Linking multidimensional functional diversity to ecological theory: A graphical hypothesis-generating framework
Functional diversity provides an appealing approach to study differences between fragmented biological communities because traits determine species’ responses to the environment, and multiple traits covary within individuals. Consequently, interest in multivariate functional diversity methods has surged over the past decade. Despite a rapidly expanding literature, rigorous hypothesis-driven studies linking the bevy of trait-based metrics to ecological theory are still rare. Major challenges are to conceptualize concurrent changes in multiple trait dimensions (“trait space”) and interpret their implications in the context of ecological theory.
To address these challenges, we present a graphical hypothesis-generating framework to predict and interpret differences in trait space among communities. Our framework is straightforward, intuitive, and quantitative: 1) design graphical hypotheses based on theory, available trait information, and system-specific ecology; 2) select a combination of quantitative tools to test these hypotheses; and 3) interpret changes in trait space in light of the results. To demonstrate its utility, we test hypotheses of community assembly and disturbance theory with three diverse datasets from aquatic invertebrate communities, including experiments and observational studies: a simulated drying experiment, a simulated top predator extinction experiment, and a field study during severe drought. This novel approach yielded insights into the ecological processes driving community dynamics in fragmented aquatic habitats that were not apparent in simpler analyses that ignored multidimensional trait structure. We posit that our framework can be applied broadly to address ecological questions across a range of systems and study designs.