Variation in the composition of corals, fishes, sponges, echinoderms, ascidians, molluscs, foraminifera and macroalgae across a pronounced in-to-offshore environmental gradient in the Jakarta Bay–Thousand Islands coral reef complex

Cleary, Daniel F. R.; Polónia, Ana R. M.; Renema, Willem; Hoeksema, Bert W.; Rachello-Dolmen, Paola G.; Moolenbeek, R.G.; Budiyanto, Agus; Yahmantoro,; Tuti, Yosephine; Giyanto, _; Draisma, Stefano G. A.; Reine, Willem F. Prud’homme van; Hariyanto, R.; Gittenberger, Adriaan; Rikoh, M.S.; Voogd, Nicole J. de

doi:10.1016/j.marpolbul.2016.04.042

Cited by 57 publications

(44 citation statements)

References 89 publications

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“…The increase in live coral and CCA cover further offshore demonstrates the influence of increasing water quality on community structure with distance from the coast. This pattern along a cross-shelf gradient is evident in other reef systems within Indonesia (Baum et al, 2015;Breckwoldt et al, 2016;Cleary et al, 2016) and around the world (Grigg, 1995;Mallela et al, 2004;Fabricius et al, 2005;Lirman and Fong, 2007) that are directly influenced by heavily populated cities on the coastline. Coastal areas along the Great Barrier Reef, Australia similarly showed community shifts from phototrophic invertebrates to macroalgae and heterotrophic invertebrates under high nutrient and sediment loads (Fabricius et al, 2012).…”

Section: Evidence Of a Cross-shelf Gradient Of Water Quality In Spermmentioning

confidence: 81%

Spatio-Temporal Patterns in Coral Reef Communities of the Spermonde Archipelago, 2012–2014, I: Comprehensive Reef Monitoring of Water and Benthic Indicators Reflect Changes in Reef Health

et al. 2018

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Pollution, fishing, and outbreaks of predators can heavily impact coastal coral reef ecosystems, leading to decreased water quality and benthic community shifts. To determine the main environmental drivers of coral reef status in the Spermonde Archipelago, Indonesia, we monitored environmental variables and coral reef benthic community structure along an on-to-offshore gradient annually from 2012 to 2014. Findings revealed that concentrations of phosphate, chlorophyll a-like fluorescence, suspended particulate matter, and light attenuation significantly decreased from on-to-offshore, while concentrations of dissolved O 2 and values of water pH significantly increased on-to-offshore. Nitrogen stable isotope signatures of sediment and an exemplary common brown alga were significantly enriched nearshore, identifying wastewater input from the city of Makassar as primary N source. In contrast to the high temporal variability in water quality, coral reef benthic community cover did not show strong temporal, but rather, spatial patterns. Turf algae was the dominant group next to live coral, and was negatively correlated to live coral, crustose coralline algae (CCA), rubble and hard substrate. Variation in benthic cover along the gradient was explained by water quality variables linked to trophic status and physico-chemical variables. As an integrated measure of reef status and structural complexity, the benthic index, based on the ratio of relative cover of live coral and CCA to other coral reef organisms, and reef rugosity were determined. The benthic index was consistently low nearshore and increased offshore, with high variability in the midshelf sites across years. Reef rugosity was also lowest nearshore and increased further offshore. Both indices dropped in 2013, increasing again in 2014, indicating a period of acute disturbance and recovery within the study and suggesting that the mid-shelf reefs are more resilient to disturbance than nearshore reefs. We thus recommend using these two indices with a selected number of environmental variables as an integral part of future reef monitoring.

