A plant’s inheritance : Soil legacy effects of crops and wild relatives in relation to plant functional traits

During their lives plants build-up a legacy for subsequent plants to inherit. The soil that supports plant growth is simultaneously conditioned by the growing plant and carries-over lasting imprints of the previous plant to influence growth of next plants. The microbial soil community and litter decomposition are two essential aspects of plant-soil legacies, as decomposition, mineralisation and microbial community composition and functioning have profound effects on subsequent plant growth by providing nutrients or by causing diseases. Characteristics of a plant may be predictive of its inheritance. Functional plant traits reflect a plant’s growth strategy and response to selection forces in the environment. During crop domestication plant characteristics have been artificially selected for, with potential unwanted side-effects as some characteristics might have been altered unintentionally. Since productivity in agriculture relies on the interactions between plant and soil, it is a necessity to study the relationship between plant traits, decomposition, microbial community composition and the possible effects on future plant growth. This thesis presents results from several experiments studying how plants influence the soil, through decomposition of plant residues, the soil microbial community assemblage, and its possible consequence for subsequent plant growth. In a crop rotation experiment plant-soil feedback effects have been studied at field scale, with legacy effects of winter cover crops as the primary focus. Influences of plant traits on litter decomposition and microbial community were studied both in a field context and under controlled greenhouse conditions. By comparing the legacy effects of crops with close-relatives from natural grasslands the effects of domestication on litter decomposition and rhizosphere microbial community composition were explored. In the field experiment, it was observed that mixtures of cover crops can perform better than the sum of their parts when the plants in the mixture complemented each other during their growth. Productivity and quality of cover crops was found to promote growth of subsequent main crops, in part through stimulation of soil fungal biomass and feedback effects of decomposing litter. In the greenhouse, growing plants were observed to suppress decomposition of root and shoot litter to varying extent depending on which plant was present and on the quality of the decomposing litter. The results also indicate that domestication has affected plant functional traits in a variety of ways, rather than having predictable effects across a range of crops. Plant functional traits are a useful approach to study legacy effects, as they predict decomposability of plant residues and partially explain the microbial community composition in the rhizosphere. Significance of plant traits as predictors varied with environmental conditions, thus interpretation of the results should be related to its context. This thesis contributes to the understanding of plant-soil interactions, with emphasis on differences and similarities of agricultural and natural ecosystems.</p

<b>R codes from Uncover Microbial Food Webs using Machine </b><b>Learning</b>

R codes used to build the different figures in Barel et al. 2023 Soil Bill. Biochem.1_ClustOfVar_ML_comparisons_loops: codes used for machine learning model comparison to select the best model for microbial feeding links inferences.2_PVS_SA: Sensitivity analysis 1 - shuffling the predictors3_PT_SA: Sensitivity analysis 2 - test the influence of species that are new or poorly characterized at the taxonomic level4_Feeding link predictions on Case Study Data: Make feeding link inferences from the Case Study data (species lists and traits) using the best model identified following step 1.5_Calculate_Link_strength_and_FW_Collections: Calculate links strength and create a food web collection for further analyses6_FW_analyses: Plot meta webs and food webs at the plot level. Calculate food web indices.For more details, please refer to the paper: Barel, J.M., Petchey, O.L., Ghaffouli, A., Jassey, V.E.J., 2023. Uncovering microbial food webs using machine learning. Soil Biology and Biochemistry 186, 109174. doi:10.1016/j.soilbio.2023.109174</p

