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While the relationship between genetic diversity and plant productivity has been established for many species, it is unclear whether environmental conditions and biotic associations alter the nature of the relationship. To address this, we investigated the interactive effects of genotypic diversity, drought and mycorrhizal association on plant productivity and plant traits. Our mesocosm study was set up at the Konza Prairie Biological Research Station, located in the south of Manhattan, Kansas. Andropogon gerardii, the focal species for our study, was planted in two levels of genotypic richness treatment: monoculture or three-genotype polyculture. A rainout shelter was constructed over half of the experimental area to impose a drought and Thiophanate-methyl fungicide was used to suppress arbuscular mycorrhizal fungi in selected pots within each genotypic richness and drought treatment. Genotypic richness and mycorrhizal association did not affect above-ground biomass of A. gerardii. Drought differentially affected the above-ground biomass, the number of flowers and bolts of A. gerardii genotypes, and the biomass and the functional traits also differed for monoculture versus polyculture. Our results suggest that drought and genotypic richness can have variable outcomes for different genotypes of a plant species.

 

Wildfire frequency and extent is increasing throughout the boreal forest-tundra ecotone as climate warms. Understanding the impacts of wildfire throughout this ecotone is required to make predictions of the rate and magnitude of changes in boreal-tundra landcover, its future flammability, and associated feedbacks to the global carbon (C) cycle and climate. We studied 48 sites spanning a gradient from tundra to low-density spruce stands that were burned in an extensive 2013 wildfire on the north slope of the Alaska Range in Denali National Park and Preserve, central Alaska. We assessed wildfire severity and C emissions, and determined the impacts of severity on understory vegetation composition, conifer tree recruitment, and active layer thickness (ALT). We also assessed conifer seed rain and used a seeding experiment to determine factors controlling post-fire tree regeneration. We found that an average of 2.18 ± 1.13 Kg C m -2 was emitted from this fire, almost 95% of which came from burning of the organic soil. On average, burn depth of the organic soil was 10.6 ± 4.5 cm and both burn depth and total C combusted increased with pre-fire conifer density. Sites with higher pre-fire conifer density were also located at warmer and drier landscape positions and associated with increased ALT post-fire, greater changes in pre- and post-fire understory vegetation communities, and higher post-fire boreal tree recruitment. Our seed rain observations and seeding experiment indicate that the recruitment potential of conifer trees is limited by seed availability in this forest-tundra ecotone. We conclude that the expected climate-induced forest infilling (i.e. increased density) at the forest-tundra ecotone could increase fire severity, but this infilling is unlikely to occur without increases in the availability of viable seed. 

Coarse woody debris (CWD) is a significant component of the forest biomass pool; hence a model is warranted to predict CWD decomposition and its role in forest carbon (C) and nutrient cycling under varying management and climatic conditions. A process-based model, CWDDAT (Coarse Woody Debris Decomposition Assessment Tool) was calibrated and validated using data from the FACE (Free Air Carbon Dioxide Enrichment) Wood Decomposition Experiment utilizing pine ( Pinus taeda ), aspen ( Populous tremuloides ) and birch ( Betula papyrifera ) on nine Experimental Forests (EF) covering a range of climate, hydrology, and soil conditions across the continental USA. The model predictions were evaluated against measured FACE log mass loss over 6 years. Four widely applied metrics of model performance demonstrated that the CWDDAT model can accurately predict CWD decomposition. The R 2 (squared Pearson’s correlation coefficient) between the simulation and measurement was 0.80 for the model calibration and 0.82 for the model validation ( P <0.01). The predicted mean mass loss from all logs was 5.4% lower than the measured mass loss and 1.4% lower than the calculated loss. The model was also used to assess the decomposition of mixed pine-hardwood CWD produced by Hurricane Hugo in 1989 on the Santee Experimental Forest in South Carolina, USA. The simulation reflected rapid CWD decomposition of the forest in this subtropical setting. The predicted dissolved organic carbon (DOC) derived from the CWD decomposition and incorporated into the mineral soil averaged 1.01 g C m -2 y -1 over the 30 years. The main agents for CWD mass loss were fungi (72.0%) and termites (24.5%), the remainder was attributed to a mix of other wood decomposers. These findings demonstrate the applicability of CWDDAT for large-scale assessments of CWD dynamics, and fine-scale considerations regarding the fate of CWD carbon. 

