Lakehead University Library Logo
    • Login
    View Item 
    •   Knowledge Commons
    • Electronic Theses and Dissertations
    • Retrospective theses
    • View Item
    •   Knowledge Commons
    • Electronic Theses and Dissertations
    • Retrospective theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    quick search

    Browse

    All of Knowledge CommonsCommunities & CollectionsBy Issue DateAuthorTitleSubjectDisciplineAdvisorCommittee MemberThis CollectionBy Issue DateAuthorTitleSubjectDisciplineAdvisorCommittee Member

    My Account

    Login

    Statistics

    View Usage Statistics

    Environmental drivers of succession in jack pine-dominated stands of boreal Ontario

    Thumbnail

    View/Open

    LongpreT2008m-1b.pdf (3.332Mb)

    Date

    2008

    Author

    Longpre, Trevor William F.

    Degree

    Master of Science

    Discipline

    Forestry and the Forest Environment

    Subject

    Taiga ecology (Ontario, Northern)
    Forest ecology (Ontario, Northern)
    Plant succession
    Photo chronosequencing

    Metadata

    Show full item record

    Abstract

    Spanning boreal Ontario, photo chronosequencing was used to observe change through time in 178 stands comprised at least in part by jack pine (Pinus bansiana Lamb.). Linked to growth and yield monitoring plot networks and a national climate model, observed succession was associated to 17 environmental attributes specific to geographic location, topography, soil conditions, and climate. Through the application of two non-parametric analytical techniques: regression trees and survival analysis, three fundamental ecological relationships to succession were identified. Deep sands were found to be the most influential ecological driver of succession in jack pine-dominated stands of boreal Ontario, followed by slope gradient and precipitation during the growing season. Derived cumulative survival probability functions for each of these variables offers tangible means by which forest forecast models in the region can be empirically refined.

    URI

    http://knowledgecommons.lakeheadu.ca/handle/2453/3715

    Collections

    • Retrospective theses

    Lakehead University Library
    Contact Us | Send Feedback

     


    Lakehead University Library
    Contact Us | Send Feedback