Landscape-scale GPP and carbon density inform patterns and impacts of an invasive tree across wet forests of Hawaii.
Plant invasion typically occurs within a landscape-scale framework of abiotic and biotic conditions, often resulting in emergent feedbacks among environment, ecosystem functions, and the dominance of invasive species. Understanding the mechanisms underlying successful invasions is an important component of conservation and management efforts, but this has been poorly investigated in a spatially explicit manner. Knowing where and why invasion patterns change throughout the landscape enables managers to use context-specific controls on the spread of invasive species. Using high-resolution airborne imaging spectroscopy, we studied plant performance in growth within and across landscapes to examine the dominance and spatial distribution of an invasive tree, Psidium cattleianum (strawberry guava), in heterogeneous environmental conditions of a submontane Hawaiian tropical forest. We assessed invader performance using the GPP ratio index, which is the relative difference in remotely sensed estimates of gross primary productivity between canopies of guava and canopies of the invaded plant community. In addition, we used airborne LiDAR data to evaluate the impacts of guava invasion on the forest aboveground carbon density in different environments. Structural equation modeling revealed that substrate type and elevation above sea level interact and amplify landscape-scale differences in productivity between the invasive species and the host plant community (GPP ratio); differences that ultimately control levels of dominance of guava. We found shifts in patterns of forest carbon storage based on both gradual increase of invader dominance and changes in environmental conditions. Overall, our results demonstrate that the remotely sensed index defined as the GPP ratio provided an innovative spatially explicit approach to track and predict the success of invasive plants based in their canopy productivity, particularly within a landscape-scale framework of varying environmental factors such as soils and elevation. This approach may help managers accurately predict where invaders of forests, scrublands, or grasslands are likely to exhibit high levels of dominance before the environment is fully invaded.