VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
1. Geosphere, Hydrosphere, Atmosphere
Bui, Dieu Tien, et al. Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods. Nature Scientific Reports. 8/15364, 2018. An eleven member team from Vietnam, Iran, the USA, China and Malaysia achieve a sophisticated analysis of interrelated geo-hydro landscape and river dynamics. After proving its veracity, the mathematical method is offered as a way to predict and mitigate future catastrophic events. We also cite as a 2010s global collaboration, via a common scientific language across locales beset by historic strife. On this website, we again ask Whom is this nascent personsphere still unknown to us? However might we altogether be able to appreciate and learn?
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices, statistical tests, and the area under the curve of success. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. (Abstract)
Cael, Barry and David Seekell. The Size-Distribution of Earth’s Lakes. Nature Scientific Reports. 6/29633, 2016. MIT and Umea University researchers discern power-law regularities across the watery world of small to large lakes.
Cannavo, Flavio and Giuseppe Nunnari. On a Possible Unified Scaling Law for Volcanic Eruption Durations. Nature Scientific Reports. 6/22289, 2016. University of Catania, Italy researchers detect even in these catastrophic events the common presence of self-organizing dynamic phases.
Volcanoes constitute dissipative systems with many degrees of freedom. Their eruptions are the result of complex processes that involve interacting chemical-physical systems. At present, due to the complexity of involved phenomena and to the lack of precise measurements, both analytical and numerical models are unable to simultaneously include the main processes involved in eruptions thus making forecasts of volcanic dynamics rather unreliable. On the other hand, accurate forecasts of some eruption parameters, such as the duration, could be a key factor in natural hazard estimation and mitigation. Analyzing a large database with most of all the known volcanic eruptions, we have determined that the duration of eruptions seems to be described by a universal distribution which characterizes eruption duration dynamics. In particular, this paper presents a plausible global power-law distribution of durations of volcanic eruptions that holds worldwide for different volcanic environments. Since the proposed model belongs to the family of the self-organized systems it may support the hypothesis that simple mechanisms can lead naturally to the emergent complexity in volcanic behaviour. (Abstract)
Cornacchia, Loreta, et al. Self-Organization of River Vegetation Leads to Emergent Buffering of River Flows and Water Levels. Proceedings of the Royal Society B. July, 2020. As complexity studies of “tangled banks” continue to reveal inherent patterns and processes, Dutch and British geoecologists based at the Royal Netherlands Institute for Sea Research quantify how they riverine environs dynamically organize themselves so as to keep up with ever changing conditions.
Global climate change will impact hydrodynamic conditions in stream ecosystems but there is limited understanding of how they interact and change. By mathematical modelling of field data, we demonstrate that bio-physical feedback between plant growth and flow redistribution causes spatial self-organization of in-channel vegetation that buffers for changed hydrological conditions. The interplay of vegetation growth and hydrodynamics results in a separation of the stream into densely vegetated, low-flow zones divided by unvegetated channels of higher flow velocities. Our results provide important evidence of how plant-driven self-organization allows stream ecosystems to adapt to changing hydrological conditions, maintaining suitable hydrodynamic conditions to support high biodiversity. (Abstract excerpt)
Crawford, John. Towards an Evolutionary-Ecology of Life in Earth. http://www-usyd-proxy.ucc.usyd.edu.au/research/opportunities/opportunities/717. Originally trained in theoretical astrophysics at the University of London, the now University of Sydney professor of sustainable agriculture, on this 2010 webpage, states he is engaged in a reconception of earth’s soil-microbe microcosm as a complex dynamical system. We note a connection with the paper herein of Jensen and Arcaute herein, who cite Crawford’s exemplary work.
Synopsis: The physical structure of soil is highly complex and it is this complexity that permits the coexistence of air and water in soil that is essential for supporting terrestrial life. That same structure provides a habitat for soil microbial community that represent the most diverse component of the terrestrial biosphere, and is responsible for processing the soil carbon store, which is the largest repository of carbon on land. We have found good evidence that the interaction between microbial activity and the binding of soil particles results in a physical restructuring of the system. This “self-organisation” of the soil-microbe complex results in on-going reconfiguration of the soil-microbe system allowing it to adapt to prevailing conditions. It may be this that is the basis for soil resilience to perturbation such as results from agriculture, and that the breakdown of self-organisation is an important factor underlying loss in soil productivity. This project will aim to develop the first theoretical framework for the evolutionary ecology of soil as a self-organising system. The student will: Critically review the literature on self-organising phenomena across different disciplines together with contemporary ecological and evolutionary theory.
Crawford, John, et al. Microbial Diversity Affects Self-Organization of the Soil–Microbe System with Consequences for Function. Journal of the Royal Society Interface. Online December, 2011. Across the vestigial empire, environmentalists from Britain and Australia quantify the presence in landscapes of formative nonlinear dynamics which along with thoroughly resident micro-life serve to insure their fecund vitality.
