Research ArticlePLANT SCIENCES

Tree growth acceleration and expansion of alpine forests: The synergistic effect of atmospheric and edaphic change

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Science Advances  31 Aug 2016:
Vol. 2, no. 8, e1501302
DOI: 10.1126/sciadv.1501302
  • Fig. 1 Study site.

    (A) Map of the region. (B) Location of study site and Hongyuan County meteorological station. (C) Forest-grassland border. (D) Small forest patches. (E) Isolated tree. Map source: Chinese Bureau of Surveying and Mapping and Google Earth. Photo credit: L. Silva.

  • Fig. 2 Basal area increment.

    Main curves: Average growth of trees sampled in old growth forests (black), forest border (red), forest patches (blue), and isolated trees (green). Shaded areas represent SEs. Inner left: Relationship between BAI and age (log-transformed axis) indicating that increased growth is partly, but not entirely (Fig. 3), influenced by ontogeny. Inner right: Average growth rates from the slopes of linear regressions (P < 0.001) of BAI over time (table S4).

  • Fig. 3 Regional curve standardization.

    Top: Ring widths aligned to cambial age, showing that growth rates are influenced by the ontogeny (size/age) of the tree. Bottom: Ring widths divided by the average value at each cambial age (that is, ring width index), showing increasing growth even when normalized by ontogeny.

  • Fig. 4 Carbon and oxygen isotopes.

    (A and B) Atmosphere-to-wood carbon isotope fractionation (Δ13C) showing significant interactions between habitat and CO2 effects. (C and D) iWUE alongside a modeled baseline of constant Ci/Ca (gray triangles), below which measured values suggest a relatively weaker stomatal control of gas exchange, attributed to increasing water availability. (E and F) Wood δ18O values reflecting changes in plant water source, attributed to thawing permafrost. Error bars represent SEs. Arrows show periods of tree growth acceleration.

  • Fig. 5 Nitrogen isotopes and C/N ratios.

    Regression lines represent changes occurred since 1900 at different habitats. Error bars represent SEs.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/8/e1501302/DC1

    fig. S1. Sketch of sampling design, showing five transects distributed across a typical alpine forest grassland transition.

    fig. S2. Relationship between tree age and size at the time of sampling (y = 0.06x + 10.08; r2 = 0.41; P < 0.01).

    fig. S3. Normalized tree ring widths and basal area increments plotted over time using individual measurements of all rings formed when trees reached two fixed diameters (10 to 11 and 30 to 31 cm).

    fig. S4. Late to early wood growth indicating stable seasonal growth patterns in recent decades.

    fig. S5. Wood and atmospheric carbon isotope composition.

    fig. S6. Meteorological data and atmospheric CO2.

    fig. S7. Relationship between tree age and wood carbon, oxygen, and nitrogen stable isotope ratios (green, isolated trees; blue, forest patches; red, forest border; black, forest interior).

    table S1. Summary of mixed-effect model examining changes in tree growth over time at different portions of the vegetation gradient.

    table S2. Summary of mixed-effect models relating environmental factors and isotopic proxies of physiological performance during the recent tree growth acceleration phase (since 1960).

    table S3. Pairwise correlations of untransformed variables performed using the entire data set.

    table S4. Linear basal area increment (BAI) trends used to estimate growth rates, summarized in Fig. 2 by habitat and time periods.

    table S5. Floristic composition and ecological characteristics of the dominant tree species.

    table S6. An information-theoretic approach to evaluate multiple mixed-effect models and derive predictions that best represent the documented changes in tree growth.

    table S7. Structure of mixed-effect models used in this study.

  • Supplementary Materials

    This PDF file includes:

    • fig. S1. Sketch of sampling design, showing five transects distributed across a typical alpine forest grassland transition.
    • fig. S2. Relationship between tree age and size at the time of sampling (y = 0.06x + 10.08; r2 = 0.41; P < 0.01).
    • fig. S3. Normalized tree ring widths and basal area increments plotted over time using individual measurements of all rings formed when trees reached two fixed diameters (10 to 11 and 30 to 31 cm).
    • fig. S4. Late to early wood growth indicating stable seasonal growth patterns in recent decades.
    • fig. S5. Wood and atmospheric carbon isotope composition.
    • fig. S6. Meteorological data and atmospheric CO2.
    • fig. S7. Relationship between tree age and wood carbon, oxygen, and nitrogen stable isotope ratios (green, isolated trees; blue, forest patches; red, forest border; black, forest interior).
    • table S1. Summary of mixed-effect model examining changes in tree growth over time at different portions of the vegetation gradient.
    • table S2. Summary of mixed-effect models relating environmental factors and isotopic proxies of physiological performance during the recent tree growth
      acceleration phase (since 1960).
    • table S3. Pairwise correlations of untransformed variables performed using the entire data set.
    • table S4. Linear basal area increment (BAI) trends used to estimate growth rates, summarized in Fig. 2 by habitat and time periods.
    • table S5. Floristic composition and ecological characteristics of the dominant tree species.
    • table S6. An information-theoretic approach to evaluate multiple mixed-effect models and derive predictions that best represent the documented changes in tree growth.
    • table S7. Structure of mixed-effect models used in this study.

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