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University of Tasmania: Social Science CSIRO Social Science Human Research Ethics Committee UTAS as Part of the IMAS Honours Project Coasts Program
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Received: 16 November 2017 Revised: 14 May 2018 Accepted: 17 May 2018 DOI: 10.1002/ece3.4301
1 2
ORIGINAL RESEARCH
3 4 5 6
1
7 9 11 12 2 13 3 4 14
David J. H. Flynn1,2 | Tim P. Lynch1
| Neville S. Barrett2 | Lincoln S. C. Wong1,2 |
Carlie Devine1 | David Hughes1
17 18 19 20 21 22
27 28 29 30 31 32
fishing, has often relied on telephone interviews selected from landline directories. However, this approach is becoming obsolete due to changes in communication technology such as a switch to unlisted mobile phones. Other methods, such as boat ramp
Correspondence Tim P. Lynch, The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Ocean & Atmosphere, Castray Esplanade, Hobart, TAS 7000, Australia. Email:
[email protected]
interviews, are often impractical due to high labor cost. We trialed an autonomous, ultra- high- resolution photosampling method as a cost effect solution for direct measurements of a recreational fishery. Our sequential photosampling was batched processed using a novel software application to produce “big data” time series movies from a spatial subset the fishery, and we validated this with a regional bus-route
Funding information University of Tasmania: Social Science, Grant/Award Number: #H0014772; CSIRO Social Science Human Research Ethics Committee, Grant/Award Number: 081/15; UTAS as Part of the IMAS Honours Project; Coasts Program
survey and interviews with participants at access points. We also compared labor costs between these two methods. Most trailer boat users were recreational fishers targeting tuna spp. Our camera system closely matched trends in temporal variation from the larger scale regional survey, but as the camera data were at much higher
33
frequency, we could additionally describe strong, daily variability in effort. Peaks
34
were normally associated with weekends, but consecutive weekend tuna fishing
35
competitions led to an anomaly of high effort across the normal weekday lulls. By
36
reducing field time and batch processing imagery, monthly labor costs for the camera
37
sampling were a quarter of the bus-route survey; costs for an individual sample 2.5%
38 39
of the bus routes. Gigapixel panoramic camera observations of fishing were repre-
40
sentative of the temporal variability of regional fishing effort and could be used to
41
develop a cost-efficient index. High-frequency sampling had the added benefit of
42
being more likely to detect abnormal patterns of use. Combinations of remote sens-
43
ing and on-site interviews may provide a solution to describing highly variable effort
44
in recreational fisheries while also validating activity and catch.
45 46
KEYWORDS
47
6
48 50 51 52 53
big data, coastal, CSIRO Ruggerdised Autonomous Gigapixel System, GigaPan, open-access fisheries, photomosaic, remote sensing, southern bluefin tuna, time interval count, time-lapse
49
Tweet: Big data seascape scale movies allow for cost-effective high-frequency monitoring of coastal recreational use #CSIRO #CRAGS #Tuna.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Ecology and Evolution. 2018;1–12.
PE: Manimaran A.
26
Collecting data on unlicensed open-access coastal activities, such as recreational
CE: Sathiyaseelan R
25
The Institute for Marine and Antarctic Studies (IMAS), University of Tasmania, Hobart, TAS, Australia
No. of pages: 12
24
2
Abstract
Dispatch: 27-6-2018
23
1 The Commonwealth Scientific and 5 Industrial Research Organisation (CSIRO) Ocean & Atmosphere, Hobart, TAS, Australia
4301
Manuscript No.
15 16
ECE3
10
Journal Name
8
Gigapixel big data movies provide cost-effective seascape scale direct measurements of open-access coastal human use such as recreational fisheries
www.ecolevol.org | 1
7
|
FLYNN et al.
2
1
1 | I NTRO D U C TI O N
the loss of broader environmental context at landscape or seascape scales (Lynch, Alderman, & Hobday, 2015) (Supporting
2 3
Worldwide, coastal zones are some of the most heavily im-
4
pacted parts of the ocean, in terms of activity types and partic-
This robotic camera system allows for much broader scaled pho-
5
ipation rates (Halpern et al., 2008). A particular focus of research
tographic assessments than simple camera traps by autonomously
6
on coastal use has been recreational fishing as this is a globally
taking, and then stitching together, multiple telephoto megapixel
7
popular activity that can significantly affect fish and inverte-
images into high-resolution gigapixel panorama. The resulting tiled
8
brate stocks (Arlinghaus, 2006; Cooke & Cowx, 2004; Lewin,
image allows fully zoomable viewing for either multiple small tar-
9
Arlinghaus, & Mehner, 2006; McPhee, Leadbitter, & Skilleter,
gets, such as Albatross nests across a colony (Lynch et al., 2015) or
10
2002), and for many species, harvest can exceed the take of the
more widely spaced larger targets, such as identifying commercial
11
commercial fishery (Giri & Hall, 2015; Lyle, Stark, & Tracey, 2014;
vs. recreational vessels around an offshore artificial reef (Wood
12
Zischke, Griffiths, & Tibbetts, 2012).
