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Question: How does regeneration response to a host‐specific, high‐severity, infrequent Dendroctonus rufipennis outbreak differ from our conceptualization of high‐severity, infrequent/low‐severity, frequent disturbance regimes in Picea engelmannii‐Abies lasiocarpa communities? Location: Southern Utah, USA. Methods: One hundred and seven plots across a high‐elevation P. engelmannii forest were sampled to reconstruct pre‐outbreak overstory and seedling bank densities, and calculate their associated metrics of diversity. Decade of establishment by seedling bank trees indicated “chronic” and “pulse” regenerators. Results: The post‐outbreak overstory and seedling bank were dominated by A. lasiocarpa. Although Pinus flexilis, Pinus ponderosa, Picea pungens, and Psuedotsuga menziesii were present in the overstory, they were virtually absent in the seedling bank. Seedling bank recruitment of A. lasiocarpa and P. engelmannii has been occurring chronically for at least the last ∼205 and ∼152 years, respectively. A pulse response of seedling bank Populus tremuloides was apparent; however, results were complicated by intense ungulate browsing. Conclusions: Despite some similarities to the high‐severity, infrequent/low‐severity, frequent conceptualization of regeneration response to disturbance, the high‐severity D. rufipennis outbreak is best described by explicitly considering host specificity and severity. Although, the outbreak simultaneously promoted both a pulse of P. tremuloides and a release of chronically regenerated A. lasiocarpa, the P. tremuloides response was generally masked by ungulate browsing, and the regeneration response came overwhelmingly from the A. lasiocarpa seedling bank. In this landscape, once dominated by P. engelmannii, the chronically regenerating seedling bank, typically thought to take advantage of canopy gaps associated with low‐severity disturbances, is poised to dominate forest reorganization in response to the host‐specific outbreak.

Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree‐ring and forest inventory data within a Bayesian state‐space model at a multi‐site, regional scale, focusing on Pinus ponderosa var. brachyptera in the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall–spring maximum temperature, and a positive effect of water‐year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%–117%, while the combined effect of climate and size‐related trends results in a 56%–91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree‐ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill.