A resource-driven stand-level survival model for loblolly pine plantations in the southern United States

Information on survival is important when building a forest growth and yield system because it affects average tree size, basal area, and, hence, the growing stock volume. Existing models for stand-level loblolly pine ( Pinus taeda ) survival in the United States do not account for how physiological constraints on growth influence stem survival. In this study, we developed a stand-level survival model as a function of light sums modified by soil moisture, vapor pressure deficit, temperature, and frost for intensively managed loblolly pine plantations in the southeastern US. The model incorporates the effects of thinning and competing hardwood vegetation. We used data from a large, region-wide set of permanent sample plots of intensively managed loblolly pine plantations, along with daily 1 km x 1 km gridded weather data, to develop the hybrid model. We evaluated the model using independent data from a wide-spacing study in Virginia and assessed its behavior under two future climate change scenarios. The hybrid model produced a good fit without substantial residual trends, and its trajectories are consistent and biologically plausible. The validation showed that the mean percent residual was within ±10% across the entire projection period. On average, there was not a substantial difference in loblolly pine survival between high- and low-emission scenarios, particularly at high and moderate elevations. Foresters can use the hybrid model to assess the effects of past and future weather conditions on the stem density in loblolly pine plantations in the southern United States. This will support climate-smart forestry. • Developed a hybrid stand-level survival model for loblolly pine. • The model accounted for the effects of thinning and competing hardwood. • Loblolly pine survival trajectories were consistent and biologically plausible. • Loblolly pine survival showed no variation under projected climate change scenarios.