Macquarie University
01whole.pdf (1.78 MB)

Environmental predictive models for shark attacks in Australian waters

Download (1.78 MB)
posted on 2022-03-28, 11:30 authored by Samantha K. Lynch
Shark attacks tend to generate disproportionate public concern and, often, calls for enhanced mitigation. However, lethal shark control measures are undesirable and a pressing challenge is to develop non-lethal mitigation strategies. This requires an improved understanding of the drivers of shark attacks. In this study, the relationships between shark attacks in Australia (1915–2015) and environmental variables were explored. Attack data from the Australian Shark Attack File and corresponding environmental data were collated, analysed, and modelled. A K-means cluster analysis revealed two attack temporal periods: 1915–1970 and 1970–2015. White shark attacks in the 1970–2015 period showed the strongest correlation with environmental variables. To identify environmental predictors of white shark attacks, a series of models were fitted, and cross-validated, using presence (attack) and randomly-generated pseudo-absence (no attack) data. The most influential variables were location (river distance, latitude, and longitude), recent rainfall, and SST anomaly. SST anomaly on non-attack dates (mean range +0.5°C to +0.7°C) was significantly (P<0.05) higher than on attack dates (mean +0.2°C). This suggests that with warmer SSTs, white sharks may seek cooler inshore locations potentially bringing them closer to humans. Warning the public of shark attack conditions may decrease attack risk without further affecting marine life.


Table of Contents

1. Introduction -- 2. Methods -- 3. Results -- 4. Discussion -- 5. Appendix -- 6. Reference list.


Empirical thesis. Bibliography: pages 45-51

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Science and Engineering, Department of Biological Sciences

Department, Centre or School

Department of Biological Sciences

Year of Award


Principal Supervisor

Nathan Hart


Copyright Samantha K. Lynch 2016. Copyright disclaimer:






1 online resource (xi, 51 pages) maps (some colour)

Former Identifiers