Rapid evolution of bacterial mutualism in the plant rhizosphere  

Li et al., 2021

While beneficial plant-microbe interactions are common in nature, direct evidence for the evolution of bacterial mutualism is scarce. Here we use experimental evolution to causally show that initially plant-antagonistic Pseudomonas protegens bacteria evolve into mutualists in the rhizosphere of Arabidopsis thaliana within six plant growth cycles (6 months). This evolutionary transition is accompanied with increased mutualist fitness via two mechanisms: (i) improved competitiveness for root exudates and (ii) enhanced tolerance to the plant-secreted antimicrobial scopoletin whose production is regulated by transcription factor MYB72. Crucially, these mutualistic adaptations are coupled with reduced phytotoxicity, enhanced transcription of MYB72 in roots, and a positive effect on plant growth. Genetically, mutualism is associated with diverse mutations in the GacS/GacA two-component regulator system, which confers high fitness benefits only in the presence of plants. Together, our results show that rhizosphere bacteria can rapidly evolve along the parasitism-mutualism continuum at an agriculturally relevant evolutionary timescale.

Panel a shows the initially antagonistic effect of Pseudomonas protegens CHA0 on A. thaliana after one plant growth cycle in the sterile sand study system (n = 5; aboveground biomass ***P = 0.0001). Panels b–f compare the effects of ancestral and evolved Pseudomonas protegens CHA0 phenotypes on plant performance-related traits in a separate plant growth assays performed on agar plates at the end of the selection experiment (n = 3 for control and n = 5 for each evolved phenotype). Different panels show the shoot biomass in grams (b), root biomass in grams (c), number of lateral roots (d), root length in cm (e), and the amount of plant ‘greenness’ in terms of green-to-white pixel ratio (f) after 14 days of bacterial inoculation. Bacterial phenotype groups are displayed in different colours (black: ancestor; dark grey: ancestral-like; light grey: transient; orange: stress-sensitive, light green: mutualist 1 and dark green: mutualist 2) and were classified and named based on K-means clustering using 14 phenotypic traits linked to growth, stress tolerance, production of bioactive compounds and antimicrobial activity. All boxplots show median (centre line), interquartile range (25–75%) and whiskers that extend 1.5 times the interquartile range overlaid with a scatter plot showing independent replicates. Statistical testing in all panels was carried out using one-way ANOVA, and asterisks above plots indicate significant differences between control and bacteria-treated plants (*P = 0.05, **P = 0.01, ***P = 0.001; n.s. = non-significant). Data for all panels are provided in the Source Data file.

https://www.nature.com/articles/s41467-021-24005-y

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Humanity's self-alienation has reached the point where it can experience its own annihilation as a supreme aesthetic pleasure.

Walter Benjamin

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Nombres del Conuco 

Tabla elaborada por Esteban Emilio Monsonyi, quien hace el siguiente comentario: es muy interesante señalar que en el caso del añú, la palabra kunú significa árbol o madera; palabra que conserva intacta la misma raíz etimológica del taíno konuko.

Fuente: https://www.academia.edu/44609090/Conuco_Fruto_del_árbol_Kalivirnae

Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks 

Windsor et al, 2021

1. Managing agricultural environments in a way that maximises the provision of multiple ecosystem services is a significant challenge in the development of sustainable and secure food systems. Advances in network ecology provide a way forward, particularly in arable landscapes, as they incorporate mutualistic and antagonistic interactions associated with crop production.
2. Here, we present an approach to identify mixes of non-crop plant species that provide multiple ecosystem services while minimising disservices. Genetic algorithms were applied to the Norwood Farm ecological network—a comprehensive dataset of antagonistic and mutualistic species interactions on an organic farm in the United Kingdom. We aimed to show how network analyses can be used to select plants supporting a high diversity of insect pollinators and parasitoids of insect pests, but low diversity of herbivores. Further to this, we wanted to understand the trade-offs in ecosystem service provision associated with conventional management practices that focus on individual ecosystem services.
3. We show that multilayer network analyses can be used to identify mixes of plant species that maximise the species richness of pollinators and parasitoids (natural enemies of insect pests), while minimising the species richness of herbivores.
4. Trade-offs between ecosystem processes were apparent with several plant species associated with a high species richness of both positive (pollinators and parasitoids) and negative (herbivores) functional taxonomic groups. As a result, optimal plant species mixes for individual ecosystem services were different from the mix simultaneously maximising pollination and parasitism of pest insects, while minimising herbivory.
5. Synthesis and applications. Plant mixes designed solely for maximising pollinator species richness are not optimal for the provision of other ecosystem services and disservices (e.g. parasitism of insect pests and herbivory). The method presented here will allow for the design of management strategies that facilitate the provision of multiple ecosystem services. To this end, we provide a protocol for practitioners to develop their own plant mixes suitable for farm-scale management. This avenue of predictive network ecology has the potential to enhance agricultural management, supporting high levels of biodiversity and food production by manipulating ecological networks in specific ways.

A conceptual representation of the genetic algorithm approach. (a) For either bipartite or multilayer networks, N initial groups of k plant species are randomly selected. (b) The plant mixes are ranked based on the optimiser function (which here is species richness but could be any property of the network of plants and interacting taxa). Individual (f) or compound (fm) optimiser functions are used depending on the scenarios but see the red text for two examples. (c) The plant species mixes that have low values of the optimiser (i.e. low species richness) are removed from the pool of potential mixes. Plant species in the remaining mixes are recombined (d) into new mixes to replace the networks that are removed during the selection stage. In (e), random plant species not already in the mix are added to the plant mixes. Finally, the new plant mixes which have been altered from the initial mix defined in (a) are then taken through the entire process again (b–e). This continues until an optimal plant mix is identified, or until the maximum number of iterations is reached. 

https://doi.org/10.1111/1365-2664.14007

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"The separation of science and non-science is not only artificial but also detrimental to the advancement of knowledge. If we want to understand nature, if we want to master our physical surroundings, then we must use all ideas, all methods, and not just a small selection of them." 

Paul Feyerabend. 1975. Against Method. Book by Paul Feyerabend.  

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Modelos Matemáticos en la Agroecología: Oportunidades y Desafíos

La agroecología se ha establecido firmemente como una disciplina científica, esto necesariamente involucra su desarrollo en diferentes áreas del conocimiento. Un área particularmente importante (y con gran potencial) es el empleo de modelos matemáticos. Aquí se muestran, haciendo énfasis en aplicaciones prácticas, algunas oportunidades que el uso de modelos matemáticos le brinda a la agroecología. De igual manera, se señalan ciertos desafíos existentes para que estos puedan ser ampliamente incorporados en la disciplina. Con este fin se presentan 3 ejemplos relacionados (respectivamente) con: 1- el diseño de experimentos, 2- el aprendizaje estadístico y 3- el modelado matemático de sistemas ecológicos. En un primer ejemplo se muestra como el diseño de experimentos permitió obtener resultados positivos y perspectivas de exploración prometedoras, en una investigación que busca soluciones a problemas relacionados al cambio climático en viñedos en transición orgánica en la zona central de Chile (Quinta Región). Posteriormente se presenta como, la combinación de datos precisos (y abundantes) en conjunto con herramientas de aprendizaje estadístico, permitió la construcción de un modelo exitoso de predicción de cosechas en manzanos orgánicos en la Región del Maule en Chile. Finalmente, se muestra como el empleo de un modelo matemático dinámico de ecología de poblaciones, permite desarrollar diseños prediales agroecológicos para solucionar algunos de los problemas asociados a la enfermedad del dragón amarrillo (HLB) en plantaciones de cítricos.