Information Appendix S1).
et al., 2016). The benefit of the system compared to simpler cam-
13
Unlike commercial fisheries, which are commonly documented by
era traps is being able to observe, in high detail from the one image
14
operators logging their catch and effort, assessments of open-access,
stream, many objects simultaneously across landscape or seascape
15
nonreporting activities, such as recreational fisheries, require sam-
scales. With so much information, data handling can become over-
16
pling. This can be both difficult and expensive due to their often large
whelming so a batch processing method called Gigapan Time ma-
17
spatial extent, high temporal variation and, for the fisheries agencies
chine has been developed. Originally used for viewing cosmological
18
undertaking the work, which often requires weekend and holiday
simulations (Yu et al., 2011), Time Machine produces a video stream
19
surveying, high labor costs (Ma et al., 2018; Pollock, Jones, & Brown,
that allows viewers to fluidly explore giga to tetra pixel-scaled videos
20
1994; Rocklin, Levrel, Drogou, Herfaut, & Veron, 2014; Venturelli,
across both space and time.
21
Hyder, & Skov, 2017). Due to these issues, assessments have typically
In Australia, annual recreational fishing participation has been es-
22
relied upon indirect data collection from off-site telephone surveys
timated at 19.5% (Henry & Lyle, 2003), which is well above the global
23
based on data frames developed around regional white pages direc-
average of around 10% (Arlinghaus, Tillner, & Bork, 2015). Around
24
tories, which traditionally have had high response rates and easily
the Australian island state of Tasmania, the annual participation rate
25
scalable sample sizes (Moore et al., 2015; Pollock et al., 1994). Such
is 29.3%, which well supersedes the national average. The Tasman
26
approaches may now risk undersampling active fishers because of de-
Peninsula in Tasmania’s southeast (Figure 1) is a popular fishing re-
27
mographically bias and overall decreased landline phone use; unlisted
gion near to the largest city and state capital of Hobart, where a steep
28
and nonregional coded mobile phones and low response success
bathymetric profile and migratory game-fish pathway overlap, provid-
29
from voice calls to mobile phones (Badcock et al., 2016; Blumberg &
ing coastal access to normally offshore fishing opportunities to rec-
30
Luke, 2009; Teixeira, Zischke, & Webley, 2016).
reational fisheries in trailer boats (Henry & Lyle, 2003). Pelagic game
31
Other forms of recreational fishery assessments employ direct
fish such as southern bluefin tuna (Thunnus maccoyii) are targeted in
32
on-site survey methods such as bus-route surveys, which use clerk
late Austral Summer and Autumn (Morton & Lyle, 2004), but as the
33
traveling around a fishery—following randomly selected predeter-
fishery is unlicensed and episodic, trends in effort and catch are poorly
34
mined schedules—interviewing and observing fishers to track fish-
understood (Lowry & Murphy, 2003; Moore et al., 2015). This partic-
35
ing effort, catch, and release (McGlennon & Kinloch, 1997; Pollock
ular recreational fishery is also of more general interest as the targets
36
et al., 1994). On-site methods that cover broad geographic areas,
are commercially important species, which include those with inter-
37
however, are seldom performed due to high labor costs (Jones &
nationally negotiated quotas (Pons et al., 2017; Zischke et al., 2012).
38
Pollock, 2012).
Recreational fishers are also often associated with other users of the
39
Given these limitations, remote sensing tools and technol-
marine environment (Farr, Stoeckl, & Sutton, 2014; Kearney, 2002) or in
40
ogies such as autonomous photography are an emerging field,
this case land-based tourists enjoying the local national parks. Hence,
41
offering direct monitoring as an alternative or supplement to on-
high use sites may be important to monitor not only for fisheries and
42
site interviews (Hartill, Payne, Rush, & Bian, 2016; Keller, Steffe,
ecological reasons but for managing social values, conflict resolution,
43
Lowry, Murphy, & Suthers, 2016; Parnell, Dayton, Fisher, Loarie,
and planning (Alessa, Kliskey, & Brown, 2008; Lynch et al., 2004).
