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Julia L. Sharp

Publications and source records attributed to Julia L. Sharp.

2 recordsLinked to original sources

Statistical classification of vegetation and water depths in montane wetlands

Relationships between water depths and density of submergent vegetation were studied in montane wetlands using statistical techniques based on clustering and an extension of regression trees. Sago pondweed ( Stuckenia pectinata ) was associated with lower average water depths than water milfoil ( Myriophyllum sibiricum ). We detected a nonlinear relationship when average water depths were used to predict percent cover in S. pectinata , with depths of 30–40 cm, producing the highest predicted average percent cover of S. pectinata ; higher and lower depths resulted in lower percent cover predictions. For M. sibiricum , higher water depths were monotonically associated with higher average percent cover. To foster more S. pectinata and less M. sibiricum , managers might employ water control structures to reduce water depths below 1 m, using both temporary drawdowns and average depths of 30–40 cm. Other species responded less markedly to water depth variation. Should decreased water depths become more common, these results suggest an increase in S. pectinata and a decrease in M. sibiricum .

Ecohydrology↗

Multi-scale clustering of functional data with application to hydraulic gradients in wetlands

A new set of methods are developed to perform cluster analysis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Kinateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time se- ries. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Different measures for comparing locations are discussed, including a method for efficiently clustering time series that are of different lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.

Journal of Data Science↗