Salabhanjika, stems from ancient Indian symbolism of fertility linking a chaste maiden with the sala tree through the ritual called dohada, or the fertilisation of plants through contact with a young woman. 

Mughal with Pahari influence 18th C.

via:  Lokinder Bisht

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Influence of plant genotype and soil on the wheat rhizosphere microbiome: Evidences for a core microbiome across eight African and European soils

Simonin et al., 2020

Here, we assessed the relative influence of wheat genotype, agricultural practices (conventional vs organic) and soil type on the rhizosphere microbiome. We characterized the prokaryotic (archaea, bacteria) and eukaryotic (fungi, protists) communities in soils from four different countries (Cameroon, France, Italy, Senegal) and determined if a rhizosphere core microbiome existed across these different countries. The wheat genotype had a limited effect on the rhizosphere microbiome (2% of variance) as the majority of the microbial taxa were consistently associated to multiple wheat genotypes grown in the same soil. Large differences in taxa richness and in community structure were observed between the eight soils studied (57% variance) and the two agricultural practices (10% variance). Despite these differences between soils, we observed that 179 taxa (2 archaea, 104 bacteria, 41 fungi, 32 protists) were consistently detected in the rhizosphere, constituting a core microbiome. In addition to being prevalent, these core taxa were highly abundant and collectively represented 50% of the reads in our dataset. Based on these results, we identify a list of key taxa as future targets of culturomics, metagenomics and wheat synthetic microbiomes. Additionally, we show that protists are an integral part of the wheat holobiont that is currently overlooked.

 https://www.biorxiv.org/content/10.1101/777383v2

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αὐτὰρ μῆλα κακοὶ φθείρουσι νο

Bad shepherds ruin their flocks.

Homer - Odyssey

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Agribusiness vs. Public Health: Disease Control in Resource-Asymmetric Conflict
Wallace et al. (2020)

In the context of modern civilization, the ecology of infectious disease cannot be described by interacting populations alone, as much of the mod- eling literature presumes. As a matter of rst principle, formalisms and their statistical applications must account for the anthrosphere from which pathogens emerge. With that objective in mind, we rst formally examine strategies for controlling outbreaks by way of environmental stochastic- ities human institutions help set. Using the Data Rate Theorem, we next explore disease control regimens under asymmetric con icts between agribusiness interests rich in resources and State public health agencies and local communities constrained by those very resources. Military the- ory describes surprising successes in the face of such an imbalance, a result we apply here. Abduction points to strategies by which public health can defeat agribusinesses in its e orts to control agriculture-led pandemics, the heavy health and scal costs of which multinationals routinely pass o to the public.

https://bit.ly/2Jcebzb
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Evolutionary games on isothermal graphs   

Allene et al., 2019.

Population structure affects the outcome of natural selection. These effects can be modeled using evolutionary games on graphs. Recently, conditions were derived for a trait to be favored under weak selection, on any weighted graph, in terms of coalescence times of random walks. Here we consider isothermal graphs, which have the same total edge weight at each node. The conditions for success on isothermal graphs take a simple form, in which the effects of graph structure are captured in the ‘effective degree’—a measure of the effective number of neighbors per individual. For two update rules (death-Birth and birth-Death), cooperative behavior is favored on a large isothermal graph if the benefit-to-cost ratio exceeds the effective degree. For two other update rules (Birth-death and Death-birth), cooperation is never favored. We relate the effective degree of a graph to its spectral gap, thereby linking evolutionary dynamics to the theory of expander graphs. Surprisingly, we find graphs of infinite average degree that nonetheless provide strong support for cooperation.

Isothermal graphs and their effective degrees. A graph is isothermal if the sum of edge weights is the same for each vertex. The effective degree 𝜅̃  of the graph, defined in Eq. (3), determines the outcome of evolutionary game dynamics. a An asymmetric isothermal graph; weights are shown for each edge. b A wheel graph, with one hub and 𝑛 wheel vertices. All connections with the hub have weight 1/𝑛. All connections in the periphery have weight (𝑛−1)/2𝑛. As 𝑛→∞, the effective degree approaches 2. A formula for arbitrary 𝑛 is derived in Supplementary Note 3. c A 30-vertex graph generated with preferential attachment⁶² and linking number 𝑚=3. Isothermal edge weights are obtained by quadratic programming (see Methods). The effective degree, 𝜅̃ ≈2.47, is less than the average topological degree, 𝑘¯=5.6. d An island model, with edges of weight 𝛼≪1 between each inter-island pair of vertices. Shown here are two islands: a 𝑘1-regular graph of size 𝑁1, and a 𝑘2-regular graph of size 𝑁2

https://www.nature.com/articles/s41467-019-13006-7 

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The butterfly counts not months but moments, and has time enough.

