2014 GSA Annual Meeting in Vancouver, British Columbia (19–22 October 2014)

Paper No. 144-4
Presentation Time: 1:45 PM

ECOLOGICAL AND STRUCTURAL CONSTRAINTS REVEALED BY SIMULATING THEORETICAL ECOSPACES


NOVACK-GOTTSHALL, Philip M., Department of Biological Sciences, Benedictine University, 5700 College Road, Lisle, IL 60532, pnovack-gottshall@ben.edu

Interest in functional diversity has increased substantially in recent years, reflecting growing awareness that organismal life habits provide useful information. In many ways, the classifications designed for these analyses are theoretical ecospaces, in which any combination of functional traits is a theoretically plausible life habit. Statistical analysis of functional diversity has largely relied on permutation tests, but these tests are limited to testing whether the functional diversity within an assemblage is statistically distinct from that of the available species pool. Monte Carlo simulations can be used to evaluate more complex models of community assembly, evolutionary diversification, among others. However, much less attention has focused on understanding the nature of the designed theoretical ecospace framework itself, which can reveal both design flaws and inherent structural limitations in the ability of the ecospace framework to identify biological patterns of interest. This process is analogous to sensitivity analysis of statistical power in classical experimental design.

Here I create different theoretical ecospace frameworks with distinct structural designs and examine their inherent structural constraints. These frameworks differ in numbers of traits and character states, types of data (factorial, numerical, binary, ordered multistate), and ability to incorporate various design constraints (such as by weighing possible states based on their frequency in species pools). Analyses demonstrate that irregularities in the resulting ecospace can be caused either by structural (design) limitations inherent to the ecospace framework itself or by ecological/evolutionary (real) constraints inherent to nature. Monte Carlo simulation allows a simple method to distinguish these types of constraints. Such sensitivity analyses must be conducted prior to using these frameworks in formal statistical analyses. I then use a powerful ecospace framework to identify real ecological and evolutionary patterns in the ecospace of Late Ordovician (Type Cincinnatian) marine assemblages. The same concerns and recommendations offered here apply equally to studies of morphological disparity.