My thesis research investigated monoterpene responses in jack pine (Pinus banksiana Lamb.) to different agents to better understand how these responses may influence the spread of the mountain pine beetle (Dendroctonus ponderosae Hopkins; MPB) in the boreal forest. The results support that monoterpenes are inducible responses in jack pine. In the first study, methyl jasmonate application elicited the greatest response in juvenile and mature trees suggesting that jasmonic acid plays a role in jack pine defence responses. In the cross-induction study, I found evidence of an increase in resistance to Grosmannia clavigera with prior jack pine budworm defoliation (Choristoneura pinus pinus Freeman; JPBW). In contrast, needle monoterpenes greatly increased after G. clavigera inoculation and continued to increase during JPBW defoliation; however, JPBW increased its feeding rate to compensate for a change in host quality. Overall, monoterpene induction in jack pine depended on the agent(s) involved and their order. The systemic responses that were observed may have implications for MPB spread in the boreal forest. / Forest Biology and Managment

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on June 26, 2009). Includes bibliographical references (p. 86-111).

With an increasing number of large area data sets, many study areas exhibit spatial non-stationarity or spatial variation in mean and variance of observed phenomena. This poses issues for a number of spatial analysis methods which assume data are stationary. The Getis and Ord’s Gi* statistic is a popular measure that, like many others, is impacted by non stationarity. The Gi* is used for locating hot and cold spots in marked data through the detection of spatial autocorrelation in values that are extreme relative to the global mean value, or the mean entire study area. This thesis describes modifications of the Getis and Ord’s Gi* local measure of spatial association, in part to account for regional differences (spatial non-stationarity) in a dataset. Instead of using data from the entire study area to calculate the mean parameter, as is done for the standard Gi*, I capture points for calculation of the mean using a circular distance band centred on the pivot location, which I call the local region (similar to the Ord and Getis Oi statistic). This approach can be applied to a single instance of a local region or to multiple spatial scales of the local region. I explore both in this paper using simulated datasets and a case study on mountain pine beetle infestation data. I find that the local region, when of a similar size to a true region (homogeneous section of the study area where the mean is approximately the same across locations), obtains similar results to the standard Gi* calculated separately on distinct regions (simulated to be distinct), but has the advantage of not needing explicit delineation of regional boundaries or partitioning into separate subareas. The results of a probability score for a multi-scale approach include high and low scores that are more evenly distributed across the study area and that are thus able to pick out more subtle variations within different regions. Through the case study I demonstrate how the multi-scale approach may be applied to a real dataset.

With an increasing number of large area data sets, many study areas exhibit spatial non-stationarity or spatial variation in mean and variance of observed phenomena. This poses issues for a number of spatial analysis methods which assume data are stationary. The Getis and Ord’s Gi* statistic is a popular measure that, like many others, is impacted by non stationarity. The Gi* is used for locating hot and cold spots in marked data through the detection of spatial autocorrelation in values that are extreme relative to the global mean value, or the mean entire study area. This thesis describes modifications of the Getis and Ord’s Gi* local measure of spatial association, in part to account for regional differences (spatial non-stationarity) in a dataset. Instead of using data from the entire study area to calculate the mean parameter, as is done for the standard Gi*, I capture points for calculation of the mean using a circular distance band centred on the pivot location, which I call the local region (similar to the Ord and Getis Oi statistic). This approach can be applied to a single instance of a local region or to multiple spatial scales of the local region. I explore both in this paper using simulated datasets and a case study on mountain pine beetle infestation data. I find that the local region, when of a similar size to a true region (homogeneous section of the study area where the mean is approximately the same across locations), obtains similar results to the standard Gi* calculated separately on distinct regions (simulated to be distinct), but has the advantage of not needing explicit delineation of regional boundaries or partitioning into separate subareas. The results of a probability score for a multi-scale approach include high and low scores that are more evenly distributed across the study area and that are thus able to pick out more subtle variations within different regions. Through the case study I demonstrate how the multi-scale approach may be applied to a real dataset.

Whitebark pine (Pinus albicaulis Engelmann) is a treeline species in the central Rocky Mountains. Its occupation of high elevations previously protected whitebark pine from long-term mountain pine beetle outbreaks. The mountain pine beetle, however, is currently reaching outbreaks of record magnitude in high-elevation whitebark pine. We used a factorial laboratory experiment to compare mountain pine beetle (Dendroctonus ponderosae Hopkins) life history characteristics between a typical host, lodgepole pine (Pinus contorta Engelmann), and whitebark pine. We tested the effects of natal host and brood host on beetle fecundity, offspring size, and brood sex-ratio. We reared mountain pine beetles from whitebark pine and from lodgepole pine, and infested half of them into their natal host and half into the other host. Fecundity was greater overall in lodgepole pine brood hosts. Among lodgepole brood hosts, beetles from whitebark pine had greater fecundity. Fecundity was also significantly related to phloem thickness, which was greater in lodgepole pine. Offspring were larger from whitebark brood hosts than from lodgepole, regardless of their parents’ natal host. Finally, sex-ratio was closer to 1:1 in lodgepole than in whitebark brood hosts. We conclude that host species affects life history of mountain pine beetle with consequences for individual beetle fitness.

