Summary: If carbon (C) sinks withdraw carbohydrates as they are transported along tree stems, carbohydrate availability may depend on local sink strength and distance from sources. Defenses, including monoterpenes – a major component of resin – limit the invasibility of pines. Since carbohydrate reserves fund monoterpene synthesis, we hypothesized that monoterpene concentrations in pine stems would decrease from the crown to the lower stem, and susceptibility to fungal infection would increase. Here, we measured carbohydrate and monoterpene concentrations along the stems of lodgepole pine trees (Pinus contorta var. latifolia) before inoculating with a blue‐stain fungus at different heights. After 6 wk, we assessed tree responses to fungal infection based on lesion length and carbohydrate mobilization. Concentrations of carbohydrates and monoterpenes in the phloem before inoculation decreased with distance from the crown, whereas lesion lengths after inoculation increased. However, trees mobilized sugars in response to fungal infection such that carbohydrate reserves near lesions were similar at all heights. Despite C mobilization, the lower stem was more vulnerable than the upper stem. Consistent with predictions based on sink–source relationships, vulnerability occurred where carbohydrates were less available, and likely resulted from C withdrawal by sinks higher in the supply chain.

In the low nutrient environment of conifer bark, subcortical beetles often carry symbiotic fungi that concentrate nutrients in host tissues. Although bark beetles are known to benefit from these symbioses, whether this is because they survive better in nutrient-rich phloem is unknown. After manipulating phloem nutrition by fertilizing lodgepole pine trees (Pinus contorta Douglas var. latifolia), we found bolts from fertilized trees to contain more living individuals, and especially more pupae and teneral adults than bolts from unfertilized trees at our southern site. At our northern site, we found that a larger proportion of mountain pine beetle (Dendroctonus ponderosae Hopkins) larvae built pupal chambers in bolts from fertilized trees than in bolts from unfertilized trees. The symbiotic fungi of the mountain pine beetle also responded to fertilization. Two mutualistic fungi of bark beetles, Grosmannia clavigera (Rob.-Jeffr. & R. W. Davidson) Zipfel, Z. W. de Beer, & M. J. Wingf. and Leptographium longiclavatum Lee, S., J. J. Kim, & C. Breuil, doubled the nitrogen concentrations near the point of infection in the phloem of fertilized trees. These fungi were less capable of concentrating nitrogen in unfertilized trees. Thus, the fungal symbionts of mountain pine beetle enhance phloem nutrition and likely mediate the beneficial effects of fertilization on the survival and development of mountain pine beetle larvae.

Bark beetle outbreaks can have serious consequences on forest ecosystem processes, biodiversity, forest structure and function, and economies. Thus, accurate and timely detection of bark beetle infestations in the early stage (known as green-attack detection) is crucial to mitigate the further impact, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that primarily rely on aerial surveys and field surveys, which can be impractical over vast areas. Existing approaches that exploit RS and ML/DL exhibit substantial diversity due to the wide range of factors involved. This article provides a comprehensive review of past and current advances in green-attack detection from three primary perspectives: bark beetle and host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. We parse existing literature based on multi- or hyperspectral analyses and distill their knowledge based on bark beetle species and attack phases with a primary emphasis on early stages of attacks, host trees, study regions, RS platforms and sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices, ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks and architectures. Although DL-based methods and the random forest algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared spectral regions, their effectiveness remains limited, and high uncertainties persist due to the subtle distinctions between healthy and attacked trees. To inspire novel solutions to these shortcomings, we delve into the principal challenges and opportunities from different perspectives, enabling a deeper understanding of the current state of research and guiding future research directions.

Most species that are negatively impacted when their densities are low aggregate to minimize this effect. Aggregation has the potential to change how Allee effects are expressed at the population level. We studied the interplay between aggregation and Allee effects in the mountain pine beetle (Dendroctonus ponderosaeHopkins), an irruptive bark beetle that aggregates to overcome tree defenses. By cooperating to surpass a critical number of attacks per tree, the mountain pine beetle is able to breach host defenses, oviposit, and reproduce. Mountain pine beetles and Hymenopteran parasitoids share some biological features, the most notable of which is obligatory host death as a consequence of parasitoid attack and development. We developed spatiotemporal models of mountain pine beetle dynamics that were based on the Nicholson–Bailey framework but which featured beetle aggregation and a tree‐level attack threshold. By fitting our models to data from a local mountain pine beetle outbreak, we demonstrate that due to aggregation, attack thresholds at the tree level can be overcome by a surprisingly low ratio of beetles per susceptible tree at the stand level. This results confirms the importance of considering aggregation in models of organisms that are subject to strong Allee effects.

Warmer climates are predicted to increase bark beetle outbreak frequency, severity, and range. Even in favorable climates, however, outbreaks can decelerate due to resource limitation, which necessitates the inclusion of competition for limited resources in analyses of climatic effects on populations. We evaluated several hypotheses of how climate impacts mountain pine beetle reproduction using an extensive 9‐year dataset, in which nearly 10,000 trees were sampled across a region of approximately 90,000 km², that was recently invaded by the mountain pine beetle in Alberta, Canada. Our analysis supports the hypothesis of a positive effect of warmer winter temperatures on mountain pine beetle overwinter survival and provides evidence that the increasing trend in minimum winter temperatures over time in North America is an important driver of increased mountain pine beetle reproduction across the region. Although we demonstrate a consistent effect of warmer minimum winter temperatures on mountain pine beetle reproductive rates that is evident at the landscape and regional scales, this effect is overwhelmed by the effect of competition for resources within trees at the site level. Our results suggest that detection of the effects of a warming climate on bark beetle populations at small spatial scales may be difficult without accounting for negative density dependence due to competition for resources.