A key challenge in small-scale fisheries that use moored fish aggregating devices (mFADs) is the ability to accurately quantify multi-sector fishing activity through fishery-independent methods. Here, we present a novel fishery-independent assessment of multi-sector fishing activity associated with a newly developed open access mFAD programme off San Juan, Puerto Rico. We identified three fishing sectors (recreational, charter and commercial) and 158 individual fishing vessels that routinely operated in the vicinity of the mFADs. The results indicate that daytime fishing activity varied by time of day, day of week, location and sector. During fishing tournaments, the data revealed that fishing activity increased threefold; across monitoring periods, for-hire charter vessels were the most consistent day-to-day user segment, and recreational activity peaked on weekends. Our study represents a new technique for rapidly identifying and detecting multi-sector fishing activity near mFADs and highlights the potential to gather comparable data wherever mFADs are deployed. The results are used to discuss how this technique can be used to assess the performance of mFADs to identify sector overlap and guide management in determining deployment patterns and facilitate the design of cost-effective surveys to estimate mFAD vessel activity, and potentially catch, of mFAD-associated species.
Un reto crucial en las pesquerías artesanales que utilizan los sistemas de agregadores de peces fijos (mFAD, por sus siglas en inglés) es el poder cuantificar con certeza la actividad pesquera multisectorial a través de métodos independientes de la pesca. En este estudio presentamos un innovador análisis independiente de la pesca para la actividad de pesca multisectorial asociada a los nuevos mFAD establecidos en Puerto Rico. Se identificaron 3 sectores pesqueros (recreacional, de alquiler y comercial) y 158 embarcaciones que rutinariamente pescaban alrededor de los mFAD. Los resultados muestran que la actividad pesquera diurna variaba por hora del día, día de la semana, lugar y sector. Durante torneos de pesca la actividad pesquera se triplicó, a lo largo de los periodos evaluados los botes de alquiler mostraron mayor consistencia por día y la actividad recreativa aumentó durante el fin de semana. Nuestro estudio plantea una nueva técnica para identificar rápidamente y detectar actividad multisectorial pesquera cerca de los mFAD y resalta el potencial de tomar datos comparables en otros lugares donde se coloquen los mFAD. Los resultados se utilizan para discutir cómo esta técnica puede ser utilizada para evaluar la ejecutoria de los mFAD, identificar solape de uso por varios sectores y guiar las decisiones en cuanto a los patrones para colocar los mFAD y facilitar el diseño de estudios costo efectivos para estimar la actividad de embarcaciones y el potencial de captura de peces alrededor de los mFAD.
Floating objects moored in coastal areas are commonly used to attract pelagic fishes to improve catch rates. Their use was first recorded in the second-century Roman poem Halieutica (
In the 1980s, mFAD (hereafter FAD) programmes in the U.S. Caribbean Sea (i.e. Puerto Rico and United States Virgin Islands) (
The FADs in Puerto Rico are moored in deep water (mean depth ± standard deviation: 448.0±135.7 m) and aggregate myriad open water species that have been identified in standardized scuba diving surveys (e.g. carangids, scombrids, coryphaenids, istophorids, thunnids and carcharhinds) (Merten unpublished data). The species most often targeted and subsequently caught near these FADs, as determined through online and direct communication catch surveys, are dolphinfish, blue and white marlin, sailfish, wahoo and various tuna species (e.g. blackfin and yellowfin tuna); it is not known whether juvenile sharks observed at the FADs during dives (
In Puerto Rico, there are 23882 registered vessels (
The purpose of this study was to provide a fishery-independent assessment of FAD use of an open access FAD array deployed off San Juan, Puerto Rico, during the first year of the programme. This study sought to address who visited the FADs (e.g. which sectors, individual charters and individual fishermen) and in what temporal and spatial frequency, and what fishing methods were used. The results are used to evaluate temporal and spatial FAD use patterns to identify sector use and help management determine future use practices and deployment patterns, and to discuss the costs and benefits of video versus traditional survey methods for estimating vessel activity, and potentially catch, of FAD-associated species.
