Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone

Abstract

Previous empirical work has shown that feedbacks between fire severity, soil organic layer thickness, tree recruitment, and forest growth are important factors controlling carbon accumulation after fire disturbance. However, current boreal forest models inadequately simulate this feedback. We address this deficiency by updating the ED2 model to include a dynamic feedback between soil organic layer thickness, tree recruitment, and forest growth. The model is validated against observations spanning monthly to centennial time scales and ranging from Alaska to Quebec. We then quantify differences in forest development after fire disturbance resulting from changes in soil organic layer accumulation, temperature, nitrogen availability, and atmospheric CO 2 . First, we find that ED2 accurately reproduces observations when a dynamic soil organic layer is included. Second, simulations indicate that the presence of a thick soil organic layer after a mild fire disturbance decreases decomposition and productivity. The combination of the biological and physical effects increases or decreases total ecosystem carbon depending on local conditions. Third, with a 48C temperature increase, some forests transition from undergoing succession to needleleaf forests to recruiting multiple cohorts of broadleaf trees, decreasing total ecosystem carbon by $40% after 300 years. However, the presence of a thick soil organic layer due to a persistently mild fire regime can prevent this transition and mediate carbon losses even under warmer temperatures. Fourth, nitrogen availability regulates successional dynamics; broadleaf species are less competitive with needleleaf trees under low nitrogen regimes. Fifth, the boreal forest shows additional short-term capacity for carbon sequestration as atmospheric CO 2 increases.

Differential declines in Alaskan boreal forest vitality related to climate and competition

“…These disparate changes suggest that boreal forest responses to climate change may strongly depend on the current state of the system, including current ecosystem composition. For example, temperature sensitivity of spring tree growth, water use, and successional strategy vary dramatically between the dominant angiosperm and gymnosperm species (Drobyshev, Gewehr, Berninger, Bergeron, & Mcglone, ; Euskirchen, Carman, & Mcguire, ; Hollingsworth, Johnstone, Bernhardt, & Chapin, ; Johnstone & Chapin, ; Trugman et al., ; Young‐Robertson, Bolton, Bhatt, Cristobal, & Thoman, ). Site‐level studies have shown that angiosperm water use greatly exceeds that of gymnosperm species in the Alaskan boreal forest, such that angiosperm species consume >20% of total snowmelt water in comparison to the <1% associated with gymnosperm species (Young‐Robertson et al., ).…”

Assessing the role of megafauna in tropical forest ecosystems and biogeochemical cycles – the potential of vegetation models

“…We have proposed VMs as an additional tool to further our knowledge on megafauna ecology and overcome some Example 5 -key process: Nutrient redistribution, availability, and resorption -life stages: from seed to adult Megafauna can interact with nutrients in different ways by: redistributing nutrients across concentration gradients, accelerating nutrient cycling and making nutrients readily available (labile nutrients), and changing nutrient resorption in leaves. Nutrient cycling in VMs is an area of recent development, particularly for nitrogen and phosphorus (Zaehle et al 2010, Smith et al 2014, Trugman et al 2016, Goll et al 2017. So far, these models have been used for simulations at the global scale or for non-tropical ecosystems.…”

Linking tree physiological constraints with predictions of carbon and water fluxes at an old‐growth coniferous forest

“…As with other ED2 studies (Medvigy and Moorcroft , Trugman et al. ), we initialized the simulation using forest inventory data (Shaw et al. ).…”

“…Recent enhancements to ED2 include a mechanistic representation of water-limited photosynthesis, where the model tracks leaf and stem water potential and used it to solve for root zone water uptake, simulate transport of water vertically through the sapwood, and calculate ultimate transpiration rates. The hydrologically enhanced version has been shown to accurately capture ecosystem dynamics at diverse locations around the globe (Trugman et al 2016.…”