Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth.

Microbial communities in the rhizosphere are distinct from those in soils and are influenced by stochastic and deterministic processes during plant development. These communities contain bacteria capable of promoting growth in host plants through various strategies. While some interactions are characterized in mechanistic detail using model systems, others can be inferred from culture-independent methods, such as 16S amplicon sequencing, using machine learning methods that account for this compositional data type. To characterize assembly processes and identify community members associated with plant growth amid the spatiotemporal variability of the rhizosphere, we grew Brassica rapa in a greenhouse time series with amended and reduced microbial treatments. Inoculation with a native soil community increased plant leaf area throughout the time series by up to 28%. Despite identifying spatially and temporally variable amplicon sequence variants (ASVs) in both treatments, inoculated communities were more highly connected and assembled more deterministically overall. Using a generalized linear modeling approach controlling for spatial variability, we identified 43 unique ASVs that were positively or negatively associated with leaf area, biomass, or growth rates across treatments and time stages. ASVs of the genus Flavobacterium dominated rhizosphere communities and showed some of the strongest positive and negative correlations with plant growth. Members of this genus, and growth-associated ASVs more broadly, exhibited variable connectivity in networks independent of growth association (positive or negative). These findings suggest host-rhizobacterial interactions vary temporally at narrow taxonomic scales and present a framework for identifying rhizobacteria that may work independently or in concert to improve agricultural yields. IMPORTANCE The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques. We identified 43 specific bacterial sequences associated with the growth of the plant Brassica rapa in different soil microbial treatments and at different stages of plant development. Most associations between bacterial abundances and plant growth were positive, although similar bacterial groups sometimes had different effects on growth. Why this happens will require more research, but overall, this study provides a way to identify native bacteria from plant roots that might be isolated and applied to boost agricultural yields.

Climate change, ecosystems and abrupt change: science priorities.

Ecologists have long studied patterns, directions and tempos of change, but there is a pressing need to extend current understanding to empirical observations of abrupt changes as climate warming accelerates. Abrupt changes in ecological systems (ACES)-changes that are fast in time or fast relative to their drivers-are ubiquitous and increasing in frequency. Powerful theoretical frameworks exist, yet applications in real-world landscapes to detect, explain and anticipate ACES have lagged. We highlight five insights emerging from empirical studies of ACES across diverse ecosystems: (i) ecological systems show ACES in some dimensions but not others; (ii) climate extremes may be more important than mean climate in generating ACES; (iii) interactions among multiple drivers often produce ACES; (iv) contingencies, such as ecological memory, frequency and sequence of disturbances, and spatial context are important; and (v) tipping points are often (but not always) associated with ACES. We suggest research priorities to advance understanding of ACES in the face of climate change. Progress in understanding ACES requires strong integration of scientific approaches (theory, observations, experiments and process-based models) and high-quality empirical data drawn from a diverse array of ecosystems. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data.

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet-multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM-Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

Detecting past changes in vegetation resilience in the context of a changing climate.

Anthropogenic climate change is continuously altering ecological responses to disturbance and must be accounted for when examining ecological resilience. One way to measure resilience in ecological datasets is by considering the amount and duration of change from a baseline created by perturbations, such as disturbances like wildfire. Recovery occurs when ecological conditions return to equilibrium, meaning that no subsequent changes can be attributed to the effects of the disturbance, but climate change often causes the recovered state to differ from the previous baseline. The palaeoecological record provides an opportunity to examine these expectations because palaeoclimates changed continuously; few periods existed when environmental conditions were stationary. Here we demonstrate a framework for examining resilience in palaeoecological records against the backdrop of a non-stationary climate by considering resilience as two components of (i) resistance (magnitude of change) and (ii) recovery (time required to return) to predicted equilibrium values. Measuring these components of resilience in palaeoecological records requires high-resolution fossil (e.g. pollen) records, local palaeoclimate reconstructions, a model to predict ecological change in response to climate change, and disturbance records measured at the same spatial scale as the ecological (e.g. vegetation history) record. Resistance following disturbance is measured as the deviation of the fossil record from the ecological state predicted by the palaeoclimate records, and recovery time is measured as the time required for the fossil record to return to predicted values. We show that some cases may involve nearly persistent equilibrium despite large climate changes, but that others can involve a shift to a new state without any complete recovery.

