Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators.

Abstract. The structure and function of Alaska's forests have changed significantly in response to a changing climate, including alterations in species composition and climate feedbacks (e.g., carbon, radiation budgets) that have important regional societal consequences and human feedbacks to forest ecosystems. In this paper we present the first comprehensive synthesis of climate-change impacts on all forested ecosystems of Alaska, highlighting changes in the most critical biophysical factors of each region. We developed a conceptual framework describing climate drivers, biophysical factors and types of change to illustrate how the biophysical and social subsystems of Alaskan forests interact and respond directly and indirectly to a changing climate. We then identify the regional and global implications to the climate system and associated socio-economic impacts, as presented in the current literature. Projections of temperature and precipitation suggest wildfire will continue to be the dominant biophysical factor in the Interior-boreal forest, leading to shifts from conifer-to deciduous-dominated forests. Based on existing research, projected increases in temperature in the Southcentral-and Kenai-boreal forests will likely increase the frequency and severity of insect outbreaks and associated wildfires, and increase the probability of establishment by invasive plant species. In the Coastal-temperate forest region snow and ice is regarded as the dominant biophysical factor. With continued warming, hydrologic changes related to more rapidly melting glaciers and rising elevation of the winter snowline will alter discharge in many rivers, which will have important v www.esajournals.org 1 November 2011 v Volume 2(11) v Article 124 consequences for terrestrial and marine ecosystem productivity. These climate-related changes will affect plant species distribution and wildlife habitat, which have regional societal consequences, and trace-gas emissions and radiation budgets, which are globally important. Our conceptual framework facilitates assessment of current and future consequences of a changing climate, emphasizes regional differences in biophysical factors, and points to linkages that may exist but that currently lack supporting research. The framework also serves as a visual tool for resource managers and policy makers to develop regional and global management strategies and to inform policies related to climate mitigation and adaptation.

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Cavity trees contribute to diverse forest structure and wildlife habitat. For a given stand, the size and density of cavity trees indicate its diversity, complexity, and suitability for wildlife habitat. Size and density of cavity trees vary with stand age, density, and structure. Using Forest Inventory and Analysis (FIA) data collected in western Oregon and western Washington, we applied correlation analysis and graphical approaches to examine relationships between cavity tree abundance and stand characteristics. Cavity tree abundance was negatively correlated with site index and percent composition of conifers, but positively correlated with stand density, quadratic mean diameter, and percent composition of hardwoods. Using FIA data, we examined the performance of Most Similar Neighbor (MSN), k nearest neighbor, and weighted MSN imputation with three variable transformations (regular, square root, and logarithmic) and Classification and Regression Tree with MSN imputation to estimate cavity tree abundance from stand attributes. There was a large reduction in mean root mean square error from 20% to 50% reference sets, but very little reduction in using the 80% reference sets, corresponding to the decreases in mean distances. The MSN imputation using square root transformation provided better estimates of cavity tree abundance for western Oregon and western Washington forests. We found that cavity trees were only 0.25 percent of live trees and 13.8 percent of dead trees in the forests of western Oregon and western Washington, thus rarer and more difficult to predict than many other forest attributes. Potential applications of MSN imputation include selecting and modeling wildlife habitat to support forest planning efforts, regional inventories, and evaluation of different management scenarios.

Plants can accumulate heavy metals when exposed to them at high levels. These metals can interfere with photosynthesis. Limited evidence suggests that increased synthesis of some heat-shock proteins (Hsps) may be a general plant response to metal stress, but the specific functions or structures protected by Hsps remain unidentified. Chloroplast small Hsps (smHsps) protect photosynthetic electron transport (Ph(et)) during heat, oxidative, and photoinhibitory stress, but it is not known if chloroplast smHsps are synthesized during metal stress and protect photosynthesis. Zea mays (corn) plants were exposed to varying soil concentrations of Cu, Ni, Pb, and Zn to determine if chloroplast smHsps are induced by heavy metals, if smHsps protect Ph(et), and any effects on chloroplast smHsp and photosynthesis. Net photosynthesis (Ph(n)) decreased with all metals-more so at higher levels and with longer exposures. Decreases in Ph(n) resulted from damage to photosynthetic metabolism, including Ph(et). All metals increased chloroplast smHsp content, which increased with time of exposure. In vitro, Ph(et) was protected from Pb (but not Ni) by purified chloroplast smHsp added to thylakoids. In vivo, Ph(n) was protected from Ni and Pb by increases in smHsp in a heat-tolerant Agrostis stolonifera selection genotype expressing additional chloroplast smHsps compared to a near-isogenic heat-sensitive genotype. These results are evidence that Hsps protect photosynthesis from heavy metals and are among the first to demonstrate specific functions protected by Hsps during metal stress.