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Nitrogen (N) is a critical element in many ecological and biogeochemical processes in forest ecosystems. Cycling of N is sensitive to changes in climate, atmospheric carbon dioxide (CO2) concentrations, and air pollution. Streamwater nitrate draining a forested ecosystem can indicate how an ecosystem is responding to these changes. We observed a pulse in streamwater nitrate concentration and export at a long-term forest research site in eastern North America that resulted in a 10-fold increase in nitrate export compared to observations over the prior decade. The pulse in streamwater nitrate occurred in a reference catchment in the 2013 water year, but was not associated with a distinct disturbance event. We analyzed a suite of environmental variables to explore possible causes. The correlation between each environmental variable and streamwater nitrate concentration was consistently higher when we accounted for the antecedent conditions of the variable prior to a given streamwater observation. In most cases, the optimal antecedent period exceeded two years. We assessed the most important variables for predicting streamwater nitrate concentration by training a machine learning model to predict streamwater nitrate concentration in the years preceding and during the streamwater nitrate pulse. The results of the correlation and machine learning analyses suggest that the pulsed increase in streamwater nitrate resulted from both (1) decreased plant uptake due to lower terrestrial gross primary production, possibly due to increased soil frost or reduced solar radiation or both; and (2) increased net N mineralization and nitrification due to warm temperatures from 2010 to 2013. Additionally, variables associated with hydrological transport of nitrate, such as maximum stream discharge, emerged as important, suggesting that hydrology played a role in the pulse. Overall, our analyses indicate that the streamwater nitrate pulse was caused by a combination of factors that occurred in the years prior to the pulse, not a single disturbance event. 

In the Baltimore urban long-term ecological research (LTER) project, (Baltimore Ecosystem Study, BES) we use the watershed approach to evaluate integrated ecosystem function. The LTER research is centered on the Gwynns Falls watershed, a 17,150 ha catchment that traverses a gradient from the urban core of Baltimore, through older urban residential (1900 - 1950) and suburban (1950- 1980) zones, rapidly suburbanizing areas and a rural/suburban fringe. Our long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls as well as several small (40 - 100 ha) watersheds located within or near to the Gwynns Falls. The longitudinal sites provide data on water and nutrient fluxes in the different land use zones of the watershed (rural/suburban, rapidly suburbanizing, old suburban, urban core) and the small watersheds provide more focused data on specific land use areas (forest, agriculture, rural/suburban, urban). Each of the gaging sites is continuously monitored for discharge and is sampled weekly for chemistry. Additional chemical sampling is carried out in a supplemental set of sites to provide a greater range of land use. Weekly analyses includes nitrate, phosphate, total nitrogen, total phosphorus, chloride and sulfate, turbidity, fecal coliforms, temperature, dissolved oxygen and pH. Cations, dissolved organic carbon and nitrogen and metals are measured on selected samples. Streamflow data for this site are posted at: http://waterdata.usgs.gov/md/nwis/nwisman?site_no=015835701 

The relationship between (a) the structure and composition of the landscape around an individual's home and (b) environmental perceptions and health outcomes has been well demonstrated (eg the value of vegetation cover to well‐being). Few studies, however, have examined how multiple landscape features (eg vegetation and water cover) relate to perceptions of multiple environmental problems (eg air or water quality) and whether those relationships hold over time. We utilized a long‐term dataset of geolocated telephone surveys in Baltimore, Maryland, to identify relationships between residents’ perceptions of environmental problems and nearby landcover. Residents of neighborhoods with more vegetation or located closer to water were less likely to perceive environmental problems. Water quality was one exception to this trend, in that people were more likely to perceive water‐quality problems when nearby water cover was greater. These trends endured over time, suggesting that these relationships are stable and therefore useful for informing policy aimed at minimizing perceived environmental problems.

 

In streams macroinvertebrate density and disturbance-sensitive taxa have decreased, and disturbance-tolerant taxa have increased. 

All animals carry specialized microbiomes, and their gut microbiota are continuously released into the environment through excretion of waste. Here we propose themeta-gutas a novel conceptual framework that addresses the ability of the gut microbiome released from an animal to function outside the host and alter biogeochemical processes mediated by microbes. We demonstrate this dynamic in the hippopotamus (hippo) and the pools they inhabit. We used natural field gradients and experimental approaches to examine fecal and pool water microbial communities and aquatic biogeochemistry across a range of hippo inputs. Sequencing using 16S RNA methods revealed community coalescence between hippo gut microbiomes and the active microbial communities in hippo pools that received high inputs of hippo feces. The shared microbiome between the hippo gut and the waters into which they excrete constitutes ameta-gutsystem that could influence the biogeochemistry of recipient ecosystems and provide a reservoir of gut microbiomes that could influence other hosts. We propose thatmeta-gutdynamics may also occur where other animal species congregate in high densities, particularly in aquatic environments.

 

Water samples from one year of weekly samples at the Baltimore Ecosystem Study core stream sites were analyzed to measure concentrations of 92 different pharmaceutical concentrations. Note that none of the sites sampled in this dataset receive effluent from wastewater treatment plants (a common source of pharmaceutical contamination). In Baisman Run, sewage is treated by onsite systems, while in the Gwynns Falls watershed, sewage is piped across the watershed boundary and treated elsewhere. 

The Baltimore Ecosystem Study stream biofilm bacterial community composition was obtained from 8 long-term sampling network sites in and near the Gwynns Falls watershed to examine how bacterial communities differ along an urban-rural gradient. Sampling was conducted at the same time as stream chemistry sampling on 18 June 2014 and 21 Oct 2014. Note: biofilm samples were taken about 50 meters east from the Carroll Park monitoring station, just under the I95 highway overpass, due to high water depth, high water flow, and lack of rock substrates for sampling. This dataset presents the number of sequences matching the taxonomic classifications in a reference database of 16S rRNA genes. See the full metadata record for detailed methods.