Search

Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios. Key Points: Consistent, reproducible estimates of monthly water withdrawals were generated at the spatial resolution of USA water service areasSpatial and temporal variability of public supply withdrawal is driven by a complex array of explanatory featuresAn operational machine learning framework was developed to estimate water withdrawals, prediction uncertainty, and feature importance [ABSTRACT FROM AUTHOR]

This article discusses the importance of integrating soil microbial communities into ecological research, conservation efforts, and ecosystem restoration. It emphasizes the need to understand the diversity and functioning of soil microbes in Australian ecosystems, particularly mycorrhizal fungi. Ongoing research projects are exploring the diversity of mycorrhizal fungi in agricultural and rainforest environments, as well as the ecological interactions that influence microbial diversity and functioning. The article also highlights the potential of soil microbes in promoting sustainable agriculture and protecting crops from climate change effects. It concludes by discussing the impacts of insect herbivory and plant pathogens on soil microbial communities and the importance of trait-based approaches in fungal ecology. A study conducted by researchers from various Australian universities and research institutions found that the richness and cover of exotic plant species were associated with increased nutrient availability and specific microbial taxa. The study also revealed that land management practices had contrasting effects on the microbiome assembly of certain plant species. Additionally, the study emphasized the important role of digging animals in soil fungal ecology and dispersal in both arid and urban ecosystems. The researchers called for further research to understand and harness the potential of soil microbial communities for ecosystem restoration and conservation in Australia. [Extracted from the article]

Classifications are typically specific to particular issues or areas, leading to patchworks of subjectively defined spatial units. Stream conservation is hindered by the lack of a universal habitat classification system and would benefit from an independent hydrology-guided spatial framework of units encompassing all aquatic habitats at multiple spatial scales within large regions. We present a system that explicitly separates the spatial framework from any particular classification developed from the framework. The framework was constructed from landscape variables that are hydrologically and biologically relevant, covered all space within the study area, and was nested hierarchically and spatially related at scales ranging from the stream reach to the entire region; classifications may be developed from any subset of the 9 basins, 107 watersheds, 459 subwatersheds, or 10,000s of valley segments or stream reaches. To illustrate the advantages of this approach, we developed a fish-guided classification generated from a framework for the Great Lakes region that produced a mosaic of habitat units which, when aggregated, formed larger patches of more general conditions at progressively broader spatial scales. We identified greater than 1,200 distinct fish habitat types at the valley segment scale, most of which were rare. Comparisons of biodiversity and species assemblages are easily examined at any scale. This system can identify and quantify habitat types, evaluate habitat quality for conservation and/or restoration, and assist managers and policymakers with prioritization of protection and restoration efforts. Similar spatial frameworks and habitat classifications can be developed for any organism in any riverine ecosystem. [ABSTRACT FROM AUTHOR]