44
& Darrow, 2010; Powers & Anson, 2016; van Poorten, Carruthers,
We trailed our CRAGS method to see whether we could develop
45
Ward, & Varkey, 2015; Wood, Lynch, Devine, Keller, & Figueira,
a high-frequency time series of temporal trends of observed trailer
46
2016). One such system is the CSIRO Ruggedised Autonomous
boat fishing effort at a concentration point for the recreational game
47
Gigapixel System (CRAGS), which is a programmable, weather-
fishery. Besides the hardware, we also tested both the suitability and
48
proof, camera trap that provides ultra-high resolution, time-lapse,
labor cost-effectiveness of a novel application of Time Machine batch
49
and high-f requency panoramic images. The CRAGS utilizes mod-
processing software to automatically produce a “big data” interactive
50
ified commercially available hardware and software known as a
movies made from our gigapixel panoramas. As the peninsula is iso-
51
GigaPan® (http://gigapan.com/) to capture photographic samples
lated and serviced by only a limited number of boat ramps, we also
52
with such high pixel density (gigapans) that wide fields of view and
aimed to validate the representative of the temporal trends we ob-
53
associated objects of interests can be examined closely without
served with CRAGS by correlating to match samples obtained from
|
3
1 2 3
COLOUR
FLYNN et al.
Australia
4 5 6 7 8
Tasmania
9 10 11 12
TAS
13
Hobart
14
43° 01' 30"
15 16
Tasman Peninsular
17 18 19 20
0 15 30
60
90
21
120 Kilometers
147° 52' 30"
Text
22
Pirates Bay
23 24 25 26 27 28
Stewarts Bay
29 30 31 32 33 34 35 36 37 38
F I G U R E 1 The Tasman Peninsula in SE Tasmania. Bus-route survey sampling occurred at ramps Pirates, Fortescue Bay, and Stuarts Bay. CRAGS location shown as camera icon and the observation area which includes the Hippolyte Rocks Marine Conservation Area is described as a triangle
Fortesque Bay
0 1.5 3
6
9
12 Kilometers
Hippolyte Rocks MCA
Tasman Is.
39 40 41
a simultaneous, regionally scaled bus-route trailer boat survey at the
Peninsula (Figure 1). This period was chosen as it corresponds with
42
three ramps on the peninsular available to large trailer boats. We also
the main tuna fishing season. For big trailer boats (>5 m length),
43
undertook on-ground interviews with trailer boat users to ground
local access to the fishery is limited to three boat ramps (Figure 1),
44
truth activity type (fishing or nonfishing) and target species. By un-
although the area may also be visited by larger vessels steaming from
45
dertaking both camera and boat ramp surveys, our over-overarching
marinas and anchorages further north (Morton & Lyle, 2004), but for
46
aim was to investigate how remote imagery may replace, compliment,
the purposes of our study, we excluded these relatively rare larger
47
or optimize conventional on-site approaches for recreational fishing.
vessels. We focused our CRAGS survey at a known game fishing concentration, the Hippolyte Rock Marine Conservation Area (HRMCA),
48 49 50
2 | M ATE R I A L S A N D M E TH O DS
which are a group of small islands about 6-km offshore (Figures 1 and 2) around which recreational fishing is permitted. While the CRAGS deployment was marginally closer to the Fortescue Bay Ramp, we
51 52
We deployed the CRAGS between 1 May 2015 and 30 June 2015
commonly observe boats approaching the HRMCA from directions
53
to assess trailer boats offshore from water adjacent to the Tasman
that corresponded to the location of all three ramps. We fixed the
|
FLYNN et al.
1 2 3
COLOUR
4
4 5 6 7 8 9 10 11 12
F I G U R E 2 An unmodified GigaPan Epic PRO unit (left) and a CRAGS deployed at Waterfall Bay, SE Tasmania (right)
13 14 16 17 18 19 20 21 22 23 24 25
LOW RESOLUTION COLOUR FIG
15
26
F I G U R E 3 Screen capture outputs from Time Machine interactive data viewer showing: a. no zoom and panoramic extent showing the Hippolyte conservation zone, b = 1/3 zoom, main Hippolyte rock ~6 km from camera, c = 1/2 zoom of a large trailer boat, d = full zoom
27 28 29 30 31 32 17 33 34 35 36 37
CRAGS to a clifftop at Waterfall Bay which overlooks the site from
CREATE Lab’s Time Machine software (CREATE Lab® Pittsburg, PA,
very high sea cliffs (~250 m), with the camera focusing on the west-
USA).
