Environmental concentrations as ratios of random variables

Human-induced environmental change increasingly threatens the stability of socio-ecological systems. Careful statistical characterization of environmental concentrations is critical to quantify and predict the consequences of such changes on human and ecosystems conditions. However, while concentrations are naturally defined as the ratio between solute mass and solvent volume, they have rarely been treated as such, typically limiting the analysis to familiar distributions generically used for any other environmental variable. To address this gap, we propose a more general framework that leverages their definition explicitly as ratios of random variables. We show that the resulting models accurately describe the behavior of nitrate plus nitrite in US rivers and salt concentration in estuaries in the Everglades by accounting for heavy tails potentially emerging when the water volume fluctuates around low values. Models that preclude the presence of heavy tails and the related high probability of extreme concentrations could significantly undermine the accuracy of diagnostic frameworks and the effectiveness of mitigation interventions, especially for soil contamination characterized by a water volume (i.e., soil moisture) frequently approaching zero.

Physical geography, isolation by distance and environmental variables shape genomic variation of wild barley (Hordeum vulgare L. ssp. spontaneum) in the Southern Levant

Determining the extent of genetic variation that reflects local adaptation in crop-wild relatives is of interest for the purpose of identifying useful genetic diversity for plant breeding. We investigated the association of genomic variation with geographical and environmental factors in wild barley (Hordeum vulgare L. ssp. spontaneum) populations of the Southern Levant using genotyping by sequencing (GBS) of 244 accessions in the Barley 1K+ collection. The inference of population structure resulted in four genetic clusters that corresponded to eco-geographical habitats and a significant association between lower gene flow rates and geographical barriers, e.g. the Judaean Mountains and the Sea of Galilee. Redundancy analysis (RDA) revealed that spatial autocorrelation explained 45% and environmental variables explained 15% of total genomic variation. Only 4.5% of genomic variation was solely attributed to environmental variation if the component confounded with spatial autocorrelation was excluded. A synthetic environmental variable combining latitude, solar radiation, and accumulated precipitation explained the highest proportion of genomic variation (3.9%). When conditioned on population structure, soil water capacity was the most important environmental variable explaining 1.18% of genomic variation. Genome scans with outlier analysis and genome-environment association studies were conducted to identify adaptation signatures. RDA and outlier methods jointly detected selection signatures in the pericentromeric regions, which have reduced recombination, of the chromosomes 3H, 4H, and 5H. However, selection signatures mostly disappeared after correction for population structure. In conclusion, adaptation to the highly diverse environments of the Southern Levant over short geographical ranges had a limited effect on the genomic diversity of wild barley. This highlighted the importance of nonselective forces in genetic differentiation.

Using remote sensing data for environmental monitoring of water objects using GIS and machine learning

The main way to understand variability of any spatial data using remote sensing is calculating spectral indices. For now, some difficulties have receiving water surface temperature due to specific properties for satellite sensors and low spatial resolution. The main sources of receiving salinity data are remote sensing data from ESA SMOS, NASA Aquarius and SMAP satellites. Using different machine learning algorithms, we can get models or equations, representing dependency between studied environmental variable and different spectral channels of remote monitoring data. After receiving and collecting remote sensing data in database this system uses machine learning algorithms to find dependency between collected field data and different spectral bands of the remote sensing data. Our goal was to form an analytical system based on remote sensors and machine learning algorithm to analyse, predict and evaluate water ecosystems for fisheries and environmental protection.

Palm functional trait responses to local environmental factors in the Colombian Amazon

Functional traits play a key role in driving plant community effects on ecosystem function. We examined nine functional traits in various palm (Arecaceae) species and their relationships with moisture, tree-fall gaps, slope, and forest type at 29 transects (500×5 m) in the northeastern region of the Colombian Amazon. Redundancy analysis of mean trait values of species within a plot weighted by their abundance and Pearson correlations were used to evaluate the relationships between traits and environmental factors. The community trait composition was correlated with local environmental factors, which explained 23% of the trait variance. We detected functional dominance of the tallest palms in soils with high moisture and in floodplain forests (p ≤0.05). Palms with relatively long leaves were dominant in the flooded forests. Acaulescent and small palms were dominant on high slopes, and in terra firme forests, long-petioled palms were dominant in forest gaps. The number of seeds per fruit was not correlated with any environmental variable. Thus, hydrology is one of the main drivers of the functional composition of neotropical palm communities at the local scale, segregating tall palms with competitive and evasive strategies from small understory palms, which are mainly stress tolerant.

specificity : an R package for generalized specificity analysis, with example analyses of Hawaiian foliar endophytic Fungi, human gut microbiome bacteria, and Antarctic glacier bacteria.

