• Aboveground Biomass Equations for the Trees of Interior Alaska

      Yarie, John; Kane, Evan; Mack, Michelle (School of Agriculture and Land Resources Management, Agricultural and Forestry Experiment Station, 2007-01)
      Calculation of forest biomass requires the use of equations that relate the mass of a tree or it's components to physical measurements that are relatively easy to obtain. In the literature individual tree relationships have been reported that estimate aboveground biomass on individual sites (e.g. Barney and Van Cleve 1973)and over large landscape areas where many data sets are combined (Jenkins et al. 2003). The equations presented in this report represent a compilation of aboveground biomass data collected within interior Alaska over the past 40 years.
    • Analyzing tree distribution and abundance in Yukon-Charley Rivers National Preserve: developing geostatistical Bayesian models with count data

      Winder, Samantha; Short, Margaret; Roland, Carl; Goddard, Scott; McIntyre, Julie (2018-05)
      Species distribution models (SDMs) describe the relationship between where a species occurs and underlying environmental conditions. For this project, I created SDMs for the five tree species that occur in Yukon-Charley Rivers National Preserve (YUCH) in order to gain insight into which environmental covariates are important for each species, and what effect each environmental condition has on that species' expected occurrence or abundance. I discuss some of the issues involved in creating SDMs, including whether or not to incorporate spatially explicit error terms, and if so, how to do so with generalized linear models (GLMs, which have discrete responses). I ran a total of 10 distinct geostatistical SDMs using Markov Chain Monte Carlo (Bayesian methods), and discuss the results here. I also compare these results from YUCH with results from a similar analysis conducted in Denali National Park and Preserve (DNPP).
    • Ornamental Trees and Shrubs for Alaska

      Babb, M. F. (School of Agriculture and Land Resources Management, Agricultural and Forestry Experiment Station, 1959-03)
      This bulletin summarizes findings from a nine year study of woody ornamentals in Alaska. Those familiar with such materials recognize that such a brief period of experimentation yields no hard and fast conclusions, even though supplemented by critical observation. and by the experience of those who have the advantage of a longer residence here.
    • Statistical analysis of species tree inference

      Dajles, Andres; Rhodes, John; Allman, Elizabeth; Goddard, Scott; Short, Margaret; Barry, Ron (2016-05)
      It is known that the STAR and USTAR algorithms are statistically consistent techniques used to infer species tree topologies from a large set of gene trees. However, if the set of gene trees is small, the accuracy of STAR and USTAR in determining species tree topologies is unknown. Furthermore, it is unknown how introducing roots on the gene trees affects the performance of STAR and USTAR. Therefore, we show that when given a set of gene trees of sizes 1, 3, 6 or 10, the STAR and USTAR algorithms with Neighbor Joining perform relatively well for two different cases: one where the gene trees are rooted at the outgroup and the STAR inferred species tree is also rooted at the outgroup, and the other where the gene trees are not rooted at the outgroup, but the USTAR inferred species tree is rooted at the outgroup. It is known that the STAR and USTAR algorithms are statistically consistent techniques used to infer species tree topologies from a large set of gene trees. However, if the set of gene trees is small, the accuracy of STAR and USTAR in determining species tree topologies is unknown. Furthermore, it is unknown how introducing roots on the gene trees affects the performance of STAR and USTAR. Therefore, we show that when given a set of gene trees of sizes 1, 3, 6 or 10, the STAR and USTAR algorithms with Neighbor Joining perform relatively well for two different cases: one where the gene trees are rooted at the outgroup and the STAR inferred species tree is also rooted at the outgroup, and the other where the gene trees are not rooted at the outgroup, but the USTAR inferred species tree is rooted at the outgroup.
    • SunStar: an implementation of the generalized STAR method

      Bettisworth, Benjamin; Chappell, Glenn; Rhodes, John; Lawlor, Orion; Hartman, Chris (2017-05)
      STAR ... is a method of computing species trees from gene trees. Later, STAR was generalized and proven to be statistically consistent given a few conditions (Allman, Degnan, and Rhodes 2013). Using these conditions, it is possible to investigate robustness in the species tree inference process, the lack of which will produce instabilities in the tree resulting from STAR. We have developed a software package that estimates support for inferred trees called SunStar.