Management assessment of mountain pine beetle infestation in Cypress Hills, SK

Abstract

Insect epidemics such as the mountain pine beetle (MPB) outbreak have a major impact on forest dynamics. In Cypress Hills, Canada, the Forest Service Branch of the Saskatchewan Ministry of Environment aims to control as many new infested trees as possible by conducting ground-based surveys around trees infested in previous years. Given the risk posed by MPB, there is a need to evaluate how well such a control strategy performs. Therefore, the goal of this study is to assess the current detection strategy compared with competing strategies (random search and search based on model predictions via machine learning), while taking management costs into account. Our model predictions via machine learning used a generalized boosted classification tree to predict locations of new infestations from ecological and environmental variables. We then ran virtual experiments to determine control efficiency under the three detection strategies. The classification tree predicts new infested locations with great accuracy (AUC = 0.93). Using model predictions for survey locations gives the highest control efficiency for larger survey areas. Overall, the current detection strategy performs well but control could be more efficient and cost-effective by increasing the survey area, as well as adding locations given by model predictions.

The Signature of Endemic Populations in the Spread of Mountain Pine Beetle Outbreaks

“…With few exceptions (such as Powell and Bentz 2014;Powell et al 2018), forecasting models tend to reconstruct beetle pressure in a heuristic way, by defining infestation indicator variables that are summed over local spatial neighbourhoods (see, e.g. Shore et al 2000;Aukema et al 2008;Robertson et al 2009;Kunegel-Lion et al 2019). Many attack dynamics regression models also employ this trick (e.g.…”

“…Ad-hoc assignments of weights to [D t ] i j often suffice in simpler predictive models (e.g. Kärvemo et al 2014;Kunegel-Lion et al 2019) but, whenever possible, it is desirable to choose a kernel derived from models of the physical flight process (Nelson et al 2008).…”

Factors governing outbreak dynamics in a forest intensively managed for mountain pine beetle

“…This gives managers guidance regarding which stands to focus on for an efficient control. For example, they could use the risk probability maps to inform survey locations 62 . These results also point out that the predictive ability of models using data from an incomplete outbreak to determine future infestations may be limited.…”