There are few allometric equations available for dipterocarp forests, despite the fact that this forest type covers extensive areas in tropical Southeast Asia. This study aims to develop a set of equations to estimate tree aboveground biomass (AGB) in dipterocarp forests in Vietnam and to validate and compare their predictive performance with allometric equations used for dipterocarps in Indonesia and pantropical areas. Diameter at breast height (DBH), total tree height (H), and wood density (WD) were used as input variables of the nonlinear weighted least square models. Akaike information criterion (AIC) and residual plots were used to select the best models; while percent bias, root mean square percentage error, and mean absolute percent error were used to compare their performance to published models. For mixed-species, the best equation was $\text{AGB}=0.06203\times {\text{DBH}}^{2.26430}\times {\mathrm{H}}^{0.51415}\times {\text{WD}}^{0.79456}$ . When applied to a random independent validation dataset, the predicted values from the generic equations and the dipterocarp equations in Indonesia overestimated the AGB for different sites, indicating the need for region-specific equations. At the genus level, the selected equations were $\text{AGB}=0.03713\times {\text{DBH}}^{2.73813}$ and $\text{AGB}=0.07483\times {\text{DBH}}^{2.54496}$ for two genera, Dipterocarpus and Shorea, respectively, in Vietnam. Compared to the mixed-species equations, the genus-specific equations improved the accuracy of the AGB estimates. Additionally, the genus-specific equations showed no significant differences in predictive performance in different regions (e.g., Indonesia, Vietnam) of Southeast Asia.

Rapid climate change over the coming century will impact suitable habitat for many tree species. In response to these changes in climate, areas that become unsuitable will see higher mortality and lower growth and recruitment. Therefore, early detection of demographic trends is critical for effective forest management. Recent 10-year remeasurement data from the United States (US) Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) Program’s national annual inventory of forest land provides an ideal data set for analyzing such trends over large areas. However, failure to distinguish between areas of future habitat contraction and expansion or persistence when estimating demographic trends may mask species’ shifts. We used remeasurement data to compare observed tree demographic rates with projected impacts of climate change for five important tree species in the Pacific Northwest. Projected impacts were based on spatial-Bayesian hierarchical models of species distributions, which were used to project areas where habitat would persist (remain climatically suitable), expand (become suitable), or contract (become unsuitable) under four future climate scenarios for the 2080s. We compared estimates of mortality and net-growth between these areas of shifting suitability and a naïve division of habitat based on elevation and latitude. Within these regions, we assessed the sustainability of mortality and determined that observational data suggest that climate change impacts were already being felt in some areas by some species. While there is an extensive literature on bioclimatic species distribution models, this work demonstrates they can be adapted to the practical problem of detecting early climate-related trends using national forest inventory data. Of the species examined, California black oak (Quercus kelloggii) had the most notable instances of observed data suggesting population declines in the core of its current range.

We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir (Abies procera), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir (Pseudotsuga menziesii var. menziesii) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km²) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak (Quercus garryana) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak (Quercus douglasii) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak (Quercus kelloggii), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.