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The tree which moves some to tears of joy is in the eyes of others only a green thing which stands in the way

William Blake to the Reverend Dr J Trusler, 23 August 1799

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www.sciencephoto.com

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Les maîtres des abeilles 

Turquie, deux rivaux pour un trésor

Some theoretical notes on agrobiodiversity: spatial heterogeneity and population interactions 

Diego Griffon and Maria-Josefina Hernandez

Ecological interactions are fundamental in ecological pest management, and these interactions form networks. The properties of these networks, where interactions of all possible nature (positive, neutral, negative) coexist, are key for management, but little is known about them. The main reasons for this lack of knowledge are the difficulties in obtaining empirical evidence. These problems may be partially bypassed using a theoretical approach. Here, by means of mathematical models that represent networks of ecological interactions in agroecosystems, we characterize some architectural features that promote the self-regulation of population densities in these networks. The results show that the key features are: spatial heterogeneity and a high proportion of positive interactions.

Biodiversity and spatial heterogeneity strongly benefit agricultural landscapes. Among others, these benefits are related to population regulation of organisms that feed on cultivated plants (Duflot et al. 2015; Fahrig et al. 2011; Letourneau et al. 2011; Rusch et al. 2016, 2010; Tscharntke et al. 2002, 2012; Vandermeer 1989). However, there is a need for further theoretical development to help us understand the processes behind these empirical observations, particularly from a mechanistic point of view. In a very general sense, in the agroecological literature it is proposed that increasing agricultural biodiversity involves an increase in the number of trophic interactions of the ecological community, which in turn promotes the stability of the whole system (Altieri 1983; Altieri and Nicholls 2000, 2004; Nicholls and Altieri 2002). On the other hand, we acknowledge that the ecological evidence concerning the relationship between the number of species (richness) and the number of trophic interactions in natural ecosystems is ambiguous (Hall and Raffaelli 1997) and, that from a theoretical point of view, the relationship between complexity and stability is an issue far from resolved (Allesina and Tang 2015; Bersier 2007; Ings et al. 2009; Namba 2015). However, when it comes to contrasting a monoculture with a multidiverse agroecosystem, these topics may have clearer answers (Griffon and Hernández 2014; Griffon and Rodríguez 2017; Rusch et al. 2016, 2010; Tscharntke et al. 2012).

In a conventional monoculture, the system is explicitly designed and managed to reduce as much as possible the unplanned associated biodiversity(typically by using insecticides, herbicides, etc.). Paradoxically this may contribute(among other things) to the long term establishment of phytophagous organisms in the system, eradicating at the same time their biological controllers (Jonsson et al. 2015; Landis, Wratten, and Gurr 2000; Levins and Vandermeer 1990). In this type of farming system most species are related directly to one (the monoculture) by a victim-exploiter relationship (i.e., predation, parasitism, parasitoidism and herbivory), where the monoculture species (the crop) typically plays the role of the victim. So, the system has a star-like architecture (i.e., many nodes connected to a central hub) with the monoculture in the center (Griffon and Torres-Alruiz 2008), which is a structure that favours the occurrence of pest situations and crops losses (Griffon and Hernández 2014; Griffon and Rodríguez 2017).

Alternatives to the ecological oversimplification of monocultures are companion crops. One of the aims of this cropping strategy is the population regulation of phytophagous and phytopathogenic species by means of ecological interactions (Altieri and Nicholls 2004). The success of this approach not only depends on the occurrence of a more complex trophic web, but also on the occurrence of other ecological interactions (competition, mutualism, amensalism and commensalism) that together make up the ecological network system, i.e., a network consisting of all types of ecological interactions (Ings et al. 2009). We have very little information on the structure of ecological networks (Pocock et al. 2012) and we also lack knowledge on how ecological networks promote the regulation of phytophagous population densities. Given the need of information, coupled with the difficulty and effort involved in achieving it in the field, this paper addresses the issue from a theoretical perspective. In order to do this, we build and numerically evaluate mathematical models that simulate networks of hypothetical ecological interactions associated with agricultural ecosystems. This is done with the objective of finding patterns that can provide guidelines on architectural features associated with self-regulation in populations.

