Caution over the use of ecological big data for conservation

When we downscaled the approach of Queiroz et al.3, we found errors in the data used to evaluate fishing exposure in these waters that were derived using a machine learning approach applied to vessel automatic identification system (AIS) location data5. In Western Australian state waters-an area lar...

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Bibliographic Details
Published in:Nature (London) 2021-07, Vol.595 (7866), p.E17-2
Main Authors: Harry, Alastair V, Braccini, J Matias
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Biodiversity
Carcharhinus obscurus
Carcharhinus plumbeus
Collaboration
Commercial fishing
Conservation
Ecologists
Ecology
Fisheries
Fishing
Fishing vessels
Learning algorithms
Machine learning
Risk assessment
Sand bars
Science
Sharks
Vessels
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Summary:When we downscaled the approach of Queiroz et al.3, we found errors in the data used to evaluate fishing exposure in these waters that were derived using a machine learning approach applied to vessel automatic identification system (AIS) location data5. In Western Australian state waters-an area larger than the Bering Sea-99.8% of longline and 100% of purse seine AIS data were incorrectly classified by the machine learning algorithm (Table 1 and Fig. 1). Contemporary longlining by a domestic tuna and billfish fishery still occurs, although these vessels were absent from the AIS data used by Queiroz et al.3. Since 2005, the intensity of this fishery has decreased and its footprint shifted to the southwest9. In Western Australia, the findings of Queiroz et al.3 risk undermining confidence in the science-based management controls that are already implemented to protect the mature biomass of long-lived dusky shark (Carcharhinus obscurus) and sandbar shark (C. plumbeus) stocks in the region12.
ISSN:0028-0836
1476-4687
DOI:10.1038/s41586-019-1444-4