Structural segregation and scales of spatial dependency in Abies amabilis forests

Larson J. Andrew, Franklin F. Jerry

    Research output: Contribution to journalArticlepeer-review


    Question: Is a mosaic structure apparent in the spatial distribution of trees in old-growth Abies amabilis forests? Location: Montane forests of the western Cascade Range, Washington, USA. Methods: Maps of tree locations were created for study areas located in two, 300-year old stands and a single 600-year old stand. Stand structure parameters were calculated using several subsample quadrats sizes (56.25 - 306.25 m2), which were drawn randomly with replacement at a density of 250 quadrats per ha from the stem maps in the computing environment. Spatial cross-covariance functions between different canopy strata were estimated using the spline cross-correlogram. Results: Negative spatial correlation (segregation) between subcanopy tree density and areas of high overstorey occupancy was detected. Understorey and midstorey tree densities were positively spatially correlated. These general trends were apparent across the range of observational scales investigated. Significant spatial correlation between canopy strata was observed at spatial scales of 12 - 44 m and extended to the largest scales in the 600-year old stand. Conclusion: The observed spatial segregation between canopy strata supports the hypothesis that old A. amabilis forests form fine-scale structural mosaics. Structural segregation at small scales may be due to competitive interactions as well as exogenous forcing of tree locations (e.g. by mortality due to pathogens or disturbance), however segregation at large scales in the 600-year old stand is likely due to exogenous factors alone. This study reinforces the idea that horizontal heterogeneity is an emergent property of old-growth forests.

    Original languageEnglish
    Pages (from-to)489-498
    Number of pages10
    JournalJournal of Vegetation Science
    Issue number4
    StatePublished - Aug 2006


    • Canopy gap
    • Forest dynamics
    • Patch
    • Spatial correlation
    • Spatial covariance
    • Spatial pattern


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