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Applying Evolutionary Meta-Strategies to Human Problems

  • Valerie GremillionEmail author
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Classes of evolutionary strategy are rarely explored, as such strategies are thought to be either highly species-specific or clearly resulting from competitive selection. But are we missing the lessons a deeper theory of evolutionary strategy provides – lessons that could help solve the global problems facing humanity? We discuss the means to such solutions through a neural-derived network approach to modeling highly detailed species-ecosystem interactions (Gremillion and Brown, An ecosystem network model for human ecological interactions. Ecological Society of America Abstracts, 86th Meeting: ID=28381, 2001) including Homo sapiens and its impacts, into ecocircuitry networks. This model identifies a set of underlying evolutionary meta-strategies that govern intrinsic strategic drivers in all organisms and potentially systems from tribes to corporations.

This approach also yields a methodology for analyzing higher-order network impacts, providing a path to address unintended consequences, and high-order network costs/benefits.

Ecocircuitry network models incorporate flows of energy, material, services, and information through many classes of inputs and outputs both biotic (living) and abiotic. We posit that the population size of any species varies with the convergence of its network interactions. To be sustainable over evolutionary time, a species or population must be relatively balanced in its positive (beneficial) and negative (detrimental) connections, or risk extinction.

Species actively manipulate their ecosystem’s circuitry through a clearly defined set of evolutionary meta-strategies, in which all species differentially alter the (1) number and (2) magnitude of both beneficial and detrimental flows while seeking to (3) decrease the variance of all flows, for stability and predictability are stabilizing. Taken together these strategies alone can change the balance of positive and negative network connections and thus population/success rates. But indirect “meta-strategies” are even more powerful and include externalizing costs, physical and other tool- and infrastructure-building, and novel strategy combinations.

We illustrate these classes of evolutionary strategies with examples from many species, as well as innovative human meta-strategies that have led to unintended consequences both beneficial and problematic. We further examine how a more encompassing ecocircuitry approach, which includes impacts of humans and their institutions, illuminates useful meta-strategies for solutions in a rapidly changing world.

Keywords

Ecocircuitry Darwinian algebra Double-binary network model Complexity Ecological network Species-ecosystem network Evolutionary strategy Meta-strategy Multivalent flows Ecosystem dynamics Functional ecology Neuron Nonlinear dynamics Ecology Species Neural circuitry Cost-benefit analysis Optimization Food webs Abiotic 

Notes

Acknowledgments

The author would like to thank E. Todd Hochman for his interdisciplinary brainstorming and assistance in graphics design; Professor James H. Brown for his in-depth collaboration on the initial model; Professor Astrid Kodric-Brown for her valuable collaboration on information flows and honeybee systems; John Smart, for his beneficial comments; the Hitchings-Elion Fellowship that funded some of the initial neuroscience thinking that led to this model; and the Department of Biology, UNM, ABQ for their essential support.

