skip to main content


Search for: All records

Creators/Authors contains: "Kao-Kniffin, Jenny"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Land-use change is highly dynamic globally and there is great uncertainty about the effects of land-use legacies on contemporary environmental performance. We used a chronosequence of urban grasslands (lawns) that were converted from agricultural and forested lands from 10 to over 130 years prior to determine if land-use legacy influences components of soil biodiversity and composition over time. We used historical aerial imagery to identify sites in Baltimore County, MD (USA) with agricultural versus forest land-use history. Soil samples were taken from these sites as well as from existing well-studied agricultural and forest sites used as historical references by the National Science Foundation Long-Term Ecological Research Baltimore Ecosystem Study program. We found that the microbiomes in lawns of agricultural origin were similar to those in agricultural reference sites, which suggests that the ecological parameters on lawns and reference agricultural systems are similar in how they influence soil microbial community dynamics. In contrast, lawns that were previously forest showed distinct shifts in soil bacterial composition upon recent conversion but reverted back in composition similar to forest soils as the lawns aged over decades. Soil fungal communities shifted after forested land was converted to lawns, but unlike bacterial communities, did not revert in composition over time. Our results show that components of bacterial biodiversity and composition are resistant to change in previously forested lawns despite urbanization processes. Therefore land-use legacy, depending on the prior use, is an important factor to consider when examining urban ecological homogenization.

     
    more » « less
  2. Whalen, Joann (Ed.)
    Abstract

    Residential landscapes are essential to the sustainability of large areas of the United States. However, spatial and temporal variation across multiple domains complicates developing policies to balance these systems’ environmental, economic, and equity dimensions. We conducted multidisciplinary studies in the Baltimore, MD, USA, metropolitan area to identify locations (hotspots) or times (hot moments) with a disproportionate influence on nitrogen export, a widespread environmental concern. Results showed high variation in the inherent vulnerability/sensitivity of individual parcels to cause environmental damage and in the knowledge and practices of individual managers. To the extent that hotspots are the result of management choices by homeowners, there are straightforward approaches to improve outcomes, e.g. fertilizer restrictions and incentives to reduce fertilizer use. If, however, hotspots arise from the configuration and inherent characteristics of parcels and neighborhoods, efforts to improve outcomes may involve more intensive and complex interventions, such as conversion to alternative ecosystem types.

     
    more » « less
    Free, publicly-accessible full text available September 29, 2024
  3. Abstract

    Microbial experimental systems provide a platform to observe how networks of groups emerge to impact plant development. We applied selection pressure for microbiome enhancement ofBrassica rapabiomass to examine adaptive bacterial group dynamics under soil nitrogen limitation. In the 9th and final generation of the experiment, selection pressure enhancedB. rapaseed yield and nitrogen use efficiency compared to our control treatment, with no effect between the random selection and control treatments. Aboveground biomass increased for both the high biomass selection and random selection plants. Soil bacterial diversity declined under highB. rapabiomass selection, suggesting a possible ecological filtering mechanism to remove bacterial taxa. Distinct sub-groups of interactions emerged among bacterial phyla such asProteobacteriaandBacteroidetesin response to selection. Extended Local Similarity Analysis and NetShift indicated greater connectivity of the bacterial community, with more edges, shorter path lengths, and altered modularity through the course of selection for enhanced plant biomass. In contrast, bacterial communities under random selection and no selection showed less complex interaction profiles of bacterial taxa. These results suggest that group-level bacterial interactions could be modified to collectively shift microbiome functions impacting the growth of the host plant under soil nitrogen limitation.

     
    more » « less
  4. Abstract

    In recent times, interest has grown in understanding how microbiomes – the collection of microorganisms in a specific environment – influence the survivability or fitness of their plant and animal hosts. The profound diversity of bacterial and fungal species found in certain environments, such as soil, provides a large pool of potential microbial partners that can interact in ways that reveal patterns of associations linking host–microbiome traits developed over time. However, most microbiome sequence data are reported as a community fingerprint, without analysis of interaction networks across microbial taxa through time.

    To address this knowledge gap, more robust tools are needed to account for microbiome dynamics that could signal a beneficial change to a plant or animal host. In this paper, we discuss applying mathematical tools, such as dynamic network modelling, which involves the use of longitudinal data to study system dynamics and microbiomes that identify potential alterations in microbial communities over time in response to an environmental change. In addition, we discuss the potential challenges and pitfalls of these methodologies, such as handling large amounts of sequencing data and accounting for random processes that influence community dynamics, as well as potential ways to address them.

    Ultimately, we argue that components of microbial community interactions can be characterized through mathematical models to reveal insights into complex dynamics associated with a plant or animal host trait. The inclusion of interaction networks in microbiome studies could provide insights into the behaviour of complex communities in tandem with host trait modification and evolution.

    A freePlain Language Summarycan be found within the Supporting Information of this article.

     
    more » « less