A Deep Zero-Inflated Model of North Atlantic Right Whale Presence To Support Blue Economy Management
Abstract
Effective modeling of endangered marine mammal species, such as the North Atlantic Right Whale, is critical for balancing marine conservation with the growing blue economy. Passive acoustic monitoring data collected by autonomous underwater vehicles provide new opportunities for localized marine species detection and oceanographic sensing, but introduce complex statistical challenges such as zero inflation, imperfect detection, and intricate dependence structures. In response, we propose the Deep Zero-Inflated Bernoulli (DeepZIB) model, a deep statistical method which jointly models latent species presence and conditional detection probabilities while learning complex habitat relationships from heterogeneous covariate information. We establish theoretical results on the model's structural properties and conduct simulation experiments to demonstrate its ability to recover underlying parameters and latent presence fields. Application to real-world passive acoustic monitoring data on the North Atlantic Right Whale along the U.S. East Coast demonstrates improved model adequacy and predictive performance in capturing the species' dynamic and spatially varying habitat. We demonstrate DeepZIB's ability to generate high-resolution, spatially and temporally varying presence maps, providing valuable insights for targeted and risk-aware management of blue economy industries, ranging from offshore and marine energy, to fisheries management and maritime transport
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BibTeX
@misc{ji2026deep,
title = {{A Deep Zero-Inflated Model of North Atlantic Right Whale Presence To Support Blue Economy Management}},
author = {Jiaxiang Ji and Laura Nazzaro and Ahmed Aziz Ezzat},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.14403},
eprint = {2606.14403},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.14403},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - A Deep Zero-Inflated Model of North Atlantic Right Whale Presence To Support Blue Economy Management
AU - Jiaxiang Ji
AU - Laura Nazzaro
AU - Ahmed Aziz Ezzat
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2606.14403
UR - https://arxiv.org/abs/2606.14403
AB - Effective modeling of endangered marine mammal species, such as the North Atlantic Right Whale, is critical for balancing marine conservation with the growing blue economy. Passive acoustic monitoring data collected by autonomous underwater vehicles provide new opportunities for localized marine species detection and oceanographic sensing, but introduce complex statistical challenges such as zero inflation, imperfect detection, and intricate dependence structures. In response, we propose the Deep Zero-Inflated Bernoulli (DeepZIB) model, a deep statistical method which jointly models latent species presence and conditional detection probabilities while learning complex habitat relationships from heterogeneous covariate information. We establish theoretical results on the model's structural properties and conduct simulation experiments to demonstrate its ability to recover underlying parameters and latent presence fields. Application to real-world passive acoustic monitoring data on the North Atlantic Right Whale along the U.S. East Coast demonstrates improved model adequacy and predictive performance in capturing the species' dynamic and spatially varying habitat. We demonstrate DeepZIB's ability to generate high-resolution, spatially and temporally varying presence maps, providing valuable insights for targeted and risk-aware management of blue economy industries, ranging from offshore and marine energy, to fisheries management and maritime transport
ER -