V. Life's Corporeal Evolution Encodes and Organizes Itself: An EarthWinian Genesis Synthesis
6. Dynamic Fractal Network Ecosystems
Lin, Hua, et al. Self-Organization of Tropical Seasonal Rain Forest in Southwest China. Ecological Modelling. 222/15, 2011. While self-organized phenomena are now recognized to span natural and societal realms, this has been difficult to quantify for ultra complex ecosystems. Here Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, researchers find a nonequilibrium thermodynamic approach that utilizies a “maximum energy dissipation theory” to reveal how a typical biota as jungle vegetation can organize themselves.
Linquist, Stefan, et al. Yes! There are Resilient Generalizations (or “Laws”) in Ecology. Quarterly Review of Biology. 91/2, 2016. University of Guelph biologists and philosophers including Ryan Gregory review the past century of environmental studies to conclude (as other fields also) that independent, generic, universally applicable natural principles really do exist, instantiate, and guide.
It is often argued that ecological communities admit of no useful generalizations or “laws” because these systems are especially prone to contingent historical events. Detractors respond that this argument assumes an overly stringent definition of laws of nature. Under a more relaxed conception, it is argued that ecological laws emerge at the level of communities and elsewhere. A brief review of this debate reveals an issue with deep philosophical roots that is unlikely to be resolved by a better understanding of generalizations in ecology. We therefore propose a strategy for transforming the conceptual question about the nature of ecological laws into a set of empirically tractable hypotheses about the relative resilience of ecological generalizations across three dimensions: taxonomy, habitat type, and scale. These hypotheses are tested using a survey of 240 meta-analyses in ecology. Our central finding is that generalizations in community ecology are just as prevalent and as resilient as those in population or ecosystem ecology. These findings should help to establish community ecology as a generality-seeking science as opposed to a science of case studies. (Abstract)
Margalef, Roman. Exosomatic Structures and Captive Energies Relevant in Succession and Evolution. Jorgensen, Sven and Felix Muller, eds. Handbook of Ecosystems Theories and Management. Boca Raton, FL: Lewis Publishers, 2000. A novel view of evolution as due to self-organized information which is contained in increasingly efficient external storage and retrieval systems.
Marquet, Pablo. Of Predators, Prey, and Power Laws. Science. 295/2229, 2002. An example of how complexity science is now articulating the intricate natural realm previously seen as intractably tangled.
As has been demonstrated, power laws are ubiquitous within local ecosystems and may hold the clue to understanding large-scale patterns in the structure and function of biodiversity. (2230)
Martinez-Garcia, Ricardo, et al.. Spatial Patterns in Ecological Systems: From Microbial Colonies to Landscapes. Emerging Topics in Life Sciences. 6/3, 2022. Instituto de Física Teórica UNESP, Brazil, Princeton University (Cornia Tarnita) and Rutgers University ecotheorists describe their current findings that Quite fulfill our 21st century survey and expectation from only patchy, spurious instances to this well grounded evident presence of whole scale as a robust confirmation as this across every everion and bioregion. See also Phase-separation Physics Underlies New Theory for the Resilience of Patchy Ecosystems by Koen Siteur, et al in PNAS (120/2, 2023.)
Self-organized spatial patterns are ubiquitous in ecological systems and allow them to adopt non-trivial spatial distributions from disordered configurations. These patterns form due to diverse nonlinear interactions among organisms and their environment which lead to the emergence of new properties unique to self-organized systems. Here, we establish two categories depending on whether the self-organization is driven by nonlinear density-dependent demographic rates or movements from microbial colonies to whole environments. (Abstract)
McGuirl, Melissa, et al. Topological Data Analysis of Zebrafish Patterns. Proceedings of the National Academy of Sciences. 117/5113, 2020. Self-organized pattern behavior is ubiquitous throughout nature from fish schooling to collective cell dynamics. (1) Biomathematicans MM and Bjorn Sandstede, Brown University, and Alexandria Volkening, Northwestern University provide an example of how widely this natural propensity has become accepted in practice. In the early 2000s, it was hardly mentioned anywhere. In 2020, a universality of local interactive agents from which a global phase arises is strongly evident. After citing this common source, the paper describes an instance by the way it shapes aquatic scale formations.
