Problem Context

The mountain pine beetle (MPB) is an insect native to forests of western North America (NRC, 2016). The MPB’s range is from northern Mexico to northern British Columbia. When MPB’s colonize a new host, fungus, which is present in the mouths of the beetles, infects the trees (NRC, 2017). The fungus spreads through the sapwood and inner bark, which impedes the trees from defending themselves (NRC, 2017). The trees usually die within 1-4 years after being infected (NRC, 2017). The MPB has been responsible for widespread pine tree mortality throughout western North America. Since the early 1990’s, the MPB has destroyed roughly half of the commercial pines in British Columbia and has since spread beyond its historical range into Alberta (NRC, 2016). The MPB has also extended its northern reach into Northern British Columbia and the territories (Nealis and Cooke, 2014). Cooke and Carroll (2017) state that the MPB population has spread eastward at an average rate of 80 km per year since the main outbreak in 2006. From 2001 to 2011, the MPB has also extended its northern front by 420 km (Nealis and Cooke, 2014).

According to Furniss and Carolin (1977), the main hosts of the MPB found in Canada are the Lodgepole, Western White, Ponderosa, and White Bark Pines and there are also records stating that MPB infects several other pine species, such as bristlecore and limber pines (Furniss and Carolin, 1977). Recent, genetic research from 2010 found that the MPB was capable of inhabiting Jack Pines (Cullingham et al., 2011) while Cale et al. (2015) found that the MPB can also affect Red Pines. Creeden et al. (2014) determined that leading up to MPB outbreaks, the locations they studied had higher monthly temperatures leading up to MPB outbreaks and were experiencing drought stress. Bater et al. (2010) concluded that LiDAR (Light Detection and Ranging) could be used to estimate tree health and mortality resulted by MPB infestation. Nealis and Cooke (2014) found that wood debris affected by a MPB infestation is extremely dry in comparison to other sites and even sites that are historically moist. In addition, they found that 5% of recent fires in British Columbia have occurred in MPB-affected stands, however, those fires accounted for more than 50% of the area burned (Nealis and Cooke, 2014). Powell and Bentz (2009) found that yearly temperatures directly affect MPB infestations, therefore temperature should be an important variable when studying MPB’s. In addition, the occurrence of a MPB outbreak exhibits a negative relationship with time, frequency and cold temperature (Sambaraju et al., 2012). Due to this, the mortality of overwintering MPB larvae is controlled by a low lethal temperature, which is -40^oC (Rosenberger et al., 2017).  

In a study conducted by Nealis and Cooke (2014), they determined that there is little knowledge about how the MPB disperses. In addition, the MPB's range expansion into the north has not followed previous estimation models and patterns due to uncertainty when trying to make predictions for climate trends, especially when looking at smaller spatial and temporal scales (Nealis and Cooke, 2014). Some other uncertainties which must be taken into account when studying MPB are the relationships between the beetle and its hosts. It may change as they become established in a new host species and destroy the trees with the best defenses (Nealis and Cooke, 2014). Bone and Altaweel (2014) concluded that there is a need for a model that can display and quantify the different stages of an MPB outbreak over large temporal resolutions. According to Assal et al. (2014), using multispectral scanner system (MSS) imagery is limited to areas with large-spread damage, as the spatial resolution of the images prevents researchers from using them to analyze smaller, localized infestations. 

Geographic information systems (GIS) are an important tool for analyzing MPB distribution and predicting future spread. GIS is also useful when looking at possible impacts due to MPB infestations, which can be done through spatial analysis. According to Assal et al. (2014), using multispectral scanner system (MSS) imagery is limited to areas with large-spread damage, as the spatial resolution of the images prevents researchers from using them to analyze smaller, localized infestations. Spectral images can be used to see the changes in reflectance over the period of an MPB infestation, as dying and dead trees will reflect different light than healthy trees (NASA, 2000). GIS is also used to analyze topography to determine which swaths of trees are most vulnerable to attack based on slope and dryness. Using satellite imagery is also much more feasible when studying a large spatial/temporal scale, and many different models can be created to apply variables over large scales. Since the MPB has spread beyond its historical range into multiple provinces and territories, it has become even more important that regions are able to share their data. This has also become a global problem. Using GIS makes it easier for municipal, provincial and federal governments to share their findings and make predictions for management plan and conservations. 

Since the beetle population is spreading, there has been growing concern as to the vulnerability of other pine species in Canada. In addition, pines represent about 10% of the total trees in Canada (CNFI, 2013), while the forest industry as a whole accounts for $23.1 billion dollars of Canada’s gross domestic product (GDP, 2017) and over 211, 000 jobs (NRCAN, 2017). Aside from the possible economic impacts that the MPB could have on the forest industry, Hrinkevich and Lewis (2011) list several other negative impacts such as: a reduction in the societal/recreational value, decreased carbon storage, reduction in watershed quality, increased fuel build-up and increased forest fires. As a result of negative economical and ecological impacts from the MPB in the Canadian forest industry, it is important to understand and predict how the MPB will spread based on future climate simulations. 

Purpose of Research

The purpose of this research is to develop a GIS-based model that will integrate multiple biophysical factors to assess the spread and future distribution of the Mountain Pine Beetle in Canada.

Research Objectives

1. To identify biophysical factors that influence MPB distribution and spread in Western Canada.
2. To develop a GIS model that will predict future spatial and temporal variations in the distribution of the MPB.
3. To apply this model to the MPB and evaluate the future distribution and spread of the MPB.
4. To evaluate the strengths and limitations of the MPB distribution model.