Seaview Survey Photo-quadrat and Image Classification Dataset

González‐Rivero, Manuel; Rodríguez-Ramírez, Alberto; Beijbom, Oscar; Dalton, Peter; Kennedy, Emma V.; Neal, Benjamin P.; Vercelloni, Julie; Bongaerts, Pim; Ganase, Anjani; Bryant, Dominic E.P.; Brown, Kristen T.; Kim, Catherine; Radice, Veronica Z.; Lopez‐Marcano, Sebastian; Dove, Sophie; Bailhache, Christophe; Beyer, Hawthorne L.; Høegh-Guldberg, Ove

doi:10.14264/uql.2019.930

Cited by 4 publications

(7 citation statements)

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“…Table A2. Descriptors for the Benthic Groups (5) and sub-groups (15) used in the analysis, along with Table 53 benthic categories to which benthos was initially classified from photo quadrats (full dataset downloadable from Gonzalez et al 2019 [45]). 'Benthic Group' and 'Benthic sub-group' identifiers in blue are the keys matching labels in the downloadable dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Research was conducted in the dry season (May to October), when the prevailing eastward flow of surface current present during the northwest monsoon is in reverse, and salinity is higher (35 PSU compared to 31 PSU during monsoon) [20]. All data presented here, including photo quadrats, are freely available to download at [45].…”

Section: Methodsmentioning

confidence: 99%

“…Classification using the trained neural networks took about 36 h per 50,000 images (200 × the speed and 1% of the cost of a manual analysis), and was completed for all sites by September 2018. All data are public access and freely available to download [45].…”

Section: Photo Quadrat Processingmentioning

confidence: 99%

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

Kennedy

Vercelloni

Neal

et al. 2020

JMSE

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Karimunjawa National Park is one of Indonesia’s oldest established marine parks. Coral reefs across the park are being impacted by fishing, tourism and declining water quality (local stressors), as well as climate change (global pressures). In this study, we apply a multivariate statistical model to detailed benthic ecological datasets collected across Karimunjawa’s coral reefs, to explore drivers of community change at the park level. Eighteen sites were surveyed in 2014 and 2018, before and after the 2016 global mass coral bleaching event. Analyses revealed that average coral cover declined slightly from 29.2 ± 0.12% (Standard Deviation, SD) to 26.3 ± 0.10% SD, with bleaching driving declines in most corals. Management zone was unrelated to coral decline, but shifts from massive morphologies toward more complex foliose and branching corals were apparent across all zones, reflecting a park-wide reduction in damaging fishing practises. A doubling of sponges and associated declines in massive corals could not be related to bleaching, suggesting another driver, likely declining water quality associated with tourism and mariculture. Further investigation of this potentially emerging threat is needed. Monitoring and management of water quality across Karimunjawa may be critical to improving resilience of reef communities to future coral bleaching.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Kennedy

Vercelloni

Neal

et al. 2020

JMSE

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“…This open access data repository [20] comprises images and benthic annotations on these images from five different global regions in the period from 2012 to 2016: Central Pacific Ocean, Western Atlantic Ocean, Central Indian Ocean, Southeast Asia and Eastern Australia (Figure 1). Within each region, multiple reefs were surveyed in a total of 22 countries.…”

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confidence: 99%

Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

et al. 2020

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Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.

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“…In 2012, 23 reefs were surveyed in the northern GBR. We resurveyed many of these individual reefs ( n = 15) in October to November 2014, 2016 ( n = 21) and 2017 ( n = 14; González‐Rivero et al, 2019). Each year, we chose to resample reefs that had been disturbed in previous years using the spatial estimates of disturbance extent described above.…”

Section: Methodsmentioning

confidence: 99%

Forecasting intensifying disturbance effects on coral reefs

Vercelloni

Liquet

Kennedy

et al. 2020

Global Change Biology

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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.

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Seaview Survey Photo-quadrat and Image Classification Dataset

Cited by 4 publications

References 0 publications

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

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

Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

Forecasting intensifying disturbance effects on coral reefs

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