Mountaintop mining is the dominant form of coal mining and the largest driver of land cover change in the central Appalachians. The waste rock from these surface mines is disposed of in the adjacent river valleys, leading to a burial of headwater streams and dramatic increases in salinity and trace metal concentrations immediately downstream. In this synoptic study we document the cumulative impact of more than 100 mining discharge outlets and approximately 28 km 2 of active and reclaimed surface coal mines on the Upper Mud River of West Virginia. We measured the concentrations of major and trace elements within the tributaries and the mainstem and found that upstream of the mines water quality was equivalent to state reference sites. However, as eight separate mining-impacted tributaries contributed their flow, conductivity and the concentrations of selenium, sulfate, magnesium, and other inorganic solutes increased at a rate directly proportional to the upstream areal extent of mining. We found strong linear correlations between the concentrations of these contaminants in the river and the proportion of the contributing watershed in surface mines. All tributaries draining mountaintop-mining-impacted catchments were characterized by high conductivity and increased sulfate concentration, while concentrations of some solutes such as Se, Sr, and N were lower in the two tributaries draining reclaimed mines. Our results demonstrate the cumulative impact of multiple mines within a single catchment and provide evidence that mines reclaimed nearly two decades ago continue to contribute significantly to water quality degradation within this watershed.environmental impact | alkaline mine drainage | total dissolved solids | water chemistry

Summary1. Soluble root exudates are notoriously difficult to collect in non-hydroponic systems because they are released in a narrow zone around roots and are rapidly assimilated by rhizosphere microbes. This has substantially limited our understanding of their rates of release and chemical composition in situ , and by extension, their ecological significance. 2.Here we describe the advantages and limitations of several commonly employed methods for measuring exudation with respect to their potential adaptability for field use in forest ecosystems. Then, we introduce a novel in situ method for measuring exudation in forest soils, and present preliminary results of the spatial and temporal dynamics of loblolly pine ( Pinus taeda L.) exudation at the Duke Forest FACTS-1 site, North Carolina, USA from April 2007 to July 2008. 3. Exudation rates varied by an order of magnitude, with the highest rates occurring in late-June 2007 and mid-July 2008, and the lowest rates occurring during late-August 2007. On an annual basis, we estimate pine roots in the upper 15 cm of soil release c. 9 g C m -2 year -1 via this flux, which represents 1-2% of net primary productivity at the site. 4. The magnitude of exudation rates did not differ across an N availability gradient but did track general patterns of below-ground C allocation at the site. Exudation was well-predicted by root morphological characteristics such as surface area and the number of root and mycorrhizal tips, further supporting a possible link between root C allocation and exudation. 5. Because all methods for estimating exudates introduce experimental artefacts, we suggest that only a limited amount of ecologically relevant information is probably gleaned from a single method. Thus, a complementary suite of experimental approaches will best enable researchers to understand consequences of changing patterns of exudation in the wake of global environmental change.

Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

A major goal of microbial ecology is to identify links between microbial community structure and microbial processes. Although this objective seems straightforward, there are conceptual and methodological challenges to designing studies that explicitly evaluate this link. Here, we analyzed literature documenting structure and process responses to manipulations to determine the frequency of structure-process links and whether experimental approaches and techniques influence link detection. We examined nine journals (published 2009-13) and retained 148 experimental studies measuring microbial community structure and processes. Many qualifying papers (112 of 148) documented structure and process responses, but few (38 of 112 papers) reported statistically testing for a link. Of these tested links, 75% were significant and typically used Spearman or Pearson's correlation analysis (68%). No particular approach for characterizing structure or processes was more likely to produce significant links. Process responses were detected earlier on average than responses in structure or both structure and process. Together, our findings suggest that few publications report statistically testing structure-process links. However, when links are tested for they often occur but share few commonalities in the processes or structures that were linked and the techniques used for measuring them.

Summary A majority of environmental studies describe microbiomes at coarse scales of taxonomic resolution (bacterial community, phylum), ignoring key ecological knowledge gained from finer‐scales and microbial indicator taxa. Here, we characterized the distribution of 940 bacterial taxa from 41 streams along an urbanization gradient (0%–83% developed watershed area) in the Raleigh‐Durham area of North Carolina (USA). Using statistical approaches derived from macro‐organismal ecology, we found that more bacterial taxa were classified as intolerant than as tolerant to increasing watershed urbanization (143 vs 48 OTUs), and we identified a threshold of 12.1% developed watershed area beyond which the majority of intolerant taxa were lost from streams. Two bacterial families strongly decreased with urbanization: Acidobacteriaceae (Acidobacteria) and Xanthobacteraceae (Alphaproteobacteria). Tolerant taxa were broadly distributed throughout the bacterial phylogeny, with members of the Comamonadaceae family (Betaproteobacteria) presenting the highest number of tolerant taxa. Shifts in microbial community structure were strongly correlated with a stream biotic index, based on macroinvertebrate composition, suggesting that microbial assemblages could be used to establish biotic criteria for monitoring aquatic ecosystems. In addition, our study shows that classic methods in community ecology can be applied to microbiome datasets to identify reliable microbial indicator taxa and determine the environmental constraints on individual taxa distributions along environmental gradients.