Another related topic must be considered. There is abundant field information that shows the positive effects of spatial heterogeneity (Batáry et al. 2011; Duflot et al. 2015; Fahrig 2013; Fahrig et al. 2011, 2015; Jonsson et al. 2015; Landis, Wratten, and Gurr 2000; Rusch et al. 2016; Tscharntke et al. 2012; Tuck et al. 2014) on the maintenance of associated agricultural biodiversity (sensu Vandermeer and Perfecto 1995; Altieri et al. 2005). In some cases, space heterogeneity may even play a more important role than intra-farm diversity in the regulation of phytophagous population densities (Fahrig et al. 2011, 2015). But surely the two components (intra and inter farm diversity) relate synergistically.

For the spatial heterogeneity to have a positive effect on the internal dynamics of agroecosystems it is necessary, on the one hand, an insidefarm design that attracts biological controllers (e.g., flower strips or beetle banks) (Altieri, Ponti, and Nicholls 2005; Nicholls and Altieri 2002) and on the other, the existence of nearby sources of organisms with enough internal complexity to provide the necessary control agents (Rusch et al. 2016, 2010; Tscharntke et al. 2012).

So, metapopulation and metacommunity dynamics seem to be crucial for the long term survival of species in heterogeneous environments (Alfonzo et al. 2009; Aberg et al. 1995; Cantrell, Cosner, and Fagan 1998; Delin and Andren 1999; Griffon, Alfonzo, and Hernandez 2010; Griffon and Hernández 2014; Gustafson and Gardner 1996; Perfecto, Vandermeer, and Wright 2009; Sisk, Haddad, and Ehrlich 1997; Tejat et al. 2002; Vandermeer and Carvajal 2001; Vandermeer and Perfecto 2007). In general terms, the spatial structure of populations, along with processes of dispersal, migration and colonization, allows the emergence of dynamics that make possible the persistence and coexistence of species (Hanski 1994, 1998; Hanski and Gilpin 1997; Hanski et al. 1996; Leibold et al. 2004). Thus, spatial heterogeneity may enhance the configuration of the complex ecological networks needed for a successful ecological pest management program (Batáry et al. 2011; Fahrig et al. 2011; Rusch et al. 2016, 2010; Tejat et al. 2002; Tscharntke et al. 2002, 2012). For this reason, in the mathematical approach used here we also include the effect of spatial heterogeneity on population dynamics.

In short, the objective of this work is to find architectural features that promote the self-regulation of population densities in ecological networks associated to agroecosystems. To do this, we built mathematical models that represent ecological networks, both for a single community and for metacommunitarian systems. We must make clear that whenever we say ‘ecological network’, we are considering the potential presence of ‘all’ types of interactions, i.e., competition, mutualism, victim-exploiter, amensalism and commensalism.

Species survivals. Initial richness, for six different initial conditions (defined in Fig 3). Blue: survival percentage in one community (no spatial heterogeneity). Grey: survival percentage of one community in a metacommunitarian background. The curves are averages of 40 simulations for each initial condition.

Uniform perturbation (example). A persistent network obtained under the 20:10:10:60 initial condition is perturbed by a little increase in the densities of each population. Left: dynamics after the perturbation. Two populations reach very low (but non zero) densities. Right: network before and after the perturbation. Notice that both networks are the same.

The more relevant results of the models evaluated and discussed in this article can be summarized as follows: (i) The conditions under which persistent networks are obtained after the iterative process are very restricted. However, when persistent  networks are obtained, they are fundamentally resilient to perturbations. (ii) Mutualistic (and positive in general) interactions have an important and extensive effect under certain (very specific) conditions. (iii) Spatial heterogeneity increases the possibility of persistence in hypothetical communities. (iv) Ecological interactions that somehow have been neglected in the past (commensalism and amensalism, the forgotten sisters), may be: 1- More frequent than generally thought, and 2- Important for the persistence of communities.

https://bit.ly/2YPVlTO

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Foto: Alex Howitt 

https://www.facebook.com/alex.howitt.5 

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Host selection shapes crop microbiome assembly and network complexity 
Xiong et al., 2020

