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RESEARCH ARTICLE (Open Access)

Defining humpback whale (Megaptera novaeangliae) potential distribution in the Great Barrier Reef Marine Park: a two-way approach

Consuelo M. Fariello https://orcid.org/0009-0008-5508-5626 A B * , Jan-Olaf Meynecke https://orcid.org/0000-0002-4639-4055 A B C * and Jasper de Bie https://orcid.org/0000-0002-8371-4089 A B C *
+ Author Affiliations
- Author Affiliations

A Whales & Climate Research Program, Griffith University, Gold Coast, QLD, Australia.

B Coastal and Marine Research Centre, Griffith University, Gold Coast, QLD, Australia.

C Cities Research Institute, Griffith University, Gold Coast, QLD, Australia.

* Correspondence to: consufariello@gmail.com

Handling Editor: Mike Calver

Pacific Conservation Biology 30, PC23032 https://doi.org/10.1071/PC23032
Submitted: 15 July 2023  Accepted: 3 May 2024  Published: 23 May 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Humpback whale (Megaptera novaeangliae) populations have been recovering from whaling but are now facing threats from changing food availability due to ocean warming and changes in habitat suitability. There is uncertainty over whether opportunistic observations can produce reliable species distribution models (SDMs) and adequately inform conservation management.

Aims

To compare SDMs for humpback whales in the Great Barrier Reef Marine Park based on different opportunistic sightings datasets and evaluate the impact different sources of opportunistic data have on our understanding of humpback whale habitat relationships.

Methods

Maximum entropy modelling (Maxent) was used to create predictive models for humpback whale distributions. Sighting data from citizen science and opportunistic observations from various other databases were used. Models were compared to evaluate disparities and predictive capabilities.

Key results

Distinct environmental variables [bathymetry, distance to the coast] were identified as the most relevant for each SDM. The best-fitting model diverged from an existing model, with humpback whale distribution predicted to be closer to shore. Areas with the highest habitat suitability were concentrated in the north-eastern coastal region across all models developed in this study.

Conclusions

This study demonstrates that, with careful application and consideration, citizen science data can enhance our understanding of humpback whale distributions and contribute to their conservation. The research underlines the importance of embracing diverse data sources in SDM, despite the challenges posed by opportunistic data.

Implications

The study provides valuable insights for conservation management and informs strategies to protect humpback whale populations in changing environmental conditions.

Keywords: citizen science, climate change, GBRMP, humpback whale, Maxent, Megaptera novaeangliae, opportunistic data, SDM, species distribution model.

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