VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
7. Dynamic Ecosystems
Pimm, Stuart. The Balance of Nature? Chicago: University of Chicago Press, 1991. A leading ecologist presciently recognizes the potential for nonlinear principles to explain complex landscapes which actually do not seek an equilibrium as long thought but are dynamically self-organized.
Ranta, Esa, et al. Ecology of Populations. Cambridge, UK: Cambridge University Press, 2006. From Scandinavia, a technical study in part on the scale independent spatial and temporal self-organization of ecosystems.
Recknagel, Freidrich, ed. Ecological Informatics. Berlin: Springer, 2003. Bioinformatics is the application of computer analysis to biological phenomena such as genetic and protein networks. This book reviews a similar employ of artificial neural networks, adaptive agents, evolutionary algorithms, and so on to characterize and understand intricate, nested ecosystems. An earlier version of the subject appeared in Ecological Modelling. 146/1-2, 2001.
Ecological Informatics is defined as interdisciplinary framework promoting the use of advanced computational technology for the elucidation of principles of information processing at and between all levels of complexity of ecosystems – from genes to ecological networks… (iii)
Reuter, Hauke. The Concepts of Emergent and Collective Properties in Individual-Based Models. Ecological Modelling. 186/4, 2005. A summary paper from a special issue on flora and fauna as the epitome of dynamic, scalar, complex systems due to many relational individuals or agents. See also Reuter in Organic Societies above.
In the second half of the 20th century a crucial paradigm shift in biological theory included the perception of ecological systems as being self-organized. (491) The important thing about life is that the local dynamics of a set of interacting entities (e.g. molecules, cells, etc.) support an emergent set of global dynamical structures which stabilize themselves by setting the boundary conditions within which the local dynamics operate. (492)
Reynolds, A. M. On the Intermittent Behaviour of Foraging Animals. Europhysics Letters. 75/4, 2006. Another insight into a creative mathematical order which suffuses the natural realm. Of course, Galilei Galileo knew this long ago. See also in the same journal issue, (S. Picoli, et al) how a similar scaling pervades human complex systems.
This suggests that the scale-free and intermittent characteristics of forager movement patterns can be understood within the context of a single unified scale-free model. (520)
Ricotta, Carlo. Self-Similar Landscape Metrics as a Synthesis of Ecological Diversity and Geometrical Complexity. Ecological Modelling. 125/2-3, 2000. A hypothesis in search of a unified science rooted in a nature suffused by universal, multifractal patterns.
Rietkerk, Max and Johan van de Koppel. Regular Pattern Formation in Real Ecosystems. Trends in Ecology and Evolution. 23/3, 2008. Another quantification by way of “striking cross-ecosystem similarities” of an innate propensity for structural self-organization throughout nature.
Localized ecological interactions can generate striking large-scale spatial patterns in ecosystems through spatial self-organization. (169)
Rinaldo, Andrea, et al. Cross-Scale Ecological Dynamics and Microbial Size Spectra in Marine Ecosystems. Proceedings of the Royal Society of London B. 269/2051, 2002. More evidence of a universal self-similarity in nature.
Why should a continuous spectrum of organism size emerge from the ecological and evolutionary processes that have shaped ecosystems over evolutionary time?...Such features may have their dynamic origin in the self-organization of complex adaptive systems, possibly to self-organized critical phenomena, because they are robust in the face of environmental fluctuations. (2051) That such a complex web of interacting factors, acting locally and over evolutionary time, should result in such universal patterns begs explanation, and suggests a tendency of ecosystems to self-organize into states that lack a characteristic size – regardless of initial conditions and of transient disturbances. (2057)
Ritchie, Mark. Scale, Heterogeneity, and the Structure and Diversity of Ecological Communities. Princeton: Princeton University Press, 2009. The fluid, intricate, diversity of land, sea and aerial fauna and flora has been evident since their naturalist study began. A Syracuse University biologist here proposes that an integral theoretical synthesis, aided by a rush of recent advances, may at last be possible. As its unifying theme and motif, the ubiquitous presence of a nested self-similarity, properly understood and quantified, can now be affirmed. As a significant aspect, it is not only animal distributions from bacteria and beetles to tuna, ungulates, and eagles that are so fractal in kind, even the terrestrial or nautical environs they reside in expresses such geometries. So may one muse that circa 2010 a consistently repetitive, indeed untangled Nature is in fact revealed, which then manifests, we are invited to observe, an independent mathematical source as an open testament for us to avail and carry forward?
In this book, I propose a new framework for predicting the structure and diversity of ecological communities that might help synthesize previous theory and data. This framework emerges out of incorporating two critical elements of the inductive approaches, scale and heterogeneity, into the analytical mathematical formalism of the more deductive approaches. (2) The emphasis on scale and heterogeneity requires a tool that can simply describe the complex physical structure of nature: fractal geometry. Fractal geometry assumes that distributions of physical material and conditions and/or biological organisms in the environment are statistically similar across a range of meaningful spatial scales. (2)
Rocha, Juan, et al. Cascading Regime Shifts Within and Across Scales. Science. 362/1379, 2018. Stockholm Resilience Centre ecological scholars including Simon Levin provide a latest finesse of complex ecosystems as they interact and transition within local and planetary bioregions and climates. The work merited a review Seeing a Global Web of Connected Systems by Marten Scheffer and Egbert van Nes (362/1357), second quote.
The potential for regime shifts and critical transitions in ecological and Earth systems, particularly in a changing climate, has received considerable attention. However, the possibility of interactions between such shifts is poorly understood. Rocha et al. used network analysis to explore whether critical transitions in ecosystems can be coupled with each other, even when far apart (see the Perspective by Scheffer and van Nes). They report different types of potential cascading effects, including domino effects and hidden feedbacks, that can be prevalent in different systems. Such cascading effects can couple the dynamics of regime shifts in distant places, which suggests that the interactions between transitions should be borne in mind in future forecasts. (Rocha summary)
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)