Refuting Creationism - Selfish Genes Evolved Cooperative Alliances

Complex human gut microbiome

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Fish swimming past coral and marine sponge. The researchers are currently using the proposed framework to investigate microbes inside marine sponges.

Researchers create new framework to help understand how microbial communities emerge - Swansea University

One of the lines of attack against the science of evolutionary biology is the lie that Richard Dawkins was advocating selfishness with his seminal book, The Selfish Gene, so 'proving' that rejection of the Christian god as the explanation for biodiversity is because 'Evolutionists' just want to sin.

It is, of course, like other creationist attacks on science, utterly devoid of any factual basis and reflects badly both on those who seek to fool their target dupes with it and on their dupes who eagerly believe them in order to justify a pretense of moral superiority.

In fact, natural selection and differential success of different alleles within a selective environment is devoid of any moral contents because it is devoid of intelligent input and genes are passive in the process. In Dawkin’s analogy, the result is as though genes behave selfishly, not that they make moral decisions or have the ability to choose which environmental selectors act on them.

One of the predictable outcomes of this gene-centred approach is that in a competition, the result which suits all competitors is cooperation since in a cooperative alliance, all the genes are winners, just as with a human group, a cooperative group is invariably more successful than a group of competing individuals in perpetual conflict.

New research shows that "Virtually all multicellular organisms on Earth live in symbiotic associations with very large and complex microbial communities known as microbiomes*" Do you have some examples of these microbiomes? Absolutely! Microbiomes are communities of microorganisms, including bacteria, fungi, viruses, and archaea, that live in close association with a host organism. Here are some examples of microbiomes found in various multicellular organisms:

1. Human Microbiome
   • Gut Microbiome: The human gastrointestinal tract houses trillions of microorganisms, primarily bacteria. These microbes play a crucial role in digesting food, synthesizing vitamins (like Vitamin K and B12), and protecting against harmful pathogens. They also influence the immune system and metabolic processes.
   • Skin Microbiome: The skin is another major site for microbial communities, with different types of bacteria and fungi adapted to various skin environments (oily, moist, dry). These microbes help protect against pathogens and contribute to skin health.
   • Oral Microbiome: The mouth hosts a diverse microbial community that helps with food digestion, prevents colonization by harmful microbes, and even contributes to overall health by influencing the body's inflammatory response.
   
   
2. Plant Microbiome
   • Rhizosphere Microbiome: The region of soil around plant roots, known as the rhizosphere, is rich in microbial life. These microorganisms help plants absorb nutrients, protect against pathogens, and improve stress resistance (like drought or high salinity).
   • Phyllosphere Microbiome: The surface of leaves, or the phyllosphere, hosts bacteria and fungi that protect plants from diseases, aid in nutrient cycling, and interact with other environmental factors such as humidity and temperature.
   
   
3. Insect Microbiome
   • Bee Gut Microbiome: Honeybees have a unique gut microbiome that aids in digesting pollen and nectar, protects against harmful pathogens, and supports overall bee health. The bee microbiome also plays a role in social behavior and colony stability.
   • Termite Gut Microbiome: Termites harbor a specialized gut microbiome rich in bacteria and protists that help them digest cellulose, the primary component of wood. These microbes enable termites to break down otherwise indigestible material into usable nutrients.
   
   
4. Coral Microbiome
   • Coral reefs are ecosystems formed by coral polyps, which live in symbiosis with a diverse microbiome consisting of bacteria, archaea, viruses, and fungi. These microbes play vital roles in nutrient cycling, defense against pathogens, and adaptation to environmental changes, like ocean acidification and warming.
5. Fish Microbiome
   • Gut Microbiome: Fish, like humans, have complex gut microbiomes that assist in digestion, immune function, and protection against disease. The composition of these microbiomes can vary based on diet, environment, and other factors.
   • Skin Microbiome: Fish skin is covered with a mucus layer that hosts a diverse microbial community. These microbes provide protection against pathogens and contribute to the health of the fish's skin and immune system.
   
   
6. Fungal Microbiome
   • Even fungi have microbiomes! For example, certain fungi, like lichens (a symbiotic relationship between a fungus and an alga or cyanobacterium), are known to host bacteria and other microorganisms that help them with nitrogen fixation, stress tolerance, and other metabolic functions.
7. Animal Microbiomes Beyond Humans
   • Ruminant Microbiome: Cows, sheep, goats, and other ruminants possess a complex microbiome in their stomachs (particularly in the rumen) that helps digest cellulose from plant cell walls, converting it into fatty acids, vitamins, and proteins essential for their nutrition.
   • Squid Light Organ Microbiome: The Hawaiian bobtail squid hosts a specific species of bioluminescent bacteria, *Vibrio fischeri*, in a specialized light organ. The bacteria produce light that helps the squid avoid predators by mimicking moonlight on the water surface, a phenomenon known as counter-illumination.

