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Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear ( Ursus thibetanus ) and red panda ( Ailurus fulgens ) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km 2 and 343 km 2 respectively, while the suitable area determined by GARP was 1074 km 2 and 714 km 2 respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study’s results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R2, whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management.

Background Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans. Methods Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China. Results The proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha − 1 , the mean absolute error ranged from 6.54 to 32.32 t∙ha − 1 , the bias ranged from − 2.14 to 1.07 t∙ha − 1 , and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha − 1 ) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area. Conclusions Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

Under China’s diversified food supply strategy and the accelerated modernization of its fisheries sector, marine ranches have become vital food sources and production bases. Their digital–intelligent transformation now represents a key pathway to improve resource efficiency, ensure food security, and promote sustainable marine economic development. Adopting a qualitative research design, this study examines China’s marine ranches using the TOE framework and a systemic grounded theory approach to identify key elements and evolutionary logic of their digital–intelligent transformation from multi-source qualitative data. It constructs a three-stage evolutionary model comprising “Technology and Facility Capacity Building Phase–Digital Resource Integration and Application Deepening Phase–Multi-stakeholder Collaboration and Systemic Governance Phase,” revealing the dynamic coupling mechanism among technological progress, organizational change, and environmental adaptation. Results indicate that the digital–intelligent transformation of marine ranches represents a systemic transition from technology-driven to collaborative governance, characterized by platform-based collaboration, factor restructuring, and institutional linkage. Based on these findings, this study proposes tiered policy and practice recommendations emphasizing institutional guidance by governments, innovation investments by enterprises, and ecological support from third-party platforms. The research not only expands the application scope of the TOE framework but also provides an applicable theoretical framework and policy reference for digital governance and sustainable development in marine fisheries.

Background The association between depression and serum total prostate-specific antigen (PSA) concentrations remains unclear. To explore whether there is a relationship between depression and PSA in American males without prostate cancer (PCa), according to the National Health and Nutrition Examination Survey (NHANES) database. Methods Three biennial cycles of survey data from 2005 to 2010 were used in our study. Multivariate adjusted regression analysis, stratified analysis, trend testing, smooth curve fitting and multiple imputation (MI) were our main research methods. Depression was assessed by the Patient Health Questionnaire-9 (PHQ-9). Results The study included a total of 4185 participants. After adjusting all covariates, whether depression was used as a continuous [β = -0.038; 95% confidence interval (CI): -0.059, -0.017; P  < 0.001] or categorical variable ( P  for trend = 0.001), especially in the mild [β = -0.239; 95% CI: -0.473, -0.006; P  = 0.044)] and moderate [β = -0.499; 95% CI: -0.907, -0.092; P  = 0.016)] depression groups, it was associated with a decrease in serum PSA concentrations. Smoothing curve fitting found the presence of a linear relationship, with PSA reduced by 0.038 ng/ml or 0.026 ng/ml (log-2 transformed total PSA) for each additional unit of depression score. Similar results were obtained for complete data after MI or data categorized by depressive symptoms. Conclusions Depression score is inversely correlated with serum total PSA concentrations among American men, and there is an interaction between depression and myocardial infarction. Clinical trial number Not applicable.

Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.

Background Existing epidemiological observational studies have suggested interesting but inconsistent clinical correlations between inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), and kidney stone disease (KSD). Herein, we implemented a two-sample bidirectional Mendelian randomization (MR) to investigate the causal relationship between IBD and KSD. Methods Data on IBD and KSD were obtained from Genome-Wide Association Studies (GWAS) summary statistics and the FinnGen consortium, respectively. Strict selection steps were used to screen for eligible instrumental SNPs. We applied inverse variance weighting (IVW) with the fix-effects model as the major method. Several sensitivity analyses were used to evaluate pleiotropy and heterogeneity. Causal relationships between IBD and KSD were explored in two opposite directions. Furthermore, we carried out multivariable MR (MVMR) to obtain the direct causal effects of IBD on KSD. Results Our results demonstrated that CD could increase the risk of KSD (IVW: OR = 1.06, 95% CI = 1.03–1.10, p  < 0.001). Similar results were found in the validation group (IVW: OR = 1.05, 95% CI = 1.01–1.08, p  = 0.013) and in the MVMR analysis. Meanwhile, no evidence of a causal association between UC and KSD was identified. The reverse MR analysis detected no causal association. Conclusions This MR study verified that CD plays a critical role in developing kidney stones and that the effect of UC on KSD needs to be further explored.

