Snowmelt timing alters shallow but not deep soil moisture in the Sierra Nevada

Roughly one-third of the Earth's land surface is seasonally covered by snow. In many of these ecosystems, the spring snowpack is melting earlier due to climatic warming and atmospheric dust deposition, which could greatly modify soil water resources during the growing season. Though snowmelt timing is known to influence soil water availability during summer, there is little known about the depth of the effects and how long the effects persist. We therefore manipulated the timing of seasonal snowmelt in a high-elevation mixed-conifer forest in a Mediterranean climate during consecutive wet and dry years. The snow-all-gone (SAG) date was advanced by 6 days in the wet year and 3 days in the dry year using black sand to reduce the snow surface albedo. To maximize variation in snowmelt timing, we also postponed the SAG date by 8 days in the wet year and 16 days in the dry year using white fabric to shade the snowpack from solar radiation. We found that deeper soil water (30-60 cm) did not show a statistically significant response to snowmelt timing. Shallow soil water (0-30 cm), however, responded strongly to snowmelt timing. The drying effect of accelerated snowmelt lasted 2 months in the 0-15 cm depth and at least 4 months in the 15-30 cm depth. Therefore, the legacy of snowmelt timing on soil moisture can persist through dry periods, and continued earlier snowmelt due to climatic warming and windblown dust could reduce near-surface water storage and availability to plants and soil biota. Key Points The hydrological signal of snowmelt timing was strongest in shallow soil Effects of snowmelt timing on soil moisture lasted 2-4 months Advancing snowmelt timing by 2-3 weeks depleted shallow soil water by one third © 2014. American Geophysical Union. All Rights Reserved

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Long-term and realistic global change manipulations had low impact on diversity of soil biota in temperate heathland

In a dry heathland ecosystem we manipulated temperature (warming), precipitation (drought) and atmospheric concentration of CO(2) in a full-factorial experiment in order to investigate changes in below-ground biodiversity as a result of future climate change. We investigated the responses in community diversity of nematodes, enchytraeids, collembolans and oribatid mites at two and eight years of manipulations. We used a structural equation modelling (SEM) approach analyzing the three manipulations, soil moisture and temperature, and seven soil biological and chemical variables. The analysis revealed a persistent and positive effect of elevated CO(2) on litter C:N ratio. After two years of treatment, the fungi to bacteria ratio was increased by warming, and the diversities within oribatid mites, collembolans and nematode groups were all affected by elevated CO(2) mediated through increased litter C:N ratio. After eight years of treatment, however, the CO(2)-increased litter C:N ratio did not influence the diversity in any of the four fauna groups. The number of significant correlations between treatments, food source quality, and soil biota diversities was reduced from six to three after two and eight years, respectively. These results suggest a remarkable resilience within the soil biota against global climate change treatments in the long term

Accelerated microbial turnover but constant growth efficiency with warming in soil

Rising temperatures are expected to reduce global soil carbon (C) stocks, driving a positive feedback to climate change1-3. However, the mechanisms underlying this prediction are not well understood, including how temperature affects microbial enzyme kinetics, growth efficiency (MGE), and turnover4,5. Here, in a laboratory study, we show that microbial turnover accelerates with warming and, along with enzyme kinetics, determines the response of microbial respiration to temperature change. In contrast, MGE, which is generally thought to decline with warming6-8, showed no temperature sensitivity. A microbial-enzyme model suggests that such temperature sensitive microbial turnover would promote soil C accumulation with warming, in contrast to reduced soil C predicted by traditional biogeochemical models. Furthermore, the effect of increased microbial turnover differs from the effects of reduced MGE, causing larger increases in soil C stocks. Our results demonstrate that the response of soil C to warming is affected by changes in microbial turnover. This control should be included in the next generation of models to improve prediction of soil C feedbacks to warming

Comparison of statistical models in a meta-analysis of fungicide treatments for the control of citrus black spot caused by Phyllosticta citricarpa

Meta-analysis has been recognised as a powerful method to synthetize existing published data from different studies through a formal statistical analysis. Several statistical models have been proposed to evaluate the effectiveness of treatments against plant diseases using meta-analysis, but the sensitivity of the estimated treatment effects to the model chosen has not been investigated in detail in the context of plant pathology. In this paper, four different statistical models were defined to analyse fungicide control trials with binary outcomes. These models were used to conduct a meta-analysis on the effectiveness of fungicide treatments against citrus black spot, a fungal disease caused by the quarantine pathogen Phyllosticta citricarpa. The models differed in the assumption made on the variability of the treatment effect (constant or variable between experimental plots) and in the method used for parameter estimation (classical or Bayesian). Odds ratios were estimated for two groups of fungicides, copper compounds and dithiocarbamates, widely applied for CBS control using each model in turn. Classical and Bayesian statistical models led to similar results, but the estimated treatment effectiveness and their associated levels of uncertainty were sensitive to the assumption made about the variability of the treatment effect. Estimated odds ratios were different depending on whether the treatment effect was assumed to be constant or variable between experimental plots. The size of the confidence intervals was underestimated when the treatment effect was assumed constant while it was variable in reality. Because of the strong between-plot variability, the 90 % percentiles of the odds ratios were much higher than the point estimates, and this result revealed that, in some plots, treatment effectiveness could be much lower than expected. Based on our results, we conclude that it is not sufficient to calculate point estimates of odds ratio when the between-plot variability of the treatment effect is strong and that, in such case, it is recommended to compute the predictive distributions of the odds ratio