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. The current, primary landscape-level models examining spread of mountain pine beetle through British Columbia are process models (Fall and Fall 2001, Riel et al. 2004). Construction of process models can be delicate. Parameters must be chosen with care. In the interest of model functionality, parameter spaces (i.e., the available range of a variable) may sometimes be truncated to values that must avoid extremes. This can be problematic when constructing models to investigate climate change. Furthermore, model validation can be challenging. Sensitivity analyses are frequently employed as direct model comparisons with established statistical techniques are not always available. Outputs may be simulation-based and relative-risk scenarios. To complement existing process models, either for mountain pine beetle or stand dynamics in general (e.g. FORECAST, (Kimmins et al. 1999)) we propose to construct a spatiotemporal statistical climate change model. Although such an approach could be employed for many bark beetles in British Columbia, we will develop a model for mountain pine beetle. We will use aerial overview survey data of tree mortality due to mountain pine beetle in a GIS. This model will be fully data-driven and optimized using likelihood techniques. Such methods will allow direct assessment selection of the best model. 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 outb ...
Aukema, Brian H.. 2008. Bark beetle response to climate change: a landscape-level risk model for British Columbia. Forest Investment Account (FIA) - Forest Science Program. Forest Investment Account Report. FIA2008MR111
Topic: FLNRORD Research Program
Keywords: Forest, Investment, Account, (FIA), British, Columbia
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