Long-term warming alters the composition of Arctic soil microbial communities

Despite the importance of Arctic soils in the global carbon cycle, we know very little of the impacts of warming on the soil microbial communities that drive carbon and nutrient cycling in these ecosystems. Over a 2-year period, we monitored the structure of soil fungal and bacterial communities in organic and mineral soil horizons in plots warmed by greenhouses for 18 years and in control plots. We found that microbial communities were stable over time but strongly structured by warming. Warming led to significant reductions in the evenness of bacterial communities, while the evenness of fungal communities increased significantly. These patterns were strongest in the organic horizon, where temperature change was greatest and were associated with a significant increase in the dominance of the Actinobacteria and significant reductions in the Gemmatimonadaceae and the Proteobacteria. Greater evenness of the fungal community with warming was associated with significant increases in the ectomycorrhizal fungi, Russula spp., Cortinarius spp., and members of the Helotiales suggesting that increased growth of the shrub Betula nana was an important mechanism driving this change. The shifts in soil microbial community structure appear sufficient to account for warming-induced changes in nutrient cycling in Arctic tundra as climate warm

Effects of observed and experimental climate change on terrestrial ecosystems in northern Canada: results from the Canadian IPY program

Published VersionTundra and taiga ecosystems comprise nearly 40 % of the terrestrial landscapes of Canada. These permafrost ecosystems have supported humans for more than 4500 years, and are currently home to ca. 115,000 people, the majority of whom are First Nations, Inuit and Métis. The responses of these ecosystems to the regional warming over the past 30–50 years were the focus of four Canadian IPY projects. Northern residents and researchers reported changes in climate and weather patterns and noted shifts in vegetation and other environmental variables. In forest-tundra areas tree growth and reproductive effort correlated with temperature, but seedling establishment was often hindered by other factors resulting in sitespecific responses. Increased shrub cover has occurred in sites across the Arctic at the plot and landscape scale, and this was supported by results from experimental warming. Experimental warming increased vegetation cover and nutrient availability in most tundra soils; however, resistance to warming was also found. Soil microbial diversity in tundra was no different than in other biomes, although there were shifts in mycorrhizal diversity in warming experiments. All sites measured were sinks for carbon during the growing season with expected seasonal and latitudinal patterns. Modeled responses of a mesic tundra system to climate change showed that the sink status will likely continue for the next 50–100 years, after which these tundra systems will likely become a net source of carbon dioxide to the atmosphere. These IPY studies were the first comprehensive assessment of the state and change in Canadian northern terrestrial ecosystems and showed that the inherent variability in these systems is reflected in their site-specific responses to changes in climate. They also showed the importance of using local traditional knowledge and science, and provided extensive data sets, sites and researchers needed to study and manage the inevitable changes in the Canadian North

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Quantifying thermal adaptation of soil microbial respiration

Abstract Quantifying the rate of thermal adaptation of soil microbial respiration is essential in determining potential for carbon cycle feedbacks under a warming climate. Uncertainty surrounding this topic stems in part from persistent methodological issues and difficulties isolating the interacting effects of changes in microbial community responses from changes in soil carbon availability. Here, we constructed a series of temperature response curves of microbial respiration (given unlimited substrate) using soils sampled from around New Zealand, including from a natural geothermal gradient, as a proxy for global warming. We estimated the temperature optima ( $${T}_{{opt}}$$ T o p t ) and inflection point ( $${T}_{\inf }$$ T inf ) of each curve and found that adaptation of microbial respiration occurred at a rate of 0.29 °C ± 0.04 1SE for $${T}_{{opt}}$$ T o p t and 0.27 °C ± 0.05 1SE for $${T}_{\inf }$$ T inf per degree of warming. Our results bolster previous findings indicating thermal adaptation is demonstrably offset from warming, and may help quantifying the potential for both limitation and acceleration of soil C losses depending on specific soil temperatures

Phospholipid fatty acid (PLFA) analysis as a tool to estimate absolute abundances from compositional 16S rRNA bacterial metabarcoding data

Microbial biodiversity monitoring through the analysis of DNA extracted from environmental samples is increasingly popular because it is perceived as being rapid, cost-effective, and flexible concerning the sample types studied. DNA can be extracted from diverse media before high-throughput sequencing of the prokaryotic 16S rRNA gene is used to characterize the taxonomic diversity and composition of the sample (known as metabarcoding). While sources of bias in metabarcoding methodologies are widely acknowledged, previous studies have focused mainly on the effects of these biases within a single substrate type, and relatively little is known of how these vary across substrates. We investigated the effect of substrate type (water, microbial mats, lake sediments, stream sediments, soil and a mock microbial community) on the relative performance of DNA metabarcoding in parallel with phospholipid fatty acid (PLFA) analysis. Quantitative estimates of the biomass of different taxonomic groups in samples were made through the analysis of PLFAs, and these were compared to the relative abundances of microbial taxa estimated from metabarcoding. Furthermore, we used the PLFA-based quantitative estimates of the biomass to adjust relative abundances of microbial groups determined by metabarcoding to provide insight into how the biomass of microbial taxa from PLFA analysis can improve understanding of microbial communities from environmental DNA samples. We used two sets of PLFA biomarkers that differed in their number of PLFAs to evaluate how PLFA biomarker selection influences biomass estimates. Metabarcoding and PLFA analysis provided significantly different views of bacterial composition, and these differences varied among substrates. We observed the most notable differences for the Gram-negative bacteria, which were overrepresented by metabarcoding in comparison to PLFA analysis. In contrast, the relative biomass and relative sequence abundances aligned reasonably well for Cyanobacteria across the tested freshwater substrates. Adjusting relative abundances of microbial taxa estimated by metabarcoding with PLFA-based quantification estimates of the microbial biomass led to significant changes in the microbial community compositions in all substrates. We recommend including independent estimates of the biomass of microbial groups to increase comparability among metabarcoding libraries from environmental samples, especially when comparing communities associated with different substrates.publishe

Intraspecific trait variation in alpine plants relates to their elevational distribution

Climate warming is shifting the distributions of mountain plant species to higher elevations. Cold-adapted plant species are under increasing pressure from novel competitors that are encroaching from lower elevations. Plant capacity to adjust to these pressures may be measurable as variation in trait values within a species. In particular, the strength and patterns of intraspecific trait variation along abiotic and biotic gradients can inform us whether and how species can adjust their anatomy and morphology to persist in a changing environment. Here, we tested whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species' height ratio, its height relative to its nearest neighbour. Variation in alpine plant trait values over elevation differed depending on a species' median elevation and the breadth of its elevational range, with species with lower median elevations and larger elevational range sizes showing greater trait variation, i.e. a steeper slope in trait values, over their elevational distributions. These effects were evidenced by significant interactions between species' elevation and their elevational preference or range for several traits: vegetative height, generative height, specific leaf area and patch area. The height ratio of focal alpine species and their neighbours decreased in the lower part of their distribution because neighbours became relatively taller at lower elevations. In contrast, species with lower elevational optima maintained a similar height ratio with neighbours throughout their range. Synthesis. We provide evidence that species from lower elevations and those with larger range sizes show greater intraspecific trait variation, which may indicate a greater ability to respond to environmental changes. Also, larger trait variation of species from lower elevations may indicate stronger competitive ability of upslope shifting species, posing one further threat to species from higher ranges

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment : An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities