Spatial convergent cross mapping to detect causal relationships from short time series.

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.

Neighborhoods have little effect on fungal attack or insect predation of developing seeds in a grassland biodiversity experiment.

Numerous observational studies have documented conspecific negative density-dependence that is consistent with the Janzen-Connell Hypothesis (JCH) of diversity maintenance. However, there have been few experimental tests of a central prediction of the JCH: that removing host-specific enemies should lead to greater increases in per capita recruitment in areas of higher host density or lower relative phylogenetic diversity. Using spatially randomized plots of high and low host biomass in a temperate grassland biodiversity experiment, we treated developing seedheads of six prairie perennials to factorial applications of fungicide and insecticide. We measured predispersal seed production, seed viability, and seedling biomass. Results were highly species-specific and idiosyncratic. Effects of insect seed predators and fungal pathogens on predispersal responses varied with neither conspecific biomass nor phylogenetic diversity, suggesting that-at least at the predispersal stage and for the insect and fungal seed predators we were able to exclude-the JCH is not sufficient to contribute to negative conspecific density-dependence for these dominant prairie species.

Sustaining multiple ecosystem functions in grassland communities requires higher biodiversity.

Society places value on the multiple functions of ecosystems from soil fertility to erosion control to wildlife-carrying capacity, and these functions are potentially threatened by ongoing biodiversity losses. Recent empirically based models using individual species' traits suggest that higher species richness is required to provide multiple ecosystem functions. However, no study to date has analyzed the observed functionality of communities of interacting species over multiple temporal scales to assess the relationship between biodiversity and multifunctionality. We use data from the longest-running biodiversity-functioning field experiment to date to test how species diversity affects the ability of grassland ecosystems to provide threshold levels of up to eight ecosystem functions simultaneously. Across years and every combination of ecosystem functions, minimum-required species richness consistently increases with the number of functions considered. Moreover, tradeoffs between functions and variability among years prevent any one community type from providing high levels of multiple functions, regardless of its diversity. Sustained multifunctionality, therefore, likely requires both higher species richness than single ecosystem functionality and a diversity of species assemblages across the landscape.