Comparing methods that quantify forest disturbances in the United States' national forest inventory.

Forest disturbances play a critical role in ecosystem dynamics. However, the methods for quantifying these disturbances at broad scales may underestimate disturbances that affect individual trees. Utilizing individual tree variables may provide early disturbance detection that directly affects tree demographics and forest dynamics. The goals of this study were to (1) describe different methods for quantifying disturbances at individual tree and condition-level scales, (2) compare the differences between disturbance variables, and (3) provide a methodology for selecting an appropriate disturbance variable from national forest inventories for diverse applications depending on user needs. To achieve these goals, we used all the remeasurements available from the USDA Forest Inventory and Analysis (FIA) database since the start of the annual inventory for the lower 48 US states. Variables used included disturbance code, treatment code, agent of mortality, and damage code. Chi-square tests of independence were used to verify how the choice of the variable that represents disturbance affects its magnitude. Disturbed plots, as classified by each disturbance variable, were mapped to observe their spatial distribution. We found that the Chi-square tests were significant when using all the states and comparing each state individually, indicating that different results exist depending on which variable is used to represent disturbance. Our results will be a useful tool to help researchers measure the magnitude and scale of disturbance since the manner in which disturbances are categorized will impact forest management plans, national and international reports of forest carbon stocks, and sequestration potential under future global change scenarios.

Modeling land use change and forest carbon stock changes in temperate forests in the United States.

BACKGROUND: Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine. RESULTS: During the study period (2000-2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change. CONCLUSIONS: Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.