V. Life's Corporeal Evolution Encodes and Organizes Itself: An EarthWinian Genesis Synthesis

6. Dynamic Fractal Network Ecosystems

Roy, Monojit, et al. Broad Scaling Region in a Spatial Ecological System. Complexity. 8/5, 2003. Scale-free patterns in dynamic ecosystems suggest they are poised near a critical state, which evidence then supports.

In summary, our individual-based spatial predator-prey model exhibits a set of scaling properties characteristic of systems near criticality. (25)

Ryan, Matthew, et al. The Use of Artificial Neural Networks (ANNs) to Simulate N2O Emissions from a Temperate Grassland Ecosystem. Ecological Modelling. 175/2, 2004. Along with many other areas such as gene regulation or social cohesion, a dynamic approach based on how the brain operates is of much utility for ecosystems studies.

Artificial neural networks are sophisticated pattern recognition systems that operate by mathematically mimicking the biological human learning process (i.e. learning by experience) where they can extract and learn the hidden relationships between system inputs and resulting outputs. (189) The grouping of the individual neurons, their configuration, the interconnection between the neurons, the weightings along these connections, and the learning algorithms employed is what makes up a functioning neural network. (190)

Saavedra, Serguei, et al. A Simple Model of Bipartite Cooperation for Ecological and Organizational Networks. Nature. 457/463, 2009. Since science now proceeds on the plane of a worldwide personal humankind, rather than by one man, real discoveries being made often pass unnoticed. A case in point might be a flurry of papers (Sugihara, Griffen, Wills, Palmer, May, Heng, Misteli) on a repetitive occurrence of the same dynamic patterns and processes across widely disparate natural and social realms. In this case, with Northwestern University’s Brian Uzzi as mentor, similar “metrics” are found in plant-animal pollination networks and manufacturer-contractor business.

Our study identifies striking similarities in the general structural characteristics of networks that are formed as a result of cooperative mechanisms operating in radically different contexts, linking partners in ecological and socio-economic systems, respectively. This empirical finding motivates the proposed simple model for bipartite cooperation, which captures the most important generic features of mutualistic interaction patterns starting from a minimal set of input parameters. At the level of partner–partner interactions, equivalent behaviour in different systems appears to be driven by similar types of interaction constraints. These correspond to complementarity in traits or characteristics, a hierarchical organization limiting the range of potential partners, and the environmental context. (465-466)

Santos, F. and J. Pacheco. Scale-Free Networks Provide a Unifying Framework for the Emergence of Cooperation. Physical Review Letters. 95/098104, 2005. A paper cited in Tom Siegfried’s A Beautiful Math which theoretically affirms an innate favorable bias to cooperate rather than compete.

We study the evolution of cooperation in the framework of evolutionary game theory, adopting the prisoner’s dilemma and snowdrift game as metaphors of cooperation between unrelated individuals. In sharp contrast with previous results we find that, whenever individuals interact following networks of contacts generated via growth and preferential attachment, leading to strong correlations between individuals, cooperation becomes the dominating trait throughout the entire range of parameters of both games, as such providing a unifying framework for the emergence of cooperation. (098104-1)

Schneider, David. The Rise of the Concept of Scale in Ecology. BioScience. 51/7, 2001. Ecosystems are best characterized by a dynamic hierarchy due to a power-law self-organized criticality.

Schramski, John, et al. Metabolic Theory Predicts Whole-Ecosystem Properties. Proceedings of the National Academy of Sciences. 112/2617, 2015. An update on this large project hosted by James Brown of the University of New Mexico, a co-author. Into the mid 2010s, decades of field and laboratory study are paying off with a general, robust synthesis such as this.

Understanding the effects of individual organisms on material cycles and energy fluxes within ecosystems is central to predicting the impacts of human-caused changes on climate, land use, and biodiversity. Here we present a theory that integrates metabolic (organism-based bottom-up) and systems (ecosystem-based top-down) approaches to characterize how the metabolism of individuals affects the flows and stores of materials and energy in ecosystems. The theory provides a robust basis for estimating the flux and storage of energy, carbon, and other materials in terrestrial, marine, and freshwater ecosystems and for quantifying the roles of different kinds of organisms and environments at scales from local ecosystems to the biosphere. Abstract excerpts)

Sendzimir, Jan, et al. Implications of Body Mass Patterns. Bissonette, John and Ilse Storch, eds. Landscape Ecology and Resource Management. Washington, DC: Island Press, 2003. In an article typical of this collection of papers, the use of complex adaptive systems theory can elucidate a self-similar pattern of ecological processes, landscape structure and species body size.

