Annual Report: 2008

I submit herewith the annual report from the Agricultural and Forestry Experiment Station, School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, for the period ending December 31, 2008. This is done in accordance with an act of Congress, approved March 2, 1887, entitled, “An act to establish agricultural experiment stations, in connection with the agricultural college established in the several states under the provisions of an act approved July 2, 1862, and under the acts supplementary thereto,” and also of the act of the Alaska Territorial Legislature, approved March 12, 1935, accepting the provisions of the act of Congress. The research reports are organized according to our strategic plan, which focuses on high-latitude soils, high-latitude agriculture, natural resources use and allocation, ecosystems management, and geographic information. These areas cross department and unit lines, linking them and unifying the research. We have also included in our financial statement information on the special grants we receive. These special grants allow us to provide research and outreach that is targeted toward economic development in Alaska. Research conducted by our graduate and undergraduate students plays an important role in these grants and the impact they make on Alaska.Financial statement -- Grants -- Students -- Research reports: Partners, Facilities, and Programs; Geographic Information; High-Latitude Agriculture; High-Latitude Soils, Management of Ecosystems; Natural Resources Use and Allocation; Index to Reports -- Publications -- Facult

Decision support systems for forest management: a comparative analysis and assessment

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.compag.2013. 12.005.[EN] Decision Support Systems (DSS) are essential tools for forest management practitioners to help take account of the many environmental, economic, administrative, legal and social aspects in forest management. The most appropriate techniques to solve a particular instance usually depend on the characteristics of the decision problem. Thus, the objective of this article is to evaluate the models and methods that have been used in developing DSS for forest management, taking into account all important features to categorize the forest problems. It is interesting to know the appropriate methods to answer specific problems, as well as the strengths and drawbacks of each method. We have also pointed out new approaches to deal with the newest trends and issues. The problem nature has been related to the temporal scale, spatial context, spatial scale, number of objectives and decision makers or stakeholders and goods and services involved. Some of these problem dimensions are inter-related, and we also found a significant relationship between various methods and problem dimensions, all of which have been analysed using contingency tables. The results showed that 63% of forest DSS use simulation modelling methods and these are particularly related to the spatial context and spatial scale and the number of people involved in taking a decision. The analysis showed how closely Multiple Criteria Decision Making (MCDM) is linked to problem types involving the consideration of the number of objectives, also with the goods and services. On the other hand, there was no significant relationship between optimization and statistical methods and problem dimensions, although they have been applied to approximately 60% and 16% of problems solved by DSS for forest management, respectively. Metaheuristics and spatial statistical methods are promising new approaches to deal with certain problem formulations and data sources. Nine out of ten DSS used an associated information system (Database and/or Geographic Information System - GIS), but the availability and quality of data continue to be an important constraining issue, and one that could cause considerable difficulty in implementing DSS in practice. Finally, the majority of DSS do not include environmental and social values and focus largely on market economic values. The results suggest a strong need to improve the capabilities of DSS in this regard, developing and applying MCDM models and incorporating them in the design of DSS for forest management in coming years.The authors acknowledge the support received from European Cooperation in Science and Technology (COST Action FP0804 - Forest Management Decision Support Systems "FORSYS"), the Ministry of Economy and Competitiveness through the research project Multiple Criteria and Group Decision Making integrated into Sustainable Management, Ref. ECO2011-27369 and Ministry of Education (Training Plan of University Teaching). We also thank the editor and reviewers for their suggestions to improve the paper.Segura Maroto, M.; Ray, D.; Maroto Álvarez, MC. (2014). Decision support systems for forest management: a comparative analysis and assessment. Computers and Electronics in Agriculture. 101:55-67. https://doi.org/10.1016/j.compag.2013.12.005S556710

Landscape-scale establishment and population spread of yellow-cedar (Callitropsis nootkatensis) at a leading northern range edge

