Abstract

Models of functional ecospace diversification within life-habit frameworks (functional-trait spaces) are increasingly used across community ecology, functional ecology, and paleoecology. In general, these models can be represented by four basic processes, three that have driven causes and one that occurs through a passive process. The driven models include redundancy (caused by forms of functional canalization), partitioning (specialization), and expansion (divergent novelty), but they also share important dynamical similarities with the passive neutral model. In this second of two companion articles, Monte Carlo simulations of these models are used to illustrate their basic statistical dynamics across a range of data structures and implementations. Ecospace frameworks with greater numbers of characters (functional traits) and ordered (multistate) character types provide more distinct dynamics and greater ability to distinguish the models, but the general dynamics tend to be congruent across all implementations. Classification-tree methods are proposed as a powerful means to select among multiple candidate models when using multivariate data sets. Well-preserved Late Ordovician (type Cincinnatian) samples from the Kope and Waynesville formations are used to illustrate how these models can be inferred in empirical applications. Initial simulations overestimate the ecological disparity of actual assemblages, confirming that actual life habits are highly constrained. Modifications incorporating more realistic assumptions (such as weighting potential life habits according to actual frequencies and adding a parameter controlling the strength of each model’s rules) provide better correspondence to actual assemblages. Samples from both formations are best fit by partitioning (and to lesser extent redundancy) models, consistent with a role for local processes. When aggregated as an entire formation, the Kope Formation pool remains best fit by the partitioning model, whereas the entire Waynesville pool is better fit by the redundancy model, implying greater beta diversity within this unit. The ‘ecospace’ package is provided to implement the simulations and to calculate their dynamics using the R statistical language.

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