Research ArticleECOLOGY

Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient

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Science Advances  04 Dec 2019:
Vol. 5, no. 12, eaaw8114
DOI: 10.1126/sciadv.aaw8114
  • Fig. 1 Approach to use remotely sensed functional diversity to inform trait-based ecology.

    (A) Trait distributions are derived from imaging spectroscopy spanning the visible, near-, and short-wave-infrared wavelengths (400 to 2500 nm). Functional diversity indices are quantified from trait distributions to understand how plant communities change across environmental gradients. We estimated functional richness (FRic) and functional divergence (FDiv). FRic corresponds to the convex hull volume and represents the trait space occupied by a community. When this volume is narrow, FRic is low (blue community), and when FRic is large, the range is wider (yellow community). FDiv represents how sample points diverge in their distances from the center of the multitrait functional space. Large values of FDiv indicate high trait differentiation within a community (yellow), and low values indicate lower divergence (blue). (B) The scale dependency of community assembly processes is evaluated by assessing variation in functional diversity across spatial scales. Functional diversity either changes with area (bold line) or is scale invariant (dashed line). (C) Community assembly processes are disentangled by testing for the relative importance of trait convergence (environmental filtering) and trait divergence (biotic interactions). (D) Functional diversity effects on ecosystem processes are tested by examining whether remotely sensed diversity indices are related to forest productivity.

  • Fig. 2 Variation of remotely sensed functional diversity indices across sites along elevation.

    Distribution of trait diversity indices within and among sites along elevation are shown for (A) FRic and (B) FDiv. Relationships between mean values per site of FRic and FDiv and elevation are indicated in (C) and (D), respectively. Black circles represent average values of FRic and FDiv in each site, and the dark vertical lines are their 95% confidence intervals. Significant regression fit is shown by a solid blue line, with gray strips showing the 95% confidence intervals of the fit and r2 indicating the amount of variation explained by elevation.

  • Fig. 3 Functional diversity–area relationships across sites.

    Scale dependency of two functional diversity measures across elevation in the nine study sites arrayed along the elevation gradient. (A) FRic and (B) FDiv. Curves represent the fit of functional diversity versus the natural log (area), green lines represent the fit for remotely sensed values, and blue lines represents the fit for simulated communities (e.g., null models; see the main text). Observed values of FRic and FDiv lower than fitted lines of the null models indicate strong environmental filtering at the site scale (e.g., trait convergence). Observed values of FRic and FDiv that are above the null expectations suggest that plant communities may be constrained by biotic interactions (e.g., trait divergence). Overlapping curves indicate no difference between simulated and measured indices and that traits are randomly distributed at the site scale.

  • Fig. 4 Changes in FRic and FDiv along elevation after controlling for scale dependency.

    (A and B) Relationship between the slope of FRic-area and FDiv-area and elevation. These slopes were calculated by fitting a log-log relation of each index with area in each site. Bars indicate 95% confidence intervals. (C and D) Standardize effect sizes (SES) of FRic and FDiv along elevation. The dashed line represents the zero value. SES that is significantly greater than, smaller than, or approaching zero indicate significant trait divergence, trait convergence, or random distribution, respectively. Mean SES values and 95% confidence intervals are shown for each site. Black circles indicate significant differences (P < 0.05) between simulated and observed communities, while open circles indicate nonsignificant differences based on Wilcoxon signed-rank tests.

  • Fig. 5 Variation on rates of NPP and GPP across sites with remotely sensed diversity indices.

    Single-trait indices correspond to the CMT values of percent of water, NSC, leaf mass per unit area (LMA), and percent of chlorophyll (Chl). Multitrait indices, FRic, and FDiv are calculated using these four traits. Black lines represent linear regression fits, blue circles represent each study site, black lines indicate the standard error of GPP or NPP in each site, and the gray strips are the 95% confidence intervals of the regression fit. Percent of explained variation (r2) by each diversity index is shown for all significant relationships (P < 0.05). GPP, gross primary productivity; NPP, net primary productivity.

  • Table 1 Results of the effect of remotely sensed functional traits and climate on NPP and GPP.

    For each model, standardized regression coefficients (β), SE, the second-order Akaike’s Information Criterion (AICc), and the differential AICc (ΔAICc) are shown. r2 is shown for significant relationships (P < 0.05). Best models (ΔAICc ≤ 2) are in bold. Single-trait indices correspond to the CMT values of percent of water, NSC, LMA, and percent of Chl. Multitrait indices, FRic, and FDiv were calculated using these four traits.

    NPP (Mg C year−1 ha−1)GPP (Mg C year−1 ha−1)
    βSEr2AICcΔAICβSEr2AICcΔAIC
    Elevation (m)−2.10.60.6644.50.1−3.81.40.5161.43.3
    MAT (°C)6.51.70.6644.4013.83.70.6658.00
    Mean annual rainfall (mm)0.82.354.09.67.34.064.36.3
    Solar radiation (GJ m−2 year−1)9.57.152.17.725.813.90.3364.16.1
    LMA (g m−2)−14.13.80.6644.60.2−30.18.20.6658.10.1
    NSC (%)−7.83.30.4448.94.5−18.96.20.5860.22.2
    Chl (mg g−1)11.84.30.5247.63.222.310.10.4163.05.0
    Water (%)−23.412.950.76.3−38.23065.97.8
    FRic3.21.30.4548.74.38.22.50.6658.10.1
    FDiv−3.862.754.29.8−17311665.37.3

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/12/eaaw8114/DC1

    Fig. S1. Geographic location of the study sites along the Amazon-Andes elevation gradient.

    Fig. S2. Variation of remotely sensed CMT values with elevation using a pixel size of 4 m2.

    Fig. S3. Spatial patterns of functional diversity indices in each study site.

    Fig. S4. Functional diversity–area relationships across sites for single-trait measurements of FRic.

    Fig. S5. Functional diversity–area relationships across sites for single-trait measurements of FDiv.

    Fig. S6. Correlations between remotely sensed indices of functional diversity and environmental variables across sites.

    Fig. S7. Correlations between CMT values derived from airborne imaging and field-based community-weighted mean traits.

    Table S1. Environmental characteristics for the nine sites along the Amazon-to-Andes elevation gradient (215 to 3537 m) in Peru.

    Table S2. Relationships between elevation, GPP, and NPP using three sampling approaches of canopy foliar traits.

    Table S3. Relationships between GPP and NPP and single-trait functional diversity indices.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Geographic location of the study sites along the Amazon-Andes elevation gradient.
    • Fig. S2. Variation of remotely sensed CMT values with elevation using a pixel size of 4 m2.
    • Fig. S3. Spatial patterns of functional diversity indices in each study site.
    • Fig. S4. Functional diversity–area relationships across sites for single-trait measurements of FRic.
    • Fig. S5. Functional diversity–area relationships across sites for single-trait measurements of FDiv.
    • Fig. S6. Correlations between remotely sensed indices of functional diversity and environmental variables across sites.
    • Fig. S7. Correlations between CMT values derived from airborne imaging and field-based community-weighted mean traits.
    • Table S1. Environmental characteristics for the nine sites along the Amazon-to-Andes elevation gradient (215 to 3537 m) in Peru.
    • Table S2. Relationships between elevation, GPP, and NPP using three sampling approaches of canopy foliar traits.
    • Table S3. Relationships between GPP and NPP and single-trait functional diversity indices.

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