Regime shifts on tropical coral reef ecosystems: future trajectories to animal-dominated states in response to anthropogenic stressors

Despite the global focus on the occurrence of regime shifts on shallow-water tropical coral reefs over the last two decades, most of this research continues to focus on changes to algal-dominated states. Here, we review recent reports (in approximately the last decade) of regime shifts to states dominated by animal groups other than zooxanthellate Scleractinian corals. We found that while there have been new reports of regime shifts to reefs dominated by Ascidacea, Porifera, Octocorallia, Zoantharia, Actiniaria and azooxanthellate Scleractinian corals, some of these changes occurred many decades ago, but have only just been reported in the literature. In most cases, these reports are over small to medium spatial scales (<4 × 104 m2 and 4 × 104 to 2 × 106 m2, respectively). Importantly, from the few studies where we were able to collect information on the persistence of the regime shifts, we determined that these non-scleractinian states are generally unstable, with further changes since the original regime shift. However, these changes were not generally back to coral dominance. While there has been some research to understand how sponge- and octocoral-dominated systems may function, there is still limited information on what ecosystem services have been disrupted or lost as a result of these shifts. Given that many coral reefs across the world are on the edge of tipping points due to increasing anthropogenic stress, we urgently need to understand the consequences of non-algal coral reef regime shifts.

Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.

Regime cycles and political change in African autocracies

ABSTRACTThis article applies a regime cycle framework to understand patterns of change and continuity in African competitive autocracies. We observe that regime change in African autocracies is rarely the result of actions carried out by rebels, opposition leaders or popular masses substantially altering the structure of power. Instead, they are more frequently carried out by senior regime cadres, resulting in controlled reshuffles of power. We argue that such regime shifts are best explained through a cyclical logic of elite collective action consisting of accommodation and consolidation, and ultimately leading to fragmentation and crisis. These dynamics indicate the stage of leader-elite relationships at a given time, and suggest when regimes may likely expand, contract, purge and fracture. We argue that, by acknowledging in which stage of the cycle a regime and its senior elites are dominant, we can gauge the likelihood as well as the potential success of a regime change. Our framework is finally applied to understand recent regime shifts in competitive autocracies across Africa.