Systems Ethology--The Life and Work of Bland Ewing

This site is dedicated to the life and work of Bland Ewing. Bland identified his research work as quantitative population ethology, but it may be more appropriate today to use the phrase systems ethology. These pages have substantial content about systems ethology, as well as a biography of Bland Ewing and connections to other related topics.

I am writing Bland’s biography, and have actively worked with him and others on his ideas on population ethology modeling. For original, see http://www.stat.wisc.edu/~yandell/ewing/. More will be added over time.

My Involvement with Bland Ewing

Bland Ewing was my first mentor. I worked for him at UC-Berkeley during the summers while I was an undergrad at Caltech in the early 1970s. By the late 1970s we lost touch, and only reconnected in the mid 1990s. At that time, Bland was suffering in a serious way from Huntington’s Disease, the same malady that took his father, grandfather, and Woody Guthrie. Bland Ewing died in the early 2000s. His mind was quite active to the last day, though he was largely confined to his apartment and later a nursing home due to low energy and difficulty walking. Bland’s short-term memory in those last years was sporadic, and he ocasionally has trouble with names, but his depth of reasoning was phenomenal.

During the early 2000s, Bland and I, along with Jim Barbieri and Bob Luck, reviewed modeling ideas that Bland first proposed in his intended dissertation. As near as any of us can tell, these ideas had not been superseded in the past quarter century. Today, in the 2020s, these seem relevant and actionable at scale given advances in data and computing capabilities.

Ewing Bibliography

Current Work

Unpublished Work from the 1970s

  • B Ewing (1973) “Population ethology,” dissertation outline, Department of Entomology, UC-Berkeley.
  • B Ewing (1974) “Population ethology,” unpublished manuscript, Department of Entomology, UC-Berkeley, Jun 1974.
  • B Ewing, P Rauch, and J Barbieri (1974) “Simulating the dynamics and structure of populations,” Lawrence Livermore Laboratory Report, UCRL-76046 (Rev. 1), Sep 1974.
  • B Ewing, P Rauch, JF Barbieri (1974) “Simulating the dynamics and structure of populations,” unpublished manuscript, Sep 1974. This paper was prepared for presentation at the Third Annual Integrated Pest Management Modelers’ Meeting: The Principles, Strategies and Tactics of Pest Population Regulation and Control in Major Crop Ecosystems, New Orleans, LA, 8-10 Jan 1975.
  • B Ewing, JF Barbieri and PA Rauch (1975) “Stimulating the Dynamics and Structure of Populations,” 1 Aug 1975. Prepared for inclusion in “The Principles, Strategies and Tactics. . . in Western Pine Beetle Ecosystem,” Progress Report, Vol. 2, 1975.
  • PA Rauch, B Ewing and DL Wood (1975) “Information transfer and the systems approach in large-scale ecological studies,” unpublished manuscript, Entomology, UC-Berkeley, 10 Aug 1975.
  • B Ewing, J Barbieri, P Rauch, D Baasch and BS Yandell (1976) “Simulating the dynamics and structure of populations,” 15 Jul 1976. Prepared for inclusion in: “The Principles, Strategies, and Tactics … in Western Pine Beetle Ecosystems” Progress Report, 1976.
  • P Bunnell (1973) A stochastic model of a lizard community. PhD Thesis, University of California, Berkeley.
  • JF Barbieri (1974) “A method for modeling sparse systems”, memorandum to JB Knox, Lawrence Livermore Laboratory, 29 Apr 1974.
  • JF Barbieri (1975) “Progress report on modeling structured ecosystems using Monte Carlo techniques,” Memorandum to JB Knox, Lawrence Livermore Laboratory, 1 Jul 1975.
  • JF Barbieri (1975) “Problems with modeling structured ecosystems and the EPA-SBNF project,” Memorandum to JB Knox, Lawrence Livermore Laboratory, 13 Jun 1975.
  • BS Yandell (1978) “Random numbers: where do they come from?” project for D Brillinger’s Time Series course, UC Berkeley, 7 Apr 1978.

Quantitative Population Ethology Software

We are developing software to implement the quantitative population ethology simulation approach using the R statistical computing system.

Please read the Practical Model Building paper for detailed information on our code. First one needs to install the R system, however. See the R Project for instructions.