V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

6. Dynamic Fractal Network Ecosystems

Rogers, Tanya, et al. Chaos is not Rare in Natural Ecosystems. Nature Ecology and Evolution. July, 2022. TR, National Marine Fisheries Service, along with Bethany Johnson and Stephan Munch, UC Santa Cruz provide a 2020s report that while Darwin’s bank remains tangled, our latest nonlinear sciences can reveal the present of an orderly basis. See also Yonatan, Yogev, et al. Complexity-Stability Trade-off in Empirical Microbial Ecosystems by Yogev Yonatan, et al in this same issue for a similar contribution..

Chaotic dynamics are thought to be rare in natural populations but this may be due to earlier empirical limitations, rather than an inherent stability of ecosystems. By way of extensive simulation testing, we applied multiple chaos detection methods to a global database and found chaotic behavior in some 30% of cases. Relative chaos was more prevalent among plankton and insects and least among birds and mammals. These results demonstrate that chaos does often occur in natural populations, and thus caution against steady-state approaches to conservation and management. (Excerpt)

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)

Scesa, Paul, et al. Defensive polyketides produced by an abundant gastropod are candidate keystone molecules in estuarine ecology. Sciences Advances. 10/44, 2024. Fourteen bioecologists at the University of Utah, California State University, NIH, UC Davis and Occidental College including Patrick Krug proceed to identity a novel, deeper phase of ecosystem functional behaviors, akin to keystone organism species. See also A New, Chemical View of Ecosystems by Molly Herring in Quanta. (March 6, 2025) for an appreciation of this vital project. As a result, a wealth of new insights are being achieved to aid in our respect and maintenance.

Secondary metabolites often function as antipredator defenses, Herein, these “keystone molecules” are shown to affect community structure and ecosystem functions, but the broader effects of animal chemistry remain largely unexplored. We study polyketides biosynthesized by sea slugs in Northern Hemisphere estuaries. Such Alderene compounds appear to have unexpected cascading effects on processes ranging from bioturbation to reproduction of species which warrants greater attention by ecologists. (Excerpt)

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)

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