Hywel T. P. Williams

Daisyworld: a review

Daisyworld is a simple planetary model designed to show the long-term effects of coupling between life and its environment. Its original form was introduced by James Lovelock as a defense against criticism that his Gaia theory of the Earth as a self-regulating homeostatic system requires teleological control rather than being an emergent property. The central premise, that living organisms can have major effects on the climate system, is no longer controversial. The Daisyworld model has attracted considerable interest from the scientific community and has now established itself as a model independent of, but still related to, the Gaia theory. Used widely as both a teaching tool and as a basis for more complex studies of feedback systems, it has also become an important paradigm for the understanding of the role of biotic components when modeling the Earth system. This paper collects the accumulated knowledge from the study of Daisyworld and provides the reader with a concise account of its important properties. We emphasize the increasing amount of exact analytic work on Daisyworld and are able to bring together and summarize these results from different systems for the first time. We conclude by suggesting what a more general model of life-environment interaction should be based on.

Artificial selection of simulated microbial ecosystems.

Recent work with microbial communities has demonstrated an adaptive response to artificial selection at the level of the ecosystem. The reasons for this response and the level at which adaptation occurs are unclear: does selection act implicitly on traits of individual species, or are higher-level traits genuinely being selected? If the ecosystem response is just the additive combination of the responses of the constituent species, then the ecosystem response could be predicted a priori, and the ecosystem-level selection process is superfluous. However, if the ecosystem response results from ecological interactions among species, then selection at a higher level is necessary. Here we perform artificial ecosystem selection experiments on an individual-based evolutionary simulation model of microbial ecology and observe a similar response to that seen with real ecosystems. We demonstrate that a significant fraction of artificially selected ecosystem responses cannot be accounted for by implicit lower-level selection of a single type of organism within the community, and that interactions among different types of organism contribute significantly to the response in the majority of cases. However, when the ecological problem posed by the artificial ecosystem selection process can be easily solved by a single dominant species, it often is.

On the origin of planetary-scale tipping points

Tipping points are recognised in many systems, including ecosystems and elements of the climate system. But can the biosphere as a whole tip and, if so, how? Past global tipping points were rare and occurred in the coupled planetary-scale dynamics of the Earth system, not in the local-scale dynamics of its weakly interacting component ecosystems. Yet, evolutionary innovations have triggered past global transformations, suggesting that tipping point theory needs to go beyond bifurcations and networks to include evolution.

Environmental regulation in a network of simulated microbial ecosystems

The Earth possesses a number of regulatory feedback mechanisms involving life. In the absence of a population of competing biospheres, it has proved hard to find a robust evolutionary mechanism that would generate environmental regulation. It has been suggested that regulation must require altruistic environmental alterations by organisms and, therefore, would be evolutionarily unstable. This need not be the case if organisms alter the environment as a selectively neutral by-product of their metabolism, as in the majority of biogeochemical reactions, but a question then arises: Why should the combined by-product effects of the biota have a stabilizing, rather than destabilizing, influence on the environment? Under certain conditions, selection acting above the level of the individual can be an effective adaptive force. Here we present an evolutionary simulation model in which environmental regulation involving higher-level selection robustly emerges in a network of interconnected microbial ecosystems. Spatial structure creates conditions for a limited form of higher-level selection to act on the collective environment-altering properties of local communities. Local communities that improve their environmental conditions achieve larger populations and are better colonizers of available space, whereas local communities that degrade their environment shrink and become susceptible to invasion. The spread of environment-improving communities alters the global environment toward the optimal conditions for growth and tends to regulate against external perturbations. This work suggests a mechanism for environmental regulation that is consistent with evolutionary theory.

Coevolutionary diversification creates nested-modular structure in phage–bacteria interaction networks

Phage and their bacterial hosts are the most diverse and abundant biological entities in the oceans, where their interactions have a major impact on marine ecology and ecosystem function. The structure of interaction networks for natural phage–bacteria communities offers insight into their coevolutionary origin. At small phylogenetic scales, observed communities typically show a nested structure, in which both hosts and phages can be ranked by their range of resistance and infectivity, respectively. A qualitatively different multi-scale structure is seen at larger phylogenetic scales; a natural assemblage sampled from the Atlantic Ocean displays large-scale modularity and local nestedness within each module. Here, we show that such ‘nested-modular’ interaction networks can be produced by a simple model of host–phage coevolution in which infection depends on genetic matching. Negative frequency-dependent selection causes diversification of hosts (to escape phages) and phages (to track their evolving hosts). This creates a diverse community of bacteria and phage, maintained by kill-the-winner ecological dynamics. When the resulting communities are visualized as bipartite networks of who infects whom, they show the nested-modular structure characteristic of the Atlantic sample. The statistical significance and strength of this observation varies depending on whether the interaction networks take into account the density of the interacting strains, with implications for interpretation of interaction networks constructed by different methods. Our results suggest that the apparently complex community structures associated with marine bacteria and phage may arise from relatively simple coevolutionary origins.

Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks.

Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems.

FALCON: a software package for analysis of nestedness in bipartite networks.

Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an “adaptive ensemble” method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts.

Evolution and the regulation of environmental variables

The idea that the biota can regulate the abiotic components of their environment to levels suitable for life has attracted criticism from neo-Darwinian theorists but is still a viable hypothesis. Here we present a model, similar to Daisyworld [1] but more general, which allows for a more extensive study of the compatibility of biotic regulation with evolutionary theory. Results obtained highlight the importance of constraints on the evolutionary process for the emergence of regulation, and set the scene for more comprehensive future study.

Artificial ecosystem selection for evolutionary optimisation

Artificial selection of microbial ecosystems for their collective function has been shown to be effective in laboratory experiments. In previous work, we used evolutionary simulation models to understand the mechanistic basis of the observed ecosystem-level response to artificial selection. Here we extend this work to consider artificial ecosystem selection as a method for evolutionary optimisation. By allowing solutions involving multiple species, artificial ecosystem selection adds a new class of multi-species solution to the available search space, while retaining all the single-species solutions achievable by lower-level selection methods. We explore the conditions where multi-species solutions (that necessitate higher-level selection) are likely to be found, and discuss the potential advantages of artificial ecosystem selection as an optimisation method.

Coevolving parasites improve host evolutionary search on structured fitness landscapes

Evidence suggests that host-parasite coevolution can often result in host diversification. However, the host traits that coevolve often have primary functions affecting growth, creating the potential for conflicting selection pressures. For example, bacteriophage often infect bacteria by binding to nutrient uptake receptors, thus diversification of bacteria due to coevolution with phage may have an impact on resource competition. This paper uses a model of bacteria and phage in a chemostat to study the impact of coevolution with phage on the evolution of host growth rates, when infection and growth are affected by the same trait. Comparing (co)evolutionary outcomes on different growth rate fitness landscapes, with and without phage, shows that coevolutionary diversification allows hosts to cross fitness valleys and improve search efficiency on rugged landscapes, although it also prevents the whole community from reaching global optima. In effect, coevolution with parasites increases exploration but decreases exploitation in host evolutionary search.