Thesis (M.S.) University of Alaska Fairbanks, 2016Yellow-cedar is a long-lived conifer of the North Pacific Coastal Temperate Rainforest region that is thought to be undergoing a continued natural range expansion in southeast Alaska. Yellow-cedar is locally rare in northeastern portions of the Alexander Archipelago, and the fairly homogenous climate and forest conditions across the region suggest that yellow-cedar's rarity could be due to its local migrational history rather than constraints on its growth. Yellow-cedar trees in northern range edge locations appear to be healthy, with few dead trees; additionally, yellow-cedar tend to be younger than co-dominant mountain and western hemlock trees, indicating recent establishment in existing forests. To explore yellow-cedar's migration in the region, and determine if the range is expanding into unoccupied habitat, I located 11 leading edge yellow-cedar populations near Juneau, Alaska. I used the geographic context of these populations to determine the topographic, climatic, and disturbance factors associated with range edge population establishment. I used those same landscape variables to model suitable habitat for the species at the range edge. Based on habitat modeling, yellow-cedar is currently only occupying 0.8 percent of its potential landscape niche in the Juneau study area. Tree ages indicate that populations are relatively young for the species, indicating recent migration, and that most populations established during the Little Ice Age climate period (1100 -- 1850). To determine if yellow-cedar is continuing to colonize unoccupied habitat in the region, I located 29 plots at the edges of yellow-cedar stands to measure regeneration and expansion into existing forest communities. Despite abundant suitable habitat, yellow-cedar stand expansion appears stagnant in recent decades. On average, seedlings only dispersed 4.65 m beyond stand boundaries and few seedlings reached mature heights both inside and outside of existing yellow-cedar stands. Mature, 100 --200-year-old trees were often observed abruptly at stand boundaries, indicating that most standboundaries have not moved in the past ~150 years. When observed, seedlings were most common in high light understory plant communities and moderately wet portions of the soil drainage gradient, consistent with the species' autecology in the region. Despite an overall lack of regeneration via seed, yellow-cedar is reproducing via asexual layering in high densities across stands. Layering may be one strategy this species employs to slowly infill habitat and/or persist on the landscape until conditions are more favorable for sexual reproduction. This study leads to a picture of yellow-cedar migration as punctuated, and relatively slow, in southeast Alaska. Yellow-cedar's migration history and currently limited spread at the northeastern range edge should be considered when planning for the conservation and management of this high value tree under future climate scenarios

Rarity of monodominance in hyperdiverse Amazonian forests.

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors

Tribal Corridor Management Planning: Model, Case Study, and Guide for Caltrans District 1, Research Report 10-01

In Northern California, tribal governments and personnel of the California Department of Transportation (Caltrans) District 1, have applied innovative context-sensitive solutions to meet a variety of transportation challenges along state highways that traverse tribal lands. This report describes and discusses the efforts under way and offer suggestions for continuing and extending these initiatives through the development of Tribal Corridor Management Plans (TCMPs). The methods employed in this project are multidisciplinary and include: (1) content analysis of existing corridor management plans; (2) literature review to identify “best practices;” (3) participant observation; (4) interviews with local stakeholders; (5) focus group interviews with Caltrans personnel; and (6) landscape analysis. This study’s authors conclude that Caltrans District 1 staff and tribal governments share common goals for highway operations; however, progress —while significant—has been somewhat hampered by geographic and administrative challenges. It is recommended that Caltrans and the tribes seek early and frequent communication and collaboration to overcome these obstacles. Further, they identify several examples of non-standard design elements that could be incorporated into highway improvements to enhance local sense of place among both residents and travelers. A preliminary TCMP for the segment of State Route 96 that lies within the boundaries of the Hoopa Valley Indian Reservation is presented as an example. Beyond its role as a guide for initiating tribal corridor projects within Caltrans District 1, the report should prove instructive for any efforts to enhance sense of place within transportation byways, particularly in Native communities

Scaling Genetic Algorithms to Large Distributed Datasets

Analysing large-scale data brings promises of new levels of scientific discovery and economic value. However, the fact that such volume of data is by its nature distributed and the need for new computational methods to be effective in the face of significant changes in data complexity and size has led to the need to develop large-scale data analytics. Genetic algorithms (GAs) have proven their flexibility in many application areas, and substantial research has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts, we reject approaches based on the centralisation of data in the main memory of a single node or requiring remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines. In this partitioned scenario, we explore two parallelisation models: PDMS, inspired by the traditional master-slave model, and PDMD, based on island models. We adopt the two models to distribute BioHEL, a popular large-scale single-node GA classifier, using the Spark distributed data processing platform. We investigate the effect of GA control parameters (population size and migration frequency).We study the accuracy, time performance and scalability of the proposed models. Our results show that our distributed genetic algorithm design provides a good tradeoff between accuracy and time. We then extend the two models using automatic termination and population sizing to enhance the distributed genetic algorithm ease-of-use. Moreover, after testing this strategy on both models, we show that the applied automation offers a promising enhancement on the performance of the initially designed GA models

Scaling Genetic Algorithms to Large Distributed Datasets

Analysing large-scale data brings promises of new levels of scientific discovery and economic value. However, the fact that such a volume of data is by its nature distributed and the need for new computational methods to be effective in the face of significant changes in data complexity and size has led to the need to develop large-scale data analytics. Genetic algorithms (GAs) have proven their flexibility in many application areas, and substantial research has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts, we reject approaches based on the centralisation of data in the main memory of a single node or requiring remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines. In this partitioned scenario, we explore two parallelisation models: PDMS, inspired by the traditional master-slave model, and PDMD, based on island models. We adopt the two models to distribute BioHEL, a popular large-scale single-node GA classifier, using the Spark distributed data processing platform. We investigate the effect of GA control parameters (population size and migration frequency). We study the accuracy, time performance and scalability of the proposed models. Our results show that our distributed genetic algorithm design provides a good tradeoff between accuracy and time

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field