Background Polyunsaturated fatty acids (PUFAs) have demonstrated significant therapeutic potential across a wide range of disease. The aim of this study was to investigate the potential impact of PUFA intake on the prevalence of erectile dysfunction (ED). Methods The study included a total of 3730 participants from the National Health and Nutrition Examination Survey (NHANES) 2001–2004. Univariate analysis, multivariate regression analysis, subgroup analysis and machine learning were utilized to explore the relationship of variables to ED. Dose response curves were constructed to observe the linear or nonlinear relationship between PUFA intake and the prevalence of ED. Propensity score matching (PSM) was used for sensitivity analysis. Finally, the potential mechanistic link between PUFA intake and ED was explored. Results Through univariate and multivariate regression analysis results before and after PSM and XGBoost algorithm model results, arachidonic acid (AA) was chosen as the main research object. The consumption of AA was found to be associated with a decreased prevalence of ED under the fully adjusted model [OR = 0.33 (0.20, 0.56), P  < 0.001]. The interaction between AA and education was found in the subgroup analysis. Dose-response curves indicated a linear negative correlation between AA intake and the prevalence of ED. The above results were confirmed in the data analysis after 1:1 PSM. In addition, AA intake was associated with a decrease in inflammatory biomarkers and homocysteine. Conclusions The results suggest that AA intake is negatively correlated with the prevalence of ED. Further, anti-inflammatory and anti-endothelial damage may play a role in this.

Seed oil and protein contents are critical agronomic traits that determine soybean quality. However, the key loci and corresponding genes controlling these quality traits remain to be elucidated. Here, we performed bulked segregant analysis by sequencing (BSA-seq) using an F4 population derived from a cross between the cultivars Heinong 35 (HN35) and Dengke 3 (DK3). A major soybean oil and protein quantitative trait locus (QTL) designated as q-OP18 was identified on chromosome 18, and the sugar transporter gene GmSWEET46 was further cloned. Haplotype analysis revealed that a single-nucleotide polymorphism (SNP) in the sixth exon of GmSWEET46 results in an amino acid change between HN35 and DK3 and is associated with seed oil and protein content, suggesting its important role in determining seed quality in soybean. GmSWEET46 is expressed during the early stages of seed and pod development and localizes to the plasma membrane, indicating its potential function as a sugar transporter. Further studies demonstrated that GmSWEET46 can regulate seed protein content, oil content, and seed size in Arabidopsis and soybean. Collectively, this study provides a novel locus and gene for regulating soybean seed traits and offers valuable resources for the breeding of high-quality and high-yielding soybean cultivars.

Land cover changes are the main factors driving the evolution of regional ecological quality. These changes must be considered in the strategic formulation of regional or national ecological policies. The forest-steppe ecotone in the Greater Khingan Mountains is an important ecological barrier in northern China. To measure the effect of ecological protection in recent years, Landsat images, object-oriented image segmentation, and convolutional neural networks were used to create land cover datasets of the forest-steppe ecotone. The Carnegie–Ames–Stanford approach (CASA) and the dimidiate pixel model were used to derive net primary productivity (NPP) and fractional vegetation cover (FVC) to assess the ecological quality of this area. The results showed that only grassland and urban land increased, whereas saline–alkali land and desert areas initially increased and then decreased from 2010 to 2018, indicating that the desertification process was substantially curbed. Total NPP increased by 26.3% (2000–2010) and 10.8% (2010–2018). However, NPP decreased slightly in the center of the study area. FVC first decreased and then increased, and the increased areas were concentrated in the forest-steppe ecotone, saline–alkali land, and desert zone in Xin Barag Left Banner. These observations indicate that the ecological quality has gradually improved due to the strict protection of forest and grassland resources and the suppression of desertification. Our results provide potential insights for land use planning and the development of environmental protection measures in the forest-steppe ecotone.