictions of species distributions based on environmental variables. This study reviews the evolution of SDM techniques from 1985 to 2023, focusing on model development and applications in conservation, climate change adaptation, and invasive species management. We employed a mixed review with bibliometric and systematic element approaches using the Scopus database, analyzing 982 documents from 275 sources. The MaxEnt model emerged as the most frequently used technique, applied in 85% of the studies due to its adaptability and accuracy. Our findings highlight the increasing trend in international collaboration, particularly between China, the United Kingdom, and Brazil. The study reveals a significant annual growth rate of 11.99%, driven by technological advancements and the urgency to address biodiversity loss. Our analysis also shows that while MaxEnt remains dominant, deep learning and other advanced computational techniques are gaining traction, reflecting a shift toward integrating AI in ecological modeling. The results emphasize the importance of global cooperation and the continued evolution of SDM methodologies, projecting further integration of real-time data sources like UAVs and satellite imagery to enhance model precision and applicability in future conservation efforts.

Post-Positivism and Its Application in Health Research

Post-positivism marks a significant shift in health research by recognizing the limitations of positivist approaches and embracing a more flexible, reflective and context-sensitive methodology. This approach acknowledges the interconnectedness of human health, influenced by various biological, psychological, social and environmental variables, which are beyond the scope of quantitative approaches alone. Post-positivism combines qualitative and quantitative methods, allowing scholars to probe the effects of health interventions, their occurrences and social contexts. The objective of this research is to explore the philosophical foundations of Post-positivism and its paradigms within health education research. This research methodology involves an extensive literature review of peer-reviewed academic articles and books to analyze the post-positivism paradigm within health education research, exploring its principles, applications and implications. The shift to post-positivism in health research has allowed for a more accurate and nuanced understanding of health issues by incorporating critical realism and reflexivity, helping minimize researcher bias. The integration of mixed-methods research and triangulation have strengthened the validity of studies, especially when the researcher examine the people experiences and the influence of social factors. Post positivism has reshaped health research by blending qualitative and quantitative methods and promoting reflexivity and ethical practices. This approach provides richer insights and more comprehensive and effective solutions that cater to both individuals and broader community needs.

Assessing how Grazing Intensity Affects the Spatial Distribution of Soil Properties

AbstractGrazing activities occupy the largest land surface, making their management crucial for addressing key issues related to the sustainability of agroecosystems. This study seeks to identify the influence of grazing intensity and vegetation cover on the spatial distribution of soil properties in a farm located in a dehesa ecosystem (SW, Spain). To achieve this, 144 soil samples were collected at 0–5 and 5–10 cm depths across six grazing intensity classes (GIC) defined by the current spatial distribution of vegetation canopy. Leveraging 25 environmental covariates, five individual machine learning algorithms (MLAs) and an ensemble model were employed, to predict soil property values. Metrics such as R2, RMSE (root mean square error), and CCC (Lin’s concordance correlation coefficient) were used to determine the best-performing model. The results revealed that high-intensity grazing classes showed higher values of soil nutrients, and SOC (soil organic carbon)than low-intensity grazing classes. Nevertheless, there no significant differencesin areas with shrub and tree canopy cover. Also, the results reveld that different between depth interval were higher in high-intensity class than in low-intensity classes. The study highlights the importance of selecting environmental variables for accurate soil property prediction. The best model depend on soil property selected and depth interval. In conclusion, this studyoffers valuable insights into grazing intensity and vegetation’s influence on soil properties in dehesa ecosystems, guiding future studies in this field.

Unlocking the habitat suitability of wild olive to improve its industrial potential: A comprehensive distribution modeling study

Wild olive (Olea europaea L.) is a highly significant forest tree species, both in Türkiye and globally. Its oil and other extracts from the fruits and leaves are vital to various industries, including culinary, cosmetics, pharmaceuticals, and healthcare, making it a valuable non-timber forest product. However, its natural distribution is restricted to Mediterranean climates, emphasizing the need for conservation efforts to support its growth and expansion. Potential distribution modelling is one of the best studies to be done to protect a species and ensure its survival. In this study, the MaxEnt method, which relies exclusively on presence data, was used to generate a potential distribution map for wild olive. The environmental variables to be included in the modeling method were determined using the Analytic Hierarchy Process (AHP), one of the multi-criteria decision-making methods. From an initial set of 29 variables, AHP selected the top 11 for the final model. The resulting model demonstrated high accuracy, with an AUC value of 0.922, successfully identifying and mapping the potential distribution areas for wild olive across Türkiye.

A Review of the Environmental Variables Included in Mand Training Interventions

A Review of the Environmental Variables Included in Mand Training Interventions

An explainable AI framework for spatiotemporal risk factor analysis in public health: a case study of cardiovascular mortality in South Korea

ABSTRACT Understanding environmental disease risk factor analysis at the district level is essential for gaining valuable insights into regional disease variations, offering a broader perspective compared to individual-level studies. Recently, explainable artificial intelligence (XAI) has received increasing attention in the analysis of factors affecting public health. However, previous purely data-driven XAI-based risk factor analyses faced challenges in capturing regional effect of environmental variables, leading to confusion regarding key spatiotemporal risk factors. Therefore, this study proposes a framework that includes two complementary XAI-based risk factor analyses following two assumptions. Regionally rescaled environmental variables must account for the unequal effects on environmental factors, which are likely influenced by variations in adaptation capacity to weather conditions and differences in exposure-response relationships to air pollutants. District-level disease distribution highlights geographic disparity in sociodemographic vulnerability, whereas temporal variation in diseases by district underscores temporal environmental impacts. Based on these two hypotheses, we rescaled environmental variables using two complementary schemes: one that employs the district-level disease distribution as the target variable, and another that utilizes the temporal residual of the disease within each district. We evaluated this framework by analyzing the association between cardiovascular age-standardized mortality rate (CVD-ASMR) and various risk factors in South Korea from 2010 to 2019, using high-performing random forest and light gradient boosting models with additive Shapley explanation. Compared to previous purely data-driven XAI-based analyses, the proposed schemes achieved significantly better results in capturing regional exposure-response relationships. In two complementary schemes, the most explainable factor to districts with high CVD-ASMR was low education level related to sociodemographic vulnerability, whereas the most explainable factors to high temporal CVD-ASMR patterns were low greenness and high air pollution levels. In addition, the two complementary schemes enabled us to reasonably analyze the interaction effect of the two risk factors, i.e. temperature and air pollutants. Furthermore, high CVD-ASMR and its high temporal variation were observed in situations of high sociodemographic vulnerability with poor air quality. These findings provide insightful public health planning for sustainable cities and society by pinpointing high-risk areas and tailoring strategies to address regional environmental challenges.