Potential changes in forest composition could reduce impacts of climate change on boreal wildfires

There is general consensus that wildfires in boreal forests will increase throughout this century in response to more severe and frequent drought conditions induced by climate change. However, prediction models generally assume that the vegetation component will remain static over the next few decades. As deciduous species are less flammable than conifer species, it is reasonable to believe that a potential expansion of deciduous species in boreal forests, either occurring naturally or through landscape management, could offset some of the impacts of climate change on the occurrence of boreal wildfires. The objective of this study was to determine the potential of this offsetting effect through a simulation experiment conducted in eastern boreal North America. Predictions of future fire activity were made using multivariate adaptive regression splines (MARS) with fire behavior indices and ecological niche models as predictor variables so as to take into account the effects of changing climate and tree distribution on fire activity. A regional climate model (RCM) was used for predictions of future fire risk conditions. The experiment was conducted under two tree dispersal scenarios: the status quo scenario, in which the distribution of forest types does not differ from the present one, and the unlimited dispersal scenario, which allows forest types to expand their range to fully occupy their climatic niche. Our results show that future warming will create climate conditions that are more prone to fire occurrence. However, unlimited dispersal of southern restricted deciduous species could reduce the impact of climate change on future fire occurrence. Hence, the use of deciduous species could be a good option for an efficient strategic fire mitigation strategy aimed at reducing fire propagation in coniferous landscapes and increasing public safety in remote populated areas of eastern boreal Canada under climate change

The CC-Bio Project: Studying the Effects of Climate Change on Quebec Biodiversity

Anticipating the effects of climate change on biodiversity is now critical for managing wild species and ecosystems. Climate change is a global driver and thus affects biodiversity globally. However, land-use planners and natural resource managers need regional or even local predictions. This provides scientists with formidable challenges given the poor documentation of biodiversity and its complex relationships with climate. We are approaching this problem in Quebec, Canada, through the CC-Bio Project (http://cc‑bio.uqar.ca/), using a boundary organization as a catalyst for team work involving climate modelers, biologists, naturalists, and biodiversity managers. In this paper we present the CC-Bio Project and its general approach, some preliminary results, the emerging hypothesis of the northern biodiversity paradox (a potential increase of biodiversity in northern ecosystems due to climate change), and an early assessment of the conservation implications generated by our team work

Dominant forest tree species are potentially vulnerable to climate change over large portions of their range even at high latitudes

Projecting suitable conditions for a species as a function of future climate provides a reasonable, although admittedly imperfect, spatially explicit estimate of species vulnerability associated with climate change. Projections emphasizing range shifts at continental scale, however, can mask contrasting patterns at local or regional scale where management and policy decisions are made. Moreover, models usually show potential for areas to become climatically unsuitable, remain suitable, or become suitable for a particular species with climate change, but each of these outcomes raises markedly different ecological and management issues. Managing forest decline at sites where climatic stress is projected to increase is likely to be the most immediate challenge resulting from climate change. Here we assess habitat suitability with climate change for five dominant tree species of eastern North American forests, focusing on areas of greatest vulnerability (loss of suitability in the baseline range) in Quebec (Canada) rather than opportunities (increase in suitability). Results show that these species are at risk of maladaptation over a remarkably large proportion of their baseline range. Depending on species, 5–21% of currently climatically suitable habitats are projected to be at risk of becoming unsuitable. This suggests that species that have traditionally defined whole regional vegetation assemblages could become less adapted to these regions, with significant impact on ecosystems and forest economy. In spite of their well-recognised limitations and the uncertainty that remains, regionally-explicit risk assessment approaches remain one of the best options to convey that message and the need for climate policies and forest management adaptation strategies

Appendix A. Climate model and emission scenario ranks based on the ratio of mean seasonal Drought Code index of 2071–2100 to 1961–1990.

Climate model and emission scenario ranks based on the ratio of mean seasonal Drought Code index of 2071–2100 to 1961–1990

Occurrences data for three Northeastern-American tree species

Occurrences data for three Northeastern-American tree specie

Description of the climate change scenarios used in this study.

<p>Description of the climate change scenarios used in this study.</p

Projected climate for three biologically-relevant variables.

<p>Graphs show probability density functions of projected climate for mean annual temperature (first column), total annual precipitation (second column) and useful precipitation (third column). The 27 climate change scenarios are plotted as gray lines. The solid and dashed black lines represent the 10^th percentile values (top row), the average values (mid row), and the 90^th percentile values (bottom row) calculated on each cell across the 27 climate change scenarios (solid lines) or the six climate change scenarios selected by the k-means algorithm (dashed lines).</p