References

  1. Andrewartha, H.G. and L.C. Birch. (1984) The Ecological Web. University of Chicago Press, Chicago.Google Scholar
  2. Baccarelli & V. Bollati. (2009) Epigenetics and environmental chemicals. Curr Opin Pediatr. 21: 243–251.CrossRefGoogle Scholar
  3. Bratsberg, B. & O. Rogeberg. (2018) Flynn effect and its reversal are both environmentally caused. PNAS 201718793; published ahead of print June 11, 2018.  https://doi.org/10.1073/pnas.1718793115 Google Scholar
  4. Brown, J.H. (1995) Macroecology. The University of Chicago Press, Chicago.Google Scholar
  5. Brown, P. and K. Caldeira. (2017) Greater future global warming inferred from Earth’s recent energy budget. Nature 552:45–50. doi: https://doi.org/10.1038/nature2467 ADSCrossRefGoogle Scholar
  6. Ceballos, G., P. Ehrlich and R. Dirzo. (2017) Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. PNAS 114:E6089–E6096.CrossRefGoogle Scholar
  7. Cohen, J. E. (1978) Food webs and niche space. Monographs in Population Biology. Princeton University Press, Princeton.Google Scholar
  8. Cribb, J. (2017) Surviving the 21st Century: Humanity’s Ten Great Challenges and How We Can Overcome Them. NY: Springer International Publishing.CrossRefGoogle Scholar
  9. Darwin, C. (1859) The Origin of Species; And, the Descent of Man. Modern Library.Google Scholar
  10. Dunne, J.A., Williams, R.J. and Martinez, N.D. (2002) Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecology letters, 5:.558–567.CrossRefGoogle Scholar
  11. Evans, J.D., Aronstein, K., Chen, Y.P., Hetru, C., Imler, J.L., Jiang, H., Kanost, M., Thompson, G.J., Zou, Z. and Hultmark, D. (2006) Immune pathways and defense mechanisms in honey bees Apis mellifera. Insect molecular biology 15:645–656.CrossRefGoogle Scholar
  12. Gould, J. L., & Gould, C. G. (1988) The honey bee. Scientific American Library.Google Scholar
  13. Graham, L. (2008) Reparations, Self-Determination, and the Seventh Generation. 21 Harv. Hum. Rts. J. 47.Google Scholar
  14. Gremillion, MAV. and J. Brown. (2001) An ecosystem network model for human ecological interactions. Ecological Society of America Abstracts, 86th Meeting: ID=28381.Google Scholar
  15. Hattab, T. et al. (2017) A unified framework to model the potential and realized distributions of invasive species within the invaded range. Diversity and Distributions 23:806–819.CrossRefGoogle Scholar
  16. Hodgkin, A. and Huxley, A. (1952) A quantitative description of membrane current and its Application to conduction and excitation in nerve. J Physiol. 117: 500–544.CrossRefGoogle Scholar
  17. Hublin, Jean-Jacques, et al. (2017) “New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens. Nature 546: 289.ADSCrossRefGoogle Scholar
  18. Ives, A.R., S.R. Carpenter, and B. Dennis. (1999) Community interaction webs and zooplankton responses to planktivory manipulations. Ecology 80:1405–1421.CrossRefGoogle Scholar
  19. Jordano, P. (1987) Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. The American Naturalist, 129: 657–677.CrossRefGoogle Scholar
  20. Kaiser-Bunbury, C., S. Muff, J. Memmot, C. Muller, A. Caflisch. (2010) The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behavior. Ecology Letters 13: 442–452CrossRefGoogle Scholar
  21. Kaiser-Bunbury, C and N Blüthgen. (2015) Integrating network ecology with applied conservation: a synthesis and guide to implementation. AoB PLANTS, v7:lv076.  https://doi.org/10.1093/aobpla/plv076
  22. Kolbert, E. (2015) The Sixth Extinction: An Unnatural History. NY: Picador.Google Scholar
  23. Levins, R. (1975) Quantitave analysis – loop analysis. In M.L. Cody ad J.L. Diamond, eds. Ecology and Evolution of Communities. Cambridge, Mass. Belknap Press.Google Scholar
  24. McRae, B., B. Dickson, T. Keitt, V. Shah. (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724.  https://doi.org/10.1890/07-1861.1 CrossRefGoogle Scholar
  25. Mora, C. et al. (2017) Global risk of deadly heat. Nature Climate Change 7:501–506.ADSCrossRefGoogle Scholar
  26. Moss, M. (2016) How the Colt Single Action Army Revolver Won the West. Popular Mechanics retrieved 5-31-18. https://www.popularmechanics.com/military/weapons/a23685/colt-single-action/
  27. Mueller, U., S. Rehner, T. Schultz. (1998) Evolution of Agriculture in Ants. Science 281:2034. ADSCrossRefGoogle Scholar
  28. Nabhan, G. P., & Buchmann, S. L. (1997) Services provided by pollinators. Nature’s Services: societal dependence on natural ecosystems, 133–150.Google Scholar
  29. Odum, H. (1983) Systems Ecology: An Introduction, Wiley-Interscience.Google Scholar
  30. Paine, R.T. (1966) Food web complexity and species diversity. American Naturalist 100:65–75.Google Scholar
  31. Paine, R.T. (1980) Food webs: linkage, interaction strength and community infrastructure. Journal of Animal Ecology 49:667–685.CrossRefGoogle Scholar
  32. Proulx, S. D. Promislow, and P. Phillips. (2005) Network thinking in ecology and evolution. TREE, v20:345–353.  https://doi.org/10.1016/j.tree.2005.04.004
  33. Schmid-Hempel, P. (1998) Parasites in social insects. Princeton University Press.Google Scholar
  34. Seeley, T. D. (1997) Honey bee colonies are group-level adaptive units. The American Naturalist, 150(S1), s22–S41.Google Scholar
  35. Raftery, A., A. Zimmer, D. Frierson, R. Startz, & P. Liu. (2017) Less than 2 °C warming by 2100 unlikely. Nature Climate Change 7:637–641.ADSCrossRefGoogle Scholar
  36. Shachak, M. and C.G. Jones. (1995) Ecological flow chains and ecological systems: concepts for linking species and ecosystem perspectives. In Linking Species and Ecosystems, C.G. Jones and J.H. Lawton, eds. Chapman and Hall, New York, New York.Google Scholar
  37. Shipley, B. (1997) Exploratory path analysis with applications in ecology and evolution. American Naturalist 149:1113–1138.CrossRefGoogle Scholar
  38. Salthe, S. (2003) Development and Evolution. MIT Press.Google Scholar
  39. Steinhauer, N. K. Kulhanek, K. Antunez, H. Human, P. Chantawannakul, M-P Chauzat, D. VanEngelsdorp. (2018) Drivers of colony losses. Curr Op in Insect Sci 26:142–148.CrossRefGoogle Scholar
  40. Stone, G., B. Gyawali, J. Sandifer. (2017). Honeybee Colony Collapse Disorder in the USA. http://digitalcommons.murraystate.edu/postersatthecapitol/2018/KSU/6/
  41. Taylor-Robinson, A.W. (2000) Vaccination against malaria: targets, strategies and potentiation of immunity to blood stage parasites. Frontiers in Bioscience 5:E16–29.Google Scholar
  42. Ulanowicz, R.E. (1997) Ecology: The Ascendant Perspective. Columbia, New York, New York.Google Scholar
  43. Van Valen, L. (1973) A new evolutionary law. Evolutionary Theory 1:1–30.Google Scholar
  44. Whorf, B. L. (1956) Language, thought and reality. Selected Writings of Benjamin Lee Whorf. J. B. Caroll (Ed). New York and London. MI T. Press and Wiley.Google Scholar
  45. Williams, R.J. and N.D. Martinez. (2000) Simple rules yield complex food webs. Nature 404:180–183.ADSCrossRefGoogle Scholar
  46. Wilson, E. O. (1971) The insect societies. Cambridge, MA: Harvard University Press.Google Scholar
  47. Wilson, E. O. (1975) Slavery in ants. Scientific American, 232:32–40.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Neurocomplexity ConsultingSanta FeUSA

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