How AI is reshaping aquatic biodiversity conservation worldwide

Image-based recognition using convolutional neural networks (CNNs), especially architectures like ResNet and YOLO, dominate species identification efforts by enabling high-precision classification from underwater imagery. Complementing this, deep learning models such as Long Short-Term Memory (LSTM) networks are deployed for bioacoustic monitoring, helping detect and classify aquatic species from underwater soundscapes.

Amid escalating climate threats and ecological degradation, artificial intelligence is emerging as a pivotal tool in safeguarding Earth’s fragile freshwater ecosystems. A newly published systematic review has mapped the fast-expanding landscape of AI applications in biodiversity conservation, underscoring their transformative impact on species monitoring, habitat assessment, and environmental policymaking. The study, titled “Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review”, was published in Biology and identifies over 300 studies spanning 2010 to 2024.

As rivers, lakes, wetlands, and groundwater ecosystems confront accelerated biodiversity loss, highlighted by an estimated 84% decline in freshwater species populations since 1970, this comprehensive review synthesizes how AI-driven innovations such as deep learning, machine learning, computer vision, and remote sensing are reshaping ecological monitoring and decision-making. The study provides a global perspective, detailing methodological trends, geographical research distribution, and technical limitations, while proposing strategies to mitigate bias and enhance the reliability of AI tools in freshwater conservation.

How is AI redefining aquatic biodiversity monitoring?

The review categorizes AI applications into four core domains: species identification, habitat modeling, ecological risk assessment, and conservation strategies. Image-based recognition using convolutional neural networks (CNNs), especially architectures like ResNet and YOLO, dominate species identification efforts by enabling high-precision classification from underwater imagery. Complementing this, deep learning models such as Long Short-Term Memory (LSTM) networks are deployed for bioacoustic monitoring, helping detect and classify aquatic species from underwater soundscapes.

Notably, the study also explores the application of environmental DNA (eDNA) and AI, highlighting the use of Support Vector Machines and transformer-based models like DNABERT to classify species based on genetic material extracted from water samples. These innovations enable scalable and automated biodiversity assessments, particularly in ecosystems where traditional taxonomic expertise is limited or monitoring is logistically difficult.

However, data limitations and model generalizability challenges persist. CNNs and transformer models require vast amounts of labeled training data, which are often scarce in aquatic contexts. Moreover, many AI models still function as "black boxes," limiting interpretability for field ecologists. To bridge this gap, the study emphasizes the need for explainability tools such as SHAP and LIMEs in ecological AI workflows.

What role does AI play in habitat modeling and environmental risk prediction?

Beyond species-level data, AI is increasingly used for predictive habitat modeling and ecological risk assessments. Algorithms such as Random Forests, Gradient Boosting Machines, and Deep Neural Networks analyze environmental parameters, including water temperature, pH, and turbidity, to forecast habitat suitability and detect ecological threats.

The review identifies AI-based ecological risk modeling as particularly effective in identifying vulnerable ecosystems exposed to pollution, invasive species, and climate-induced stress. Ensemble learning models, such as XGBoost and Bagging techniques, demonstrate strong performance in integrating diverse data sources and reducing prediction variance. These models support mitigation efforts by quantifying the likelihood of ecological degradation and prioritizing intervention zones.

In the domain of water quality monitoring, deep learning models such as LSTMs and Autoencoders are employed to predict changes in key indicators and detect anomalies like nutrient surges or algal blooms. Although these models are promising, their success is heavily dependent on access to dense and accurate sensor networks, an infrastructural gap in many regions.

Remote sensing also features prominently in AI-driven habitat assessment. CNNs are used to classify ecological zones from satellite and drone imagery, while time-series analysis helps detect pollution events and environmental shifts over time. However, limitations in image resolution, atmospheric interference, and generalizability remain significant barriers.

Can AI democratize conservation and strengthen global monitoring?

Perhaps one of the most transformative areas highlighted in the study is AI’s contribution to conservation planning and citizen science. AI-powered decision support systems and simulation-based optimizers are now being used to model conservation scenarios, integrating species data, climate projections, and socio-economic variables. This enables more adaptive and dynamic conservation strategies aligned with evolving policy and ecological conditions.

Spatial optimization tools like MARXAN and Zonation, enhanced with machine learning techniques, are employed to identify priority areas for protected zone expansion. These systems consider multiple ecological objectives, such as biodiversity richness and climate resilience, allowing more nuanced conservation designs. However, the complexity of these systems and dependency on comprehensive datasets raise concerns about equitable participation and stakeholder engagement.

The review also underscores the potential of lightweight AI models embedded in mobile apps to empower citizen scientists in species identification and ecological data collection. This integration not only democratizes biodiversity monitoring but expands geographic coverage in data-poor regions. Nevertheless, the quality of citizen-generated data is often inconsistent, requiring validation protocols to ensure accuracy and reliability.

From a policy standpoint, the study calls for interdisciplinary collaboration to establish standardized methodologies and evaluation protocols. It emphasizes the need for transparency in model training and reporting, use of open-access datasets, and publication of both successful and unsuccessful AI applications to reduce publication bias and enhance reproducibility.