Microbiomes are essential for the health, growth, and survival of virtually all multicellular organisms. They are involved in processes like digestion, immunity, nutrient absorption, and protection against pathogens, showcasing a remarkable diversity of symbiotic relationships across the tree of life.

Evidence of these cooperative alliances abound in nature not least in the cooperative alliances of microorganisms known as microbiomes, and, more significantly from a human perspective is the essential cooperative alliance that make multicellular life work. Indeed, multicellular life would not exist were it not for the cooperative alliances between prokaryote cells that produced the eukaryotes cells of which all multicellular organisms as themselves cooperative alliances.

This new work by Dr Miguel Lurgi and his colleague Dr Gui Araujo, from the Biosciences Department of the Faculty of Science and Engineering, Swansea University, UK, with collaborators from the French Scientific Research Council, the University of New South Wales in Australia, and the Institute for Marine and Antarctic Studies, also in Australia offers an explanation for how these microbiomes evolved.

Researchers create new framework to help understand how microbial communities emerge

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Virtually all multicellular organisms on Earth live in symbiotic associations with very large and complex microbial communities known as microbiomes.

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New research has just been published aimed at offering a complete understanding how those relationships form.

Computational ecologist Dr Miguel Lurgi explores how associations between complex bacterial communities and multicellular hosts emerge in nature by combining theory with empirical work.

For his latest research Dr Lurgi and his colleague Dr Gui Araujo, from the Biosciences Department of the Faculty of Science and Engineering, teamed up with collaborators from the French Scientific Research Council, the University of New South Wales in Australia, and the Institute for Marine and Antarctic Studies, also in Australia.

They set about devising a theoretical framework to gain further knowledge on the emergence of host-associated complex microbiomes. Their insights have just been published by prestigious journal Trends in Microbiology.

We argue that microbiome assembly is a product of ecology and evolution acting together. Our research aims at bringing together ecological and evolutionary theory on one hand, and microbial and symbiont ecology and evolution on the other, to create a holistic picture of the assembly of complex symbioses. These symbiotic relationships constitute one of the most ancient associations between multicellular organisms and groups of microbes, and, in many cases, they are fundamental to the persistence of both the host and the microbiome.

Dr Miguel Lurgi, corresponding author
Department of Biosciences
Swansea University, Swansea, UK.

The researchers are currently using the proposed framework to investigate microbes inside marine sponges. They are also looking at extending these findings to other microbiomes, eventually allowing for a unified understanding of the intricate nature of symbiotic relationships of multiple species within different groups of hosts and across taxa.

Dr Lurgi is head of the Computational Ecology Lab at Swansea and has been awarded a Leverhulme Trust award for his research project The origin of complex symbioses.

My main research focus is on the mechanisms behind the emergence of complexity in ecological networks. I develop theoretical models of ecological communities and network dynamics to better understand these mechanisms and the biodiversity patterns they give rise to.

Dr Miguel Lurgi.

Dr Lurgi and Dr Araujo are now working on developing the mathematical foundations of the ideas presented in the current paper and have just presented the work at the 19th International Symposium of Microbial Ecology, in South Africa.

Read the paper A mechanistic framework for complex microbe-host symbioses in full

Highlights
As in all complex ecosystems, multispecies symbiotic associations are shaped by ecological and evolutionary forces acting at several temporal, spatial, and organisational scales.

Microbiome assembly inside plant and animal hosts is shaped by, and in turn shapes, the traits of both microbes and hosts. Theoretical frameworks combining ecological and evolutionary mechanisms are essential to provide a better understanding of the assembly of complex symbiotic microbial communities.

The generation of testable predictions from theory relies on the identification of key mechanisms playing a fundamental role on the questions and patterns addressed. In microbiome research this amounts to processes giving rise to their complex organisation.

Methods for matching empirical patterns to model outcomes through model selection and data analysis can reveal potential sets of mechanisms and conditions capable of generating observed patterns of organisation in complex microbiomes.

