V. Life's Corporeal Evolution Encodes and Organizes Itself: An EarthWinian Genesis Synthesis
6. Dynamic Fractal Network Ecosystems
Fath, Brian, et al. Ecosystem Growth and Development. BioSystems. 77/1-3, 2004. Further thoughts on ecosystem organization by way of far-from-equilibrium thermodynamics with co-authors Sven Jorgensen, Bernard Patten and Milan Straskraba.
Feagin, R. A., et al. Individual versus Community Level Processes and Pattern Formation in a Model of Sand Dune Succession. Ecological Modelling. 183/4, 2005. This specific study provides a microcosm of nature’s reciprocal interplay of entity (plant, person) and relevant group. Pierre Teilhard de Chardin termed this “creative union.” In so doing, nature can teach a common principle and wisdom that could well serve our human abide.
The results showed that the plant patterns were due to individual plant responses to their environment within their local neighborhood, yet these responses were constrained by the global history of the community. (Abstract 435) The results of this study are an important contribution to the theoretical debate over whether individualistic or community-unit processes drive the formation of pattern in plant communities. The model demonstrates that within sand dune plant communities, both processes affect pattern formation. (447)
Filotas, Elise, et al. Viewing Forests through the Lens of Complex Systems Science. Ecosphere. 5/1, 2014. Research ecologists from Spain, Italy, Canada, and the USA, including Lael Parrott present a review and tutorial to date with regard to arboreal canopies and biomes by virtue of this new integral vista. See Jose Ibarra, et al for a 2020 update and fulfillment.
Complex systems science provides a transdisciplinary framework to study systems characterized by (1) heterogeneity, (2) hierarchy, (3) self-organization, (4) openness, (5) adaptation, (6) memory, (7) non-linearity, and (8) uncertainty. Complex systems thinking has inspired both theory and applied strategies for improving ecosystem resilience and adaptability, but applications in forest ecology and management are just beginning to emerge. We review the properties of complex systems using four well-studied forest biomes (temperate, boreal, tropical and Mediterranean) as examples. The lens of complex systems science yields insights into facets of forest structure and dynamics that facilitate comparisons among ecosystems. These biomes share the main properties of complex systems but differ in specific ecological properties, disturbance regimes, and human uses. We show how this approach can help forest scientists and managers to conceptualize forests as integrated social-ecological systems and provide concrete examples of how to manage forests as complex adaptive systems. (Abstract)
Fortin, Marie-Josee, et al. Network Ecology in Dynamic Landscapes. Proceedings of the Royal Society B. April, 2021. M-J F, and Chris Brimacombe, University of Toronto and Mark Dale, University of Northern British Columbia advance mathematical methods by which to quantify all manner of ecosystem structures and processes by way of their innate active network topologies. For much more see a new book Quantitative Analysis of Ecological Networks (Cambridge University Press, 2021) by M. Dale and M-J Fortin.
Network ecology is an emerging field that allows researchers to conceptualize and analyze ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the ‘ecological network dynamics framework’. This framework stresses that the interplay between species interaction and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links). (Abstract excerpt)
Fortuna, Miguel, et al. Evolving Digital Ecological Networks. PLoS Computational Biology. 9/3, 2013. Systems theorists Fortuna, Princeton University, with Luis Zaman, Aaron Wagner and Charles Ofria, Michigan State University, open another window onto the added dimension of intrinsic self-organizing phenomena and forces that serve to channel life’s developmental emergence. These endogenous, independent principles then are seen in effect at each and every scale and creaturely instance.
Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. (Abstract)
Franklin, Oskar, et al. Organizing Principles for Vegetation Dynamics. Nature Plants. 6/5, 2020. We cite this entry by a 29 member international team with postings in Austria, Sweden, the UK, Australia, the USA, Japan, Finland, Switzerland, China, France, the Netherlands, Israel, Luxembourg, and South Africa including Roderick Dewar and Ehud Meron as a good example of how the 21st century project to detect and quantify common self-organized patterns and processes across natural environs is achieving its grand goal. We note that the implied, independent mathematical source from whence these features arise also needs to be realized.