Uncovering microbial food webs using machine learning

Microbial trophic interactions are an important aspect of microbiomes in any ecosystem. They can reveal how microbial diversity modulates ecosystem functioning. However, uncovering microbial feeding interactions is a challenge because direct observation of predation is difficult with classical approaches such as behaviour and gut contents analyses. To overcome this issue, recent developments in trait-matching and machine-learning ap- proaches are promising for successfully inferring microbial feeding links. Here, we tested the ability of six machine-learning algorithms for predicting microbial feeding links, based on species traits and taxonomy. By incorporating organism speed, size and abundance into the model predictions, we further estimated the prob- ability of feeding links occurring. We found that the model trained with the boosted regression trees algorithm predicted feeding links between microbes best. Sensitivity analyses showed that feeding link predictions were robust against faulty predictors in the training set, and capable of predicting feeding links for empirical datasets containing up to 50% of new taxa. We cross-validated the feeding link predictions using an empirical dataset from a Sphagnum-dominated peatland with direct feeding observations for two dominant testate amoeba pred- ators. The feeding habits of the two testate amoeba species were comparable between microscopic observations and model predictions. Machine learning thus offers a means to develop robust models for studying microbial food webs. It offers a route to combine traditional observations with DNA-based sampling strategies to upscale soil biodiversity research along ecological gradients

Come Rain, Come Shine: Peatland Carbon Dynamics Shift Under Extreme Precipitation

International audiencePrecipitation patterns are becoming increasingly extreme, particularly at northern latitudes. Current climate models predict that this trend will continue in the future. While droughts have been repeatedly studied in many ecosystems over the last decades, the consequences of increasingly intense, but less frequent rainfall events, on carbon (C) cycling are not well understood. At northern latitudes, peatlands store one third of the terrestrial carbon and their functioning is highly dependent on water. Shifts in rainfall regimes could disrupt peatland C dynamics and speed-up the rates of C loss. How will these immense stocks of C be able to withstand and recover from extreme rainfall? We tested the resistance and resilience effects of extreme precipitation regimes on peatland carbon dioxide (CO 2) and methane (CH 4) fluxes, pore water dissolved organic carbon (DOC) and litter decomposition rates by exposing intact peat cores to extreme, springtime rainfall patterns in a controlled mesocosm experiment. We find that more intense but less frequent rainfall destabilized water table dynamics, with cascading effects on peatland C fluxes. Decomposition and respiration rates increased with a deeper mean water table depth (WTD) and larger WTD fluctuations. We observed similar patterns for CO 2 uptake, which were likely mediated by improved vascular plant performance. After a three-week recovery period, CO 2 fluxes still displayed responses to the earlier WTD dynamics, suggesting lagged effects of precipitation regime shifts. Furthermore, we found that CH 4 emissions decreased with deeper mean WTD, but this showed a high resilience once WTD dynamics stabilised. Not only do our results illustrate that shifting rainfall patterns translate in altered WTD dynamics and, consequentially, influence C fluxes, they also demonstrate that exposure to altered rainfall early in the growing season can have lasting effects on CO 2 exchange. Even though the increased CO 2 assimilation under extreme precipitation patterns signals peatland resistance under changing climatic conditions, it may instead mark the onset of vascular plant encroachment and the associated C loss

Legume presence reduces the decomposition rate of non-legume roots

Living plants can enhance litter decomposition rates via a priming effect by releasing root exudates which provide energy to saprotrophic microbes and thereby enable them to degrade litter faster. The strength of this effect, however, is expected to be dependent on the litter properties. To test whether the presence of a growing plant affects the decomposition rate of dead roots with different traits, we used dead roots of seven species (3 grasses, 3 legumes, 1 forb) as litter and quantified litter mass loss after eight weeks of incubation in soil with or without a growing white clover (Trifolium repens) plant. We expected root decomposition to be faster in the presence of T. repens, especially for roots with high C:N ratio. We found that the presence of T. repens slowed down the decomposition of grass and forb roots (negative priming), while it did not significantly affect the decomposition of legume roots. Our results show that root decomposition can be slowed down in the presence of a living plant and that this effect depends on the properties of the decomposing roots, with a pronounced reduction in root litter poor in N and P, but not in the relatively nutrient-rich legume root litters. Negative priming effect of legume plants on non-legume litter decomposition may have resulted from preferential substrate utilisation by soil microbes</p

Spatial heterogeneity in root litter and soil legacies differentially affect legume root traits

