Biodiversity is rapidly declining worldwide, and there is consensus that this can decrease ecosystem functioning and services. It remains unclear, though, whether few or many of the species in an ecosystem are needed to sustain the provisioning of ecosystem services. It has been hypothesized that most species would promote ecosystem services if many times, places, functions and environmental changes were considered; however, no previous study has considered all of these factors together. Here we show that 84% of the 147 grassland plant species studied in 17 biodiversity experiments promoted ecosystem functioning at least once. Different species promoted ecosystem functioning during different years, at different places, for different functions and under different environmental change scenarios. Furthermore, the species needed to provide one function during multiple years were not the same as those needed to provide multiple functions within one year. Our results indicate that even more species will be needed to maintain ecosystem functioning and services than previously suggested by studies that have either (1) considered only the number of species needed to promote one function under one set of environmental conditions, or (2) separately considered the importance of biodiversity for providing ecosystem functioning across multiple years, places, functions or environmental change scenarios. Therefore, although species may appear functionally redundant when one function is considered under one set of environmental conditions, many species are needed to maintain multiple functions at multiple times and places in a changing world.

We introduce glmulti, an R package for automated model selection and multi-model inference with glm and related functions. From a list of explanatory variables, the provided function glmulti builds all possible unique models involving these variables and, optionally, their pairwise interactions. Restrictions can be specified for candidate models, by excluding specific terms, enforcing marginality, or controlling model complexity. Models are fitted with standard R functions like glm. The n best models and their support (e.g., (Q)AIC, (Q)AICc, or BIC) are returned, allowing model selection and multi-model inference through standard R functions. The package is optimized for large candidate sets by avoiding memory limitation, facilitating parallelization and providing, in addition to exhaustive screening, a compiled genetic algorithm method. This article briefly presents the statistical framework and introduces the package, with applications to simulated and real data.

The competition-colonization trade-off model is often used to explain the coexistence of species. Yet its applicability has been severely criticized, mainly because the original model assumed a strict competitive hierarchy of species and did not allow for any preemptive effect. We considered the impact of relaxing both of these limitations on coexistence. Relaxing trade-off intensity makes coexistence less likely and introduces a minimum colonization rate below which any coexistence is impossible. Allowing for preemption introduces a limit to dissimilarity between species. Surprisingly, preemption does not impede coexistence as one could presume from previous studies, but can actually increase the likelihood of coexistence. Its effect on coexistence depends on whether or not species in the regional pool are strongly limited in their colonization ability. Preemption is predicted to favour coexistence when: (i) species are not strongly limited in their colonization ability; and (ii) the competitive trade-off is not infinitely intense.

Keystone species are defined as having disproportionate importance in their community. This concept has proved useful and is now often used in conservation ecology. Here, we introduce the concept of keystone communities (and ecosystems) within metacommunities (and metaecosystems). We define keystone and burden communities as communities with impacts disproportionately large (positive or negative respectively) relative to their weight in the metacommunity. We show how a simple metric, based on the effects of single-community removals, can characterise communities along a 'keystoneness' axis. We illustrate the usefulness of this approach with examples from two different theoretical models. We further distinguish environmental heterogeneity from species trait heterogeneity as determinants of keystoneness. We suggest that the concept of keystone communities/ecosystems will be highly beneficial, not only as a fundamental step towards understanding species interactions in a spatial context, but also as a tool for the management of disturbed landscapes.