The development and evaluation of a successful strategy for rare plant management and habitat rehabilitation depends largely on the accuracy with which population status and trends can be quantified. Population Viability Analysis (PVA) has been used to estimate the long term prognosis for species, relying on the quantification of past trends in demographics to estimate future population status. Since population demographic structure is often strongly influenced by environmental conditions, reliability of PVA can be compromised when there is significant environmental variability. The saddle region of the Big Island of Hawai’i comprises one of the rarest ecosystems in the world and houses 15 federally listed threatened and endangered plant species. Mean annual rainfall is 32.2 ±16.7cm with a minimum and maximum annual rainfall of 4.1 and 74.8 cm, respectively, over a 29 year period. To assess the efficacy of management and to provide feedback to optimize management techniques, a monitoring program was initiated in 2007 to collect PVA compatible demographic data.
Results/Conclusions
Preliminary analysis of 4-year demographic data sets show significant shifts in structure for a remnant population of Tetramolopium arenarium, ssp. arenarium (ANOVA, p<0.05). Adult abundance from the previous year changed by factors of 0.43, 0.71, and 0.35 in 2008, 2009, and 2010, respectively. Juvenile abundance changed by factors of 0.33, 3.08, and 1.56 for the same years. During the years prior, mean annual precipitation differed from the 29-year average by factors of 0.64, 0.42, and 1.15. Results suggest that, in addition to natural life history processes, population structure is strongly and differentially influenced by environmental stochasticity, specifically by intra-and inter-annual variation in the magnitude and timing of rain events. For this reason, it is essential to have robust and long-term data sets for both population demographics and environmental variables to adequately model stochasticity in this dryland ecosystem. Only with this level of understanding will accurate assessment and optimization of rare species management be possible.