Abstract
Virtually all multicellular organisms on Earth live in symbiotic associations with complex microbial communities: the microbiome. This ancient relationship is of fundamental importance for both the host and the microbiome. Recently, the analyses of numerous microbiomes have revealed an incredible diversity and complexity of symbionts, with different mechanisms identified as potential drivers of this diversity. However, the interplay of ecological and evolutionary forces generating these complex associations is still poorly understood. Here we explore and summarise the suite of ecological and evolutionary mechanisms identified as relevant to different aspects of microbiome complexity and diversity. We argue that microbiome assembly is a dynamic product of ecology and evolution at various spatio-temporal scales. We propose a theoretical framework to classify mechanisms and build mechanistic host-microbiome models to link them to empirical patterns. We develop a cohesive foundation for the theoretical understanding of the combined effects of ecology and evolution on the assembly of complex symbioses.

Complex symbioses: a shared history of ecology and evolution
Complex symbioses encompassing microbiomes (see Glossary) and their multicellular hosts are a ubiquitous and essential aspect of life since the emergence of multicellularity [1]. Microbiomes act as ‘multifunctional organs’ for hosts by providing essential metabolic functions and engaging in a multitude of roles crucial for development, protection, health, nutrition, and more. The hosts, in turn, offer an entire ecosystem for the microbes, providing resources, habitats, and means of transportation [2]. In addition to being vital to the biology and ecology of individual hosts, the microbiome is a fundamental component of a host’s entire evolutionary history [3]. Host and microbial species can shape and integrate each other in a coevolutionary process to such an extent that they can potentially become a single evolutionary unit [4]. In addition, microbiome composition can change through the acquisition or loss of community members, which can provide functional flexibility to the symbiosis (e.g., to allow for temporary adaptation to an environmental change) [5]. Consequently, to describe and understand the evolution and function of all multicellular life, it is essential to understand the nature of complex host-microbiome associations.

Describing the elements of a complex microbiome’s function and composition involves uncovering the mechanisms underlying their emergence and maintenance. This challenge requires an understanding of the dynamical interplay between the ecology and evolution of both hosts and microbes [6]. The complexity of these communities is made from intricate causal links and feedback loops operating across spatial scales, from single microbes to host communities, and existing across several temporal scales, from a microbe’s lifespan to an entire evolutionary history [7–9]. Thus, to understand complex symbioses, and their origins, it is necessary to consider the joint dynamical influences of a combined set of ecological and evolutionary mechanisms that operate across multiple scales [10].

Advances in DNA sequencing technologies have generated large amounts of information on the diversity and composition of the microbial world [11,12]. These advances should go hand in hand with the development of theory to build a mechanistic understanding of the processes generating the organisational patterns observed in microbial communities [9,13]. A first step towards this understanding is a comprehensive description of microbiome assembly mechanisms organised under a coherent framework. Once these mechanisms have been identified, such a framework can be extended to provide pattern-generating dynamic models and a basis for data analysis that would allow researchers to explain observed patterns through a mechanistic lens. Last, comparing outputs from different models can help in testing competing hypotheses.

In this review, we propose a conceptual unifying framework to incorporate ecological and evolutionary mechanisms of host-microbiome assembly into mathematical models. Our goal is to allow for a formal quantitative examination of their role in the assembly of complex symbioses. This framework is grounded in previous ecological theory aimed at understanding biodiversity across scales. It incorporates biological mechanisms as model elements, combining (i) the complex temporal ecological dynamics of microbes and resources mediated by hosts, (ii) the microbial community assembly process via speciation, dispersal, and invasions, (iii) the demographic dynamics of host communities, and (iv) the evolution of host and microbe traits through time. Additionally, we discuss the use and selection of models to generate empirical patterns and test specific hypotheses behind the emergence of these patterns.

Classifying mechanisms of host-microbiome assembly
Previous research on host-associated microbial communities has highlighted the need to move beyond descriptions of observed patterns of microbiome organisation and tackle the challenge of unveiling the specific ecological and evolutionary mechanisms underlying their assembly and composition [14–16]. Building towards this goal, recent studies have focused on developing a better understanding of different sets of mechanisms playing a role on specifics aspects of microbiome organisation. Adair and Douglas [16] provide a comprehensive discussion of ecological mechanisms (both deterministic and stochastic) behind the assembly of host-associated microbiomes by microbe colonisers from the external environment. Their focus is exclusively on ecological processes. Kohl [14] focuses on identifying mechanisms driving the pattern of phylosymbiosis observed in host-associated microbial communities, providing a theoretical conceptualisation of the shared evolutionary history between host and microbiome in terms of selection, drift, dispersal, and diversification. Culp and Goodman [15] provide an in-depth review of the specific mechanism of microbial cross-feeding and its role on the ecology and evolution driving microbiome composition. As useful as these (and other) previous efforts are at organising our knowledge on the mechanisms of microbiome assembly, in order to structure a unifying framework of complex symbioses it is desirable to first provide a comprehensive synthesis of both the ecological and evolutionary mechanisms of host-microbiome assembly. The focus is on a seamless integration into a theoretical modelling framework of microbiome assembly. Thus, as a first step towards developing a mechanistic framework for complex microbe-host symbioses, we present a systematic classification of the ecological and evolutionary mechanisms of host-microbiome assembly identified in previous empirical and experimental studies (Table 1).

