Forecasting boreal vegetation dynamics: understory plants, state changes and biome shifts

A working group of the Canadian Institute of Ecology and Evolution. Summer/Fall 2019.

Participants: Steven Cumming, Stephen Mayor, Jenifer Baltzer, Josie Hughes, Nicholas Coops, Alison Munson, Florent Mouillot, Merritt Turetsky, Gordon McNickle, Eliot McIntire, Allan Carroll

Project Summary

Many Canadians are concerned about the future of our boreal forests, the wildlife that they support, the communities established there, and the resources and wealth that they export to the majority who live beyond its southern fringes. These forests are almost all on public lands; where large private sector firms operate, for example on forest management areas, they do so under license from the Crown. All our levels government share responsibility for the management and conservation of these lands. To meet this responsibility, they need to forecast what the forests will “look like” in the future, if one action or another is taken. To be specific, they need to forecast detailed maps of vegetation patterns, not just of trees, but also shrubs, grasses, mosses and lichens. This “ecological forecasting” would be difficult enough under historical conditions, but those conditions no longer apply. Climate warming is expected to lead fairly soon to substantial reorganisations of the plant communities, including so-called biome shifts where, for example, forest is replaced by grassland. These changes are hard to predict from knowledge of present conditions alone. What is required are detailed mechanistic models of the processes driving historical and future conditions, including how the surface vegetation and soils interact with the atmosphere and the climate system. Such models exist, but not at the spatial resolution necessary for many problems in management and conservation, such as sustaining boreal caribou, migratory birds, or northern communities under risk of fire. We propose to A) work out in detail what ecological forecasting capability is needed to support decision-making; B) identify the critical gaps in data or scientific knowledge that prevent us from making these forecasts; and C) develop a program of research and development that could fill these gaps over the next 5 to 10 years.

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