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Summer Research Project 2017

  • Title: Group Project: Functional traits and early National Ecological Observatory Network (NEON) data
  • Group Project Leader: Sydne Record
  • Mentors: John Grady; Sydne Record
  • Collaborators: Sydne Record
  • Project Description:

    “Trait-based” research is a central focus of contemporary community ecology. It is now commonplace to use traits to predict responses of species to climatic change and habitat loss, to assess how assemblages and food webs will reorganize following colonization or emigration of new species (e.g., invasion), and to unravel large-scale patterns in species distributions across geographic gradients. This summer project will explore trait variation associated with early National Ecological Observatory Network (NEON) organismal and remote sensing data through two projects. Both projects are part of a larger collaboration with scientists at Michigan State University, University of Florida Gainesville, Portland State University, and University of Arizona.

    The goal of the first project will be to understand how functional diversity of small mammals, beetles, and plants varies across NEON sites. To this end, the REU student will contribute to the ongoing compilation of a functional trait database for species documented at NEON sites by scouring the literature and existing databases. Data collected will be compiled as part of an ongoing NEON EAGER project entitled, “Intraspecific trait variation and community structure at a continental scale.”

    The goal of the second project is to test size-energy rules in forests using field data and remotely sensed NEON data. Within a species, trees can vary in size by many orders of magnitude, from tiny seedlings to multi-ton adults. This intraspecific variation in tree size has important consequences for forest structure and function, as it governs patterns of tree abundance, growth rate and energy flux. Recent theoretical models make predictions about size-energy patterns of forests, but available evidence indicates deviation from predictions for the smallest and largest trees. The goal of this project is to examine seedling and canopy tree demography at Harvard forest and explore their theoretical implications. We will link early remote sensing data from NEON with boots-on-the-ground measurements at Harvard plots. Students will gain valuable field experience and be exposed to new methods in remote sensing and data analysis. They will also grapple with current theoretical issues that are being hotly debated in the ecological literature.

    The student selected for this project will be co-mentored by Drs. John Grady and Sydne Record of Bryn Mawr College, who will both be present during the eleven-week summer period at Harvard Forest in 2017. A typical week for the student will entail some time in the field and some time at the computer. Drs. Grady and Record will meet with the student a minimum of three times per week. Dr. Grady will assist the summer student in database compilation, locating transects, ID and tagging of seedlings, and recording demographic and canopy data. The student will learn identification of forest tree species, remote sensing techniques and experimental protocol, as well as theoretical issues motivating the research. The student will analyze data using R statistical software and will present his or her findings in a symposium at the final week at HF. There will be opportunities to develop an independent project that can be extended into a year-long independent study or a senior thesis. On average the student can expect to spend about 50% of the time doing field work and 50% of the time doing data entry and data analysis.

    General Requirements: The student working on this project must be willing to
    1. Participate actively in field studies, including ~8 hours per field day in a forest environment with biting insects and hot, humid conditions.
    2. Be able to hike with scientific gear (30-45 lb. pack) in rough, forested terrain.
    3. Be willing to spend many hours searching the literature and online materials for trait information to populate the trait database.
    4. Have, or be willing to develop, a basic understanding of Excel and R for graphical and statistical analysis.
    5. Think critically about theoretical issues in forest demographics and community ecology and link them to field work and database activities.

  • Readings:

    Farrior, C., Bohlman, S., Hubbell, S. & Pacala, S. Dominance of the suppressed: Power-law size structure in tropical forests. Science 351, 155-157 (2016).

    Flynn, D.F.B, et al. Loss of functional diversity under land use intensification across multiple taxa. Ecology Letters 12, 22-33 (2009).

    Ruger, N., Condit, R. Testing metabolic theory with models of tree growth that include light competition. Functional Ecology, 26, 759-765 (2012).

    Shingleton A. W. Allometry: The study of biological scaling. Nature Education Knowledge 3 (10): 2. (2010).

    Swenson, N.G., et al. The biogeography and filtering of woody plant functional diversity in North and South America. Global Ecology and Biogeography 21, 798-808 (2012).

    West, G. B., Enquist, B. J. & Brown, J. H. A general quantitative theory of forest structure and dynamics. Proceedings of the National Academy of Sciences 106, 7040-7045 (2009).

    Westoby, M. The self-thinning rule. Advances in Ecological Research 14, 167-220. (1984)

  • Research Category: Regional Studies, Physiological Ecology, Population Dynamics, and Species Interactions, Large Experiments and Permanent Plot Studies