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

6. Dynamic Fractal Network Ecosystems

Green, David, et al. Complexity in Landscape Ecology. Berlin: Springer, 2006. Research ecologists provide a good primer on how natural environments are graced by and thoroughly express the principles of dynamical systems of self-organization, fractal scale-invariance, modularity, and cellular automata. A proper grasp of this endemic viability will then contribute to a global biosphere sustainability.

Greenbaum, Gili and Nina Fefferman. Application of Networks Methods for Understanding Evolutionary dynamics in Discrete Habitats. Molecular Ecology. 26/11, 2017. Ben-Gurion University of the Negev and University of Tennessee bioecologists provide another tutorial entry to an effective application of interactive network phenomena to ecosystem populations. Once again, the invitation is that these common connective aspects, although invisible, are in fact a major formative property. See also The Multilayer Nature of Ecological Networks by Shai Pilosof, et al in Nature Ecology & Evolution (1/0101, 2017), along with Complex Networks in Ecology by Gili Greenbaum, et al in Israel Journal of Ecology & Evolution (61/2, 2015).

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. In the last decades, network theory – a branch of discrete mathematics concerned with complex interactions between discrete elements – has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. (Abstract)

Griffen, Blaine and John Drake. Scaling Rules for the Final Decline to Extinction. Proceedings of the Royal Society B. 276/1361, 2009. From the University of Georgia Odum School of Ecology, a report that the same geometries, as if due to an original, complementary genetic code, seem to occur at each and every phase of life and death.

The consistency of certain spatial and temporal dynamics among highly different nonlinear ecological systems is astounding. Such scaling rules have been found to characterize individual behaviour; the use of space by organisms; life-history patterns of individuals; population growth, regulation and abundance; community stability; and, by integrating across species ensembles, even patterns of biodiversity across regional landscapes. (1361)

Guimaraes, Paulo. The Structure of Ecological Networks across Levels of Organization. Annual Review of Ecology, Evolution and Systematics. 51/433, 2020. As a Universidade de São Paulo researcher provides a latest exposition of how nature’s interactive networks, as a heretofore overlooked feature, suffuse and serve to join, connect, inform and sustain living systems. As we have noted, here is another way that Darwin’s famous tangled bank can yet gain a consistent comprehension.

Interactions connect the units of ecological systems, by which to form networks. Individual-based networks characterize variation in niches within populations as they merge with each other to compose species-based networks and food webs in ecological communities. At spatiotemporal scales, networks portray the structure of ecological interactions across landscapes and over macroevolutionary time, which I review across multiple levels. By such studies, regularities in network structure are seen to emerge due to the fundamental architectural patterns shared by complex networks. (Abstract excerpt)

Hagstrom, George and Simon Levin. Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns, Critical Transitions, and Public Goods. Ecosystems. Online February, 2017. Princeton University, Ecology and Evolutionary Biology professors write a 20th Anniversary Paper for this journal which attests to how much these nonlinear dynamics act to organize the breadth and depth of environments and their creaturely inhabitants. We cite its Abstract and the Ecosystems vision.

Complex adaptive systems provide a unified framework for explaining ecosystem phenomena. In the past 20 years, complex adaptive systems have been sharpened from an abstract concept into a series of tools that can be used to solve concrete problems. These advances have been led by the development of new techniques for coupling ecological and evolutionary dynamics, for integrating dynamics across multiple scales of organization, and for using data to infer the complex interactions among different components of ecological systems. Focusing on the development and usage of these new methods, we discuss how they have led to an improved understanding of three universal features of complex adaptive systems, emergent patterns; tipping points and critical phenomena; and cooperative behavior. Many of these are currently undergoing dramatic changes due to anthropogenic perturbations, and we take the opportunity to discuss how complex adaptive systems can be used to improve the management of public goods and to better preserve critical ecosystem services. (Abstract)

