Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens.

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.

Difference in microbial community structure along a gradient of crater altitude: insights from the Nushan volcano.

The variation in the soil microbial community along the altitude gradient has been widely documented. However, the structure and function of the microbial communities distributed along the altitude gradient in the crater still need to be determined. We gathered soil specimens from different elevations within the Nushan volcano crater to bridge this knowledge gap. We investigated the microbial communities of bacteria and fungi in the soil. It is noteworthy that the microbial alpha diversity peaks in the middle of the crater. However, network analysis shows that bacterial (nodes 760 vs 613 vs 601) and fungal (nodes 328 vs 224 vs 400) communities are most stable at the bottom and top of the crater, respectively. Furthermore, the soil microbial network exhibited a decline, followed by an increase across varying altitudes. The core microorganisms displayed the highest correlation with pH and alkaline phosphatase (AP, as determined through redundancy analysis (RDA) and Mantel tests for correlation analysis. The fungal community has a higher number of core microorganisms, while the bacterial core microorganisms demonstrate greater susceptibility to environmental factors. In conclusion, we utilized Illumina sequencing techniques to assess the disparities in the structure and function of bacteria and fungi in the soil.IMPORTANCEThese findings serve as a foundation for future investigations on microbial communities present in volcanic soil.

The dynamic changes in phytoplankton and environmental factors within Dongping Lake (China) before and after the South-to-North Water Diversion Project.

Dongping Lake is one of the most important regulation and storage lakes along the eastern route of the South-to-North Water Diversion Project in China, the water quality condition of which directly influences the safety of water diverting, because it serves as a Yangtze River water redistribution control point. However, the changes in algae, and in environmental factors affecting their community structures, before and after the water diversion project are rarely reported. In this study, the temporal variations of phytoplankton abundance were examined based on monthly samples collected at three stations from May 2010 to April 2022. The total abundance of algae greatly decreased after the water diversion project was implemented, with a relatively stable biodiversity and evenness before and after the water translocation. Multiple statistical methods were used together with the water quality indices (WQIs) and the nutrient status index (TSIM) to evaluate overall water condition and analyse relationships among environmental factors. The WQIs demonstrated a general "Good" water quality with a seasonal differentiation, and that water conditions during water transfer periods were better than during non-water transfer periods, which may be ascribed to the improved hydraulic conditions and purified water environment during water transfer periods. Redundancy analysis showed that water temperature, ammonia nitrogen, water transparency, and total phosphorus were the most important environmental factors, with relatively decreased contribution rates towards phytoplankton communities after the water translocation. Importantly, some dominant phytoplankton genera of Chlorophyta, Bacillariophyceae, and Cyanophyceae were similarly affected by water transparency, and nitrogen and phosphorus nutrients in summer after the water translocation. These research findings helped us gain a comprehensive understanding of the changing patterns of water quality and microalgae and their relationships before and after the water diversion project, providing a guidance for future lake management in regulating hydraulic conditions and improving water quality of Dongping Lake.

Driving mechanism of groundwater quality and probabilistic health risk quantification in the central Yinchuan Plain.

The environmental changes from climatic, terrestrial and anthropogenic drivers can significantly influence the groundwater quality that may pose a threat to human health. However, the driving mechanism of groundwater quality and potential health risk still remains to be studied. In this paper, 165 groundwater samples were analyzed to evaluate the groundwater quality, driving mechanism, and probabilistic health risk in the central Yinchuan Plain by applying fuzzy comprehensive evaluation method (FCEM), redundance analysis (RDA) and Monte Carlo simulation. The results showed that hydrochemical evolution of groundwater were strongly influenced by water-rock interaction, evaporation and human activities. While 55.2% of groundwater samples reached the drinking water quality standard (Class I, II and III), 44.8% of samples exceeded the standard limits of Class III water quality (Class IV and V), indicating a high pollution level of groundwater. Mn, TDS, NH4+, NO3-, Fe, F-, NO2-, As were among major indicators that influence the groundwater quality due to the natural and anthropogenic processes. The RDA analysis revealed that climatic factors (PE: 10.9%, PRE: 1.1%), GE chemical properties (ORP: 20.7%, DO: 2.4%), hydrogeological factors (BD: 16.5%, K: 4.1%), and terrestrial factors (elevation: 1.2%; distanced: 5.6%, distancerl: 1.5%, NDVI: 1.2%) were identified as major driving factors influencing the groundwater quality in the study area. The HHRA suggested that TCR values of arsenic in infants, children and teens greatly exceeded the acceptable risk threshold of 1E-4, indicating a high cancer risk with a basic trend: infants > children > teens, while TCR values of adults were within the acceptable risk level. THI values of four age groups in the RME scenario were nearly ten times higher than those in the CTE scenario, displaying a great health effect on all age groups (HQ > 1). The present study provides novel insights into the driving mechanism of groundwater quality and potential health hazard in arid and semi-arid regions.

Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review.

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02-15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Evolution characteristics and molecular constraints of microbial communities during coal biogasification.

This study investigates the production of biomethane, and variation in microbial community and coal molecular structures using gas chromatography, 16S rRNA high-throughput sequencing and Fourier transform infrared spectroscopy. Additionally, the factors influencing microbial community structure at a molecular level are discussed. The results demonstrate that bituminous coal exhibits a higher biomethane yield than anthracite coal. In bituminous coal samples, Escherichia and Proteiniphilum are the predominant bacteria at day 0, while Macellibacteroides dominates from days 5 to 35. Methanofollis is the dominated archaea during days 0 to 15, followed by Methanosarcina on day 35. In anthracite coal samples, Soehngenia is the dominant bacterial genus at day 0; however, it transitions to mainly Soehngenia and Aminobacterium within days 5-15 before evolving into Acetomicrobium on day 35. Methanocorpusculum is predominantly found in archaeal communities during days 0-15 but shifts to Methanosarcina on day 35. Alpha diversity analysis reveals that bacterial communities have higher species abundance and diversity compared to archaeal communities. Redundancy analysis indicates a significant correlation between coal molecular structure and bacterial community composition (P value < 0.05), whereas no correlation exists with archaeal community composition (P value > 0.05). The research findings provide theoretical support for revealing the biological gasification mechanisms of coal.

Morphological Enrichment and Environmental Factors Correlation of Heavy Metals in Dominant Plants in Typical Manganese Ore Areas in Guizhou, China.

Bioavailable heavy metal and their efficient phytoremediation in mining areas have major implications for environmental and human health. In this study, we investigated 12 dominant plants in a typical Mn ore area of Zunyi, Guizhou Province, China, to determine the heavy metal contents, morphologies, and environmental factors affecting Mn, Cd, Pb, Cu, Zn, and Cr in the plant parts and rhizosphere soil. The bioavailabilities and degrees of metals were evaluated using the ratios of the secondary to primary phase distributions and potential ecological risk indices. Principal component analysis, cluster analysis, positive matrix factorisation modelling, and redundancy analysis were used to trace the origins and correlations among the metals. The results indicate that the bioavailabilities were the highest for Mn and Cd in the study area, and all of the target heavy metals had bioavailabilities above the moderate ecological harm level. Statistical modelling indicates that there are four main pollution sources: mining, smelting, processing operations, and atmospheric deposition. The dominant plants had high heavy metal enrichments, bioconcentration factors, and translocation factors for Mn, Cu, Cr, Cd, and Zn. The redundancy analysis indicates that soil total N, total P, and pH affect metal absorption and distributions in Compositae and non-Compositae plants in low-N, low-P, and slightly alkaline mining environments. This study provides a feasible basis for the screening of heavy metal enrichment plants and the improvement of remediation technology in manganese ore area under the extreme environment of poor nutrition.

Response of Water Quality to Land Use and Landscape Pattern in the Ganjiang River Watershed.

Analysing the impact of landscape composition and structure on water quality at different scales is of great significance to water quality protection. The aim of this study was to determine scale-dependent impacts of land use/landscape patterns on water quality. The Ganjiang River, the largest water system in the Poyang Lake watershed, the largest freshwater lake in China. The response of water quality to land use and landscape patterns in the Ganjiang River watershed was explored based on land use and water quality data using redundancy and Spearman correlation analyses. Considering upstream monitoring of the entire Ganjiang River watershed; watersheds at the county level administrative region; and 1, 2, 5, 10, 15, 20, and 30 km-radius circular buffer zones, a total of nine scales of land use/landscape patterns that influence water quality in the Ganjiang River watershed were analysed. Results indicated that the county-level scale and the land use type of the 20 km-radius buffer zone upstream of the monitoring site were closely linked to water quality (96.28% and 93.23%, respectively). Among the land use types, construction land and cultivated land were the main output sources of pollutants. Regarding landscape pattern index, the greater the fragmentation of the landscape, the heavier was the water pollution load; the more the patches per unit area, the more stable was the ecosystem and the lower was the pollutant concentration. In addition, the eco-hydrological system of the Ganjiang River watershed was revealed to some extent through multi-angle analysis. These conclusions can serve as a reference for government departments to formulate land management and water quality protection measures.

Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area.

Combining the results of base models to create a meta-model is one of the ensemble approaches known as stacking. In this study, stacking of five base learners, including eXtreme gradient boosting, random forest, feed-forward neural networks, generalized linear models with Lasso or Elastic Net regularization, and support vector machines, was used to study the spatial variation of Mn, Cd, Pb, and nitrate in Qom-Kahak Aquifers, Iran. The stacking strategy proved to be an effective substitute predictor for existing machine learning approaches due to its high accuracy and stability when compared to individual learners. Contrarily, there was not any best-performing base model for all of the involved parameters. For instance, in the case of cadmium, random forest produced the best results, with adjusted R2 and RMSE of 0.108 and 0.014, as opposed to 0.337 and 0.013 obtained by the stacking method. The Mn and Cd showed a tight link with phosphate by the redundancy analysis (RDA). This demonstrates the effect of phosphate fertilizers on agricultural operations. In order to analyze the causes of groundwater pollution, spatial methodologies can be used with multivariate analytic techniques, such as RDA, to help uncover hidden sources of contamination that would otherwise go undetected. Lead has a larger health risk than nitrate, according to the probabilistic health risk assessment, which found that 34.4% and 6.3% of the simulated values for children and adults, respectively, were higher than HQ = 1. Furthermore, cadmium exposure risk affected 84% of children and 47% of adults in the research area.

Evolution and health risk of indicator microorganisms in landscape water replenished by reclaimed water.

As an important means to solve water shortage, reclaimed water has been widely used for landscape water supply. However, with the emergence of large-scale epidemic diseases such as SARS, avian influenza and COVID-19 in recent years, people are increasingly concerned about the public health safety of reclaimed water discharged into landscape water, especially the pathogenic microorganisms in it. In this study, the water quality and microorganisms of the Old Summer Palace, a landscape water body with reclaimed water as the only replenishment water source, were tracked through long-term dynamic monitoring. And the health risks of indicator microorganisms were analyzed using Quantitative Microbial Risk Assessment (QMRA). It was found that the concentration of indicator microorganisms Enterococcus (ENT), Escherichia coli (EC) and Fecal coliform (FC) generally showed an upward trend along the direction of water flow and increased by more than 0.6 log at the end of the flow. The concentrations of indicator microorganisms were higher in summer and autumn than those in spring. And there was a positive correlation between the concentration of indicator microorganisms and COD. Further research suggested that increased concentration of indicator microorganisms also led to increased health risks, which were more than 30% higher in other areas of the park than the water inlet area and required special attention. In addition, (water) surface operation exposure pathway had much higher health risks than other pathways and people in related occupations were advised to take precautions to reduce the risks.

Genomic signals of local adaptation in Picea crassifolia.

BACKGROUND: Global climate change poses a grave threat to biodiversity and underscores the importance of identifying the genes and corresponding environmental factors involved in the adaptation of tree species for the purposes of conservation and forestry. This holds particularly true for spruce species, given their pivotal role as key constituents of the montane, boreal, and sub-alpine forests in the Northern Hemisphere. RESULTS: Here, we used transcriptomes, species occurrence records, and environmental data to investigate the spatial genetic distribution of and the climate-associated genetic variation in Picea crassifolia. Our comprehensive analysis employing ADMIXTURE, principal component analysis (PCA) and phylogenetic methodologies showed that the species has a complex population structure with obvious differentiation among populations in different regions. Concurrently, our investigations into isolation by distance (IBD), isolation by environment (IBE), and niche differentiation among populations collectively suggests that local adaptations are driven by environmental heterogeneity. By integrating population genomics and environmental data using redundancy analysis (RDA), we identified a set of climate-associated single-nucleotide polymorphisms (SNPs) and showed that environmental isolation had a more significant impact than geographic isolation in promoting genetic differentiation. We also found that the candidate genes associated with altitude, temperature seasonality (Bio4) and precipitation in the wettest month (Bio13) may be useful for forest tree breeding. CONCLUSIONS: Our findings deepen our understanding of how species respond to climate change and highlight the importance of integrating genomic and environmental data in untangling local adaptations.