In 2015, the Government of Puerto Rico (PR) deployed eight spherical steel FADs (147 cm diameter). The FADs were moored in a line that spanned approximately 60.06 km from Loiza to Manati; individual FAD depths are as follows: FAD A, 305 m; FAD B, 402 m; FAD C, 349 m; FAD D, 260 m; FAD E, 550 m; FAD F, 600 m; FAD G, 598 m; FAD H, 520 m (
Time-lapse cameras were programmed (Supplementary material, Table S1) and mounted at the base of the mast on three of the FADs nearest the two largest ports in the study area and nearest an active boat ramp (FAD B, Cangrejos; FAD D, San Juan; FAD F, Dorado boat ramp) (
FAD | Monitoring start date | Monitoring end date | Full days | No. vessel days1 | Total images | Vessel images | Detection rate | Vessels classified | Vessel movements (r/t*) | Early visit | Late visit |
---|---|---|---|---|---|---|---|---|---|---|---|
F | 8/30/2015 | 9/18/2015 | 20 | 0 | 33600 | 1746 | 5.2% | 56 | n/a | 0542 | 1740 |
F | 10/3/2015 | 10/19/2015 | 17 | 0 | 28560 | 709 | 2.5% | 20 | 11r | 0547 | 1715 |
F | 1/30/2016 | 2/16/2016 | 18 | 5 | 30240 | 215 | 0.7% | 10 | 7r | 0747 | 1625 |
B | 1/30/2016 | 2/21/2016 | 23 | 2 | 38640 | 744 | 1.9% | 31 | 10t | 0701 | 1618 |
D | 1/30/2016 | 2/20/2016 | 22 | 4 | 36960 | 1126 | 3.0% | 35 | 10t | 0617 | 1823 |
F | 6/15/2016 | 7/3/2016 | 19 | 9 | 31920 | 188 | 0.6% | 4 | 3r | 0741 | 1519 |
D | 6/15/2016 | 7/4/2016 | 20 | 1 | 33600 | 954 | 2.8% | 7 | 7r/3t | 0716 | 1745 |
Every recorded day (0530-1930) was imported into video editing software (FCPX v10.2.3) and labelled image by image for presence or absence of vessels. Images with vessels were then classified following these steps: (1) the first image of a vessel proximal to a FAD where distinct features could be identified (i.e. vessel make and model, vessel name and unique vessel attributes) was saved to an archive (i.e. as a reference image) and given a unique label to allow that vessel to be cross-identified if it occurred in subsequent images; (2) reference images were cross-identified with each subsequent vessel image throughout the entire dataset; (3) images with vessels present, but not close enough to allow cross-identification or unique identification, were physically labelled as unknown vessels (i.e. to allow the presence and absence of unknown vessels to be calculated); (4) certain ship types (e.g. tankers, cargo vessels, cruise ships and sailboats) were excluded from the database and never labelled because their size and behaviour were easily distinguished from those of fishing vessels. All labelled vessels were assumed to be fishing vessels and engaged in some form of fishing (e.g. searching, trolling, hand-lining, jigging, drifting, free-diving, running, setting lines, following birds, tying up to the FAD or passing the FADs were all categorized as apparent fishing activities). All apparent fishing vessel activities were described in three ways: per vessel, sector or as a whole (number of images, average amount of time present per day and visits per day).
All labelled vessels were categorized according to three sectors. Charter vessels were identified from experience and using the web (
Analysis and statistics
The metadata from each video were imported into Microsoft Excel ver.2013. Within Excel, timestamps were converted to a particular format (YYYY-MM-DD HH:MM:SS) and all data were imported into Google BigQuery (
Time series data were categorized by time of day (TOD: a, early am, 0530-0859; b, late am, 0900-1229; c, early pm, 1230-1559; d, late pm, 1600-1930), by day of week (DOW: Monday, 1; Tuesday, 2; Wednesday, 3; Thursday, 4; Friday, 5; Saturday, 6; Sunday, 7) and whether there was a distinct event associated with the day (e.g. holiday, tournament or none) for each monitoring period per FAD. Data were analysed in R studio version 0.99.903. Comparisons of activity between FAD locations were only considered with full recording days and when all FADs were recording simultaneously. The association of vessel activity with the factors TOD and DOW was considered using a logistic regression model (
logit(yi,j,k) = β0 + β1TODi,t + β2DOWj,t + β3(TOD*DOW)i,j,t
where yi,j,k is the probability that a vessel is detected at TODi, DOWj and photo index t. All inference was conducted at 5% significance. In this model, TODa and DOW1 were treated as the common reference groups, with which the rest of the TOD/DOW combinations were compared. To control familywise error rate while investigating vessel activity relative to each combination of TOD and DOW, simultaneous confidence intervals were constructed using a contrast matrix.
