Lisa M. Ellsworth

Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii

Frequent wildfires in tropical landscapes dominated by non-native invasive grasses threaten surrounding ecosystems and developed areas. To better manage fire, accurate estimates of the spatial and temporal variability in fuels areurgentlyneeded.Wequantifiedthespatialvariabilityinliveanddeadfinefuelloadsandmoisturesatfourguineagrass (Megathyrsus maximus) dominated sites. To assess temporal variability, we sampled these four sites each summer for 3 years(2008-2010)andalsosampledfuelloads,moisturesandweathervariablesbiweeklyatthreesitesfor1year.Liveand dead fine fuel loads ranged spatially from 0.85 to 8.66 and 1.50 to 25.74Mgha � 1 respectively, and did not vary by site or year. Biweekly live and dead fuel moistures varied by 250 and 54% respectively, and were closely correlated (P,0.05) with soil moisture, relative humidity, air temperature and precipitation. Overall, fine fuels and moistures exhibited tremendous variability, highlighting the importance of real-time, site-specific data for fire prevention and management. However, tight correlations with commonly quantified weather variables demonstrates the capacity to accurately predict fuel variables across large landscapes to better inform management and research on fire potential in guinea grass ecosystems in Hawaii and throughout the tropics.

Vegetation Response to Juniper Reduction and Grazing Exclusion in Sagebrush-Steppe Habitat in Eastern Oregon☆

ABSTRACT Western juniper expansion is one of the largest threats to conserving sagebrush steppe ecosystems in the northwestern United States. Juniper expansion has degraded the sagebrush steppe by altering fire regimes and outcompeting shrubs and herbaceous vegetation for limited resources. We characterized the effect of juniper removal in a severely degraded sagebrush steppe habitat for 3 yr following juniper cutting. In addition, we measured the effect of low-intensity seasonal grazing on plant community recovery through cattle exclusion treatments. We monitored plant community composition (exotic annual grasses, preferred grasses, preferred forbs, and shrubs); fuel loads; and juniper recruitment in a factorial design of juniper removal and grazing exclusion. We found that although there were significant differences between cut and uncut juniper treatments, there were no consistent trends across all 3 yr. Our results suggest that other factors, such as timing of precipitation, may also have strong short-term effects on plant community composition. We detected no significant grazing effects during the study period, suggesting the current grazing regime is appropriate for the area. The cutting of juniper increased total fuel loads and herbaceous fuel loads. Compared with open interspace, a twofold increase in juniper seedlings and saplings was detected beneath juniper piles, which will act as sources for future juniper encroachment.

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.