Determining relative contributions of vegetation and topography to burn severity from LANDSAT imagery

Allocation Strategies of Carbon, Nitrogen, and Phosphorus at Species and Community Levels With Recovery After Wildfire

Plant stoichiometry and nutrient allocation can reflect a plant's adaptation to environmental nutrient changes. However, the allocation strategies of carbon (C), nitrogen (N), and phosphorus (P) between leaf and fine root in response to wildfire have been poorly studied. Our primary objective was to elucidate the trade-off of elemental allocation between above- and belowground parts in response to the soil nutrient changes after a wildfire. We explored the allocation sloping exponents of C, N, and P between leaf and fine root at the species and community levels at four recovery periods (year 2, 10, 20, and 30) after moderately severe wildfire and one unburned treatment in boreal forests in Great Xing'an Mountains, northeast China. Compared with the unburned treatment, leaf C concentration decreased and fine root C increased at year 2 after recovery. The leaf N concentration at year 10 after recovery was higher than that of unburned treatment. Plant growth tended to be limited by P concentration at year 10 after recovery. Nutrient allocation between leaf and fine root differed between species and community levels, especially in the early recovery periods (i.e., 2 and 10 years). At the community level, the nutrient concentrations of the leaf changed more as compared to that of the fine root at year 2 after recovery when the fine root nutrients changed more than those of the leaf. The different C, N, and P allocation strategies advanced the understanding of plant adaptation to soil nutrient changes during the postfire ecosystem restoration.

Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem

Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level.

Does Environment Filtering or Seed Limitation Determine Post-fire Forest Recovery Patterns in Boreal Larch Forests?

Wildfire is a primary natural disturbance in boreal forests, and post-fire vegetation recovery rate influences carbon, water, and energy exchange between the land and atmosphere in the region. Seed availability and environmental filtering are two important determinants in regulating post-fire vegetation recovery in boreal forests. Quantifying how these determinants change over time is helpful for understanding post-fire forest successional trajectory. Time series of remote sensing data offer considerable potential in monitoring the trajectory of post-fire vegetation recovery dynamics beyond current field surveys about structural attributes, which generally lack a temporal perspective across large burned areas. We used a time series of the normalized difference vegetation index (NDVI) and normalized difference shortwave infrared reflectance index (NDSWIR) derived from Landsat images to investigate post-fire dynamics in a Chinese boreal larch forest. An adjacent, unburned patch of a similar forest type and environmental conditions was selected as a control to separate interannual fluctuation in NDVI and NDSWIR caused by climate from changes due to wildfire. Temporal anomalies in NDVI and NDSWIR showed that more than 10 years were needed for ecosystems to recover to a pre-fire state. The boosted regression tree analysis showed that fire severity exerted a persistent, dominant influence on vegetation recovery during the early post-fire successional stage and explained more than 60% of variation in vegetation recovery, whereas distance to the nearest unburned area and environmental conditions exhibited a relatively small influence. This result indicated that the legacy effects of fire disturbance, which control seed availability for tree recruitment, would persist for decades. The influence of environmental filtering could increase with succession and could mitigate the initial heterogeneity in recovery caused by wildfire.

Eighteen-Year Farming Management Moderately Shapes the Soil Microbial Community Structure but Promotes Habitat-Specific Taxa

Soil microbes have critical influence on the productivity and sustainability of agricultural ecosystems, yet the magnitude and direction to which management practices affect the soil microbial community remain unclear. This work aimed to examine the impacts of three farming systems, conventional grain cropping (CON), organic grain cropping (ORG), and grain cropping-pasture rotation (ICL), on the soil microbial community structure and putative gene abundances of N transformations using high-throughput 16S rRNA gene and ITS sequencing approaches. Two additional systems, a forest plantation (PF) and an abandoned agricultural field subject to natural succession (SUC), were also included for better assessment of the soil microbial community in terms of variation scale and regulatory importance of management intensity vs. plant type. Farming systems significantly affected the biodiversity of soil fungi but not bacteria, with Shannon index being the lowest in ORG. Bacterial and fungal communities in three cropping systems clustered and separated from those in PF and SUC, suggesting that management practices as such played minor roles in shaping the soil microbial community compared to plant type (i.e., woody vs. herbaceous plants). However, management practices prominently regulated habitat-specific taxa. Lecanoromycetes, a class of Ascomycota accounted for ∼10% of total fungal population in ORG, but almost nil in the other four systems. ORG also enriched bacteria belonging to the phyla, Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, and Gemmatimonadetes. Further, PICRUSt predicted that N-cycle community compositions varied with farming systems; compared to CON, ORG and ICL were more divergent from PF and SUC. Soil pH, together with inorganic N, extractable organic C, and soil organic C:N ratio explained < 50% of the total variations in both bacterial and fungal communities. Our data indicates that while moderately affecting the overall structure of the soil microbial community, management practices, particularly fertilization and the source of N (synthetic vs. organic), were important in regulating the presence and abundance of habitat-specific taxa.

