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Genomics-­Enhanced Forecasting Tools to Secure Canada’s NearTerm Lignocellulosic Feedstock Supply for Bioenergy using the Mountain Pine Beetle System


Generating solutions




Competition in Applied Genomics Research in Bioproducts or Crops (ABC)

Genome Centre(s)



Project Leader(s)

Fiscal Year Project Launched


Project Description

The recent mountain pine beetle outbreak in British Columbia, now spreading into Alberta, has caused unprecedented damage to the Canadian forest industry. The current infestation has affected more than 14 million hectares of pine forests and is the largest such epidemic in recorded history. Conifer forests are Canada’s largest renewable source of ligno-cellulose, used for energy production, paper and wood products. Understanding the biology of the mountain pine beetle in order to use that knowledge for anticipating and helping to control future outbreaks is an important contribution to Canadian forest economics, particularly related to energy production. Although massive amounts of dead timber from the mountain pine beetle epidemic have created an unexpected surplus of potential energy feedstock, this will not necessarily provide a sustainable feedstock supply in the future. Before strategic investments are made in the forest industry, current methods of predicting feedstock need to be improved.

The mountain pine beetle infestation has three interacting components: the host trees, lodgepole pine and jack pine, the beetle itself and multiple beetle-associated tree-killing fungal species.

Our study has four parts. First, we will carry out extensive genomic studies of all three organisms, with an emphasis on genes that are important in their interaction. Second, we will use this information to build a map that shows the inter-relationships of populations of these organisms in relation to geographic location, time, environment and climate. Third, we will use the above information to create models that could forecast the likelihood of a mountain pine beetle outbreak in any location at a particular time. Finally, we will use all of this information to make an analysis of the economics of forest use for energy production. A unique aspect of this project is our ability to combine all the genetic and genomic data with geographic and economic information to provide a detailed picture of the threat of a mountain pine beetle outbreak. The overall goal of our applied genomics project is to generate new genomics-based information and tools for improved prediction of renewable energy feedstock supply from conifer forests, using the current mountain pine beetle epidemic as an example of an important host pest-pathogen system.

Integrated GE3LS Research: A model of the effects of epidemics on forest feedstock
GE3LS Project Leaders: Brian Aukema, Barry Cooke, Tim Williamson – Natural Resources Canada Grant Hauer, University of Alberta
Conifer forests are Canada’s largest renewable source of lignocellulose. Their sustainable use for bioenergy production would form a strategic part of economic diversification within the forestry sector and help alleviate our dependence on fossil fuels. Drawing on feedstock from conifer forests affected by the mountain pine beetle epidemic to generate biofuels would also help mitigate some of the negative economic and environmental impacts of such large-scale disturbance on this valuable resource. Our integrated team of investigators with experience in environmental risk assessment will use genomic data in developing models for forecasting the possibility of future  pinebeetle outbreaks in both time and geographical location.

We will model the spread and likelihood of a pine-beetle outbreak using genomic data for the pine beetle and its associated fungal species. Then, we will use these data to devise environmental-risk models to improve our capacity to estimate the likelihood of outbreaks of mountain pine beetle infestations. This will allow more accurate estimates of fibre supply. We will then combine this information with estimates of forestdeterioration rates due to the pine beetle with economic variables such as prices of bioenergy, costs, and discount rates. Finally we will weave all this information into a model that will elucidate the viability of the bioenergy industry given the variability of prices and of feedstock availability.

We anticipate that this project will help clarify the economic potential of the bioenergy industry in Canada and inform decisions regarding investment in bioenergy facilities.