Leaf litter chemistry and its effects on soil microorganisms in different ages of Zanthoxylum planispinum var. Dintanensis.

BACKGROUND: Leaf litter is the products of metabolism during the growth and development of plantation, and it is also an important component of nutrient cycling in plantation ecosystems. However, leaf litter chemistry and its effects on soil microorganisms in different ages, as well as the interactions between chemical components in leaf litter have been rarely reported. Based on this, this paper took Zanthoxylum planispinum var. dintanensis (hereafter Z. planispinum) plantations of 5-7, 10-12, 20-22, and 28-32 years old as the objects. By using one-way ANOVA, Pearson correlation analysis and redundancy analysis, we investigated leaf litter chemistry and its effects on soil microorganisms in different ages, and to reveal internal correlation of various chemical components in leaf litter, which can provide a scientific basis for the regulation of soil microbial activity in plantations. RESULTS: The variation of organic carbon with plantation age was more stable compared to total nitrogen and phosphorus of leaf litter. Nitrogen resorption was stronger than phosphorus resorption efficiency in Z. planispinum, and resorption efficiencies of leaf nitrogen and phosphorus for different ages were lower than the global average. Total nitrogen was highly significantly positively correlated with lignin, and total potassium was significantly positively correlated with tannin, suggesting the increase of inorganic substances in leaf litter would promote the accumulation of secondary metabolites. The leaf litter chemical traits explained up to 72% of soil microorganisms, where lignin was positively correlated with fungi and negatively correlated with bacteria, indicating that fungi are able to decompose lower quality litter and can break down complex and stable organic compounds more rapidly than bacteria. The nutrient elements carbon and nitrogen in the leaf litter and their interrelationship also have a great impact on soil microorganisms, because carbon is not only the element that provides energy, but also the element with the largest content in the microbiota. CONCLUSIONS: The sustained increase in inorganic nutrients of leaf litter did not favor the decomposition of secondary metabolites, but rather inhibited the degradation of leaf litter. The significant positive effect of the leaf litter chemistry on soil microorganisms indicates the important role of leaf litter in promoting nutrient cycling in Z. planispinum plantations.

Fate of selenium in a Se-enriched region of North China: Translocation, bioaccumulation, source, and health benefits.

There are limited studies on the translocation and bioaccumulation of selenium (Se) in weak alkaline cultivated Se-enriched soil, and the sources and speciation of Se in wheat grains remain unclear. In this study, we measured the Se levels in soils, roots, stems, and wheat grains from Se-enriched cultivated land in Ci County, China, which has a high incidence of esophageal cancer. The Se levels in the roots were higher than those in the soils, indicating that wheat plants bioaccumulated high concentrations of Se from the soil (enrichment coefficient [EC] range from the soil to the root: 0.94-3.29). Redundancy analysis indicated that the bioaccumulated factor, translocation coefficient, and EC were mainly controlled by phosphorus, pH, and Fe2O3 (contribution rates: 37.5%, 19.5%, and 15.9%, respectively). Linear regression analysis revealed that the sources of Se in grains were mainly from the water-soluble fraction (R2 = 0.55, at p < 0.05), the weakly acidic fraction (R2 = 0.84, at p < 0.05), the reducible fraction (R2 = 0.84, at p < 0.05), and the oxidizable fraction (R2 = 0.70, at p < 0.05), as well as from atmospheric deposition (R2 = 0.37, at p < 0.01). There is a significant correlation between the Se from atmospheric deposition and the oxidizable fraction (R2 = 0.62, at p < 0.01) and the residual fraction (R2 = 0.33, at p < 0.01). The contribution of Se input flux from atmospheric deposition was 5.50 g/hm2 for one year. Furthermore, the average content of organic Se in wheat grains was 58.93%. The Se concentrations found in wheat grains were considered beneficial for human health based on a comparison with the Chinese Society of Nutrition standard and worldwide levels. The results of this study will increase the overall knowledge on the theme, which could help prevent and control the harmful effects of undesirable concentrations of Se on human health.

Statistical Inference in Redundancy Analysis: A Direct Covariance Structure Modeling Approach.

