Data Science can be used to inform environmental management and policy. We have been working with several NSW Government stakeholders to give insight into how the Sydney community interacts with it's beautiful harbour.

1. INNOVATIVE ENVIRONMENTAL
DATA SCIENCE IN AUSTRALIAS
MOST ICONIC WATERWAY
LUKE HEDGE
THE UNIVERSITY OF NEW SOUTH WALES
DRLUKEHEDGE
SYDNEY HARBOUR

2. 50 %| WORLD’S COASTLINE ALTERED
©Shutterstock | pokki1

3. POPULATION | 4.4 M© image | Rodney Campbell

4. A SYSTEMATIC SCIENTIFIC REVIEW
>20, 000 journal articles searched
310 publications found
four universities
two government agencies
one national museum
15 scientist authors
© image | Rodney Campbell

5. PURERESE
ARCH
APPLIED RESE
ARCH
200 Publications
110 Publications
©Kingsley Griffin
©Deposit Photos
© image | Rodney Campbell

6. 92 Publications 91 Publications 7 Publications 28 Publications
ROCKY REEF SEAFLOOR SEAGRASS ROCKY SHORES
OPEN WATER MANGROVE BEACHES FRESHWATER
92 PAPERS 91 PAPERS 7 PAPERS 28 PAPERS
32 PAPERS 26 PAPERS 4 PAPERS 1 PAPERS
APPLIED RESEARCH BASIC RESEARCH
© image | Rodney Campbell

7. 0 50 100 150
ECOLOGY
CHEMISTRY
BIOLOGY
MANAGEMENT
OCEANOGRAPHY
GEOLOGY
FISHERIES
© image | Rodney Campbell

8. © image | Rodney Campbell

9. WHERE ARE THE HABITATS ?
WHERE ARE THE IMPACTS ?
WHERE DO THEY OVERLAP ?
CREDIBLE
INTERPRETABLE
ACTIONABLE

15. © image | Vitaly Korovin

16. CREDIBLE
INTERPRETABLE
ACTIONABLE

17. Species distribution models
A model that relates
environmental predictors
to known species locations
across a landscape
To provide
understanding or
prediction

18. Fig. 1. (a) Example presence-only data—atlas records of where the tree species An-
gophora costata has been reported to be present, west of Sydney, Australia. The study
region is shaded. (b) A map of minimum temperature (◦
C) over the study region. Vari-
ables such as this are used to model how intensity of A. costata presence relates to the
environment. (c) A species distribution model, modeling the association between A. costata
and a suite of environmental variables. This is the fitted intensity function for A. costata
records per km2
, modeled as a quadratic function of four environmental variables using a
point process model as in Section 4.
example is given in Figure 1(a). This figure gives all locations where a par-
perform well in characterizing
the natural distributions of
species (within their current
range)
Occurrence points Enviro predictor Model prediction
Warton and Sheppard (2010) Annals App. Stat.
Elith et al (2009) Annu. Rev. Ecol. Evol. Syst. 2009.
Warton and Aarts (2013) J. Anim. Ecol.
useful ecological insight and
strong predictive capability

