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106,107 ✉ replying to A. V. Harry & J. M. Braccini Nature (2021) Our global analysis 1 estimated the overlap and fishing exposure risk (FEI) using the space use of satellite-tracked sharks and longline fishing effort monitored by the automatic identification system (AIS). In the accompanying Comment, Harry and Braccini 2 draw attention to two localized shark-longline vessel overlap hotspots in Australian waters, stating that 47 fishing vessels were misclassified as longline and purse seine vessels in the Global Fishing Watch (GFW) 3 2012-2016 AIS fishing effort data product that we used. This, they propose 2 , results in misi-dentifications that highlight fishing exposure hotspots that are subject to an unexpected level of sensitivity in the analysis and they suggest that misidentifications could broadly affect the calculations of fishing exposure and the central conclusions of our study 1. We acknowledged in our previously published paper 1 that gear reclassifications were likely to occur for a small percentage of the more than 70,000 vessels studied, however, here we demonstrate that even using much larger numbers of vessel reclassifications than those proposed by Harry and Braccini 2 , the central results and conclusions of our paper 1 do not change. In our use of a third-party dataset such as GFW 3 , we stated clearly 1 that the dataset is undergoing continuous refinement to correct for acknowledged contamination of some gear types with others in some regions (for example, drifting longlines with bottom-set longlines off New Zealand 1). The characterization of GFW vessels (gear) is under-taken using two convolutional neural networks that were trained 3 on 45,441 marine vessels (fishing and non-fishing) that identified six Published online: xx xx xxxx Check for updates Q1 Q2 Q3 Q4 Q5 A list of affiliations appears at the end of the paper.
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Nature | Vol 595 | 8 July 2021 | E17
Matters arising
Caution over the use of ecological big data
for conservation
Alastair V. Harry1,2 ✉ & J. Matias Braccini1
arising from N. Queiroz et al. Nature (2019)
Highly collaborative and data-intensive ecology studies are at the fore-
front of innovative solutions to global issues in conservation and natural
resource management
. In their spatial risk assessment of industrial-
ized fishing, Queiroz etal.
use big data and collaborative science to
outline a global conservation blueprint for pelagic sharks. In Austral-
ian waters, their analysis incorrectly identified global risk ‘hotspots’
in areas that are not subject to fishing and where spatial closures and
other management measures are already in place to protect sharks.
We highlight the potential for large-scale global analyses to misdirect
conservation efforts if not aligned with regional needs and priorities.
Although ecologists have enthusiastically adopted collaborative,
data-driven approaches in recent years, limited attention has been
given to the challenges in this emergent field, including the potential
for these often highly impactful studies to confound management and
conservation actions4. We applaud the collaborative effort by Quei-
roz etal.3 in assimilating satellite tagging data on 1,800 large pelagic
and neritic sharks generated by 153 authors. However, we also caution
against the use of data-intensive methods for guiding policy at the
global scale without proper acknowledgement of their risks, complexi-
ties and limitations.
In their paper, Queiroz etal.
identify Australia’s North West Shelf
(NWS) as a global fishing exposure hotspot for sharks on the basis
of spatial overlap with purported drifting longline and purse seine
fishing vessel movements, despite no such fishing having occurred
during the past two decades in this area. When we downscaled the
approach of Queiroz etal.3, we found errors in the data used to evalu-
ate 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). Incor-
rect classifications included movement data from other types of com-
mercial fishing vessels as well as non-fishing vessels. For example, 95%
of the data for purse seines in Western Australia waters were attributed
to the movements of the research vessel of our agency (which, inci-
dentally, does not undertake purse seine or drifting longline surveys).
The area of the NWS identified as highest risk falls within a spatial
closure of 0.8millionkm
in which directed shark fishing has been
prohibited since 2005
. Although an area to the northeast remains
open to shark fishing, none has occurred since 20096 and a network of
State and Commonwealth marine reserves has since been implemented
over much of that area. Fishery-independent surveys carried out over
a 17-year period confirm stable or increasing relative abundance and
size of large sharks in the region
. Historically, the waters adjacent
to the NWS shelf were indeed important fishing grounds for foreign
drifting longline vessels before their exclusion from Australian waters
in 19977, and for Australian vessels in the subsequent years8. Contem-
porary 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.
The approach of Queiroz etal.3 fared better at the scale of the entire
Australian Exclusive Economic Zone and offshore territories (10.2mil-
lion km2), where the tuna and billfish longline fleet operating off east-
ern Australia was correctly classified (Fig.1). However, 51% of drifting
longline data were still incorrect (Table1) and, notably, several demersal
trawlers were also misclassified as being part of the longline fleet. Data
from these vessels led to the incorrect identification of another pelagic
longline risk hotspot within the Great Barrier Reef Marine Park (Fig.1),
where this fishing method is not permitted. In the case of both the
NWS and Great Barrier Reef, the fishing exposure hotspots identified
were due to fewer than five vessels being misclassified, highlighting a
presumably unexpected level of sensitivity in the analysis.