Redundancy analysis (RA) is a multivariate method that maximizes the mean variance of a set of criterion variables explained by a small number of redundancy variates (i.e., linear combinations of a set of predictor variables). However, two challenges exist in RA. First, inferential information for the RA estimates might not be readily available. Second, the existing methods addressing the dimensionality problem in RA are limited for various reasons. To aid the applications of RA, we propose a direct covariance structure modeling approach to RA. The proposed approach (1) provides inferential information for the RA estimates, and (2) allows the researcher to use a simple yet practical criterion to address the dimensionality problem in RA. We illustrate our approach with an artificial example, validate some standard error estimates by simulations, and demonstrate our new criterion in a real example. Finally, we conclude with future research topics.

The role of harmonized Landsat Sentinel-2 (HLS) products to reveal multiple trajectories and determinants of cropland abandonment in subtropical mountainous areas.

Cropland abandonment is a widespread land-change process globally, which can stem from the accelerated outmigration of the population from rural to urban areas, socio-economic and political changes, catastrophes, and other trigger events. Clouds limit the utility of optical satellite data to monitor cropland abandonment in highly fragmented mountain agricultural landscapes of tropical and subtropical regions, including the south of China. Taking Nanjing County of China as an example, we developed a novel approach by utilizing multisource satellite (Landsat and Sentinel-2) imagery to map multiple trajectories of cropland abandonment (transitioning from cropland to grassland, shrubs and forest) in subtropical mountainous landscapes. Then, we employed a redundancy analysis (RDA) to identify the spatial association of cropland abandonment considering agricultural productivity, physiography, locational characteristics and economic factors. Results indicate the great suitability of harmonized Landsat 8 and Sentinel-2 images to distinguish multiple trajectories of cropland abandonment in subtropical mountainous areas. Our framework of mapping cropland abandonment resulted in good producer's (78.2%) and user's (81.3%) accuracies. The statistical analysis showed 31.85% of croplands cultivated in 2000 were abandoned by 2018, and more than a quarter of townships experienced cropland abandonment with high abandoned rates (>38%). Cropland abandonment mainly occurred in relatively unfavorable areas for agricultural production, for instance with a slope above 6°. Slope and the proximity to the nearest settlement explained 65.4% and 8.1% of the variation of cropland abandonment at the township level, respectively. The developed approaches on both mapping cropland abandonment and modeling determinants can be highly relevant to monitor multiple trajectories of cropland abandonment and ascribe their determinants not only in mountainous China but also elsewhere and thus promote the formulation of land-use policies that aim to steer cropland abandonment.

Chiral pesticides levels in peri-urban area near Yangtze River and their correlations with water quality and microbial communities.

Pesticides are considered to be the second-largest non-point source pollution in water. Our research assayed the river network of typical agricultural areas in the middle and lower Yangtze River as the study area. Pesticides residues in aquatic environment were determined by QuEChERS, combined with high-performance liquid chromatography tandem mass spectrometry, or gas chromatograph-mass spectrometer. At chiral pesticides' levels, we detected pesticides contents in water, classified and counted the types of pesticides, and analyzed their environmental risk assessment. Furthermore, potential correlations between chiral pesticides concentrations and water quality indicators were assayed. Additionally, we explored their relations with microbial communities at species levels. Enantiomers of Diclofop-methyl, Ethiprole, Difenoconazole and Epoxiconazole were enantioselectively distributed. More interestingly, due to various chiral environment of the sampling site, the enantiomers of Tebuconazole Acetochlor, Glufosinate ammonium and Bifenthrin had completely different distributions at different sites. Based on that, the chiral pesticides Diclofop-methyl, Bifenthrin, Ethiprole, Tebuconazole and Difenoconazole are enantioselective to the risk of aquatic environment. Generally, enantiomeric selectivity had high positive correlations with total nitrogen and phosphorus. Then we found that chiral fate behavior of Tebuconazole and Paichongding in water might be affected by prokaryotes. In addition, the chiral behavior of Diclofop-methyl, Propiconazole, Difenoconazole, and Tebuconazole isomers in water might be negatively affected by eukaryotes. That research helped us to comprehensively understand the impact of non-point source pollution of chiral pesticides in aquatic environment and provided basic data support for developing biological and water quality indicators for monitoring pollution in aquatic environment.

Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin.

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales.

The effects of water quality parameters, water level changes, and mixing on zooplankton community dynamics in a tropical high-mountain Lake Ardibo, Ethiopia.

