Standing dead trees (SDT) are an important component of forest ecosystems. However, it can be a challenge to develop reliable estimates of population parameters because dead trees are generally lower in abundance and have more complex spatial distributions (e.g., are more clustered) than live trees. In addition, most forest inventories are designed for sampling live trees. Previous studies (e.g., Bull et al. 1990) have recommend using a relatively higher sampling intensity or larger plot sizes for dead versus live trees, but this is more time consuming and costly. Adding new plots, increasing plot sizes or otherwise modifying plot designs can be especially costly in the case of large scale (e.g., national forest inventories) and other permanent plot network. This thesis sought to explore approaches to improving estimation of standing dead tree abundance, other than adding more plots or modifying plot designs, in the context of the US National Forest Inventory and Analysis Program (USDA Forest Service 2008) and US Forest Health Monitoring (FHM) Program (now merged with the FIA plot design) and other similar permanent plot networks. One major consequence of using sampling plots that are either too small or too few for sampling standing dead trees is that it is likely that there will be a large proportion of zero observations in data, typically referred to as "zero-inflated" data. Excess zero observations increases variation of estimates of standing dead tree parameters. To reduce this variability caused by zero-inflated data, a new model, Expected-Zero Hurdle (EZ-Hurdle) method, is proposed. The EZ-Hurdle method replaced the observed zero proportion in data with an expected zero probability obtained from auxiliary information describing the distance from a random point (plot center) to the nearest standing dead tree. The EZ-Hurdle method greatly improved the precision and showed less average bias than fixed-area sampling, with or without adjustment using the standard Hurdle model when tested with both simulation and field studies. The EZ-Hurdle method improved the precision without adding fixed-area plot but it required additional information to explain the uncertainty caused by zero observations in data. Especially, EZ-Hurdle methods improved the precision when only additional information was applied without adding points. Therefore, it can be applied to improve the precision of estimates without changing plot design such as FIA and FHM program. The EZ-Hurdle method performs best when the density of standing dead trees is low or a small fixed-area plot size is used to collect the data because the expected zero probability which is modeled from auxiliary information showed less variation than observed zero proportion in data. Although EZ-Hurdle method showed better precision, it is less cost and sampling efficiency than fixed-area sampling method due to time to search the nearest standing dead tree. Therefore, distance-limited EZ-Hurdle method which restricts the search radius to find the nearest standing dead tree was proposed to reduce time to collect auxiliary information. Distance-limited EZ-Hurdle method showed better precision than fixed-area sampling for all circumstances such as densities and spatial patterns. It also has better time efficiency than the original EZ-Hurdle method. Therefore, the EZ-Hurdle method with a distance-limited method can be an alternative method to improve the precision for estimating the density of standing dead trees without changes of plot design using reasonable cost and time to collect the data.
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