Arable plant communities as a surrogate of crop rhizosphere microbiota.

Soil microbiota is a crucial component of agroecosystem biodiversity, enhancing plant growth and providing important services in agriculture. However, its characterization is demanding and relatively expensive. In this study, we evaluated whether arable plant communities can be used as a surrogate of bacterial and fungal communities of the rhizosphere of Elephant Garlic (Allium ampeloprasum L.), a traditional crop plant of central Italy. We sampled plant, bacterial, and fungal communities, i.e., the groups of such organisms co-existing in space and time, in 24 plots located in eight fields and four farms. At the plot level, no correlations in species richness emerged, while the composition of plant communities was correlated with that of both bacterial and fungal communities. As regards plants and bacteria, such correlation was mainly driven by similar responses to geographic and environmental factors, while fungal communities seemed to be correlated in species composition with both plants and bacteria due to biotic interactions. All the correlations in species composition were unaffected by the number of fertilizer and herbicide applications, i.e., agricultural intensity. Besides correlations, we detected a predictive relationship of plant community composition towards fungal community composition. Our results highlight the potential of arable plant communities to be used as a surrogate of crop rhizosphere microbial communities in agroecosystems.

Epiphytic and Endophytic Fungi Colonizing Seeds of Two Poaceae Weed Species and Fusarium spp. Seed Degradation Potential In Vitro.

Fungi colonizing the surface and endosphere of two widespread Poaceae weed species, Avena fatua and Echinochloa crus-galli, were isolated to compare the taxonomic composition between the plant species, location, and year of the seed collection. The seed-degrading potential of Fusarium isolated from the seeds was tested by inoculating seeds of E. crus-galli with spore suspension. Molecular identification of epiphytic and endophytic fungal genera was performed by sequencing the ITS region of rDNA. Endophytes comprised of significantly lower fungal richness compared to epiphytes. A significant taxonomic overlap was observed between the endosphere and seed surface. The most abundant genera were Alternaria, Fusarium, Cladosporium, and Sarocladium. Analysis of similarities and hierarchical clustering showed that microbial communities were more dissimilar between the two plant species than between the years. Fusarium isolates with a high potential to infect and degrade E. crus-galli seeds in laboratory conditions belong to F. sporotrichioides and F. culmorum.

Who is the culprit: Is pest infestation responsible for crop yield losses close to semi-natural habitats?

Semi-natural habitats (SNHs) are becoming increasingly scarce in modern agricultural landscapes. This may reduce natural ecosystem services such as pest control with its putatively positive effect on crop production. In agreement with other studies, we recently reported wheat yield reductions at field borders which were linked to the type of SNH and the distance to the border. In this experimental landscape-wide study, we asked whether these yield losses have a biotic origin while analyzing fungal seed and fungal leaf pathogens, herbivory of cereal leaf beetles, and weed cover as hypothesized mediators between SNHs and yield. We established experimental winter wheat plots of a single variety within conventionally managed wheat fields at fixed distances either to a hedgerow or to an in-field kettle hole. For each plot, we recorded the fungal infection rate on seeds, fungal infection and herbivory rates on leaves, and weed cover. Using several generalized linear mixed-effects models as well as a structural equation model, we tested the effects of SNHs at a field scale (SNH type and distance to SNH) and at a landscape scale (percentage and diversity of SNHs within a 1000-m radius). In the dry year of 2016, we detected one putative biotic culprit: Weed cover was negatively associated with yield values at a 1-m and 5-m distance from the field border with a SNH. None of the fungal and insect pests, however, significantly affected yield, neither solely nor depending on type of or distance to a SNH. However, the pest groups themselves responded differently to SNH at the field scale and at the landscape scale. Our findings highlight that crop losses at field borders may be caused by biotic culprits; however, their negative impact seems weak and is putatively reduced by conventional farming practices.

Using functional traits to model annual plant community dynamics.

Predicting the response of biological communities to changes in the environment or management is a fundamental pursuit of community ecology. Meeting this challenge requires the integration of multiple processes: habitat filtering, niche differentiation, biotic interactions, competitive exclusion, and stochastic demographic events. Most approaches to this long-standing problem focus either on the role of the environment, using trait-based filtering approaches, or on quantifying biotic interactions with process-based community dynamics models. We introduce a novel approach that uses functional traits to parameterize a process-based model. By combining the two approaches we make use of the extensive literature on traits and community filtering as a convenient means of reducing the parameterization requirements of a complex population dynamics model whilst retaining the power to capture the processes underlying community assembly. Using arable weed communities as a case study, we demonstrate that this approach results in predictions that show realistic distributions of traits and that trait selection predicted by our simulations is consistent with in-field observations. We demonstrate that trait-based filtering approaches can be combined with process-based models to derive the emergent distribution of traits. While initially developed to predict the impact of crop management on functional shifts in weed communities, our approach has the potential to be applied to other annual plant communities if the generality of relationships between traits and model parameters can be confirmed.

Surveying the arable plant diversity of conventionally managed farmland: a comparison of methods.

Agricultural intensification has led to drastic population declines in Europe's arable plant vegetation, and continuous monitoring is a prerequisite for assessing measures to increase and conserve remnant populations of endangered arable plant species. Unfortunately, strong variation in plot sizes and in-field locations makes comparison of current arable plant monitoring approaches difficult. This study compares different relevé approaches in conventionally managed arable fields in Northwest German farmland with respect to plant species detection success and time expenditure. We compared species detection rate and expenditure of time of six different relevé types in 45 conventionally managed arable fields (each 15 fields of wheat, maize, and rapeseed): field "Interior" plots (50 × 2 m); field edge plots: "Edge_30" (30 × 2 m), "Edge_50" (50 × 2 m), and "Edge_500" (500 × 1 m); "Subplots" (four dispersed plots of 5 × 1 m); and "Corner" plots (50 × 2 m). To determine species detection rate, the species richness recorded with a survey method was related to the field's total plant species number as estimated from a survey of the entire field edge zone. With a species detection rate of 8.3% (median), interior plots were inadequate for characterizing the field's arable plant vegetation. Edge_500 plots yielded the highest proportion of the field's arable plant species pool (75.6%, including taxa of conservation value), followed by "Corner" plots (45.8%) and "Sublots" (32.6%). Edge_50 and Edge_30 plots detected less than 25% of the field's species pool. The average time needed for a relevé was 20 min in Edge500 plots and 5-11 min in the other plot types. We suggest implementing Edge_500 plots as a standard monitoring approach in conventionally managed farmland due to its favorable ratio of detection success to expenditure of time. Our findings should be compared to methodological studies conducted in other regions, in different farmland management systems, and in landscapes of variable complexities.