The diversity and abundance of the zooplankton community are affected by changes in physico-chemical parameters, trophic interactions, water level changes, and periodic mixing. In this study, the influence of environmental variables, water level changes, and periodic mixing on zooplankton distribution and abundance was seasonally studied for Lake Ardibo from October 2020 to September 2021 collected from 3 sites. Results on the physico-chemical variables indicated that all variables, except turbidity, varied significantly (p < 0.05) in all sampling seasons. A total of 33 zooplankton species were recorded, comprising 18 rotifers, 11 cladocerans, and 4 cyclopoid copepods. Zooplankton abundance significantly varied seasonally with peak abundance (4232.13 ind. L-1) recorded in the dry season and lowest numbers (402.42 ind. L-1) during the long rainy period. Redundancy analysis (RDA) results indicated that total phosphorus, ammonia, water temperature, silicon dioxide, and conductivity were the most significant driving forces for the seasonal successions in the abundance and distribution of zooplankton communities. Cyclopoid copepod abundance was significantly (p < 0.05) higher in the dry season, which could be correlated with the partial mixing (atelomixis) phenomenon that occurred during the dry period.

Use of multivariate analysis to identify phytoplankton bioindicators of stream water quality in the monomodal equatorial agroecological zone of Cameroon.

The aquatic ecosystem is compromised by many contaminants that may reduce ecosystem functions and severely affect human health. This study aimed at determining suitable phytoplankton bioindicators of water quality for biomonitoring of freshwater streams in the monomodal agroecological zone of Cameroon. Water physicochemical and hydrological parameters, together with phytoplankton abundance and diversity, were measured from June 2016 to May 2017 along the Benoe Stream. Principal component analysis and redundancy analysis were used to determine phytoplankton spatial and temporal distribution and identify indicator species. The Shannon-Wiener diversity and Pielou's evenness indices indicated a clean to mildly polluted stream with a diverse phytoplankton community consisting of 84 genera belonging to 51 families that was dominated by the Bacillariophyta (64%), followed by Chlorophyta (13%) and Cyanophyta (10%). The total dissolved solids, electrical conductivity, stream water velocity, and discharge were the most important stream characteristics affecting the abundance of the dominant phytoplankton genera. Seasonal variations in the stream characteristics as well as spatial community distribution along an urban-small-scale farming - large-scale farming gradient were unveiled and their influence on the phytoplankton relative abundances. Increased abundance of Synedra ulna was indicative of low TDS and EC, which was the contrary for Gyrosigma baltium dominance. High Pleurosira laevis abundance was associated with the urban zone while high Diatoma sp. and Oscillatoria sp. abundances were related to the large-scale farming zone of the stream. These phytoplankton species have good potential for use as bioindicators for stream water quality monitoring in the monomodal agroecological zone.

Integrated Environmental and Genomic Analysis Reveals the Drivers of Local Adaptation in African Indigenous Chickens.

Breeding for climate resilience is currently an important goal for sustainable livestock production. Local adaptations exhibited by indigenous livestock allow investigating the genetic control of this resilience. Ecological niche modeling (ENM) provides a powerful avenue to identify the main environmental drivers of selection. Here, we applied an integrative approach combining ENM with genome-wide selection signature analyses (XPEHH and Fst) and genotype-environment association (redundancy analysis), with the aim of identifying the genomic signatures of adaptation in African village chickens. By dissecting 34 agro-climatic variables from the ecosystems of 25 Ethiopian village chicken populations, ENM identified six key drivers of environmental challenges: One temperature variable-strongly correlated with elevation, three precipitation variables as proxies for water availability, and two soil/land cover variables as proxies of food availability for foraging chickens. Genome analyses based on whole-genome sequencing (n = 245), identified a few strongly supported genomic regions under selection for environmental challenges related to altitude, temperature, water scarcity, and food availability. These regions harbor several gene clusters including regulatory genes, suggesting a predominantly oligogenic control of environmental adaptation. Few candidate genes detected in relation to heat-stress, indicates likely epigenetic regulation of thermo-tolerance for a domestic species originating from a tropical Asian wild ancestor. These results provide possible explanations for the rapid past adaptation of chickens to diverse African agro-ecologies, while also representing new landmarks for sustainable breeding improvement for climate resilience. We show that the pre-identification of key environmental drivers, followed by genomic investigation, provides a powerful new approach for elucidating adaptation in domestic animals.