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Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics

The ability of random environmental variation to stabilize competitor coexistence was pointed out long ago and, in recent years, has received considerable attention. Analyses have focused on variations in the log abundances of species, with mean logarithmic growth rates when rare, 𝔼[r], used as metrics for persistence. However, invasion probabilities and the times to extinction are not single-valued functions of 𝔼[r] and, in some cases, decrease as 𝔼[r] increases. Here, we present a synthesis of stochasticity-induced stabilization (SIS) phenomena based on the ratio between the expected arithmetic growth μ and its variance g. When the diffusion approximation holds, explicit formulas for invasion probabilities and persistence times are single-valued, monotonic functions of μ/g. The storage effect in the lottery model, together with other well-known examples drawn from population genetics, microbiology, and ecology (including discrete and continuous dynamics, with overlapping and non-overlapping generations), are placed together, reviewed, and explained within this new, transparent theoretical framework. We also clarify the relationships between life-history strategies and SIS, and study the dynamics of extinction when SIS fails

Savannas are highly variable systems, and predicting variation, especially in the tree layer, represents a major unresolved challenge for forecasting biosphere responses to global change. Prediction to date has focused on disentangling interactions between resource limitation and chronic disturbances to identify what determines local savanna vegetation heterogeneity. By focusing at too fine a scale, this approach overlooks: sample size limitation arising fromsparse tree distributions; stochasticity in demographic and environmental processes that is preserved as heterogeneity among tree populations with slow dynamics; and spatial self-organization. Renewedfocus on large (1–50 ha) permanent plots and on spatial patterns of tree-layer variability at even larger landscape spatial scales (≥1000s of ha) promises to resolve these limitations, consistent with the goal of predicting large-scale biosphere responses to global change.

[This corrects the article DOI: 10.1371/journal.pone.0207216.].

Ecological drift causes species abundances to fluctuate randomly, lowering diversity within communities and increasing differences among otherwise equivalent communities. Despite broad interest in ecological drift, ecologists have little experimental evidence of its consequences in nature, where competitive forces modulate species abundances. We manipulated drift by imposing 40-fold variation in the size of experimentally assembled annual plant communities and holding their edge-to-interior ratios comparable. Drift over three generations was greater than predicted by neutral models, causing high extinction rates and fast divergence in composition among smaller communities. Competitive asymmetries drove populations of most species to small enough sizes that demographic stochasticity could markedly influence dynamics, increasing the importance of drift in communities. The strong effects of drift occurred despite stabilizing niche differences, which cause species to have greater population growth rates when at low local abundance. Overall, the importance of ecological drift appears greater in non-neutral communities than previously recognized, and varies with community size and the type and strength of density dependence.

Temporal asynchrony among species helps diversity to stabilize ecosystem functioning, but identifying the mechanisms that determine synchrony remains a challenge. Here, we refine and test theory showing that synchrony depends on three factors: species responses to environmental variation, interspecific interactions, and demographic stochasticity. We then conduct simulation experiments with empirical population models to quantify the relative influence of these factors on the synchrony of dominant species in five semiarid grasslands. We found that the average synchrony of per capita growth rates, which can range from 0 (perfect asynchrony) to 1 (perfect synchrony), was higher when environmental variation was present (0.62) rather than absent (0.43). Removing interspecific interactions and demographic stochasticity had small effects on synchrony. For the dominant species in these plant communities, where species interactions and demographic stochasticity have little influence, synchrony reflects the covariance in species' responses to the environment.

[This corrects the article DOI: 10.1371/journal.pone.0144378.].

Phylogenomic analyses have helped resolve many recalcitrant relationships in the angiosperm tree of life, yet phylogenetic resolution of the backbone of the Leguminosae, one of the largest and most economically and ecologically important families, remains poor due to generally limited molecular data and incomplete taxon sampling of previous studies. Here, we resolve many of the Leguminosae’s thorniest nodes through comprehensive analysis of plastome-scale data using multiple modified coding and noncoding data sets of 187 species representing almost all major clades of the family. Additionally,we thoroughly characterize conflicting phylogenomic signal across the plastome in light of the family’s complex history of plastome evolution. Most analyses produced largely congruent topologies with strong statistical support and provided strong support for resolution of some long-controversial deep relationships among the early diverging lineages of the subfamilies Caesalpinioideae and Papilionoideae. The robust phylogenetic backbone reconstructed in this study establishes a framework for future studies on legume classification, evolution, and diversification. However, conflicting phylogenetic signal was detected and quantified at several key nodes that prevent the confident resolution of these nodes using plastome data alone.

[This corrects the article DOI: 10.1371/journal.pcbi.1006195.].

The authors have provided a corrected version here. thumbnail Download: * PPT PowerPoint slide * PNG larger image * TIFF original image Fig 1. The small grids on the right show the predicted pattern of results in Exp 1 under the four hypotheses. https://doi.org/10.1371/journal.pone.0186922.g001 Citation: Locke SM, Landy MS (2017) Correction: Temporal causal inference with stochastic audiovisual sequences.