Multiscale predictors of small tree survival across a heterogeneous tropical landscape.

Uncertainties about controls on tree mortality make forest responses to land-use and climate change difficult to predict. We tracked biomass of tree functional groups in tropical forest inventories across Puerto Rico and the U.S. Virgin Islands, and with random forests we ranked 86 potential predictors of small tree survival (young or mature stems 2.5-12.6 cm diameter at breast height). Forests span dry to cloud forests, range in age, geology and past land use and experienced severe drought and storms. When excluding species as a predictor, top predictors are tree crown ratio and height, two to three species traits and stand to regional factors reflecting local disturbance and the system state (widespread recovery, drought, hurricanes). Native species, and species with denser wood, taller maximum height, or medium typical height survive longer, but short trees and species survive hurricanes better. Trees survive longer in older stands and with less disturbed canopies, harsher geoclimates (dry, edaphically dry, e.g., serpentine substrates, and highest-elevation cloud forest), or in intervals removed from hurricanes. Satellite image phenology and bands, even from past decades, are top predictors, being sensitive to vegetation type and disturbance. Covariation between stand-level species traits and geoclimate, disturbance and neighboring species types may explain why most neighbor variables, including introduced vs. native species, had low or no importance, despite univariate correlations with survival. As forests recovered from a hurricane in 1998 and earlier deforestation, small trees of introduced species, which on average have lighter wood, died at twice the rate of natives. After hurricanes in 2017, the total biomass of trees ≥12.7 cm dbh of the introduced species Spathodea campanulata spiked, suggesting that more frequent hurricanes might perpetuate this light-wooded species commonness. If hurricane recovery favors light-wooded species while drought favors others, climate change influences on forest composition and ecosystem services may depend on the frequency and severity of extreme climate events.

Plant diversity patterns in neotropical dry forests and their conservation implications.

Seasonally dry tropical forests are distributed across Latin America and the Caribbean and are highly threatened, with less than 10% of their original extent remaining in many countries. Using 835 inventories covering 4660 species of woody plants, we show marked floristic turnover among inventories and regions, which may be higher than in other neotropical biomes, such as savanna. Such high floristic turnover indicates that numerous conservation areas across many countries will be needed to protect the full diversity of tropical dry forests. Our results provide a scientific framework within which national decision-makers can contextualize the floristic significance of their dry forest at a regional and continental scale.

Root biomass allocation in the world's upland forests.

Because the world's forests play a major role in regulating nutrient and carbon cycles, there is much interest in estimating their biomass. Estimates of aboveground biomass based on well-established methods are relatively abundant; estimates of root biomass based on standard methods are much less common. The goal of this work was to determine if a reliable method to estimate root biomass density for forests could be developed based on existing data from the literature. The forestry literature containing root biomass measurements was reviewed and summarized and relationships between both root biomass density (Mg ha-1) and root:shoot ratios (R/S) as dependent variables and various edaphic and climatic independent variables, singly and in combination, were statistically tested. None of the tested independent variables of aboveground biomass density, latitude, temperature, precipitation, temperature:precipitation ratios, tree type, soil texture, and age had important explanatory value for R/S. However, linear regression analysis showed that aboveground biomass density, age, and latitudinal category were the most important predictors of root biomass density, and together explained 84% of the variation. A comparison of root biomass density estimates based on our equations with those based on use of generalized R/S ratios for forests in the United States indicated that our method tended to produce estimates that were about 20% higher.