Rabindranath Tagore
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Pollen from a variety of common plants as seen under a microscope
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Data-driven contact structures: from homogeneous mixing to multilayer networks

Alberto Aleta, Guilherme Ferraz de Arruda, and Yamir Moreno

The modeling of the spreading of communicable diseases has experienced signi cant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare di erent scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and nally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are di erent. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population.

 

Modeling the contact patterns of the population. Panel A: Schematic view of the di erent models considered. If the only information available is the average number of contacts per individual, homogeneous mixing can be assumed (H). If there is information about the average number of contacts between individuals with age a and a´, then a classical group-interaction model can be implemented (M). On the other hand, if the full contact distribution of the population is known, regardless of their age, it is possible to build the contact network of the population (C). Lastly, when both the contact distribution and the interaction patterns between di erent age groups are known, the individual heterogeneity and the global mixing patterns can be combined to create a multilayer network in which each layer represents a di erent age group (C+M). Panel B: Demographic structure of Italy in 2005 [55]. Panel C: Age-contact patterns in Italy obtained in the POLYMOD study. Panel D: Contact distribution in Italy obtained in the POLYMOD study. The distribution is fi tted to a right-censored negative binomial distribution since the maximum number of contacts that could be reported was 45. 

https://arxiv.org/abs/2003.06946

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Coccomyces dentatus
https://es.wikipedia.org/wiki/Coccomyces_dentatus
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Coexistence of nestedness and modularity in host–pathogen infection networks      
Sergi Valverde, Blai Vidiella, Raúl Montañez, Aurora Fraile, Soledad Sacristán & Fernando García-Arenal

The long-term coevolution of hosts and pathogens in their environment forms a complex web of multi-scale interactions. Understanding how environmental heterogeneity affects the structure of host–pathogen networks is a prerequisite for predicting disease dynamics and emergence. Although nestedness is common in ecological networks, and theory suggests that nested ecosystems are less prone to dynamic instability, why nestedness varies in time and space is not fully understood. Many studies have been limited by a focus on single habitats and the absence of a link between spatial variation and structural heterogeneity such as nestedness and modularity. Here we propose a neutral model for the evolution of host–pathogen networks in multiple habitats. In contrast to previous studies, our study proposes that local modularity can coexist with global nestedness, and shows that real ecosystems are found in a continuum between nested-modular and nested networks driven by intraspecific competition. Nestedness depends on neutral mechanisms of community assembly, whereas modularity is contingent on local adaptation and competition. The structural pattern may change spatially and temporally but remains stable over evolutionary timescales. We validate our theoretical predictions with a longitudinal study of plant–virus interactions in a heterogeneous agricultural landscape.

 

https://www.nature.com/articles/s41559-020-1130-9
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The bacterial community in potato is recruited from soil and partly inherited across generations

Buchholz et al., 2019.

Strong efforts have been made to understand the bacterial communities in potato plants and the rhizosphere. Research has focused on the effect of the environment and plant genotype on bacterial community structures and dynamics, while little is known about the origin and assembly of the bacterial community, especially in potato tubers. The tuber microbiota, however, may be of special interest as it could play an important role in crop quality, such as storage stability. Here, we used 16S rRNA gene amplicon sequencing to study the bacterial communities that colonize tubers of different potato cultivars commonly used in Austrian potato production over three generations and grown in different soils. Statistical analysis of sequencing data showed that the bacterial community of potato tubers has changed over generations and has become more similar to the soil bacterial community, while the impact of the potato cultivar on the bacterial assemblage has lost significance over time. The communities in different tuber parts did not differ significantly, while the soil bacterial community showed significant differences to the tuber microbiota composition. Additionally, the presence of OTUs in subsequent tuber generation points to vertical transmission of a subset of the tuber microbiota. Four OTUs were common to all tuber generations and all potato varieties. In summary, we conclude that the microbiota of potato tubers is recruited from the soil largely independent from the plant variety. Furthermore, the bacterial assemblage in potato tubers consists of bacteria transmitted from one tuber generation to the next and bacteria recruited from the soil.

