N‐dimensional hypervolumes to study stability of complex ecosystems

Abstract

Although our knowledge on the stabilising role of biodiversity and on how it is affected by perturbations has greatly improved, we still lack a comprehensive view on ecosystem stability that is transversal to different habitats and perturbations. Hence, we propose a framework that takes advantage of the multiplicity of components of an ecosystem and their contribution to stability. Ecosystem components can range from species or functional groups, to different functional traits, or even the cover of different habitats in a landscape mosaic. We make use of n-dimensional hypervolumes to define ecosystem states and assess how much they shift after environmental changes have occurred. We demonstrate the value of this framework with a study case on the effects of environmental change on Alpine ecosystems. Our results highlight the importance of a multidimensional approach when studying ecosystem stability and show that our framework is flexible enough to be applied to different types of ecosystem components, which can have important implications for the study of ecosystem stability and transient dynamics.

Behavioral Hypervolumes of Predator Groups and Predator-Predator Interactions Shape Prey Survival Rates and Selection on Prey Behavior

“…Like other aspects of trait-based ecology (Blonder et al 2014; Barros et al 2016), we predict that the multidimensional behavioral diversity exhibited by individuals, groups, or even communities could be important metrics for predicting higher-order ecological phenomena.…”

“…Hypervolumes and other extrema statistics have been used in a variety of other subfields of ecology, and their pros and cons are consequently quite well documented (Blonder et al 2014; Barros et al 2016). First, when comparing hypervolumes across groups, the number of dimensions used will shape hypervolume estimates and thus should be held constant to ensure comparability.…”

Elevation predicts the functional composition of alpine plant communities based on vegetative traits, but not based on floral traits

“…Data on various traits allow to comprehensively track functional changes and transitions in communities as response to environmental changes or as cause for changes in ecosystem processes (Allan et al 2015;Tilman et al 1997). Thus, knowledge on the dispersion of several traits along elevational gradients can be valuable to understand and predict responses of plant communities to climate warming (Barros et al 2016;Read et al 2014;Sundqvist et al 2013).…”

“…The framework of n-dimensional hypervolumes considers, unlike other multivariate approaches, each trait equally, does not reduce the number of dimensions, and thus represents a direct representation of functional community composition (Barros et al 2016;Carmona et al 2016;Junker et al 2016;Kuppler et al 2017;Lamanna et al 2014). Accordingly, hypervolumes have been shown to be a valuable approach to track changes in community composition (Barros et al 2016).…”

“…The framework of n-dimensional hypervolumes considers, unlike other multivariate approaches, each trait equally, does not reduce the number of dimensions, and thus represents a direct representation of functional community composition (Barros et al 2016;Carmona et al 2016;Junker et al 2016;Kuppler et al 2017;Lamanna et al 2014). Accordingly, hypervolumes have been shown to be a valuable approach to track changes in community composition (Barros et al 2016). Although the framework of n-dimensional hypervolumes has been proposed 60 years ago to describe niches (Hutchinson 1957), just recently, it became accessible through the development of statistical approaches that calculate the size and overlap of n-dimensional hypervolumes (for description of the different available approaches, see Blonder et al 2014;Carmona et al 2016;Junker et al 2016;Swanson et al 2015).…”