Other Goals of This Initiative

Advancing Genomic and Physiological Parameters as Sensitive Biomarkers of Change:

Genomic, physiological and biochemical sciences are fields of investigation that are rapidly evolving and rely on advanced technologies. The Alaska Science Center is expanding its genomics capability with next generation sequencing and bioinformatics. This will enable the Center to sequence large, complex genomes and conduct transcriptome and gene expression studies. In addition, the Center is exploring physiological and biochemical tools that assess the health status and body condition of wildlife species and the responses to changing food webs.

Data Management and Integration:

The Changes Arctic Ecosystems (CAE) Initiative is testing new approaches to data management and integration that support data access by team scientists, sharing of data products among partner agencies, and long-term archival of data and data products.

Capacity Building for Modeling, Hydrology and Landscape Ecology:

The USGS and resource management agencies in Alaska are increasingly using Bayesian Networks (BNs), Structured Decision Making (SDM), and other similar knowledge-based modeling approaches. BNs provide a versatile framework for integrating empirical data with expert opinion, which often is required when modeling ecosystems where much is unknown. SDM provides an objective, quantitative means to integrate ecological knowledge with multiple management objectives and constraints, to arrive at an optimal management decision. SDM is an integral component of adaptive management. The CAE Initiative is developing additional USGS capacity in these modeling approaches. In addition, the Alaska Science Center is building capacity in hydrology and landscape ecology.

The Role of Monitoring in this Initiative:

Monitoring of physical attributes via sampling and remote sensing, as well as monitoring of biological attributes, is a foundation to this research initiative. As much as possible existing datasets will be used. The CAE initiative will evaluate data bases from long-term monitoring efforts conducted by the USGS, DOI partner bureaus, and others and provide feedback. Linking documented relationships with down-scaled weather models or generalized circulation models is key to predicting future population response to climate-induced change, one of the major ecosystem change drivers in the Arctic. Using long-term datasets in model experiments improves design of data collection methods and data analysis.