Extensive wildfires, climate change, and an abrupt state change in subalpine ribbon forests, Colorado.

Ecosystems may shift abruptly when the effects of climate change and disturbance interact, and landscapes with regularly patterned vegetation may be especially vulnerable to abrupt shifts. Here we use a fossil pollen record from a regularly patterned ribbon forest (alternating bands of forests and meadows) in Colorado to examine whether past changes in wildfire and climate produced abrupt vegetation shifts. Comparing the percentages of conifer pollen with sedimentary δ18 O data (interpreted as an indicator of temperature or snow accumulation) indicates a first-order linear relationship between vegetation composition and climate change with no detectable lags over the past 2,500 yr (r = 0.55, P < 0.001). Additionally, however, we find that the vegetation changed abruptly within a century of extensive wildfires, which were recognized in a previous study to have burned approximately 80% of the surrounding 1,000 km2 landscape 1,000 yr ago when temperatures rose ~0.5°C. The vegetation change was larger than expected from the effects of climate change alone. Pollen assemblages changed from a composition associated with closed subalpine forests to one similar to modern ribbon forests. Fossil pollen assemblages then remained like those from modern ribbon forests for the following ~1,000 yr, providing a clear example of how extensive disturbances can trigger persistent new vegetation states and alter how vegetation responds to climate.

Medieval warming initiated exceptionally large wildfire outbreaks in the Rocky Mountains.

Many of the largest wildfires in US history burned in recent decades, and climate change explains much of the increase in area burned. The frequency of extreme wildfire weather will increase with continued warming, but many uncertainties still exist about future fire regimes, including how the risk of large fires will persist as vegetation changes. Past fire-climate relationships provide an opportunity to constrain the related uncertainties, and reveal widespread burning across large regions of western North America during past warm intervals. Whether such episodes also burned large portions of individual landscapes has been difficult to determine, however, because uncertainties with the ages of past fires and limited spatial resolution often prohibit specific estimates of past area burned. Accounting for these challenges in a subalpine landscape in Colorado, we estimated century-scale fire synchroneity across 12 lake-sediment charcoal records spanning the past 2,000 y. The percentage of sites burned only deviated from the historic range of variability during the Medieval Climate Anomaly (MCA) between 1,200 and 850 y B.P., when temperatures were similar to recent decades. Between 1,130 and 1,030 y B.P., 83% (median estimate) of our sites burned when temperatures increased ∼0.5 °C relative to the preceding centuries. Lake-based fire rotation during the MCA decreased to an estimated 120 y, representing a 260% higher rate of burning than during the period of dendroecological sampling (360 to -60 y B.P.). Increased burning, however, did not persist throughout the MCA. Burning declined abruptly before temperatures cooled, indicating possible fuel limitations to continued burning.

Conifer expansion reduces the competitive ability and herbivore defense of aspen by modifying light environment and soil chemistry.

Disturbance patterns strongly influence plant community structure. What remains less clear, particularly at a mechanistic level, is how changes in disturbance cycles alter successional outcomes in plant communities. There is evidence that fire suppression is resulting in longer fire return intervals in subalpine forests and that these lengthened intervals increase competitive interactions between aspen and conifer species. We conducted a field and greenhouse study to compare photosynthesis, growth and defense responses of quaking aspen and subalpine fir regeneration under light reductions and shifts in soil chemistry that occur as conifers increase in dominance. The studies demonstrated that aspen regeneration was substantially more sensitive to light and soil resource limitations than that of subalpine fir. For aspen, light reductions and/or shifts in soil chemistry limited height growth, biomass gain, photosynthesis and the production of defense compounds (phenolic glycosides and condensed tannins). Biomass gain and phenolic glycoside concentrations were co-limited by light reduction and changes in soil chemistry. In contrast, subalpine fir seedlings tended to be more tolerant of low light conditions and showed no sensitivity to changes in soil chemistry. Unlike aspen, subalpine fir increased its root to shoot ratio on conifer soils, which may partially explain its maintenance of growth and defense. The results suggest that increasing dominance of conifers in subalpine forests alters light conditions and soil chemistry in a way that places greater physiological and growth constraints on aspen than subalpine fir, with a likely outcome being more successful recruitment of conifers and losses in aspen cover.