The paper introduces the Deep Zero-Inflated Bernoulli (DeepZIB) model, a deep statistical framework that jointly estimates latent North Atlantic Right Whale presence and detection probabilities from passive acoustic monitoring data. It addresses challenges such as zero inflation, imperfect detection, and complex habitat dependencies, and demonstrates improved predictive performance and model adequacy through simulations and real-world application along the U.S. East Coast. The model produces high-resolution, spatiotemporal presence maps that can inform targeted, risk-aware management of blue economy activities.
By Jiaxiang Ji, Laura Nazzaro, Ahmed Aziz Ezzat
arXiv:2609.13281v1 Announce Type: cross
Abstract: Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across bro...
By Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa, Rita Leit\~ao, Gabrielle Arrieta, M\'onica A. Silva, Matthew Graham, Ana M. G. Ferreira
arXiv:2609.13206v1 Announce Type: cross
Abstract: Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such...
By Makayla McDevitt, Maike Sonnewald, Stephanie Dutkiewicz
The paper presents a lightweight ResNet-based two-stage cascade for passive acoustic monitoring of killer whales. First, it detects vocalizations, then it classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. The pipeline achieves high macro‑F1 scores on the DCLDE 2027 dataset and improves real‑time inference speed, while active learning adapts the detector to new acoustic environments.
By Daniela Ruiz, Manuel Castellote, Zhongqi Miao, Carl Chalmers, Bruno Demuro, Rahul Dodhia, Pablo Arbelaez, Juan M. Lavista
arXiv:2608.21281v1 Announce Type: new
Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging...
By Abigail G. Grassick, Jerome Tze-Hou Hsu, Ethan Lin, Ziang Liu, Max Whitton, Madelyn Hair, Liam Gutierrez, Haozheng Yu, Kristin Branson, Vivek Jayaraman, Michael A. Gil, Andrew M. Hein, Jennifer J. Sun
arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.
By Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh
arXiv:2606. 08484v1 Announce Type: cross Abstract: Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning.
By Shufeng Kong, Tao Yu, Yuanyuan Wei, Caihua Liu, Junwen Bai, Yingheng Wang, Marc Grimson, Daniel Fink, Carla P. Gomes
Nereus is a new large-scale underwater dataset that supports fine-grained object attribute understanding and grounded counting perception. It contains 88,000 images, 2.68 million question-answer pairs, and annotations for 136,000 marine organism objects spanning 2,690 species and 1,515 genera, along with 65,000 point annotations and 67,000 grounded counting QA pairs. Experiments with multimodal models show that Nereus improves downstream adaptation over existing underwater-domain data and provides a foundation for future research on fine-grained marine perception.
By Jingtao Deng, Zihao Jin, Yuehe Chen, Feixiang He, Shuiwang Li, Dan Zeng
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
By Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang
arXiv:2607. 16731v1 Announce Type: new Abstract: Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical oceanography, held back by the complexity of the processes involved and by data scarcity.
By Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat, Stefano Ciavatta
OceanMoE is a structured conditional sparse Mixture-of-Experts framework designed for long‑horizon multivariate ocean forecasting. It fuses cross‑variable information to build target‑specific local representations and performs content‑conditioned sparse routing at each spatial location, with the number of active experts adjusted by router confidence. Experiments on ORAS5 data show that OceanMoE reduces aggregate forecasting error and maintains lower geometric‑mean normalized RMSE compared to baselines, while expert allocation varies with prediction targets and locations.
By Yishun Zhu, Jian Wang
The paper introduces a divide-and-conquer neural Bayes estimation framework for spatial and spatiotemporal point process models. It uses transformer-based architectures on partitions of the domain and an attention mechanism to combine local summaries, enabling direct inference from raw point pattern data. Simulation studies show reliable inference with better scalability than existing neural and classical likelihood-based methods, and the approach is applied to whale call data in Cape Cod Bay, producing parameter estimates and credible intervals at negligible cost after training.
By Bokgyeong Kang, Andrew Zammit-Mangion, Erin M. Schliep, Robert S. Schick