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 C omplexity 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 Irreproducible Results and the Breeding of Pigs (or Nondegenerate Limit Random Variables in Biology). BioScience 26: For an ecological example of extensions of these ideas see The Unified Neutral Theory of Biodiversity and Biogeography. Stephen P. Hubbell. Princeton University Press For an evolutionary theory example of extensions see Gavrilets, S, R. Acton and J. Gravner Dynamics of speciation and diversification in a metapopulation. Evolution 54:

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.