Initially, seed potatoes of seven potato cultivars (Agata, Agria, Ditta, Fabiola, Fontane, Lady Claire and Hermes) were used for 16S rRNA gene amplicon sequencing (T0) and were grown in parallel in commercial potting soil. At maturity, tubers were harvested and used for 16S rRNA gene amplicon sequencing (T1). Tubers of four varieties (Agata, Fabiola, Hermes and Lady Claire) were planted in pots with five different soil types (commercial potting soil and four different farmland soils). Again, tubers were harvested at maturity and used for bacterial community sequencing (T2). The 16S rRNA gene amplicon sequencing data of different potato tuber generations and potato cultivars were combined to different datasets depending on the research question (datasets 1–4), as indicated by differently colored boxes. 

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223691
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À partir de ce moment, il est possible de dire que la peste fut notre affaire à tous. 

Albert Camus (1947). La Peste.
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A mutualistic interaction between Streptomyces bacteria, strawberry plants and pollinating bees

Kim et al., 2019

Microbes can establish mutualistic interactions with plants and insects. Here we track the movement of an endophytic strain of Streptomyces bacteria throughout a managed strawberry ecosystem. We show that a Streptomyces isolate found in the rhizosphere and on flowers protects both the plant and pollinating honeybees from pathogens (phytopathogenic fungus Botrytis cinerea and pathogenic bacteria, respectively). The pollinators can transfer the Streptomyces bacteria among flowers and plants, and Streptomyces can move into the plant vascular bundle from the flowers and from the rhizosphere. Our results present a tripartite mutualism between Streptomyces, plant and pollinator partners.

Microbial diversity of strawberry flowers and pollen. a Pyrosequencing of microbes in strawberry flowers (n = 9, 13 independent experiments) and b pollen (n = 2, 9 independent experiments). Taxonomic assignment was conducted at the family level with the Silva database (http://www.arb-silva.de/) and a cutoff of 97% similarity. Flower and pollen samples were collected from November 2013 (0 week) to March 2014 (24 week). Heatmap of hierarchical clustering of bacterial communities by 16S rRNA region. c Flower samples and d pollen samples. Heatmap color (purple to yellow) displayed from low to high abundance of each OTU. e Beta diversity tree (Minkowski distance) of samples with gray mold disease incidence. f Venn diagram of common OTU numbers in flower and pollen samples during the period of low gray mold disease incidence. g Gray mold incidence over a growing season as related to Streptomyces OTU read numbers. Gray mold incidence, bars represent standard error of nine blocks, each block contains 150 plants. Star (*) indicates statistically significant differences between disease incidence and OTU numbers of Streptomyces globisporus NRRL B-2872, which is identical to SP6C4 and SF7B6 by t-test (P value < 0.05). Bars represent standard error. a, b, e, f, g Source data are provided as a Source Data file.

https://www.nature.com/articles/s41467-019-12785-3

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Luis Buñuel - Tierra sin pan (Las Hurdes)
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Complex responses of global insect pests to climate warming  
Lehmann et al., 2020

Although it is well known that insects are sensitive to temperature, how they will be affected by ongoing global warming remains uncertain because these responses are multifaceted and ecologically complex. We reviewed the effects of climate warming on 31 globally important phytophagous (plant‐eating) insect pests to determine whether general trends in their responses to warming were detectable. We included four response categories (range expansion, life history, population dynamics, and trophic interactions) in this assessment. For the majority of these species, we identified at least one response to warming that affects the severity of the threat they pose as pests. Among these insect species, 41% showed responses expected to lead to increased pest damage, whereas only 4% exhibited responses consistent with reduced effects; notably, most of these species (55%) demonstrated mixed responses. This means that the severity of a given insect pest may both increase and decrease with ongoing climate warming. Overall, our analysis indicated that anticipating the effects of climate warming on phytophagous insect pests is far from straightforward. Rather, efforts to mitigate the undesirable effects of warming on insect pests must include a better understanding of how individual species will respond, and the complex ecological mechanisms underlying their responses.

Four major categories of responses to climate warming. (a) Range changes include range expansions or shifts (latitudinal or altitudinal). (b) Life‐history changes primarily consist of alterations to biological timing events or the number of annual generations. (c) Population dynamics reflect population size, and damage is expected to increase whenever temperature limits performance, but if threshold temperatures are reached, control and related feedback mechanisms may be triggered. T_present reflects current temperature fluctuations over a time period (eg a year or a day), whereas T_future reflects future temperatures over the same period. (d) Trophic interactions reflect temperature responses of organisms and trophic groups (plants = dashed green line, herbivores = solid red line, predators = dashed blue line). Because vital rates (ie rates of important life‐history traits, such as growth, dispersal, and reproduction) may vary, climate warming could strongly affect trophic relationships.  

https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/fee.2160
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