Meron, Ehud. Nonlinear Physics of Ecosystems. Boca Raton: CRC Press, 2015. In this volume which seeks to join these far-removed yet intimately related domains, a Blaustein Institute for Desert Research and Ben-Gurion University physicist specifies and explains how living environments exemplify a spontaneous self-organization via complex dynamical systems of self-similar pattern formation. Nature’s entanglement can at last be shown to exhibit an integral universality of scales, periodicities, fractal shapes, symmetry breaks, reciprocities, and so on across vegetation, species, communities, and biodiversities.
Miele, Vincent and Catherine Matias. Revealing the Hidden Structure of Dynamic Ecological Networks. arXiv:1701.01335. University of Lyon and University of Paris Diderot biomathematicians conceive a method by which to discern nature’s vital, interactive relations between creatures and environs, which are not readily perceptible by a particulate focus.
Recent technological advances and long-term data studies provide interaction data that can be modelled through dynamic networks, i.e a sequence of different snapshots of an evolving ecological network. Most often time is the parameter along which these networks evolve but any other one-dimensional gradient (temperature, altitude, depth, humidity) could be considered. Here we propose a statistical tool to analyse the underlying structure of these networks and follow its evolution dynamics. It consists in extracting the main features of these networks and summarise them into a high-level view. We analyse a dynamic animal contact network and a seasonal food web and in both cases we show that our approach allows for the identification of a backbone organisation as well as interesting temporal variations at the individual level. (Abstract)
Montiglio, Puerre-Olivier, et al. Nested Interaction Networks Represent a Missing Link in the Study of Behavioural and Community Ecology. arXiv:1804.00927. The six person team has postings in Canada, Germany, USA and the UK, and cites themselves as an EcoEvoInteract Scientific Network. When we began this section around 2000, complex ecosystem patterns were sparsely evident and not well quantified. Network theories were in early stages, and not widely applied. This posting in the later 2010s is a good example of their pervasive utility as flora and fauna become treated just the same as physiologies. For example, an illustration displays wholes within wholes from genes to phenotypes to populations, via a typical node-link reciprocity.
Interactions are ubiquitous across biological systems. These interactions can be abstracted as patterns of connections among distinct units which form a hierarchy of biological organisation: gene and protein networks shape phenotypic traits and together constitute individuals, individuals are embedded within populations, populations within communities, and communities within ecosystems. The concept of nested biological networks is implicit in a variety of disciplines ranging from the study of genetic circuits regulating phenotypic trait expression, to the study of predator-prey interactions influencing community composition. Here, we formalise nested networks as having nodes that can contain, or be embedded in, other nodes, and where edges can bridge connections between sets of embedded nodes. We focus on two phenomena in particular: (i) indirect connections among units can arise from the structure of connections at higher or lower levels of organisation, (ii) the propagation of effects across neighbouring hierarchical levels of organization. (Abstract)
Montoya, Jose and Ricard Sole. Small World Patterns in Food Webs. Journal of Theoretical Biology. 214/3, 2002. The property of complex networks to develop a power-law distribution of interlinked nodes is here shown to apply for dynamic ecosystem communities.
Mouillot, David, et al. The Fractal Model. OIKOS. 90/3, 2000. An exploration of a self-similar geometry which can be applied to characterize species abundance and distribution.
Nathan, Marco. Molecular Ecosystems. Biology & Philosophy. Online September, 2013. A University of Denver philosopher traces in the recent literature how the term “ecosystem” is used in an analogous way for life’s other, often much removed, scalar phases such as biomolecular phenomena. Nathan avers that this is a valid metaphor for living systems which repeat their form and function, patterns and processes, at each sequential domain. Thus it is fair to enter a “microcosm – macrocosm” identity, along with a similar tendency to “individuation” at each nested instance. To note once more, as such a holistic purview becomes possible, all evolution and each organism do indeed reveal a necessary reiteration and recapitulation, as intuited by every age and culture.
In sum, we can distinguish between two models of ecosystem individuation: a physiognomic approach – which begins with a preliminary circumscription of the system and then works “inwards” to explore the ecological structure of the territory – and a component-specific approach, which starts by selecting the population(s) of reference and then works “outwards,” looking at their distribution and the totality of their interactions. Recent advances in genetics and molecular biology revealed that development is the product of a series of discrete and interacting modules, which allow the tinkering of finely-tuned and complex processes without wreaking havoc to the entire organism. I argue that our two modes of ecological individuation – physiognomic and component-specific – correspond to two distinct strategies for individuating developmental modules. (6)
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