For cj=(c0,j,… cp,j), β=(β0,… βp) our j=1,...,m null hypotheses are: Hj:cjT β=0.
Vessel activity, in terms of average vessel visits per day, was then compared by distinct day, looking solely at days when official fishing tournaments were held over weekends (n=7) versus non-tournament weekends (n=38) using a chi-square analysis.
To test vessel range detection, one of the cameras used during this study was redeployed on FAD B but with internal time calibrated to the time of a handheld GPS (Garmin s76) and video operation set to continuous. The vessel used was a 20’ centre console with dual outboards and dual outriggers (height of centre console above the water, 2.4 m; freeboard aft, 0.6 m; length of outriggers, 3.9 m). The vessel was then oriented to the direction of the camera and slowly motored away while waypoints were taken frequently. The waypoints were plotted in ArcMap v.10.4.1. and matched to the time associated with the images.
All images for labelled vessels were visually classified based on FAD proximity relative to reference images acquired from range detection (Supplementary material, Fig. S1). There were three designations: adjacent (<70 m), close (~70-132 m) and far (>132 m) from a FAD. In addition, each image classified for proximity was labelled based on fishing mode. There were three fishing modes: spearfishing, drift fishing (i.e. troll and pole live baiting, vertical jigging, hand-lining and fly fishing) and trolling. Data were then analysed cumulatively and by large and small vessels in R.
Individual vessel dynamics were examined to calculate the total number of images, time spent per day, daily visits, re-visitation times (e.g. time between successive appearances at a single FAD on the same day, between days, months, weeks or FAD locations) and movement time between FADs. Revisits were defined as any visit greater than 30 minutes apart.
Catch reports gathered from an online survey from FADs that overlapped with camera deployments were tallied and compared with the imagery for matches. These data were used to relate preliminary catch quantity and composition while vessels fished the FADs.
A qualitative cost-benefit analysis was conducted between the video monitoring technique and traditional port, phone, mail and web-based survey (PPMW) techniques (personal communication QuanTech, Inc.). All surveys were assessed based on the costs associated with personnel and methods and the benefits of each approach.
A total of 158 unique vessels were characterized in 150 days recorded by the cameras from 29 August 2015 to 5 July 2016. When considering only full days recorded, a total of 139 days captured 233520 images. Overall, data were collected over 81 calendar days or 22.1% of the year. The maximum number of days during a monitoring period in which no vessels were captured on camera was 9; only the first two deployments had at least one vessel captured every day (
Cumulatively, the vessel detection rate for full recording days was 2.4% (n=5680) but varied by FAD location and time of year (
Vessel activity varied significantly by TOD and DOW (logistic regression: P<0.001).
Source of variation | DF | SS | MS | F | P |
---|---|---|---|---|---|
TOD | 3 | 35 | 11.64 | 498.05 | <0.001 |
Day | 6 | 36 | 5.96 | 255.03 | <0.001 |
Interaction | 18 | 16 | 0.86 | 36.93 | <0.001 |
Error | 233492 | 5458 | 0.023 |
Monitoring start and end date | Days (#) | FAD F | FAD D | FAD B | |||
---|---|---|---|---|---|---|---|
(hrs/d) | (vessels/d) | (hrs/d) | (vessels/d) | (hrs/day) | (vessels/d) | ||
8/30/15 – 9/18/15 | 20 | 2.56±1.93 | 4.04±2.53 | - | - | - | - |
10/3/15 – 10/19/15 | 17 | 1.67±2.00 | 1.58±1.50 | - | - | - | - |
1/30/16 – 2/16/16 | 18 | 0.52±0.81 | 1.68±1.79 | 1.75±2.37 | 4.47±4.64 | 0.98±1.23 | 3.31±3.09 |
6/15/16 – 7/3/16 | 19 | 0.19±0.30 | 1.50±0.78 | 1.12±1.25 | 2.00±1.32 | - | - |
The proportion of vessel types, classified by size or sector, varied by TOD (chi-square: P<0.001) (
Vessels that were only detected on one day (103 vessels or 64.7% of the total) represented 25.5% of all vessel detections and were mostly detected on weekends (65.5%). The majority (57.3%) of these vessels were small recreational, followed by large recreational (31.1%), and then small commercial fishing vessels (11.6%). Vessels detected more than six days (=10 vessels or 6.2% of total) represented 23.7% of all vessel detections and were detected consistently across all days of the week. Of these vessels, five were recreational, four were charter and one was a commercial fishing vessel.