Over the last two decades, mountain pine beetle (Dendroctonus ponderosae) populations reached epidemic levels across much of western North America, including high elevations where cool temperatures previously limited beetle persistence. Many high-elevation pine species are susceptible hosts and experienced high levels of mortality in recent outbreaks, but co-occurring Great Basin bristlecone pine (Pinus longaeva), the longest-living non-clonal organism, were not attacked. I assessed Great Basin bristlecone pine resistance to mountain pine beetle by evaluating mountain pine beetle host selection behavior and reproductive success in this species. To evaluate mountain pine beetle host selection preference for Great Basin bristlecone pine, I used no-choice 48-hour attack box experiments that confined pioneering female beetles onto pairs of living Great Basin bristlecone and limber pine (P. flexilis), a susceptible host tree species. To investigate the effect of induced tree defenses on host selection behavior, I repeated the tests on paired sections of Great Basin bristlecone and limber pines that had been recently cut, thereby removing their capacity for induced defensive reactions to an attack. Mountain pine beetles avoided Great Basin bristlecone pine relative to limber pine, suggesting that Great Basin bristlecone pine has a high level of resistance to mountain pine beetle due at least in part to stimuli that repel pioneering females from initiating attacks, even when induced defenses are compromised. To investigate mountain pine beetle reproductive success in Great Basin bristlecone pine, I compared the mating success, fecundity, and brood production of mountain pine beetle parents placed in cut Great Basin bristlecone pine bolts with that of mountain pine beetles placed in cut bolts of limber pine and lodgepole pine (P. contorta), two susceptible species. Initial reproductive development was similar in all three tree species, but nearly all brood in Great Basin bristlecone pine died before emerging. The extensive offspring mortality observed in Great Basin bristlecone pine may be a key evolutionary driver behind mountain pine beetle aversion to the species. These findings suggest that Great Basin bristlecone pine is a highly resistant species with low vulnerability to climate-driven increases in mountain pine beetle outbreaks at high elevations.

Climate change is likely to disrupt the timing of developmental events (phenology) in insect populations in which development time is largely determined by temperature. Shifting phenology puts insects at risk of being exposed to seasonal weather extremes during sensitive life stages and losing synchrony with biotic resources. Additionally, warming may result in loss of developmental synchronization within a population, making it difficult to find mates or mount mass attacks against well-defended resources at low population densities. It is unknown whether genetic evolution of development time can occur rapidly enough to moderate these effects. The work presented here is largely motivated by the need to understand how mountain pine beetle (MPB) populations will respond to climate change. MPB is an important forest pest from both an economic and ecological perspective, because MPB outbreaks often result in massive timber loss. Recent MPB range expansion and increased outbreak frequency have been linked to warming temperatures. We present a novel approach to modeling the evolution of phenology by allowing the parameters of a phenology model to evolve in response to selection on emergence time and density. We also develop a temperature-dependent phenology model for MPB that accounts for multiple types of developmental variation: variation that persists throughout a life stage, random variation, and variation due to the MPB oviposition mechanism. This model is parameterized using MPB development time data from constant temperature laboratory experiments. We use Laplace's method to approximate steady distributions of the evolution model under stable temperatures. Here the mean phenotype allows for parents and offspring to be oviposited at exactly the same time of year in consecutive generations. These results are verified numerically for both MPB and a two-stage model insect. The evolution model is also applied to investigate the evolution of phenology for MPB and the two-stage model insect under warming temperatures. The model predicts that local populations can only adapt to climate change if development time can adapt so that individuals can complete exactly one generation per year and if the rate of temperature change is moderate.