Substantial progress has been made in understanding individual plant processes. But the greater challenge is to predict vegetation dynamics in a changing environment. We propose that three general organizing principles — natural selection, self-organization and entropy maximization — can facilitate the development of reliable dynamic vegetation models (DVMs). Here, we aim to clarify their theoretical basis, along with potentials and limit, for improving our understanding of vegetation dynamics and our ability to predict vegetation change. (Abstract excerpt)
Gamarra, Javier. Metapopulations in Multifractal Landscapes. Proceedings of the Royal Society B. 272/1815, 2005. By using the parameter of lacunarity – a measure of landscape texture or spatial aggregation – the pervasive presence of a fractal self-similarity can be quantified and expressed.
Garlaschelli, Diego, et al. Universal Scaling Relations in Food Webs. Nature. 423/165, 2003. From predator-prey cycles to river networks and vascular systems, a common power-law self-similarity organizes the same efficient pattern.
Gautestad, Arlid and Ivar Mysterud. Intrinsic Scaling Complexity in Animal Dispersion and Abundance. American Naturalist. 165/1, 2005. Progress toward a generalized theoretical framework for populations kinetics and spatial dynamics based on statistical mechanics and complex systems.
We describe, simulate, and discuss three testable aspects of a model for multiscaled habitat use at the individual level: (1) scale-free distribution of movement steps under influence of self-reinforcing site fidelity, (2) fractal spatial dispersion of intra-home range relocations, and (3) nonasymptotic expansion of observed intra-home range patch use with increasing set of relocations. (44)
Getzin, Stephan, et al. Discovery of Fairy Circles in Australia Supports Self-Organization Theory. Proceedings of the National Academy of Sciences. Early Edition, March, 2016. An eleven person team from Germany, Israel, and Australia including Ehud Meron quantify how these curious flora patterns are an exemplary manifestation of innate physical topologies and dynamics.
Pattern-formation theory predicts that vegetation gap patterns, such as the fairy circles of Namibia, emerge through the action of pattern-forming biomass–water feedbacks and that such patterns should be found elsewhere in water-limited systems around the world. We report here the exciting discovery of fairy-circle patterns in the remote outback of Australia. Using fieldwork, remote sensing, spatial pattern analysis, mathematical modeling, and pattern-formation theory we show that the Australian gap patterns share with their Namibian counterparts the same characteristics but are driven by a different biomass–water feedback. These observations are in line with a central universality principle of pattern-formation theory and support the applicability of this theory to wider contexts of spatial self-organization in ecology. (Significance)
Gilarranz, Luis, et al. Effects of Network Modularity on the Spread of Perturbation Impact in Experimental Metapopulations. Science. 357/199, 2017. Spanish National Research Group CSIC, McGill University, and University of Seville integrative ecologists including Jordi Bascompte argue for a common propensity for creatures to form distinctly viable community subassemblies in an environment. See also The Importance of Being Modular by Marta Sales-Pardo in the same issue.
Networks with a modular structure are expected to have a lower risk of global failure. However, this theoretical result has remained untested until now. We used an experimental microarthropod metapopulation to test the effect of modularity on the response to perturbation. We perturbed one local population and measured the spread of the impact of this perturbation, both within and between modules. Our results show the buffering capacity of modular networks. To assess the generality of our findings, we then analyzed a dynamical model of our system. We show that in the absence of perturbations, modularity is negatively correlated with metapopulation size. However, even when a small local perturbation occurs, this negative effect is offset by a buffering effect that protects the majority of the nodes from the perturbation. (Abstract)
Green, David and Suzanne Sadedin. Interactions Matter – Complexity in Landscapes and Ecosystems. Ecological Complexity. 2/2, 2005. From a new journal, a survey of this “new paradigm for ecology” which finds ecosystems to exemplify nonlinear, self-organizing, critically poised, nested networks.
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