Search

Understanding the processes and mechanisms that shape the distribution patterns and variations of biodiversity along spatial gradients continues to be a priority for ecological research. We focused on the biodiversity of benthic diatom communities within a large near‐natural watershed. The objectives are: (1) to explore the overall spatial patterns of benthic diatom biodiversity; (2) to investigate the effects associated with watercourse position and environmental variables, as well as both common and rare species on two facets (i.e., taxonomic and functional) of alpha and beta diversity; and (3) to unveil the mechanisms underlying their spatial variations. Alpha diversity indices along the stream watercourse showed a clear increasing trend from upstream to downstream sites. Results of random forest regression identified conductivity as the primary factor influencing functional alpha diversity, while elevation emerged as the predominant factor for taxonomic alpha diversity. Beta diversity partitioning revealed that taxonomic beta diversity generally exceeded functional beta diversity. These diversity measures exhibited different patterns along the watercourse position: taxonomic beta diversity remained relatively consistent along the watercourse, whereas functional total beta diversity and its two components of middle stream sites were lower than those of upstream and downstream sites. Functional beta diversity was sustained by dominant and common species, while rare species made significant contributions to taxonomic beta diversity. Both taxonomic and functional beta diversity and its components displayed a stronger influence from spatial factors than from local environmental, geo‐climatic, and nutrient variables. Collectively, taxonomic and functional alpha and beta diversity demonstrated distinct responses to the main environmental gradients and spatial factors within our catchment, highlighting their different insights into diatom diversity. Furthermore, research is required to assess the generalizability of our findings to similar ecosystems. In addition, this study presents opportunities for expansion to include other taxa (e.g., macroinvertebrates and fish) to gain a comprehensive understanding of the driving mechanisms behind stream biodiversity. [ABSTRACT FROM AUTHOR]

The metacommunity theory proposes that community structure and biodiversity are influenced by both local processes (such as environmental filtering) and regional processes (such as dispersal). Despite the extensive use of traditional bioassessments based on species‐environment relationships, the impact of dispersal processes on these assessments has been largely overlooked. This study aims to compare correlations between various bioassessment indices, including Shannon Weiner (H′), Biological Monitoring Working Party (BMWP), average score per taxon (ASPT), biotic index (BI), and EPT taxa index (EPT), based on macroinvertebrates collected from 147 sampling sites in a subtropical Chinese near‐natural catchment. Modified indices were calculated by removing species strongly influenced by dispersal processes to address the influence of dispersal processes. Their relationship with environmental factors was then compared to the original indices. The study employed random forest regression (RFR) to compare the explanatory power of environmental factors using the two sets of indices. The spearman rank correlation analysis was conducted to examine the correlation between indices and environmental factors. The river health assessment was performed based on both modified and original indices. The results reveal significant differences between original and modified indices (especially H′ and BI) providing a more accurate reflection of environmental conditions. Furthermore, the sensitivity of the different indices to various environmental factors varied, leading to differences in the bioassessment results between the modified and the original indices. Notably, original H′, BMWP, and ASPT overestimated the bioassessment results, whereas the original BI underestimated them. These findings offer valuable insights into bioassessment and river health assessment evaluation within the catchment and other interconnected freshwater ecosystems, such as lakes, reservoirs, and wetlands. Our study underscores the importance of assessing and mitigating the impact of dispersal processes on bioassessment to obtain a more precise representation of the status of freshwater ecosystems. [ABSTRACT FROM AUTHOR]

Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent the intricate relationships that exist between soil qualities and environmental influences. Therefore, in this study, we investigated the major determinants of CP release from different land use/types such as farmland, desert, forest soils, and rivers. The study utilizes the structural equation model (SEM), multiple linear regression (MLR), and three machine learning (ML) models (Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost)) to predict the release of CP from different soils by using soil iron (Fe), aluminum (Al), calcium (Ca), pH, total organic carbon (TOC) and precipitation as independent variables. Results show that colloidal-cations (Fe, Al, Ca) and colloidal-TOC strongly influence CP release, while bioclimatic variables (precipitation) and pH have weaker effects. XGBoost outperforms the other models with an R2 of 0.94 and RMSE of 0.09. SHapley Additive Explanations described the outcomes since XGBoost is accurate. The relative relevance ranking indicated that colloidal TOC had the highest ranking in predicting CP. This was supported by the analysis of partial dependence plots, which showed that an increase in colloidal TOC increased soil CP release. According to our research, the SHAP XGBoost model provides significant information that can help determine the variables that considerably influence CP contents as compared to RF, SVM, and MLR. [Display omitted] • SHAP XGboost performed better than RF, SVM and MLR. • Colloidal TOC ranked as the most important factor followed by Fe, Ca, Al. • Increasing colloidal TOC leads to an increase in the release of Colloidal P. [ABSTRACT FROM AUTHOR]

Soil colloids have been shown to play a critical role in soil phosphorus (P) mobility and transport. However, identifying the potential mechanisms behind colloidal P (P coll) release and the key influencing factors remains a blind spot. Herein, a machine learning approach (random forest (RF) coupled with partial dependence plot analyses) was applied to determine the effects of different soil physicochemical parameters on P coll content in three colloidal subfractions (i.e., nano- (NC): 1–20 nm, fine- (FC): 20–220 nm and medium-sized colloids (MC): 220–450 nm) based on a regional dataset of 12 farmlands in Zhejiang Province, China. RF successfully predicted P coll content (R2 = 0.98). Results showed that colloidal- organic carbon (OC coll) and minerals were the major determinants of total P coll content (1–450 nm); their critical values for increasing P coll release were 87.0 mg L−1 for OC coll , 11.0 mg L−1 for iron (Fe coll) or aluminium (Al coll), 2.6 mg L−1 for calcium (Ca coll), 9.0 mg L−1 for magnesium (Mg coll), 2.5 mg L−1 for silicon (Si coll), and 1.4 mg L−1 for manganese (Mn coll). Among three colloidal subfractions, the major factors determining P coll were soil Olsen-P (P Olsen ; 125.0 mg kg−1), Ca coll (2.5 mg L−1), and colloidal P saturation (21.0%) in NC; Mn coll (1.5 mg L−1), Mg coll (6.8 mg L−1), and P Olsen (135.0 mg kg−1) in FC; while Mn coll (1.5 mg L−1), Al coll (2.5 mg L−1), and Fe coll (3.8 mg L−1) in MC, respectively. OC coll had a considerable effect in the three fractions, with critical values of 80.0 mg L−1 in NC or FC, and 50.0 mg L−1 in MC. Our study concluded that the information gleaned using the RF model can be used as crucial evidence to identify the key determinants of different size fractionated P coll contents. However, we still need to discover one or more easy-to-measure parameters that can help us better predict P coll. [Display omitted] • Effects of different soil physicochemical factors on colloidal P content were investigated. • Random Forest showed good performance to predict colloidal P content in agricultural soils. • Colloidal organic carbon and minerals were the key determinants of colloidal P content. [ABSTRACT FROM AUTHOR]