High-dose verbenone, verbenone plus nonhost volatiles (NHVs), and both semiochemicals in combination with aggregant-baited funnel traps were tested for stand- level protection against mountain pine beetle attack for two consecutive years (2004-2005) at three seral high elevation whitebark pine sites in the Greater Yellowstone Ecosystem. In 2004, two 0.25-hectare treatments comprised of 25 high-dose verbenone pouches or verbenone pouches combined with single baited funnel traps were tested in a push-pull strategy. In 2005, 25 high-dose verbenone and 25 NHV pouches, or verbenone and NHV in combination with baited funnel trap clusters were tested. In both years, treatments were compared to 0.25-hectare control plots with no semiochemicals or funnel traps. The proportion of trees attacked by mountain pine beetle in treated plots was significantly reduced, when compared to control plots, at only one site treated with verbenone in 2004, and at only one site in 2005. High-dose verbenone alone, verbenone and NHVs, and both semiochemicals combined with baited funnel traps in a push-pull strategy did not consistently reduce the proportion of mountain pine beetle attacked trees relative to control plots. No covariates tested, including stand density, beetle population size, or tree size were consistently significant in explaining proportion of trees attacked.

The mountain pine beetle (MPB, Dendroctonus ponderosae), a tree-killing bark beetle, has historically been part of the normal disturbance regime in lodgepole pine (Pinus contorta) forests. In recent years, warm winters and summers have allowed MPB populations to achieve synchronous emergence and successful attacks, resulting in widespread population outbreaks and resultant tree mortality across western North America. We develop an age-structured forest demographic model that incorporates temperature-dependent MPB infestations: the Susceptible-Infested-Juvenile (SIJ) model. Stability of fixed points is analyzed as a function of population growth rates, and indicates the existence of periodic outbreaks that intensify as growth rates increase. We devise analytical methods to predict outbreak severity and duration as well as outbreak return time. To assess the vulnerability of natural resources to climate change, we develop a thermally-driven mechanistic model to predict MPB population growth rates using a distributional model of beetle phenology in conjunction with criteria for successful tree colonization. The model uses projected daily minimum and maximum temperatures for the years 2025 to 2085 generated by three separate global climate models. Growth rates are calculated each year for an area defined by latitude range 42° N to 49° N and longitude range 108° W to 117° W on a Cartesian grid of approximately 4km resolution. Using these growth rates, we analyze how the optimal thermal window for beetle development is changing with respect to elevation as a result of climate change induced warming. We also use our combined model to evaluate if thermal regimes exist that would promote life cycle bivoltinism and discuss how yearly growth rates would change as a result. Outbreaks of MPB are largely driven by host tree stand demographics and spatial effects of beetle dispersal. We augment the SIJ model to account for the spatial effects of MPB dispersal throughout a forest landscape by coupling it with a Gaussian redistribution kernel. The new model generates a train of sustained solitary waves of infestation that move through a forest with constant speed. We convert the resulting integrodifference equation into a partial differential equation and search for travelling wave solutions. The resulting differential equation provides predictions of the shape of an outbreak wave profile and of peak infestation as functions of wave speed, which can be calculated analytically. These results culminate in the derivation of an explicit formula for predicting the severity of an outbreak based on the net reproductive rate of MPB and host searching efficiency.

This dissertation describes the development of CAMBIUM, an agent-based forest sector model for large-scale strategic analysis. This model is designed as a decision support tool for assessing the effect that changes in forest product demand and resource inventories can have on the structure and economic viability of the forest sector. CAMBIUM complements existing forest sector models by modeling aggregate product supply as an emergent property of individual companies’ production decisions and stand-level ecological processes. Modeling the forest products sector as a group of interacting autonomous agents makes it possible to introduce production capacity dynamics and the potential for mill insolvencies as factors in modeling the effects of market and forest inventory based disturbances. This thesis contains four main manuscripts. In the first manuscript I develop and test a dispersal algorithm that projects aggregated forest inventory information onto a lattice grid. This method can be used to generate ecologically and statistically consistent datasets where high-quality spatial inventory data is otherwise unavailable. The second manuscript utilizes this dataset in developing a provincial-level resource dynamics model for assessing the timber supply effects of introducing weevil-resistant spruce. This model employs a stand-level approach to simulating weevil infestation and associated merchantable volume losses. Provincial-level impacts are determined by simulating harvest activities over a 350 year time horizon. In the third manuscript I shift the focus to interactions between forest companies. I analyze the effects of strategic decisions on sector structure by developing CAMBIUM as an agent-based model of competition and industry structure evolution. The forest sector is modeled as a group of autonomous, interacting agents that evolve and compete within the limitations posed by resource inventories and product demand. In the final manuscript I calibrate CAMBIUM to current conditions in the British Columbia forest sector. Industry agents compete for roundwood inputs, as well as for profits in finished product markets for pulp, panel products, and lumber. To test the relevance and utility of this model, CAMBIUM is used to quantify the cumulative impacts of a market downturn for forest products and mountain pine beetle induced timber supply fluctuations on the structure of the forest sector.