TRIA-Net: 10 years of collaborative research on turning risk into action for the mountain pine beetle epidemic

James, Patrick M. A.; Huber, Dezene P. W.

doi:10.1139/cjfr-2019-0384

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…With appropriate funding and coordination, multi-disciplinary collaborations can be built into networks such as the Turning Risk Into Action for the Mountain Pine Beetle Epidemic network (TRIA network; http://tria-net.srv.ualberta.ca/ resteam/). This network involves different research specialists (e.g., genomicists, ecologists, socio-ecologists) who work together to predict mountain pine beetle dynamics and their social impacts (James and Huber 2019). Adopting an interdisciplinary approach is not only beneficial for large partnerships as small collaborations can also benefit from involving multiple academic disciplines.…”

Section: ) Build Diverse Teams That Are Flexible Cohesive and Supportivementioning

confidence: 99%

Bridging the divide between ecological forecasts and environmental decision making

et al. 2021

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The rate of human-induced environmental change continues to accelerate, stimulating the need for rapid and science-based decision making. The recent availability of cyberinfrastructure, open-source data and novel techniques has increased opportunities to use ecological forecasts to predict environmental change. But to effectively inform environmental decision making, forecasts should not only be reliable, but should also be designed to address the needs of decision makers with their assumptions, uncertainties, and results clearly communicated. To help researchers better integrate forecasting into decision making, we outline ten practical guidelines to help navigate the interdisciplinary and collaborative nature of forecasting in social-ecological systems. Some guidelines focus on improving forecasting skills, including how to build better models, account for uncertainties and use technologies to improve their utility, while others are developed to facilitate the integration of forecasts with decision making, including how to form effective partnerships and how to design forecasts relevant to the specific decision being addressed. We hope these guidelines help researchers make forecasts more accurate, precise, transparent, and most pressingly, useful for informing environmental decisions.

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Section: ) Build Diverse Teams That Are Flexible Cohesive and Supportivementioning

confidence: 99%

Bridging the divide between ecological forecasts and environmental decision making

et al. 2021

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“…Eastward expansion has facilitated infestations within populations of lodgepole pine, jack pine (P. banksiana), and their hybrid (P. contorta x P. banksiana); hosts with limited defensive capacity due to a lack of coevolutionary interactions with MPB (7)(8)(9)(10). MPB poses an alarming continental threat, as jack pine is the predominant pine species that extends across the boreal forest to the Great Lakes and the Atlantic Coast of Canada (10)(11)(12)(13)(14). Efforts to mitigate the spread of MPB have been hampered by the lack of effective control methods (15).…”

Section: Introductionmentioning

confidence: 99%

Genomic virulence features of Beauveria bassiana as a biocontrol agent for the mountain pine beetle population

Fernandez

Ritland

et al. 2023

Preprint

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Background: The mountain pine beetle, Dendroctonus ponderosae, is an irruptive bark beetle that causes extensive mortality to many pine species within the forests of western North America. Driven by climate change and wildfire suppression, a recent MPB outbreak has spread across more than 18 million hectares, including areas to the east of the Rocky Mountains that comprise populations and species of pines not previously affected. Despite its impacts, there are few tactics available to control MPB populations. Beauveria bassiana is an entomopathogenic fungus used as a biological agent in agriculture and forestry and has potential as a management tactic for the mountain pine beetle population. This work investigates the phenotypic and genomic variation between B. bassianastrains to identify optimal strains against a specific insect. Results: Using comparative genome and transcriptome analyses of eight B. bassiana isolates, we have identified the genetic basis of virulence, which includes oosporein production. Genes unique to the more virulent strains included functions in biosynthesis of mycotoxins, membrane transporters, and transcription factors. Significant differential expression of genes related to virulence, transmembrane transport, and stress response was identified between the different strains, as well as up to nine-fold upregulation of genes involved in the biosynthesis of oosporein. Differential correlation analysis revealed transcription factors that may be involved in regulating oosporein production. Conclusion: This study provides a foundation for the selection and/or engineering of the most effect strain of B. bassiana for the biological control of mountain pine beetle and other insect pests populations.

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“…In the province of British Columbia (BC), Canada, an unprecedented insect outbreak of mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins) began in the early 1990s and reached a peak between 2005 and 2006, which facilitated a massive migration of beetles into the province of Alberta 2 of 23 (AB) [9,12,14]. The cumulative forest area in BC alone that has been attacked by the MPB, since the ongoing outbreak that began in 1990, is estimated to be over 25 million hectares [15,16]. An indigenous insect to western North American forests, the MPB is a bark beetle that feeds mainly on lodgepole pine (Pinus contorta var.…”

Section: Introductionmentioning

confidence: 99%

Integrating Neighborhood Effect and Supervised Machine Learning Techniques to Model and Simulate Forest Insect Outbreaks in British Columbia, Canada

Harati

Pérez

Molowny‐Horas

2020

Forests

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Background and Objectives: Modelling and simulation of forest land cover change due to epidemic insect outbreaks are powerful tools that can be used in planning and preparing strategies for forest management. In this study, we propose an integrative approach to model land cover changes at a provincial level, using as a study case the simulation of the spatiotemporal dynamics of mountain pine beetle (MPB) infestation over the lodgepole pine forest of British Columbia (BC), Canada. This paper aims to simulate land cover change by applying supervised machine learning techniques to maps of MPB-driven deforestation. Materials and Methods: We used a 16-year series (1999–2014) of spatial information on annual mortality of pine trees due to MPB attacks, provided by the BC Ministry of Forests. We used elevation, aspect, slope, ruggedness, and weighted neighborhood of infestation as predictors. We implemented (a) generalized linear regression (GLM), and (b) random forest (RF) algorithms to simulate forestland cover changes due to MPB between 2005 and 2014. To optimize the ability of our models to predict MPB infestation in 2020, a cross-validation procedure was implemented. Results: Simulating infestations from 2008 to 2014, RF algorithms produced less error than GLM. Our simulations for the year 2020 confirmed the predictions from the BC Ministry of Forest by forecasting a slower rate of spread in future MPB infestations in the province. Conclusions: Integrating neighborhood effects as variables in model calibration allows spatiotemporal complexities to be simulated.

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TRIA-Net: 10 years of collaborative research on turning risk into action for the mountain pine beetle epidemic

Cited by 5 publications

References 11 publications

Bridging the divide between ecological forecasts and environmental decision making

Bridging the divide between ecological forecasts and environmental decision making

Genomic virulence features of Beauveria bassiana as a biocontrol agent for the mountain pine beetle population

Integrating Neighborhood Effect and Supervised Machine Learning Techniques to Model and Simulate Forest Insect Outbreaks in British Columbia, Canada

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