No abstract

Biogeochemical models increasingly consider the microbial control of carbon cycling in soil. The major current challenge is to validate mechanistic descriptions of microbial processes and predicted system responses against experimental observations. We analyzed soil biochemical models of different complexity regarding parameter identifiability using information geometry, i.e. a model is geometrically interpreted as a manifold embedded in data space. The most complex model (PECCAD) was used as a test case to reveal parsimonious process formulations. All models showed sloppiness, i.e. most individual parameter values cannot be inferred from the observed data.We derived a less complex model formulation of PECCAD with effective inferable parameter combinations and identified structural model limitations.

Microplastics (MP) are pervasive in the environment. There is ample evidence of negative MP effects on biota in aquatic ecosystems, though little is known about MP effects in terrestrial ecosystems. Given numerous entry routes of MP into soils, soil organisms are likely to be exposed to MP. We compared potential toxicological effects of MP from (i) low-density polyethylene (LDPE) (mean diameter ± standard deviation: 57 ± 40 µm) and (ii) a blend of biodegradable polymers polylactide (PLA) and poly(butylene adipateco-terephthalate) (PBAT) (40 ± 31 µm) on the reproduction and body length of the soil-dwelling bacterivorous nematode Caenorhabditis elegans. Feed suspensions without (control) or with MP (treatments) at concentrations of 1, 10, and 100 mg MP L −1 were prepared and nematodes were exposed to those suspensions on agar plates until completion of their reproductive phase (∼6 days). Using Nile red-stained PLA/PBAT MP particles and fluorescence microscopy, we demonstrated the ingestion of MP by C. elegans into pharynges and intestines. Under MP exposure, nematodes had fewer offspring (up to 22.9%) compared to nematodes in the control group. This decline was independent on the plastic type. We detected a tendency toward greater decreases in offspring at higher concentrations. Despite hints of negative effects on nematode body length under MP exposure, we could not derive a consistent pattern. We conclude that in MP-contaminated soils, the reproduction of nematodes, central actors in the soil food web, can be affected, with potentially negative implications for key soil functions, e.g., the regulation of soil biogeochemical cycles.

No abstract

The objective of our study was to test whether limited microbial degradation at low pesticide concentrations could explain the discrepancy between overall degradability demonstrated in laboratory tests and their actual persistence in the environment. Studies on pesticide degradation are often performed using unrealistically high application rates seldom found in natural environments. Nevertheless, biodegradation rates determined for higher pesticide doses cannot necessarily be extrapolated to lower concentrations. In this context, we wanted to (i) compare the kinetics of pesticide degradation at different concentrations in arable land and (ii) clarify whether there is a concentration threshold below which the expression of the functional genes involved in the degradation pathway is inhibited without further pesticide degradation taking place. We set up an incubation experiment for four weeks using 14C-ring labeled 2-methyl-4chlorophenoxyacetic acid (MCPA) as a model compound in concentrations from 30 to 20,000 µg kg −1 soil. To quantify the abundance of putative microorganisms involved in MCPA degradation and their degradation activity, tfdA gene copy numbers (DNA) and transcripts (mRNA) were determined by quantitative real-time PCR. Mineralization dynamics of MCPA derived-C were analyzed by monitoring 14CO 2 production and 14C assimilation by soil microorganisms. We identified two different concentration thresholds for growth and activity with respect to MCPA degradation using tfdA gene and mRNA transcript abundance as growth and activity indices, respectively. The tfdA gene expression started to increase between 1,000 and 5,000 µg MCPA kg −1 dry soil, but an actual increase in tfdA sequences could only be determined at a concentration of 20,000 µg. Accordingly, we observed a clear shift from catabolic to anabolic utilization of MCPA-derived C in the concentration range of 1,000 to 5,000 µg kg −1. Concentrations ≥1,000 µg kg −1 were mainly associated with delayed mineralization, while concentrations ≤1,000 µg kg −1 showed rapid absolute dissipation. The persistence of pesticides at low concentrations cannot, therefore,

Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel “gene-centric” model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.

Trait-based models have improved the understanding and prediction of soil organic matter dynamics in terrestrial ecosystems. Microscopic observations and pore scale models are now increasingly used to quantify and elucidate the effects of soil heterogeneity on microbial processes. Combining both approaches provides a promising way to accurately capture spatial microbial-physicochemical interactions and to predict overall system behavior. The present study aims to quantify controls on carbon (C) turnover in soil due to the mm-scale spatial distribution of microbial decomposer communities in soil. A new spatially explicit trait-based model (SpatC) has been developed that captures the combined dynamics of microbes and soil organic matter (SOM) by taking into account microbial life-history traits and SOM accessibility. Samples of spatial distributions of microbes at µm-scale resolution were generated using a spatial statistical model based on Log Gaussian Cox Processes which was originally used to analyze distributions of bacterial cells in soil thin sections. These µm-scale distribution patterns were then aggregated to derive distributions of microorganisms at mm-scale. We performed Monte-Carlo simulations with microbial distributions that differ in mm-scale spatial heterogeneity and functional community composition (oligotrophs, copiotrophs, and copiotrophic cheaters). Our modeling approach revealed that the spatial distribution of soil microorganisms triggers spatiotemporal patterns of C utilization and microbial succession. Only strong spatial clustering of decomposer communities induces a diffusion limitation of the substrate supply on the microhabitat scale, which significantly reduces the total decomposition of C compounds and the overall microbial growth. However, decomposer communities act as functionally redundant microbial guilds with only slight changes in C utilization. The combined statistical and process-based modeling approach derives distribution patterns of microorganisms at the mm-scale from microbial biogeography at microhabitat scale (µm) and quantifies the emergent macroscopic (cm) microbial and C dynamics. Thus, it effectively links observable process dynamics to the spatial control by microbial communities. Our study highlights a powerful approach that can provide further insights into the biological control of soil organic matter turnover.

No abstract