Thesis (Ph.D.)--University of Washington, 2014Many research studies have characterized the primary productivity of tropical forests and contributed to highlighting the complexity of underlying drivers of the ecological system. However, few studies have explored how productivity changes across multiple scales and how the drivers controlling productivity might differ depending on climatic and edaphic factors. Most know that modeling of the earth's surface using remote sensing within a geospatial format is limited by the spatial resolution of the technology and also the relative small temporal resolution of forestry inventory information. However even when we construct our models from this information knowing errors have probably been incorporated, we have a tendency to overlook those limitations because we generally don't have access to information containing fewer errors. This is especially critical to remember and understand when trying to model a system which is not completely understood or where robust information may not exist. Therefore it is helpful to be able to identify any critical thresholds of productivity so that one can determine when tipping points may occur in complex ecosystems. Determining the critical thresholds and tipping points for productivity would therefore allow us to then recognize the empirical indicators that may trigger a system or its components to shift from one state to another. This would then allow us to better understand the heterogeneity that exits in productivities at the local scales. To search for potential thresholds and tipping points for productivity across scales, a study was designed to search for any relationships between empirical productivity data from tropical forest studies and other parameters such as climatic and edaphic variables. This study used the tools provided by meta-analysis, spatial modeling and quantification of human impacts at the local level to identify which combination of variables might reveal potential thresholds of the productivity. The performance of these variables was then used within a modeling environment to understand the underlying assumptions and how forest cover at the local scale is impacted by anthropogenic activities in relation to policy implementations. At the global level those variables that best explain the spatial heterogeneity of total productivities at plot scales was based on using a meta-analysis of aggregated field data from 96 natural forests from the American, Asian and African tropics. These data suggested that 73% of the variance in total net primary productivity (NPPt) could be explained by different combinations of four variables: soil-order, soil-texture, precipitation group and mean air temperature. If variations in NPPt by soil order, soil texture, precipitation group, and mean air temperature are not factored into modeling activities, regional estimates could over- or under-estimates total productivity potentials. At the regional level, underlying assumptions about a modeling environment were tested to determine how 20, 15, 10, five and one-km sampling resolutions using different occupancy selection criteria altered the distribution and importance of input variables as well as which variables were significant within the prediction model. Variances explained by predictive models were similar across cell sizes although relative importance of variables differed by sampling resolution. Partial dependence plots were used to search for potential thresholds or tipping points of NPP change as affected by an independent variable such as minimum daytime temperature. Applying different cell occupancy selection rules significantly changed the overall distribution of NPP values. Finally, policy additionality was measured by investigating anthropogenic activities within the Mount Halimun Salak National Park in reducing deforestation by implementing spatially explicit use zones. Results showed that for the period 2003 - 2013, strict conservation areas had a 6.2% lower rate of deforestation relative to all other use zones combined. The relative rate of deforestation was higher in the Special Research & Training zone, which is a designated area for local communities to acquire livelihood resources. Deforestation was lowest in the Rehabilitation zone which are forests designated as areas to restore lands characterized as degraded and deforested
