Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach

To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Temperature and soils predict the distribution of plant species along the Himalayan elevational gradient

Tropical montane systems are characterized by a high plant species diversity and complex environmental gradients. Climate warming may force species to track suitable climatic conditions and shift their distribution upward, which may be particularly problematic for species with narrow elevational ranges. To better understand the fate of montane plant species in the face of climate change, we evaluated a) which environmental factors best predict the distribution of 277 plant species along the Himalayan elevational gradient in Nepal, and b) whether species elevational ranges increase with increasing elevation. To this end, we developed ecological niche models using MaxEnt by combining species survey and presence data with 19 environmental predictors. Key environmental factors that best predicted the distribution of Himalayan plant species were mean annual temperature (for 54.5% of the species) followed by soil clay content (10.2%) and slope (9.4%). Although temperature is the best predictor, it is associated with many other covariates that may explain species distribution, such as irradiance and potential evapotranspiration. Species at both ends of the Himalayan elevational gradient had narrower elevational ranges than species in the middle. Our results suggest that with further global warming, most Himalayan plant species have to migrate upward, which is especially critical for upland species with narrow distribution ranges.

Occurrence of Diarrheal diseases among under-five children and associated socio-demographic and household environmental factors: An Investigation Based on National Family Health Survey-4 in Rural India

Background: Diarrheal disease is a major population health problem that is the leading reason for mortality and morbidity among children aged 0-59 months in rural India. Therefore, the rationale of this study was to identify the socio-demographic, environmental predictors associated with diarrhea among under-five children in rural India. Methods: A total of 188,521 living children (0-59 months) were utilized from the National Family Health Survey-4, 2015–2016. Bivariate and binary logistic regression analysis was carried out from the available NFHS-4 data for selected socio-demographic and environmental predictors to identify the relationship of occurrence of diarrhea using STATA 13.1. Results: In rural India, children aged 12-23 months [AOR: 0.897, 95% CI (0.876, 0.983)], 24-35 months [AOR: 0.579, 95% CI (0.543, 0.617)], 36-47 months [AOR: 0.394, 95% CI (0.367, 0.424)], 48-59 months [AOR: 0.313, 95% CI (0.289, 0.339)] were significantly less likely to suffer diarrheal disease. Female children [AOR: 0.897, 95% CI (0.859, 0.937)], children belonged to Scheduled Tribe [AOR: 0.811, 95% CI (0.755, 0.872)], Other Backward Classes [AOR: 0.902, 95% CI (0.851, 0.956)] were less likelihood to experience diarrhea significantly. Diarrhea disease was also significantly more likely to occur among Muslim children [AOR: 1.217, 95% CI (1.128, 1.313)], other religion [AOR: 1.163, 95% CI (1.062, 1.272)] children in central region [AOR: 1.510, 95% CI (1.410, 1.617)], east region [AOR: 1.077, 95% CI (1.002, 1.157)], and west region [AOR: 1.201, 95% CI (1.095, 1.317)], children with low birth weight [AOR: 1.135, 95% CI (1.074, 1.149)], undernourished [AOR: 1.097, 95% CI (1.038, 1.197)], improper stool disposal [AOR: 1.061, 95% CI (1.002, 1.124)], and rudimentary roof materials [AOR: 1.113, 95% CI (1.048, 1.182)]. Conclusions: In the rural part of India, diarrhea has occurred frequently now. The different socio-demographic and environmental factors are influencing this disease. For reducing the vulnerability of diarrhea the socio-demographic and environmental factors should be improved or monitoring by effective community education. The government and different NGOs should focus on improved drinking water sources, sanitation facility which may reduce the vulnerability of the disease.

The main drivers for the occurrence of six red-listed epiphytic bryophytes and lichens in the boreo-nemoral forest landscape, Latvia

Forest landscape plays a significant role in rare cryptogam distribution. However, data about the environmental demands of rare epiphytic bryophytes and lichens in boreo-nemoral forest landscapes are not complete. In this study, we focused on finding the main environmental predictors influencing the occurrence of three red-listed epiphytic bryophytes and three red-listed epiphytic lichens in the Latvian boreo-nemoral forest landscape. We obtained the records of species from the Natural Data Management System OZOLS database, which is a national information system on all rare taxa. We analyzed the occurrence of species in relation to forest stand age and area, forest type, heterogeneity and tree bark pH class. We found that selected red-listed bryophyte and lichen occurrence was mainly influenced by forest stand age and area. However, each of the red-listed epiphytic bryophyte and lichen has their own ecological demands in the boreo-nemoral landscape.