ern side of HRMCA (Figure 1). For each sample, CRAGS captured
Movies are fully interactive and are able to be zoomed into,
a series of 68 photographs through a telephoto lens, which when
paused, and played at various speeds and are also time and date-
stitched together provide a high-resolution panoramic image with
stamped (Figure 3). Using this interactive movie feature of this
40
a wide field of view (~140°) (Supporting Information Appendix S1).
software, each GigaPan movie was systematically paused at each
41 8 We estimated the size of the area CRAGS’ sampled compared to the regional area accessible by trailer boats using google earth polygons 42
sample, fully zoomed, and scanned left to right, top to bottom, for
43
and the University of New Hampshire KML extension tool—https://
were recorded for each sample with objects larger than ~10 m in
extension.unh.edu/kmlTools/index.cfm.
length (not trailer boats) or requiring longer than 10 seconds to dis-
38 39
44 45 46 47 48 49 50 51 52 53
objects in the seascape by the lead author (Figure 3). Vessel counts
The time of the sample start point was randomly selected, and
tinguish (e.g., low resolution and image artifact) being discounted.
then, sampling was conducted sequentially throughout the survey
Samples were categorized into two strata: weekdays and weekends/
daylight hours (06:00–18:00) only with nights “blanked” (Supporting
public holidays using the time and date stamp provided on each
Information Appendix S1). Photographic sessions were programmed
GigaPan scene (Figure 3).
to be separated by a recurring 93-min gap, thus producing a forward
The count of boats for each sample was then extrapolated into
shift in sample capture time and minimizing potential autocorrela-
an estimate of daily average boating effort (hours ± SE) using the in-
tions between boating activities and sampling times. Photographs
stantaneous count method (Equation 1).
were batch processed to both form panoramic images for each sample and be compiled into an interactive time-lapse movie using
ê i = ̄Ii × T
(1)
|
5
FLYNN et al.
1
where ê i is the extrapolated boating hours for the ith day, ̄Ii is the
selected without replacement. Equal sampling weight was provided
2
average number of trailer boats observed across the instantaneous
to each ramp due to lack of prior knowledge of use rates.
3
samples for day i, and T is the daily sampling period (daylight hours).
At each boat ramp, surveys were conducted by first counting
4
As bad weather can be a major factor affecting the decision to boat
parked boat trailers, then by monitoring vessel entry and exits during
5
or fish (Forbes, Tracey, & Lyle, 2009), boating effort was assumed to
a 1-hr sampling period (Kinloch, McGlennon, Nicoll, & Pike, 1997;
6
be 0 when adverse weather made observations of boats impossible. Next, total effort for each day- t ype strata (Ê ) within sam-
Pollock et al., 1994). The 1-hr period was chosen due to travel time
7 8
pling months (k) was calculated to estimate the monthly effort
completed for all ramps within the daily sampling frame. Interviews
9
(Equation 2). As CRAGS allowed for continuous sampling throughout
were conducted with all parties launching and retrieving vessels
the survey period, the sampling probability (πk) was set to 1.
during the clerk’s wait time, determining party size, target species/
jk
10 11 12
Ê jk =
13
n ∑ i=1
(
ê i 𝜋k
)
group, and activity (i.e., fishing or not) for estimates of fishing effort. (2)
which could then be adjusted to fishing effort via the proportion of Standard error and effort variance for monthly estimates were
15
calculated (Equation 3) according to Pollock et al. (1994) for strata.
16 17
� � 1 ∑n � � nj −1 i=1 (̂ei − ē i )2 × (Njk )2 SEjk = � nj
18 19 20
As variation for each strata was calculated separately, we used
23
a sum of squared standard errors approach (Equation 4) to estimate
24
the total monthly variation.
25 26
29 30 31 32 33
(3)
number of days of strata j in month k.
22
SEk =
√
interviewees activities (Pollock et al., 1994; Robson & Jones, 1989) (Supporting Information Appendix S3). Party size between ramps was tested for any difference using a one factor ANOVA.
where nj is the number of sampled days of strata j; Njk is the total
21
28
This well-established procedure produced a monthly extrapolated estimation of boat effort in standardized units of effort hours ± SE,
14
27
and distance between sites, allowing for a bus-route sample to be
The bus-route survey hence provides an estimation of all fishing effort from trailer boat around this isolated peninsular. The CRAGS data are thus a high-frequency subset of the entire boat fishing effort for the period that was extrapolated by the regional survey. This allowed us to both test the relative use of the HRMCA compared to wider peninsular use and the ability of our remote observations with our CRAGs unit of an actual subset of the fishery to track larger temporal patterns of peninsular wide effort.