Range detection was calculated as 492 m. Within 30 m, detailed vessel attributes were distinguishable and classifiable. At 70 m, detailed attributes were no longer classifiable but larger features such as the presence of outriggers, the centre console, and the colour of the outboards and hull were still detectable. At 132 m, the vessel was still clearly visible as a centre console and other features, such as the black outboard engines could be seen. At locations greater than 132 m, the vessel was still detectable but only as a moving object on the horizon.
Cumulatively, labelled images (n=4553) showed vessels far (>132 m, 45%; n=2038) and near (70-132 m, 30%; n=1353), rather than close to the FADs (<70 m, 25%; n=1162). Among these images, observed fishing modes were troll (73.7%), drift (23.8%) and spearfishing (2.5%). When images were examined by large (n=1593) and small vessels (n=2960) there were significant differences in both proximity and fishing mode (chi-square: P<0.001) (
Only 11.6% (n=19) of the detected vessels moved between monitored FADs during the monitoring periods; 4.2% (n=7) of the vessels were detected moving between FADs on the same day (all FAD D to B=9.24 km); and only one vessel moved between FAD D and B each day during three separate visits. The least amount of time between FAD visits on the same day was 22 min; the greatest was 262 min. Movements from FAD D or FAD B to FAD F were not detected on the same day, but one vessel did move between FAD F and FAD D (=23.36 km) during the same monitoring period.
Across all monitoring periods, 27 vessels revisited a FAD more than once on the same day. In total, there were 111 repeat events (FAD F, n=55; FAD D, n=37; FAD B, n=19) (74.41±56.79 min).
Survey Method | Personnel | Method | Data Size (gb) | Technique | |||||
---|---|---|---|---|---|---|---|---|---|
No. (#) | SalaryA (USD/hr) | Field1 (hrs) | Desk2 (hrs) | Cost | Resources | Pros | Cons | ||
Video | 2F 1D | $45FX $45DX | 3 | 40 | $2070C ($5000)D | Boat; FAD; Camera; SD card; Batteries | 7 | in situ; programmable; potential for automation; potential for web streaming; easily comparable to other locations with FADs | Potential lens obstruction or field of view issues; need batteries or power source; need demographic data from other data sources |
Port | 11F 3D | $35F $60D | 880B | 520 | $70000C ($15000)D | Forms; Clipboards; Pens; Transportation | 1 | Obtain more information about trip profiles and boats | Only public access sites are sampled; no data from boats at private docks |
Phone | 4D | $60D | N/A | 260E | $18000C ($8000)D | Phone service; Office space | 2 | Easy for domestic calls; reach people at private access sites and those that fish odd hours | Difficult and expensive to call internationally; low response rates without pre-notification |
3D | $60D | 0 | 270F | $24750C ($8000)D | Printer; Postage | 3 | Reach people at private access sites; no valid phone number or e-mail needed | Printing and mailing surveys is expensive; need valid addresses | |
Web | 1D | $100D | 0 | 170 | $18000J ($11000)D | Web domain; Web server; E-mail licence | 2 | Can target entire study group; once developed, reused for little cost | No contact or data without valid emails |
A Average salary for field biologists and office employees at QuanTech Inc; B Each site is visited every day for 4 hours; C Cost includes hourly costs and other direct costs such as supplies; D One time cost to train staff and develop survey protocol; E Hours to survey based on a sample size of 1000 registered vessels over a one-week period to ask about trips from a three-week period; F Hours to survey based on a sample size of 1000 registered vessels by mail asking about trips from a three-week period; includes time required to design paper surveys; H Cost to print and mail an advanced letter, survey, reminder postcard and second survey for non-respondents; J Cost to register domain, develop web survey, lease web server space and maintain survey for 2 months; X Based on salary for lead author to conduct this study; 1 Approximate time needed for deploying and retrieving the equipment in the study area on 1 FAD; 2 Approximate time needed to store, process, analyse and acquire results from one camera deployment.
