Temperature-Dependent Development of the Mountain Pine Beetle (Coleoptera: Scolytidae) and Simulation of Its Phenology

Bentz, Barbara; Logan, Jesse A.; Amman, Gene D.

doi:10.4039/ent1231083-5

Cited by 225 publications

(211 citation statements)

References 24 publications

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“…The mountain pine beetle's life cycle is primarily controlled by temperature (Logan and Bentz, 1999;Powell and Logan, 2005). We employed a process model (developed from laboratory measurements of life-stage development rates as functions of temperature) that simulates the timing of all eight life stages of the mountain pine beetle (Bentz et al, 1991;Amman, 1986, Logan et al, 1995;Logan and Powell, 2001). The model computes a developmental index in each life stage by combining the annual course of hourly temperatures with the life-stage development rate.…”

Section: Adaptive Seasonality: Temperature Effects On the Lifecycle Omentioning

confidence: 99%

Forest ecosystems, disturbance, and climatic change in Washington State, USA

et al. 2010

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C limatic change is likely to affect Pacific Northwest (PNW) forests in several important ways. In this paper, we address the role of climate in four forest ecosystem processes and project the effects of future climatic change on these processes. First, we analyze how climate affects Douglas-fir growth across the region to understand potential changes in future growth. In areas where Douglas-fir is not water-limited, future growth will continue to vary with interannual climate variability, but in places where Douglas-fir is water-limited, growth is likely to decline due to projected increase in summer potential evapotranspiration. Second, we use existing analyses of climatic controls on future potential tree species ranges to highlight areas where species turnover may be greatest. By the mid 21 st century, some areas of the interior Columbia Basin and eastern Cascades are likely to have climates poorly suited to pine species that are susceptible to mountain pine beetle, and if these pines are climatically stressed, they may be more vulnerable to pine beetle attack. Climatic suitability for Douglas-fir is also likely to change, with substantial decreases in climatically suitable area in the Puget Trough and the Okanogan Highlands. Third, using regression approaches, we examine the relationships between climate and the area burned by fire in the PNW and in eight Washington ecosystems and project future area burned in response to changing climate. Area burned is significantly related to both temperature and precipitation in summer, but more physiologically relevant variables, such as water balance deficit, perform as well or better in models. Regional area burned is likely to double or even triple by the end of the 2040s, although Washington ecosystems have different sensitivities to climate and thus different responses to climatic change. Fourth, we evaluate the influence of climatic change on mountain pine beetle (MPB) outbreaks by quantifying both host-tree vulnerability and pine beetle adaptive seasonality. Host-tree vulnerability is closely related to vapor pressure deficit (VPD), and future projections support the hypothesis that summer VPD will increase over a significant portion of the range of host tree species. Due to the increased host 255vulnerability, MPB populations are expected to become more viable at higher elevations leading to increased incidence of MPB outbreaks. The increased rates of disturbance by fire and mountain pine beetle are likely to be more significant agents of changes in forest structure and composition in the 21st century than species turnover or declines in productivity. This suggests that understanding future disturbance regimes is critical for successful adaptation to climate change.

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Section: Adaptive Seasonality: Temperature Effects On the Lifecycle Omentioning

confidence: 99%

Forest ecosystems, disturbance, and climatic change in Washington State, USA

et al. 2010

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“…There are additional processes, not included in our model, that govern dynamics in natural stands. These include competition with other bark beetles (Safranyik and Carroll, 2006), variable redistribution survivorship S (Burnell, 1997), evidence that host selection can change with beetle density (Wallin and Raffa, 2004) and temperature-dependent beetle phenology (Bentz et al, 1991). While strategic models that leave out too many ecological factors may be unable to predict risk may be unable to predict risk (Nelson et al, 2008), we believe that the next step is to validate the vigorstructured model against mountain pine beetle infestation data.…”

Section: Discussionmentioning

confidence: 99%

A Structured Threshold Model for Mountain Pine Beetle Outbreak

Lewis

Nelson

2009

Bull. Math. Biol.

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A vigor-structured model for mountain pine beetle outbreak dynamics within a forest stand is proposed and analyzed. This model explicitly tracks the changing vigor structure in the stand. All model parameters, other than beetle vigor preference, were determined by fitting model components to empirical data. An abrupt threshold for tree mortality to beetle densities allows for model simplification. Based on initial beetle density, model outcomes vary from decimation of the entire stand in a single year, to inability of the beetles to infect any trees. An intermediate outcome involves an initial infestation which subsequently dies out before the entire stand is killed. A model extension is proposed for dynamics of beetle aggregation. This involves a stochastic formulation.

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“…A wellsynchronized adult emergence facilitates mass attack, and is important in the development of MPB outbreaks because the insects must overcome host defenses to successfully colonize healthy trees (Raffa et al 2008). Temperature directly influences MPB development rate (Bentz et al 1991;Régnière et al 2012b), and stage-specific development thresholds help synchronize adult emergence (Powell and Logan 2005). Mortality due to extreme cold also conditions MPB population success (Safranyik and Linton 1998).…”

Section: The Insectmentioning

confidence: 99%

“…Empirically driven, statistical approaches have been proposed (Safranyik et al 1975;Aukema et al 2008;Preisler et al 2012;Reyes et al 2012), and mechanistic models have also been developed (Bentz et al 1991;Gilbert et al 2004;Régnière and Bentz 2007;Powell and Bentz 2009), to analyze the role of temperature in MPB population outbreaks using historic and future climate data (Logan and Bentz 1999;Logan and Powell 2001;Hicke et al 2006;Safranyik et al 2010). While empirical models have good descriptive power for the range of conditions for which they were derived, they need to be used with caution under unobserved multivariate contexts such as encountered when crossing ecoregional boundaries.…”

Section: The Modelmentioning

confidence: 99%