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Due to fire suppression policies, timber harvest, and other management practices over the last century, many low- to mid-elevation forests in semiarid parts of the western United States have accumulated high fuel loads and dense, multi-layered canopies that are dominated by shade-tolerant and fire-sensitive conifers. To a great extent, the future status of western US forests will depend on tree species' responses to patterns and trends in fire activity and fire behavior and postfire management decisions. This is especially the case in the North American Mediterranean-climate zone ( NAMCZ), which supports the highest precipitation variability in North America and a 4- to 6-month annual drought, and has seen greater-than-average increases in air temperature and fire activity over the last three decades. We established 1490 survey plots in 14 burned areas on 10 National Forests across a range of elevations, forest types, and fire severities in the central and northern NAMCZ to provide insight into factors that promote natural tree regeneration after wildfires and the differences in postfire responses of the most common conifer species. We measured site characteristics, seedling densities, woody shrub, and tree growth. We specified a zero-inflated negative binomial mixed model with random effects to understand the importance of each measured variable in predicting conifer regeneration. Across all fires, 43% of all plots had no conifer regeneration. Ten of the 14 fires had median conifer seedling densities that did not meet Forest Service stocking density thresholds for mixed conifer forests. When regeneration did occur, it was dominated by shade-tolerant but fire-sensitive firs ( Abies spp.), Douglas-fir ( Pseudotsuga menziesii) and incense cedar ( Calocedrus decurrens). Seedling densities of conifer species were lowest in sites that burned at high severity, principally due to the biotic consequences of high severity fire, for example, increased distances to live seed trees and competition with fire-following shrubs. We developed a second model specifically for forest managers and restoration practitioners who work in yellow pine and mixed conifer forests in the central NAMCZ to assess potential natural regeneration in the years immediately following a fire, allowing them to prioritize which areas may need active postfire forest restoration and supplemental planting. [ABSTRACT FROM AUTHOR]

In 2000, fourty seed trees which were located at Sanghdeh Forest of Caspian Region with altitude ranged between 1700 to 2300 m-above sea level, were selected. 200 seeds were collected from each tree, subsequently. The seeds were sown in 2000 and in 2001 repeatedly, in plastic bage containg forest soil and kept in a forest nursery located at the same forest at 1550 m. above sea level altitude. Seed germination and seedling survival was measured in 2000 and 2001. According to T-Test data analysis, there were significant differences between seed germinatin and seedling survival of the treatments in both years. Total seed germination (TSG) in 2000 was 9.22% more than TSG in 2001. Seed germination was correlated significantly and negatively with the seed trees age and diameter. Seed trees with moderate age and at diameter class of 30 cm, performed the highest percentage of seed germination in 2000 (without seed storage). There was minimum reduction in seed germination for seeds collected from trees at moderate diameter class and stored for one year.

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Abstract: We propose a scalable technique called Seeded Clustering that allows us to maintain R-tree indices by bulk insertion while keeping pace with high data arrival rates. Our approach uses a seed tree, which is copied from the top k levels of a target R-tree, to classify input data objects into clusters. We then build an R-tree for each of the clusters and insert the input R-trees into the target R-tree in bulk one at a time. We present detailed algorithms for the seeded clustering and bulk insertion. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods. [Copyright &y& Elsevier]