Background and Aims: Plants affect the soil environment via litter inputs and changes in biotic communities, which feed back to subsequent plant growth. Here we investigated the individual contributions of litter and biotic communities to soil feedback effects, and plant ability to respond to spatial heterogeneity in soil legacy. Methods: We tested for localised and systemic responses of Trifolium repens to soil biotic and root litter legacy of seven grassland species by exposing half of a root system to control soil and the other half to specific inoculum or root litter. Results: Soil inoculation triggered a localised reduction in root length while litter locally increased root biomass independent of inoculum or litter species identity. Nodule formation was locally suppressed in response to soil conditioned by another legume (Vicia cracca) and showed a trend towards systemic reduction in response to conspecific soil. V. cracca litter also caused a systemic response with thinner roots produced in the part of the root system not directly exposed to the litter. Conclusions: Spatial heterogeneity in root litter distribution and soil communities generate distinct local and systemic responses in root morphology and nodulation. These responses can influence plant-mutualist interactions and nutrient cycling, and should be included in plant co-existence models

Uncovering microbial food webs using machine learning

Microbial trophic interactions are an important aspect of microbiomes in any ecosystem. They can reveal how microbial diversity modulates ecosystem functioning. However, uncovering microbial feeding interactions is a challenge because direct observation of predation is difficult with classical approaches such as behaviour and gut contents analyses. To overcome this issue, recent developments in trait-matching and machine-learning approaches are promising for successfully inferring microbial feeding links. Here, we tested the ability of six machine-learning algorithms for predicting microbial feeding links, based on species traits and taxonomy. By incorporating organism speed, size and abundance into the model predictions, we further estimated the probability of feeding links occurring. We found that the model trained with the boosted regression trees algorithm predicted feeding links between microbes best. Sensitivity analyses showed that feeding link predictions were robust against faulty predictors in the training set, and capable of predicting feeding links for empirical datasets containing up to 50% of new taxa. We cross-validated the feeding link predictions using an empirical dataset from a Sphagnum-dominated peatland with direct feeding observations for two dominant testate amoeba predators. The feeding habits of the two testate amoeba species were comparable between microscopic observations and model predictions. Machine learning thus offers a means to develop robust models for studying microbial food webs. It offers a route to combine traditional observations with DNA-based sampling strategies to upscale soil biodiversity research along ecological gradients

Correction to: Plant presence reduces root and shoot litter decomposition rates of crops and wild relatives

The authors wish to report an error in the original version of the paper. In figure 4, panels a) and b) are incorrectly titled respectively "Crop shoots" and "Crop roots". The correct titles should be a) "Wild shoots", and b) "Wild roots" as presented on the next page. No other aspects of the results presented in the publication were affected. The caption of the figure stays unchanged. (Figure presented.).</p

Plant presence reduces root and shoot litter decomposition rates of crops and wild relatives

Aims: Roots contribute greatly to carbon cycling in agriculture. Measuring aboveground litter decomposition could approximate belowground turn-over if drivers of decomposition, f.e. litter traits and plant presence, influence shoot and root decomposition in a comparable manner. We tested coordination of above- and belowground litter traits and decomposition rates for six pairs of crops and closely related wild plants and studied the influence of plant presence on decomposition. Methods: Above- and belowground traits were measured, compared and related to decomposition rates. Shoot and root litters were incubated in presence of the same plant species as the litter species (own) or in presence of two other plant species (a grass or forb). Results: Shoots decomposed 1.43–1.98 times faster than (resp.) wild plant and crop roots. Decomposition correlated negatively with litter carbon and lignin concentrations, except crop root decomposition which correlated negatively with nitrogen concentration. Unexpectedly, plant presence reduced litter decomposition, with strongest effects for root litters in presence of forbs. Conclusions: Carbon cycling might be slower than predicted solely based on shoots decomposition rates, especially in presence of growing plants. While root decomposition of wild plants can be approximated by shoot decomposition, crop shoots are a poor proxy for crop root decomposition.</p

Correction to: Plant presence reduces root and shoot litter decomposition rates of crops and wild relatives

The authors wish to report an error in the original version of the paper. In figure 4, panels a) and b) are incorrectly titled respectively "Crop shoots" and "Crop roots". The correct titles should be a) "Wild shoots", and b) "Wild roots" as presented on the next page. No other aspects of the results presented in the publication were affected. The caption of the figure stays unchanged. (Figure presented.).</p