Crowding, climate, and the case for social distancing among trees

Denser forests across the USA experience more damage from insects and pathogens

Forests across much of the United States are becoming denser. Trees growing in denser stands experience more competition for essential resources, which can make them more vulnerable to disturbances. Forest density can be expressed in terms of basal area, a metric that has been used to assess vulnerability of some forests to damage by certain insects or pathogens. A raster map of total tree basal area (TBA) for the conterminous United States was compared with annual (2000-2019) survey maps of forest damage due to insects and pathogens. Across each of four regions, median TBA was significantly higher within forest areas defoliated or killed by insects or pathogens than in areas without recorded damage. Therefore, TBA may serve as a regional-scale indicator of forest health and a first filter for identifying areas that merit finer-scale analysis of forest conditions.

Predictive accuracy of post-fire conifer death declines over time in models based on crown and bole injury

A key uncertainty of empirical models of post-fire tree mortality is understanding the drivers of elevated post-fire mortality several years following fire, known as delayed mortality. Delayed mortality can represent a substantial fraction of mortality, particularly for large trees that are a conservation focus in western US coniferous forests. Current post-fire tree mortality models have undergone limited evaluation of how injury level and time since fire interact to influence model accuracy and predictor variable importance. Less severe injuries potentially serve as an indicator for vulnerability to additional stressors such as bark beetle attack or moisture stress. We used a collection of 164,293 individual tree records to examine post-fire tree mortality in eight western USA conifers: Abies concolor, Abies grandis, Calocedrus decurrens, Larix occidentalis, Pinus contorta, Pinus lambertiana, Pinus ponderosa, and Pseudotsuga menziesii. We evaluated the importance of fire injury predictors on discriminating between surviving trees versus immediate and delayed post-fire mortality. We fit balanced random forest models for each species using cumulative tree mortality from 1 to 5-years post-fire. We compared these results to multi-class random forest models using first-year mortality, 2-5-year mortality, and survival 5-years post-fire as a response variable. Crown volume scorched, diameter at breast height, and relative bark char height, were used as predictor variables. The cumulative mortality models all predicted trees that died within 1-year of fire with high accuracy but failed to predict 2-5-year mortality. The multi-class models were an improvement but had lower accuracy for predicting 2-5-year mortality. Multi-class model accuracies ranged from 85% to 95% across all species for predicting 1-year post-fire mortality, 42%-71% for predicting 2-5-year mortality, and 64%-85% for predicting trees that lived past 5-years. Our study highlights the differences in tree species tolerance to fire injury and suggests that including second-order predictors such as beetle attack or climatic water stress before and after fire will be critical to improve accuracy and better understand the mechanisms and patterns of fire-caused tree death. Random forest models have potential for management applications such as post-fire harvesting and simulating future stand dynamics.