BioComplexity: New Approaches to Big,
Bad Problems, or the Same Old Dreck?
Louis J. Gross
The Institute for Environmental
Modeling
Departments of Ecology and
Evolutionary Biology and Mathematics
University of Tennessee
What is biocomplexity?
Bio – pertaining to life, so must include some
aspect of this.
Complexity – A complex system is one in
which a well trained scientist knowledgable
about the system of concern cannot rapidly
intuit how the system will behave. By rapidly
here, I mean with the aid of simple
computational tools (pencil and paper,
computer, calculator, etc.) in a few minutes.
Routes to complexity
Complexity should include systems that may
be quite easily described, but have
underlying complicated responses (e.g. the
chaotic dynamics models of single or multiple
populations) that one cannot intuit easily, as
well as systems that most of us would agree
are complicated due to multiple interacting
factors (food webs with many components,
ecosystems with dynamic and spatial
responses on multiple scales).
Urn Schemes and Null Models:
Cohen, Joel E. 1976. Irreproducible Results and
the Breeding of Pigs (or Nondegenerate Limit
Random Variables in Biology). BioScience
26:391-394.
For an ecological example of extensions of these
ideas see The Unified Neutral Theory of
Biodiversity and Biogeography. Stephen P.
Hubbell. Princeton University Press. 2001.
For an evolutionary theory example of extensions
see Gavrilets, S, R. Acton and J. Gravner. 2000.
Dynamics of speciation and diversification in a
metapopulation. Evolution 54:1493-1501
What isn’t biocomplex?
• Single organism biomass growth
(determinant species).
• Projecting species ranges and occurrence
from habitat data
• Succession trajectory (sere)
• Recreational use patterns
Complexity – the hype
Complexity theory is just the latest "buzz
word" hot topic and will go the way of game
theory, catastrophe theory, chaos theory,
self-organized criticality (and Stephen
Wolfram’s latest “New Kind of Science”) to
name a few.
Complexity – the hype
All these were supposedly going to provide "the
answers" to major scientific questions. All have
contributed but never lived up to the hype. They
merely provide a variety of constructs to allow
us to phrase our questions in new ways, and
tools to potentially allow us to tease apart
similarities in behavior between systems arising
in very different scientific fields.
So how do we approach problems
identified as complex?
Example: Recent request made to me:
The Park Service is beginning the process of
developing conceptual ecological models to
identify and monitor ecological vital signs as one
might monitor human vital signs such pulse,
blood pressure, and temperature to assess a
person's health.
Products sought are: conceptual diagrammatic
representations of these systems with various
stressors, documentation on what the diagrams
mean, and a preliminary suggestion on which
ecological variables are most vital as indicators
and early warnings for monitoring.
Major concerns include aspects of air quality and
atmospheric deposition, invasive species, impacts
of development, water quality and quantity, fire,
visitor impacts, climate change, threatened and
endangered species, and restoration ecology.
My response to this conceptual modeling
request:
It is preferable to focus on key components at a
variety of trophic levels, and the associated basic
environmental issues affecting those key
components. Before starting any of this, one
needs to very carefully specify the objectives and
evaluation procedures of the modeling effort. If
the objective of is to decide on the key
components of the system, then you should
involve people with long field experience in the
system from a variety of stakeholder groups.
Deciding on the key components to include is not
inherently a modeling problem but a social
choice one. This is based upon intuition of
experts in natural history (and cultural history),
people who are directly impacted, and those
stakeholders representing society as a whole.
Modeling can inform the process, but does not
define it.