Coral Reef Monitoring, Reef Assessment Technologies, and Ecosystem-Based Management

Obura, David; Aeby, Greta S.; Amornthammarong, Natchanon; Appeltans, Ward; Bax, Nicholas J.; Bishop, Joe; Brainard, Russell E.; Chan, Samuel J. W.; Fletcher, Pamela J.; Gordon, Timothy; Gramer, Lewis J.; Gudka, Mishal; Halas, John C.; Hendee, James C.; Hodgson, Gregor; Huang, Danwei; Jankulak, Mike; Jones, Albert C.; Kimura, Tadashi; Levy, Joshua; Miloslavich, Patricia; Chou, Loke Ming; Muller‐Karger, Frank E.; Osuka, Kennedy; Samoilys, Melita; Simpson, Stephen D.; Tun, Karenne; Wongbusarakum, Supin

doi:10.3389/fmars.2019.00580

Cited by 118 publications

(95 citation statements)

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“…Long-term continuity in monitoring is essential to identify baseline patterns, detect early warning signals and ensure robust forecasting of the ecosystem trajectories [2]. Novel technologies, therefore, need to preserve the long-term integrity of monitoring archives [28]. To evaluate this, we contrasted automated estimations of coral cover (XL-CSS data) against manual estimations from different monitoring programs using a linear mixed-effect regression (LME).…”

Section: Data Continuity In Coral Reef Monitoringmentioning

confidence: 99%

“…The number of open-access online tools for automated image annotation and robust demonstrations of their applications across conservation disciplines [16,30,45,46] is rapidly increasing. Critical steps are now needed to capitalise on these efforts [28,47] and maximise the advantages of a global monitoring and conservation science empowered by machine learning.…”

Section: Implications Of Automated Benthic Assessments For Coral Reefmentioning

confidence: 99%

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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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Section: Data Continuity In Coral Reef Monitoringmentioning

confidence: 99%

Section: Implications Of Automated Benthic Assessments For Coral Reefmentioning

confidence: 99%

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

et al. 2020

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“…an oceans perspective) the model provides a tangible way to forge equivalence between 'blue economy' and the SDGs [6]. It also identifies specific requirements for monitoring, how science and knowledge can be used, and helps identify gaps [34,48]. Among countries, where formal joint management may be impossible, commitment to the SDGs and application of a common model [2] could help assure alignment and mutually supportive actions [ 4 9,50].…”

Section: Resultsmentioning

confidence: 99%

“…for coral reefs [ 3 3,34], in large part due to greater complexity and variation in human dimensions over natural ones [35]. Efforts to redress this have been varied, such as the SocMon programme of the Global Coral Reef Monitoring Network [36], and capacities for monitoring, data management and integration are improving to the point of making 'integrated monitoring' a reality [34,38]. The SDG model supports these efforts, not only helping to identify key components of the model and variables defining their interactions that monitoring programmes should measure, but also providing a bridge to societal benefit priorities motivating large scale aggregation [35,38].…”

Section: The Local Contextmentioning

confidence: 99%

“…The SDG indicator framework identifies indicators at three levels of indicator maturity, from low (Tier 3) to operational (Tier 1) [3], paralleling the 'readiness' framework of essential ocean variables [34,41], providing practical steps guiding how these indicators can be matured and made more ready for use. However, many of the proposed SDG indicators are not relevant to local contexts, or cannot readily be equated with more tangible/local indicators (Table S1).…”

Section: The National and International Contextmentioning

confidence: 99%

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A Plot for Sustainability – The Sustainable Development Goals as A Narrative<strong> </strong>

Obura¹

2019

Preprint

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The Sustainable Development Goals, while complex at first sight, express a simple narrative about the relationships between people and nature. This paper illustrates this in the context of a coral reef land or seascape supporting coastal people. Coral reefs, their health described by measures of coral and fish diversity and abundance, provide key services and benefits to people. These services directly support 10s of millions of jobs in multiple economic sectors in coastal and distant states, protect and harbor communities and cities across tropical coastlines, sustain use of living and non-living resources, provide transport infrastructure and valuable natural products, and in future may provide energy solutions. Through these multiple benefits, coral reefs contribute to reducing hunger and poverty, thus improving health, and potentially strengthening gender and social equality. However, access and use result in pressures that may drive decline in coral reef health. Broader land and seascape factors also affect reef health, including land-use change and altered freshwater flows, as well as climate change. Managing this complex system requires appropriate awareness and knowledge, governance mechanisms and investments by stakeholders. This narrative can be used from local to global levels, motivating actions and policy at and across these scales to sustain ecosystem function and use, for the oceans what is also increasingly called a blue economy.

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Representation and complementarity of the long‐term coral monitoring on the Great Barrier Reef

Mellin

Peterson

Puotinen

et al. 2020

Ecological Applications

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Effective environmental management hinges on efficient and targeted monitoring, which in turn should adapt to increasing disturbance regimes that now characterize most ecosystems. Habitats and biodiversity of Australia’s Great Barrier Reef (GBR), the world’s largest coral reef ecosystem, are in declining condition, prompting a review of the effectiveness of existing coral monitoring programs. Applying a regional model of coral cover (i.e., the most widely used proxy for coral reef condition globally) within major benthic communities, we assess the representation and complementarity of existing long‐term coral reef monitoring programs on the GBR. We show that existing monitoring has captured up to 45% of the environmental diversity on the GBR, while some geographic areas (including major hotspots of cyclone activity over the last 30 yr) have remained unmonitored. Further, we identified complementary groups of reefs characterized by similar benthic community composition and similar coral cover trajectories since 1996. The mosaic of their distribution across the GBR reflects spatial variation in the cumulative impact of multiple acute disturbances, as well as spatial gradients in coral recovery potential. Representation and complementarity, in combination with other performance assessment criteria, can inform the cost‐effective design and stratification of future surveys. Based on these results, we formulate recommendations to assist with the design of future long‐term coral reef monitoring programs.

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Coral Reef Monitoring, Reef Assessment Technologies, and Ecosystem-Based Management

Cited by 118 publications

References 100 publications

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

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

A Plot for Sustainability – The Sustainable Development Goals as A Narrative<strong> </strong>

Representation and complementarity of the long‐term coral monitoring on the Great Barrier Reef

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Coral Reef Monitoring, Reef Assessment Technologies, and Ecosystem-Based Management

Cited by 118 publications

References 100 publications

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

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

A Plot for Sustainability &ndash; The Sustainable Development Goals as A Narrative<strong> </strong>

Representation and complementarity of the long‐term coral monitoring on the Great Barrier Reef

Contact Info

Product

Resources

About

A Plot for Sustainability – The Sustainable Development Goals as A Narrative<strong> </strong>