Post 5: AI for Marine Conservation

Welcome all!

In this week's blog, we will be delving into the prosperous world of artificial intelligence (AI) for marine conservation. The integration of cutting-edge technologies plays a pivotal role in bolstering our efforts to protect and sustainably manage ocean ecosystems. We will delve into the realm of AI, a powerful tool with the capacity to revolutionise the way we approach marine conservation. From advanced data analytics to autonomous monitoring systems, AI offers a suite of sophisticated solutions aimed at enhancing our understanding of marine environments and mitigating anthropogenic impacts. Join us as we embark on an exploration of the scientific principles and practical applications driving AI innovation in the conservation of our oceans.

What is Artificial Intelligence?

AI is a branch of computer science that aims to create intelligent systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. AI encompasses a wide range of techniques and approaches, including machine learning and deep learning.

Machine learning is a subset of AI that focuses on developing algorithms that can learn from and make predictions or decisions based on data. These algorithms improve their performance over time as they are exposed to more data. Supervised learning, unsupervised learning, and reinforcement learning are common types of machine learning techniques.

Deep learning is a specialized form of machine learning inspired by the structure and function of the human brain's neural networks. Deep learning algorithms, known as artificial neural networks, consist of interconnected layers of nodes that process data and extract features hierarchically. These algorithms have shown remarkable success in tasks such as image recognition, natural language processing, and speech recognition (1).

AI Applications in Marine Conservation

AI has emerged as a powerful ally in the realm of marine conservation, offering innovative solutions to address the complex challenges facing our oceans. Leveraging advanced technologies such as machine learning and deep learning, AI enables researchers and conservationists to monitor, analyse, and protect marine ecosystems with unprecedented precision and efficiency. From monitoring and surveillance to data analysis and species identification, AI applications are transforming our approach to understanding and managing marine environments.

Monitoring and Surveillance: AI-powered drones and satellites have emerged as invaluable tools for monitoring marine habitats and detecting illegal activities such as poaching. Equipped with advanced sensors and imaging technologies, drones and satellites can capture high-resolution images and data over vast oceanic areas. AI algorithms analyse this data in real-time to detect changes in marine ecosystems, such as coral bleaching, illegal fishing activities, or the presence of endangered species. By automating the process of monitoring and surveillance, AI enables more efficient and cost-effective conservation efforts, allowing for timely intervention and protection of marine biodiversity.

Data Analysis: The vast amount of data collected from marine environments poses a significant challenge for conservationists. AI algorithms offer powerful solutions for processing and analysing these large datasets to identify patterns, trends, and anomalies in marine ecosystems. Machine learning techniques, such as clustering and classification, enable the extraction of meaningful insights from complex data sources, including oceanographic measurements, biodiversity surveys, and environmental sensor data. By automating data analysis tasks, AI accelerates scientific discovery and enhances our understanding of marine ecosystems, facilitating evidence-based conservation strategies.

Species Identification: AI plays a crucial role in species identification from underwater images and videos, facilitating biodiversity monitoring efforts in marine environments. Convolutional neural networks (CNNs), a type of deep learning algorithm, can analyse vast amounts of visual data to accurately classify and identify marine species based on their unique features and characteristics. By automating the process of species identification, AI enables researchers to rapidly assess biodiversity hotspots, monitor population trends, and detect invasive species, aiding in the conservation and management of marine ecosystems.

Ocean Modelling: AI techniques are increasingly utilised to develop predictive models for understanding ocean dynamics and climate change impacts. AI algorithms can analyse vast amounts of oceanographic data, including sea surface temperatures, currents, and salinity levels, to forecast future changes in marine ecosystems. By integrating AI with physical ocean models, researchers can simulate complex interactions between oceanic processes and climate variables, enabling more accurate predictions of sea level rise, ocean acidification, and extreme weather events. These predictive models provide valuable insights for policymakers and stakeholders, guiding adaptation and mitigation efforts to protect marine biodiversity and coastal communities in the face of climate change.

(2), (3)

Benefits of AI in Marine Conservation

Increased Efficiency: AI technologies streamline marine conservation efforts by automating repetitive tasks and optimising data analysis processes. By leveraging machine learning algorithms, AI systems can efficiently process vast amounts of data collected from marine environments, such as satellite imagery, underwater surveys, and environmental sensor data. This automation reduces the time and resources required for data analysis, allowing conservationists to focus their efforts on strategic planning and decision-making. Furthermore, AI-driven automation enables real-time monitoring and response to environmental changes, enhancing the effectiveness of conservation interventions and ensuring timely protection of marine biodiversity.

Enhanced Accuracy: AI algorithms offer superior accuracy and reliability compared to traditional methods, transforming decision-making in marine conservation. Machine learning models can identify subtle patterns and trends in complex datasets that may go unnoticed by human analysts, leading to more informed and precise conservation strategies. By reviewing historical data and learning from past experiences, AI systems can predict future environmental trends and potential conservation outcomes with greater accuracy. This enhanced predictive capability enables proactive conservation measures and mitigates risks to marine ecosystems, ultimately improving the overall effectiveness of conservation efforts.

Scalability: One of the key advantages of AI in marine conservation is its scalability, allowing for broader and more comprehensive monitoring and conservation initiatives. AI-powered solutions can be deployed across large geographic areas, from remote marine reserves to expansive oceanic regions, facilitating widespread data collection and analysis. This scalability enables conservationists to assess and monitor marine biodiversity on a global scale, identifying conservation priorities and implementing targeted interventions where they are needed most. Additionally, AI technologies can adapt to changing environmental conditions and evolving conservation challenges, ensuring the long-term sustainability and resilience of marine ecosystems in the face of ongoing threats and pressures.