19. Ecological Applications, 24(1), 2014, pp. 71–83
Ó 2014 by the Ecological Society of America
Prediction of fishing effort distributions
using boosted regression trees
CANDAN U. SOYKAN,1,2,3
TOMOHARU EGUCHI,1
SUZANNE KOHIN,2
AND HEIDI DEWAR
2
1
Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service,
National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, California 92037 USA
2
Fisheries Resources Division, Southwest Fisheries Science Center, National Marine Fisheries Service,
National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, California 92037 USA
Abstract. Concerns about bycatch of protected species have become a dominant factor
shaping fisheries management. However, efforts to mitigate bycatch are often hindered by a
lack of data on the distributions of fishing effort and protected species. One approach to
overcoming this problem has been to overlay the distribution of past fishing effort with known
locations of protected species, often obtained through satellite telemetry and occurrence data,
to identify potential bycatch hotspots. This approach, however, generates static bycatch risk
maps, calling into question their ability to forecast into the future, particularly when dealing
with spatiotemporally dynamic fisheries and highly migratory bycatch species. In this study,
we use boosted regression trees to model the spatiotemporal distribution of fishing effort for
two distinct fisheries in the North Pacific Ocean, the albacore (Thunnus alalunga) troll fishery
and the California drift gillnet fishery that targets swordfish (Xiphias gladius). Our results
suggest that it is possible to accurately predict fishing effort using ,10 readily available
predictor variables (cross-validated correlations between model predictions and observed data
;0.6). Although the two fisheries are quite different in their gears and fishing areas, their
respective models had high predictive ability, even when input data sets were restricted to a
fraction of the full time series. The implications for conservation and management are
encouraging: Across a range of target species, fishing methods, and spatial scales, even a
relatively short time series of fisheries data may suffice to accurately predict the location of
fishing effort into the future. In combination with species distribution modeling of bycatch
species, this approach holds promise as a mitigation tool when observer data are limited. Even
in data-rich regions, modeling fishing effort and bycatch may provide more accurate estimates
of bycatch risk than partial observer coverage for fisheries and bycatch species that are heavily
influenced by dynamic oceanographic conditions.
Key words: albacore; bycatch mitigation; dynamic oceanographic conditions; fisheries management;
marine spatial planning; species distribution modeling; swordfish.
INTRODUCTION or negligible given the costs and logistics associated with
such efforts. Although such obstacles impede direct
FIG. 1. Maps of cumulative fishing effort: (A) West Coast drift gillnet (DGN; measured as number of gear sets) and (B) North
Pacific albacore troll (AT; measured as number of days fished) fisheries. Individual grid cells are 100
3100
for the drift gillnet fishery
and 18318 for the albacore troll fishery. The drift gillnet fishery data cover the period 1981–2001, and the albacore troll fishery data
cover the period 1991–2010. Grid cells with fewer than three total sets or days fished have been censored for confidentiality.
January 2014 75PREDICTING FISHING EFFORT DISTRIBUTIONS

20. City of Sydney
Rose Bay
Lane Cove River
Manly
Sydney Institute of
Marine Science

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Sydney Harbour
573 surveys
6 months
12 000 ‘events’
15 personnel

22. predictors
0
1
2
3
4
prediction
occurrences
model number of presence points n and their location (yi). This has not
previously been proposed for the analysis of presence-only data, despite
the extensive literature on the analysis of presence-only data. We consider
inhomogeneous Poisson point process models [Cressie (1993); Diggle (2003)],
which make the following two assumptions:
1. The locations of the n point events (y1,...,yn) are independent.
2. The intensity at point yi [λ(yi), denoted as λi for convenience], the lim-
iting expected number of presences per unit area [Cressie (1993)], can
be modeled as a function of the k explanatory variables. We assume a
log-linear specification:
log(λi) = β0 +
k
j=1
xijβj,(2.1)
although note that the linearity assumption can be relaxed in the usual
way (e.g., using quadratic terms or splines). The parameters of the model
for the λi are stored in the vector β = (β0,β1,...,βk).
Note that the process being modeled here is locations where an organism has
been reported rather than locations where individuals of the organism occur.
Hence, the independence assumption would only be violated by interactions
between records of sightings rather than by interactions between individ-
ual organisms per se. The atlas data of Figure 1 consist of 721 A. costata
records accumulated over a period of 35 years in a region of 86,000 km2, so
model
explanation | correlation
MaxEnt
Boosting
GLM | GAM
Random Forrest

23. 5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
Fishers (km2
)
Mean weather conditions

24. Boat based fishery
Weekday
Weekend
W
eekend
W
eekday
Recreational Intensity
*link *link
M
orningM
idday
Afternoon

27. 0.000000 0.000005 0.000010 0.000015
Recreational Activities (m2
)
Mean weather conditions

28. 0.000000 0.000005 0.000010 0.000015 0.000020
Recreational Activities−
non boat related (m2
)
Mean weather conditions