The study and management of ecosystems represents the most dynamic field of contemporary ecology. Ecosystem research bridges fundamental ecology, environmental ecology and environmental problem-solving. The scope of ecosystem science extends from bounded systems such as watersheds to spatially complex landscapes, to the Earth itself, and crosses temporal scales from seconds to millennia. Ecosystem science has strong links to other disciplines including landscape ecology, global ecology, biogeochemistry, aquatic ecology, soil science, hydrology, ecological economics and conservation biology. Studies of ecosystems employ diverse approaches, including theory and modeling, long-term investigations, comparative research and large experiments. (Ecosystems journal)

Hammond, Sean and Karl Niklas. Modeling Forest Self-assembly Dynamics Using Allometric and Physical First Principles. Science. 61/663, 2011. Charles Darwin’s apt phrase “tangled bank” is often used to convey nature’s seemingly chaotic flora. Some century and a half later, Cornell University botanists now describe a computational method that reveals a heretofore elusive, endemic regularity springing from an implicate mathematical source. Galileo Galilei would say tell me about it, for we seem at the cusp of realizing a profoundly new nature with such an algorithmic, read genetic, testament. The cover image for the issue depicts a forest as if rising from an instructive program.

Computer models are used by ecologists for studying a broad range of research questions, from long-term forest dynamics to the functional traits that theoretically give one species an advantage over others. Despite their increasing popularity, these models have been criticized for simulating complex biological phenomena, involving numerous biotic and abiotic variables, using seeming overly simplistic computational approaches. In this article, we review the usefulness and limitations of spatially explicit individual-based models for forested ecosystems by focusing on the attributes of a recent model, called SERA (spatially explicit reiterative algorithm), that employs seven allometric formulas and a few physical principles. Despite its simplicity, SERA successfully predicts forest self-assembly and dynamics. (Abstract, 663)

Harte, John and Erica Newman. Maximum Information Entropy: A Foundation for Ecological Theory. Trends in Ecology and Evolution. 29/7, 2014. UC Berkeley environmentalists finesse thermodynamic effects by way of probability distributions so as to achieve a better, more optimum, analysis of dynamic ecosystems. A later paper with Andrew Rominger is Metabolic Partitioning Across Individuals in Ecological Communities in Global Ecology and Biogeography (Online July 2017).

The maximum information entropy (MaxEnt) principle is a successful method of statistical inference that has recently been applied to ecology. Here, we show how MaxEnt can accurately predict patterns such as species–area relationships (SARs) and abundance distributions in macroecology and be a foundation for ecological theory. We discuss the conceptual foundation of the principle, why it often produces accurate predictions of probability distributions in science despite not incorporating explicit mechanisms, and how mismatches between predictions and data can shed light on driving mechanisms in ecology. We also review possible future extensions of the maximum entropy theory of ecology (METE), a potentially important foundation for future developments in ecological theory. (Abstract)

Harte, John, et al. Metabolic Partitioning Across Individuals in Ecological Communities. Global Ecology and Biogeography. Online July, 2017. Environmentalists Harte, and Andrew Rominger, UC Berkeley, and Erica Newman, University of Arizona, continue to effectively apply maximum entropy principles across real ecosystems. See Harte & Newman 2014 for more theory.

The mechanistic origin and shape of body-size distributions within communities are of considerable interest in ecology. A recently proposed light-limitation model provides a good fit to the distribution of tree sizes in a tropical forest plot. The maximum entropy theory of ecology (METE) also predicts size distributions, but without explicit mechanistic assumptions, and thus its predictions should hold in ecosystems generally, regardless of whether they are light limited. A comparison of the form and success of the predictions of the model and the theory can provide insight into the role that mechanisms play in shaping patterns in macroecology. The prediction by the METE of the size distribution of organisms is remarkably similar in form to that of the model: power-law behaviour in the size range where the light-limitation model predicts a power law, and exponential behaviour in the size range where the model predicts an exponential tail. (Abstract)

Harte, John, et al. Self-Similarity in the Distribution and Abundance of Species. Science. 284/334, 1999. The article discusses how species are distributed by a nested webwork in biome habitats, an arrangement which reflects the utility of fractal geometry to describe natural systems.

