The rampant loss of biodiversity is starting to be recognized as a global crisis rivaling the climate emergency. To address this crisis, scientists need robust methods to measure the diversity in a system. Importantly, these methods should not only count species but capture the variety of different functions that the species in a system can perform. In this paper, we propose a machine learning method by which existing data from ecosystem monitoring can be reanalyzed to reveal changes of functional biodiversity over time.
The dynamics of trait-based metacommunities have attracted much attention, but not much is known about how dispersal and spatial environmental variability mutually interact with each other to drive coexistence patterns and diversity. Here, we present …
Metacommunity ecology currently lacks a consistent functional trait perspective across trophic levels. To foster new cross-taxa experiments and field studies, we present hypotheses on how three trait dimensions change along gradients of density of …
Theoretical Ecology, Biodiversity and trait-based modelling