• Plant microbiomes are essential to host health and productivity but the ecological processes that govern crop microbiome assembly are not fully known.
• Here we examined bacterial communities across 684 samples from soils (rhizosphere and bulk soil) and multiple compartment niches (rhizoplane, root endosphere, phylloplane, and leaf endosphere) in maize (Zea mays)‐wheat (Triticum aestivum)/barley (Hordeum vulgare) rotation system under different fertilization practices at two contrasting sites.
• Our results demonstrate that microbiome assembly is shaped predominantly by compartment niche and host species rather than by site or fertilization practice. From soils to epiphytes to endophytes, host selection pressure sequentially increased and bacterial diversity and network complexity consequently reduced, with the strongest host effect in leaf endosphere. Source tracking indicates that crop microbiome is mainly derived from soils and gradually enriched and filtered at different plant compartment niches. Moreover, crop microbiomes were dominated by a few dominant taxa (~0.5% of bacterial phylotypes), with Bacilli identified as the important biomarker taxa for wheat and barley and Methylobacteriaceae for maize.
• Our work provides comprehensive empirical evidence on host selection, potential sources and enrichment processes for crop microbiome assembly, and has important implications for future crop management and manipulation of crop microbiome for sustainable agriculture.

https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.16890
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Uniendo los Puntos

Agrobiodiversidad en un contexto de múltiples crisis ambientales

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I must emphasize ecosystems cannot be described in hierarchical terms. Whether plant-animal communities contain "dominant" individuals within a species can be argued at length. But to rank species within an ecosystem, between species, is anthropomorphism at its crudest.

Murray Bookchin

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Genetic correlations and ecological networks shape coevolving mutualisms 

Assis et al., 2020

Ecological interactions shape the evolution of multiple species traits in populations. These traits are often linked to each other through genetic correlations, affecting how each trait evolves through selection imposed by interacting partners. Here, we integrate quantitative genetics, coevolutionary theory and network science to explore how trait correlations affect the coevolution of mutualistic species not only in pairs of species but also in species‐rich networks across space. We show that genetic correlations may determine the pace of coevolutionary change, affect species abundances and fuel divergence among populations of the same species. However, this trait divergence promoted by genetic correlations is partially buffered by the nested structure of species‐rich mutualisms. Our study, therefore, highlights how coevolution and its ecological consequences may result from conflicting processes at different levels of organisation, ranging from genes to communities. 

https://onlinelibrary.wiley.com/doi/10.1111/ele.13605

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"Natura"

Bruno Latour

 

TRILLIUM

By Louise Gluck

 

When I woke up I was in a forest. The dark

seemed natural, the sky through the pine trees

thick with many lights.

 

I knew nothing; I could do nothing but see.

And as I watched, all the lights of heaven

faded to make a single thing, a fire

burning through the cool firs.

Then it wasn’t possible any longer

to stare at heaven and not be destroyed.

 

Are there souls that ne­­­ed

death’s presence, as I require protection?

I think if I speak long enough

I will answer that question, I will see

whatever they see, a ladder

reaching through the firs, whatever

calls them to exchange their lives—

 

Think what I understand already.

I woke up ignorant in a forest;

only a moment ago, I didn’t know my voice

if one were given me

would be so full of grief, my sentences

like cries strung together.

I didn’t even know I felt grief

until that word came, until I felt

rain streaming from me.

The Nexus Between Plant and Plant Microbiome: Revelation of the Networking Strategies
Babalola et al., 2020

The diversity of plant-associated microbes is enormous and complex. These microbiomes are structured and form complex interconnected microbial networks that are important in plant health and ecosystem functioning. Understanding the composition of the microbiome and their core function is important in unraveling their networking strategies and their potential influence on plant performance. The network is altered by the host plant species, which in turn influence the microbial interaction dynamics and co-evolution. We discuss the plant microbiome and the complex interplay among microbes and between their host plants. We provide an overview of how plant performance is influenced by the microbiome diversity and function.

https://bit.ly/3h5ddTL
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The Anthropocene

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Photo: Tharmaplan Tilaxan

Photo:  Filip Jandourek

 Photo: Alexander Grir

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