From interesting details to dynamical relevance: toward more effective use of empirical insights in theory construction

• Published in: Oikos 2001-07, Vol.94 (1), p.39-50
• Main Author: Schmitz, Oswald J.
• Format: Article
• Language: English
• Subjects: Costs and Gains of Recent Progress in Ecology; Ecological competition; Ecological modeling; Ecology; Herbivores; Population ecology; Predation; Predators; Species; Synecology; Trophic relationships
• ISSN: 0030-1299; 1600-0706
• DOI: 10.1034/j.1600-0706.2001.11312.x

A perennial challenge in ecology is to develop dynamical systems models that appropriately abstract and characterize the dynamics of natural systems. Deriving an appropriate model of system dynamics can be a long and iterative process whose outcome depends critically on the quality of empirical data describing the long-term behavior of a natural system. Most ecological time series are insufficient to offer insight into the way organizational hierarchies and spatial scales are causally linked to natural system dynamics. Moreover, the classic tradition of hypothesis testing in ecology is not likely to lead to those key insights. This because empirical research is geared almost exclusively toward testing model predictions based on underlying causal relationships assumed by theorists. So, empirical research relies heavily on theory for guidance on what is or is not dynamically relevant. I argue here that it is entirely possible to reduce much of this guesswork involved with deciding on causal structure by giving empirical research a new role in theory development. In this role, natural history and field observations are used to develop stochastic, individual-based and spatially explicit computational models or IBMs that can explore the range of contingency and complexity inherent in real-world systems. IBMs can be used to run simulations allowing deductions to be made about the causal linkages between organizational hierarchies, spatial scales, and dynamics. These deductions can be tested under field conditions using experiments that manipulate the putative causal structure and evaluate the dynamical consequences. The emerging insights from this stage can then be used to inspire an analytical construct that embodies the dynamically relevant scales and mechanisms. In essence, computational modeling serves as an intermediate step in theory development in that a wide range of possibly important biological details are considered and then reduced to a subset that is dynamically relevant.