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As wildfires become larger and more severe across western North America, it grows increasingly important to understand how they will affect the biogeochemical processes influencing ecosystem recovery. Soil nitrogen (N) cycling is a key process constraining recovery rates. In addition to its direct responses to fire, N cycling can also respond to other post-fire transformations, including increases or decreases in microbial biomass, soil moisture, and pH. To examine the short-term effects of wildfire on belowground processes in the northern Sierra Nevada, we collected soil samples along a gradient from unburned to high fire severity over 10  months following a wildfire. This included immediate pre- and post-fire sampling for many variables at most sites. While season and soil moisture did not substantially alter pH, microbial biomass, net N mineralisation, and nitrification in unburned locations, they interacted with burn severity in complex ways to constrain N cycling after fire. In areas that burned, pH increased (at least initially) after fire, and there were non-monotonic changes in microbial biomass. Net N mineralisation also had variable responses to wetting in burned locations. These changes suggest burn severity and precipitation patterns can interact to alter N cycling rates following fire. 

Deciduous shrubs are expanding across the graminoid-dominated nutrient-poor arctic tundra. Absorptive root traits of shrubs are key determinants of nutrient acquisition strategy from tundra soils, but the variations of shrub root traits within and among common shrub genera across the arctic climatic gradient are not well resolved. Consequently, the impacts of arctic shrub expansion on belowground nutrient cycling remain largely unclear. Here, we collected roots from 170 plots of three commonly distributed shrub genera ( Alnus , Betula , and Salix ) and a widespread sedge ( Eriophorum vaginatum ) along a climatic gradient in northern Alaska. Absorptive root traits that are relevant to the strategy of plant nutrient acquisition were determined. The influence of aboveground dominant vegetation cover on the standing root biomass, root productivity, vertical rooting profile, as well as the soil nitrogen (N) pool in the active soil layer was examined. We found consistent root trait variation among arctic plant genera along the sampling transect. Alnus and Betula had relatively thicker and less branched, but more frequently ectomycorrhizal colonized absorptive roots than Salix , suggesting complementarity between root efficiency and ectomycorrhizal dependence among the co-existing shrubs. Shrub-dominated plots tended to have more productive absorptive roots than sedge-dominated plots. At the northern sites, deep absorptive roots (>20 cm depth) were more frequent in birch-dominated plots. We also found shrub roots extensively proliferated into the adjacent sedge-dominated plots. The soil N pool in the active layer generally decreased from south to north but did not vary among plots dominated by different shrub or sedge genera. Our results reveal diverse nutrient acquisition strategies and belowground impacts among different arctic shrubs, suggesting that further identifying the specific shrub genera in the tundra landscape will ultimately provide better predictions of belowground dynamics across the changing arctic. 

The formation of novel and disappeared climates between the last glacial maximum (LGM) and the present is important to consider to understand the expansion and contraction of species niches and distributions, as well as the formation and loss of communities and ecological interactions over time. Our choice in climate data resolution has the potential to complicate predictions of the ecological impacts of climate change, since climate varies from local to global scales and this spatial variation is reflected in climate data. To address this issue, we downscaled LGM and modern (1975–2005) 30‐year averaged climate data to 60‐m resolution for the entire state of Alaska for 10 different climate variables, and then upsampled each variable to coarser resolutions (60 m to 12 km). We modeled the distributions of novel and disappeared climates to evaluate the locations and fractional area of novel and disappeared climates for each of our climate variables and resolutions. Generally, novel and disappeared climates were located in southern Alaska, although there were cases where some disappeared climates existed within coastal and interior Alaska. Climate resolution affected the fractional area of novel and disappeared climates in three patterns: As the spatial resolution of climate became coarser, the fractional area of novel and disappeared climates (a) increased, (b) decreased, or (c) had no explainable relationship. Overall, we found the use of coarser climate data increased the fractional area of novel and disappeared climates due to decreased environmental variability and removal of climate extremes. Our results reinforce the importance of downscaling coarse climate data and suggest that studies analyzing the effects of climate change on ecosystems may overestimate or underestimate their conclusions when utilizing coarse climate data.

 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.