This fishery-independent assessment of multi-sector fishing activity relative to an open access FAD array off San Juan, PR, is the first such study using video monitoring. Generally, activity was highest during late morning, on weekends, near major ports and during fishing tournaments. Ocean access influenced FAD fleet activity composition, and the results showed differences in FAD exploitation by TOD, DOW, fishing mode and proximity to the FADs by sector and size of vessel. This approach illustrates how video techniques can be used to generate high-resolution data useful for describing spatio-temporal trends in activity relative to a fixed structure in the ocean. This methodology has the potential to be extended beyond monitoring moored FADs to other features of interest, such as coastal marine reserves or ports of interest.
However, this study also had a number of limitations. First, while sampling was done over the course of a full year, data were not collected continuously and were unevenly distributed between locations, so they did not allow seasonal or interannual comparisons, time scales that FAD use may vary on. Additionally, these data were collected during the first year of a new programme and the results could be inflated due to fishers’ interest in moving offshore, whether for commercial or recreational purposes, to target pelagic species as new FADs were deployed (
Activity varied significantly by TOD during this study. Generally, daytime activity began just before sunrise (e.g. civil twilight), increased through the late morning, and then decreased steadily through the early and late afternoon.
Activity varied significantly by DOW during this study. It was highest on weekends and during fishing tournaments, where vessel visits per day increased threefold. In other Caribbean locations with FADs, such as Dominica and Haiti, large recreational fishing sectors (e.g. private recreational fishermen and charter boats) do not exist in the same concentration as in PR (
Activity varied significantly by sector. Large charter and recreational vessel activity peaked during late morning (0900-1230), while smaller commercial and recreational vessel activity peaked during early morning (0530-0859) (
During the study, fewer daily movements were detected between FADs than in repeat occurrences at the same FAD, results that suggest vessels utilize FADs closest to their home port. In Guadeloupe,
On a per FAD basis, ocean access determined FAD activity dynamics. At FAD D, the vast majority of repeats (81%) occurred within an hour. At FADs F and B, half of the repeats were greater than an hour apart. Points of entry at these locations are a boat ramp and a shallow channel that periodically shoals (
Video monitoring represents a new method for quantitatively sampling fishing vessel activity which has costs and benefits when compared with traditional PPMW techniques (
One of the motivating factors for this work was that the new method and information obtained would have direct application for describing spatio-temporal fishing activity relative to FADs in other locations. Video monitoring provided insights into FAD fishing activity down to the level of an individual vessel, information that can be used to improve FAD rules and regulations, better understand FAD fishing activity, better determine FAD deployment locations, and structure surveys that minimize costs but maximize the potential to approximate FAD catch and effort in the future.
Given the results of this study, local management agencies [e.g. the Caribbean Fisheries Management Council and the Department of Natural and Environmental Resources (DNER)], and fishing marinas should consider introducing additional questions to established port and tournament-sampling strategies in order to better evaluate FAD use patterns and performance. For example, management agencies could place an emphasis on quantifying the frequency of gear types being used at the FADs, and marinas that host fishing tournaments can determine whether fish were caught within a mile of a FAD during their tournaments. Gathering these types of data and comparing them with in situ camera data can provide FAD performance values that, when combined with catch, can be used to justify the need and longevity of the programme in PR (or any other location where FADs exist). This could also be useful for quantifying the performance of individual FADs, in terms of both activity levels and catch success, to better determine the amount, spacing and proper location to deploy FADs in the future.