29. CREDIBLE
INTERPRETABLE
ACTIONABLE

30. ©Jessica Merrett

31. ©Jessica Merrett

32. ©Jessica Merrett

33. CREDIBLE
INTERPRETABLE
ACTIONABLE

35. Al As Cr
Cu Fe Mn
Pb Sn
1000
2000
3000
2
4
6
8
4
8
12
0
10
20
30
40
50
2000
4000
6000
10
20
30
40
50
10
20
30
40
50
1
2
3
4
20
40
60
80
Ba Cl NH QB RB Ba Cl NH QB RB Ba Cl NH QB RB
Ba Cl NH QB RB Ba Cl NH QB RB Ba Cl NH QB RB
Ba Cl NH QB RB Ba Cl NH QB RB Ba Cl NH QB RB
Location
Metalconcentration PDL
PDL
Ref
Sn
As
Zn
©Luke Hedge

36. Al As Cr
Cu Fe Mn
1000
2000
3000
2
4
6
8
4
8
12
50 6000
50
Ba Cl NH QB RB Ba Cl NH QB RB Ba Cl NH QB RB
As
©Luke Hedge

37. 1.0
1.5
2.0
2.5
Clontarf
North Harbour
Quarantine Bay
Rose Bay
ShannonDiversity(H)
Boating Infrastructure Reference
©Luke Hedge

38. Crustacea
Mollusca
Other
0
50
100
150
200
0
100
200
0
25
50
75
100
0
100
200
300
ClontarfNorthHarbour
QuarantineBay
RoseBay
ClontarfNorthHarbour
QuarantineBay
RoseBay
Abundance
Boating Infrastructure
Reference
CRUSTACEA MOLLUSCA
OTHER POLYCHAETE
©Luke Hedge

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Clontarf NorthHarbour Balmoral
0
10
20
Metal concentration (mm/kg)
Cu
Clontarf NorthHarbour Balmoral
Zn
©Luke Hedge

43. 140
150
160
170
180
190
Grainsize (µm)
Balmoral
©Luke Hedge

44. 7.1 7.2 7.3 7.4 7.5 7.6
mm/kg
Cu
Rose Bay
©Luke Hedge

45. Al As Cr Cu Fe Mn Pb Sn Zn
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0.0
0.5
1.0
1.5
2.0
2.5
0
1
2
3
MooringsControl
0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60
Distance
semivariance Spatial Autocorrelation (Variograms)
©Luke Hedge

46. 80
0 10 20 30
Nearest Mooring (m)
2 14 24
80
0 10 20 30
Nearest Mooring (m)
10 23 35
120
140
160
180
5 10 15
Nearest Mooring (m)
Grainsize(D10)
a. Sample data (mm/kg)
4
6
8
10
9 13 16
b. Metal
concentration (mm/kg)
Cu
120
140
160
180
5 10 15
Nearest Mooring (m)
Grainsize(D10)
a. Sample data (mm/kg)
6
8
10
12
14
17 22 26
b. Metal
concentration (mm/kg)
Pb
Balmoral
Cu Pb
North Harbour
120
5 10 15
Nearest Mooring (m)
9 13 16
50
100
150
200
4 8 12 16
Nearest Mooring (m)
Grainsize(D10)
a. Sample data (mm/kg)
10
20
30
40
5 18 28
b. Metal
concentration (mm/kg)
Cu
North Harbour
©Luke Hedge

47. Maldanidae sp. Ostracod sp. Nematode sp. Bivalve sp. 1
Syllidae sp. Amphipoda sp. 1 Nereididae sp. Nemertean sp.
0
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20
30
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10
15
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6
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10
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30
0
20
40
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3
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9
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6
9
0
2
4
6
8
0 10 20 0 10 20 0 10 20 0 10 20
0 10 20 0 10 20 0 10 20 0 10 20
0 10 20 0 10 20 0 10 20 0 10 20
Distance from Moorings (m)
Abundance
Location
Clontarf
North Harbour
©Luke Hedge

48. georgianus tricuspidata australis trachylepis testacea
0
5
10
15
20
25
0
50
100
0
3
6
9
0
5
10
15
20
0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30
Distance from Moorings (m)
Abundance
©Brendan Lanham

49. INTERPRETABLE
ACTIONABLE
CREDIBLE
©Deposit Photos | 263Ben

50. DRLUKEHEDGE
L.HEDGE@UNSW.EDU.AU
©Deposit Photos | 263Ben