AIMS_GreatPlains_approach3_EEAS

This study was conducted in the South Fork of King’s Creek at Konza Prairie Biological Station, one of the most extensively studied prairie stream ecosystems. Kings Creek, a 5th-order intermittent stream, drains a 1,059-ha tallgrass prairie in the Kansas Flint Hills. The USGS gage (06879560; established in 1979) on the mainstem provides long-term hydrological data. The region has a mid-continental climate, with an annual mean temperature ranging from 5.6 to 16.7 °C and annual precipitation between 30 and 114 cm. The landscape is dominated by unplowed tallgrass prairie, with a band of deciduous trees lining the downstream reach of King’s Creek. The catchment soil features alternating limestone and shale layers, contributing to the area's complex subsurface hydrology and biodiversity.
This synoptic survey was conducted to support the sampling objectives of the Aquatic Intermittency Effects on Microbiomes in Streams (AIMS) Project. In June 2021, a field team collected hydrology and biogeochemistry datasets across 50 locations within a sub-drainage of the South Fork of King’s Creek. Site selection aimed to balance multiple priorities: (i) targeting existing monitoring infrastructure with long-term data (n=14); (ii) including locations near known springs and tributary junctions (n=9); and (iii) incorporating a range of drainage areas and topographic wetness index (TWI) values (n=27), both of which are linked to flow permanence.
At each site, samples of water (if present), biofilm, leaf litter, and sediment were collected. Microbial enzyme activities associated with organic matter decomposition (β-glucosidase, phenol oxidase, and peroxidase) and the mineralization of organic nitrogen (N-acetylglucosaminidase) and phosphorus (phosphatase) were measured. Enzyme activities were quantified fluorometrically using 4-methylumbelliferyl (MUB)-linked substrates or colorimetrically using 4-nitrophenyl (pNP)-linked and 3,4-dihydroxyphenylalanine (L-DOPA)-linked substrates.
Additional details are available on the AIMS OSF site: https://osf.io/e7s9j/