We have demonstrated that self-similarity theory provides an overarching framework within which empirically supported patterns in ecology are unified, new and plausible results are derived, and the connection between the SAR (species-area relationship) and the lognormal abundance distribution is questioned. (336)

Hatton, Ian, et al. The Predator-Prey Power Law: Biomass Scaling Across Terrestrial and Aquatic Biomes. Science. 349/1070, 2015. When we first posted this section in 2004, evidence for common principles was as patchy as many ecosystems. By 2017 however, McGill University, University of Guelph, Perimeter Institute (Matteo Smerlak), Tanzania Wildlife Research Institute, and Centre for Biodiversity Theory and Modeling, CNRS, France researchers can quantify a universality of patterns and process which appear independently of local environs. See also a commentary Energy Flows in Ecosystems by Just Cebrian in the same issue.

Ecosystems exhibit surprising regularities in structure and function across terrestrial and aquatic biomes worldwide. We assembled a global data set for 2260 communities of large mammals, invertebrates, plants, and plankton. We find that predator and prey biomass follow a general scaling law with exponents consistently near ¾. This pervasive pattern implies that the structure of the biomass pyramid becomes increasingly bottom-heavy at higher biomass. Similar exponents are obtained for community production-biomass relations, suggesting conserved links between ecosystem structure and function. These exponents are similar to many body mass allometries, and yet ecosystem scaling emerges independently from individual-level scaling, which is not fully understood. These patterns suggest a greater degree of ecosystem-level organization than previously recognized and a more predictive approach to ecological theory. (Abstract)

Coarsening refers to any relaxation process wherein the characteristic length scale grows over time. Examples of coarsening phenomena abound in condensed matter physics: domain growth in quenched magnets; Ostwald ripening in alloys and emulsions; bubble coalescence in soap froths; phase separation in binary mixtures; etc. In these systems, excess free energy is stored in localized defects whose size spontaneously increases over time so as to reduce their density and hence the total free energy. The relevance of coarsening phenomena extends to astrophysics, with galactic clustering, socio-dynamics, e.g. in simplified models of consensus formation or of racial segregation, and in many other branches of sciences. Key universal features of coarsening are dynamic scaling and asymptotic self-similarity. (2)
.
A general scaling law for ecology Despite the huge diversity of ecological communities, they can have unexpected patterns in common. Hatton et al. describe a general scaling law that relates total predator and prey biomass in terrestrial and aquatic animal communities (see the Perspective by Cebrian). They draw on data from many thousands of population counts of animal communities ranging from plankton to large mammals, across a wide range of biomes. They find a ubiquitous pattern of biomass scaling, which may suggest an underlying organization in ecosystems. It seems that communities follow systematic changes in structure and dynamics across environmental gradients. (Science Editor)

Hatton et al show that this pattern – that is, a decreasing predator to prey biomass ratio with increasing prey biomass – applies universally in both aquatic and terrestrial ecosystems. Furthermore, they demonstrate that this universal pattern emerges from a sublinear scaling that is independent of the ecosystem considered. (Cebrian 1053)

Higgins, Paul, et al. Dynamics of Climate and Ecosystem Coupling. Philosophical Transactions of the Royal Society of London B. 357/647, 2002. The paper argues that no biotic phenomena can be studied in isolation from its environment.

Interactions between subunits of the global climate-biosphere system (e.g., atmosphere, ocean, biosphere and cryosphere) often lead to behavior that is not evident when each subunit is viewed in isolation. This newly evident behavior is an emergent property of the coupled subsystems. (647)

Ho, Mae-Wan and Robert Ulanowicz. Sustainable Systems as Organisms? BioSystems. 82/1, 2005. A biophysicist and an ecologist, who have each contributed to a “thermodynamics of organized complexity,” propose that sustainable ecosystems are best seen as organisms because they express similar attributes of energy flow and storage, dynamic cycles, a nested hierarchy, and so on. As a consequence, this viable state ought to be a goal for our communities and economies.

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next  [More Pages]