Higher productivity in forests with mixed mycorrhizal strategies

Abstract

Decades of theory and empirical studies have demonstrated links between biodiversity and ecosystem functioning, yet the putative processes that underlie these patterns remain elusive. This is especially true for forest ecosystems, where the functional traits of plant species are challenging to quantify. We analyzed 74,563 forest inventory plots that span 35 ecoregions in the contiguous USA and found that in ~77% of the ecoregions mixed mycorrhizal plots were more productive than plots where either arbuscular or ectomycorrhizal fungal-associated tree species were dominant. Moreover, the positive effects of mixing mycorrhizal strategies on forest productivity were more pronounced at low than high tree species richness. We conclude that at low richness different mycorrhizal strategies may allow tree species to partition nutrient uptake and thus can increase community productivity, whereas at high richness other dimensions of functional diversity can enhance resource partitioning and community productivity. Our findings highlight the importance of mixed mycorrhizal strategies, in addition to that of taxonomic diversity in general, for maintaining ecosystem functioning in forests.

Fig. 1. Conceptual figure illustrating the hypothetical relationships between AM tree dominance and forest productivity, as well as the underlying resource-use scenarios in tree species that coexist in local communities.

a Hypothetical overall relationship. b Hypothetical relationship at low tree species diversity. c Hypothetical relationship at high tree species diversity. We expect a concave-negative relationship between AM tree dominance and forest productivity, which would be more evident at low than at high tree species diversity. At low diversity, we expect that productivity would be higher in communities with both mycorrhizal types than in those dominated by a single mycorrhizal type, because the large niche differences between AM and ECM tree species would maximize the occupied resource space. At high diversity, we expect that resource space would be well occupied by a large number of species, and the positive effects of species diversity on productivity may outweigh that of mycorrhizal composition, which would weaken the relationship between AM tree dominance and productivity. In our illustration, the boxes represent the total available resource space, circles represent the resource space occupied by tree species (orange circles represent AM tree species, green circles represent ECM tree species), and grey areas represent unconsumed resources. We do not illustrate that the resource space occupied by specific species could change with species richness, which merits additional exploration. Rather, we consider simpler cases by assuming that each species has a fixed niche size to abstract some fundamental effects of mixed vs. single mycorrhizal strategies.

Fig. 2. Observed relationship between AM tree dominance and forest productivity.

AM tree dominance is quantified as AM proportion based on tree basal area. The black curve was simple regression fitted across all forest plots, whereas other curves were simple regressions fitted for plots within each ecoregion. Each grey circle represents the data of one forest plot (n = 74,563). Inset frequency chart: The frequencies of each form of relationship observed across all ecoregions. Inset map: The colored map indicates the distribution of each form of relationship across ecoregions. The significance of the relationships in the frequency chart and colored map was based on regressions with environmental variables fitted as covariates. NS non-significant. See Supplementary Table S1 for overall statistical results and Supplementary Data 1 and Supplementary Fig. S14 for results and figures of each ecoregion.

Fig. 3. Relative variable importance from random forest model explaining forest productivity.

Relative variable importance is the mean decrease in squared error caused by each of the variables, rescaled such that it sums up to the total pseudo-R² of the whole model. The overall explained variation (R²) of forest productivity is 0.69. Source data are provided as a Source Data file.

Fig. 4. Relationships between AM tree dominance and productivity in forests with low vs. high species richness.

a Forests with low tree species richness (richness ≤ 5). b Forests with high tree species richness (richness > 5). We fitted general linear models with ecoregion, AM proportion (linear and quadratic terms), interactions between AM proportion and ecoregion, stand age, elevation, slope, climatic variables, and soil pH as explanatory variables (see Supplementary Table S1 for statistical results).

Fig. 5. Structural equation models of climate, soil pH, and AM proportion as predictors of forest productivity.

a Forests with low tree species richness (richness ≤ 5). b Forests with high tree species richness (richness > 5). Solid black arrows represent positive paths (p < 0.05, piecewise SEM), solid red arrows represent negative paths (p < 0.05, piecewise SEM), and solid blue arrows represent non-significant paths (p > 0.05, piecewise SEM). In addition, we included the interactive effects of T.SEAS and soil pH on AM proportion and productivity, with dashed black and red arrows representing positive and negative effects, respectively. We report the path coefficients as standardized effect sizes. Overall fit of piecewise SEM was evaluated using Shipley’s test of d-separation: Fisher’s C = 3.466 & p = 0.177 for low-richness forests; Fisher’s C = 5.206 & p = 0.074 for high-richness forests (if p > 0.05, then no paths are missing and the model is a good fit). AM proportion², quadratic AM proportion; MAT, mean annual temperature; MAP, mean annual precipitation; T.SEAS, temperature seasonality. Note that the arrows from AM proportion to AM proportion² reflect deterministic relations based on a calculation rather than hypothesized causal relationships.