Beta Diversity Patterns of Post-fire Forests in Central Yunnan Plateau, Southwest China: Disturbances Intensify the Priority Effect in the Community Assembly

Post-fire succession is an ideal case for studying effects of disturbance on community assembly, and the key is to disentangle the contributions of assembly processes to the variation of community composition, namely beta diversity, and the contingent scales. The central Yunnan Plateau of Southwest China is characterized by monsoon related seasonal drought, and frequent forest fires. We sampled five fire sites burned in different years and a middle aged forest, measured species composition dissimilarity and its species turnover and nestedness components, within each fire site and across all sites. Results indicated species turnover as the primary component of beta diversity within all communities. There was no trend of change with year-since-fire (YSF) in beta diversity among early post-fire communities, but beta diversity in the middle aged community was significantly higher. Species turnover patterns across fire sites revealed a weak dispersal limit effect, which was stronger at lower than upper slope position for woody plants, and reverse for herbs. At the site scale, the species dissimilarity and turnover both enlarged with increasing slope position difference, especially in the middle-aged community, but the species nestedness had no consistent trend among sites, except a decreasing trend in the middle-aged forest. (Partial) Mantel tests indicated habitat filtering [primarily indicating total nitrogen (TN) and slope position] played a much stronger role than dispersal limit and YSF (indicating competition intensity) for the post-fire forest assembly at the landscape scale, for both woody and herbaceous layers. However, at the site scale, Mantel tests indicated a diminishing effect of soil nutrient filtering with increasing YSF, while effects of topography and spatial distance in the middle aged community was stronger. This divergence suggests the primary assembly mechanism gradually shift away from the soil constraint. While the seasonal drought and the mountain topography dominate the environmental legacy, our results imply that fires may reinforce a priority effect in the forests assembly in this region, by creating a habitat filtering (e.g., moisture and nitrogen limitation) effect on species composition in post-fire communities.

Emerging spatial patterns in Antarctic prokaryotes

Recent advances in knowledge of patterns of biogeography in terrestrial eukaryotic organisms have led to a fundamental paradigm shift in understanding of the controls and history of life on land in Antarctica, and its interactions over the long term with the glaciological and geological processes that have shaped the continent. However, while it has long been recognized that the terrestrial ecosystems of Antarctica are dominated by microbes and their processes, knowledge of microbial diversity and distributions has lagged far behind that of the macroscopic eukaryote organisms. Increasing human contact with and activity in the continent is leading to risks of biological contamination and change in a region whose isolation has protected it for millions of years at least; these risks may be particularly acute for microbial communities which have, as yet, received scant recognition and attention. Even a matter apparently as straightforward as Protected Area designation in Antarctica requires robust biodiversity data which, in most parts of the continent, remain almost completely unavailable. A range of important contributing factors mean that it is now timely to reconsider the state of knowledge of Antarctic terrestrial prokaryotes. Rapid advances in molecular biological approaches are increasingly demonstrating that bacterial diversity in Antarctica may be far greater than previously thought, and that there is overlap in the environmental controls affecting both Antarctic prokaryotic and eukaryotic communities. Bacterial dispersal mechanisms and colonization patterns remain largely unaddressed, although evidence for regional evolutionary differentiation is rapidly accruing and, with this, there is increasing appreciation of patterns in regional bacterial biogeography in this large part of the globe. In this review, we set out to describe the state of knowledge of Antarctic prokaryote diversity patterns, drawing analogy with those of eukaryote groups where appropriate. Based on our synthesis, it is clear that spatial patterns of Antarctic prokaryotes can be unique at local scales, while the limited evidence available to date supports the group exhibiting overall regional biogeographical patterns similar to the eukaryotes. We further consider the applicability of the concept of “functional redundancy” for the Antarctic microbial community and highlight the requirements for proper consideration of their important and distinctive roles in Antarctic terrestrial ecosystems.

Defining fire environment zones in the boreal forests of northeastern China

Fire activity in boreal forests will substantially increase with prolonged growing seasons under a warming climate. This trend poses challenges to managing fires in boreal forest landscapes. A fire environment zone map offers a basis for evaluating these fire-related problems and designing more effective fire management plans to improve the allocation of management resources across a landscape. Toward that goal, we identified three fire environment zones across boreal forest landscapes in northeastern China using analytical methods to identify spatial clustering of the environmental variables of climate, vegetation, topography, and human activity. The three fire environment zones were found to be in strong agreement with the spatial distributions of the historical fire data (occurrence, size, and frequency) for 1966-2005. This paper discusses how the resulting fire environment zone map can be used to guide forest fire management and fire regime prediction.

Development of standard fuel models in boreal forests of Northeast China through calibration and validation

Understanding the fire prediction capabilities of fuel models is vital to forest fire management. Various fuel models have been developed in the Great Xing'an Mountains in Northeast China. However, the performances of these fuel models have not been tested for historical occurrences of wildfires. Consequently, the applicability of these models requires further investigation. Thus, this paper aims to develop standard fuel models. Seven vegetation types were combined into three fuel models according to potential fire behaviors which were clustered using Euclidean distance algorithms. Fuel model parameter sensitivity was analyzed by the Morris screening method. Results showed that the fuel model parameters 1-hour time-lag loading, dead heat content, live heat content, 1-hour time-lag SAV(Surface Area-to-Volume), live shrub SAV, and fuel bed depth have high sensitivity. Two main sensitive fuel parameters: 1-hour time-lag loading and fuel bed depth, were determined as adjustment parameters because of their high spatio-temporal variability. The FARSITE model was then used to test the fire prediction capabilities of the combined fuel models (uncalibrated fuel models). FARSITE was shown to yield an unrealistic prediction of the historical fire. However, the calibrated fuel models significantly improved the capabilities of the fuel models to predict the actual fire with an accuracy of 89%. Validation results also showed that the model can estimate the actual fires with an accuracy exceeding 56% by using the calibrated fuel models. Therefore, these fuel models can be efficiently used to calculate fire behaviors, which can be helpful in forest fire management.