Given the noticeable differences in sector use by TOD and DOW, another strategy could be to manage the daily timing of FAD fishing by sector to realize the full productivity of all sectors [i.e. maximize catch of locally sourced seafood (commercial sector; early am); maximize the opportunity of catching billfish (charter fleet; late am to sunset); and maximize fishing opportunities (recreational fleet; late am to sunset)]. This could take the form of separate licensing by sector and allocating specific fishing days and times based on the type of licence purchased (
The technique described herein has potential to be compared with the co-occurrence of FAD fish biomass and diel movements of fish (e.g. scombrids, coryphaenids, istophorids and thunnids) targeted at the FADs. Multi-frequency echo sounder buoys are commonly used on dFADs by industrial fishermen to estimate the amount of fish biomass by depth based on signal intensity at a FAD (
It is important that future research incorporate fishery-dependent socio-economic and catch data simultaneously with in situ vessel detection to provide a more complete picture of the FAD fleet dynamics and performance in this region; these techniques can be scaled up and applied to any location where FADs exist. The most economical surveys to add would be phone-and web-based surveys. Lastly, in order to realize the full benefits of this fishery-independent survey technique for FAD management, it is important to increase recording time, automate the detection of vessels through image recognition (
This research was funded by the Federal Aid in Sport Fish Restoration (Dingell-Johnson) programme of the United States Fish and Wildlife Service grants (Grant No: PR-F-F14AF00688) administered by the Puerto Rico Department of Natural and Environmental Resources, and Beyond Our Shores, Inc, Collaborative FAD Research Programme. We would like to thank QuanTech, Inc. for their assistance with the survey comparison cost-benefit analysis. We would also like to thank Fundación Legado Azul for their support of this research. This work would not have been possible without the help of the following fishermen and their charter operations: Captain Luis Lagradier (Puerto Rico Sport Fishing Charters), Captain Luis Burgos (Caribbean Fishing Academy), Captain Rafa Terraza (Billfish Fishing Charters), and Captain Luis Iglesias (Bill Wraps Fishing Charters). Thank you for your support and collaboration in FAD research in the US Caribbean Sea.
The following supplementary material is available through the online version of this article and at the following link:
Table S1. – Camera type, power source and setup used in this study.
Fig. S1. – A camera was redeployed to conduct a range test to determine approximately how far a vessel in an image was from the FAD. Image (1) is a vessel that is 70 metres from the FAD; Image (2) is the same vessel 132 m from the FAD. These photos were used to determine the proximity of vessels to the FADs.
Fig. S2. – Clockwise from upper left: (1) Vessel #55 earliest buoy visit 0542; (2) Vessel #143 latest buoy visit 1823; (3) Yellow arrows indicate highest number of vessels present in single image; all were unclassifiable and designated as unknown; (4) tanker.
Fig. S3. – Clockwise from upper left: (1) Vessel #89 known charter vessel trolling; (2) Vessel #132 likely recreational vessel trolling named “g_yacht_blue_pin_strips_dbl_window”; (3) Vessel #9 known commercial vessel trolling; (4) Vessel #23 likely recreational vessel drift fishing with handlines named “g_blank_boat”.
Fig. S4. – Clockwise from upper left: (1) Vessel #69 likely recreational spearfishermen with spears and floats visible; (2) Vessel #12 known commercial vessel with gaff in hand; (3) Vessel #19 likely recreational vessel jigging and drift fishing with sargassum present; (4) Unknown vessel trolling away from the buoy with a fish visible (it appears to be a dolphinfish) in the foreground.
Fig. S5. – Clockwise from upper left: (1) Vessel #50 present in squall; this vessel was named “g_very_sm_lowris_centercons”; (2) Vessel #57 shown tying up to buoy F; (3) Example of image with vessel and birds blocking a portion of the field of view; (4) Example of image with vessel with a bird blocking a greater portion of the field of view.
Fig. S6. – Clockwise from upper left: (1) Unknown vessel present with foggy lens and bird present; (2) A dolphinfish is present in the lower left portion of the image; (3) An unidentified tern bird with a fish in its beak; (4) An unidentified marine animal.