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Predicting multi-species Bark Beetle (Coleoptera: Curculionidae: Scolytinae) occurrence in Alaska: open-access big GIS-data mining to provide robust inferenceUniversity of Kansas, 2021-07-03Native bark beetles (Coleoptera: Curculionidae: Scolytinae) are a multi-species complex that rank among the key disturbances of coniferous forests of western North America. Many landscape-level variables are known to influence beetle outbreaks, such as suitable climatic conditions, spatial arrangement of incipient populations, topography, abundance of mature host trees, and disturbance history that include former outbreaks and fire. We assembled the first open access data, which can be used in open source GIS platforms, for understanding the ecology of the bark beetle organism in Alaska. We used boosted classification and regression tree as a machine learning data mining algorithm to model-predict the relationship between 14 environmental variables, as model predictors, and 838 occurrence records of 68 bark beetle species compared to pseudo-absence locations across the state of Alaska. The model predictors include topography- and climate-related predictors as well as feature proximities and anthropogenic factors. We were able to model, predict, and map the multi-species bark beetle occurrences across the state of Alaska on a 1-km spatial resolution in addition to providing a good quality environmental dataset freely accessible for the public. About 16% of the mixed forest and 59% of evergreen forest are expected to be occupied by the bark beetles based on current climatic conditions and biophysical attributes of the landscape. The open access dataset that we prepared, and the machine learning modeling approach that we used, can provide a foundation for future research not only on scolytines but for other multi-species questions of concern, such as forest defoliators, and small and big game wildlife species worldwide.
Mathematical Modeling and Simulation with MATLABThis textbook attempts to provide you with an overview of the commonly used basic mathematical models, as well as a wide range of applications. It offers a perspective that brings you back to the modeling process and the assumptions that go into it.
BUILDING CAPACITY FOR CLIMATE ADAPTATION Assessing the Vulnerability of Transportation Infrastructure to Sea Level Rise for Safety Enhancement in RITI CommunitiesSea level rise (SLR) and more frequent extreme weather events are an emerging concern for transportation infrastructures in coastal areas. In particular, the livelihoods and transportation safety of vulnerable populations such as indigenous rural communities may be at higher risk to sea-level rise and exacerbated coastal flooding due to their heavy dependence on natural resources, settlements in relatively isolated fringe land, limited accessibility to services, and alternative economic activities, as well as lack of resources and tools for adaptation. Despite existing studies on sea-level rise’s impacts, there is a lack of understanding of how the impacts of tidal flooding and sea-level rise may be unevenly distributed both spatially and socially, and how vulnerable (e.g. rural, relatively isolated) communities have experienced such impacts and perceive future risks. Using survey data, this project helps to better understand the current experience and risk perception of different communities when facing sea-level rise and more frequent coastal flooding. It helps to understand different communities’ perceived travel challenges with coastal flooding, the social sensitivity to different types of challenges, and the priorities and concerns to access various types of resources with the projected sea-level rise. The findings could be used to develop adaptation strategies that improve communities’ safe access to highly valued resources and activities.
Alaska Earthquake Center Quarterly Technical Report April-June 2021This series of technical quarterly reports from the Alaska Earthquake Center (AEC) includes detailed summaries and updates on Alaska seismicity, the AEC seismic network and stations, field work, our social media presence, and lists publications and presentations by AEC staff. Multiple AEC staff members contributed to this report. It is issued in the following month after the completion of each quarter Q1: January-March, Q2: April-June, Q3: July-September, and Q4: October-December.
Conditions for staggering and delaying outplantings of the kelps Saccharina latissima and Alaria marginata for maricultureWe describe a method for production of kelp using meiospore seeding creating flexibility for extended storage time prior to outplanting. One bottleneck to expansion of the kelp farming industry is the lack of flexibility in timing of seeded twine production, which is dependent on the fertility of wild sporophytes. We tested methods to slow gametophyte growth and reproduction of early life stages by manipulating temperature of the kelp Saccharina latissima. Reducing temperature from 12 C to 4 C reduced gametophyte size, sporophyte size, egg production, and sporophyte production and subsequently was the best candidate condition for storage experiments of seeded twine. Next, we examined how storage of Alaria marginata and S. latissima seeded twine at 4 C under differing nutrient concentrations affected the viability of sporelings after being moved into optimal growth conditions. Seeded twine storage at 4 C with no alteration to culturing media showed no negative effects in sporophyte density and sporophyte length for both species. This method for seeded twine storage, “cold banking,” allowed seeded twine storage for at least an additional 36 days compared to standard methods, with a total of 56 days spent in the hatchery providing opportunity for outplanting timing and staggering to enhance aquaculture efficiency.