Governments have designated national parks to protect the natural environment against ecosystem destruction and improve individuals’ emotional and recreational life. National parks enhance environment-friendly awareness by conducting ecotourism activities and individuals with environment-friendly awareness are inclined to continue to visit national parks as ecotourism destinations. The New Environmental Paradigm (NEP) is a widely used measure of environmental concern, suitable for measuring the environment-friendly attitude and revisit intention of visitors of national parks. Therefore, the study carried out structural equation modeling (SEM) to investigate the relationship between the NEP, national park conservation consciousness and environment-friendly behavioral intention. Based on the results, an implication is presented to induce national parks to cultivate individual environment-friendly awareness and for visitors to pursue sustainable, environment-friendly tourism behavior. The findings indicate that national parks are to expand educational programs and facilities for eco-tourists visiting national parks to maintain a balanced relationship between themselves and nature and have a strong environmental awareness to preserve the natural environment.

The southeastern region of the United States exhibits an unusual trend of decreasing tree species richness (TSR) from higher to lower latitudes over the Florida peninsula. This trend contradicts the widely marked latitudinal diversity gradient where species richness is highest in tropical zones and decreases towards extratropical regions. This study aims to assess the environmental factors that prompt this atypical inverse latitudinal gradient seen in TSR using the USDA Forest Service’s Forest Inventory and Analysis (FIA) database. Fifteen variables under four categories of forested area, groundwater, soil properties, and climate groups were examined to model TSR in the region. Generalized linear models (GLMs) with Poisson distributions first assessed individual variables to test explanatory power then the LASSO regularization method was utilized to extract two subsets of the most influential variables to predict TSR. Forest area and four climate variables (mean annual temperature, precipitation seasonality, mean temperature of coldest quarter, and mean precipitation of driest quarter) were the top five variables during the initial GLM assessment implying their potential individual influence in regulating TSR. Two subsets of LASSO models contained seven and three predictor variables, respectively. Frist subset includes seven predictors, presented in highest to low standardized coefficient, mean temperature of coldest quarter, forested area, precipitation seasonality, mean precipitation of driest quarter, water table depth, spodosol, and available water storage. The other subset further excluded four lowest influential variables from the first set, leaving the top three variables from the first subset. The first subset of the LASSO model predicted TSR with 63.4% explained deviance while the second subset reproduced 60.2% of deviance explained. With only three variables used, the second model outperformed the first model evaluated by the AIC value. We conclude that forest patch area, mean temperature of coldest quarter, and precipitation seasonality are the highly influential variables of TSR among environmental factors in the southeastern region of U.S., but evolutionary or historic cause should be further incorporated to fully understand tree species diversity pattern in this region.

The Upper Trinity River Basin (TRB) is the most populated river basin and one of the largest water suppliers in Texas. However, sediment and nutrient loads are reducing the capacity of reservoirs and degrading water quality. The objectives of this study are to calibrate and validate the Soil and Water Assessment Tool (SWAT) model for ten study watersheds within the Upper TRB in order to assess nutrient loads into major reservoirs in the basin and to predict the effects of point source elimination and urbanization on nutrient loads through scenario analyses. SWAT performed reasonably well for the current condition except for two out of five tributaries in the Eagle Mountain watershed and total phosphorous OPEN ACCESS Water 2015, 7 5690 in Richland-Chambers. The impacts of simulated scenarios varied within watersheds. Point-source elimination achieved reductions ranging from 0.3% to 24% in total phosphorus and 1% to 56% in total nitrogen received by the reservoirs. Population and development projections were used to examine the impacts of urbanization on each watershed. Projected urbanization in 2030 had large effects on simulated total phosphorus loads in some watersheds, ranging from a reduction of 1% to an increase of 111%. Projected urbanization also affected simulated total nitrogen loads, from a reduction of 3% to an increase of 24%. One limitation of this study is the lack of long-term, up-to-date water quality data due to discontinued water-quality monitoring stations. Although careful considerations were given to the adjustment of parameter values reflecting various aspects of the nutrient processes, further data collection will enhance modeling study for assessment of these watersheds’ water resources and environmental problem.

Inland wetlands store disproportionate soil organic carbon (SOC), yet spatially explicit information is scarce for temperate monsoon riverine wetlands, where recurrent erosive flooding shapes carbon accumulation. To map depth-resolved SOC stocks (kg C m⁻²) and evaluate satellite covariates, we combined stocks measured at 494 sites in the Han, Geum, and Yeongsan–Seomjin river basins, South Korea, with multi-temporal Sentinel-2 and terrain covariates in random forest models at 20 m for the organic layer, 0–30 cm, and 0–100 cm. Cross-validated R² (squared correlation) was low (0.21, 0.18, and 0.15, respectively) but within the range reported for wetland SOC mapping. Satellite covariates raised R² by 0.02–0.07 and reduced the root mean square error (RMSE) relative to a non-satellite baseline; dormant-season reflectance added no information. The April normalized difference vegetation index (NDVI) ranked highest for both mineral-soil layers, while hydrology and parent material gained importance at 0–100 cm. NDVI showed a threshold-type partial-dependence response despite weak rank correlations; one plausible interpretation is that vegetation cover indicates the geomorphic stability of the ground surface. The model-based regional stocks are 3.11 and 6.08 Mt C at 0–30 and 0–100 cm (27.7 and 54.1 t C ha⁻¹), at the low end of published values for Asian monsoon riverine wetlands. The maps provide a baseline for carbon assessment.