SE21k + SE22k
… + SE2jk
(4)
9 As samples were obtained in a serial and continuous fashion, a time series model using rolling averages was also applied to the data (Diggle, 1995) (Equation 5). smt =
To validate the CRAGS method high-frequency tracking of effort metrics over time, we undertook a Spearman rank correlation between time-matched effort estimations by CRAGS and the bus- route survey. For graphing, bus-route extrapolation estimates were overlaid onto extrapolations from daily averages of boat fishing effort from CRAGS. We also carefully logged all time spent on the proj-
ê t−1 + ê t + ê t+1 3
(5)
34
where smt is the smoothed average of extrapolated effort ê per
35
sample t which is weighted across three samples, each separated by
36
93 min. This provided a smoothed average within days.
ect to compare total labor costs between CRAGS and the bus route for both data collection and processing to determine effort across the comparative month as well as for cost per sample.
3 | R E S U LT S
37
We also conducted a bus-route style survey to estimate fishing
38
effort from trailer boats for the region (Pollock et al., 1994; Robson
39
& Jones, 1989). While CRAGS samples were collected throughout
The CRAGS operated successfully and continuously throughout
40
May and June, the comparable bus-route survey was only conducted
the study, capturing 5.45 ± 1.98 SE (May) and 4.83 ± 0.88 SE (June)
41
in May due to logistical constraints. We surveyed the three major
panoramas per day, making a total of 314 samples. We made 895
42
boat ramps in the region at Pirates Bay; Fortescue Bay; and Stewarts
observations of boats but 51 samples across 21 days returned zero
43
Bay between 3 May 2015 and 30 May 2015 (Figure 1).
counts of boats (17% of total samples), and these were concentrated
44
A total of 20 bus-route samples were scheduled. We undertook
on weekdays—with 44 zero-count samples across 18 days—while on
45
random stratified sampling with day type divided into weekdays or
weekends and public holiday there were only seven samples across
46
weekend/public holidays. To reflect expected higher recreational
3 days with no activity. Inclement weather rendered 27 samples un-
47
effort during weekends and public holidays (McCluskey & Lewison,
usable. The images and batch-processed Time Machine movies con-
48
2008), the sampling probability (πk) was weighted toward this strata
sumed 427.4 GB of memory.
49
over weekdays at 12 to eight samples (Supporting Information
50
Appendix S2). We stratified the sample time into morning (a.m.,
Based on boat ramp interviews (Figure 4), the average trailer ̂ was 2.87 people per vessel throughout May boat party size (P)
51
06:00–11:59) and afternoon (p.m., 12:00–18:00), with equal proba-
(±0.133 SE, n = 84), and party size between sites showed no signifi-
52
bilities of sampling. To minimize temporal autocorrelation, the start-
cant difference (df = 2, F = 3.02, p = 0.68). The average monthly boat
53
ing location and travel direction of the route were also randomly
fishing effort (hours) extrapolated for our CRAGS observations of
|
FLYNN et al.
1 2 3 4 5 6 7 8
LOW RESOLUTION FIG
6
9 10 11
F I G U R E 4 Extrapolated monthly trailer boat effort (hours ± SE) for the entire Tasman Peninsula, assessed by a bus-route survey (May) and for the subset of the area around the Hippolyte HRM sampled with CRAGS (May and June)
12 13 14 15 16 17 18
HRMCA was 5,629 hr (±711 SE), with 4,096 hr (±607 SE) in May and
competitions, the “Tuna Club of Tasmania’s’ Far South Classic” (16–
19
1,534 hr (±370 SE) in June (Figure 4)—this could be extrapolated to
17th May) and Rally #4 Northern invitational weekend rally (23rd
20
fisher hours by multiplying by the average party size but as no in-
May).
21
terviews were conducted in June, we left both the CRAGS and bus
22
route extrapolations as boat fishing effort only.
A total of 16 bus-route samples were conducted; logistic failures led to the abandonment of four (20%) of the planned samples.
