|Forest Investment Account (FIA) - Forest Science Program|
|FIA Project Y092277|
|Bark beetle response to climate change: a landscape-level risk model for British Columbia|
|Project lead: Aukema, Brian (University of Northern British Columbia)|
|Contributing Authors: Raffa, Kenneth F.; Aukema, Brian H.; Bentz, Barbara J.; Carroll, Allan L.; Hicke, Jeffrey A.; Turner, Monica G.; Romme, William H.|
|Subject: Forest Investment Account (FIA), British Columbia|
|Series: Forest Investment Account (FIA) - Forest Science Program|
|Insects and diseases exert immense economic loss to forests through reduced tree growth and mortality. In recent years, these problems have been exacerbated by climate change. For example, in British Columbia, warmer summers and an amelioration of winter temperatures have allowed the mountain pine beetle to expand its range into new areas of British Columbia and Alberta east of the Rocky Mountains. A significant concern is subsequent expansion of this insect into jack pine within the boreal forest. Another concern, as yet unstudied, is the potential expansion of mountain pine beetle northward through lodgepole pine into northern British Columbia and the Yukon.|
Range expansion and/or increased tree mortality is not limited to mountain pine beetle. Indeed, many other insects such as Douglas fir beetle, Western balsam bark beetle, and spruce beetle have reached record population levels in British Columbia in recent years. Such population dynamics are likely due to a phenomenon known as the Moran effect, in which populations with the same density-dependent structure erupt simultaneously when synchronized by a landscape-level exogenous variable. The most plausible exogenous variable in forest systems is temperature, especially for uni- or semi-voltine insects dependent upon phenological synchrony. A changing climate, especially increasing temperatures, may not only extend ranges but also bring about simultaneous populations eruptions.
There has been a plethora of work on models examining population dynamics of the mountain pine beetle. Predominant approaches have included deterministic process models with relative risk, simulation outputs (Riel et al. 2004), analytical process models designed to mirror system behaviour (Powell et al. 1996, Powell et al. 1998, Logan et al. 1998), simulations at landscape scales (Barclay et al. 2005), and both spatial and aspatial stochastic models at stand and landscape levels (Safranyik et al. 1975, Preisler and Mitchell 1993). The latter class of models has been underdeveloped, due in part to the lack of large area, spatially explicit datasets (Aukema et al. 2006, Nelson et al. 2006) and computational challenges in working with autoregressive models that explicitly incorporate organism abundance in space and time (He et al. 2003). None of these models have been used to predict climate change scenarios.
To complement existing process models, either for mountain pine beetle or stand dynamics in general (e.g. FORECAST, (Kimmins et al. 1999)) we are constructing a spatiotemporal statistical climate change model. Although such an approach could be employed for many bark beetles in British Columbia, we are developing a model for mountain pine beetle. We are using aerial overview survey data of tree mortality due to mountain pine beetle in a GIS. Out models will be fully data-driven and optimized using likelihood techniques. Such methods will allow direct assessment selection of the best models. We will explicitly incorporate temperature through coefficients that will account for observed temperatures from climate stations, interpolated temperatures where data do not exist, and/or outputs from various climate change scenarios. Outputs will be absolute estimates of outbreak probability on annual bases, with standard errors, at clearly defined scales.
There are no changes to the project description, team, or contributions from the originally approved project plans following the first year of work. We are currently completing all of work proposed in Year I (development of platform and proof of concept). We look forward to year two and expanding our model. This work continues to exhibit a high probability of success:
(1) The lead proponents have considerable expertise in landscape ecology, climate change, statistics, and their interface and a successful publication record in the peer-reviewed literature.
(2) They have experience in processing, rasterizing, and analyzing aerial survey overview data.
(3) The framework for the spatiotemporal regression model has been published in (Aukema et al. 2006). A refinement has been made for likelihood methods (Zhu et al. 2007).
(4) The mountain pine beetle outbreak continues to demonstrate strong spatial and temporal dependence (i.e., a progressing outbreak and dispersal) (Aukema et al. 2006). Such biological properties are amenable to outbreak forecasting.
|Related projects:  FSP_Y081277,  FSP_Y103277|
Executive summary (94Kb)
Article in BioScience (6.0Mb)
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Updated August 16, 2010
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