As illustrated here, although patterns identified in global analy-
ses may be broadly informative, they can also be incorrect or misin
formative at regional levels where there is the scope for misallocating
resources for conservation and management. Framed alternatively,
Received: 6 November 2019
Accepted: 16 March 2021
Published online: 7 July 2021
Check for updates
1Fisheries & Agriculture Resource Management, Department of Primary Industries and Regional Development Western Australia, Hillarys, Western Australia, Australia. 2Centre for Sustainable
Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Murdoch, Western Australia, Australia. e-mail:
Table 1 | Summary of machine-learning classiied ishing
effort data
Western Australia Australia and offshore
Total area (millionkm2)2.27 10.2
Gear type Longline Purse seine Longline Purse seine
Total classified vessels 11 376 15
Incorrectly classified
vessels 9 3 24 11
Fishing hours 41,074 2,650 190,355 7,511
Incorrect fishing hours (%) 99.82% 100% 51% 82%
The machine-learning classiied ishing effort data used by Queiroz etal.3 to evaluate the
risks to sharks from ishing in Western Australian and Australian maritime jurisdictions. The
table shows the total number of vessels classiied as using longlines or purse seine, and their
respective ishing hours, along with the number of vessels and percentage of ishing hours
found to be incorrect. Australia and offshore territories includes all offshore and sub-Antarctic
territories and the Australian Antarctic Territory.
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This is likely to be due to more favourable weather conditions for smaller vessels travelling from Indonesia, particularly to Zones 6 and 7. • the daily numbers of illegal FFVs increased slightly overall during the first four months of the study to around 18 FFVs, after which time FFV numbers steadily declined to about 3 FFVs per day in June 2007. However, to place these results in context, FFV numbers had declined from about 60 FFVs per day in September 2005 to about 15 FFVs at the start of the present study in June 2006. Results for particular Zones can be summarised as follows (see Section 6.3.1): • The number of FFVs in Zone 1 was generally low, averaging around 2.5 FFVs per day but reaching 5-6 FFVs between January and March 2007. • For Zone 2, estimated daily FFVs were extremely low, with none seen in the last 8 months of the 13 month study period. • For Zone 6, the number of FFVs was highest in September 2006 (13-15), which was similar to FFV numbers in the previous 18 month study. However, after September 2006 numbers dramatically declined to less than one vessel in June 2007. It is important to note that the period of decline coincides with a seasonal decline in FFVs observed in previous years. However, the magnitude of decline clearly indicates a reduction in FFV numbers. • For Zone 7, the number of FFVs showed a significant and consistent decline from around 10 FFVs per day in June 2006 to less than 1 vessel per day in June 2007. To place these results in context with the results of Salini et al. (2007), FFV numbers declined from a peak of around 42 per day in January 2006. A number of assumptions are required to make any estimate of FFV fleet size. Some attempt is made at this in Section 7, but there is little information on which to base good estimates of either “residence time” per visit or numbers of visits per month by each vessel. A plausible range of values for these is used and leads to a coarse estimate of FFV fleet size in the range between 15 and 180 depending on the assumptions used. This demonstrates a significant reduction in fleet size estimated to be between 55 and 600 in 2005-2006. These assumptions can be refined as outlined under Recommendation 6 and would lead to more precise estimates. Based on the analyses of surveillance and apprehension data from June 2006 – June 2007 inclusive, we make the following recommendations for improving surveillance data gathering: Recommendation 1: We recommend that data for the months since June 2007 be analysed using the methods in this report, to provide estimates for at least one more year to confirm the decline in FFVs is real and not due to artefacts such as seasonal incursions. Recommendation 2: We recommend that BPC adopt the methods outlined in this report (using estimator nC or nD) into their normal practice. The new ‘quick’ estimator, nQ, is useful for obtaining ‘ball park’ estimates of FFV numbers, but should not be used for accurately estimating FFV numbers. Recommendation 3: We recommend that a further analysis of the current database be undertaken to provide empirical estimates of detection probabilities as a function of distance from the aircraft's track, in order to validate or refine the swath widths currently in use. Recommendation 4: We recommend that BPC routinely collect the time and position of the aircraft at the point where any FFV sighting takes place. Recommendation 5: The data field recording “Type of vessel” needs to be redefined to provide an unambiguous and clearer distinction between the different types of fishing vessels. Recommendation 6: More effort needs to be given to the estimation of “residence time” and “number of visits per month” in order to provide reliable estimates of the size of the fishing fleet involved. The extent to which apprehension effort may influence the residence time should also be explored. Recommendation 7: We recommend carrying out statistical evaluation of all the estimators and providing “hard” evidence on their performance, in particular which one is the most appropriate for the given dataset which has a biased nature of sampling because flight path is not random in the surveillance area. This will allow us to access to which extent the quasi-Poisson assumption is valid which is vital for our calculation of the standard errors. The study should include bias, variance, and mean square errors (MSE). This would require high statistical skills and extensive simulation studies. Recommendation 8: We recommend that a further study be undertaken to estimate the size of the catch of shark species from these illegal FFVs in order to improve the quality of stock assessment of sharks in northern Australia, and to improve current ecosystem models that can predict the ecological effects of IUU on the broader ecosystem.
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