23
The total subset area observed by CRAGS was 9.6 km2 which
Trailer boat effort was estimated to be 19,650 hr (±2,858 SE) in May
24
compared to 318 km2 that was easily available to trailer boats around
(Figure 4). No trailers at individual ramps, all during weekday sam-
25
the Tasman Peninsular, and this corresponds to around 3% of the
ples, were recorded four times. The total number of trailers sighted
26
total area. Compared to the total fishing effort for May, extrapolated
per ramp ranged from 0 to 16 during weekdays and 1–54 for week-
27
from the regional bus-route survey, the relatively small area around
ends, with the highest number recorded on 16 May 2015 at Pirates
28
the HRM (~3%) received a large amount of the fishing effort (~23%).
Bay. During high- intensity days (particularly weekends/holidays
29
The successful high daily frequency of CRAGS sampling permit-
mornings), there were often large overflows from ramp car parks,
30
ted the examination of fine-scale temporal variation which displayed
with parked cars and boat trailers extending as much as one kilome-
31
a ragged sinusoid oscillation (Figure 5). Effort was generally low
ter from the boat ramp.
32
within the weekday strata and then increased rapidly to large peaks
During the bus-route survey, 84 interviews were conducted.
33
on weekends (Figure 5). One exception to this was between the
The majority of mariner (90.5% n = 96) indicated fishing was their
34
18th and 22nd May, which had more effort on weekdays than other
primary activity (Table 1). Overall, 64.3% (n = 54) indicated their
35
periods (Figure 5). Bracketing this period were 2-weekend fishing
primary target were tuna, with 17.9% (n = 15) specifically targeting
37 38 39 40 41 42 43 44 45 46 47 48
Rolling mean daily effort in hours
36 600 500 400 300 200 100 0
49 50 51 52 53
Day Weekdays
Weekends + Public Holidays
F I G U R E 5 High-frequency, interdaily, extrapolated CRAGS estimations of boat effort hours around the Hippolytes (HRM)
18
|
7
1 2 3
COLOUR
FLYNN et al.
4 5 6 7 8 9 10 11 12 13 14 15
F I G U R E 6 Daily boating effort (hours) extrapolated using bus-route access point survey (bar) and CRAGS (line). Effort estimate from bus-route survey was calculated from morning (a.m., dark gray) and afternoon (p.m., light gray) survey. CRAGS survey was conducted between May and June, while the bus-route survey was only conducted in May
16 17 18
TA B L E 1 Interviewees reported primary target taxa or activity
TA B L E 2 Ranking of effort for paired samples between CRAGS and the bus route
Primary target taxa/ species/activity
Interview responses
%
Tuna (unspecified)
37
44.0
Date
CRAGS hours (rank)
Bus-route hours (rank)
Δ Rank
Southern Bluefin Tuna
15
17.9
2/05/2015
330 (9)
1,208 (10)
−1
2
2.38
3/05/2015
303 (8)
1,471 (11)
−3
All Tuna
54
64.3
12/05/2015
0 (1)
48 (2)
−1
25
Flathead
5
5.95
14/05/2015
0 (1)
24 (1)
0
26
Southern rock lobster
9
10.7
16/05/2015
387 (11)
27
Squid/calamari
4
4.76
19/05/2015
93 (4)
28
Abalone
1
1.19
29
20/05/2015
Small Pelagics
1
1.19
30
22/05/2015
Snotty Trevalla
2
2.38
31
23/05/2015
All nearshore species
22
26.2
24/05/2015
Surfing
1
1.19
25/05/2015
SCUBA
3
3.57
29/05/2015
Demon Cave visitors
3
3.57
Pleasure Cruise
1
1.19
All nonextractive
8
9.52
19 20 21 22 23 24
32 33 34 35 36 37 38 39
Albacore Tuna
Note. “Tuna” responses denote anglers targeting all species within the family Thunnini.
1,794 (12)
−1
89 (4)
0
57 (3)
117 (5)
−2
228 (7)
283 (6)
1
513 (12)
414 (8)
4
345 (10)
1,155 (9)
1
122 (6)
323 (7)
−1
102 (5)
79 (3)
1
a daily estimation for tabulation (Table 2). The proportional difference between estimated efforts using the two methods ranged from 0.78- to 4.18-fold difference; however, ranks estimates between CRAGS and bus-route samples were strongly associated (n = 16, ρ(12) = 0.87,
40 41
southern bluefin tuna (Thunnus maccoyii). Other common recre-
42
ational targets included flathead (Platycephalus spp., 5.95%),
separately for correlation, which remained strong for both a.m. (n = 9,
43
Southern rock lobster (Jasus edwardsii, 10.7%), and squid/calamari
ρ(7) = 0.80, p =
