Forecasting intensifying disturbance effects on coral reefs

Abstract

Anticipating future changes of an ecosystem's dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef-building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here we used statistical modelling to explore the effects of broad-scale climate-related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive HC mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of HC, soft corals (SC) and algae. Discrete disturbances increased the prevalence of algae relative to HC while the interaction between cyclones and heat stress was the main driver of the increase in SC relative to algae and HC. Predictions from disturbance scenarios included relative increases in algae versus SC that varied by the frequency and types of disturbance interactions. However, high uncertainty of compositional changes in the presence of several disturbances shows that responses of algae and SC to the decline in HC needs further research. Better understanding of the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching this goal. Stan and reef composition models and R codes to compute and extract outputs of the Bayesian model and to implement the model in R are accessible in the Supporting Information. O RCI D Julie Vercelloni https://orcid.org/0000-0001-5227-014X Marji Puotinen https://orcid.org/0000-0002-1383-697X S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Vercelloni J, Liquet B, Kennedy EV, et al. Forecasting intensifying disturbance effects on coral reefs.

Coral Reef Community Changes in Karimunjawa National Park, Indonesia: Assessing the Efficacy of Management in the Face of Local and Global Stressors

“…Data analysis on abundance (percent cover) of benthic groups within the Karimunjawa National Park were performed using the R software (R Core Team 2016), following a methodology developed by Vercelloni et al [53]. Benthic groups were aggregated into Hard coral, Soft coral, Algae, Sponge and Sediment by summing different sub-groups to produce group relative abundances (Appendix A, Table A2).…”

“…Quadrats were grouped into 100 m sub-transects, a) in order to exclude any data where transect line deviated, so that the exact same section of reef (to within 5 m, the GPS error range) could be directly compared between years, and b) so that data analysis took place at an appropriate ecological scale [53]. Sub-transects were generated using hierarchical clustering based on Euclidean distance between geo-located quadrats within transects [53]. We retained sub-transects composed of a minimum of three images per surveyed year and repeated in 2014 and 2018.…”

Shifting Baselines: Physiological legacies contribute to the response of reef coral to frequent heat waves

“…For example assessment of symbiont community (Suggett, Warner, & Leggat, 2017) and cellular memory (Brown, Dunne, Edwards, Sweet, & Phongsuwan, 2015) in the coral holobiont during and after thermal stress, to test for shifting physiological status that may ameliorate or exacerbate coral bleaching. Thus, physiotype tracking – or multivariate physiological and molecular time series data – provide the capacity for identification of the cellular mechanisms that contribute to resilience, acclimatization, and/or resistance that can allow for better predictions of coral ecological responses to intensifying climate change and other regional disturbances (Vercelloni et al, 2020).…”

Correcting Misclassification Errors in Crowdsourced Ecological Data: A Bayesian Perspective

“…However, coral reefs are negatively affected by pressures such as climate change, which have caused a substantial decline in hard coral abundance (Ainsworth et al, 2016;De'ath et al, 2012;GBRMPA, 2014a,b). In addition, monitoring the GBR is especially challenging because it extends over 346, 000 km 2 and traditional marine surveys are expensive (Nichols & Williams, 2006;Nygård et al, 2016;Roelfsema & Phinn, 2010;Vercelloni et al, 2020). To address these issues, Peterson et al (2020) demonstrated how image-based coral cover data elicited from citizens can be combined with other professional data sources to improve the spatio-temporal data coverage in the GBR and increase the information gained to inform management.…”