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Roemermann, C.

Publications and source records attributed to Roemermann, C..

2 recordsLinked to original sources

Fungal community composition links rhizosphere microbiome organization to plant phenotype in response to moderate warming

BackgroundGlobal warming increasingly challenges plant performance and ecosystem function, and plant responses to elevated temperature are shaped not only by intrinsic plasticity, but also by interactions with rhizosphere microbial communities. However, it remains unclear how warming-induced microbiome reorganization relates to plant performance, whether microbiome community composition predicts plant phenotypic responses to elevated temperature better than simple host-microbiome origin matching, and which bacterial or fungal community features contribute most strongly to this prediction. ResultsWe implemented a full factorial design manipulating plant genotype (P), microbial inoculum (M), and temperature (T) using natural Arabidopsis thaliana ecotypes and their corresponding rhizosphere microbiomes from contrasting climatic regions. By integrating high-throughput plant phenotyping, microbial community profiling, microbiome-phenotype coupling analyses, and predictive modeling, we found that elevated temperature induced a coherent thermomorphogenic shift in plant architecture, while plant phenotypic variation remained determined primarily by genotype over the 14-day vegetative growth period covered in this study. Overall, cold-origin genotypes showed larger temperature-induced trait shifts than warm-origin genotypes. Rhizosphere microbial communities were structured predominantly by inoculum origin, but were also affected by warming. Evidence for a increased thermomorphogenic growth when genotypes were combined with inoculum from their home site was limited. Instead, microbiome-phenotype associations were better captured by community composition than by matching status, with fungal community variation showing stronger and more consistent associations with plant phenotype than bacterial variation and providing more robust predictions of plant phenotypes across models. ConclusionsWarming reorganized plant-rhizosphere systems within persistent host- and inoculum-associated baselines. Plant phenotypic variation during vegetative development under elevated temperature was linked more closely to microbiome composition, especially in fungi, than to simple host-microbiome matching of origins. These findings provide a framework for identifying microbiome features associated with plant performance under climate warming.

plant biology↗

Prediction in trait-based ecology: global simulations of specific leaf area using a trait-based dynamic vegetation model

AbstractPredicting plant community functional traits is considered a Holy Grail of trait-based ecology because traits underpin ecosystem processes. Previous statistical, machine learning, and optimality approaches have produced global plant trait predictions. However, the utility of trait-based vegetation models, which include demographic processes and can represent trait diversity, remains unexplored at this scale. We use aDGVM2-LL, a trait- and individual-based dynamic global vegetation model (DGVM). aDGVM2-LL simulates community assembly, which is driven by natural selection, biotic, and abiotic conditions; simulated specific leaf area (SLA) is an emergent outcome of community assembly. We examine: 1) how well aDGVM2-LL can simulate global SLA by examining deviations from trait data, and 2) explore drivers of strong deviations. Compared to GBIF-derived SLA data, aDGVM2-LL displays mean SLA differences of -2.9 (m2/kg)(GBIF range ca. 4 - 35 m2/kg), a root mean square error (RMSE) of 7.25, and normalised mean absolute error (nMAE) of 26.54%. Published statistical, machine learning, and optimality approaches displayed differences with GBIF-derived trait data which range between (mean : -4.83 - 2.67, RMSE: 4.41 - 6.68, nMAE: 13.41% - 25.20%). Thus, aDGVM2-LL mean differences are comparable with published predictions while RMSEs and nMAEs are higher. Large aDGVM2-LL mismatches occur in areas where the model incorrectly simulates the relative abundances of deciduous vs. evergreen leaf phenologies. Correcting mismatches in leaf phenological abundances strongly reduces the range of mean SLA differences (-0.14 - 0.43), RMSEs (5.85 - 5.90), and nMAEs (15.44% - 20.61%). These results show that an eco-evolutionary, process-based approach can reasonably simulate global SLA values, particularly when leaf phenological abundances are accurate. Our results highlight the general importance of the global drivers of leaf phenology for leaf traits. The correct simulation of the relative abundances of deciduous and evergreen leaf phenologies is crucial to predict contemporary and future SLA.

ecology↗