The fine-scale structure of herbaceous vegetation, the medium-scale mosaic of forest patches, and the grand geological sweep of the landscape have an appealing cohesiveness and fit, like nested Russian dolls. (129)

Seuront, Laurent. Fractals and Multifractals in Ecology and Aquatic Science. Boca Rotan: CRC Press, 2009. With theoretical and evidential depth, a Flinders University, Adelaide, biologist and oceanographer articulates the self-organizing mathematics of a newly intelligible, untangled nature where the same patterns recur everywhere on land and sea. A gloss of main topics can attest: About Geometries, Self-Similar and Self-Affine Fractals, Frequency Distributions, Fractal-Related Clarifications (re self-organized criticality), Estimating Dimensions, and Multifractals. The volume is thus intended as a handbook to aid ecologists in these novel appreciations.

Shachak, Moshe and Bertrand Boeken. Patterns of Biotic Community Organization and Reorganization. Ecological Complexity. 7/4, 2010. Ben Gurion University ecologists report that complex adaptive systems theory, from Simon Levin, can well model variable shrub vegetation patterns in the face of ever-changing environments.

Recent developments in the field of complex systems provide new frontiers for the study of ecological organization. One of the hallmarks of complexity is that global phenomena emerge out of local interactions that affect global properties and behavior of systems. Non-linear interactions provide important sources for large-scale order that emerges from self-organization. In ecological systems three global phenomena of self-organization result from local interactions among species. On the community level, local interactions determine species assemblage organization, i.e. the distribution of species and their abundance in time and space. On the ecosystem level, the global phenomenon is food web organization, which is the source of functional properties such as energy flow and nutrient cycling. On the landscape level, order appears in the form of biotically induced mosaics of patches such as vegetation patterns in arid and semi-arid lands. (433-434)

Shade, Ashley, et al. Macroecology to Unite All Life, Large and Small. Trends in Ecology & Evolution. Online September, 2018. As many other fields lately seek an integral synthesis to fulfill and cap decades of diverse studies, eleven scientists from the USA, Denmark, Germany and the Czech Republic here propose a comprehensive systems ecology. A salient principle of metabolic rates across microbial to “macrobial” phases is taken as a good guide. A glossary includes terms as Abundance-occupancy, Metagenomics, Mesocosm, Morphospecies, Taxonomic Units, and so on. See also An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science by Debra Peters, et al in BioScience (68/9, 2018) and herein for another effort.

Macroecology is the study of the mechanisms underlying general patterns of ecology across scales. Research in microbial ecology and macroecology have long been detached. Here, we argue that it is time to bridge the gap, as they share a common currency of species and individuals, and a common goal of understanding the causes and consequences of changes in biodiversity. Microbial ecology and macroecology will mutually benefit from a unified research agenda and shared datasets that span the entirety of the biodiversity of life and the geographic expanse of the Earth. (Abstract)

Siteur, Koen, et al. Phase-separation physics underlies new theory for the resilience of patchy ecosystems. PNAS. 120/2, 2023. A century and six decades later, in our global phase, Dutch and Chinese ecotheorists at last reach the deep roots of “tangled banks” by an integrations with condensed matter phenomena as it actively proceeds through transitional emergences. The second quote goes on to record the consequence of critical states.

Spatial self-organization of ecosystems into large-scales enables diverse organisms to cope with variable environmental conditions and to buffer degradation. Scale-dependent feedbacks have provided a framework for self-organized formations such as arid areas or mussel beds. Here, we cite an alternative approach by way of the complications of a biotic or abiotic basis such as herbivores, sediment, or nutrients. Building on physical theory for phase-separation dynamics, we show that patchy phases are more vulnerable at small spatial scales. By this view, the initiation of coarse aggregations can offer a new indicator to signal tipping points and radical habitat loss.

Our study contributes a better perspective based on self-organized patchiness to understand irregular ecosystems that lack feedbacks associated with spatial Turing patterns and disturbances due to scale-free modes that typify self-organized criticality.

Solari, Aldo, et al. On Skipjack Tuna Dynamics: Similarity at Several Scales. Seuront, Laurent and Peter Strutton, eds. Handbook of Scaling Methods in Aquatic Ecology. Boca Rotan, FL: CRC Press, 2004. Of special interest is the finding that these tuna populations have invariant structures because their nautical environment itself is an iteration of temperature patterns, surface waves, wind effects, and so on, which carry on into the depths of a “fractal ocean.”

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