Challenges and Considerations of AI

Data Quality and Bias: One of the primary challenges in AI-driven marine conservation is ensuring the quality and integrity of the data used to train and deploy AI models. Issues such as incomplete or inaccurate data, sampling biases, and data gaps can affect the reliability and effectiveness of AI-driven conservation efforts. Additionally, AI algorithms may inadvertently perpetuate biases present in the training data, leading to unfair or discriminatory outcomes. Addressing these concerns requires careful consideration of data collection methods, data validation processes, and algorithmic transparency to mitigate bias and ensure the ethical use of AI in marine conservation.

Technological Limitations: While AI technologies hold immense potential for advancing marine conservation, they also face several technological limitations that must be addressed. Current AI algorithms may struggle with complex environmental variables, noisy data, and limited computational resources, particularly in remote or inaccessible marine environments. Additionally, AI models may lack interpretability, making it challenging to understand and validate their decision-making processes. Continued research and development efforts are needed to overcome these limitations and enhance the capabilities of AI technologies for marine conservation. Collaboration between scientists, technologists, policymakers, and stakeholders is essential to drive innovation and ensure the responsible deployment of AI in marine conservation efforts (5).

Successful Application of Ai in Marine Conservation

Project OceanMind: OceanMind is a non-profit organisation that uses AI and satellite technology to combat illegal fishing activities and promote sustainable fisheries management. By utilising satellite imagery, vessel tracking data, and other sources of marine data, OceanMind's AI algorithms can identify suspicious fishing vessels and detect illegal fishing activities in near real-time. This technology has been instrumental in supporting law enforcement agencies and fisheries authorities worldwide, leading to numerous successful prosecutions of illegal fishing operations and the protection of marine biodiversity. For example, OceanMind's collaboration with the government of Palau resulted in the interception and apprehension of several illegal fishing vessels, safeguarding the country's valuable marine resources and contributing to global efforts to combat illegal, unreported, and unregulated (IUU) fishing.

Marine Debris Tracker: The Marine Debris Tracker is a mobile application developed by the NOAA Marine Debris Program and the Southeast Atlantic Marine Debris Initiative that utilises AI and citizen science to monitor and track marine debris pollution. The app allows users to report and document instances of marine debris they encounter during coastal clean-up activities, beach walks, or boating trips. AI algorithms analyse the data collected through the app to identify patterns and hotspots of marine debris accumulation, enabling conservationists to prioritise clean-up efforts and implement targeted interventions. The Marine Debris Tracker has been widely adopted by citizen scientists and conservation organizations worldwide, leading to significant reductions in marine debris pollution and increased public awareness of the issue.

Coral Reef Monitoring with AI: Researchers at the Queensland University of Technology (QUT) have developed AI-powered underwater drones equipped with specialised cameras to monitor and assess the health of coral reefs. These drones use AI algorithms to analyse underwater images and videos in real-time, identifying coral species, assessing coral bleaching, and detecting signs of disease or degradation. By automating the process of coral reef monitoring, these AI-powered drones enable researchers to collect large amounts of data more efficiently and accurately than traditional methods. This technology has been deployed in various coral reef ecosystems worldwide, providing valuable insights into the impacts of climate change, pollution, and other threats on coral reef health and resilience.

(Press on links for further reading on the projects)

Wrapping up AI for our Oceans

The future of AI in marine conservation holds immense promise, with numerous opportunities for further innovation and development. As AI technologies continue to evolve, we can expect advancements in areas such as predictive modelling, habitat restoration, and ecosystem monitoring. Collaborative efforts between scientists, policymakers, investors, tech developers, and other stakeholders will be crucial in harnessing the full potential of AI for marine conservation. By fostering interdisciplinary partnerships and sharing data, expertise, and resources, we can overcome challenges, drive innovation, and accelerate progress towards sustainable ocean management.

In closing, I extend my heartfelt gratitude to you, the readers, for embarking on this insightful journey into the realm of AI and its profound impact on marine conservation. I do this to increase my knowledge on relevant topics and also help spread the amazing work being done in the industry (it ain't all doom and gloom). I invite you to share your thoughts, comments, and experiences in the ongoing dialogue about the role of AI in marine conservation. Join us next week as we delve into the innovative technologies combating marine pollution, continuing our exploration of solutions to safeguard the health and biodiversity of our precious marine ecosystems. Together, let's inspire positive change and make a lasting difference for our oceans.

"Ad astra per aspera."

Sources

1. Chowdhary, K.R., 2020. Fundamentals of artificial intelligence (pp. 603-649). New Delhi: Springer India.

2. Ditria, E.M., Buelow, C.A., Gonzalez-Rivero, M. and Connolly, R.M., 2022. Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective. Frontiers in Marine Science, 9, p.918104.

3. Alloghani, M.A., 2023. Using AI to Monitor Marine Environmental Pollution: Systematic Review. Artificial Intelligence and Sustainability, pp.87-97.

4. Silvestro, D., Goria, S., Sterner, T. and Antonelli, A., 2022. Improving biodiversity protection through artificial intelligence. Nature sustainability, 5(5), pp.415-424.

5. Shivaprakash, K.N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., Jadeyegowda, M. and Kiesecker, J.M., 2022. Potential for artificial intelligence (AI) and machine learning (ML) applications in biodiversity conservation, managing forests, and related services in India. Sustainability, 14(12), p.7154.