Species diversity is a major determinant of ecosystem productivity, stability, invasibility, and nutrient dynamics. Hundreds of studies spanning terrestrial, aquatic, and marine ecosystems show that high-diversity mixtures are approximately twice as productive as monocultures of the same species and that this difference increases through time. These impacts of higher diversity have multiple causes, including interspecific complementarity, greater use of limiting resources, decreased herbivory and disease, and nutrient-cycling feedbacks that increase nutrient stores and supply rates over the long term. These experimentally observed effects of diversity are consistent with predictions based on a variety of theories that share a common feature: All have trade-off-based mechanisms that allow long-term coexistence of many different competing species. Diversity loss has an effect as great as, or greater than, the effects of herbivory, fire, drought, nitrogen addition, elevated CO2, and other drivers of environmental change. The preservation, conservation, and restoration of biodiversity should be a high global priority.

Biodiversity enhances many of nature's benefits to people, including the regulation of climate and the production of wood in forests, livestock forage in grasslands and fish in aquatic ecosystems. Yet people are now driving the sixth mass extinction event in Earth's history. Human dependence and influence on biodiversity have mainly been studied separately and at contrasting scales of space and time, but new multiscale knowledge is beginning to link these relationships.

Recent developments in complex systems analysis have led to new techniques for detecting causal relationships using relatively short time series, on the order of 30 sequential observations. Although many ecological observation series are even shorter, perhaps fewer than ten sequential observations, these shorter time series are often highly replicated in space (i.e., plot replication). Here, we combine the existing techniques of convergent cross mapping (CCM) and dewdrop regression to build a novel test of causal relations that leverages spatial replication, which we call multispatial CCM. Using examples from simulated and real-world ecological data, we test the ability of multispatial CCM to detect causal relationships between processes. We find that multispatial CCM successfully detects causal relationships with as few as five sequential observations, even in the presence of process noise and observation error. Our results suggest that this technique may constitute a useful test for causality in systems where experiments are difficult to perform and long time series are not available. This new technique is available in the multispatialCCM package for the R programming language.

Biodiversity loss decreases ecosystem functioning at the local scales at which species interact, but it remains unclear how biodiversity loss affects ecosystem functioning at the larger scales of space and time that are most relevant to biodiversity conservation and policy. Theory predicts that additional insurance effects of biodiversity on ecosystem functioning could emerge across time and space if species respond asynchronously to environmental variation and if species become increasingly dominant when and where they are most productive. Even if only a few dominant species maintain ecosystem functioning within a particular time and place, ecosystem functioning may be enhanced by many different species across many times and places (β-diversity). Here, we develop and apply a new approach to estimate these previously unquantified insurance effects of biodiversity on ecosystem functioning that arise due to species turnover across times and places. In a long-term (18-year) grassland plant diversity experiment, we find that total insurance effects are positive in sign and substantial in magnitude, amounting to 19% of the net biodiversity effect, mostly due to temporal insurance effects. Species loss can therefore reduce ecosystem functioning both locally and by eliminating species that would otherwise enhance ecosystem functioning across temporally fluctuating and spatially heterogeneous environments.

Most species in all ecosystems are rare in some form.