Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States.

Understanding ecosystem processes and the influence of regional scale drivers can provide useful information for managing forest ecosystems. Examining more local scale drivers of forest biomass and water yield can also provide insights for identifying and better understanding the effects of climate change and management on forests. We used diverse multi-scale datasets, functional models and Geographically Weighted Regression (GWR) to model ecosystem processes at the watershed scale and to interpret the influence of ecological drivers across the Southeastern United States (SE US). Aboveground forest biomass (AGB) was determined from available geospatial datasets and water yield was estimated using the Water Supply and Stress Index (WaSSI) model at the watershed level. Our geostatistical model examined the spatial variation in these relationships between ecosystem processes, climate, biophysical, and forest management variables at the watershed level across the SE US. Ecological and management drivers at the watershed level were analyzed locally to identify whether drivers contribute positively or negatively to aboveground forest biomass and water yield ecosystem processes and thus identifying potential synergies and tradeoffs across the SE US region. Although AGB and water yield drivers varied geographically across the study area, they were generally significantly influenced by climate (rainfall and temperature), land-cover factor1 (Water and barren), land-cover factor2 (wetland and forest), organic matter content high, rock depth, available water content, stand age, elevation, and LAI drivers. These drivers were positively or negatively associated with biomass or water yield which significantly contributes to ecosystem interactions or tradeoff/synergies. Our study introduced a spatially-explicit modelling framework to analyze the effect of ecosystem drivers on forest ecosystem structure, function and provision of services. This integrated model approach facilitates multi-scale analyses of drivers and interactions at the local to regional scale.

The role of composition, invasives, and maintenance emissions on urban forest carbon stocks.

There are few field-based, empirical studies quantifying the effect of invasive trees and palms and maintenance-related carbon emissions on changes in urban forest carbon stocks. We estimated carbon (C) stock changes and tree maintenance-related C emissions in a subtropical urban forest by re-measuring a subsample of residential permanent plots during 2009 and 2011, using regional allometric biomass equations, and surveying residential homeowners near Orlando, FL, USA. The effect of native, non-native, invasive tree species and palms on C stocks and sequestration was also quantified. Findings show 17.8 tC/ha in stocks and 1.2 tC/ha/year of net sequestration. The most important species both by frequency of C stocks and sequestration were Quercus laurifolia Michx. and Quercus virginiana Mill., accounting for 20% of all the trees measured; 60% of carbon stocks and over 75% of net C sequestration. Palms contributed to less than 1% of the total C stocks. Natives comprised two-thirds of the tree population and sequestered 90% of all C, while invasive trees and palms accounted for 5 % of net C sequestration. Overall, invasive and exotic trees had a limited contribution to total C stocks and sequestration. Annual tree-related maintenance C emissions were 0.1% of total gross C sequestration. Plot-level tree, palm, and litter cover were correlated to C stocks and net sequestration. Findings can be used to complement existing urban forest C offset accounting and monitoring protocols and to better understand the role of invasive woody plants on urban ecosystem service provision.

A framework for identifying carbon hotspots and forest management drivers.

Spatial analyses of ecosystem system services that are directly relevant to both forest management decision making and conservation in the subtropics are rare. Also, frameworks that identify and map carbon stocks and corresponding forest management drivers using available regional, national, and international-level forest inventory datasets could provide insights into key forest structural characteristics and management practices that are optimal for carbon storage. To address this need we used publicly available USDA Forest Service Forest Inventory and Analysis data and spatial analyses to develop a framework for mapping "carbon hotspots" (i.e. areas of significantly high tree and understory aboveground carbon stocks) across a range of forest types using the state of Florida, USA as an example. We also analyzed influential forest management variables (e.g. forest types, fire, hurricanes, tenure, management activities) using generalized linear mixed modeling to identify drivers associated with these hotspots. Most of the hotspots were located in the northern third of the state some in peri-urban areas, and there were no identifiable hotspots in South Florida. Forest silvicultural treatments (e.g. site preparation, thinning, logging, etc) were not significant predictors of hotspots. Forest types, site quality, and stand age were however significant predictors. Higher site quality and stand age increased the probability of forests being classified as a hotspot. Disturbance type and time since disturbance were not significant predictors in our analyses. This framework can use globally available forest inventory datasets to analyze and map ecosystems service provision areas and bioenergy supplies and identify forest management practices that optimize these services in forests.