Biology subjects

Bohan, D. A.

Publications and source records attributed to Bohan, D. A..

5 recordsLinked to original sources

bioRxiv2022

NGS data reveals the phyllosphere microbiome of wheat plants infected by the fungal pathogen Zymoseptoria tritici

The fungal pathogen Zymoseptoria tritici is the causal agent of Septoria tritici blotch (STB), a major wheat disease in Western Europe. Microorganisms inhabiting wheat leaves might act as beneficial, biocontrol or facilitating agents that could limit or stimulate the development of Z. tritici. Improving our understanding of microbial communities in the wheat phyllosphere would lead to new insights into STB management. This resource announcement provides fungal and bacterial metabarcoding datasets obtained by sampling wheat leaves differing in the presence of symptoms caused by Z. tritici. Tissues were sampled from three wheat commercial varieties on three sampling dates during a cropping season. Weeds around wheat fields were sampled as well. In total, more than 450 leaf samples were collected. The pathogen Z. tritici was quantified using qPCR. We provide the raw metabarcoding datasets, the Amplicon Sequence Variant (ASV) tables obtained after bioinformatic processing, the metadata associated to each sample (sampling date, wheat variety and tissue health condition), a preliminary descriptive analysis of the data, and the code used for bioinformatic and descriptive statistical analysis.

microbiology↗

bioRxiv2020

Combined effect of crop rotation and carabid beetles on weed dynamics in arable fields

Weed management is a resource-intensive practice in arable agriculture, with direct and long-term impacts on both productivity and biodiversity (e.g. plant, pollinators and farmland wildlife). In conventional systems, weed control relies on crop management and herbicide inputs, but for more sustainable production systems, use of herbicides needs to be reduced. This requires a good understanding of the processes that regulate arable weed dynamics in arable fields. We adopted a systems framework to understand and model interacting components that drive the weed dynamics in 168 arable fields. Within this framework, we built a structural equation model (SEM) to quantify the direct and indirect effects of crop rotation (i.e. crops in the previous three years and the current year) and carabid beetles (Coleoptera: Carabidae) on weed density, seed abundance and accumulation in the seedbank. We included results from a mechanistic approach to infer interactions between seed-feeding carabid beetles and seeds to estimate predation pressure in each field. Our results show that weeds in arable fields are regulated by crop type, sowing season, and activity density of carabid beetles. We found a direct effect of crop rotation, including both past and current field management practice, on weed abundance in the field and its seedbank. There was also an indirect effect of crops on weed seed accumulation in the seedbank via the effect of seed-eating carabid beetles. The efficiency of weed control by carabid beetles depended on the cumulative predation pressure, which indicates the importance of functional diversity as well as abundance. Farmers and agronomists can capitalise on the ecosystem services provided by carabid beetles by adapting agronomic practices and crop rotation to maintain a rich fauna of seed-eating carabids in fields and potentially across the agricultural landscapes. When integrated with rotational management practices, this ecosystem services can improve the efficiency of weed management and contribute to the sustainability of cropping systems.

ecology↗

bioRxiv2020

Inferring species interactions from ecological survey data: a mechanistic approach to predict quantitative food webs of seed-feeding by carabid beetles

O_LIEcological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, e.g. predation, are short-lived and difficult to observe or detect. There are different methods for inferring the presence of interactions, which we lack methods to predict interaction strengths and so use weighted network analysis. C_LIO_LIHere, we develop a trait-based approach suitable for creating quantitative networks, i.e. with varying interaction strengths. We developed the method for seed-feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other interactions. C_LIO_LIWe used existing literature data from experimental seed-feeding trials to predict a per-individual interaction cost index based on carabid and seed size with frequency-dependent prey selection and assuming bottom up control. This was scaled up to the population level to create predicted inferred weighted networks using the abundance of carabids and seeds in samples from arable fields and energetic intake rates of carabids from the literature. From these weighted networks, we also calculated a novel measure of predation pressure. C_LIO_LIWe applied it to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how networks and predation pressure varied amongst fields. C_LIO_LIInferred networks are valuable because (i) they provide null models for the structuring of food webs to test against empirical species interaction data, e.g. DNA analysis of carabid gut regurgitates, and (ii) they allow weighted inferred networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This would permit network analysis even at times and in places when interactions were not directly assessed. C_LI

ecology↗

bioRxiv2020

Standardizing carabid pitfall catches for trapping effort; a meta-analysis

O_LICarabid beetles (Coleoptera: Carabidae) provide important ecological services and are frequently used as a bio-indicator in monitoring environmental quality. The abundance and diversity of carabids is usually determined using pitfall trapping, but trap catches are difficult to compare between studies due to variation in trapping effort. The standardization of the catch for trapping effort has not been previously addressed in a global analysis of studies in the literature. C_LIO_LIThe aims of this study are (i) to define a method for estimating the effect of trapping effort on the size of the pitfall catch, and (ii) to explore factors related to study designs, sampling method, study origin, and level of data aggregation to determine how these factors affect the catch per unit effort in pitfall trapping. C_LIO_LIWe conducted a meta-analysis on the activity-density and diversity of carabids across studies, based on published data from Europe and North-America to analyse whether standardization of catch measurements might be possible. Data were extracted from 104 publications, spanning a period of 42 years. C_LIO_LIThe total catch was proportional to the number of trap days, and ranged from 0.19-9.53 beetles/(trap day) across studies (95% range), with a mean of 1.33 beetles/(trap day). The number of species was allometrically related to the trapping effort defined as the product of the number of traps, their perimeter and the time of exposure in the field, and characterized by a power exponent of 0.25. Species richness ranged across studies from 2.30-13.18 species/(m day)0.25 (95% range) with a mean of 7.15 species/(m day)0.25. The size of the catch and the number of species were higher in crops with narrow as compared to wide rows. There was no significant change in abundance or diversity of carabids in arable land over the 42 years covered. We also found that increasing trapping effort may not yield more accurate results. C_LIO_LIThe results show that it is possible to standardize activity-density-based catches and species diversity for trapping effort across studies using a power transformation, allowing meta-analysis of such data, e.g. to elucidate factors affecting abundance and diversity of the focal taxa. C_LI

ecology↗

bioRxiv2020

Microbial association networks give relevant insights into plant pathobiomes

Interactions between plant pathogens and other plant-associated microorganisms regulate disease. Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine (Vitis vinifera) and the fungal pathogen causing powdery mildew (Erysiphe necator) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection.

ecology↗