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Section: Evidence Of a Cross-shelf Gradient Of Water Quality In Spermmentioning

confidence: 81%

Spatio-Temporal Patterns in Coral Reef Communities of the Spermonde Archipelago, 2012–2014, I: Comprehensive Reef Monitoring of Water and Benthic Indicators Reflect Changes in Reef Health

et al. 2018

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“…Additionally, the marked on-to-offshore gradient in water quality corresponds clearly with the benthic communities of the reef system. In a gradient analysis of Thousands Island in Jakarta Bay, Cleary et al (2015) identified a similar gradient in water quality and benthic community but also emphasize changes in species composition and their abundances among sites.…”

Section: Discussionmentioning

confidence: 76%

Spatio-Temporal Patterns in the Coral Reef Communities of the Spermonde Archipelago, 2012–2014, II: Fish Assemblages Display Structured Variation Related to Benthic Condition

et al. 2018

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The Spermonde Archipelago is a complex of ∼70 mostly populated islands off Southwest Sulawesi, Indonesia, in the center of the Coral Triangle. The reefs in this area are exposed to a high level of anthropogenic disturbances. Previous studies have shown that variation in the benthos is strongly linked to water quality and distance from the mainland. However, little is known about the fish assemblages of the region and if their community structure also follows a relationship with benthic structure and distance from shore. In this study, we used eight islands of the archipelago, varying in distance from 1 to 55 km relative to the mainland, and 3 years of surveys, to describe benthic and fish assemblages and to examine the spatial and temporal influence of benthic composition on the structure of the fish assemblages. Cluster analysis indicated that distinct groups of fish were associated with distance, while few species were present across the entire range of sites. Relating fish communities to benthic composition using a multivariate generalized linear model confirmed that fish groups relate to structural complexity (rugosity) or differing benthic groups; either algae, reef builders (coral and crustose coralline algae) or invertebrates and rubble. From these relationships we can identify sets of fish species that may be lost given continued degradation of the Spermonde reefs. Lastly, the incorporation of water quality, benthic and fish indices indicates that local coral reefs responded positively after an acute disturbance in 2013 with increases in reef builders and fish diversity over relatively short (1 year) time frames. This study contributes an important, missing component (fish community structure) to the growing literature on the Spermonde Archipelago, a system that features environmental pressures common in the greater Southeast Asian region.

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“…That does not allow comparison among different reefs and their responses to specific stressors. However, Cleary et al [62][63][64] present evidence to the contrary. A further important goal is to refine discrimination acuity and reliability sufficiently to use photographic images of larger reef sectors.…”

mentioning

confidence: 95%

Neural Network Recognition of Marine Benthos and Corals

et al. 2020

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We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016–2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored the current applications of deep learning for corals and benthic image classification by discussing the most recent studies conducted by researchers. We review the developments in the field, point out their current limitations, and outline their timelines and unique potential. We also discussed a few future research directions in the fields of deep learning. Future needs are the age detection of single species, in order to track trends in their population recruitment, decline, and recovery. Fine resolution, at the polyp level, is still to be developed, in order to allow separation of species with similar macroscopic features. That refinement of DL will allow such comparisons and their analyses. We conclude that the usefulness of future, more refined automatic identification will allow reef comparison, and tracking long term changes in species diversity. The hitherto unused addition of intraspecific coral color parameters, will add the inclusion of physiological coral responses to environmental conditions and change thereof. The core aim of this review was to underscore the strength and reliability of the DL approach for documenting coral reef features based on an evaluation of the currently available published uses of this method. We expect that this review will encourage researchers from computer vision and marine societies to collaborate on similar long-term joint ventures.

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Variation in the composition of corals, fishes, sponges, echinoderms, ascidians, molluscs, foraminifera and macroalgae across a pronounced in-to-offshore environmental gradient in the Jakarta Bay–Thousand Islands coral reef complex

Cited by 57 publications

References 89 publications

Spatio-Temporal Patterns in Coral Reef Communities of the Spermonde Archipelago, 2012–2014, I: Comprehensive Reef Monitoring of Water and Benthic Indicators Reflect Changes in Reef Health

Spatio-Temporal Patterns in Coral Reef Communities of the Spermonde Archipelago, 2012–2014, I: Comprehensive Reef Monitoring of Water and Benthic Indicators Reflect Changes in Reef Health

Spatio-Temporal Patterns in the Coral Reef Communities of the Spermonde Archipelago, 2012–2014, II: Fish Assemblages Display Structured Variation Related to Benthic Condition

Neural Network Recognition of Marine Benthos and Corals

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