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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An EarthWinian Genesis Synthesis

6. Dynamic Fractal Network Ecosystems

Pahl-Wostl, Claudia. The Dynamic Nature of Ecosystems. Chichester, UK: Wiley, 1995. In response to the old fragmented, Newtonian mechanistic paradigm, a holistic, relational approach is proposed that can engage the pervasive “pattern of interactions” in self-organized environments.

Pascual, Mercedes and Frederic Guichard. Criticality and Disturbance in Spatial Ecological Systems. Trends in Ecology and Evolution. 20/2, 2005. Three modes are identified: phase transitions, self-organized criticality and ‘robust’ criticality; with regard to spatial patterns, temporal dynamics and threshold behavior.

Criticality has been an appealing ecological concept from two different perspectives: first, as an explanation for scale-invariant patterns in nature, and second, as a mechanism underlying drastic change, in the form of either large unpredictable temporal fluctuations (SOC) or sudden state shifts by small perturbations (classical phase transitions). (94)

Pascual, Mercedes and Jennifer Dunne, eds. Ecological Networks. Oxford: Oxford University Press, 2006. A collection from the Santa Fe Institute that marks a new level and phase of mathematical quantification and experimental verification. Darwin’s “tangled bank” is at last discernible in both structure and dynamics via the many varieties of complex systems theory.

Peacor, Scott, et al. Phenotypic Plasticity and Species Coexistence: Modeling Food Webs as Complex Adaptive Systems. Pascual, Mercedes and Jennifer Dunne, eds. Ecological Networks. Oxford: Oxford University Press, 2006. The chapters in this book, from the certain experience of their authors, each convey a somewhat different terminology and emphasis. Some talk of network nodes and links. In this paper, following Holland, Gell-Mann, Morowitz, Bar-Yam, and especially Levin, ecosystems are said to involve diverse individuals or agents whose local interactions gives rise to an emergent, hierarchical scale.

Following a CAS approach, we present a computational tool in which the dynamics and structure of a model community emerge from interacting individuals that adapt to their environment. (247)

Peters, Debra, et al. An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science. BioScience. 68/9, 2018. A significant proposal by twenty-two working environmentalists across the USA such as the Dept. of Agriculture, UCLA, ASU, and other flora and fauna agencies and colleges, that the time has come, as Ashley Shade et al below agree, to strive for a whole Earth systemic synthesis across every micro to macro natural realm.

The Earth is a complex system comprising many interacting spatial and temporal scales. We developed a transdisciplinary data-model integration (TDMI) approach to understand, predict, and manage for these complex dynamics that focuses on spatiotemporal modeling and cross-scale interactions. Our approach employs human-centered machine-learning strategies supported by a data science integration system (DSIS). Applied to ecological problems, our approach integrates knowledge and data on (a) biological processes, (b) spatial heterogeneity in the land surface template, and (c) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, and theory). (Abstract)

Pigolotti, Simone, et al. Stochastic Spatial Models in Ecology. Journal of Statistical Physics. 172/1, 2018. SP, Okinawa Institute of Science, Massimo Cencini and Consiglio Nazionale delle Ricerch, Rome, Daniel Molina, Basque Center for Applied Mathematics, and Miguel Munoz, University of Granada, Spain provide a good example of later 2010s (re)unifications across this widest span from lively physical substrates to active flora and fauna environments.

Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. In this review, we emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories. (Abstract excerpt)

Pimm, Stuart. The Balance of Nature? Chicago: University of Chicago Press, 1991. A leading ecologist presciently recognizes the potential for nonlinear principles to explain complex landscapes which actually do not seek an equilibrium as long thought but are dynamically self-organized.

Ranta, Esa, et al. Ecology of Populations. Cambridge, UK: Cambridge University Press, 2006. From Scandinavia, a technical study in part on the scale independent spatial and temporal self-organization of ecosystems.

Recknagel, Freidrich, ed. Ecological Informatics. Berlin: Springer, 2003. Bioinformatics is the application of computer analysis to biological phenomena such as genetic and protein networks. This book reviews a similar employ of artificial neural networks, adaptive agents, evolutionary algorithms, and so on to characterize and understand intricate, nested ecosystems. An earlier version of the subject appeared in Ecological Modelling. 146/1-2, 2001.

Ecological Informatics is defined as interdisciplinary framework promoting the use of advanced computational technology for the elucidation of principles of information processing at and between all levels of complexity of ecosystems – from genes to ecological networks… (iii)

Reuter, Hauke. The Concepts of Emergent and Collective Properties in Individual-Based Models. Ecological Modelling. 186/4, 2005. A summary paper from a special issue on flora and fauna as the epitome of dynamic, scalar, complex systems due to many relational individuals or agents. See also Reuter in Organic Societies above.

In the second half of the 20th century a crucial paradigm shift in biological theory included the perception of ecological systems as being self-organized. (491) The important thing about life is that the local dynamics of a set of interacting entities (e.g. molecules, cells, etc.) support an emergent set of global dynamical structures which stabilize themselves by setting the boundary conditions within which the local dynamics operate. (492)

Reynolds, A. M. On the Intermittent Behaviour of Foraging Animals. Europhysics Letters. 75/4, 2006. Another insight into a creative mathematical order which suffuses the natural realm. Of course, Galilei Galileo knew this long ago. See also in the same journal issue, (S. Picoli, et al) how a similar scaling pervades human complex systems.

This suggests that the scale-free and intermittent characteristics of forager movement patterns can be understood within the context of a single unified scale-free model. (520)

Ricotta, Carlo. Self-Similar Landscape Metrics as a Synthesis of Ecological Diversity and Geometrical Complexity. Ecological Modelling. 125/2-3, 2000. A hypothesis in search of a unified science rooted in a nature suffused by universal, multifractal patterns.

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