Coarse woody debris (CWD) is an important component in forests, hosting a variety of organisms that have critical roles in nutrient cycling and carbon (C) storage. We developed a process-based model using literature, field observations, and expert knowledge to assess woody debris decomposition in forests and the movement of wood C into the soil and atmosphere. The sensitivity analysis was conducted against the primary ecological drivers (wood properties and ambient conditions) used as model inputs. The analysis used eighty-nine climate datasets from North America, from tropical (14.2° N) to boreal (65.0° N) zones, with large ranges in annual mean temperature (26.5°C in tropical to -11.8°C in boreal), annual precipitation (6,143 to 181 mm), annual snowfall (0 to 612 kg m -2 ), and altitude (3 to 2,824 m above mean see level). The sensitivity analysis showed that CWD decomposition was strongly affected by climate, geographical location and altitude, which together regulate the activity of both microbial and invertebrate wood-decomposers. CWD decomposition rate increased with increments in temperature and precipitation, but decreased with increases in latitude and altitude. CWD decomposition was also sensitive to wood size, density, position (standing vs downed), and tree species. The sensitivity analysis showed that fungi are the most important decomposers of woody debris, accounting for over 50% mass loss in nearly all climatic zones in North America. The model includes invertebrate decomposers, focusing mostly on termites, which can have an important role in CWD decomposition in tropical and some subtropical regions. The role of termites in woody debris decomposition varied widely, between 0 and 40%, from temperate areas to tropical regions. Woody debris decomposition rates simulated for eighty-nine locations in North America were within the published range of woody debris decomposition rates for regions in northern hemisphere from 1.6° N to 68.3° N and in Australia. 

Abstract Aims Determining the ecological consequences of interactions between slow changes in long-term climate means and amplified variability in climate is an important research frontier in plant ecology. We combined the recent approach of climate sensitivity functions with a revised hydrological ‘bucket model’ to improve predictions on how plant species will respond to changes in the mean and variance of groundwater resources. Methods We leveraged spatiotemporal variation in long-term datasets of riparian vegetation cover and groundwater levels to build the first groundwater sensitivity functions for common plant species of dryland riparian corridors. Our results demonstrate the value of this approach to identifying which plant species will thrive (or fail) in an increasingly variable climate layered with declining groundwater stores. Important Findings Riparian plant species differed in sensitivity to both the mean and variance in groundwater levels. Rio Grande cottonwood (Populus deltoides ssp. wislizenii) cover was predicted to decline with greater inter-annual groundwater variance, while coyote willow (Salix exigua) and other native wetland species were predicted to benefit from greater year-to-year variance. No non-native species were sensitive to groundwater variance, but patterns for Russian olive (Elaeagnus angustifolia) predict declines under deeper mean groundwater tables. Warm air temperatures modulated groundwater sensitivity for cottonwood, which was more sensitive to variability in groundwater in years/sites with warmer maximum temperatures than in cool sites/periods. Cottonwood cover declined most with greater intra-annual coefficients of variation (CV) in groundwater, but was not significantly correlated with inter-annual CV, perhaps due to the short time series (16 years) relative to cottonwood lifespan. In contrast, non-native tamarisk (Tamarix chinensis) cover increased with both intra- and inter-annual CV in groundwater. Altogether, our results predict that changes in groundwater variability and mean will affect riparian plant communities through the differential sensitivities of individual plant species to mean versus variance in groundwater stores.