Soils are complex ecosystems and the pore-scale physical structure regulates key processes that support terrestrial life. These include maintaining an appropriate mixture of air and water in soil, nutrient cycling and carbon sequestration. There is evidence that this structure is not random, although the organizing mechanism is not known. Using X-ray microtomography and controlled microcosms, we provide evidence that organization of pore-scale structure arises spontaneously out of the interaction between microbial activity, particle aggregation and resource flows in soil. A simple computational model shows that these interactions give rise to self-organization involving both physical particles and microbes that gives soil unique material properties. (Abstract, 1)
De Arcangelis, Lucilla, et al. Statistical Physics Approach to Earthquake Occurrence and Forecasting. Physics Reports. Vol. 628, 2016. Second University of Naples, and University of Grenoble, physicists post a technical study of these geological events, especially for the Italian peninsula. Circa 2016, even such extreme phenomena can be seen to reflect common mathematical features. As the Abstract cites, since the 1990s when these projects began, the fields of statistical mechanics and nonlinear complexity theory have merged as a robust analytical method. As a result, a scale-invariant, self-organizing, fractal universality is found to hold, as this website attests, across every cosmic, planetary, and civilizational domain. As we may learn, this worldwide knowledge can then be fed back to predict and mitigate catastrophes. A final section looks at Seismicity Triggered by Nuclear Explosions. What can ever be done, as we peoples get this far and close, so that men don’t blow it up before we figure it out?
There is striking evidence that the dynamics of the Earth crust is controlled by a wide variety of mutually dependent mechanisms acting at different spatial and temporal scales. The interplay of these mechanisms produces instabilities in the stress field, leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution towards instability before a single event is very difficult to monitor. On the other hand, collective behavior in stress transfer and relaxation within the Earth crust leads to emergent properties described by stable phenomenological laws for a population of many earthquakes in size, time and space domains. This observation has stimulated a statistical mechanics approach to earthquake occurrence, applying ideas and methods as scaling laws, universality, fractal dimension, renormalization group, to characterize the physics of earthquakes. In this review we also briefly discuss how the statistical mechanics approach can be applied to non-tectonic earthquakes and to other natural stochastic processes, such as volcanic eruptions and solar flares. (Abstract excerpt)
De Domenico, Manilo and Vito Latora. Scaling and Universality in River Flow Dynamics. arXiv:1011.5685v1.. Posted November 2010 by University of Catania systems physicists who analyze waterways by non-Gaussian probability density functions to find them robustly distinguished by nature’s geometries.
Summing up, in this Letter we have found a novel scaling for river dynamics which relates the non-Gaussian fluctuations occurring in the flows at different time scales. Both the scaling behavior and the break up of the scaling are features common to all the rivers we have considered. River flow fluctuations are strongly dependent on rainfall dynamics, and therefore multifractal cascade models are the best candidates for their characterization. (4)
Deyasi, Krishanu, et al. Network Similarity and Statistical Analysis of Earthquake Seismic Data. arXiv:1611.06318. We note because Indian Institute of Science and University of Hyogo, Japan theorist proceed to interpret this natural phenomena in terms of scale-free complex networks.
Diego, Perugini and Poli Giampiero. Chaotic Dynamics and Fractals in Magmatic Interaction Processes. Earth and Planetary Sciences Letters. 175/1, 2000. The constant mathematical basis holds in this volcanic realm.
It is shown that fractal geometry and chaotic dynamics can be considered suitable techniques in studying complex petrological phenomena. (93)
Dodds, Peter and Daniel Rothman. Scaling, Universality, and Geomorphology. Annual Review of Earth and Planetary Sciences. 28/571, 2000. A survey article on general power-law principles as exemplified in river networks, landform topographies and alluvial sediments.
The idea, (universality) in a nutshell, is that many complicated phenomena, sometimes from vastly different fields, exhibit the same scaling laws. (572)
Donges, Jonathan, et al. The Backbone of the Climate Network. EPL Europhysics Letters. 87/48007, 2009. A contribution from the Potsdam Institute for Climate Research and Humboldt University toward a better understanding of global weather patterns if appreciated as dynamic nonlinear system. Network science can then reveal as not possible earlier phenomena such as a global temperature field, oceanic surface circulation, El Nino and La Nina effects and so on. Compare then, e.g., with the “earthquake network” studies of Abe & Suzuki herein for examples of nature’s universal repetition from soil to sky.
In the last decade, the complex network paradigm has proven to be a fruitful tool for the investigation of complex systems in various areas of science, e.g., the internet and world wide web in computer science, food webs, gene expression and neural networks in biology, and citation networks in social science. (48007) The methodology developed in this letter has the potential to be universally applicable to extract the energy, matter or information flow structure in any spatially extended dynamical system from observations taken from the real world, experiments and simulations. (48007-2)