Qiaoqi Sun’s research while affiliated with Northeast Forestry University and other places

The occurrence, frequency, and severity of drought are accelerating due to global warming. Understanding the vulnerability of plantation forests to climate change, particularly to drought events, is critical to revealing the underlying mechanisms of tree resilience, recovery, and acclimation, which are important for plantation management. How the stand age affects the climate sensitivity of tree growth, as well as the direction, magnitude, and duration of the drought legacy, in plantation forests in northeast China is still unclear. In this study, we used MODIS-derived NDVI time series with gridded climate data from 2000 to 2020 to fill this knowledge gap. The selected plantation forests were dominated by four coniferous species: Korean pine (Pinus koraiensis), Scots pine (Pinus sylvestris), Japanese larch (Larix kaempferi), and Dahurian larch (Larix gmelinii). The results show that the climate sensitivity of tree growth differed among species and age groups. The growth of Korean pine and Scots pine was mostly dependent upon precipitation, while the growth of Japanese larch and Dahurian larch was determined primarily by temperature. Old Japanese larch (21–40 years) and Dahurian larch trees (31–60 years) were more sensitive to temperature and precipitation than young conspecifics, whereas old Korean pine (41–60 years) and Scots pine (31–60 years) were less sensitive to precipitation and temperature than young conspecifics. Furthermore, the legacy of drought lasted one year for Korean pine, Japanese larch, and Dahurian larch and over three years for Scots pine. Old trees were more severely affected by drought, particularly Scots pine and Dahurian larch. The findings of the study can help improve plantation forest management for better adaptation to future climate change.

Inter-annual variability in growing season temperature and precipitation, together with snow coverage duration, determine vegetation growth in boreal ecosystems. However, little is known about the impact of concurrent and antecedent climate, particularly snow cover duration, on vegetation growth in a boreal riparian forest. Additionally, significant uncertainty exists regarding whether the distance to a river (as a proxy of groundwater availability) further modifies these climatic legacy effects on vegetation growth. To fill this knowledge gap, we quantified the responses of different vegetation types (shrub, deciduous coniferous and broadleaf forests) to concurrent and antecedent climate variables in a boreal riparian forest, and further determined the magnitude and duration of climate legacies in relation to distance to a river, using MODIS-derived NDVI time series with gridded climate data from 2001 to 2020. Results showed that higher temperature and precipitation and longer snow cover duration increased vegetation growth. For deciduous coniferous forests and broadleaf forests, the duration of temperature legacy was about one year, precipitation legacy about two years and snow cover duration legacy was 3 to 4 years. Further, distance to a river modified the concurrent and antecedent temperature and snow cover duration legacy effects on vegetation growth, but not that of precipitation. Specifically, temperature and snow cover duration legacies were shorter at the sites near a river compared to sites at greater distance to a river. Our research highlights the importance of snow cover duration on vegetation growth and that closeness to a river can buffer adverse climate impacts by shortening the strength and duration of climate legacies in a boreal riparian forest.

Climate change has driven an increase in the frequency and severity of fires in Eurasian boreal forests. A growing number of field studies have linked the change in fire regime to post-fire recruitment failure and permanent forest loss. In this study we used four burned area and two forest loss datasets to calculate the landscape-scale fire return interval (FRI) and associated risk of permanent forest loss. We then used machine learning to predict how the FRI will change under a high emissions scenario (SSP3–7.0) by the end of the century. We found that there are currently 133,000 km² forest at high, or extreme, risk of fire-induced forest loss, with a further 3 M km² at risk by the end of the century. This has the potential to degrade or destroy some of the largest remaining intact forests in the world, negatively impact the health and economic wellbeing of people living in the region, as well as accelerate global climate change.

Climate change has driven an increase in the frequency and severity of fires in Eurasian boreal forests. A growing number of field studies have linked the change in fire regime to post-fire recruitment failure and permanent forest loss. In this study we used four burnt area and two forest loss datasets to calculate the landscape-scale fire return interval (FRI) and associated risk of permanent forest loss. We then used machine learning to predict how the FRI will change under a high emissions scenario (SSP3-7.0) by the end of the century. We found that there is currently 133 000 km2 at high, or extreme, risk of fire-induced forest loss, with a further 3 M km2 at risk by the end of the century. This has the potential to degrade or destroy some of the largest remaining intact forests in the world, negatively impact the health and economic wellbeing of people living in the region, as well as accelerate global climate change.

Climate change and land-use are driving large changes in forest ecosystems around the globe. In the boreal biome it is likely that increases in temperature and the associated lengthening of the growing season will cause the forest to expand into the northern tundra and upwards in elevation, whilst potentially contracting at its southern limits. This increase in temperature is also driving an increase in the frequency and severity of boreal forest fires. A growing number of studies have observed the failure of forest species to re-establish after a stand-replacing fire event, which results in the shift to a non-forested ecosystem. In this chapter, this process is referred to as post-fire recruitment failure. We provide multiple lines of evidence for boreal forests, and more specifically for southern Siberia forests, that a possible regional tipping point is unfolding, which could lead to the rapid replacement of large areas of forest ecosystems with low-stature non-forest ecosystems. This change would come with significant consequences for the carbon balance, surface albedo and the resulting altered energy balance.

Prolonged dry periods and increased temperatures that result from anthropogenic climate change have been shown to increase the frequency and severity of wildfires in the boreal region. There is growing evidence that such changes in fire regime can reduce forest resilience and drive shifts in post-fire plant successional trajectories. The response of post-fire vegetation communities to climate variability is under-studied, despite being a critical phase determining the ultimate successional conclusion. This study investigated the responses of post-fire recruited species to climate change and inter-annual variability at 16 study sites that experienced high-severity fire events, mostly in early 2000, within the Scots pine forest-steppe zone of southeastern Siberia, Russia. These sites were originally dominated by Scots pine, and by 2018, they were recruited by different successional species. Additionally, three mature Scots pine stands were included for comparison. A Bayesian Additive Regression Trees (BART) approach was used to model the relationship between Landsat-derived Normalized Difference Vegetation Index (NDVI) time series, temperature and precipitation in the 15 years after a stand-replacing fire. Using the resulting BART models, together with six projected climate scenarios with increased temperature and enhanced inner-annual precipitation variability, we simulated NDVI at 5-year intervals for 15 years post-fire. Our results show that the BART models performed well, with in-sample Pseudo-R2 varying from 0.49 to 0.95 for fire-disturbed sites. Increased temperature enhanced greenness across all sites and across all three time periods since fires, exhibiting a positive feedback in a warming environment. Repeatedly dry spring periods reduced NDVI at all the sites and wetter summer periods following such dry springs could not compensate for this, indicating that a prolonged dry spring has a strong impact consistently over the entire early developmental stages from the initial 5 years to 15 years post-fire. Further, young forests showed higher climate sensitivity compared to the mature forest, irrespective of species and projected climatic conditions. Our findings suggest that a dry spring not only increases fire risk, but also delays recovery of boreal forests in southern Siberia. It also highlights the importance of changing rainfall seasonality as well as total rainfall in a changing climate for post-fire recovery of forest.