Understanding the factors that influence microbes' environmental distributions is important for determining drivers of microbial community composition. Species distributions are governed by parameters that influence their dispersal or survival. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogenies. In microbial ecology, "specificity" is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao's (1982, 2010) Quadratic Entropy can be conveniently applied to calculate specificity of a focal species to many different environmental variables, including 1-dimensional variables (vectors), dissimilarity matrices, phylogenies, and ontologies. We present our R package "specificity" for performing the above analyses, and apply it to four real-life microbial datasets to demonstrate its application: fungi living within the leaves of native Hawaiian plants, bacteria from the human gut microbiome, bacteria living within Antarctic glacier ice, and the Earth Microbiome Project data set. Finally, we present an interactive visualization and data exploration tool for these analyses, available as a companion R-package called "specificity.shiny".

Does glacial retreat impact benthic chironomid communities? A case study from Rocky Mountain National Park, Colorado

The aim of this study was to determine which environmental variables are responsible for modern benthic chironomid distributions in a glacial setting. The chironomid communities from nine alpine lakes were assessed, and forty-three individual taxa were extracted and identified. Surface water temperature and nitrate were strongly and negatively correlated (−0.82, p = 0.007), suggesting that glacial meltwater (the driver that explains both surface water temperature (SWT) (°C) and nitrate (NO3 + NO2-N)) is the environmental variable that explains the most variance (15%). On average, lakes receiving glacial meltwater were 2.62 °C colder and contained 66% more NO3 + NO2-N than lakes only receiving meltwater from snow. The presence of taxa from the tribe Diamesinae indicates very cold input from running water, and these taxa may be used as a qualitative indicator species for the existence of glacial meltwater within a lake catchment. Heterotrissocladius, Diamesa spp., and Pseudodiamesa were present in the coldest lakes. Chironomus, Diplocladius, and Protanypus were assemblages found in cold lakes affiliated with the littoral zone or alpine streams. The modern benthic chironomid communities collected from the alpine of subalpine lakes of Rocky Mountain National Park, Colorado, represent a range of climatic and trophic influences and capture the transition from cold oligotrophic lakes to warmer and eutrophic conditions.

ENVIRONMENTAL IMPACT ASSESSMENT, PERSONAL HYGIENE, AND OCCUPATIONAL HISTORY ON THE INCIDENCE OF DERMATITIS

The environment is a very influential factor in the incidence of dermatitis. Dermatitis is a skin disease that is acute, sub-acute/ or chronic caused by inflammation of the skin that occurs due to exogenous and endogenous factors. The problem in this study is the high prevalence of dermatitis in the community, reaching 623 cases. The purpose of this study is to analyze the analysis of the impact of environmental studies, personal hygiene, and work history on the incidence of dermatitis in the community in the Darul prosperous sub-district, Nagan Raya district. This research method is an analytic design with a Cross-sectional design. This research was carried out in December 2020 which became the population of people with Dermatitis with a sample of 86 respondents. This study was analyzed using univariate and bivariate, then tested by Chi-Square test. The results of the study after being tested stated that there was an influence of environment, personal hygiene, and work history on the incidence of dermatitis as evidenced by the P.value 0.05. The conclusion after analyzing the effect of the model on the impact of work history, personal hygiene, and the environment there is an influence on the incidence of dermatitis. Among all these variables the most powerful influence is the environmental variable. Suggestions to the Puskesmas to further improve policies in dealing with the incidence of dermatitis by approaching and empowering the community so that a dermatitis-free society is achieved.

Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China

In deserts, pond cladocerans suffer harsh conditions like low and erratic rainfall, high evaporation, and highly variable salinity, and they have limited species richness. The limited species can take advantage of ephippia or resting eggs for being dispersed with winds in such habitats. Thus, environmental selection is assumed to play a major role in community assembly, especially at a fine spatial scale. Located in Inner Mongolia, the Ulan Buh desert has plenty of temporary water bodies and a few permanent lakes filled by groundwater. To determine species diversity and the role of environmental selection in community assembly in such a harsh environment, we sampled 37 sand ponds in June 2012. Fourteen species of Cladocera were found in total, including six pelagic species, eight littoral species, and two benthic species. These cladocerans were mainly temperate and cosmopolitan fauna. Our classification and regression tree model showed that conductivity, dissolved oxygen, and pH were the main factors correlated with species richness in the sand ponds. Spatial analysis using a PCNM model demonstrated a broad-scale spatial structure in the cladoceran communities. Conductivity was the most significant environmental variable explaining cladoceran community variation. Two species, Moina cf. brachiata and Ceriodaphnia reticulata occurred commonly, with an overlap at intermediate conductivity. Our results, therefore, support that environmental selection plays a major role in structuring cladoceran communities in deserts.