Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict aboveground live biomass in Southern California using remote sensing data (Landsat Normalized Difference Vegetation Index (NDVI)) and a suite of geophysical variables. By substituting the NDVI and precipitation predictors for any given year, we were able to apply the model to each year from 2000 to 2019. Using a total of 980 field plots, our model had a k-fold cross-validation R2 of 0.51 and an RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE = 9.7) for Sierran mixed-conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers, we calculated the mean year 2010 shrubland biomasses for the four national forests that ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomasses from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measurements in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.
Anthropogenic nitrogen (N) and sulfur (S) deposition can negatively affect ecosystem functions and lichen biomonitors can be a cost-effective way to monitor air pollution exposure across the landscape. Interior dry forests of the southwestern United States face increasing development pressures; however, this region differs from others with well-developed biomonitoring programs in having drier climates and a greater fraction of deposition delivered in dry forms. We measured throughfall N and S deposition at 12 sites in Utah and 10 in New Mexico and co-located collection of 6 lichen species. Throughfall N deposition ranged from 0.76 to 6.96 kg/ha/ year and S deposition from 0.57 to 1.44 kg/ha/year with elevated levels near human development that were not predicted by commonly used simulation models. Throughfall N was 4.6 and 1.6 times higher in summer compared with fall-spring in Utah and New Mexico and S deposition was 3.9 and 1.8 times higher in summer. Lichen N and S concentrations ranged from 0.97 to 2.7% and 0.09 to 0.33%. Replicate samples within plots showed high variability in N and S concentrations with within-plot coefficients of variation for N ranging between 5 and 10% and for S between 7 and 15%. In Utah, N and S concentrations in lichen species were correlated with each other in most cases, with R2 ranging from 0.52 to 0.85. N concentrations in Melanohalea exasperatula and Melanohalea subolivacea could be correlated with average annual throughfall N deposition in Utah (R2 = 0.58 and 0.31). Those relationships were improved by focusing on deposition in fall-spring prior to lichen sampling in Utah (R2 for M. exasperatula, M. subolivacea, and X. montana = 0.59, 0.42, and 0.28). In New Mexico, lichens exhibited greater coefficients of variability within plots than between plots and could not be correlated with throughfall N deposition. In neither study area was S correlated between lichens and throughfall deposition, which may be the result of low S deposition over a narrow deposition range or complex lichen assimilation of S. Lichen biomonitoring for N deposition in the region shows promise, but could potentially be improved by sampling more thalli to reduce within-plot variability, repeated lichen collection synchronized with throughfall changeouts to explore temporal variability, and washing lichen collections to distinguish N and S that has been incorporated by the thalli from dry deposition that may accumulate on lichen surfaces.
United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment (NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.