Suding et al. (2004) demonstrate how conceptual advances in alternative ecosystem states theory have led to a greater understanding of why degraded systems are often resilient to restoration management. In their review they pose one (of several) ‘outstanding’ questions (Box 3 in Suding et al. 2004): “Are there predictable characteristics that indicate when a system will follow a successional pathway and/or that indicate the presence or absence of alternative ecosystem states?” We suggest that the persistence of alternative stable states might be predicted from simple consideration of assembly rules for systems structured along a gradient of environmental adversity. We raise the hypothesis that strongly abiotically‐ or disturbance‐structured assemblages, with nonrandom trait under‐dispersion (Weiher and Keddy 1995), are more likely to exhibit catastrophic phase shifts in community structure than assemblages which are weakly structured by environmental adversity.

Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.

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