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Article << Previous     |     Next >>   Contents Vol 22(8)

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

Lisa M. Ellsworth A D , Creighton M. Litton A , Andrew D. Taylor B and J. Boone Kauffman C

A Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, 1910 East-West Road, Honolulu, HI 96822, USA.
B Department of Biology, University of Hawaii at Manoa, 2538 McCarthy Mall, Edmondson 216, Honolulu, HI 96822, USA.
C Department of Fisheries and Wildlife, Oregon State University, 104 Nash Hall, Corvallis, OR 97331, USA.
D Corresponding author. Email: lmellsworth@gmail.com

International Journal of Wildland Fire 22(8) 1083-1092 http://dx.doi.org/10.1071/WF12051
Submitted: 31 March 2012  Accepted: 21 March 2013   Published: 6 September 2013


 
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Abstract

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 are urgently needed. We quantified the spatial variability in live and dead fine fuel loads and moistures at four guinea grass (Megathyrsus maximus) dominated sites. To assess temporal variability, we sampled these four sites each summer for 3 years (2008–2010) and also sampled fuel loads, moistures and weather variables biweekly at three sites for 1 year. Live and dead fine fuel loads ranged spatially from 0.85 to 8.66 and 1.50 to 25.74 Mg ha–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.

Additional keywords: fire behaviour, fire modelling, fuels modelling, invasive species.


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