We classify these mechanisms into different categories reflecting the separation between ecological and evolutionary processes. We further divide ecological processes into host- and microbe-related as a natural way of thinking about the processes concerning each of these distinct types of organisms involved in the symbioses. On the one hand, it allows for the grouping of ecological mechanisms comprising the dynamical states that generate interactions, reproduction, dispersal and death of individual microbes and hosts. On the other hand, it groups together evolutionary mechanisms driving the change in species characteristics and the emergence of innovation over the course of many reproductive and colonisation events through time. Since the two types of objects being modelled via these processes are the hosts and the microbes, each ecological mechanism is defined as relating to either host or microbes.

Microbe-related ecological mechanisms
These mechanisms encompass microbial traits and functions that are relevant to their interactions with other microbes and the host environment (Table 1). For example, temporal patterns of succession and different stages of microbiome composition are shaped by mechanisms of microbe-microbe interactions, microbial function, and priority effects [17,18]. Functional trait-based relationships and competition for common resources between microbes within hosts can cause continuous turnover and patterns of change in microbiome composition associated to different stages of host development [19,20]. Incorporating microbe-related ecological mechanisms into modelling frameworks should focus on defining the relevant features of microbial taxa (e.g., taxonomic versus functional), how they relate to each other and the host, their traits and distribution. This information should then be used to define their explicit interactions (including interaction types) with other microbes and the hosts. Host-related ecological mechanisms

These mechanisms derive from the biology and ecology of hosts, encompassing both their bodies as the environmental setting for the microbiome and the dynamic changes and patterns influencing this environment over time (Table 1). Patterns of microbiome richness, abundance (i.e., microbial density), and complexity are strongly influenced by mechanisms of host selection and host’s life-traits [21,22]. Mechanisms of host selection are diverse and can shape their microbiome in different ways. Hosts can, for example, alter microbial densities directly through genetic interactions [23] or control microbiome composition through substrate availability determined by the host’s physiological state [24]. Composition of the microbiome is also strongly influenced by aspects of host behaviour, such as diet [25,26], which can explain substantial variations in alpha- and beta-diversity of microbiomes across several vertebrate clades [27]. The incorporation of host-related ecological mechanisms into modelling frameworks involves formal definitions of the host, translated as model parameters and conditions of the environment in which the microbial community exists.

Evolutionary mechanisms
These mechanisms concern the maintenance and change of information within communities as they experience different conditions across space and time (Table 1). This information is encoded in the genes that make up the species and can affect the outcome of assembly. Patterns of co-phylogeny and co-diversification emerge as a consequence of a shared history of hosts and microbes through the action of evolutionary mechanisms [28–31]. Homogeneous coevolution can produce convergence of microbiomes in individuals of a single host species that live in geographically distinct locations [32] or different host species that share the same diet [25]. By contrast, genetic differences across individuals of the same host species living in different environments can drive a divergence of microbiome composition in response to adaptations to different environmental conditions [33]. The unique nature of microbial evolution, being faster than host evolution and capable of lateral gene transfer, is an additional source of complexity in host-microbe associations. Host-microbe interactions can potentially evolve to change between being mutualistic, commensal, and parasitic [34]. Incorporating evolutionary mechanisms into modelling frameworks involves changing model parameters governing species traits over time, with the corresponding filtering of resulting phenotypes via selection.