Environmental predictors of pulmonary nontuberculous mycobacteria (NTM) sputum positivity among persons with cystic fibrosis in the state of Florida

Nontuberculous mycobacteria (NTM) are opportunistic human pathogens that are commonly found in soil and water, and exposure to these organisms may cause pulmonary nontuberculous mycobacterial disease. Persons with cystic fibrosis (CF) are at high risk for developing pulmonary NTM infections, and studies have shown that prolonged exposure to certain environments can increase the risk of pulmonary NTM. It is therefore important to determine the risk associated with different geographic areas. Using annualized registry data obtained from the Cystic Fibrosis Foundation Patient Registry for 2010 through 2017, we conducted a geospatial analysis of NTM infections among persons with CF in Florida. A Bernoulli model in SaTScan was used to identify clustering of ZIP codes with higher than expected numbers of NTM culture positive individuals. Generalized linear mixed models with a binomial distribution were used to test the association of environmental variables and NTM culture positivity. We identified a significant cluster of M. abscessus and predictors of NTM sputum positivity, including annual precipitation and soil mineral levels.

Taxonomic and functional dissimilarities of soil bacterial communities are more related to environmental dissimilarity than geographic distance

The processes governing soil bacteria biogeography are still not fully understood. It remains unknown how the importance of environmental filtering and dispersal differs between bacterial taxonomic and functional biogeography, and whether their importance is scale-dependent. We sampled soils at 195 plots across the Tibet plateau, with distances among plots ranging from 20 m to 1 550 km. Taxonomic composition of bacterial community was characterized by 16S amplicon sequencing, and functional community composition by qPCR targeting 9 functional groups involved in N dynamics. Twelve climatic and soil characteristics were also measured. Both taxonomic and functional dissimilarities were more related to environmental dissimilarity than geographic distance. Taxonomic dissimilarity was mostly explained by soil pH and organic matter, while functional dissimilarity was mostly linked to moisture, temperature and N, P and C availabilities. The roles of environmental filtering and dispersal were, however, scale-dependent and varied between taxonomic and functional dissimilarities, with distance affecting taxonomic dissimilarity over short distances (<~300 km) and functional dissimilarity over long distances (>~600 km). The importance of different environmental predictors varied across scales more for functional than taxonomic dissimilarity. Our results demonstrate how biodiversity dimension (taxonomic versus functional) and spatial scale strongly influence the conclusions derived from bacterial biogeography studies.

Region-Specific Associations between Environmental Factors and Escherichia coli in Freshwater Beaches in Toronto and Niagara Region, Canada

Poor freshwater beach quality, measured by Escherichia coli (E. coli) levels, poses a risk of recreational water illness. This study linked environmental data to E. coli geometric means collected at 18 beaches in Toronto (2008–2019) and the Niagara Region (2011–2019) to examine the environmental predictors of E. coli. We developed region-specific models using mixed effects models to examine E. coli as a continuous variable and recommended thresholds of E. coli concentration (100 CFU/100 mL and 200 CFU/100 mL). Substantial clustering of E. coli values at the beach level was observed in Toronto, while minimal clustering was seen in Niagara, suggesting an important beach-specific effect in Toronto beaches. Air temperature and turbidity (measured directly or visually observed) were positively associated with E. coli in all models in both regions. In Toronto, waterfowl counts, rainfall, stream discharge and water temperature were positively associated with E. coli levels, while solar irradiance and water level were negatively associated. In Niagara, wave height and water level had a positive association with E. coli, while rainfall was negatively associated. The differences in regional models suggest the importance of a region-specific approach to addressing beach water quality. The results can guide beach monitoring and management practices, including predictive modelling.

Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks

The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

Residential Area Sociodemographic and Breast Cancer Screening Venue Location Built Environmental Features Associated with Women’s Use of Closest Venue in Greater Sydney, Australia

Understanding environmental predictors of women’s use of closest breast screening venue versus other site(s) may assist optimal venue placement. This study assessed relationships between residential-area sociodemographic measures, venue location features, and women’s use of closest versus other venues. Data of 320,672 Greater Sydney screening attendees were spatially joined to residential state suburbs (SSCs) (n = 799). SSC-level sociodemographic measures included proportions of: women speaking English at home; university-educated; full-time employed; and dwellings with motor-vehicles. A geographic information system identified each woman’s closest venue to home, and venue co-location with bus-stop, train-station, hospital, general practitioner, and shop(s). Multilevel logistic models estimated associations between environmental measures and closest venue attendance. Attendance at closest venue was 59.4%. Closest venue attendance was positively associated with SSC-level women speaking English but inversely associated with SSC-level women university-educated, full-time employed, and dwellings with motor-vehicles. Mobile venue co-location with general practitioner and shop was positively, but co-location with bus-stop and hospital was inversely associated with attendance. Attendance was positively associated with fixed venue co-location with train-station and hospital but inversely associated with venue co-location with bus-stop, general practitioner, and shop. Program planners should consider these features when optimising service locations to enhance utilisation. Some counterintuitive results necessitate additional investigation.