Fig 1.
Sample plot vegetation top cover by plant functional type (A) and species (B), and repeated cover by plant functional type (C) and species (D) in the MAT sample plots. Points (plus symbols) are individual plots. Points in (B) and (D) are colored by species’ functional type. Moss and lichen were not identified to species.
Fig 2.
Plot-level LAVP distribution in sample plots (A) and LAVP for each PFT (B), and species (C) in the MAT sample plots. Boxplots in (C) are colored by species’ functional type assignment. Significant differences among PFTs or taxa (Dunn’s Test) in (B) and (C) are indicated by different letters.
Fig 3.
(A) Observed peak-season NDVI in the MAT sample plots regressed against plot-level vertically projected leaf area (LAvp) using the LAI~NDVI model (Eq 4), solved for NDVI. Black line and text show the best fit parameters estimated using nls with LAVP calculated using vascular species only, red shows the pan-Arctic parameters used in Shaver (2007, 2013), and blue shows best fit parameters with LAVP calculations that include moss. (B) Observed peak-season NDVI regressed against NDVI predicted from a multiple regression model using top cover (first pin hits only) or repeated cover of eight PFTs or (C) individual species as predictors. Goodness-of-fit statistics are inset. Regression coefficients are in Tables 1 and 2.
Table 1.
Summary of linear mixture models examining the influence of CT top cover (proportion of first pin hits) and repeated (proportion of all pin hits) cover on NDVI.
Table 2.
Summary of linear mixture models examining the influence of species top cover (proportion of first pin hits) and repeated (proportion of all pin hits) cover on NDVI.
Fig 4.
Reflectance signatures of CT-specific monotypic plots (A) and leaves from focal species (B). Significant differences among CTs or taxa (Dunn’s post-hoc test) in (A) and (B) are indicated by different letters. Orange bars in top left panel indicate regression coefficients (estimate ± se) from a multiple linear regression between CT top cover in the MAT sample plots and plot-level NDVI. The congruence between regression coefficients for the dominant CT’s and reflectance values from the monotypic plots supports our assertion that NDVI in mixed plots can be approximated as linear combinations of the product of CT top cover values.
Fig 5.
The relationship between components of peak-season plot-level CO2 exchange in the MAT sample plots and NDVI (A), LAVP calculated without moss contributions (B), and LAVP calculated with moss (C). Goodness-of-fit and model statistics are inset.
Table 3.
Summary of regression models examining the influence of plant functional type top cover (proportion of first pin hits) on plot-level CO2 exchange.
Table 4.
Summary of regression models examining the influence of plot top cover (proportion of first pin hits) on plot-level light curve parameters.
Fig 6.
(A) Relationship between LAI predicted using the LAI~NDVI model parameterized with field plot data and observed LAVP. Black points and lines show the fit with LAVP calculated without moss included, whereas the blue line shows the fit with LAVP calculated with moss contributions. (B) Relationship between GPP600 predicted by the Tundra GPP Model (Eq(5)) using LAVP without moss (black), LAVP with moss (blue), or LAI modeled from NDVI (red) and GPP600 estimated from field measured light curves. In all plots, the dotted line represents a 1:1 relationship.