Now, because creationists love some mathematics to misrepresent, (and because the HTML is a challenge) here is how the team represented the microbiome associated with sponges:

Box 3

Mechanism selection and pattern-generation: the example of the sponge microbiome

Sponges (Porifera) are an ideal system to study the coevolution of complex symbioses. They are considered one of the most ancient extant metazoans and have had intimate relationships with their microbiome for hundreds of millions of years. Many eco-evolutionary patterns have been described for these symbioses, including that sponge microbiomes encompass a wide range of richness, but have a high correlation of microbiome composition with host species [21]. Sponges can also be generally separated based on their microbiomes into two types: one characterised by low microbial abundance (LMA), and another characterised by high microbial abundance (HMA). Among many mechanisms thought to define HMA and LMA sponges, we focus on two ecological ones: water pumping rate (‘Host properties’ in Table 1 in the main text) and host selection on microbes (‘Host selection/enrichment’ in Table 1 in the main text). HMA sponges have, in general, lower rates of water pumping and a more effective selection and enrichment of desired microbes [29,31]. Thus, as an example of a simple hypothesis to find the essential mechanisms underlying the HMA–LMA abundance pattern, we construct a model to investigate whether a divergence of microbial abundance can be explained just by these two ecological mechanisms. This is a relatively simple model, which assumes independence of microbial types (i.e., microbes do not interact) and is purely ecological. However, it clearly illustrates how mechanism selection and pattern generation work in our proposed framework.

In this model, we assume a pool of microbes in the water column, each defined by three random numbers:

(i) D_i: the abundance (i.e., density) of the i-th microbe type in the water.

(ii) ω_i: a positive natural growth rate of the i-th microbe type.

(iii) t_i: a microbial trait describing the value of microbe i to its host, from less to more capable of assisting in host survival.

Each host (i.e., sponge) pumps microbes through its body and cultivates those microbes that best contribute to supporting its health and fitness. Thus, two host parameters characterise HMA and LMA sponges:

(i) \(\in _h\): the pumping rate of host \(h\).

(ii)\(\delta _h\): the selection strength of host \(h\), denoting its capacity to enrich microbes according to their trait.

Representing the abundance/density of each microbe type inside each host \(x _{ih}\) (see Box 1 in the main text), we can write a simple equation for host-mediated microbe dynamics: \begin{equation} \frac{d x_{ih}}{dt} = x_{ih} \omega_i \delta^{t_i}_h + \in_h \left( D_i - x_{ih} \right) - \gamma x_{ih}^2 \tag{I} \end{equation}
Where:
(i) \(\omega_i \delta^{t_i}_h\) is the growth rate of microbe i. Microbes are enriched to a higher growth rate according to \(\delta _h \) driven by trait \(t_i\).
(ii) \(\in _h ({D_i} - {x_{ih}})\) is the flux of microbes resulting from the balance of the influx of water microbes and efflux of internal microbes, pumped with rate \(\in _h\).
(iii) \(\gamma\) represents the self-regulation of microbe populations.

A typical HMA host would be defined with small pumping rate and large selection strength, while an LMA host would be the opposite. Their total microbial abundance can be calculated from the microbe dynamics at equilibrium.

If such a model can reproduce the HMA–LMA abundance pattern (Figure I), this would suggest that the focus of mechanism selection is on water pumping rates and microbial enrichment. Further, this model would constitute a basis to investigate other patterns relating to HMA and LMA sponges. If this model is unable to reproduce the pattern, then it tells us that the underlying reason involves another mechanistic setting. In this example, the pattern is reproduced only if both mechanisms are present together. A computer code implementation of this model and a tutorial on how to use it is available in the supplemental information online and also at: https://github.com/computational-ecology-lab/sponge-microbiome-abundances.

Figure I The emergence of the high microbial abundance (HMA)–low microbial abundance (LMA) pattern requires differences in both host-trait mechanisms.

A set of 100 samples is simulated with the model described in this box for 500 microbial types. Left: abundances/densities of each microbe type for HMA and LMA sponges in a simulation of the dynamics through time until equilibrium of a single sample. The temporal scale (x-axis) reflects the scales of parameter values. Right: ratio of abundances for each sample for three models: one in which both water pumping rates (\(\in_h\)) and host selection (\(\delta_h\)) differ between HMA and LMA hosts (both, blue dots), the second one in which only water pumping rates differ (pump, orange dots), and a final version in which only host selection differs across host types (selection, green dots).

A lot of stuff for creationists to try to deny there. First there is the refutation of the nonsensical claim that 'selfish' genes are genes for selfishness and therefore can't produce cooperation or altruism.

Secondly, there is the mathematical model for how an evolutionary process produces cooperative microbiomes upon which multicellular organisms have evolved to depend, again in a cooperative, symbiotic alliance.

And thirdly there is the subtle evidence that the scientists have no doubt that the process of evolution as explained by the Theory of Evolution, is responsible for the evolution of these microbiomes, with no god or magic designer required in the explanation and, above all, no evidence that they found the TOE less than adequate for explaining the observations and turned instead to the childish superstitions of magic and evidence-free supernatural magicians.

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