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Thermally induced mass coral bleaching is globally responsible for major losses of coral cover. Coral recovery from mass coral disturbances like the 2016 bleaching event hinges on successful recruitment of new coral colonies to the existing population. Juvenile corals as a life history stage represent survival and growth of new recruits. As such, habitat preferences of juvenile corals and how environmental parameters interact to drive coral recovery following a mass bleaching disturbance are important research areas. To expand our knowledge on this topic, we compared juvenile coral densities from before the 2016 bleaching event with those after the disturbance and identified abiotic and biotic characteristics of 21 reefs in the inner Seychelles that predict juvenile coral densities. Our results show that following the 2016 bleaching event, juvenile coral densities were significantly reduced by about 70%, with a particularly large decline in juvenile Acropora. Macroalgae present a large obstacle to survival of juvenile corals in a post-bleaching setting, but their influence varies as a function of herbivore biomass, reef structure, and reef type. Higher biomass of herbivorous fish weakens the negative effect of macroalgae on juvenile corals, and structural complexity on granitic reefs is a strong positive predictor of juvenile coral density. However, structural complexity on carbonate or patch reefs was negatively related to juvenile coral density, highlighting the importance of considering interactive terms in analyses. Our study emphasises the importance of habitat for juvenile coral abundance at both fine and seascape scales, adding to the literature on drivers of reef rebound potential following severe coral bleaching. Keywords Coral reef recovery Á Recruitment Á Coral bleaching Á Coral reef ecology Á Macroalgae Á Seychelles Topic Editor Morgan S. Pratchett

Social–ecological systems (SES) exhibit complex cause‐and‐effect relationships. Capturing, interpreting, and responding to signals that indicate changes in ecosystems is key for sustainable management in SES. Breaks in this signal–response chain, when feedbacks are missing, will allow change to continue until a point when abrupt ecological surprises may occur. In these situations, societies and local ecosystems can often become uncoupled. In this paper, we demonstrate how the red loop–green loop (RL–GL) concept can be used to uncover missing feedbacks and to better understand past social–ecological dynamics. Reinstating these feedbacks in order to recouple the SES may ultimately create more sustainable systems on local scales. The RL–GL concept can uncover missing feedbacks through the characterization of SES dynamics along a spectrum of human resource dependence. Drawing on diverse qualitative and quantitative data sources, we classify SES dynamics throughout the history of Jamaican coral reefs along the RL–GL spectrum. We uncover missing feedbacks in red‐loop and red‐trap scenarios from around the year 600 until now. The Jamaican coral reef SES dynamics have moved between all four dynamic states described in the RL–GL concept: green loop, green trap, red loop and red trap. We then propose mechanisms to guide the current unsustainable red traps back to more sustainable green loops, involving mechanisms of seafood trade and ecological monitoring. By gradually moving away from seafood exports, Jamaica may be able to return to green‐loop dynamics between the local society and their locally sourced seafood. We discuss the potential benefits and drawbacks of this proposed intervention and give indications of why an export ban may insure against future missing feedbacks and could prolong the sustainability of the Jamaican coral reef ecosystem. Our approach demonstrates how the RL–GL approach can uncover missing feedbacks in a coral reef SES, a way the concept has not been used before. We advocate for how the RL–GL concept in a feedback setting can be used to synthesize various types of data and to gain an understanding of past, present and future sustainability that can be applied in diverse social–ecological settings. A free Plain Language Summary can be found within the Supporting Information of this article.

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Thresholds and tipping points are frequently used concepts to address the risks of global change pressures and their mitigation. It is tempting to also consider them to understand biodiversity change and design measures to ensure biotic integrity. Here, we argue that thresholds and tipping points do not work well in the context of biodiversity change for conceptual, ethical, and empirical reasons. Defining a threshold for biodiversity change (a maximum tolerable degree of turnover or loss) neglects that ecosystem multifunctionality often relies on the complete entangled web of species interactions and invokes the ethical issue of declaring some biodiversity dispensable. Alternatively defining a threshold for pressures on biodiversity might seem more straightforward as it addresses the causes of biodiversity change. However, most biodiversity change appears to be gradual and accumulating over time rather than reflecting a disproportionate change when transgressing a pressure threshold. Moreover, biodiversity change is not in synchrony with environmental change, but massively delayed through inertia inflicted by population dynamics and demography. In consequence, formulating environmental management targets as preventing the transgression of thresholds is less useful in the context of biodiversity change, as such thresholds neither capture how biodiversity responds to anthropogenic pressures nor how it links to ecosystem functioning. Instead, addressing biodiversity change requires reflecting the spatiotemporal complexity of altered local community dynamics and temporal turnover in composition leading to shifts in distributional ranges and species interactions.

Cell size is a master trait in the functional ecology of phytoplankton correlating with numerous morphological, physiological, and life-cycle characteristics of species that constrain their nutrient use, growth, and edibility. In contrast to well-known spatial patterns in cell size at macroecological scales or temporal changes in experimental contexts, few data sets allow testing temporal changes in cell sizes within ecosystems. To analyze the temporal changes of intraspecific and community-wide cell size, we use the phytoplankton data derived from the Lower Saxony Wadden Sea monitoring program, which comprises sample-and species-specific measurements of cell volume from 1710 samples collected over 14 yr. We find significant reductions in both the cell volume of most species and the weighted mean cell size of communities. Mainly diatoms showed this decline, whereas the size of dinoflagellates seemed to be less responsive. The magnitude of the trend indicates that cell volumes are about 30% smaller now than a decade ago. This interannual trend is overlayed by seasonal cycles with smaller cells typically observed in summer. In the subset of samples including environmental conditions, small community cell size was strongly related to high temperatures and low total phosphorus concentration. We conclude that cell size captures ongoing changes in phytoplankton communities beyond the changes in species composition. In addition, based on the changes in species biovolumes revealed by our analysis, we warn that using standard cell size values in phytoplankton assessment will not only miss temporal changes in size, but also lead to systematic errors in biomass estimates over time.

The current policy and goals aimed to conserve biodiversity and manage biodiversity change are often formulated at the global scale. At smaller scales however, biodiversity change is more nuanced leading to a plethora of trends in different metrics of alpha diversity and temporal turnover. Therefore, largescale policy targets do not translate easily into local to regional management decisions for biodiversity. Using long-term monitoring data from the Wadden Sea (Southern North Sea), joining structural equation models and general dissimilarity models enabled a better overview of the drivers of biodiversity change. Few commonalities emerged as birds, fish, macroinvertebrates, and phytoplankton differed in their response to certain drivers of change. These differences were additionally dependent upon the biodiversity aspect in question and which environmental data were recorded in each monitoring program. No single biodiversity metric or model sufficed to capture all ongoing change, which requires an explicitly multivariate approaches to biodiversity assessment in local ecosystem management.